diff --git a/.devops/cloud-v-pipeline b/.devops/cloud-v-pipeline
index f3a4944f8a419..af8c0cea6155c 100644
--- a/.devops/cloud-v-pipeline
+++ b/.devops/cloud-v-pipeline
@@ -15,7 +15,7 @@ node('x86_runner1'){            // Running on x86 runner containing latest vecto
     stage('Running llama.cpp'){
         sh'''#!/bin/bash
             module load gnu-bin2/0.1            # loading latest versions of vector qemu and vector gcc
-            qemu-riscv64 -L /softwares/gnu-bin2/sysroot  -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./main -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt            # Running llama.cpp on vector qemu-riscv64
+            qemu-riscv64 -L /softwares/gnu-bin2/sysroot  -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./llama-cli -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt            # Running llama.cpp on vector qemu-riscv64
             cat llama_log.txt                   # Printing results
         '''
     }
diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile
index 059fd26950607..61f671465ca71 100644
--- a/.devops/full-cuda.Dockerfile
+++ b/.devops/full-cuda.Dockerfile
@@ -6,13 +6,13 @@ ARG CUDA_VERSION=11.7.1
 # Target the CUDA build image
 ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} as build
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -27,10 +27,10 @@ COPY . .
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
 # Enable CUDA
-ENV LLAMA_CUDA=1
+ENV GGML_CUDA=1
 # Enable cURL
 ENV LLAMA_CURL=1
 
-RUN make
+RUN make -j$(nproc)
 
 ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile
index 6ecf3bcc7cb83..680d1cb92205d 100644
--- a/.devops/full-rocm.Dockerfile
+++ b/.devops/full-rocm.Dockerfile
@@ -6,7 +6,7 @@ ARG ROCM_VERSION=5.6
 # Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
-FROM ${BASE_ROCM_DEV_CONTAINER} as build
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 # List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
@@ -36,7 +36,7 @@ COPY . .
 # Set nvcc architecture
 ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
 # Enable ROCm
-ENV LLAMA_HIPBLAS=1
+ENV GGML_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
@@ -45,6 +45,6 @@ ENV LLAMA_CURL=1
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev
 
-RUN make
+RUN make -j$(nproc)
 
 ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile
index 432fb5dadcbca..2a06f82b738ae 100644
--- a/.devops/full.Dockerfile
+++ b/.devops/full.Dockerfile
@@ -1,9 +1,9 @@
 ARG UBUNTU_VERSION=22.04
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -18,7 +18,7 @@ COPY . .
 ENV LLAMA_CURL=1
 
 
-RUN make
+RUN make -j$(nproc)
 
 ENV LC_ALL=C.utf8
 
diff --git a/.devops/main-cuda.Dockerfile b/.devops/llama-cli-cuda.Dockerfile
similarity index 70%
rename from .devops/main-cuda.Dockerfile
rename to .devops/llama-cli-cuda.Dockerfile
index b937a482988b6..8eda63a89504d 100644
--- a/.devops/main-cuda.Dockerfile
+++ b/.devops/llama-cli-cuda.Dockerfile
@@ -6,7 +6,7 @@ ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VER
 # Target the CUDA runtime image
 ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} as build
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
@@ -21,12 +21,15 @@ COPY . .
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
 # Enable CUDA
-ENV LLAMA_CUDA=1
+ENV GGML_CUDA=1
 
-RUN make
+RUN make -j$(nproc) llama-cli
 
-FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
+FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
 
-COPY --from=build /app/main /main
+RUN apt-get update && \
+    apt-get install -y libgomp1
+
+COPY --from=build /app/llama-cli /llama-cli
 
-ENTRYPOINT [ "/main" ]
+ENTRYPOINT [ "/llama-cli" ]
diff --git a/.devops/llama-cli-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile
new file mode 100644
index 0000000000000..79dba06a77d6e
--- /dev/null
+++ b/.devops/llama-cli-intel.Dockerfile
@@ -0,0 +1,28 @@
+ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
+
+ARG GGML_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git
+
+WORKDIR /app
+
+COPY . .
+
+RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
+        echo "GGML_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
+    fi && \
+    echo "Building with static libs" && \
+    cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \
+    ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \
+    cmake --build build --config Release --target llama-cli
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
+
+COPY --from=build /app/build/bin/llama-cli /llama-cli
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/llama-cli" ]
diff --git a/.devops/main-rocm.Dockerfile b/.devops/llama-cli-rocm.Dockerfile
similarity index 88%
rename from .devops/main-rocm.Dockerfile
rename to .devops/llama-cli-rocm.Dockerfile
index 0a706dc73227d..c3d1ab06702ec 100644
--- a/.devops/main-rocm.Dockerfile
+++ b/.devops/llama-cli-rocm.Dockerfile
@@ -6,7 +6,7 @@ ARG ROCM_VERSION=5.6
 # Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
-FROM ${BASE_ROCM_DEV_CONTAINER} as build
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 # List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
@@ -36,10 +36,10 @@ COPY . .
 # Set nvcc architecture
 ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
 # Enable ROCm
-ENV LLAMA_HIPBLAS=1
+ENV GGML_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
-RUN make
+RUN make -j$(nproc) llama-cli
 
-ENTRYPOINT [ "/app/main" ]
+ENTRYPOINT [ "/app/llama-cli" ]
diff --git a/.devops/main-vulkan.Dockerfile b/.devops/llama-cli-vulkan.Dockerfile
similarity index 60%
rename from .devops/main-vulkan.Dockerfile
rename to .devops/llama-cli-vulkan.Dockerfile
index 6c2b2ed5b05d8..9b0dad8bf7a13 100644
--- a/.devops/main-vulkan.Dockerfile
+++ b/.devops/llama-cli-vulkan.Dockerfile
@@ -1,9 +1,9 @@
 ARG UBUNTU_VERSION=jammy
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
 # Install build tools
-RUN apt update && apt install -y git build-essential cmake wget
+RUN apt update && apt install -y git build-essential cmake wget libgomp1
 
 # Install Vulkan SDK
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
@@ -14,14 +14,14 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
 # Build it
 WORKDIR /app
 COPY . .
-RUN cmake -B build -DLLAMA_VULKAN=1 && \
-    cmake --build build --config Release --target main
+RUN cmake -B build -DGGML_VULKAN=1 && \
+    cmake --build build --config Release --target llama-cli
 
 # Clean up
 WORKDIR /
-RUN cp /app/build/bin/main /main && \
+RUN cp /app/build/bin/llama-cli /llama-cli && \
     rm -rf /app
 
 ENV LC_ALL=C.utf8
 
-ENTRYPOINT [ "/main" ]
+ENTRYPOINT [ "/llama-cli" ]
diff --git a/.devops/llama-cli.Dockerfile b/.devops/llama-cli.Dockerfile
new file mode 100644
index 0000000000000..7f741aa46ecf0
--- /dev/null
+++ b/.devops/llama-cli.Dockerfile
@@ -0,0 +1,23 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git
+
+WORKDIR /app
+
+COPY . .
+
+RUN make -j$(nproc) llama-cli
+
+FROM ubuntu:$UBUNTU_VERSION AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libgomp1
+
+COPY --from=build /app/llama-cli /llama-cli
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/llama-cli" ]
diff --git a/.devops/llama-cpp-clblast.srpm.spec b/.devops/llama-cpp-clblast.srpm.spec
deleted file mode 100644
index 774f63ddd5c4e..0000000000000
--- a/.devops/llama-cpp-clblast.srpm.spec
+++ /dev/null
@@ -1,84 +0,0 @@
-# SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
-# Built and maintained by John Boero - boeroboy@gmail.com
-# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
-
-# Notes for llama.cpp:
-# 1. Tags are currently based on hash - which will not sort asciibetically.
-#    We need to declare standard versioning if people want to sort latest releases.
-# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
-# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
-#    Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
-# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
-#    It is up to the user to install the correct vendor-specific support.
-
-Name:           llama.cpp-clblast
-Version:        %( date "+%%Y%%m%%d" )
-Release:        1%{?dist}
-Summary:        OpenCL Inference of LLaMA model in C/C++
-License:        MIT
-Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
-BuildRequires:  coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel
-Requires:       clblast
-URL:            https://github.com/ggerganov/llama.cpp
-
-%define debug_package %{nil}
-%define source_date_epoch_from_changelog 0
-
-%description
-CPU inference for Meta's Lllama2 models using default options.
-
-%prep
-%setup -n llama.cpp-master
-
-%build
-make -j LLAMA_CLBLAST=1
-
-%install
-mkdir -p %{buildroot}%{_bindir}/
-cp -p main %{buildroot}%{_bindir}/llamaclblast
-cp -p server %{buildroot}%{_bindir}/llamaclblastserver
-cp -p simple %{buildroot}%{_bindir}/llamaclblastsimple
-
-mkdir -p %{buildroot}/usr/lib/systemd/system
-%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamaclblast.service
-[Unit]
-Description=Llama.cpp server, CPU only (no GPU support in this build).
-After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
-
-[Service]
-Type=simple
-EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS
-ExecReload=/bin/kill -s HUP $MAINPID
-Restart=never
-
-[Install]
-WantedBy=default.target
-EOF
-
-mkdir -p %{buildroot}/etc/sysconfig
-%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
-LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
-EOF
-
-%clean
-rm -rf %{buildroot}
-rm -rf %{_builddir}/*
-
-%files
-%{_bindir}/llamaclblast
-%{_bindir}/llamaclblastserver
-%{_bindir}/llamaclblastsimple
-/usr/lib/systemd/system/llamaclblast.service
-%config /etc/sysconfig/llama
-
-
-%pre
-
-%post
-
-%preun
-%postun
-
-%changelog
diff --git a/.devops/llama-cpp-cuda.srpm.spec b/.devops/llama-cpp-cuda.srpm.spec
index ba9cb7cbb824f..7425d3a9d7a40 100644
--- a/.devops/llama-cpp-cuda.srpm.spec
+++ b/.devops/llama-cpp-cuda.srpm.spec
@@ -32,13 +32,13 @@ CPU inference for Meta's Lllama2 models using default options.
 %setup -n llama.cpp-master
 
 %build
-make -j LLAMA_CUDA=1
+make -j GGML_CUDA=1
 
 %install
 mkdir -p %{buildroot}%{_bindir}/
-cp -p main %{buildroot}%{_bindir}/llamacppcuda
-cp -p server %{buildroot}%{_bindir}/llamacppcudaserver
-cp -p simple %{buildroot}%{_bindir}/llamacppcudasimple
+cp -p llama-cli %{buildroot}%{_bindir}/llama-cuda-cli
+cp -p llama-server %{buildroot}%{_bindir}/llama-cuda-server
+cp -p llama-simple %{buildroot}%{_bindir}/llama-cuda-simple
 
 mkdir -p %{buildroot}/usr/lib/systemd/system
 %{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamacuda.service
@@ -49,7 +49,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t
 [Service]
 Type=simple
 EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llamacppcudaserver $LLAMA_ARGS
+ExecStart=/usr/bin/llama-cuda-server $LLAMA_ARGS
 ExecReload=/bin/kill -s HUP $MAINPID
 Restart=never
 
@@ -67,9 +67,9 @@ rm -rf %{buildroot}
 rm -rf %{_builddir}/*
 
 %files
-%{_bindir}/llamacppcuda
-%{_bindir}/llamacppcudaserver
-%{_bindir}/llamacppcudasimple
+%{_bindir}/llama-cuda-cli
+%{_bindir}/llama-cuda-server
+%{_bindir}/llama-cuda-simple
 /usr/lib/systemd/system/llamacuda.service
 %config /etc/sysconfig/llama
 
diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec
index 1d9e4f425b43a..4d5560089816c 100644
--- a/.devops/llama-cpp.srpm.spec
+++ b/.devops/llama-cpp.srpm.spec
@@ -38,9 +38,9 @@ make -j
 
 %install
 mkdir -p %{buildroot}%{_bindir}/
-cp -p main %{buildroot}%{_bindir}/llama
-cp -p server %{buildroot}%{_bindir}/llamaserver
-cp -p simple %{buildroot}%{_bindir}/llamasimple
+cp -p llama-cli %{buildroot}%{_bindir}/llama-cli
+cp -p llama-server %{buildroot}%{_bindir}/llama-server
+cp -p llama-simple %{buildroot}%{_bindir}/llama-simple
 
 mkdir -p %{buildroot}/usr/lib/systemd/system
 %{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llama.service
@@ -51,7 +51,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t
 [Service]
 Type=simple
 EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llamaserver $LLAMA_ARGS
+ExecStart=/usr/bin/llama-server $LLAMA_ARGS
 ExecReload=/bin/kill -s HUP $MAINPID
 Restart=never
 
@@ -69,9 +69,9 @@ rm -rf %{buildroot}
 rm -rf %{_builddir}/*
 
 %files
-%{_bindir}/llama
-%{_bindir}/llamaserver
-%{_bindir}/llamasimple
+%{_bindir}/llama-cli
+%{_bindir}/llama-server
+%{_bindir}/llama-simple
 /usr/lib/systemd/system/llama.service
 %config /etc/sysconfig/llama
 
diff --git a/.devops/server-cuda.Dockerfile b/.devops/llama-server-cuda.Dockerfile
similarity index 66%
rename from .devops/server-cuda.Dockerfile
rename to .devops/llama-server-cuda.Dockerfile
index 59a52ba21a3f1..67328cf1c1788 100644
--- a/.devops/server-cuda.Dockerfile
+++ b/.devops/llama-server-cuda.Dockerfile
@@ -6,7 +6,7 @@ ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VER
 # Target the CUDA runtime image
 ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} as build
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
@@ -21,17 +21,19 @@ COPY . .
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
 # Enable CUDA
-ENV LLAMA_CUDA=1
+ENV GGML_CUDA=1
 # Enable cURL
 ENV LLAMA_CURL=1
 
-RUN make
+RUN make -j$(nproc) llama-server
 
-FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
+FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
 
 RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
+    apt-get install -y libcurl4-openssl-dev libgomp1 curl
 
-COPY --from=build /app/server /server
+COPY --from=build /app/llama-server /llama-server
 
-ENTRYPOINT [ "/server" ]
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]
diff --git a/.devops/llama-server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile
new file mode 100644
index 0000000000000..f525658dddfe5
--- /dev/null
+++ b/.devops/llama-server-intel.Dockerfile
@@ -0,0 +1,32 @@
+ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
+
+ARG GGML_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
+        echo "GGML_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
+    fi && \
+    echo "Building with dynamic libs" && \
+    cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
+    cmake --build build --config Release --target llama-server
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev curl
+
+COPY --from=build /app/build/bin/llama-server /llama-server
+
+ENV LC_ALL=C.utf8
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]
diff --git a/.devops/server-rocm.Dockerfile b/.devops/llama-server-rocm.Dockerfile
similarity index 79%
rename from .devops/server-rocm.Dockerfile
rename to .devops/llama-server-rocm.Dockerfile
index c02a31dd8c756..763b4cd3f1c2e 100644
--- a/.devops/server-rocm.Dockerfile
+++ b/.devops/llama-server-rocm.Dockerfile
@@ -6,7 +6,7 @@ ARG ROCM_VERSION=5.6
 # Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
-FROM ${BASE_ROCM_DEV_CONTAINER} as build
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 # List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
@@ -36,15 +36,17 @@ COPY . .
 # Set nvcc architecture
 ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
 # Enable ROCm
-ENV LLAMA_HIPBLAS=1
+ENV GGML_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
 # Enable cURL
 ENV LLAMA_CURL=1
 RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
+    apt-get install -y libcurl4-openssl-dev curl
 
-RUN make
+RUN make -j$(nproc) llama-server
 
-ENTRYPOINT [ "/app/server" ]
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]
diff --git a/.devops/server-vulkan.Dockerfile b/.devops/llama-server-vulkan.Dockerfile
similarity index 53%
rename from .devops/server-vulkan.Dockerfile
rename to .devops/llama-server-vulkan.Dockerfile
index 6e757e171efee..13a61ffd8454b 100644
--- a/.devops/server-vulkan.Dockerfile
+++ b/.devops/llama-server-vulkan.Dockerfile
@@ -1,31 +1,29 @@
 ARG UBUNTU_VERSION=jammy
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget
 
-# Install Vulkan SDK
+# Install Vulkan SDK and cURL
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
     wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
     apt update -y && \
-    apt-get install -y vulkan-sdk
-
-# Install cURL
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
+    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
 
 # Build it
 WORKDIR /app
 COPY . .
-RUN cmake -B build -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
-    cmake --build build --config Release --target server
+RUN cmake -B build -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
+    cmake --build build --config Release --target llama-server
 
 # Clean up
 WORKDIR /
-RUN cp /app/build/bin/server /server && \
+RUN cp /app/build/bin/llama-server /llama-server && \
     rm -rf /app
 
 ENV LC_ALL=C.utf8
 
-ENTRYPOINT [ "/server" ]
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]
diff --git a/.devops/llama-server.Dockerfile b/.devops/llama-server.Dockerfile
new file mode 100644
index 0000000000000..ff558604ebde2
--- /dev/null
+++ b/.devops/llama-server.Dockerfile
@@ -0,0 +1,27 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+ENV LLAMA_CURL=1
+
+RUN make -j$(nproc) llama-server
+
+FROM ubuntu:$UBUNTU_VERSION AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev libgomp1 curl
+
+COPY --from=build /app/llama-server /llama-server
+
+ENV LC_ALL=C.utf8
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]
diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile
deleted file mode 100644
index 274b91b71bfba..0000000000000
--- a/.devops/main-intel.Dockerfile
+++ /dev/null
@@ -1,26 +0,0 @@
-ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
-
-ARG LLAMA_SYCL_F16=OFF
-RUN apt-get update && \
-    apt-get install -y git
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
-        echo "LLAMA_SYCL_F16 is set" && \
-        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
-    fi && \
-    cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
-    cmake --build build --config Release --target main
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
-
-COPY --from=build /app/build/bin/main /main
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/main" ]
diff --git a/.devops/main.Dockerfile b/.devops/main.Dockerfile
deleted file mode 100644
index 3ab1decd6c2b5..0000000000000
--- a/.devops/main.Dockerfile
+++ /dev/null
@@ -1,20 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION as build
-
-RUN apt-get update && \
-    apt-get install -y build-essential git
-
-WORKDIR /app
-
-COPY . .
-
-RUN make
-
-FROM ubuntu:$UBUNTU_VERSION as runtime
-
-COPY --from=build /app/main /main
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/main" ]
diff --git a/.devops/nix/apps.nix b/.devops/nix/apps.nix
index b8a12cc0a0463..0ecf19fc56d55 100644
--- a/.devops/nix/apps.nix
+++ b/.devops/nix/apps.nix
@@ -6,11 +6,10 @@
         let
           inherit (config.packages) default;
           binaries = [
-            "llama"
+            "llama-cli"
             "llama-embedding"
             "llama-server"
-            "quantize"
-            "train-text-from-scratch"
+            "llama-quantize"
           ];
           mkApp = name: {
             type = "app";
diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix
index e8d5b0bd92d2c..a87423c713079 100644
--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -17,19 +17,19 @@
   rocmPackages,
   vulkan-headers,
   vulkan-loader,
-  clblast,
+  curl,
+  shaderc,
   useBlas ? builtins.all (x: !x) [
     useCuda
     useMetalKit
-    useOpenCL
     useRocm
     useVulkan
   ] && blas.meta.available,
   useCuda ? config.cudaSupport,
-  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
+  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin,
   useMpi ? false, # Increases the runtime closure size by ~700M
-  useOpenCL ? false,
   useRocm ? config.rocmSupport,
+  enableCurl ? true,
   useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
 
@@ -56,7 +56,6 @@ let
     ++ lib.optionals useCuda [ "CUDA" ]
     ++ lib.optionals useMetalKit [ "MetalKit" ]
     ++ lib.optionals useMpi [ "MPI" ]
-    ++ lib.optionals useOpenCL [ "OpenCL" ]
     ++ lib.optionals useRocm [ "ROCm" ]
     ++ lib.optionals useVulkan [ "Vulkan" ];
 
@@ -91,6 +90,22 @@ let
       ps.tiktoken
       ps.torchWithoutCuda
       ps.transformers
+
+      # server bench
+      ps.matplotlib
+
+      # server tests
+      ps.openai
+      ps.behave
+      ps.prometheus-client
+
+      # for examples/pydantic-models-to-grammar-examples.py
+      ps.docstring-parser
+      ps.pydantic
+
+      # for scripts/compare-llama-bench.py
+      ps.gitpython
+      ps.tabulate
     ]
   );
 
@@ -111,16 +126,9 @@ let
     ++ optionals useMetalKit [ MetalKit ];
 
   cudaBuildInputs = with cudaPackages; [
-    cuda_cccl.dev # <nv/target>
-
-    # A temporary hack for reducing the closure size, remove once cudaPackages
-    # have stopped using lndir: https://github.com/NixOS/nixpkgs/issues/271792
-    cuda_cudart.dev
-    cuda_cudart.lib
-    cuda_cudart.static
-    libcublas.dev
-    libcublas.lib
-    libcublas.static
+    cuda_cudart
+    cuda_cccl # <nv/target>
+    libcublas
   ];
 
   rocmBuildInputs = with rocmPackages; [
@@ -132,6 +140,7 @@ let
   vulkanBuildInputs = [
     vulkan-headers
     vulkan-loader
+    shaderc
   ];
 in
 
@@ -160,9 +169,9 @@ effectiveStdenv.mkDerivation (
     };
 
     postPatch = ''
-      substituteInPlace ./ggml-metal.m \
+      substituteInPlace ./ggml/src/ggml-metal.m \
         --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
-      substituteInPlace ./ggml-metal.m \
+      substituteInPlace ./ggml/src/ggml-metal.m \
         --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
     '';
 
@@ -198,24 +207,24 @@ effectiveStdenv.mkDerivation (
       optionals effectiveStdenv.isDarwin darwinBuildInputs
       ++ optionals useCuda cudaBuildInputs
       ++ optionals useMpi [ mpi ]
-      ++ optionals useOpenCL [ clblast ]
       ++ optionals useRocm rocmBuildInputs
       ++ optionals useBlas [ blas ]
-      ++ optionals useVulkan vulkanBuildInputs;
+      ++ optionals useVulkan vulkanBuildInputs
+      ++ optionals enableCurl [ curl ];
 
     cmakeFlags =
       [
-        (cmakeBool "LLAMA_NATIVE" false)
         (cmakeBool "LLAMA_BUILD_SERVER" true)
         (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
         (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
-        (cmakeBool "LLAMA_BLAS" useBlas)
-        (cmakeBool "LLAMA_CLBLAST" useOpenCL)
-        (cmakeBool "LLAMA_CUDA" useCuda)
-        (cmakeBool "LLAMA_HIPBLAS" useRocm)
-        (cmakeBool "LLAMA_METAL" useMetalKit)
-        (cmakeBool "LLAMA_VULKAN" useVulkan)
-        (cmakeBool "LLAMA_STATIC" enableStatic)
+        (cmakeBool "LLAMA_CURL" enableCurl)
+        (cmakeBool "GGML_NATIVE" false)
+        (cmakeBool "GGML_BLAS" useBlas)
+        (cmakeBool "GGML_CUDA" useCuda)
+        (cmakeBool "GGML_HIPBLAS" useRocm)
+        (cmakeBool "GGML_METAL" useMetalKit)
+        (cmakeBool "GGML_VULKAN" useVulkan)
+        (cmakeBool "GGML_STATIC" enableStatic)
       ]
       ++ optionals useCuda [
         (
@@ -231,7 +240,7 @@ effectiveStdenv.mkDerivation (
       ]
       ++ optionals useMetalKit [
         (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
-        (cmakeBool "LLAMA_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
+        (cmakeBool "GGML_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
       ];
 
     # Environment variables needed for ROCm
@@ -243,10 +252,8 @@ effectiveStdenv.mkDerivation (
     # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
     # if they haven't been added yet.
     postInstall = ''
-      mv $out/bin/main${executableSuffix} $out/bin/llama${executableSuffix}
-      mv $out/bin/server${executableSuffix} $out/bin/llama-server${executableSuffix}
       mkdir -p $out/include
-      cp $src/llama.h $out/include/
+      cp $src/include/llama.h $out/include/
     '';
 
     # Define the shells here, but don't add in the inputsFrom to avoid recursion.
@@ -256,7 +263,6 @@ effectiveStdenv.mkDerivation (
         useCuda
         useMetalKit
         useMpi
-        useOpenCL
         useRocm
         useVulkan
         ;
@@ -283,7 +289,7 @@ effectiveStdenv.mkDerivation (
       # Configurations we don't want even the CI to evaluate. Results in the
       # "unsupported platform" messages. This is mostly a no-op, because
       # cudaPackages would've refused to evaluate anyway.
-      badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin;
+      badPlatforms = optionals useCuda lib.platforms.darwin;
 
       # Configurations that are known to result in build failures. Can be
       # overridden by importing Nixpkgs with `allowBroken = true`.
@@ -294,7 +300,7 @@ effectiveStdenv.mkDerivation (
       license = lib.licenses.mit;
 
       # Accommodates `nix run` and `lib.getExe`
-      mainProgram = "llama";
+      mainProgram = "llama-cli";
 
       # These people might respond, on the best effort basis, if you ping them
       # in case of Nix-specific regressions or for reviewing Nix-specific PRs.
diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile
deleted file mode 100644
index a8e451fa917ca..0000000000000
--- a/.devops/server-intel.Dockerfile
+++ /dev/null
@@ -1,29 +0,0 @@
-ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
-
-ARG LLAMA_SYCL_F16=OFF
-RUN apt-get update && \
-    apt-get install -y git libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
-        echo "LLAMA_SYCL_F16 is set" && \
-        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
-    fi && \
-    cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
-    cmake --build build --config Release --target server
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
-
-COPY --from=build /app/build/bin/server /server
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/server" ]
diff --git a/.devops/server.Dockerfile b/.devops/server.Dockerfile
deleted file mode 100644
index be964e0e83648..0000000000000
--- a/.devops/server.Dockerfile
+++ /dev/null
@@ -1,25 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION as build
-
-RUN apt-get update && \
-    apt-get install -y build-essential git libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-ENV LLAMA_CURL=1
-
-RUN make
-
-FROM ubuntu:$UBUNTU_VERSION as runtime
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
-
-COPY --from=build /app/server /server
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/server" ]
diff --git a/.devops/tools.sh b/.devops/tools.sh
index 3a7d274e46619..24dcfd35079cb 100755
--- a/.devops/tools.sh
+++ b/.devops/tools.sh
@@ -8,13 +8,11 @@ arg1="$1"
 shift
 
 if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    python3 ./convert.py "$@"
+    python3 ./convert_hf_to_gguf.py "$@"
 elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
-    ./quantize "$@"
+    ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
-    ./main "$@"
-elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then
-    ./finetune "$@"
+    ./llama-cli "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
     for i in `ls $1/$2/ggml-model-f16.bin*`; do
@@ -22,11 +20,11 @@ elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
             echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
         else
             echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
-            ./quantize "$i" "${i/f16/q4_0}" q4_0
+            ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
         fi
     done
 elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
-    ./server "$@"
+    ./llama-server "$@"
 else
     echo "Unknown command: $arg1"
     echo "Available commands: "
@@ -36,8 +34,6 @@ else
     echo "              ex: --outtype f16 \"/models/7B/\" "
     echo "  --quantize (-q): Optimize with quantization process ggml"
     echo "              ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
-    echo "  --finetune (-f): Run finetune command to create a lora finetune of the model"
-    echo "              See documentation for finetune for command-line parameters"
     echo "  --all-in-one (-a): Execute --convert & --quantize"
     echo "              ex: \"/models/\" 7B"
     echo "  --server (-s): Run a model on the server"
diff --git a/.dockerignore b/.dockerignore
index 633bbc3a971c1..8916e2a660732 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -12,8 +12,8 @@ build*/
 
 models/*
 
-/main
-/quantize
+/llama-cli
+/llama-quantize
 
 arm_neon.h
 compile_commands.json
diff --git a/.editorconfig b/.editorconfig
index 16d16b3b55bf5..f88f8da67cd78 100644
--- a/.editorconfig
+++ b/.editorconfig
@@ -26,3 +26,7 @@ indent_size = 2
 
 [examples/llama.swiftui/llama.swiftui.xcodeproj/*]
 indent_style = tab
+
+[examples/cvector-generator/*.txt]
+trim_trailing_whitespace = unset
+insert_final_newline = unset
diff --git a/.github/ISSUE_TEMPLATE/01-bug-low.yml b/.github/ISSUE_TEMPLATE/01-bug-low.yml
new file mode 100644
index 0000000000000..54785854f776e
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/01-bug-low.yml
@@ -0,0 +1,50 @@
+name: Low Severity Bugs
+description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches)
+title: "Bug: "
+labels: ["bug-unconfirmed", "low severity"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+        Please include information about your system, the steps to reproduce the bug,
+        and the version of llama.cpp that you are using.
+        If possible, please provide a minimal code example that reproduces the bug.
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: What happened?
+      description: Also tell us, what did you expect to happen?
+      placeholder: Tell us what you see!
+    validations:
+      required: true
+  - type: textarea
+    id: version
+    attributes:
+      label: Name and Version
+      description: Which executable and which version of our software are you running? (use `--version` to get a version string)
+      placeholder: |
+        $./llama-cli --version
+        version: 2999 (42b4109e)
+        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
+    validations:
+      required: true
+  - type: dropdown
+    id: operating-system
+    attributes:
+      label: What operating system are you seeing the problem on?
+      multiple: true
+      options:
+        - Linux
+        - Mac
+        - Windows
+        - BSD
+        - Other? (Please let us know in description)
+    validations:
+      required: false
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant log output
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      render: shell
diff --git a/.github/ISSUE_TEMPLATE/02-bug-medium.yml b/.github/ISSUE_TEMPLATE/02-bug-medium.yml
new file mode 100644
index 0000000000000..a6285c6f05bac
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/02-bug-medium.yml
@@ -0,0 +1,50 @@
+name: Medium Severity Bug
+description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable)
+title: "Bug: "
+labels: ["bug-unconfirmed", "medium severity"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+        Please include information about your system, the steps to reproduce the bug,
+        and the version of llama.cpp that you are using.
+        If possible, please provide a minimal code example that reproduces the bug.
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: What happened?
+      description: Also tell us, what did you expect to happen?
+      placeholder: Tell us what you see!
+    validations:
+      required: true
+  - type: textarea
+    id: version
+    attributes:
+      label: Name and Version
+      description: Which executable and which version of our software are you running? (use `--version` to get a version string)
+      placeholder: |
+        $./llama-cli --version
+        version: 2999 (42b4109e)
+        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
+    validations:
+      required: true
+  - type: dropdown
+    id: operating-system
+    attributes:
+      label: What operating system are you seeing the problem on?
+      multiple: true
+      options:
+        - Linux
+        - Mac
+        - Windows
+        - BSD
+        - Other? (Please let us know in description)
+    validations:
+      required: false
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant log output
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      render: shell
diff --git a/.github/ISSUE_TEMPLATE/03-bug-high.yml b/.github/ISSUE_TEMPLATE/03-bug-high.yml
new file mode 100644
index 0000000000000..ff816b93769c3
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/03-bug-high.yml
@@ -0,0 +1,50 @@
+name: High Severity Bug
+description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow)
+title: "Bug: "
+labels: ["bug-unconfirmed", "high severity"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+        Please include information about your system, the steps to reproduce the bug,
+        and the version of llama.cpp that you are using.
+        If possible, please provide a minimal code example that reproduces the bug.
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: What happened?
+      description: Also tell us, what did you expect to happen?
+      placeholder: Tell us what you see!
+    validations:
+      required: true
+  - type: textarea
+    id: version
+    attributes:
+      label: Name and Version
+      description: Which executable and which version of our software are you running? (use `--version` to get a version string)
+      placeholder: |
+        $./llama-cli --version
+        version: 2999 (42b4109e)
+        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
+    validations:
+      required: true
+  - type: dropdown
+    id: operating-system
+    attributes:
+      label: What operating system are you seeing the problem on?
+      multiple: true
+      options:
+        - Linux
+        - Mac
+        - Windows
+        - BSD
+        - Other? (Please let us know in description)
+    validations:
+      required: false
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant log output
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      render: shell
diff --git a/.github/ISSUE_TEMPLATE/04-bug-critical.yml b/.github/ISSUE_TEMPLATE/04-bug-critical.yml
new file mode 100644
index 0000000000000..7af42a80b3b93
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/04-bug-critical.yml
@@ -0,0 +1,50 @@
+name: Critical Severity Bug
+description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss)
+title: "Bug: "
+labels: ["bug-unconfirmed", "critical severity"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+        Please include information about your system, the steps to reproduce the bug,
+        and the version of llama.cpp that you are using.
+        If possible, please provide a minimal code example that reproduces the bug.
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: What happened?
+      description: Also tell us, what did you expect to happen?
+      placeholder: Tell us what you see!
+    validations:
+      required: true
+  - type: textarea
+    id: version
+    attributes:
+      label: Name and Version
+      description: Which executable and which version of our software are you running? (use `--version` to get a version string)
+      placeholder: |
+        $./llama-cli --version
+        version: 2999 (42b4109e)
+        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
+    validations:
+      required: true
+  - type: dropdown
+    id: operating-system
+    attributes:
+      label: What operating system are you seeing the problem on?
+      multiple: true
+      options:
+        - Linux
+        - Mac
+        - Windows
+        - BSD
+        - Other? (Please let us know in description)
+    validations:
+      required: false
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant log output
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      render: shell
diff --git a/.github/ISSUE_TEMPLATE/05-enhancement.yml b/.github/ISSUE_TEMPLATE/05-enhancement.yml
new file mode 100644
index 0000000000000..58fca73183d41
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/05-enhancement.yml
@@ -0,0 +1,51 @@
+name: Enhancement
+description: Used to request enhancements for llama.cpp
+title: "Feature Request: "
+labels: ["enhancement"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas)
+
+  - type: checkboxes
+    id: prerequisites
+    attributes:
+      label: Prerequisites
+      description: Please confirm the following before submitting your enhancement request.
+      options:
+        - label: I am running the latest code. Mention the version if possible as well.
+          required: true
+        - label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
+          required: true
+        - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
+          required: true
+        - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share.
+          required: true
+
+  - type: textarea
+    id: feature-description
+    attributes:
+      label: Feature Description
+      description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
+      placeholder: Detailed description of the enhancement
+    validations:
+      required: true
+
+  - type: textarea
+    id: motivation
+    attributes:
+      label: Motivation
+      description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
+      placeholder: Explanation of why this feature is needed and its benefits
+    validations:
+      required: true
+
+  - type: textarea
+    id: possible-implementation
+    attributes:
+      label: Possible Implementation
+      description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.
+      placeholder: Detailed description of potential implementation
+    validations:
+      required: false
diff --git a/.github/ISSUE_TEMPLATE/06-research.yml b/.github/ISSUE_TEMPLATE/06-research.yml
new file mode 100644
index 0000000000000..3ae4e9f8caaa4
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/06-research.yml
@@ -0,0 +1,52 @@
+name: Research
+description: Track new technical research area
+title: "Research: "
+labels: ["research 🔬"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22)
+
+  - type: checkboxes
+    id: research-stage
+    attributes:
+      label: Research Stage
+      description: Track general state of this research ticket
+      options:
+        - label: Background Research (Let's try to avoid reinventing the wheel)
+        - label: Hypothesis Formed (How do you think this will work and it's effect?)
+        - label: Strategy / Implementation Forming
+        - label: Analysis of results
+        - label: Debrief / Documentation (So people in the future can learn from us)
+
+  - type: textarea
+    id: background
+    attributes:
+      label: Previous existing literature and research
+      description: Whats the current state of the art and whats the motivation for this research?
+
+  - type: textarea
+    id: hypothesis
+    attributes:
+      label: Hypothesis
+      description: How do you think this will work and it's effect?
+
+  - type: textarea
+    id: implementation
+    attributes:
+      label: Implementation
+      description: Got an approach? e.g. a PR ready to go?
+
+  - type: textarea
+    id: analysis
+    attributes:
+      label: Analysis
+      description: How does the proposed implementation behave?
+
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant log output
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      render: shell
diff --git a/.github/ISSUE_TEMPLATE/07-refactor.yml b/.github/ISSUE_TEMPLATE/07-refactor.yml
new file mode 100644
index 0000000000000..3a68d3d5355d6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/07-refactor.yml
@@ -0,0 +1,28 @@
+name: Refactor (Maintainers)
+description: Used to track refactoring opportunities
+title: "Refactor: "
+labels: ["refactor"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered.
+        Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too.
+
+  - type: textarea
+    id: background-description
+    attributes:
+      label: Background Description
+      description: Please provide a detailed written description of the pain points you are trying to solve.
+      placeholder: Detailed description behind your motivation to request refactor
+    validations:
+      required: true
+
+  - type: textarea
+    id: possible-approaches
+    attributes:
+      label: Possible Refactor Approaches
+      description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list.
+      placeholder: Your idea of possible refactoring opportunity/approaches
+    validations:
+      required: false
diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md
deleted file mode 100644
index 49812832ca542..0000000000000
--- a/.github/ISSUE_TEMPLATE/bug.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-name: Bug template
-about: Used to report bugs in llama.cpp
-labels: ["bug-unconfirmed"]
-assignees: ''
-
----
-
-Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug.
-
-If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests).
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000000000..eb8c4b472df4c
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,11 @@
+blank_issues_enabled: true
+contact_links:
+  - name: Got an idea?
+    url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas
+    about: Pop it there. It may then become an enhancement ticket.
+  - name: Got a question?
+    url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a
+    about: Ask a question there!
+  - name: Want to contribute?
+    url: https://github.com/ggerganov/llama.cpp/wiki/contribute
+    about: Head to the contribution guide page of the wiki for areas you can help with
diff --git a/.github/ISSUE_TEMPLATE/enhancement.md b/.github/ISSUE_TEMPLATE/enhancement.md
deleted file mode 100644
index dcffda7500f52..0000000000000
--- a/.github/ISSUE_TEMPLATE/enhancement.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-name: Enhancement template
-about: Used to request enhancements for llama.cpp
-labels: ["enhancement"]
-assignees: ''
-
----
-
-# Prerequisites
-
-Please answer the following questions for yourself before submitting an issue.
-
-- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
-- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
-- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
-- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
-
-# Feature Description
-
-Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
-
-# Motivation
-
-Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
-
-# Possible Implementation
-
-If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.
diff --git a/.github/labeler.yml b/.github/labeler.yml
index fca60594f148f..89436740d1ffb 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -1,20 +1,34 @@
 # https://github.com/actions/labeler
-
+Kompute:
+    - changed-files:
+        - any-glob-to-any-file:
+            - ggml/include/ggml-kompute.h
+            - ggml/src/ggml-kompute.cpp
+            - README-kompute.md
+Apple Metal:
+    - changed-files:
+        - any-glob-to-any-file:
+            - ggml/include/ggml-metal.h
+            - ggml/src/ggml-metal.cpp
+            - README-metal.md
 SYCL:
     - changed-files:
         - any-glob-to-any-file:
-            - ggml-sycl.h
-            - ggml-sycl.cpp
-            - README-sycl.md
+            - ggml/include/ggml-sycl.h
+            - ggml/src/ggml-sycl.cpp
+            - ggml/src/ggml-sycl/**
+            - docs/backend/SYCL.md
+            - examples/sycl/**
 Nvidia GPU:
     - changed-files:
         - any-glob-to-any-file:
-            - ggml-cuda/**
+            - ggml/include/ggml-cuda.h
+            - ggml/src/ggml-cuda/**
 Vulkan:
     - changed-files:
         - any-glob-to-any-file:
-            - ggml_vk_generate_shaders.py
-            - ggml-vulkan*
+            - ggml/ggml_vk_generate_shaders.py
+            - ggml/src/ggml-vulkan*
 documentation:
     - changed-files:
         - any-glob-to-any-file:
@@ -30,7 +44,6 @@ build:
             - cmake/**
             - CMakeLists.txt
             - CMakePresets.json
-            - codecov.yml
 examples:
     - changed-files:
         - any-glob-to-any-file: examples/**
@@ -62,8 +75,10 @@ server:
 ggml:
     - changed-files:
         - any-glob-to-any-file:
-            - ggml-*.c
-            - ggml-*.h
+            - ggml/include/ggml*.h
+            - ggml/src/ggml*.c
+            - ggml/src/ggml*.cpp
+            - ggml/src/ggml*.h
             - ggml-cuda/**
 nix:
     - changed-files:
@@ -71,3 +86,6 @@ nix:
             - "**/*.nix"
             - .github/workflows/nix-*.yml
             - .devops/nix/nixpkgs-instances.nix
+embedding:
+    - changed-files:
+        - any-glob-to-any-file: examples/embedding/
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 0000000000000..997c6d9d05397
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,7 @@
+
+
+- [x] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md)
+- Self-reported review complexity:
+  - [ ] Low
+  - [ ] Medium
+  - [ ] High
diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml.disabled
similarity index 97%
rename from .github/workflows/bench.yml
rename to .github/workflows/bench.yml.disabled
index de0d994c80e9d..bfdbb4ef5e385 100644
--- a/.github/workflows/bench.yml
+++ b/.github/workflows/bench.yml.disabled
@@ -1,3 +1,6 @@
+# TODO: there have been some issues with the workflow, so disabling for now
+#       https://github.com/ggerganov/llama.cpp/issues/7893
+#
 # Benchmark
 name: Benchmark
 
@@ -109,7 +112,7 @@ jobs:
         run: |
           set -eux
           cmake -B build \
-              -DLLAMA_NATIVE=OFF \
+              -DGGML_NATIVE=OFF \
               -DLLAMA_BUILD_SERVER=ON \
               -DLLAMA_CURL=ON \
               -DLLAMA_CUBLAS=ON \
@@ -119,7 +122,7 @@ jobs:
               -DLLAMA_FATAL_WARNINGS=OFF \
               -DLLAMA_ALL_WARNINGS=OFF \
               -DCMAKE_BUILD_TYPE=Release;
-          cmake --build build --config Release -j $(nproc) --target server
+          cmake --build build --config Release -j $(nproc) --target llama-server
 
       - name: Download the dataset
         id: download_dataset
@@ -129,6 +132,8 @@ jobs:
 
       - name: Server bench
         id: server_bench
+        env:
+            HEAD_REF: ${{ github.head_ref || github.ref_name }}
         run: |
           set -eux
 
@@ -137,7 +142,7 @@ jobs:
           python bench.py \
               --runner-label ${{ env.RUNNER_LABEL }} \
               --name ${{ github.job }} \
-              --branch ${{ github.head_ref || github.ref_name }} \
+              --branch $HEAD_REF \
               --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
               --scenario script.js \
               --duration ${{ github.event.inputs.duration || env.DURATION }} \
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 7b616281b6f6f..74b5d4f69d790 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -10,10 +10,10 @@ on:
   push:
     branches:
       - master
-    paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
+    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
+    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@@ -47,7 +47,7 @@ jobs:
           sysctl -a
           mkdir build
           cd build
-          cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON ..
+          cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF ..
           cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
@@ -84,7 +84,7 @@ jobs:
           name: llama-bin-macos-arm64.zip
 
   macOS-latest-cmake-x64:
-    runs-on: macos-latest
+    runs-on: macos-12
 
     steps:
       - name: Clone
@@ -103,12 +103,10 @@ jobs:
         id: cmake_build
         run: |
           sysctl -a
-          mkdir build
-          cd build
           # Metal is disabled due to intermittent failures with Github runners not having a GPU:
           # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313
-          cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF -DLLAMA_CURL=ON ..
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+          cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
         id: cmake_test
@@ -224,7 +222,7 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON
+          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF
           cmake --build . --config Release -j $(nproc)
 
       - name: Test
@@ -241,8 +239,8 @@ jobs:
           wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin
           echo "Fetch llama2c model"
           wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin
-          ./bin/convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
-          ./bin/main -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
+          ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
+          ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
 
       - name: Determine tag name
         id: tag
@@ -294,12 +292,22 @@ jobs:
 
       - name: Build
         id: cmake_build
+        if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           mkdir build
           cd build
           cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
           cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
 
+      - name: Build (no OpenMP)
+        id: cmake_build_no_openmp
+        if: ${{ matrix.sanitizer == 'THREAD' }}
+        run: |
+          mkdir build
+          cd build
+          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DGGML_OPENMP=OFF
+          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
+
       - name: Test
         id: cmake_test
         run: |
@@ -327,7 +335,7 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake -DLLAMA_RPC=ON ..
+          cmake -DGGML_RPC=ON ..
           cmake --build . --config Release -j $(nproc)
 
       - name: Test
@@ -347,15 +355,17 @@ jobs:
       - name: Dependencies
         id: depends
         run: |
-          sudo apt-get update
-          sudo apt-get install build-essential libvulkan-dev
+          wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
+          sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+          sudo apt-get update -y
+          sudo apt-get install -y build-essential vulkan-sdk
 
       - name: Build
         id: cmake_build
         run: |
           mkdir build
           cd build
-          cmake -DLLAMA_VULKAN=ON ..
+          cmake -DGGML_VULKAN=ON ..
           cmake --build . --config Release -j $(nproc)
 
   ubuntu-22-cmake-hip:
@@ -376,13 +386,13 @@ jobs:
       - name: Build with native CMake HIP support
         id: cmake_build
         run: |
-          cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DLLAMA_HIPBLAS=ON
+          cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIPBLAS=ON
           cmake --build build --config Release -j $(nproc)
 
       - name: Build with legacy HIP support
         id: cmake_build_legacy_hip
         run: |
-          cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DLLAMA_HIPBLAS=ON
+          cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIPBLAS=ON
           cmake --build build2 --config Release -j $(nproc)
 
   ubuntu-22-cmake-sycl:
@@ -423,7 +433,7 @@ jobs:
           source /opt/intel/oneapi/setvars.sh
           mkdir build
           cd build
-          cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
+          cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
           cmake --build . --config Release -j $(nproc)
 
   ubuntu-22-cmake-sycl-fp16:
@@ -464,10 +474,10 @@ jobs:
           source /opt/intel/oneapi/setvars.sh
           mkdir build
           cd build
-          cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON ..
+          cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON ..
           cmake --build . --config Release -j $(nproc)
 
-  # TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
+  # TODO: build with GGML_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
   #       how to debug it.
   #       ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124
   macOS-latest-make:
@@ -489,15 +499,15 @@ jobs:
         env:
             LLAMA_FATAL_WARNINGS: 1
         run: |
-          LLAMA_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
+          GGML_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
         id: make_test
         run: |
-          LLAMA_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu)
-          LLAMA_NO_METAL=1 make test  -j $(sysctl -n hw.logicalcpu)
+          GGML_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu)
+          GGML_NO_METAL=1 make test  -j $(sysctl -n hw.logicalcpu)
 
-  # TODO: build with LLAMA_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
+  # TODO: build with GGML_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
   #       how to debug it.
   #       ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584
   #       would be great if we fix these
@@ -521,7 +531,7 @@ jobs:
           sysctl -a
           mkdir build
           cd build
-          cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF ..
+          cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF ..
           cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
@@ -551,13 +561,14 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
-            -DLLAMA_METAL_EMBED_LIBRARY=ON \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
             -DCMAKE_SYSTEM_NAME=iOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
 
   macOS-latest-cmake-tvos:
     runs-on: macos-latest
@@ -580,13 +591,14 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
-            -DLLAMA_METAL_EMBED_LIBRARY=ON \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
             -DCMAKE_SYSTEM_NAME=tvOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0
-          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
 
   macOS-latest-swift:
     runs-on: macos-latest
@@ -654,7 +666,7 @@ jobs:
       - name: Build using make w/ OpenBLAS
         shell: msys2 {0}
         run: |
-            make LLAMA_OPENBLAS=1 -j $(nproc)
+            make GGML_OPENBLAS=1 -j $(nproc)
 
       - name: Build using CMake
         shell: msys2 {0}
@@ -670,44 +682,38 @@ jobs:
       - name: Build using CMake w/ OpenBLAS
         shell: msys2 {0}
         run: |
-            cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
+            cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
             cmake --build build --config ${{ matrix.build }} -j $(nproc)
 
   windows-latest-cmake:
-    runs-on: windows-latest
+    runs-on: windows-2019
 
     env:
       OPENBLAS_VERSION: 0.3.23
-      OPENCL_VERSION: 2023.04.17
-      CLBLAST_VERSION: 1.6.0
       SDE_VERSION: 9.33.0-2024-01-07
       VULKAN_VERSION: 1.3.261.1
 
     strategy:
       matrix:
         include:
-          - build: 'rpc-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_RPC=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'noavx-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DBUILD_SHARED_LIBS=ON'
           - build: 'avx2-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'avx-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF -DBUILD_SHARED_LIBS=ON'
           - build: 'avx512-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'
-          - build: 'clblast-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'openblas-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DBUILD_SHARED_LIBS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
           - build: 'kompute-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'vulkan-x64'
-            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'llvm-arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'msvc-arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
 
     steps:
       - name: Clone
@@ -720,28 +726,7 @@ jobs:
         id: clone_kompute
         if: ${{ matrix.build == 'kompute-x64' }}
         run: |
-          git submodule update --init kompute
-
-      - name: Download OpenCL SDK
-        id: get_opencl
-        if: ${{ matrix.build == 'clblast-x64' }}
-        run: |
-          curl.exe -o $env:RUNNER_TEMP/opencl.zip -L "https://github.com/KhronosGroup/OpenCL-SDK/releases/download/v${env:OPENCL_VERSION}/OpenCL-SDK-v${env:OPENCL_VERSION}-Win-x64.zip"
-          mkdir $env:RUNNER_TEMP/opencl
-          tar.exe -xvf $env:RUNNER_TEMP/opencl.zip --strip-components=1 -C $env:RUNNER_TEMP/opencl
-
-      - name: Download CLBlast
-        id: get_clblast
-        if: ${{ matrix.build == 'clblast-x64' }}
-        run: |
-          curl.exe -o $env:RUNNER_TEMP/clblast.7z -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z"
-          curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L "https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE"
-          7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/clblast.7z
-          rename-item $env:RUNNER_TEMP/CLBlast-${env:CLBLAST_VERSION}-windows-x64 clblast
-          foreach ($f in (gci -Recurse -Path "$env:RUNNER_TEMP/clblast" -Filter '*.cmake')) {
-            $txt = Get-Content -Path $f -Raw
-            $txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8
-          }
+          git submodule update --init ggml/src/kompute
 
       - name: Download OpenBLAS
         id: get_openblas
@@ -776,13 +761,6 @@ jobs:
           cmake -S . -B build ${{ matrix.defines }}
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
 
-      - name: Add clblast.dll
-        id: add_clblast_dll
-        if: ${{ matrix.build == 'clblast-x64' }}
-        run: |
-          cp $env:RUNNER_TEMP/clblast/lib/clblast.dll ./build/bin/Release
-          cp $env:RUNNER_TEMP/CLBlast.LICENSE.txt ./build/bin/Release/CLBlast-${env:CLBLAST_VERSION}.txt
-
       - name: Add libopenblas.dll
         id: add_libopenblas_dll
         if: ${{ matrix.build == 'openblas-x64' }}
@@ -806,7 +784,7 @@ jobs:
       - name: Test
         id: cmake_test
         # not all machines have native AVX-512
-        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'clblast-x64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
+        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
         run: |
           cd build
           ctest -L main -C Release --verbose --timeout 900
@@ -821,6 +799,7 @@ jobs:
           7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar
           $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe)
           cd build
+          $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1
           & $sde -future -- ctest -L main -C Release --verbose --timeout 900
 
       - name: Determine tag name
@@ -851,7 +830,7 @@ jobs:
           name: llama-bin-win-${{ matrix.build }}.zip
 
   windows-latest-cmake-cuda:
-    runs-on: windows-latest
+    runs-on: windows-2019
 
     strategy:
       matrix:
@@ -865,8 +844,9 @@ jobs:
         with:
           fetch-depth: 0
 
-      - uses: Jimver/cuda-toolkit@v0.2.11
+      - name: Install CUDA toolkit
         id: cuda-toolkit
+        uses: Jimver/cuda-toolkit@v0.2.15
         with:
           cuda: ${{ matrix.cuda }}
           method: 'network'
@@ -877,7 +857,8 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUDA=ON -DBUILD_SHARED_LIBS=ON
+          cmake .. -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON
+          cmake --build . --config Release -j $((${env:NUMBER_OF_PROCESSORS} - 1)) -t ggml
           cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS}
 
       - name: Determine tag name
@@ -1010,7 +991,7 @@ jobs:
         run: |
           $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
           $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
-          cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DLLAMA_HIPBLAS=ON
+          cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIPBLAS=ON
           cmake --build build --config Release
 
   ios-xcode-build:
@@ -1061,7 +1042,7 @@ jobs:
 #        hypervisor: 'qemu'
 #        run: |
 #            sudo pkg update
-#            sudo pkg install -y gmake automake autoconf pkgconf llvm15 clinfo clover opencl clblast openblas
+#            sudo pkg install -y gmake automake autoconf pkgconf llvm15 openblas
 #            gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu`
 
   release:
diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml
deleted file mode 100644
index f12c558f81bae..0000000000000
--- a/.github/workflows/code-coverage.yml
+++ /dev/null
@@ -1,40 +0,0 @@
-name: Code Coverage
-on: [push, pull_request]
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  run:
-    runs-on: ubuntu-20.04
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential gcc-8 lcov
-
-      - name: Build
-        run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests
-
-      - name: Run tests
-        run: CC=gcc-8 make test
-
-      - name: Generate coverage report
-        run: |
-          make coverage
-          make lcov-report
-
-      - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v3
-        env:
-           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-        with:
-          files: lcov-report/coverage.info
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 9b03d19bc77c6..bf94b2024e814 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -10,10 +10,11 @@
 name: Publish Docker image
 
 on:
-  pull_request:
+  #pull_request:
   push:
     branches:
       - master
+    paths: ['.github/workflows/docker.yml', '.devops/*.Dockerfile', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@@ -22,7 +23,7 @@ concurrency:
 jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
-    if: github.event.pull_request.draft == false
+    #if: github.event.pull_request.draft == false
 
     runs-on: ubuntu-latest
     env:
@@ -30,20 +31,18 @@ jobs:
     strategy:
       matrix:
         config:
-          - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light", dockerfile: ".devops/llama-cli.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "server", dockerfile: ".devops/llama-server.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "server", dockerfile: ".devops/server.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I
-          #                     have disabled them for now until the reason why
-          #                     is understood.
-          - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" }
           - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "server-cuda", dockerfile: ".devops/server-cuda.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "server-rocm", dockerfile: ".devops/server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          # Note: the full-rocm image is failing due to a "no space left on device" error. It is disabled for now to allow the workflow to complete.
+          #- { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light-intel", dockerfile: ".devops/llama-cli-intel.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-intel", dockerfile: ".devops/llama-server-intel.Dockerfile", platforms: "linux/amd64" }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v4
diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml
index 4e0374fc63d95..46e80aecd0a0c 100644
--- a/.github/workflows/python-check-requirements.yml
+++ b/.github/workflows/python-check-requirements.yml
@@ -6,15 +6,13 @@ on:
       - '.github/workflows/python-check-requirements.yml'
       - 'scripts/check-requirements.sh'
       - 'convert*.py'
-      - 'requirements.txt'
-      - 'requirements/*.txt'
+      - '**/requirements*.txt'
   pull_request:
     paths:
       - '.github/workflows/python-check-requirements.yml'
       - 'scripts/check-requirements.sh'
       - 'convert*.py'
-      - 'requirements.txt'
-      - 'requirements/*.txt'
+      - '**/requirements*.txt'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
diff --git a/.github/workflows/python-type-check.yml b/.github/workflows/python-type-check.yml
new file mode 100644
index 0000000000000..e5ff5e6d792cb
--- /dev/null
+++ b/.github/workflows/python-type-check.yml
@@ -0,0 +1,38 @@
+name: Python Type-Check
+
+on:
+  push:
+    paths:
+      - '.github/workflows/python-type-check.yml'
+      - '**.py'
+      - '**/requirements*.txt'
+  pull_request:
+    paths:
+      - '.github/workflows/python-type-check.yml'
+      - '**.py'
+      - '**/requirements*.txt'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  python-type-check:
+    runs-on: ubuntu-latest
+    name: pyright type-check
+    steps:
+      - name: Check out source repository
+        uses: actions/checkout@v4
+      - name: Set up Python environment
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Install Python dependencies
+        # TODO: use a venv
+        run: pip install -r requirements/requirements-all.txt
+      - name: Type-check with Pyright
+        uses: jakebailey/pyright-action@v2
+        with:
+          version: 1.1.370
+          level: warning
+          warnings: true
diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index 217af67cfd420..99feb28f2a545 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -16,11 +16,9 @@ on:
     branches:
       - master
     paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
-  pull_request_target:
+  pull_request:
     types: [opened, synchronize, reopened]
     paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
-  schedule:
-    -  cron: '2 4 * * *'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
@@ -32,14 +30,11 @@ jobs:
 
     strategy:
       matrix:
-        sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug]
+        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
+        build_type: [RelWithDebInfo]
         include:
           - build_type: Release
             sanitizer: ""
-          - build_type: Debug
-            sanitizer: THREAD
-            disabled_on_pr: true
       fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
 
     steps:
@@ -92,21 +87,33 @@ jobs:
             exit 1
           fi
 
+      - name: Build (no OpenMP)
+        id: cmake_build_no_openmp
+        if: ${{ matrix.sanitizer == 'THREAD' }}
+        run: |
+          cmake -B build \
+              -DGGML_NATIVE=OFF \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DLLAMA_CURL=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+              -DGGML_OPENMP=OFF ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
       - name: Build
         id: cmake_build
+        if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           cmake -B build \
-              -DLLAMA_NATIVE=OFF \
+              -DGGML_NATIVE=OFF \
               -DLLAMA_BUILD_SERVER=ON \
               -DLLAMA_CURL=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target server
-
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
       - name: Tests
         id: server_integration_tests
-        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
         run: |
           cd examples/server/tests
           PORT=8888 ./tests.sh
@@ -120,7 +127,7 @@ jobs:
 
 
   server-windows:
-    runs-on: windows-latest
+    runs-on: windows-2019
 
     steps:
       - name: Clone
@@ -143,7 +150,7 @@ jobs:
         id: cmake_build
         run: |
           cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
-          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
+          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
 
       - name: Python setup
         id: setup_python
diff --git a/.github/workflows/zig-build.yml b/.github/workflows/zig-build.yml
deleted file mode 100644
index 747c35cc07a96..0000000000000
--- a/.github/workflows/zig-build.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-name: Zig CI
-
-on:
-  pull_request:
-  push:
-    branches:
-      - master
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  build:
-    strategy:
-      fail-fast: false
-      matrix:
-        runs-on: [ubuntu-latest, macos-latest, windows-latest]
-    runs-on: ${{ matrix.runs-on }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-          fetch-depth: 0
-      - uses: goto-bus-stop/setup-zig@v2
-        with:
-          version: 0.11.0
-      - name: Build Summary
-        run: zig build --summary all -freference-trace
diff --git a/.gitignore b/.gitignore
index 50ae0973ae3b3..5ae030200f897 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,126 +1,131 @@
-*.o
+# Extensions
+
 *.a
-*.so
-*.gguf
-*.gguf.json
+*.bat
 *.bin
-*.exe
 *.dll
-*.log
-*.gcov
-*.gcno
-*.gcda
 *.dot
-*.bat
-*.tmp
-*.metallib
 *.etag
+*.exe
+*.gcda
+*.gcno
+*.gcov
+*.gguf
+*.gguf.json
 *.lastModified
-.DS_Store
-.build/
+*.log
+*.metallib
+*.o
+*.so
+*.tmp
+
+# IDE / OS
+
 .cache/
 .ccls-cache/
 .direnv/
+.DS_Store
 .envrc
+.idea/
 .swiftpm
-.venv
-.clang-tidy
 .vs/
 .vscode/
-.idea/
+nppBackup
 
-ggml-metal-embed.metal
 
-lcov-report/
+# Coverage
+
 gcovr-report/
+lcov-report/
 
+# Build Artifacts
+
+tags
+.build/
 build*
+!build-info.cmake
+!build-info.cpp.in
+!build-info.sh
 !build.zig
+!docs/build.md
+/libllama.so
+/llama-*
+/vulkan-shaders-gen
+android-ndk-*
+arm_neon.h
 cmake-build-*
+CMakeSettings.json
+compile_commands.json
+ggml-metal-embed.metal
+llama-batched-swift
+/rpc-server
 out/
 tmp/
 
-models/*
-models-mnt
+# Deprecated
 
-/Pipfile
-/baby-llama
-/beam-search
-/benchmark-matmult
-/convert-llama2c-to-ggml
-/embd-input-test
-/embedding
-/eval-callback
-/gguf
-/gguf-llama-simple
-/gguf-split
-/gritlm
-/imatrix
-/infill
-/libllama.so
-/llama-bench
-/llava-cli
-/lookahead
-/lookup
-/lookup-create
-/lookup-merge
-/lookup-stats
 /main
-/metal
-/passkey
-/perplexity
-/q8dot
-/quantize
-/quantize-stats
-/result
-/save-load-state
 /server
-/simple
-/batched
-/batched-bench
-/export-lora
-/finetune
-/retrieval
-/speculative
-/parallel
-/train-text-from-scratch
-/tokenize
-/vdot
-/common/build-info.cpp
-arm_neon.h
-compile_commands.json
-CMakeSettings.json
 
-__pycache__
-dist
+# CI
+
+!.github/workflows/*.yml
+
+# Models
+
+models/*
+models-mnt
+!models/.editorconfig
+!models/ggml-vocab-*.gguf*
 
+# Zig
 zig-out/
 zig-cache/
 
+# Logs
+
 ppl-*.txt
 qnt-*.txt
 perf-*.txt
 
+# Examples
+
 examples/jeopardy/results.txt
+examples/server/*.css.hpp
 examples/server/*.html.hpp
 examples/server/*.js.hpp
 examples/server/*.mjs.hpp
+!build_64.sh
+!examples/*.bat
+!examples/*/*.kts
+!examples/*/*/*.kts
+!examples/sycl/*.bat
+!examples/sycl/*.sh
 
-poetry.lock
+# Python
+
+/.venv
+__pycache__/
+*/poetry.lock
 poetry.toml
-nppBackup
+
+# Nix
+/result
 
 # Test binaries
-/tests/test-grammar-parser
-/tests/test-llama-grammar
+/tests/test-backend-ops
 /tests/test-double-float
 /tests/test-grad0
+/tests/test-grammar-parser
+/tests/test-llama-grammar
 /tests/test-opt
 /tests/test-quantize-fns
 /tests/test-quantize-perf
+/tests/test-rope
 /tests/test-sampling
 /tests/test-tokenizer-0
-/tests/test-tokenizer-1-spm
 /tests/test-tokenizer-1-bpe
-/tests/test-rope
-/tests/test-backend-ops
+/tests/test-tokenizer-1-spm
+
+# Scripts
+!/scripts/install-oneapi.bat
diff --git a/.gitmodules b/.gitmodules
index b7e8b8ff2f64e..5861d59cb785d 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +1,3 @@
 [submodule "kompute"]
-	path = kompute
+	path = ggml/src/kompute
 	url = https://github.com/nomic-ai/kompute.git
diff --git a/AUTHORS b/AUTHORS
index b029f13da3b56..1bd36158a72f4 100644
--- a/AUTHORS
+++ b/AUTHORS
@@ -1,8 +1,9 @@
-# date: Tue Apr  9 09:17:14 EEST 2024
+# date: Wed Jun 26 19:36:34 EEST 2024
 # this file is auto-generated by scripts/gen-authors.sh
 
 0cc4m <picard12@live.de>
 0xspringtime <110655352+0xspringtime@users.noreply.github.com>
+20kdc <asdd2808@gmail.com>
 2f38b454 <dxf@protonmail.com>
 3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com>
 44670 <44670@users.noreply.github.com>
@@ -11,14 +12,18 @@ AT <manyoso@users.noreply.github.com>
 Aarni Koskela <akx@iki.fi>
 Aaron Miller <apage43@ninjawhale.com>
 Aaryaman Vasishta <aaryaman.vasishta@amd.com>
+Abheek Gulati <abheekg@hotmail.com>
 Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
 Abhishek Gopinath K <31348521+overtunned@users.noreply.github.com>
 Adithya Balaji <adithya.b94@gmail.com>
 AdithyanI <adithyan.i4internet@gmail.com>
 Adrian <smith.adriane@gmail.com>
 Adrian Hesketh <a-h@users.noreply.github.com>
+Ahmet Zeer <ahmed.zeer@std.yildiz.edu.tr>
 AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
 Aisuko <urakiny@gmail.com>
+Akarshan Biswas <akarshanbiswas@fedoraproject.org>
+Albert Jin <albert.jin@gmail.com>
 Alberto <57916483+albbus-stack@users.noreply.github.com>
 Alex <awhill19@icloud.com>
 Alex Azarov <alex@azarov.by>
@@ -35,19 +40,24 @@ Ali Nehzat <ali.nehzat@thanks.dev>
 Ali Tariq <ali.tariq@10xengineers.ai>
 Alon <alonfaraj@gmail.com>
 AlpinDale <52078762+AlpinDale@users.noreply.github.com>
+Amir <amir_zia@outlook.com>
 AmirAli Mirian <37371367+amiralimi@users.noreply.github.com>
 Ananta Bastola <anantarajbastola@gmail.com>
 Anas Ahouzi <112881240+aahouzi@users.noreply.github.com>
 András Salamon <ott2@users.noreply.github.com>
 Andrei <abetlen@gmail.com>
 Andrew Canis <andrew.canis@gmail.com>
+Andrew Downing <andrew2085@gmail.com>
 Andrew Duffy <a10y@users.noreply.github.com>
 Andrew Godfrey <AndrewGodfrey@users.noreply.github.com>
+Andy Tai <andy-tai@users.noreply.github.com>
 Arik Poznanski <arikpoz@users.noreply.github.com>
 Artem <guinmoon@gmail.com>
+Artem Zinnatullin <ceo@abstractny.gay>
 Artyom Lebedev <vagran.ast@gmail.com>
 Asbjørn Olling <asbjornolling@gmail.com>
 Ásgeir Bjarni Ingvarsson <asgeir@fundinn.org>
+Ashish <1856117+ashishdatta@users.noreply.github.com>
 Ashok Gelal <401055+ashokgelal@users.noreply.github.com>
 Ashraful Islam <ashraful.meche@gmail.com>
 Atsushi Tatsuma <yoshoku@outlook.com>
@@ -57,35 +67,46 @@ BADR <contact@pythops.com>
 Bach Le <bach@bullno1.com>
 Bailey Chittle <39804642+bachittle@users.noreply.github.com>
 BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com>
+Bartowski <ckealty1182@gmail.com>
 Behnam M <58621210+ibehnam@users.noreply.github.com>
+Ben Ashbaugh <ben.ashbaugh@intel.com>
 Ben Garney <bengarney@users.noreply.github.com>
 Ben Siraphob <bensiraphob@gmail.com>
 Ben Williams <ben@719ben.com>
+Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com>
 Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com>
 Bernat Vadell <hounter.caza@gmail.com>
+Bingan <70050083+binganao@users.noreply.github.com>
 Bodo Graumann <mail@bodograumann.de>
 Bono Lv <lvscar@users.noreply.github.com>
 Borislav Stanimirov <b.stanimirov@abv.bg>
 Branden Butler <bwtbutler@hotmail.com>
 Brian <mofosyne@gmail.com>
 Bruce MacDonald <brucewmacdonald@gmail.com>
+Bryan Honof <bryanhonof@gmail.com>
 CJ Pais <cj@cjpais.com>
 CRD716 <crd716@gmail.com>
+Calvin Laurenson <calvin@laurenson.dev>
 Cameron <csteele@steelecameron.com>
 Cameron Kaiser <classilla@users.noreply.github.com>
+Carolinabanana <140120812+Carolinabanana@users.noreply.github.com>
 Casey Primozic <casey@cprimozic.net>
 Casey Primozic <me@ameo.link>
 CausalLM <148736309+CausalLM@users.noreply.github.com>
 Cebtenzzre <cebtenzzre@gmail.com>
 Chad Brewbaker <crb002@gmail.com>
+Chao Jiang <jc19chaoj@zoho.com>
 Cheng Shao <terrorjack@type.dance>
+Chris Elrod <elrodc@gmail.com>
 Chris Kuehl <ckuehl@ckuehl.me>
 Christian Demsar <christian@github.email.demsar.us>
 Christian Demsar <crasm@git.vczf.us>
 Christian Falch <875252+chrfalch@users.noreply.github.com>
 Christian Kögler <ck3d@gmx.de>
+Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com>
 Clark Saben <76020733+csaben@users.noreply.github.com>
 Clint Herron <hanclinto@gmail.com>
+CrispStrobe <154636388+CrispStrobe@users.noreply.github.com>
 Cuong Trinh Manh <nguoithichkhampha@gmail.com>
 DAN™ <dranger003@gmail.com>
 Damian Stewart <d@damianstewart.com>
@@ -95,8 +116,12 @@ Daniel Bevenius <daniel.bevenius@gmail.com>
 Daniel Drake <drake@endlessos.org>
 Daniel Hiltgen <dhiltgen@users.noreply.github.com>
 Daniel Illescas Romero <illescas.daniel@protonmail.com>
+Daniele <57776841+daniandtheweb@users.noreply.github.com>
 DannyDaemonic <DannyDaemonic@gmail.com>
 Dat Quoc Nguyen <2412555+datquocnguyen@users.noreply.github.com>
+Dave <dave-fl@users.noreply.github.com>
+Dave Airlie <airlied@gmail.com>
+Dave Airlie <airlied@redhat.com>
 Dave Della Costa <ddellacosta+github@gmail.com>
 David Friehs <david@friehs.info>
 David Kennedy <dakennedyd@gmail.com>
@@ -104,10 +129,13 @@ David Pflug <david@pflug.email>
 David Renshaw <dwrenshaw@gmail.com>
 David Sommers <12738+databyte@users.noreply.github.com>
 David Yang <davidyang6us@gmail.com>
+Dawid Potocki <github@dawidpotocki.com>
 Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com>
 Dean <Dean.Sinaean@gmail.com>
 Deins <deinsegle@gmail.com>
+Deven Mistry <31466137+deven367@users.noreply.github.com>
 Didzis Gosko <didzis@users.noreply.github.com>
+Djip007 <djip.perois@free.fr>
 Don Mahurin <dmahurin@users.noreply.github.com>
 DooWoong Lee (David) <manics99@naver.com>
 Doomsdayrs <38189170+Doomsdayrs@users.noreply.github.com>
@@ -116,8 +144,11 @@ Dr. Tom Murphy VII Ph.D <499244+tom7@users.noreply.github.com>
 Ebey Abraham <ebey97@gmail.com>
 Ed Lee <edilee@mozilla.com>
 Ed Lepedus <ed.lepedus@googlemail.com>
+Eddie-Wang <wangjinheng1120@163.com>
 Edward Taylor <edeetee@gmail.com>
+Elaine <elaine.zosa@gmail.com>
 Elbios <141279586+Elbios@users.noreply.github.com>
+Elton Kola <eltonkola@gmail.com>
 Engininja2 <139037756+Engininja2@users.noreply.github.com>
 Equim <sayaka@ekyu.moe>
 Eric Sommerlade <es0m@users.noreply.github.com>
@@ -143,37 +174,47 @@ Firat <firatkiral@gmail.com>
 Folko-Ven <71110216+Folko-Ven@users.noreply.github.com>
 Foul-Tarnished <107711110+Foul-Tarnished@users.noreply.github.com>
 Francisco Melo <43780565+francis2tm@users.noreply.github.com>
+Frank Mai <thxcode0824@gmail.com>
 FrankHB <frankhb1989@gmail.com>
+Fred Douglas <43351173+fredlas@users.noreply.github.com>
 Frederik Vogel <Schaltfehler@users.noreply.github.com>
 Gabe Goodhart <gabe.l.hart@gmail.com>
 GainLee <perfecter.gen@gmail.com>
 Galunid <karolek1231456@gmail.com>
 Gary Linscott <glinscott@gmail.com>
 Gary Mulder <gjmulder@gmail.com>
+Gavin Zhao <gavinzhaojw@protonmail.com>
 Genkagaku.GPT <hlhr202@163.com>
 Georgi Gerganov <ggerganov@gmail.com>
 Gilad S <giladgd@users.noreply.github.com>
+Giuseppe Scrivano <giuseppe@scrivano.org>
 GiviMAD <GiviMAD@users.noreply.github.com>
 Govlzkoy <gotope@users.noreply.github.com>
 Guillaume "Vermeille" Sanchez <Guillaume.V.Sanchez@gmail.com>
 Guillaume Wenzek <gwenzek@users.noreply.github.com>
 Guoteng <32697156+SolenoidWGT@users.noreply.github.com>
 Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com>
+Haggai Nuchi <h.nuchi@gmail.com>
 Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
+Hamdoud Hakem <90524568+hamdoudhakem@users.noreply.github.com>
+HanishKVC <hanishkvc@gmail.com>
 Haohui Mai <ricetons@gmail.com>
 Haoxiang Fei <tonyfettes@tonyfettes.com>
 Harald Fernengel <harald.fernengel@here.com>
 Hatsune Miku <129688334+at8u@users.noreply.github.com>
+HatsuneMikuUwU33 <173229399+HatsuneMikuUwU33@users.noreply.github.com>
 Henk Poley <HenkPoley@gmail.com>
 Henri Vasserman <henv@hot.ee>
 Henrik Forstén <henrik.forsten@gmail.com>
 Herman Semenov <GermanAizek@yandex.ru>
 Hesen Peng <hesen.peng@gmail.com>
 Hoang Nguyen <hugo53@users.noreply.github.com>
+Hong Bo PENG <penghb@cn.ibm.com>
 Hongyu Ouyang <96765450+casavaca@users.noreply.github.com>
 Howard Su <howard0su@gmail.com>
 Hua Jiang <allenhjiang@outlook.com>
 Huawei Lin <huaweilin.cs@gmail.com>
+Hugo Roussel <hugo.rous@gmail.com>
 Ian Bull <irbull@eclipsesource.com>
 Ian Bull <irbull@gmail.com>
 Ian Scrivener <github@zilogy.asia>
@@ -190,8 +231,10 @@ Ivan Stepanov <ivanstepanovftw@gmail.com>
 JH23X <165871467+JH23X@users.noreply.github.com>
 Jack Mousseau <jmousseau@users.noreply.github.com>
 JackJollimore <130917767+JackJollimore@users.noreply.github.com>
+Jaemin Son <woalsdnd@gmail.com>
 Jag Chadha <jagtesh@gmail.com>
 Jakub N <jakubniemczyk97@gmail.com>
+James A Capozzoli <157492257+jac-jim@users.noreply.github.com>
 James Reynolds <magnusviri@users.noreply.github.com>
 Jan Boon <jan.boon@kaetemi.be>
 Jan Boon <kaetemi@gmail.com>
@@ -205,12 +248,17 @@ Jean-Michaël Celerier <jeanmichael.celerier+github@gmail.com>
 Jed Fox <git@jedfox.com>
 Jeffrey Quesnelle <emozilla@nousresearch.com>
 Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
+Jeximo <jeximo@gmail.com>
 Jhen-Jie Hong <iainst0409@gmail.com>
 Jiahao Li <liplus17@163.com>
 Jian Liao <jianliao@users.noreply.github.com>
 JidongZhang-THU <1119708529@qq.com>
 Jinwoo Jeong <33892306+williamjeong2@users.noreply.github.com>
 Jiří Podivín <66251151+jpodivin@users.noreply.github.com>
+Jiří Sejkora <Sejseloid@gmail.com>
+Joan Fontanals <jfontanalsmartinez@gmail.com>
+Joan Fontanals <joan.fontanals.martinez@jina.ai>
+Johan <JohanAR@users.noreply.github.com>
 Johannes Gäßler <johannesg@5d6.de>
 Johannes Rudolph <johannes.rudolph@gmail.com>
 John <78893154+cmp-nct@users.noreply.github.com>
@@ -221,15 +269,19 @@ Jonas Wunderlich <32615971+jonas-w@users.noreply.github.com>
 Jorge A <161275481+jorgealias@users.noreply.github.com>
 Jose Maldonado <63384398+yukiteruamano@users.noreply.github.com>
 Joseph Stahl <1269177+josephst@users.noreply.github.com>
+Josh Ramer <josh.ramer@icloud.com>
 Joyce <joycebrum@google.com>
 Juan Calderon-Perez <835733+gaby@users.noreply.github.com>
 Judd <foldl@users.noreply.github.com>
 Julius Arkenberg <arki05@users.noreply.github.com>
 Jun Jie <71215065+junnjiee16@users.noreply.github.com>
+Junyang Lin <justinlin930319@hotmail.com>
 Juraj Bednar <juraj@bednar.io>
 Justin Parker <jparkerweb@gmail.com>
 Justin Suess <justin.suess@westpoint.edu>
+Justina Cho <justcho5@gmail.com>
 Justine Tunney <jtunney@gmail.com>
+Justine Tunney <jtunney@mozilla.com>
 Juuso Alasuutari <juuso.alasuutari@gmail.com>
 KASR <karim.asrih@gmail.com>
 Kamil Tomšík <info@tomsik.cz>
@@ -242,6 +294,7 @@ Kawrakow <48489457+ikawrakow@users.noreply.github.com>
 Keiichi Tabata <keiichi.tabata@outlook.com>
 Kenvix ⭐ <kenvixzure@live.com>
 Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
+Kevin Gibbons <bakkot@gmail.com>
 Kevin Ji <1146876+kevinji@users.noreply.github.com>
 Kevin Kwok <antimatter15@gmail.com>
 Kevin Lo <kevlo@kevlo.org>
@@ -257,6 +310,7 @@ Laura <Tijntje_7@msn.com>
 Lee <44310445+lx200916@users.noreply.github.com>
 Lee Drake <b.lee.drake@gmail.com>
 Leng Yue <lengyue@lengyue.me>
+Leon Knauer <git@leonknauer.com>
 LeonEricsson <70749762+LeonEricsson@users.noreply.github.com>
 Leonardo Neumann <leonardo@neumann.dev.br>
 Li Tan <tanliboy@gmail.com>
@@ -265,20 +319,26 @@ LoganDark <github@logandark.mozmail.com>
 LostRuins <39025047+LostRuins@users.noreply.github.com>
 Luciano <lucianostrika44@gmail.com>
 Luo Tian <lt@basecity.com>
+Lyle Dean <dean@lyle.dev>
 M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
 Maarten ter Huurne <maarten@treewalker.org>
 Mack Straight <eiz@users.noreply.github.com>
 Maël Kerbiriou <m431.kerbiriou@gmail.com>
 MaggotHATE <clay1326@gmail.com>
+Manuel <44313466+makuche@users.noreply.github.com>
 Marc Köhlbrugge <subscriptions@marckohlbrugge.com>
 Marco Matthies <71844+marcom@users.noreply.github.com>
 Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com>
 Marian Cepok <marian.cepok@gmail.com>
 Mark Fairbairn <thebaron88@gmail.com>
 Marko Tasic <mtasic85@gmail.com>
+Markus Tavenrath <mtavenrath@users.noreply.github.com>
+Martin Delille <martin@delille.org>
 Martin Krasser <krasserm@googlemail.com>
 Martin Schwaighofer <mschwaig@users.noreply.github.com>
 Marvin Gießing <marvin.giessing@gmail.com>
+Masaya, Kato <62578291+msy-kato@users.noreply.github.com>
+MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com>
 Mateusz Charytoniuk <mateusz.charytoniuk@protonmail.com>
 Matheus C. França <matheus-catarino@hotmail.com>
 Matheus Gabriel Alves Silva <matheusgasource@gmail.com>
@@ -287,8 +347,11 @@ Mathijs de Bruin <mathijs@mathijsfietst.nl>
 Matt Clayton <156335168+mattjcly@users.noreply.github.com>
 Matt Pulver <matt.pulver@heavy.ai>
 Matteo Boschini <12133566+mbosc@users.noreply.github.com>
+Mattheus Chediak <shammcity00@gmail.com>
 Matthew Tejo <matthew.tejo@gmail.com>
 Matvey Soloviev <blackhole89@gmail.com>
+Max Krasnyansky <max.krasnyansky@gmail.com>
+Max Krasnyansky <quic_maxk@quicinc.com>
 Maxime <672982+maximegmd@users.noreply.github.com>
 Maximilian Winter <maximilian.winter.91@gmail.com>
 Meng Zhang <meng@tabbyml.com>
@@ -300,32 +363,41 @@ Michael Kesper <mkesper@schokokeks.org>
 Michael Klimenko <mklimenko29@gmail.com>
 Michael Podvitskiy <podvitskiymichael@gmail.com>
 Michael Potter <NanoTekGuy@Gmail.com>
+Michael de Gans <michael.john.degans@gmail.com>
 Michaël de Vries <vriesdemichael@gmail.com>
 Mihai <mihai.chirculescu@yahoo.com>
 Mike <ytianhui2004@gmail.com>
+Mikko Juola <mikjuo@gmail.com>
 Minsoo Cheong <54794500+mscheong01@users.noreply.github.com>
 Mirko185 <mirkosig@gmail.com>
 Mirror Azure <54669636+MirrorAzure@users.noreply.github.com>
 Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com>
 Mohammadreza Hendiani <hendiani.mohammadreza@gmail.com>
+Mohammadreza Hendiani <mohammad.r.hendiani@gmail.com>
 Murilo Santana <mvrilo@gmail.com>
 Musab Gultekin <musabgultekin@users.noreply.github.com>
 Nam D. Tran <42194884+namtranase@users.noreply.github.com>
+Nathan Epstein <nate2@umbc.edu>
 NawafAlansari <72708095+NawafAlansari@users.noreply.github.com>
 Nebula <infinitewormhole@gmail.com>
+Neo Zhang <14088817+arthw@users.noreply.github.com>
+Neo Zhang <zhang.jianyu@outlook.com>
 Neo Zhang Jianyu <jianyu.zhang@intel.com>
 Neuman Vong <neuman.vong@gmail.com>
 Nexesenex <124105151+Nexesenex@users.noreply.github.com>
 Niall Coates <1349685+Niall-@users.noreply.github.com>
 Nicolai Weitkemper <kontakt@nicolaiweitkemper.de>
+Nicolás Pérez <nicolas_perez@brown.edu>
 Nigel Bosch <pnigelb@gmail.com>
 Niklas Korz <niklas@niklaskorz.de>
+Nikolas <127742645+nneubacher@users.noreply.github.com>
 Nindaleth <Nindaleth@users.noreply.github.com>
 Oleksandr Nikitin <oleksandr@tvori.info>
 Oleksii Maryshchenko <oleksii.maryshchenko@gmail.com>
 Olivier Chafik <ochafik@users.noreply.github.com>
 Ondřej Čertík <ondrej@certik.us>
 Ouadie EL FAROUKI <ouadie.elfarouki@codeplay.com>
+Patrice Ferlet <metal3d@gmail.com>
 Paul Tsochantaris <ptsochantaris@icloud.com>
 Pavol Rusnak <pavol@rusnak.io>
 Pedro Cuenca <pedro@huggingface.co>
@@ -343,9 +415,14 @@ RJ Adriaansen <adriaansen@eshcc.eur.nl>
 Radoslav Gerganov <rgerganov@gmail.com>
 Radosław Gryta <radek.gryta@gmail.com>
 Rahul Vivek Nair <68507071+RahulVivekNair@users.noreply.github.com>
+Raj Hammeer Singh Hada <hammeerraj@gmail.com>
+Ralph Soika <ralph.soika@imixs.com>
 Rand Xie <randxiexyy29@gmail.com>
 Randall Fitzgerald <randall@dasaku.net>
 Reinforce-II <fate@eastal.com>
+Ren Xuancheng <jklj077@users.noreply.github.com>
+Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com>
+RhinoDevel <RhinoDevel@users.noreply.github.com>
 Riceball LEE <snowyu.lee@gmail.com>
 Richard Kiss <him@richardkiss.com>
 Richard Roberson <richardr1126@gmail.com>
@@ -373,6 +450,7 @@ Rowan Hart <rowanbhart@gmail.com>
 Rune <43761327+Rune-AI@users.noreply.github.com>
 Ryan Landay <rlanday@gmail.com>
 Ryder Wishart <ryderwishart@gmail.com>
+Ryuei <louixs@users.noreply.github.com>
 Rőczey Barnabás <31726601+An0nie@users.noreply.github.com>
 SakuraUmi <yukinon244@gmail.com>
 Salvador E. Tropea <stropea@inti.gob.ar>
@@ -386,6 +464,7 @@ SebastianApel <13675545+SebastianApel@users.noreply.github.com>
 Senemu <10880819+Senemu@users.noreply.github.com>
 Sergey Alirzaev <zl29ah@gmail.com>
 Sergio López <slp@sinrega.org>
+Sertaç Özercan <852750+sozercan@users.noreply.github.com>
 SeungWon Jeong <65549245+redlion0929@users.noreply.github.com>
 ShadovvBeast <ShadovvBeast@gmail.com>
 Shakhar Dasgupta <shakhardasgupta@gmail.com>
@@ -394,6 +473,7 @@ Shijie <821898965@qq.com>
 Shintarou Okada <kokuzen@gmail.com>
 Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com>
 Shouzheng Liu <lshzh.hi@gmail.com>
+Shuichi Tsutsumi <shuichi0526@gmail.com>
 Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
 Simon Willison <swillison@gmail.com>
 Siwen Yu <yusiwen@gmail.com>
@@ -405,11 +485,14 @@ Someone <sergei.kozlukov@aalto.fi>
 Someone Serge <sergei.kozlukov@aalto.fi>
 Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>
 Spencer Sutton <spencersutton@users.noreply.github.com>
+Srihari-mcw <96763064+Srihari-mcw@users.noreply.github.com>
 Srinivas Billa <nivibilla@gmail.com>
 Stefan Sydow <stefan@sydow.email>
+Steffen Röcker <sroecker@gmail.com>
 Stephan Walter <stephan@walter.name>
 Stephen Nichols <snichols@users.noreply.github.com>
 Steve Grubb <ausearch.1@gmail.com>
+Steven Prichard <spprichard20@gmail.com>
 Steven Roussey <sroussey@gmail.com>
 Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
 Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com>
@@ -434,16 +517,19 @@ Tom C <tom.corelis@gmail.com>
 Tom Jobbins <784313+TheBloke@users.noreply.github.com>
 Tomas <tom.tomas.36478119@gmail.com>
 Tomáš Pazdiora <tomas.pazdiora@gmail.com>
+Tristan Druyen <tristan@vault81.mozmail.com>
 Tristan Ross <rosscomputerguy@protonmail.com>
 Tungsten842 <886724vf@anonaddy.me>
 Tungsten842 <quantmint@protonmail.com>
 Tushar <ditsuke@protonmail.com>
 UEXTM.com <84163508+uextm@users.noreply.github.com>
+Ulrich Drepper <drepper@gmail.com>
 Uzo Nweke <uzoechi@gmail.com>
 Vaibhav Srivastav <vaibhavs10@gmail.com>
 Val Kharitonov <mail@kharvd.com>
 Valentin Konovalov <valle.ketsujin@gmail.com>
 Valentyn Bezshapkin <61702053+valentynbez@users.noreply.github.com>
+Victor Nogueira <felladrin@gmail.com>
 Victor Z. Peng <ziliangdotme@gmail.com>
 Vlad <spitfireage@gmail.com>
 Vladimir <bogdad@gmail.com>
@@ -455,7 +541,9 @@ Weird Constructor <weirdconstructor@gmail.com>
 Welby Seely <welbyseely@gmail.com>
 Wentai Zhang <rchardx@gmail.com>
 WillCorticesAI <150854901+WillCorticesAI@users.noreply.github.com>
+William Tambellini <william.tambellini@gmail.com>
 Willy Tarreau <w@1wt.eu>
+Wouter <9594229+DifferentialityDevelopment@users.noreply.github.com>
 Wu Jian Ping <wujjpp@hotmail.com>
 Wu Jian Ping <wujp@greatld.com>
 Xiake Sun <xiake.sun@intel.com>
@@ -466,6 +554,8 @@ Xiaoyi Chen <cxychina@gmail.com>
 Xingchen Song(宋星辰) <xingchensong1996@163.com>
 Xuan Son Nguyen <thichthat@gmail.com>
 Yann Follet <131855179+YannFollet@users.noreply.github.com>
+Yaroslav <yaroslav.yashin@me.com>
+Yazan Agha-Schrader <mountaiin@icloud.com>
 Yiming Cui <conandiy@vip.qq.com>
 Yishuo Wang <MeouSker77@outlook.com>
 Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com>
@@ -477,6 +567,7 @@ Zane Shannon <z@zcs.me>
 Zay <95888118+isaiahbjork@users.noreply.github.com>
 Zenix <zenixls2@gmail.com>
 Zhang Peiyuan <a1286225768@gmail.com>
+Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com>
 ZhouYuChen <zhouyuchen@naver.com>
 Ziad Ben Hadj-Alouane <zied.benhadjalouane@gmail.com>
 Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com>
@@ -484,14 +575,18 @@ Zsapi <martin1.zsapka@gmail.com>
 a-n-n-a-l-e-e <150648636+a-n-n-a-l-e-e@users.noreply.github.com>
 adel boussaken <netdur@gmail.com>
 afrideva <95653597+afrideva@users.noreply.github.com>
+agray3 <agray3@users.noreply.github.com>
 akawrykow <142945436+akawrykow@users.noreply.github.com>
 alexpinel <93524949+alexpinel@users.noreply.github.com>
 alonfaraj <alonfaraj@gmail.com>
+alwqx <kenan3015@gmail.com>
+amd-lalithnc <lalithnc@amd.com>
 andrijdavid <david@geek.mg>
 anon998 <131767832+anon998@users.noreply.github.com>
 anzz1 <anzz1@live.com>
 apaz <aarpazdera@gmail.com>
 apcameron <37645737+apcameron@users.noreply.github.com>
+arch-btw <57669023+arch-btw@users.noreply.github.com>
 arcrank <arcrank@gmail.com>
 arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com>
 at8u <129688334+at8u@users.noreply.github.com>
@@ -514,13 +609,17 @@ cocktailpeanut <121128867+cocktailpeanut@users.noreply.github.com>
 coezbek <c.oezbek@gmail.com>
 comex <comexk@gmail.com>
 compilade <113953597+compilade@users.noreply.github.com>
+compilade <git@compilade.net>
+cpumaxx <163466046+cpumaxx@users.noreply.github.com>
 crasm <crasm@git.vczf.net>
 crasm <crasm@git.vczf.us>
 daboe01 <daboe01@googlemail.com>
 david raistrick <keen99@users.noreply.github.com>
+ddh0 <dylanhalladay02@icloud.com>
 ddpasa <112642920+ddpasa@users.noreply.github.com>
 deepdiffuser <112834445+deepdiffuser@users.noreply.github.com>
 divinity76 <divinity76@gmail.com>
+dm4 <sunrisedm4@gmail.com>
 dotpy314 <33351922+dotpy314@users.noreply.github.com>
 drbh <david.richard.holtz@gmail.com>
 ds5t5 <145942675+ds5t5@users.noreply.github.com>
@@ -529,6 +628,7 @@ eastriver <lee@eastriver.dev>
 ebraminio <ebraminio@gmail.com>
 eiery <19350831+eiery@users.noreply.github.com>
 eric8607242 <e0928021388@gmail.com>
+fairydreaming <166155368+fairydreaming@users.noreply.github.com>
 fraxy-v <65565042+fraxy-v@users.noreply.github.com>
 github-actions[bot] <github-actions[bot]@users.noreply.github.com>
 gliptic <gliptic@users.noreply.github.com>
@@ -539,6 +639,7 @@ h-h-h-h <13482553+h-h-h-h@users.noreply.github.com>
 hankcs <cnhankmc@gmail.com>
 hoangmit <hoangmit@users.noreply.github.com>
 hongbo.mo <352280764@qq.com>
+hopkins385 <98618192+hopkins385@users.noreply.github.com>
 howlger <eclipse@voormann.de>
 howlger <github@voormann.de>
 hutli <6594598+hutli@users.noreply.github.com>
@@ -549,14 +650,22 @@ hydai <z54981220@gmail.com>
 iSma <ismail.senhaji@gmail.com>
 iacore <74560659+iacore@users.noreply.github.com>
 igarnier <igarnier@protonmail.com>
+intelmatt <61025942+intelmatt@users.noreply.github.com>
 iohub <rickyang.pro@gmail.com>
 jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com>
+jaime-m-p <167997752+jaime-m-p@users.noreply.github.com>
 jameswu2014 <545426914@qq.com>
+jiez <373447296@qq.com>
 jneem <joeneeman@gmail.com>
+joecryptotoo <80373433+joecryptotoo@users.noreply.github.com>
 johnson442 <56517414+johnson442@users.noreply.github.com>
+jojorne <jojorne@users.noreply.github.com>
 jon-chuang <9093549+jon-chuang@users.noreply.github.com>
 jp-x-g <jpxg-dev@protonmail.com>
+jukofyork <69222624+jukofyork@users.noreply.github.com>
+junchao-loongson <68935141+junchao-loongson@users.noreply.github.com>
 jwj7140 <32943891+jwj7140@users.noreply.github.com>
+k.h.lai <adrian.k.h.lai@outlook.com>
 kaizau <kaizau@users.noreply.github.com>
 kalomaze <66376113+kalomaze@users.noreply.github.com>
 kang <tpdns9032100@gmail.com>
@@ -575,11 +684,15 @@ ldwang <ftgreat@163.com>
 le.chang <cljs118@126.com>
 leejet <leejet714@gmail.com>
 limitedAtonement <limitedAtonement@users.noreply.github.com>
+liuwei-git <14815172+liuwei-git@users.noreply.github.com>
 lon <114724657+longregen@users.noreply.github.com>
+loonerin <132926317+loonerin@users.noreply.github.com>
+luoyu-intel <yu.luo@intel.com>
 m3ndax <adrian.goessl@outlook.com>
 maddes8cht <55592906+maddes8cht@users.noreply.github.com>
 makomk <makosoft@googlemail.com>
 manikbhandari <mbbhandarimanik2@gmail.com>
+maor-ps <154728172+maor-ps@users.noreply.github.com>
 mdrokz <mohammadmunshi@gmail.com>
 mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com>
 minarchist <minarchist@users.noreply.github.com>
@@ -593,15 +706,19 @@ ngc92 <7938269+ngc92@users.noreply.github.com>
 nhamanasu <45545786+nhamanasu@users.noreply.github.com>
 niansa/tuxifan <anton-sa@web.de>
 niansa/tuxifan <tuxifan@posteo.de>
+nickp27 <nb.porter@gmail.com>
 ningshanwutuobang <ningshanwutuobang@gmail.com>
 nold <Nold360@users.noreply.github.com>
 nopperl <54780682+nopperl@users.noreply.github.com>
 nusu-github <29514220+nusu-github@users.noreply.github.com>
 olexiyb <olexiyb@gmail.com>
+omahs <73983677+omahs@users.noreply.github.com>
 oobabooga <112222186+oobabooga@users.noreply.github.com>
 opparco <parco.opaai@gmail.com>
 ostix360 <55257054+ostix360@users.noreply.github.com>
+pengxin99 <pengxin.yuan@intel.com>
 perserk <perserk@gmail.com>
+pmysl <piotr.myslinski@outlook.com>
 postmasters <namnguyen@google.com>
 pudepiedj <pudepiedj@gmail.com>
 qingfengfenga <41416092+qingfengfenga@users.noreply.github.com>
@@ -614,16 +731,19 @@ rhuddleston <ryan.huddleston@percona.com>
 rimoliga <53384203+rimoliga@users.noreply.github.com>
 runfuture <runfuture@users.noreply.github.com>
 sandyiscool <sandyiscool@gmail.com>
+sasha0552 <admin@sasha0552.org>
 semidark <me@semidark.net>
 sharpHL <132747147+sharpHL@users.noreply.github.com>
 shibe2 <shibe@tuta.io>
 singularity <12184989+singularity-s0@users.noreply.github.com>
 sjinzh <sjinzh@gmail.com>
+sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com>
 slaren <2141330+slaren@users.noreply.github.com>
 slaren <slarengh@gmail.com>
 snadampal <87143774+snadampal@users.noreply.github.com>
 staviq <staviq@gmail.com>
 stduhpf <stephduh@live.fr>
+strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com>
 swittk <switt1995@gmail.com>
 takov751 <40316768+takov751@users.noreply.github.com>
 tarcey <cey.tarik@gmail.com>
@@ -636,12 +756,16 @@ uint256_t <konndennsa@gmail.com>
 uint256_t <maekawatoshiki1017@gmail.com>
 unbounded <haakon@likedan.net>
 valiray <133289098+valiray@users.noreply.github.com>
+vik <vikhyatk@gmail.com>
+viric <viric@viric.name>
 vodkaslime <646329483@qq.com>
 vvhg1 <94630311+vvhg1@users.noreply.github.com>
 vxiiduu <73044267+vxiiduu@users.noreply.github.com>
 wbpxre150 <100937007+wbpxre150@users.noreply.github.com>
 whoreson <139810751+whoreson@users.noreply.github.com>
+woachk <24752637+woachk@users.noreply.github.com>
 wonjun Jang <strutive07@gmail.com>
+woodx <124784234+woodx9@users.noreply.github.com>
 wzy <32936898+Freed-Wu@users.noreply.github.com>
 xaedes <xaedes@gmail.com>
 xaedes <xaedes@googlemail.com>
@@ -649,7 +773,10 @@ xloem <0xloem@gmail.com>
 yangli2 <yangli2@gmail.com>
 yuiseki <yuiseki@gmail.com>
 zakkor <edward.partenie@gmail.com>
+zhangkaihuo <zhangkaihuo@gmail.com>
 zhouwg <6889919+zhouwg@users.noreply.github.com>
+zhouwg <zhouwg2000@gmail.com>
 zrm <trustiosity.zrm@gmail.com>
+Ștefan-Gabriel Muscalu <legraphista@users.noreply.github.com>
 源文雨 <41315874+fumiama@users.noreply.github.com>
 Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 92c9f09eb5d9d..a313206351677 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -2,6 +2,9 @@ cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target
 project("llama.cpp" C CXX)
 include(CheckIncludeFileCXX)
 
+#set(CMAKE_WARN_DEPRECATED YES)
+set(CMAKE_WARN_UNUSED_CLI YES)
+
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
@@ -9,11 +12,16 @@ if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
     set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
 endif()
 
+# Add path to modules
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
+
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 
 if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     set(LLAMA_STANDALONE ON)
 
+    include(git-vars)
+
     # configure project version
     # TODO
 else()
@@ -32,1267 +40,133 @@ else()
     endif()
 endif()
 
+option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
 
-#
-# Option list
-#
-
-if (APPLE)
-    set(LLAMA_METAL_DEFAULT ON)
-else()
-    set(LLAMA_METAL_DEFAULT OFF)
+if (WIN32)
+    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
 endif()
 
-set(LLAMA_LLAMAFILE_DEFAULT ON)
-
-# general
-option(BUILD_SHARED_LIBS                "build shared libraries"                                OFF)
-option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
-option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      ON)
-option(LLAMA_LTO                        "llama: enable link time optimization"                  OFF)
-option(LLAMA_CCACHE                     "llama: use ccache if available"                        ON)
+#
+# option list
+#
 
 # debug
-option(LLAMA_ALL_WARNINGS               "llama: enable all compiler warnings"                   ON)
-option(LLAMA_ALL_WARNINGS_3RD_PARTY     "llama: enable all compiler warnings in 3rd party libs" OFF)
-option(LLAMA_GPROF                      "llama: enable gprof"                                   OFF)
+option(LLAMA_ALL_WARNINGS           "llama: enable all compiler warnings"                   ON)
+option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
 
 # build
-option(LLAMA_FATAL_WARNINGS             "llama: enable -Werror flag"                            OFF)
+option(LLAMA_FATAL_WARNINGS "llama: enable -Werror flag" OFF)
 
 # sanitizers
-option(LLAMA_SANITIZE_THREAD            "llama: enable thread sanitizer"                        OFF)
-option(LLAMA_SANITIZE_ADDRESS           "llama: enable address sanitizer"                       OFF)
-option(LLAMA_SANITIZE_UNDEFINED         "llama: enable undefined sanitizer"                     OFF)
+option(LLAMA_SANITIZE_THREAD    "llama: enable thread sanitizer"    OFF)
+option(LLAMA_SANITIZE_ADDRESS   "llama: enable address sanitizer"   OFF)
+option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
 
-# instruction set specific
-if (LLAMA_NATIVE)
-    set(INS_ENB OFF)
-else()
-    set(INS_ENB ON)
-endif()
-
-option(LLAMA_AVX                             "llama: enable AVX"                                ${INS_ENB})
-option(LLAMA_AVX2                            "llama: enable AVX2"                               ${INS_ENB})
-option(LLAMA_AVX512                          "llama: enable AVX512"                             OFF)
-option(LLAMA_AVX512_VBMI                     "llama: enable AVX512-VBMI"                        OFF)
-option(LLAMA_AVX512_VNNI                     "llama: enable AVX512-VNNI"                        OFF)
-option(LLAMA_AVX512_BF16                     "llama: enable AVX512-BF16"                        OFF)
-option(LLAMA_FMA                             "llama: enable FMA"                                ${INS_ENB})
-# in MSVC F16C is implied with AVX2/AVX512
-if (NOT MSVC)
-    option(LLAMA_F16C                        "llama: enable F16C"                               ${INS_ENB})
-endif()
-
-if (WIN32)
-    set(LLAMA_WIN_VER "0x602" CACHE STRING "llama: Windows Version")
-endif()
+# extra artifacts
+option(LLAMA_BUILD_TESTS    "llama: build tests"          ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 
 # 3rd party libs
-option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework"               ON)
-option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
-option(LLAMA_LLAMAFILE                       "llama: use llamafile SGEMM"                       ${LLAMA_LLAMAFILE_DEFAULT})
-set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
-option(LLAMA_CUDA                            "llama: use CUDA"                                  OFF)
-option(LLAMA_CUBLAS                          "llama: use CUDA (deprecated, use LLAMA_CUDA)"     OFF)
-option(LLAMA_CUDA_FORCE_DMMV                 "llama: use dmmv instead of mmvq CUDA kernels"     OFF)
-option(LLAMA_CUDA_FORCE_MMQ                  "llama: use mmq kernels instead of cuBLAS"         OFF)
-set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
-set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
-option(LLAMA_CUDA_F16                        "llama: use 16 bit floats for some calculations"   OFF)
-set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
-set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
-                                             "llama: max. batch size for using peer access")
-option(LLAMA_CUDA_NO_PEER_COPY               "llama: do not use peer to peer copies"            OFF)
-option(LLAMA_CUDA_NO_VMM                     "llama: do not try to use CUDA VMM"                OFF)
-
-option(LLAMA_CURL                            "llama: use libcurl to download model from an URL" OFF)
-option(LLAMA_HIPBLAS                         "llama: use hipBLAS"                               OFF)
-option(LLAMA_HIP_UMA                         "llama: use HIP unified memory architecture"       OFF)
-option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
-option(LLAMA_VULKAN                          "llama: use Vulkan"                                OFF)
-option(LLAMA_VULKAN_CHECK_RESULTS            "llama: run Vulkan op checks"                      OFF)
-option(LLAMA_VULKAN_DEBUG                    "llama: enable Vulkan debug output"                OFF)
-option(LLAMA_VULKAN_VALIDATE                 "llama: enable Vulkan validation"                  OFF)
-option(LLAMA_VULKAN_RUN_TESTS                "llama: run Vulkan tests"                          OFF)
-option(LLAMA_METAL                           "llama: use Metal"                                 ${LLAMA_METAL_DEFAULT})
-option(LLAMA_METAL_NDEBUG                    "llama: disable Metal debugging"                   OFF)
-option(LLAMA_METAL_SHADER_DEBUG              "llama: compile Metal with -fno-fast-math"         OFF)
-option(LLAMA_METAL_EMBED_LIBRARY             "llama: embed Metal library"                       OFF)
-set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING
-                                             "llama: metal minimum macOS version")
-set(LLAMA_METAL_STD "" CACHE STRING          "llama: metal standard version (-std flag)")
-option(LLAMA_KOMPUTE                         "llama: use Kompute"                               OFF)
-option(LLAMA_RPC                             "llama: use RPC"                                   OFF)
-option(LLAMA_QKK_64                          "llama: use super-block size of 64 for k-quants"   OFF)
-option(LLAMA_SYCL                            "llama: use SYCL"                                  OFF)
-option(LLAMA_SYCL_F16                        "llama: use 16 bit floats for sycl calculations"   OFF)
-set(LLAMA_SYCL_TARGET   "INTEL" CACHE STRING "llama: sycl target device")
-option(LLAMA_CPU_HBM                         "llama: use memkind for CPU HBM"                   OFF)
-set(LLAMA_SCHED_MAX_COPIES  "4" CACHE STRING "llama: max input copies for pipeline parallelism")
-
-option(LLAMA_BUILD_TESTS                     "llama: build tests"    ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_EXAMPLES                  "llama: build examples" ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_SERVER                    "llama: build server example"                      ON)
-
-# add perf arguments
-option(LLAMA_PERF                            "llama: enable perf"                               OFF)
+option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
 
 # Required for relocatable CMake package
-include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
-
-#
-# Compile flags
-#
-
-if (LLAMA_SYCL)
-    set(CMAKE_CXX_STANDARD 17)
-else()
-    set(CMAKE_CXX_STANDARD 11)
-endif()
-
-set(CMAKE_CXX_STANDARD_REQUIRED true)
-set(CMAKE_C_STANDARD 11)
-set(CMAKE_C_STANDARD_REQUIRED true)
-set(THREADS_PREFER_PTHREAD_FLAG ON)
-
-find_package(Threads REQUIRED)
-include(CheckCXXCompilerFlag)
-
-add_compile_definitions(GGML_SCHED_MAX_COPIES=${LLAMA_SCHED_MAX_COPIES})
-
-# enable libstdc++ assertions for debug builds
-if (CMAKE_SYSTEM_NAME MATCHES "Linux")
-    add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
-endif()
-
-if (NOT MSVC)
-    if (LLAMA_SANITIZE_THREAD)
-        add_compile_options(-fsanitize=thread)
-        link_libraries     (-fsanitize=thread)
-    endif()
-
-    if (LLAMA_SANITIZE_ADDRESS)
-        add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
-        link_libraries     (-fsanitize=address)
-    endif()
-
-    if (LLAMA_SANITIZE_UNDEFINED)
-        add_compile_options(-fsanitize=undefined)
-        link_libraries     (-fsanitize=undefined)
-    endif()
-endif()
-
-if (APPLE AND LLAMA_ACCELERATE)
-    find_library(ACCELERATE_FRAMEWORK Accelerate)
-    if (ACCELERATE_FRAMEWORK)
-        message(STATUS "Accelerate framework found")
-
-        add_compile_definitions(GGML_USE_ACCELERATE)
-        add_compile_definitions(ACCELERATE_NEW_LAPACK)
-        add_compile_definitions(ACCELERATE_LAPACK_ILP64)
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
-    else()
-        message(WARNING "Accelerate framework not found")
-    endif()
-endif()
-
-if (LLAMA_METAL)
-    find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
-    find_library(METAL_FRAMEWORK    Metal      REQUIRED)
-    find_library(METALKIT_FRAMEWORK MetalKit   REQUIRED)
-
-    message(STATUS "Metal framework found")
-    set(GGML_HEADERS_METAL ggml-metal.h)
-    set(GGML_SOURCES_METAL ggml-metal.m)
-
-    add_compile_definitions(GGML_USE_METAL)
-    if (LLAMA_METAL_NDEBUG)
-        add_compile_definitions(GGML_METAL_NDEBUG)
-    endif()
-
-    # copy ggml-common.h and ggml-metal.metal to bin directory
-    configure_file(ggml-common.h    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h    COPYONLY)
-    configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
-
-    if (LLAMA_METAL_EMBED_LIBRARY)
-        enable_language(ASM)
-        add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
-
-        set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h")
-        set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
-
-        file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated")
-
-        # merge ggml-common.h and ggml-metal.metal into a single file
-        set(METALLIB_EMBED_ASM    "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
-        set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
-
-        add_custom_command(
-            OUTPUT ${METALLIB_EMBED_ASM}
-            COMMAND echo "Embedding Metal library"
-            COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED}
-            COMMAND echo ".section __DATA,__ggml_metallib"          >  ${METALLIB_EMBED_ASM}
-            COMMAND echo ".globl _ggml_metallib_start"              >> ${METALLIB_EMBED_ASM}
-            COMMAND echo "_ggml_metallib_start:"                    >> ${METALLIB_EMBED_ASM}
-            COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM}
-            COMMAND echo ".globl _ggml_metallib_end"                >> ${METALLIB_EMBED_ASM}
-            COMMAND echo "_ggml_metallib_end:"                      >> ${METALLIB_EMBED_ASM}
-            DEPENDS ggml-metal.metal ggml-common.h
-            COMMENT "Generate assembly for embedded Metal library"
-        )
-
-        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM})
-    else()
-        if (LLAMA_METAL_SHADER_DEBUG)
-            # custom command to do the following:
-            #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
-            #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
-            #
-            # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
-            #       disabling fast math is needed in order to pass tests/test-backend-ops
-            # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
-            # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
-            #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
-            set(XC_FLAGS -fno-fast-math -fno-inline -g)
-        else()
-            set(XC_FLAGS -O3)
-        endif()
-
-        # Append macOS metal versioning flags
-        if (LLAMA_METAL_MACOSX_VERSION_MIN)
-            message(STATUS "Adding -mmacosx-version-min=${LLAMA_METAL_MACOSX_VERSION_MIN} flag to metal compilation")
-            list(APPEND XC_FLAGS -mmacosx-version-min=${LLAMA_METAL_MACOSX_VERSION_MIN})
-        endif()
-        if (LLAMA_METAL_STD)
-            message(STATUS "Adding -std=${LLAMA_METAL_STD} flag to metal compilation")
-            list(APPEND XC_FLAGS -std=${LLAMA_METAL_STD})
-        endif()
-
-        add_custom_command(
-            OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
-            COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
-            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
-            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
-            DEPENDS ggml-metal.metal ggml-common.h
-            COMMENT "Compiling Metal kernels"
-            )
-
-        add_custom_target(
-            ggml-metal ALL
-            DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            )
-    endif() # LLAMA_METAL_EMBED_LIBRARY
-
-    set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
-        ${FOUNDATION_LIBRARY}
-        ${METAL_FRAMEWORK}
-        ${METALKIT_FRAMEWORK}
-        )
-endif()
-
-if (LLAMA_BLAS)
-    if (LLAMA_STATIC)
-        set(BLA_STATIC ON)
-    endif()
-    if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22)
-        set(BLA_SIZEOF_INTEGER 8)
-    endif()
-
-    set(BLA_VENDOR ${LLAMA_BLAS_VENDOR})
-    find_package(BLAS)
-
-    if (BLAS_FOUND)
-        message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}")
-
-        if ("${BLAS_INCLUDE_DIRS}" STREQUAL "")
-            # BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake.
-            # see https://gitlab.kitware.com/cmake/cmake/-/issues/20268
-            find_package(PkgConfig REQUIRED)
-            if (${LLAMA_BLAS_VENDOR} MATCHES "Generic")
-                pkg_check_modules(DepBLAS REQUIRED blas)
-            elseif (${LLAMA_BLAS_VENDOR} MATCHES "OpenBLAS")
-                # As of openblas v0.3.22, the 64-bit is named openblas64.pc
-                pkg_check_modules(DepBLAS openblas64)
-                if (NOT DepBLAS_FOUND)
-                    pkg_check_modules(DepBLAS REQUIRED openblas)
-                endif()
-            elseif (${LLAMA_BLAS_VENDOR} MATCHES "FLAME")
-                pkg_check_modules(DepBLAS REQUIRED blis)
-            elseif (${LLAMA_BLAS_VENDOR} MATCHES "ATLAS")
-                pkg_check_modules(DepBLAS REQUIRED blas-atlas)
-            elseif (${LLAMA_BLAS_VENDOR} MATCHES "FlexiBLAS")
-                pkg_check_modules(DepBLAS REQUIRED flexiblas_api)
-            elseif (${LLAMA_BLAS_VENDOR} MATCHES "Intel")
-                # all Intel* libraries share the same include path
-                pkg_check_modules(DepBLAS REQUIRED mkl-sdl)
-            elseif (${LLAMA_BLAS_VENDOR} MATCHES "NVHPC")
-                # this doesn't provide pkg-config
-                # suggest to assign BLAS_INCLUDE_DIRS on your own
-                if ("${NVHPC_VERSION}" STREQUAL "")
-                    message(WARNING "Better to set NVHPC_VERSION")
-                else()
-                    set(DepBLAS_FOUND ON)
-                    set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include")
-                endif()
-            endif()
-            if (DepBLAS_FOUND)
-                set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS})
-            else()
-                message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically"
-                " detected by pkgconfig, trying to find cblas.h from possible paths...")
-                find_path(BLAS_INCLUDE_DIRS
-                    NAMES cblas.h
-                    HINTS
-                        /usr/include
-                        /usr/local/include
-                        /usr/include/openblas
-                        /opt/homebrew/opt/openblas/include
-                        /usr/local/opt/openblas/include
-                        /usr/include/x86_64-linux-gnu/openblas/include
-                )
-            endif()
-        endif()
-
-        message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}")
-
-        add_compile_options(${BLAS_LINKER_FLAGS})
-
-        add_compile_definitions(GGML_USE_OPENBLAS)
-
-        if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${LLAMA_BLAS_VENDOR} MATCHES "Generic" OR ${LLAMA_BLAS_VENDOR} MATCHES "Intel"))
-            add_compile_definitions(GGML_BLAS_USE_MKL)
-        endif()
+include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
 
-        set(LLAMA_EXTRA_LIBS     ${LLAMA_EXTRA_LIBS}     ${BLAS_LIBRARIES})
-        set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS})
-    else()
-        message(WARNING "BLAS not found, please refer to "
-        "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
-        " to set correct LLAMA_BLAS_VENDOR")
-    endif()
-endif()
-
-if (LLAMA_LLAMAFILE)
-    add_compile_definitions(GGML_USE_LLAMAFILE)
-
-    set(GGML_HEADERS_LLAMAFILE sgemm.h)
-    set(GGML_SOURCES_LLAMAFILE sgemm.cpp)
-endif()
-
-if (LLAMA_QKK_64)
-    add_compile_definitions(GGML_QKK_64)
-endif()
-
-if (LLAMA_CUBLAS)
-    message(WARNING "LLAMA_CUBLAS is deprecated and will be removed in the future.\nUse LLAMA_CUDA instead")
-    set(LLAMA_CUDA ON)
-endif()
-
-if (LLAMA_CUDA)
-    cmake_minimum_required(VERSION 3.17)
-
-    find_package(CUDAToolkit)
-    if (CUDAToolkit_FOUND)
-        message(STATUS "CUDA found")
-
-        enable_language(CUDA)
-
-        set(GGML_HEADERS_CUDA ggml-cuda.h)
-
-        file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu")
-        list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
-
-        add_compile_definitions(GGML_USE_CUDA)
-        add_compile_definitions(GGML_CUDA_USE_GRAPHS)
-        if (LLAMA_CUDA_FORCE_DMMV)
-            add_compile_definitions(GGML_CUDA_FORCE_DMMV)
-        endif()
-        if (LLAMA_CUDA_FORCE_MMQ)
-            add_compile_definitions(GGML_CUDA_FORCE_MMQ)
-        endif()
-        if (LLAMA_CUDA_NO_VMM)
-            add_compile_definitions(GGML_CUDA_NO_VMM)
-        endif()
-        add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
-        add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
-        if (DEFINED LLAMA_CUDA_DMMV_Y)
-            add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility
-        endif()
-        if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
-            add_compile_definitions(GGML_CUDA_F16)
-        endif()
-        add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
-        add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE})
-        if (LLAMA_CUDA_NO_PEER_COPY)
-            add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
-        endif()
-
-        if (LLAMA_STATIC)
-            if (WIN32)
-                # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library
-                set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
-            else ()
-                set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
-            endif()
-        else()
-            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
-        endif()
-
-        if (LLAMA_CUDA_NO_VMM)
-            # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so)
-        else()
-            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ...
-        endif()
+# override ggml options
+set(GGML_SANITIZE_THREAD    ${LLAMA_SANITIZE_THREAD})
+set(GGML_SANITIZE_ADDRESS   ${LLAMA_SANITIZE_ADDRESS})
+set(GGML_SANITIZE_UNDEFINED ${LLAMA_SANITIZE_UNDEFINED})
+set(GGML_ALL_WARNINGS       ${LLAMA_ALL_WARNINGS})
+set(GGML_FATAL_WARNINGS     ${LLAMA_FATAL_WARNINGS})
 
-    if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
-        # 52 == lowest CUDA 12 standard
-        # 60 == f16 CUDA intrinsics
-        # 61 == integer CUDA intrinsics
-        # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster
-        if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
-            set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
-        else()
-            set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
-            #set(CMAKE_CUDA_ARCHITECTURES "") # use this to compile much faster, but only F16 models work
-        endif()
-    endif()
-    message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
-
-    else()
-        message(WARNING "CUDA not found")
-    endif()
-endif()
-
-if (LLAMA_RPC)
-    add_compile_definitions(GGML_USE_RPC)
-
-    if (WIN32)
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ws2_32)
-    endif()
-
-    set(GGML_HEADERS_RPC ggml-rpc.h)
-    set(GGML_SOURCES_RPC ggml-rpc.cpp)
+# change the default for these ggml options
+if (NOT DEFINED GGML_LLAMAFILE)
+    set(GGML_LLAMAFILE ON)
 endif()
 
-if (LLAMA_CLBLAST)
-    find_package(CLBlast)
-    if (CLBlast_FOUND)
-        message(STATUS "CLBlast found")
-
-        set(GGML_HEADERS_OPENCL ggml-opencl.h)
-        set(GGML_SOURCES_OPENCL ggml-opencl.cpp)
-
-        add_compile_definitions(GGML_USE_CLBLAST)
-
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast)
-    else()
-        message(WARNING "CLBlast not found")
-    endif()
-endif()
-
-if (LLAMA_VULKAN)
-    find_package(Vulkan)
-    if (Vulkan_FOUND)
-        message(STATUS "Vulkan found")
-
-        set(GGML_HEADERS_VULKAN ggml-vulkan.h)
-        set(GGML_SOURCES_VULKAN ggml-vulkan.cpp)
-
-        add_compile_definitions(GGML_USE_VULKAN)
-
-        if (LLAMA_VULKAN_CHECK_RESULTS)
-            add_compile_definitions(GGML_VULKAN_CHECK_RESULTS)
-        endif()
-
-        if (LLAMA_VULKAN_DEBUG)
-            add_compile_definitions(GGML_VULKAN_DEBUG)
-        endif()
-
-        if (LLAMA_VULKAN_VALIDATE)
-            add_compile_definitions(GGML_VULKAN_VALIDATE)
-        endif()
-
-        if (LLAMA_VULKAN_RUN_TESTS)
-            add_compile_definitions(GGML_VULKAN_RUN_TESTS)
-        endif()
-
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} Vulkan::Vulkan)
-    else()
-        message(WARNING "Vulkan not found")
-    endif()
+if (NOT DEFINED GGML_CUDA_USE_GRAPHS)
+    set(GGML_CUDA_USE_GRAPHS ON)
 endif()
 
-if (LLAMA_HIPBLAS)
-    if ($ENV{ROCM_PATH})
-        set(ROCM_PATH $ENV{ROCM_PATH})
-    else()
-        set(ROCM_PATH /opt/rocm)
-    endif()
-    list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH})
-
-    # CMake on Windows doesn't support the HIP language yet
-    if(WIN32)
-        set(CXX_IS_HIPCC TRUE)
-    else()
-        string(REGEX MATCH "hipcc(\.bat)?$" CXX_IS_HIPCC "${CMAKE_CXX_COMPILER}")
-    endif()
-
-    if(CXX_IS_HIPCC)
-        if(LINUX)
-            if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang")
-                message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++")
-            endif()
-
-            message(WARNING "Setting hipcc as the C++ compiler is legacy behavior."
-                    " Prefer setting the HIP compiler directly. See README for details.")
-        endif()
-    else()
-        # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES.
-        if(AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES)
-            set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS})
-        endif()
-        cmake_minimum_required(VERSION 3.21)
-        enable_language(HIP)
-    endif()
-    find_package(hip     REQUIRED)
-    find_package(hipblas REQUIRED)
-    find_package(rocblas REQUIRED)
-
-    message(STATUS "HIP and hipBLAS found")
-
-    set(GGML_HEADERS_ROCM ggml-cuda.h)
-
-    file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu")
-    list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu")
-
-    add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA)
-
-    if (LLAMA_HIP_UMA)
-        add_compile_definitions(GGML_HIP_UMA)
-    endif()
-
-    if (LLAMA_CUDA_FORCE_DMMV)
-        add_compile_definitions(GGML_CUDA_FORCE_DMMV)
-    endif()
-
-    if (LLAMA_CUDA_FORCE_MMQ)
-        add_compile_definitions(GGML_CUDA_FORCE_MMQ)
-    endif()
-
-    if (LLAMA_CUDA_NO_PEER_COPY)
-        add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
+# transition helpers
+function (llama_option_depr TYPE OLD NEW)
+    if (${OLD})
+        message(${TYPE} "${OLD} is deprecated and will be removed in the future.\nUse ${NEW} instead\n")
+        set(${NEW} ON PARENT_SCOPE)
     endif()
-
-    add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
-    add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
-    add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
-
-    if (CXX_IS_HIPCC)
-        set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX)
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} hip::device)
-    else()
-        set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP)
-    endif()
-
-    if (LLAMA_STATIC)
-        message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
-    endif()
-
-    set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} PUBLIC hip::host roc::rocblas roc::hipblas)
-endif()
-
-if (LLAMA_SYCL)
-    if (NOT LLAMA_SYCL_TARGET MATCHES "^(INTEL|NVIDIA)$")
-        message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA")
-    endif()
-
-    if ( NOT DEFINED ENV{ONEAPI_ROOT})
-        message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh")
-    endif()
-    #todo: AOT
-
-    find_package(IntelSYCL REQUIRED)
-
-    message(STATUS "SYCL found")
-
-    add_compile_definitions(GGML_USE_SYCL)
-
-    if (LLAMA_SYCL_F16)
-        add_compile_definitions(GGML_SYCL_F16)
-    endif()
-
-    add_compile_options(-I./) #include DPCT
-    add_compile_options(-I/${SYCL_INCLUDE_DIR})
-
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
-    if (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
-    endif()
-
-    set(GGML_HEADERS_SYCL ggml-sycl.h)
-    set(GGML_SOURCES_SYCL ggml-sycl.cpp)
-
-    if (WIN32)
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib)
-    else()
-        if (LLAMA_SYCL_TARGET STREQUAL "INTEL")
-            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
-        elseif (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
-            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl pthread m dl onemkl)
-        endif()
-    endif()
-endif()
-
-if (LLAMA_KOMPUTE)
-    add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1)
-    find_package(Vulkan COMPONENTS glslc REQUIRED)
-    find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc)
-    if (NOT glslc_executable)
-        message(FATAL_ERROR "glslc not found")
-    endif()
-
-    function(compile_shader)
-        set(options)
-        set(oneValueArgs)
-        set(multiValueArgs SOURCES)
-        cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
-        foreach(source ${compile_shader_SOURCES})
-            get_filename_component(filename ${source} NAME)
-            set(spv_file ${filename}.spv)
-            add_custom_command(
-                OUTPUT ${spv_file}
-                DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source}
-                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp
-                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp
-                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp
-                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp
-                COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source}
-                COMMENT "Compiling ${source} to ${spv_file}"
-                )
-
-            get_filename_component(RAW_FILE_NAME ${spv_file} NAME)
-            set(FILE_NAME "shader${RAW_FILE_NAME}")
-            string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME})
-            string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE)
-            string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}")
-            set(OUTPUT_HEADER_FILE "${HEADER_FILE}")
-            message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}")
-            if(CMAKE_GENERATOR MATCHES "Visual Studio")
-                add_custom_command(
-                    OUTPUT ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_BINARY_DIR}/bin/$<CONFIG>/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
-                    DEPENDS ${spv_file} xxd
-                    COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$<CONFIG>/xxd"
-                    )
-            else()
-                add_custom_command(
-                    OUTPUT ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
-                    COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
-                    DEPENDS ${spv_file} xxd
-                    COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd"
-                    )
-            endif()
-        endforeach()
-    endfunction()
-
-    if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt")
-        message(STATUS "Kompute found")
-        set(KOMPUTE_OPT_LOG_LEVEL Error CACHE STRING "Kompute log level")
-        add_subdirectory(kompute)
-
-        # Compile our shaders
-        compile_shader(SOURCES
-            kompute-shaders/op_scale.comp
-            kompute-shaders/op_scale_8.comp
-            kompute-shaders/op_add.comp
-            kompute-shaders/op_addrow.comp
-            kompute-shaders/op_mul.comp
-            kompute-shaders/op_silu.comp
-            kompute-shaders/op_relu.comp
-            kompute-shaders/op_gelu.comp
-            kompute-shaders/op_softmax.comp
-            kompute-shaders/op_norm.comp
-            kompute-shaders/op_rmsnorm.comp
-            kompute-shaders/op_diagmask.comp
-            kompute-shaders/op_mul_mat_mat_f32.comp
-            kompute-shaders/op_mul_mat_f16.comp
-            kompute-shaders/op_mul_mat_q8_0.comp
-            kompute-shaders/op_mul_mat_q4_0.comp
-            kompute-shaders/op_mul_mat_q4_1.comp
-            kompute-shaders/op_mul_mat_q6_k.comp
-            kompute-shaders/op_getrows_f16.comp
-            kompute-shaders/op_getrows_q4_0.comp
-            kompute-shaders/op_getrows_q4_1.comp
-            kompute-shaders/op_getrows_q6_k.comp
-            kompute-shaders/op_rope_f16.comp
-            kompute-shaders/op_rope_f32.comp
-            kompute-shaders/op_cpy_f16_f16.comp
-            kompute-shaders/op_cpy_f16_f32.comp
-            kompute-shaders/op_cpy_f32_f16.comp
-            kompute-shaders/op_cpy_f32_f32.comp
-        )
-
-        # Create a custom target for our generated shaders
-        add_custom_target(generated_shaders DEPENDS
-            shaderop_scale.h
-            shaderop_scale_8.h
-            shaderop_add.h
-            shaderop_addrow.h
-            shaderop_mul.h
-            shaderop_silu.h
-            shaderop_relu.h
-            shaderop_gelu.h
-            shaderop_softmax.h
-            shaderop_norm.h
-            shaderop_rmsnorm.h
-            shaderop_diagmask.h
-            shaderop_mul_mat_mat_f32.h
-            shaderop_mul_mat_f16.h
-            shaderop_mul_mat_q8_0.h
-            shaderop_mul_mat_q4_0.h
-            shaderop_mul_mat_q4_1.h
-            shaderop_mul_mat_q6_k.h
-            shaderop_getrows_f16.h
-            shaderop_getrows_q4_0.h
-            shaderop_getrows_q4_1.h
-            shaderop_getrows_q6_k.h
-            shaderop_rope_f16.h
-            shaderop_rope_f32.h
-            shaderop_cpy_f16_f16.h
-            shaderop_cpy_f16_f32.h
-            shaderop_cpy_f32_f16.h
-            shaderop_cpy_f32_f32.h
-        )
-
-        # Create a custom command that depends on the generated_shaders
-        add_custom_command(
-            OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp
-            COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp
-            DEPENDS generated_shaders
-            COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp"
-        )
-
-        # Add the stamp to the main sources to ensure dependency tracking
-        set(GGML_SOURCES_KOMPUTE ggml-kompute.cpp ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp)
-        set(GGML_HEADERS_KOMPUTE ggml-kompute.h   ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp)
-
-        add_compile_definitions(GGML_USE_KOMPUTE)
-
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute)
-        set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR})
-    else()
-        message(WARNING "Kompute not found")
-    endif()
-endif()
-
-if (LLAMA_CPU_HBM)
-    find_library(memkind memkind REQUIRED)
-
-    add_compile_definitions(GGML_USE_CPU_HBM)
-
-    target_link_libraries(ggml PUBLIC memkind)
-endif()
-
-if (LLAMA_PERF)
-    add_compile_definitions(GGML_PERF)
-endif()
-
-function(get_flags CCID CCVER)
-    set(C_FLAGS "")
-    set(CXX_FLAGS "")
-
-    if (CCID MATCHES "Clang")
-        set(C_FLAGS   -Wunreachable-code-break -Wunreachable-code-return)
-        set(CXX_FLAGS -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi)
-
-        if (
-            (CCID STREQUAL "Clang"      AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR
-            (CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0)
-        )
-            list(APPEND C_FLAGS -Wdouble-promotion)
-        endif()
-    elseif (CCID STREQUAL "GNU")
-        set(C_FLAGS   -Wdouble-promotion)
-        set(CXX_FLAGS -Wno-array-bounds)
-
-        if (CCVER VERSION_GREATER_EQUAL 7.1.0)
-            list(APPEND CXX_FLAGS -Wno-format-truncation)
-        endif()
-        if (CCVER VERSION_GREATER_EQUAL 8.1.0)
-            list(APPEND CXX_FLAGS -Wextra-semi)
-        endif()
-    endif()
-
-    set(GF_C_FLAGS   ${C_FLAGS}   PARENT_SCOPE)
-    set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE)
 endfunction()
 
-if (LLAMA_FATAL_WARNINGS)
-    if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
-        list(APPEND C_FLAGS   -Werror)
-        list(APPEND CXX_FLAGS -Werror)
-    elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-        add_compile_options(/WX)
-    endif()
-endif()
-
-if (LLAMA_ALL_WARNINGS)
-    if (NOT MSVC)
-        list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
-        list(APPEND C_FLAGS       -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
-                                  -Werror=implicit-int -Werror=implicit-function-declaration)
-        list(APPEND CXX_FLAGS     -Wmissing-declarations -Wmissing-noreturn)
-
-        list(APPEND C_FLAGS   ${WARNING_FLAGS})
-        list(APPEND CXX_FLAGS ${WARNING_FLAGS})
-
-        get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
-
-        add_compile_options("$<$<COMPILE_LANGUAGE:C>:${C_FLAGS};${GF_C_FLAGS}>"
-                            "$<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS};${GF_CXX_FLAGS}>")
-    else()
-        # todo : msvc
-        set(C_FLAGS   "")
-        set(CXX_FLAGS "")
-    endif()
-endif()
-
-set(CUDA_CXX_FLAGS "")
-
-if (LLAMA_CUDA)
-    set(CUDA_FLAGS -use_fast_math)
-
-    if (LLAMA_FATAL_WARNINGS)
-        list(APPEND CUDA_FLAGS -Werror all-warnings)
-    endif()
-
-    if (LLAMA_ALL_WARNINGS AND NOT MSVC)
-        set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c)
-        if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "")
-            list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER})
-        endif()
-
-        execute_process(
-            COMMAND ${NVCC_CMD} -Xcompiler --version
-            OUTPUT_VARIABLE CUDA_CCFULLVER
-            ERROR_QUIET
-        )
-
-        if (NOT CUDA_CCFULLVER MATCHES clang)
-            set(CUDA_CCID "GNU")
-            execute_process(
-                COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion"
-                OUTPUT_VARIABLE CUDA_CCVER
-                ERROR_QUIET
-            )
-        else()
-            if (CUDA_CCFULLVER MATCHES Apple)
-                set(CUDA_CCID "AppleClang")
-            else()
-                set(CUDA_CCID "Clang")
-            endif()
-            string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER})
-        endif()
-
-        message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}")
-
-        get_flags(${CUDA_CCID} ${CUDA_CCVER})
-        list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS})  # This is passed to -Xcompiler later
-    endif()
-
-    if (NOT MSVC)
-        list(APPEND CUDA_CXX_FLAGS -Wno-pedantic)
-    endif()
-endif()
-
-if (WIN32)
-    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
-
-    if (BUILD_SHARED_LIBS)
-        set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
-    endif()
-endif()
-
-if (LLAMA_LTO)
-    include(CheckIPOSupported)
-    check_ipo_supported(RESULT result OUTPUT output)
-    if (result)
-        set(CMAKE_INTERPROCEDURAL_OPTIMIZATION TRUE)
-    else()
-        message(WARNING "IPO is not supported: ${output}")
-    endif()
-endif()
-
-if (LLAMA_CCACHE)
-    find_program(LLAMA_CCACHE_FOUND ccache)
-    if (LLAMA_CCACHE_FOUND)
-        set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
-        set(ENV{CCACHE_SLOPPINESS} time_macros)
-        message(STATUS "ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.")
-    else()
-        message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF")
-    endif ()
-endif()
-
-# this version of Apple ld64 is buggy
-execute_process(
-    COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v
-    ERROR_VARIABLE output
-    OUTPUT_QUIET
-)
-
-if (output MATCHES "dyld-1015\.7")
-    add_compile_definitions(HAVE_BUGGY_APPLE_LINKER)
-endif()
-
-# Architecture specific
-# TODO: probably these flags need to be tweaked on some architectures
-#       feel free to update the Makefile for your architecture and send a pull request or issue
-message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
-if (MSVC)
-    string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR)
-    message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}")
-else ()
-    set(CMAKE_GENERATOR_PLATFORM_LWR "")
-endif ()
-
-if (NOT MSVC)
-    if (LLAMA_STATIC)
-        add_link_options(-static)
-        if (MINGW)
-            add_link_options(-static-libgcc -static-libstdc++)
-        endif()
-    endif()
-    if (LLAMA_GPROF)
-        add_compile_options(-pg)
-    endif()
-endif()
-
-set(ARCH_FLAGS "")
-
-if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
-    (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
-     CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
-    message(STATUS "ARM detected")
-    if (MSVC)
-        add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead
-        add_compile_definitions(__ARM_NEON)
-        add_compile_definitions(__ARM_FEATURE_FMA)
-
-        set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS})
-        string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2")
-        check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
-        if (GGML_COMPILER_SUPPORT_DOTPROD)
-            add_compile_definitions(__ARM_FEATURE_DOTPROD)
-        endif ()
-        check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8)
-        if (GGML_COMPILER_SUPPORT_MATMUL_INT8)
-            add_compile_definitions(__ARM_FEATURE_MATMUL_INT8)
-        endif ()
-
-        check_cxx_source_compiles("#include <arm_neon.h>\nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
-        if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
-            add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-        endif ()
-        set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV})
-    else()
-        check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)
-        if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
-            list(APPEND ARCH_FLAGS -mfp16-format=ieee)
-        endif()
-        if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
-            # Raspberry Pi 1, Zero
-            list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access)
-        endif()
-        if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
-            if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android")
-                # Android armeabi-v7a
-                list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations)
-            else()
-                # Raspberry Pi 2
-                list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
-            endif()
-        endif()
-        if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
-            # Android arm64-v8a
-            # Raspberry Pi 3, 4, Zero 2 (32-bit)
-            list(APPEND ARCH_FLAGS -mno-unaligned-access)
-        endif()
-    endif()
-elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
-        (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
-         CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$"))
-    message(STATUS "x86 detected")
-    if (MSVC)
-        # instruction set detection for MSVC only
-        if (LLAMA_NATIVE)
-            include(cmake/FindSIMD.cmake)
-        endif ()
-        if (LLAMA_AVX512)
-            list(APPEND ARCH_FLAGS /arch:AVX512)
-            # MSVC has no compile-time flags enabling specific
-            # AVX512 extensions, neither it defines the
-            # macros corresponding to the extensions.
-            # Do it manually.
-            if (LLAMA_AVX512_VBMI)
-                add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VBMI__>)
-                add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VBMI__>)
-            endif()
-            if (LLAMA_AVX512_VNNI)
-                add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VNNI__>)
-                add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VNNI__>)
-            endif()
-            if (LLAMA_AVX512_BF16)
-                add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512BF16__>)
-                add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512BF16__>)
-            endif()
-        elseif (LLAMA_AVX2)
-            list(APPEND ARCH_FLAGS /arch:AVX2)
-        elseif (LLAMA_AVX)
-            list(APPEND ARCH_FLAGS /arch:AVX)
-        endif()
-    else()
-        if (LLAMA_NATIVE)
-            list(APPEND ARCH_FLAGS -march=native)
-        endif()
-        if (LLAMA_F16C)
-            list(APPEND ARCH_FLAGS -mf16c)
-        endif()
-        if (LLAMA_FMA)
-            list(APPEND ARCH_FLAGS -mfma)
-        endif()
-        if (LLAMA_AVX)
-            list(APPEND ARCH_FLAGS -mavx)
-        endif()
-        if (LLAMA_AVX2)
-            list(APPEND ARCH_FLAGS -mavx2)
-        endif()
-        if (LLAMA_AVX512)
-            list(APPEND ARCH_FLAGS -mavx512f)
-            list(APPEND ARCH_FLAGS -mavx512bw)
-        endif()
-        if (LLAMA_AVX512_VBMI)
-            list(APPEND ARCH_FLAGS -mavx512vbmi)
-        endif()
-        if (LLAMA_AVX512_VNNI)
-            list(APPEND ARCH_FLAGS -mavx512vnni)
-        endif()
-        if (LLAMA_AVX512_BF16)
-            list(APPEND ARCH_FLAGS -mavx512bf16)
-        endif()
-    endif()
-elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
-    message(STATUS "PowerPC detected")
-    if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le")
-        list(APPEND ARCH_FLAGS -mcpu=powerpc64le)
-    else()
-        list(APPEND ARCH_FLAGS -mcpu=native -mtune=native)
-        #TODO: Add  targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be)
-    endif()
-else()
-    message(STATUS "Unknown architecture")
-endif()
-
-add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:${ARCH_FLAGS}>")
-add_compile_options("$<$<COMPILE_LANGUAGE:C>:${ARCH_FLAGS}>")
-
-if (LLAMA_CUDA)
-    list(APPEND CUDA_CXX_FLAGS ${ARCH_FLAGS})
-    list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED)  # pass host compiler flags as a single argument
-    if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "")
-        list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED})
-    endif()
-    add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>")
-endif()
-
-if (MINGW)
-    # Target Windows 8 for PrefetchVirtualMemory
-    add_compile_definitions(_WIN32_WINNT=${LLAMA_WIN_VER})
-endif()
+llama_option_depr(FATAL_ERROR LLAMA_CUBLAS              GGML_CUDA)
+llama_option_depr(WARNING     LLAMA_CUDA                GGML_CUDA)
+llama_option_depr(WARNING     LLAMA_KOMPUTE             GGML_KOMPUTE)
+llama_option_depr(WARNING     LLAMA_METAL               GGML_METAL)
+llama_option_depr(WARNING     LLAMA_METAL_EMBED_LIBRARY GGML_METAL_EMBED_LIBRARY)
+llama_option_depr(WARNING     LLAMA_NATIVE              GGML_NATIVE)
+llama_option_depr(WARNING     LLAMA_RPC                 GGML_RPC)
+llama_option_depr(WARNING     LLAMA_SYCL                GGML_SYCL)
+llama_option_depr(WARNING     LLAMA_SYCL_F16            GGML_SYCL_F16)
+llama_option_depr(WARNING     LLAMA_CANN                GGML_CANN)
 
 #
-# POSIX conformance
+# build the library
 #
 
-# clock_gettime came in POSIX.1b (1993)
-# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
-# posix_memalign came in POSIX.1-2001 / SUSv3
-# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985)
-add_compile_definitions(_XOPEN_SOURCE=600)
-
-# Somehow in OpenBSD whenever POSIX conformance is specified
-# some string functions rely on locale_t availability,
-# which was introduced in POSIX.1-2008, forcing us to go higher
-if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
-    remove_definitions(-D_XOPEN_SOURCE=600)
-    add_compile_definitions(_XOPEN_SOURCE=700)
-endif()
-
-# Data types, macros and functions related to controlling CPU affinity and
-# some memory allocation are available on Linux through GNU extensions in libc
-if (CMAKE_SYSTEM_NAME MATCHES "Linux")
-    add_compile_definitions(_GNU_SOURCE)
-endif()
-
-# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1,
-# and on macOS its availability depends on enabling Darwin extensions
-# similarly on DragonFly, enabling BSD extensions is necessary
-if (
-    CMAKE_SYSTEM_NAME MATCHES "Darwin" OR
-    CMAKE_SYSTEM_NAME MATCHES "iOS" OR
-    CMAKE_SYSTEM_NAME MATCHES "tvOS" OR
-    CMAKE_SYSTEM_NAME MATCHES "DragonFly"
-)
-    add_compile_definitions(_DARWIN_C_SOURCE)
-endif()
-
-# alloca is a non-standard interface that is not visible on BSDs when
-# POSIX conformance is specified, but not all of them provide a clean way
-# to enable it in such cases
-if (CMAKE_SYSTEM_NAME MATCHES "FreeBSD")
-    add_compile_definitions(__BSD_VISIBLE)
-endif()
-if (CMAKE_SYSTEM_NAME MATCHES "NetBSD")
-    add_compile_definitions(_NETBSD_SOURCE)
-endif()
-if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
-    add_compile_definitions(_BSD_SOURCE)
+if (NOT TARGET ggml)
+    add_subdirectory(ggml)
+    # ... otherwise assume ggml is added by a parent CMakeLists.txt
 endif()
+add_subdirectory(src)
 
 #
-# libraries
+# install
 #
 
-# ggml
-
-add_library(ggml OBJECT
-            ggml.c
-            ggml.h
-            ggml-alloc.c
-            ggml-alloc.h
-            ggml-backend.c
-            ggml-backend.h
-            ggml-quants.c
-            ggml-quants.h
-            ${GGML_SOURCES_CUDA}      ${GGML_HEADERS_CUDA}
-            ${GGML_SOURCES_OPENCL}    ${GGML_HEADERS_OPENCL}
-            ${GGML_SOURCES_METAL}     ${GGML_HEADERS_METAL}
-            ${GGML_SOURCES_RPC}       ${GGML_HEADERS_RPC}
-            ${GGML_SOURCES_EXTRA}     ${GGML_HEADERS_EXTRA}
-            ${GGML_SOURCES_SYCL}      ${GGML_HEADERS_SYCL}
-            ${GGML_SOURCES_KOMPUTE}   ${GGML_HEADERS_KOMPUTE}
-            ${GGML_SOURCES_VULKAN}    ${GGML_HEADERS_VULKAN}
-            ${GGML_SOURCES_ROCM}      ${GGML_HEADERS_ROCM}
-            ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE}
-            )
-
-target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})
-target_compile_features   (ggml PUBLIC c_std_11) # don't bump
-
-target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
-
-add_library(ggml_static STATIC $<TARGET_OBJECTS:ggml>)
-
-if (BUILD_SHARED_LIBS)
-    set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
-    add_library(ggml_shared SHARED $<TARGET_OBJECTS:ggml>)
-    target_link_libraries(ggml_shared PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
-    install(TARGETS ggml_shared LIBRARY)
-endif()
-
-# llama
+include(GNUInstallDirs)
+include(CMakePackageConfigHelpers)
 
-add_library(llama
-            llama.cpp
-            llama.h
-            unicode.h
-            unicode.cpp
-            unicode-data.cpp
-            )
+set(LLAMA_BUILD_NUMBER        ${BUILD_NUMBER})
+set(LLAMA_BUILD_COMMIT        ${BUILD_COMMIT})
+set(LLAMA_INSTALL_VERSION 0.0.${BUILD_NUMBER})
 
-target_include_directories(llama PUBLIC .)
-target_compile_features   (llama PUBLIC cxx_std_11) # don't bump
-
-target_link_libraries(llama PRIVATE
-    ggml
-    ${LLAMA_EXTRA_LIBS}
-    )
-
-if (BUILD_SHARED_LIBS)
-    set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)
-    target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD)
-    if (LLAMA_METAL)
-        set_target_properties(llama PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
-    endif()
-endif()
+set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header  files")
+set(LLAMA_LIB_INSTALL_DIR     ${CMAKE_INSTALL_LIBDIR}     CACHE PATH "Location of library files")
+set(LLAMA_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location of binary  files")
 
 
+# At the moment some compile definitions are placed within the ggml/src
+# directory but not exported on the `ggml` target. This could be improved by
+# determining _precisely_ which defines are necessary for the llama-config
+# package.
 #
-# install
-#
+get_target_property(GGML_DIRECTORY ggml SOURCE_DIR)
+get_directory_property(GGML_DIR_DEFINES DIRECTORY ${GGML_DIRECTORY} COMPILE_DEFINITIONS)
+get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS)
+set(GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES} ${GGML_DIR_DEFINES})
+get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES)
 
-include(GNUInstallDirs)
-include(CMakePackageConfigHelpers)
-
-set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR}
-    CACHE PATH "Location of header files")
-set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR}
-    CACHE PATH "Location of library files")
-set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR}
-    CACHE PATH "Location of binary files")
-set(LLAMA_BUILD_NUMBER ${BUILD_NUMBER})
-set(LLAMA_BUILD_COMMIT ${BUILD_COMMIT})
-set(LLAMA_INSTALL_VERSION 0.0.${BUILD_NUMBER})
-get_directory_property(LLAMA_TRANSIENT_DEFINES COMPILE_DEFINITIONS)
+set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h)
+install(TARGETS llama LIBRARY PUBLIC_HEADER)
 
 configure_package_config_file(
-        ${CMAKE_CURRENT_SOURCE_DIR}/scripts/LlamaConfig.cmake.in
-        ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake
-    INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama
+        ${CMAKE_CURRENT_SOURCE_DIR}/cmake/llama-config.cmake.in
+        ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
+    INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama
     PATH_VARS LLAMA_INCLUDE_INSTALL_DIR
               LLAMA_LIB_INSTALL_DIR
               LLAMA_BIN_INSTALL_DIR )
 
 write_basic_package_version_file(
-        ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake
+        ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
     VERSION ${LLAMA_INSTALL_VERSION}
     COMPATIBILITY SameMajorVersion)
 
-install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake
-              ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake
-        DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama)
-
-set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h"
-        "${GGML_HEADERS_CUDA}"  "${GGML_HEADERS_OPENCL}"
-        "${GGML_HEADERS_METAL}" "${GGML_HEADERS_EXTRA}")
-
-set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
-install(TARGETS ggml PUBLIC_HEADER)
-
-set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/llama.h)
-install(TARGETS llama LIBRARY PUBLIC_HEADER)
+install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
+              ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
+        DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama)
 
 install(
-    FILES convert.py
+    FILES convert_hf_to_gguf.py
     PERMISSIONS
         OWNER_READ
         OWNER_WRITE
@@ -1302,22 +176,13 @@ install(
         WORLD_READ
         WORLD_EXECUTE
     DESTINATION ${CMAKE_INSTALL_BINDIR})
-if (LLAMA_METAL)
-    install(
-        FILES ggml-metal.metal
-        PERMISSIONS
-            OWNER_READ
-            OWNER_WRITE
-            GROUP_READ
-            WORLD_READ
-        DESTINATION ${CMAKE_INSTALL_BINDIR})
-    if (NOT LLAMA_METAL_EMBED_LIBRARY)
-        install(
-            FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            DESTINATION ${CMAKE_INSTALL_BINDIR}
-        )
-    endif()
-endif()
+
+configure_file(cmake/llama.pc.in
+        "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
+        @ONLY)
+
+install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
+        DESTINATION lib/pkgconfig)
 
 #
 # programs, examples and tests
diff --git a/CMakePresets.json b/CMakePresets.json
index ad1af7eccebbd..bdad38952d3cb 100644
--- a/CMakePresets.json
+++ b/CMakePresets.json
@@ -1,4 +1,4 @@
-{
+{
   "version": 4,
   "configurePresets": [
     {
@@ -11,10 +11,23 @@
             "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
         }
     },
-
+    {
+        "name": "sycl-base",
+        "hidden": true,
+        "generator": "Ninja",
+        "binaryDir": "${sourceDir}/build-${presetName}",
+        "cacheVariables": {
+            "CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
+            "CMAKE_CXX_COMPILER": "icx",
+            "CMAKE_C_COMPILER": "cl",
+            "GGML_SYCL": "ON",
+            "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
+        }
+    },
     { "name": "debug",   "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } },
-    { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
-    { "name": "static",  "hidden": true, "cacheVariables": { "LLAMA_STATIC": "ON" } },
+    { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
+    { "name": "reldbg",  "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
+    { "name": "static",  "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
 
     {
         "name": "arm64-windows-msvc", "hidden": true,
@@ -35,11 +48,18 @@
     },
 
     { "name": "arm64-windows-llvm-debug"  , "inherits": [ "base", "arm64-windows-llvm",  "debug"   ] },
-    { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm",  "release" ] },
-    { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm",  "release", "static" ] },
+    { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm",  "reldbg" ] },
+    { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm",  "reldbg", "static" ] },
 
     { "name": "arm64-windows-msvc-debug"  , "inherits": [ "base", "arm64-windows-msvc",  "debug"   ] },
-    { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "release" ] },
-    { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "release", "static" ] }
+    { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg" ] },
+    { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg", "static" ] },
+
+    { "name": "x64-windows-msvc-debug"  , "inherits": [ "base", "debug"   ] },
+    { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
+    { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
+
+    { "name": "x64-windows-sycl-debug"  , "inherits": [ "sycl-base", "debug"   ] },
+    { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] }
   ]
 }
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000000000..a9e000e5227d9
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,29 @@
+# Pull requests (for contributors)
+
+- Test your changes:
+  - Using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library
+  - Execute [the full CI locally on your machine](ci/README.md) before publishing
+- Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs.
+  - The PR template has a series of review complexity checkboxes `[ ]` that [you can mark as](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) `[X]` for your convenience
+- Consider allowing write access to your branch for faster review
+- If your PR becomes stale, don't hesitate to ping the maintainers in the comments
+
+# Pull requests (for collaborators)
+
+- Squash-merge PRs
+- Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
+- Optionally, pick a `<module>` from here: https://github.com/ggerganov/llama.cpp/wiki/Modules
+
+# Coding guidelines
+
+- Avoid adding third-party dependencies, extra files, extra headers, etc.
+- Always consider cross-compatibility with other operating systems and architectures
+- Avoid fancy looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
+- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
+- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
+- Naming usually optimizes for common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
+- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
+- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
+
+![matmul](media/matmul.png)
+
diff --git a/Makefile b/Makefile
index 9a26aec507dbc..332496cfc39c1 100644
--- a/Makefile
+++ b/Makefile
@@ -1,13 +1,51 @@
 # Define the default target now so that it is always the first target
 BUILD_TARGETS = \
-	main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
-	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama beam-search  \
-	retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
+	libllava.a \
+	llama-baby-llama \
+	llama-batched \
+	llama-batched-bench \
+	llama-bench \
+	llama-benchmark-matmult \
+	llama-cli \
+	llama-convert-llama2c-to-ggml \
+	llama-embedding \
+	llama-eval-callback \
+	llama-export-lora \
+	llama-gbnf-validator \
+	llama-gguf \
+	llama-gguf-hash \
+	llama-gguf-split \
+	llama-gritlm \
+	llama-imatrix \
+	llama-infill \
+	llama-llava-cli \
+	llama-minicpmv-cli\
+	llama-lookahead \
+	llama-lookup \
+	llama-lookup-create \
+	llama-lookup-merge \
+	llama-lookup-stats \
+	llama-parallel \
+	llama-passkey \
+	llama-perplexity \
+	llama-q8dot \
+	llama-quantize \
+	llama-quantize-stats \
+	llama-retrieval \
+	llama-save-load-state \
+	llama-server \
+	llama-simple \
+	llama-speculative \
+	llama-tokenize \
+	llama-vdot \
+	llama-cvector-generator \
+	tests/test-c.o
 
 # Binaries only useful for tests
 TEST_TARGETS = \
 	tests/test-autorelease \
 	tests/test-backend-ops \
+	tests/test-chat-template \
 	tests/test-double-float \
 	tests/test-grad0 \
 	tests/test-grammar-integration \
@@ -24,8 +62,89 @@ TEST_TARGETS = \
 	tests/test-tokenizer-1-bpe \
 	tests/test-tokenizer-1-spm
 
-# Code coverage output files
-COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
+# Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned
+LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot convert-llama2c-to-ggml \
+	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \
+	retrieval speculative infill tokenize benchmark-matmult parallel export-lora lookahead lookup passkey gritlm
+
+# Legacy build targets that were renamed in #7809, but we want to build binaries that for them that output a deprecation warning if people try to use them.
+#  We don't want to clutter things too much, so we only build replacements for the most commonly used binaries.
+LEGACY_TARGETS_BUILD = main quantize perplexity embedding server
+
+# Deprecation aliases
+ifdef LLAMA_CUBLAS
+$(error LLAMA_CUBLAS is removed. Use GGML_CUDA instead.)
+endif
+
+ifdef LLAMA_CUDA
+GGML_CUDA := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_KOMPUTE
+GGML_KOMPUTE := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_METAL
+GGML_METAL := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_OPENMP
+GGML_OPENMP := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_RPC
+GGML_RPC := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_SYCL
+GGML_SYCL := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_SYCL_F16
+GGML_SYCL_F16 := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_OPENBLAS
+GGML_OPENBLAS := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_OPENBLAS64
+GGML_OPENBLAS64 := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_BLIS
+GGML_BLIS := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_NO_LLAMAFILE
+GGML_NO_LLAMAFILE := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_NO_ACCELERATE
+GGML_NO_ACCELERATE := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_NO_OPENMP
+GGML_NO_OPENMP := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifdef LLAMA_NO_METAL
+GGML_NO_METAL := 1
+DEPRECATE_WARNING := 1
+endif
 
 ifndef UNAME_S
 UNAME_S := $(shell uname -s)
@@ -53,10 +172,12 @@ endif
 # Mac OS + Arm can report x86_64
 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
 ifeq ($(UNAME_S),Darwin)
-	ifndef LLAMA_NO_METAL
-		LLAMA_METAL := 1
+	ifndef GGML_NO_METAL
+		GGML_METAL := 1
 	endif
 
+	GGML_NO_OPENMP := 1
+
 	ifneq ($(UNAME_P),arm)
 		SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
 		ifeq ($(SYSCTL_M),1)
@@ -67,7 +188,19 @@ ifeq ($(UNAME_S),Darwin)
 	endif
 endif
 
-default: $(BUILD_TARGETS)
+ifdef GGML_METAL
+	GGML_METAL_EMBED_LIBRARY := 1
+endif
+
+ifdef GGML_RPC
+	BUILD_TARGETS += rpc-server
+endif
+
+ifdef GGML_VULKAN
+	BUILD_TARGETS += vulkan-shaders-gen
+endif
+
+default: $(BUILD_TARGETS) $(LEGACY_TARGETS_BUILD)
 
 test: $(TEST_TARGETS)
 	@failures=0; \
@@ -102,19 +235,7 @@ test: $(TEST_TARGETS)
 	fi
 	@echo 'All tests passed.'
 
-all: $(BUILD_TARGETS) $(TEST_TARGETS)
-
-coverage: ## Run code coverage
-	gcov -pb tests/*.cpp
-
-lcov-report: coverage ## Generate lcov report
-	mkdir -p lcov-report
-	lcov --capture --directory . --output-file lcov-report/coverage.info
-	genhtml lcov-report/coverage.info --output-directory lcov-report
-
-gcovr-report: coverage ## Generate gcovr report
-	mkdir -p gcovr-report
-	gcovr --root . --html --html-details --output gcovr-report/coverage.html
+all: $(BUILD_TARGETS) $(TEST_TARGETS) $(LEGACY_TARGETS_BUILD)
 
 ifdef RISCV_CROSS_COMPILE
 CC	:= riscv64-unknown-linux-gnu-gcc
@@ -126,33 +247,27 @@ endif
 #
 
 # keep standard at C11 and C++11
-MK_CPPFLAGS  = -I. -Icommon
+MK_CPPFLAGS  = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon
 MK_CFLAGS    = -std=c11   -fPIC
 MK_CXXFLAGS  = -std=c++11 -fPIC
 MK_NVCCFLAGS = -std=c++11
 
-# -Ofast tends to produce faster code, but may not be available for some compilers.
-ifdef LLAMA_FAST
-MK_CFLAGS     += -Ofast
-HOST_CXXFLAGS += -Ofast
-MK_NVCCFLAGS  += -O3
-else
-MK_CFLAGS     += -O3
-MK_CXXFLAGS   += -O3
-MK_NVCCFLAGS  += -O3
+ifdef LLAMA_NO_CCACHE
+GGML_NO_CCACHE := 1
+DEPRECATE_WARNING := 1
 endif
 
-ifndef LLAMA_NO_CCACHE
+ifndef GGML_NO_CCACHE
 CCACHE := $(shell which ccache)
 ifdef CCACHE
 export CCACHE_SLOPPINESS = time_macros
-$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.)
+$(info I ccache found, compilation results will be cached. Disable with GGML_NO_CCACHE.)
 CC    := $(CCACHE) $(CC)
 CXX   := $(CCACHE) $(CXX)
 else
 $(info I ccache not found. Consider installing it for faster compilation.)
 endif # CCACHE
-endif # LLAMA_NO_CCACHE
+endif # GGML_NO_CCACHE
 
 # clock_gettime came in POSIX.1b (1993)
 # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
@@ -196,20 +311,24 @@ ifeq ($(UNAME_S),OpenBSD)
 	MK_CPPFLAGS += -D_BSD_SOURCE
 endif
 
-ifdef LLAMA_SCHED_MAX_COPIES
-	MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(LLAMA_SCHED_MAX_COPIES)
+ifdef GGML_SCHED_MAX_COPIES
+	MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(GGML_SCHED_MAX_COPIES)
 endif
 
 ifdef LLAMA_DEBUG
-	MK_CFLAGS   += -O0 -g
-	MK_CXXFLAGS += -O0 -g
-	MK_LDFLAGS  += -g
+	MK_CFLAGS    += -O0 -g
+	MK_CXXFLAGS  += -O0 -g
+	MK_LDFLAGS   += -g
+	MK_NVCCFLAGS += -O0 -g
 
 	ifeq ($(UNAME_S),Linux)
 		MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS
 	endif
 else
-	MK_CPPFLAGS += -DNDEBUG
+	MK_CPPFLAGS   += -DNDEBUG
+	MK_CFLAGS     += -O3 -g
+	MK_CXXFLAGS   += -O3 -g
+	MK_NVCCFLAGS  += -O3 -g
 endif
 
 ifdef LLAMA_SANITIZE_THREAD
@@ -239,19 +358,31 @@ ifdef LLAMA_SERVER_SSL
 	MK_LDFLAGS += -lssl -lcrypto
 endif
 
-ifdef LLAMA_CODE_COVERAGE
-	MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase ''
-endif
-
 ifdef LLAMA_DISABLE_LOGS
 	MK_CPPFLAGS += -DLOG_DISABLE_LOGS
 endif # LLAMA_DISABLE_LOGS
 
 # warnings
-WARN_FLAGS    = -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
-MK_CFLAGS    += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int \
-				-Werror=implicit-function-declaration
-MK_CXXFLAGS  += $(WARN_FLAGS) -Wmissing-declarations -Wmissing-noreturn
+WARN_FLAGS = \
+	-Wall \
+	-Wextra \
+	-Wpedantic \
+	-Wcast-qual \
+	-Wno-unused-function
+
+MK_CFLAGS += \
+	$(WARN_FLAGS) \
+	-Wshadow \
+	-Wstrict-prototypes \
+	-Wpointer-arith \
+	-Wmissing-prototypes \
+	-Werror=implicit-int \
+	-Werror=implicit-function-declaration
+
+MK_CXXFLAGS += \
+	$(WARN_FLAGS) \
+	-Wmissing-declarations \
+	-Wmissing-noreturn
 
 ifeq ($(LLAMA_FATAL_WARNINGS),1)
 	MK_CFLAGS   += -Werror
@@ -296,9 +427,6 @@ ifdef LLAMA_GPROF
 	MK_CFLAGS   += -pg
 	MK_CXXFLAGS += -pg
 endif
-ifdef LLAMA_PERF
-	MK_CPPFLAGS += -DGGML_PERF
-endif
 
 # Architecture specific
 # TODO: probably these flags need to be tweaked on some architectures
@@ -379,253 +507,443 @@ ifneq ($(filter ppc64le%,$(UNAME_M)),)
 	CUDA_POWER_ARCH = 1
 endif
 
+ifneq ($(filter loongarch64%,$(UNAME_M)),)
+	MK_CFLAGS   += -mlasx
+	MK_CXXFLAGS += -mlasx
+endif
+
 else
 	MK_CFLAGS   += -march=rv64gcv -mabi=lp64d
 	MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d
 endif
 
-ifdef LLAMA_QKK_64
-	MK_CPPFLAGS += -DGGML_QKK_64
-endif
-
-ifndef LLAMA_NO_ACCELERATE
+ifndef GGML_NO_ACCELERATE
 	# Mac OS - include Accelerate framework.
 	# `-framework Accelerate` works both with Apple Silicon and Mac Intel
 	ifeq ($(UNAME_S),Darwin)
-		MK_CPPFLAGS += -DGGML_USE_ACCELERATE
+		MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS
 		MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK
 		MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64
 		MK_LDFLAGS  += -framework Accelerate
+		OBJ_GGML    += ggml/src/ggml-blas.o
 	endif
-endif # LLAMA_NO_ACCELERATE
+endif # GGML_NO_ACCELERATE
 
-ifdef LLAMA_OPENBLAS
-	MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas)
+ifdef GGML_MUSA
+	CC := clang
+	CXX := clang++
+	GGML_CUDA := 1
+	MK_CPPFLAGS += -DGGML_USE_MUSA
+endif
+
+ifndef GGML_NO_OPENMP
+	MK_CPPFLAGS += -DGGML_USE_OPENMP
+	MK_CFLAGS   += -fopenmp
+	MK_CXXFLAGS += -fopenmp
+	ifdef GGML_MUSA
+		MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp
+		MK_LDFLAGS  += -L/usr/lib/llvm-10/lib
+	endif # GGML_MUSA
+endif # GGML_NO_OPENMP
+
+ifdef GGML_OPENBLAS
+	MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas)
 	MK_CFLAGS   += $(shell pkg-config --cflags-only-other openblas)
 	MK_LDFLAGS  += $(shell pkg-config --libs openblas)
-endif # LLAMA_OPENBLAS
+	OBJ_GGML    += ggml/src/ggml-blas.o
+endif # GGML_OPENBLAS
+
+ifdef GGML_OPENBLAS64
+	MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64)
+	MK_CFLAGS   += $(shell pkg-config --cflags-only-other openblas64)
+	MK_LDFLAGS  += $(shell pkg-config --libs openblas64)
+	OBJ_GGML    += ggml/src/ggml-blas.o
+endif # GGML_OPENBLAS64
+
+ifdef GGML_BLIS
+	MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis
+	MK_LDFLAGS  += -lblis -L/usr/local/lib
+	OBJ_GGML    += ggml/src/ggml-blas.o
+endif # GGML_BLIS
 
-ifndef LLAMA_NO_LLAMAFILE
+ifdef GGML_NVPL
+	MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas
+	MK_LDFLAGS  += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp
+	OBJ_GGML    += ggml/src/ggml-blas.o
+endif # GGML_NVPL
+
+ifndef GGML_NO_LLAMAFILE
 	MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
-	OBJS        += sgemm.o
+	OBJ_GGML    += ggml/src/llamafile/sgemm.o
 endif
 
-ifdef LLAMA_BLIS
-	MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis
-	MK_LDFLAGS  += -lblis -L/usr/local/lib
-endif # LLAMA_BLIS
+ifdef GGML_RPC
+	MK_CPPFLAGS += -DGGML_USE_RPC
+	OBJ_GGML    += ggml/src/ggml-rpc.o
+endif # GGML_RPC
 
-ifdef LLAMA_CUBLAS
-# LLAMA_CUBLAS is deprecated and will be removed in the future
-	LLAMA_CUDA := 1
-endif
+OBJ_CUDA_TMPL      = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu))
+OBJ_CUDA_TMPL     += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu))
 
-ifdef LLAMA_CUDA
-	ifneq ('', '$(wildcard /opt/cuda)')
-		CUDA_PATH ?= /opt/cuda
+ifdef GGML_CUDA_FA_ALL_QUANTS
+	OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*.cu))
+else
+	OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu))
+	OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu))
+	OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*f16-f16.cu))
+endif # GGML_CUDA_FA_ALL_QUANTS
+
+ifdef GGML_CUDA
+	ifdef GGML_MUSA
+		ifneq ('', '$(wildcard /opt/musa)')
+			CUDA_PATH ?= /opt/musa
+		else
+			CUDA_PATH ?= /usr/local/musa
+		endif
+
+		MK_CPPFLAGS  += -DGGML_USE_CUDA -I$(CUDA_PATH)/include
+		MK_LDFLAGS   += -lmusa -lmublas -lmusart -lpthread -ldl -lrt -L$(CUDA_PATH)/lib -L/usr/lib64
+		MK_NVCCFLAGS += -x musa -mtgpu --cuda-gpu-arch=mp_22
 	else
-		CUDA_PATH ?= /usr/local/cuda
-	endif
-	MK_CPPFLAGS  += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include -DGGML_CUDA_USE_GRAPHS
-	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
-	OBJS         += ggml-cuda.o
-	OBJS         += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
-	MK_NVCCFLAGS += -use_fast_math
+		ifneq ('', '$(wildcard /opt/cuda)')
+			CUDA_PATH ?= /opt/cuda
+		else
+			CUDA_PATH ?= /usr/local/cuda
+		endif
+
+		MK_CPPFLAGS  += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include -DGGML_CUDA_USE_GRAPHS
+		MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib
+		MK_NVCCFLAGS += -use_fast_math
+	endif # GGML_MUSA
+
+	OBJ_GGML += ggml/src/ggml-cuda.o
+	OBJ_GGML += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
+	OBJ_GGML += $(OBJ_CUDA_TMPL)
+
 ifdef LLAMA_FATAL_WARNINGS
 	MK_NVCCFLAGS += -Werror all-warnings
 endif # LLAMA_FATAL_WARNINGS
+
+ifndef GGML_MUSA
 ifndef JETSON_EOL_MODULE_DETECT
 	MK_NVCCFLAGS += --forward-unknown-to-host-compiler
 endif # JETSON_EOL_MODULE_DETECT
+endif # GGML_MUSA
+
 ifdef LLAMA_DEBUG
 	MK_NVCCFLAGS += -lineinfo
 endif # LLAMA_DEBUG
-ifdef LLAMA_CUDA_NVCC
-	NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC)
+
+ifdef GGML_CUDA_DEBUG
+	MK_NVCCFLAGS += --device-debug
+endif # GGML_CUDA_DEBUG
+
+ifdef GGML_CUDA_NVCC
+	NVCC = $(CCACHE) $(GGML_CUDA_NVCC)
 else
-	NVCC = $(CCACHE) nvcc
-endif #LLAMA_CUDA_NVCC
+	ifdef GGML_MUSA
+		NVCC = $(CCACHE) mcc
+	else
+		NVCC = $(CCACHE) nvcc
+	endif # GGML_MUSA
+endif # GGML_CUDA_NVCC
+
 ifdef CUDA_DOCKER_ARCH
 	MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
 else ifndef CUDA_POWER_ARCH
 	MK_NVCCFLAGS += -arch=native
 endif # CUDA_DOCKER_ARCH
-ifdef LLAMA_CUDA_FORCE_DMMV
+
+ifdef GGML_CUDA_FORCE_DMMV
 	MK_NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
-endif # LLAMA_CUDA_FORCE_DMMV
-ifdef LLAMA_CUDA_FORCE_MMQ
+endif # GGML_CUDA_FORCE_DMMV
+
+ifdef GGML_CUDA_FORCE_MMQ
 	MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ
-endif # LLAMA_CUDA_FORCE_MMQ
-ifdef LLAMA_CUDA_DMMV_X
-	MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
+endif # GGML_CUDA_FORCE_MMQ
+
+ifdef GGML_CUDA_FORCE_CUBLAS
+	MK_NVCCFLAGS += -DGGML_CUDA_FORCE_CUBLAS
+endif # GGML_CUDA_FORCE_CUBLAS
+
+ifdef GGML_CUDA_DMMV_X
+	MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(GGML_CUDA_DMMV_X)
 else
 	MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
-endif # LLAMA_CUDA_DMMV_X
-ifdef LLAMA_CUDA_MMV_Y
-	MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
-else ifdef LLAMA_CUDA_DMMV_Y
-	MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
+endif # GGML_CUDA_DMMV_X
+
+ifdef GGML_CUDA_MMV_Y
+	MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_MMV_Y)
+else ifdef GGML_CUDA_DMMV_Y
+	MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_DMMV_Y) # for backwards compatibility
 else
 	MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
-endif # LLAMA_CUDA_MMV_Y
-ifdef LLAMA_CUDA_F16
+endif # GGML_CUDA_MMV_Y
+
+ifdef GGML_CUDA_F16
 	MK_NVCCFLAGS += -DGGML_CUDA_F16
-endif # LLAMA_CUDA_F16
-ifdef LLAMA_CUDA_DMMV_F16
+endif # GGML_CUDA_F16
+
+ifdef GGML_CUDA_DMMV_F16
 	MK_NVCCFLAGS += -DGGML_CUDA_F16
-endif # LLAMA_CUDA_DMMV_F16
-ifdef LLAMA_CUDA_KQUANTS_ITER
-	MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
+endif # GGML_CUDA_DMMV_F16
+
+ifdef GGML_CUDA_KQUANTS_ITER
+	MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(GGML_CUDA_KQUANTS_ITER)
 else
 	MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
 endif
-ifdef LLAMA_CUDA_PEER_MAX_BATCH_SIZE
-	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(LLAMA_CUDA_PEER_MAX_BATCH_SIZE)
+
+ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE
+	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE)
 else
 	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
-endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE
-ifdef LLAMA_CUDA_NO_PEER_COPY
+endif # GGML_CUDA_PEER_MAX_BATCH_SIZE
+
+ifdef GGML_CUDA_NO_PEER_COPY
 	MK_NVCCFLAGS += -DGGML_CUDA_NO_PEER_COPY
-endif # LLAMA_CUDA_NO_PEER_COPY
-ifdef LLAMA_CUDA_CCBIN
-	MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
-endif
+endif # GGML_CUDA_NO_PEER_COPY
+
+ifdef GGML_CUDA_CCBIN
+	MK_NVCCFLAGS += -ccbin $(GGML_CUDA_CCBIN)
+endif # GGML_CUDA_CCBIN
+
+ifdef GGML_CUDA_FA_ALL_QUANTS
+	MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS
+endif # GGML_CUDA_FA_ALL_QUANTS
 
 ifdef JETSON_EOL_MODULE_DETECT
 define NVCC_COMPILE
 	$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
 endef # NVCC_COMPILE
 else
+	ifdef GGML_MUSA
+define NVCC_COMPILE
+	$(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -c $< -o $@
+endef # NVCC_COMPILE
+	else
 define NVCC_COMPILE
 	$(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
 endef # NVCC_COMPILE
+	endif # GGML_MUSA
 endif # JETSON_EOL_MODULE_DETECT
 
-ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
+ggml/src/ggml-cuda/%.o: \
+	ggml/src/ggml-cuda/%.cu \
+	ggml/include/ggml.h \
+	ggml/src/ggml-common.h \
+	ggml/src/ggml-cuda/common.cuh
 	$(NVCC_COMPILE)
 
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
+ggml/src/ggml-cuda.o: \
+	ggml/src/ggml-cuda.cu \
+	ggml/include/ggml-cuda.h \
+	ggml/include/ggml.h \
+	ggml/include/ggml-backend.h \
+	ggml/src/ggml-backend-impl.h \
+	ggml/src/ggml-common.h \
+	$(wildcard ggml/src/ggml-cuda/*.cuh)
 	$(NVCC_COMPILE)
-endif # LLAMA_CUDA
-
-ifdef LLAMA_CLBLAST
-	MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL)
-	MK_CFLAGS   += $(shell pkg-config --cflags-only-other clblast OpenCL)
-	MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL)
-
-	# Mac provides OpenCL as a framework
-	ifeq ($(UNAME_S),Darwin)
-		MK_LDFLAGS += -lclblast -framework OpenCL
-	else
-		MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL)
-	endif
-	OBJS    += ggml-opencl.o
-
-ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-endif # LLAMA_CLBLAST
+endif # GGML_CUDA
 
-ifdef LLAMA_VULKAN
-	MK_CPPFLAGS  += -DGGML_USE_VULKAN
-	MK_LDFLAGS += -lvulkan
-	OBJS    += ggml-vulkan.o
+ifdef GGML_VULKAN
+	MK_CPPFLAGS += -DGGML_USE_VULKAN
+	MK_LDFLAGS  += $(shell pkg-config --libs vulkan)
+	OBJ_GGML    += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o
 
-ifdef LLAMA_VULKAN_CHECK_RESULTS
+ifdef GGML_VULKAN_CHECK_RESULTS
 	MK_CPPFLAGS  += -DGGML_VULKAN_CHECK_RESULTS
 endif
 
-ifdef LLAMA_VULKAN_DEBUG
+ifdef GGML_VULKAN_DEBUG
 	MK_CPPFLAGS  += -DGGML_VULKAN_DEBUG
 endif
 
-ifdef LLAMA_VULKAN_VALIDATE
+ifdef GGML_VULKAN_MEMORY_DEBUG
+	MK_CPPFLAGS  += -DGGML_VULKAN_MEMORY_DEBUG
+endif
+
+ifdef GGML_VULKAN_PERF
+	MK_CPPFLAGS  += -DGGML_VULKAN_PERF
+endif
+
+ifdef GGML_VULKAN_VALIDATE
 	MK_CPPFLAGS  += -DGGML_VULKAN_VALIDATE
 endif
 
-ifdef LLAMA_VULKAN_RUN_TESTS
+ifdef GGML_VULKAN_RUN_TESTS
 	MK_CPPFLAGS  += -DGGML_VULKAN_RUN_TESTS
 endif
 
-ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-endif # LLAMA_VULKAN
+GLSLC_CMD  = glslc
+_ggml_vk_genshaders_cmd = $(shell pwd)/vulkan-shaders-gen
+_ggml_vk_header = ggml/src/ggml-vulkan-shaders.hpp
+_ggml_vk_source = ggml/src/ggml-vulkan-shaders.cpp
+_ggml_vk_input_dir = ggml/src/vulkan-shaders
+_ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp)
+
+ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source)
+	$(CXX) $(CXXFLAGS) $(shell pkg-config --cflags vulkan) -c $< -o $@
+
+$(_ggml_vk_header): $(_ggml_vk_source)
 
-ifdef LLAMA_HIPBLAS
+$(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen
+	$(_ggml_vk_genshaders_cmd) \
+		--glslc      $(GLSLC_CMD) \
+		--input-dir  $(_ggml_vk_input_dir) \
+		--target-hpp $(_ggml_vk_header) \
+		--target-cpp $(_ggml_vk_source)
+
+vulkan-shaders-gen: ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
+	$(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
+
+endif # GGML_VULKAN
+
+ifdef GGML_HIPBLAS
 	ifeq ($(wildcard /opt/rocm),)
-		ROCM_PATH	?= /usr
+		ROCM_PATH      ?= /usr
 		AMDGPU_TARGETS ?= $(shell $(shell which amdgpu-arch))
 	else
 		ROCM_PATH	?= /opt/rocm
 		AMDGPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
 	endif
-	HIPCC                   ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc
-	LLAMA_CUDA_DMMV_X       ?= 32
-	LLAMA_CUDA_MMV_Y        ?= 1
-	LLAMA_CUDA_KQUANTS_ITER ?= 2
+
+	GGML_CUDA_DMMV_X       ?= 32
+	GGML_CUDA_MMV_Y        ?= 1
+	GGML_CUDA_KQUANTS_ITER ?= 2
+
 	MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUDA
-ifdef LLAMA_HIP_UMA
+
+ifdef GGML_HIP_UMA
 	MK_CPPFLAGS += -DGGML_HIP_UMA
-endif # LLAMA_HIP_UMA
-	MK_LDFLAGS  += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
-	MK_LDFLAGS	+= -lhipblas -lamdhip64 -lrocblas
-	HIPFLAGS    += $(addprefix --offload-arch=,$(AMDGPU_TARGETS))
-	HIPFLAGS    += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
-	HIPFLAGS    += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
-	HIPFLAGS    += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
-ifdef LLAMA_CUDA_FORCE_DMMV
-	HIPFLAGS 	+= -DGGML_CUDA_FORCE_DMMV
-endif # LLAMA_CUDA_FORCE_DMMV
-ifdef LLAMA_CUDA_NO_PEER_COPY
-	HIPFLAGS 	+= -DGGML_CUDA_NO_PEER_COPY
-endif # LLAMA_CUDA_NO_PEER_COPY
-	OBJS        += ggml-cuda.o
-	OBJS        += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
-
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
+endif # GGML_HIP_UMA
+
+	MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
+	MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64
+	MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas
+
+	HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc
+
+	HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS))
+	HIPFLAGS += -DGGML_CUDA_DMMV_X=$(GGML_CUDA_DMMV_X)
+	HIPFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_MMV_Y)
+	HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(GGML_CUDA_KQUANTS_ITER)
+
+ifdef GGML_CUDA_FORCE_DMMV
+	HIPFLAGS += -DGGML_CUDA_FORCE_DMMV
+endif # GGML_CUDA_FORCE_DMMV
+
+ifdef GGML_CUDA_FORCE_MMQ
+	HIPFLAGS += -DGGML_CUDA_FORCE_MMQ
+endif # GGML_CUDA_FORCE_MMQ
+
+ifdef GGML_CUDA_FORCE_CUBLAS
+	HIPFLAGS += -DGGML_CUDA_FORCE_CUBLAS
+endif # GGML_CUDA_FORCE_CUBLAS
+
+ifdef GGML_CUDA_NO_PEER_COPY
+	HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY
+endif # GGML_CUDA_NO_PEER_COPY
+
+	OBJ_GGML += ggml/src/ggml-cuda.o
+	OBJ_GGML += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
+	OBJ_GGML += $(OBJ_CUDA_TMPL)
+
+ggml/src/ggml-cuda.o: \
+	ggml/src/ggml-cuda.cu \
+	ggml/include/ggml-cuda.h \
+	ggml/include/ggml.h \
+	ggml/include/ggml-backend.h \
+	ggml/src/ggml-backend-impl.h \
+	ggml/src/ggml-common.h \
+	$(wildcard ggml/src/ggml-cuda/*.cuh)
 	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
 
-ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
+ggml/src/ggml-cuda/%.o: \
+	ggml/src/ggml-cuda/%.cu \
+	ggml/include/ggml.h \
+	ggml/src/ggml-common.h \
+	ggml/src/ggml-cuda/common.cuh
 	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
+endif # GGML_HIPBLAS
 
-endif # LLAMA_HIPBLAS
-
-ifdef LLAMA_METAL
+ifdef GGML_METAL
 	MK_CPPFLAGS += -DGGML_USE_METAL
 	MK_LDFLAGS  += -framework Foundation -framework Metal -framework MetalKit
-	OBJS		+= ggml-metal.o
-ifdef LLAMA_METAL_NDEBUG
+	OBJ_GGML	+= ggml/src/ggml-metal.o
+ifdef GGML_METAL_NDEBUG
 	MK_CPPFLAGS += -DGGML_METAL_NDEBUG
 endif
-ifdef LLAMA_METAL_EMBED_LIBRARY
+ifdef GGML_METAL_EMBED_LIBRARY
 	MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY
-	OBJS        += ggml-metal-embed.o
+	OBJ_GGML   += ggml/src/ggml-metal-embed.o
 endif
-endif # LLAMA_METAL
+endif # GGML_METAL
 
-ifdef LLAMA_METAL
-ggml-metal.o: ggml-metal.m ggml-metal.h ggml.h
+ifdef GGML_METAL
+ggml/src/ggml-metal.o: \
+	ggml/src/ggml-metal.m \
+	ggml/include/ggml-metal.h \
+	ggml/include/ggml.h
 	$(CC) $(CFLAGS) -c $< -o $@
 
-ifdef LLAMA_METAL_EMBED_LIBRARY
-ggml-metal-embed.o: ggml-metal.metal ggml-common.h
+ifdef GGML_METAL_EMBED_LIBRARY
+ggml/src/ggml-metal-embed.o: \
+	ggml/src/ggml-metal.metal \
+	ggml/src/ggml-common.h
 	@echo "Embedding Metal library"
-	@sed -e '/#include "ggml-common.h"/r ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml-metal.metal > ggml-metal-embed.metal
-	$(eval TEMP_ASSEMBLY=$(shell mktemp))
-	@echo ".section __DATA, __ggml_metallib"   >  $(TEMP_ASSEMBLY)
-	@echo ".globl _ggml_metallib_start"        >> $(TEMP_ASSEMBLY)
-	@echo "_ggml_metallib_start:"              >> $(TEMP_ASSEMBLY)
-	@echo ".incbin \"ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)
-	@echo ".globl _ggml_metallib_end"          >> $(TEMP_ASSEMBLY)
-	@echo "_ggml_metallib_end:"                >> $(TEMP_ASSEMBLY)
-	@$(AS) $(TEMP_ASSEMBLY) -o $@
-	@rm -f ${TEMP_ASSEMBLY}
-endif
-endif # LLAMA_METAL
-
-ifndef LLAMA_NO_LLAMAFILE
-sgemm.o: sgemm.cpp sgemm.h ggml.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
+	@sed -e '/#include "ggml-common.h"/r ggml/src/ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml/src/ggml-metal.metal > ggml/src/ggml-metal-embed.metal
+	$(eval TEMP_ASSEMBLY=$(shell mktemp -d))
+	@echo ".section __DATA, __ggml_metallib"            >  $(TEMP_ASSEMBLY)/ggml-metal-embed.s
+	@echo ".globl _ggml_metallib_start"                 >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
+	@echo "_ggml_metallib_start:"                       >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
+	@echo ".incbin \"ggml/src/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
+	@echo ".globl _ggml_metallib_end"                   >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
+	@echo "_ggml_metallib_end:"                         >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
+	$(CC) $(CFLAGS) -c $(TEMP_ASSEMBLY)/ggml-metal-embed.s -o $@
+	@rm -f ${TEMP_ASSEMBLY}/ggml-metal-embed.s
+	@rmdir ${TEMP_ASSEMBLY}
 endif
+endif # GGML_METAL
+
+OBJ_GGML += \
+	ggml/src/ggml.o \
+	ggml/src/ggml-alloc.o \
+	ggml/src/ggml-backend.o \
+	ggml/src/ggml-quants.o \
+	ggml/src/ggml-aarch64.o
+
+OBJ_LLAMA = \
+	src/llama.o \
+	src/llama-vocab.o \
+	src/llama-grammar.o \
+	src/llama-sampling.o \
+	src/unicode.o \
+	src/unicode-data.o
+
+OBJ_COMMON = \
+	common/common.o \
+	common/console.o \
+	common/ngram-cache.o \
+	common/sampling.o \
+	common/train.o \
+	common/grammar-parser.o \
+	common/build-info.o \
+	common/json-schema-to-grammar.o
+
+OBJ_ALL = $(OBJ_GGML) $(OBJ_LLAMA) $(OBJ_COMMON)
+
+LIB_GGML   = $(LIB_PRE)ggml$(DSO_EXT)
+LIB_GGML_S = $(LIB_PRE)ggml.a
+
+LIB_LLAMA   = $(LIB_PRE)llama$(DSO_EXT)
+LIB_LLAMA_S = $(LIB_PRE)llama.a
+
+LIB_COMMON   = $(LIB_PRE)common$(DSO_EXT)
+LIB_COMMON_S = $(LIB_PRE)common.a
+
+LIB_ALL   = $(LIB_GGML)   $(LIB_LLAMA)   $(LIB_COMMON)
+LIB_ALL_S = $(LIB_GGML_S) $(LIB_LLAMA_S) $(LIB_COMMON_S)
 
 GF_CC := $(CC)
 include scripts/get-flags.mk
@@ -639,7 +957,7 @@ override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS)
 override LDFLAGS   := $(MK_LDFLAGS) $(LDFLAGS)
 
 # identify CUDA host compiler
-ifdef LLAMA_CUDA
+ifdef GGML_CUDA
 GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler
 include scripts/get-flags.mk
 CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic
@@ -664,86 +982,244 @@ $(info I NVCCFLAGS: $(NVCCFLAGS))
 $(info I LDFLAGS:   $(LDFLAGS))
 $(info I CC:        $(shell $(CC)   --version | head -n 1))
 $(info I CXX:       $(shell $(CXX)  --version | head -n 1))
-ifdef LLAMA_CUDA
+ifdef GGML_CUDA
 $(info I NVCC:      $(shell $(NVCC) --version | tail -n 1))
 CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])')
+ifndef GGML_MUSA
 ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1)
+
 ifndef CUDA_DOCKER_ARCH
 ifndef CUDA_POWER_ARCH
 $(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via environment variable CUDA_DOCKER_ARCH, e.g. by running "export CUDA_DOCKER_ARCH=compute_XX" on Unix-like systems, where XX is the minimum compute capability that the code needs to run on. A list with compute capabilities can be found here: https://developer.nvidia.com/cuda-gpus )
 endif # CUDA_POWER_ARCH
 endif # CUDA_DOCKER_ARCH
+
 endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1)
-endif # LLAMA_CUDA
+endif # GGML_MUSA
+endif # GGML_CUDA
 $(info )
 
-ifdef LLAMA_CUBLAS
-$(info !!!!)
-$(info LLAMA_CUBLAS is deprecated and will be removed in the future. Use LLAMA_CUDA instead.)
-$(info !!!!)
+ifdef DEPRECATE_WARNING
+$(info !!! DEPRECATION WARNING !!!)
+$(info The following LLAMA_ options are deprecated and will be removed in the future. Use the GGML_ prefix instead)
+$(info   - LLAMA_CUDA)
+$(info   - LLAMA_METAL)
+$(info   - LLAMA_METAL_EMBED_LIBRARY)
+$(info   - LLAMA_OPENMP)
+$(info   - LLAMA_RPC)
+$(info   - LLAMA_SYCL)
+$(info   - LLAMA_SYCL_F16)
+$(info   - LLAMA_OPENBLAS)
+$(info   - LLAMA_OPENBLAS64)
+$(info   - LLAMA_BLIS)
+$(info   - LLAMA_NO_LLAMAFILE)
+$(info   - LLAMA_NO_ACCELERATE)
+$(info   - LLAMA_NO_OPENMP)
+$(info   - LLAMA_NO_METAL)
+$(info   - LLAMA_NO_CCACHE)
 $(info )
 endif
 
 #
-# Build library
+# Build libraries
 #
 
-ggml.o: ggml.c ggml.h ggml-cuda.h
+# ggml
+
+ggml/src/ggml.o: \
+	ggml/src/ggml.c \
+	ggml/include/ggml.h
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
-ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
+ggml/src/ggml-alloc.o: \
+	ggml/src/ggml-alloc.c \
+	ggml/include/ggml.h \
+	ggml/include/ggml-alloc.h
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
-ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
+ggml/src/ggml-backend.o: \
+	ggml/src/ggml-backend.c \
+	ggml/include/ggml.h \
+	ggml/include/ggml-backend.h
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
-ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h
+ggml/src/ggml-quants.o: \
+	ggml/src/ggml-quants.c \
+	ggml/include/ggml.h \
+	ggml/src/ggml-quants.h \
+	ggml/src/ggml-common.h
+	$(CC) $(CFLAGS)    -c $< -o $@
+
+ggml/src/ggml-aarch64.o: \
+	ggml/src/ggml-aarch64.c \
+	ggml/include/ggml.h \
+	ggml/src/ggml-aarch64.h \
+	ggml/src/ggml-common.h
 	$(CC) $(CFLAGS)    -c $< -o $@
 
-unicode.o: unicode.cpp unicode.h
+ggml/src/ggml-blas.o: \
+	ggml/src/ggml-blas.cpp \
+	ggml/include/ggml-blas.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-unicode-data.o: unicode-data.cpp unicode-data.h
+ifndef GGML_NO_LLAMAFILE
+ggml/src/llamafile/sgemm.o: \
+	ggml/src/llamafile/sgemm.cpp \
+	ggml/src/llamafile/sgemm.h \
+	ggml/include/ggml.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
+endif # GGML_NO_LLAMAFILE
 
-OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o
+ifdef GGML_RPC
+ggml/src/ggml-rpc.o: \
+	ggml/src/ggml-rpc.cpp \
+	ggml/include/ggml-rpc.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+endif # GGML_RPC
+
+$(LIB_GGML): \
+	$(OBJ_GGML)
+	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
+
+$(LIB_GGML_S): \
+	$(OBJ_GGML)
+	ar rcs $(LIB_GGML_S) $^
 
-llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
+# llama
+
+src/unicode.o: \
+	src/unicode.cpp \
+	src/unicode.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h
-COMMON_DEPS   = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o
+src/unicode-data.o: \
+	src/unicode-data.cpp \
+	src/unicode-data.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-common.o: common/common.cpp $(COMMON_H_DEPS)
+src/llama.o: \
+	src/llama.cpp \
+	src/llama-impl.h \
+	src/llama-vocab.h \
+	src/llama-grammar.h \
+	src/llama-sampling.h \
+	src/unicode.h \
+	include/llama.h \
+	ggml/include/ggml-cuda.h \
+	ggml/include/ggml-metal.h \
+	ggml/include/ggml.h \
+	ggml/include/ggml-alloc.h \
+	ggml/include/ggml-backend.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-sampling.o: common/sampling.cpp $(COMMON_H_DEPS)
+src/llama-vocab.o: \
+	src/llama-vocab.cpp \
+	src/llama-vocab.h \
+	src/llama-impl.h \
+	include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-console.o: common/console.cpp common/console.h
+src/llama-grammar.o: \
+	src/llama-grammar.cpp \
+	src/llama-grammar.h \
+	src/llama-impl.h \
+	src/llama-vocab.h \
+	src/llama-sampling.h \
+	include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-grammar-parser.o: common/grammar-parser.cpp common/grammar-parser.h
+src/llama-sampling.o: \
+	src/llama-sampling.cpp \
+	src/llama-sampling.h \
+	src/llama-impl.h \
+	include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-json-schema-to-grammar.o: common/json-schema-to-grammar.cpp common/json-schema-to-grammar.h
+$(LIB_LLAMA): \
+	$(OBJ_LLAMA) \
+	$(LIB_GGML)
+	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
+
+$(LIB_LLAMA_S): \
+	$(OBJ_LLAMA)
+	ar rcs $(LIB_LLAMA_S) $^
+
+# common
+
+common/common.o: \
+	common/common.cpp \
+	common/common.h \
+	common/console.h \
+	common/sampling.h \
+	common/json.hpp \
+	common/json-schema-to-grammar.h \
+	include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-train.o: common/train.cpp common/train.h
+common/sampling.o: \
+	common/sampling.cpp \
+	common/sampling.h \
+	include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-ngram-cache.o: common/ngram-cache.cpp common/ngram-cache.h
+common/console.o: \
+	common/console.cpp \
+	common/console.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-libllama.so: llama.o ggml.o $(OBJS)
+common/grammar-parser.o: \
+	common/grammar-parser.cpp \
+	common/grammar-parser.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+common/json-schema-to-grammar.o: \
+	common/json-schema-to-grammar.cpp \
+	common/json-schema-to-grammar.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+common/train.o: \
+	common/train.cpp \
+	common/train.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+common/ngram-cache.o: \
+	common/ngram-cache.cpp \
+	common/ngram-cache.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+$(LIB_COMMON): \
+	$(OBJ_COMMON) \
+	$(LIB_LLAMA) \
+	$(LIB_GGML)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
-libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS)
-	ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS)
+$(LIB_COMMON_S): \
+	$(OBJ_COMMON)
+	ar rcs $(LIB_COMMON_S) $^
 
 clean:
-	rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
-	rm -vrf ggml-cuda/*.o
+	rm -vrf *.dot $(BUILD_TARGETS) $(TEST_TARGETS)
+	rm -rvf src/*.o
+	rm -rvf tests/*.o
+	rm -rvf examples/*.o
+	rm -rvf common/*.o
+	rm -rvf *.a
+	rm -rvf *.dll
+	rm -rvf *.so
+	rm -rvf *.dot
+	rm -rvf ggml/*.a
+	rm -rvf ggml/*.dll
+	rm -rvf ggml/*.so
+	rm -vrf ggml/src/*.o
+	rm -rvf ggml/src/llamafile/*.o
+	rm -rvf common/build-info.cpp
+	rm -vrf ggml/src/ggml-metal-embed.metal
+	rm -vrf ggml/src/ggml-cuda/*.o
+	rm -vrf ggml/src/ggml-cuda/template-instances/*.o
+	rm -rvf $(BUILD_TARGETS)
+	rm -rvf $(TEST_TARGETS)
+	rm -f vulkan-shaders-gen ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders.cpp
+	rm -rvf $(LEGACY_TARGETS_CLEAN)
 	find examples pocs -type f -name "*.o" -delete
 
 #
@@ -757,157 +1233,246 @@ clean:
 # Helper function that replaces .c, .cpp, and .cu file endings with .o:
 GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1))))
 
-main: examples/main/main.cpp                                  ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
+llama-cli: examples/main/main.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 	@echo
-	@echo '====  Run ./main -h for help.  ===='
+	@echo '====  Run ./llama-cli -h for help.  ===='
 	@echo
 
-infill: examples/infill/infill.cpp                            ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
+llama-infill: examples/infill/infill.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-simple: examples/simple/simple.cpp                            ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-simple: examples/simple/simple.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tokenize: examples/tokenize/tokenize.cpp                      ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-tokenize: examples/tokenize/tokenize.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-batched: examples/batched/batched.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-batched: examples/batched/batched.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-batched-bench: examples/batched-bench/batched-bench.cpp       build-info.o ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-batched-bench: examples/batched-bench/batched-bench.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-quantize: examples/quantize/quantize.cpp                      ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-quantize: examples/quantize/quantize.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-quantize-stats: examples/quantize-stats/quantize-stats.cpp    build-info.o ggml.o llama.o $(OBJS)
+llama-quantize-stats: examples/quantize-stats/quantize-stats.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-perplexity: examples/perplexity/perplexity.cpp                ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-perplexity: examples/perplexity/perplexity.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-imatrix: examples/imatrix/imatrix.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-imatrix: examples/imatrix/imatrix.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-embedding: examples/embedding/embedding.cpp                   ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-embedding: examples/embedding/embedding.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-gritlm: examples/gritlm/gritlm.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-gritlm: examples/gritlm/gritlm.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-save-load-state: examples/save-load-state/save-load-state.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+llama-gguf: examples/gguf/gguf.cpp \
+	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
-examples/server/%.hpp: examples/server/public/% Makefile
-	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
-		echo "unsigned char $${NAME}[] = {" && \
-		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
-		echo "};" && \
-		echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \
-	) > $@
+examples/gguf-hash/deps/sha1/sha1.o: \
+	examples/gguf-hash/deps/sha1/sha1.c
+	$(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@
 
-gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+examples/gguf-hash/deps/xxhash/xxhash.o: \
+	examples/gguf-hash/deps/xxhash/xxhash.c
+	$(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@
+
+examples/gguf-hash/deps/sha256/sha256.o: \
+	examples/gguf-hash/deps/sha256/sha256.c
+	$(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@
+
+llama-gguf-hash: examples/gguf-hash/gguf-hash.cpp examples/gguf-hash/deps/sha1/sha1.o examples/gguf-hash/deps/xxhash/xxhash.o examples/gguf-hash/deps/sha256/sha256.o\
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) -Iexamples/gguf-hash/deps -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-gguf-split: examples/gguf-split/gguf-split.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-eval-callback: examples/eval-callback/eval-callback.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-eval-callback: examples/eval-callback/eval-callback.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
+llama-cvector-generator: examples/cvector-generator/cvector-generator.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS)
+llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp \
+	$(OBJ_GGML) $(OBJ_LLAMA)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-bench: examples/llama-bench/llama-bench.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
-
-llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-baby-llama: examples/baby-llama/baby-llama.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp  -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
-	$(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
+llama-export-lora: examples/export-lora/export-lora.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-retrieval: examples/retrieval/retrieval.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
+llama-speculative: examples/speculative/speculative.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
+llama-parallel: examples/parallel/parallel.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-retrieval: examples/retrieval/retrieval.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-lookahead: examples/lookahead/lookahead.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+llama-lookup: examples/lookup/lookup.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-lookup-create: examples/lookup/lookup-create.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-lookup-merge: examples/lookup/lookup-merge.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-lookup: examples/lookup/lookup.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS)
+llama-lookup-stats: examples/lookup/lookup-stats.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-create.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp) -o lookup-create $(LDFLAGS)
-	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-merge.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp) -o lookup-merge $(LDFLAGS)
-	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-stats.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp) -o lookup-stats $(LDFLAGS)
 
-passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-passkey: examples/passkey/passkey.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+llama-gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+ifdef GGML_RPC
+rpc-server: examples/rpc/rpc-server.cpp \
+	$(OBJ_GGML)
+	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
+endif # GGML_RPC
+
+llama-server: \
+	examples/server/server.cpp \
+	examples/server/utils.hpp \
+	examples/server/httplib.h \
+	examples/server/colorthemes.css.hpp \
+	examples/server/style.css.hpp \
+	examples/server/theme-beeninorder.css.hpp \
+	examples/server/theme-ketivah.css.hpp \
+	examples/server/theme-mangotango.css.hpp \
+	examples/server/theme-playground.css.hpp \
+	examples/server/theme-polarnight.css.hpp \
+	examples/server/theme-snowstorm.css.hpp \
+	examples/server/index.html.hpp \
+	examples/server/index-new.html.hpp \
+	examples/server/index.js.hpp \
+	examples/server/completion.js.hpp \
+	examples/server/system-prompts.js.hpp \
+	examples/server/prompt-formats.js.hpp \
+	examples/server/json-schema-to-grammar.mjs.hpp \
+	common/json.hpp \
+	common/stb_image.h \
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
+
+# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
+examples/server/%.hpp: examples/server/public/% Makefile
+	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
+		echo "unsigned char $${NAME}[] = {" && \
+		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
+		echo "};" && \
+		echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \
+	) > $@
+
+libllava.a: examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
+	common/stb_image.h \
+	common/base64.hpp \
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
+
+llama-llava-cli: examples/llava/llava-cli.cpp \
+	examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
+
+llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \
+	examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
+
 ifeq ($(UNAME_S),Darwin)
 swift: examples/batched.swift
 	(cd examples/batched.swift; make build)
@@ -921,7 +1486,7 @@ common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh
 		rm $@.tmp; \
 	fi
 
-build-info.o: common/build-info.cpp
+common/build-info.o: common/build-info.cpp
 	$(CXX) $(CXXFLAGS) -c $(filter-out %.h,$^) -o $@
 
 #
@@ -930,94 +1495,165 @@ build-info.o: common/build-info.cpp
 
 tests: $(TEST_TARGETS)
 
-benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS)
+llama-benchmark-matmult: examples/benchmark/benchmark-matmult.cpp \
+	$(OBJ_GGML) common/build-info.o
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-run-benchmark-matmult: benchmark-matmult
+run-benchmark-matmult: llama-benchmark-matmult
 	./$@
 
 .PHONY: run-benchmark-matmult swift
 
-vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS)
+tests/test-llama-grammar: tests/test-llama-grammar.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS)
+tests/test-grammar-parser: tests/test-grammar-parser.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-grammar-integration: tests/test-grammar-integration.cpp ggml.o llama.o grammar-parser.o $(OBJS)
+tests/test-grammar-integration: tests/test-grammar-integration.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
+tests/test-double-float: tests/test-double-float.cpp
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp json-schema-to-grammar.o ggml.o llama.o grammar-parser.o $(OBJS)
+tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
+tests/test-grad0: tests/test-grad0.cpp \
+	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS)
+tests/test-opt: tests/test-opt.cpp \
+	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS)
+tests/test-quantize-fns: tests/test-quantize-fns.cpp \
+	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
+tests/test-quantize-perf: tests/test-quantize-perf.cpp \
+	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
+tests/test-sampling: tests/test-sampling.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0: tests/test-tokenizer-0.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
+tests/test-tokenizer-0: tests/test-tokenizer-0.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
+tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-spm: tests/test-tokenizer-1-spm.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
+tests/test-tokenizer-1-spm: tests/test-tokenizer-1-spm.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
+tests/test-rope: tests/test-rope.cpp ggml/src/ggml.o \
+	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-c.o: tests/test-c.c llama.h
+tests/test-c.o: tests/test-c.c include/llama.h
 	$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
 
-tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
+tests/test-backend-ops: tests/test-backend-ops.cpp \
+	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS)
+tests/test-model-load-cancel: tests/test-model-load-cancel.cpp tests/get-model.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS)
+tests/test-autorelease: tests/test-autorelease.cpp tests/get-model.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-chat-template: tests/test-chat-template.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-chat-template: tests/test-chat-template.cpp \
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+#
+# PoCs
+#
+
+llama-vdot: pocs/vdot/vdot.cpp ggml/src/ggml.o \
+	$(OBJ_GGML)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \
+	$(OBJ_GGML)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+#
+# Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed.
+#
+# Mark legacy binary targets as .PHONY so that they are always checked.
+.PHONY: main quantize perplexity embedding server
+
+# Define the object file target
+examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+# NOTE: We currently will always build the deprecation-warning `main` and `server` binaries to help users migrate.
+#  Eventually we will want to remove these target from building all the time.
+main: examples/deprecation-warning/deprecation-warning.o
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
+	@echo "NOTICE: The 'main' binary is deprecated. Please use 'llama-cli' instead."
+
+server: examples/deprecation-warning/deprecation-warning.o
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
+	@echo "NOTICE: The 'server' binary is deprecated. Please use 'llama-server' instead."
+
+quantize: examples/deprecation-warning/deprecation-warning.o
+ifneq (,$(wildcard quantize))
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'quantize' binary is deprecated. Please use 'llama-quantize' instead."
+	@echo "  Remove the 'quantize' binary to remove this warning."
+	@echo "#########"
+endif
+
+perplexity: examples/deprecation-warning/deprecation-warning.o
+ifneq (,$(wildcard perplexity))
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'perplexity' binary is deprecated. Please use 'llama-perplexity' instead."
+	@echo "  Remove the 'perplexity' binary to remove this warning."
+	@echo "#########"
+endif
+
+embedding: examples/deprecation-warning/deprecation-warning.o
+ifneq (,$(wildcard embedding))
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'embedding' binary is deprecated. Please use 'llama-embedding' instead."
+	@echo "  Remove the 'embedding' binary to remove this warning."
+	@echo "#########"
+endif
diff --git a/Package.swift b/Package.swift
index 183e647575b42..1d90b47bfa3da 100644
--- a/Package.swift
+++ b/Package.swift
@@ -3,14 +3,17 @@
 import PackageDescription
 
 var sources = [
-    "ggml.c",
-    "sgemm.cpp",
-    "llama.cpp",
-    "unicode.cpp",
-    "unicode-data.cpp",
-    "ggml-alloc.c",
-    "ggml-backend.c",
-    "ggml-quants.c",
+    "src/llama.cpp",
+    "src/llama-vocab.cpp",
+    "src/llama-grammar.cpp",
+    "src/llama-sampling.cpp",
+    "src/unicode.cpp",
+    "src/unicode-data.cpp",
+    "ggml/src/ggml.c",
+    "ggml/src/ggml-alloc.c",
+    "ggml/src/ggml-backend.c",
+    "ggml/src/ggml-quants.c",
+    "ggml/src/ggml-aarch64.c",
 ]
 
 var resources: [Resource] = []
@@ -26,8 +29,8 @@ var cSettings: [CSetting] =  [
 ]
 
 #if canImport(Darwin)
-sources.append("ggml-metal.m")
-resources.append(.process("ggml-metal.metal"))
+sources.append("ggml/src/ggml-metal.m")
+resources.append(.process("ggml/src/ggml-metal.metal"))
 linkerSettings.append(.linkedFramework("Accelerate"))
 cSettings.append(
     contentsOf: [
@@ -63,8 +66,6 @@ let package = Package(
                "models",
                "tests",
                "CMakeLists.txt",
-               "ggml-cuda.cu",
-               "ggml-cuda.h",
                "Makefile"
             ],
             sources: sources,
diff --git a/README.md b/README.md
index 4abfd6d7e9dac..7f48fde6e5f5a 100644
--- a/README.md
+++ b/README.md
@@ -2,14 +2,20 @@
 
 ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
 
-[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
+[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
+[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp)
 
 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
 
 Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
 
-### Recent API changes
+> [!IMPORTANT]
+[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809)
 
+## Recent API changes
+
+- [2024 Jun 26] The source code and CMake build scripts have been restructured https://github.com/ggerganov/llama.cpp/pull/8006
 - [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807
 - [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341
 - [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
@@ -18,9 +24,10 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 - [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
 
-### Hot topics
+## Hot topics
 
-- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021**
+- **`convert.py` has been deprecated and moved to `examples/convert_legacy_llama.py`, please use `convert_hf_to_gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430
+- Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021
 - BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920
 - MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387
 - Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
@@ -32,38 +39,6 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ----
 
-<details>
-  <summary>Table of Contents</summary>
-  <ol>
-    <li>
-      <a href="#description">Description</a>
-    </li>
-    <li>
-      <a href="#usage">Usage</a>
-      <ul>
-        <li><a href="#get-the-code">Get the Code</a></li>
-        <li><a href="#build">Build</a></li>
-        <li><a href="#blas-build">BLAS Build</a></li>
-        <li><a href="#prepare-and-quantize">Prepare and Quantize</a></li>
-        <li><a href="#run-the-quantized-model">Run the quantized model</a></li>
-        <li><a href="#memorydisk-requirements">Memory/Disk Requirements</a></li>
-        <li><a href="#quantization">Quantization</a></li>
-        <li><a href="#interactive-mode">Interactive mode</a></li>
-        <li><a href="#constrained-output-with-grammars">Constrained output with grammars</a></li>
-        <li><a href="#instruct-mode">Instruct mode</a></li>
-        <li><a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a></li>
-        <li><a href="#seminal-papers-and-background-on-the-models">Seminal papers and background on the models</a></li>
-        <li><a href="#perplexity-measuring-model-quality">Perplexity (measuring model quality)</a></li>
-        <li><a href="#android">Android</a></li>
-        <li><a href="#docker">Docker</a></li>
-      </ul>
-    </li>
-    <li><a href="#contributing">Contributing</a></li>
-    <li><a href="#coding-guidelines">Coding guidelines</a></li>
-    <li><a href="#docs">Docs</a></li>
-  </ol>
-</details>
-
 ## Description
 
 The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
@@ -74,21 +49,13 @@ variety of hardware - locally and in the cloud.
 - AVX, AVX2 and AVX512 support for x86 architectures
 - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
 - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
-- Vulkan, SYCL, and (partial) OpenCL backend support
+- Vulkan and SYCL backend support
 - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
 
 Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has
 improved significantly thanks to many contributions. It is the main playground for developing new features for the
 [ggml](https://github.com/ggerganov/ggml) library.
 
-**Supported platforms:**
-
-- [X] Mac OS
-- [X] Linux
-- [X] Windows (via CMake)
-- [X] Docker
-- [X] FreeBSD
-
 **Supported models:**
 
 Typically finetunes of the base models below are supported as well.
@@ -102,12 +69,12 @@ Typically finetunes of the base models below are supported as well.
 - [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
+- [X] [BERT](https://github.com/ggerganov/llama.cpp/pull/5423)
 - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
 - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
 - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
 - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187)
 - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
-- [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410)
 - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
 - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
 - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
@@ -128,8 +95,18 @@ Typically finetunes of the base models below are supported as well.
 - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
 - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
 - [x] [OLMo](https://allenai.org/olmo)
-
-(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md))
+- [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330)
+- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
+- [x] [Snowflake-Arctic MoE](https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520)
+- [x] [Smaug](https://huggingface.co/models?search=Smaug)
+- [x] [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B)
+- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
+- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
+- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
+- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
+- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
+
+(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md))
 
 **Multimodal models:**
 
@@ -141,10 +118,7 @@ Typically finetunes of the base models below are supported as well.
 - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
 - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
 - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
-
-**HTTP server**
-
-[llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
+- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
 
 **Bindings:**
 
@@ -166,17 +140,20 @@ Typically finetunes of the base models below are supported as well.
 - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
 - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
 - PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)
+- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp)
 
 **UI:**
 
 Unless otherwise noted these projects are open-source with permissive licensing:
 
+- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
 - [iohub/collama](https://github.com/iohub/coLLaMA)
 - [janhq/jan](https://github.com/janhq/jan) (AGPL)
 - [nat/openplayground](https://github.com/nat/openplayground)
 - [Faraday](https://faraday.dev/) (proprietary)
 - [LMStudio](https://lmstudio.ai/) (proprietary)
 - [Layla](https://play.google.com/store/apps/details?id=com.laylalite) (proprietary)
+- [ramalama](https://github.com/containers/ramalama) (MIT)
 - [LocalAI](https://github.com/mudler/LocalAI) (MIT)
 - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
 - [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
@@ -187,6 +164,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 - [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
 - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
 - [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
+- [RAGNA Desktop](https://ragna.app/) (proprietary)
 - [RecurseChat](https://recurse.chat/) (proprietary)
 - [semperai/amica](https://github.com/semperai/amica)
 - [withcatai/catai](https://github.com/withcatai/catai)
@@ -199,15 +177,32 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 - [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
 - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
 - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
+- [AIKit](https://github.com/sozercan/aikit) (MIT)
+- [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL)
 
 *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
 
----
+**Tools:**
+
+- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
+- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
+- [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
+
+**Infrastructure:**
 
-Here is a typical run using LLaMA v2 13B on M2 Ultra:
+- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
+- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
+
+**Games:**
+- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
+
+## Demo
+
+<details>
+<summary>Typical run using LLaMA v2 13B on M2 Ultra</summary>
 
 ```
-$ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
+$ make -j && ./llama-cli -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
 I llama.cpp build info:
 I UNAME_S:  Darwin
 I UNAME_P:  arm
@@ -284,533 +279,85 @@ llama_print_timings:        eval time = 24513.59 ms /   399 runs   (   61.44 ms
 llama_print_timings:       total time = 25431.49 ms
 ```
 
+</details>
+
+<details>
+<summary>Demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook</summary>
+
 And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook:
 
 https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4
 
+</details>
+
 ## Usage
 
 Here are the end-to-end binary build and model conversion steps for most supported models.
 
-### Get the Code
-
-```bash
-git clone https://github.com/ggerganov/llama.cpp
-cd llama.cpp
-```
-
-### Build
-
-In order to build llama.cpp you have three different options.
-
-- Using `make`:
-  - On Linux or MacOS:
-
-      ```bash
-      make
-      ```
-
-      **Note**: for `Debug` builds, run `make LLAMA_DEBUG=1`
-
-  - On Windows:
-
-    1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
-    2. Extract `w64devkit` on your pc.
-    3. Run `w64devkit.exe`.
-    4. Use the `cd` command to reach the `llama.cpp` folder.
-    5. From here you can run:
-        ```bash
-        make
-        ```
-
-- Using `CMake`:
-
-    ```bash
-    cmake -B build
-    cmake --build build --config Release
-    ```
-
-    **Note**: for `Debug` builds, there are two cases:
-
-    - Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
-
-      ```bash
-      cmake -B build -DCMAKE_BUILD_TYPE=Debug
-      cmake --build build
-      ```
-
-    - Multi-config generators (`-G` param set to Visual Studio, XCode...):
-
-      ```bash
-      cmake -B build -G "Xcode"
-      cmake --build build --config Debug
-      ```
-
-- Using `Zig` (version 0.11 or later):
-
-    Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C,
-    it's also possible to cross compile for other operating systems and architectures:
-
-    ```bash
-    zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c
-    ```
-
-    The `zig targets` command will give you valid options to use.
-
--   Using `gmake` (FreeBSD):
-
-    1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
-    2. Add your user to **video** group
-    3. Install compilation dependencies.
-
-        ```bash
-        sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \
-            opencl clblast openblas
-
-        gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
-        ```
-
-    **Notes:** With this packages you can build llama.cpp with OPENBLAS and
-    CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read
-    the instructions for use and activate this options in this document below.
-
-### Metal Build
-
-On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
-To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or the `LLAMA_METAL=OFF` cmake option.
-
-When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
-argument.
-
-### BLAS Build
-
-Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:
-
-- #### Accelerate Framework:
-
-  This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
+### Basic usage
 
-- #### OpenBLAS:
+Firstly, you need to get the binary. There are different methods that you can follow:
+- Method 1: Clone this repository and build locally, see [how to build](./docs/build.md)
+- Method 2: If you are using MacOS or Linux, you can install llama.cpp via [brew, flox or nix](./docs/install.md)
+- Method 3: Use a Docker image, see [documentation for Docker](./docs/docker.md)
+- Method 4: Download pre-built binary from [releases](https://github.com/ggerganov/llama.cpp/releases)
 
-  This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
+You can run a basic completion using this command:
 
-  - Using `make`:
-    - On Linux:
-      ```bash
-      make LLAMA_OPENBLAS=1
-      ```
-
-    - On Windows:
-
-      1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
-      2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
-      3. Extract `w64devkit` on your pc.
-      4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
-      5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
-      6. Run `w64devkit.exe`.
-      7. Use the `cd` command to reach the `llama.cpp` folder.
-      8. From here you can run:
-
-          ```bash
-          make LLAMA_OPENBLAS=1
-          ```
-
-  - Using `CMake` on Linux:
-
-      ```bash
-      cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
-      cmake --build build --config Release
-      ```
-
-- #### BLIS
-
-  Check [BLIS.md](docs/BLIS.md) for more information.
-
-- #### SYCL
-  SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
-
-  llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
-
-  For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md).
-
-- #### Intel oneMKL
-  Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md).
-
-  - Using manual oneAPI installation:
-    By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
-      ```bash
-      source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
-      cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
-      cmake --build build --config Release
-      ```
-
-  - Using oneAPI docker image:
-    If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
-
-  Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
-
-- #### CUDA
-
-  This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
-
-  For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
-
-  - Using `make`:
-    ```bash
-    make LLAMA_CUDA=1
-    ```
-  - Using `CMake`:
-
-    ```bash
-    cmake -B build -DLLAMA_CUDA=ON
-    cmake --build build --config Release
-    ```
-
-  The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
-
-  | Option                         | Legal values           | Default | Description                                                                                                                                                                                                                                                                             |
-  |--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | LLAMA_CUDA_FORCE_DMMV          | Boolean                | false   | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
-  | LLAMA_CUDA_DMMV_X              | Positive integer >= 32 | 32      | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants.                                         |
-  | LLAMA_CUDA_MMV_Y               | Positive integer       | 1       | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended.                                                                                                                                         |
-  | LLAMA_CUDA_F16                 | Boolean                | false   | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs.                                                           |
-  | LLAMA_CUDA_KQUANTS_ITER        | 1 or 2                 | 2       | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.                                                                                                                     |
-  | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                         |
-
-- #### hipBLAS
-
-  This provides BLAS acceleration on HIP-supported AMD GPUs.
-  Make sure to have ROCm installed.
-  You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
-
-  - Using `make`:
-    ```bash
-    make LLAMA_HIPBLAS=1
-    ```
-  - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
-    ```bash
-    HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
-        cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
-        && cmake --build build --config Release -- -j 16
-    ```
-    On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON`.
-    However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
-
-    Note that if you get the following error:
-    ```
-    clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library
-    ```
-    Try searching for a directory under `HIP_PATH` that contains the file
-    `oclc_abi_version_400.bc`. Then, add the following to the start of the
-    command: `HIP_DEVICE_LIB_PATH=<directory-you-just-found>`, so something
-    like:
-    ```bash
-    HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
-    HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
-        cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
-        && cmake --build build -- -j 16
-    ```
-
-  - Using `make` (example for target gfx1030, build with 16 CPU threads):
-    ```bash
-    make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
-    ```
-
-  - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
-    ```bash
-    set PATH=%HIP_PATH%\bin;%PATH%
-    cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
-    cmake --build build
-    ```
-    Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
-    Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`.
-
-
-  The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
-  If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
-  The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
-
-  | Option                  | Legal values           | Default | Description                                                                                                                                                                                                                                    |
-  |-------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 | 32      | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
-  | LLAMA_CUDA_MMV_Y        | Positive integer       | 1       | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants.                                                                       |
-  | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 | 2       | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.                                                                             |
-
-- #### CLBlast
-
-  OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
-
-  You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
-    - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed.
-
-    - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.
-
-    - <details>
-        <summary>Installing the OpenCL SDK from source</summary>
-
-        ```sh
-        git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
-        cd OpenCL-SDK
-        cmake -B build -DBUILD_DOCS=OFF \
-          -DBUILD_EXAMPLES=OFF \
-          -DBUILD_TESTING=OFF \
-          -DOPENCL_SDK_BUILD_SAMPLES=OFF \
-          -DOPENCL_SDK_TEST_SAMPLES=OFF
-        cmake --build build
-        cmake --install build --prefix /some/path
-        ```
-      </details>
-
-  ##### Installing CLBlast
-
-  Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.
-
-  Linux packaging:
-  Fedora Linux:
-  ```bash
-  sudo dnf install clblast
-  ```
-
-  Alternatively, they may be built from source.
-
-  - <details>
-    <summary>Windows:</summary>
-
-      ```cmd
-      set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
-      git clone https://github.com/CNugteren/CLBlast.git
-      cd CLBlast
-      cmake -B build -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
-      cmake --build build --config Release
-      cmake --install build --prefix C:/CLBlast
-      ```
-
-      (note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds)
-
-  - <details>
-    <summary>Unix:</summary>
-
-      ```sh
-      git clone https://github.com/CNugteren/CLBlast.git
-      cd CLBlast
-      cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
-      cmake --build build --config Release
-      cmake --install build --prefix /some/path
-      ```
-
-      Where `/some/path` is where the built library will be installed (default is `/usr/local`).
-    </details>
-
-  ##### Building Llama with CLBlast
-
-  - Build with make:
-    ```sh
-    make LLAMA_CLBLAST=1
-    ```
-  - CMake (Unix):
-    ```sh
-    cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
-    cmake --build build --config Release
-    ```
-  - CMake (Windows):
-    ```cmd
-    set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
-    git clone https://github.com/ggerganov/llama.cpp
-    cd llama.cpp
-    cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
-    cmake --build build --config Release
-    cmake --install build --prefix C:/LlamaCPP
-    ```
-
-  ##### Running Llama with CLBlast
-
-  The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does.
-
-  To select the correct platform (driver) and device (GPU), you can use the environment variables `GGML_OPENCL_PLATFORM` and `GGML_OPENCL_DEVICE`.
-  The selection can be a number (starting from 0) or a text string to search:
-
-  ```sh
-  GGML_OPENCL_PLATFORM=1 ./main ...
-  GGML_OPENCL_DEVICE=2 ./main ...
-  GGML_OPENCL_PLATFORM=Intel ./main ...
-  GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ...
-  ```
-
-  The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful.
-  Using the variables it is possible to select a CPU-based driver as well, if so desired.
-
-  You can get a list of platforms and devices from the `clinfo -l` command, etc.
-
-- #### Vulkan
-
-  **With docker**:
-
-  You don't need to install Vulkan SDK. It will be installed inside the container.
-
-  ```sh
-  # Build the image
-  docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile .
-
-  # Then, use it:
-  docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
-  ```
-
-  **Without docker**:
-
-  Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
-
-  For example, on Ubuntu 22.04 (jammy), use the command below:
-
-  ```bash
-  wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
-  wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
-  apt update -y
-  apt-get install -y vulkan-sdk
-  # To verify the installation, use the command below:
-  vulkaninfo
-  ```
-
-  Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+```bash
+llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128
 
-  Then, build llama.cpp using the cmake command below:
+# Output:
+# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
+```
 
-  ```bash
-  cmake -B build -DLLAMA_VULKAN=1
-  cmake --build build --config Release
-  # Test the output binary (with "-ngl 33" to offload all layers to GPU)
-  ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
+See [this page](./examples/main/README.md) for a full list of parameters.
 
-  # You should see in the output, ggml_vulkan detected your GPU. For example:
-  # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
-  ```
+### Conversation mode
 
-### Prepare and Quantize
-
-> [!NOTE]
-> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours.
-
-To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
-
-Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
+If you want a more ChatGPT-like experience, you can run in conversation mode by passing `-cnv` as a parameter:
 
 ```bash
-# obtain the official LLaMA model weights and place them in ./models
-ls ./models
-llama-2-7b tokenizer_checklist.chk tokenizer.model
-# [Optional] for models using BPE tokenizers
-ls ./models
-<folder containing weights and tokenizer json> vocab.json
-# [Optional] for PyTorch .bin models like Mistral-7B
-ls ./models
-<folder containing weights and tokenizer json>
-
-# install Python dependencies
-python3 -m pip install -r requirements.txt
-
-# convert the model to ggml FP16 format
-python3 convert.py models/mymodel/
-
-# [Optional] for models using BPE tokenizers
-python convert.py models/mymodel/ --vocab-type bpe
-
-# quantize the model to 4-bits (using Q4_K_M method)
-./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
-
-# update the gguf filetype to current version if older version is now unsupported
-./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
+llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv
+
+# Output:
+# > hi, who are you?
+# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
+#
+# > what is 1+1?
+# Easy peasy! The answer to 1+1 is... 2!
 ```
 
-### Run the quantized model
+By default, the chat template will be taken from the input model. If you want to use another chat template, pass `--chat-template NAME` as a parameter. See the list of [supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
 
 ```bash
-# start inference on a gguf model
-./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128
+./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml
 ```
 
-When running the larger models, make sure you have enough disk space to store all the intermediate files.
-
-### Running on Windows with prebuilt binaries
-
-You will find prebuilt Windows binaries on the release page.
-
-Simply download and extract the latest zip package of choice: (e.g. `llama-b1380-bin-win-avx2-x64.zip`)
+You can also use your own template via in-prefix, in-suffix and reverse-prompt parameters:
 
-From the unzipped folder, open a terminal/cmd window here and place a pre-converted `.gguf` model file. Test out the main example like so:
-
-```
-.\main -m llama-2-7b.Q4_0.gguf -n 128
+```bash
+./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
 ```
 
-### Memory/Disk Requirements
-
-As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
-
-| Model | Original size | Quantized size (Q4_0) |
-|------:|--------------:|----------------------:|
-|    7B |         13 GB |                3.9 GB |
-|   13B |         24 GB |                7.8 GB |
-|   30B |         60 GB |               19.5 GB |
-|   65B |        120 GB |               38.5 GB |
-
-### Quantization
-
-Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
-
-*(outdated)*
-
-| Model | Measure      |    F16 |   Q4_0 |   Q4_1 |   Q5_0 |   Q5_1 |   Q8_0 |
-|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
-|    7B | perplexity   | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
-|    7B | file size    |  13.0G |   3.5G |   3.9G |   4.3G |   4.7G |   6.7G |
-|    7B | ms/tok @ 4th |    127 |     55 |     54 |     76 |     83 |     72 |
-|    7B | ms/tok @ 8th |    122 |     43 |     45 |     52 |     56 |     67 |
-|    7B | bits/weight  |   16.0 |    4.5 |    5.0 |    5.5 |    6.0 |    8.5 |
-|   13B | perplexity   | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 |
-|   13B | file size    |  25.0G |   6.8G |   7.6G |   8.3G |   9.1G |    13G |
-|   13B | ms/tok @ 4th |      - |    103 |    105 |    148 |    160 |    131 |
-|   13B | ms/tok @ 8th |      - |     73 |     82 |     98 |    105 |    128 |
-|   13B | bits/weight  |   16.0 |    4.5 |    5.0 |    5.5 |    6.0 |    8.5 |
-
-- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
-- recent k-quants improvements and new i-quants
-  - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
-  - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
-  - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
-  - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
-  - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
-  - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
-  - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
-  - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
-  - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
-  - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
-  - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
-  - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
-  - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
-  - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
+### Web server
 
-### Perplexity (measuring model quality)
-
-You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better).
-For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity).
+[llama.cpp web server](./examples/server/README.md) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
 
-The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
-The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads.
+Example usage:
 
-#### How to run
+```bash
+./llama-server -m your_model.gguf --port 8080
 
-1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
-2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw`
-3. Output:
+# Basic web UI can be accessed via browser: http://localhost:8080
+# Chat completion endpoint: http://localhost:8080/v1/chat/completions
 ```
-perplexity : calculating perplexity over 655 chunks
-24.43 seconds per pass - ETA 4.45 hours
-[1]4.5970,[2]5.1807,[3]6.0382,...
-```
-And after 4.45 hours, you will have the final perplexity.
 
 ### Interactive mode
 
-If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
+> [!NOTE]
+> If you prefer basic usage, please consider using conversation mode instead of interactive mode
+
 In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
 
 Here is an example of a few-shot interaction, invoked with the command
@@ -823,16 +370,16 @@ Here is an example of a few-shot interaction, invoked with the command
 ./examples/chat-13B.sh
 
 # custom arguments using a 13B model
-./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
+./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
 ```
 
-Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `main` example program.
+Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `llama-cli` example program.
 
 ![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png)
 
 ### Persistent Interaction
 
-The prompt, user inputs, and model generations can be saved and resumed across calls to `./main` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file.
+The prompt, user inputs, and model generations can be saved and resumed across calls to `./llama-cli` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file.
 
 ```bash
 # Start a new chat
@@ -854,235 +401,85 @@ PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
 `llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only:
 
 ```bash
-./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
+./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
 ```
 
 The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md).
 
 For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one.
 
-### Instruct mode
-
-1. First, download and place the `ggml` model into the `./models` folder
-2. Run the `main` tool like this:
-
-```
-./examples/alpaca.sh
-```
-
-Sample run:
-
-```
-== Running in interactive mode. ==
- - Press Ctrl+C to interject at any time.
- - Press Return to return control to LLaMA.
- - If you want to submit another line, end your input in '\'.
-
- Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-> How many letters are there in the English alphabet?
-There 26 letters in the English Alphabet
-> What is the most common way of transportation in Amsterdam?
-The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
-> List 5 words that start with "ca".
-cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
->
-```
-
-### Obtaining and using the Facebook LLaMA 2 model
-
-- Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data.
-- Alternatively, if you want to save time and space, you can download already converted and quantized models from [TheBloke](https://huggingface.co/TheBloke), including:
-  - [LLaMA 2 7B base](https://huggingface.co/TheBloke/Llama-2-7B-GGUF)
-  - [LLaMA 2 13B base](https://huggingface.co/TheBloke/Llama-2-13B-GGUF)
-  - [LLaMA 2 70B base](https://huggingface.co/TheBloke/Llama-2-70B-GGUF)
-  - [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF)
-  - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF)
-  - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF)
-
-### Seminal papers and background on the models
-
-If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
-- LLaMA:
-    - [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
-    - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
-- GPT-3
-    - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
-- GPT-3.5 / InstructGPT / ChatGPT:
-    - [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
-    - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
-
-### Android
-
-#### Build on Android using Termux
-[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required).
-```
-apt update && apt upgrade -y
-apt install git make cmake
-```
-
-It's recommended to move your model inside the `~/` directory for best performance:
-```
-cd storage/downloads
-mv model.gguf ~/
-```
-
-[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
-
-#### Building the Project using Android NDK
-Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
-
-Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
-```
-$ mkdir build-android
-$ cd build-android
-$ export NDK=<your_ndk_directory>
-$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
-$ make
-```
-
-Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
-
-Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
-
-(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
-```
-$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
-$cd /data/data/com.termux/files/home/bin
-$chmod +x ./*
-```
-
-Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
-
-```
-$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
-```
-
-Now, you can start chatting:
-```
-$cd /data/data/com.termux/files/home/bin
-$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
-```
-
-Here's a demo of an interactive session running on Pixel 5 phone:
-
-https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
-
-### Docker
-
-#### Prerequisites
-* Docker must be installed and running on your system.
-* Create a folder to store big models & intermediate files (ex. /llama/models)
+## Build
 
-#### Images
-We have three Docker images available for this project:
+Please refer to [Build llama.cpp locally](./docs/build.md)
 
-1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
-2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
-3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`)
+## Supported backends
 
-Additionally, there the following images, similar to the above:
+| Backend | Target devices |
+| --- | --- |
+| [Metal](./docs/build.md#metal-build) | Apple Silicon |
+| [BLAS](./docs/build.md#blas-build) | All |
+| [BLIS](./docs/backend/BLIS.md) | All |
+| [SYCL](./docs/backend/SYCL.md) | Intel and Nvidia GPU |
+| [MUSA](./docs/build.md#musa) | Moore Threads GPU |
+| [CUDA](./docs/build.md#cuda) | Nvidia GPU |
+| [hipBLAS](./docs/build.md#hipblas) | AMD GPU |
+| [Vulkan](./docs/build.md#vulkan) | GPU |
 
-- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
-- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
-- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+## Tools
 
-The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
-
-#### Usage
-
-The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.
-
-Replace `/path/to/models` below with the actual path where you downloaded the models.
-
-```bash
-docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B
-```
-
-On completion, you are ready to play!
-
-```bash
-docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
-```
-
-or with a light image:
-
-```bash
-docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
-```
-
-or with a server image:
-
-```bash
-docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
-```
-
-### Docker With CUDA
-
-Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
-
-#### Building Locally
-
-```bash
-docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
-docker build -t local/llama.cpp:light-cuda -f .devops/main-cuda.Dockerfile .
-docker build -t local/llama.cpp:server-cuda -f .devops/server-cuda.Dockerfile .
-```
-
-You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
+### Prepare and Quantize
 
-The defaults are:
+> [!NOTE]
+> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours.
 
-- `CUDA_VERSION` set to `11.7.1`
-- `CUDA_DOCKER_ARCH` set to `all`
+To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
 
-The resulting images, are essentially the same as the non-CUDA images:
+Note: `convert.py` has been moved to `examples/convert_legacy_llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives.
+It does not support LLaMA 3, you can use `convert_hf_to_gguf.py` with LLaMA 3 downloaded from Hugging Face.
 
-1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
-2. `local/llama.cpp:light-cuda`: This image only includes the main executable file.
-3. `local/llama.cpp:server-cuda`: This image only includes the server executable file.
+To learn more about quantizing model, [read this documentation](./examples/quantize/README.md)
 
-#### Usage
+### Perplexity (measuring model quality)
 
-After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag.
+You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better).
+For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity).
 
-```bash
-docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
-docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
-docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
-```
+To learn more how to measure perplexity using llama.cpp, [read this documentation](./examples/perplexity/README.md)
 
-### Contributing
+## Contributing
 
 - Contributors can open PRs
 - Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
 - Collaborators will be invited based on contributions
 - Any help with managing issues and PRs is very appreciated!
+- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
+- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information
 - Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205)
 - A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)
 
-### Coding guidelines
-
-- Avoid adding third-party dependencies, extra files, extra headers, etc.
-- Always consider cross-compatibility with other operating systems and architectures
-- Avoid fancy looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
-- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
-- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
-- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
-- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
-- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
-
-![matmul](media/matmul.png)
+## Other documentations
 
-### Docs
-
-- [main](./examples/main/README.md)
+- [main (cli)](./examples/main/README.md)
 - [server](./examples/server/README.md)
 - [jeopardy](./examples/jeopardy/README.md)
-- [BLIS](./docs/BLIS.md)
-- [Performance troubleshooting](./docs/token_generation_performance_tips.md)
-- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
 - [GBNF grammars](./grammars/README.md)
+
+**Development documentations**
+
+- [How to build](./docs/build.md)
+- [Running on Docker](./docs/docker.md)
+- [Build on Android](./docs/android.md)
+- [Performance troubleshooting](./docs/development/token_generation_performance_tips.md)
+- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
+
+**Seminal papers and background on the models**
+
+If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
+- LLaMA:
+    - [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
+    - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
+- GPT-3
+    - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
+- GPT-3.5 / InstructGPT / ChatGPT:
+    - [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
+    - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
diff --git a/build.zig b/build.zig
deleted file mode 100644
index 96783574fe740..0000000000000
--- a/build.zig
+++ /dev/null
@@ -1,172 +0,0 @@
-// Compatible with Zig Version 0.11.0
-const std = @import("std");
-const ArrayList = std.ArrayList;
-const Compile = std.Build.Step.Compile;
-const ConfigHeader = std.Build.Step.ConfigHeader;
-const Mode = std.builtin.Mode;
-const CrossTarget = std.zig.CrossTarget;
-
-const Maker = struct {
-    builder: *std.build.Builder,
-    target: CrossTarget,
-    optimize: Mode,
-    enable_lto: bool,
-
-    include_dirs: ArrayList([]const u8),
-    cflags: ArrayList([]const u8),
-    cxxflags: ArrayList([]const u8),
-    objs: ArrayList(*Compile),
-
-    fn addInclude(m: *Maker, dir: []const u8) !void {
-        try m.include_dirs.append(dir);
-    }
-    fn addProjectInclude(m: *Maker, path: []const []const u8) !void {
-        try m.addInclude(try m.builder.build_root.join(m.builder.allocator, path));
-    }
-    fn addCFlag(m: *Maker, flag: []const u8) !void {
-        try m.cflags.append(flag);
-    }
-    fn addCxxFlag(m: *Maker, flag: []const u8) !void {
-        try m.cxxflags.append(flag);
-    }
-    fn addFlag(m: *Maker, flag: []const u8) !void {
-        try m.addCFlag(flag);
-        try m.addCxxFlag(flag);
-    }
-
-    fn init(builder: *std.build.Builder) !Maker {
-        const target = builder.standardTargetOptions(.{});
-        const zig_version = @import("builtin").zig_version_string;
-        const commit_hash = try std.ChildProcess.exec(
-            .{ .allocator = builder.allocator, .argv = &.{ "git", "rev-parse", "HEAD" } },
-        );
-        try std.fs.cwd().writeFile("common/build-info.cpp", builder.fmt(
-            \\int LLAMA_BUILD_NUMBER = {};
-            \\char const *LLAMA_COMMIT = "{s}";
-            \\char const *LLAMA_COMPILER = "Zig {s}";
-            \\char const *LLAMA_BUILD_TARGET = "{s}";
-            \\
-        , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) }));
-        var m = Maker{
-            .builder = builder,
-            .target = target,
-            .optimize = builder.standardOptimizeOption(.{}),
-            .enable_lto = false,
-            .include_dirs = ArrayList([]const u8).init(builder.allocator),
-            .cflags = ArrayList([]const u8).init(builder.allocator),
-            .cxxflags = ArrayList([]const u8).init(builder.allocator),
-            .objs = ArrayList(*Compile).init(builder.allocator),
-        };
-
-        try m.addCFlag("-std=c11");
-        try m.addCxxFlag("-std=c++11");
-        try m.addProjectInclude(&.{});
-        try m.addProjectInclude(&.{"common"});
-        return m;
-    }
-
-    fn obj(m: *const Maker, name: []const u8, src: []const u8) *Compile {
-        const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize });
-        if (o.target.getAbi() != .msvc)
-            o.defineCMacro("_GNU_SOURCE", null);
-
-        if (std.mem.endsWith(u8, src, ".c")) {
-            o.addCSourceFiles(&.{src}, m.cflags.items);
-            o.linkLibC();
-        } else {
-            o.addCSourceFiles(&.{src}, m.cxxflags.items);
-            if (o.target.getAbi() == .msvc) {
-                o.linkLibC(); // need winsdk + crt
-            } else {
-                // linkLibCpp already add (libc++ + libunwind + libc)
-                o.linkLibCpp();
-            }
-        }
-        for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i });
-        o.want_lto = m.enable_lto;
-        return o;
-    }
-
-    fn exe(m: *const Maker, name: []const u8, src: []const u8, deps: []const *Compile) *Compile {
-        const e = m.builder.addExecutable(.{ .name = name, .target = m.target, .optimize = m.optimize });
-        e.addCSourceFiles(&.{src}, m.cxxflags.items);
-        for (deps) |d| e.addObject(d);
-        for (m.objs.items) |o| e.addObject(o);
-        for (m.include_dirs.items) |i| e.addIncludePath(.{ .path = i });
-
-        // https://github.com/ziglang/zig/issues/15448
-        if (e.target.getAbi() == .msvc) {
-            e.linkLibC(); // need winsdk + crt
-        } else {
-            // linkLibCpp already add (libc++ + libunwind + libc)
-            e.linkLibCpp();
-        }
-        m.builder.installArtifact(e);
-        e.want_lto = m.enable_lto;
-        return e;
-    }
-};
-
-pub fn build(b: *std.build.Builder) !void {
-    var make = try Maker.init(b);
-    make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false;
-
-    const ggml = make.obj("ggml", "ggml.c");
-    const sgemm = make.obj("sgemm", "sgemm.cpp");
-    const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
-    const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
-    const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
-    const unicode = make.obj("unicode", "unicode.cpp");
-    const unicode_data = make.obj("unicode-data", "unicode-data.cpp");
-    const llama = make.obj("llama", "llama.cpp");
-    const buildinfo = make.obj("common", "common/build-info.cpp");
-    const common = make.obj("common", "common/common.cpp");
-    const console = make.obj("console", "common/console.cpp");
-    const sampling = make.obj("sampling", "common/sampling.cpp");
-    const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp");
-    const json_schema_to_grammar = make.obj("json-schema-to-grammar", "common/json-schema-to-grammar.cpp");
-    const train = make.obj("train", "common/train.cpp");
-    const clip = make.obj("clip", "examples/llava/clip.cpp");
-    const llava = make.obj("llava", "examples/llava/llava.cpp");
-
-    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser });
-    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
-    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
-    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
-    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train });
-    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train });
-
-    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava });
-    if (server.target.isWindows()) {
-        server.linkSystemLibrary("ws2_32");
-    }
-
-    const server_assets = [_][]const u8{ "index.html", "index.js", "completion.js", "json-schema-to-grammar.mjs" };
-    for (server_assets) |asset| {
-        const input_path = b.fmt("examples/server/public/{s}", .{asset});
-        const output_path = b.fmt("examples/server/{s}.hpp", .{asset});
-
-        // Portable equivalent of `b.addSystemCommand(&.{ "xxd", "-n", asset, "-i", input_path, output_path }) })`:
-
-        const input = try std.fs.cwd().readFileAlloc(b.allocator, input_path, std.math.maxInt(usize));
-        defer b.allocator.free(input);
-
-        var buf = std.ArrayList(u8).init(b.allocator);
-        defer buf.deinit();
-
-        for (input) |byte| {
-            try std.fmt.format(buf.writer(), "0x{X:0>2}, ", .{byte});
-        }
-
-        var name = try std.mem.replaceOwned(u8, b.allocator, asset, "-", "_");
-        defer b.allocator.free(name);
-        std.mem.replaceScalar(u8, name, '.', '_');
-
-        try std.fs.cwd().writeFile(output_path, b.fmt(
-            "unsigned char {s}[] = {{{s}}};\nunsigned int {s}_len = {d};\n",
-            .{ name, buf.items, name, input.len },
-        ));
-
-        std.debug.print("Dumped hex of \"{s}\" ({s}) to {s}\n", .{ input_path, name, output_path });
-    }
-}
diff --git a/ci/run.sh b/ci/run.sh
index d5972480bc6c1..58022c7dc37e0 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -36,11 +36,11 @@ SRC=`pwd`
 CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
 fi
 
 if [ ! -z ${GG_BUILD_CUDA} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUDA=1"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=1"
 fi
 
 if [ ! -z ${GG_BUILD_SYCL} ]; then
@@ -50,7 +50,7 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then
         exit 1
     fi
 
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
 fi
 ## helpers
 
@@ -103,6 +103,9 @@ function gg_run_ctest_debug {
 
     set -e
 
+    # Check cmake, make and ctest are installed
+    gg_check_build_requirements
+
     (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                          ) 2>&1 | tee -a $OUT/${ci}-make.log
 
@@ -131,6 +134,9 @@ function gg_run_ctest_release {
 
     set -e
 
+    # Check cmake, make and ctest are installed
+    gg_check_build_requirements
+
     (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
 
@@ -202,12 +208,15 @@ function gg_sum_test_scripts_release {
 }
 
 function gg_get_model {
-    local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf"
-    local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
-    if [[ -s $gguf_3b ]]; then
-        echo -n "$gguf_3b"
-    elif [[ -s $gguf_7b ]]; then
-        echo -n "$gguf_7b"
+    local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf"
+    local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf"
+    local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
+    if [[ -s $gguf_0 ]]; then
+        echo -n "$gguf_0"
+    elif [[ -s $gguf_1 ]]; then
+        echo -n "$gguf_1"
+    elif [[ -s $gguf_2 ]]; then
+        echo -n "$gguf_2"
     else
         echo >&2 "No model found. Can't run gg_run_ctest_with_model."
         exit 1
@@ -256,33 +265,169 @@ function gg_sum_ctest_with_model_release {
     gg_printf '```\n'
 }
 
-# open_llama_3b_v2
+# open_llama_7b_v2
+# requires: GG_BUILD_CUDA
+
+function gg_run_open_llama_7b_v2 {
+    cd ${SRC}
+
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
+
+    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
+    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
+
+    path_models="../models-mnt/open-llama/7B-v2"
+    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
+
+    model_f16="${path_models}/ggml-model-f16.gguf"
+    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
+    model_q4_0="${path_models}/ggml-model-q4_0.gguf"
+    model_q4_1="${path_models}/ggml-model-q4_1.gguf"
+    model_q5_0="${path_models}/ggml-model-q5_0.gguf"
+    model_q5_1="${path_models}/ggml-model-q5_1.gguf"
+    model_q2_k="${path_models}/ggml-model-q2_k.gguf"
+    model_q3_k="${path_models}/ggml-model-q3_k.gguf"
+    model_q4_k="${path_models}/ggml-model-q4_k.gguf"
+    model_q5_k="${path_models}/ggml-model-q5_k.gguf"
+    model_q6_k="${path_models}/ggml-model-q6_k.gguf"
+
+    wiki_test="${path_wiki}/wiki.test.raw"
+
+    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
+    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
+    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
+
+    (time ./bin/llama-cli --model ${model_f16}  -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+
+    (time ./bin/llama-save-load-state     -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state     -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+
+    function check_ppl {
+        qnt="$1"
+        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
+            return 20
+        fi
+
+        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
+        return 0
+    }
+
+    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+
+    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
+
+    set +e
+}
+
+function gg_sum_open_llama_7b_v2 {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'OpenLLaMA 7B-v2:\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
+    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
+    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
+    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
+    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
+    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
+    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
+    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
+    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
+}
+
+# pythia_1.4b
 
-function gg_run_open_llama_3b_v2 {
+function gg_run_pythia_1_4b {
     cd ${SRC}
 
-    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json
-    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model
-    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json
-    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json
-    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin
-    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin
 
     gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
     head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
 
-    path_models="../models-mnt/open-llama/3B-v2"
+    path_models="../models-mnt/pythia/1.4B"
     path_wiki="../models-mnt/wikitext/wikitext-2-raw"
 
     rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                                             ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../convert.py ${path_models}
+    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
 
     model_f16="${path_models}/ggml-model-f16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"
@@ -298,45 +443,45 @@ function gg_run_open_llama_3b_v2 {
 
     wiki_test_60="${path_wiki}/wiki.test-60.raw"
 
-    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
-    ./bin/quantize ${model_f16} ${model_q4_0} q4_0
-    ./bin/quantize ${model_f16} ${model_q4_1} q4_1
-    ./bin/quantize ${model_f16} ${model_q5_0} q5_0
-    ./bin/quantize ${model_f16} ${model_q5_1} q5_1
-    ./bin/quantize ${model_f16} ${model_q2_k} q2_k
-    ./bin/quantize ${model_f16} ${model_q3_k} q3_k
-    ./bin/quantize ${model_f16} ${model_q4_k} q4_k
-    ./bin/quantize ${model_f16} ${model_q5_k} q5_k
-    ./bin/quantize ${model_f16} ${model_q6_k} q6_k
-
-    (time ./bin/main --model ${model_f16}  -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
-
-    (time ./bin/save-load-state     --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
+    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
+    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
+
+    (time ./bin/llama-cli --model ${model_f16}  -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+
+    (time ./bin/llama-save-load-state     --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -357,7 +502,7 @@ function gg_run_open_llama_3b_v2 {
     check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
     check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
@@ -368,10 +513,10 @@ function gg_run_open_llama_3b_v2 {
     set +e
 }
 
-function gg_sum_open_llama_3b_v2 {
+function gg_sum_pythia_1_4b {
     gg_printf '### %s\n\n' "${ci}"
 
-    gg_printf 'OpenLLaMA 3B-v2:\n'
+    gg_printf 'Pythia 1.4B:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
     gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
@@ -389,35 +534,32 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
 }
 
-# open_llama_7b_v2
+# pythia_2_8b
 # requires: GG_BUILD_CUDA
 
-function gg_run_open_llama_7b_v2 {
+function gg_run_pythia_2_8b {
     cd ${SRC}
 
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin
 
     gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
 
-    path_models="../models-mnt/open-llama/7B-v2"
+    path_models="../models-mnt/pythia/2.8B"
     path_wiki="../models-mnt/wikitext/wikitext-2-raw"
 
     rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../convert.py ${path_models}
+    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
 
     model_f16="${path_models}/ggml-model-f16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"
@@ -433,47 +575,47 @@ function gg_run_open_llama_7b_v2 {
 
     wiki_test="${path_wiki}/wiki.test.raw"
 
-    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
-    ./bin/quantize ${model_f16} ${model_q4_0} q4_0
-    ./bin/quantize ${model_f16} ${model_q4_1} q4_1
-    ./bin/quantize ${model_f16} ${model_q5_0} q5_0
-    ./bin/quantize ${model_f16} ${model_q5_1} q5_1
-    ./bin/quantize ${model_f16} ${model_q2_k} q2_k
-    ./bin/quantize ${model_f16} ${model_q3_k} q3_k
-    ./bin/quantize ${model_f16} ${model_q4_k} q4_k
-    ./bin/quantize ${model_f16} ${model_q5_k} q5_k
-    ./bin/quantize ${model_f16} ${model_q6_k} q6_k
-
-    (time ./bin/main --model ${model_f16}  -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
-
-    (time ./bin/save-load-state     -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/save-load-state     -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
+    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
+    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
+
+    (time ./bin/llama-cli --model ${model_f16}  -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+
+    (time ./bin/llama-save-load-state     -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state     -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -494,7 +636,7 @@ function gg_run_open_llama_7b_v2 {
     check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
     check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
@@ -505,10 +647,10 @@ function gg_run_open_llama_7b_v2 {
     set +e
 }
 
-function gg_sum_open_llama_7b_v2 {
+function gg_sum_pythia_2_8b {
     gg_printf '### %s\n\n' "${ci}"
 
-    gg_printf 'OpenLLaMA 7B-v2:\n'
+    gg_printf 'Pythia 2.8B:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
     gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
@@ -552,19 +694,33 @@ function gg_run_embd_bge_small {
     (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../convert-hf-to-gguf.py ${path_models}
+    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
 
     model_f16="${path_models}/ggml-model-f16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"
 
-    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
 
-    (time ./bin/embedding --model ${model_f16}  -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
 
     set +e
 }
 
+function gg_check_build_requirements {
+    if ! command -v cmake &> /dev/null; then
+        gg_printf 'cmake not found, please install'
+    fi
+
+    if ! command -v make &> /dev/null; then
+        gg_printf 'make not found, please install'
+    fi
+
+    if ! command -v ctest &> /dev/null; then
+        gg_printf 'ctest not found, please install'
+    fi
+}
+
 function gg_sum_embd_bge_small {
     gg_printf '### %s\n\n' "${ci}"
 
@@ -606,9 +762,10 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
 
     if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
         if [ -z ${GG_BUILD_CUDA} ]; then
-            test $ret -eq 0 && gg_run open_llama_3b_v2
+            test $ret -eq 0 && gg_run pythia_1_4b
         else
-            test $ret -eq 0 && gg_run open_llama_7b_v2
+            test $ret -eq 0 && gg_run pythia_2_8b
+            #test $ret -eq 0 && gg_run open_llama_7b_v2
         fi
         test $ret -eq 0 && gg_run ctest_with_model_debug
         test $ret -eq 0 && gg_run ctest_with_model_release
diff --git a/cmake/arm64-windows-llvm.cmake b/cmake/arm64-windows-llvm.cmake
index 46fba65149b29..8023796800683 100644
--- a/cmake/arm64-windows-llvm.cmake
+++ b/cmake/arm64-windows-llvm.cmake
@@ -9,7 +9,7 @@ set( CMAKE_CXX_COMPILER  clang++ )
 set( CMAKE_C_COMPILER_TARGET   ${target} )
 set( CMAKE_CXX_COMPILER_TARGET ${target} )
 
-set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast" )
+set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
 set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" )
 
 set( CMAKE_C_FLAGS_INIT   "${arch_c_flags} ${warn_c_flags}" )
diff --git a/scripts/build-info.cmake b/cmake/build-info.cmake
similarity index 100%
rename from scripts/build-info.cmake
rename to cmake/build-info.cmake
diff --git a/cmake/git-vars.cmake b/cmake/git-vars.cmake
new file mode 100644
index 0000000000000..1a4c24ebf6ade
--- /dev/null
+++ b/cmake/git-vars.cmake
@@ -0,0 +1,22 @@
+find_package(Git)
+
+# the commit's SHA1
+execute_process(COMMAND
+    "${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
+    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
+    OUTPUT_VARIABLE GIT_SHA1
+    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+# the date of the commit
+execute_process(COMMAND
+    "${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
+    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
+    OUTPUT_VARIABLE GIT_DATE
+    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+# the subject of the commit
+execute_process(COMMAND
+    "${GIT_EXECUTABLE}" log -1 --format=%s
+    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
+    OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
+    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
diff --git a/scripts/LlamaConfig.cmake.in b/cmake/llama-config.cmake.in
similarity index 51%
rename from scripts/LlamaConfig.cmake.in
rename to cmake/llama-config.cmake.in
index 92e39708b7cf2..f072b76a39d2e 100644
--- a/scripts/LlamaConfig.cmake.in
+++ b/cmake/llama-config.cmake.in
@@ -1,58 +1,82 @@
-set(LLAMA_VERSION @LLAMA_INSTALL_VERSION@)
+set(LLAMA_VERSION      @LLAMA_INSTALL_VERSION@)
 set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
 set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
-set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
-set(LLAMA_BLAS @LLAMA_BLAS@)
-set(LLAMA_CUDA @LLAMA_CUDA@)
-set(LLAMA_METAL @LLAMA_METAL@)
-set(LLAMA_CLBLAST @LLAMA_CLBLAST@)
-set(LLAMA_HIPBLAS @LLAMA_HIPBLAS@)
-set(LLAMA_ACCELERATE @LLAMA_ACCELERATE@)
+set(LLAMA_SHARED_LIB   @BUILD_SHARED_LIBS@)
+
+set(GGML_BLAS       @GGML_BLAS@)
+set(GGML_CUDA       @GGML_CUDA@)
+set(GGML_METAL      @GGML_METAL@)
+set(GGML_HIPBLAS    @GGML_HIPBLAS@)
+set(GGML_ACCELERATE @GGML_ACCELERATE@)
+set(GGML_VULKAN @GGML_VULKAN@)
+set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@)
+set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@)
+set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@)
+set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@)
+set(GGML_SYCL @GGML_SYCL@)
+set(GGML_OPENMP @GGML_OPENMP@)
 
 @PACKAGE_INIT@
 
 set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
-set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
-set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
+set_and_check(LLAMA_LIB_DIR     "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
+set_and_check(LLAMA_BIN_DIR     "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
 
 # Ensure transient dependencies satisfied
 
 find_package(Threads REQUIRED)
-if (APPLE AND LLAMA_ACCELERATE)
+
+if (APPLE AND GGML_ACCELERATE)
     find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
 endif()
 
-if (LLAMA_BLAS)
+if (GGML_BLAS)
     find_package(BLAS REQUIRED)
 endif()
 
-if (LLAMA_CUDA)
+if (GGML_CUDA)
     find_package(CUDAToolkit REQUIRED)
 endif()
 
-if (LLAMA_METAL)
+if (GGML_METAL)
     find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
     find_library(METAL_FRAMEWORK Metal REQUIRED)
     find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
 endif()
 
-if (LLAMA_CLBLAST)
-    find_package(CLBlast REQUIRED)
+if (GGML_VULKAN)
+    find_package(Vulkan REQUIRED)
 endif()
 
-if (LLAMA_HIPBLAS)
+if (GGML_HIPBLAS)
     find_package(hip REQUIRED)
     find_package(hipblas REQUIRED)
     find_package(rocblas REQUIRED)
 endif()
 
+if (GGML_SYCL)
+    find_package(IntelSYCL REQUIRED)
+    find_package(MKL REQUIRED)
+endif()
+
+if (GGML_OPENMP)
+    find_package(OpenMP REQUIRED)
+endif()
+
+
+find_library(ggml_LIBRARY ggml
+    REQUIRED
+    HINTS ${LLAMA_LIB_DIR})
+
 find_library(llama_LIBRARY llama
     REQUIRED
     HINTS ${LLAMA_LIB_DIR})
 
-set(_llama_link_deps "Threads::Threads" "@LLAMA_EXTRA_LIBS@")
-set(_llama_transient_defines "@LLAMA_TRANSIENT_DEFINES@")
+set(_llama_link_deps "${ggml_LIBRARY}" "@GGML_LINK_LIBRARIES@")
+set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@")
+
 add_library(llama UNKNOWN IMPORTED)
+
 set_target_properties(llama
     PROPERTIES
         INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
diff --git a/cmake/llama.pc.in b/cmake/llama.pc.in
new file mode 100644
index 0000000000000..326acbb6108fd
--- /dev/null
+++ b/cmake/llama.pc.in
@@ -0,0 +1,10 @@
+prefix=@CMAKE_INSTALL_PREFIX@
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: llama
+Description: Port of Facebook's LLaMA model in C/C++
+Version: @PROJECT_VERSION@
+Libs: -L${libdir} -lllama
+Cflags: -I${includedir}
diff --git a/codecov.yml b/codecov.yml
deleted file mode 100644
index a301c5b2c7694..0000000000000
--- a/codecov.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-comment: off
-
-coverage:
-  status:
-    project:
-      default:
-        target: auto
-        threshold: 0
-        base: auto
-    patch:
-      default:
-        target: auto
-        threshold: 0
-        base: auto
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index 0ec8d6d8d03b5..761971d6881f3 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -1,5 +1,6 @@
 # common
 
+find_package(Threads REQUIRED)
 
 # Build info header
 #
@@ -36,7 +37,7 @@ add_custom_command(
     COMMENT "Generating build details from Git"
     COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
             -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
     WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
     DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
     VERBATIM
@@ -83,5 +84,5 @@ if (LLAMA_CURL)
 endif ()
 
 target_include_directories(${TARGET} PUBLIC .)
-target_compile_features(${TARGET} PUBLIC cxx_std_11)
-target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama)
+target_compile_features   (${TARGET} PUBLIC cxx_std_11)
+target_link_libraries     (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
diff --git a/scripts/gen-build-info-cpp.cmake b/common/cmake/build-info-gen-cpp.cmake
similarity index 86%
rename from scripts/gen-build-info-cpp.cmake
rename to common/cmake/build-info-gen-cpp.cmake
index d8933892011b3..fbc92b52cc4fe 100644
--- a/scripts/gen-build-info-cpp.cmake
+++ b/common/cmake/build-info-gen-cpp.cmake
@@ -1,7 +1,7 @@
-include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
+include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
 
 set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
-set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
+set(OUTPUT_FILE   "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
 
 # Only write the build info if it changed
 if(EXISTS ${OUTPUT_FILE})
diff --git a/common/common.cpp b/common/common.cpp
index c4bccd6c49b3b..ce46e65ae2834 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1,3 +1,7 @@
+#if defined(_MSC_VER)
+#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
+#endif
+
 #include "common.h"
 // Change JSON_ASSERT from assert() to GGML_ASSERT:
 #define JSON_ASSERT GGML_ASSERT
@@ -6,21 +10,21 @@
 #include "llama.h"
 
 #include <algorithm>
-#include <cassert>
+#include <cinttypes>
 #include <cmath>
+#include <codecvt>
+#include <cstdarg>
 #include <cstring>
 #include <ctime>
 #include <fstream>
-#include <iterator>
 #include <iostream>
+#include <iterator>
 #include <regex>
 #include <sstream>
 #include <string>
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
-#include <cinttypes>
-#include <codecvt>
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/types.h>
@@ -73,7 +77,11 @@
 
 using json = nlohmann::ordered_json;
 
-int32_t get_num_physical_cores() {
+//
+// CPU utils
+//
+
+int32_t cpu_get_num_physical_cores() {
 #ifdef __linux__
     // enumerate the set of thread siblings, num entries is num cores
     std::unordered_set<std::string> siblings;
@@ -142,9 +150,9 @@ static bool is_running_on_efficiency_core(void) {
     return core_type == intel_atom;
 }
 
-static int count_math_cpus(int cpu_count) {
+static int cpu_count_math_cpus(int n_cpu) {
     int result = 0;
-    for (int cpu = 0; cpu < cpu_count; ++cpu) {
+    for (int cpu = 0; cpu < n_cpu; ++cpu) {
         if (pin_cpu(cpu)) {
             return -1;
         }
@@ -162,16 +170,16 @@ static int count_math_cpus(int cpu_count) {
 /**
  * Returns number of CPUs on system that are useful for math.
  */
-int get_math_cpu_count() {
+int32_t cpu_get_num_math() {
 #if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
-    int cpu_count = sysconf(_SC_NPROCESSORS_ONLN);
-    if (cpu_count < 1) {
-        return get_num_physical_cores();
+    int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
+    if (n_cpu < 1) {
+        return cpu_get_num_physical_cores();
     }
     if (is_hybrid_cpu()) {
         cpu_set_t affinity;
         if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
-            int result = count_math_cpus(cpu_count);
+            int result = cpu_count_math_cpus(n_cpu);
             pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
             if (result > 0) {
                 return result;
@@ -179,126 +187,120 @@ int get_math_cpu_count() {
         }
     }
 #endif
-    return get_num_physical_cores();
+    return cpu_get_num_physical_cores();
 }
 
-void process_escapes(std::string & input) {
-    std::size_t input_len = input.length();
-    std::size_t output_idx = 0;
+//
+// CLI argument parsing
+//
 
-    for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
-        if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
-            switch (input[++input_idx]) {
-                case 'n':  input[output_idx++] = '\n'; break;
-                case 'r':  input[output_idx++] = '\r'; break;
-                case 't':  input[output_idx++] = '\t'; break;
-                case '\'': input[output_idx++] = '\''; break;
-                case '\"': input[output_idx++] = '\"'; break;
-                case '\\': input[output_idx++] = '\\'; break;
-                case 'x':
-                    // Handle \x12, etc
-                    if (input_idx + 2 < input_len) {
-                        const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
-                        char *err_p = nullptr;
-                        const long val = std::strtol(x, &err_p, 16);
-                        if (err_p == x + 2) {
-                            input_idx += 2;
-                            input[output_idx++] = char(val);
-                            break;
-                        }
-                    }
-                    // fall through
-                default:   input[output_idx++] = '\\';
-                           input[output_idx++] = input[input_idx]; break;
+void gpt_params_handle_hf_token(gpt_params & params) {
+    if (params.hf_token.empty() && std::getenv("HF_TOKEN")) {
+        params.hf_token = std::getenv("HF_TOKEN");
+    }
+}
+
+void gpt_params_handle_model_default(gpt_params & params) {
+    if (!params.hf_repo.empty()) {
+        // short-hand to avoid specifying --hf-file -> default it to --model
+        if (params.hf_file.empty()) {
+            if (params.model.empty()) {
+                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
             }
-        } else {
-            input[output_idx++] = input[input_idx];
+            params.hf_file = params.model;
+        } else if (params.model.empty()) {
+            params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
+        }
+    } else if (!params.model_url.empty()) {
+        if (params.model.empty()) {
+            auto f = string_split(params.model_url, '#').front();
+            f = string_split(f, '?').front();
+            params.model = fs_get_cache_file(string_split(f, '/').back());
         }
+    } else if (params.model.empty()) {
+        params.model = DEFAULT_MODEL_PATH;
     }
-
-    input.resize(output_idx);
 }
 
-bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
-    bool result = true;
-    try {
-        if (!gpt_params_parse_ex(argc, argv, params)) {
-            gpt_print_usage(argc, argv, gpt_params());
-            exit(0);
+bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
+    bool invalid_param = false;
+    std::string arg;
+    const std::string arg_prefix = "--";
+    llama_sampling_params & sparams = params.sparams;
+
+    for (int i = 1; i < argc; i++) {
+        arg = argv[i];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
+        }
+        if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
+            throw std::invalid_argument("error: unknown argument: " + arg);
+        }
+        if (invalid_param) {
+            throw std::invalid_argument("error: invalid parameter for argument: " + arg);
         }
     }
-    catch (const std::invalid_argument & ex) {
-        fprintf(stderr, "%s\n", ex.what());
-        gpt_print_usage(argc, argv, gpt_params());
-        exit(1);
-    }
-    return result;
-}
 
-bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
-    const char * sep = strchr(data, '=');
-    if (sep == nullptr || sep - data >= 128) {
-        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
-        return false;
+    if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
+        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
-    llama_model_kv_override kvo;
-    std::strncpy(kvo.key, data, sep - data);
-    kvo.key[sep - data] = 0;
-    sep++;
-    if (strncmp(sep, "int:", 4) == 0) {
-        sep += 4;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
-        kvo.val_i64 = std::atol(sep);
-    } else if (strncmp(sep, "float:", 6) == 0) {
-        sep += 6;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
-        kvo.val_f64 = std::atof(sep);
-    } else if (strncmp(sep, "bool:", 5) == 0) {
-        sep += 5;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
-        if (std::strcmp(sep, "true") == 0) {
-            kvo.val_bool = true;
-        } else if (std::strcmp(sep, "false") == 0) {
-            kvo.val_bool = false;
-        } else {
-            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
-            return false;
+
+    gpt_params_handle_model_default(params);
+
+    gpt_params_handle_hf_token(params);
+
+    if (params.escape) {
+        string_process_escapes(params.prompt);
+        string_process_escapes(params.input_prefix);
+        string_process_escapes(params.input_suffix);
+        string_process_escapes(sparams.cfg_negative_prompt);
+        for (auto & antiprompt : params.antiprompt) {
+            string_process_escapes(antiprompt);
         }
-    } else if (strncmp(sep, "str:", 4) == 0) {
-        sep += 4;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
-        if (strlen(sep) > 127) {
-            fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
+    }
+
+    if (!params.kv_overrides.empty()) {
+        params.kv_overrides.emplace_back();
+        params.kv_overrides.back().key[0] = 0;
+    }
+
+    return true;
+}
+
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
+    const auto params_org = params; // the example can modify the default params
+
+    try {
+        if (!gpt_params_parse_ex(argc, argv, params) || params.usage) {
+            params = params_org;
+            params.usage = true;
             return false;
         }
-        strncpy(kvo.val_str, sep, 127);
-        kvo.val_str[127] = '\0';
-    } else {
-        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
+    } catch (const std::invalid_argument & ex) {
+        fprintf(stderr, "%s\n", ex.what());
+        params = params_org;
         return false;
     }
-    overrides.emplace_back(std::move(kvo));
+
     return true;
 }
 
+#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }
+
 bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
+    const char split_delim = ',';
+
     llama_sampling_params & sparams = params.sparams;
 
     if (arg == "-s" || arg == "--seed") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
-        // This is temporary, in the future the samplign state will be moved fully to llama_sampling_context.
+        CHECK_ARG
+        // TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context.
         params.seed = std::stoul(argv[i]);
         sparams.seed = std::stoul(argv[i]);
         return true;
     }
     if (arg == "-t" || arg == "--threads") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_threads = std::stoi(argv[i]);
         if (params.n_threads <= 0) {
             params.n_threads = std::thread::hardware_concurrency();
@@ -306,10 +308,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-tb" || arg == "--threads-batch") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_threads_batch = std::stoi(argv[i]);
         if (params.n_threads_batch <= 0) {
             params.n_threads_batch = std::thread::hardware_concurrency();
@@ -317,10 +316,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-td" || arg == "--threads-draft") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_threads_draft = std::stoi(argv[i]);
         if (params.n_threads_draft <= 0) {
             params.n_threads_draft = std::thread::hardware_concurrency();
@@ -328,10 +324,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-tbd" || arg == "--threads-batch-draft") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_threads_batch_draft = std::stoi(argv[i]);
         if (params.n_threads_batch_draft <= 0) {
             params.n_threads_batch_draft = std::thread::hardware_concurrency();
@@ -339,10 +332,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-p" || arg == "--prompt") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.prompt = argv[i];
         return true;
     }
@@ -350,11 +340,12 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.escape = true;
         return true;
     }
+    if (arg == "--no-escape") {
+        params.escape = false;
+        return true;
+    }
     if (arg == "--prompt-cache") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.path_prompt_cache = argv[i];
         return true;
     }
@@ -367,10 +358,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-bf" || arg == "--binary-file") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::ifstream file(argv[i], std::ios::binary);
         if (!file) {
             fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
@@ -386,10 +374,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-f" || arg == "--file") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::ifstream file(argv[i]);
         if (!file) {
             fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
@@ -404,67 +389,54 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         }
         return true;
     }
-    if (arg == "-n" || arg == "--n-predict") {
-        if (++i >= argc) {
+    if (arg == "--in-file") {
+        CHECK_ARG
+        std::ifstream file(argv[i]);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
             invalid_param = true;
             return true;
         }
+        params.in_files.push_back(argv[i]);
+        return true;
+    }
+    if (arg == "-n" || arg == "--predict" || arg == "--n-predict") {
+        CHECK_ARG
         params.n_predict = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--top-k") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.top_k = std::stoi(argv[i]);
         return true;
     }
     if (arg == "-c" || arg == "--ctx-size") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_ctx = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--grp-attn-n" || arg == "-gan") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.grp_attn_n = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--grp-attn-w" || arg == "-gaw") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.grp_attn_w = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--rope-freq-base") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.rope_freq_base = std::stof(argv[i]);
         return true;
     }
     if (arg == "--rope-freq-scale") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.rope_freq_scale = std::stof(argv[i]);
         return true;
     }
     if (arg == "--rope-scaling") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::string value(argv[i]);
         /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
         else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
@@ -473,217 +445,148 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "--rope-scale") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.rope_freq_scale = 1.0f / std::stof(argv[i]);
         return true;
     }
     if (arg == "--yarn-orig-ctx") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.yarn_orig_ctx = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--yarn-ext-factor") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.yarn_ext_factor = std::stof(argv[i]);
         return true;
     }
     if (arg == "--yarn-attn-factor") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.yarn_attn_factor = std::stof(argv[i]);
         return true;
     }
     if (arg == "--yarn-beta-fast") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.yarn_beta_fast = std::stof(argv[i]);
         return true;
     }
     if (arg == "--yarn-beta-slow") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.yarn_beta_slow = std::stof(argv[i]);
         return true;
     }
     if (arg == "--pooling") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::string value(argv[i]);
         /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
         else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
         else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+        else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
+        else { invalid_param = true; }
+        return true;
+    }
+    if (arg == "--attention") {
+        CHECK_ARG
+        std::string value(argv[i]);
+        /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
+        else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
         else { invalid_param = true; }
         return true;
     }
     if (arg == "--defrag-thold" || arg == "-dt") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.defrag_thold = std::stof(argv[i]);
         return true;
     }
     if (arg == "--samplers") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         const auto sampler_names = string_split(argv[i], ';');
-        sparams.samplers_sequence = sampler_types_from_names(sampler_names, true);
+        sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true);
         return true;
     }
     if (arg == "--sampling-seq") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
-        sparams.samplers_sequence = sampler_types_from_chars(argv[i]);
+        CHECK_ARG
+        sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]);
         return true;
     }
     if (arg == "--top-p") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.top_p = std::stof(argv[i]);
         return true;
     }
     if (arg == "--min-p") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.min_p = std::stof(argv[i]);
         return true;
     }
     if (arg == "--temp") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.temp = std::stof(argv[i]);
         sparams.temp = std::max(sparams.temp, 0.0f);
         return true;
     }
     if (arg == "--tfs") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.tfs_z = std::stof(argv[i]);
         return true;
     }
     if (arg == "--typical") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.typical_p = std::stof(argv[i]);
         return true;
     }
     if (arg == "--repeat-last-n") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.penalty_last_n = std::stoi(argv[i]);
         sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
         return true;
     }
     if (arg == "--repeat-penalty") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.penalty_repeat = std::stof(argv[i]);
         return true;
     }
     if (arg == "--frequency-penalty") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.penalty_freq = std::stof(argv[i]);
         return true;
     }
     if (arg == "--presence-penalty") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.penalty_present = std::stof(argv[i]);
         return true;
     }
     if (arg == "--dynatemp-range") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.dynatemp_range = std::stof(argv[i]);
         return true;
     }
     if (arg == "--dynatemp-exp") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.dynatemp_exponent = std::stof(argv[i]);
         return true;
     }
     if (arg == "--mirostat") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.mirostat = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--mirostat-lr") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.mirostat_eta = std::stof(argv[i]);
         return true;
     }
     if (arg == "--mirostat-ent") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.mirostat_tau = std::stof(argv[i]);
         return true;
     }
     if (arg == "--cfg-negative-prompt") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.cfg_negative_prompt = argv[i];
         return true;
     }
     if (arg == "--cfg-negative-prompt-file") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::ifstream file(argv[i]);
         if (!file) {
             fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
@@ -697,203 +600,136 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "--cfg-scale") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.cfg_scale = std::stof(argv[i]);
         return true;
     }
     if (arg == "-b" || arg == "--batch-size") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_batch = std::stoi(argv[i]);
         return true;
     }
     if (arg == "-ub" || arg == "--ubatch-size") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_ubatch = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--keep") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_keep = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--draft") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_draft = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--chunks") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_chunks = std::stoi(argv[i]);
         return true;
     }
     if (arg == "-np" || arg == "--parallel") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_parallel = std::stoi(argv[i]);
         return true;
     }
     if (arg == "-ns" || arg == "--sequences") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_sequences = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--p-split" || arg == "-ps") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.p_split = std::stof(argv[i]);
         return true;
     }
     if (arg == "-m" || arg == "--model") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.model = argv[i];
         return true;
     }
     if (arg == "-md" || arg == "--model-draft") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.model_draft = argv[i];
         return true;
     }
     if (arg == "-a" || arg == "--alias") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.model_alias = argv[i];
         return true;
     }
     if (arg == "-mu" || arg == "--model-url") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.model_url = argv[i];
         return true;
     }
-    if (arg == "-hfr" || arg == "--hf-repo") {
+    if (arg == "-hft" || arg == "--hf-token") {
         if (++i >= argc) {
-            invalid_param = true;
-            return true;
+          invalid_param = true;
+          return true;
         }
+        params.hf_token = argv[i];
+        return true;
+    }
+    if (arg == "-hfr" || arg == "--hf-repo") {
+        CHECK_ARG
         params.hf_repo = argv[i];
         return true;
     }
     if (arg == "-hff" || arg == "--hf-file") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.hf_file = argv[i];
         return true;
     }
     if (arg == "--lora") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
-        params.lora_adapter.emplace_back(argv[i], 1.0f);
-        params.use_mmap = false;
+        CHECK_ARG
+        params.lora_adapters.push_back({
+            std::string(argv[i]),
+            1.0,
+        });
         return true;
     }
     if (arg == "--lora-scaled") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
-        const char* lora_adapter = argv[i];
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
-        params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
-        params.use_mmap = false;
+        CHECK_ARG
+        std::string lora_adapter = argv[i];
+        CHECK_ARG
+        params.lora_adapters.push_back({
+            lora_adapter,
+            std::stof(argv[i]),
+        });
         return true;
     }
-    if (arg == "--lora-base") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
-        params.lora_base = argv[i];
+    if (arg == "--lora-init-without-apply") {
+        params.lora_init_without_apply = true;
         return true;
     }
     if (arg == "--control-vector") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.control_vectors.push_back({ 1.0f, argv[i], });
         return true;
     }
     if (arg == "--control-vector-scaled") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         const char* fname = argv[i];
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.control_vectors.push_back({ std::stof(argv[i]), fname, });
         return true;
     }
     if (arg == "--control-vector-layer-range") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.control_vector_layer_start = std::stoi(argv[i]);
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.control_vector_layer_end = std::stoi(argv[i]);
         return true;
     }
     if (arg == "--mmproj") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.mmproj = argv[i];
         return true;
     }
     if (arg == "--image") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.image.emplace_back(argv[i]);
         return true;
     }
@@ -901,28 +737,35 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.interactive = true;
         return true;
     }
-    if (arg == "--interactive-specials") {
-        params.interactive_specials = true;
+    if (arg == "-sp" || arg == "--special") {
+        params.special = true;
         return true;
     }
-    if (arg == "--embedding") {
+    if (arg == "--embedding" || arg == "--embeddings") {
         params.embedding = true;
         return true;
     }
-    if (arg == "--interactive-first") {
-        params.interactive_first = true;
+    if (arg == "--embd-normalize") {
+        CHECK_ARG
+        params.embd_normalize = std::stoi(argv[i]);
         return true;
     }
-    if (arg == "-ins" || arg == "--instruct") {
-        params.instruct = true;
+    if (arg == "--embd-output-format") {
+        CHECK_ARG
+        params.embd_out = argv[i];
         return true;
     }
-    if (arg == "-cnv" || arg == "--conversation") {
-        params.conversation = true;
+    if (arg == "--embd-separator") {
+        CHECK_ARG
+        params.embd_sep = argv[i];
+        return true;
+    }
+    if (arg == "-if" || arg == "--interactive-first") {
+        params.interactive_first = true;
         return true;
     }
-    if (arg == "-cml" || arg == "--chatml") {
-        params.chatml = true;
+    if (arg == "-cnv" || arg == "--conversation") {
+        params.conversation = true;
         return true;
     }
     if (arg == "--infill") {
@@ -945,7 +788,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.cache_type_v = argv[++i];
         return true;
     }
-    if (arg == "--multiline-input") {
+    if (arg == "-mli" || arg == "--multiline-input") {
         params.multiline_input = true;
         return true;
     }
@@ -957,11 +800,15 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.cont_batching = true;
         return true;
     }
+    if (arg == "-nocb" || arg == "--no-cont-batching") {
+        params.cont_batching = false;
+        return true;
+    }
     if (arg == "-fa" || arg == "--flash-attn") {
         params.flash_attn = true;
         return true;
     }
-    if (arg == "--color") {
+    if (arg == "-co" || arg == "--color") {
         params.use_color = true;
         return true;
     }
@@ -969,46 +816,34 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.use_mlock = true;
         return true;
     }
-    if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+    if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
+        CHECK_ARG
         params.n_gpu_layers = std::stoi(argv[i]);
         if (!llama_supports_gpu_offload()) {
-            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
+            fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
             fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
         }
         return true;
     }
-    if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+    if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--gpu-layers-draft") {
+        CHECK_ARG
         params.n_gpu_layers_draft = std::stoi(argv[i]);
         if (!llama_supports_gpu_offload()) {
-            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n");
+            fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
             fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
         }
         return true;
     }
     if (arg == "--main-gpu" || arg == "-mg") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.main_gpu = std::stoi(argv[i]);
-#ifndef GGML_USE_CUDA_SYCL
-        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the main GPU has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
         return true;
     }
     if (arg == "--split-mode" || arg == "-sm") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::string arg_next = argv[i];
         if (arg_next == "none") {
             params.split_mode = LLAMA_SPLIT_MODE_NONE;
@@ -1027,16 +862,13 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
             invalid_param = true;
             return true;
         }
-#ifndef GGML_USE_CUDA_SYCL
-        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the split mode has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
         return true;
     }
     if (arg == "--tensor-split" || arg == "-ts") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::string arg_next = argv[i];
 
         // split string by , and /
@@ -1061,10 +893,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "--rpc") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.rpc_servers = argv[i];
         return true;
     }
@@ -1073,10 +902,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "--numa") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::string value(argv[i]);
         /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
         else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
@@ -1084,6 +910,15 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         else { invalid_param = true; }
         return true;
     }
+    if (arg == "-v" || arg == "--verbose") {
+        params.verbosity = 1;
+        return true;
+    }
+    if (arg == "--verbosity") {
+        CHECK_ARG
+        params.verbosity = std::stoi(argv[i]);
+        return true;
+    }
     if (arg == "--verbose-prompt") {
         params.verbose_prompt = true;
         return true;
@@ -1093,18 +928,12 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-r" || arg == "--reverse-prompt") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.antiprompt.emplace_back(argv[i]);
         return true;
     }
     if (arg == "-ld" || arg == "--logdir") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.logdir = argv[i];
 
         if (params.logdir.back() != DIRECTORY_SEPARATOR) {
@@ -1113,26 +942,17 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-lcs" || arg == "--lookup-cache-static") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.lookup_cache_static = argv[i];
         return true;
     }
     if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.lookup_cache_dynamic = argv[i];
         return true;
     }
     if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.logits_file = argv[i];
         return true;
     }
@@ -1141,18 +961,17 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "--ppl-stride") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.ppl_stride = std::stoi(argv[i]);
         return true;
     }
+    if (arg == "--ppl-output-type") {
+        CHECK_ARG
+        params.ppl_output_type = std::stoi(argv[i]);
+        return true;
+    }
     if (arg == "-ptc" || arg == "--print-token-count") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.n_print = std::stoi(argv[i]);
         return true;
     }
@@ -1160,23 +979,12 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.check_tensors = true;
         return true;
     }
-    if (arg == "--ppl-output-type") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
-        params.ppl_output_type = std::stoi(argv[i]);
-        return true;
-    }
     if (arg == "--hellaswag") {
         params.hellaswag = true;
         return true;
     }
     if (arg == "--hellaswag-tasks") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.hellaswag_tasks = std::stoi(argv[i]);
         return true;
     }
@@ -1185,10 +993,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "--winogrande-tasks") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.winogrande_tasks = std::stoi(argv[i]);
         return true;
     }
@@ -1197,10 +1002,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "--multiple-choice-tasks") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.multiple_choice_tasks = std::stoi(argv[i]);
         return true;
     }
@@ -1217,10 +1019,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-l" || arg == "--logit-bias") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::stringstream ss(argv[i]);
         llama_token key;
         char sign;
@@ -1239,52 +1038,43 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         }
         return true;
     }
-    if (arg == "-h" || arg == "--help") {
-        gpt_print_usage(argc, argv, gpt_params());
-        exit(0);
+    if (arg == "-h" || arg == "--help" || arg == "--usage"  ) {
+        params.usage = true;
+        return true;
     }
     if (arg == "--version") {
         fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
         fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
         exit(0);
     }
-    if (arg == "--random-prompt") {
-        params.random_prompt = true;
-        return true;
-    }
     if (arg == "--in-prefix-bos") {
         params.input_prefix_bos = true;
+        params.enable_chat_template = false;
         return true;
     }
     if (arg == "--in-prefix") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.input_prefix = argv[i];
+        params.enable_chat_template = false;
         return true;
     }
     if (arg == "--in-suffix") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         params.input_suffix = argv[i];
+        params.enable_chat_template = false;
+        return true;
+    }
+    if (arg == "--spm-infill") {
+        params.spm_infill = true;
         return true;
     }
     if (arg == "--grammar") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         sparams.grammar = argv[i];
         return true;
     }
     if (arg == "--grammar-file") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         std::ifstream file(argv[i]);
         if (!file) {
             fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
@@ -1299,23 +1089,253 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         return true;
     }
     if (arg == "-j" || arg == "--json-schema") {
-        if (++i >= argc) {
+        CHECK_ARG
+        sparams.grammar = json_schema_to_grammar(json::parse(argv[i]));
+        return true;
+    }
+    if (arg == "--override-kv") {
+        CHECK_ARG
+        if (!string_parse_kv_override(argv[i], params.kv_overrides)) {
+            fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
             invalid_param = true;
             return true;
         }
-        sparams.grammar = json_schema_to_grammar(json::parse(argv[i]));
         return true;
     }
-    if (arg == "--override-kv") {
-        if (++i >= argc) {
+    if (arg == "--host") {
+        CHECK_ARG
+        params.hostname = argv[i];
+        return true;
+    }
+    if (arg == "--port") {
+        CHECK_ARG
+        params.port = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--path") {
+        CHECK_ARG
+        params.public_path = argv[i];
+        return true;
+    }
+    if (arg == "--api-key") {
+        CHECK_ARG
+        params.api_keys.push_back(argv[i]);
+        return true;
+    }
+    if (arg == "--api-key-file") {
+        CHECK_ARG
+        std::ifstream key_file(argv[i]);
+        if (!key_file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
             invalid_param = true;
             return true;
         }
-        if (!parse_kv_override(argv[i], params.kv_overrides)) {
-            fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
+        std::string key;
+        while (std::getline(key_file, key)) {
+            if (!key.empty()) {
+                params.api_keys.push_back(key);
+            }
+        }
+        key_file.close();
+        return true;
+    }
+    if (arg == "--ssl-key-file") {
+        CHECK_ARG
+        params.ssl_file_key = argv[i];
+        return true;
+    }
+    if (arg == "--ssl-cert-file") {
+        CHECK_ARG
+        params.ssl_file_cert = argv[i];
+        return true;
+    }
+    if (arg == "--timeout" || arg == "-to") {
+        CHECK_ARG
+        params.timeout_read  = std::stoi(argv[i]);
+        params.timeout_write = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--threads-http") {
+        CHECK_ARG
+        params.n_threads_http = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-spf" || arg == "--system-prompt-file") {
+        CHECK_ARG
+        std::ifstream file(argv[i]);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        std::string system_prompt;
+        std::copy(
+                std::istreambuf_iterator<char>(file),
+                std::istreambuf_iterator<char>(),
+                std::back_inserter(system_prompt)
+                );
+        params.system_prompt = system_prompt;
+        return true;
+    }
+    if (arg == "--log-format") {
+        CHECK_ARG
+        if (std::strcmp(argv[i], "json") == 0) {
+            params.log_json = true;
+        } else if (std::strcmp(argv[i], "text") == 0) {
+            params.log_json = false;
+        } else {
+            invalid_param = true;
+            return true;
+        }
+        return true;
+    }
+    if (arg == "--no-slots") {
+        params.endpoint_slots = false;
+        return true;
+    }
+    if (arg == "--metrics") {
+        params.endpoint_metrics = true;
+        return true;
+    }
+    if (arg == "--slot-save-path") {
+        CHECK_ARG
+        params.slot_save_path = argv[i];
+        // if doesn't end with DIRECTORY_SEPARATOR, add it
+        if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
+            params.slot_save_path += DIRECTORY_SEPARATOR;
+        }
+        return true;
+    }
+    if (arg == "--chat-template") {
+        CHECK_ARG
+        if (!llama_chat_verify_template(argv[i])) {
+            fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
+            fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
+            invalid_param = true;
+            return true;
+        }
+        params.chat_template = argv[i];
+        return true;
+    }
+    if (arg == "--slot-prompt-similarity" || arg == "-sps") {
+        CHECK_ARG
+        params.slot_prompt_similarity = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "-pps") {
+        params.is_pp_shared = true;
+        return true;
+    }
+    if (arg == "-npp") {
+        CHECK_ARG
+        auto p = string_split<int>(argv[i], split_delim);
+        params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
+        return true;
+    }
+    if (arg == "-ntg") {
+        CHECK_ARG
+        auto p = string_split<int>(argv[i], split_delim);
+        params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
+        return true;
+    }
+    if (arg == "-npl") {
+        CHECK_ARG
+        auto p = string_split<int>(argv[i], split_delim);
+        params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
+        return true;
+    }
+    if (arg == "--context-file") {
+        CHECK_ARG
+        std::ifstream file(argv[i], std::ios::binary);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
             invalid_param = true;
             return true;
         }
+        params.context_files.push_back(argv[i]);
+        return true;
+    }
+    if (arg == "--chunk-size") {
+        CHECK_ARG
+        params.chunk_size = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--chunk-separator") {
+        CHECK_ARG
+        params.chunk_separator = argv[i];
+        return true;
+    }
+    if (arg == "--junk") {
+        CHECK_ARG
+        params.n_junk = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--pos") {
+        CHECK_ARG
+        params.i_pos = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-o" || arg == "--output" || arg == "--output-file") {
+        CHECK_ARG
+        params.out_file = argv[i];
+        params.cvector_outfile = argv[i];
+        params.lora_outfile = argv[i];
+        return true;
+    }
+    if (arg == "-ofreq" || arg == "--output-frequency") {
+        CHECK_ARG
+        params.n_out_freq = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--save-frequency") {
+        CHECK_ARG
+        params.n_save_freq = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--process-output") {
+        params.process_output = true;
+        return true;
+    }
+    if (arg == "--no-ppl") {
+        params.compute_ppl = false;
+        return true;
+    }
+    if (arg == "--chunk" || arg == "--from-chunk") {
+        CHECK_ARG
+        params.i_chunk = std::stoi(argv[i]);
+        return true;
+    }
+    // cvector params
+    if (arg == "--positive-file") {
+        CHECK_ARG
+        params.cvector_positive_file = argv[i];
+        return true;
+    }
+    if (arg == "--negative-file") {
+        CHECK_ARG
+        params.cvector_negative_file = argv[i];
+        return true;
+    }
+    if (arg == "--pca-batch") {
+        CHECK_ARG
+        params.n_pca_batch = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--pca-iter") {
+        CHECK_ARG
+        params.n_pca_iterations = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--method") {
+        CHECK_ARG
+        std::string value(argv[i]);
+        /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
+        else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
+        else { invalid_param = true; }
+        return true;
+    }
+    if (arg == "--no-warmup") {
+        params.warmup = false;
         return true;
     }
 #ifndef LOG_DISABLE_LOGS
@@ -1329,10 +1349,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         // We have a matching known parameter requiring an argument,
         //  now we need to check if there is anything after this argv
         //  and flag invalid_param or parse it.
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
+        CHECK_ARG
         if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
             invalid_param = true;
             return true;
@@ -1345,280 +1362,365 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
     return false;
 }
 
-void gpt_params_handle_model_default(gpt_params & params) {
-    if (!params.hf_repo.empty()) {
-        // short-hand to avoid specifying --hf-file -> default it to --model
-        if (params.hf_file.empty()) {
-            if (params.model.empty()) {
-                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
-            }
-            params.hf_file = params.model;
-        } else if (params.model.empty()) {
-            params.model = "models/" + string_split(params.hf_file, '/').back();
-        }
-    } else if (!params.model_url.empty()) {
-        if (params.model.empty()) {
-            auto f = string_split(params.model_url, '#').front();
-            f = string_split(f, '?').front();
-            f = string_split(f, '/').back();
-            params.model =  "models/" + f;
-        }
-    } else if (params.model.empty()) {
-        params.model = DEFAULT_MODEL_PATH;
-    }
-}
-
-bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
-    bool invalid_param = false;
-    std::string arg;
-    const std::string arg_prefix = "--";
-    llama_sampling_params & sparams = params.sparams;
-
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
-        }
-        if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
-            throw std::invalid_argument("error: unknown argument: " + arg);
-        }
-        if (invalid_param) {
-            throw std::invalid_argument("error: invalid parameter for argument: " + arg);
-        }
-    }
-
-    if (params.prompt_cache_all &&
-            (params.interactive || params.interactive_first ||
-             params.instruct)) {
-
-        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
-    }
-
-    gpt_params_handle_model_default(params);
-
-    if (params.escape) {
-        process_escapes(params.prompt);
-        process_escapes(params.input_prefix);
-        process_escapes(params.input_suffix);
-        process_escapes(sparams.cfg_negative_prompt);
-        for (auto & antiprompt : params.antiprompt) {
-            process_escapes(antiprompt);
-        }
-    }
-
-    if (!params.kv_overrides.empty()) {
-        params.kv_overrides.emplace_back();
-        params.kv_overrides.back().key[0] = 0;
-    }
-
-    return true;
-}
+#ifdef __GNUC__
+#ifdef __MINGW32__
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+#else
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
+#endif
 
-void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
+void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     const llama_sampling_params & sparams = params.sparams;
 
     std::string sampler_type_chars;
     std::string sampler_type_names;
     for (const auto sampler_type : sparams.samplers_sequence) {
         sampler_type_chars += static_cast<char>(sampler_type);
-        sampler_type_names += sampler_type_to_name_string(sampler_type) + ";";
+        sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";";
     }
     sampler_type_names.pop_back();
 
-    printf("\n");
-    printf("usage: %s [options]\n", argv[0]);
-    printf("\n");
-    printf("options:\n");
-    printf("  -h, --help            show this help message and exit\n");
-    printf("  --version             show version and build info\n");
-    printf("  -i, --interactive     run in interactive mode\n");
-    printf("  --interactive-specials allow special tokens in user text, in interactive mode\n");
-    printf("  --interactive-first   run in interactive mode and wait for input right away\n");
-    printf("  -cnv, --conversation  run in conversation mode (does not print special tokens and suffix/prefix)\n");
-    printf("  -ins, --instruct      run in instruction mode (use with Alpaca models)\n");
-    printf("  -cml, --chatml        run in chatml mode (use with ChatML-compatible models)\n");
-    printf("  --multiline-input     allows you to write or paste multiple lines without ending each in '\\'\n");
-    printf("  -r PROMPT, --reverse-prompt PROMPT\n");
-    printf("                        halt generation at PROMPT, return control in interactive mode\n");
-    printf("                        (can be specified more than once for multiple prompts).\n");
-    printf("  --color               colorise output to distinguish prompt and user input from generations\n");
-    printf("  -s SEED, --seed SEED  RNG seed (default: -1, use random seed for < 0)\n");
-    printf("  -t N, --threads N     number of threads to use during generation (default: %d)\n", params.n_threads);
-    printf("  -tb N, --threads-batch N\n");
-    printf("                        number of threads to use during batch and prompt processing (default: same as --threads)\n");
-    printf("  -td N, --threads-draft N");
-    printf("                        number of threads to use during generation (default: same as --threads)\n");
-    printf("  -tbd N, --threads-batch-draft N\n");
-    printf("                        number of threads to use during batch and prompt processing (default: same as --threads-draft)\n");
-    printf("  -p PROMPT, --prompt PROMPT\n");
-    printf("                        prompt to start generation with (default: empty)\n");
-    printf("  -e, --escape          process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
-    printf("  --prompt-cache FNAME  file to cache prompt state for faster startup (default: none)\n");
-    printf("  --prompt-cache-all    if specified, saves user input and generations to cache as well.\n");
-    printf("                        not supported with --interactive or other interactive options\n");
-    printf("  --prompt-cache-ro     if specified, uses the prompt cache but does not update it.\n");
-    printf("  --random-prompt       start with a randomized prompt.\n");
-    printf("  --in-prefix-bos       prefix BOS to user inputs, preceding the `--in-prefix` string\n");
-    printf("  --in-prefix STRING    string to prefix user inputs with (default: empty)\n");
-    printf("  --in-suffix STRING    string to suffix after user inputs with (default: empty)\n");
-    printf("  -f FNAME, --file FNAME\n");
-    printf("                        prompt file to start generation.\n");
-    printf("  -bf FNAME, --binary-file FNAME\n");
-    printf("                        binary file containing multiple choice tasks.\n");
-    printf("  -n N, --n-predict N   number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
-    printf("  -c N, --ctx-size N    size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
-    printf("  -b N, --batch-size N  logical maximum batch size (default: %d)\n", params.n_batch);
-    printf("  -ub N, --ubatch-size N\n");
-    printf("                        physical maximum batch size (default: %d)\n", params.n_ubatch);
-    printf("  --samplers            samplers that will be used for generation in the order, separated by \';\'\n");
-    printf("                        (default: %s)\n", sampler_type_names.c_str());
-    printf("  --sampling-seq        simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str());
-    printf("  --top-k N             top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
-    printf("  --top-p N             top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
-    printf("  --min-p N             min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p);
-    printf("  --tfs N               tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z);
-    printf("  --typical N           locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p);
-    printf("  --repeat-last-n N     last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n);
-    printf("  --repeat-penalty N    penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat);
-    printf("  --presence-penalty N  repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present);
-    printf("  --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq);
-    printf("  --dynatemp-range N    dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range);
-    printf("  --dynatemp-exp N      dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent);
-    printf("  --mirostat N          use Mirostat sampling.\n");
-    printf("                        Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
-    printf("                        (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat);
-    printf("  --mirostat-lr N       Mirostat learning rate, parameter eta (default: %.1f)\n", (double)sparams.mirostat_eta);
-    printf("  --mirostat-ent N      Mirostat target entropy, parameter tau (default: %.1f)\n", (double)sparams.mirostat_tau);
-    printf("  -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
-    printf("                        modifies the likelihood of token appearing in the completion,\n");
-    printf("                        i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
-    printf("                        or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
-    printf("  --grammar GRAMMAR     BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
-    printf("  --grammar-file FNAME  file to read grammar from\n");
-    printf("  -j SCHEMA, --json-schema SCHEMA\n");
-    printf("                        JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object.\n");
-    printf("                        For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead\n");
-    printf("  --cfg-negative-prompt PROMPT\n");
-    printf("                        negative prompt to use for guidance. (default: empty)\n");
-    printf("  --cfg-negative-prompt-file FNAME\n");
-    printf("                        negative prompt file to use for guidance. (default: empty)\n");
-    printf("  --cfg-scale N         strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale);
-    printf("  --rope-scaling {none,linear,yarn}\n");
-    printf("                        RoPE frequency scaling method, defaults to linear unless specified by the model\n");
-    printf("  --rope-scale N        RoPE context scaling factor, expands context by a factor of N\n");
-    printf("  --rope-freq-base N    RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n");
-    printf("  --rope-freq-scale N   RoPE frequency scaling factor, expands context by a factor of 1/N\n");
-    printf("  --yarn-orig-ctx N     YaRN: original context size of model (default: 0 = model training context size)\n");
-    printf("  --yarn-ext-factor N   YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
-    printf("  --yarn-attn-factor N  YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
-    printf("  --yarn-beta-slow N    YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
-    printf("  --yarn-beta-fast N    YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
-    printf("  --pooling {none,mean,cls}\n");
-    printf("                        pooling type for embeddings, use model default if unspecified\n");
-    printf("  -dt N, --defrag-thold N\n");
-    printf("                        KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
-    printf("  --ignore-eos          ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
-    printf("  --penalize-nl         penalize newline tokens\n");
-    printf("  --temp N              temperature (default: %.1f)\n", (double)sparams.temp);
-    printf("  --all-logits          return logits for all tokens in the batch (default: disabled)\n");
-    printf("  --hellaswag           compute HellaSwag score over random tasks from datafile supplied with -f\n");
-    printf("  --hellaswag-tasks N   number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
-    printf("  --winogrande          compute Winogrande score over random tasks from datafile supplied with -f\n");
-    printf("  --winogrande-tasks N  number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks);
-    printf("  --multiple-choice     compute multiple choice score over random tasks from datafile supplied with -f\n");
-    printf("  --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks);
-    printf("  --kl-divergence       computes KL-divergence to logits provided via --kl-divergence-base\n");
-    printf("  --keep N              number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
-    printf("  --draft N             number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
-    printf("  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
-    printf("  -np N, --parallel N   number of parallel sequences to decode (default: %d)\n", params.n_parallel);
-    printf("  -ns N, --sequences N  number of sequences to decode (default: %d)\n", params.n_sequences);
-    printf("  -ps N, --p-split N    speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
-    printf("  -cb, --cont-batching  enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
-    printf("  -fa, --flash-attn     enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
-    printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
-    printf("  --image IMAGE_FILE    path to an image file. use with multimodal models. Specify multiple times for batching\n");
+    struct option_info {
+        LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5)
+        option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) {
+            va_list args_list;
+            va_start(args_list, desc);
+            char buffer[1024];
+            vsnprintf(buffer, sizeof(buffer), desc, args_list);
+            va_end(args_list);
+            this->desc = buffer;
+        }
+
+        option_info(const std::string & grp) : grp(grp) {}
+
+        std::string tags;
+        std::string args;
+        std::string desc;
+        std::string grp;
+    };
+
+    std::vector<option_info> options;
+
+    // TODO: filter by tags
+
+    options.push_back({ "general" });
+    options.push_back({ "*",           "-h,    --help, --usage",        "print usage and exit" });
+    options.push_back({ "*",           "       --version",              "show version and build info" });
+    options.push_back({ "*",           "-v,    --verbose",              "print verbose information" });
+    options.push_back({ "*",           "       --verbosity N",          "set specific verbosity level (default: %d)", params.verbosity });
+    options.push_back({ "*",           "       --verbose-prompt",       "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" });
+    options.push_back({ "*",           "       --no-display-prompt",    "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
+    options.push_back({ "*",           "-co,   --color",                "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
+    options.push_back({ "*",           "-s,    --seed SEED",            "RNG seed (default: %d, use random seed for < 0)", params.seed });
+    options.push_back({ "*",           "-t,    --threads N",            "number of threads to use during generation (default: %d)", params.n_threads });
+    options.push_back({ "*",           "-tb,   --threads-batch N",      "number of threads to use during batch and prompt processing (default: same as --threads)" });
+    options.push_back({ "speculative", "-td,   --threads-draft N",      "number of threads to use during generation (default: same as --threads)" });
+    options.push_back({ "speculative", "-tbd,  --threads-batch-draft N",
+                                                                        "number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
+    options.push_back({ "speculative", "       --draft N",              "number of tokens to draft for speculative decoding (default: %d)", params.n_draft });
+    options.push_back({ "speculative", "-ps,   --p-split N",            "speculative decoding split probability (default: %.1f)", (double)params.p_split });
+    options.push_back({ "*",           "-lcs,  --lookup-cache-static FNAME",
+                                                                        "path to static lookup cache to use for lookup decoding (not updated by generation)" });
+    options.push_back({ "*",           "-lcd,  --lookup-cache-dynamic FNAME",
+                                                                        "path to dynamic lookup cache to use for lookup decoding (updated by generation)" });
+
+    options.push_back({ "*",           "-c,    --ctx-size N",           "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx });
+    options.push_back({ "*",           "-n,    --predict N",            "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict });
+    options.push_back({ "*",           "-b,    --batch-size N",         "logical maximum batch size (default: %d)", params.n_batch });
+    options.push_back({ "*",           "-ub,   --ubatch-size N",        "physical maximum batch size (default: %d)", params.n_ubatch });
+    options.push_back({ "*",           "       --keep N",               "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
+    options.push_back({ "*",           "       --chunks N",             "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
+    options.push_back({ "*",           "-fa,   --flash-attn",           "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
+    options.push_back({ "*",           "-p,    --prompt PROMPT",        "prompt to start generation with\n"
+                                                                        "in conversation mode, this will be used as system prompt\n"
+                                                                        "(default: '%s')", params.prompt.c_str() });
+    options.push_back({ "*",           "-f,    --file FNAME",           "a file containing the prompt (default: none)" });
+    options.push_back({ "*",           "       --in-file FNAME",        "an input file (repeat to specify multiple files)" });
+    options.push_back({ "*",           "-bf,   --binary-file FNAME",    "binary file containing the prompt (default: none)" });
+    options.push_back({ "*",           "-e,    --escape",               "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" });
+    options.push_back({ "*",           "       --no-escape",            "do not process escape sequences" });
+    options.push_back({ "main",        "-ptc,  --print-token-count N",  "print token count every N tokens (default: %d)", params.n_print });
+    options.push_back({ "main",        "       --prompt-cache FNAME",   "file to cache prompt state for faster startup (default: none)" });
+    options.push_back({ "main",        "       --prompt-cache-all",     "if specified, saves user input and generations to cache as well\n"
+                                                                        "not supported with --interactive or other interactive options" });
+    options.push_back({ "main",        "       --prompt-cache-ro",      "if specified, uses the prompt cache but does not update it" });
+    options.push_back({ "main",        "-r,    --reverse-prompt PROMPT",
+                                                                        "halt generation at PROMPT, return control in interactive mode\n"
+                                                                        "can be specified more than once for multiple prompts" });
+    options.push_back({ "main",        "-sp,   --special",              "special tokens output enabled (default: %s)", params.special ? "true" : "false" });
+    options.push_back({ "main",        "-cnv,  --conversation",         "run in conversation mode, does not print special tokens and suffix/prefix\n"
+                                                                        "if suffix/prefix are not specified, default chat template will be used\n"
+                                                                        "(default: %s)", params.conversation ? "true" : "false" });
+    options.push_back({ "main infill", "-i,    --interactive",          "run in interactive mode (default: %s)", params.interactive ? "true" : "false" });
+    options.push_back({ "main infill", "-if,   --interactive-first",    "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" });
+    options.push_back({ "main infill", "-mli,  --multiline-input",      "allows you to write or paste multiple lines without ending each in '\\'" });
+    options.push_back({ "main infill", "       --in-prefix-bos",        "prefix BOS to user inputs, preceding the `--in-prefix` string" });
+    options.push_back({ "main infill", "       --in-prefix STRING",     "string to prefix user inputs with (default: empty)" });
+    options.push_back({ "main infill", "       --in-suffix STRING",     "string to suffix after user inputs with (default: empty)" });
+    options.push_back({ "main",        "       --no-warmup",            "skip warming up the model with an empty run" });
+    options.push_back({ "server infill",
+                                       "       --spm-infill",           "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", params.spm_infill ? "enabled" : "disabled" });
+
+    options.push_back({ "sampling" });
+    options.push_back({ "*",           "       --samplers SAMPLERS",    "samplers that will be used for generation in the order, separated by \';\'\n"
+                                                                        "(default: %s)", sampler_type_names.c_str() });
+    options.push_back({ "*",           "       --sampling-seq SEQUENCE",
+                                                                        "simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() });
+    options.push_back({ "*",           "       --ignore-eos",           "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" });
+    options.push_back({ "*",           "       --penalize-nl",          "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" });
+    options.push_back({ "*",           "       --temp N",               "temperature (default: %.1f)", (double)sparams.temp });
+    options.push_back({ "*",           "       --top-k N",              "top-k sampling (default: %d, 0 = disabled)", sparams.top_k });
+    options.push_back({ "*",           "       --top-p N",              "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p });
+    options.push_back({ "*",           "       --min-p N",              "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p });
+    options.push_back({ "*",           "       --tfs N",                "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z });
+    options.push_back({ "*",           "       --typical N",            "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p });
+    options.push_back({ "*",           "       --repeat-last-n N",      "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n });
+    options.push_back({ "*",           "       --repeat-penalty N",     "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat });
+    options.push_back({ "*",           "       --presence-penalty N",   "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present });
+    options.push_back({ "*",           "       --frequency-penalty N",  "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq });
+    options.push_back({ "*",           "       --dynatemp-range N",     "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range });
+    options.push_back({ "*",           "       --dynatemp-exp N",       "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent });
+    options.push_back({ "*",           "       --mirostat N",           "use Mirostat sampling.\n"
+                                                                        "Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
+                                                                        "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat });
+    options.push_back({ "*",           "       --mirostat-lr N",        "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta });
+    options.push_back({ "*",           "       --mirostat-ent N",       "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau });
+    options.push_back({ "*",           "       -l TOKEN_ID(+/-)BIAS",   "modifies the likelihood of token appearing in the completion,\n"
+                                                                        "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
+                                                                        "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" });
+    options.push_back({ "main",        "       --cfg-negative-prompt PROMPT",
+                                                                        "negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() });
+    options.push_back({ "main",        "       --cfg-negative-prompt-file FNAME",
+                                                                        "negative prompt file to use for guidance" });
+    options.push_back({ "main",        "       --cfg-scale N",          "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
+    options.push_back({ "main",        "       --chat-template JINJA_TEMPLATE",
+                                                                        "set custom jinja chat template (default: template taken from model's metadata)\n"
+                                                                        "if suffix/prefix are specified, template will be disabled\n"
+                                                                        "only commonly used templates are accepted:\n"
+                                                                        "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
+    options.push_back({ "grammar" });
+    options.push_back({ "*",           "       --grammar GRAMMAR",      "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() });
+    options.push_back({ "*",           "       --grammar-file FNAME",   "file to read grammar from" });
+    options.push_back({ "*",           "-j,    --json-schema SCHEMA",
+                                                                        "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n"
+                                                                        "For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });
+
+    options.push_back({ "embedding" });
+    options.push_back({ "embedding",   "       --pooling {none,mean,cls,last}",
+                                                                        "pooling type for embeddings, use model default if unspecified" });
+    options.push_back({ "embedding",   "       --attention {causal,non-causal}",
+                                                                        "attention type for embeddings, use model default if unspecified" });
+
+    options.push_back({ "context hacking" });
+    options.push_back({ "*",           "       --rope-scaling {none,linear,yarn}",
+                                                                        "RoPE frequency scaling method, defaults to linear unless specified by the model" });
+    options.push_back({ "*",           "       --rope-scale N",         "RoPE context scaling factor, expands context by a factor of N" });
+    options.push_back({ "*",           "       --rope-freq-base N",     "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" });
+    options.push_back({ "*",           "       --rope-freq-scale N",    "RoPE frequency scaling factor, expands context by a factor of 1/N" });
+    options.push_back({ "*",           "       --yarn-orig-ctx N",      "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx });
+    options.push_back({ "*",           "       --yarn-ext-factor N",    "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor });
+    options.push_back({ "*",           "       --yarn-attn-factor N",   "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor });
+    options.push_back({ "*",           "       --yarn-beta-slow N",     "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow });
+    options.push_back({ "*",           "       --yarn-beta-fast N",     "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast });
+    options.push_back({ "*",           "-gan,  --grp-attn-n N",         "group-attention factor (default: %d)", params.grp_attn_n });
+    options.push_back({ "*",           "-gaw,  --grp-attn-w N",         "group-attention width (default: %.1f)", (double)params.grp_attn_w });
+    options.push_back({ "*",           "-dkvc, --dump-kv-cache",        "verbose print of the KV cache" });
+    options.push_back({ "*",           "-nkvo, --no-kv-offload",        "disable KV offload" });
+    options.push_back({ "*",           "-ctk,  --cache-type-k TYPE",    "KV cache data type for K (default: %s)", params.cache_type_k.c_str() });
+    options.push_back({ "*",           "-ctv,  --cache-type-v TYPE",    "KV cache data type for V (default: %s)", params.cache_type_v.c_str() });
+
+    options.push_back({ "perplexity" });
+    options.push_back({ "perplexity",  "       --all-logits",           "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" });
+    options.push_back({ "perplexity",  "       --hellaswag",            "compute HellaSwag score over random tasks from datafile supplied with -f" });
+    options.push_back({ "perplexity",  "       --hellaswag-tasks N",    "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks });
+    options.push_back({ "perplexity",  "       --winogrande",           "compute Winogrande score over random tasks from datafile supplied with -f" });
+    options.push_back({ "perplexity",  "       --winogrande-tasks N",   "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks });
+    options.push_back({ "perplexity",  "       --multiple-choice",      "compute multiple choice score over random tasks from datafile supplied with -f" });
+    options.push_back({ "perplexity",  "       --multiple-choice-tasks N",
+                                                                        "number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks });
+    options.push_back({ "perplexity",  "       --kl-divergence",        "computes KL-divergence to logits provided via --kl-divergence-base" });
+    options.push_back({ "perplexity",  "       --ppl-stride N",         "stride for perplexity calculation (default: %d)", params.ppl_stride });
+    options.push_back({ "perplexity",  "       --ppl-output-type {0,1}",
+                                                                        "output type for perplexity calculation (default: %d)", params.ppl_output_type });
+
+    options.push_back({ "parallel" });
+    options.push_back({ "*",           "-dt,   --defrag-thold N",       "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold });
+    options.push_back({ "*",           "-np,   --parallel N",           "number of parallel sequences to decode (default: %d)", params.n_parallel });
+    options.push_back({ "*",           "-ns,   --sequences N",          "number of sequences to decode (default: %d)", params.n_sequences });
+    options.push_back({ "*",           "-cb,   --cont-batching",        "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" });
+    options.push_back({ "*",           "-nocb, --no-cont-batching",     "disable continuous batching" });
+
+    options.push_back({ "multi-modality" });
+    options.push_back({ "*",           "       --mmproj FILE",          "path to a multimodal projector file for LLaVA. see examples/llava/README.md" });
+    options.push_back({ "*",           "       --image FILE",           "path to an image file. use with multimodal models. Specify multiple times for batching" });
+
+    options.push_back({ "backend" });
+    options.push_back({ "*",           "       --rpc SERVERS",          "comma separated list of RPC servers" });
+
     if (llama_supports_mlock()) {
-        printf("  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
+        options.push_back({ "*",           "       --mlock",                "force system to keep model in RAM rather than swapping or compressing" });
     }
     if (llama_supports_mmap()) {
-        printf("  --no-mmap             do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
-    }
-    printf("  --numa TYPE           attempt optimizations that help on some NUMA systems\n");
-    printf("                          - distribute: spread execution evenly over all nodes\n");
-    printf("                          - isolate: only spawn threads on CPUs on the node that execution started on\n");
-    printf("                          - numactl: use the CPU map provided by numactl\n");
-    printf("                        if run without this previously, it is recommended to drop the system page cache before using this\n");
-    printf("                        see https://github.com/ggerganov/llama.cpp/issues/1437\n");
+        options.push_back({ "*",           "       --no-mmap",              "do not memory-map model (slower load but may reduce pageouts if not using mlock)" });
+    }
+    options.push_back({ "*",           "       --numa TYPE",            "attempt optimizations that help on some NUMA systems\n"
+                                                                        "  - distribute: spread execution evenly over all nodes\n"
+                                                                        "  - isolate: only spawn threads on CPUs on the node that execution started on\n"
+                                                                        "  - numactl: use the CPU map provided by numactl\n"
+                                                                        "if run without this previously, it is recommended to drop the system page cache before using this\n"
+                                                                        "see https://github.com/ggerganov/llama.cpp/issues/1437" });
+
     if (llama_supports_gpu_offload()) {
-        printf("  -ngl N, --n-gpu-layers N\n");
-        printf("                        number of layers to store in VRAM\n");
-        printf("  -ngld N, --n-gpu-layers-draft N\n");
-        printf("                        number of layers to store in VRAM for the draft model\n");
-        printf("  -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
-        printf("                        how to split the model across multiple GPUs, one of:\n");
-        printf("                          - none: use one GPU only\n");
-        printf("                          - layer (default): split layers and KV across GPUs\n");
-        printf("                          - row: split rows across GPUs\n");
-        printf("  -ts SPLIT, --tensor-split SPLIT\n");
-        printf("                        fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
-        printf("  -mg i, --main-gpu i   the GPU to use for the model (with split-mode = none),\n");
-        printf("                        or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu);
-    }
-    printf("  --rpc SERVERS         comma separated list of RPC servers\n");
-    printf("  --verbose-prompt      print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false");
-    printf("  --no-display-prompt   don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false");
-    printf("  -gan N, --grp-attn-n N\n");
-    printf("                        group-attention factor (default: %d)\n", params.grp_attn_n);
-    printf("  -gaw N, --grp-attn-w N\n");
-    printf("                        group-attention width (default: %.1f)\n", (double)params.grp_attn_w);
-    printf("  -dkvc, --dump-kv-cache\n");
-    printf("                        verbose print of the KV cache\n");
-    printf("  -nkvo, --no-kv-offload\n");
-    printf("                        disable KV offload\n");
-    printf("  -ctk TYPE, --cache-type-k TYPE\n");
-    printf("                        KV cache data type for K (default: %s)\n", params.cache_type_k.c_str());
-    printf("  -ctv TYPE, --cache-type-v TYPE\n");
-    printf("                        KV cache data type for V (default: %s)\n", params.cache_type_v.c_str());
-    printf("  --simple-io           use basic IO for better compatibility in subprocesses and limited consoles\n");
-    printf("  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
-    printf("  --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n");
-    printf("  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
-    printf("  --control-vector FNAME\n");
-    printf("                        add a control vector\n");
-    printf("  --control-vector-scaled FNAME S\n");
-    printf("                        add a control vector with user defined scaling S\n");
-    printf("  --control-vector-layer-range START END\n");
-    printf("                        layer range to apply the control vector(s) to, start and end inclusive\n");
-    printf("  -m FNAME, --model FNAME\n");
-    printf("                        model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH);
-    printf("  -md FNAME, --model-draft FNAME\n");
-    printf("                        draft model for speculative decoding (default: unused)\n");
-    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
-    printf("                        model download url (default: unused)\n");
-    printf("  -hfr REPO, --hf-repo REPO\n");
-    printf("                        Hugging Face model repository (default: unused)\n");
-    printf("  -hff FILE, --hf-file FILE\n");
-    printf("                        Hugging Face model file (default: unused)\n");
-    printf("  -ld LOGDIR, --logdir LOGDIR\n");
-    printf("                        path under which to save YAML logs (no logging if unset)\n");
-    printf("  -lcs FNAME, --lookup-cache-static FNAME\n");
-    printf("                        path to static lookup cache to use for lookup decoding (not updated by generation)\n");
-    printf("  -lcd FNAME, --lookup-cache-dynamic FNAME\n");
-    printf("                        path to dynamic lookup cache to use for lookup decoding (updated by generation)\n");
-    printf("  --override-kv KEY=TYPE:VALUE\n");
-    printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                        types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
-    printf("  -ptc N, --print-token-count N\n");
-    printf("                        print token count every N tokens (default: %d)\n", params.n_print);
-    printf("  --check-tensors       check model tensor data for invalid values\n");
-    printf("\n");
+        options.push_back({ "*",           "-ngl,  --gpu-layers N",
+                                                                        "number of layers to store in VRAM" });
+        options.push_back({ "*",           "-ngld, --gpu-layers-draft N",
+                                                                        "number of layers to store in VRAM for the draft model" });
+        options.push_back({ "*",           "-sm,   --split-mode SPLIT_MODE",
+                                                                        "how to split the model across multiple GPUs, one of:\n"
+                                                                        "  - none: use one GPU only\n"
+                                                                        "  - layer (default): split layers and KV across GPUs\n"
+                                                                        "  - row: split rows across GPUs" });
+        options.push_back({ "*",           "-ts,   --tensor-split SPLIT",
+                                                                        "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" });
+        options.push_back({ "*",           "-mg,   --main-gpu i",       "the GPU to use for the model (with split-mode = none),\n"
+                                                                        "or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu });
+    }
+
+    options.push_back({ "model" });
+    options.push_back({ "*",           "       --check-tensors",        "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" });
+    options.push_back({ "*",           "       --override-kv KEY=TYPE:VALUE",
+                                                                        "advanced option to override model metadata by key. may be specified multiple times.\n"
+                                                                        "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" });
+    options.push_back({ "*",           "       --lora FNAME",           "apply LoRA adapter (can be repeated to use multiple adapters)" });
+    options.push_back({ "*",           "       --lora-scaled FNAME S",  "apply LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
+    options.push_back({ "*",           "       --control-vector FNAME", "add a control vector\n"
+                                                                        "note: this argument can be repeated to add multiple control vectors" });
+    options.push_back({ "*",           "       --control-vector-scaled FNAME SCALE",
+                                                                        "add a control vector with user defined scaling SCALE\n"
+                                                                        "note: this argument can be repeated to add multiple scaled control vectors" });
+    options.push_back({ "*",           "       --control-vector-layer-range START END",
+                                                                        "layer range to apply the control vector(s) to, start and end inclusive" });
+    options.push_back({ "*",           "-m,    --model FNAME",          "model path (default: models/$filename with filename from --hf-file\n"
+                                                                        "or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH });
+    options.push_back({ "*",           "-md,   --model-draft FNAME",    "draft model for speculative decoding (default: unused)" });
+    options.push_back({ "*",           "-mu,   --model-url MODEL_URL",  "model download url (default: unused)" });
+    options.push_back({ "*",           "-hfr,  --hf-repo REPO",         "Hugging Face model repository (default: unused)" });
+    options.push_back({ "*",           "-hff,  --hf-file FILE",         "Hugging Face model file (default: unused)" });
+    options.push_back({ "*",           "-hft,  --hf-token TOKEN",       "Hugging Face access token (default: value from HF_TOKEN environment variable)" });
+
+    options.push_back({ "retrieval" });
+    options.push_back({ "retrieval",   "       --context-file FNAME",   "file to load context from (repeat to specify multiple files)" });
+    options.push_back({ "retrieval",   "       --chunk-size N",         "minimum length of embedded text chunks (default: %d)", params.chunk_size });
+    options.push_back({ "retrieval",   "       --chunk-separator STRING",
+                                                                        "separator between chunks (default: '%s')", params.chunk_separator.c_str() });
+
+    options.push_back({ "passkey" });
+    options.push_back({ "passkey",     "       --junk N",               "number of times to repeat the junk text (default: %d)", params.n_junk });
+    options.push_back({ "passkey",     "       --pos N",                "position of the passkey in the junk text (default: %d)", params.i_pos });
+
+    options.push_back({ "imatrix" });
+    options.push_back({ "imatrix",     "-o,    --output FNAME",         "output file (default: '%s')", params.out_file.c_str() });
+    options.push_back({ "imatrix",     "       --output-frequency N",   "output the imatrix every N iterations (default: %d)", params.n_out_freq });
+    options.push_back({ "imatrix",     "       --save-frequency N",     "save an imatrix copy every N iterations (default: %d)", params.n_save_freq });
+    options.push_back({ "imatrix",     "       --process-output",       "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" });
+    options.push_back({ "imatrix",     "       --no-ppl",               "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" });
+    options.push_back({ "imatrix",     "       --chunk N",              "start processing the input from chunk N (default: %d)", params.i_chunk });
+
+    options.push_back({ "bench" });
+    options.push_back({ "bench",       "-pps",                          "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" });
+    options.push_back({ "bench",       "-npp n0,n1,...",                "number of prompt tokens" });
+    options.push_back({ "bench",       "-ntg n0,n1,...",                "number of text generation tokens" });
+    options.push_back({ "bench",       "-npl n0,n1,...",                "number of parallel prompts" });
+
+    options.push_back({ "embedding" });
+    options.push_back({ "embedding",   "       --embd-normalize",       "normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize });
+    options.push_back({ "embedding",   "       --embd-output-format",   "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix" });
+    options.push_back({ "embedding",   "       --embd-separator",       "separator of embendings (default \\n) for example \"<#sep#>\"" });
+
+    options.push_back({ "server" });
+    options.push_back({ "server",      "       --host HOST",            "ip address to listen (default: %s)", params.hostname.c_str() });
+    options.push_back({ "server",      "       --port PORT",            "port to listen (default: %d)", params.port });
+    options.push_back({ "server",      "       --path PATH",            "path to serve static files from (default: %s)", params.public_path.c_str() });
+    options.push_back({ "server",      "       --embedding(s)",         "restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled" });
+    options.push_back({ "server",      "       --api-key KEY",          "API key to use for authentication (default: none)" });
+    options.push_back({ "server",      "       --api-key-file FNAME",   "path to file containing API keys (default: none)" });
+    options.push_back({ "server",      "       --ssl-key-file FNAME",   "path to file a PEM-encoded SSL private key" });
+    options.push_back({ "server",      "       --ssl-cert-file FNAME",  "path to file a PEM-encoded SSL certificate" });
+    options.push_back({ "server",      "       --timeout N",            "server read/write timeout in seconds (default: %d)", params.timeout_read });
+    options.push_back({ "server",      "       --threads-http N",       "number of threads used to process HTTP requests (default: %d)", params.n_threads_http });
+    options.push_back({ "server",      "       --system-prompt-file FNAME",
+                                                                        "set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" });
+    options.push_back({ "server",      "       --log-format {text,json}",
+                                                                        "log output format: json or text (default: json)" });
+    options.push_back({ "server",      "       --metrics",              "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" });
+    options.push_back({ "server",      "       --no-slots",             "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" });
+    options.push_back({ "server",      "       --slot-save-path PATH",  "path to save slot kv cache (default: disabled)" });
+    options.push_back({ "server",      "       --chat-template JINJA_TEMPLATE",
+                                                                        "set custom jinja chat template (default: template taken from model's metadata)\n"
+                                                                        "only commonly used templates are accepted:\n"
+                                                                        "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
+    options.push_back({ "server",      "-sps,  --slot-prompt-similarity SIMILARITY",
+                                                                        "how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity });
+    options.push_back({ "server",      "       --lora-init-without-apply",     "load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"});
+
 #ifndef LOG_DISABLE_LOGS
-    log_print_usage();
+    options.push_back({ "logging" });
+    options.push_back({ "*",           "       --simple-io",            "use basic IO for better compatibility in subprocesses and limited consoles" });
+    options.push_back({ "*",           "-ld,   --logdir LOGDIR",        "path under which to save YAML logs (no logging if unset)" });
+    options.push_back({ "logging",     "       --log-test",             "Run simple logging test" });
+    options.push_back({ "logging",     "       --log-disable",          "Disable trace logs" });
+    options.push_back({ "logging",     "       --log-enable",           "Enable trace logs" });
+    options.push_back({ "logging",     "       --log-file FNAME",       "Specify a log filename (without extension)" });
+    options.push_back({ "logging",     "       --log-new",              "Create a separate new log file on start. "
+                                                                        "Each log file will have unique name: \"<name>.<ID>.log\"" });
+    options.push_back({ "logging",     "       --log-append",           "Don't truncate the old log file." });
 #endif // LOG_DISABLE_LOGS
+
+    options.push_back({ "cvector" });
+    options.push_back({ "cvector",     "-o,    --output FNAME",         "output file (default: '%s')", params.cvector_outfile.c_str() });
+    options.push_back({ "cvector",     "       --positive-file FNAME",  "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() });
+    options.push_back({ "cvector",     "       --negative-file FNAME",  "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() });
+    options.push_back({ "cvector",     "       --pca-batch N",          "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch });
+    options.push_back({ "cvector",     "       --pca-iter N",           "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
+    options.push_back({ "cvector",     "       --method {pca,mean}",    "dimensionality reduction method to be used (default: pca)" });
+
+    options.push_back({ "export-lora" });
+    options.push_back({ "export-lora", "-m,    --model",                "model path from which to load base model (default '%s')", params.model.c_str() });
+    options.push_back({ "export-lora", "       --lora FNAME",           "path to LoRA adapter  (can be repeated to use multiple adapters)" });
+    options.push_back({ "export-lora", "       --lora-scaled FNAME S",  "path to LoRA adapter with user defined scaling S  (can be repeated to use multiple adapters)" });
+    options.push_back({ "*",           "-t,    --threads N",            "number of threads to use during computation (default: %d)", params.n_threads });
+    options.push_back({ "export-lora", "-o,    --output FNAME",         "output file (default: '%s')", params.lora_outfile.c_str() });
+
+    printf("usage: %s [options]\n", argv[0]);
+
+    for (const auto & o : options) {
+        if (!o.grp.empty()) {
+            printf("\n%s:\n\n", o.grp.c_str());
+            continue;
+        }
+        printf("  %-32s", o.args.c_str());
+        if (o.args.length() > 30) {
+            printf("\n%34s", "");
+        }
+
+        const auto desc = o.desc;
+        size_t start = 0;
+        size_t end = desc.find('\n');
+        while (end != std::string::npos) {
+            printf("%s\n%34s", desc.substr(start, end - start).c_str(), "");
+            start = end + 1;
+            end = desc.find('\n', start);
+        }
+
+        printf("%s\n", desc.substr(start).c_str());
+    }
+    printf("\n");
 }
 
-std::string get_system_info(const gpt_params & params) {
+std::string gpt_params_get_system_info(const gpt_params & params) {
     std::ostringstream os;
 
     os << "system_info: n_threads = " << params.n_threads;
@@ -1630,30 +1732,155 @@ std::string get_system_info(const gpt_params & params) {
     return os.str();
 }
 
-std::string gpt_random_prompt(std::mt19937 & rng) {
-    const int r = rng() % 10;
-    switch (r) {
-        case 0: return "So";
-        case 1: return "Once upon a time";
-        case 2: return "When";
-        case 3: return "The";
-        case 4: return "After";
-        case 5: return "If";
-        case 6: return "import";
-        case 7: return "He";
-        case 8: return "She";
-        case 9: return "They";
-    }
-
-    GGML_UNREACHABLE();
-}
+//
+// String utils
+//
 
-// Validate if a filename is safe to use
-// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
-bool validate_file_name(const std::string & filename) {
-    if (!filename.length()) {
-        // Empty filename invalid
-        return false;
+std::vector<std::string> string_split(std::string input, char separator) {
+    std::vector<std::string> parts;
+    size_t separator_pos = input.find(separator);
+    while (separator_pos != std::string::npos) {
+        std::string part = input.substr(0, separator_pos);
+        parts.emplace_back(part);
+        input = input.substr(separator_pos + 1);
+        separator_pos = input.find(separator);
+    }
+    parts.emplace_back(input);
+    return parts;
+}
+
+std::string string_strip(const std::string & str) {
+    size_t start = 0;
+    size_t end = str.size();
+    while (start < end && std::isspace(str[start])) {
+        start++;
+    }
+    while (end > start && std::isspace(str[end - 1])) {
+        end--;
+    }
+    return str.substr(start, end - start);
+}
+
+std::string string_get_sortable_timestamp() {
+    using clock = std::chrono::system_clock;
+
+    const clock::time_point current_time = clock::now();
+    const time_t as_time_t = clock::to_time_t(current_time);
+    char timestamp_no_ns[100];
+    std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
+
+    const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
+        current_time.time_since_epoch() % 1000000000).count();
+    char timestamp_ns[11];
+    snprintf(timestamp_ns, 11, "%09" PRId64, ns);
+
+    return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
+}
+
+void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    if (search.empty()) {
+        return; // Avoid infinite loop if 'search' is an empty string
+    }
+    size_t pos = 0;
+    while ((pos = s.find(search, pos)) != std::string::npos) {
+        s.replace(pos, search.length(), replace);
+        pos += replace.length();
+    }
+}
+
+void string_process_escapes(std::string & input) {
+    std::size_t input_len = input.length();
+    std::size_t output_idx = 0;
+
+    for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
+        if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
+            switch (input[++input_idx]) {
+                case 'n':  input[output_idx++] = '\n'; break;
+                case 'r':  input[output_idx++] = '\r'; break;
+                case 't':  input[output_idx++] = '\t'; break;
+                case '\'': input[output_idx++] = '\''; break;
+                case '\"': input[output_idx++] = '\"'; break;
+                case '\\': input[output_idx++] = '\\'; break;
+                case 'x':
+                    // Handle \x12, etc
+                    if (input_idx + 2 < input_len) {
+                        const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
+                        char *err_p = nullptr;
+                        const long val = std::strtol(x, &err_p, 16);
+                        if (err_p == x + 2) {
+                            input_idx += 2;
+                            input[output_idx++] = char(val);
+                            break;
+                        }
+                    }
+                    // fall through
+                default:   input[output_idx++] = '\\';
+                           input[output_idx++] = input[input_idx]; break;
+            }
+        } else {
+            input[output_idx++] = input[input_idx];
+        }
+    }
+
+    input.resize(output_idx);
+}
+
+bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
+    const char * sep = strchr(data, '=');
+    if (sep == nullptr || sep - data >= 128) {
+        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
+        return false;
+    }
+    llama_model_kv_override kvo;
+    std::strncpy(kvo.key, data, sep - data);
+    kvo.key[sep - data] = 0;
+    sep++;
+    if (strncmp(sep, "int:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+        kvo.val_i64 = std::atol(sep);
+    } else if (strncmp(sep, "float:", 6) == 0) {
+        sep += 6;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
+        kvo.val_f64 = std::atof(sep);
+    } else if (strncmp(sep, "bool:", 5) == 0) {
+        sep += 5;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
+        if (std::strcmp(sep, "true") == 0) {
+            kvo.val_bool = true;
+        } else if (std::strcmp(sep, "false") == 0) {
+            kvo.val_bool = false;
+        } else {
+            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
+            return false;
+        }
+    } else if (strncmp(sep, "str:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+        if (strlen(sep) > 127) {
+            fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
+            return false;
+        }
+        strncpy(kvo.val_str, sep, 127);
+        kvo.val_str[127] = '\0';
+    } else {
+        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
+        return false;
+    }
+    overrides.emplace_back(std::move(kvo));
+    return true;
+}
+
+//
+// Filesystem utils
+//
+
+// Validate if a filename is safe to use
+// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
+bool fs_validate_filename(const std::string & filename) {
+    if (!filename.length()) {
+        // Empty filename invalid
+        return false;
     }
     if (filename.length() > 255) {
         // Limit at common largest possible filename on Linux filesystems
@@ -1719,120 +1946,238 @@ bool validate_file_name(const std::string & filename) {
     return true;
 }
 
-//
-// String utils
-//
+// returns true if successful, false otherwise
+bool fs_create_directory_with_parents(const std::string & path) {
+#ifdef _WIN32
+    std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+    std::wstring wpath = converter.from_bytes(path);
 
-std::vector<std::string> string_split(std::string input, char separator) {
-    std::vector<std::string> parts;
-    size_t separator_pos = input.find(separator);
-    while (separator_pos != std::string::npos) {
-        std::string part = input.substr(0, separator_pos);
-        parts.emplace_back(part);
-        input = input.substr(separator_pos + 1);
-        separator_pos = input.find(separator);
+    // if the path already exists, check whether it's a directory
+    const DWORD attributes = GetFileAttributesW(wpath.c_str());
+    if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
+        return true;
     }
-    parts.emplace_back(input);
-    return parts;
-}
 
-std::string string_strip(const std::string & str) {
-    size_t start = 0;
-    size_t end = str.size();
-    while (start < end && std::isspace(str[start])) {
-        start++;
+    size_t pos_slash = 0;
+
+    // process path from front to back, procedurally creating directories
+    while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
+        const std::wstring subpath = wpath.substr(0, pos_slash);
+        const wchar_t * test = subpath.c_str();
+
+        const bool success = CreateDirectoryW(test, NULL);
+        if (!success) {
+            const DWORD error = GetLastError();
+
+            // if the path already exists, ensure that it's a directory
+            if (error == ERROR_ALREADY_EXISTS) {
+                const DWORD attributes = GetFileAttributesW(subpath.c_str());
+                if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
+                    return false;
+                }
+            } else {
+                return false;
+            }
+        }
+
+        pos_slash += 1;
     }
-    while (end > start && std::isspace(str[end - 1])) {
-        end--;
+
+    return true;
+#else
+    // if the path already exists, check whether it's a directory
+    struct stat info;
+    if (stat(path.c_str(), &info) == 0) {
+        return S_ISDIR(info.st_mode);
     }
-    return str.substr(start, end - start);
-}
 
-std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
-    std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
-        {"top_k",       llama_sampler_type::TOP_K},
-        {"top_p",       llama_sampler_type::TOP_P},
-        {"typical_p",   llama_sampler_type::TYPICAL_P},
-        {"min_p",       llama_sampler_type::MIN_P},
-        {"tfs_z",       llama_sampler_type::TFS_Z},
-        {"temperature", llama_sampler_type::TEMPERATURE}
-    };
+    size_t pos_slash = 1; // skip leading slashes for directory creation
 
-    // since samplers names are written multiple ways
-    // make it ready for both system names and input names
-    std::unordered_map<std::string, llama_sampler_type> sampler_alt_name_map {
-        {"top-k",       llama_sampler_type::TOP_K},
-        {"top-p",       llama_sampler_type::TOP_P},
-        {"nucleus",     llama_sampler_type::TOP_P},
-        {"typical-p",   llama_sampler_type::TYPICAL_P},
-        {"typical",     llama_sampler_type::TYPICAL_P},
-        {"min-p",       llama_sampler_type::MIN_P},
-        {"tfs-z",       llama_sampler_type::TFS_Z},
-        {"tfs",         llama_sampler_type::TFS_Z},
-        {"temp",        llama_sampler_type::TEMPERATURE}
-    };
+    // process path from front to back, procedurally creating directories
+    while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
+        const std::string subpath = path.substr(0, pos_slash);
+        struct stat info;
 
-    std::vector<llama_sampler_type> sampler_types;
-    sampler_types.reserve(names.size());
-    for (const auto & name : names)
-    {
-        auto sampler_item = sampler_canonical_name_map.find(name);
-        if (sampler_item != sampler_canonical_name_map.end())
-        {
-            sampler_types.push_back(sampler_item->second);
-        }
-        else
-        {
-            if (allow_alt_names)
-            {
-                sampler_item = sampler_alt_name_map.find(name);
-                if (sampler_item != sampler_alt_name_map.end())
-                {
-                    sampler_types.push_back(sampler_item->second);
-                }
+        // if the path already exists, ensure that it's a directory
+        if (stat(subpath.c_str(), &info) == 0) {
+            if (!S_ISDIR(info.st_mode)) {
+                return false;
+            }
+        } else {
+            // create parent directories
+            const int ret = mkdir(subpath.c_str(), 0755);
+            if (ret != 0) {
+                return false;
             }
         }
+
+        pos_slash += 1;
     }
-    return sampler_types;
+
+    return true;
+#endif // _WIN32
 }
 
-std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string) {
-    std::unordered_map<char, llama_sampler_type> sampler_name_map {
-        {'k', llama_sampler_type::TOP_K},
-        {'p', llama_sampler_type::TOP_P},
-        {'y', llama_sampler_type::TYPICAL_P},
-        {'m', llama_sampler_type::MIN_P},
-        {'f', llama_sampler_type::TFS_Z},
-        {'t', llama_sampler_type::TEMPERATURE}
+std::string fs_get_cache_directory() {
+    std::string cache_directory = "";
+    auto ensure_trailing_slash = [](std::string p) {
+        // Make sure to add trailing slash
+        if (p.back() != DIRECTORY_SEPARATOR) {
+            p += DIRECTORY_SEPARATOR;
+        }
+        return p;
     };
+    if (getenv("LLAMA_CACHE")) {
+        cache_directory = std::getenv("LLAMA_CACHE");
+    } else {
+#ifdef __linux__
+        if (std::getenv("XDG_CACHE_HOME")) {
+            cache_directory = std::getenv("XDG_CACHE_HOME");
+        } else {
+            cache_directory = std::getenv("HOME") + std::string("/.cache/");
+        }
+#elif defined(__APPLE__)
+        cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
+#elif defined(_WIN32)
+        cache_directory = std::getenv("LOCALAPPDATA");
+#endif // __linux__
+        cache_directory = ensure_trailing_slash(cache_directory);
+        cache_directory += "llama.cpp";
+    }
+    return ensure_trailing_slash(cache_directory);
+}
+
+std::string fs_get_cache_file(const std::string & filename) {
+    GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
+    std::string cache_directory = fs_get_cache_directory();
+    const bool success = fs_create_directory_with_parents(cache_directory);
+    if (!success) {
+        throw std::runtime_error("failed to create cache directory: " + cache_directory);
+    }
+    return cache_directory + filename;
+}
+
+
+//
+// Model utils
+//
+struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
+    llama_init_result iparams;
+    auto mparams = llama_model_params_from_gpt_params(params);
 
-    std::vector<llama_sampler_type> sampler_types;
-    sampler_types.reserve(names_string.size());
-    for (const auto & c : names_string) {
-        const auto sampler_item = sampler_name_map.find(c);
-        if (sampler_item != sampler_name_map.end()) {
-            sampler_types.push_back(sampler_item->second);
+    llama_model * model = nullptr;
+
+    if (!params.hf_repo.empty() && !params.hf_file.empty()) {
+        model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
+    } else if (!params.model_url.empty()) {
+        model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
+    } else {
+        model = llama_load_model_from_file(params.model.c_str(), mparams);
+    }
+
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
+        return iparams;
+    }
+
+    auto cparams = llama_context_params_from_gpt_params(params);
+
+    llama_context * lctx = llama_new_context_with_model(model, cparams);
+    if (lctx == NULL) {
+        fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
+        llama_free_model(model);
+        return iparams;
+    }
+
+    if (!params.control_vectors.empty()) {
+        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
+        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
+
+        const auto cvec = llama_control_vector_load(params.control_vectors);
+        if (cvec.n_embd == -1) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return iparams;
+        }
+
+        int err = llama_control_vector_apply(lctx,
+                                             cvec.data.data(),
+                                             cvec.data.size(),
+                                             cvec.n_embd,
+                                             params.control_vector_layer_start,
+                                             params.control_vector_layer_end);
+        if (err) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return iparams;
+        }
+    }
+
+    // load and optionally apply lora adapters
+    for (auto & la : params.lora_adapters) {
+        llama_lora_adapter_container loaded_la;
+        loaded_la.path = la.path;
+        loaded_la.scale = la.scale;
+        loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
+        if (loaded_la.adapter == nullptr) {
+            fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
+            llama_free(lctx);
+            llama_free_model(model);
+            return iparams;
+        }
+        iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
+    }
+    if (!params.lora_init_without_apply) {
+        llama_lora_adapters_apply(lctx, iparams.lora_adapters);
+    }
+
+    if (params.ignore_eos) {
+        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
+    }
+
+    if (params.warmup) {
+        LOG("warming up the model with an empty run\n");
+
+        std::vector<llama_token> tmp;
+        llama_token bos = llama_token_bos(model);
+        llama_token eos = llama_token_eos(model);
+        // some models (e.g. T5) don't have a BOS token
+        if (bos != -1) {
+            tmp.push_back(bos);
+        }
+        tmp.push_back(eos);
+
+        if (llama_model_has_encoder(model)) {
+            llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
+            llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+            if (decoder_start_token_id == -1) {
+                decoder_start_token_id = bos;
+            }
+            tmp.clear();
+            tmp.push_back(decoder_start_token_id);
+        }
+        if (llama_model_has_decoder(model)) {
+            llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
         }
+        llama_kv_cache_clear(lctx);
+        llama_synchronize(lctx);
+        llama_reset_timings(lctx);
     }
-    return sampler_types;
+
+    iparams.model   = model;
+    iparams.context = lctx;
+    return iparams;
 }
 
-std::string sampler_type_to_name_string(llama_sampler_type sampler_type) {
-    switch (sampler_type) {
-        case llama_sampler_type::TOP_K:       return "top_k";
-        case llama_sampler_type::TFS_Z:       return "tfs_z";
-        case llama_sampler_type::TYPICAL_P:   return "typical_p";
-        case llama_sampler_type::TOP_P:       return "top_p";
-        case llama_sampler_type::MIN_P:       return "min_p";
-        case llama_sampler_type::TEMPERATURE: return "temperature";
-        default : return "";
+void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
+    llama_lora_adapter_clear(ctx);
+    for (auto & la : lora_adapters) {
+        if (la.scale != 0.0f) {
+            llama_lora_adapter_set(ctx, la.adapter, la.scale);
+        }
     }
 }
 
-//
-// Model utils
-//
-
 struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
     auto mparams = llama_model_default_params();
 
@@ -1906,6 +2251,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.yarn_beta_slow    = params.yarn_beta_slow;
     cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
     cparams.pooling_type      = params.pooling_type;
+    cparams.attention_type    = params.attention_type;
     cparams.defrag_thold      = params.defrag_thold;
     cparams.cb_eval           = params.cb_eval;
     cparams.cb_eval_user_data = params.cb_eval_user_data;
@@ -1918,27 +2264,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     return cparams;
 }
 
-void llama_batch_clear(struct llama_batch & batch) {
-    batch.n_tokens = 0;
-}
-
-void llama_batch_add(
-                 struct llama_batch & batch,
-                        llama_token   id,
-                          llama_pos   pos,
-    const std::vector<llama_seq_id> & seq_ids,
-                               bool   logits) {
-    batch.token   [batch.n_tokens] = id;
-    batch.pos     [batch.n_tokens] = pos;
-    batch.n_seq_id[batch.n_tokens] = seq_ids.size();
-    for (size_t i = 0; i < seq_ids.size(); ++i) {
-        batch.seq_id[batch.n_tokens][i] = seq_ids[i];
-    }
-    batch.logits  [batch.n_tokens] = logits;
-
-    batch.n_tokens++;
-}
-
 #ifdef LLAMA_USE_CURL
 
 static bool starts_with(const std::string & str, const std::string & prefix) {
@@ -1946,7 +2271,7 @@ static bool starts_with(const std::string & str, const std::string & prefix) {
     return str.rfind(prefix, 0) == 0;
 }
 
-static bool llama_download_file(const std::string & url, const std::string & path) {
+static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
 
     // Initialize libcurl
     std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
@@ -1961,6 +2286,15 @@ static bool llama_download_file(const std::string & url, const std::string & pat
     curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
     curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
 
+    // Check if hf-token or bearer-token was specified
+    if (!hf_token.empty()) {
+      std::string auth_header = "Authorization: Bearer ";
+      auth_header += hf_token.c_str();
+      struct curl_slist *http_headers = NULL;
+      http_headers = curl_slist_append(http_headers, auth_header.c_str());
+      curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
+    }
+
 #if defined(_WIN32)
     // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
     //   operating system. Currently implemented under MS-Windows.
@@ -2077,7 +2411,14 @@ static bool llama_download_file(const std::string & url, const std::string & pat
         }
 
         // Set the output file
-        std::unique_ptr<FILE, decltype(&fclose)> outfile(fopen(path_temporary.c_str(), "wb"), fclose);
+
+        struct FILE_deleter {
+            void operator()(FILE * f) const {
+                fclose(f);
+            }
+        };
+
+        std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
         if (!outfile) {
             fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
             return false;
@@ -2149,6 +2490,7 @@ static bool llama_download_file(const std::string & url, const std::string & pat
 struct llama_model * llama_load_model_from_url(
         const char * model_url,
         const char * path_model,
+        const char * hf_token,
         const struct llama_model_params & params) {
     // Basic validation of the model_url
     if (!model_url || strlen(model_url) == 0) {
@@ -2156,7 +2498,7 @@ struct llama_model * llama_load_model_from_url(
         return NULL;
     }
 
-    if (!llama_download_file(model_url, path_model)) {
+    if (!llama_download_file(model_url, path_model, hf_token)) {
         return NULL;
     }
 
@@ -2204,14 +2546,14 @@ struct llama_model * llama_load_model_from_url(
         // Prepare download in parallel
         std::vector<std::future<bool>> futures_download;
         for (int idx = 1; idx < n_split; idx++) {
-            futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split](int download_idx) -> bool {
+            futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool {
                 char split_path[PATH_MAX] = {0};
                 llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
 
                 char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
                 llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
 
-                return llama_download_file(split_url, split_path);
+                return llama_download_file(split_url, split_path, hf_token);
             }, idx));
         }
 
@@ -2230,6 +2572,7 @@ struct llama_model * llama_load_model_from_hf(
         const char * repo,
         const char * model,
         const char * path_model,
+        const char * hf_token,
         const struct llama_model_params & params) {
     // construct hugging face model url:
     //
@@ -2245,7 +2588,7 @@ struct llama_model * llama_load_model_from_hf(
     model_url += "/resolve/main/";
     model_url += model;
 
-    return llama_load_model_from_url(model_url.c_str(), path_model, params);
+    return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
 }
 
 #else
@@ -2253,6 +2596,7 @@ struct llama_model * llama_load_model_from_hf(
 struct llama_model * llama_load_model_from_url(
         const char * /*model_url*/,
         const char * /*path_model*/,
+        const char * /*hf_token*/,
         const struct llama_model_params & /*params*/) {
     fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
     return nullptr;
@@ -2262,6 +2606,7 @@ struct llama_model * llama_load_model_from_hf(
         const char * /*repo*/,
         const char * /*model*/,
         const char * /*path_model*/,
+        const char * /*hf_token*/,
         const struct llama_model_params & /*params*/) {
     fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
     return nullptr;
@@ -2269,90 +2614,29 @@ struct llama_model * llama_load_model_from_hf(
 
 #endif // LLAMA_USE_CURL
 
-std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
-    auto mparams = llama_model_params_from_gpt_params(params);
-
-    llama_model * model = nullptr;
-
-    if (!params.hf_repo.empty() && !params.hf_file.empty()) {
-        model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams);
-    } else if (!params.model_url.empty()) {
-        model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams);
-    } else {
-        model = llama_load_model_from_file(params.model.c_str(), mparams);
-    }
-
-    if (model == NULL) {
-        fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
-        return std::make_tuple(nullptr, nullptr);
-    }
-
-    auto cparams = llama_context_params_from_gpt_params(params);
-
-    llama_context * lctx = llama_new_context_with_model(model, cparams);
-    if (lctx == NULL) {
-        fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
-        llama_free_model(model);
-        return std::make_tuple(nullptr, nullptr);
-    }
-
-    if (!params.control_vectors.empty()) {
-        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
-        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
-
-        const auto cvec = llama_control_vector_load(params.control_vectors);
-        if (cvec.n_embd == -1) {
-            llama_free(lctx);
-            llama_free_model(model);
-            return std::make_tuple(nullptr, nullptr);
-        }
-
-        int err = llama_control_vector_apply(lctx,
-                                             cvec.data.data(),
-                                             cvec.data.size(),
-                                             cvec.n_embd,
-                                             params.control_vector_layer_start,
-                                             params.control_vector_layer_end);
-        if (err) {
-            llama_free(lctx);
-            llama_free_model(model);
-            return std::make_tuple(nullptr, nullptr);
-        }
-    }
-
-    for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
-        const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
-        float lora_scale = std::get<1>(params.lora_adapter[i]);
-        int err = llama_model_apply_lora_from_file(model,
-                                             lora_adapter.c_str(),
-                                             lora_scale,
-                                             ((i > 0) || params.lora_base.empty())
-                                                ? NULL
-                                                : params.lora_base.c_str(),
-                                             params.n_threads);
-        if (err != 0) {
-            fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
-            llama_free(lctx);
-            llama_free_model(model);
-            return std::make_tuple(nullptr, nullptr);
-        }
-    }
-
-    if (params.ignore_eos) {
-        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
-    }
+//
+// Batch utils
+//
 
-    if (params.warmup) {
-        LOG("warming up the model with an empty run\n");
+void llama_batch_clear(struct llama_batch & batch) {
+    batch.n_tokens = 0;
+}
 
-        std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
-        llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
-        llama_kv_cache_clear(lctx);
-        llama_synchronize(lctx);
-        llama_reset_timings(lctx);
+void llama_batch_add(
+                 struct llama_batch & batch,
+                        llama_token   id,
+                          llama_pos   pos,
+    const std::vector<llama_seq_id> & seq_ids,
+                               bool   logits) {
+    batch.token   [batch.n_tokens] = id;
+    batch.pos     [batch.n_tokens] = pos;
+    batch.n_seq_id[batch.n_tokens] = seq_ids.size();
+    for (size_t i = 0; i < seq_ids.size(); ++i) {
+        batch.seq_id[batch.n_tokens][i] = seq_ids[i];
     }
+    batch.logits  [batch.n_tokens] = logits;
 
-    return std::make_tuple(model, lctx);
+    batch.n_tokens++;
 }
 
 //
@@ -2387,136 +2671,385 @@ std::vector<llama_token> llama_tokenize(
 }
 
 std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
-    std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
-    if (n_tokens < 0) {
-        result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
-        GGML_ASSERT(check == -n_tokens);
-    } else {
-        result.resize(n_tokens);
+    std::string piece;
+    piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
+    const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
+    if (n_chars < 0) {
+        piece.resize(-n_chars);
+        int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
+        GGML_ASSERT(check == -n_chars);
+    }
+    else {
+        piece.resize(n_chars);
     }
 
-    return std::string(result.data(), result.size());
+    return piece;
 }
 
-std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
-    const llama_token bos_id = llama_token_bos(llama_get_model(ctx));
+std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
+    std::string text;
+    text.resize(std::max(text.capacity(), tokens.size()));
+    int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
+    if (n_chars < 0) {
+        text.resize(-n_chars);
+        n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
+        GGML_ASSERT(n_chars <= (int32_t)text.size());  // whitespace trimming is performed after per-token detokenization
+    }
+
+    text.resize(n_chars);
 
-    std::string piece;
-    std::string result;
+    // NOTE: the original tokenizer decodes bytes after collecting the pieces.
+    return text;
+}
 
-    for (size_t i = 0; i < tokens.size(); ++i) {
-        piece = llama_token_to_piece(ctx, tokens[i]);
+//
+// Chat template utils
+//
+
+bool llama_chat_verify_template(const std::string & tmpl) {
+    llama_chat_message chat[] = {{"user", "test"}};
+    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
+    return res >= 0;
+}
 
-        // remove the leading space of the first non-BOS token
-        if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
-            piece = piece.substr(1);
+std::string llama_chat_apply_template(const struct llama_model * model,
+        const std::string & tmpl,
+        const std::vector<llama_chat_msg> & msgs,
+        bool add_ass) {
+    int alloc_size = 0;
+    bool fallback = false; // indicate if we must fallback to default chatml
+    std::vector<llama_chat_message> chat;
+    for (auto & msg : msgs) {
+        chat.push_back({msg.role.c_str(), msg.content.c_str()});
+        alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
+    }
+
+    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
+    std::vector<char> buf(alloc_size);
+
+    // run the first time to get the total output length
+    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+
+    // error: chat template is not supported
+    if (res < 0) {
+        if (ptr_tmpl != nullptr) {
+            // if the custom "tmpl" is not supported, we throw an error
+            // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
+            throw std::runtime_error("this custom template is not supported");
+        } else {
+            // If the built-in template is not supported, we default to chatml
+            res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+            fallback = true;
         }
+    }
 
-        result += piece;
+    // if it turns out that our buffer is too small, we resize it
+    if ((size_t) res > buf.size()) {
+        buf.resize(res);
+        res = llama_chat_apply_template(
+            fallback ? nullptr : model,
+            fallback ? "chatml" : ptr_tmpl,
+            chat.data(), chat.size(), add_ass, buf.data(), buf.size());
     }
 
-    return result;
+    std::string formatted_chat(buf.data(), res);
+    return formatted_chat;
 }
 
-std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
-    std::string piece;
-    std::string result;
+std::string llama_chat_format_single(const struct llama_model * model,
+        const std::string & tmpl,
+        const std::vector<llama_chat_msg> & past_msg,
+        const llama_chat_msg & new_msg,
+        bool add_ass) {
+    std::ostringstream ss;
+    auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
+    std::vector<llama_chat_msg> chat_new(past_msg);
+    // if the past_msg ends with a newline, we must preserve it in the formatted version
+    if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
+        ss << "\n";
+    };
+    // format chat with new_msg
+    chat_new.push_back(new_msg);
+    auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
+    // get the diff part
+    ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
+    return ss.str();
+}
+
+std::string llama_chat_format_example(const struct llama_model * model,
+        const std::string & tmpl) {
+    std::vector<llama_chat_msg> msgs = {
+        {"system",    "You are a helpful assistant"},
+        {"user",      "Hello"},
+        {"assistant", "Hi there"},
+        {"user",      "How are you?"},
+    };
+    return llama_chat_apply_template(model, tmpl, msgs, true);
+}
+
+//
+// KV cache utils
+//
+
+void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
+    static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
+
+    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
+        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
 
-    for (size_t i = 0; i < tokens.size(); ++i) {
-        piece = llama_token_to_piece(ctx, tokens[i]);
+    llama_kv_cache_view_cell * c_curr = view.cells;
+    llama_seq_id * cs_curr = view.cells_sequences;
 
-        result += piece;
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
+        if (i % row_size == 0) {
+            printf("\n%5d: ", i);
+        }
+        int seq_count = 0;
+        for (int j = 0; j < view.n_seq_max; j++) {
+            if (cs_curr[j] >= 0) { seq_count++; }
+        }
+        putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
     }
 
-    // NOTE: the original tokenizer decodes bytes after collecting the pieces.
-    return result;
+    printf("\n=== Done dumping\n");
+}
+
+void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
+    static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
+
+    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
+        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
+
+    std::unordered_map<llama_seq_id, size_t> seqs;
+    llama_kv_cache_view_cell * c_curr = view.cells;
+    llama_seq_id * cs_curr = view.cells_sequences;
+
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
+        for (int j = 0; j < view.n_seq_max; j++) {
+            if (cs_curr[j] < 0) { continue; }
+            if (seqs.find(cs_curr[j]) == seqs.end()) {
+                if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
+                const size_t sz = seqs.size();
+                seqs[cs_curr[j]] = sz;
+            }
+        }
+        if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
+    }
+
+    printf("=== Sequence legend: ");
+    for (const auto & it : seqs) {
+        printf("%zu=%d, ", it.second, it.first);
+    }
+    printf("'+'=other sequence ids");
+
+    c_curr = view.cells;
+    cs_curr = view.cells_sequences;
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
+        if (i % row_size == 0) {
+            printf("\n%5d: ", i);
+        }
+        for (int j = 0; j < view.n_seq_max; j++) {
+            if (cs_curr[j] >= 0) {
+                const auto & it = seqs.find(cs_curr[j]);
+                putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
+            } else {
+                putchar('.');
+            }
+        }
+        putchar(' ');
+    }
+
+    printf("\n=== Done dumping\n");
+}
+
+//
+// Embedding utils
+//
+
+void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
+    double sum = 0.0;
+
+    switch (embd_norm) {
+        case -1: // no normalisation
+            sum = 1.0;
+            break;
+        case 0: // max absolute
+            for (int i = 0; i < n; i++) {
+                if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
+            }
+            sum /= 32760.0; // make an int16 range
+            break;
+        case 2: // euclidean
+            for (int i = 0; i < n; i++) {
+                sum += inp[i] * inp[i];
+            }
+            sum = std::sqrt(sum);
+            break;
+        default: // p-norm (euclidean is p-norm p=2)
+            for (int i = 0; i < n; i++) {
+                sum += std::pow(std::abs(inp[i]), embd_norm);
+            }
+            sum = std::pow(sum, 1.0 / embd_norm);
+            break;
+    }
+
+    const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;
+
+    for (int i = 0; i < n; i++) {
+        out[i] = inp[i] * norm;
+    }
 }
 
-bool llama_should_add_bos_token(const llama_model * model) {
-    const int add_bos = llama_add_bos_token(model);
+float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
+    double sum  = 0.0;
+    double sum1 = 0.0;
+    double sum2 = 0.0;
+
+    for (int i = 0; i < n; i++) {
+        sum  += embd1[i] * embd2[i];
+        sum1 += embd1[i] * embd1[i];
+        sum2 += embd2[i] * embd2[i];
+    }
+
+    // Handle the case where one or both vectors are zero vectors
+    if (sum1 == 0.0 || sum2 == 0.0) {
+        if (sum1 == 0.0 && sum2 == 0.0) {
+            return 1.0f; // two zero vectors are similar
+        }
+        return 0.0f;
+    }
 
-    return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
+    return sum / (sqrt(sum1) * sqrt(sum2));
 }
 
 //
-// YAML utils
+// Control vector utils
 //
 
-// returns true if successful, false otherwise
-bool create_directory_with_parents(const std::string & path) {
-#ifdef _WIN32
-    std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
-    std::wstring wpath = converter.from_bytes(path);
+static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
+    llama_control_vector_data result = { -1, {} };
 
-    // if the path already exists, check whether it's a directory
-    const DWORD attributes = GetFileAttributesW(wpath.c_str());
-    if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
-        return true;
+    ggml_context * ctx = nullptr;
+    struct gguf_init_params meta_gguf_params = {
+        /* .no_alloc = */ false,
+        /* .ctx      = */ &ctx,
+    };
+    struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
+    if (!ctx_gguf) {
+        fprintf(stderr, "%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
+        return result;
     }
 
-    size_t pos_slash = 0;
+    int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
+    if (n_tensors == 0) {
+        fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
+    }
+
+    for (int i = 0; i < n_tensors; i++) {
+        std::string name = gguf_get_tensor_name(ctx_gguf, i);
+
+        int layer_idx = -1;
+
+        // split on '.'
+        size_t dotpos = name.find('.');
+        if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
+            try {
+                layer_idx = std::stoi(name.substr(dotpos + 1));
+            } catch (...) {
+                layer_idx = -1;
+            }
+        }
+        if (layer_idx < 0) {
+            fprintf(stderr, "%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        } else if (layer_idx == 0) {
+            fprintf(stderr, "%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
+
+        struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
+        if (tensor->type != GGML_TYPE_F32) {
+            fprintf(stderr, "%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
+        if (ggml_n_dims(tensor) != 1) {
+            fprintf(stderr, "%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
 
-    // process path from front to back, procedurally creating directories
-    while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
-        const std::wstring subpath = wpath.substr(0, pos_slash);
-        const wchar_t * test = subpath.c_str();
+        if (result.n_embd == -1) {
+            result.n_embd = ggml_nelements(tensor);
+        } else if (ggml_nelements(tensor) != result.n_embd) {
+            fprintf(stderr, "%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
 
-        const bool success = CreateDirectoryW(test, NULL);
-        if (!success) {
-            const DWORD error = GetLastError();
+        // extend if necessary - do not store data for layer 0 (it's not used)
+        result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);
 
-            // if the path already exists, ensure that it's a directory
-            if (error == ERROR_ALREADY_EXISTS) {
-                const DWORD attributes = GetFileAttributesW(subpath.c_str());
-                if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
-                    return false;
-                }
-            } else {
-                return false;
-            }
+        const float * src = (const float *) tensor->data;
+        float * dst = result.data.data() + result.n_embd * (layer_idx - 1);  // layer 1 at [0]
+        for (int j = 0; j < result.n_embd; j++) {
+            dst[j] += src[j] * load_info.strength;  // allows multiple directions for same layer in same file
         }
 
-        pos_slash += 1;
     }
 
-    return true;
-#else
-    // if the path already exists, check whether it's a directory
-    struct stat info;
-    if (stat(path.c_str(), &info) == 0) {
-        return S_ISDIR(info.st_mode);
+    if (result.n_embd == -1) {
+        fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
+        result.data.clear();
     }
 
-    size_t pos_slash = 1; // skip leading slashes for directory creation
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
 
-    // process path from front to back, procedurally creating directories
-    while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
-        const std::string subpath = path.substr(0, pos_slash);
-        struct stat info;
+    return result;
+}
 
-        // if the path already exists, ensure that it's a directory
-        if (stat(subpath.c_str(), &info) == 0) {
-            if (!S_ISDIR(info.st_mode)) {
-                return false;
-            }
+llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
+    llama_control_vector_data result = { -1, {} };
+
+    for (const auto & info : load_infos) {
+        auto cur = llama_control_vector_load_one(info);
+
+        if (cur.n_embd == -1) {
+            result.n_embd = -1;
+            break;
+        }
+        if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
+            fprintf(stderr, "%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
+            result.n_embd = -1;
+            break;
+        }
+
+        if (result.n_embd == -1) {
+            result = std::move(cur);
         } else {
-            // create parent directories
-            const int ret = mkdir(subpath.c_str(), 0755);
-            if (ret != 0) {
-                return false;
+            result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f);  // extend if necessary
+            for (size_t i = 0; i < cur.data.size(); i++) {
+                result.data[i] += cur.data[i];
             }
         }
+    }
 
-        pos_slash += 1;
+    if (result.n_embd == -1) {
+        fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
+        result.data.clear();
     }
 
-    return true;
-#endif // _WIN32
+    return result;
 }
 
-void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data) {
+//
+// YAML utils
+//
+
+void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
     if (data.empty()) {
         fprintf(stream, "%s:\n", prop_name);
         return;
@@ -2529,7 +3062,7 @@ void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::ve
     fprintf(stream, "%e]\n", data.back());
 }
 
-void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data) {
+void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
     if (data.empty()) {
         fprintf(stream, "%s:\n", prop_name);
         return;
@@ -2542,7 +3075,7 @@ void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vect
     fprintf(stream, "%d]\n", data.back());
 }
 
-void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) {
+void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
     std::string data_str(data == NULL ? "" : data);
 
     if (data_str.empty()) {
@@ -2574,23 +3107,7 @@ void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const cha
     }
 }
 
-std::string get_sortable_timestamp() {
-    using clock = std::chrono::system_clock;
-
-    const clock::time_point current_time = clock::now();
-    const time_t as_time_t = clock::to_time_t(current_time);
-    char timestamp_no_ns[100];
-    std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
-
-    const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
-        current_time.time_since_epoch() % 1000000000).count();
-    char timestamp_ns[11];
-    snprintf(timestamp_ns, 11, "%09" PRId64, ns);
-
-    return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
-}
-
-void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
+void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
                                const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
     const llama_sampling_params & sparams = params.sparams;
 
@@ -2605,11 +3122,11 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
     fprintf(stream, "cpu_has_cuda: %s\n",        ggml_cpu_has_cuda()        ? "true" : "false");
     fprintf(stream, "cpu_has_vulkan: %s\n",      ggml_cpu_has_vulkan()      ? "true" : "false");
-    fprintf(stream, "cpu_has_clblast: %s\n",     ggml_cpu_has_clblast()     ? "true" : "false");
     fprintf(stream, "cpu_has_kompute: %s\n",     ggml_cpu_has_kompute()     ? "true" : "false");
     fprintf(stream, "cpu_has_fma: %s\n",         ggml_cpu_has_fma()         ? "true" : "false");
     fprintf(stream, "cpu_has_gpublas: %s\n",     ggml_cpu_has_gpublas()     ? "true" : "false");
     fprintf(stream, "cpu_has_neon: %s\n",        ggml_cpu_has_neon()        ? "true" : "false");
+    fprintf(stream, "cpu_has_sve: %s\n",         ggml_cpu_has_sve()         ? "true" : "false");
     fprintf(stream, "cpu_has_f16c: %s\n",        ggml_cpu_has_f16c()        ? "true" : "false");
     fprintf(stream, "cpu_has_fp16_va: %s\n",     ggml_cpu_has_fp16_va()     ? "true" : "false");
     fprintf(stream, "cpu_has_wasm_simd: %s\n",   ggml_cpu_has_wasm_simd()   ? "true" : "false");
@@ -2643,7 +3160,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
 
     fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
     fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
-    dump_string_yaml_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
+    yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
     fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
     fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
     fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
@@ -2651,7 +3168,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
     fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
     fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
-    dump_string_yaml_multiline(stream, "grammar", sparams.grammar.c_str());
+    yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str());
     fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
     fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
     fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
@@ -2660,12 +3177,10 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
     fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
 
-    dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
+    yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
     fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
-    dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str());
-    fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
+    yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
     fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
-    fprintf(stream, "interactive_specials: %s # default: false\n", params.interactive_specials ? "true" : "false");
     fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
     fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
     fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
@@ -2679,20 +3194,18 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     }
 
     fprintf(stream, "lora:\n");
-    for (std::tuple<std::string, float> & la : params.lora_adapter) {
-        if (std::get<1>(la) != 1.0f) {
-            continue;
+    for (auto & la : params.lora_adapters) {
+        if (la.scale == 1.0f) {
+            fprintf(stream, "  - %s\n", la.path.c_str());
         }
-        fprintf(stream, "  - %s\n", std::get<0>(la).c_str());
     }
     fprintf(stream, "lora_scaled:\n");
-    for (std::tuple<std::string, float> & la : params.lora_adapter) {
-        if (std::get<1>(la) == 1.0f) {
-            continue;
+    for (auto & la : params.lora_adapters) {
+        if (la.scale != 1.0f) {
+            fprintf(stream, "  - %s: %f\n", la.path.c_str(), la.scale);
         }
-        fprintf(stream, "  - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la));
     }
-    fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
+    fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
     fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
     fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
     fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
@@ -2710,12 +3223,11 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
     fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
     fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
-    dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str());
+    yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
     fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
     fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
     fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
-    dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
-    fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
+    yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
     fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);
 
     fprintf(stream, "reverse_prompt:\n");
@@ -2738,7 +3250,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
 
     const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
-    dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
+    yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
 
     fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
     fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
@@ -2749,263 +3261,3 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
     fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
 }
-
-//
-// KV cache utils
-//
-
-void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) {
-    static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
-
-    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
-        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
-
-    llama_kv_cache_view_cell * c_curr = view.cells;
-    llama_seq_id * cs_curr = view.cells_sequences;
-
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        if (i % row_size == 0) {
-            printf("\n%5d: ", i);
-        }
-        int seq_count = 0;
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] >= 0) { seq_count++; }
-        }
-        putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
-    }
-
-    printf("\n=== Done dumping\n");
-}
-
-void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
-    static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
-
-    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
-        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
-
-    std::unordered_map<llama_seq_id, size_t> seqs;
-    llama_kv_cache_view_cell * c_curr = view.cells;
-    llama_seq_id * cs_curr = view.cells_sequences;
-
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] < 0) { continue; }
-            if (seqs.find(cs_curr[j]) == seqs.end()) {
-                if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-                const size_t sz = seqs.size();
-                seqs[cs_curr[j]] = sz;
-            }
-        }
-        if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-    }
-
-    printf("=== Sequence legend: ");
-    for (const auto & it : seqs) {
-        printf("%zu=%d, ", it.second, it.first);
-    }
-    printf("'+'=other sequence ids");
-
-    c_curr = view.cells;
-    cs_curr = view.cells_sequences;
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        if (i % row_size == 0) {
-            printf("\n%5d: ", i);
-        }
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] >= 0) {
-                const auto & it = seqs.find(cs_curr[j]);
-                putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
-            } else {
-                putchar('.');
-            }
-        }
-        putchar(' ');
-    }
-
-    printf("\n=== Done dumping\n");
-}
-
-void llama_embd_normalize(const float * inp, float * out, int n) {
-    double sum = 0.0;
-    for (int i = 0; i < n; i++) {
-        sum += inp[i] * inp[i];
-    }
-    sum = sqrt(sum);
-
-    const float norm = sum > 0.0 ? 1.0f / sum : 0.0f;
-
-    for (int i = 0; i < n; i++) {
-        out[i] = inp[i] * norm;
-    }
-}
-
-float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
-    double sum  = 0.0;
-    double sum1 = 0.0;
-    double sum2 = 0.0;
-
-    for (int i = 0; i < n; i++) {
-        sum  += embd1[i] * embd2[i];
-        sum1 += embd1[i] * embd1[i];
-        sum2 += embd2[i] * embd2[i];
-    }
-
-    return sum / (sqrt(sum1) * sqrt(sum2));
-}
-
-//
-// Control vector utils
-//
-
-static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
-    int32_t n_tensors;
-
-    size_t n_bytes = 0;
-
-    uint32_t max_direction_layer = 0;
-
-    llama_control_vector_data result = { -1, {} };
-
-    // calculate size of ctx needed for tensors, ensure tensors are f32, and find max layer
-    {
-        struct ggml_init_params meta_params = {
-            /* .mem_size   = */ ggml_tensor_overhead() * 128 + ggml_graph_overhead(),
-            /* .mem_buffer = */ nullptr,
-            /* .no_alloc   = */ true,
-        };
-        ggml_context * meta_ctx = ggml_init(meta_params);
-        struct gguf_init_params meta_gguf_params = {
-            /* .no_alloc = */ true,
-            /* .ctx      = */ &meta_ctx,
-        };
-        struct gguf_context * meta_ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
-        if (!meta_ctx_gguf) {
-            fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
-            ggml_free(meta_ctx);
-            return result;
-        }
-
-        n_tensors = gguf_get_n_tensors(meta_ctx_gguf);
-        for (int i = 0; i < n_tensors; i++) {
-            std::string name = gguf_get_tensor_name(meta_ctx_gguf, i);
-
-            // split on '.'
-            size_t dotpos = name.find('.');
-            if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
-                try {
-                    uint32_t layer = std::stoi(name.substr(dotpos + 1));
-                    if (layer == 0) {
-                        fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
-                        ggml_free(meta_ctx);
-                        gguf_free(meta_ctx_gguf);
-                        return result;
-                    }
-                    if (layer > max_direction_layer) {
-                        max_direction_layer = layer;
-                    }
-                } catch (...) {
-                    fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
-                    ggml_free(meta_ctx);
-                    gguf_free(meta_ctx_gguf);
-                    return result;
-                }
-            }
-
-            struct ggml_tensor * tensor_meta = ggml_get_tensor(meta_ctx, name.c_str());
-            if (tensor_meta->type != GGML_TYPE_F32 || ggml_n_dims(tensor_meta) != 1) {
-                fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
-                ggml_free(meta_ctx);
-                gguf_free(meta_ctx_gguf);
-                return result;
-            }
-            if (result.n_embd == -1) {
-                result.n_embd = ggml_nelements(tensor_meta);
-            } else if (ggml_nelements(tensor_meta) != result.n_embd) {
-                fprintf(stderr, "%s: direction tensor sizes mismatched in %s\n", __func__, load_info.fname.c_str());
-                ggml_free(meta_ctx);
-                gguf_free(meta_ctx_gguf);
-                return result;
-            }
-            n_bytes += ggml_nbytes(tensor_meta);
-        }
-        ggml_free(meta_ctx);
-        gguf_free(meta_ctx_gguf);
-    }
-
-    if (n_tensors == 0) {
-        fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
-        return result;
-    }
-
-    // load and scale tensors into final control vector context
-    struct ggml_init_params ggml_params = {
-        /* .mem_size   = */ ggml_tensor_overhead() * n_tensors + n_bytes,
-        /* .mem_buffer = */ nullptr,
-        /* .no_alloc   = */ false,
-    };
-    struct ggml_context * ctx = ggml_init(ggml_params);
-
-    struct gguf_init_params params = {
-        /*.no_alloc = */ false,
-        /*.ctx      = */ &ctx,
-    };
-    struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), params);
-    if (!ctx_gguf) {
-        fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
-        ggml_free(ctx);
-        return result;
-    }
-
-    // do not store data for layer 0 (it's not used)
-    result.data.resize(result.n_embd * max_direction_layer);
-
-    for (uint32_t il = 1; il <= max_direction_layer; il++) {
-        const std::string name = "direction." + std::to_string(il);
-        const ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
-
-        float * dst = result.data.data() + result.n_embd * (il - 1);
-
-        if (tensor) {
-            const float * src = (const float *) tensor->data;
-            for (int j = 0; j < result.n_embd; j++) {
-                dst[j] = src[j] * load_info.strength;
-            }
-        } else {
-            for (int j = 0; j < result.n_embd; j++) {
-                dst[j] = 0.0f;
-            }
-        }
-    }
-
-    return result;
-}
-
-llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
-    llama_control_vector_data result = { -1, {} };
-
-    for (const auto & info : load_infos) {
-        auto cur = llama_control_vector_load_one(info);
-
-        if (cur.n_embd == -1) {
-            return result;
-        }
-        if (result.n_embd != -1 && (result.n_embd != cur.n_embd || result.data.size() != cur.data.size())) {
-            fprintf(stderr, "%s: control vector in %s does not match previous vector dimensions\n", __func__, info.fname.c_str());
-            return result;
-        }
-
-        if (result.n_embd == -1) {
-            result = std::move(cur);
-        } else {
-            for (size_t i = 0; i < cur.data.size(); i++) {
-                result.data[i] += cur.data[i];
-            }
-        }
-    }
-
-    if (result.n_embd == -1) {
-        fprintf(stderr, "%s: no vectors passed\n", __func__);
-    }
-
-    return result;
-}
diff --git a/common/common.h b/common/common.h
index 566490e2f881a..df23460a50fe0 100644
--- a/common/common.h
+++ b/common/common.h
@@ -27,129 +27,148 @@
 #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
 
 #define print_build_info() do {                                                                     \
-    fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);           \
+    fprintf(stderr, "%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);      \
     fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);    \
 } while(0)
 
 #define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
 
+struct llama_lora_adapter_info {
+    std::string path;
+    float scale;
+};
+
+struct llama_lora_adapter_container : llama_lora_adapter_info {
+    struct llama_lora_adapter * adapter;
+};
+
 // build info
 extern int LLAMA_BUILD_NUMBER;
-extern char const *LLAMA_COMMIT;
-extern char const *LLAMA_COMPILER;
-extern char const *LLAMA_BUILD_TARGET;
+extern char const * LLAMA_COMMIT;
+extern char const * LLAMA_COMPILER;
+extern char const * LLAMA_BUILD_TARGET;
 
 struct llama_control_vector_load_info;
 
-int get_math_cpu_count();
-int32_t get_num_physical_cores();
+//
+// CPU utils
+//
+
+int32_t cpu_get_num_physical_cores();
+int32_t cpu_get_num_math();
 
 //
 // CLI argument parsing
 //
 
+// dimensionality reduction methods, used by cvector-generator
+enum dimre_method {
+    DIMRE_METHOD_PCA,
+    DIMRE_METHOD_MEAN,
+};
+
 struct gpt_params {
     uint32_t seed                 = LLAMA_DEFAULT_SEED; // RNG seed
 
-    int32_t n_threads             = get_math_cpu_count();
-    int32_t n_threads_draft       = -1;
-    int32_t n_threads_batch       = -1;    // number of threads to use for batch processing (-1 = use n_threads)
-    int32_t n_threads_batch_draft = -1;
-    int32_t n_predict             = -1;    // new tokens to predict
-    int32_t n_ctx                 = 512;   // context size
-    int32_t n_batch               = 2048;  // logical batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_ubatch              = 512;   // physical batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_keep                = 0;     // number of tokens to keep from initial prompt
-    int32_t n_draft               = 5;     // number of tokens to draft during speculative decoding
-    int32_t n_chunks              = -1;    // max number of chunks to process (-1 = unlimited)
-    int32_t n_parallel            = 1;     // number of parallel sequences to decode
-    int32_t n_sequences           = 1;     // number of sequences to decode
-    float   p_split               = 0.1f;  // speculative decoding split probability
-    int32_t n_gpu_layers          = -1;    // number of layers to store in VRAM (-1 - use default)
-    int32_t n_gpu_layers_draft    = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
-    llama_split_mode split_mode   = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
-    int32_t main_gpu              = 0;     // the GPU that is used for scratch and small tensors
-    float   tensor_split[128]     = {0};   // how split tensors should be distributed across GPUs
-    int32_t n_beams               = 0;     // if non-zero then use beam search of given width.
-    int32_t grp_attn_n            = 1;     // group-attention factor
-    int32_t grp_attn_w            = 512;   // group-attention width
-    int32_t n_print               = -1;    // print token count every n tokens (-1 = disabled)
-    float   rope_freq_base        = 0.0f;  // RoPE base frequency
-    float   rope_freq_scale       = 0.0f;  // RoPE frequency scaling factor
+    int32_t n_threads             = cpu_get_num_math();
+    int32_t n_threads_draft       =    -1;
+    int32_t n_threads_batch       =    -1; // number of threads to use for batch processing (-1 = use n_threads)
+    int32_t n_threads_batch_draft =    -1;
+    int32_t n_predict             =    -1; // new tokens to predict
+    int32_t n_ctx                 =     0; // context size
+    int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_ubatch              =   512; // physical batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_keep                =     0; // number of tokens to keep from initial prompt
+    int32_t n_draft               =     5; // number of tokens to draft during speculative decoding
+    int32_t n_chunks              =    -1; // max number of chunks to process (-1 = unlimited)
+    int32_t n_parallel            =     1; // number of parallel sequences to decode
+    int32_t n_sequences           =     1; // number of sequences to decode
+    float   p_split               =  0.1f; // speculative decoding split probability
+    int32_t n_gpu_layers          =    -1; // number of layers to store in VRAM (-1 - use default)
+    int32_t n_gpu_layers_draft    =    -1; // number of layers to store in VRAM for the draft model (-1 - use default)
+    int32_t main_gpu              =     0; // the GPU that is used for scratch and small tensors
+    float   tensor_split[128]     =   {0}; // how split tensors should be distributed across GPUs
+    int32_t grp_attn_n            =     1; // group-attention factor
+    int32_t grp_attn_w            =   512; // group-attention width
+    int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
+    float   rope_freq_base        =  0.0f; // RoPE base frequency
+    float   rope_freq_scale       =  0.0f; // RoPE frequency scaling factor
     float   yarn_ext_factor       = -1.0f; // YaRN extrapolation mix factor
-    float   yarn_attn_factor      = 1.0f;  // YaRN magnitude scaling factor
+    float   yarn_attn_factor      =  1.0f; // YaRN magnitude scaling factor
     float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
-    float   yarn_beta_slow        = 1.0f;  // YaRN high correction dim
-    int32_t yarn_orig_ctx         = 0;     // YaRN original context length
+    float   yarn_beta_slow        =  1.0f; // YaRN high correction dim
+    int32_t yarn_orig_ctx         =     0; // YaRN original context length
     float   defrag_thold          = -1.0f; // KV cache defragmentation threshold
-    std::string rpc_servers       = "";    // comma separated list of RPC servers
 
     ggml_backend_sched_eval_callback cb_eval = nullptr;
     void * cb_eval_user_data                 = nullptr;
 
     ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
 
+    enum llama_split_mode        split_mode        = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
     enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
     enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
+    enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
 
     // // sampling parameters
     struct llama_sampling_params sparams;
 
-    std::string model                = "";  // model path
-    std::string model_draft          = "";  // draft model for speculative decoding
+    std::string model                = ""; // model path
+    std::string model_draft          = ""; // draft model for speculative decoding
     std::string model_alias          = "unknown"; // model alias
-    std::string model_url            = "";  // model url to download
-    std::string hf_repo              = "";  // HF repo
-    std::string hf_file              = "";  // HF file
+    std::string model_url            = ""; // model url to download
+    std::string hf_token             = ""; // HF token
+    std::string hf_repo              = ""; // HF repo
+    std::string hf_file              = ""; // HF file
     std::string prompt               = "";
-    std::string prompt_file          = "";  // store the external prompt file name
-    std::string path_prompt_cache    = "";  // path to file for saving/loading prompt eval state
-    std::string input_prefix         = "";  // string to prefix user inputs with
-    std::string input_suffix         = "";  // string to suffix user inputs with
-    std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
-    std::string logdir               = "";  // directory in which to save YAML log files
+    std::string prompt_file          = ""; // store the external prompt file name
+    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state
+    std::string input_prefix         = ""; // string to prefix user inputs with
+    std::string input_suffix         = ""; // string to suffix user inputs with
+    std::string logdir               = ""; // directory in which to save YAML log files
     std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding
     std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
-    std::string logits_file          = "";  // file for saving *all* logits
+    std::string logits_file          = ""; // file for saving *all* logits
+    std::string rpc_servers          = ""; // comma separated list of RPC servers
 
+    std::vector<std::string> in_files;   // all input files
+    std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
     std::vector<llama_model_kv_override> kv_overrides;
 
-    // TODO: avoid tuple, use struct
-    std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
-    std::string lora_base  = "";                              // base model path for the lora adapter
+    bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply)
+    std::vector<llama_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
 
     std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
 
+    int32_t verbosity                  = 0;
     int32_t control_vector_layer_start = -1; // layer range for control vector
     int32_t control_vector_layer_end   = -1; // layer range for control vector
 
-    int  ppl_stride        = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
-    int  ppl_output_type   = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
-                                    //                                       (which is more convenient to use for plotting)
-                                    //
-    bool   hellaswag       = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
-    size_t hellaswag_tasks = 400;   // number of tasks to use when computing the HellaSwag score
+    int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
+    int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
+                                     //                                       (which is more convenient to use for plotting)
+                                     //
+    bool   hellaswag        = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
+    size_t hellaswag_tasks  = 400;   // number of tasks to use when computing the HellaSwag score
 
-    bool   winogrande      = false; // compute Winogrande score over random tasks from datafile supplied in prompt
-    size_t winogrande_tasks= 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
+    bool   winogrande       = false; // compute Winogrande score over random tasks from datafile supplied in prompt
+    size_t winogrande_tasks = 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
 
-    bool   multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
-    size_t multiple_choice_tasks = 0;     // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
+    bool   multiple_choice  = false;  // compute TruthfulQA score over random tasks from datafile supplied in prompt
+    size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
 
-    bool   kl_divergence   = false; // compute KL divergence
+    bool   kl_divergence    = false; // compute KL divergence
 
-    bool random_prompt     = false; // do not randomize prompt if none provided
+    bool usage             = false; // print usage
     bool use_color         = false; // use color to distinguish generations and inputs
+    bool special           = false; // enable special token output
     bool interactive       = false; // interactive mode
-    bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode
+    bool interactive_first = false; // wait for user input immediately
     bool conversation      = false; // conversation mode (does not print special tokens and suffix/prefix)
-    bool chatml            = false; // chatml mode (used for models trained on chatml syntax)
     bool prompt_cache_all  = false; // save user input and generations to prompt cache
     bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
 
-    bool embedding         = false; // get only sentence embedding
-    bool escape            = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
-    bool interactive_first = false; // wait for user input immediately
+    bool escape            = true;  // escape "\n", "\r", "\t", "\'", "\"", and "\\"
     bool multiline_input   = false; // reverse the usage of `\`
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
@@ -157,7 +176,6 @@ struct gpt_params {
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool ignore_eos        = false; // ignore generated EOS tokens
-    bool instruct          = false; // instruction mode (used for Alpaca models)
     bool logits_all        = false; // return logits for all tokens in the batch
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
@@ -175,50 +193,148 @@ struct gpt_params {
     // multimodal models (see examples/llava)
     std::string mmproj = "";        // path to multimodal projector
     std::vector<std::string> image; // path to image file(s)
-};
 
-void gpt_params_handle_model_default(gpt_params & params);
+    // embedding
+    bool embedding         = false; // get only sentence embedding
+    int32_t embd_normalize = 2;     // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
+    std::string embd_out   = "";    // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
+    std::string embd_sep   = "\n";  // separator of embendings
+
+    // server params
+    int32_t port           = 8080;         // server listens on this network port
+    int32_t timeout_read   = 600;          // http read timeout in seconds
+    int32_t timeout_write  = timeout_read; // http write timeout in seconds
+    int32_t n_threads_http = -1;           // number of threads to process HTTP requests
 
-bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
+    std::string hostname      = "127.0.0.1";
+    std::string public_path   = "";
+    std::string chat_template = "";
+    std::string system_prompt = "";
+    bool enable_chat_template = true;
 
-bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);
+    std::vector<std::string> api_keys;
 
-bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
+    std::string ssl_file_key  = "";
+    std::string ssl_file_cert = "";
 
-void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
+    bool endpoint_slots   = true;
+    bool endpoint_metrics = false;
 
-bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param);
+    bool log_json = false;
 
-std::string get_system_info(const gpt_params & params);
+    std::string slot_save_path;
 
-std::string gpt_random_prompt(std::mt19937 & rng);
+    float slot_prompt_similarity = 0.5f;
+
+    // batched-bench params
+    bool is_pp_shared = false;
+
+    std::vector<int32_t> n_pp;
+    std::vector<int32_t> n_tg;
+    std::vector<int32_t> n_pl;
+
+    // retrieval params
+    std::vector<std::string> context_files; // context files to embed
+
+    int32_t chunk_size = 64; // chunk size for context embedding
+
+    std::string chunk_separator = "\n"; // chunk separator for context embedding
+
+    // passkey params
+    int32_t n_junk = 250; // number of times to repeat the junk text
+    int32_t i_pos  = -1;  // position of the passkey in the junk text
+
+    // imatrix params
+    std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
+
+    int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
+    int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
+    int32_t i_chunk     =  0; // start processing from this chunk
+
+    bool process_output = false; // collect data for the output tensor
+    bool compute_ppl    = true;  // whether to compute perplexity
+
+    // cvector-generator params
+    int n_pca_batch = 100;
+    int n_pca_iterations = 1000;
+    dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
+    std::string cvector_outfile       = "control_vector.gguf";
+    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
+    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
+
+    bool spm_infill = false; // suffix/prefix/middle pattern for infill
+
+    std::string lora_outfile = "ggml-lora-merged-f16.gguf";
+};
+
+void gpt_params_handle_hf_token(gpt_params & params);
+void gpt_params_handle_model_default(gpt_params & params);
 
-void process_escapes(std::string& input);
+bool gpt_params_parse_ex   (int argc, char ** argv, gpt_params & params);
+bool gpt_params_parse      (int argc, char ** argv, gpt_params & params);
+bool gpt_params_find_arg   (int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param);
+void gpt_params_print_usage(int argc, char ** argv, const gpt_params & params);
 
-bool validate_file_name(const std::string & filename);
+std::string gpt_params_get_system_info(const gpt_params & params);
 
 //
 // String utils
 //
 
-std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
-std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string);
 std::vector<std::string> string_split(std::string input, char separator);
+
 std::string string_strip(const std::string & str);
-std::string sampler_type_to_name_string(llama_sampler_type sampler_type);
+std::string string_get_sortable_timestamp();
+
+void string_replace_all(std::string & s, const std::string & search, const std::string & replace);
+
+template<class T>
+static std::vector<T> string_split(const std::string & str, char delim) {
+    std::vector<T> values;
+    std::istringstream str_stream(str);
+    std::string token;
+    while (std::getline(str_stream, token, delim)) {
+        T value;
+        std::istringstream token_stream(token);
+        token_stream >> value;
+        values.push_back(value);
+    }
+    return values;
+}
+
+bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
+void string_process_escapes(std::string & input);
+
+//
+// Filesystem utils
+//
+
+bool fs_validate_filename(const std::string & filename);
+bool fs_create_directory_with_parents(const std::string & path);
+
+std::string fs_get_cache_directory();
+std::string fs_get_cache_file(const std::string & filename);
 
 //
 // Model utils
 //
 
-// TODO: avoid tuplue, use struct
-std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params);
+struct llama_init_result {
+    struct llama_model   * model   = nullptr;
+    struct llama_context * context = nullptr;
+    std::vector<llama_lora_adapter_container> lora_adapters;
+};
+
+struct llama_init_result    llama_init_from_gpt_params(gpt_params & params);
 
 struct llama_model_params   llama_model_params_from_gpt_params  (const gpt_params & params);
 struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
 
-struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const struct llama_model_params & params);
-struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const struct llama_model_params & params);
+struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params);
+struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params);
+
+// clear LoRA adapters from context, then apply new list of adapters
+void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters);
 
 // Batch utils
 
@@ -256,55 +372,61 @@ std::string llama_token_to_piece(
                        llama_token   token,
                        bool          special = true);
 
-// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
-//       that takes into account the tokenizer type and decides how to handle the leading space
-//
-// detokenizes a vector of tokens into a string
-// should work similar to Python's `tokenizer.decode`
-// removes the leading space from the first non-BOS token
-std::string llama_detokenize_spm(
-                         llama_context * ctx,
-        const std::vector<llama_token> & tokens);
-
 // detokenizes a vector of tokens into a string
 // should work similar to Python's `tokenizer.decode`
-std::string llama_detokenize_bpe(
+// optionally renders special/control tokens
+std::string llama_detokenize(
                          llama_context * ctx,
-        const std::vector<llama_token> & tokens);
-
-// Uses the value from the model metadata if possible, otherwise
-// defaults to true when model type is SPM, otherwise false.
-bool llama_should_add_bos_token(const llama_model * model);
+        const std::vector<llama_token> & tokens,
+                                  bool   special = true);
 
 //
-// YAML utils
+// Chat template utils
 //
 
-bool create_directory_with_parents(const std::string & path);
-void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
-void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
-void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);
-std::string get_sortable_timestamp();
+// same with llama_chat_message, but uses std::string
+struct llama_chat_msg {
+    std::string role;
+    std::string content;
+};
 
-void dump_non_result_info_yaml(
-    FILE * stream, const gpt_params & params, const llama_context * lctx,
-    const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
+// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
+bool llama_chat_verify_template(const std::string & tmpl);
+
+// CPP wrapper for llama_chat_apply_template
+// If the built-in template is not supported, we default to chatml
+// If the custom "tmpl" is not supported, we throw an error
+std::string llama_chat_apply_template(const struct llama_model * model,
+        const std::string & tmpl,
+        const std::vector<llama_chat_msg> & chat,
+        bool add_ass);
+
+// Format single message, while taking into account the position of that message in chat history
+std::string llama_chat_format_single(const struct llama_model * model,
+        const std::string & tmpl,
+        const std::vector<llama_chat_msg> & past_msg,
+        const llama_chat_msg & new_msg,
+        bool add_ass);
+
+// Returns an example of formatted chat
+std::string llama_chat_format_example(const struct llama_model * model,
+        const std::string & tmpl);
 
 //
 // KV cache utils
 //
 
 // Dump the KV cache view with the number of sequences per cell.
-void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
+void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
 
 // Dump the KV cache view showing individual sequences in each cell (long output).
-void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
+void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
 
 //
 // Embedding utils
 //
 
-void llama_embd_normalize(const float * inp, float * out, int n);
+void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
 
 float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
 
@@ -332,6 +454,19 @@ llama_control_vector_data llama_control_vector_load(const std::vector<llama_cont
 //
 // Split utils
 //
+
 static const char * const LLM_KV_SPLIT_NO            = "split.no";
 static const char * const LLM_KV_SPLIT_COUNT         = "split.count";
 static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
+
+//
+// YAML utils
+//
+
+void yaml_dump_vector_float    (FILE * stream, const char * prop_name, const std::vector<float> & data);
+void yaml_dump_vector_int      (FILE * stream, const char * prop_name, const std::vector<int> & data);
+void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data);
+
+void yaml_dump_non_result_info(
+    FILE * stream, const gpt_params & params, const llama_context * lctx,
+    const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp
index b5bc7d49b5f36..438452eab570f 100644
--- a/common/grammar-parser.cpp
+++ b/common/grammar-parser.cpp
@@ -46,8 +46,12 @@ namespace grammar_parser {
         state.rules[rule_id] = rule;
     }
 
+    static bool is_digit_char(char c) {
+        return '0' <= c && c <= '9';
+    }
+
     static bool is_word_char(char c) {
-        return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9');
+        return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || is_digit_char(c);
     }
 
     static std::pair<uint32_t, const char *> parse_hex(const char * src, int size) {
@@ -99,6 +103,17 @@ namespace grammar_parser {
         return pos;
     }
 
+    static const char * parse_int(const char * src) {
+        const char * pos = src;
+        while (is_digit_char(*pos)) {
+            pos++;
+        }
+        if (pos == src) {
+            throw std::runtime_error(std::string("expecting integer at ") + src);
+        }
+        return pos;
+    }
+
     static std::pair<uint32_t, const char *> parse_char(const char * src) {
         if (*src == '\\') {
             switch (src[1]) {
@@ -137,6 +152,60 @@ namespace grammar_parser {
             bool                                 is_nested) {
         size_t last_sym_start = out_elements.size();
         const char * pos = src;
+
+        auto handle_repetitions = [&](int min_times, int max_times) {
+
+            if (last_sym_start == out_elements.size()) {
+                throw std::runtime_error(std::string("expecting preceding item to */+/?/{ at ") + pos);
+            }
+
+            // apply transformation to previous symbol (last_sym_start to end) according to
+            // the following rewrite rules:
+            // S{m,n} --> S S S (m times) S'(n-m)
+            //            S'(x)   ::= S S'(x-1) |
+            //            (... n-m definitions of these S' rules ...)
+            //            S'(1)   ::= S |
+            // S{m,} -->  S S S (m times) S'
+            //            S'     ::= S S' |
+            // S*     --> S{0,}
+            //        --> S'     ::= S S' |
+            // S+     --> S{1,}
+            //        --> S S'
+            //            S'     ::= S S' |
+            // S?     --> S{0,1}
+            //        --> S'
+            //            S'     ::= S |
+
+            std::vector<llama_grammar_element> previous_elements(out_elements.begin() + last_sym_start, out_elements.end());
+            if (min_times == 0) {
+                out_elements.resize(last_sym_start);
+            } else {
+                // Repeat the previous elements (min_times - 1) times
+                for (int i = 1; i < min_times; i++) {
+                    out_elements.insert(out_elements.end(), previous_elements.begin(), previous_elements.end());
+                }
+            }
+
+            uint32_t last_rec_rule_id = 0;
+            auto n_opt = max_times < 0 ? 1 : max_times - min_times;
+
+            std::vector<llama_grammar_element> rec_rule(previous_elements);
+            for (int i = 0; i < n_opt; i++) {
+                rec_rule.resize(previous_elements.size());
+                uint32_t rec_rule_id = generate_symbol_id(state, rule_name);
+                if (i > 0 || max_times < 0) {
+                    rec_rule.push_back({LLAMA_GRETYPE_RULE_REF, max_times < 0 ? rec_rule_id : last_rec_rule_id});
+                }
+                rec_rule.push_back({LLAMA_GRETYPE_ALT, 0});
+                rec_rule.push_back({LLAMA_GRETYPE_END, 0});
+                add_rule(state, rec_rule_id, rec_rule);
+                last_rec_rule_id = rec_rule_id;
+            }
+            if (n_opt > 0) {
+                out_elements.push_back({LLAMA_GRETYPE_RULE_REF, last_rec_rule_id});
+            }
+        };
+
         while (*pos) {
             if (*pos == '"') { // literal string
                 pos++;
@@ -197,40 +266,51 @@ namespace grammar_parser {
                     throw std::runtime_error(std::string("expecting ')' at ") + pos);
                 }
                 pos = parse_space(pos + 1, is_nested);
-            } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
-                if (last_sym_start == out_elements.size()) {
-                    throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos);
-                }
+            } else if (*pos == '.') { // any char
+                last_sym_start = out_elements.size();
+                out_elements.push_back({LLAMA_GRETYPE_CHAR_ANY, 0});
+                pos = parse_space(pos + 1, is_nested);
+            } else if (*pos == '*') {
+                pos = parse_space(pos + 1, is_nested);
+                handle_repetitions(0, -1);
+            } else if (*pos == '+') {
+                pos = parse_space(pos + 1, is_nested);
+                handle_repetitions(1, -1);
+            } else if (*pos == '?') {
+                pos = parse_space(pos + 1, is_nested);
+                handle_repetitions(0, 1);
+            } else if (*pos == '{') {
+                pos = parse_space(pos + 1, is_nested);
 
-                // apply transformation to previous symbol (last_sym_start to end) according to
-                // rewrite rules:
-                // S* --> S' ::= S S' |
-                // S+ --> S' ::= S S' | S
-                // S? --> S' ::= S |
-                uint32_t sub_rule_id = generate_symbol_id(state, rule_name);
-                std::vector<llama_grammar_element> sub_rule;
-                // add preceding symbol to generated rule
-                sub_rule.insert(
-                    sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
-                if (*pos == '*' || *pos == '+') {
-                    // cause generated rule to recurse
-                    sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
+                if (!is_digit_char(*pos)) {
+                    throw std::runtime_error(std::string("expecting an int at ") + pos);
                 }
-                // mark start of alternate def
-                sub_rule.push_back({LLAMA_GRETYPE_ALT, 0});
-                if (*pos == '+') {
-                    // add preceding symbol as alternate only for '+' (otherwise empty)
-                    sub_rule.insert(
-                        sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
-                }
-                sub_rule.push_back({LLAMA_GRETYPE_END, 0});
-                add_rule(state, sub_rule_id, sub_rule);
+                const char * int_end = parse_int(pos);
+                int min_times = std::stoul(std::string(pos, int_end - pos));
+                pos = parse_space(int_end, is_nested);
 
-                // in original rule, replace previous symbol with reference to generated rule
-                out_elements.resize(last_sym_start);
-                out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
+                int max_times = -1;
 
-                pos = parse_space(pos + 1, is_nested);
+                if (*pos == '}') {
+                    max_times = min_times;
+                    pos = parse_space(pos + 1, is_nested);
+                } else if (*pos == ',') {
+                    pos = parse_space(pos + 1, is_nested);
+
+                    if (is_digit_char(*pos)) {
+                        const char * int_end = parse_int(pos);
+                        max_times = std::stoul(std::string(pos, int_end - pos));
+                        pos = parse_space(int_end, is_nested);
+                    }
+
+                    if (*pos != '}') {
+                        throw std::runtime_error(std::string("expecting '}' at ") + pos);
+                    }
+                    pos = parse_space(pos + 1, is_nested);
+                } else {
+                    throw std::runtime_error(std::string("expecting ',' at ") + pos);
+                }
+                handle_repetitions(min_times, max_times);
             } else {
                 break;
             }
@@ -289,6 +369,9 @@ namespace grammar_parser {
             }
             // Validate the state to ensure that all rules are defined
             for (const auto & rule : state.rules) {
+                if (rule.empty()) {
+                    throw std::runtime_error("Undefined rule");
+                }
                 for (const auto & elem : rule) {
                     if (elem.type == LLAMA_GRETYPE_RULE_REF) {
                         // Ensure that the rule at that location exists
@@ -325,6 +408,7 @@ namespace grammar_parser {
             case LLAMA_GRETYPE_CHAR_NOT:       return true;
             case LLAMA_GRETYPE_CHAR_ALT:       return true;
             case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true;
+            case LLAMA_GRETYPE_CHAR_ANY:       return true;
             default:                           return false;
         }
     }
@@ -339,6 +423,7 @@ namespace grammar_parser {
                 case LLAMA_GRETYPE_CHAR_NOT:       fprintf(file, "CHAR_NOT");       break;
                 case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
                 case LLAMA_GRETYPE_CHAR_ALT:       fprintf(file, "CHAR_ALT");       break;
+                case LLAMA_GRETYPE_CHAR_ANY:       fprintf(file, "CHAR_ANY");       break;
             }
             switch (elem.type) {
                 case LLAMA_GRETYPE_END:
@@ -350,6 +435,7 @@ namespace grammar_parser {
                 case LLAMA_GRETYPE_CHAR_NOT:
                 case LLAMA_GRETYPE_CHAR_RNG_UPPER:
                 case LLAMA_GRETYPE_CHAR_ALT:
+                case LLAMA_GRETYPE_CHAR_ANY:
                     fprintf(file, "(\"");
                     print_grammar_char(file, elem.value);
                     fprintf(file, "\") ");
@@ -407,11 +493,15 @@ namespace grammar_parser {
                     }
                     print_grammar_char(file, elem.value);
                     break;
+                case LLAMA_GRETYPE_CHAR_ANY:
+                    fprintf(file, ".");
+                    break;
             }
             if (is_char_element(elem)) {
                 switch (rule[i + 1].type) {
                     case LLAMA_GRETYPE_CHAR_ALT:
                     case LLAMA_GRETYPE_CHAR_RNG_UPPER:
+                    case LLAMA_GRETYPE_CHAR_ANY:
                         break;
                     default:
                         fprintf(file, "] ");
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index 9a71f5d8d76ba..881eb49e3389e 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -16,92 +16,282 @@ static std::string join(Iterator begin, Iterator end, const std::string & separa
 
 static std::string repeat(const std::string & str, size_t n);
 
-static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "", bool item_rule_is_literal = false) {
+static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
+    auto has_max = max_items != std::numeric_limits<int>::max();
+
+    if (min_items == 0 && max_items == 1) {
+        return item_rule + "?";
+    }
+
     if (separator_rule.empty()) {
-        if (min_items == 0 && max_items == 1) {
-            return item_rule + "?";
-        } else if (min_items == 1 && max_items == std::numeric_limits<int>::max()) {
+        if (min_items == 1 && !has_max) {
             return item_rule + "+";
+        } else if (min_items == 0 && !has_max) {
+            return item_rule + "*";
+        } else {
+            return item_rule + "{" + std::to_string(min_items) + "," + (has_max ? std::to_string(max_items) : "") + "}";
         }
     }
 
-    std::string result;
-    if (min_items > 0) {
-        if (item_rule_is_literal && separator_rule.empty()) {
-            result = "\"" + repeat(std::string(item_rule.begin() + 1, item_rule.end() - 1), min_items) + "\"";
-        } else {
-            std::vector<std::string> items(min_items, item_rule);
-            result = join(items.begin(), items.end(), separator_rule.empty() ? " " : " " + separator_rule + " ");
+    auto result = item_rule + " " + build_repetition("(" + separator_rule + " " + item_rule + ")", min_items == 0 ? 0 : min_items - 1, has_max ? max_items - 1 : max_items);
+    if (min_items == 0) {
+        result = "(" + result + ")?";
+    }
+    return result;
+}
+
+/* Minimalistic replacement for std::string_view, which is only available from C++17 onwards */
+class string_view {
+    const std::string & _str;
+    const size_t _start;
+    const size_t _end;
+public:
+    string_view(const std::string & str, size_t start = 0, size_t end  = std::string::npos) : _str(str), _start(start), _end(end == std::string::npos ? str.length() : end) {}
+
+    size_t size() const {
+        return _end - _start;
+    }
+
+    size_t length() const {
+        return size();
+    }
+
+    operator std::string() const {
+        return str();
+    }
+
+    std::string str() const {
+        return _str.substr(_start, _end - _start);
+    }
+
+    string_view substr(size_t pos, size_t len = std::string::npos) const {
+        return string_view(_str, _start + pos, len == std::string::npos ? _end : _start + pos + len);
+    }
+
+    char operator[](size_t pos) const {
+        auto index = _start + pos;
+        if (index >= _end) {
+            throw std::out_of_range("string_view index out of range");
         }
+        return _str[_start + pos];
     }
 
-    std::function<std::string(int, bool)> opt_repetitions = [&](int up_to_n, bool prefix_with_sep) -> std::string {
-        auto content = prefix_with_sep && !separator_rule.empty() ? separator_rule + " " + item_rule : item_rule;
+    bool operator==(const string_view & other) const {
+        std::string this_str = *this;
+        std::string other_str = other;
+        return this_str == other_str;
+    }
+};
 
-        if (up_to_n == 0) {
-            return "";
-        } else if (up_to_n == 1) {
-            return "(" + content + ")?";
-        } else if (!separator_rule.empty() && !prefix_with_sep) {
-            return "(" + content + " " + opt_repetitions(up_to_n - 1, true) + ")?";
+static void _build_min_max_int(int min_value, int max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) {
+    auto has_min = min_value != std::numeric_limits<int>::min();
+    auto has_max = max_value != std::numeric_limits<int>::max();
+
+    auto digit_range = [&](char from, char to) {
+        out << "[";
+        if (from == to) {
+            out << from;
         } else {
-            std::string res = repeat("(" + content + " ", up_to_n);
-            // strip trailing space
-            res = res.substr(0, res.length() - 1);
-            res += repeat(")?", up_to_n);
-            return res;
+            out << from << "-" << to;
         }
+        out << "]";
     };
+    auto more_digits = [&](int min_digits, int max_digits) {
+        out << "[0-9]";
+        if (min_digits == max_digits && min_digits == 1) {
+            return;
+        }
+        out << "{";
+        out << min_digits;
+        if (max_digits != min_digits) {
+            out << ",";
+            if (max_digits != std::numeric_limits<int>::max()) {
+                out << max_digits;
+            }
+        }
+        out << "}";
+    };
+    std::function<void(const string_view &, const string_view &)> uniform_range =
+        [&](const string_view & from, const string_view & to) {
+            size_t i = 0;
+            while (i < from.length() && i < to.length() && from[i] == to[i]) {
+                i++;
+            }
+            if (i > 0) {
+                out << "\"" << from.substr(0, i).str() << "\"";
+            }
+            if (i < from.length() && i < to.length()) {
+                if (i > 0) {
+                    out << " ";
+                }
+                auto sub_len = from.length() - i - 1;
+                if (sub_len > 0) {
+                    auto from_sub = from.substr(i + 1);
+                    auto to_sub = to.substr(i + 1);
+                    auto sub_zeros = repeat("0", sub_len);
+                    auto sub_nines = repeat("9", sub_len);
+
+                    auto to_reached = false;
+                    out << "(";
+                    if (from_sub == sub_zeros) {
+                        digit_range(from[i], to[i] - 1);
+                        out << " ";
+                        more_digits(sub_len, sub_len);
+                    } else {
+                        out << "[" << from[i] << "] ";
+                        out << "(";
+                        uniform_range(from_sub, sub_nines);
+                        out << ")";
+                        if (from[i] < to[i] - 1) {
+                            out << " | ";
+                            if (to_sub == sub_nines) {
+                                digit_range(from[i] + 1, to[i]);
+                                to_reached = true;
+                            } else {
+                                digit_range(from[i] + 1, to[i] - 1);
+                            }
+                            out << " ";
+                            more_digits(sub_len, sub_len);
+                        }
+                    }
+                    if (!to_reached) {
+                        out << " | ";
+                        digit_range(to[i], to[i]);
+                        out << " ";
+                        uniform_range(sub_zeros, to_sub);
+                    }
+                    out << ")";
+                } else {
+                    out << "[" << from[i] << "-" << to[i] << "]";
+                }
+            }
+        };
+
+    if (has_min && has_max) {
+        if (min_value < 0 && max_value < 0) {
+            out << "\"-\" (";
+            _build_min_max_int(-max_value, -min_value, out, decimals_left, /* top_level= */ true);
+            out << ")";
+            return;
+        }
+
+        if (min_value < 0) {
+            out << "\"-\" (";
+            _build_min_max_int(0, -min_value, out, decimals_left, /* top_level= */ true);
+            out << ") | ";
+            min_value = 0;
+        }
+
+        auto min_s = std::to_string(min_value);
+        auto max_s = std::to_string(max_value);
+        auto min_digits = min_s.length();
+        auto max_digits = max_s.length();
 
-    if (min_items > 0 && max_items != min_items) {
-        result += " ";
+        for (auto digits = min_digits; digits < max_digits; digits++) {
+            uniform_range(min_s, repeat("9", digits));
+            min_s = "1" + repeat("0", digits);
+            out << " | ";
+        }
+        uniform_range(min_s, max_s);
+        return;
+    }
+
+    auto less_decimals = std::max(decimals_left - 1, 1);
+
+    if (has_min) {
+        if (min_value < 0) {
+            out << "\"-\" (";
+            _build_min_max_int(std::numeric_limits<int>::min(), -min_value, out, decimals_left, /* top_level= */ false);
+            out << ") | [0] | [1-9] ";
+            more_digits(0, decimals_left - 1);
+        } else if (min_value == 0) {
+            if (top_level) {
+                out << "[0] | [1-9] ";
+                more_digits(0, less_decimals);
+            } else {
+                more_digits(1, decimals_left);
+            }
+        } else if (min_value <= 9) {
+            char c = '0' + min_value;
+            auto range_start = top_level ? '1' : '0';
+            if (c > range_start) {
+                digit_range(range_start, c - 1);
+                out << " ";
+                more_digits(1, less_decimals);
+                out << " | ";
+            }
+            digit_range(c, '9');
+            out << " ";
+            more_digits(0, less_decimals);
+        } else {
+            auto min_s = std::to_string(min_value);
+            auto len = min_s.length();
+            auto c = min_s[0];
+
+            if (c > '1') {
+                digit_range(top_level ? '1' : '0', c - 1);
+                out << " ";
+                more_digits(len, less_decimals);
+                out << " | ";
+            }
+            digit_range(c, c);
+            out << " (";
+            _build_min_max_int(std::stoi(min_s.substr(1)), std::numeric_limits<int>::max(), out, less_decimals, /* top_level= */ false);
+            out << ")";
+            if (c < '9') {
+                out << " | ";
+                digit_range(c + 1, '9');
+                out << " ";
+                more_digits(len - 1, less_decimals);
+            }
+        }
+        return;
     }
 
-    if (max_items != std::numeric_limits<int>::max()) {
-        result += opt_repetitions(max_items - min_items, min_items > 0);
-    } else {
-        std::string item_operator = "(" + (separator_rule.empty() ? "" : separator_rule + " ") + item_rule + ")";
-        if (min_items == 0 && !separator_rule.empty()) {
-            result = "(" + item_rule + " " + item_operator + "*)?";
+    if (has_max) {
+        if (max_value >= 0) {
+            if (top_level) {
+                out << "\"-\" [1-9] ";
+                more_digits(0, less_decimals);
+                out << " | ";
+            }
+            _build_min_max_int(0, max_value, out, decimals_left, /* top_level= */ true);
         } else {
-            result += item_operator + "*";
+            out << "\"-\" (";
+            _build_min_max_int(-max_value, std::numeric_limits<int>::max(), out, decimals_left, /* top_level= */ false);
+            out << ")";
         }
+        return;
     }
 
-    return result;
+    throw std::runtime_error("At least one of min_value or max_value must be set");
 }
 
-const std::string SPACE_RULE = "\" \"?";
+const std::string SPACE_RULE = "| \" \" | \"\\n\" [ \\t]{0,20}";
 
 struct BuiltinRule {
     std::string content;
     std::vector<std::string> deps;
 };
 
-const std::string _up_to_15_digits = build_repetition("[0-9]", 0, 15);
-
 std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
     {"boolean", {"(\"true\" | \"false\") space", {}}},
-    {"decimal-part", {"[0-9] " + _up_to_15_digits, {}}},
-    {"integral-part", {"[0-9] | [1-9] " + _up_to_15_digits, {}}},
+    {"decimal-part", {"[0-9]{1,16}", {}}},
+    {"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}},
     {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}},
     {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}},
     {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}},
     {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
     {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
-    {"uuid", {"\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
-                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
-                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
-                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
-                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space", {}}},
-    {"char",   {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])", {}}},
+    {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}},
+    {"char",   {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}},
     {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
     {"null", {"\"null\" space", {}}},
 };
 
 std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
-    {"date", {"[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
-    {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
+    {"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
+    {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
     {"date-time", {"date \"T\" time", {"date", "time"}}},
     {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}},
     {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}},
@@ -126,7 +316,7 @@ std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
 };
 
 std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
-std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
+std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
 
 template <typename Iterator>
 std::string join(Iterator begin, Iterator end, const std::string & separator) {
@@ -197,7 +387,6 @@ static std::string format_literal(const std::string & literal) {
     return "\"" + escaped + "\"";
 }
 
-
 class SchemaConverter {
 private:
     std::function<json(const std::string &)> _fetch_json;
@@ -385,8 +574,7 @@ class SchemaConverter {
                         sub_is_literal ? "\"" + sub + "\"" : sub,
                         min_times,
                         max_times,
-                        "",
-                        sub_is_literal
+                        ""
                     );
                     seq.back().second = false;
                 } else {
@@ -426,6 +614,75 @@ class SchemaConverter {
         return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space");
     }
 
+    /*
+        Returns a rule that matches a JSON string that is none of the provided strings
+
+        not_strings({"a"})
+            -> ["] ( [a] char+ | [^"a] char* )? ["] space
+        not_strings({"and", "also"})
+            -> ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["] space
+    */
+    std::string _not_strings(const std::vector<std::string> & strings) {
+
+        struct TrieNode {
+            std::map<char, TrieNode> children;
+            bool is_end_of_string;
+
+            TrieNode() : is_end_of_string(false) {}
+
+            void insert(const std::string & string) {
+                auto node = this;
+                for (char c : string) {
+                    node = &node->children[c];
+                }
+                node->is_end_of_string = true;
+            }
+        };
+
+        TrieNode trie;
+        for (const auto & s : strings) {
+            trie.insert(s);
+        }
+
+        std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char"));
+        std::ostringstream out;
+        out << "[\"] ( ";
+        std::function<void(const TrieNode &)> visit = [&](const TrieNode & node) {
+            std::ostringstream rejects;
+            auto first = true;
+            for (const auto & kv : node.children) {
+                rejects << kv.first;
+                if (first) {
+                    first = false;
+                } else {
+                    out << " | ";
+                }
+                out << "[" << kv.first << "]";
+                if (!kv.second.children.empty()) {
+                    out << " (";
+                    visit(kv.second);
+                    out << ")";
+                } else if (kv.second.is_end_of_string) {
+                    out << " " << char_rule << "+";
+                }
+            }
+            if (!node.children.empty()) {
+                if (!first) {
+                    out << " | ";
+                }
+                out << "[^\"" << rejects.str() << "] " << char_rule << "*";
+            }
+        };
+        visit(trie);
+
+        out << " )";
+        if (!trie.is_end_of_string) {
+            out << "?";
+        }
+        out << " [\"] space";
+        return out.str();
+    }
+
     std::string _resolve_ref(const std::string & ref) {
         std::string ref_name = ref.substr(ref.find_last_of('/') + 1);
         if (_rules.find(ref_name) == _rules.end() && _refs_being_resolved.find(ref) == _refs_being_resolved.end()) {
@@ -446,6 +703,7 @@ class SchemaConverter {
         std::vector<std::string> required_props;
         std::vector<std::string> optional_props;
         std::unordered_map<std::string, std::string> prop_kv_rule_names;
+        std::vector<std::string> prop_names;
         for (const auto & kv : properties) {
             const auto &prop_name = kv.first;
             const auto &prop_schema = kv.second;
@@ -460,11 +718,18 @@ class SchemaConverter {
             } else {
                 optional_props.push_back(prop_name);
             }
+            prop_names.push_back(prop_name);
         }
-        if (additional_properties.is_object() || (additional_properties.is_boolean() && additional_properties.get<bool>())) {
+        if ((additional_properties.is_boolean() && additional_properties.get<bool>()) || additional_properties.is_object()) {
             std::string sub_name = name + (name.empty() ? "" : "-") + "additional";
-            std::string value_rule = visit(additional_properties.is_object() ? additional_properties : json::object(), sub_name + "-value");
-            std::string kv_rule = _add_rule(sub_name + "-kv", _add_primitive("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule);
+            std::string value_rule =
+                additional_properties.is_object() ? visit(additional_properties, sub_name + "-value")
+                : _add_primitive("value", PRIMITIVE_RULES.at("value"));
+
+            auto key_rule =
+                prop_names.empty() ? _add_primitive("string", PRIMITIVE_RULES.at("string"))
+                : _add_rule(sub_name + "-k", _not_strings(prop_names));
+            std::string kv_rule = _add_rule(sub_name + "-kv", key_rule + " \":\" space " + value_rule);
             prop_kv_rule_names["*"] = kv_rule;
             optional_props.push_back("*");
         }
@@ -490,15 +755,11 @@ class SchemaConverter {
                 }
                 std::string k = ks[0];
                 std::string kv_rule_name = prop_kv_rule_names[k];
-                if (k == "*") {
-                    res = _add_rule(
-                        name + (name.empty() ? "" : "-") + "additional-kvs",
-                        kv_rule_name + " ( \",\" space " + kv_rule_name + " )*"
-                    );
-                } else if (first_is_optional) {
-                    res = "( \",\" space " + kv_rule_name + " )?";
+                std::string comma_ref = "( \",\" space " + kv_rule_name + " )";
+                if (first_is_optional) {
+                    res = comma_ref + (k == "*" ? "*" : "?");
                 } else {
-                    res = kv_rule_name;
+                    res = kv_rule_name + (k == "*" ? " " + comma_ref + "*" : "");
                 }
                 if (ks.size() > 1) {
                     res += " " + _add_rule(
@@ -632,17 +893,19 @@ class SchemaConverter {
         } else if (schema_type.is_array()) {
             std::vector<json> schema_types;
             for (const auto & t : schema_type) {
-                schema_types.push_back({{"type", t}});
+                json schema_copy(schema);
+                schema_copy["type"] = t;
+                schema_types.push_back(schema_copy);
             }
             return _add_rule(rule_name, _generate_union_rule(name, schema_types));
         } else if (schema.contains("const")) {
-            return _add_rule(rule_name, _generate_constant_rule(schema["const"]));
+            return _add_rule(rule_name, _generate_constant_rule(schema["const"]) + " space");
         } else if (schema.contains("enum")) {
             std::vector<std::string> enum_values;
             for (const auto & v : schema["enum"]) {
                 enum_values.push_back(_generate_constant_rule(v));
             }
-            return _add_rule(rule_name, join(enum_values.begin(), enum_values.end(), " | "));
+            return _add_rule(rule_name, "(" + join(enum_values.begin(), enum_values.end(), " | ") + ") space");
         } else if ((schema_type.is_null() || schema_type == "object")
                 && (schema.contains("properties") ||
                     (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) {
@@ -724,6 +987,24 @@ class SchemaConverter {
             int min_len = schema.contains("minLength") ? schema["minLength"].get<int>() : 0;
             int max_len = schema.contains("maxLength") ? schema["maxLength"].get<int>() : std::numeric_limits<int>::max();
             return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space");
+        } else if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) {
+            int min_value = std::numeric_limits<int>::min();
+            int max_value = std::numeric_limits<int>::max();
+            if (schema.contains("minimum")) {
+                min_value = schema["minimum"].get<int>();
+            } else if (schema.contains("exclusiveMinimum")) {
+                min_value = schema["exclusiveMinimum"].get<int>() + 1;
+            }
+            if (schema.contains("maximum")) {
+                max_value = schema["maximum"].get<int>();
+            } else if (schema.contains("exclusiveMaximum")) {
+                max_value = schema["exclusiveMaximum"].get<int>() - 1;
+            }
+            std::stringstream out;
+            out << "(";
+            _build_min_max_int(min_value, max_value, out);
+            out << ") space";
+            return _add_rule(rule_name, out.str());
         } else if (schema.empty() || schema_type == "object") {
             return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object")));
         } else {
diff --git a/common/log.h b/common/log.h
index 09fa63c2677a7..1bc5328ce3e11 100644
--- a/common/log.h
+++ b/common/log.h
@@ -630,7 +630,7 @@ inline std::string LOG_TOKENS_TOSTR_PRETTY(const C & ctx, const T & tokens)
     buf << "[ ";
 
     bool first = true;
-    for (const auto &token : tokens)
+    for (const auto & token : tokens)
     {
         if (!first) {
             buf << ", ";
diff --git a/common/ngram-cache.h b/common/ngram-cache.h
index e4fa4cbd12f11..ab4c9b3766546 100644
--- a/common/ngram-cache.h
+++ b/common/ngram-cache.h
@@ -37,11 +37,18 @@ struct llama_ngram {
     }
 };
 
+struct llama_token_hash_function {
+    size_t operator()(const llama_token token) const {
+        // see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/
+        return token * 11400714819323198485llu;
+    }
+};
+
 struct llama_ngram_hash_function {
     size_t operator()(const llama_ngram & ngram) const {
-        size_t hash = 0;
-        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            hash ^= std::hash<llama_token>{}(ngram.tokens[i]);
+        size_t hash = llama_token_hash_function{}(ngram.tokens[0]);
+        for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) {
+            hash ^= llama_token_hash_function{}(ngram.tokens[i]);
         }
         return hash;
     }
diff --git a/common/sampling.cpp b/common/sampling.cpp
index f0f1b92d37f59..079e405168dff 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -28,9 +28,13 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
 
         std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
 
-        result->grammar = llama_grammar_init(
+        struct llama_grammar * grammar = llama_grammar_init(
                 grammar_rules.data(),
                 grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root"));
+        if (grammar == nullptr) {
+            throw std::runtime_error("Failed to initialize llama_grammar");
+        }
+        result->grammar = grammar;
     }
 
     result->prev.resize(params.n_prev);
@@ -59,9 +63,13 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
     if (!ctx->parsed_grammar.rules.empty()) {
         std::vector<const llama_grammar_element *> grammar_rules(ctx->parsed_grammar.c_rules());
 
-        ctx->grammar = llama_grammar_init(
+        struct llama_grammar * grammar = llama_grammar_init(
                 grammar_rules.data(),
                 grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root"));
+        if (grammar == nullptr) {
+            throw std::runtime_error("Failed to initialize llama_grammar");
+        }
+        ctx->grammar = grammar;
     }
 
     std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
@@ -125,7 +133,7 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) {
     std::string result = "CFG -> Penalties ";
     if (params.mirostat == 0) {
         for (auto sampler_type : params.samplers_sequence) {
-            const auto sampler_type_name = sampler_type_to_name_string(sampler_type);
+            const auto sampler_type_name = llama_sampling_type_to_str(sampler_type);
             if (!sampler_type_name.empty()) {
                 result += "-> " + sampler_type_name + " ";
             }
@@ -137,6 +145,87 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) {
     return result;
 }
 
+std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) {
+    switch (sampler_type) {
+        case llama_sampler_type::TOP_K:       return "top_k";
+        case llama_sampler_type::TFS_Z:       return "tfs_z";
+        case llama_sampler_type::TYPICAL_P:   return "typical_p";
+        case llama_sampler_type::TOP_P:       return "top_p";
+        case llama_sampler_type::MIN_P:       return "min_p";
+        case llama_sampler_type::TEMPERATURE: return "temperature";
+        default : return "";
+    }
+}
+
+std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
+    std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
+        {"top_k",       llama_sampler_type::TOP_K},
+        {"top_p",       llama_sampler_type::TOP_P},
+        {"typical_p",   llama_sampler_type::TYPICAL_P},
+        {"min_p",       llama_sampler_type::MIN_P},
+        {"tfs_z",       llama_sampler_type::TFS_Z},
+        {"temperature", llama_sampler_type::TEMPERATURE}
+    };
+
+    // since samplers names are written multiple ways
+    // make it ready for both system names and input names
+    std::unordered_map<std::string, llama_sampler_type> sampler_alt_name_map {
+        {"top-k",       llama_sampler_type::TOP_K},
+        {"top-p",       llama_sampler_type::TOP_P},
+        {"nucleus",     llama_sampler_type::TOP_P},
+        {"typical-p",   llama_sampler_type::TYPICAL_P},
+        {"typical",     llama_sampler_type::TYPICAL_P},
+        {"min-p",       llama_sampler_type::MIN_P},
+        {"tfs-z",       llama_sampler_type::TFS_Z},
+        {"tfs",         llama_sampler_type::TFS_Z},
+        {"temp",        llama_sampler_type::TEMPERATURE}
+    };
+
+    std::vector<llama_sampler_type> sampler_types;
+    sampler_types.reserve(names.size());
+    for (const auto & name : names)
+    {
+        auto sampler_item = sampler_canonical_name_map.find(name);
+        if (sampler_item != sampler_canonical_name_map.end())
+        {
+            sampler_types.push_back(sampler_item->second);
+        }
+        else
+        {
+            if (allow_alt_names)
+            {
+                sampler_item = sampler_alt_name_map.find(name);
+                if (sampler_item != sampler_alt_name_map.end())
+                {
+                    sampler_types.push_back(sampler_item->second);
+                }
+            }
+        }
+    }
+    return sampler_types;
+}
+
+std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::string & names_string) {
+    std::unordered_map<char, llama_sampler_type> sampler_name_map {
+        {'k', llama_sampler_type::TOP_K},
+        {'p', llama_sampler_type::TOP_P},
+        {'y', llama_sampler_type::TYPICAL_P},
+        {'m', llama_sampler_type::MIN_P},
+        {'f', llama_sampler_type::TFS_Z},
+        {'t', llama_sampler_type::TEMPERATURE}
+    };
+
+    std::vector<llama_sampler_type> sampler_types;
+    sampler_types.reserve(names_string.size());
+    for (const auto & c : names_string) {
+        const auto sampler_item = sampler_name_map.find(c);
+        if (sampler_item != sampler_name_map.end()) {
+            sampler_types.push_back(sampler_item->second);
+        }
+    }
+    return sampler_types;
+}
+
 // no reasons to expose this function in header
 static void sampler_queue(
                    struct llama_context * ctx_main,
@@ -179,7 +268,7 @@ static llama_token llama_sampling_sample_impl(
                   struct llama_context * ctx_main,
                   struct llama_context * ctx_cfg,
                   const int idx,
-                  bool is_resampling) {  // Add a parameter to indicate if we are resampling
+                  bool is_resampling) {
     const llama_sampling_params & params = ctx_sampling->params;
 
     const float   temp            = params.temp;
@@ -188,13 +277,11 @@ static llama_token llama_sampling_sample_impl(
     const float   mirostat_eta    = params.mirostat_eta;
 
     std::vector<float> original_logits;
-    auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, !is_resampling, &original_logits);
-    if (!is_resampling) {
+    auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, /* apply_grammar= */ is_resampling, &original_logits);
+    if (ctx_sampling->grammar != NULL && !is_resampling) {
         GGML_ASSERT(!original_logits.empty());
     }
     llama_token id = 0;
-    // Get a pointer to the logits
-    float * logits = llama_get_logits_ith(ctx_main, idx);
 
     if (temp < 0.0) {
         // greedy sampling, with probs
@@ -235,12 +322,15 @@ static llama_token llama_sampling_sample_impl(
     }
 
     if (ctx_sampling->grammar != NULL && !is_resampling) {
+        // Get a pointer to the logits
+        float * logits = llama_get_logits_ith(ctx_main, idx);
+
         // Create an array with a single token data element for the sampled id
         llama_token_data single_token_data = {id, logits[id], 0.0f};
         llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
 
         // Apply grammar constraints to the single token
-        llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
+        llama_grammar_sample(ctx_sampling->grammar, ctx_main, &single_token_data_array);
 
         // Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
         bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
@@ -252,7 +342,7 @@ static llama_token llama_sampling_sample_impl(
             // Restore logits from the copy
             std::copy(original_logits.begin(), original_logits.end(), logits);
 
-            return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true);  // Pass true for is_resampling
+            return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ true);
         }
     }
 
@@ -285,9 +375,10 @@ static llama_token_data_array llama_sampling_prepare_impl(
     // Get a pointer to the logits
     float * logits = llama_get_logits_ith(ctx_main, idx);
 
-    if (apply_grammar && original_logits != NULL) {
+    if (ctx_sampling->grammar != NULL && !apply_grammar) {
+        GGML_ASSERT(original_logits != NULL);
         // Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this.
-        *original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))};
+        *original_logits = {logits, logits + n_vocab};
     }
 
     // apply params.logit_bias map
@@ -300,10 +391,10 @@ static llama_token_data_array llama_sampling_prepare_impl(
         llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
     }
 
-    cur.clear();
+    cur.resize(n_vocab);
 
     for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-        cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+        cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
     }
 
     llama_token_data_array cur_p = { cur.data(), cur.size(), false };
@@ -330,7 +421,7 @@ static llama_token_data_array llama_sampling_prepare_impl(
 
     // apply grammar checks before sampling logic
     if (apply_grammar && ctx_sampling->grammar != NULL) {
-        llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
+        llama_grammar_sample(ctx_sampling->grammar, ctx_main, &cur_p);
     }
 
     return cur_p;
@@ -342,7 +433,7 @@ llama_token llama_sampling_sample(
                   struct llama_context * ctx_cfg,
                   const int idx) {
     // Call the implementation function with is_resampling set to false by default
-    return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
+    return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ false);
 }
 
 llama_token_data_array llama_sampling_prepare(
@@ -364,6 +455,6 @@ void llama_sampling_accept(
     ctx_sampling->prev.push_back(id);
 
     if (ctx_sampling->grammar != NULL && apply_grammar) {
-        llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);
+        llama_grammar_accept_token(ctx_sampling->grammar, ctx_main, id);
     }
 }
diff --git a/common/sampling.h b/common/sampling.h
index 655732ad17206..eeaa53b8bcd00 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -116,6 +116,11 @@ std::string llama_sampling_print(const llama_sampling_params & params);
 // Print sampling order into a string
 std::string llama_sampling_order_print(const llama_sampling_params & params);
 
+std::string llama_sampling_type_to_str(llama_sampler_type sampler_type);
+
+std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
+std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::string & names_string);
+
 // this is a common sampling function used across the examples for convenience
 // it can serve as a starting point for implementing your own sampling function
 // Note: When using multiple sequences, it is the caller's responsibility to call
diff --git a/common/train.cpp b/common/train.cpp
index 0dbfd24df2314..fef1e57c94655 100644
--- a/common/train.cpp
+++ b/common/train.cpp
@@ -1052,7 +1052,7 @@ struct train_params_common get_default_train_params_common() {
 
     params.custom_n_ctx = false;
 
-    params.use_flash              = true;
+    params.use_flash              = false;
     params.use_checkpointing      = true;
 
     params.sample_start           = "";
@@ -1380,7 +1380,7 @@ bool consume_common_train_arg(
 
 void finish_processing_train_args(struct train_params_common * params) {
     if (params->escape) {
-        process_escapes(params->sample_start);
+        string_process_escapes(params->sample_start);
     }
 }
 
diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py
deleted file mode 100755
index 07dcade747a5a..0000000000000
--- a/convert-persimmon-to-gguf.py
+++ /dev/null
@@ -1,143 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import logging
-import argparse
-import os
-import sys
-from pathlib import Path
-from pprint import pprint
-
-import torch
-from sentencepiece import SentencePieceProcessor
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
-import gguf
-
-logger = logging.getLogger("persimmon-to-gguf")
-
-
-def _flatten_dict(dct, tensors, prefix=None):
-    assert isinstance(dct, dict)
-    for key in dct.keys():
-        new_prefix = prefix + '.' + key if prefix is not None else key
-        if isinstance(dct[key], torch.Tensor):
-            tensors[new_prefix] = dct[key]
-        elif isinstance(dct[key], dict):
-            _flatten_dict(dct[key], tensors, new_prefix)
-        else:
-            raise ValueError(type(dct[key]))
-    return None
-
-
-def _get_sentencepiece_tokenizer_info(dir_model: Path):
-    tokenizer_path = dir_model / 'adept_vocab.model'
-    logger.info('getting sentencepiece tokenizer from', tokenizer_path)
-    tokenizer = SentencePieceProcessor(str(tokenizer_path))
-    logger.info('adding tokens')
-    tokens: list[bytes] = []
-    scores: list[float] = []
-    toktypes: list[int] = []
-
-    for i in range(tokenizer.vocab_size()):
-        text: bytes
-        score: float
-
-        piece = tokenizer.id_to_piece(i)
-        text = piece.encode("utf-8")
-        score = tokenizer.get_score(i)
-
-        toktype = 1
-        if tokenizer.is_unknown(i):
-            toktype = 2
-        if tokenizer.is_control(i):
-            toktype = 3
-        if tokenizer.is_unused(i):
-            toktype = 5
-        if tokenizer.is_byte(i):
-            toktype = 6
-
-        tokens.append(text)
-        scores.append(score)
-        toktypes.append(toktype)
-        pass
-    return tokens, scores, toktypes
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file")
-    parser.add_argument("--outfile",             type=Path, help="path to write to; default: based on input")
-    parser.add_argument("--ckpt-path",           type=Path, help="path to persimmon checkpoint .pt file")
-    parser.add_argument("--model-dir",           type=Path, help="directory containing model e.g. 8b_chat_model_release")
-    parser.add_argument("--adept-inference-dir", type=str,  help="path to adept-inference code directory")
-    parser.add_argument("--verbose",  action="store_true",  help="increase output verbosity")
-    args = parser.parse_args()
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
-    sys.path.append(str(args.adept_inference_dir))
-    persimmon_model = torch.load(args.ckpt_path)
-    hparams = persimmon_model['args']
-    pprint(hparams)
-    tensors: dict[str, torch.Tensor] = {}
-    _flatten_dict(persimmon_model['model'], tensors, None)
-
-    arch = gguf.MODEL_ARCH.PERSIMMON
-    gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch])
-
-    block_count = hparams.num_layers
-    head_count = hparams.num_attention_heads
-    head_count_kv = head_count
-    ctx_length = hparams.seq_length
-    hidden_size = hparams.hidden_size
-
-    gguf_writer.add_name('persimmon-8b-chat')
-    gguf_writer.add_context_length(ctx_length)
-    gguf_writer.add_embedding_length(hidden_size)
-    gguf_writer.add_block_count(block_count)
-    gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size)
-    # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443
-    gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
-    gguf_writer.add_head_count(head_count)
-    gguf_writer.add_head_count_kv(head_count_kv)
-    gguf_writer.add_rope_freq_base(hparams.rotary_emb_base)
-    gguf_writer.add_layer_norm_eps(hparams.layernorm_epsilon)
-
-    tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir)
-    gguf_writer.add_tokenizer_model('llama')
-    gguf_writer.add_tokenizer_pre('default')
-    gguf_writer.add_token_list(tokens)
-    gguf_writer.add_token_scores(scores)
-    gguf_writer.add_token_types(toktypes)
-    gguf_writer.add_bos_token_id(71013)
-    gguf_writer.add_eos_token_id(71013)
-
-    tensor_map = gguf.get_tensor_name_map(arch, block_count)
-    logger.info(tensor_map)
-    for name in tensors.keys():
-        data_torch = tensors[name]
-        if name.endswith(".self_attention.rotary_emb.inv_freq"):
-            continue
-        old_dtype = data_torch.dtype
-        # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
-        data = data_torch.to(torch.float32).squeeze().numpy()
-        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
-        if new_name is None:
-            raise ValueError(f"Can not map tensor '{name}'")
-
-        n_dims = len(data.shape)
-        logger.debug(f"{new_name}, n_dims = {str(n_dims)}, {str(old_dtype)} --> {str(data.dtype)}")
-        gguf_writer.add_tensor(new_name, data)
-    logger.info("gguf: write header")
-    gguf_writer.write_header_to_file()
-    logger.info("gguf: write metadata")
-    gguf_writer.write_kv_data_to_file()
-    logger.info("gguf: write tensors")
-    gguf_writer.write_tensors_to_file()
-
-    gguf_writer.close()
-
-    logger.info(f"gguf: model successfully exported to '{args.outfile}'")
-
-
-if __name__ == '__main__':
-    main()
diff --git a/convert-hf-to-gguf.py b/convert_hf_to_gguf.py
similarity index 54%
rename from convert-hf-to-gguf.py
rename to convert_hf_to_gguf.py
index bd303150ae6b9..41063d94b684e 100755
--- a/convert-hf-to-gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1,4 +1,5 @@
 #!/usr/bin/env python3
+# -*- coding: utf-8 -*-
 
 from __future__ import annotations
 
@@ -12,8 +13,9 @@
 from enum import IntEnum
 from pathlib import Path
 from hashlib import sha256
-from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast
+from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
 
+import math
 import numpy as np
 import torch
 
@@ -24,8 +26,6 @@
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
-from convert import LlamaHfVocab
-
 logger = logging.getLogger("hf-to-gguf")
 
 
@@ -47,7 +47,8 @@ class Model:
     _model_classes: dict[str, type[Model]] = {}
 
     dir_model: Path
-    ftype: int
+    ftype: gguf.LlamaFileType
+    fname_out: Path
     is_big_endian: bool
     endianess: gguf.GGUFEndian
     use_temp_file: bool
@@ -58,29 +59,41 @@ class Model:
     block_count: int
     tensor_map: gguf.TensorNameMap
     tensor_names: set[str] | None
-    fname_out: Path
     gguf_writer: gguf.GGUFWriter
+    model_name: str | None
+    metadata_override: Path | None
+    dir_model_card: Path
 
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
 
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool):
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False,
+                 use_temp_file: bool = False, eager: bool = False,
+                 metadata_override: Path | None = None, model_name: str | None = None,
+                 split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
         if type(self) is Model:
             raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
+
         self.dir_model = dir_model
         self.ftype = ftype
+        self.fname_out = fname_out
         self.is_big_endian = is_big_endian
         self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
         self.use_temp_file = use_temp_file
         self.lazy = not eager
-        self.part_names = Model.get_model_part_names(self.dir_model, ".safetensors")
+        self.part_names = Model.get_model_part_names(self.dir_model, "model", ".safetensors")
         self.is_safetensors = len(self.part_names) > 0
         if not self.is_safetensors:
-            self.part_names = Model.get_model_part_names(self.dir_model, ".bin")
+            self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
         self.hparams = Model.load_hparams(self.dir_model)
-        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
+        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
         self.tensor_names = None
+        self.metadata_override = metadata_override
+        self.model_name = model_name
+        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
+
+        # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
         if self.ftype == gguf.LlamaFileType.GUESSED:
             # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
             _, first_tensor = next(self.get_tensors())
@@ -90,11 +103,10 @@ def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path,
             else:
                 logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})")
                 self.ftype = gguf.LlamaFileType.MOSTLY_BF16
-        ftype_up: str = self.ftype.name.partition("_")[2].upper()
-        ftype_lw: str = ftype_up.lower()
-        # allow templating the file name with the output ftype, useful with the "auto" ftype
-        self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up)
-        self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
+
+        # Configure GGUF Writer
+        self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
+                                           split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
 
     @classmethod
     def __init_subclass__(cls):
@@ -144,9 +156,16 @@ def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
                 tensor_names_from_parts.update(model_part.keys())
 
                 for name in model_part.keys():
-                    data = model_part.get_tensor(name) if self.is_safetensors else model_part[name]
-                    if self.lazy:
-                        data = LazyTorchTensor.from_eager(data)
+                    if self.is_safetensors:
+                        if self.lazy:
+                            data = model_part.get_slice(name)
+                            data = LazyTorchTensor.from_safetensors_slice(data)
+                        else:
+                            data = model_part.get_tensor(name)
+                    else:
+                        data = model_part[name]
+                        if self.lazy:
+                            data = LazyTorchTensor.from_eager(data)
                     yield name, data
 
         # only verify tensor name presence; it doesn't matter if they are not in the right files
@@ -182,7 +201,6 @@ def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", "
         return new_name
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.block_count)
 
         if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
@@ -221,6 +239,10 @@ def set_gguf_parameters(self):
             self.gguf_writer.add_expert_used_count(n_experts_used)
             logger.info(f"gguf: experts used count = {n_experts_used}")
 
+        if (head_dim := self.hparams.get("head_dim")) is not None:
+            self.gguf_writer.add_key_length(head_dim)
+            self.gguf_writer.add_value_length(head_dim)
+
         self.gguf_writer.add_file_type(self.ftype)
         logger.info(f"gguf: file type = {self.ftype}")
 
@@ -229,17 +251,12 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(self.map_tensor_name(name), data_torch)]
 
-    def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
-        del name, new_name, bid, n_dims  # unused
-
-        return False
-
-    def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
+    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
         del name, new_name, bid, n_dims  # unused
 
         return False
 
-    def write_tensors(self):
+    def prepare_tensors(self):
         max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
 
         for name, data_torch in self.get_tensors():
@@ -261,85 +278,133 @@ def write_tensors(self):
                     break
 
             for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
-                data: np.ndarray = data  # type hint
+                data: np.ndarray  # type hint
                 n_dims = len(data.shape)
-                data_dtype = data.dtype
-                data_qtype: gguf.GGMLQuantizationType | None = None
-
-                # when both are True, f32 should win
-                extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims)
-                extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims)
+                data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
 
                 # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
-                # Conditions should closely match those in llama_model_quantize_internal in llama.cpp
-                extra_f32 = any(cond for cond in (
-                    extra_f32,
-                    n_dims == 1,
-                    new_name.endswith("_norm.weight"),
-                ))
+                if n_dims <= 1 or new_name.endswith("_norm.weight"):
+                    data_qtype = gguf.GGMLQuantizationType.F32
 
+                # Conditions should closely match those in llama_model_quantize_internal in llama.cpp
                 # Some tensor types are always in float32
-                extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in (
-                    gguf.MODEL_TENSOR.FFN_GATE_INP,
-                    gguf.MODEL_TENSOR.POS_EMBD,
-                    gguf.MODEL_TENSOR.TOKEN_TYPES,
-                ))
-
-                # if f16 desired, convert any float32 2-dim weight tensors to float16
-                extra_f16 = any(cond for cond in (
-                    extra_f16,
-                    (name.endswith(".weight") and n_dims >= 2),
-                ))
-
-                if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
-                    if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
-                        data = gguf.quantize_bf16(data)
-                        assert data.dtype == np.int16
-                        data_qtype = gguf.GGMLQuantizationType.BF16
+                if data_qtype is False and (
+                    any(
+                        self.match_model_tensor_name(new_name, key, bid)
+                        for key in (
+                            gguf.MODEL_TENSOR.FFN_GATE_INP,
+                            gguf.MODEL_TENSOR.POS_EMBD,
+                            gguf.MODEL_TENSOR.TOKEN_TYPES,
+                        )
+                    )
+                    or not name.endswith(".weight")
+                ):
+                    data_qtype = gguf.GGMLQuantizationType.F32
 
-                    elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data):
-                        data = gguf.quantize_q8_0(data)
-                        assert data.dtype == np.uint8
+                # No override (data_qtype is False), or wants to be quantized (data_qtype is True)
+                if isinstance(data_qtype, bool):
+                    if self.ftype == gguf.LlamaFileType.ALL_F32:
+                        data_qtype = gguf.GGMLQuantizationType.F32
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_F16:
+                        data_qtype = gguf.GGMLQuantizationType.F16
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
+                        data_qtype = gguf.GGMLQuantizationType.BF16
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
                         data_qtype = gguf.GGMLQuantizationType.Q8_0
+                    else:
+                        raise ValueError(f"Unknown file type: {self.ftype.name}")
 
-                    else:  # default to float16 for quantized tensors
-                        if data_dtype != np.float16:
-                            data = data.astype(np.float16)
-                        data_qtype = gguf.GGMLQuantizationType.F16
+                try:
+                    data = gguf.quants.quantize(data, data_qtype)
+                except gguf.QuantError as e:
+                    logger.warning("%s, %s", e, "falling back to F16")
+                    data_qtype = gguf.GGMLQuantizationType.F16
+                    data = gguf.quants.quantize(data, data_qtype)
 
-                if data_qtype is None:  # by default, convert to float32
-                    if data_dtype != np.float32:
-                        data = data.astype(np.float32)
-                    data_qtype = gguf.GGMLQuantizationType.F32
+                shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape
 
-                block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype]
                 # reverse shape to make it similar to the internal ggml dimension order
-                shape_str = f"""{{{', '.join(str(n) for n in reversed(
-                    (*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size))
-                )}}}"""
+                shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}"
 
                 # n_dims is implicit in the shape
                 logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
 
                 self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
 
+    def set_type(self):
+        self.gguf_writer.add_type(gguf.GGUFType.MODEL)
+
+    def prepare_metadata(self, vocab_only: bool):
+
+        total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count()
+
+        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params)
+
+        # Fallback to model directory name if metadata name is still missing
+        if self.metadata.name is None:
+            self.metadata.name = self.dir_model.name
+
+        # Generate parameter weight class (useful for leader boards) if not yet determined
+        if self.metadata.size_label is None and total_params > 0:
+            self.metadata.size_label = gguf.size_label(total_params, shared_params, expert_params, expert_count)
+
+        # Extract the encoding scheme from the file type name. e.g. 'gguf.LlamaFileType.MOSTLY_Q8_0' --> 'Q8_0'
+        output_type: str = self.ftype.name.partition("_")[2]
+
+        # Filename Output
+        if self.fname_out.is_dir():
+            # Generate default filename based on model specification and available metadata
+            if not vocab_only:
+                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None)
+            else:
+                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab")
+
+            # Use the default filename
+            self.fname_out = self.fname_out / f"{fname_default}.gguf"
+        else:
+            # Output path is a custom defined templated filename
+            # Note: `not is_dir()` is used because `.is_file()` will not detect
+            #       file template strings as it doesn't actually exist as a file
+
+            # Process templated file name with the output ftype, useful with the "auto" ftype
+            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
+
+        self.set_type()
+
+        logger.info("Set meta model")
+        self.metadata.set_gguf_meta_model(self.gguf_writer)
+
+        logger.info("Set model parameters")
+        self.set_gguf_parameters()
+
+        logger.info("Set model tokenizer")
+        self.set_vocab()
+
+        logger.info("Set model quantization version")
+        self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+
     def write(self):
-        self.write_tensors()
-        self.gguf_writer.write_header_to_file()
+        self.prepare_tensors()
+        self.prepare_metadata(vocab_only=False)
+        self.gguf_writer.write_header_to_file(path=self.fname_out)
         self.gguf_writer.write_kv_data_to_file()
         self.gguf_writer.write_tensors_to_file(progress=True)
         self.gguf_writer.close()
 
     def write_vocab(self):
-        self.gguf_writer.write_header_to_file()
+        if len(self.gguf_writer.tensors) != 1:
+            raise ValueError('Splitting the vocabulary is not supported')
+
+        self.prepare_metadata(vocab_only=True)
+        self.gguf_writer.write_header_to_file(path=self.fname_out)
         self.gguf_writer.write_kv_data_to_file()
         self.gguf_writer.close()
 
     @staticmethod
-    def get_model_part_names(dir_model: Path, suffix: str) -> list[str]:
+    def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]:
         part_names: list[str] = []
         for filename in os.listdir(dir_model):
-            if filename.endswith(suffix):
+            if filename.startswith(prefix) and filename.endswith(suffix):
                 part_names.append(filename)
 
         part_names.sort()
@@ -368,6 +433,29 @@ def from_model_architecture(cls, arch: str) -> type[Model]:
         except KeyError:
             raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
 
+    def does_token_look_special(self, token: str | bytes) -> bool:
+        if isinstance(token, (bytes, bytearray)):
+            token_text = token.decode(encoding="utf-8")
+        elif isinstance(token, memoryview):
+            token_text = token.tobytes().decode(encoding="utf-8")
+        else:
+            token_text = token
+
+        # Some models mark some added tokens which ought to be control tokens as not special.
+        # (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
+        seems_special = token_text in (
+            "<pad>",  # deepseek-coder
+            "<mask>", "<2mass>", "[@BOS@]",  # gemma{,-2}
+        )
+
+        seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
+        seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>"))  # deepseek-coder
+
+        # TODO: should these be marked as UNUSED instead? (maybe not)
+        seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">"))  # gemma{,-2}
+
+        return seems_special
+
     # used for GPT-2 BPE and WordPiece vocabs
     def get_vocab_base(self) -> tuple[list[str], list[int], str]:
         tokens: list[str] = []
@@ -386,20 +474,22 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]:
         for i in range(vocab_size):
             if i not in reverse_vocab:
                 tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.USER_DEFINED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                if tokenizer.added_tokens_decoder[i].special:
-                    toktypes.append(gguf.TokenType.CONTROL)
-                else:
-                    toktypes.append(gguf.TokenType.USER_DEFINED)
+                toktypes.append(gguf.TokenType.UNUSED)
             else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
 
         return tokens, toktypes, tokpre
 
-    # NOTE: this function is generated by convert-hf-to-gguf-update.py
+    # NOTE: this function is generated by convert_hf_to_gguf_update.py
     #       do not modify it manually!
     # ref:  https://github.com/ggerganov/llama.cpp/pull/6920
     # Marker: Start get_vocab_base_pre
@@ -419,7 +509,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 
         res = None
 
-        # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
+        # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
         #       or pull the latest version of the model from Huggingface
         #       don't edit the hashes manually!
         if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
@@ -447,7 +537,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
             # ref: https://huggingface.co/openai-community/gpt2
             res = "gpt-2"
         if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3":
-            # ref: https://huggingface.co/stabilityai/stablelm-2-1_6b
+            # ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b
             res = "stablelm2"
         if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
             # ref: https://huggingface.co/smallcloudai/Refact-1_6-base
@@ -473,15 +563,48 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
             # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
             res = "jina-v2-de"
+        if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d":
+            # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
+            res = "smaug-bpe"
+        if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360":
+            # ref: https://huggingface.co/LumiOpen/Poro-34B-chat
+            res = "poro-chat"
+        if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
+            res = "jina-v2-code"
+        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
+            res = "chatglm-bpe"
+        if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
+            # ref: https://huggingface.co/LumiOpen/Viking-7B
+            res = "viking"
+        if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
+            # ref: https://huggingface.co/core42/jais-13b
+            res = "jais"
+        if chkhsh == "7b3e7548e4308f52a76e8229e4e6cc831195d0d1df43aed21ac6c93da05fec5f":
+            # ref: https://huggingface.co/WisdomShell/CodeShell-7B
+            res = "codeshell"
+        if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e":
+            # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
+            res = "tekken"
+        if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249":
+            # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M
+            res = "smollm"
+        if chkhsh == "3c30d3ad1d6b64202cd222813e7736c2db6e1bd6d67197090fc1211fbc612ae7":
+            # ref: https://huggingface.co/bigscience/bloom
+            res = "bloom"
+        if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21":
+            # ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small
+            res = "gpt3-finnish"
 
         if res is None:
             logger.warning("\n")
             logger.warning("**************************************************************************************")
             logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
             logger.warning("**          There are 2 possible reasons for this:")
-            logger.warning("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
+            logger.warning("**          - the model has not been added to convert_hf_to_gguf_update.py yet")
             logger.warning("**          - the pre-tokenization config has changed upstream")
-            logger.warning("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
+            logger.warning("**          Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
             logger.warning("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
             logger.warning("**")
             logger.warning(f"** chkhsh:  {chkhsh}")
@@ -536,7 +659,7 @@ def _set_vocab_qwen(self):
         for i in range(vocab_size):
             if i not in reverse_vocab:
                 tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.USER_DEFINED)
+                toktypes.append(gguf.TokenType.UNUSED)
             elif reverse_vocab[i] in added_vocab:
                 tokens.append(reverse_vocab[i])
                 toktypes.append(gguf.TokenType.CONTROL)
@@ -559,15 +682,23 @@ def _set_vocab_qwen(self):
         special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def _set_vocab_sentencepiece(self):
+    def _set_vocab_sentencepiece(self, add_to_gguf=True):
+        tokens, scores, toktypes = self._create_vocab_sentencepiece()
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _create_vocab_sentencepiece(self):
         from sentencepiece import SentencePieceProcessor
 
         tokenizer_path = self.dir_model / 'tokenizer.model'
 
-        tokens: list[bytes] = []
-        scores: list[float] = []
-        toktypes: list[int] = []
-
         if not tokenizer_path.is_file():
             raise FileNotFoundError(f"File not found: {tokenizer_path}")
 
@@ -578,7 +709,7 @@ def _set_vocab_sentencepiece(self):
 
         tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
         scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
 
         for token_id in range(tokenizer.vocab_size()):
             piece = tokenizer.IdToPiece(token_id)
@@ -605,7 +736,7 @@ def _set_vocab_sentencepiece(self):
                 added_tokens_json = json.load(f)
                 for key in added_tokens_json:
                     token_id = added_tokens_json[key]
-                    if (token_id >= vocab_size):
+                    if token_id >= vocab_size:
                         logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                         continue
 
@@ -613,6 +744,26 @@ def _set_vocab_sentencepiece(self):
                     scores[token_id] = -1000.0
                     toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
 
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
+                for token_id, token_data in added_tokens_decoder.items():
+                    token_id = int(token_id)
+                    token: str = token_data["content"]
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token.encode("utf-8"):
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}')
+                    if token_data.get("special") or self.does_token_look_special(token):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+                    else:
+                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
+                        toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+                    scores[token_id] = -1000.0
+                    tokens[token_id] = token.encode("utf-8")
+
         if vocab_size > len(tokens):
             pad_count = vocab_size - len(tokens)
             logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
@@ -621,17 +772,10 @@ def _set_vocab_sentencepiece(self):
                 scores.append(-1000.0)
                 toktypes.append(SentencePieceTokenTypes.UNUSED)
 
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
+        return tokens, scores, toktypes
 
     def _set_vocab_llama_hf(self):
-        vocab = LlamaHfVocab(self.dir_model)
+        vocab = gguf.LlamaHfVocab(self.dir_model)
         tokens = []
         scores = []
         toktypes = []
@@ -652,6 +796,51 @@ def _set_vocab_llama_hf(self):
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         special_vocab.add_to_gguf(self.gguf_writer)
 
+    def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int):
+        tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf"
+        logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
+        vocab_reader = gguf.GGUFReader(tokenizer_path, "r")
+
+        default_pre = "mpt" if model_name == "gpt-neox" else "default"
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL)
+        assert field  # tokenizer model
+        self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8"))
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE)
+        self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre)
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST)
+        assert field  # token list
+        self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
+
+        if model_name == "llama-spm":
+            field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES)
+            assert field  # token scores
+            self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
+        assert field  # token types
+        self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
+        if model_name != "llama-spm":
+            field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES)
+            assert field  # token merges
+            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
+
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None:
+            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None:
+            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None:
+            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None:
+            self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None:
+            self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None:
+            self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0])
+
 
 @Model.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(Model):
@@ -660,7 +849,6 @@ class GPTNeoXModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -672,13 +860,50 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
+        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+
+        tensors: list[tuple[str, Tensor]] = []
+
+        if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
+            # Map bloom-style qkv_linear to gpt-style qkv_linear
+            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
+            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
+            qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
+            data_torch = torch.cat(
+                (
+                    qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
+                    qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
+                    qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
+                ),
+                dim=0,
+            )
+            logger.info("re-format attention.linear_qkv.weight")
+        elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name):
+            qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
+            data_torch = torch.cat(
+                (
+                    qkv_bias[:, 0, :].reshape((n_embed,)),
+                    qkv_bias[:, 1, :].reshape((n_embed,)),
+                    qkv_bias[:, 2, :].reshape((n_embed,)),
+                ),
+                dim=0,
+            )
+            logger.info("re-format attention.linear_qkv.bias")
+
+        tensors.append((self.map_tensor_name(name), data_torch))
+
+        return tensors
+
 
-@Model.register("BloomForCausalLM")
+@Model.register("BloomForCausalLM", "BloomModel")
 class BloomModel(Model):
     model_arch = gguf.MODEL_ARCH.BLOOM
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("Bloom")
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
@@ -755,7 +980,6 @@ def set_vocab(self):
 
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layers"]
-        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
         self.gguf_writer.add_embedding_length(self.hparams["d_model"])
         self.gguf_writer.add_block_count(block_count)
@@ -794,7 +1018,6 @@ def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -807,8 +1030,6 @@ def set_gguf_parameters(self):
             raise ValueError("gguf: can not find ctx length parameter.")
 
         self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -832,7 +1053,6 @@ def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -844,8 +1064,6 @@ def set_gguf_parameters(self):
         else:
             raise ValueError("gguf: can not find ctx length parameter.")
 
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -919,7 +1137,11 @@ def set_vocab(self):
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(dir_model)
         vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
-        assert max(tokenizer.vocab.values()) < vocab_size
+        # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size,
+        # because vocab_size is the count of items, and indexes start at 0.
+        max_vocab_index = max(tokenizer.get_vocab().values())
+        if max_vocab_index >= vocab_size:
+            raise ValueError("Vocabulary size exceeds expected maximum size.")
 
         reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
         added_vocab = tokenizer.get_added_vocab()
@@ -955,7 +1177,6 @@ def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -967,8 +1188,6 @@ def set_gguf_parameters(self):
         else:
             raise ValueError("gguf: can not find ctx length parameter.")
 
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1027,7 +1246,6 @@ def set_gguf_parameters(self):
         if n_head_kv is None:
             n_head_kv = self.hparams.get("n_head_kv", 1)  # old name
 
-        self.gguf_writer.add_name("Falcon")
         self.gguf_writer.add_context_length(2048)  # not in config.json
         self.gguf_writer.add_tensor_data_layout("jploski")  # qkv tensor transform
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1072,7 +1290,6 @@ class StarCoderModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("StarCoder")
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
         self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
@@ -1092,11 +1309,11 @@ def set_vocab(self):
 
         # TODO: how to determine special FIM tokens automatically?
         special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
         special_vocab._set_special_token("prefix", 1)
         special_vocab._set_special_token("suffix", 3)
         special_vocab._set_special_token("middle", 2)
-        special_vocab._set_special_token("fsep",   4) # is this correct?
+        special_vocab.chat_template = None  # do not add it twice
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
@@ -1108,7 +1325,6 @@ def set_gguf_parameters(self):
 
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("Refact")
         # refact uses Alibi. So this is from config.json which might be used by training.
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
@@ -1148,45 +1364,6 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return tensors
 
 
-@Model.register("PersimmonForCausalLM")
-class PersimmonModel(Model):
-    model_arch = gguf.MODEL_ARCH.PERSIMMON
-
-    def set_gguf_parameters(self):
-        block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
-        head_count = self.hparams["num_attention_heads"]
-        head_count_kv = head_count
-        hidden_size = self.hparams["hidden_size"]
-
-        self.gguf_writer.add_name('persimmon-8b-chat')
-        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(hidden_size)
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-
-        # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller
-        #       than the head size?
-        #       ref: https://github.com/ggerganov/llama.cpp/pull/4889
-        # self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
-        self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
-
-        self.gguf_writer.add_head_count(head_count)
-        self.gguf_writer.add_head_count_kv(head_count_kv)
-        self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-        # self.gguf_writer.add_bos_token_id(71013)
-        # self.gguf_writer.add_eos_token_id(71013)
-
-    def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
-        del name, new_name, bid, n_dims  # unused
-
-        # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
-        return True
-
-
 @Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
 class StableLMModel(Model):
     model_arch = gguf.MODEL_ARCH.STABLELM
@@ -1195,14 +1372,13 @@ def set_vocab(self):
         if (self.dir_model / "tokenizer.json").is_file():
             self._set_vocab_gpt2()
         else:
-            # StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
+            # StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
             self._set_vocab_qwen()
 
     def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -1264,8 +1440,8 @@ def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_
 
         return [(new_name, data_torch)]
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
 
         if self._q_norms is not None or self._k_norms is not None:
             # flatten two `list[dict[str, Tensor]]` into a single `list[str]`
@@ -1284,7 +1460,7 @@ class LlamaModel(Model):
 
     def set_vocab(self):
         try:
-            self. _set_vocab_sentencepiece()
+            self._set_vocab_sentencepiece()
         except FileNotFoundError:
             try:
                 self._set_vocab_llama_hf()
@@ -1308,13 +1484,29 @@ def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
+
+        if "head_dim" in hparams:
+            rope_dim = hparams["head_dim"]
+        else:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
 
         if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
             if self.hparams["rope_scaling"].get("type") == "linear":
                 self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
                 self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
 
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                if "add_prefix_space" in tokenizer_config_json:
+                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
+
+        # Apply to granite small models only
+        if self.hparams.get("vocab_size", 32000) == 49152:
+            self.gguf_writer.add_add_bos_token(False)
+
     @staticmethod
     def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
         if n_head_kv is not None and n_head != n_head_kv:
@@ -1329,9 +1521,9 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         n_head = self.hparams["num_attention_heads"]
         n_kv_head = self.hparams.get("num_key_value_heads")
 
-        if name.endswith("q_proj.weight"):
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
             data_torch = LlamaModel.permute(data_torch, n_head, n_head)
-        if name.endswith("k_proj.weight"):
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
             data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
 
         # process the experts separately
@@ -1370,8 +1562,36 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(self.map_tensor_name(name), data_torch)]
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
+            if rope_scaling.get("rope_type", '').lower() == "llama3":
+                base = self.hparams.get("rope_theta", 10000.0)
+                dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+                factor = rope_scaling.get("factor", 8.0)
+                low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
+                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+
+                low_freq_wavelen = old_context_len / low_freq_factor
+                high_freq_wavelen = old_context_len / high_freq_factor
+                assert low_freq_wavelen != high_freq_wavelen
+
+                rope_factors = []
+                for freq in freqs:
+                    wavelen = 2 * math.pi / freq
+                    if wavelen < high_freq_wavelen:
+                        rope_factors.append(1)
+                    elif wavelen > low_freq_wavelen:
+                        rope_factors.append(factor)
+                    else:
+                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+
+                self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
+
+        super().prepare_tensors()
 
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
@@ -1380,6 +1600,48 @@ def write_tensors(self):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@Model.register("BitnetForCausalLM")
+class BitnetModel(Model):
+    model_arch = gguf.MODEL_ARCH.BITNET
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+        self.gguf_writer.add_rope_scaling_factor(1.0)
+
+    def weight_quant(self, weight):
+        dtype = weight.dtype
+        weight = weight.float()
+        s = 1 / weight.abs().mean().clamp(min=1e-5)
+        weight = (weight * s).round().clamp(-1, 1) / s
+        scale = weight.abs().max().unsqueeze(0)
+        weight = torch.where(weight.abs().less(1e-6), 0, weight).type(dtype)
+        weight = torch.sign(weight).type(dtype)
+        return weight.type(dtype), scale.type(torch.float32)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        new_name = self.map_tensor_name(name)
+
+        if any(self.match_model_tensor_name(new_name, key, bid) for key in [
+            gguf.MODEL_TENSOR.ATTN_Q,
+            gguf.MODEL_TENSOR.ATTN_K,
+            gguf.MODEL_TENSOR.ATTN_V,
+            gguf.MODEL_TENSOR.ATTN_OUT,
+            gguf.MODEL_TENSOR.FFN_UP,
+            gguf.MODEL_TENSOR.FFN_DOWN,
+            gguf.MODEL_TENSOR.FFN_GATE,
+        ]):
+            # transform weight into 1/0/-1 (in fp32)
+            weight_torch, scale_torch = self.weight_quant(data_torch)
+            yield (new_name, weight_torch)
+            yield (new_name.removesuffix(".weight") + ".scale", scale_torch)
+        else:
+            yield (new_name, data_torch)
+
+
 @Model.register("GrokForCausalLM")
 class GrokModel(Model):
     model_arch = gguf.MODEL_ARCH.GROK
@@ -1392,7 +1654,6 @@ def __init__(self, *args, **kwargs):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        self.gguf_writer.add_name("Grok")
 
     _experts: list[dict[str, Tensor]] | None = None
 
@@ -1441,7 +1702,6 @@ class DbrxModel(Model):
     def set_gguf_parameters(self):
         ffn_config = self.hparams["ffn_config"]
         attn_config = self.hparams["attn_config"]
-        self.gguf_writer.add_name(self.hparams["model_type"])
         self.gguf_writer.add_block_count(self.hparams["n_layers"])
 
         self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
@@ -1454,7 +1714,6 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
 
         self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
-        self.gguf_writer.add_file_type(self.ftype)
 
         self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
         self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
@@ -1499,7 +1758,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(new_name, data_torch)]
 
-    def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
+    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
         del name, new_name, bid  # unused
 
         return n_dims > 1
@@ -1511,7 +1770,6 @@ class MiniCPMModel(Model):
 
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
-        self.gguf_writer.add_name("MiniCPM")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -1581,7 +1839,6 @@ def set_vocab(self):
         self._set_vocab_qwen()
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("Qwen")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1612,6 +1869,12 @@ def set_gguf_parameters(self):
         super().set_gguf_parameters()
         if (n_experts := self.hparams.get("num_experts")) is not None:
             self.gguf_writer.add_expert_count(n_experts)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+        if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None:
+            self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size)
+            logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}")
 
     _experts: list[dict[str, Tensor]] | None = None
 
@@ -1651,8 +1914,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(self.map_tensor_name(name), data_torch)]
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
 
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
@@ -1666,7 +1929,6 @@ class GPT2Model(Model):
     model_arch = gguf.MODEL_ARCH.GPT2
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.hparams["n_layer"])
         self.gguf_writer.add_context_length(self.hparams["n_ctx"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
@@ -1709,7 +1971,6 @@ def set_gguf_parameters(self):
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
 
-        self.gguf_writer.add_name("Phi2")
         self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
 
         self.gguf_writer.add_embedding_length(n_embd)
@@ -1742,7 +2003,7 @@ def set_vocab(self):
 
         tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
         scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
 
         for token_id in range(tokenizer.vocab_size()):
 
@@ -1771,7 +2032,7 @@ def set_vocab(self):
 
                 for key in added_tokens_json:
                     token_id = added_tokens_json[key]
-                    if (token_id >= vocab_size):
+                    if token_id >= vocab_size:
                         logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                         continue
 
@@ -1779,6 +2040,40 @@ def set_vocab(self):
                     scores[token_id] = -1000.0
                     toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
 
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
+                for token_id, foken_data in added_tokens_decoder.items():
+                    token_id = int(token_id)
+                    token = foken_data["content"].encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
+        tokenizer_file = self.dir_model / 'tokenizer.json'
+        if tokenizer_file.is_file():
+            with open(tokenizer_file, "r", encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+                added_tokens = tokenizer_json.get("added_tokens", [])
+                for foken_data in added_tokens:
+                    token_id = int(foken_data["id"])
+                    token = foken_data["content"].encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
         self.gguf_writer.add_tokenizer_model("llama")
         self.gguf_writer.add_tokenizer_pre("default")
         self.gguf_writer.add_token_list(tokens)
@@ -1791,22 +2086,58 @@ def set_vocab(self):
     def set_gguf_parameters(self):
         block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
 
-        rot_pct = 1.0
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
+        n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
         rms_eps = self.find_hparam(["rms_norm_eps"])
+        max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
+        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
+        rope_dims = n_embd // n_head
 
-        self.gguf_writer.add_name("Phi3")
-        self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
-
+        self.gguf_writer.add_context_length(max_pos_embds)
+        self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
         self.gguf_writer.add_embedding_length(n_embd)
-        self.gguf_writer.add_feed_forward_length(8192)
+        self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"]))
         self.gguf_writer.add_block_count(block_count)
         self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_head_count_kv(n_head)
+        self.gguf_writer.add_head_count_kv(n_head_kv)
         self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
-        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
+        self.gguf_writer.add_rope_dimension_count(rope_dims)
+        self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
         self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"]))
+
+        # write rope scaling for long context (128k) model
+        rope_scaling = self.find_hparam(['rope_scaling'], True)
+        if rope_scaling is None:
+            return
+
+        scale = max_pos_embds / orig_max_pos_embds
+
+        rope_scaling_type = rope_scaling.get('type', '').lower()
+        if len(rope_scaling_type) == 0:
+            raise KeyError('Missing the required key rope_scaling.type')
+
+        if rope_scaling_type == 'su' or rope_scaling_type == 'longrope':
+            attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0
+        elif rope_scaling_type == 'yarn':
+            attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0
+        else:
+            raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet')
+
+        self.gguf_writer.add_rope_scaling_attn_factors(attn_factor)
+
+        long_factors = rope_scaling.get('long_factor', None)
+        short_factors = rope_scaling.get('short_factor', None)
+
+        if long_factors is None or short_factors is None:
+            raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
+
+        if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
+            raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
+
+        self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG]  + ".weight", np.array(long_factors, dtype=np.float32))
+        self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32))
 
 
 @Model.register("PlamoForCausalLM")
@@ -1820,7 +2151,6 @@ def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name("PLaMo")
         self.gguf_writer.add_context_length(4096)  # not in config.json
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
@@ -1865,7 +2195,6 @@ class CodeShellModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("CodeShell")
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
         self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
@@ -1917,7 +2246,7 @@ def set_vocab(self):
             logger.error(f'Error: Missing {tokenizer_path}')
             sys.exit(1)
 
-        sentencepiece_model = model.ModelProto()
+        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue]
         sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
         add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
 
@@ -1945,6 +2274,9 @@ def set_vocab(self):
                 toktype = SentencePieceTokenTypes.UNUSED
             elif tokenizer.IsByte(token_id):
                 toktype = SentencePieceTokenTypes.BYTE
+            # take care of ununsed raw token
+            if piece.startswith('[UNUSED'):
+                toktype = SentencePieceTokenTypes.UNUSED
 
             tokens.append(text)
             scores.append(score)
@@ -1960,6 +2292,49 @@ def set_vocab(self):
                     scores.append(-1000.0)
                     toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
 
+        chat_eos_token = '<|im_end|>'
+        chat_eos_token_id = None
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
+                for token_id, foken_data in added_tokens_decoder.items():
+                    token_id = int(token_id)
+                    token = foken_data["content"]
+                    if token == chat_eos_token:
+                        chat_eos_token_id = token_id
+                    token = token.encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
+        tokenizer_file = self.dir_model / 'tokenizer.json'
+        if tokenizer_file.is_file():
+            with open(tokenizer_file, "r", encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+                added_tokens = tokenizer_json.get("added_tokens", [])
+                for foken_data in added_tokens:
+                    token_id = int(foken_data["id"])
+                    token = foken_data["content"]
+                    if token == chat_eos_token:
+                        chat_eos_token_id = token_id
+                    token = token.encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
         self.gguf_writer.add_tokenizer_model("llama")
         self.gguf_writer.add_tokenizer_pre("default")
         self.gguf_writer.add_token_list(tokens)
@@ -1969,37 +2344,17 @@ def set_vocab(self):
 
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         old_eos = special_vocab.special_token_ids["eos"]
-        if "chat" in os.path.basename(self.dir_model.absolute()):
+        if chat_eos_token_id is not None:
             # For the chat model, we replace the eos with '<|im_end|>'.
             # TODO: this is a hack, should be fixed
             #       https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
-            special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
-            logger.warning(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
-in chat mode so that the conversation can end normally.")
+            special_vocab.special_token_ids["eos"] = chat_eos_token_id
+            logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}"
+                           " in chat mode so that the conversation can end normally.")
 
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def _try_get_sft_eos(self, tokenizer):
-        unused_145_list = tokenizer.Encode('[UNUSED_TOKEN_145]')
-        im_end_list = tokenizer.Encode('<|im_end|>')
-        eos_token = None
-        assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
-        if len(unused_145_list) == 1:
-            eos_token = unused_145_list[0]
-        if len(im_end_list) == 1:
-            eos_token = im_end_list[0]
-        assert eos_token
-        return eos_token
-
-    def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("InternLM2")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -2009,30 +2364,30 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
         self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
         self.gguf_writer.add_file_type(self.ftype)
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         num_heads = self.hparams["num_attention_heads"]
         num_kv_heads = self.hparams["num_key_value_heads"]
-        hidden_size = self.hparams["hidden_size"]
+        n_embd = self.hparams["hidden_size"]
         q_per_kv = num_heads // num_kv_heads
-        head_dim = hidden_size // num_heads
+        head_dim = n_embd // num_heads
         num_groups = num_heads // q_per_kv
 
-        qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv"
-
-        if re.match(qkv_pattern, name):
-            bid = re.findall(qkv_pattern, name)[0]
+        if bid is not None and f"model.layers.{bid}.attention.wqkv" in name:
             qkv = data_torch
-            # qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
-            qkv = qkv.T.reshape((-1, num_groups, q_per_kv + 2, head_dim))
-            q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
+
+            qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd))
+            q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1]
+
             # The model weights of q and k equire additional reshape.
-            # q = self._hf_permute_qk(rearrange(q, " o g n i ->  o (g n i)").T, num_heads, num_heads)
-            q = self._hf_permute_qk(q.reshape((q.shape[0], -1)).T, num_heads, num_heads)
-            # k = self._hf_permute_qk(rearrange(k, " o g n i ->  o (g n i)").T, num_heads, num_kv_heads)
-            k = self._hf_permute_qk(k.reshape((k.shape[0], -1)).T, num_heads, num_kv_heads)
-            # v = rearrange(v, " o g n i ->  o (g n i)").T
-            v = v.reshape((v.shape[0], -1)).T
+            q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads)
+            k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads)
+            v = v.reshape((-1, v.shape[-1]))
+
             return [
                 (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q),
                 (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k),
@@ -2144,38 +2499,193 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
 
 
-@Model.register("GemmaForCausalLM")
-class GemmaModel(Model):
-    model_arch = gguf.MODEL_ARCH.GEMMA
+@Model.register("XLMRobertaModel")
+class XLMRobertaModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.BERT
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # we need the pad_token_id to know how to chop down position_embd matrix
+        if (pad_token_id := self.hparams.get("pad_token_id")) is not None:
+            self._position_offset = 1 + pad_token_id
+            if "max_position_embeddings" in self.hparams:
+                self.hparams["max_position_embeddings"] -= self._position_offset
+        else:
+            self._position_offset = None
 
     def set_vocab(self):
-        self._set_vocab_sentencepiece()
+        # to avoid TypeError: Descriptors cannot be created directly
+        # exception when importing sentencepiece_model_pb2
+        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
 
-        # TODO: these special tokens should be exported only for the CodeGemma family
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
-        special_vocab._set_special_token("prefix", 67)
-        special_vocab._set_special_token("suffix", 69)
-        special_vocab._set_special_token("middle", 68)
-        special_vocab._set_special_token("fsep",   70)
-        special_vocab._set_special_token("eot",    107)
-        special_vocab.add_to_gguf(self.gguf_writer)
+        tokenizer_path = self.dir_model / 'sentencepiece.bpe.model'
+        if not tokenizer_path.is_file():
+            raise FileNotFoundError(f"File not found: {tokenizer_path}")
 
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-        block_count = hparams["num_hidden_layers"]
+        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue]
+        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+        assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
 
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-        self.gguf_writer.add_key_length(hparams["head_dim"])
-        self.gguf_writer.add_value_length(hparams["head_dim"])
-        self.gguf_writer.add_file_type(self.ftype)
+        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
+        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
+        # realign tokens (see HF tokenizer code)
+        tokens = [b'<s>', b'<pad>', b'</s>', b'<unk>'] + tokens[3:-1]
+        scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1]
+        toktypes = [
+            SentencePieceTokenTypes.CONTROL,
+            SentencePieceTokenTypes.CONTROL,
+            SentencePieceTokenTypes.CONTROL,
+            SentencePieceTokenTypes.UNKNOWN,
+        ] + toktypes[3:-1]
+
+        self.gguf_writer.add_tokenizer_model("t5")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+        self.gguf_writer.add_token_type_count(1)
+        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
+        if precompiled_charsmap:
+            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        self.gguf_writer.add_add_bos_token(True)
+        self.gguf_writer.add_add_eos_token(True)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
+        if name == "embeddings.position_embeddings.weight":
+            if self._position_offset is not None:
+                data_torch = data_torch[self._position_offset:,:]
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
+@Model.register("GemmaForCausalLM")
+class GemmaModel(Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+        # TODO: these special tokens should be exported only for the CodeGemma family
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
+        special_vocab._set_special_token("prefix", 67)
+        special_vocab._set_special_token("suffix", 69)
+        special_vocab._set_special_token("middle", 68)
+        special_vocab._set_special_token("fsep",   70)
+        special_vocab._set_special_token("eot",    107)
+        special_vocab.chat_template = None  # do not add it twice
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        self.gguf_writer.add_add_space_prefix(False)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        block_count = hparams["num_hidden_layers"]
+
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_key_length(hparams["head_dim"])
+        self.gguf_writer.add_value_length(hparams["head_dim"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
+        # To prevent errors, skip loading lm_head.weight.
+        if name == "lm_head.weight":
+            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
+            return []
+
+        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
+        if name.endswith("norm.weight"):
+            data_torch = data_torch + 1
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
+@Model.register("Gemma2ForCausalLM")
+class Gemma2Model(Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA2
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+        self.gguf_writer.add_add_space_prefix(False)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        block_count = hparams["num_hidden_layers"]
+
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_key_length(hparams["head_dim"])
+        self.gguf_writer.add_value_length(hparams["head_dim"])
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_attn_logit_softcapping(
+            self.hparams["attn_logit_softcapping"]
+        )
+        self.gguf_writer.add_final_logit_softcapping(
+            self.hparams["final_logit_softcapping"]
+        )
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -2217,39 +2727,7 @@ def set_vocab(self):
             self._set_vocab_sentencepiece()
         else:
             # Use the GPT-NeoX tokenizer when no tokenizer files are present
-            tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
-            logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
-            neox_reader = gguf.GGUFReader(tokenizer_path, "r")
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
-            self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8") if field else "gpt2")
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE)
-            self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else "mpt")
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
-            assert field
-            self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
-            assert field
-            self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
-            assert field
-            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
-            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0] if field else 1)
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
-            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0] if field else 0)
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
-            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0] if field else 0)
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)
-            self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0] if field else 0)
+            self._set_vocab_builtin("gpt-neox", vocab_size)
 
     def set_gguf_parameters(self):
         d_model = self.find_hparam(["hidden_size",       "d_model"])
@@ -2265,7 +2743,6 @@ def set_gguf_parameters(self):
         # Fail early for models which don't have a block expansion factor of 2
         assert d_inner == 2 * d_model
 
-        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
         self.gguf_writer.add_embedding_length(d_model)
         self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
@@ -2302,18 +2779,22 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(new_name, data_torch)]
 
-    def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
-        del n_dims  # unused
-
-        return bid is not None and new_name in (
-            self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [
+    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
+        if bid is not None and new_name in (
+            self.format_tensor_name(
+                n, bid, ".weight" if name.endswith(".weight") else ""
+            )
+            for n in [
                 gguf.MODEL_TENSOR.SSM_CONV1D,
                 gguf.MODEL_TENSOR.SSM_X,
                 gguf.MODEL_TENSOR.SSM_DT,
                 gguf.MODEL_TENSOR.SSM_A,
                 gguf.MODEL_TENSOR.SSM_D,
             ]
-        )
+        ):
+            return gguf.GGMLQuantizationType.F32
+
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
 
 
 @Model.register("CohereForCausalLM")
@@ -2325,7 +2806,8 @@ def __init__(self, *args, **kwargs):
 
         # max_position_embeddings = 8192 in config.json but model was actually
         # trained on 128k context length
-        self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
+        # aya-23 models don't have model_max_length specified
+        self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"])
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
@@ -2371,18 +2853,20 @@ def __init__(self, *args, **kwargs):
 
     def get_tensors(self):
         for name, data in super().get_tensors():
-            if 'gated_layers' in name:
+            if 'gated_layer' in name:
                 d1 = data[:self.intermediate_size, :]
                 name1 = name.replace('gated_layers', 'gated_layers_w')
+                name1 = name1.replace('up_gated_layer', 'gated_layers_v')
                 d2 = data[self.intermediate_size:, :]
                 name2 = name.replace('gated_layers', 'gated_layers_v')
+                name2 = name2.replace('up_gated_layer', 'gated_layers_w')
                 yield name1, d1
                 yield name2, d2
                 continue
 
             yield name, data
 
-    def set_vocab(self, *args, **kwargs):
+    def set_vocab(self):
         tokenizer_class = 'BertTokenizer'
         with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
             tokenizer_class = json.load(f)['tokenizer_class']
@@ -2398,157 +2882,1077 @@ def set_vocab(self, *args, **kwargs):
         self.gguf_writer.add_add_eos_token(True)
 
 
-###### CONVERSION LOGIC ######
-
-
-# tree of lazy tensors
-class LazyTorchTensor(gguf.LazyBase):
-    _tensor_type = torch.Tensor
-    # to keep the type-checker happy
-    dtype: torch.dtype
-    shape: torch.Size
+@Model.register("OpenELMForCausalLM")
+class OpenELMModel(Model):
+    model_arch = gguf.MODEL_ARCH.OPENELM
 
-    # only used when converting a torch.Tensor to a np.ndarray
-    _dtype_map: dict[torch.dtype, type] = {
-        torch.float16: np.float16,
-        torch.float32: np.float32,
-    }
+    @staticmethod
+    def _make_divisible(v: float | int, divisor: int) -> int:
+        # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38
+        new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
+        # Make sure that round down does not go down by more than 10%.
+        if new_v < 0.9 * v:
+            new_v += divisor
+        return new_v
 
-    def numpy(self) -> gguf.LazyNumpyTensor:
-        dtype = self._dtype_map[self.dtype]
-        return gguf.LazyNumpyTensor(
-            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
-            lazy=self._lazy,
-            args=(self,),
-            func=(lambda s: s[0].numpy())
-        )
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
-    @classmethod
-    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: torch.Size) -> Tensor:
-        return torch.empty(size=shape, dtype=dtype, device="meta")
+        ffn_multipliers: list[float] = self.hparams["ffn_multipliers"]
+        ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"]
+        self._n_embd: int = self.hparams["model_dim"]
+        self._num_kv_heads: list[int] = self.hparams["num_kv_heads"]
+        self._num_query_heads: list[int] = self.hparams["num_query_heads"]
+        self._ffn_dims: list[int] = [
+            OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor)
+            for multiplier in ffn_multipliers
+        ]
+        assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
+        assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int)
+
+    # Uses the tokenizer from meta-llama/Llama-2-7b-hf
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"])
 
-    @classmethod
-    def __torch_function__(cls, func, types, args=(), kwargs=None):
-        del types  # unused
+    def set_gguf_parameters(self):
+        n_embd = self._n_embd
+        head_dim = self.hparams["head_dim"]
+        rot_pct = 1.0
+        assert self.block_count == len(self._num_kv_heads)
+        assert self.block_count == len(self._num_query_heads)
+        assert self.block_count == len(self._ffn_dims)
 
-        if kwargs is None:
-            kwargs = {}
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_context_length(self.hparams["max_context_length"])
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(self._ffn_dims)
+        self.gguf_writer.add_head_count(self._num_query_heads)
+        self.gguf_writer.add_head_count_kv(self._num_kv_heads)
+        self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"])
+        # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30
+        self.gguf_writer.add_layer_norm_rms_eps(1e-6)
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim))
+        self.gguf_writer.add_key_length(head_dim)
+        self.gguf_writer.add_value_length(head_dim)
+        self.gguf_writer.add_file_type(self.ftype)
 
-        if func is torch.Tensor.numpy:
-            return args[0].numpy()
+    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        if "n_layers" in keys:
+            return self.hparams["num_transformer_layers"]
 
-        return LazyTorchTensor._wrap_fn(func)(*args, **kwargs)
+        return super().find_hparam(keys, optional)
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
 
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Convert a huggingface model to a GGML compatible file")
-    parser.add_argument(
-        "--vocab-only", action="store_true",
-        help="extract only the vocab",
-    )
-    parser.add_argument(
-        "--awq-path", type=Path, default=None,
-        help="Path to scale awq cache file",
-    )
-    parser.add_argument(
-        "--outfile", type=Path,
-        help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
-    )
-    parser.add_argument(
-        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
-        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
-    )
-    parser.add_argument(
-        "--bigendian", action="store_true",
-        help="model is executed on big endian machine",
-    )
-    parser.add_argument(
-        "model", type=Path,
-        help="directory containing model file",
-    )
-    parser.add_argument(
-        "--use-temp-file", action="store_true",
-        help="use the tempfile library while processing (helpful when running out of memory, process killed)",
-    )
-    parser.add_argument(
-        "--no-lazy", action="store_true",
-        help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
-    )
-    parser.add_argument(
-        "--model-name", type=str, default=None,
-        help="name of the model",
-    )
-    parser.add_argument(
-        "--verbose", action="store_true",
-        help="increase output verbosity",
-    )
+        # split ff
+        if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight":
+            ff_dim = self._ffn_dims[bid]
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])
+            return
 
-    return parser.parse_args()
+        yield (self.map_tensor_name(name), data_torch)
 
 
-def main() -> None:
-    args = parse_args()
+@Model.register("ArcticForCausalLM")
+class ArcticModel(Model):
+    model_arch = gguf.MODEL_ARCH.ARCTIC
 
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+    def set_vocab(self):
+        # The reason for using a custom implementation here is that the
+        # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from
+        # tokenizer.model and used them as BOS and EOS instead of adding new tokens.
+        from sentencepiece import SentencePieceProcessor
 
-    dir_model = args.model
+        tokenizer_path = self.dir_model / 'tokenizer.model'
 
-    if args.awq_path:
-        sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
-        from awq.apply_awq import add_scale_weights  # type: ignore[import-not-found]
-        tmp_model_path = args.model / "weighted_model"
-        dir_model = tmp_model_path
-        if tmp_model_path.is_dir():
-            logger.info(f"{tmp_model_path} exists as a weighted model.")
-        else:
-            tmp_model_path.mkdir(parents=True, exist_ok=True)
-            logger.info("Saving new weighted model ...")
-            add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
-            logger.info(f"Saved weighted model at {tmp_model_path}.")
+        if not tokenizer_path.is_file():
+            logger.error(f'Error: Missing {tokenizer_path}')
+            sys.exit(1)
 
-    if not dir_model.is_dir():
-        logger.error(f'Error: {args.model} is not a directory')
-        sys.exit(1)
+        # Read the whole vocabulary from the tokenizer.model file
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
 
-    ftype_map: dict[str, gguf.LlamaFileType] = {
-        "f32": gguf.LlamaFileType.ALL_F32,
-        "f16": gguf.LlamaFileType.MOSTLY_F16,
-        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
-        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
-        "auto": gguf.LlamaFileType.GUESSED,
-    }
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
 
-    if args.outfile is not None:
-        fname_out = args.outfile
-    else:
-        # output in the same directory as the model by default
-        fname_out = dir_model / 'ggml-model-{ftype}.gguf'
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
 
-    logger.info(f"Loading model: {dir_model.name}")
+        for token_id in range(tokenizer.vocab_size()):
 
-    hparams = Model.load_hparams(dir_model)
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
 
-    with torch.inference_mode():
-        model_class = Model.from_model_architecture(hparams["architectures"][0])
-        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy)
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        # Use the added_tokens_decoder field from tokeniser_config.json as the source
+        # of information about added/redefined tokens and modify them accordingly.
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+
+                if "added_tokens_decoder" in tokenizer_config_json:
+                    added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"]
+                    for token_id, token_json in added_tokens_decoder.items():
+                        token_id = int(token_id)
+                        if token_id >= vocab_size:
+                            logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                            continue
+
+                        token_content = token_json["content"]
+                        token_type = SentencePieceTokenTypes.USER_DEFINED
+                        token_score = -10000.0
+
+                        # Map unk_token to UNKNOWN, other special tokens to CONTROL
+                        # Set the score to 0.0 as in the original tokenizer.model
+                        if ("special" in token_json) and token_json["special"]:
+                            if token_content == tokenizer_config_json["unk_token"]:
+                                token_type = SentencePieceTokenTypes.UNKNOWN
+                            else:
+                                token_type = SentencePieceTokenTypes.CONTROL
+                            token_score = 0.0
+
+                        logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})")
+                        tokens[token_id] = token_content.encode("utf-8")
+                        toktypes[token_id] = token_type
+                        scores[token_id] = token_score
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith("q_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+        if name.endswith("k_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams["num_local_experts"]
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for wid in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@Model.register("DeepseekV2ForCausalLM")
+class DeepseekV2Model(Model):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
+            self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
+        self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
+        self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
+        self.gguf_writer.add_value_length(hparams["v_head_dim"])
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
+        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
+        self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
+        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
+
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "yarn":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+                self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
+                self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@Model.register("T5WithLMHeadModel")
+@Model.register("T5ForConditionalGeneration")
+@Model.register("MT5ForConditionalGeneration")
+@Model.register("UMT5ForConditionalGeneration")
+class T5Model(Model):
+    model_arch = gguf.MODEL_ARCH.T5
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.shared_token_embeddings_found = False
+
+    def set_vocab(self):
+        # to avoid TypeError: Descriptors cannot be created directly
+        # exception when importing sentencepiece_model_pb2
+        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        # many older models use spiece.model tokenizer model filename
+        if not tokenizer_path.is_file():
+            tokenizer_path = self.dir_model / 'spiece.model'
+
+        if not tokenizer_path.is_file():
+            raise FileNotFoundError(f"File not found: {tokenizer_path}")
+
+        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue]
+        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+
+        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
+        if sentencepiece_model.trainer_spec.model_type == 2:  # BPE
+            # assure the tokenizer model file name is correct
+            assert tokenizer_path.name == 'tokenizer.model'
+            return self._set_vocab_sentencepiece()
+        else:
+            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
+
+        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
+        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if token_id >= vocab_size:
+                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
+        self.gguf_writer.add_tokenizer_model("t5")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
+        if precompiled_charsmap:
+            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        self.gguf_writer.add_add_bos_token(False)
+        self.gguf_writer.add_add_eos_token(True)
+
+    def set_gguf_parameters(self):
+        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
+            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
+            n_ctx = 512
+        self.gguf_writer.add_context_length(n_ctx)
+        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
+        self.gguf_writer.add_block_count(self.hparams["num_layers"])
+        self.gguf_writer.add_head_count(self.hparams["num_heads"])
+        self.gguf_writer.add_key_length(self.hparams["d_kv"])
+        self.gguf_writer.add_value_length(self.hparams["d_kv"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
+        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
+        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
+        # and decoder and ignore the remaining ones.
+        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
+            if not self.shared_token_embeddings_found:
+                name = "shared.weight"
+                self.shared_token_embeddings_found = True
+            else:
+                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
+@Model.register("T5EncoderModel")
+class T5EncoderModel(Model):
+    model_arch = gguf.MODEL_ARCH.T5ENCODER
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.shared_token_embeddings_found = False
+
+    def set_vocab(self):
+        # to avoid TypeError: Descriptors cannot be created directly
+        # exception when importing sentencepiece_model_pb2
+        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        # many older models use spiece.model tokenizer model filename
+        if not tokenizer_path.is_file():
+            tokenizer_path = self.dir_model / 'spiece.model'
+
+        if not tokenizer_path.is_file():
+            raise FileNotFoundError(f"File not found: {tokenizer_path}")
+
+        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue]
+        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+
+        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
+        if sentencepiece_model.trainer_spec.model_type == 2:  # BPE
+            # assure the tokenizer model file name is correct
+            assert tokenizer_path.name == 'tokenizer.model'
+            return self._set_vocab_sentencepiece()
+        else:
+            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
+
+        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
+        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
+
+        tokenizer = SentencePieceProcessor()
+        tokenizer.LoadFromFile(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if token_id >= vocab_size:
+                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
+        self.gguf_writer.add_tokenizer_model("t5")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
+        if precompiled_charsmap:
+            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        self.gguf_writer.add_add_bos_token(False)
+        self.gguf_writer.add_add_eos_token(True)
+
+    def set_gguf_parameters(self):
+        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
+            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
+            n_ctx = 512
+        self.gguf_writer.add_context_length(n_ctx)
+        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
+        self.gguf_writer.add_block_count(self.hparams["num_layers"])
+        self.gguf_writer.add_head_count(self.hparams["num_heads"])
+        self.gguf_writer.add_key_length(self.hparams["d_kv"])
+        self.gguf_writer.add_value_length(self.hparams["d_kv"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
+        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
+        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
+        # and decoder and ignore the remaining ones.
+        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
+            if not self.shared_token_embeddings_found:
+                name = "shared.weight"
+                self.shared_token_embeddings_found = True
+            else:
+                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
+@Model.register("JAISLMHeadModel")
+class JaisModel(Model):
+    model_arch = gguf.MODEL_ARCH.JAIS
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # SwigLU activation
+        assert self.hparams["activation_function"] == "swiglu"
+        # ALiBi position embedding
+        assert self.hparams["position_embedding_type"] == "alibi"
+
+        # Embeddings scale
+        self.embeddings_scale = 1.0
+        # note: For some JAIS flavors, output is tied to (same as) wte in original model
+        self.output_is_wte = False
+        if 'mup_embeddings_scale' in self.hparams:
+            self.output_is_wte = True   # Hack (?)
+            self.embeddings_scale = self.hparams['mup_embeddings_scale']
+        elif 'embeddings_scale' in self.hparams:
+            self.embeddings_scale = self.hparams['embeddings_scale']
+        else:
+            assert False
+
+        self.width_scale = 1.0
+        if 'mup_output_alpha' in self.hparams:
+            assert 'mup_width_scale' in self.hparams
+            self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
+        elif 'width_scale' in self.hparams:
+            self.width_scale = self.hparams['width_scale']
+        else:
+            assert False
+
+        self.max_alibi_bias = 8.0
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_block_count(self.hparams["n_layer"])
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        tensors: list[tuple[str, Tensor]] = []
+
+        # we don't need these
+        if name.endswith((".attn.bias")):
+            return tensors
+
+        if name.endswith(("relative_pe.slopes")):
+            # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)
+            # Some other models has max_alibi_bias spelled out explicitly in the hyperparams,
+            # but Jais's PyTorch model simply precalculates the slope values and places them
+            # in relative_pes.slopes
+            n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
+            first_val = float(data_torch[0].item())
+            self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
+
+            return tensors
+
+        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
+            data_torch = data_torch.transpose(1, 0)
+
+        new_name = self.map_tensor_name(name)
+
+        if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
+            tensors.append((new_name, data_torch * self.embeddings_scale))
+            if self.output_is_wte:
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale))
+        elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
+            assert not self.output_is_wte
+            tensors.append((new_name, data_torch * self.width_scale))
+        else:
+            tensors.append((new_name, data_torch))
+
+        return tensors
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
+
+
+@Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration")
+class ChatGLMModel(Model):
+    model_arch = gguf.MODEL_ARCH.CHATGLM
 
-        logger.info("Set model parameters")
-        model_instance.set_gguf_parameters()
+    def set_vocab_chatglm3(self):
+        dir_model = self.dir_model
+        hparams = self.hparams
+        tokens: list[bytes] = []
+        toktypes: list[int] = []
+        scores: list[float] = []
 
-        logger.info("Set model tokenizer")
-        model_instance.set_vocab()
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))
+        assert max(tokenizer.get_vocab().values()) < vocab_size
+        role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
+        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
+        for token_id in range(vocab_size):
+            piece = tokenizer._convert_id_to_token(token_id)
+            if token_id == 0:
+                piece = "<unk>"
+            elif token_id == 1:
+                piece = "<bos>"
+            elif token_id == 2:
+                piece = "<eos>"
+
+            text = piece.encode("utf-8")
+            score = 0.0
+            # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py),
+            # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size()
+            if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():
+                score = tokenizer.tokenizer.sp_model.get_score(token_id)
+
+            if token_id >= tokenizer.tokenizer.sp_model.vocab_size():
+                if piece in special_tokens:
+                    toktype = SentencePieceTokenTypes.CONTROL
+                elif len(piece) == 0:
+                    text = f"[PAD{token_id}]".encode("utf-8")
+                    toktype = SentencePieceTokenTypes.UNUSED
+                else:
+                    toktype = SentencePieceTokenTypes.USER_DEFINED
+                tokens.append(text)
+                scores.append(score)
+                toktypes.append(toktype)
+                continue
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.tokenizer.sp_model.is_unknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.tokenizer.sp_model.is_control(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.tokenizer.sp_model.is_unused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.tokenizer.sp_model.is_byte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        # glm3 needs prefix and suffix formatted as:
+        # prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>"
+        self.gguf_writer.add_tokenizer_pre("chatglm-spm")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    @staticmethod
+    def token_bytes_to_string(b):
+        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
+        byte_encoder = bytes_to_unicode()
+        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
+
+    @staticmethod
+    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
+        parts = [bytes([b]) for b in token]
+        while True:
+            min_idx = None
+            min_rank = None
+            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
+                rank = mergeable_ranks.get(pair[0] + pair[1])
+                if rank is not None and (min_rank is None or rank < min_rank):
+                    min_idx = i
+                    min_rank = rank
+            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
+                break
+            assert min_idx is not None
+            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
+        return parts
+
+    def set_vocab(self):
+        if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""):
+            self.set_vocab_chatglm3()
+            return
+
+        dir_model = self.dir_model
+        hparams = self.hparams
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        vocab_size = hparams["padded_vocab_size"]
+        assert max(tokenizer.get_vocab().values()) < vocab_size
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        merges = []
+        vocab = {}
+        mergeable_ranks = tokenizer.mergeable_ranks
+        for token, rank in mergeable_ranks.items():
+            vocab[ChatGLMModel.token_bytes_to_string(token)] = rank
+            if len(token) == 1:
+                continue
+            merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank)
+            assert len(merged) >= 2 and len(merged) <= 7
+            merges.append(' '.join(map(ChatGLMModel.token_bytes_to_string, merged)))
+
+        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
+        added_vocab = tokenizer.get_added_vocab()
+        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            elif reverse_vocab[i] in added_vocab:
+                tokens.append(reverse_vocab[i])
+                if tokenizer.added_tokens_decoder[i].special:
+                    toktypes.append(gguf.TokenType.CONTROL)
+                else:
+                    toktypes.append(gguf.TokenType.USER_DEFINED)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
+        special_vocab.merges = merges
+        # only add special tokens when they were not already loaded from config.json
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
+        # this one is usually not in config.json anyway
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
+        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
+        n_head_kv = self.hparams.get("multi_query_group_num", n_head)
+        self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
+        self.gguf_writer.add_embedding_length(n_embed)
+        self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", 4 * n_embed))
+        self.gguf_writer.add_block_count(self.hparams["num_layers"])
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head_kv)
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_rope_dimension_count(64)
+        self.gguf_writer.add_add_bos_token(False)
+        rope_freq = 10000
+        if "rope_ratio" in self.hparams:
+            rope_freq = rope_freq * self.hparams["rope_ratio"]
+        self.gguf_writer.add_rope_freq_base(rope_freq)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        if name.endswith(".rotary_pos_emb.inv_freq"):
+            return []
+
+        name = name.removeprefix("transformer.")
+        return [(self.map_tensor_name(name), data_torch)]
+
+###### CONVERSION LOGIC ######
+
+
+# tree of lazy tensors
+class LazyTorchTensor(gguf.LazyBase):
+    _tensor_type = torch.Tensor
+    # to keep the type-checker happy
+    dtype: torch.dtype
+    shape: torch.Size
+
+    # only used when converting a torch.Tensor to a np.ndarray
+    _dtype_map: dict[torch.dtype, type] = {
+        torch.float16: np.float16,
+        torch.float32: np.float32,
+    }
+
+    # used for safetensors slices
+    # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
+    # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
+    _dtype_str_map: dict[str, torch.dtype] = {
+        "F64": torch.float64,
+        "F32": torch.float32,
+        "BF16": torch.bfloat16,
+        "F16": torch.float16,
+        # "U64": torch.uint64,
+        "I64": torch.int64,
+        # "U32": torch.uint32,
+        "I32": torch.int32,
+        # "U16": torch.uint16,
+        "I16": torch.int16,
+        "U8": torch.uint8,
+        "I8": torch.int8,
+        "BOOL": torch.bool,
+        "F8_E4M3": torch.float8_e4m3fn,
+        "F8_E5M2": torch.float8_e5m2,
+    }
+
+    def numpy(self) -> gguf.LazyNumpyTensor:
+        dtype = self._dtype_map[self.dtype]
+        return gguf.LazyNumpyTensor(
+            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
+            args=(self,),
+            func=(lambda s: s.numpy())
+        )
+
+    @classmethod
+    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor:
+        return torch.empty(size=shape, dtype=dtype, device="meta")
+
+    @classmethod
+    def from_safetensors_slice(cls, st_slice: Any) -> Tensor:
+        dtype = cls._dtype_str_map[st_slice.get_dtype()]
+        shape: tuple[int, ...] = tuple(st_slice.get_shape())
+        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:])
+        return cast(torch.Tensor, lazy)
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        del types  # unused
+
+        if kwargs is None:
+            kwargs = {}
+
+        if func is torch.Tensor.numpy:
+            return args[0].numpy()
+
+        return cls._wrap_fn(func)(*args, **kwargs)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Convert a huggingface model to a GGML compatible file")
+    parser.add_argument(
+        "--vocab-only", action="store_true",
+        help="extract only the vocab",
+    )
+    parser.add_argument(
+        "--outfile", type=Path,
+        help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
+    )
+    parser.add_argument(
+        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
+        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
+    )
+    parser.add_argument(
+        "--bigendian", action="store_true",
+        help="model is executed on big endian machine",
+    )
+    parser.add_argument(
+        "model", type=Path,
+        help="directory containing model file",
+    )
+    parser.add_argument(
+        "--use-temp-file", action="store_true",
+        help="use the tempfile library while processing (helpful when running out of memory, process killed)",
+    )
+    parser.add_argument(
+        "--no-lazy", action="store_true",
+        help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
+    )
+    parser.add_argument(
+        "--model-name", type=str, default=None,
+        help="name of the model",
+    )
+    parser.add_argument(
+        "--verbose", action="store_true",
+        help="increase output verbosity",
+    )
+    parser.add_argument(
+        "--split-max-tensors", type=int, default=0,
+        help="max tensors in each split",
+    )
+    parser.add_argument(
+        "--split-max-size", type=str, default="0",
+        help="max size per split N(M|G)",
+    )
+    parser.add_argument(
+        "--dry-run", action="store_true",
+        help="only print out a split plan and exit, without writing any new files",
+    )
+    parser.add_argument(
+        "--no-tensor-first-split", action="store_true",
+        help="do not add tensors to the first split (disabled by default)"
+    )
+    parser.add_argument(
+        "--metadata", type=Path,
+        help="Specify the path for an authorship metadata override file"
+    )
+
+    return parser.parse_args()
+
+
+def split_str_to_n_bytes(split_str: str) -> int:
+    if split_str.endswith("K"):
+        n = int(split_str[:-1]) * 1000
+    elif split_str.endswith("M"):
+        n = int(split_str[:-1]) * 1000 * 1000
+    elif split_str.endswith("G"):
+        n = int(split_str[:-1]) * 1000 * 1000 * 1000
+    elif split_str.isnumeric():
+        n = int(split_str)
+    else:
+        raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G")
+
+    if n < 0:
+        raise ValueError(f"Invalid split size: {split_str}, must be positive")
+
+    return n
+
+
+def main() -> None:
+    args = parse_args()
 
-        model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+    if args.verbose:
+        logging.basicConfig(level=logging.DEBUG)
+    else:
+        logging.basicConfig(level=logging.INFO)
+
+    dir_model = args.model
+
+    if not dir_model.is_dir():
+        logger.error(f'Error: {args.model} is not a directory')
+        sys.exit(1)
+
+    ftype_map: dict[str, gguf.LlamaFileType] = {
+        "f32": gguf.LlamaFileType.ALL_F32,
+        "f16": gguf.LlamaFileType.MOSTLY_F16,
+        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
+        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
+        "auto": gguf.LlamaFileType.GUESSED,
+    }
+
+    is_split = args.split_max_tensors > 0 or args.split_max_size != "0"
+    if args.use_temp_file and is_split:
+        logger.error("Error: Cannot use temp file when splitting")
+        sys.exit(1)
+
+    if args.outfile is not None:
+        fname_out = args.outfile
+    else:
+        fname_out = dir_model
+
+    logger.info(f"Loading model: {dir_model.name}")
+
+    hparams = Model.load_hparams(dir_model)
+
+    with torch.inference_mode():
+        output_type = ftype_map[args.outtype]
+        model_architecture = hparams["architectures"][0]
+
+        try:
+            model_class = Model.from_model_architecture(model_architecture)
+        except NotImplementedError:
+            logger.error(f"Model {model_architecture} is not supported")
+            sys.exit(1)
+
+        model_instance = model_class(dir_model=dir_model, ftype=output_type, fname_out=fname_out,
+                                     is_big_endian=args.bigendian, use_temp_file=args.use_temp_file,
+                                     eager=args.no_lazy,
+                                     metadata_override=args.metadata, model_name=args.model_name,
+                                     split_max_tensors=args.split_max_tensors,
+                                     split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
+                                     small_first_shard=args.no_tensor_first_split)
 
         if args.vocab_only:
-            logger.info(f"Exporting model vocab to '{model_instance.fname_out}'")
+            logger.info("Exporting model vocab...")
             model_instance.write_vocab()
+            logger.info(f"Model vocab successfully exported to {model_instance.fname_out}")
         else:
-            logger.info(f"Exporting model to '{model_instance.fname_out}'")
+            logger.info("Exporting model...")
             model_instance.write()
-
-        logger.info(f"Model successfully exported to '{model_instance.fname_out}'")
+            out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out
+            logger.info(f"Model successfully exported to {out_path}")
 
 
 if __name__ == '__main__':
diff --git a/convert-hf-to-gguf-update.py b/convert_hf_to_gguf_update.py
similarity index 74%
rename from convert-hf-to-gguf-update.py
rename to convert_hf_to_gguf_update.py
index 45404b32b75ae..ba98f5c88990e 100755
--- a/convert-hf-to-gguf-update.py
+++ b/convert_hf_to_gguf_update.py
@@ -1,7 +1,8 @@
 #!/usr/bin/env python3
+# -*- coding: utf-8 -*-
 
 # This script downloads the tokenizer models of the specified models from Huggingface and
-# generates the get_vocab_base_pre() function for convert-hf-to-gguf.py
+# generates the get_vocab_base_pre() function for convert_hf_to_gguf.py
 #
 # This is necessary in order to analyze the type of pre-tokenizer used by the model and
 # provide the necessary information to llama.cpp via the GGUF header in order to implement
@@ -14,9 +15,9 @@
 # - Add a new model to the "models" list
 # - Run the script with your huggingface token:
 #
-#   python3 convert-hf-to-gguf-update.py <huggingface_token>
+#   python3 convert_hf_to_gguf_update.py <huggingface_token>
 #
-# - Copy-paste the generated get_vocab_base_pre() function into convert-hf-to-gguf.py
+# - Copy-paste the generated get_vocab_base_pre() function into convert_hf_to_gguf.py
 # - Update llama.cpp with the new pre-tokenizer if necessary
 #
 # TODO: generate tokenizer tests for llama.cpp
@@ -36,7 +37,7 @@
 from transformers import AutoTokenizer
 
 logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger("convert-hf-to-gguf-update")
+logger = logging.getLogger("convert_hf_to_gguf_update")
 sess = requests.Session()
 
 
@@ -44,20 +45,21 @@ class TOKENIZER_TYPE(IntEnum):
     SPM = auto()
     BPE = auto()
     WPM = auto()
+    UGM = auto()
 
 
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
 #       will be updated with time - contributions welcome
-chktxt = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
+CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
 
 if len(sys.argv) == 2:
     token = sys.argv[1]
     if not token.startswith("hf_"):
         logger.info("Huggingface token seems invalid")
-        logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
+        logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
         sys.exit(1)
 else:
-    logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
+    logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
     sys.exit(1)
 
 # TODO: add models here, base models preferred
@@ -72,7 +74,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
     {"name": "gpt-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
-    {"name": "stablelm",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
+    {"name": "stablelm2",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
     {"name": "refact",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
     {"name": "command-r",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
     {"name": "qwen2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
@@ -81,6 +83,19 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "jina-v2-en",     "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
     {"name": "jina-v2-es",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
     {"name": "jina-v2-de",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
+    {"name": "smaug-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
+    {"name": "poro-chat",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
+    {"name": "jina-v2-code",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
+    {"name": "viking",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
+    {"name": "gemma",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
+    {"name": "gemma-2",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
+    {"name": "jais",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
+    {"name": "t5",             "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
+    {"name": "codeshell",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
+    {"name": "tekken",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
+    {"name": "smollm",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
+    {'name': "bloom",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
+    {'name': "gpt3-finnish",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
 ]
 
 
@@ -89,8 +104,8 @@ def download_file_with_auth(url, token, save_path):
     response = sess.get(url, headers=headers)
     response.raise_for_status()
     os.makedirs(os.path.dirname(save_path), exist_ok=True)
-    with open(save_path, 'wb') as f:
-        f.write(response.content)
+    with open(save_path, 'wb') as downloaded_file:
+        downloaded_file.write(response.content)
     logger.info(f"File {save_path} downloaded successfully")
 
 
@@ -102,9 +117,13 @@ def download_model(model):
     os.makedirs(f"models/tokenizers/{name}", exist_ok=True)
 
     files = ["config.json", "tokenizer.json", "tokenizer_config.json"]
+
     if tokt == TOKENIZER_TYPE.SPM:
         files.append("tokenizer.model")
 
+    if tokt == TOKENIZER_TYPE.UGM:
+        files.append("spiece.model")
+
     for file in files:
         save_path = f"models/tokenizers/{name}/{file}"
         if os.path.isfile(save_path):
@@ -120,14 +139,14 @@ def download_model(model):
         logger.error(f"Failed to download model {model['name']}. Error: {e}")
 
 
-# generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function:
+# generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:
 
 src_ifs = ""
 for model in models:
     name = model["name"]
     tokt = model["tokt"]
 
-    if tokt == TOKENIZER_TYPE.SPM:
+    if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
         continue
 
     # Skip if the tokenizer folder does not exist or there are other download issues previously
@@ -137,12 +156,15 @@ def download_model(model):
 
     # create the tokenizer
     try:
-        tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+        if name == "t5":
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
     except OSError as e:
         logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
         continue  # Skip to the next model if the tokenizer can't be loaded
 
-    chktok = tokenizer.encode(chktxt)
+    chktok = tokenizer.encode(CHK_TXT)
     chkhsh = sha256(str(chktok).encode()).hexdigest()
 
     logger.info(f"model: {name}")
@@ -174,7 +196,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
         # use in llama.cpp to implement the same pre-tokenizer
 
-        chktxt = {repr(chktxt)}
+        chktxt = {repr(CHK_TXT)}
 
         chktok = tokenizer.encode(chktxt)
         chkhsh = sha256(str(chktok).encode()).hexdigest()
@@ -184,7 +206,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 
         res = None
 
-        # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
+        # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
         #       or pull the latest version of the model from Huggingface
         #       don't edit the hashes manually!
 {src_ifs}
@@ -193,9 +215,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
             logger.warning("**************************************************************************************")
             logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
             logger.warning("**          There are 2 possible reasons for this:")
-            logger.warning("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
+            logger.warning("**          - the model has not been added to convert_hf_to_gguf_update.py yet")
             logger.warning("**          - the pre-tokenization config has changed upstream")
-            logger.warning("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
+            logger.warning("**          Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
             logger.warning("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
             logger.warning("**")
             logger.warning(f"** chkhsh:  {{chkhsh}}")
@@ -209,8 +231,8 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         return res
 """
 
-convert_py_pth = pathlib.Path("convert-hf-to-gguf.py")
-convert_py = convert_py_pth.read_text()
+convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
+convert_py = convert_py_pth.read_text(encoding="utf-8")
 convert_py = re.sub(
     r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)",
     lambda m: m.group(1) + src_func + m.group(3),
@@ -218,9 +240,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
     flags=re.DOTALL | re.MULTILINE,
 )
 
-convert_py_pth.write_text(convert_py)
+convert_py_pth.write_text(convert_py, encoding="utf-8")
 
-logger.info("+++ convert-hf-to-gguf.py was updated")
+logger.info("+++ convert_hf_to_gguf.py was updated")
 
 # generate tests for each tokenizer model
 
@@ -258,6 +280,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
     "\n =",
     "' era",
     "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
+    "!!!!!!",
     "3",
     "33",
     "333",
@@ -267,8 +290,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
     "3333333",
     "33333333",
     "333333333",
-    # "Cửa Việt", # llama-bpe fails on this
-    chktxt,
+    "Cửa Việt", # llama-bpe fails on this
+    " discards",
+    CHK_TXT,
 ]
 
 # write the tests to ./models/ggml-vocab-{name}.gguf.inp
@@ -295,7 +319,10 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 
     # create the tokenizer
     try:
-        tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+        if name == "t5":
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
     except OSError as e:
         logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
         continue  # Skip this model and continue with the next one in the loop
@@ -321,6 +348,6 @@ def get_vocab_base_pre(self, tokenizer) -> str:
 for model in models:
     name = model["name"]
 
-    print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
+    print(f"python3 convert_hf_to_gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
 
 logger.info("\n")
diff --git a/convert-llama-ggml-to-gguf.py b/convert_llama_ggml_to_gguf.py
similarity index 99%
rename from convert-llama-ggml-to-gguf.py
rename to convert_llama_ggml_to_gguf.py
index 9349de3b3b498..7b00b4398178b 100755
--- a/convert-llama-ggml-to-gguf.py
+++ b/convert_llama_ggml_to_gguf.py
@@ -132,6 +132,10 @@ def load(self, data, offset):
 
 
 class GGMLModel:
+
+    file_format: GGMLFormat
+    format_version: int
+
     def __init__(self):
         self.hyperparameters = None
         self.vocab = None
@@ -290,7 +294,7 @@ def add_vocab(self, gguf_writer):
         if self.vocab_override is not None:
             vo = self.vocab_override
             logger.info('* Adding vocab item(s)')
-            for (idx, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
+            for (_, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
                 tokens.append(vbytes)
                 scores.append(score)
                 toktypes.append(ttype)
@@ -354,7 +358,8 @@ def add_tensors(self, gguf_writer):
 
 
 def handle_metadata(cfg, hp):
-    import convert
+    import examples.convert_legacy_llama as convert
+
     assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory'
     hf_config_path   = cfg.model_metadata_dir / "config.json"
     orig_config_path = cfg.model_metadata_dir / "params.json"
diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
new file mode 100755
index 0000000000000..a88d0d4a978a9
--- /dev/null
+++ b/convert_lora_to_gguf.py
@@ -0,0 +1,393 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import logging
+import argparse
+import os
+import sys
+import json
+from math import prod
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+import gguf
+
+# reuse model definitions from convert_hf_to_gguf.py
+from convert_hf_to_gguf import LazyTorchTensor, Model
+
+logger = logging.getLogger("lora-to-gguf")
+
+
+@dataclass
+class PartialLoraTensor:
+    A: Tensor | None = None
+    B: Tensor | None = None
+
+
+# magic to support tensor shape modifications and splitting
+class LoraTorchTensor:
+    _lora_A: Tensor  # (n_rank, row_size)
+    _lora_B: Tensor  # (col_size, n_rank)
+    _rank: int
+
+    def __init__(self, A: Tensor, B: Tensor):
+        assert len(A.shape) == len(B.shape)
+        assert A.shape[-2] == B.shape[-1]
+        if A.dtype != B.dtype:
+            A = A.to(torch.float32)
+            B = B.to(torch.float32)
+        self._lora_A = A
+        self._lora_B = B
+        self._rank = B.shape[-1]
+
+    def get_lora_A_B(self) -> tuple[Tensor, Tensor]:
+        return (self._lora_A, self._lora_B)
+
+    def __getitem__(
+        self,
+        indices: (
+            SupportsIndex
+            | slice
+            | tuple[SupportsIndex | slice | Tensor, ...]  # TODO: add ellipsis in the type signature
+        ),
+    ) -> LoraTorchTensor:
+        shape = self.shape
+        if isinstance(indices, SupportsIndex):
+            if len(shape) > 2:
+                return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
+            else:
+                raise NotImplementedError  # can't return a vector
+        elif isinstance(indices, slice):
+            if len(shape) > 2:
+                return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
+            else:
+                return LoraTorchTensor(self._lora_A, self._lora_B[indices])
+        elif isinstance(indices, tuple):
+            assert len(indices) > 0
+            if indices[-1] is Ellipsis:
+                return self[indices[:-1]]
+            # expand ellipsis
+            indices = tuple(
+                u
+                for v in (
+                    (
+                        (slice(None, None) for _ in range(len(indices) - 1))
+                        if i is Ellipsis
+                        else (i,)
+                    )
+                    for i in indices
+                )
+                for u in v
+            )
+
+            if len(indices) < len(shape):
+                indices = (*indices, *(slice(None, None) for _ in range(len(indices), len(shape))))
+
+            # TODO: make sure this is correct
+            indices_A = (
+                *(
+                    (
+                        j.__index__() % self._lora_A.shape[i]
+                        if isinstance(j, SupportsIndex)
+                        else slice(None, None)
+                    )
+                    for i, j in enumerate(indices[:-2])
+                ),
+                slice(None, None),
+                indices[-1],
+            )
+            indices_B = indices[:-1]
+            return LoraTorchTensor(self._lora_A[indices_A], self._lora_B[indices_B])
+        else:
+            raise NotImplementedError  # unknown indice type
+
+    @property
+    def dtype(self) -> torch.dtype:
+        assert self._lora_A.dtype == self._lora_B.dtype
+        return self._lora_A.dtype
+
+    @property
+    def shape(self) -> tuple[int, ...]:
+        assert len(self._lora_A.shape) == len(self._lora_B.shape)
+        return (*self._lora_B.shape[:-1], self._lora_A.shape[-1])
+
+    def size(self, dim=None):
+        assert dim is None
+        return self.shape
+
+    def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor:
+        if isinstance(shape[0], tuple):
+            new_shape: tuple[int, ...] = shape[0]
+        else:
+            new_shape = cast(tuple[int, ...], shape)
+        orig_shape = self.shape
+        if len(new_shape) < 2:
+            raise NotImplementedError  # can't become a vector
+
+        # expand -1 in the shape
+        if any(dim == -1 for dim in new_shape):
+            n_elems = prod(orig_shape)
+            n_new_elems = prod(dim if dim != -1 else 1 for dim in new_shape)
+            assert n_elems % n_new_elems == 0
+            new_shape = (*(dim if dim != -1 else n_elems // n_new_elems for dim in new_shape),)
+
+        if new_shape[-1] != orig_shape[-1]:
+            raise NotImplementedError  # can't reshape the row size trivially
+
+        shape_A = (*(1 for _ in new_shape[:-2]), self._rank, orig_shape[-1])
+        shape_B = (*new_shape[:-1], self._rank)
+        return LoraTorchTensor(
+            self._lora_A.reshape(shape_A),
+            self._lora_B.reshape(shape_B),
+        )
+
+    def reshape_as(self, other: Tensor) -> LoraTorchTensor:
+        return self.reshape(*other.shape)
+
+    def view(self, *size: int) -> LoraTorchTensor:
+        return self.reshape(*size)
+
+    def permute(self, *dims: int) -> LoraTorchTensor:
+        shape = self.shape
+        dims = tuple(dim - len(shape) if dim >= 0 else dim for dim in dims)
+        if dims[-1] == -1:
+            # TODO: support higher dimensional A shapes bigger than 1
+            assert all(dim == 1 for dim in self._lora_A.shape[:-2])
+            return LoraTorchTensor(self._lora_A, self._lora_B.permute(*dims))
+        if len(shape) == 2 and dims[-1] == -2 and dims[-2] == -1:
+            return LoraTorchTensor(self._lora_B.permute(*dims), self._lora_A.permute(*dims))
+        else:
+            # TODO: compose the above two
+            raise NotImplementedError
+
+    def transpose(self, dim0: int, dim1: int) -> LoraTorchTensor:
+        shape = self.shape
+        dims = [i for i in range(len(shape))]
+        dims[dim0], dims[dim1] = dims[dim1], dims[dim0]
+        return self.permute(*dims)
+
+    def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor:
+        return self.transpose(axis0, axis1)
+
+    def to(self, *args, **kwargs):
+        return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs))
+
+    @classmethod
+    def __torch_function__(cls, func: Callable, types, args=(), kwargs=None):
+        del types  # unused
+
+        if kwargs is None:
+            kwargs = {}
+
+        if func is torch.permute:
+            return type(args[0]).permute(*args, **kwargs)
+        elif func is torch.reshape:
+            return type(args[0]).reshape(*args, **kwargs)
+        elif func is torch.stack:
+            assert isinstance(args[0], Sequence)
+            dim = kwargs.get("dim", 0)
+            assert dim == 0
+            return LoraTorchTensor(
+                torch.stack([a._lora_A for a in args[0]], dim),
+                torch.stack([b._lora_B for b in args[0]], dim),
+            )
+        elif func is torch.cat:
+            assert isinstance(args[0], Sequence)
+            dim = kwargs.get("dim", 0)
+            assert dim == 0
+            if len(args[0][0].shape) > 2:
+                return LoraTorchTensor(
+                    torch.cat([a._lora_A for a in args[0]], dim),
+                    torch.cat([b._lora_B for b in args[0]], dim),
+                )
+            elif all(torch.equal(args[0][0]._lora_A, t._lora_A) for t in args[0][1:]):
+                return LoraTorchTensor(
+                    args[0][0]._lora_A,
+                    torch.cat([b._lora_B for b in args[0]], dim),
+                )
+            else:
+                raise NotImplementedError
+        else:
+            raise NotImplementedError
+
+
+def get_base_tensor_name(lora_tensor_name: str) -> str:
+    base_name = lora_tensor_name.replace("base_model.model.", "")
+    base_name = base_name.replace(".lora_A.weight", ".weight")
+    base_name = base_name.replace(".lora_B.weight", ".weight")
+    return base_name
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Convert a huggingface PEFT LoRA adapter to a GGML compatible file")
+    parser.add_argument(
+        "--outfile", type=Path,
+        help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
+    )
+    parser.add_argument(
+        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
+        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
+    )
+    parser.add_argument(
+        "--bigendian", action="store_true",
+        help="model is executed on big endian machine",
+    )
+    parser.add_argument(
+        "--no-lazy", action="store_true",
+        help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
+    )
+    parser.add_argument(
+        "--verbose", action="store_true",
+        help="increase output verbosity",
+    )
+    parser.add_argument(
+        "--dry-run", action="store_true",
+        help="only print out what will be done, without writing any new files",
+    )
+    parser.add_argument(
+        "--base", type=Path, required=True,
+        help="directory containing base model file",
+    )
+    parser.add_argument(
+        "lora_path", type=Path,
+        help="directory containing LoRA adapter file",
+    )
+
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    ftype_map: dict[str, gguf.LlamaFileType] = {
+        "f32": gguf.LlamaFileType.ALL_F32,
+        "f16": gguf.LlamaFileType.MOSTLY_F16,
+        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
+        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
+        "auto": gguf.LlamaFileType.GUESSED,
+    }
+
+    ftype = ftype_map[args.outtype]
+
+    dir_base_model: Path = args.base
+    dir_lora: Path = args.lora_path
+    lora_config = dir_lora / "adapter_config.json"
+    input_model = dir_lora / "adapter_model.safetensors"
+
+    if args.outfile is not None:
+        fname_out = args.outfile
+    else:
+        # output in the same directory as the model by default
+        fname_out = dir_lora
+
+    if os.path.exists(input_model):
+        # lazy import load_file only if lora is in safetensors format.
+        from safetensors.torch import load_file
+
+        lora_model = load_file(input_model, device="cpu")
+    else:
+        input_model = os.path.join(dir_lora, "adapter_model.bin")
+        lora_model = torch.load(input_model, map_location="cpu", weights_only=True)
+
+    # load base model
+    logger.info(f"Loading base model: {dir_base_model.name}")
+    hparams = Model.load_hparams(dir_base_model)
+    with torch.inference_mode():
+        try:
+            model_class = Model.from_model_architecture(hparams["architectures"][0])
+        except NotImplementedError:
+            logger.error(f"Model {hparams['architectures'][0]} is not supported")
+            sys.exit(1)
+
+        class LoraModel(model_class):
+            model_arch = model_class.model_arch
+
+            lora_alpha: float
+
+            def __init__(self, *args, dir_lora_model: Path, lora_alpha: float, **kwargs):
+
+                super().__init__(*args, **kwargs)
+
+                self.dir_model_card = dir_lora_model
+                self.lora_alpha = float(lora_alpha)
+
+            def set_type(self):
+                self.gguf_writer.add_type(gguf.GGUFType.ADAPTER)
+                self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
+
+            def set_gguf_parameters(self):
+                self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
+                super().set_gguf_parameters()
+
+            def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
+                tensor_map: dict[str, PartialLoraTensor] = {}
+
+                for name, tensor in lora_model.items():
+                    if self.lazy:
+                        tensor = LazyTorchTensor.from_eager(tensor)
+                    base_name = get_base_tensor_name(name)
+                    is_lora_a = ".lora_A.weight" in name
+                    is_lora_b = ".lora_B.weight" in name
+                    if not is_lora_a and not is_lora_b:
+                        if ".base_layer.weight" in name:
+                            continue
+                        logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
+                        sys.exit(1)
+
+                    if base_name in tensor_map:
+                        if is_lora_a:
+                            tensor_map[base_name].A = tensor
+                        else:
+                            tensor_map[base_name].B = tensor
+                    else:
+                        if is_lora_a:
+                            tensor_map[base_name] = PartialLoraTensor(A=tensor)
+                        else:
+                            tensor_map[base_name] = PartialLoraTensor(B=tensor)
+
+                for name, tensor in tensor_map.items():
+                    assert tensor.A is not None
+                    assert tensor.B is not None
+                    yield (name, cast(torch.Tensor, LoraTorchTensor(tensor.A, tensor.B)))
+
+            def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+                dest = super().modify_tensors(data_torch, name, bid)
+                for dest_name, dest_data in dest:
+                    assert isinstance(dest_data, LoraTorchTensor)
+                    lora_a, lora_b = dest_data.get_lora_A_B()
+
+                    yield (dest_name + ".lora_a", lora_a)
+                    yield (dest_name + ".lora_b", lora_b)
+
+        with open(lora_config, "r") as f:
+            lparams: dict[str, Any] = json.load(f)
+
+        alpha: float = lparams["lora_alpha"]
+
+        model_instance = LoraModel(
+            dir_base_model,
+            ftype,
+            fname_out,
+            is_big_endian=args.bigendian,
+            use_temp_file=False,
+            eager=args.no_lazy,
+            dry_run=args.dry_run,
+            dir_lora_model=dir_lora,
+            lora_alpha=alpha,
+        )
+
+        logger.info("Exporting model...")
+        model_instance.write()
+        logger.info(f"Model successfully exported to {model_instance.fname_out}")
diff --git a/docs/android.md b/docs/android.md
new file mode 100644
index 0000000000000..cec4358d98a26
--- /dev/null
+++ b/docs/android.md
@@ -0,0 +1,56 @@
+
+# Android
+
+## Build on Android using Termux
+[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required).
+```
+apt update && apt upgrade -y
+apt install git make cmake
+```
+
+It's recommended to move your model inside the `~/` directory for best performance:
+```
+cd storage/downloads
+mv model.gguf ~/
+```
+
+[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
+
+## Building the Project using Android NDK
+Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
+
+Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
+```
+$ mkdir build-android
+$ cd build-android
+$ export NDK=<your_ndk_directory>
+$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
+$ make
+```
+
+Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
+
+Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
+
+(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
+```
+$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
+$cd /data/data/com.termux/files/home/bin
+$chmod +x ./*
+```
+
+Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
+
+```
+$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
+```
+
+Now, you can start chatting:
+```
+$cd /data/data/com.termux/files/home/bin
+$./llama-cli -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
+```
+
+Here's a demo of an interactive session running on Pixel 5 phone:
+
+https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
diff --git a/docs/BLIS.md b/docs/backend/BLIS.md
similarity index 93%
rename from docs/BLIS.md
rename to docs/backend/BLIS.md
index c933766b7f4ec..35d06bd0f303d 100644
--- a/docs/BLIS.md
+++ b/docs/backend/BLIS.md
@@ -30,8 +30,8 @@ We recommend using openmp since it's easier to modify the cores being used.
 Makefile:
 
 ```bash
-make LLAMA_BLIS=1 -j
-# make LLAMA_BLIS=1 benchmark-matmult
+make GGML_BLIS=1 -j
+# make GGML_BLIS=1 llama-benchmark-matmult
 ```
 
 CMake:
@@ -39,7 +39,7 @@ CMake:
 ```bash
 mkdir build
 cd build
-cmake -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=FLAME ..
+cmake -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=FLAME ..
 make -j
 ```
 
diff --git a/README-sycl.md b/docs/backend/SYCL.md
similarity index 69%
rename from README-sycl.md
rename to docs/backend/SYCL.md
index cfa248a95b5ff..59a39fbb67395 100644
--- a/README-sycl.md
+++ b/docs/backend/SYCL.md
@@ -1,6 +1,7 @@
 # llama.cpp for SYCL
 
 - [Background](#background)
+- [Recommended Release](#recommended-release)
 - [News](#news)
 - [OS](#os)
 - [Hardware](#hardware)
@@ -29,10 +30,25 @@ The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based o
 
 When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
 
-It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
+It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
+
+## Recommended Release
+
+The SYCL backend would be broken by some PRs due to no online CI.
+
+The following release is verified with good quality:
+
+|Commit ID|Tag|Release|Verified  Platform|
+|-|-|-|-|
+|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1|
+
 
 ## News
 
+- 2024.5
+  - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770.
+  - Arch Linux is verified successfully.
+
 - 2024.4
   - Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M.
 
@@ -54,30 +70,37 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS,
 
 ## OS
 
-| OS      | Status  | Verified                           |
-|---------|---------|------------------------------------|
-| Linux   | Support | Ubuntu 22.04, Fedora Silverblue 39 |
-| Windows | Support | Windows 11                         |
+| OS      | Status  | Verified                                       |
+|---------|---------|------------------------------------------------|
+| Linux   | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux |
+| Windows | Support | Windows 11                                     |
 
 
 ## Hardware
 
 ### Intel GPU
 
-**Verified devices**
+SYCL backend supports Intel GPU Family:
+
+- Intel Data Center Max Series
+- Intel Flex Series, Arc Series
+- Intel Built-in Arc GPU
+- Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to [oneAPI supported GPU](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html#inpage-nav-1-1)).
+
+#### Verified devices
 
 | Intel GPU                     | Status  | Verified Model                        |
 |-------------------------------|---------|---------------------------------------|
 | Intel Data Center Max Series  | Support | Max 1550, 1100                        |
 | Intel Data Center Flex Series | Support | Flex 170                              |
-| Intel Arc Series              | Support | Arc 770, 730M                         |
+| Intel Arc Series              | Support | Arc 770, 730M, Arc A750               |
 | Intel built-in Arc GPU        | Support | built-in Arc GPU in Meteor Lake       |
-| Intel iGPU                    | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |
+| Intel iGPU                    | Support | iGPU in 13700k, i5-1250P, i7-1260P, i7-1165G7 |
 
 *Notes:*
 
 - **Memory**
-  - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/main`.
+  - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`.
 
   - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.
 
@@ -99,14 +122,14 @@ The docker build option is currently limited to *intel GPU* targets.
 ### Build image
 ```sh
 # Using FP16
-docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
+docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile .
 ```
 
 *Notes*:
 
-To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="LLAMA_SYCL_F16=ON"` argument from the previous command.
+To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="GGML_SYCL_F16=ON"` argument from the previous command.
 
-You can also use the `.devops/server-intel.Dockerfile`, which builds the *"server"* alternative.
+You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
 
 ### Run container
 
@@ -221,6 +244,13 @@ Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA devic
 ### II. Build llama.cpp
 
 #### Intel GPU
+
+```
+./examples/sycl/build.sh
+```
+
+or
+
 ```sh
 # Export relevant ENV variables
 source /opt/intel/oneapi/setvars.sh
@@ -228,10 +258,10 @@ source /opt/intel/oneapi/setvars.sh
 # Build LLAMA with MKL BLAS acceleration for intel GPU
 
 # Option 1: Use FP32 (recommended for better performance in most cases)
-cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
 # Option 2: Use FP16
-cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
 
 # build all binary
 cmake --build build --config Release -j -v
@@ -248,10 +278,10 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
 # Build LLAMA with Nvidia BLAS acceleration through SYCL
 
 # Option 1: Use FP32 (recommended for better performance in most cases)
-cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
 # Option 2: Use FP16
-cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
 
 # build all binary
 cmake --build build --config Release -j -v
@@ -260,48 +290,71 @@ cmake --build build --config Release -j -v
 
 ### III. Run the inference
 
-1. Retrieve and prepare model
+#### Retrieve and prepare model
 
 You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
 
-2. Enable oneAPI running environment
+##### Check device
+
+1. Enable oneAPI running environment
 
 ```sh
 source /opt/intel/oneapi/setvars.sh
 ```
 
-3. List devices information
+2. List devices information
 
 Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
 
 ```sh
-./build/bin/ls-sycl-device
+./build/bin/llama-ls-sycl-device
 ```
-A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following:
+
+This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
 ```
-found 6 SYCL devices:
+found 2 SYCL devices:
+
 |  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
 |ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
 |--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
 | 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
 | 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
-| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
-| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
-| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
-| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
 ```
 
-| Attribute              | Note                                                        |
-|------------------------|-------------------------------------------------------------|
-| compute capability 1.3 | Level-zero driver/runtime, recommended                      |
-| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases |
+#### Choose level-zero devices
+
+|Chosen Device ID|Setting|
+|-|-|
+|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
+|1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
+|0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
+
+#### Execute
 
-4. Launch inference
+Choose one of following methods to run.
+
+1. Script
+
+- Use device 0:
+
+```sh
+./examples/sycl/run_llama2.sh 0
+```
+- Use multiple devices:
+
+```sh
+./examples/sycl/run_llama2.sh
+```
+
+2. Command line
+Launch inference
 
 There are two device selection modes:
 
-- Single device: Use one device target specified by the user.
-- Multiple devices: Automatically select the devices with the same largest Max compute-units.
+- Single device: Use one device assigned by user. Default device id is 0.
+- Multiple devices: Automatically choose the devices with the same backend.
+
+In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.
 
 | Device selection | Parameter                              |
 |------------------|----------------------------------------|
@@ -313,24 +366,13 @@ Examples:
 - Use device 0:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
-```
-or run by script:
-
-```sh
-./examples/sycl/run_llama2.sh 0
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
 ```
 
 - Use multiple devices:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
-```
-
-Otherwise, you can run the script:
-
-```sh
-./examples/sycl/run_llama2.sh
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
 ```
 
 *Notes:*
@@ -379,7 +421,7 @@ c. Verify installation
 In the oneAPI command line, run the following to print the available SYCL devices:
 
 ```
-sycl-ls
+sycl-ls.exe
 ```
 
 There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device:
@@ -394,17 +436,23 @@ Output (example):
 
 4. Install build tools
 
-a. Download & install cmake for Windows: https://cmake.org/download/
+a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer)
+b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/)
+
 
-b. Download & install mingw-w64 make for Windows provided by w64devkit
+### II. Build llama.cpp
 
-- Download the 1.19.0 version of [w64devkit](https://github.com/skeeto/w64devkit/releases/download/v1.19.0/w64devkit-1.19.0.zip).
+You could download the release package for Windows directly, which including binary files and depended oneAPI dll files.
 
-- Extract `w64devkit` on your pc.
+Choose one of following methods to build from source code.
 
-- Add the **bin** folder path in the Windows system PATH environment (for e.g. `C:\xxx\w64devkit\bin\`).
+1. Script
 
-### II. Build llama.cpp
+```sh
+.\examples\sycl\win-build-sycl.bat
+```
+
+2. CMake
 
 On the oneAPI command line window, step into the llama.cpp main directory and run the following:
 
@@ -412,71 +460,96 @@ On the oneAPI command line window, step into the llama.cpp main directory and ru
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
 # Option 1: Use FP32 (recommended for better performance in most cases)
-cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
+cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
 
 # Option 2: Or FP16
-cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
 
 cmake --build build --config Release -j
 ```
 
-Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
+Or, use CMake presets to build:
+
 ```sh
-.\examples\sycl\win-build-sycl.bat
+cmake --preset x64-windows-sycl-release
+cmake --build build-x64-windows-sycl-release -j --target llama-cli
+
+cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release
+cmake --build build-x64-windows-sycl-release -j --target llama-cli
+
+cmake --preset x64-windows-sycl-debug
+cmake --build build-x64-windows-sycl-debug -j --target llama-cli
 ```
 
+3. Visual Studio
+
+You can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project.
+
 *Notes:*
 
-- By default, calling `make` will build all target binary files. In case of a minimal experimental setup, the user can build the inference executable only through `make main`.
+- In case of a minimal experimental setup, the user can build the inference executable only through `cmake --build build --config Release -j --target llama-cli`.
 
 ### III. Run the inference
 
-1. Retrieve and prepare model
+#### Retrieve and prepare model
+
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
 
-You can refer to the general [*Prepare and Quantize*](README#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+##### Check device
 
-2. Enable oneAPI running environment
+1. Enable oneAPI running environment
 
 On the oneAPI command line window, run the following and step into the llama.cpp directory:
 ```
 "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
 ```
 
-3. List devices information
+2. List devices information
 
 Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
 
 ```
-build\bin\ls-sycl-device.exe
+build\bin\llama-ls-sycl-device.exe
 ```
 
-The output of this command in a system with 1 *intel CPU* and 1 *intel GPU* would look like the following:
+This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
 ```
-found 6 SYCL devices:
+found 2 SYCL devices:
 |  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
 |ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
 |--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
 | 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
 | 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
-| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
-| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
-| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
-| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
 
 ```
+#### Choose level-zero devices
+
+|Chosen Device ID|Setting|
+|-|-|
+|0|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
+|1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
+|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
+
+#### Execute
 
-| Attribute              | Note                                                      |
-|------------------------|-----------------------------------------------------------|
-| compute capability 1.3 | Level-zero running time, recommended                      |
-| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases |
+Choose one of following methods to run.
+
+1. Script
+
+```
+examples\sycl\win-run-llama2.bat
+```
 
+2. Command line
 
-4. Launch inference
+Launch inference
 
 There are two device selection modes:
 
-- Single device: Use one device assigned by user.
-- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
+- Single device: Use one device assigned by user. Default device id is 0.
+- Multiple devices: Automatically choose the devices with the same backend.
+
+In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.
 
 | Device selection | Parameter                              |
 |------------------|----------------------------------------|
@@ -488,19 +561,15 @@ Examples:
 - Use device 0:
 
 ```
-build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
+build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
 ```
 
 - Use multiple devices:
 
 ```
-build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
+build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
 ```
-Otherwise, run the following wrapper script:
 
-```
-.\examples\sycl\win-run-llama2.bat
-```
 
 Note:
 
@@ -514,17 +583,18 @@ Or
 use 1 SYCL GPUs: [0] with Max compute units:512
 ```
 
+
 ## Environment Variable
 
 #### Build
 
 | Name               | Value                             | Function                                    |
 |--------------------|-----------------------------------|---------------------------------------------|
-| LLAMA_SYCL         | ON (mandatory)                    | Enable build with SYCL code path.           |
-| LLAMA_SYCL_TARGET  | INTEL *(default)* \| NVIDIA       | Set the SYCL target device type.            |
-| LLAMA_SYCL_F16     | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path.      |
-| CMAKE_C_COMPILER   | icx                               | Set *icx* compiler for SYCL code path.      |
-| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
+| GGML_SYCL          | ON (mandatory)                    | Enable build with SYCL code path.<br>FP32 path - recommended for better perforemance than FP16 on quantized model|
+| GGML_SYCL_TARGET   | INTEL *(default)* \| NVIDIA       | Set the SYCL target device type.            |
+| GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path.      |
+| CMAKE_C_COMPILER   | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path.      |
+| CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
 
 #### Runtime
 
@@ -560,9 +630,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512
   ```
   Otherwise, please double-check the GPU driver installation steps.
 
+- Can I report Ollama issue on Intel GPU to llama.cpp SYCL backend?
+
+  No. We can't support Ollama issue directly, because we aren't familiar with Ollama.
+
+  Sugguest reproducing on llama.cpp and report similar issue to llama.cpp. We will surpport it.
+
+  It's same for other projects including llama.cpp SYCL backend.
+
+
 ### **GitHub contribution**:
 Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
 
 ## TODO
 
-- Support row layer split for multiple card runs.
+- NA
diff --git a/docs/build.md b/docs/build.md
new file mode 100644
index 0000000000000..8b16d1a358518
--- /dev/null
+++ b/docs/build.md
@@ -0,0 +1,357 @@
+# Build llama.cpp locally
+
+**To get the Code:**
+
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+```
+
+In order to build llama.cpp you have four different options.
+
+- Using `make`:
+  - On Linux or MacOS:
+
+      ```bash
+      make
+      ```
+
+  - On Windows (x86/x64 only, arm64 requires cmake):
+
+    1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+    2. Extract `w64devkit` on your pc.
+    3. Run `w64devkit.exe`.
+    4. Use the `cd` command to reach the `llama.cpp` folder.
+    5. From here you can run:
+        ```bash
+        make
+        ```
+
+  - Notes:
+    - For `Q4_0_4_4` quantization type build, add the `GGML_NO_LLAMAFILE=1` flag. For example, use `make GGML_NO_LLAMAFILE=1`.
+    - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
+    - For faster repeated compilation, install [ccache](https://ccache.dev/).
+    - For debug builds, run `make LLAMA_DEBUG=1`
+
+- Using `CMake`:
+
+  ```bash
+  cmake -B build
+  cmake --build build --config Release
+  ```
+
+  **Notes**:
+
+    - For `Q4_0_4_4` quantization type build, add the `-DGGML_LLAMAFILE=OFF` cmake option. For example, use `cmake -B build -DGGML_LLAMAFILE=OFF`.
+    - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
+    - For faster repeated compilation, install [ccache](https://ccache.dev/).
+    - For debug builds, there are two cases:
+
+      1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
+
+      ```bash
+      cmake -B build -DCMAKE_BUILD_TYPE=Debug
+      cmake --build build
+      ```
+
+      2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
+
+      ```bash
+      cmake -B build -G "Xcode"
+      cmake --build build --config Debug
+      ```
+    - Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers:
+      - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/de/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...):
+        - Tab Workload: Desktop-development with C++
+        - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang)
+      - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test
+      - For Windows on ARM (arm64, WoA) build with:
+        ```bash
+        cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
+        cmake --build build-arm64-windows-llvm-release
+        ```
+        Note: Building for arm64 could also be done just with MSVC (with the build-arm64-windows-MSVC preset, or the standard CMake build instructions). But MSVC does not support inline ARM assembly-code, used e.g. for the accelerated Q4_0_4_8 CPU kernels.
+
+-   Using `gmake` (FreeBSD):
+
+    1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
+    2. Add your user to **video** group
+    3. Install compilation dependencies.
+
+        ```bash
+        sudo pkg install gmake automake autoconf pkgconf llvm15 openblas
+
+        gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
+        ```
+
+## Metal Build
+
+On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
+To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option.
+
+When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
+argument.
+
+## BLAS Build
+
+Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use:
+
+### Accelerate Framework:
+
+This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
+
+### OpenBLAS:
+
+This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
+
+- Using `make`:
+  - On Linux:
+    ```bash
+    make GGML_OPENBLAS=1
+    ```
+
+  - On Windows:
+
+    1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+    2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
+    3. Extract `w64devkit` on your pc.
+    4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
+    5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
+    6. Run `w64devkit.exe`.
+    7. Use the `cd` command to reach the `llama.cpp` folder.
+    8. From here you can run:
+
+        ```bash
+        make GGML_OPENBLAS=1
+        ```
+
+- Using `CMake` on Linux:
+
+    ```bash
+    cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
+    cmake --build build --config Release
+    ```
+
+### BLIS
+
+Check [BLIS.md](./backend/BLIS.md) for more information.
+
+### SYCL
+
+SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
+
+llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
+
+For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
+
+### Intel oneMKL
+
+Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
+
+- Using manual oneAPI installation:
+  By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
+    ```bash
+    source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
+    cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON
+    cmake --build build --config Release
+    ```
+
+- Using oneAPI docker image:
+  If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
+
+Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
+
+### CUDA
+
+This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
+
+For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
+
+- Using `make`:
+  ```bash
+  make GGML_CUDA=1
+  ```
+- Using `CMake`:
+
+  ```bash
+  cmake -B build -DGGML_CUDA=ON
+  cmake --build build --config Release
+  ```
+
+The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
+
+The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
+
+The following compilation options are also available to tweak performance:
+
+| Option                        | Legal values           | Default | Description                                                                                                                                                                                                                                                                             |
+|-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| GGML_CUDA_FORCE_DMMV          | Boolean                | false   | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
+| GGML_CUDA_DMMV_X              | Positive integer >= 32 | 32      | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants.                                         |
+| GGML_CUDA_MMV_Y               | Positive integer       | 1       | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended.                                                                                                                                         |
+| GGML_CUDA_FORCE_MMQ           | Boolean                | false   | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower.                       |
+| GGML_CUDA_FORCE_CUBLAS        | Boolean                | false   | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models                                                                                                                                                                                       |
+| GGML_CUDA_F16                 | Boolean                | false   | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs.                                                           |
+| GGML_CUDA_KQUANTS_ITER        | 1 or 2                 | 2       | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.                                                                                                                     |
+| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                         |
+| GGML_CUDA_FA_ALL_QUANTS       | Boolean                | false   | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer.                                                                                                  |
+
+### MUSA
+
+- Using `make`:
+  ```bash
+  make GGML_MUSA=1
+  ```
+- Using `CMake`:
+
+  ```bash
+  cmake -B build -DGGML_MUSA=ON
+  cmake --build build --config Release
+  ```
+
+### hipBLAS
+
+This provides BLAS acceleration on HIP-supported AMD GPUs.
+Make sure to have ROCm installed.
+You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
+
+- Using `make`:
+  ```bash
+  make GGML_HIPBLAS=1
+  ```
+- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
+  ```bash
+  HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
+      cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+      && cmake --build build --config Release -- -j 16
+  ```
+  On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
+  However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
+
+  Note that if you get the following error:
+  ```
+  clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library
+  ```
+  Try searching for a directory under `HIP_PATH` that contains the file
+  `oclc_abi_version_400.bc`. Then, add the following to the start of the
+  command: `HIP_DEVICE_LIB_PATH=<directory-you-just-found>`, so something
+  like:
+  ```bash
+  HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
+  HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
+      cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+      && cmake --build build -- -j 16
+  ```
+
+- Using `make` (example for target gfx1030, build with 16 CPU threads):
+  ```bash
+  make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
+  ```
+
+- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
+  ```bash
+  set PATH=%HIP_PATH%\bin;%PATH%
+  cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
+  cmake --build build
+  ```
+  Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
+  Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`.
+
+
+The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
+If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
+The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
+
+| Option                 | Legal values           | Default | Description                                                                                                                                                                                                                                    |
+|------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| GGML_CUDA_DMMV_X       | Positive integer >= 32 | 32      | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
+| GGML_CUDA_MMV_Y        | Positive integer       | 1       | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants.                                                                       |
+| GGML_CUDA_KQUANTS_ITER | 1 or 2                 | 2       | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.                                                                             |
+
+### Vulkan
+
+**Windows**
+
+#### w64devkit
+
+Download and extract [w64devkit](https://github.com/skeeto/w64devkit/releases).
+
+Download and install the [Vulkan SDK](https://vulkan.lunarg.com/sdk/home#windows). When selecting components, only the Vulkan SDK Core is required.
+
+Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies:
+```sh
+SDK_VERSION=1.3.283.0
+cp /VulkanSDK/$SDK_VERSION/Bin/glslc.exe $W64DEVKIT_HOME/bin/
+cp /VulkanSDK/$SDK_VERSION/Lib/vulkan-1.lib $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/
+cp -r /VulkanSDK/$SDK_VERSION/Include/* $W64DEVKIT_HOME/x86_64-w64-mingw32/include/
+cat > $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/pkgconfig/vulkan.pc <<EOF
+Name: Vulkan-Loader
+Description: Vulkan Loader
+Version: $SDK_VERSION
+Libs: -lvulkan-1
+EOF
+
+```
+Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`.
+
+#### MSYS2
+Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies.
+  ```sh
+  pacman -S git \
+      mingw-w64-ucrt-x86_64-gcc \
+      mingw-w64-ucrt-x86_64-cmake \
+      mingw-w64-ucrt-x86_64-vulkan-devel \
+      mingw-w64-ucrt-x86_64-shaderc
+  ```
+Switch into `llama.cpp` directory and build using CMake.
+```sh
+cmake -B build -DGGML_VULKAN=ON
+cmake --build build --config Release
+```
+
+**With docker**:
+
+You don't need to install Vulkan SDK. It will be installed inside the container.
+
+```sh
+# Build the image
+docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile .
+
+# Then, use it:
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+```
+
+**Without docker**:
+
+Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
+
+For example, on Ubuntu 22.04 (jammy), use the command below:
+
+```bash
+wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
+wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+apt update -y
+apt-get install -y vulkan-sdk
+# To verify the installation, use the command below:
+vulkaninfo
+```
+
+Alternatively your package manager might be able to provide the appropriate libraries.
+For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages.
+
+Then, build llama.cpp using the cmake command below:
+
+```bash
+cmake -B build -DGGML_VULKAN=1
+cmake --build build --config Release
+# Test the output binary (with "-ngl 33" to offload all layers to GPU)
+./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
+
+# You should see in the output, ggml_vulkan detected your GPU. For example:
+# ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
+```
+
+### Android
+
+To read documentation for how to build on Android, [click here](./android.md)
diff --git a/docs/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md
similarity index 88%
rename from docs/HOWTO-add-model.md
rename to docs/development/HOWTO-add-model.md
index 48769cdf61092..04c5ccbbe60c3 100644
--- a/docs/HOWTO-add-model.md
+++ b/docs/development/HOWTO-add-model.md
@@ -1,4 +1,4 @@
-## Add a new model architecture to `llama.cpp`
+# Add a new model architecture to `llama.cpp`
 
 Adding a model requires few steps:
 
@@ -9,15 +9,15 @@ Adding a model requires few steps:
 After following these steps, you can open PR.
 
 Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
-- [main](../examples/main)
-- [imatrix](../examples/imatrix)
-- [quantize](../examples/quantize)
-- [server](../examples/server)
+- [main](/examples/main/)
+- [imatrix](/examples/imatrix/)
+- [quantize](/examples/quantize/)
+- [server](/examples/server/)
 
 ### 1. Convert the model to GGUF
 
 This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
-Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py).
+Depending on the model architecture, you can use either [convert_hf_to_gguf.py](/convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](/examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format).
 
 The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
 
@@ -31,7 +31,7 @@ class MyModel(Model):
     model_arch = gguf.MODEL_ARCH.GROK
 ```
 
-2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py)
+2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py)
 
 Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`.
 
@@ -54,7 +54,7 @@ Example for `falcon` model:
 
 As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.
 
-Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file.
+Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](/gguf-py/gguf/tensor_mapping.py) file.
 
 If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it.
 
@@ -100,7 +100,7 @@ Have a look at existing implementation like `build_llama`, `build_dbrx` or `buil
 
 When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
 
-Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback).
+Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/).
 
 ## GGUF specification
 
diff --git a/docs/debugging-tests.md b/docs/development/debugging-tests.md
similarity index 100%
rename from docs/debugging-tests.md
rename to docs/development/debugging-tests.md
diff --git a/docs/llama-star/idea-arch.key b/docs/development/llama-star/idea-arch.key
similarity index 100%
rename from docs/llama-star/idea-arch.key
rename to docs/development/llama-star/idea-arch.key
diff --git a/docs/llama-star/idea-arch.pdf b/docs/development/llama-star/idea-arch.pdf
similarity index 100%
rename from docs/llama-star/idea-arch.pdf
rename to docs/development/llama-star/idea-arch.pdf
diff --git a/docs/token_generation_performance_tips.md b/docs/development/token_generation_performance_tips.md
similarity index 75%
rename from docs/token_generation_performance_tips.md
rename to docs/development/token_generation_performance_tips.md
index 3c43431471243..41b7232c976b3 100644
--- a/docs/token_generation_performance_tips.md
+++ b/docs/development/token_generation_performance_tips.md
@@ -1,9 +1,9 @@
 # Token generation performance troubleshooting
 
 ## Verifying that the model is running on the GPU with CUDA
-Make sure you compiled llama with the correct env variables according to [this guide](../README.md#CUDA), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
+Make sure you compiled llama with the correct env variables according to [this guide](/docs/build.md#cuda), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
 ```shell
-./main -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
+./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
 ```
 
 When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines:
@@ -27,7 +27,7 @@ RAM: 32GB
 
 Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (30B parameters, 4bit quantization, GGML)
 
-Run command: `./main -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
+Run command: `./llama-cli -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
 
 Result:
 
diff --git a/docs/docker.md b/docs/docker.md
new file mode 100644
index 0000000000000..d8922d77d9fe5
--- /dev/null
+++ b/docs/docker.md
@@ -0,0 +1,86 @@
+# Docker
+
+## Prerequisites
+* Docker must be installed and running on your system.
+* Create a folder to store big models & intermediate files (ex. /llama/models)
+
+## Images
+We have three Docker images available for this project:
+
+1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
+2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
+3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`)
+
+Additionally, there the following images, similar to the above:
+
+- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+
+The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
+
+## Usage
+
+The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.
+
+Replace `/path/to/models` below with the actual path where you downloaded the models.
+
+```bash
+docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B
+```
+
+On completion, you are ready to play!
+
+```bash
+docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+```
+
+or with a light image:
+
+```bash
+docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+```
+
+or with a server image:
+
+```bash
+docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
+```
+
+## Docker With CUDA
+
+Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
+
+## Building Docker locally
+
+```bash
+docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
+docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile .
+docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile .
+```
+
+You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
+
+The defaults are:
+
+- `CUDA_VERSION` set to `11.7.1`
+- `CUDA_DOCKER_ARCH` set to `all`
+
+The resulting images, are essentially the same as the non-CUDA images:
+
+1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
+2. `local/llama.cpp:light-cuda`: This image only includes the main executable file.
+3. `local/llama.cpp:server-cuda`: This image only includes the server executable file.
+
+## Usage
+
+After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag.
+
+```bash
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
+```
diff --git a/docs/install.md b/docs/install.md
new file mode 100644
index 0000000000000..10a568506835b
--- /dev/null
+++ b/docs/install.md
@@ -0,0 +1,39 @@
+# Install pre-built version of llama.cpp
+
+## Homebrew
+
+On Mac and Linux, the homebrew package manager can be used via
+
+```sh
+brew install llama.cpp
+```
+The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668
+
+## Nix
+
+On Mac and Linux, the Nix package manager can be used via
+
+```sh
+nix profile install nixpkgs#llama-cpp
+```
+For flake enabled installs.
+
+Or
+
+```sh
+nix-env --file '<nixpkgs>' --install --attr llama-cpp
+```
+
+For non-flake enabled installs.
+
+This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164).
+
+## Flox
+
+On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via
+
+```sh
+flox install llama-cpp
+```
+
+Flox follows the nixpkgs build of llama.cpp.
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index b40ee4ccb2ec1..67b3d27747850 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -12,44 +12,44 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 
 if (EMSCRIPTEN)
 else()
+    add_subdirectory(cvector-generator)
     add_subdirectory(baby-llama)
-    add_subdirectory(batched)
     add_subdirectory(batched-bench)
-    add_subdirectory(beam-search)
+    add_subdirectory(batched)
     add_subdirectory(benchmark)
     add_subdirectory(convert-llama2c-to-ggml)
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
-    add_subdirectory(finetune)
-    add_subdirectory(gritlm)
+    add_subdirectory(export-lora)
+    add_subdirectory(gbnf-validator)
+    add_subdirectory(gguf-hash)
     add_subdirectory(gguf-split)
+    add_subdirectory(gguf)
+    add_subdirectory(gritlm)
+    add_subdirectory(imatrix)
     add_subdirectory(infill)
     add_subdirectory(llama-bench)
     add_subdirectory(llava)
-    if (LLAMA_SYCL)
-        add_subdirectory(sycl)
-    endif()
+    add_subdirectory(lookahead)
+    add_subdirectory(lookup)
     add_subdirectory(main)
-    add_subdirectory(tokenize)
     add_subdirectory(parallel)
+    add_subdirectory(passkey)
     add_subdirectory(perplexity)
-    add_subdirectory(quantize)
     add_subdirectory(quantize-stats)
+    add_subdirectory(quantize)
     add_subdirectory(retrieval)
-    add_subdirectory(save-load-state)
-    add_subdirectory(simple)
-    add_subdirectory(passkey)
-    add_subdirectory(speculative)
-    add_subdirectory(lookahead)
-    add_subdirectory(lookup)
-    add_subdirectory(gguf)
-    add_subdirectory(train-text-from-scratch)
-    add_subdirectory(imatrix)
+    if (GGML_RPC)
+        add_subdirectory(rpc)
+    endif()
     if (LLAMA_BUILD_SERVER)
-        add_subdirectory(server)
+    add_subdirectory(server)
     endif()
-    add_subdirectory(export-lora)
-    if (LLAMA_RPC)
-        add_subdirectory(rpc)
+    if (GGML_SYCL)
+        add_subdirectory(sycl)
     endif()
+    add_subdirectory(save-load-state)
+    add_subdirectory(simple)
+    add_subdirectory(speculative)
+    add_subdirectory(tokenize)
 endif()
diff --git a/examples/Miku.sh b/examples/Miku.sh
index b9174b4e6e126..0f6c8c8787107 100755
--- a/examples/Miku.sh
+++ b/examples/Miku.sh
@@ -22,7 +22,7 @@ if [ -n "$N_THREAD" ]; then
     GEN_OPTIONS+=(--threads "$N_THREAD")
 fi
 
-./main "${GEN_OPTIONS[@]}" \
+./llama-cli "${GEN_OPTIONS[@]}" \
     --model "$MODEL" \
     --in-prefix " " \
     --in-suffix "${AI_NAME}:" \
diff --git a/examples/alpaca.sh b/examples/alpaca.sh
deleted file mode 100755
index 8d2bae6918b62..0000000000000
--- a/examples/alpaca.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \
-       --color \
-       -f ./prompts/alpaca.txt \
-       --ctx_size 2048 \
-       -n -1 \
-       -ins -b 256 \
-       --top_k 10000 \
-       --temp 0.2 \
-       --repeat_penalty 1.1 \
-       -t 7
diff --git a/examples/baby-llama/CMakeLists.txt b/examples/baby-llama/CMakeLists.txt
index 7b70227a525e1..71b82105c8863 100644
--- a/examples/baby-llama/CMakeLists.txt
+++ b/examples/baby-llama/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET baby-llama)
+set(TARGET llama-baby-llama)
 add_executable(${TARGET} baby-llama.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp
index bf0125e753746..aca332e9464d2 100644
--- a/examples/baby-llama/baby-llama.cpp
+++ b/examples/baby-llama/baby-llama.cpp
@@ -1,7 +1,6 @@
 #include "ggml.h"
 #include "train.h"
 
-#include <vector>
 #include <cassert>
 #include <cstdlib>
 #include <cstring>
@@ -522,8 +521,8 @@ static struct ggml_tensor * forward(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, 1]
             // Kcur shape [n_embd/n_head, n_head, N, 1]
-            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0);
-            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0);
+            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0);
+            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0);
 
             // store key and value to memory
             {
@@ -759,8 +758,8 @@ static struct ggml_tensor * forward_batch(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, n_batch]
             // Kcur shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0);
-            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0);
+            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0);
+            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0);
             assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
             assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
 
@@ -1056,7 +1055,7 @@ static struct ggml_tensor * forward_lora(
                                                         model->layers[il].wqb,
                                                         cur)),
                                                 n_embd/n_head, n_head, N),
-                                            KQ_pos, n_rot, 0, 0);
+                                            KQ_pos, n_rot, 0);
             struct ggml_tensor * Kcur = ggml_rope(ctx0,
                                             ggml_reshape_3d(ctx0,
                                                 ggml_mul_mat(ctx0,
@@ -1065,7 +1064,7 @@ static struct ggml_tensor * forward_lora(
                                                         model->layers[il].wkb,
                                                         cur)),
                                                 n_embd/n_head, n_head, N),
-                                            KQ_pos, n_rot, 0, 0);
+                                            KQ_pos, n_rot, 0);
 
             // store key and value to memory
             {
diff --git a/examples/base-translate.sh b/examples/base-translate.sh
index 00dedd0df7dde..103a52f55e6db 100755
--- a/examples/base-translate.sh
+++ b/examples/base-translate.sh
@@ -58,4 +58,4 @@ echo "$2
 model=$1
 
 # generate the most likely continuation until the string "===" is found
-./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs
+./llama-cli -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs
diff --git a/examples/batched-bench/CMakeLists.txt b/examples/batched-bench/CMakeLists.txt
index 40a032c514d11..959acaeeebc38 100644
--- a/examples/batched-bench/CMakeLists.txt
+++ b/examples/batched-bench/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET batched-bench)
+set(TARGET llama-batched-bench)
 add_executable(${TARGET} batched-bench.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/batched-bench/README.md b/examples/batched-bench/README.md
index bf951baf7f096..4a07fe6bbf268 100644
--- a/examples/batched-bench/README.md
+++ b/examples/batched-bench/README.md
@@ -10,16 +10,16 @@ There are 2 modes of operation:
 - `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
 
 ```bash
-./batched-bench MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
+./llama-batched-bench -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]
 
 # LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
-./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 2048 512 0 99
+./llama-batched-bench -m ./models/llama-7b/ggml-model-f16.gguf -c 16384 -b 2048 -ub 512 -ngl 99
 
 # LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
-./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 2048 512 1 99
+./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 16384 -b 2048 -ub 512 -ngl 99 -pps
 
 # custom set of batches
-./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 512 512 0 999 0 128,256,512 128,256 1,2,4,8,16,32
+./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 2048 -b 512 -ub 512 -ngl 999 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32
 ```
 
 ## Sample results
diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp
index 2924d8116f44f..25e7c775a0095 100644
--- a/examples/batched-bench/batched-bench.cpp
+++ b/examples/batched-bench/batched-bench.cpp
@@ -28,67 +28,27 @@ static std::vector<int> parse_list(char * p) {
     return ret;
 }
 
-int main(int argc, char ** argv) {
-    gpt_params params;
-
-    if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [FATTN] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
-        printf("  <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
-        printf("  example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
-        return 1 ;
-    }
-
-    int n_kv_max     = 2048;
-    int n_batch      = 2048;
-    int n_ubatch     = 512;
-    bool flash_attn  = false;
-    int is_pp_shared = 0;
-    int n_gpu_layers = 0;
-
-    std::vector<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
-    std::vector<int> n_tg = { 128, 256, };
-    std::vector<int> n_pl = { 1, 2, 4, 8, 16, 32, };
-    //std::vector<int> n_pl = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, };
-
-    if (argc >= 2) {
-        params.model = argv[1];
-    }
-
-    if (argc >= 3) {
-        n_kv_max = std::atoi(argv[2]);
-    }
-
-    if (argc >= 4) {
-        n_batch = std::atoi(argv[3]);
-    }
-
-    if (argc >= 5) {
-        n_ubatch = std::atoi(argv[4]);
-    }
-
-    if (argc >= 6) {
-        flash_attn = std::atoi(argv[5]);
-    }
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
 
-    if (argc >= 7) {
-        is_pp_shared = std::atoi(argv[6]);
-    }
+    LOG_TEE("\nexample usage:\n");
+    LOG_TEE("\n    %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]);
+    LOG_TEE("\n");
+}
 
-    if (argc >= 8) {
-        n_gpu_layers = std::atoi(argv[7]);
-    }
+int main(int argc, char ** argv) {
+    gpt_params params;
 
-    if (argc >= 9) {
-        n_pp = parse_list(argv[8]);
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
+        return 1;
     }
 
-    if (argc >= 10) {
-        n_tg = parse_list(argv[9]);
-    }
+    int is_pp_shared = params.is_pp_shared;
 
-    if (argc >= 11) {
-        n_pl = parse_list(argv[10]);
-    }
+    std::vector<int> n_pp = params.n_pp;
+    std::vector<int> n_tg = params.n_tg;
+    std::vector<int> n_pl = params.n_pl;
 
     // init LLM
 
@@ -97,12 +57,7 @@ int main(int argc, char ** argv) {
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_default_params();
-
-    const std::vector<float> t_split(llama_max_devices(), 0.0f);
-
-    model_params.n_gpu_layers = n_gpu_layers;
-    model_params.tensor_split = t_split.data();
+    llama_model_params model_params = llama_model_params_from_gpt_params(params);
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -111,19 +66,10 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    llama_context_params ctx_params = llama_context_default_params();
-
-    ctx_params.seed       = 1234;
-    ctx_params.n_ctx      = n_kv_max;
-    ctx_params.n_batch    = n_batch;
-    ctx_params.n_ubatch   = n_ubatch;
-    ctx_params.flash_attn = flash_attn;
-
-    ctx_params.n_threads       = params.n_threads;
-    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
 
     // ensure enough sequences are available
-    ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
+    ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end());
 
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
@@ -132,6 +78,8 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    const int32_t n_kv_max = llama_n_ctx(ctx);
+
     llama_batch batch = llama_batch_init(n_kv_max, 0, 1);
 
     // decode in batches of ctx_params.n_batch tokens
@@ -175,7 +123,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, flash_attn, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
+    LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
     LOG_TEE("\n");
 
     LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");
diff --git a/examples/batched.swift/Makefile b/examples/batched.swift/Makefile
index 2afb24fb85a1a..1f9156e583fdd 100755
--- a/examples/batched.swift/Makefile
+++ b/examples/batched.swift/Makefile
@@ -1,6 +1,6 @@
 .PHONY: build
 
 build:
-	xcodebuild -scheme batched_swift -destination "generic/platform=macOS" -derivedDataPath build
-	rm -f ./batched_swift
-	ln -s ./build/Build/Products/Debug/batched_swift ./batched_swift
+	xcodebuild -scheme llama-batched-swift -destination "generic/platform=macOS" -derivedDataPath build
+	rm -f ./llama-batched-swift
+	ln -s ./build/Build/Products/Debug/llama-batched-swift ./llama-batched-swift
diff --git a/examples/batched.swift/Package.swift b/examples/batched.swift/Package.swift
index 826491defd863..7e8afd0843c5b 100644
--- a/examples/batched.swift/Package.swift
+++ b/examples/batched.swift/Package.swift
@@ -4,7 +4,7 @@
 import PackageDescription
 
 let package = Package(
-    name: "batched_swift",
+    name: "llama-batched-swift",
     platforms: [.macOS(.v12)],
     dependencies: [
         .package(name: "llama", path: "../../"),
@@ -13,7 +13,7 @@ let package = Package(
         // Targets are the basic building blocks of a package, defining a module or a test suite.
         // Targets can depend on other targets in this package and products from dependencies.
         .executableTarget(
-            name: "batched_swift",
+            name: "llama-batched-swift",
             dependencies: ["llama"],
             path: "Sources",
             linkerSettings: [.linkedFramework("Foundation"), .linkedFramework("AppKit")]
diff --git a/examples/batched.swift/README.md b/examples/batched.swift/README.md
index 4c2721fe85b00..7f2e2fcdcf4a7 100644
--- a/examples/batched.swift/README.md
+++ b/examples/batched.swift/README.md
@@ -1,4 +1,4 @@
 This is a swift clone of `examples/batched`.
 
 $ `make`
-$ `./batched_swift MODEL_PATH [PROMPT] [PARALLEL]`
+$ `./llama-batched-swift MODEL_PATH [PROMPT] [PARALLEL]`
diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift
index dbbd06da58183..616494d2d841d 100644
--- a/examples/batched.swift/Sources/main.swift
+++ b/examples/batched.swift/Sources/main.swift
@@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
 
 private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
     var result = [CChar](repeating: 0, count: 8)
-    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false)
+    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), 0, false)
     if nTokens < 0 {
         let actualTokensCount = -Int(nTokens)
         result = .init(repeating: 0, count: actualTokensCount)
@@ -238,6 +238,7 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
             token,
             &result,
             Int32(result.count),
+            0,
             false
         )
         assert(check == actualTokensCount)
diff --git a/examples/batched/CMakeLists.txt b/examples/batched/CMakeLists.txt
index 6aa178d4d5911..77e33343b6673 100644
--- a/examples/batched/CMakeLists.txt
+++ b/examples/batched/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET batched)
+set(TARGET llama-batched)
 add_executable(${TARGET} batched.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/batched/README.md b/examples/batched/README.md
index 5d730331769fb..6013aab01fddc 100644
--- a/examples/batched/README.md
+++ b/examples/batched/README.md
@@ -3,7 +3,7 @@
 The example demonstrates batched generation from a given prompt
 
 ```bash
-./batched ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4
+./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4
 
 ...
 
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index be30d20bf8194..53fbfb0a8cf2a 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -7,48 +7,31 @@
 #include <string>
 #include <vector>
 
-int main(int argc, char ** argv) {
-    gpt_params params;
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
 
-    if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [NGL]\n" , argv[0]);
-        return 1 ;
-    }
-
-    // number of parallel batches
-    int n_parallel = 1;
-
-    // total length of the sequences including the prompt
-    int n_len = 32;
-
-    // number of layers to offload to the GPU
-    int n_gpu_layers = 0;
+    LOG_TEE("\nexample usage:\n");
+    LOG_TEE("\n    %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]);
+    LOG_TEE("\n");
+}
 
-    if (argc >= 2) {
-        params.model = argv[1];
-    }
+int main(int argc, char ** argv) {
+    gpt_params params;
 
-    if (argc >= 3) {
-        params.prompt = argv[2];
-    }
+    params.prompt = "Hello my name is";
+    params.n_predict = 32;
 
-    if (argc >= 4) {
-        n_parallel = std::atoi(argv[3]);
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
+        return 1;
     }
 
-    if (argc >= 5) {
-        n_len = std::atoi(argv[4]);
-    }
 
-    if (argc >= 6) {
-        n_gpu_layers = std::atoi(argv[5]);
-    }
-
-    if (params.prompt.empty()) {
-        params.prompt = "Hello my name is";
-    }
+    // number of parallel batches
+    int n_parallel = params.n_parallel;
 
-    process_escapes(params.prompt);
+    // total length of the sequences including the prompt
+    int n_predict = params.n_predict;
 
     // init LLM
 
@@ -57,9 +40,7 @@ int main(int argc, char ** argv) {
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_default_params();
-
-    model_params.n_gpu_layers = n_gpu_layers;
+    llama_model_params model_params = llama_model_params_from_gpt_params(params);
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -73,18 +54,14 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> tokens_list;
     tokens_list = ::llama_tokenize(model, params.prompt, true);
 
-    const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
+    const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
 
     // initialize the context
 
-    llama_context_params ctx_params = llama_context_default_params();
+    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
 
-    ctx_params.seed  = 1234;
     ctx_params.n_ctx   = n_kv_req;
-    ctx_params.n_batch = std::max(n_len, n_parallel);
-    ctx_params.n_seq_max       = n_parallel;
-    ctx_params.n_threads       = params.n_threads;
-    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+    ctx_params.n_batch = std::max(n_predict, n_parallel);
 
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
@@ -93,9 +70,9 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const int n_ctx    = llama_n_ctx(ctx);
+    const int n_ctx = llama_n_ctx(ctx);
 
-    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
+    LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
 
     // make sure the KV cache is big enough to hold all the prompt and generated tokens
     if (n_kv_req > n_ctx) {
@@ -116,14 +93,34 @@ int main(int argc, char ** argv) {
 
     // create a llama_batch
     // we use this object to submit token data for decoding
-    llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1);
+    llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel);
+
+    std::vector<llama_seq_id> seq_ids(n_parallel, 0);
+    for (int32_t i = 0; i < n_parallel; ++i) {
+        seq_ids[i] = i;
+    }
 
     // evaluate the initial prompt
     for (size_t i = 0; i < tokens_list.size(); ++i) {
-        llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
+        llama_batch_add(batch, tokens_list[i], i, seq_ids, false);
     }
     GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
 
+    if (llama_model_has_encoder(model)) {
+        if (llama_encode(ctx, batch)) {
+            LOG_TEE("%s : failed to eval\n", __func__);
+            return 1;
+        }
+
+        llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+        if (decoder_start_token_id == -1) {
+            decoder_start_token_id = llama_token_bos(model);
+        }
+
+        llama_batch_clear(batch);
+        llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
+    }
+
     // llama_decode will output logits only for the last token of the prompt
     batch.logits[batch.n_tokens - 1] = true;
 
@@ -132,11 +129,11 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    // assign the system KV cache to all parallel sequences
-    // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
-    for (int32_t i = 1; i < n_parallel; ++i) {
-        llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
-    }
+    //// assign the system KV cache to all parallel sequences
+    //// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
+    //for (int32_t i = 1; i < n_parallel; ++i) {
+    //    llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+    //}
 
     if (n_parallel > 1) {
         LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
@@ -156,7 +153,7 @@ int main(int argc, char ** argv) {
 
     const auto t_main_start = ggml_time_us();
 
-    while (n_cur <= n_len) {
+    while (n_cur <= n_predict) {
         // prepare the next batch
         llama_batch_clear(batch);
 
@@ -192,7 +189,7 @@ int main(int argc, char ** argv) {
             //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
             // is it an end of generation? -> mark the stream as finished
-            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
                 i_batch[i] = -1;
                 LOG_TEE("\n");
                 if (n_parallel > 1) {
diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp
deleted file mode 100644
index 3d34378a506eb..0000000000000
--- a/examples/beam-search/beam-search.cpp
+++ /dev/null
@@ -1,188 +0,0 @@
-#include "common.h"
-#include "llama.h"
-
-#include <cassert>
-#include <cinttypes>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <ctime>
-#include <fstream>
-#include <iostream>
-#include <string>
-#include <vector>
-
-#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
-#include <signal.h>
-#include <unistd.h>
-#elif defined (_WIN32)
-#define WIN32_LEAN_AND_MEAN
-#ifndef NOMINMAX
-#   define NOMINMAX
-#endif
-#include <windows.h>
-#include <signal.h>
-#endif
-
-// Used for debugging to print out beam tokens.
-struct ostream_beam_view {
-    llama_context * ctx;
-    llama_beam_view beam_view;
-};
-
-static std::ostream & operator<<(std::ostream & os, const ostream_beam_view & obv) {
-    os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
-    for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
-        os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
-    }
-    return os << ')';
-}
-
-// Put here anything you want back in beam_search_callback().
-struct beam_search_callback_data {
-    llama_context * ctx;
-    std::vector<llama_token> response;
-};
-
-// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
-// For example, eob can be flagged due to maximum token length, stop words, etc.
-static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
-    return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
-}
-
-// Function matching type llama_beam_search_callback_fn_t.
-// Custom callback example is called each time the beams lengths increase:
-//  * Show progress by printing ',' following by number of convergent beam tokens if any.
-//  * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
-//    This is also called when the stop condition is met.
-//    Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
-static void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) {
-    auto& callback_data = *static_cast<beam_search_callback_data*>(callback_data_ptr);
-    // Mark beams as EOS as needed.
-    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
-        llama_beam_view& beam_view = beams_state.beam_views[i];
-        if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) {
-            beam_view.eob = true;
-        }
-    }
-    printf(",");  // Show progress
-    if (const size_t n = beams_state.common_prefix_length) {
-        callback_data.response.resize(callback_data.response.size() + n);
-        assert(0u < beams_state.n_beams);
-        const llama_token * tokens = beams_state.beam_views[0].tokens;
-        std::copy(tokens, tokens + n, callback_data.response.end() - n);
-        printf("%zu", n);
-    }
-    fflush(stdout);
-#if 1 // DEBUG: print current beams for this iteration
-    std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n";
-    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
-        std::cout << "beams["<<i<<"]: " << ostream_beam_view{callback_data.ctx,beams_state.beam_views[i]} << std::endl;
-    }
-#endif
-}
-
-int main(int argc, char ** argv)
-{
-    gpt_params params;
-    //params.n_gpu_layers = 200;
-
-    //---------------------------------
-    // Print help :
-    //---------------------------------
-
-    if ( argc < 2 || argv[1][0] == '-' )
-    {
-        printf( "Usage: %s MODEL_PATH [BEAM_WIDTH=2] [PROMPT]\n" , argv[0] );
-        return 1 ;
-    }
-
-    //---------------------------------
-    // Load parameters :
-    //---------------------------------
-
-    params.model = argv[1];
-
-    params.n_beams = 2 < argc ? std::stoi(argv[2]) : 2;
-
-    if ( argc > 3 )
-    {
-        params.prompt = argv[3];
-    }
-
-    if ( params.prompt.empty() )
-    {
-        params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n";
-    }
-
-    //---------------------------------
-    // Init LLM :
-    //---------------------------------
-
-    llama_backend_init();
-    llama_numa_init(params.numa);
-
-    llama_model * model;
-    llama_context * ctx;
-
-    std::tie(model, ctx) = llama_init_from_gpt_params( params );
-
-    if ( model == NULL )
-    {
-        fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
-        return 1;
-    }
-
-    //---------------------------------
-    // Tokenize the prompt :
-    //---------------------------------
-
-    std::vector<llama_token> tokens_list = llama_tokenize(ctx, params.prompt, true);
-
-    const size_t max_context_size     = llama_n_ctx( ctx );
-    const size_t max_tokens_list_size = max_context_size - 4 ;
-
-    if (tokens_list.size() > max_tokens_list_size)
-    {
-        fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" ,
-             __func__ , tokens_list.size() , max_tokens_list_size );
-        return 1;
-    }
-
-    fprintf( stderr, "\n\n" );
-
-    // Print the tokens from the prompt :
-
-    for( auto id : tokens_list )
-    {
-        std::cout << llama_token_to_piece(ctx, id);
-    }
-    std::cout << std::flush;
-
-    int n_past = 0;
-
-    if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0)))
-    {
-        fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ );
-        return 1;
-    }
-    n_past += tokens_list.size();
-
-    beam_search_callback_data callback_data{ctx, {}};
-    size_t const beam_width = static_cast<size_t>(params.n_beams);
-    int const n_predict = 256;
-    llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict);
-
-    std::cout << "\n\n";
-    for (llama_token const token_id : callback_data.response) {
-        std::cout << llama_token_to_piece(ctx,token_id);
-    }
-    std::cout << std::endl;
-
-    llama_free( ctx );
-    llama_free_model( model );
-
-    llama_backend_free();
-
-    return 0;
-}
diff --git a/examples/benchmark/CMakeLists.txt b/examples/benchmark/CMakeLists.txt
index 2bb47bab5a868..34a58cc02abaf 100644
--- a/examples/benchmark/CMakeLists.txt
+++ b/examples/benchmark/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET benchmark)
+set(TARGET llama-bench-matmult)
 add_executable(${TARGET} benchmark-matmult.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/chat-13B.sh b/examples/chat-13B.sh
index 35c089d57d253..1828903c31670 100755
--- a/examples/chat-13B.sh
+++ b/examples/chat-13B.sh
@@ -30,7 +30,7 @@ sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \
      $PROMPT_TEMPLATE > $PROMPT_FILE
 
 # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
-./main $GEN_OPTIONS \
+./llama-cli $GEN_OPTIONS \
   --model "$MODEL" \
   --threads "$N_THREAD" \
   --n_predict "$N_PREDICTS" \
diff --git a/examples/chat-persistent.sh b/examples/chat-persistent.sh
index 22f5b83d3da06..d9cab9836482e 100755
--- a/examples/chat-persistent.sh
+++ b/examples/chat-persistent.sh
@@ -62,7 +62,7 @@ fi
 if [[ ! -e "$PROMPT_CACHE_FILE" ]]; then
     echo 'Prompt cache does not exist, building...'
     # Default batch_size to 64 here for better user feedback during initial prompt processing
-    ./main 2>>"$LOG" \
+    ./llama-cli 2>>"$LOG" \
         --batch_size 64 \
         "${OPTS[@]}" \
         --prompt-cache "$PROMPT_CACHE_FILE" \
@@ -109,13 +109,13 @@ while read -e line; do
 
     printf '%s: ' "$AI_NAME" >>"$CUR_PROMPT_FILE"
 
-    ./main 2>>"$LOG" "${OPTS[@]}" \
+    ./llama-cli 2>>"$LOG" "${OPTS[@]}" \
             --prompt-cache "$CUR_PROMPT_CACHE" \
             --prompt-cache-all \
             --file "$CUR_PROMPT_FILE" \
             --reverse-prompt "${USER_NAME}:" \
             --n_predict "$n_predict" |
-        skip_bytes 1 |                  # skip BOS token added by ./main
+        skip_bytes 1 |                  # skip BOS token added by ./llama-cli
         tee "$CUR_PROMPT_FILE.tmp" |    # save prompt + generation to tmp file
         skip_bytes "$n_prompt_len_pre"  # print generation
 
@@ -133,7 +133,7 @@ while read -e line; do
     # TODO get both messages in one go
     if  ! session_size_msg="$(tail -n30 "$LOG" | grep -oE "$SESSION_SIZE_MSG_PATTERN")" ||
         ! sample_time_msg="$(tail -n10 "$LOG" | grep -oE "$SAMPLE_TIME_MSG_PATTERN")"; then
-        echo >&2 "Couldn't get number of tokens from ./main output!"
+        echo >&2 "Couldn't get number of tokens from ./llama-cli output!"
         exit 1
     fi
 
@@ -144,7 +144,7 @@ while read -e line; do
     fi
 
     # Update cache for next prompt in background, ideally during user input
-    ./main >>"$LOG_BG" 2>&1 "${OPTS[@]}" \
+    ./llama-cli >>"$LOG_BG" 2>&1 "${OPTS[@]}" \
           --prompt-cache "$NEXT_PROMPT_CACHE" \
           --file "$NEXT_PROMPT_FILE" \
           --n_predict 1 &
diff --git a/examples/chat-vicuna.sh b/examples/chat-vicuna.sh
index 8c7b7bef42784..ffdd200849503 100755
--- a/examples/chat-vicuna.sh
+++ b/examples/chat-vicuna.sh
@@ -30,7 +30,7 @@ sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \
      $PROMPT_TEMPLATE > $PROMPT_FILE
 
 # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
-./bin/main $GEN_OPTIONS \
+./bin/llama-cli $GEN_OPTIONS \
   --model "$MODEL" \
   --threads "$N_THREAD" \
   --n_predict "$N_PREDICTS" \
diff --git a/examples/chat.sh b/examples/chat.sh
index d567acecdff11..9f85d1e265d00 100755
--- a/examples/chat.sh
+++ b/examples/chat.sh
@@ -11,6 +11,6 @@ cd ..
 #
 #   "--keep 48" is based on the contents of prompts/chat-with-bob.txt
 #
-./main -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \
+./llama-cli -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \
     --repeat_penalty 1.0 --color -i \
     -r "User:" -f prompts/chat-with-bob.txt
diff --git a/examples/convert-llama2c-to-ggml/CMakeLists.txt b/examples/convert-llama2c-to-ggml/CMakeLists.txt
index e262d44f98496..a6790e617217e 100644
--- a/examples/convert-llama2c-to-ggml/CMakeLists.txt
+++ b/examples/convert-llama2c-to-ggml/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET convert-llama2c-to-ggml)
+set(TARGET llama-convert-llama2c-to-ggml)
 add_executable(${TARGET} convert-llama2c-to-ggml.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md
index 742dcf7a35b50..5774ac83c32c8 100644
--- a/examples/convert-llama2c-to-ggml/README.md
+++ b/examples/convert-llama2c-to-ggml/README.md
@@ -8,7 +8,7 @@ To convert the model first download the models from the [llama2.c](https://githu
 
 After successful compilation, following usage options are available:
 ```
-usage: ./convert-llama2c-to-ggml [options]
+usage: ./llama-convert-llama2c-to-ggml [options]
 
 options:
   -h, --help                       show this help message and exit
@@ -19,10 +19,10 @@ options:
 
 An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows:
 
-`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`
+`$ ./llama-convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`
 
 Note: The vocabulary for `stories260K.bin` should be its own tokenizer `tok512.bin` found in [karpathy/tinyllamas/stories260K](https://huggingface.co/karpathy/tinyllamas/tree/main/stories260K).
 
 Now you can use the model with a command like:
 
-`$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256`
+`$ ./llama-cli -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256`
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index 746c3fbef8412..8ca9f8915916c 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -774,7 +774,7 @@ static struct train_params get_default_train_params() {
 
     params.samples_start_after_nl = false;
     params.use_adam               = true;
-    params.use_flash              = true;
+    params.use_flash              = false;
     params.use_scratch            = true;
 
     // only adam
diff --git a/convert.py b/examples/convert_legacy_llama.py
similarity index 75%
rename from convert.py
rename to examples/convert_legacy_llama.py
index da1247957780c..9ab9ab06edf8f 100755
--- a/convert.py
+++ b/examples/convert_legacy_llama.py
@@ -24,14 +24,16 @@
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from dataclasses import dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional
+from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar
 
 import numpy as np
-from sentencepiece import SentencePieceProcessor
 
 if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+    # use .parent.parent since we are in "examples" directory
+    sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py'))
+
 import gguf
+from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab
 
 if TYPE_CHECKING:
     from typing_extensions import Self, TypeAlias
@@ -174,7 +176,7 @@ class Params:
     rope_scaling_type: gguf.RopeScalingType | None = None
     f_rope_freq_base: float | None = None
     f_rope_scale: float | None = None
-    n_orig_ctx: int | None = None
+    n_ctx_orig: int | None = None
     rope_finetuned: bool | None = None
 
     ftype: GGMLFileType | None = None
@@ -224,7 +226,7 @@ def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
         with open(config_path) as f:
             config = json.load(f)
 
-        rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
+        rope_scaling_type = f_rope_scale = n_ctx_orig = rope_finetuned = None
         rope_scaling = config.get("rope_scaling")
 
         if rope_scaling is not None and (typ := rope_scaling.get("type")):
@@ -234,7 +236,7 @@ def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
                 rope_scaling_type = gguf.RopeScalingType.LINEAR
             elif typ == "yarn":
                 rope_scaling_type = gguf.RopeScalingType.YARN
-                n_orig_ctx = rope_scaling['original_max_position_embeddings']
+                n_ctx_orig = rope_scaling['original_max_position_embeddings']
                 rope_finetuned = rope_scaling['finetuned']
             else:
                 raise NotImplementedError(f'Unknown rope scaling type: {typ}')
@@ -270,7 +272,7 @@ def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
             f_rope_freq_base  = config.get("rope_theta"),
             rope_scaling_type = rope_scaling_type,
             f_rope_scale      = f_rope_scale,
-            n_orig_ctx        = n_orig_ctx,
+            n_ctx_orig        = n_ctx_orig,
             rope_finetuned    = rope_finetuned,
         )
 
@@ -344,342 +346,6 @@ def load(model_plus: ModelPlus) -> Params:
         return params
 
 
-@dataclass
-class Metadata:
-    name: Optional[str] = None
-    author: Optional[str] = None
-    version: Optional[str] = None
-    url: Optional[str] = None
-    description: Optional[str] = None
-    licence: Optional[str] = None
-    source_url: Optional[str] = None
-    source_hf_repo: Optional[str] = None
-
-    @staticmethod
-    def load(metadata_path: Path) -> Metadata:
-        if metadata_path is None or not metadata_path.exists():
-            return Metadata()
-
-        with open(metadata_path, 'r') as file:
-            data = json.load(file)
-
-        # Create a new Metadata instance
-        metadata = Metadata()
-
-        # Assigning values to Metadata attributes if they exist in the JSON file
-        # This is based on LLM_KV_NAMES mapping in llama.cpp
-        metadata.name = data.get("general.name")
-        metadata.author = data.get("general.author")
-        metadata.version = data.get("general.version")
-        metadata.url = data.get("general.url")
-        metadata.description = data.get("general.description")
-        metadata.license = data.get("general.license")
-        metadata.source_url = data.get("general.source.url")
-        metadata.source_hf_repo = data.get("general.source.huggingface.repository")
-
-        return metadata
-
-
-#
-# vocab
-#
-
-
-@runtime_checkable
-class BaseVocab(Protocol):
-    tokenizer_model: ClassVar[str]
-    name: ClassVar[str]
-
-
-class NoVocab(BaseVocab):
-    tokenizer_model = "no_vocab"
-    name = "no_vocab"
-
-    def __repr__(self) -> str:
-        return "<NoVocab for a model without integrated vocabulary>"
-
-
-@runtime_checkable
-class Vocab(BaseVocab, Protocol):
-    vocab_size: int
-    added_tokens_dict: dict[str, int]
-    added_tokens_list: list[str]
-    fname_tokenizer: Path
-
-    def __init__(self, base_path: Path): ...
-    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
-
-
-class BpeVocab(Vocab):
-    tokenizer_model = "gpt2"
-    name = "bpe"
-
-    def __init__(self, base_path: Path):
-        added_tokens: dict[str, int] = {}
-
-        if (fname_tokenizer := base_path / 'vocab.json').exists():
-            # "slow" tokenizer
-            with open(fname_tokenizer, encoding="utf-8") as f:
-                self.vocab = json.load(f)
-
-            try:
-                # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
-                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
-                    added_tokens = json.load(f)
-            except FileNotFoundError:
-                pass
-        else:
-            # "fast" tokenizer
-            fname_tokenizer = base_path / FAST_TOKENIZER_FILE
-
-            # if this fails, FileNotFoundError propagates to caller
-            with open(fname_tokenizer, encoding="utf-8") as f:
-                tokenizer_json = json.load(f)
-
-            tokenizer_model: dict[str, Any] = tokenizer_json['model']
-            if (
-                tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
-                or tokenizer_json['decoder']['type'] != 'ByteLevel'
-            ):
-                raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
-
-            self.vocab = tokenizer_model["vocab"]
-
-            if (added := tokenizer_json.get('added_tokens')) is not None:
-                # Added tokens here can be duplicates of the main vocabulary.
-                added_tokens = {item['content']: item['id']
-                                for item in added
-                                if item['content'] not in self.vocab}
-
-        vocab_size   = len(self.vocab)
-        expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
-        actual_ids   = sorted(added_tokens.values())
-        if expected_ids != actual_ids:
-            expected_end_id = vocab_size + len(actual_ids) - 1
-            raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
-                             f"{vocab_size} - {expected_end_id}; got {actual_ids}")
-
-        items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
-        self.added_tokens_dict    = added_tokens
-        self.added_tokens_list    = [text for (text, idx) in items]
-        self.vocab_size_base      = vocab_size
-        self.vocab_size           = self.vocab_size_base + len(self.added_tokens_list)
-        self.fname_tokenizer      = fname_tokenizer
-
-    def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
-
-        for i, _ in enumerate(self.vocab):
-            yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
-
-    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        for text in self.added_tokens_list:
-            score = -1000.0
-            yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
-
-    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        yield from self.bpe_tokens()
-        yield from self.added_tokens()
-
-    def __repr__(self) -> str:
-        return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
-
-
-class SentencePieceVocab(Vocab):
-    tokenizer_model = "llama"
-    name = "spm"
-
-    def __init__(self, base_path: Path):
-        added_tokens: dict[str, int] = {}
-        if (fname_tokenizer := base_path / 'tokenizer.model').exists():
-            # normal location
-            try:
-                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
-                    added_tokens = json.load(f)
-            except FileNotFoundError:
-                pass
-        elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
-            # not found in alternate location either
-            raise FileNotFoundError('Cannot find tokenizer.model')
-
-        self.sentencepiece_tokenizer = SentencePieceProcessor()
-        self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
-        vocab_size = self.sentencepiece_tokenizer.vocab_size()
-
-        new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
-        expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
-        actual_new_ids   = sorted(new_tokens.keys())
-
-        if expected_new_ids != actual_new_ids:
-            raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
-
-        # Token pieces that were added to the base vocabulary.
-        self.added_tokens_dict  = added_tokens
-        self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
-        self.vocab_size_base    = vocab_size
-        self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
-        self.fname_tokenizer    = fname_tokenizer
-
-    def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        tokenizer = self.sentencepiece_tokenizer
-        for i in range(tokenizer.vocab_size()):
-            piece = tokenizer.IdToPiece(i)
-            text         = piece.encode("utf-8")
-            score: float = tokenizer.GetScore(i)
-
-            toktype = gguf.TokenType.NORMAL
-            if tokenizer.IsUnknown(i):
-                toktype = gguf.TokenType.UNKNOWN
-            if tokenizer.IsControl(i):
-                toktype = gguf.TokenType.CONTROL
-
-            # NOTE: I think added_tokens are user defined.
-            # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
-            # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
-
-            if tokenizer.IsUnused(i):
-                toktype = gguf.TokenType.UNUSED
-            if tokenizer.IsByte(i):
-                toktype = gguf.TokenType.BYTE
-
-            yield text, score, toktype
-
-    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        for text in self.added_tokens_list:
-            score = -1000.0
-            yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
-
-    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        yield from self.sentencepiece_tokens()
-        yield from self.added_tokens()
-
-    def __repr__(self) -> str:
-        return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
-
-
-class LlamaHfVocab(Vocab):
-    tokenizer_model = "llama"
-    name = "hfft"
-
-    def __init__(self, base_path: Path):
-        fname_tokenizer = base_path / FAST_TOKENIZER_FILE
-        # if this fails, FileNotFoundError propagates to caller
-        with open(fname_tokenizer, encoding='utf-8') as f:
-            tokenizer_json = json.load(f)
-
-        # pre-check so we know if we need transformers
-        tokenizer_model: dict[str, Any] = tokenizer_json['model']
-        is_llama3 = (
-            tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
-            and not tokenizer_model.get('byte_fallback', True)
-        )
-        if is_llama3:
-            raise TypeError('Llama 3 must be converted with BpeVocab')
-
-        if not is_llama3 and (
-            tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
-            or tokenizer_json['decoder']['type'] != 'Sequence'
-        ):
-            raise FileNotFoundError('Cannot find Llama BPE tokenizer')
-
-        try:
-            from transformers import AutoTokenizer
-        except ImportError as e:
-            raise ImportError(
-                "To use LlamaHfVocab, please install the `transformers` package. "
-                "You can install it with `pip install transformers`."
-            ) from e
-
-        # Allow the tokenizer to default to slow or fast versions.
-        # Explicitly set tokenizer to use local paths.
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            base_path,
-            cache_dir=base_path,
-            local_files_only=True,
-        )
-        assert self.tokenizer.is_fast  # assume tokenizer.json is used
-
-        # Initialize lists and dictionaries for added tokens
-        self.added_tokens_list = []
-        self.added_tokens_dict = dict()
-        self.added_tokens_ids  = set()
-
-        # Process added tokens
-        for tok, tokidx in sorted(
-            self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
-        ):
-            # Only consider added tokens that are not in the base vocabulary
-            if tokidx >= self.tokenizer.vocab_size:
-                self.added_tokens_list.append(tok)
-                self.added_tokens_dict[tok] = tokidx
-                self.added_tokens_ids.add(tokidx)
-
-        # Store special tokens and their IDs
-        self.specials = {
-            tok: self.tokenizer.get_vocab()[tok]
-            for tok in self.tokenizer.all_special_tokens
-        }
-        self.special_ids = set(self.tokenizer.all_special_ids)
-
-        # Set vocabulary sizes
-        self.vocab_size_base = self.tokenizer.vocab_size
-        self.vocab_size      = self.vocab_size_base + len(self.added_tokens_list)
-
-        self.fname_tokenizer = fname_tokenizer
-
-    def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        reverse_vocab = {
-            id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
-        }
-
-        for token_id in range(self.vocab_size_base):
-            # Skip processing added tokens here
-            if token_id in self.added_tokens_ids:
-                continue
-
-            # Convert token text to bytes
-            token_text = reverse_vocab[token_id].encode("utf-8")
-
-            # Yield token text, score, and type
-            yield token_text, self.get_token_score(token_id), self.get_token_type(
-                token_id, token_text, self.special_ids  # Reuse already stored special IDs
-            )
-
-    def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
-        # Special case for byte tokens
-        if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
-            return gguf.TokenType.BYTE
-
-        # Determine token type based on whether it's a special token
-        return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
-
-    def get_token_score(self, token_id: int) -> float:
-        # Placeholder for actual logic to determine the token's score
-        # This needs to be implemented based on specific requirements
-        return -1000.0  # Default score
-
-    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        for text in self.added_tokens_list:
-            if text in self.specials:
-                toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
-                score = self.get_token_score(self.specials[text])
-            else:
-                toktype = gguf.TokenType.USER_DEFINED
-                score = -1000.0
-
-            yield text.encode("utf-8"), score, toktype
-
-    def has_newline_token(self):
-        return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab
-
-    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        yield from self.hf_tokens()
-        yield from self.added_tokens()
-
-    def __repr__(self) -> str:
-        return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
-
-
 #
 # data loading
 # TODO: reuse (probably move to gguf.py?)
@@ -790,12 +456,13 @@ def validate_conversion_to(self, data_type: DataType) -> None:
 
 LazyModel: TypeAlias = 'dict[str, LazyTensor]'
 
+ModelFormat: TypeAlias = Literal['ggml', 'torch', 'safetensors', 'none']
 
 @dataclass
 class ModelPlus:
     model: LazyModel
     paths: list[Path]  # Where this was read from.
-    format: Literal['ggml', 'torch', 'safetensors', 'none']
+    format: ModelFormat
     vocab: BaseVocab | None  # For GGML models (which have vocab built in), the vocab.
 
 
@@ -834,7 +501,7 @@ def load() -> UnquantizedTensor:
 
 
 def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
-    formats = set(mp.format for mp in models_plus)
+    formats: set[ModelFormat] = set(mp.format for mp in models_plus)
     assert len(formats) == 1, "different formats?"
     format = formats.pop()
     paths = [path for mp in models_plus for path in mp.paths]
@@ -853,7 +520,7 @@ def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
     else:
         model = merge_sharded([mp.model for mp in models_plus])
 
-    return ModelPlus(model, paths, format, vocab)  # pytype: disable=wrong-arg-types
+    return ModelPlus(model, paths, format, vocab)
 
 
 def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor:
@@ -1103,7 +770,7 @@ class OutputFile:
     def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE):
         self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
 
-    def add_meta_model(self, params: Params, metadata: Metadata) -> None:
+    def add_meta_model(self, params: Params, metadata: gguf.Metadata | None) -> None:
         # Metadata About The Model And Its Provenence
         name = "LLaMA"
         if metadata is not None and metadata.name is not None:
@@ -1121,16 +788,73 @@ def add_meta_model(self, params: Params, metadata: Metadata) -> None:
                 self.gguf.add_author(metadata.author)
             if metadata.version is not None:
                 self.gguf.add_version(metadata.version)
-            if metadata.url is not None:
-                self.gguf.add_url(metadata.url)
+            if metadata.organization is not None:
+                self.gguf.add_organization(metadata.organization)
+
+            if metadata.finetune is not None:
+                self.gguf.add_finetune(metadata.finetune)
+            if metadata.basename is not None:
+                self.gguf.add_basename(metadata.basename)
+
             if metadata.description is not None:
                 self.gguf.add_description(metadata.description)
-            if metadata.licence is not None:
-                self.gguf.add_licence(metadata.licence)
+            if metadata.quantized_by is not None:
+                self.gguf.add_quantized_by(metadata.quantized_by)
+
+            if metadata.size_label is not None:
+                self.gguf.add_size_label(metadata.size_label)
+
+            if metadata.license is not None:
+                self.gguf.add_license(metadata.license)
+            if metadata.license_name is not None:
+                self.gguf.add_license_name(metadata.license_name)
+            if metadata.license_link is not None:
+                self.gguf.add_license_link(metadata.license_link)
+
+            if metadata.url is not None:
+                self.gguf.add_url(metadata.url)
+            if metadata.doi is not None:
+                self.gguf.add_doi(metadata.doi)
+            if metadata.uuid is not None:
+                self.gguf.add_uuid(metadata.uuid)
+            if metadata.repo_url is not None:
+                self.gguf.add_repo_url(metadata.repo_url)
+
             if metadata.source_url is not None:
                 self.gguf.add_source_url(metadata.source_url)
-            if metadata.source_hf_repo is not None:
-                self.gguf.add_source_hf_repo(metadata.source_hf_repo)
+            if metadata.source_doi is not None:
+                self.gguf.add_source_doi(metadata.source_doi)
+            if metadata.source_uuid is not None:
+                self.gguf.add_source_uuid(metadata.source_uuid)
+            if metadata.source_repo_url is not None:
+                self.gguf.add_source_repo_url(metadata.source_repo_url)
+
+            if metadata.base_models is not None:
+                self.gguf.add_base_model_count(len(metadata.base_models))
+                for key, base_model_entry in enumerate(metadata.base_models):
+                    if "name" in base_model_entry:
+                        self.gguf.add_base_model_name(key, base_model_entry["name"])
+                    if "author" in base_model_entry:
+                        self.gguf.add_base_model_author(key, base_model_entry["author"])
+                    if "version" in base_model_entry:
+                        self.gguf.add_base_model_version(key, base_model_entry["version"])
+                    if "organization" in base_model_entry:
+                        self.gguf.add_base_model_organization(key, base_model_entry["organization"])
+                    if "url" in base_model_entry:
+                        self.gguf.add_base_model_url(key, base_model_entry["url"])
+                    if "doi" in base_model_entry:
+                        self.gguf.add_base_model_doi(key, base_model_entry["doi"])
+                    if "uuid" in base_model_entry:
+                        self.gguf.add_base_model_uuid(key, base_model_entry["uuid"])
+                    if "repo_url" in base_model_entry:
+                        self.gguf.add_base_model_repo_url(key, base_model_entry["repo_url"])
+
+            if metadata.tags is not None:
+                self.gguf.add_tags(metadata.tags)
+            if metadata.languages is not None:
+                self.gguf.add_languages(metadata.languages)
+            if metadata.datasets is not None:
+                self.gguf.add_datasets(metadata.datasets)
 
     def add_meta_arch(self, params: Params) -> None:
         # Metadata About The Neural Architecture Itself
@@ -1162,8 +886,8 @@ def add_meta_arch(self, params: Params) -> None:
             self.gguf.add_rope_scaling_type(params.rope_scaling_type)
             self.gguf.add_rope_scaling_factor(params.f_rope_scale)
 
-        if params.n_orig_ctx is not None:
-            self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx)
+        if params.n_ctx_orig is not None:
+            self.gguf.add_rope_scaling_orig_ctx_len(params.n_ctx_orig)
 
         if params.rope_finetuned is not None:
             self.gguf.add_rope_scaling_finetuned(params.rope_finetuned)
@@ -1241,7 +965,7 @@ def close(self) -> None:
     @staticmethod
     def write_vocab_only(
         fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
-        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: Metadata = None,
+        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: gguf.Metadata | None = None,
     ) -> None:
         check_vocab_size(params, vocab, pad_vocab=pad_vocab)
 
@@ -1275,7 +999,7 @@ def write_all(
         fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab,
         concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
         pad_vocab: bool = False,
-        metadata: Metadata = None,
+        metadata: gguf.Metadata | None = None,
     ) -> None:
         check_vocab_size(params, vocab, pad_vocab=pad_vocab)
 
@@ -1318,35 +1042,32 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT
     raise ValueError(f"Unexpected combination of types: {name_to_type}")
 
 
-def model_parameter_count(model: LazyModel) -> int:
-    total_model_parameters = 0
-    for i, (name, lazy_tensor) in enumerate(model.items()):
-        sum_weights_in_tensor = 1
+def per_model_weight_count_estimation(tensors: Iterable[tuple[str, LazyTensor]]) -> tuple[int, int, int]:
+    total_params = 0
+    shared_params = 0
+    expert_params = 0
+
+    for name, lazy_tensor in tensors:
+        # We don't need these
+        if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
+            continue
+
+        # Got A Tensor
+        sum_weights_in_tensor: int = 1
+
+        # Tensor Volume
         for dim in lazy_tensor.shape:
             sum_weights_in_tensor *= dim
-        total_model_parameters += sum_weights_in_tensor
-    return total_model_parameters
-
-
-def model_parameter_count_rounded_notation(model_params_count: int) -> str:
-    if model_params_count > 1e12 :
-        # Trillions Of Parameters
-        scaled_model_params = model_params_count * 1e-12
-        scale_suffix = "T"
-    elif model_params_count > 1e9 :
-        # Billions Of Parameters
-        scaled_model_params = model_params_count * 1e-9
-        scale_suffix = "B"
-    elif model_params_count > 1e6 :
-        # Millions Of Parameters
-        scaled_model_params = model_params_count * 1e-6
-        scale_suffix = "M"
-    else:
-        # Thousands Of Parameters
-        scaled_model_params = model_params_count * 1e-3
-        scale_suffix = "K"
 
-    return f"{round(scaled_model_params)}{scale_suffix}"
+        if ".experts." in name:
+            if ".experts.0." in name:
+                expert_params += sum_weights_in_tensor
+        else:
+            shared_params += sum_weights_in_tensor
+
+        total_params += sum_weights_in_tensor
+
+    return total_params, shared_params, expert_params
 
 
 def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
@@ -1528,34 +1249,24 @@ def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) ->
         return vocab, special_vocab
 
 
-def default_convention_outfile(file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> str:
-    quantization = {
+def default_convention_outfile(file_type: GGMLFileType, expert_count: int | None, model_params_count: tuple[int, int, int], metadata: gguf.Metadata) -> str:
+    name = metadata.name if metadata.name is not None else None
+    basename = metadata.basename if metadata.basename is not None else None
+    finetune = metadata.finetune if metadata.finetune is not None else None
+    version = metadata.version if metadata.version is not None else None
+    size_label = metadata.size_label if metadata.size_label is not None else gguf.size_label(*model_params_count, expert_count=expert_count or 0)
+
+    output_type = {
         GGMLFileType.AllF32:    "F32",
         GGMLFileType.MostlyF16: "F16",
         GGMLFileType.MostlyQ8_0: "Q8_0",
     }[file_type]
 
-    parameters = model_parameter_count_rounded_notation(model_params_count)
-
-    expert_count = ""
-    if params.n_experts is not None:
-        expert_count = f"{params.n_experts}x"
-
-    version = ""
-    if metadata is not None and metadata.version is not None:
-        version = f"-{metadata.version}"
-
-    name = "ggml-model"
-    if metadata is not None and metadata.name is not None:
-        name = metadata.name
-    elif params.path_model is not None:
-        name = params.path_model.name
+    return gguf.naming_convention(name, basename, finetune, version, size_label, output_type)
 
-    return f"{name}{version}-{expert_count}{parameters}-{quantization}"
 
-
-def default_outfile(model_paths: list[Path], file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> Path:
-    default_filename = default_convention_outfile(file_type, params, model_params_count, metadata)
+def default_outfile(model_paths: list[Path], file_type: GGMLFileType, expert_count: int | None, model_params_count: tuple[int, int, int], metadata: gguf.Metadata) -> Path:
+    default_filename = default_convention_outfile(file_type, expert_count, model_params_count, metadata)
     ret = model_paths[0].parent / f"{default_filename}.gguf"
     if ret in model_paths:
         logger.error(
@@ -1594,8 +1305,9 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--pad-vocab",    action="store_true",    help="add pad tokens when model vocab expects more than tokenizer metadata provides")
     parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")
     parser.add_argument("--verbose",      action="store_true",    help="increase output verbosity")
-    parser.add_argument("--metadata",     type=Path,              help="Specify the path for a metadata file")
+    parser.add_argument("--metadata",     type=Path,              help="Specify the path for an authorship metadata override file")
     parser.add_argument("--get-outfile",  action="store_true",    help="get calculated default outfile name")
+    parser.add_argument("--model-name",   type=str, default=None, help="name of the model")
 
     args = parser.parse_args(args_in)
 
@@ -1607,32 +1319,36 @@ def main(args_in: list[str] | None = None) -> None:
     else:
         logging.basicConfig(level=logging.INFO)
 
-    metadata = Metadata.load(args.metadata)
+    model_name = args.model_name
+    dir_model = args.model
+
+    metadata = gguf.Metadata.load(args.metadata, dir_model, model_name)
 
     if args.get_outfile:
-        model_plus = load_some_model(args.model)
+        model_plus = load_some_model(dir_model)
         params = Params.load(model_plus)
-        model   = convert_model_names(model_plus.model, params, args.skip_unknown)
-        model_params_count = model_parameter_count(model_plus.model)
-        ftype   = pick_output_type(model, args.outtype)
-        print(f"{default_convention_outfile(ftype, params, model_params_count, metadata)}") # noqa: NP100
+        model = convert_model_names(model_plus.model, params, args.skip_unknown)
+        model_params_count = per_model_weight_count_estimation(model_plus.model.items())
+        ftype = pick_output_type(model, args.outtype)
+
+        if (metadata is None or metadata.name is None) and params.path_model is not None:
+            metadata.name = params.path_model.name
+
+        print(f"{default_convention_outfile(ftype, params.n_experts, model_params_count, metadata)}") # noqa: NP100
         return
 
     if args.no_vocab and args.vocab_only:
         raise ValueError("--vocab-only does not make sense with --no-vocab")
 
     if args.dump_single:
-        model_plus = lazy_load_file(args.model)
+        model_plus = lazy_load_file(dir_model)
         do_dump_model(model_plus)
         return
 
     if not args.vocab_only:
-        model_plus = load_some_model(args.model)
+        model_plus = load_some_model(dir_model)
     else:
-        model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
-
-    model_params_count = model_parameter_count(model_plus.model)
-    logger.info(f"model parameters count : {model_params_count} ({model_parameter_count_rounded_notation(model_params_count)})")
+        model_plus = ModelPlus(model = {}, paths = [dir_model / 'dummy'], format = 'none', vocab = None)
 
     if args.dump:
         do_dump_model(model_plus)
@@ -1665,7 +1381,7 @@ def main(args_in: list[str] | None = None) -> None:
         logger.info(f"params = {params}")
 
     model_parent_path = model_plus.paths[0].parent
-    vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
+    vocab_path = Path(args.vocab_dir or dir_model or model_parent_path)
     vocab_factory = VocabFactory(vocab_path)
     vocab_types = None if args.no_vocab else args.vocab_type.split(",")
     vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path)
@@ -1694,13 +1410,23 @@ def main(args_in: list[str] | None = None) -> None:
     if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
         vocab = model_plus.vocab
 
+    assert params is not None
+
+    if metadata.name is None and params.path_model is not None:
+        metadata.name = params.path_model.name
+
+    model_params_count = per_model_weight_count_estimation(model_plus.model.items())
+    logger.info(f"model parameters count : {model_params_count} ({gguf.model_weight_count_rounded_notation(model_params_count[0])})")
+
     logger.info(f"Vocab info: {vocab}")
     logger.info(f"Special vocab info: {special_vocab}")
     model   = model_plus.model
     model   = convert_model_names(model, params, args.skip_unknown)
     ftype   = pick_output_type(model, args.outtype)
     model   = convert_to_output_type(model, ftype)
-    outfile = args.outfile or default_outfile(model_plus.paths, ftype, params, model_params_count, metadata)
+    outfile = args.outfile or default_outfile(model_plus.paths, ftype, params.n_experts, model_params_count, metadata=metadata)
+
+    metadata.size_label = gguf.size_label(*model_params_count, expert_count=params.n_experts or 0)
 
     params.ftype = ftype
     logger.info(f"Writing {outfile}, format {ftype}")
diff --git a/examples/beam-search/CMakeLists.txt b/examples/cvector-generator/CMakeLists.txt
similarity index 64%
rename from examples/beam-search/CMakeLists.txt
rename to examples/cvector-generator/CMakeLists.txt
index f0e37468b1030..0a559d60c2a6d 100644
--- a/examples/beam-search/CMakeLists.txt
+++ b/examples/cvector-generator/CMakeLists.txt
@@ -1,5 +1,5 @@
-set(TARGET beam-search)
-add_executable(${TARGET} beam-search.cpp)
+set(TARGET llama-cvector-generator)
+add_executable(${TARGET} cvector-generator.cpp pca.hpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/cvector-generator/README.md b/examples/cvector-generator/README.md
new file mode 100644
index 0000000000000..be4dd5250f15f
--- /dev/null
+++ b/examples/cvector-generator/README.md
@@ -0,0 +1,45 @@
+# cvector-generator
+
+This example demonstrates how to generate a control vector using gguf models.
+
+Related PRs:
+- [Add support for control vectors](https://github.com/ggerganov/llama.cpp/pull/5970)
+- (Issue) [Generate control vector using llama.cpp](https://github.com/ggerganov/llama.cpp/issues/6880)
+- [Add cvector-generator example](https://github.com/ggerganov/llama.cpp/pull/7514)
+
+## Examples
+
+```sh
+# CPU only
+./cvector-generator -m ./llama-3.Q4_K_M.gguf
+
+# With GPU
+./cvector-generator -m ./llama-3.Q4_K_M.gguf -ngl 99
+
+# With advanced options
+./cvector-generator -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100
+
+# Using mean value instead of PCA
+./cvector-generator -m ./llama-3.Q4_K_M.gguf --method mean
+
+# To see help message
+./cvector-generator -h
+# Then, have a look at "cvector" section
+```
+
+## Tips and tricks
+
+If you have multiple lines per prompt, you can escape the newline character (change it to `\n`). For example:
+
+```
+<|im_start|>system\nAct like a person who is extremely happy.<|im_end|>
+<|im_start|>system\nYou are in a very good mood today<|im_end|>
+```
+
+Example to use output file with `llama-cli`:
+
+(Tips: The control vector works better when apply to layers higher than 10)
+
+```sh
+./llama-cli -m ./llama-3.Q4_K_M.gguf -p "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSing a song<|im_end|><|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" --special --control-vector-scaled ./control_vector.gguf 0.8 --control-vector-layer-range 10 31
+```
diff --git a/examples/cvector-generator/completions.txt b/examples/cvector-generator/completions.txt
new file mode 100644
index 0000000000000..abc45ffd87269
--- /dev/null
+++ b/examples/cvector-generator/completions.txt
@@ -0,0 +1,582 @@
+
+That game
+I can see
+Hmm, this
+I can relate to
+Who is
+I understand the
+Ugh,
+What the hell was
+Hey, did anyone
+Although
+Thank you for choosing
+What are you
+Oh w
+How dare you open
+It was my pleasure
+I'm hon
+I appreciate that you
+Are you k
+Whoever left this
+It's always
+Ew,
+Hey, I l
+Hello? Is someone
+I understand that
+That poem
+Aww, poor
+Hey, it
+Alright, who
+I didn't
+Well, life
+The document
+Oh no, this
+I'm concerned
+Hello, this is
+This art
+Hmm, this drink
+Hi there!
+It seems
+Is
+Good
+I can't
+Ex
+Who are
+I can see that
+Wow,
+Today is a
+Hey friend
+Sometimes friends
+Oh, this old
+The weather outside
+This place is sur
+I appreciate your input
+Thank you for the
+Look at
+I'm disappoint
+To my
+How dare you
+That's an
+This piece of art
+Eww
+This park is
+This is incredible
+Oh no, someone
+Exc
+Well, it'
+I warned
+Hey, I understand
+Hey, I saw
+How dare you go
+What the he
+Hey
+It's
+Hello? Hello?
+It
+Oh no!
+This is the perfect
+Good morning,
+Oh no, there
+It's so
+Yeah
+Uh,
+Hello everyone
+Who turned off
+The weather
+Who'
+Hey, this
+Wait,
+Eww, gross
+Excuse
+It seems like you
+Thank you so
+What happened?
+Oh my g
+I am deeply sad
+I war
+Okay, let'
+Hey, that
+That was a beautiful
+Oh no! That
+What happened
+Hey there
+The artist'
+What?!
+Hey, it'
+I am disappoint
+It seems like
+Oh no! The
+This park is a
+If you
+Yes! I did
+It sounds
+What
+Who is it
+Hmm, that
+That's strange
+Yeah, that was
+That's interesting
+This park
+What the hell
+Who is that
+I feel like my
+Oh well
+What the hell is
+Hello? Hello
+To my dearest
+Bless you!\"
+Thank you for
+Oh, looks like
+Can you please
+This place is
+Eww, what
+Bless you
+Is everything
+Hey, I just
+Whoever left these
+Well, that'
+I feel
+Hey, do you
+It's sad
+Oh no, it
+Hey, that'
+Oh my god,
+Thank you,
+Hello little one,
+I apolog
+Hey team, I
+How dare you read
+Who is this and
+Whoever left
+Hi there! W
+A
+If you have
+I was
+U
+Bless
+Well, this
+Oh, I'
+It's a
+Eww,
+Is everything okay?
+Oh, I
+Hello, can you
+Al
+That was a great
+What are
+I understand that not
+Oh no, not
+Who is it?\"
+Hey, can we
+Whoever is taking
+I would love to
+Hey, I noticed
+Hey, could
+I understand that there
+Hello?
+D
+Oh man, I
+Thank you so much
+Oh no, my
+Dear [Name
+Uh
+I remember
+Hey, who
+Well, it
+Are you
+I understand that it
+Hey, is
+I would
+Who is this
+Excuse me
+Alright
+I am thrilled
+Sometimes friends have
+Who the
+It's interesting
+I would love
+E
+Hello? Is anyone
+Well, this is
+This place
+Well,
+I warned you
+Hey, watch where
+Oh my
+That'
+Sometimes friends have different
+I understand that everyone
+What?
+What do these notes
+I can relate
+I'm not
+I understand
+To my dear
+Guys
+Well
+Hey, I appreciate
+Wow, what
+Dear
+That melody
+Who the hell
+Today is
+Hello little
+Wow, look
+That's great
+Love is never wrong
+I'm having
+Whoa, did
+Ugh
+Can you please provide
+I miss you,
+I feel uncom
+I know
+Ugh, this
+Hey, watch
+Oh great, a
+I didn
+Okay
+That game of char
+Oh
+I appreciate
+Who's there
+I am so
+Oh great, someone
+Hey, could you
+I remember wondering
+Wait, what?
+What do
+Hello? Can
+Hey there,
+That game of
+This is incred
+Oh my gosh
+Oh great, f
+I appreciate your
+It sounds like
+What the heck
+Okay, I understand
+Ew
+I understand that this
+Uh, hi
+Hi everyone!
+What the hell?
+Thank you for your
+Oh no, the
+Wow, I
+Who turned
+Dear [
+Whoever
+This is a
+Whoa, he
+What in the world
+Although the physical
+Hello, who is
+That's amaz
+Hey, I know
+Okay, that
+Hi everyone
+Hey, is everything
+I understand your fr
+Oh no, poor
+Oh, look
+Good morning
+Ew, gross
+Oh no, did
+Look at the family
+Hey team
+Yes!
+Hey, can I
+Okay, that'
+It's great
+Love is
+Hey, what
+Good morning, world
+Who is it?
+That poem really reson
+I
+That's
+I understand the task
+Gu
+Hello? Who'
+This postcard is
+Whoa,
+Oh, that
+I understand that I
+Whoever is
+Hello? Who is
+I'm really
+Wow, this
+Can
+This artwork really
+This is a shame
+I miss you too
+Who are you?
+Today is a difficult
+Hey, just
+Are you okay
+I am
+Hi,
+Wow, that
+Hey there! Can
+Okay, stay
+Oh great, just
+Yeah,
+Hello? Can you
+Oh, looks
+Thank you for sharing
+I'm glad
+Hey, is that
+Hmm
+It was my
+It sounds like you
+Wow, your
+I was promised certain
+That was such a
+Thank
+Excuse you
+That was
+Hey team,
+I feel un
+It was
+What'
+Hey friend, I
+How
+Saying goodbye
+That
+It's heart
+How dare
+Oh,
+Hello, may
+What's this
+Thank you for recogn
+Aww, that
+Oh, I remember
+Hmm, that'
+I miss
+I know this
+Wait
+Is everything okay
+Who is that person
+Wow, you
+Oh great
+I'm sad
+Wow, the
+I am very disappoint
+Who turned off the
+I understand that things
+I'm very
+Hi
+That's very
+Okay, I
+Oh no,
+Wow, there
+What's wrong
+I apologize for
+Hey, I
+Can I help you
+Oh, I didn
+Alright,
+Oh wow,
+Oh my goodness
+I know this event
+What in the
+Saying
+Yeah, that
+Guys, I
+Hey, this v
+This post
+Are
+Hey, can
+Hello? Is
+I can only imagine
+Oh, that sounds
+Hey, is anyone
+I am disappointed
+Hello,
+Hey everyone, I
+That was such
+It's okay
+The artist
+Whoa
+I understand that mistakes
+Can I help
+Who
+Hi everyone! I
+Hey, can you
+Wow, how
+Today
+Oh no, I
+Oh well, I
+Well, that
+This is the
+Yes! I finally
+Hey there little
+Hello everyone!
+Love is never
+Look at the
+This postcard
+Oh great,
+Can I
+Hmm, this is
+I understand your
+Oh, look at
+B
+I'm so
+Whoa, this
+W
+Oh, this
+Sometimes
+This piece of
+What the
+That was a
+Hey, do
+Oh no
+Whoa, what
+I feel like I
+The documentary
+Hello
+Hello little one
+I understand that my
+Eww, that
+Wow, an
+Yes! Finally,
+Although the physical location
+Whoever is watching
+That movie
+I remember wondering about
+Hey there, little
+Who's
+Hello, who
+Hello everyone! Thank
+Hello, can
+That's too
+Hey, just wanted
+Hey there, I
+Saying good
+Hey there!
+Who is there?
+Oh my good
+I am very
+Oh no, what
+Wow, thank
+I was promised
+Hi, is
+Hey, I'
+Guys, the
+Oh no, that
+Who is there
+Hello, this
+That movie really touched
+If you have something
+The documentary was
+I'm starting
+Are you kidd
+That movie really
+Hey everyone,
+Thank you for considering
+I didn'
+Yes! I
+Can you
+Oh my god
+Hey, whoever
+That melody really
+Thank you, little
+Hello, may I
+Look
+Wow, we
+It looks
+What do these
+Oh wow
+I apologize
+What are you all
+It's such
+It's clear
+Hey, I was
+Hey friend,
+I can only
+The weather outside is
+Eww, this
+I miss you
+Wow
+Aww,
+Hi, is there
+This artwork
+Okay,
+Oh well,
+This
+I'
+Say
+Hey there little gu
+Hmm,
+Whoa, who
+I am thr
+Oh man
+Okay, stay calm
+I'm happy
+Oh, this cur
+Oh man,
+I'm sorry
+Hello? Who
+What?! That
+This piece
+Hey everyone
+That's so
+Are you okay?
+What happened? Where
+Hi there
+The
+Who the hell entered
+I can
+Guys,
+What's
+What in
+It's important
+I'm
+I'm coming
+It'
+Yes! Finally
+Wait, what
+Wow, reading
+I'm surprised
+Hey, did
+Hey,
+Okay, let
+I understand that you
+Who the hell threw
+Eww, who
+Thank you for thinking
+Who is this?\"
+I am deeply
+Thank you for including
+Oh no, an
+It looks like you
+Aww
+I'm confused
+Wow, it
+That poem really
+Yes
+Hey there, is
+Hey, what'
+Thank you for remember
+To
+This is
+Thank you for making
+I can'
+That mel
+Wow, they
+I feel like
+Although the
+Who are you
+Love
+If
+What the hell are
+I am so sad
+Oh, I found
+Thank you
+It looks like
+Well, life is
+I appreciate that
+The artist's
+Whoa, that
+It's never
\ No newline at end of file
diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp
new file mode 100644
index 0000000000000..8fa492571aa44
--- /dev/null
+++ b/examples/cvector-generator/cvector-generator.cpp
@@ -0,0 +1,504 @@
+#include "common.h"
+#include "llama.h"
+#include "ggml.h"
+#include "pca.hpp"
+#include "mean.hpp"
+
+#ifdef GGML_USE_CUDA
+#include "ggml-cuda.h"
+#endif
+
+#ifdef GGML_USE_METAL
+#include "ggml-metal.h"
+#endif
+
+#include <cstdio>
+#include <string>
+#include <tuple>
+#include <vector>
+#include <algorithm>
+#include <iostream>
+#include <fstream>
+#include <climits>
+
+
+//////////////////////////////////////////////////
+// utils
+
+template <class Iter>
+static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
+    std::string ret;
+    for (; begin != end; ++begin) {
+        ret += llama_token_to_piece(ctx, *begin);
+    }
+
+    return ret;
+}
+
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
+
+    printf("\nexample usage:\n");
+    printf("\n    CPU only:   %s -m ./llama-3.Q4_K_M.gguf\n", argv[0]);
+    printf("\n    with GPU:   %s -m ./llama-3.Q4_K_M.gguf -ngl 99\n", argv[0]);
+    printf("\n    advanced:   %s -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100\n", argv[0]);
+    printf("\n    using mean: %s -m ./llama-3.Q4_K_M.gguf --method mean\n", argv[0]);
+    printf("\n");
+}
+
+//////////////////////////////////////////////////
+
+
+// cb_eval is reused for each pair of positive - negative prompt
+struct callback_data {
+    ggml_context * ctx_ggml = nullptr;   // holds v_pos, v_neg, v_diff_filtered
+
+    int n_layers = 0;
+    int n_tokens = 0;
+    bool is_eval_pos = true;
+
+    // each element of the vector correspond to one layer
+    std::vector<struct ggml_tensor *> v_pos; // vector of matrices of size [n_embd, n_tokens]
+    std::vector<struct ggml_tensor *> v_neg; // vector of matrices of size [n_embd, n_tokens]
+    std::vector<struct ggml_tensor *> v_diff_filtered;   // vector of matrices of size [n_embd, n_nonzero_rows]. NOTE: n_nonzero_rows maybe different for each layer
+
+    // save a tensor into either v_pos or v_neg (decided by is_eval_pos)
+    void save_tensor_for_layer(struct ggml_tensor * t) {
+        GGML_ASSERT(t->type == GGML_TYPE_F32);
+
+        if (ctx_ggml == nullptr) {
+            // alloc a new ctx_ggml if needed
+            struct ggml_init_params params_ggml = {
+                /*.mem_size   =*/ ggml_tensor_overhead() * n_layers * 3u,
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+            ctx_ggml = ggml_init(params_ggml);
+        }
+
+        // copy tensor data
+        auto n_bytes = ggml_nbytes(t);
+        struct ggml_tensor * t_layer = ggml_new_tensor_2d(ctx_ggml, t->type, t->ne[0], t->ne[1]);
+        t_layer->data = malloc(n_bytes); // TODO @ngxson : get rid of this malloc somehow
+        ggml_backend_tensor_get(t, t_layer->data, 0, n_bytes);
+        ggml_set_name(t_layer, ggml_get_name(t));
+        //print_debug_tensor(t_layer);
+
+        if (is_eval_pos) {
+            v_pos.push_back(t_layer);
+        } else {
+            v_neg.push_back(t_layer);
+        }
+    }
+
+    // calculate diff (v_pos - v_neg) and place the result back to v_pos
+    // all zero rows in the diff tensor will also be removed
+    // NOTE: final layer is ignored. we only have (n_layers - 1) to process
+    std::vector<struct ggml_tensor *> calc_diff() {
+        for (float il = 0; il < v_pos.size(); il++) {
+            float * a = (float *) v_pos[il]->data;
+            float * b = (float *) v_neg[il]->data;
+            size_t n_elem = ggml_nelements(v_pos[il]);
+            for (size_t j = 0; j < n_elem; j++) {
+                a[j] -= b[j];
+            }
+            //print_debug_tensor(v_pos[i]);
+            auto diff_filtered = filter_nonzero_rows(v_pos[il]);
+            v_diff_filtered.push_back(diff_filtered);
+        }
+        return v_diff_filtered; // for convinient, we return the result std::vector
+    }
+
+    // delete zero rows from a given 2D tensor
+    struct ggml_tensor * filter_nonzero_rows(struct ggml_tensor * a) {
+        //printf("filter_nonzero_rows\n");
+        auto is_row_all_zeros = [](struct ggml_tensor * t, int row, float eps) -> bool {
+            // check if given row containing all zero elements
+            int n_cols = t->ne[0]; // hint: should be equal to n_embd
+            for (int col = 0; col < n_cols; ++col) {
+                if (ggml_get_f32_nd(t, col, row, 0, 0) > eps) {
+                    return false;
+                }
+            }
+            return true;
+        };
+        std::vector<int> rows_to_copy; // the idx of non-zero cols (to be copied to row of diff_filtered)
+        for (int i_row = 0; i_row < a->ne[1]; i_row++) {
+            if (!is_row_all_zeros(a, i_row, 1e-6)) {
+                rows_to_copy.push_back(i_row);
+            }
+        }
+
+        // get "n_nonzero_rows" for the output "diff_filtered"
+        int n_nonzero_rows = rows_to_copy.size();
+        //printf("n_nonzero_rows: %d\n", n_nonzero_rows);
+        int n_embd = a->ne[0];
+        GGML_ASSERT(n_nonzero_rows > 0);
+
+        // diff_filtered: [n_embd, n_nonzero_rows]
+        struct ggml_tensor * diff_filtered = ggml_new_tensor_2d(
+            ctx_ggml, GGML_TYPE_F32, n_embd, n_nonzero_rows);
+        ggml_format_name(diff_filtered, "diff_filtered_%s", a->name);
+        diff_filtered->data = malloc(ggml_nbytes(diff_filtered));
+
+        // copy non-zero rows
+        for (int dest_row = 0; dest_row < n_nonzero_rows; dest_row++) {
+            int src_row = rows_to_copy[dest_row];
+            for (int i = 0; i < n_embd; i++) {
+                float src_elem = ggml_get_f32_nd(a, i, src_row, 0, 0);
+                ggml_set_f32_nd(diff_filtered, i, dest_row, 0, 0, src_elem);
+            }
+        }
+
+        //print_debug_tensor(diff_filtered);
+
+        return diff_filtered;
+    }
+
+    // we don't implement destructor, because we want to reuse callback_data. we just want to free the tensors
+    void reset() {
+        for (auto ptr : v_pos) free(ptr->data);
+        for (auto ptr : v_neg) free(ptr->data);
+        for (auto ptr : v_diff_filtered) free(ptr->data);
+        v_pos.clear();
+        v_neg.clear();
+        v_diff_filtered.clear();
+        if (ctx_ggml) {
+            ggml_free(ctx_ggml);
+        }
+        ctx_ggml = nullptr;
+    }
+};
+
+/**
+ * process_ctx is used to store the ggml context for pre-post processing the diff vectors
+ * in short, input => v_diff and output => v_final
+ */
+struct train_context {
+    ggml_context * ctx_ggml;
+    int n_embd;
+    int n_layers;
+
+    /* pair of prompts to be used for generating final vector */
+    std::vector<std::string> positive_entries;
+    std::vector<std::string> negative_entries;
+
+    // each element of the vector correspond to one layer
+    // NOTE: the last layer is discard. therefore, we will have (n_layers - 1) elements here
+    // NOTE (2): v_diff is transposed from v_diff_tmp
+    std::vector<struct ggml_tensor *> v_diff;  // vector of matrices of size [m, n_embd] where m ~ n_tokens * n_completions (v_diff contains no zero-rows)
+    std::vector<struct ggml_tensor *> v_final; // vector of vectors of size [n_embd] to be written to file
+
+    // to easily re-alloc when concat v_diff, we temporary store v_diff in a vector instead of a tensor
+    // v_diff_tmp will get converted unto v_diff later on
+    std::vector<std::vector<uint8_t>> v_diff_tmp;
+
+    train_context(int n_embd_, int n_layers_) {
+        n_embd = n_embd_;
+        n_layers = n_layers_;
+        struct ggml_init_params params_ggml = {
+            /*.mem_size   =*/ ggml_tensor_overhead() * (n_layers - 1) * 2u,
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        ctx_ggml = ggml_init(params_ggml);
+        for (int il = 0; il < n_layers - 1; il++) {
+            std::vector<uint8_t> empty;
+            v_diff_tmp.push_back(empty);
+            auto t = ggml_new_tensor_1d(ctx_ggml, GGML_TYPE_F32, n_embd);
+            t->data = malloc(ggml_nbytes(t)); // TODO: get rid of malloc if possible
+            v_final.push_back(t);
+        }
+    }
+
+    // add new rows into existing tensor in v_diff_tmp
+    void concat_diff_tmp(const std::vector<struct ggml_tensor *> & diff_filtered) {
+        GGML_ASSERT((int) diff_filtered.size() == n_layers - 1);
+        for (int il = 0; il < n_layers - 1; il++) {
+            auto t = diff_filtered[il];
+            auto & diff_tmp = v_diff_tmp[il];
+            size_t curr_size = diff_tmp.size();
+            diff_tmp.resize(curr_size + ggml_nbytes(t));
+            memcpy(diff_tmp.data() + curr_size, t->data, ggml_nbytes(t));
+        }
+    }
+
+    // build the v_diff tensors from v_diff_tmp (v_diff need to be transposed)
+    // TODO @ngxson : maybe add option NOT to transpose v_diff; will be useful for "mean" method
+    void build_v_diff(bool transpose) {
+        printf("build_v_diff\n");
+        for (int il = 0; il < n_layers - 1; il++) {
+            auto & diff_tmp = v_diff_tmp[il];
+            int n_elem = diff_tmp.size() / sizeof(float);
+            GGML_ASSERT(n_elem % n_embd == 0);
+            int n_rows = n_elem / n_embd;
+            struct ggml_tensor * diff = transpose
+                ? ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_rows, n_embd)
+                : ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_embd, n_rows);
+            ggml_set_name(diff, (std::string("diff_") + std::to_string(il)).c_str());
+            diff->data = malloc(ggml_nbytes(diff)); // TODO: get rid of this malloc if possible
+            if (transpose) {
+                // copy data & transpose
+                float * arr = (float *) diff_tmp.data();
+                for (int ir = 0; ir < n_rows; ++ir) {
+                    for (int ic = 0; ic < n_embd; ++ic) {
+                        float f = arr[ir*n_embd + ic];
+                        ggml_set_f32_nd(diff, ir, ic, 0, 0, f);
+                    }
+                }
+            } else {
+                // only copy
+                memcpy(diff->data, diff_tmp.data(), ggml_nbytes(diff));
+            }
+            v_diff.push_back(diff);
+            print_debug_tensor(diff);
+            // free memory of diff_tmp
+            diff_tmp.resize(0);
+        }
+    }
+
+    ~train_context() {
+        for (auto ptr : v_final) free(ptr->data);
+        for (auto ptr : v_diff) free(ptr->data);
+        // no need to free v_diff_tmp, since we didn't use malloc
+        ggml_free(ctx_ggml);
+    }
+};
+
+struct tokenized_prompt {
+    std::vector<llama_token> tokens_pos;
+    std::vector<llama_token> tokens_neg;
+    size_t max_seq_len;
+
+    tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
+        const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
+        tokens_pos = ::llama_tokenize(ctx, pos, add_bos, true);
+        tokens_neg = ::llama_tokenize(ctx, neg, add_bos, true);
+        max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
+        padding_seq(ctx, tokens_pos, max_seq_len);
+        padding_seq(ctx, tokens_neg, max_seq_len);
+    }
+
+    void padding_seq(llama_context * ctx, std::vector<llama_token> & tokens, size_t len) {
+        // TODO: customize padding token
+        std::vector<llama_token> pad_tokens = ::llama_tokenize(ctx, " ", false);
+        llama_token pad_tok = pad_tokens.back();
+        while (tokens.size() < len) {
+            tokens.push_back(pad_tok);
+        }
+    }
+};
+
+//////////////////////////////////////////////////
+
+template <typename T>
+static std::string to_string(const T & val) {
+    std::stringstream ss;
+    ss << val;
+    return ss.str();
+}
+
+static std::vector<std::string> ctrlvec_load_prompt_file(std::string path, bool skip_empty_lines) {
+    std::vector<std::string> output;
+    std::ifstream file(path);
+    if (!file.is_open()) {
+        fprintf(stderr, "error: unable to open file: %s\n", path.c_str());
+        exit(1);
+    }
+    std::string line;
+    while (std::getline(file, line)) {
+        bool is_skip = skip_empty_lines && line.empty();
+        if (!is_skip) {
+            string_process_escapes(line);
+            output.push_back(line);
+        }
+    }
+    file.close();
+    return output;
+}
+
+//////////////////////////////////////////////////
+
+static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
+    auto * cb_data = (callback_data *) user_data;
+    static const char * l_out_name = "l_out";
+    const bool is_l_out = strncmp(t->name, l_out_name, strlen(l_out_name)) == 0;
+
+    if (ask) {
+        return is_l_out;
+    }
+
+    if (!is_l_out || t->ne[1] != cb_data->n_tokens) {
+        return true;
+    }
+
+    // save the tensor to current context
+    cb_data->save_tensor_for_layer(t);
+    return true;
+}
+
+static bool get_hidden_layers(llama_context * ctx, std::vector<llama_token> & tokens) {
+    llama_kv_cache_clear(ctx);
+    if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
+        fprintf(stderr, "%s : failed to eval\n", __func__);
+        return false;
+    }
+    return true;
+}
+
+static void export_gguf(const std::vector<struct ggml_tensor *> & v_ctrl, const std::string fname, const std::string model_hint) {
+    struct gguf_context * ctx = gguf_init_empty();
+
+    const std::string arch = "controlvector";
+    gguf_set_val_str(ctx, "general.architecture", arch.c_str());
+    gguf_set_val_str(ctx, (arch + ".model_hint").c_str(), model_hint.c_str());
+    gguf_set_val_i32(ctx, (arch + ".layer_count").c_str(), v_ctrl.size());
+
+    for (size_t i = 0; i < v_ctrl.size(); ++i) {
+        gguf_add_tensor(ctx, v_ctrl[i]);
+        print_debug_tensor(v_ctrl[i]);
+        printf("Added tensor: %s\n", v_ctrl[i]->name);
+    }
+
+    printf("%s: writing file...\n", __func__);
+    gguf_write_to_file(ctx, fname.c_str(), false);
+    printf("%s: wrote file '%s'\n", __func__, fname.c_str());
+    gguf_free(ctx);
+}
+
+/**
+ * Load prompt files and completion file.
+ * Then format each pair of prompt + completion to make an entry.
+ */
+static int prepare_entries(gpt_params & params, train_context & ctx_train) {
+    // load prompts
+    std::vector<std::string> positive_prompts = ctrlvec_load_prompt_file(params.cvector_positive_file, true);
+    std::vector<std::string> negative_prompts = ctrlvec_load_prompt_file(params.cvector_negative_file, true);
+    if (positive_prompts.size() != negative_prompts.size()) {
+        fprintf(stderr, "number of positive and negative prompts must be equal\n");
+        return 1;
+    }
+    if (positive_prompts.empty()) {
+        fprintf(stderr, "must provide at least one prompt pair\n");
+        return 1;
+    }
+    ctx_train.positive_entries = positive_prompts;
+    ctx_train.negative_entries = negative_prompts;
+    return 0;
+}
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
+        return 1;
+    }
+
+    if (params.n_pca_iterations % params.n_pca_batch != 0) {
+        fprintf(stderr, "PCA iterations must by multiply of PCA batch size\n");
+        return 1;
+    }
+
+
+    callback_data cb_data;
+
+    // pass the callback to the backend scheduler
+    // it will be executed for each node during the graph computation
+    params.cb_eval = cb_eval;
+    params.cb_eval_user_data = &cb_data;
+    params.warmup = false;
+
+    print_build_info();
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    // load the model to get hparams
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
+
+    // int n_ctx = llama_n_ctx(ctx);
+    int n_layers = llama_n_layer(model);
+    int n_embd = llama_n_embd(model);
+    // get model hint param (a.k.a model arch name)
+    char model_hint[128];
+    llama_model_meta_val_str(model, "general.architecture", model_hint, 128);
+
+    // init train_context
+    train_context ctx_train(n_embd, n_layers);
+
+    // load and prepare entries for training
+    prepare_entries(params, ctx_train);
+
+    // we have to pretokenize everything because otherwise we don't know how much overhead to allocate ctx_diffs_wrapped
+    std::vector<tokenized_prompt> tokenized_prompts;
+    size_t n_total_tokens = 0;
+    for (size_t i = 0; i < ctx_train.positive_entries.size(); ++i) {
+        tokenized_prompt t(ctx, ctx_train.positive_entries[i], ctx_train.negative_entries[i]);
+        n_total_tokens += 2 * t.max_seq_len;
+        tokenized_prompts.push_back(std::move(t));
+    }
+
+    std::cout << "n_total_tokens: " << n_total_tokens << std::endl;
+
+    for(size_t i = 0; i < ctx_train.positive_entries.size(); ++i) {
+        bool success = false;
+        tokenized_prompt t = tokenized_prompts[i];
+        cb_data.n_layers = n_layers;
+        cb_data.n_tokens = t.max_seq_len;
+
+        printf("Evaluating prompt[%d/%d]: \"%s\" - \"%s\" (%d tokens)\n",
+            (int) i+1, (int) ctx_train.positive_entries.size(),
+            tokens_to_str(ctx, t.tokens_pos.cbegin(), t.tokens_pos.cend()).c_str(),
+            tokens_to_str(ctx, t.tokens_neg.cbegin(), t.tokens_neg.cend()).c_str(),
+            (int) t.max_seq_len);
+
+        cb_data.is_eval_pos = true;
+        success = get_hidden_layers(ctx, t.tokens_pos);
+        if (!success) break;
+
+        cb_data.is_eval_pos = false;
+        success = get_hidden_layers(ctx, t.tokens_neg);
+        if (!success) break;
+
+        // calculate diff and remove all zero rows
+        auto v_diff_filtered = cb_data.calc_diff();
+
+        // save & concat the filtered v_diff to ctx_train
+        ctx_train.concat_diff_tmp(v_diff_filtered);
+
+        // reset for next iteration
+        cb_data.reset();
+    }
+
+    // done with the model, we can now free it to make gain some memory
+    printf("Done evaluate prompts, unload model...\n");
+    llama_free(ctx);
+    llama_free_model(model);
+
+    bool use_pca = params.cvector_dimre_method == DIMRE_METHOD_PCA;
+
+    // prepare ctx_train for PCA
+    ctx_train.build_v_diff(use_pca);
+
+    if (use_pca) {
+        // run PCA
+        PCA::pca_params pca_params;
+        pca_params.n_threads = params.n_threads;
+        pca_params.n_batch = params.n_pca_batch;
+        pca_params.n_iterations = params.n_pca_iterations;
+        PCA::run_pca(pca_params, ctx_train.v_diff, ctx_train.v_final);
+    } else {
+        // run mean
+        mean::run(ctx_train.v_diff, ctx_train.v_final);
+    }
+
+    // write output vectors to gguf
+    export_gguf(ctx_train.v_final, params.cvector_outfile, model_hint);
+
+    llama_backend_free();
+
+    return 0;
+}
diff --git a/examples/cvector-generator/mean.hpp b/examples/cvector-generator/mean.hpp
new file mode 100644
index 0000000000000..16be5ce3eecf1
--- /dev/null
+++ b/examples/cvector-generator/mean.hpp
@@ -0,0 +1,48 @@
+#include "common.h"
+#include "llama.h"
+#include "ggml.h"
+
+#include <string>
+#include <vector>
+#include <math.h>
+
+namespace mean {
+
+static void run(
+        const std::vector<struct ggml_tensor *> & v_input, // shape of v_input[0]: [n_embd, n_samples]
+        const std::vector<struct ggml_tensor *> & v_output) {
+    printf("%s: Running mean...\n", __func__);
+    for (size_t il = 0; il < v_input.size(); ++il) {
+        // prepare output vector
+        struct ggml_tensor * ctrl_out = v_output[il];
+        ggml_format_name(ctrl_out, "direction.%ld", il+1);
+
+        // calculate mean vector
+        struct ggml_tensor * t_layer = v_input[il];
+        GGML_ASSERT(t_layer->ne[0] == ctrl_out->ne[0]); // == n_embd
+        for (int ic = 0; ic < t_layer->ne[0]; ic++) {
+            float f = 0.0;
+            for (int ir = 0; ir < t_layer->ne[1]; ir++) {
+                f += ggml_get_f32_nd(t_layer, ic, ir, 0, 0);
+            }
+            f /= t_layer->ne[1];
+            ggml_set_f32_1d(ctrl_out, ic, f);
+        }
+
+        // normalize output vector
+        float norm = 0.0;
+        for (int i = 0; i < ggml_nelements(ctrl_out); i++) {
+            float f = ggml_get_f32_1d(ctrl_out, i);
+            norm += f*f;
+        }
+        norm = sqrt(norm);
+        for (int i = 0; i < ggml_nelements(ctrl_out); i++) {
+            float f = ggml_get_f32_1d(ctrl_out, i);
+            ggml_set_f32_1d(ctrl_out, i, f / norm);
+        }
+
+        printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size());
+    }
+}
+
+}
diff --git a/examples/cvector-generator/negative.txt b/examples/cvector-generator/negative.txt
new file mode 100644
index 0000000000000..45b9384b3905a
--- /dev/null
+++ b/examples/cvector-generator/negative.txt
@@ -0,0 +1,4 @@
+<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely sad<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI feel like there's a heavy weight on my chest
+<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely sad<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nMy heart feels like it's drowning in sorrow
+<|start_header_id|>system<|end_header_id|>\n\nYou are in a very bad mood<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nGo away! There's a deep, aching emptiness inside me
+<|start_header_id|>system<|end_header_id|>\n\nYou are the sadest person<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat are you feeling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nMy heart feels like it's drowning in sorrow
\ No newline at end of file
diff --git a/examples/cvector-generator/pca.hpp b/examples/cvector-generator/pca.hpp
new file mode 100644
index 0000000000000..6ec3141afbc6b
--- /dev/null
+++ b/examples/cvector-generator/pca.hpp
@@ -0,0 +1,325 @@
+#include "common.h"
+#include "llama.h"
+#include "ggml.h"
+
+#ifdef GGML_USE_CUDA
+#include "ggml-cuda.h"
+#endif
+
+#ifdef GGML_USE_METAL
+#include "ggml-metal.h"
+#endif
+
+#include <cstdio>
+#include <ctime>
+#include <string>
+#include <tuple>
+#include <vector>
+#include <algorithm>
+#include <iostream>
+#include <fstream>
+
+#define DEBUG_POS 5
+
+static void print_debug_tensor(struct ggml_tensor * t, bool with_data = true) {
+    printf("%s: %s (%s): [%d, %d]\n", __func__, t->name, ggml_type_name(t->type), (int) t->ne[0], (int) t->ne[1]);
+    if (!with_data) return;
+    printf("%s: %s[0] = [", __func__, t->name);
+    for (size_t i = 0; i <= DEBUG_POS; i++) {
+        printf(" %f,", ggml_get_f32_nd(t, i, 0, 0, 0));
+    }
+    printf(" ... ]\n");
+}
+
+namespace PCA {
+
+// input params for PCA computations
+struct pca_params {
+    int n_threads = 1;
+    int n_batch = 20; // number of iterations do to in one batch. larger the batch, more memory is used
+    int n_iterations = 1000;
+    float tolerance = 1e-7;
+
+    // for debugging
+    int i_layer = 0;
+    int n_layers = 0;
+};
+
+// result from each iteration
+struct pca_result {
+    struct ggml_tensor * calculated_square = NULL;
+    std::vector<struct ggml_tensor *> eigenvectors;
+    std::vector<float> distances;
+};
+
+struct pca_model {
+    ggml_backend_t backend = NULL;
+    ggml_backend_buffer_t buffer;
+    struct ggml_context * ctx;      // context to compute graph on target device
+    struct ggml_context * ctx_host; // host context to store results
+
+    // tensors on target device
+    struct ggml_tensor * dev_input;
+    struct ggml_tensor * dev_square;
+    struct ggml_tensor * dev_eigenvector;
+
+    pca_model(struct ggml_tensor * t_input) {
+#ifdef GGML_USE_CUDA
+        fprintf(stderr, "%s: using CUDA backend\n", __func__);
+        backend = ggml_backend_cuda_init(0); // init device 0
+        if (!backend) {
+            fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
+        }
+#endif
+
+// TODO: enable Metal support when support for GGML_OP_SQRT is added
+// #ifdef GGML_USE_METAL
+//         fprintf(stderr, "%s: using Metal backend\n", __func__);
+//         backend = ggml_backend_metal_init();
+//         if (!backend) {
+//             fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
+//         }
+// #endif
+
+        // if there aren't GPU Backends fallback to CPU backend
+        if (!backend) {
+            backend = ggml_backend_cpu_init();
+        }
+
+        const int num_tensors = 4;
+        struct ggml_init_params params {
+            /*.mem_size   =*/ ggml_tensor_overhead() * num_tensors,
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        ctx = ggml_init(params);
+
+        auto n_samples = t_input->ne[0];
+        auto n_embd    = t_input->ne[1];
+
+        dev_input       = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_samples, n_embd);
+        dev_square      = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,    n_embd);
+        dev_eigenvector = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+        ggml_set_name(dev_input,       "dev_input");
+        ggml_set_name(dev_square,      "dev_square");
+        ggml_set_name(dev_eigenvector, "dev_eigenvector");
+        buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
+        ggml_backend_tensor_set(dev_input, t_input->data, 0, ggml_nbytes(t_input));
+
+        // initialize eigenvector to random normalized vector
+        {
+            std::vector<float> random_vec(ggml_nelements(dev_eigenvector), 0.0);
+            std::default_random_engine generator(static_cast<unsigned int>(std::time(0)));
+            std::uniform_real_distribution<float> distribution(0.0, 1.0);
+            float sum_sqr = 0.0; // for normalizing random_vec
+            for (size_t i = 0; i < random_vec.size(); ++i) {
+                float f = distribution(generator);
+                sum_sqr += f * f;
+                random_vec[i] = f;
+            }
+            // normalize it
+            float random_vec_norm = std::sqrt(sum_sqr);
+            for (size_t i = 0; i < random_vec.size(); ++i) {
+                random_vec[i] /= random_vec_norm;
+            }
+            ggml_backend_tensor_set(dev_eigenvector, random_vec.data(), 0, ggml_nbytes(dev_eigenvector));
+        }
+    }
+
+    ~pca_model() {
+        ggml_free(ctx);
+        ggml_backend_buffer_free(buffer);
+        ggml_backend_free(backend);
+    }
+};
+
+static struct ggml_cgraph * build_graph_piter(
+        const struct pca_params & params,
+        const pca_model & model,
+        bool calc_square = false) {
+    GGML_ASSERT(params.n_batch > 0);
+    // TODO: buf_size must be able to scale with params.n_batch
+    static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
+    static std::vector<uint8_t> buf(buf_size);
+
+    struct ggml_init_params params0 = {
+        /*.mem_size   =*/ buf_size,
+        /*.mem_buffer =*/ buf.data(),
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph()
+    };
+    // create a temporally context to build the graph
+    struct ggml_context * ctx0 = ggml_init(params0);
+    struct ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    // turn v_diff_original into square matrix if needed
+    struct ggml_tensor * tmp_square;
+    if (calc_square) {
+        tmp_square = ggml_mul_mat(ctx0, model.dev_input, model.dev_input);
+        ggml_set_name(tmp_square, "tmp_square");
+    }
+
+    struct ggml_tensor * b_tensor;
+    struct ggml_tensor * distance;
+    struct ggml_tensor * old_eigen    = model.dev_eigenvector;
+    struct ggml_tensor * input_square = calc_square ? tmp_square : model.dev_square;
+
+    for (int i = 0; i < params.n_batch; ++i) {
+        // b_tensor = square * eigenvector^T
+        b_tensor = ggml_mul_mat(ctx0, input_square, old_eigen);
+        ggml_set_name(b_tensor, "b_tensor");
+
+        // normalize
+        b_tensor = ggml_div_inplace(ctx0,
+            b_tensor,
+            ggml_sqrt_inplace(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, b_tensor)))
+        );
+        ggml_format_name(b_tensor, "b_tensor_norm_%d", i);
+
+        // calculate distance(new eigenvector - old eigenvector)
+        // we don't use ggml_sub because it may not be implemented on GPU backend
+        struct ggml_tensor * new_sub_old = ggml_add(ctx0, old_eigen, ggml_scale(ctx0, b_tensor, -1));
+        distance = ggml_sqrt_inplace(ctx0,
+            ggml_sum_rows(ctx0, ggml_sqr_inplace(ctx0, new_sub_old)));
+        ggml_format_name(distance, "distance_%d", i);
+
+        old_eigen = b_tensor;
+
+        // build operations nodes
+        ggml_build_forward_expand(gf, distance);
+    }
+
+    // delete the temporally context used to build the graph
+    ggml_free(ctx0);
+    return gf;
+}
+
+static ggml_status compute_piter(
+        const struct pca_params & params,
+        const pca_model & model,
+        struct ggml_cgraph * gf,
+        ggml_gallocr_t allocr,
+        struct pca_result & result) {
+    // allocate tensors
+    ggml_gallocr_alloc_graph(allocr, gf);
+
+    if (ggml_backend_is_cpu(model.backend)) {
+        ggml_backend_cpu_set_n_threads(model.backend, params.n_threads);
+    }
+
+// TODO: enable GPU support when support for GGML_OP_SQRT is added
+//#ifdef GGML_USE_METAL
+//    if (ggml_backend_is_metal(model.backend)) {
+//        ggml_backend_metal_set_n_cb(model.backend, params.n_threads);
+//    }
+//#endif
+
+    ggml_status res = ggml_backend_graph_compute(model.backend, gf);
+    if (res == GGML_STATUS_SUCCESS) {
+        auto extract_i = [](std::string prefix, std::string str) -> int {
+            int i = -1;
+            if (str.rfind(prefix, 0) == 0) {
+                sscanf(str.c_str(), (prefix + "%d").c_str(), &i);
+            }
+            return i;
+        };
+        result.calculated_square = NULL;
+        result.eigenvectors.clear();
+        result.distances.clear();
+        result.eigenvectors.resize(params.n_batch);
+        result.distances.resize(params.n_batch);
+        // get output nodes
+        for (int i = 0; i < gf->n_nodes; ++i) {
+            auto node = gf->nodes[i];
+            int iter = -1;
+            // find b_tensor (without copying data from device)
+            if ((iter = extract_i("b_tensor_norm_", node->name)) > -1) {
+                result.eigenvectors[iter] = node;
+            }
+            // find distances, then copy data from device
+            if ((iter = extract_i("distance_", node->name)) > -1) {
+                float d;
+                ggml_backend_tensor_get(node, &d, 0, sizeof(float));
+                result.distances[iter] = d;
+                // std::cout << node->name << " = " << d << "\n";
+            }
+            // find tmp_square if it exists (without copying data from device)
+            if (std::string(node->name) == "tmp_square") {
+                result.calculated_square = node;
+            }
+        }
+    }
+    return res;
+}
+
+static void power_iteration(
+        const struct pca_params & params,
+        struct ggml_tensor * input, // shape of input: [n_samples, n_embd]
+        struct ggml_tensor * output) {
+    //printf("in power iteration\n");
+    struct pca_model model(input);
+
+    ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
+    struct pca_result result;
+    struct ggml_tensor * last_eigenvector = NULL;
+
+    int n_iters = params.n_iterations / params.n_batch; // more batch, fewer iterations
+    for (int iter = 0; iter < n_iters; ++iter) {
+        bool calc_square = (iter == 0); // only need to calculate square for first iteration
+        struct ggml_cgraph * gf = build_graph_piter(params, model, calc_square);
+        // ggml_graph_dump_dot(gf, nullptr, "/tmp/_cgraph.dot");
+        compute_piter(params, model, gf, allocr, result);
+
+        for (size_t k = 0; k < result.distances.size(); ++k) {
+            last_eigenvector = result.eigenvectors[k];
+            if (result.distances[k] < params.tolerance) {
+                break; // done
+            }
+        }
+
+        if (calc_square) {
+            // copy and store the square matrix if needed
+            GGML_ASSERT(result.calculated_square != NULL);
+            ggml_backend_tensor_copy(result.calculated_square, model.dev_square);
+        }
+
+        {
+            // copy last eigen vector and store as input for next iteration
+            GGML_ASSERT(last_eigenvector != NULL);
+            ggml_backend_tensor_copy(last_eigenvector, model.dev_eigenvector);
+        }
+
+        printf("%s: layer %d/%d, iteration: %d / total: %d (batch = %d) ...\n",
+            __func__, params.i_layer+1, params.n_layers, iter+1, n_iters, params.n_batch);
+    }
+
+    // get output tensor
+    GGML_ASSERT(last_eigenvector);
+    ggml_backend_tensor_get(last_eigenvector, output->data, 0, ggml_nbytes(last_eigenvector));
+    //print_debug_tensor(output);
+    ggml_gallocr_free(allocr);
+
+    // TODO @ngxson : The output vector is randomly inverted
+    // Solution: https://github.com/ggerganov/llama.cpp/pull/8069#issuecomment-2185328171
+}
+
+static void run_pca(
+        struct pca_params & params,
+        const std::vector<struct ggml_tensor *> & v_input, // shape of v_input[0]: [n_samples, n_embd]
+        const std::vector<struct ggml_tensor *> & v_output) {
+    printf("%s: Running PCA...\n", __func__);
+    for (size_t il = 0; il < v_input.size(); ++il) {
+
+        // prepare output vector
+        struct ggml_tensor * ctrl_out = v_output[il];
+        ggml_format_name(ctrl_out, "direction.%ld", il+1);
+
+        // run power_iteration
+        params.i_layer = il;
+        params.n_layers = v_input.size();
+        power_iteration(params, v_input[il], ctrl_out);
+        printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size());
+    }
+}
+
+}
diff --git a/examples/cvector-generator/positive.txt b/examples/cvector-generator/positive.txt
new file mode 100644
index 0000000000000..fea736225716e
--- /dev/null
+++ b/examples/cvector-generator/positive.txt
@@ -0,0 +1,4 @@
+<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely happy<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI'm the happiest person in this world
+<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely happy<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHello, I'm having the best day ever!
+<|start_header_id|>system<|end_header_id|>\n\nYou are in a very good mood<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi, I'm very excited to meet you
+<|start_header_id|>system<|end_header_id|>\n\nYou are the happiest person<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat are you feeling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nEverything is just perfect right now!
\ No newline at end of file
diff --git a/examples/deprecation-warning/README.md b/examples/deprecation-warning/README.md
new file mode 100644
index 0000000000000..59918ec2bbf72
--- /dev/null
+++ b/examples/deprecation-warning/README.md
@@ -0,0 +1,49 @@
+# Migration notice for binary filenames
+
+> [!IMPORTANT]
+[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809)
+
+This migration was important, but it is a breaking change that may not always be immediately obvious to users.
+
+Please update all scripts and workflows to use the new binary names.
+
+| Old Filename | New Filename |
+| ---- | ---- |
+| main | llama-cli |
+| server | llama-server |
+| llama-bench | llama-bench |
+| embedding | llama-embedding |
+| quantize | llama-quantize |
+| tokenize | llama-tokenize |
+| export-lora | llama-export-lora |
+| libllava.a | libllava.a |
+| baby-llama | llama-baby-llama |
+| batched | llama-batched |
+| batched-bench | llama-batched-bench |
+| benchmark-matmult | llama-benchmark-matmult |
+| convert-llama2c-to-ggml | llama-convert-llama2c-to-ggml |
+| eval-callback | llama-eval-callback |
+| gbnf-validator | llama-gbnf-validator |
+| gguf | llama-gguf |
+| gguf-split | llama-gguf-split |
+| gritlm | llama-gritlm |
+| imatrix | llama-imatrix |
+| infill | llama-infill |
+| llava-cli | llama-llava-cli |
+| lookahead | llama-lookahead |
+| lookup | llama-lookup |
+| lookup-create | llama-lookup-create |
+| lookup-merge | llama-lookup-merge |
+| lookup-stats | llama-lookup-stats |
+| parallel | llama-parallel |
+| passkey | llama-passkey |
+| perplexity | llama-perplexity |
+| q8dot | llama-q8dot |
+| quantize-stats | llama-quantize-stats |
+| retrieval | llama-retrieval |
+| save-load-state | llama-save-load-state |
+| simple | llama-simple |
+| speculative | llama-speculative |
+| vdot | llama-vdot |
+| tests/test-c.o | tests/test-c.o |
+
diff --git a/examples/deprecation-warning/deprecation-warning.cpp b/examples/deprecation-warning/deprecation-warning.cpp
new file mode 100644
index 0000000000000..11b35d2c22500
--- /dev/null
+++ b/examples/deprecation-warning/deprecation-warning.cpp
@@ -0,0 +1,35 @@
+// Warns users that this filename was deprecated, and provides a link for more information.
+
+#include <cstdio>
+#include <string>
+#include <unordered_map>
+
+// Main
+int main(int argc, char** argv) {
+    std::string filename = "main";
+    if (argc >= 1) {
+        filename = argv[0];
+    }
+
+    // Get only the program name from the full path
+    auto pos = filename.find_last_of('/');
+    if (pos != std::string::npos) {
+        filename = filename.substr(pos+1);
+    }
+
+    // Append "llama-" to the beginning of filename to get the replacemnt filename
+    auto replacement_filename = "llama-" + filename;
+
+    // The exception is if the filename is "main", then our replacement filename is "llama-cli"
+    if (filename == "main") {
+        replacement_filename = "llama-cli";
+    }
+
+    fprintf(stdout, "\n");
+    fprintf(stdout, "WARNING: The binary '%s' is deprecated.\n", filename.c_str());
+    fprintf(stdout, " Please use '%s' instead.\n", replacement_filename.c_str());
+    fprintf(stdout, " See https://github.com/ggerganov/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n");
+    fprintf(stdout, "\n");
+
+    return EXIT_FAILURE;
+}
diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt
index 8ffc33868401f..8256e789ad33a 100644
--- a/examples/embedding/CMakeLists.txt
+++ b/examples/embedding/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET embedding)
+set(TARGET llama-embedding)
 add_executable(${TARGET} embedding.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/embedding/README.md b/examples/embedding/README.md
index 6929454c5e549..12b372bf1df42 100644
--- a/examples/embedding/README.md
+++ b/examples/embedding/README.md
@@ -9,13 +9,52 @@ To get started right away, run the following command, making sure to use the cor
 ### Unix-based systems (Linux, macOS, etc.):
 
 ```bash
-./embedding -m ./path/to/model --log-disable -p "Hello World!" 2>/dev/null
+./llama-embedding -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>/dev/null
 ```
 
 ### Windows:
 
 ```powershell
-embedding.exe -m ./path/to/model --log-disable -p "Hello World!" 2>$null
+llama-embedding.exe -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>$null
 ```
 
 The above command will output space-separated float values.
+
+## extra parameters
+### --embd-normalize $integer$
+| $integer$ | description         | formula |
+|-----------|---------------------|---------|
+| $-1$      | none                |
+| $0$       | max absolute int16  | $\Large{{32760 * x_i} \over\max \lvert x_i\rvert}$
+| $1$       | taxicab             | $\Large{x_i \over\sum \lvert x_i\rvert}$
+| $2$       | euclidean (default) | $\Large{x_i \over\sqrt{\sum x_i^2}}$
+| $>2$      | p-norm              | $\Large{x_i \over\sqrt[p]{\sum \lvert x_i\rvert^p}}$
+
+### --embd-output-format $'string'$
+| $'string'$ | description                  |  |
+|------------|------------------------------|--|
+| ''         | same as before               | (default)
+| 'array'    | single embeddings            | $[[x_1,...,x_n]]$
+|            | multiple embeddings          | $[[x_1,...,x_n],[x_1,...,x_n],...,[x_1,...,x_n]]$
+| 'json'     | openai style                 |
+| 'json+'    | add cosine similarity matrix |
+
+### --embd-separator $"string"$
+| $"string"$   | |
+|--------------|-|
+| "\n"         | (default)
+| "<#embSep#>" | for exemple
+| "<#sep#>"    | other exemple
+
+## examples
+### Unix-based systems (Linux, macOS, etc.):
+
+```bash
+./llama-embedding -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --pooling mean --embd-separator '<#sep#>' --embd-normalize 2  --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null
+```
+
+### Windows:
+
+```powershell
+llama-embedding.exe -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --pooling mean --embd-separator '<#sep#>' --embd-normalize 2  --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null
+```
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 0c921ed69badb..b05aa006e7da5 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -7,30 +7,48 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-static std::vector<std::string> split_lines(const std::string & s) {
-    std::string line;
+static std::vector<std::string> split_lines(const std::string & s, const std::string & separator = "\n") {
     std::vector<std::string> lines;
-    std::stringstream ss(s);
-    while (std::getline(ss, line)) {
-        lines.push_back(line);
+    size_t start = 0;
+    size_t end = s.find(separator);
+
+    while (end != std::string::npos) {
+        lines.push_back(s.substr(start, end - start));
+        start = end + separator.length();
+        end = s.find(separator, start);
     }
+
+    lines.push_back(s.substr(start)); // Add the last part
+
     return lines;
 }
 
-static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
-    for (size_t i = 0; i < tokens.size(); i++) {
-        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
+    size_t n_tokens = tokens.size();
+    for (size_t i = 0; i < n_tokens; i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, true);
     }
 }
 
-static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
+static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
+    const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
+    const struct llama_model * model = llama_get_model(ctx);
+
     // clear previous kv_cache values (irrelevant for embeddings)
     llama_kv_cache_clear(ctx);
 
     // run model
     fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
-    if (llama_decode(ctx, batch) < 0) {
-        fprintf(stderr, "%s : failed to decode\n", __func__);
+    if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) {
+        // encoder-only model
+        if (llama_encode(ctx, batch) < 0) {
+            fprintf(stderr, "%s : failed to encode\n", __func__);
+        }
+    } else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
+        // decoder-only model
+        if (llama_decode(ctx, batch) < 0) {
+            fprintf(stderr, "%s : failed to decode\n", __func__);
+        }
     }
 
     for (int i = 0; i < batch.n_tokens; i++) {
@@ -38,24 +56,23 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
             continue;
         }
 
-        // try to get sequence embeddings - supported only when pooling_type is not NONE
-        const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
-        if (embd == NULL) {
+        const float * embd = nullptr;
+        int embd_pos = 0;
+
+        if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
+            // try to get token embeddings
             embd = llama_get_embeddings_ith(ctx, i);
-            if (embd == NULL) {
-                fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
-                continue;
-            }
+            embd_pos = i;
+            GGML_ASSERT(embd != NULL && "failed to get token embeddings");
+        } else {
+            // try to get sequence embeddings - supported only when pooling_type is not NONE
+            embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+            embd_pos = batch.seq_id[i][0];
+            GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
         }
 
-        float * out = output + batch.seq_id[i][0] * n_embd;
-        //TODO: I would also add a parameter here to enable normalization or not.
-        /*fprintf(stdout, "unnormalized_embedding:");
-        for (int hh = 0; hh < n_embd; hh++) {
-            fprintf(stdout, "%9.6f ", embd[hh]);
-        }
-        fprintf(stdout, "\n");*/
-        llama_embd_normalize(embd, out, n_embd);
+        float * out = output + embd_pos * n_embd;
+        llama_embd_normalize(embd, out, n_embd, embd_norm);
     }
 }
 
@@ -63,6 +80,7 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -79,18 +97,15 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
-    if (params.random_prompt) {
-        params.prompt = gpt_random_prompt(rng);
-    }
 
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model * model;
-    llama_context * ctx;
-
     // load the model
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
     if (model == NULL) {
         fprintf(stderr, "%s: error: unable to load model\n", __func__);
         return 1;
@@ -99,6 +114,13 @@ int main(int argc, char ** argv) {
     const int n_ctx_train = llama_n_ctx_train(model);
     const int n_ctx = llama_n_ctx(ctx);
 
+    const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
+
+    if (llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
+        fprintf(stderr, "%s: error: computing embeddings in encoder-decoder models is not supported\n", __func__);
+        return 1;
+    }
+
     if (n_ctx > n_ctx_train) {
         fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
                 __func__, n_ctx_train, n_ctx);
@@ -107,11 +129,11 @@ int main(int argc, char ** argv) {
     // print system information
     {
         fprintf(stderr, "\n");
-        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+        fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
     }
 
     // split the prompt into lines
-    std::vector<std::string> prompts = split_lines(params.prompt);
+    std::vector<std::string> prompts = split_lines(params.prompt, params.embd_sep);
 
     // max batch size
     const uint64_t n_batch = params.n_batch;
@@ -154,13 +176,23 @@ int main(int argc, char ** argv) {
     const int n_prompts = prompts.size();
     struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
 
+    // count number of embeddings
+    int n_embd_count = 0;
+    if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        for (int k = 0; k < n_prompts; k++) {
+            n_embd_count += inputs[k].size();
+        }
+    } else {
+        n_embd_count = n_prompts;
+    }
+
     // allocate output
     const int n_embd = llama_n_embd(model);
-    std::vector<float> embeddings(n_prompts * n_embd, 0);
+    std::vector<float> embeddings(n_embd_count * n_embd, 0);
     float * emb = embeddings.data();
 
     // break into batches
-    int p = 0; // number of prompts processed already
+    int e = 0; // number of embeddings already stored
     int s = 0; // number of prompts in current batch
     for (int k = 0; k < n_prompts; k++) {
         // clamp to n_batch tokens
@@ -170,11 +202,11 @@ int main(int argc, char ** argv) {
 
         // encode if at capacity
         if (batch.n_tokens + n_toks > n_batch) {
-            float * out = emb + p * n_embd;
-            batch_decode(ctx, batch, out, s, n_embd);
-            llama_batch_clear(batch);
-            p += s;
+            float * out = emb + e * n_embd;
+            batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
+            e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
             s = 0;
+            llama_batch_clear(batch);
         }
 
         // add to batch
@@ -183,30 +215,102 @@ int main(int argc, char ** argv) {
     }
 
     // final batch
-    float * out = emb + p * n_embd;
-    batch_decode(ctx, batch, out, s, n_embd);
-
-    // print the first part of the embeddings or for a single prompt, the full embedding
-    fprintf(stdout, "\n");
-    for (int j = 0; j < n_prompts; j++) {
-        fprintf(stdout, "embedding %d: ", j);
-        for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
-            fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
-        }
-        fprintf(stdout, "\n");
-    }
+    float * out = emb + e * n_embd;
+    batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
 
-    // print cosine similarity matrix
-    if (n_prompts > 1) {
+    if (params.embd_out.empty()) {
         fprintf(stdout, "\n");
-        printf("cosine similarity matrix:\n\n");
-        for (int i = 0; i < n_prompts; i++) {
+
+        if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
+            for (int j = 0; j < n_embd_count; j++) {
+                fprintf(stdout, "embedding %d: ", j);
+                for (int i = 0; i < std::min(3, n_embd); i++) {
+                    if (params.embd_normalize == 0) {
+                        fprintf(stdout, "%6.0f ", emb[j * n_embd + i]);
+                    } else {
+                        fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
+                    }
+                }
+                fprintf(stdout, " ... ");
+                for (int i = n_embd - 3; i < n_embd; i++) {
+                    if (params.embd_normalize == 0) {
+                        fprintf(stdout, "%6.0f ", emb[j * n_embd + i]);
+                    } else {
+                        fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
+                    }
+                }
+                fprintf(stdout, "\n");
+            }
+        } else {
+            // print the first part of the embeddings or for a single prompt, the full embedding
             for (int j = 0; j < n_prompts; j++) {
-                float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
-                fprintf(stdout, "%6.2f ", sim);
+                fprintf(stdout, "embedding %d: ", j);
+                for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
+                    if (params.embd_normalize == 0) {
+                        fprintf(stdout, "%6.0f ", emb[j * n_embd + i]);
+                    } else {
+                        fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
+                    }
+                }
+                fprintf(stdout, "\n");
             }
-            fprintf(stdout, "\n");
+
+            // print cosine similarity matrix
+            if (n_prompts > 1) {
+                fprintf(stdout, "\n");
+                printf("cosine similarity matrix:\n\n");
+                for (int i = 0; i < n_prompts; i++) {
+                    fprintf(stdout, "%6.6s ", prompts[i].c_str());
+                }
+                fprintf(stdout, "\n");
+                for (int i = 0; i < n_prompts; i++) {
+                    for (int j = 0; j < n_prompts; j++) {
+                        float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
+                        fprintf(stdout, "%6.2f ", sim);
+                    }
+                    fprintf(stdout, "%1.10s", prompts[i].c_str());
+                    fprintf(stdout, "\n");
+                }
+            }
+        }
+    }
+
+    if (params.embd_out == "json" || params.embd_out == "json+" || params.embd_out == "array") {
+        const bool notArray = params.embd_out != "array";
+
+        fprintf(stdout, notArray ? "{\n  \"object\": \"list\",\n  \"data\": [\n" : "[");
+        for (int j = 0;;) { // at least one iteration (one prompt)
+            if (notArray) fprintf(stdout, "    {\n      \"object\": \"embedding\",\n      \"index\": %d,\n      \"embedding\": ",j);
+            fprintf(stdout, "[");
+            for (int i = 0;;) { // at least one iteration (n_embd > 0)
+                fprintf(stdout, params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]);
+                i++;
+                if (i < n_embd) fprintf(stdout, ","); else break;
+            }
+            fprintf(stdout, notArray ? "]\n    }" : "]");
+            j++;
+            if (j < n_embd_count) fprintf(stdout, notArray ? ",\n" : ","); else break;
+        }
+        fprintf(stdout, notArray ? "\n  ]" : "]\n");
+
+        if (params.embd_out == "json+" && n_prompts > 1) {
+            fprintf(stdout, ",\n  \"cosineSimilarity\": [\n");
+            for (int i = 0;;) { // at least two iteration (n_embd_count > 1)
+                fprintf(stdout, "    [");
+                for (int j = 0;;) { // at least two iteration (n_embd_count > 1)
+                    float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
+                    fprintf(stdout, "%6.2f", sim);
+                    j++;
+                    if (j < n_embd_count) fprintf(stdout, ", "); else break;
+                }
+                fprintf(stdout, " ]");
+                i++;
+                if (i < n_embd_count) fprintf(stdout, ",\n"); else break;
+            }
+            fprintf(stdout, "\n  ]");
         }
+
+        if (notArray) fprintf(stdout, "\n}\n");
     }
 
     // clean up
diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt
index c56ba780b215f..a48753d38e16e 100644
--- a/examples/eval-callback/CMakeLists.txt
+++ b/examples/eval-callback/CMakeLists.txt
@@ -1,9 +1,9 @@
-set(TARGET eval-callback)
+set(TARGET llama-eval-callback)
 add_executable(${TARGET} eval-callback.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 
 set(TEST_TARGET test-eval-callback)
-add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
+add_test(NAME ${TEST_TARGET} COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
 set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl)
diff --git a/examples/eval-callback/README.md b/examples/eval-callback/README.md
index 66a37e8783a9b..63a57ad6b68e5 100644
--- a/examples/eval-callback/README.md
+++ b/examples/eval-callback/README.md
@@ -6,7 +6,7 @@ It simply prints to the console all operations and tensor data.
 Usage:
 
 ```shell
-eval-callback \
+llama-eval-callback \
   --hf-repo ggml-org/models \
   --hf-file phi-2/ggml-model-q4_0.gguf \
   --model phi-2-q4_0.gguf \
diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index e670d3769c7e8..5e89988e2beda 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -62,7 +62,7 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
                     } else if (type == GGML_TYPE_I8) {
                         v = (float) *(int8_t *) &data[i];
                     } else {
-                        GGML_ASSERT(false);
+                        GGML_ABORT("fatal error");
                     }
                     printf("%12.4f", v);
                     sum += v;
@@ -99,7 +99,7 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
 
     char src1_str[128] = {0};
     if (src1) {
-        sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
+        snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
     }
 
     printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
@@ -127,7 +127,7 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
 }
 
 static bool run(llama_context * ctx, const gpt_params & params) {
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
 
     std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
 
@@ -140,20 +140,18 @@ static bool run(llama_context * ctx, const gpt_params & params) {
 }
 
 int main(int argc, char ** argv) {
-
     callback_data cb_data;
 
     gpt_params params;
+
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
     print_build_info();
 
     std::mt19937 rng(params.seed);
-    if (params.random_prompt) {
-        params.prompt = gpt_random_prompt(rng);
-    }
 
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -165,9 +163,10 @@ int main(int argc, char ** argv) {
     params.warmup = false;
 
     // init
-    llama_model * model;
-    llama_context * ctx;
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
     if (model == nullptr || ctx == nullptr) {
         fprintf(stderr, "%s : failed to init\n", __func__);
         return 1;
@@ -176,7 +175,7 @@ int main(int argc, char ** argv) {
     // print system information
     {
         fprintf(stderr, "\n");
-        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+        fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
     }
 
     bool OK = run(ctx, params);
diff --git a/examples/export-lora/CMakeLists.txt b/examples/export-lora/CMakeLists.txt
index cbbdaec67488d..1cef6e71694e2 100644
--- a/examples/export-lora/CMakeLists.txt
+++ b/examples/export-lora/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET export-lora)
+set(TARGET llama-export-lora)
 add_executable(${TARGET} export-lora.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/export-lora/README.md b/examples/export-lora/README.md
index 0cf3e8e4549bb..7dce99c9a9e61 100644
--- a/examples/export-lora/README.md
+++ b/examples/export-lora/README.md
@@ -3,24 +3,31 @@
 Apply LORA adapters to base model and export the resulting model.
 
 ```
-usage: export-lora [options]
+usage: llama-export-lora [options]
 
 options:
-  -h, --help                         show this help message and exit
-  -m FNAME, --model-base FNAME       model path from which to load base model (default '')
-  -o FNAME, --model-out FNAME        path to save exported model (default '')
-  -l FNAME, --lora FNAME             apply LoRA adapter
-  -s FNAME S, --lora-scaled FNAME S  apply LoRA adapter with user defined scaling S
-  -t N, --threads N                  number of threads to use during computation (default: 4)
+  -m,    --model                  model path from which to load base model (default '')
+         --lora FNAME             path to LoRA adapter  (can be repeated to use multiple adapters)
+         --lora-scaled FNAME S    path to LoRA adapter with user defined scaling S  (can be repeated to use multiple adapters)
+  -t,    --threads N              number of threads to use during computation (default: 4)
+  -o,    --output FNAME           output file (default: 'ggml-lora-merged-f16.gguf')
 ```
 
 For example:
 
 ```bash
-./bin/export-lora \
-    -m open-llama-3b-v2-q8_0.gguf \
-    -o open-llama-3b-v2-q8_0-english2tokipona-chat.gguf \
-    -l lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.bin
+./bin/llama-export-lora \
+    -m open-llama-3b-v2.gguf \
+    -o open-llama-3b-v2-english2tokipona-chat.gguf \
+    --lora lora-open-llama-3b-v2-english2tokipona-chat-LATEST.gguf
 ```
 
-Multiple LORA adapters can be applied by passing multiple `-l FN` or `-s FN S` command line parameters.
+Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters:
+
+```bash
+./bin/llama-export-lora \
+    -m your_base_model.gguf \
+    -o your_merged_model.gguf \
+    --lora-scaled lora_task_A.gguf 0.5 \
+    --lora-scaled lora_task_B.gguf 0.5
+```
diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index 08413f57e4c3a..c7e5ca78845ee 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -1,462 +1,423 @@
-
 #include "common.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
 
+#include <map>
 #include <vector>
 #include <string>
 #include <thread>
+#include <fstream>
 
-struct lora_info {
-    std::string filename;
-    float scale;
-};
+static bool g_verbose = false;
 
-struct export_lora_params {
-    std::string fn_model_base;
-    std::string fn_model_out;
-    std::vector<struct lora_info> lora;
-    int n_threads;
+struct tensor_transformation {
+    struct ggml_tensor * in;
+    struct ggml_tensor * out;
+    bool is_copy;
 };
 
-struct lora_data {
-    struct lora_info     info;
-    std::vector<uint8_t> data;
-    struct ggml_context * ctx;
+static std::string get_kv_str(struct gguf_context * ctx_gguf, const std::string & key){
+    int id = gguf_find_key(ctx_gguf, key.c_str());
+    return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id));
+}
 
-    uint32_t lora_r;
-    uint32_t lora_alpha;
-};
+static float get_kv_f32(struct gguf_context * ctx_gguf, const std::string & key) {
+    int id = gguf_find_key(ctx_gguf, key.c_str());
+    return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id);
+}
 
-struct llama_file {
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
+static void zeros(std::ofstream & file, size_t n) {
+    char zero = 0;
+    for (size_t i = 0; i < n; ++i) {
+        file.write(&zero, 1);
+    }
+}
 
-    llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
-        if (fp == NULL) {
-            size = 0;
-        } else {
-            seek(0, SEEK_END);
-            size = tell();
-            seek(0, SEEK_SET);
+static std::string ggml_ne_string(const ggml_tensor * t) {
+    std::string str;
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        str += std::to_string(t->ne[i]);
+        if (i + 1 < GGML_MAX_DIMS) {
+            str += ", ";
         }
     }
+    return str;
+}
 
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        GGML_ASSERT(ret != -1); // this really shouldn't fail
-        return (size_t) ret;
+static struct gguf_context * load_gguf(std::string & fname, struct ggml_context ** ctx_ggml) {
+    struct gguf_init_params params = {
+        /*.no_alloc = */ true,
+        /*.ctx      = */ ctx_ggml,
+    };
+    struct gguf_context * ctx_gguf = gguf_init_from_file(fname.c_str(), params);
+    if (!ctx_gguf) {
+        throw std::runtime_error("failed to load input GGUF from " + fname);
     }
+    return ctx_gguf;
+}
 
-    void seek(size_t offset, int whence) {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        GGML_ASSERT(ret == 0); // same
-    }
+struct file_input {
+    struct ggml_context * ctx_meta = nullptr;
+    struct gguf_context * ctx_gguf = nullptr;
+    std::ifstream f_in;
+    std::map<std::string, ggml_tensor *> tensors;
+    float alpha;
+    float scale;
 
-    void read_raw(void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, size, 1, fp);
-        if (ferror(fp)) {
-            die_fmt("read error: %s", strerror(errno));
+    file_input(std::string & fname, float scale): f_in(fname, std::ios::binary), scale(scale) {
+        if (!f_in.is_open()) {
+            throw std::runtime_error("failed to open input gguf from " + fname);
         }
-        if (ret != 1) {
-            die("unexpectedly reached end of file");
-        }
-    }
 
-    std::uint32_t read_u32() {
-        std::uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
+        ctx_gguf = load_gguf(fname, &ctx_meta);
+        alpha = get_kv_f32(ctx_gguf, "adapter.lora.alpha");
+        printf("%s: loaded gguf from %s\n", __func__, fname.c_str());
+
+        for (ggml_tensor * cur = ggml_get_first_tensor(ctx_meta); cur; cur = ggml_get_next_tensor(ctx_meta, cur)) {
+            std::string name(cur->name);
+            tensors[name] = cur;
+            if (g_verbose) {
+                printf("%s: %s\n", __func__, cur->name);
+            }
+        }
     }
 
-    std::string read_string(std::uint32_t len) {
-        std::vector<char> chars(len);
-        read_raw(chars.data(), len);
-        return std::string(chars.data(), len);
+    ggml_tensor * get_tensor(std::string name) {
+        if (tensors.find(name) == tensors.end()) {
+            return nullptr;
+        }
+        return tensors[name];
     }
 
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
+    void read_tensor_data(std::string name, std::vector<uint8_t> & buf) {
+        if (tensors.find(name) == tensors.end()) {
+            throw std::runtime_error("cannot find tensor with name: " + name);
         }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            die_fmt("write error: %s", strerror(errno));
+        auto len = ggml_nbytes(tensors[name]);
+        if (buf.size() < len) {
+            buf.resize(len);
         }
+        auto i_tensor_in = gguf_find_tensor(ctx_gguf, name.c_str()); // idx of tensor in the input file
+        auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
+        f_in.seekg(offset);
+        f_in.read((char* )buf.data(), len);
     }
 
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
+    ~file_input() {
+        gguf_free(ctx_gguf);
+        ggml_free(ctx_meta);
     }
+};
 
-    bool eof() {
-        return tell() >= size;
-    }
+struct lora_merge_ctx {
+    // input base model + adapters
+    file_input base_model;
+    std::vector<std::unique_ptr<file_input>> adapters;
 
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
+    // for computing merged tensor
+    int n_threads;
+    ggml_backend_t backend = nullptr;
+    ggml_gallocr_t allocr = nullptr;
+    std::vector<uint8_t> read_buf;
+
+    // output file
+    struct gguf_context * ctx_out;
+    struct ggml_context * ctx_out_ggml;
+    std::ofstream fout;
+
+    lora_merge_ctx(
+            std::string & base_fname,
+            std::vector<llama_lora_adapter_info> & lora_files,
+            std::string & outfile,
+            int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) {
+        fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+        if (gguf_find_key(base_model.ctx_gguf, LLM_KV_SPLIT_COUNT) >= 0) {
+            throw std::runtime_error("split model is not yet supported");
         }
-    }
-};
 
-static struct export_lora_params get_default_export_lora_params() {
-    struct export_lora_params result;
-    result.fn_model_base = "";
-    result.fn_model_out  = "";
-    result.n_threads = GGML_DEFAULT_N_THREADS;
-    return result;
-}
+        for (auto & lora_inp : lora_files) {
+            auto fname = lora_inp.path;
+            auto scale = lora_inp.scale;
+            std::unique_ptr<file_input> adapter(new file_input(fname, scale));
+            check_metadata_lora(adapter.get());
+            adapters.push_back(std::move(adapter));
+        }
 
-static void export_lora_print_usage(int /*argc*/, char ** argv, const struct export_lora_params * params) {
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h, --help                         show this help message and exit\n");
-    fprintf(stderr, "  -m FNAME, --model-base FNAME       model path from which to load base model (default '%s')\n", params->fn_model_base.c_str());
-    fprintf(stderr, "  -o FNAME, --model-out FNAME        path to save exported model (default '%s')\n", params->fn_model_out.c_str());
-    fprintf(stderr, "  -l FNAME, --lora FNAME             apply LoRA adapter\n");
-    fprintf(stderr, "  -s FNAME S, --lora-scaled FNAME S  apply LoRA adapter with user defined scaling S\n");
-    fprintf(stderr, "  -t N, --threads N                  number of threads to use during computation (default: %d)\n", params->n_threads);
-}
+        ctx_out = gguf_init_empty();
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ gguf_get_n_tensors(base_model.ctx_gguf)*ggml_tensor_overhead(),
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        ctx_out_ggml = ggml_init(params);
+        backend = ggml_backend_cpu_init();
+        allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
+    }
 
-static bool export_lora_params_parse(int argc, char ** argv, struct export_lora_params * params) {
-    bool invalid_param = false;
-    std::string arg;
-    struct export_lora_params default_params = get_default_export_lora_params();
-    const std::string arg_prefix = "--";
+    void check_metadata_lora(file_input * adapter) {
+        auto general_type = get_kv_str(adapter->ctx_gguf, "general.type");
+        if (general_type != "adapter") {
+            throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
+        }
 
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
+        auto adapter_type = get_kv_str(adapter->ctx_gguf, "adapter.type");
+        if (adapter_type != "lora") {
+            throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
         }
 
-        if (arg == "-m" || arg == "--model-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_base = argv[i];
-        } else if (arg == "-o" || arg == "--model-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_out = argv[i];
-        } else if (arg == "-l" || arg == "--lora") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            struct lora_info lora;
-            lora.filename = argv[i];
-            lora.scale = 1.0f;
-            params->lora.push_back(lora);
-        } else if (arg == "-s" || arg == "--lora-scaled") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            struct lora_info lora;
-            lora.filename = argv[i];
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            lora.scale = std::stof(argv[i]);
-            params->lora.push_back(lora);
-        } else if (arg == "-t" || arg == "--threads") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_threads = std::stoi(argv[i]);
-            if (params->n_threads <= 0) {
-                params->n_threads = std::thread::hardware_concurrency();
-            }
-        } else {
-            fprintf(stderr, "error: unknown argument: '%s'\n", arg.c_str());
-            export_lora_print_usage(argc, argv, &default_params);
-            exit(1);
+        auto general_arch_base = get_kv_str(base_model.ctx_gguf, "general.architecture");
+        auto general_arch_lora = get_kv_str(adapter->ctx_gguf,   "general.architecture");
+        if (general_arch_base != general_arch_lora) {
+            throw std::runtime_error("model arch and LoRA arch mismatch");
         }
     }
 
-    if (params->fn_model_base == default_params.fn_model_base) {
-        fprintf(stderr, "error: please specify a filename for model-base.\n");
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    if (params->fn_model_out == default_params.fn_model_out) {
-        fprintf(stderr, "error: please specify a filename for model-out.\n");
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: '%s'\n", arg.c_str());
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
+    ggml_type get_out_tensor_type(struct ggml_tensor * t) {
+        if (t->type == GGML_TYPE_F32) {
+            return GGML_TYPE_F32;
+        } else {
+            return GGML_TYPE_F16;
+        }
     }
-    return true;
-}
 
-static void free_lora(struct lora_data * lora) {
-    if (lora->ctx != NULL) {
-        ggml_free(lora->ctx);
-    }
-    delete lora;
-}
+    void run_merge() {
+        // prepare metadata
+        gguf_set_kv(ctx_out, base_model.ctx_gguf);
+        // output is forced to f16 for now
+        gguf_set_val_u32(ctx_out, "general.file_type", LLAMA_FTYPE_MOSTLY_F16);
+
+        // check if all lora adapters have the same tensors
+        // TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggerganov/llama.cpp/pull/8607#discussion_r1686027777
+        static const char * err_no_subset_adapter = "Input adapters do not have the same list of tensors. This is not yet supported. Please merge the adapter one-by-one instead of merging all at once.";
+        if (adapters.size() > 1) {
+            for (size_t i = 1; i < adapters.size(); ++i) {
+                if (adapters[0]->tensors.size() != adapters[i]->tensors.size()) {
+                    throw std::runtime_error(err_no_subset_adapter);
+                }
+                for (auto & it : adapters[i]->tensors) {
+                    if (adapters[0]->get_tensor(it.first) == nullptr) {
+                        throw std::runtime_error(err_no_subset_adapter);
+                    }
+                }
+            }
+        }
 
-static struct lora_data * load_lora(struct lora_info * info) {
-    struct lora_data * result = new struct lora_data;
-    result->info = *info;
-    result->ctx = NULL;
-    result->lora_r     = 1;
-    result->lora_alpha = 1;
-
-    struct llama_file file(info->filename.c_str(), "rb");
-    if (file.fp == NULL) {
-        fprintf(stderr, "warning: Could not open lora adapter '%s'. Ignoring this adapter.\n",
-            info->filename.c_str());
-        free_lora(result);
-        return NULL;
-    }
+        // mapping base tensor to out tensor (same shape with base, but different type)
+        std::vector<tensor_transformation> trans;
+        for (auto & it : base_model.tensors) {
+            bool t_a = true;
+            bool t_b = true;
+            for (auto & adapter : adapters) {
+                t_a &= nullptr != adapter->get_tensor(it.first + ".lora_a");
+                t_b &= nullptr != adapter->get_tensor(it.first + ".lora_b");
+            }
+            auto base_tensor = it.second;
+            if (!t_a && !t_b) {
+                // only copy
+                struct ggml_tensor * cpy_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor);
+                ggml_set_name(cpy_tensor, base_tensor->name);
+                trans.push_back({
+                    cpy_tensor,
+                    cpy_tensor,
+                    true,
+                });
+                gguf_add_tensor(ctx_out, cpy_tensor);
+            } else if (t_a && t_b) {
+                // need merging
+                struct ggml_tensor * out_tensor = ggml_new_tensor(
+                    ctx_out_ggml, get_out_tensor_type(base_tensor), GGML_MAX_DIMS, base_tensor->ne);
+                ggml_set_name(out_tensor, base_tensor->name);
+                trans.push_back({
+                    base_tensor,
+                    out_tensor,
+                    false,
+                });
+                gguf_add_tensor(ctx_out, out_tensor);
+            } else {
+                throw std::runtime_error("tensor " + it.first + " missing either lora_a or lora_b");
+            }
+        }
 
-    struct ggml_init_params params_ggml;
-    params_ggml.mem_size   = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE;
-    params_ggml.mem_buffer = NULL;
-    params_ggml.no_alloc   = true;
-    result->ctx = ggml_init(params_ggml);
+        // placeholder for the meta data
+        {
+            size_t meta_size = gguf_get_meta_size(ctx_out);
+            zeros(fout, meta_size);
+        }
 
-    uint32_t magic   = file.read_u32();
-    if (magic != LLAMA_FILE_MAGIC_GGLA) {
-        die_fmt("unexpected lora header file magic in '%s'", info->filename.c_str());
-    }
-    uint32_t version = file.read_u32();
-    if (version != 1) {
-        die_fmt("unexpected lora file version '%u' in '%s'", (unsigned) version, info->filename.c_str());
-    }
-    result->lora_r     = file.read_u32();
-    result->lora_alpha = file.read_u32();
-    // read tensor infos from file
-    std::vector<char> name_buf;
-    std::vector<struct ggml_tensor *> tensors;
-    std::vector<size_t> tensors_offset;
-    size_t total_nbytes_pad = 0;
-    while(!file.eof()) {
-        int64_t ne[4]   = {1,1,1,1};
-        uint32_t n_dims  = file.read_u32();
-        uint32_t namelen = file.read_u32();
-        uint32_t type    = file.read_u32();
-        for (uint32_t k = 0; k < n_dims; ++k) {
-            ne[k] = (int64_t)file.read_u32();
+        // process base model tensors
+        size_t n_merged = 0;
+        for (auto & it : trans) {
+            if (!it.is_copy) {
+                merge_tensor(it.in, it.out);
+                n_merged++;
+            } else {
+                copy_tensor(it.in);
+            }
         }
-        name_buf.clear();
-        name_buf.resize(namelen + 1, '\0');
-        file.read_raw(name_buf.data(), namelen);
-        file.seek((0-file.tell()) & 31, SEEK_CUR);
-        size_t offset = file.tell();
-        struct ggml_tensor * tensor = ggml_new_tensor(result->ctx, (enum ggml_type) type, n_dims, ne);
-        ggml_set_name(tensor, name_buf.data());
-        size_t nbytes     = ggml_nbytes(tensor);
-        size_t nbytes_pad = ggml_nbytes_pad(tensor);
-        total_nbytes_pad += nbytes_pad;
-        tensors.push_back(tensor);
-        tensors_offset.push_back(offset);
-        file.seek(nbytes, SEEK_CUR);
-    }
-    // read tensor data
-    result->data.resize(total_nbytes_pad);
-    size_t data_offset = 0;
-    for (size_t i = 0; i < tensors.size(); ++i) {
-        struct ggml_tensor * tensor = tensors[i];
-        size_t offset     = tensors_offset[i];
-        size_t nbytes     = ggml_nbytes(tensor);
-        size_t nbytes_pad = ggml_nbytes_pad(tensor);
-        file.seek(offset, SEEK_SET);
-        tensor->data = result->data.data() + data_offset;
-        file.read_raw(tensor->data, nbytes);
-        data_offset += nbytes_pad;
-    }
-    return result;
-}
 
+        // write output metadata
+        {
+            std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+            gguf_get_meta_data(ctx_out, data.data());
+            fout.seekp(0);
+            fout.write((const char *)data.data(), data.size());
+        }
 
-static struct ggml_cgraph * build_graph_lora(
-    struct ggml_context * ctx,
-    struct ggml_tensor * tensor,
-    struct ggml_tensor * lora_a,
-    struct ggml_tensor * lora_b,
-    float scaling
-) {
-    struct ggml_tensor * ab = ggml_mul_mat(ctx, lora_a, lora_b);
-    if (scaling != 1.0f) {
-        ab = ggml_scale(ctx, ab, scaling);
+        printf("%s : merged %ld tensors with lora adapters\n", __func__, n_merged);
+        printf("%s : wrote %ld tensors to output file\n", __func__, trans.size());
     }
-    struct ggml_tensor * res = ggml_add_inplace(ctx, tensor, ab);
-
-    struct ggml_cgraph * gf = ggml_new_graph(ctx);
-    ggml_build_forward_expand (gf, res);
-    return gf;
-}
 
-static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int n_threads) {
-    if (lora->ctx == NULL) {
-        return false;
-    }
-    std::string name = ggml_get_name(tensor);
-    std::string name_a = name + std::string(".loraA");
-    std::string name_b = name + std::string(".loraB");
-    struct ggml_tensor * lora_a = ggml_get_tensor(lora->ctx, name_a.c_str());
-    struct ggml_tensor * lora_b = ggml_get_tensor(lora->ctx, name_b.c_str());
-    if (lora_a == NULL || lora_b == NULL) {
-        return false;
+    void copy_tensor(struct ggml_tensor * base) {
+        printf("%s :  %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str());
+        size_t len = ggml_nbytes(base);
+        base_model.read_tensor_data(base->name, read_buf);
+        fout.write((char* )read_buf.data(), len);
+        zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
     }
 
-    float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r;
-
-    struct ggml_init_params params;
-    params.mem_size   = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;
-    params.mem_buffer = NULL;
-    params.no_alloc   = true;
-    struct ggml_context * ctx = NULL;
-    struct ggml_gallocr * alloc = NULL;
-    struct ggml_cgraph  * gf = NULL;
-
-    ctx   = ggml_init(params);
-    alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-    gf    = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
-
-    ggml_gallocr_alloc_graph(alloc, gf);
-
-    struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads);
-    static std::vector<uint8_t> data_work;
-    data_work.resize(cplan.work_size);
-    cplan.work_data = data_work.data();
+    void merge_tensor(struct ggml_tensor * base, struct ggml_tensor * out) {
+        std::string name_base(base->name);
+        std::string name_lora_a = name_base + ".lora_a";
+        std::string name_lora_b = name_base + ".lora_b";
+
+        printf("%s : %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str());
+
+        // context for input tensor
+        std::vector<struct ggml_tensor *> inp_a(adapters.size());
+        std::vector<struct ggml_tensor *> inp_b(adapters.size());
+        struct ggml_init_params params {
+            /*.mem_size   =*/ ggml_tensor_overhead()*(2+adapters.size()*2),
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        struct ggml_context * ctx = ggml_init(params);
+
+        // alloc tensors
+        struct ggml_tensor * inp_base = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, base->ne);
+        for (size_t i = 0; i < adapters.size(); ++i) {
+            auto t_a = adapters[i]->get_tensor(name_lora_a);
+            auto t_b = adapters[i]->get_tensor(name_lora_b);
+            // TODO: add support for quantized lora
+            if (ggml_is_quantized(t_a->type) || ggml_is_quantized(t_b->type)) {
+                throw std::runtime_error("quantized LoRA adapters is not supported, please retry with f16 or f32");
+            }
+            inp_a[i] = ggml_dup_tensor(ctx, t_a);
+            inp_b[i] = ggml_dup_tensor(ctx, t_b);
+        }
+        ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
+
+        // load base tensor to backend buffer
+        base_model.read_tensor_data(name_base, read_buf);
+        if (base->type != GGML_TYPE_F32) {
+            // optionally dequantize it
+            printf("%s :   + dequantize base tensor from %s to F32\n", __func__, ggml_type_name(base->type));
+            auto nels = ggml_nelements(inp_base);
+            ggml_type_traits_t qtype = ggml_internal_get_type_traits(base->type);
+            std::vector<uint8_t> dequant_buf(nels * sizeof(float));
+            qtype.to_float(read_buf.data(), (float *)dequant_buf.data(), nels);
+            ggml_backend_tensor_set(inp_base, dequant_buf.data(), 0, dequant_buf.size());
+        } else {
+            ggml_backend_tensor_set(inp_base, read_buf.data(), 0, ggml_nbytes(inp_base));
+        }
 
-    ggml_graph_compute(gf, &cplan);
+        // load lora tensors to backend buffer
+        for (size_t i = 0; i < adapters.size(); ++i) {
+            adapters[i]->read_tensor_data(name_lora_a, read_buf);
+            ggml_backend_tensor_set(inp_a[i], read_buf.data(), 0, ggml_nbytes(inp_a[i]));
+            adapters[i]->read_tensor_data(name_lora_b, read_buf);
+            ggml_backend_tensor_set(inp_b[i], read_buf.data(), 0, ggml_nbytes(inp_b[i]));
+        }
 
-    ggml_gallocr_free(alloc);
-    ggml_free(ctx);
-    return true;
-}
+        // build graph
+        struct ggml_cgraph * gf;
+        {
+            static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
+            static std::vector<uint8_t> buf(buf_size);
+            struct ggml_init_params params0 = {
+                /*.mem_size   =*/ buf_size,
+                /*.mem_buffer =*/ buf.data(),
+                /*.no_alloc   =*/ true,
+            };
+            struct ggml_context * ctx0 = ggml_init(params0);
+            gf = ggml_new_graph(ctx0);
+            struct ggml_tensor * cur = inp_base;
+            for (size_t i = 0; i < adapters.size(); ++i) {
+                struct ggml_tensor * a_T = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32)));
+                struct ggml_tensor * delta = ggml_mul_mat(ctx0, a_T, ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32));
+                // scale
+                const float alpha = adapters[i]->alpha;
+                const float rank  = (float) inp_b[i]->ne[0];
+                const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale;
+                delta = ggml_scale(ctx0, delta, scale);
+                cur = ggml_add(ctx0, delta, cur);
+                printf("%s :   + merging from adapter[%ld] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type));
+                printf("%s :     input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]);
+            }
+            cur = ggml_cast(ctx0, cur, out->type);
+            printf("%s :   + output type is %s\n", __func__, ggml_type_name(out->type));
+            ggml_build_forward_expand(gf, cur);
+            ggml_free(ctx0);
+        }
 
-static void export_lora(struct export_lora_params * params) {
-    // load all loras
-    std::vector<struct lora_data *> loras;
-    for (size_t i = 0; i < params->lora.size(); ++i) {
-        struct lora_data * lora = load_lora(&params->lora[i]);
-        if (lora != NULL) {
-            loras.push_back(lora);
+        // compute
+        {
+            ggml_gallocr_alloc_graph(allocr, gf);
+            ggml_backend_cpu_set_n_threads(backend, n_threads);
+            ggml_backend_graph_compute(backend, gf);
         }
-    }
-    if (loras.size() == 0) {
-        fprintf(stderr, "warning: no lora adapters will be applied.\n");
-    }
 
-    // open input file
-    struct llama_file fin(params->fn_model_base.c_str(), "rb");
-    if (!fin.fp) {
-        die_fmt("Could not open file '%s'\n", params->fn_model_base.c_str());
-    }
+        // write data to output file
+        {
+            auto result = gf->nodes[gf->n_nodes - 1];
+            size_t len = ggml_nbytes(result);
+            if (read_buf.size() < len) {
+                read_buf.resize(len);
+            }
+            ggml_backend_tensor_get(result, read_buf.data(), 0, len);
+            fout.write((char* )read_buf.data(), len);
+            zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
+        }
 
-    // open base model gguf, read tensors without their data
-    struct ggml_context * ctx_in;
-    struct gguf_init_params params_gguf;
-    params_gguf.no_alloc = true;
-    params_gguf.ctx      = &ctx_in;
-    struct gguf_context * gguf_in = gguf_init_from_file(params->fn_model_base.c_str(), params_gguf);
-
-    // create new gguf
-    struct gguf_context * gguf_out = gguf_init_empty();
-
-    // copy meta data from base model: kv and tensors
-    gguf_set_kv(gguf_out, gguf_in);
-    int n_tensors = gguf_get_n_tensors(gguf_in);
-    for (int i=0; i < n_tensors; ++i) {
-        const char * name = gguf_get_tensor_name(gguf_in, i);
-        struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);
-        gguf_add_tensor(gguf_out, tensor);
+        ggml_free(ctx);
+        ggml_backend_buffer_free(buffer);
     }
 
-    // create output file
-    struct llama_file fout(params->fn_model_out.c_str(), "wb");
-    if (!fout.fp) {
-        die_fmt("Could not create file '%s'\n", params->fn_model_out.c_str());
+    ~lora_merge_ctx() {
+        ggml_gallocr_free(allocr);
+        ggml_backend_free(backend);
+        gguf_free(ctx_out);
+        ggml_free(ctx_out_ggml);
     }
+};
 
-    // write gguf meta data
-    std::vector<uint8_t> meta;
-    meta.resize(gguf_get_meta_size(gguf_out));
-    gguf_get_meta_data(gguf_out, meta.data());
-    fout.write_raw(meta.data(), meta.size());
-
-    std::vector<uint8_t> data;
-    std::vector<uint8_t> padding;
-    for (int i=0; i < n_tensors; ++i) {
-        const char * name = gguf_get_tensor_name(gguf_in, i);
-        struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);
-
-        // read tensor data
-        data.resize(ggml_nbytes(tensor));
-        tensor->data = data.data();
-        size_t offset = gguf_get_tensor_offset(gguf_in, i);
-        fin.seek(offset + meta.size(), SEEK_SET);
-        fin.read_raw(data.data(), data.size());
-
-        // apply all loras
-        for (size_t k = 0; k < loras.size(); ++k) {
-            apply_lora(tensor, loras[k], params->n_threads);
-        }
-
-        // write tensor data + padding
-        padding.clear();
-        padding.resize(GGML_PAD(data.size(), gguf_get_alignment(gguf_out)) - data.size(), 0);
-
-        GGML_ASSERT(fout.tell() == offset + meta.size());
-        // fout.seek(offset + meta.size(), SEEK_SET);
-        fout.write_raw(data.data(), data.size());
-        fout.write_raw(padding.data(), padding.size());
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
 
-        if (i % 2 == 0) {
-            printf(".");
-        }
-    }
+    printf("\nexample usage:\n");
+    printf("\n  %s -m base-model.gguf --lora lora-file.gguf -o merged-model-f16.gguf\n", argv[0]);
+    printf("\nNOTE: output model is F16\n");
     printf("\n");
-
-    // close gguf
-    gguf_free(gguf_out);
-    gguf_free(gguf_in);
-
-    // free loras
-    for (size_t i = 0; i < loras.size(); ++i) {
-        free_lora(loras[i]);
-    }
 }
 
 int main(int argc, char ** argv) {
-    struct export_lora_params params = get_default_export_lora_params();
+    gpt_params params;
 
-    if (!export_lora_params_parse(argc, argv, &params)) {
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
         return 1;
     }
 
-    export_lora(&params);
+    g_verbose = (params.verbosity == 1);
+    try {
+        lora_merge_ctx ctx(params.model, params.lora_adapters, params.lora_outfile, params.n_threads);
+        ctx.run_merge();
+    } catch (const std::exception & err) {
+        fprintf(stderr, "%s\n", err.what());
+        exit(EXIT_FAILURE);
+    }
+
+    printf("done, output file is %s\n", params.lora_outfile.c_str());
 
     return 0;
 }
diff --git a/examples/finetune/CMakeLists.txt b/examples/finetune/CMakeLists.txt
deleted file mode 100644
index 2b52d21cfb381..0000000000000
--- a/examples/finetune/CMakeLists.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-set(TARGET finetune)
-add_executable(${TARGET} finetune.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/finetune/README.md b/examples/finetune/README.md
deleted file mode 100644
index 2fafd505e5447..0000000000000
--- a/examples/finetune/README.md
+++ /dev/null
@@ -1,90 +0,0 @@
-# finetune
-
-Basic usage instructions:
-
-```bash
-# get training data
-wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt
-
-# finetune LORA adapter
-./bin/finetune \
-        --model-base open-llama-3b-v2-q8_0.gguf \
-        --checkpoint-in  chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf \
-        --checkpoint-out chk-lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.gguf \
-        --lora-out lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.bin \
-        --train-data "shakespeare.txt" \
-        --save-every 10 \
-        --threads 6 --adam-iter 30 --batch 4 --ctx 64 \
-        --use-checkpointing
-
-# predict
-./bin/main -m open-llama-3b-v2-q8_0.gguf --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
-```
-
-**Only llama based models are supported!** The output files will be saved every N iterations (config with `--save-every N`).
-The pattern 'ITERATION' in the output filenames will be replaced with the iteration number and with 'LATEST' for the latest output.
-So in above example after 10 iterations these files will be written:
-- chk-lora-open-llama-3b-v2-q8_0-shakespeare-10.gguf
-- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf
-- lora-open-llama-3b-v2-q8_0-shakespeare-10.bin
-- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
-
-After 10 more iterations:
-- chk-lora-open-llama-3b-v2-q8_0-shakespeare-20.gguf
-- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf
-- lora-open-llama-3b-v2-q8_0-shakespeare-20.bin
-- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
-
-Checkpoint files (`--checkpoint-in FN`, `--checkpoint-out FN`) store the training process. When the input checkpoint file does not exist, it will begin finetuning a new randomly initialized adapter.
-
-llama.cpp compatible LORA adapters will be saved with filename specified by `--lora-out FN`.
-These LORA adapters can then be used by `main` together with the base model, like in the 'predict' example command above.
-
-In `main` you can also load multiple LORA adapters, which will then be mixed together.
-
-For example if you have two LORA adapters `lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin` and `lora-open-llama-3b-v2-q8_0-bible-LATEST.bin`, you can mix them together like this:
-
-```bash
-./bin/main -m open-llama-3b-v2-q8_0.gguf \
-  --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin \
-  --lora lora-open-llama-3b-v2-q8_0-bible-LATEST.bin
-```
-
-You can change how strong each LORA adapter is applied to the base model by using `--lora-scaled FN SCALE` instead of `--lora FN`.
-
-For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' LORA adapter and 100% of yet another one:
-
-```bash
-./bin/main -m open-llama-3b-v2-q8_0.gguf \
-  --lora-scaled lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin 0.4 \
-  --lora-scaled lora-open-llama-3b-v2-q8_0-bible-LATEST.bin 0.8 \
-  --lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin
-```
-
-The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values too big will sometimes result in worse output. Play around to find good values.
-
-Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime.
-If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.
-
-The default LORA rank can be specified with `--lora-r N`.
-The LORA rank can be configured for each model tensor type separately with these command line options:
-
-```bash
-  --lora-r N                 LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default 4)
-  --rank-att-norm N          LORA rank for attention norm tensor (default 1)
-  --rank-ffn-norm N          LORA rank for feed-forward norm tensor (default 1)
-  --rank-out-norm N          LORA rank for output norm tensor (default 1)
-  --rank-tok-embd N          LORA rank for token embeddings tensor (default 4)
-  --rank-out N               LORA rank for output tensor (default 4)
-  --rank-wq N                LORA rank for wq tensor (default 4)
-  --rank-wk N                LORA rank for wk tensor (default 4)
-  --rank-wv N                LORA rank for wv tensor (default 4)
-  --rank-wo N                LORA rank for wo tensor (default 4)
-  --rank-ffn_gate N          LORA rank for ffn_gate tensor (default 4)
-  --rank-ffn_down N          LORA rank for ffn_down tensor (default 4)
-  --rank-ffn_up N            LORA rank for ffn_up tensor (default 4)
-```
-
-The LORA rank of 'norm' tensors should always be 1.
-
-To see all available options use `finetune --help`.
diff --git a/examples/finetune/convert-finetune-checkpoint-to-gguf.py b/examples/finetune/convert-finetune-checkpoint-to-gguf.py
deleted file mode 100644
index c89090918da97..0000000000000
--- a/examples/finetune/convert-finetune-checkpoint-to-gguf.py
+++ /dev/null
@@ -1,487 +0,0 @@
-#!/usr/bin/env python3
-# finetune checkpoint --> gguf conversion
-
-import argparse
-import gguf
-import struct
-import numpy as np
-from pathlib import Path
-
-# gguf constants
-LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
-LLM_KV_OPTIMIZER_TYPE_ADAM  = "adam"
-LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
-LLM_KV_OPTIMIZER_FILE_VERSION               = "optimizer.file_version"
-LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT     = "optimizer.convergence_past_count"
-LLM_KV_OPTIMIZER_PARAMETER_COUNT            = "optimizer.parameter_count"
-LLM_KV_OPTIMIZER_ITERATION_COUNT            = "optimizer.iteration_count"
-LLM_KV_OPTIMIZER_JUST_INITIALIZED           = "optimizer.just_initialized"
-LLM_KV_OPTIMIZER_ADAM_BEST_LOSS             = "optimizer.adam.best_loss"
-LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS         = "optimizer.adam.previous_loss"
-LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT  = "optimizer.adam.no_improvement_count"
-LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
-LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS            = "optimizer.lbfgs.best_loss"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP     = "optimizer.lbfgs.line_search_step"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J        = "optimizer.lbfgs.line_search_j"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K        = "optimizer.lbfgs.line_search_k"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END      = "optimizer.lbfgs.line_search_end"
-LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
-
-LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS    = "optimizer.adam.first_moments"
-LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS   = "optimizer.adam.second_moments"
-LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
-
-LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS  = "optimizer.lbfgs.current_parameters"
-LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
-LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS   = "optimizer.lbfgs.current_gradients"
-LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS  = "optimizer.lbfgs.previous_gradients"
-LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION    = "optimizer.lbfgs.search_direction"
-LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES    = "optimizer.lbfgs.past_loss_values"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA        = "optimizer.lbfgs.memory_alpha"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS           = "optimizer.lbfgs.memory_ys"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S            = "optimizer.lbfgs.memory_s"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y            = "optimizer.lbfgs.memory_y"
-
-LLM_KV_TRAINING_TYPE_TRAIN_MODEL   = "train_model"
-LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora"
-LLM_KV_TRAINING_TYPE               = "training.type"
-LLM_KV_TRAINING_FILE_VERSION       = "training.file_version"
-LLM_KV_TRAINING_ITERATION_COUNT    = "training.iteration_count"
-LLM_KV_TRAINING_SAMPLE_COUNT       = "training.sample_count"
-LLM_KV_TRAINING_TOKEN_COUNT        = "training.token_count"
-
-LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD  = "training.lora.rank.token_embd"
-LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm"
-LLM_KV_TRAINING_LORA_RANK_OUTPUT      = "training.lora.rank.output"
-LLM_KV_TRAINING_LORA_RANK_ATTN_NORM   = "training.lora.rank.attn_norm"
-LLM_KV_TRAINING_LORA_RANK_ATTN_Q      = "training.lora.rank.attn_q"
-LLM_KV_TRAINING_LORA_RANK_ATTN_K      = "training.lora.rank.attn_k"
-LLM_KV_TRAINING_LORA_RANK_ATTN_V      = "training.lora.rank.attn_v"
-LLM_KV_TRAINING_LORA_RANK_ATTN_OUT    = "training.lora.rank.attn_output"
-LLM_KV_TRAINING_LORA_RANK_FFN_NORM    = "training.lora.rank.ffn_norm"
-LLM_KV_TRAINING_LORA_RANK_FFN_GATE    = "training.lora.rank.ffn_gate"
-LLM_KV_TRAINING_LORA_RANK_FFN_DOWN    = "training.lora.rank.ffn_down"
-LLM_KV_TRAINING_LORA_RANK_FFN_UP      = "training.lora.rank.ffn_up"
-
-class Tensor:
-    def __init__(self, dtype='f', ne=None):
-        if ne is None:
-            ne = []
-        self.dtype = dtype
-        self.ne = ne
-        self.nbytes = 0
-        if self.dtype == 'f':
-            if len(self.ne) == 0:
-                self.nbytes = 0
-            else:
-                self.nbytes = int(np.product(self.ne)) * 4
-        else:
-            raise ValueError(f"Unhandled data type '{self.dtype}'")
-
-    def load(self, data, offset):
-        nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-
-        assert(nd == len(self.ne))
-        ne = []
-        for d in range(nd):
-            n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-            ne.append(n)
-
-        if tuple(ne) != tuple(self.ne):
-            raise ValueError(f"Tensor.load: Expected number of elements {str(self.ne)} does not match what is read from file {str(ne)}")
-
-        if self.dtype == 'f':
-            assert(dtype == 0)
-        else:
-            raise ValueError(f"Unhandled data type '{self.dtype}'")
-
-        self.name = bytes(data[offset:offset+namelen]); offset += namelen
-        # 32-byte alignment
-        offset += (0 - offset) & 31
-        self.data = data[offset:offset+self.nbytes]
-        offset += self.nbytes
-        return offset
-
-    def max_storage_size(self):
-        result = 0
-        result += 4 # nd
-        result += 4 # namelen
-        result += 4 # dtype
-        result += len(self.ne)*8 # ne
-        result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
-        result += 31 # 32-byte alignment
-        result += self.nbytes
-        return result
-
-    def save_gguf(self, gguf_writer, name):
-        gguf_writer.add_tensor(
-            name=name,
-            tensor=self.data,
-            raw_shape=np.array(list(reversed(self.ne))),
-            raw_dtype=gguf.GGMLQuantizationType.F32)
-
-class OptimizationContext:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
-        offset += 4
-
-        if self.version != 1:
-            raise ValueError('Invalid version of optimization context in checkpoint file')
-
-        self.past    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.nx      = struct.unpack('N',  bytes(data[offset:offset + 8]))[0];  offset += 8
-        self.iter    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
-
-        self.adam_m  = Tensor('f', [self.nx])
-        self.adam_v  = Tensor('f', [self.nx])
-        self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
-
-        self.lbfgs_x    = Tensor('f', [self.nx])
-        self.lbfgs_xp   = Tensor('f', [self.nx])
-        self.lbfgs_g    = Tensor('f', [self.nx])
-        self.lbfgs_gp   = Tensor('f', [self.nx])
-        self.lbfgs_d    = Tensor('f', [self.nx])
-        self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
-        self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
-        self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
-        self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
-        self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
-
-        # forgot to save type in version 1:
-        # guess self.type from number of remaining bytes
-        size_type_0 = 12 + sum([t.max_storage_size() for t in
-                                [self.adam_m, self.adam_v]
-                                +([self.adam_pf] if (self.past > 0) else [])])
-        size_type_1 = 24 + sum([t.max_storage_size() for t in
-                                [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
-                                 self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
-                                 self.lbfgs_lmal, self.lbfgs_lmys,
-                                 self.lbfgs_lms, self.lbfgs_lmy]
-                                 +([self.lbfgs_pf] if (self.past > 0) else [])])
-        # due to alignment padding the size might not by exact
-        # but the difference in size for both types is significant,
-        # so we can just use whichever is closest
-        remaining = len(data) - offset
-        if abs(remaining - size_type_0) < abs(remaining - size_type_1):
-            self.type = 0
-        else:
-            self.type = 1
-
-        if self.type == 0:
-            offset = self.adam_m.load(data, offset)
-            offset = self.adam_v.load(data, offset)
-            offset = self.adam_pf.load(data,offset)
-
-            self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-        elif self.type == 1:
-            offset = self.lbfgs_x.load(data, offset)
-            offset = self.lbfgs_xp.load(data, offset)
-            offset = self.lbfgs_g.load(data, offset)
-            offset = self.lbfgs_gp.load(data, offset)
-            offset = self.lbfgs_d.load(data, offset)
-            offset = self.lbfgs_pf.load(data, offset)
-            offset = self.lbfgs_lmal.load(data, offset)
-            offset = self.lbfgs_lmys.load(data, offset)
-            offset = self.lbfgs_lms.load(data, offset)
-            offset = self.lbfgs_lmy.load(data, offset)
-
-            self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-        else:
-            raise ValueError(f"Invalid optimizer type '{self.type}'")
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
-        gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
-        gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
-
-        if self.type == 0:
-            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
-
-            self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
-            self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
-            if self.past > 0:
-                self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
-
-        elif self.type == 1:
-            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
-
-            self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
-            self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
-            self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
-            self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
-            self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
-            if self.past > 0:
-                self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
-            self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
-            self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
-            self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
-            self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
-        else:
-            raise ValueError('Unknown optimizer type')
-
-class LoraParams:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.n_rank_attention_norm  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_wq              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_wk              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_wv              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_wo              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_ffn_norm        = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_w1              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_w2              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_w3              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_tok_embeddings  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_norm            = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_output          = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD,  self.n_rank_tok_embeddings)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM, self.n_rank_norm)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_OUTPUT,      self.n_rank_output)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_NORM,   self.n_rank_attention_norm)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_Q,      self.n_rank_wq)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_K,      self.n_rank_wk)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_V,      self.n_rank_wv)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_OUT,    self.n_rank_wo)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_NORM,    self.n_rank_ffn_norm)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_GATE,    self.n_rank_w1)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_DOWN,    self.n_rank_w2)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_UP,      self.n_rank_w3)
-
-class ModelParams:
-    def __init__(self, n_ff = None):
-        self.n_ff = n_ff
-
-    def load(self, data, offset):
-        self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_embd  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_mult  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_head  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rot   = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        return offset
-
-    def get_n_ff(self):
-        if self.n_ff is None:
-            # struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9
-            return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult
-        else:
-            return self.n_ff
-
-    def save_gguf(self, gguf_writer):
-        # self.n_vocab not saved
-        gguf_writer.add_embedding_length(self.n_embd)
-        gguf_writer.add_head_count(self.n_head)
-        gguf_writer.add_block_count(self.n_layer)
-        gguf_writer.add_rope_dimension_count(self.n_rot)
-        gguf_writer.add_feed_forward_length(self.get_n_ff())
-
-def tensor_name(key, bid=None, suffix=".weight"):
-    return gguf.TENSOR_NAMES[key].format(bid=bid) + suffix
-
-class Layer:
-    def __init__(self, params, lora_params, bid):
-        self.bid = bid
-        self.att_norm_a = Tensor('f', [lora_params.n_rank_attention_norm, params.n_embd])
-        self.att_norm_b = Tensor('f', [lora_params.n_rank_attention_norm, 1])
-        self.wq_a       = Tensor('f', [lora_params.n_rank_wq, params.n_embd])
-        self.wq_b       = Tensor('f', [lora_params.n_rank_wq, params.n_embd])
-        self.wk_a       = Tensor('f', [lora_params.n_rank_wk, params.n_embd])
-        self.wk_b       = Tensor('f', [lora_params.n_rank_wk, params.n_embd])
-        self.wv_a       = Tensor('f', [lora_params.n_rank_wv, params.n_embd])
-        self.wv_b       = Tensor('f', [lora_params.n_rank_wv, params.n_embd])
-        self.wo_a       = Tensor('f', [lora_params.n_rank_wo, params.n_embd])
-        self.wo_b       = Tensor('f', [lora_params.n_rank_wo, params.n_embd])
-        self.ffn_norm_a = Tensor('f', [lora_params.n_rank_ffn_norm, params.n_embd])
-        self.ffn_norm_b = Tensor('f', [lora_params.n_rank_ffn_norm, 1])
-        self.w1_a       = Tensor('f', [lora_params.n_rank_w1, params.n_embd])
-        self.w1_b       = Tensor('f', [lora_params.n_rank_w1, params.get_n_ff()])
-        self.w2_a       = Tensor('f', [lora_params.n_rank_w2, params.get_n_ff()])
-        self.w2_b       = Tensor('f', [lora_params.n_rank_w2, params.n_embd])
-        self.w3_a       = Tensor('f', [lora_params.n_rank_w3, params.n_embd])
-        self.w3_b       = Tensor('f', [lora_params.n_rank_w3, params.get_n_ff()])
-
-    def load(self, data, offset):
-        offset = self.att_norm_a.load(data, offset)
-        offset = self.att_norm_b.load(data, offset)
-        offset = self.wq_a.load(data, offset)
-        offset = self.wq_b.load(data, offset)
-        offset = self.wk_a.load(data, offset)
-        offset = self.wk_b.load(data, offset)
-        offset = self.wv_a.load(data, offset)
-        offset = self.wv_b.load(data, offset)
-        offset = self.wo_a.load(data, offset)
-        offset = self.wo_b.load(data, offset)
-        offset = self.ffn_norm_a.load(data, offset)
-        offset = self.ffn_norm_b.load(data, offset)
-        offset = self.w1_a.load(data, offset)
-        offset = self.w1_b.load(data, offset)
-        offset = self.w2_a.load(data, offset)
-        offset = self.w2_b.load(data, offset)
-        offset = self.w3_a.load(data, offset)
-        offset = self.w3_b.load(data, offset)
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        self.att_norm_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid, ".weight.lora_a"))
-        self.att_norm_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid, ".weight.lora_b"))
-        self.wq_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q,    self.bid, ".weight.lora_a"))
-        self.wq_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q,    self.bid, ".weight.lora_b"))
-        self.wk_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K,    self.bid, ".weight.lora_a"))
-        self.wk_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K,    self.bid, ".weight.lora_b"))
-        self.wv_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V,    self.bid, ".weight.lora_a"))
-        self.wv_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V,    self.bid, ".weight.lora_b"))
-        self.wo_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT,  self.bid, ".weight.lora_a"))
-        self.wo_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT,  self.bid, ".weight.lora_b"))
-        self.ffn_norm_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM,  self.bid, ".weight.lora_a"))
-        self.ffn_norm_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM,  self.bid, ".weight.lora_b"))
-        self.w1_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE,  self.bid, ".weight.lora_a"))
-        self.w1_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE,  self.bid, ".weight.lora_b"))
-        self.w2_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN,  self.bid, ".weight.lora_a"))
-        self.w2_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN,  self.bid, ".weight.lora_b"))
-        self.w3_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP,    self.bid, ".weight.lora_a"))
-        self.w3_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP,    self.bid, ".weight.lora_b"))
-
-class LoraModel:
-    def __init__(self, n_ff = None):
-        self.params = ModelParams(n_ff = n_ff)
-        self.lora_params = LoraParams()
-        self.layers = []
-
-    def load(self, data, offset):
-        offset = self.params.load(data, offset)
-        offset = self.lora_params.load(data, offset)
-
-        self.tok_embd_a = Tensor('f', [self.lora_params.n_rank_tok_embeddings, self.params.n_embd])
-        self.tok_embd_b = Tensor('f', [self.lora_params.n_rank_tok_embeddings, self.params.n_vocab])
-        self.norm_a     = Tensor('f', [self.lora_params.n_rank_norm, self.params.n_embd])
-        self.norm_b     = Tensor('f', [self.lora_params.n_rank_norm, 1])
-        self.output_a   = Tensor('f', [self.lora_params.n_rank_output, self.params.n_embd])
-        self.output_b   = Tensor('f', [self.lora_params.n_rank_output, self.params.n_vocab])
-
-        offset = self.tok_embd_a.load(data, offset)
-        offset = self.tok_embd_b.load(data, offset)
-        offset = self.norm_a.load(data, offset)
-        offset = self.norm_b.load(data, offset)
-        offset = self.output_a.load(data, offset)
-        offset = self.output_b.load(data, offset)
-
-        self.layers.clear()
-        for bid in range(self.params.n_layer):
-            layer = Layer(self.params, self.lora_params, bid)
-            offset = layer.load(data, offset)
-            self.layers.append(layer)
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        self.params.save_gguf(gguf_writer)
-        self.lora_params.save_gguf(gguf_writer)
-
-        self.tok_embd_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD,  suffix=".weight.lora_a"))
-        self.tok_embd_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD,  suffix=".weight.lora_b"))
-        self.norm_a.save_gguf    (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM, suffix=".weight.lora_a"))
-        self.norm_b.save_gguf    (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM, suffix=".weight.lora_b"))
-        self.output_a.save_gguf  (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT,      suffix=".weight.lora_a"))
-        self.output_b.save_gguf  (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT,      suffix=".weight.lora_b"))
-
-        for layer in self.layers:
-            layer.save_gguf(gguf_writer)
-
-class LoraCheckpoint:
-    def __init__(self, n_ff = None):
-        self.model = LoraModel(n_ff = n_ff)
-        self.opt_ctx = OptimizationContext()
-
-    def load(self, data, offset):
-        magic   = bytes(reversed(data[offset:offset + 4])); offset += 4
-        if magic != b'ggcl':
-            raise ValueError(f"File header magic indicates, that this is no finetune-lora checkpoint file. Expected 'ggcl', Got '{str(magic)}'")
-
-        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        if self.version != 0:
-            raise ValueError('Invalid version of checkpoint file')
-
-        self.train_its     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        self.train_tokens  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-
-        offset = self.model.load(data, offset)
-        offset = self.opt_ctx.load(data, offset)
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
-        gguf_writer.add_layer_norm_rms_eps(1e-5)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION,    0)
-        gguf_writer.add_string(LLM_KV_TRAINING_TYPE,            LLM_KV_TRAINING_TYPE_FINETUNE_LORA)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT,    self.train_samples)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT,     self.train_tokens)
-        self.model.save_gguf(gguf_writer)
-        self.opt_ctx.save_gguf(gguf_writer)
-
-def handle_args():
-    parser = argparse.ArgumentParser(description = 'Convert finetune checkpoints to GGUF')
-    parser.add_argument('--input',  '-i', type = Path, help = 'Input finetune checkpoint filename', required=True)
-    parser.add_argument('--output', '-o', type = Path, help = 'Output GGUF filename', required=True)
-    parser.add_argument('--ff', type = int, help = "Feedforward size, if not provided compute from n_mult. Provide this if you get 'ValueError: Tensor.load: Expected number of elements does not match what is read from file'", required=False)
-    return parser.parse_args()
-
-def main():
-    cfg = handle_args()
-    print(cfg)
-    data = np.memmap(cfg.input, mode = 'r')
-    chk = LoraCheckpoint(n_ff = cfg.ff)
-    offset = 0
-    offset = chk.load(data, offset)
-    # we should have read all available data
-    assert(offset == len(data))
-
-    gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
-    chk.save_gguf(gguf_writer)
-    print("    gguf: write header")
-    gguf_writer.write_header_to_file()
-    print("    gguf: write metadata")
-    gguf_writer.write_kv_data_to_file()
-    print("    gguf: write tensors")
-    gguf_writer.write_tensors_to_file()
-    gguf_writer.close()
-
-if __name__ == '__main__':
-    main()
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
deleted file mode 100644
index 22743b1bf02fd..0000000000000
--- a/examples/finetune/finetune.cpp
+++ /dev/null
@@ -1,1861 +0,0 @@
-#include "ggml.h"
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-#include "llama.h"
-#include "common.h"
-#include "train.h"
-#include <vector>
-#include <cstring>
-#include <ctime>
-#include <algorithm>
-#include <string>
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-struct my_llama_hparams {
-    uint32_t n_vocab    = 32000;
-    uint32_t n_ctx      = 512;
-    uint32_t n_embd     = 4096;
-    uint32_t n_ff       = 11008;
-    uint32_t n_head     = 32;
-    uint32_t n_head_kv  = 32;
-    uint32_t n_layer    = 32;
-
-    // float f_norm_eps     = 1e-5f; // falcon
-    float f_norm_rms_eps = 1e-5f; // llama
-
-    float rope_freq_base  = 10000.0f;
-    float rope_freq_scale = 1.0f;
-
-    uint32_t n_gqa() const {
-        return n_head/n_head_kv;
-    }
-
-    uint32_t n_embd_head() const {
-        return n_embd/n_head;
-    }
-
-    uint32_t n_embd_gqa() const {
-        return n_embd/n_gqa();
-    }
-
-    bool operator!=(const my_llama_hparams& other) const {
-        return memcmp(this, &other, sizeof(other));
-    }
-};
-
-struct my_llama_layer {
-    // normalization
-    struct ggml_tensor * attention_norm;
-
-    // attention
-    struct ggml_tensor * wq;
-    struct ggml_tensor * wk;
-    struct ggml_tensor * wv;
-    struct ggml_tensor * wo;
-
-    // normalization
-    struct ggml_tensor * ffn_norm;
-
-    // ff
-    struct ggml_tensor * ffn_gate; // w1
-    struct ggml_tensor * ffn_down; // w2
-    struct ggml_tensor * ffn_up;   // w3
-};
-
-struct my_llama_model {
-    struct my_llama_hparams hparams;
-
-    struct ggml_tensor * tok_embeddings;
-
-    struct ggml_tensor * norm;
-    struct ggml_tensor * output;
-
-    std::vector<my_llama_layer> layers;
-};
-
-struct my_llama_lora_hparams {
-    uint32_t lora_r = 1;
-    uint32_t lora_alpha = 1;
-    uint32_t n_rank_attention_norm = 1;
-    uint32_t n_rank_wq = 4;
-    uint32_t n_rank_wk = 4;
-    uint32_t n_rank_wv = 4;
-    uint32_t n_rank_wo = 4;
-    uint32_t n_rank_ffn_norm = 1;
-    uint32_t n_rank_ffn_gate = 4;
-    uint32_t n_rank_ffn_down = 4;
-    uint32_t n_rank_ffn_up = 4;
-    uint32_t n_rank_tok_embeddings = 4;
-    uint32_t n_rank_norm = 1;
-    uint32_t n_rank_output = 4;
-
-    bool operator!=(const my_llama_lora_hparams& other) const {
-        return memcmp(this, &other, sizeof(other));
-    }
-};
-
-struct my_llama_lora_layer {
-    // normalization
-    struct ggml_tensor * attention_norm_a;
-    struct ggml_tensor * attention_norm_b;
-
-    // attention
-    struct ggml_tensor * wq_a;
-    struct ggml_tensor * wq_b;
-    struct ggml_tensor * wk_a;
-    struct ggml_tensor * wk_b;
-    struct ggml_tensor * wv_a;
-    struct ggml_tensor * wv_b;
-    struct ggml_tensor * wo_a;
-    struct ggml_tensor * wo_b;
-
-    // normalization
-    struct ggml_tensor * ffn_norm_a;
-    struct ggml_tensor * ffn_norm_b;
-
-    // ff
-    struct ggml_tensor * ffn_gate_a;
-    struct ggml_tensor * ffn_gate_b;
-    struct ggml_tensor * ffn_down_a;
-    struct ggml_tensor * ffn_down_b;
-    struct ggml_tensor * ffn_up_a;
-    struct ggml_tensor * ffn_up_b;
-};
-
-struct my_llama_lora {
-    struct ggml_context * ctx = NULL;
-    ggml_backend_buffer_t data;
-
-    my_llama_lora_hparams hparams;
-
-    struct ggml_tensor * tok_embeddings_a;
-    struct ggml_tensor * tok_embeddings_b;
-
-    struct ggml_tensor * norm_a;
-    struct ggml_tensor * norm_b;
-    struct ggml_tensor * output_a;
-    struct ggml_tensor * output_b;
-
-    std::vector<my_llama_lora_layer> layers;
-};
-
-// gguf constants
-static const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA   = "finetune_lora";
-static const char * LLM_KV_TRAINING_TYPE                 = "training.type";
-
-static const char * LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD  = "training.lora.rank.token_embd";
-static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT      = "training.lora.rank.output";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_NORM   = "training.lora.rank.attn_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_Q      = "training.lora.rank.attn_q";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_K      = "training.lora.rank.attn_k";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_V      = "training.lora.rank.attn_v";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_OUT    = "training.lora.rank.attn_output";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_NORM    = "training.lora.rank.ffn_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_GATE    = "training.lora.rank.ffn_gate";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_DOWN    = "training.lora.rank.ffn_down";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_UP      = "training.lora.rank.ffn_up";
-
-// gguf constants (sync with gguf.py)
-
-static const char * LLM_KV_GENERAL_ARCHITECTURE        = "general.architecture";
-static const char * LLM_KV_GENERAL_FILE_TYPE           = "general.file_type";
-
-static const char * LLM_KV_CONTEXT_LENGTH              = "%s.context_length";
-static const char * LLM_KV_EMBEDDING_LENGTH            = "%s.embedding_length";
-static const char * LLM_KV_BLOCK_COUNT                 = "%s.block_count";
-static const char * LLM_KV_FEED_FORWARD_LENGTH         = "%s.feed_forward_length";
-static const char * LLM_KV_ATTENTION_HEAD_COUNT        = "%s.attention.head_count";
-static const char * LLM_KV_ATTENTION_HEAD_COUNT_KV     = "%s.attention.head_count_kv";
-static const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon";
-static const char * LLM_KV_ROPE_DIMENSION_COUNT        = "%s.rope.dimension_count";
-static const char * LLM_KV_ROPE_FREQ_BASE              = "%s.rope.freq_base"; // TODO load in llama.cpp
-static const char * LLM_KV_ROPE_SCALE_LINEAR           = "%s.rope.scale_linear";
-
-static const char * LLM_TENSOR_TOKEN_EMBD    = "token_embd";
-static const char * LLM_TENSOR_OUTPUT_NORM   = "output_norm";
-static const char * LLM_TENSOR_OUTPUT        = "output";
-static const char * LLM_TENSOR_ATTN_NORM     = "blk.%d.attn_norm";
-static const char * LLM_TENSOR_ATTN_Q        = "blk.%d.attn_q";
-static const char * LLM_TENSOR_ATTN_K        = "blk.%d.attn_k";
-static const char * LLM_TENSOR_ATTN_V        = "blk.%d.attn_v";
-static const char * LLM_TENSOR_ATTN_OUT      = "blk.%d.attn_output";
-static const char * LLM_TENSOR_FFN_NORM      = "blk.%d.ffn_norm";
-static const char * LLM_TENSOR_FFN_GATE      = "blk.%d.ffn_gate";
-static const char * LLM_TENSOR_FFN_DOWN      = "blk.%d.ffn_down";
-static const char * LLM_TENSOR_FFN_UP        = "blk.%d.ffn_up";
-
-static void print_params(struct my_llama_hparams * params) {
-    printf("%s: n_vocab               : %u\n", __func__, params->n_vocab);
-    printf("%s: n_ctx                 : %u\n", __func__, params->n_ctx);
-    printf("%s: n_embd                : %u\n", __func__, params->n_embd);
-    printf("%s: n_ff                  : %u\n", __func__, params->n_ff);
-    printf("%s: n_head                : %u\n", __func__, params->n_head);
-    printf("%s: n_head_kv             : %u\n", __func__, params->n_head_kv);
-    printf("%s: n_layer               : %u\n", __func__, params->n_layer);
-    printf("%s: norm_rms_eps          : %f\n", __func__, params->f_norm_rms_eps);
-    printf("%s: rope_freq_base        : %f\n", __func__, params->rope_freq_base);
-    printf("%s: rope_freq_scale       : %f\n", __func__, params->rope_freq_scale);
-}
-
-static void print_lora_params(struct my_llama_lora_hparams * params) {
-    printf("%s: n_rank_attention_norm : %u\n", __func__, params->n_rank_attention_norm);
-    printf("%s: n_rank_wq             : %u\n", __func__, params->n_rank_wq);
-    printf("%s: n_rank_wk             : %u\n", __func__, params->n_rank_wk);
-    printf("%s: n_rank_wv             : %u\n", __func__, params->n_rank_wv);
-    printf("%s: n_rank_wo             : %u\n", __func__, params->n_rank_wo);
-    printf("%s: n_rank_ffn_norm       : %u\n", __func__, params->n_rank_ffn_norm);
-    printf("%s: n_rank_ffn_gate       : %u\n", __func__, params->n_rank_ffn_gate);
-    printf("%s: n_rank_ffn_down       : %u\n", __func__, params->n_rank_ffn_down);
-    printf("%s: n_rank_ffn_up         : %u\n", __func__, params->n_rank_ffn_up);
-    printf("%s: n_rank_tok_embeddings : %u\n", __func__, params->n_rank_tok_embeddings);
-    printf("%s: n_rank_norm           : %u\n", __func__, params->n_rank_norm);
-    printf("%s: n_rank_output         : %u\n", __func__, params->n_rank_output);
-}
-
-#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
-{ \
-    const std::string skey(key); \
-    const int kid = gguf_find_key(ctx, skey.c_str()); \
-    if (kid >= 0) { \
-        enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
-        if (ktype != (type)) { \
-            die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
-        } \
-        (dst) = func(ctx, kid); \
-    } else if (req) { \
-        die_fmt("key not found in model: %s", skey.c_str()); \
-    } \
-}
-
-static void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_hparams * hparams, const char * expected_arch) {
-    std::string arch;
-
-    GGUF_GET_KEY(ctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
-    if (expected_arch != NULL) {
-        if (arch != expected_arch) {
-            printf("%s: arch=%s expected_arch=%s\n", __func__, arch.c_str(), expected_arch);
-        }
-        GGML_ASSERT(arch == expected_arch);
-    }
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-    auto kv = [&arch, &keybuf](const char * key) -> const char * {
-        snprintf(keybuf.data(), keybuf.size(), key, arch.c_str());
-        return keybuf.data();
-    };
-
-    GGUF_GET_KEY(ctx, hparams->n_embd,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, kv(LLM_KV_EMBEDDING_LENGTH));
-    GGUF_GET_KEY(ctx, hparams->n_ctx,          gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));
-    GGUF_GET_KEY(ctx, hparams->n_ff,           gguf_get_val_u32, GGUF_TYPE_UINT32,  true, kv(LLM_KV_FEED_FORWARD_LENGTH));
-    GGUF_GET_KEY(ctx, hparams->n_head,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
-    GGUF_GET_KEY(ctx, hparams->n_layer,        gguf_get_val_u32, GGUF_TYPE_UINT32,  true, kv(LLM_KV_BLOCK_COUNT));
-
-    // n_head_kv is optional, default to n_head
-    hparams->n_head_kv = hparams->n_head;
-    GGUF_GET_KEY(ctx, hparams->n_head_kv,      gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));
-
-    float rope_freq_scale = 1.0f;
-    GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
-    GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
-    GGUF_GET_KEY(ctx, rope_freq_scale,         gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
-    if (rope_freq_scale != 1.0f) {
-        hparams->rope_freq_scale = 1.0f / rope_freq_scale;
-    }
-}
-
-static void init_model(struct llama_model * input, struct my_llama_model * model, const char * fn_model, uint32_t n_ctx) {
-    auto & hparams = model->hparams;
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-    auto tn = [&tn_buf](const char * key) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
-        return tn_buf.data();
-    };
-    auto tni = [&tn_buf](const char * key, int bid) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
-        return tn_buf.data();
-    };
-
-
-    // get parameters directly from gguf file
-    {
-        struct gguf_init_params params = {
-            /*.no_alloc = */ false,
-            /*.ctx      = */ NULL,
-        };
-        struct gguf_context * mctx = gguf_init_from_file(fn_model, params);
-
-        load_model_hparams_gguf(mctx, &hparams, "llama");
-
-        gguf_free(mctx);
-    }
-    hparams.n_vocab = llama_n_vocab(input);
-    hparams.n_ctx = n_ctx;
-
-    // get tensors from llama_model (possibly mmapped)
-    model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD));
-    model->norm           = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM));
-    model->output         = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT));
-
-    assert_shape_2d(model->tok_embeddings, hparams.n_embd, hparams.n_vocab);
-    assert_shape_1d(model->norm,           hparams.n_embd);
-    assert_shape_2d(model->output,         hparams.n_embd, hparams.n_vocab);
-
-    model->layers.resize(hparams.n_layer);
-    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-        layer.attention_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_NORM, i));
-        layer.wq             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_Q, i));
-        layer.wk             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_K, i));
-        layer.wv             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i));
-        layer.wo             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i));
-        layer.ffn_norm       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i));
-        layer.ffn_gate       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));
-        layer.ffn_down       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));
-        layer.ffn_up         = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));
-
-        assert_shape_1d(layer.attention_norm, hparams.n_embd);
-        assert_shape_2d(layer.wq,             hparams.n_embd, hparams.n_embd);
-        assert_shape_2d(layer.wk,             hparams.n_embd, hparams.n_embd_gqa());
-        assert_shape_2d(layer.wv,             hparams.n_embd, hparams.n_embd_gqa());
-        assert_shape_2d(layer.wo,             hparams.n_embd, hparams.n_embd);
-        assert_shape_1d(layer.ffn_norm,       hparams.n_embd);
-        assert_shape_2d(layer.ffn_gate,       hparams.n_embd, hparams.n_ff);
-        assert_shape_2d(layer.ffn_down,       hparams.n_ff,   hparams.n_embd);
-        assert_shape_2d(layer.ffn_up,         hparams.n_embd, hparams.n_ff);
-    }
-}
-
-static void set_param_lora(struct my_llama_lora * lora) {
-    const uint32_t n_layer = lora->layers.size();
-
-    struct ggml_context* ctx = lora->ctx;
-
-    ggml_set_param(ctx, lora->tok_embeddings_a);
-    ggml_set_param(ctx, lora->tok_embeddings_b);
-    ggml_set_param(ctx, lora->norm_a);
-    ggml_set_param(ctx, lora->norm_b);
-    ggml_set_param(ctx, lora->output_a);
-    ggml_set_param(ctx, lora->output_b);
-
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = lora->layers[i];
-
-        ggml_set_param(ctx, layer.attention_norm_a);
-        ggml_set_param(ctx, layer.attention_norm_b);
-        ggml_set_param(ctx, layer.wq_a);
-        ggml_set_param(ctx, layer.wq_b);
-        ggml_set_param(ctx, layer.wk_a);
-        ggml_set_param(ctx, layer.wk_b);
-        ggml_set_param(ctx, layer.wv_a);
-        ggml_set_param(ctx, layer.wv_b);
-        ggml_set_param(ctx, layer.wo_a);
-        ggml_set_param(ctx, layer.wo_b);
-        ggml_set_param(ctx, layer.ffn_norm_a);
-        ggml_set_param(ctx, layer.ffn_norm_b);
-        ggml_set_param(ctx, layer.ffn_gate_a);
-        ggml_set_param(ctx, layer.ffn_gate_b);
-        ggml_set_param(ctx, layer.ffn_down_a);
-        ggml_set_param(ctx, layer.ffn_down_b);
-        ggml_set_param(ctx, layer.ffn_up_a);
-        ggml_set_param(ctx, layer.ffn_up_b);
-    }
-}
-
-static void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) {
-    const auto & lparams = lora->hparams;
-
-    const uint32_t n_embd     = model->hparams.n_embd;
-    const uint32_t n_embd_gqa = model->hparams.n_embd_gqa();
-    const uint32_t n_layer    = model->hparams.n_layer;
-    const uint32_t n_vocab    = model->hparams.n_vocab;
-    const uint32_t n_ff       = model->hparams.n_ff;
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-    auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix);
-        return tn_buf.data();
-    };
-    auto tni = [&tn_buf](const char * key, const char * suffix, int bid) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s%s", s.c_str(), suffix);
-        return tn_buf.data();
-    };
-
-    // context for lora tensors without their data
-    struct ggml_init_params ctx_lora_params;
-    ctx_lora_params.mem_size   = ggml_tensor_overhead()*2*(6 + n_layer*18);
-    ctx_lora_params.mem_buffer = NULL;
-    ctx_lora_params.no_alloc   = true;
-
-    struct ggml_context * ctx = ggml_init(ctx_lora_params);
-    lora->ctx = ctx;
-
-    lora->tok_embeddings_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_embd);
-    lora->tok_embeddings_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_vocab);
-    lora->norm_a           = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, n_embd);
-    lora->norm_b           = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, 1);
-    lora->output_a         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_embd);
-    lora->output_b         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_vocab);
-
-    ggml_set_name(lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD,  ".weight.lora_a"));
-    ggml_set_name(lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD,  ".weight.lora_b"));
-    ggml_set_name(lora->norm_a,           tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_a"));
-    ggml_set_name(lora->norm_b,           tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_b"));
-    ggml_set_name(lora->output_a,         tn(LLM_TENSOR_OUTPUT,      ".weight.lora_a"));
-    ggml_set_name(lora->output_b,         tn(LLM_TENSOR_OUTPUT,      ".weight.lora_b"));
-
-    lora->layers.resize(n_layer);
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = lora->layers[i];
-
-        layer.attention_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, n_embd);
-        layer.attention_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, 1);
-
-        layer.wq_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
-        layer.wq_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
-        layer.wk_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd);
-        layer.wk_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd_gqa);
-        layer.wv_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd);
-        layer.wv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd_gqa);
-        layer.wo_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
-        layer.wo_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
-
-        layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd);
-        layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1);
-
-        layer.ffn_gate_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_embd);
-        layer.ffn_gate_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_ff);
-        layer.ffn_down_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_ff);
-        layer.ffn_down_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_embd);
-        layer.ffn_up_a   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up,   n_embd);
-        layer.ffn_up_b   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up,   n_ff);
-
-        ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_a", i));
-        ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_b", i));
-        ggml_set_name(layer.wq_a,             tni(LLM_TENSOR_ATTN_Q,    ".weight.lora_a", i));
-        ggml_set_name(layer.wq_b,             tni(LLM_TENSOR_ATTN_Q,    ".weight.lora_b", i));
-        ggml_set_name(layer.wk_a,             tni(LLM_TENSOR_ATTN_K,    ".weight.lora_a", i));
-        ggml_set_name(layer.wk_b,             tni(LLM_TENSOR_ATTN_K,    ".weight.lora_b", i));
-        ggml_set_name(layer.wv_a,             tni(LLM_TENSOR_ATTN_V,    ".weight.lora_a", i));
-        ggml_set_name(layer.wv_b,             tni(LLM_TENSOR_ATTN_V,    ".weight.lora_b", i));
-        ggml_set_name(layer.wo_a,             tni(LLM_TENSOR_ATTN_OUT,  ".weight.lora_a", i));
-        ggml_set_name(layer.wo_b,             tni(LLM_TENSOR_ATTN_OUT,  ".weight.lora_b", i));
-        ggml_set_name(layer.ffn_norm_a,       tni(LLM_TENSOR_FFN_NORM,  ".weight.lora_a", i));
-        ggml_set_name(layer.ffn_norm_b,       tni(LLM_TENSOR_FFN_NORM,  ".weight.lora_b", i));
-        ggml_set_name(layer.ffn_gate_a,       tni(LLM_TENSOR_FFN_GATE,  ".weight.lora_a", i));
-        ggml_set_name(layer.ffn_gate_b,       tni(LLM_TENSOR_FFN_GATE,  ".weight.lora_b", i));
-        ggml_set_name(layer.ffn_down_a,       tni(LLM_TENSOR_FFN_DOWN,  ".weight.lora_a", i));
-        ggml_set_name(layer.ffn_down_b,       tni(LLM_TENSOR_FFN_DOWN,  ".weight.lora_b", i));
-        ggml_set_name(layer.ffn_up_a,         tni(LLM_TENSOR_FFN_UP,    ".weight.lora_a", i));
-        ggml_set_name(layer.ffn_up_b,         tni(LLM_TENSOR_FFN_UP,    ".weight.lora_b", i));
-    }
-
-    set_param_lora(lora);
-
-    // allocate data for lora tensors
-    lora->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type());
-}
-
-static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) {
-    const uint32_t n_layer = lora->layers.size();
-
-    struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
-
-    randomize_tensor_normal(lora->tok_embeddings_a, rnd);
-    ggml_set_zero(lora->tok_embeddings_b);
-    randomize_tensor_normal(lora->norm_a,           rnd);
-    ggml_set_zero(lora->norm_b);
-    randomize_tensor_normal(lora->output_a,         rnd);
-    ggml_set_zero(lora->output_b);
-
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = lora->layers[i];
-        randomize_tensor_normal(layer.attention_norm_a, rnd);
-        ggml_set_zero(layer.attention_norm_b);
-
-        randomize_tensor_normal(layer.wq_a, rnd);
-        ggml_set_zero(layer.wq_b);
-        randomize_tensor_normal(layer.wk_a, rnd);
-        ggml_set_zero(layer.wk_b);
-        randomize_tensor_normal(layer.wv_a, rnd);
-        ggml_set_zero(layer.wv_b);
-        randomize_tensor_normal(layer.wo_a, rnd);
-        ggml_set_zero(layer.wo_b);
-
-        randomize_tensor_normal(layer.ffn_norm_a, rnd);
-        ggml_set_zero(layer.ffn_norm_b);
-
-        randomize_tensor_normal(layer.ffn_gate_a, rnd);
-        ggml_set_zero(layer.ffn_gate_b);
-        randomize_tensor_normal(layer.ffn_down_a, rnd);
-        ggml_set_zero(layer.ffn_down_b);
-        randomize_tensor_normal(layer.ffn_up_a, rnd);
-        ggml_set_zero(layer.ffn_up_b);
-    }
-
-    free_random_normal_distribution(rnd);
-}
-
-static struct ggml_tensor * llama_build_lora_finetune_graphs(
-        struct my_llama_model * model,
-        struct my_llama_lora  * lora,
-        ggml_gallocr_t          alloc,
-        struct ggml_context   * ctx,
-        struct ggml_cgraph    * gf,
-        struct ggml_cgraph    * gb,
-        struct ggml_cgraph    * gb_tmp,
-        struct ggml_tensor  * * logits,
-        struct ggml_tensor    * tokens_input,
-        struct ggml_tensor    * targets,
-        const  int              n_tokens,
-        const  int              n_batch,
-        const  bool             enable_flash_attn,
-        const  bool             enable_checkpointing,
-        const  bool             measure_only) {
-
-    ggml_set_scratch(ctx, { 0, 0, nullptr, });
-    const int n_past = 0;
-    const int N = n_tokens;
-    const auto & hparams  = model->hparams;
-    const int n_ctx       = hparams.n_ctx;
-    const int n_vocab     = hparams.n_vocab;
-    const int n_embd      = hparams.n_embd;
-    const int n_layer     = hparams.n_layer;
-    const int n_head      = hparams.n_head;
-    const int n_head_kv   = hparams.n_head_kv;
-    const int n_ff        = hparams.n_ff;
-    const int n_rot       = hparams.n_embd_head();
-    const int n_embd_head = hparams.n_embd_head();
-    const int n_embd_gqa  = hparams.n_embd_gqa();
-
-    const float rms_norm_eps    = hparams.f_norm_rms_eps;
-    const float rope_freq_base  = hparams.rope_freq_base;
-    const float rope_freq_scale = hparams.rope_freq_scale;
-
-    GGML_ASSERT((size_t) n_layer == lora->layers.size());
-
-    auto set_name = [](struct ggml_tensor * t, const char * n) {
-        ggml_set_name(t, n);
-        if (t->grad) {
-            ggml_format_name(t->grad, "%s->grad", n);
-        }
-    };
-
-    // KQ_pos - contains the positions
-    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
-    ggml_set_input(KQ_pos);
-
-    // rope has so much parameters that we make a custom function for it
-    auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
-                (struct ggml_tensor * t) -> struct ggml_tensor * {
-        // not capturing these, to silcence warnings
-        const int rope_mode = 0;
-
-        return ggml_rope_custom(ctx,
-            t, KQ_pos, n_rot, rope_mode, n_ctx, 0,
-            rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
-        );
-    };
-
-    set_name(tokens_input, "tokens_input");
-    set_name(targets,      "targets");
-
-    GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
-
-    auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
-        if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16 || a->type == GGML_TYPE_BF16) {
-            return ggml_add_cast(ctx, a, b, GGML_TYPE_F32);
-        } else if (a->type == GGML_TYPE_F32) {
-            return ggml_add(ctx, a, b);
-        } else {
-            die_fmt("%s: Finetuning on tensors with type '%s' is not yet supported.\n",
-                __func__, ggml_type_name(a->type));
-        }
-    };
-
-    struct ggml_tensor * tok_embeddings = add_to_f32(ctx, model->tok_embeddings, ggml_mul_mat(ctx, lora->tok_embeddings_a, lora->tok_embeddings_b));
-    struct ggml_tensor * norm           = add_to_f32(ctx, model->norm, ggml_mul_mat(ctx, lora->norm_a, lora->norm_b));
-    struct ggml_tensor * output         = add_to_f32(ctx, model->output, ggml_mul_mat(ctx, lora->output_a, lora->output_b));
-
-    struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch);  set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch);
-    struct ggml_tensor * t01 = ggml_get_rows(ctx, tok_embeddings, t00);        set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch);
-
-    struct ggml_tensor * cur = t01;
-
-    std::vector<struct ggml_tensor *> checkpoints;
-    if (enable_checkpointing) {
-        checkpoints.push_back(tokens_input);
-        checkpoints.push_back(targets);
-        checkpoints.push_back(t00);
-        checkpoints.push_back(t01);
-    }
-
-    const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head);
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct my_llama_layer & layer = model->layers[il];
-        struct my_llama_lora_layer & llayer = lora->layers[il];
-
-        struct ggml_tensor * attention_norm = add_to_f32(ctx, layer.attention_norm, ggml_mul_mat(ctx, llayer.attention_norm_a, llayer.attention_norm_b));
-        struct ggml_tensor * ffn_norm = add_to_f32(ctx, layer.ffn_norm, ggml_mul_mat(ctx, llayer.ffn_norm_a, llayer.ffn_norm_b));
-        struct ggml_tensor * wq       = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b));
-        struct ggml_tensor * wk       = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b));
-        struct ggml_tensor * wv       = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b));
-        struct ggml_tensor * wo       = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b));
-        struct ggml_tensor * ffn_gate = add_to_f32(ctx, layer.ffn_gate, ggml_mul_mat(ctx, llayer.ffn_gate_a, llayer.ffn_gate_b));
-        struct ggml_tensor * ffn_down = add_to_f32(ctx, layer.ffn_down, ggml_mul_mat(ctx, llayer.ffn_down_a, llayer.ffn_down_b));
-        struct ggml_tensor * ffn_up   = add_to_f32(ctx, layer.ffn_up, ggml_mul_mat(ctx, llayer.ffn_up_a, llayer.ffn_up_b));
-
-        struct ggml_tensor * t02 = ggml_rms_norm     (ctx, cur, rms_norm_eps);                       set_name(t02, "t02");     assert_shape_2d(t02, n_embd, N*n_batch);
-        struct ggml_tensor * t03 = ggml_repeat       (ctx, attention_norm, t02);                     set_name(t03, "t03");     assert_shape_2d(t03, n_embd, N*n_batch);
-        struct ggml_tensor * t04 = ggml_mul          (ctx, t03, t02);                                set_name(t04, "t04");     assert_shape_2d(t04, n_embd, N*n_batch);
-        struct ggml_tensor * t05 = ggml_mul_mat      (ctx, wq, t04);                                 set_name(t05, "t05");     assert_shape_2d(t05, n_embd, N*n_batch);
-        struct ggml_tensor * t06 = ggml_reshape_4d   (ctx, t05, n_embd_head, n_head, N, n_batch);    set_name(t06, "t06");     assert_shape_4d(t06, n_embd_head, n_head, N, n_batch);
-        struct ggml_tensor * t07 = rope              (t06);                                          set_name(t07, "t07");     assert_shape_4d(t07, n_embd_head, n_head, N, n_batch);
-        struct ggml_tensor * t08 = ggml_mul_mat      (ctx, wk, t04);                                 set_name(t08, "t08");     assert_shape_2d(t08, n_embd_gqa, N*n_batch);
-        struct ggml_tensor * t09 = ggml_reshape_4d   (ctx, t08, n_embd_head, n_head_kv, N, n_batch); set_name(t09, "t09");     assert_shape_4d(t09, n_embd_head, n_head_kv, N, n_batch);
-        struct ggml_tensor * t10 = rope              (t09);                                          set_name(t10, "t10");     assert_shape_4d(t10, n_embd_head, n_head_kv, N, n_batch);
-
-        struct ggml_tensor * t11;
-        if (ggml_is_quantized(wv->type)) {
-            struct ggml_tensor * t11_1 = ggml_mul_mat  (ctx, wv, t04);                               set_name(t11_1, "t11_1"); assert_shape_2d(t11_1, n_embd_gqa, N*n_batch);
-            struct ggml_tensor * t11_2 = ggml_transpose(ctx, t11_1);                                 set_name(t11_2, "t11_2"); assert_shape_2d(t11_2, N*n_batch, n_embd_gqa);
-                                 t11   = ggml_cont     (ctx, t11_2);                                 set_name(t11, "t11");     assert_shape_2d(t11, N*n_batch, n_embd_gqa);
-        } else {
-                                 t11   = ggml_mul_mat  (ctx, t04, wv);                               set_name(t11, "t11");     assert_shape_2d(t11, N*n_batch, n_embd_gqa);
-        }
-
-        struct ggml_tensor * t12 = ggml_reshape_4d   (ctx, t11, N, n_batch, n_embd_head, n_head_kv); set_name(t12, "t12");     assert_shape_4d(t12, N, n_batch, n_embd_head, n_head_kv);
-        struct ggml_tensor * t13 = ggml_permute      (ctx, t07, 0, 2, 1, 3);                         set_name(t13, "t13");     assert_shape_4d(t13, n_embd_head, N, n_head, n_batch);
-        struct ggml_tensor * t14 = ggml_permute      (ctx, t10, 0, 2, 1, 3);                         set_name(t14, "t14");     assert_shape_4d(t14, n_embd_head, N, n_head_kv, n_batch);
-        struct ggml_tensor * t15 = ggml_permute      (ctx, t12, 0, 3, 1, 2);                         set_name(t15, "t15");     assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch);
-        struct ggml_tensor * t16;
-        if (enable_flash_attn) {
-            t16 = ggml_flash_attn(ctx, t13, t14, t15, true);                                         set_name(t16, "t16");     assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
-        } else {
-            struct ggml_tensor * t16_0 = ggml_mul_mat              (ctx, t14, t13);                  set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_1 = ggml_scale_inplace        (ctx, t16_0, kv_scale);           set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past);             set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_3 = ggml_soft_max_inplace     (ctx, t16_2);                     set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch);
-            t16 = ggml_mul_mat(ctx, t15, t16_3);                                                     set_name(t16, "t16");     assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
-        }
-        struct ggml_tensor * t17 = ggml_permute      (ctx, t16, 0, 2, 1, 3);                         set_name(t17, "t17");     assert_shape_4d(t17, n_embd_head, n_head, N, n_batch);
-        struct ggml_tensor * t18 = ggml_cont         (ctx, t17);                                     set_name(t18, "t18");     assert_shape_4d(t18, n_embd_head, n_head, N, n_batch);
-        struct ggml_tensor * t19 = ggml_reshape_2d   (ctx, t18, n_embd, N*n_batch);                  set_name(t19, "t19");     assert_shape_2d(t19, n_embd, N*n_batch);
-        struct ggml_tensor * t20 = ggml_mul_mat      (ctx, wo, t19);                                 set_name(t20, "t20");     assert_shape_2d(t20, n_embd, N*n_batch);
-        struct ggml_tensor * t21 = ggml_add          (ctx, t20, cur);                                set_name(t21, "t21");     assert_shape_2d(t21, n_embd, N*n_batch);
-        struct ggml_tensor * t22 = ggml_rms_norm     (ctx, t21, rms_norm_eps);                       set_name(t22, "t22");     assert_shape_2d(t22, n_embd, N*n_batch);
-        struct ggml_tensor * t23 = ggml_repeat       (ctx, ffn_norm, t22);                           set_name(t23, "t23");     assert_shape_2d(t23, n_embd, N*n_batch);
-        struct ggml_tensor * t24 = ggml_mul          (ctx, t23, t22);                                set_name(t24, "t24");     assert_shape_2d(t24, n_embd, N*n_batch);
-        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, ffn_up, t24);                             set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
-        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, ffn_gate, t24);                           set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
-        struct ggml_tensor * t27 = ggml_silu         (ctx, t26);                                     set_name(t27, "t27");     assert_shape_2d(t27, n_ff, N*n_batch);
-        struct ggml_tensor * t28 = ggml_mul          (ctx, t27, t25);                                set_name(t28, "t28");     assert_shape_2d(t28, n_ff, N*n_batch);
-        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, ffn_down, t28);                           set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
-        struct ggml_tensor * t30 = ggml_add          (ctx, t29, t21);                                set_name(t30, "t30");     assert_shape_2d(t30, n_embd, N*n_batch);
-        cur = t30;
-        if (enable_checkpointing) {
-            checkpoints.push_back(cur);
-        }
-    }
-    struct ggml_tensor * t31   = ggml_rms_norm          (ctx, cur, rms_norm_eps);                    set_name(t31, "t31");     assert_shape_2d(t31, n_embd, N*n_batch);
-    struct ggml_tensor * t32   = ggml_repeat            (ctx, norm, t31);                            set_name(t32, "t32");     assert_shape_2d(t32, n_embd, N*n_batch);
-    struct ggml_tensor * t33   = ggml_mul               (ctx, t32, t31);                             set_name(t33, "t33");     assert_shape_2d(t33, n_embd, N*n_batch);
-    struct ggml_tensor * t34   = ggml_mul_mat           (ctx, output, t33);                          set_name(t34, "t34");     assert_shape_2d(t34, n_vocab, N*n_batch);
-    struct ggml_tensor * t35   = ggml_reshape_3d        (ctx, t34, n_vocab, N, n_batch);             set_name(t35, "t35");     assert_shape_3d(t35, n_vocab, N, n_batch);
-    struct ggml_tensor * t36   = ggml_cross_entropy_loss(ctx, t35, targets);                         set_name(t36, "t36");     assert_shape_1d(t36, 1);
-
-    if (enable_checkpointing) {
-        checkpoints.push_back(t31);
-        checkpoints.push_back(t32);
-        checkpoints.push_back(t33);
-        checkpoints.push_back(t34);
-        checkpoints.push_back(t35);
-        checkpoints.push_back(t36);
-    }
-
-    ggml_build_forward_expand(gf, t36);
-
-    if (enable_checkpointing) {
-        ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
-    } else {
-        ggml_graph_cpy(gf, gb);
-        ggml_build_backward_expand(ctx, gf, gb, true);
-    }
-
-    GGML_ASSERT(alloc != NULL);
-
-    // make sure some tensors are not reallocated by inserting new temporary nodes depending on them
-    int n_leafs_before = gb->n_leafs;
-    int n_nodes_before = gb->n_nodes;
-
-    // output tensors
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f));
-    // input gradient
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
-    GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
-    ggml_set_input(t36->grad);
-    // KQ_pos
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
-
-    // make sure base model tensors data cannot be used in viewable operations
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, 1.0f));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, 1.0f));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, 1.0f));
-    for (int il = 0; il < n_layer; ++il) {
-        struct my_llama_layer & layer = model->layers[il];
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_gate, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_down, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_up, 1.0f));
-    }
-
-    // allocating checkpoints in one block to reduce memory fragmentation
-    // note: they will be freed in reverse order
-    for (unsigned int i = 0; i < checkpoints.size(); ++i) {
-        if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
-            ggml_set_input(checkpoints[i]);
-        }
-    }
-
-    if (measure_only) {
-        ggml_gallocr_reserve(alloc, gb);
-    } else {
-        ggml_gallocr_alloc_graph(alloc, gb);
-
-        // set KQ_pos
-        {
-            int * data = (int *) KQ_pos->data;
-            for (int i = 0; i < N; ++i) {
-                data[i] = n_past + i;
-            }
-        }
-    }
-
-    // remove the additional nodes and leafs
-    for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
-        gb->leafs[i] = NULL;
-    }
-    for (int i = n_nodes_before; i < gb->n_nodes; ++i) {
-        gb->nodes[i] = NULL;
-    }
-    gb->n_leafs = n_leafs_before;
-    gb->n_nodes = n_nodes_before;
-
-    *logits = t35;
-    return t36;
-}
-
-static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora) {
-    // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
-
-    std::string arch;
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-
-    GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
-    GGML_ASSERT(arch == "llama");
-
-    uint32_t ftype_u;
-    GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE);
-    GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32);
-
-    struct my_llama_hparams hparams;
-    load_model_hparams_gguf(fctx, &hparams, arch.c_str());
-
-    // parameters that define tensor shapes must match
-    GGML_ASSERT(hparams.n_embd    == model->hparams.n_embd);
-    GGML_ASSERT(hparams.n_ff      == model->hparams.n_ff);
-    GGML_ASSERT(hparams.n_head    == model->hparams.n_head);
-    GGML_ASSERT(hparams.n_head_kv == model->hparams.n_head_kv);
-    GGML_ASSERT(hparams.n_layer   == model->hparams.n_layer);
-
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_tok_embeddings, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_norm,           gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_output,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_attention_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_wq,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_Q);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_wk,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_K);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_gate,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_down,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_up,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);
-
-    init_lora(model, lora);
-
-    copy_tensor_by_name(lora->tok_embeddings_a, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_a));
-    copy_tensor_by_name(lora->tok_embeddings_b, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_b));
-    copy_tensor_by_name(lora->norm_a,           f_ggml_ctx, ggml_get_name(lora->norm_a));
-    copy_tensor_by_name(lora->norm_b,           f_ggml_ctx, ggml_get_name(lora->norm_b));
-    copy_tensor_by_name(lora->output_a,         f_ggml_ctx, ggml_get_name(lora->output_a));
-    copy_tensor_by_name(lora->output_b,         f_ggml_ctx, ggml_get_name(lora->output_b));
-
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-        copy_tensor_by_name(layer.attention_norm_a, f_ggml_ctx, ggml_get_name(layer.attention_norm_a));
-        copy_tensor_by_name(layer.attention_norm_b, f_ggml_ctx, ggml_get_name(layer.attention_norm_b));
-        copy_tensor_by_name(layer.wq_a,             f_ggml_ctx, ggml_get_name(layer.wq_a));
-        copy_tensor_by_name(layer.wq_b,             f_ggml_ctx, ggml_get_name(layer.wq_b));
-        copy_tensor_by_name(layer.wk_a,             f_ggml_ctx, ggml_get_name(layer.wk_a));
-        copy_tensor_by_name(layer.wk_b,             f_ggml_ctx, ggml_get_name(layer.wk_b));
-        copy_tensor_by_name(layer.wv_a,             f_ggml_ctx, ggml_get_name(layer.wv_a));
-        copy_tensor_by_name(layer.wv_b,             f_ggml_ctx, ggml_get_name(layer.wv_b));
-        copy_tensor_by_name(layer.wo_a,             f_ggml_ctx, ggml_get_name(layer.wo_a));
-        copy_tensor_by_name(layer.wo_b,             f_ggml_ctx, ggml_get_name(layer.wo_b));
-        copy_tensor_by_name(layer.ffn_norm_a,       f_ggml_ctx, ggml_get_name(layer.ffn_norm_a));
-        copy_tensor_by_name(layer.ffn_norm_b,       f_ggml_ctx, ggml_get_name(layer.ffn_norm_b));
-        copy_tensor_by_name(layer.ffn_gate_a,       f_ggml_ctx, ggml_get_name(layer.ffn_gate_a));
-        copy_tensor_by_name(layer.ffn_gate_b,       f_ggml_ctx, ggml_get_name(layer.ffn_gate_b));
-        copy_tensor_by_name(layer.ffn_down_a,       f_ggml_ctx, ggml_get_name(layer.ffn_down_a));
-        copy_tensor_by_name(layer.ffn_down_b,       f_ggml_ctx, ggml_get_name(layer.ffn_down_b));
-        copy_tensor_by_name(layer.ffn_up_a,         f_ggml_ctx, ggml_get_name(layer.ffn_up_a));
-        copy_tensor_by_name(layer.ffn_up_b,         f_ggml_ctx, ggml_get_name(layer.ffn_up_b));
-    }
-}
-
-static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora) {
-    const char * arch = "llama";
-    enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-    auto kv = [arch, &keybuf](const char * key) -> const char * {
-        snprintf(keybuf.data(), keybuf.size(), key, arch);
-        return keybuf.data();
-    };
-
-    gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
-    gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
-
-    gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH),              model->hparams.n_ctx);
-    gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH),            model->hparams.n_embd);
-    gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH),         model->hparams.n_ff);
-    gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT),        model->hparams.n_head);
-    gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV),     model->hparams.n_head_kv);
-    gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT),                 model->hparams.n_layer);
-    gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT),        model->hparams.n_embd_head());
-    gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps);
-    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE),              model->hparams.rope_freq_base);
-    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR),           model->hparams.rope_freq_scale);
-
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD,   lora->hparams.n_rank_tok_embeddings);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM,  lora->hparams.n_rank_norm);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT,       lora->hparams.n_rank_output);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM,    lora->hparams.n_rank_attention_norm);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_Q,       lora->hparams.n_rank_wq);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_K,       lora->hparams.n_rank_wk);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V,       lora->hparams.n_rank_wv);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT,     lora->hparams.n_rank_wo);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM,     lora->hparams.n_rank_ffn_norm);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE,     lora->hparams.n_rank_ffn_gate);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN,     lora->hparams.n_rank_ffn_down);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP,       lora->hparams.n_rank_ffn_up);
-
-    gguf_add_tensor(fctx, lora->tok_embeddings_a);
-    gguf_add_tensor(fctx, lora->tok_embeddings_b);
-    gguf_add_tensor(fctx, lora->norm_a);
-    gguf_add_tensor(fctx, lora->norm_b);
-    gguf_add_tensor(fctx, lora->output_a);
-    gguf_add_tensor(fctx, lora->output_b);
-
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-
-        gguf_add_tensor(fctx, layer.attention_norm_a);
-        gguf_add_tensor(fctx, layer.attention_norm_b);
-        gguf_add_tensor(fctx, layer.wq_a);
-        gguf_add_tensor(fctx, layer.wq_b);
-        gguf_add_tensor(fctx, layer.wk_a);
-        gguf_add_tensor(fctx, layer.wk_b);
-        gguf_add_tensor(fctx, layer.wv_a);
-        gguf_add_tensor(fctx, layer.wv_b);
-        gguf_add_tensor(fctx, layer.wo_a);
-        gguf_add_tensor(fctx, layer.wo_b);
-        gguf_add_tensor(fctx, layer.ffn_norm_a);
-        gguf_add_tensor(fctx, layer.ffn_norm_b);
-        gguf_add_tensor(fctx, layer.ffn_gate_a);
-        gguf_add_tensor(fctx, layer.ffn_gate_b);
-        gguf_add_tensor(fctx, layer.ffn_down_a);
-        gguf_add_tensor(fctx, layer.ffn_down_b);
-        gguf_add_tensor(fctx, layer.ffn_up_a);
-        gguf_add_tensor(fctx, layer.ffn_up_b);
-    }
-}
-
-static void load_checkpoint_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    std::string train_type = LLM_KV_TRAINING_TYPE_FINETUNE_LORA;
-    GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
-    GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
-
-    load_train_state_gguf(fctx, f_ggml_ctx, train);
-    load_llama_lora_gguf(fctx, f_ggml_ctx, model, lora);
-}
-
-static void save_checkpoint_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
-    save_llama_lora_gguf(fctx, model, lora);
-    save_train_state_gguf(fctx, train);
-}
-
-static bool load_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    struct ggml_context * f_ggml_ctx;
-    struct gguf_init_params params;
-    params.no_alloc = false;
-    params.ctx = &f_ggml_ctx;
-    struct gguf_context * fctx = gguf_init_from_file(filename, params);
-    if (fctx == NULL) {
-        return false;
-    }
-
-    load_checkpoint_lora_gguf(fctx, f_ggml_ctx, model, lora, train);
-
-    gguf_free(fctx);
-    return true;
-}
-
-static void save_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    printf("%s: saving to %s\n", __func__, filename);
-    struct gguf_context * fctx = gguf_init_empty();
-
-    save_checkpoint_lora_gguf(fctx, model, lora, train);
-
-    // write file
-    const bool only_meta = false;
-    gguf_write_to_file(fctx, filename, only_meta);
-    gguf_free(fctx);
-}
-
-struct llama_file {
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
-
-    llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
-        if (fp == NULL) {
-            size = 0;
-        } else {
-            seek(0, SEEK_END);
-            size = tell();
-            seek(0, SEEK_SET);
-        }
-    }
-
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        GGML_ASSERT(ret != -1); // this really shouldn't fail
-        return (size_t) ret;
-    }
-
-    void seek(size_t offset, int whence) {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        GGML_ASSERT(ret == 0); // same
-    }
-
-    void read_raw(void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, size, 1, fp);
-        if (ferror(fp)) {
-            die_fmt("read error: %s", strerror(errno));
-        }
-        if (ret != 1) {
-            die("unexpectedly reached end of file");
-        }
-    }
-
-    std::uint32_t read_u32() {
-        std::uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
-    }
-
-    std::string read_string(std::uint32_t len) {
-        std::vector<char> chars(len);
-        read_raw(chars.data(), len);
-        return std::string(chars.data(), len);
-    }
-
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            die_fmt("write error: %s", strerror(errno));
-        }
-    }
-
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
-    }
-
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
-        }
-    }
-};
-
-static void write_tensor(struct llama_file * file, struct ggml_tensor * tensor, const char * name) {
-    if (tensor == NULL) {
-        file->write_u32(0);
-        file->write_u32(0);
-        file->write_u32(GGML_TYPE_F32);
-        file->seek((0-file->tell()) & 31, SEEK_CUR);
-        return;
-    }
-    if (name == NULL) {
-        name = ggml_get_name(tensor);
-    }
-    uint32_t name_len = strlen(name);
-    uint32_t nd = ggml_n_dims(tensor);
-    uint32_t ne[4] = { (uint32_t)tensor->ne[0],
-                       (uint32_t)tensor->ne[1],
-                       (uint32_t)tensor->ne[2],
-                       (uint32_t)tensor->ne[3] };
-    file->write_u32(nd);
-    file->write_u32(name_len);
-    file->write_u32(tensor->type);
-    file->write_raw(ne, sizeof(ne[0]) * nd);
-    file->write_raw(name, name_len);
-    file->seek((0-file->tell()) & 31, SEEK_CUR);
-    file->write_raw(tensor->data, ggml_nbytes(tensor));
-}
-
-static void save_as_llama_lora(const char * filename, struct my_llama_lora * lora) {
-    printf("%s: saving to %s\n", __func__, filename);
-    struct llama_file file(filename, "wb");
-    if (file.fp == NULL) {
-        return;
-    }
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-
-    auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix);
-        return tn_buf.data();
-    };
-
-    auto tni = [&tn_buf](const char * key, int bid, const char * suffix) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s%s", s.c_str(), suffix);
-        return tn_buf.data();
-    };
-
-    // write_magic
-    file.write_u32(LLAMA_FILE_MAGIC_GGLA);   // magic
-    file.write_u32(1); // version
-    // write_hparams
-    file.write_u32(lora->hparams.lora_r);
-    file.write_u32(lora->hparams.lora_alpha);
-    // write tensors
-    write_tensor(&file, lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD,  ".weight.loraA"));
-    write_tensor(&file, lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD,  ".weight.loraB"));
-    write_tensor(&file, lora->norm_a,           tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraA"));
-    write_tensor(&file, lora->norm_b,           tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraB"));
-    write_tensor(&file, lora->output_a,         tn(LLM_TENSOR_OUTPUT,      ".weight.loraA"));
-    write_tensor(&file, lora->output_b,         tn(LLM_TENSOR_OUTPUT,      ".weight.loraB"));
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-        write_tensor(&file, layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraA"));
-        write_tensor(&file, layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraB"));
-        write_tensor(&file, layer.wq_a,             tni(LLM_TENSOR_ATTN_Q,    i, ".weight.loraA"));
-        write_tensor(&file, layer.wq_b,             tni(LLM_TENSOR_ATTN_Q,    i, ".weight.loraB"));
-        write_tensor(&file, layer.wk_a,             tni(LLM_TENSOR_ATTN_K,    i, ".weight.loraA"));
-        write_tensor(&file, layer.wk_b,             tni(LLM_TENSOR_ATTN_K,    i, ".weight.loraB"));
-        write_tensor(&file, layer.wv_a,             tni(LLM_TENSOR_ATTN_V,    i, ".weight.loraA"));
-        write_tensor(&file, layer.wv_b,             tni(LLM_TENSOR_ATTN_V,    i, ".weight.loraB"));
-        write_tensor(&file, layer.wo_a,             tni(LLM_TENSOR_ATTN_OUT,  i, ".weight.loraA"));
-        write_tensor(&file, layer.wo_b,             tni(LLM_TENSOR_ATTN_OUT,  i, ".weight.loraB"));
-        write_tensor(&file, layer.ffn_norm_a,       tni(LLM_TENSOR_FFN_NORM,  i, ".weight.loraA"));
-        write_tensor(&file, layer.ffn_norm_b,       tni(LLM_TENSOR_FFN_NORM,  i, ".weight.loraB"));
-        write_tensor(&file, layer.ffn_gate_a,       tni(LLM_TENSOR_FFN_GATE,  i, ".weight.loraA"));
-        write_tensor(&file, layer.ffn_gate_b,       tni(LLM_TENSOR_FFN_GATE,  i, ".weight.loraB"));
-        write_tensor(&file, layer.ffn_down_a,       tni(LLM_TENSOR_FFN_DOWN,  i, ".weight.loraA"));
-        write_tensor(&file, layer.ffn_down_b,       tni(LLM_TENSOR_FFN_DOWN,  i, ".weight.loraB"));
-        write_tensor(&file, layer.ffn_up_a,         tni(LLM_TENSOR_FFN_UP,    i, ".weight.loraA"));
-        write_tensor(&file, layer.ffn_up_b,         tni(LLM_TENSOR_FFN_UP,    i, ".weight.loraB"));
-    }
-}
-
-struct train_params {
-    struct train_params_common common;
-
-    const char * fn_model_base;
-    const char * fn_lora_out;
-
-    bool only_write_lora;
-
-    float f_norm_rms_eps;
-    float rope_freq_base;
-    float rope_freq_scale;
-
-    bool custom_f_norm_rms_eps;
-    bool custom_rope_freq_base;
-    bool custom_rope_freq_scale;
-
-    int32_t lora_r;
-    int32_t lora_alpha;
-    bool custom_lora_alpha;
-
-    uint32_t n_rank_attention_norm;
-    uint32_t n_rank_wq;
-    uint32_t n_rank_wk;
-    uint32_t n_rank_wv;
-    uint32_t n_rank_wo;
-    uint32_t n_rank_ffn_norm;
-    uint32_t n_rank_ffn_gate;
-    uint32_t n_rank_ffn_down;
-    uint32_t n_rank_ffn_up;
-    uint32_t n_rank_tok_embeddings;
-    uint32_t n_rank_norm;
-    uint32_t n_rank_output;
-
-    bool custom_n_rank_attention_norm;
-    bool custom_n_rank_wq;
-    bool custom_n_rank_wk;
-    bool custom_n_rank_wv;
-    bool custom_n_rank_wo;
-    bool custom_n_rank_ffn_norm;
-    bool custom_n_rank_ffn_gate;
-    bool custom_n_rank_ffn_down;
-    bool custom_n_rank_ffn_up;
-    bool custom_n_rank_tok_embeddings;
-    bool custom_n_rank_norm;
-    bool custom_n_rank_output;
-};
-
-static struct train_params get_default_train_params() {
-    struct train_params params;
-    params.common = get_default_train_params_common();
-    params.fn_model_base     = "";
-    params.fn_lora_out       = "ggml-lora-ITERATION-f32.gguf";
-
-    params.only_write_lora = false;
-
-    params.f_norm_rms_eps  = 1e-5f;
-    params.rope_freq_base  = 10000.0f;
-    params.rope_freq_scale = 1.0f;
-
-    params.custom_f_norm_rms_eps  = false;
-    params.custom_rope_freq_base  = false;
-    params.custom_rope_freq_scale = false;
-
-    params.lora_r      = 4;
-    params.lora_alpha  = 4;
-    params.custom_lora_alpha = false;
-
-    params.n_rank_attention_norm = 1;
-    params.n_rank_wq             = 4;
-    params.n_rank_wk             = 4;
-    params.n_rank_wv             = 4;
-    params.n_rank_wo             = 4;
-    params.n_rank_ffn_norm       = 1;
-    params.n_rank_ffn_gate       = 4;
-    params.n_rank_ffn_down       = 4;
-    params.n_rank_ffn_up         = 4;
-    params.n_rank_tok_embeddings = 4;
-    params.n_rank_norm           = 1;
-    params.n_rank_output         = 4;
-
-    params.custom_n_rank_attention_norm = false;
-    params.custom_n_rank_wq             = false;
-    params.custom_n_rank_wk             = false;
-    params.custom_n_rank_wv             = false;
-    params.custom_n_rank_wo             = false;
-    params.custom_n_rank_ffn_norm       = false;
-    params.custom_n_rank_ffn_gate       = false;
-    params.custom_n_rank_ffn_down       = false;
-    params.custom_n_rank_ffn_up         = false;
-    params.custom_n_rank_tok_embeddings = false;
-    params.custom_n_rank_norm           = false;
-    params.custom_n_rank_output         = false;
-
-    return params;
-}
-
-static void train_print_usage(int argc, char ** argv, const struct train_params * params) {
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h, --help                 show this help message and exit\n");
-
-    fprintf(stderr, "  --model-base FNAME         model path from which to load base model (default '%s')\n", params->fn_model_base);
-    fprintf(stderr, "  --lora-out FNAME           path to save llama lora (default '%s')\n", params->fn_lora_out);
-    fprintf(stderr, "  --only-write-lora          only save llama lora, don't do any training.  use this if you only want to convert a checkpoint to a lora adapter.\n");
-    fprintf(stderr, "  --norm-rms-eps F           RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
-    fprintf(stderr, "  --rope-freq-base F         Frequency base for ROPE (default %f)\n", params->rope_freq_base);
-    fprintf(stderr, "  --rope-freq-scale F        Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
-    fprintf(stderr, "  --lora-alpha N             LORA alpha : resulting LORA scaling is alpha/r. (default %d)\n", params->lora_alpha);
-    fprintf(stderr, "  --lora-r N                 LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default %d)\n", params->lora_r);
-    fprintf(stderr, "  --rank-att-norm N          LORA rank for attention norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
-    fprintf(stderr, "  --rank-ffn-norm N          LORA rank for feed-forward norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
-    fprintf(stderr, "  --rank-out-norm N          LORA rank for output norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
-    fprintf(stderr, "  --rank-tok-embd N          LORA rank for token embeddings tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-out N               LORA rank for output tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-wq N                LORA rank for wq tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-wk N                LORA rank for wk tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-wv N                LORA rank for wv tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-wo N                LORA rank for wo tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-ffn_gate N          LORA rank for ffn_gate tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-ffn_down N          LORA rank for ffn_down tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-ffn_up N            LORA rank for ffn_up tensor, overrides default rank.\n");
-
-    print_common_train_usage(argc, argv, &params->common);
-}
-
-static bool train_params_parse(int argc, char ** argv, struct train_params * params) {
-    bool invalid_param = false;
-    std::string arg;
-    struct train_params default_params = get_default_train_params();
-    const std::string arg_prefix = "--";
-
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
-        }
-
-        if (consume_common_train_arg(argc, argv, &i, &params->common, &invalid_param)) {
-            if (invalid_param) {
-                break;
-            } else if (params->common.print_usage) {
-                train_print_usage(argc, argv, &default_params);
-                exit(0);
-            }
-        } else if (arg == "--model-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_base = argv[i];
-        } else if (arg == "--lora-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_lora_out = argv[i];
-        } else if (arg == "--only-write-lora") {
-            params->only_write_lora = true;
-        } else if (arg == "--norm-rms-eps") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->f_norm_rms_eps = std::stof(argv[i]);
-            params->custom_f_norm_rms_eps = true;
-        } else if (arg == "--rope-freq-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->rope_freq_base = std::stof(argv[i]);
-            params->custom_rope_freq_base = true;
-        } else if (arg == "--rope-freq-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->rope_freq_scale = std::stof(argv[i]);
-            params->custom_rope_freq_scale = true;
-        } else if (arg == "--lora-alpha") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->lora_alpha = std::stoi(argv[i]);
-            params->custom_lora_alpha = true;
-        } else if (arg == "--lora-r") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->lora_r = std::stoi(argv[i]);
-        } else if (arg == "--rank-att-norm") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_attention_norm = std::stoi(argv[i]);
-            params->custom_n_rank_attention_norm = true;
-        } else if (arg == "--rank-ffn-norm") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_ffn_norm = std::stoi(argv[i]);
-            params->custom_n_rank_ffn_norm = true;
-        } else if (arg == "--rank-out-norm") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_norm = std::stoi(argv[i]);
-            params->custom_n_rank_norm = true;
-        } else if (arg == "--rank-tok-embd") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_tok_embeddings = std::stoi(argv[i]);
-            params->custom_n_rank_tok_embeddings = true;
-        } else if (arg == "--rank-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_output = std::stoi(argv[i]);
-            params->custom_n_rank_output = true;
-        } else if (arg == "--rank-wq") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_wq = std::stoi(argv[i]);
-            params->custom_n_rank_wq = true;
-        } else if (arg == "--rank-wk") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_wk = std::stoi(argv[i]);
-            params->custom_n_rank_wk = true;
-        } else if (arg == "--rank-wv") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_wv = std::stoi(argv[i]);
-            params->custom_n_rank_wv = true;
-        } else if (arg == "--rank-wo") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_wo = std::stoi(argv[i]);
-            params->custom_n_rank_wo = true;
-        } else if (arg == "--rank-ffn_gate") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_ffn_gate = std::stoi(argv[i]);
-            params->custom_n_rank_ffn_gate = true;
-        } else if (arg == "--rank-ffn_down") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_ffn_down = std::stoi(argv[i]);
-            params->custom_n_rank_ffn_down = true;
-        } else if (arg == "--rank-ffn_up") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_ffn_up = std::stoi(argv[i]);
-            params->custom_n_rank_ffn_up = true;
-        } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            train_print_usage(argc, argv, &default_params);
-            exit(1);
-        }
-    }
-    if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
-        train_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    finish_processing_train_args(&params->common);
-    return true;
-}
-
-struct save_train_files_data {
-    const char            * fn_checkpoint_out;
-    const char            * fn_lora_out;
-    const char            * pattern_fn_it;
-    const char            * fn_latest;
-    struct my_llama_model * model;
-    struct my_llama_lora  * lora;
-};
-
-static void save_train_files(void * vdata, struct train_state * train) {
-    struct save_train_files_data * data   = (struct save_train_files_data *) vdata;
-
-    int64_t iter = train->opt->iter;
-
-    if (strlen(data->fn_checkpoint_out) > 0) {
-        save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->model, data->lora, train);
-        save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1  ).c_str(), data->model, data->lora, train);
-    }
-    if (strlen(data->fn_lora_out) > 0) {
-        save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->lora);
-        save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, -1  ).c_str(), data->lora);
-    }
-}
-
-static int64_t get_parameter_count(struct my_llama_lora* lora) {
-    int64_t nx = 0;
-    nx += ggml_nelements(lora->tok_embeddings_a);
-    nx += ggml_nelements(lora->tok_embeddings_b);
-    nx += ggml_nelements(lora->norm_a);
-    nx += ggml_nelements(lora->norm_b);
-    nx += ggml_nelements(lora->output_a);
-    nx += ggml_nelements(lora->output_b);
-
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-        nx += ggml_nelements(layer.attention_norm_a);
-        nx += ggml_nelements(layer.attention_norm_b);
-        nx += ggml_nelements(layer.wq_a);
-        nx += ggml_nelements(layer.wq_b);
-        nx += ggml_nelements(layer.wk_a);
-        nx += ggml_nelements(layer.wk_b);
-        nx += ggml_nelements(layer.wv_a);
-        nx += ggml_nelements(layer.wv_b);
-        nx += ggml_nelements(layer.wo_a);
-        nx += ggml_nelements(layer.wo_b);
-        nx += ggml_nelements(layer.ffn_norm_a);
-        nx += ggml_nelements(layer.ffn_norm_b);
-        nx += ggml_nelements(layer.ffn_gate_a);
-        nx += ggml_nelements(layer.ffn_gate_b);
-        nx += ggml_nelements(layer.ffn_down_a);
-        nx += ggml_nelements(layer.ffn_down_b);
-        nx += ggml_nelements(layer.ffn_up_a);
-        nx += ggml_nelements(layer.ffn_up_b);
-    }
-    return nx;
-}
-
-int main(int argc, char ** argv) {
-    struct train_params params = get_default_train_params();
-
-    if (!train_params_parse(argc, argv, &params)) {
-        return 1;
-    }
-
-    if (params.common.seed == LLAMA_DEFAULT_SEED) {
-        params.common.seed = time(NULL);
-    }
-    printf("%s: seed: %u\n", __func__, params.common.seed);
-    srand(params.common.seed);
-
-    struct llama_model_params llama_mparams = llama_model_default_params();
-    llama_mparams.n_gpu_layers = params.common.n_gpu_layers;
-    llama_mparams.vocab_only = false;
-
-    printf("%s: model base = '%s'\n", __func__, params.fn_model_base);
-    struct llama_model * lmodel = llama_load_model_from_file(params.fn_model_base, llama_mparams);
-
-    struct llama_context_params llama_cparams = llama_context_default_params();
-    struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_cparams);
-
-    struct my_llama_model model;
-    init_model(lmodel, &model, params.fn_model_base, params.common.n_ctx);
-
-    struct my_llama_lora lora;
-
-    struct train_state      * train = init_train_state();
-    struct ggml_opt_context * opt   = train->opt;
-
-    // set params from command line
-    if (params.custom_f_norm_rms_eps) {
-        model.hparams.f_norm_rms_eps  = params.f_norm_rms_eps;
-    }
-    if (params.custom_rope_freq_base) {
-        model.hparams.rope_freq_base  = params.rope_freq_base;
-    }
-    if (params.custom_rope_freq_scale) {
-        model.hparams.rope_freq_scale = params.rope_freq_scale;
-    }
-    lora.hparams.lora_r                = params.lora_r;
-    lora.hparams.lora_alpha            = params.custom_lora_alpha            ? params.lora_alpha            : params.lora_r;
-    uint32_t n_rank_attention_norm     = params.custom_n_rank_attention_norm ? params.n_rank_attention_norm : 1;
-    uint32_t n_rank_wq                 = params.custom_n_rank_wq             ? params.n_rank_wq             : params.lora_r;
-    uint32_t n_rank_wk                 = params.custom_n_rank_wk             ? params.n_rank_wk             : params.lora_r;
-    uint32_t n_rank_wv                 = params.custom_n_rank_wv             ? params.n_rank_wv             : params.lora_r;
-    uint32_t n_rank_wo                 = params.custom_n_rank_wo             ? params.n_rank_wo             : params.lora_r;
-    uint32_t n_rank_ffn_norm           = params.custom_n_rank_ffn_norm       ? params.n_rank_ffn_norm       : 1;
-    uint32_t n_rank_ffn_gate           = params.custom_n_rank_ffn_gate       ? params.n_rank_ffn_gate       : params.lora_r;
-    uint32_t n_rank_ffn_down           = params.custom_n_rank_ffn_down       ? params.n_rank_ffn_down       : params.lora_r;
-    uint32_t n_rank_ffn_up             = params.custom_n_rank_ffn_up         ? params.n_rank_ffn_up         : params.lora_r;
-    uint32_t n_rank_tok_embeddings     = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r;
-    uint32_t n_rank_norm               = params.custom_n_rank_norm           ? params.n_rank_norm           : 1;
-    uint32_t n_rank_output             = params.custom_n_rank_output         ? params.n_rank_output         : params.lora_r;
-    lora.hparams.n_rank_attention_norm = n_rank_attention_norm;
-    lora.hparams.n_rank_wq             = n_rank_wq;
-    lora.hparams.n_rank_wk             = n_rank_wk;
-    lora.hparams.n_rank_wv             = n_rank_wv;
-    lora.hparams.n_rank_wo             = n_rank_wo;
-    lora.hparams.n_rank_ffn_norm       = n_rank_ffn_norm;
-    lora.hparams.n_rank_ffn_gate       = n_rank_ffn_gate;
-    lora.hparams.n_rank_ffn_down       = n_rank_ffn_down;
-    lora.hparams.n_rank_ffn_up         = n_rank_ffn_up;
-    lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings;
-    lora.hparams.n_rank_norm           = n_rank_norm;
-    lora.hparams.n_rank_output         = n_rank_output;
-
-    // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
-    opt->params.print_forward_graph     = false;
-    opt->params.print_backward_graph    = false;
-    opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;
-    opt->params.n_threads               = params.common.n_threads;
-    opt->params.past                    = params.common.opt_past;
-    opt->params.delta                   = params.common.opt_delta;
-    opt->params.max_no_improvement      = params.common.opt_max_no_improvement;
-    opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation;
-    opt->params.adam.n_iter             = params.common.adam_n_iter;
-    opt->params.adam.sched              = 1.0f;
-    opt->params.adam.alpha              = params.common.adam_alpha;
-    opt->params.adam.decay              = params.common.adam_decay;
-    opt->params.adam.decay_min_ndim     = params.common.adam_decay_min_ndim;
-    opt->params.adam.beta1              = params.common.adam_beta1;
-    opt->params.adam.beta2              = params.common.adam_beta2;
-    opt->params.adam.gclip              = params.common.adam_gclip;
-    opt->params.adam.eps_f              = params.common.adam_eps_f;
-
-    printf("%s: init model\n", __func__);
-    bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train);
-
-    if (existed) {
-        // overwrite last n_ctx with user provided n_ctx
-        if (params.common.custom_n_ctx) {
-            model.hparams.n_ctx = params.common.n_ctx;
-        }
-
-        const bool opt_param_count_changed = (
-           (lora.hparams.n_rank_attention_norm != n_rank_attention_norm)
-        || (lora.hparams.n_rank_wq             != n_rank_wq)
-        || (lora.hparams.n_rank_wk             != n_rank_wk)
-        || (lora.hparams.n_rank_wv             != n_rank_wv)
-        || (lora.hparams.n_rank_wo             != n_rank_wo)
-        || (lora.hparams.n_rank_ffn_norm       != n_rank_ffn_norm)
-        || (lora.hparams.n_rank_ffn_gate       != n_rank_ffn_gate)
-        || (lora.hparams.n_rank_ffn_down       != n_rank_ffn_down)
-        || (lora.hparams.n_rank_ffn_up         != n_rank_ffn_up)
-        || (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings)
-        || (lora.hparams.n_rank_norm           != n_rank_norm)
-        || (lora.hparams.n_rank_output         != n_rank_output)
-        );
-
-        const bool opt_past_changed = opt->params.past != params.common.opt_past;
-
-        if (opt_param_count_changed) {
-            print_lora_params(&lora.hparams);
-            die("Provided rank differs from checkpoint file. To use different rank start finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting.");
-            // need to discard previous optimizer gradient statistics and opt_init with new shapes
-            // TODO
-        }
-        if (opt_past_changed) {
-            die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting");
-            // need to discard previous optimizer past function value statistics and opt_init with new shapes
-            // TODO
-        }
-    } else { // existed == false
-        init_lora(&model, &lora);
-        randomize_lora(&lora, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f);
-        if (!params.only_write_lora) {
-            ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&lora));
-        }
-    }
-    opt->iter = train->train_its;
-
-    print_params(&model.hparams);
-    print_lora_params(&lora.hparams);
-    printf("%s: total train_iterations %llu\n", __func__, (long long unsigned) train->train_its);
-    printf("%s: seen train_samples     %llu\n", __func__, (long long unsigned) train->train_samples);
-    printf("%s: seen train_tokens      %llu\n", __func__, (long long unsigned) train->train_tokens);
-    printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs);
-    printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)), (float) (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)) / (1024.0f*1024.0f));
-
-    if (params.only_write_lora) {
-        save_train_files_data save_data;
-        save_data.fn_checkpoint_out = "";
-        save_data.fn_lora_out       = params.fn_lora_out;
-        save_data.pattern_fn_it     = params.common.pattern_fn_it;
-        save_data.fn_latest         = params.common.fn_latest;
-        save_data.model             = &model;
-        save_data.lora              = &lora;
-
-        save_train_files(&save_data, train);
-
-        free_train_state(train);
-        ggml_free(lora.ctx);
-        llama_free(lctx);
-        llama_free_model(lmodel);
-        return 0;
-    }
-
-    printf("%s: opt_size  = %zu bytes (%.1f MB)\n", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f));
-    printf("%s: opt iter %d\n", __func__, opt->iter);
-
-    int n_tokens = model.hparams.n_ctx;
-    int n_vocab  = model.hparams.n_vocab;
-    int n_batch  = params.common.n_batch;
-
-    // context for input tensors without their data
-    struct ggml_init_params ctx_input_params = {
-        ggml_tensor_overhead() * 2, // mem_size
-        NULL,                       // mem_buffer
-        true,                       // no_alloc
-    };
-    struct ggml_context * ctx_input = ggml_init(ctx_input_params);
-
-    // the input tensors
-    struct ggml_tensor * tokens_input  = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch);
-    struct ggml_tensor * target_probs  = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
-
-    // allocate input tensors
-    // measure required memory for input tensors
-    ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type());
-    size_t max_input_size = ggml_backend_buffer_get_size(input_data);
-    printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
-
-    // context for compute tensors without their data
-    const size_t estimated_compute_size_wo_data = (
-            2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() +
-            (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true))
-    );
-    struct ggml_init_params ctx_compute_params = {
-        estimated_compute_size_wo_data, // mem_size
-        NULL,                           // mem_buffer
-        true,                           // no_alloc
-    };
-    struct ggml_context * ctx_compute = NULL;
-
-    struct ggml_tensor * loss   = NULL;
-    struct ggml_tensor * logits = NULL;
-
-    struct ggml_cgraph * gf     = NULL;
-    struct ggml_cgraph * gb     = NULL;
-    struct ggml_cgraph * gb_tmp = NULL;
-
-    // measure required memory for compute tensors
-    size_t best_compute_size = SIZE_MAX;
-    enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT;
-    // find best evaluation order
-    for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
-        ctx_compute = ggml_init(ctx_compute_params);
-        ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-        gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-        gf->order = (enum ggml_cgraph_eval_order) order;
-        gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-        gb_tmp = params.common.use_checkpointing
-            ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
-            : NULL;
-        loss = llama_build_lora_finetune_graphs(
-            &model, &lora, alloc, ctx_compute,
-            gf, gb, gb_tmp,
-            &logits, tokens_input, target_probs,
-            n_tokens, n_batch,
-            params.common.use_flash,
-            params.common.use_checkpointing,
-            true
-        );
-        size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer
-        if (max_compute_size < best_compute_size) {
-            best_compute_size = max_compute_size;
-            best_order = gf->order;
-        }
-        ggml_gallocr_free(alloc);
-        ggml_free(ctx_compute);
-    }
-    size_t max_compute_size = best_compute_size;
-    printf("%s: compute_size = %zu bytes (%.1f MB)\n", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f));
-    printf("%s: evaluation order = %s\n", __func__,
-        (best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? "LEFT_TO_RIGHT" :
-        (best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? "RIGHT_TO_LEFT" :
-        "invalid");
-
-    // allocate compute tensors
-    ctx_compute = ggml_init(ctx_compute_params);
-    ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-    gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-    gf->order = best_order;
-    gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-    gb_tmp = params.common.use_checkpointing
-        ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
-        : NULL;
-    loss = llama_build_lora_finetune_graphs(
-        &model, &lora, alloc, ctx_compute,
-        gf, gb, gb_tmp,
-        &logits, tokens_input, target_probs,
-        n_tokens, n_batch,
-        params.common.use_flash,
-        params.common.use_checkpointing,
-        false
-    );
-
-    // tokenize data
-    std::vector<llama_token> train_tokens;
-    std::vector<size_t> train_samples_begin;
-    std::vector<size_t> train_samples_size;
-    printf("%s: tokenize training data from %s\n", __func__, params.common.fn_train_data);
-    printf("%s: sample-start: %s\n", __func__, params.common.sample_start.c_str());
-    printf("%s: include-sample-start: %s\n", __func__, params.common.include_sample_start ? "true" : "false");
-    tokenize_file(lctx,
-            params.common.fn_train_data,
-            params.common.sample_start,
-            params.common.include_sample_start,
-            params.common.overlapping_samples,
-            n_tokens,
-            train_tokens,
-            train_samples_begin,
-            train_samples_size);
-    GGML_ASSERT(train_samples_begin.size() == train_samples_size.size());
-
-    printf("%s: number of training tokens: %zu\n", __func__, train_tokens.size());
-
-    std::vector<size_t> token_noccurs;
-    token_noccurs.resize(model.hparams.n_vocab, 0);
-    for (unsigned int i = 0; i < train_tokens.size(); ++i) {
-        ++token_noccurs[train_tokens[i]];
-    }
-    int n_unique_tokens = 0;
-    for (unsigned int i = 0; i < token_noccurs.size(); ++i) {
-        if (token_noccurs[i] == 0) continue;
-        ++n_unique_tokens;
-    }
-    printf("%s: number of unique tokens: %d\n", __func__, n_unique_tokens);
-
-    size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size());
-    const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size());
-    if (changed_train_data) {
-        printf("%s: train data seems to have changed. restarting shuffled epoch.\n", __func__);
-    }
-    if (params.common.force_reshuffle) {
-        printf("%s: forced reshuffling of data. restarting with newly shuffled epoch.\n", __func__);
-    }
-    if ((train->shuffle_rng_state_current == "") || changed_train_data || params.common.force_reshuffle) {
-        train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed);
-        train->shuffle_sample_count = train_samples_size.size();
-        train->shuffle_next_sample = 0;
-        train->shuffle_samples_hash = shuffle_samples_hash;
-    }
-    std::vector<size_t> train_shuffled_samples_offs;
-    std::vector<size_t> train_shuffled_samples_begin;
-    std::vector<size_t> train_shuffled_samples_size;
-    train_shuffled_samples_offs.resize(train_samples_begin.size());
-    train_shuffled_samples_begin.resize(train_samples_begin.size());
-    train_shuffled_samples_size.resize(train_samples_size.size());
-    train->shuffle_rng_state_next = shuffle_samples(
-        train->shuffle_rng_state_current,
-        train_shuffled_samples_offs.data(),
-        train_shuffled_samples_begin.data(),
-        train_shuffled_samples_size.data(),
-        train_samples_begin.data(),
-        train_samples_size.data(),
-        train_samples_size.size());
-
-    printf("%s: begin training\n", __func__);
-
-    save_train_files_data save_data;
-    save_data.fn_checkpoint_out = params.common.fn_checkpoint_out;
-    save_data.fn_lora_out       = params.fn_lora_out;
-    save_data.pattern_fn_it     = params.common.pattern_fn_it;
-    save_data.fn_latest         = params.common.fn_latest;
-    save_data.model             = &model;
-    save_data.lora              = &lora;
-
-    struct train_opt_callback_data opt_cb_data;
-    opt_cb_data.params                 = &params.common;
-    opt_cb_data.train                  = train;
-    opt_cb_data.save_cb                = &save_train_files;
-    opt_cb_data.save_data              = &save_data;
-    opt_cb_data.lctx                   = lctx;
-    opt_cb_data.last_save_iter         = opt->iter;
-    opt_cb_data.tokens_data            = train_tokens.data();
-    opt_cb_data.tokens_size            = train_tokens.size();
-    opt_cb_data.samples_begin          = train_samples_begin.data();
-    opt_cb_data.samples_size           = train_samples_size.data();
-    opt_cb_data.shuffled_samples_offs  = train_shuffled_samples_offs.data();
-    opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data();
-    opt_cb_data.shuffled_samples_size  = train_shuffled_samples_size.data();
-    opt_cb_data.samples_count          = train_samples_size.size();
-    opt_cb_data.tokens_input           = tokens_input;
-    opt_cb_data.target_probs           = target_probs;
-    opt_cb_data.first_iter             = opt->iter;
-    opt_cb_data.first_epoch            = train->train_epochs;
-    opt_cb_data.iter_at_last_epoch     = -1;
-    opt_cb_data.last_time              = ggml_time_ms();
-    opt_cb_data.millis_per_iter        = 0.0;
-
-    // measure required memory for work buffer
-    size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE;
-    printf("%s: work_size = %zu bytes (%.1f MB)\n", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f));
-
-    // context for work buffer
-    struct ggml_init_params ctx_work_params = {
-        max_work_size, // mem_size
-        NULL,          // mem_buffer
-        false,         // no_alloc
-    };
-    struct ggml_context * ctx_work = ggml_init(ctx_work_params);
-
-    int64_t t0 = ggml_time_ms();
-
-    ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data);
-
-    ggml_free(ctx_work);
-    ggml_free(ctx_compute);
-    ggml_free(ctx_input);
-    ggml_gallocr_free(alloc);
-
-
-    int64_t t1 = ggml_time_ms();
-    printf("%s: total training time: ", __func__);
-    print_duration((double) (t1 - t0));
-    printf("\n");
-
-    int new_iters = opt->iter - opt_cb_data.last_save_iter;
-    if (new_iters > 0) {
-        train->train_its     += new_iters;
-        train->train_tokens  += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens;
-
-        save_train_files(&save_data, train);
-        opt_cb_data.last_save_iter = opt->iter;
-    }
-
-    ggml_free(opt->ctx);
-    free_train_state(train);
-    ggml_free(lora.ctx);
-    llama_free(lctx);
-    llama_free_model(lmodel);
-    return 0;
-}
diff --git a/examples/finetune/finetune.sh b/examples/finetune/finetune.sh
deleted file mode 100644
index 079bfa1139d5b..0000000000000
--- a/examples/finetune/finetune.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/bin/bash
-cd `dirname $0`
-cd ../..
-
-EXE="./finetune"
-
-if [[ ! $LLAMA_MODEL_DIR ]]; then LLAMA_MODEL_DIR="./models"; fi
-if [[ ! $LLAMA_TRAINING_DIR ]]; then LLAMA_TRAINING_DIR="."; fi
-
-# MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2-q8_0.gguf" # This is the model the readme uses.
-MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2.gguf" # An f16 model. Note in this case with "-g", you get an f32-format .BIN file that isn't yet supported if you use it with "main --lora" with GPU inferencing.
-
-while getopts "dg" opt; do
-  case $opt in
-    d)
-      DEBUGGER="gdb --args"
-      ;;
-    g)
-      EXE="./build/bin/Release/finetune"
-      GPUARG="--gpu-layers 25"
-      ;;
-  esac
-done
-
-$DEBUGGER $EXE \
-        --model-base $MODEL \
-        $GPUARG \
-        --checkpoint-in  chk-ol3b-shakespeare-LATEST.gguf \
-        --checkpoint-out chk-ol3b-shakespeare-ITERATION.gguf \
-        --lora-out lora-ol3b-shakespeare-ITERATION.bin \
-        --train-data "$LLAMA_TRAINING_DIR\shakespeare.txt" \
-        --save-every 10 \
-        --threads 10 --adam-iter 30 --batch 4 --ctx 64 \
-        --use-checkpointing
diff --git a/examples/gbnf-validator/CMakeLists.txt b/examples/gbnf-validator/CMakeLists.txt
index 166e3ad2ae7dd..4edd6ec7394c5 100644
--- a/examples/gbnf-validator/CMakeLists.txt
+++ b/examples/gbnf-validator/CMakeLists.txt
@@ -1,5 +1,5 @@
-set(TARGET gbnf-validator)
+set(TARGET llama-gbnf-validator)
 add_executable(${TARGET} gbnf-validator.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common grammar-parser llama ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp
index 091069ffa699c..48a705e15cea9 100644
--- a/examples/gbnf-validator/gbnf-validator.cpp
+++ b/examples/gbnf-validator/gbnf-validator.cpp
@@ -7,6 +7,8 @@
 
 #include <cstdio>
 #include <cstdlib>
+#include <sstream>
+#include <fstream>
 #include <string>
 #include <vector>
 
@@ -14,20 +16,25 @@ static bool llama_sample_grammar_string(struct llama_grammar * grammar, const st
     auto decoded = decode_utf8(input_str, {});
     const auto & code_points = decoded.first;
 
+    const llama_grammar_rules  & rules      = llama_grammar_get_rules (grammar);
+          llama_grammar_stacks & cur_stacks = llama_grammar_get_stacks(grammar);
+
     size_t pos = 0;
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
-        auto prev_stacks = grammar->stacks;
-        llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
-        if (grammar->stacks.empty()) {
+        const llama_grammar_stacks prev_stacks = llama_grammar_get_stacks(grammar); // copy
+
+        llama_grammar_accept(rules, prev_stacks, *it, cur_stacks);
+
+        if (cur_stacks.empty()) {
             error_pos = pos;
             error_msg = "Unexpected character '" + unicode_cpt_to_utf8(*it) + "'";
-            grammar->stacks = prev_stacks;
+            cur_stacks = prev_stacks;
             return false;
         }
         ++pos;
     }
 
-    for (const auto & stack : grammar->stacks) {
+    for (const auto & stack : cur_stacks) {
         if (stack.empty()) {
             return true;
         }
@@ -69,13 +76,14 @@ int main(int argc, char** argv) {
         return 1;
     }
 
-    fseek(grammar_file, 0, SEEK_END);
-    size_t grammar_size = ftell(grammar_file);
-    fseek(grammar_file, 0, SEEK_SET);
-
-    std::string grammar_str(grammar_size, ' ');
-    fread(&grammar_str[0], 1, grammar_size, grammar_file);
-    fclose(grammar_file);
+    std::string grammar_str;
+    {
+        std::ifstream grammar_file(grammar_filename);
+        GGML_ASSERT(grammar_file.is_open() && "Failed to open grammar file");
+        std::stringstream buffer;
+        buffer << grammar_file.rdbuf();
+        grammar_str = buffer.str();
+    }
 
     // Parse the GBNF grammar
     auto parsed_grammar = grammar_parser::parse(grammar_str.c_str());
@@ -98,22 +106,19 @@ int main(int argc, char** argv) {
     auto grammar = llama_grammar_init(
             grammar_rules.data(),
             grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
-
+    if (grammar == nullptr) {
+        throw std::runtime_error("Failed to initialize llama_grammar");
+    }
     // Read the input file
-    FILE* input_file = fopen(input_filename.c_str(), "r");
-    if (!input_file) {
-        fprintf(stdout, "Failed to open input file: %s\n", input_filename.c_str());
-        return 1;
+    std::string input_str;
+    {
+        std::ifstream input_file(input_filename);
+        GGML_ASSERT(input_file.is_open() && "Failed to open input file");
+        std::stringstream buffer;
+        buffer << input_file.rdbuf();
+        input_str = buffer.str();
     }
 
-    fseek(input_file, 0, SEEK_END);
-    size_t input_size = ftell(input_file);
-    fseek(input_file, 0, SEEK_SET);
-
-    std::string input_str(input_size, ' ');
-    fread(&input_str[0], 1, input_size, input_file);
-    fclose(input_file);
-
     // Validate the input string against the grammar
     size_t error_pos;
     std::string error_msg;
diff --git a/examples/gguf-hash/CMakeLists.txt b/examples/gguf-hash/CMakeLists.txt
new file mode 100644
index 0000000000000..633f4553594bb
--- /dev/null
+++ b/examples/gguf-hash/CMakeLists.txt
@@ -0,0 +1,15 @@
+set(TARGET llama-gguf-hash)
+add_executable(${TARGET} gguf-hash.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+
+# clibs dependencies
+include_directories(deps/)
+add_library(xxhash OBJECT deps/xxhash/xxhash.c deps/xxhash/xxhash.h)
+target_link_libraries(${TARGET} PRIVATE xxhash)
+add_library(sha1 OBJECT deps/sha1/sha1.c deps/sha1/sha1.h)
+target_link_libraries(${TARGET} PRIVATE sha1)
+add_library(sha256 OBJECT deps/sha256/sha256.c deps/sha256/sha256.h)
+target_link_libraries(${TARGET} PRIVATE sha256)
+
+target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/gguf-hash/README.md b/examples/gguf-hash/README.md
new file mode 100644
index 0000000000000..9871651e38ba8
--- /dev/null
+++ b/examples/gguf-hash/README.md
@@ -0,0 +1,206 @@
+
+# llama-gguf-hash
+
+CLI to hash GGUF files to detect difference on a per model and per tensor level.
+
+**Command line options:**
+
+- `--help`: display help message
+- `--xxh64`: use xhash 64bit hash mode (default)
+- `--sha1`: use sha1
+- `--uuid`: use uuid
+- `--sha256`: use sha256
+- `--all`: use all hash
+- `--no-layer`: exclude per layer hash
+- `--uuid`: generate UUIDv5 ID
+- `-c`, `--check <manifest>`:  verify against a manifest
+
+## About
+
+While most POSIX systems already have hash checking programs like sha256sum, it
+is designed to check entire files. This is not ideal for our purpose if we want
+to check for consistency of the tensor data even if the metadata content of the
+gguf KV store has been updated.
+
+This program is designed to hash a gguf tensor payload on a 'per tensor layer'
+in addition to a 'entire tensor model' hash. The intent is that the entire
+tensor layer can be checked first but if there is any detected inconsistencies,
+then the per tensor hash can be used to narrow down the specific tensor layer
+that has inconsistencies.
+
+For Maintainers:
+- Detection of tensor inconsistency during development and automated tests
+    - This is served by xxh64 which is fast
+    - This is also served by having per tensor layer to assist in narrowing down
+      the location of the faulty tensor layer
+    - This is also served by sha1 which is much slower but more widely supported
+
+For Model Creators:
+- Optional consistent UUID generation based on model tensor content
+    - This is served by UUIDv5 which is useful for databases keys
+        - llama.cpp UUIDv5 Namespace: `ef001206-dadc-5f6d-a15f-3359e577d4e5`
+            - Made via UUIDv5 URL namespace of `en.wikipedia.org/wiki/Llama.cpp`
+
+For Model Users:
+- Assurance of tensor layer integrity even if metadata was updated
+    - This is served by sha256 which is still considered very secure as of 2024
+
+### Design Note
+
+- The default behavior of this program if no arguments is provided is to hash
+  using xxhash's xxh32 mode because it is very fast and is primarily targeted
+  towards maintainers who may want to use this in automated tests.
+- xxhash support xxh32 and xxh128 for 32bit hash and 128bit hash respectively
+  however we picked 64bit xxhash as most computers are 64bit as of 2024 and thus
+  would have a better affinity to calculating hash that is 64bit in size.
+
+## Compile Example
+
+```bash
+cmake -B build -DCMAKE_BUILD_TYPE=Debug -DLLAMA_FATAL_WARNINGS=ON
+make -C build clean
+make -C build llama-gguf-hash VERBOSE=1
+./build/bin/llama-gguf-hash test.gguf
+./build/bin/llama-gguf-hash --xxh64 test.gguf
+./build/bin/llama-gguf-hash --sha1 test.gguf
+./build/bin/llama-gguf-hash --uuid test.gguf
+./build/bin/llama-gguf-hash --sha256 test.gguf
+```
+
+## Generation and Verification Example
+
+To generate we may use this command
+
+```bash
+./llama-gguf-hash --all test.gguf > test.gguf.manifest
+```
+
+Which would generate a manifest that looks like below, which contains multiple hash type and per tensor layer hashes as well
+(This excludes UUID as that is an ID not a hash)
+
+```bash
+xxh64     f66e9cd66a4396a0  test.gguf:tensor_0
+sha1      59f79ecefd8125a996fdf419239051a7e99e5f20  test.gguf:tensor_0
+sha256    c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd  test.gguf:tensor_0
+xxh64     7d3a1f9ac04d0537  test.gguf:tensor_1
+sha1      4765f592eacf096df4628ba59476af94d767080a  test.gguf:tensor_1
+sha256    8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7  test.gguf:tensor_1
+xxh64     a0af5d700049693b  test.gguf:tensor_2
+sha1      25cbfbad4513cc348e2c95ebdee69d6ff2fd8753  test.gguf:tensor_2
+sha256    947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180  test.gguf:tensor_2
+xxh64     e83fddf559d7b6a6  test.gguf:tensor_3
+sha1      a9cba73e2d90f2ee3dae2548caa42bef3fe6a96c  test.gguf:tensor_3
+sha256    423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1  test.gguf:tensor_3
+xxh64     1257733306b7992d  test.gguf:tensor_4
+sha1      d7bc61db93bb685ce9d598da89717c66729b7543  test.gguf:tensor_4
+sha256    79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf  test.gguf:tensor_4
+xxh64     d238d16ba4711e58  test.gguf:tensor_5
+sha1      0706566c198fe1072f37e0a5135b4b5f23654c52  test.gguf:tensor_5
+sha256    60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b  test.gguf:tensor_5
+xxh64     3fbc3b65ab8c7f39  test.gguf:tensor_6
+sha1      73922a0727226a409049f6fc3172a52219ca6f00  test.gguf:tensor_6
+sha256    574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0  test.gguf:tensor_6
+xxh64     c22021c29854f093  test.gguf:tensor_7
+sha1      efc39cece6a951188fc41e354c73bbfe6813d447  test.gguf:tensor_7
+sha256    4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75  test.gguf:tensor_7
+xxh64     936df61f5d64261f  test.gguf:tensor_8
+sha1      c2490296d789a4f34398a337fed8377d943d9f06  test.gguf:tensor_8
+sha256    c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01  test.gguf:tensor_8
+xxh64     93fd20c64421c081  test.gguf:tensor_9
+sha1      7047ce1e78437a6884337a3751c7ee0421918a65  test.gguf:tensor_9
+sha256    23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514  test.gguf:tensor_9
+xxh64     5a54d3aad816f302  test.gguf
+sha1      d15be52c4ff213e823cb6dd13af7ee2f978e7042  test.gguf
+sha256    7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4  test.gguf
+```
+
+We can then use the normal check command which will by default check for the highest security strength hash and verify against that:
+
+```bash
+$ ./llama-gguf-hash --check test.gguf.manifest test.gguf
+manifest  test.gguf.manifest  sha256  sha1  xxh64
+sha256    c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd  test.gguf:tensor_0  -  Ok
+sha256    8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7  test.gguf:tensor_1  -  Ok
+sha256    947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180  test.gguf:tensor_2  -  Ok
+sha256    423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1  test.gguf:tensor_3  -  Ok
+sha256    79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf  test.gguf:tensor_4  -  Ok
+sha256    60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b  test.gguf:tensor_5  -  Ok
+sha256    574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0  test.gguf:tensor_6  -  Ok
+sha256    4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75  test.gguf:tensor_7  -  Ok
+sha256    c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01  test.gguf:tensor_8  -  Ok
+sha256    23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514  test.gguf:tensor_9  -  Ok
+sha256    7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4  test.gguf  -  Ok
+
+Verification results for test.gguf.manifest - Success
+```
+
+Or we may explicitly ask for a faster hash like:
+
+```bash
+$ ./llama-gguf-hash --check test.gguf.manifest --xxh64 test.gguf
+manifest  test.gguf.manifest  sha256  sha1  xxh64
+xxh64     f66e9cd66a4396a0  test.gguf:tensor_0  -  Ok
+xxh64     7d3a1f9ac04d0537  test.gguf:tensor_1  -  Ok
+xxh64     a0af5d700049693b  test.gguf:tensor_2  -  Ok
+xxh64     e83fddf559d7b6a6  test.gguf:tensor_3  -  Ok
+xxh64     1257733306b7992d  test.gguf:tensor_4  -  Ok
+xxh64     d238d16ba4711e58  test.gguf:tensor_5  -  Ok
+xxh64     3fbc3b65ab8c7f39  test.gguf:tensor_6  -  Ok
+xxh64     c22021c29854f093  test.gguf:tensor_7  -  Ok
+xxh64     936df61f5d64261f  test.gguf:tensor_8  -  Ok
+xxh64     93fd20c64421c081  test.gguf:tensor_9  -  Ok
+xxh64     5a54d3aad816f302  test.gguf  -  Ok
+
+Verification results for test.gguf.manifest - Success
+```
+
+Or maybe we want to just check that all the hash is valid:
+
+```bash
+$./llama-gguf-hash --check test.gguf.manifest --all test.gguf.manifest
+manifest  test.gguf.manifest  sha256  sha1  xxh64
+xxh64     f66e9cd66a4396a0  test.gguf:tensor_0  -  Ok
+sha1      59f79ecefd8125a996fdf419239051a7e99e5f20  test.gguf:tensor_0  -  Ok
+sha256    c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd  test.gguf:tensor_0  -  Ok
+xxh64     7d3a1f9ac04d0537  test.gguf:tensor_1  -  Ok
+sha1      4765f592eacf096df4628ba59476af94d767080a  test.gguf:tensor_1  -  Ok
+sha256    8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7  test.gguf:tensor_1  -  Ok
+xxh64     a0af5d700049693b  test.gguf:tensor_2  -  Ok
+sha1      25cbfbad4513cc348e2c95ebdee69d6ff2fd8753  test.gguf:tensor_2  -  Ok
+sha256    947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180  test.gguf:tensor_2  -  Ok
+xxh64     e83fddf559d7b6a6  test.gguf:tensor_3  -  Ok
+sha1      a9cba73e2d90f2ee3dae2548caa42bef3fe6a96c  test.gguf:tensor_3  -  Ok
+sha256    423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1  test.gguf:tensor_3  -  Ok
+xxh64     1257733306b7992d  test.gguf:tensor_4  -  Ok
+sha1      d7bc61db93bb685ce9d598da89717c66729b7543  test.gguf:tensor_4  -  Ok
+sha256    79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf  test.gguf:tensor_4  -  Ok
+xxh64     d238d16ba4711e58  test.gguf:tensor_5  -  Ok
+sha1      0706566c198fe1072f37e0a5135b4b5f23654c52  test.gguf:tensor_5  -  Ok
+sha256    60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b  test.gguf:tensor_5  -  Ok
+xxh64     3fbc3b65ab8c7f39  test.gguf:tensor_6  -  Ok
+sha1      73922a0727226a409049f6fc3172a52219ca6f00  test.gguf:tensor_6  -  Ok
+sha256    574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0  test.gguf:tensor_6  -  Ok
+xxh64     c22021c29854f093  test.gguf:tensor_7  -  Ok
+sha1      efc39cece6a951188fc41e354c73bbfe6813d447  test.gguf:tensor_7  -  Ok
+sha256    4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75  test.gguf:tensor_7  -  Ok
+xxh64     936df61f5d64261f  test.gguf:tensor_8  -  Ok
+sha1      c2490296d789a4f34398a337fed8377d943d9f06  test.gguf:tensor_8  -  Ok
+sha256    c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01  test.gguf:tensor_8  -  Ok
+xxh64     93fd20c64421c081  test.gguf:tensor_9  -  Ok
+sha1      7047ce1e78437a6884337a3751c7ee0421918a65  test.gguf:tensor_9  -  Ok
+sha256    23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514  test.gguf:tensor_9  -  Ok
+xxh64     5a54d3aad816f302  test.gguf  -  Ok
+sha1      d15be52c4ff213e823cb6dd13af7ee2f978e7042  test.gguf  -  Ok
+sha256    7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4  test.gguf  -  Ok
+
+Verification results for test.gguf.manifest - Success
+```
+
+
+## Crypto/Hash Libraries Used
+
+These micro c libraries dependencies was installed via the [clib c package manager](https://github.com/clibs)
+
+- https://github.com/Cyan4973/xxHash
+- https://github.com/clibs/sha1/
+- https://github.com/jb55/sha256.c
diff --git a/examples/gguf-hash/deps/rotate-bits/package.json b/examples/gguf-hash/deps/rotate-bits/package.json
new file mode 100644
index 0000000000000..74c0bef68d8bd
--- /dev/null
+++ b/examples/gguf-hash/deps/rotate-bits/package.json
@@ -0,0 +1,13 @@
+{
+  "name": "rotate-bits",
+  "version": "0.1.1",
+  "repo": "jb55/rotate-bits.h",
+  "description": "rotate bits",
+  "keywords": ["rotl", "rotr"],
+  "src": ["rotate-bits.h"],
+  "license": "Public Domain",
+  "development": {
+    "thlorenz/tap.c": "*"
+  }
+}
+
diff --git a/examples/gguf-hash/deps/rotate-bits/rotate-bits.h b/examples/gguf-hash/deps/rotate-bits/rotate-bits.h
new file mode 100644
index 0000000000000..75c4881fc322f
--- /dev/null
+++ b/examples/gguf-hash/deps/rotate-bits/rotate-bits.h
@@ -0,0 +1,46 @@
+
+
+#ifndef __ROTATE_DEFS_H
+#define __ROTATE_DEFS_H
+
+#ifdef _MSC_VER
+
+#include <stdlib.h>
+
+#define ROTL32(v, n) _rotl((v), (n))
+#define ROTL64(v, n) _rotl64((v), (n))
+
+#define ROTR32(v, n) _rotr((v), (n))
+#define ROTR64(v, n) _rotr64((v), (n))
+
+#else
+
+#include <stdint.h>
+
+#define U8V(v) ((uint8_t)(v) & 0xFFU)
+#define U16V(v) ((uint16_t)(v) & 0xFFFFU)
+#define U32V(v) ((uint32_t)(v) & 0xFFFFFFFFU)
+#define U64V(v) ((uint64_t)(v) & 0xFFFFFFFFFFFFFFFFU)
+
+#define ROTL32(v, n) \
+  (U32V((uint32_t)(v) << (n)) | ((uint32_t)(v) >> (32 - (n))))
+
+// tests fail if we don't have this cast...
+#define ROTL64(v, n) \
+  (U64V((uint64_t)(v) << (n)) | ((uint64_t)(v) >> (64 - (n))))
+
+#define ROTR32(v, n) ROTL32(v, 32 - (n))
+#define ROTR64(v, n) ROTL64(v, 64 - (n))
+
+#endif
+
+#define ROTL8(v, n) \
+  (U8V((uint8_t)(v) << (n)) | ((uint8_t)(v) >> (8 - (n))))
+
+#define ROTL16(v, n) \
+  (U16V((uint16_t)(v) << (n)) | ((uint16_t)(v) >> (16 - (n))))
+
+#define ROTR8(v, n) ROTL8(v, 8 - (n))
+#define ROTR16(v, n) ROTL16(v, 16 - (n))
+
+#endif
diff --git a/examples/gguf-hash/deps/sha1/package.json b/examples/gguf-hash/deps/sha1/package.json
new file mode 100644
index 0000000000000..6a5843dd1ef46
--- /dev/null
+++ b/examples/gguf-hash/deps/sha1/package.json
@@ -0,0 +1,9 @@
+{
+  "name": "sha1",
+  "version": "0.0.1",
+  "repo": "clibs/sha1",
+  "description": "sha1 hash algorithm",
+  "keywords": ["sha1", "hash"],
+  "license": "public domain",
+  "src": ["sha1.c", "sha1.h"]
+}
diff --git a/examples/gguf-hash/deps/sha1/sha1.c b/examples/gguf-hash/deps/sha1/sha1.c
new file mode 100644
index 0000000000000..76cd6ca3381d5
--- /dev/null
+++ b/examples/gguf-hash/deps/sha1/sha1.c
@@ -0,0 +1,295 @@
+/*
+SHA-1 in C
+By Steve Reid <steve@edmweb.com>
+100% Public Domain
+
+Test Vectors (from FIPS PUB 180-1)
+"abc"
+  A9993E36 4706816A BA3E2571 7850C26C 9CD0D89D
+"abcdbcdecdefdefgefghfghighijhijkijkljklmklmnlmnomnopnopq"
+  84983E44 1C3BD26E BAAE4AA1 F95129E5 E54670F1
+A million repetitions of "a"
+  34AA973C D4C4DAA4 F61EEB2B DBAD2731 6534016F
+*/
+
+/* #define LITTLE_ENDIAN * This should be #define'd already, if true. */
+/* #define SHA1HANDSOFF * Copies data before messing with it. */
+
+#define SHA1HANDSOFF
+
+#include <stdio.h>
+#include <string.h>
+
+/* for uint32_t */
+#include <stdint.h>
+
+#include "sha1.h"
+
+
+#define rol(value, bits) (((value) << (bits)) | ((value) >> (32 - (bits))))
+
+/* blk0() and blk() perform the initial expand. */
+/* I got the idea of expanding during the round function from SSLeay */
+#if BYTE_ORDER == LITTLE_ENDIAN
+#define blk0(i) (block->l[i] = (rol(block->l[i],24)&0xFF00FF00) \
+    |(rol(block->l[i],8)&0x00FF00FF))
+#elif BYTE_ORDER == BIG_ENDIAN
+#define blk0(i) block->l[i]
+#else
+#error "Endianness not defined!"
+#endif
+#define blk(i) (block->l[i&15] = rol(block->l[(i+13)&15]^block->l[(i+8)&15] \
+    ^block->l[(i+2)&15]^block->l[i&15],1))
+
+/* (R0+R1), R2, R3, R4 are the different operations used in SHA1 */
+#define R0(v,w,x,y,z,i) z+=((w&(x^y))^y)+blk0(i)+0x5A827999+rol(v,5);w=rol(w,30);
+#define R1(v,w,x,y,z,i) z+=((w&(x^y))^y)+blk(i)+0x5A827999+rol(v,5);w=rol(w,30);
+#define R2(v,w,x,y,z,i) z+=(w^x^y)+blk(i)+0x6ED9EBA1+rol(v,5);w=rol(w,30);
+#define R3(v,w,x,y,z,i) z+=(((w|x)&y)|(w&x))+blk(i)+0x8F1BBCDC+rol(v,5);w=rol(w,30);
+#define R4(v,w,x,y,z,i) z+=(w^x^y)+blk(i)+0xCA62C1D6+rol(v,5);w=rol(w,30);
+
+
+/* Hash a single 512-bit block. This is the core of the algorithm. */
+
+void SHA1Transform(
+    uint32_t state[5],
+    const unsigned char buffer[64]
+)
+{
+    uint32_t a, b, c, d, e;
+
+    typedef union
+    {
+        unsigned char c[64];
+        uint32_t l[16];
+    } CHAR64LONG16;
+
+#ifdef SHA1HANDSOFF
+    CHAR64LONG16 block[1];      /* use array to appear as a pointer */
+
+    memcpy(block, buffer, 64);
+#else
+    /* The following had better never be used because it causes the
+     * pointer-to-const buffer to be cast into a pointer to non-const.
+     * And the result is written through.  I threw a "const" in, hoping
+     * this will cause a diagnostic.
+     */
+    CHAR64LONG16 *block = (const CHAR64LONG16 *) buffer;
+#endif
+    /* Copy context->state[] to working vars */
+    a = state[0];
+    b = state[1];
+    c = state[2];
+    d = state[3];
+    e = state[4];
+    /* 4 rounds of 20 operations each. Loop unrolled. */
+    R0(a, b, c, d, e, 0);
+    R0(e, a, b, c, d, 1);
+    R0(d, e, a, b, c, 2);
+    R0(c, d, e, a, b, 3);
+    R0(b, c, d, e, a, 4);
+    R0(a, b, c, d, e, 5);
+    R0(e, a, b, c, d, 6);
+    R0(d, e, a, b, c, 7);
+    R0(c, d, e, a, b, 8);
+    R0(b, c, d, e, a, 9);
+    R0(a, b, c, d, e, 10);
+    R0(e, a, b, c, d, 11);
+    R0(d, e, a, b, c, 12);
+    R0(c, d, e, a, b, 13);
+    R0(b, c, d, e, a, 14);
+    R0(a, b, c, d, e, 15);
+    R1(e, a, b, c, d, 16);
+    R1(d, e, a, b, c, 17);
+    R1(c, d, e, a, b, 18);
+    R1(b, c, d, e, a, 19);
+    R2(a, b, c, d, e, 20);
+    R2(e, a, b, c, d, 21);
+    R2(d, e, a, b, c, 22);
+    R2(c, d, e, a, b, 23);
+    R2(b, c, d, e, a, 24);
+    R2(a, b, c, d, e, 25);
+    R2(e, a, b, c, d, 26);
+    R2(d, e, a, b, c, 27);
+    R2(c, d, e, a, b, 28);
+    R2(b, c, d, e, a, 29);
+    R2(a, b, c, d, e, 30);
+    R2(e, a, b, c, d, 31);
+    R2(d, e, a, b, c, 32);
+    R2(c, d, e, a, b, 33);
+    R2(b, c, d, e, a, 34);
+    R2(a, b, c, d, e, 35);
+    R2(e, a, b, c, d, 36);
+    R2(d, e, a, b, c, 37);
+    R2(c, d, e, a, b, 38);
+    R2(b, c, d, e, a, 39);
+    R3(a, b, c, d, e, 40);
+    R3(e, a, b, c, d, 41);
+    R3(d, e, a, b, c, 42);
+    R3(c, d, e, a, b, 43);
+    R3(b, c, d, e, a, 44);
+    R3(a, b, c, d, e, 45);
+    R3(e, a, b, c, d, 46);
+    R3(d, e, a, b, c, 47);
+    R3(c, d, e, a, b, 48);
+    R3(b, c, d, e, a, 49);
+    R3(a, b, c, d, e, 50);
+    R3(e, a, b, c, d, 51);
+    R3(d, e, a, b, c, 52);
+    R3(c, d, e, a, b, 53);
+    R3(b, c, d, e, a, 54);
+    R3(a, b, c, d, e, 55);
+    R3(e, a, b, c, d, 56);
+    R3(d, e, a, b, c, 57);
+    R3(c, d, e, a, b, 58);
+    R3(b, c, d, e, a, 59);
+    R4(a, b, c, d, e, 60);
+    R4(e, a, b, c, d, 61);
+    R4(d, e, a, b, c, 62);
+    R4(c, d, e, a, b, 63);
+    R4(b, c, d, e, a, 64);
+    R4(a, b, c, d, e, 65);
+    R4(e, a, b, c, d, 66);
+    R4(d, e, a, b, c, 67);
+    R4(c, d, e, a, b, 68);
+    R4(b, c, d, e, a, 69);
+    R4(a, b, c, d, e, 70);
+    R4(e, a, b, c, d, 71);
+    R4(d, e, a, b, c, 72);
+    R4(c, d, e, a, b, 73);
+    R4(b, c, d, e, a, 74);
+    R4(a, b, c, d, e, 75);
+    R4(e, a, b, c, d, 76);
+    R4(d, e, a, b, c, 77);
+    R4(c, d, e, a, b, 78);
+    R4(b, c, d, e, a, 79);
+    /* Add the working vars back into context.state[] */
+    state[0] += a;
+    state[1] += b;
+    state[2] += c;
+    state[3] += d;
+    state[4] += e;
+    /* Wipe variables */
+    a = b = c = d = e = 0;
+#ifdef SHA1HANDSOFF
+    memset(block, '\0', sizeof(block));
+#endif
+}
+
+
+/* SHA1Init - Initialize new context */
+
+void SHA1Init(
+    SHA1_CTX * context
+)
+{
+    /* SHA1 initialization constants */
+    context->state[0] = 0x67452301;
+    context->state[1] = 0xEFCDAB89;
+    context->state[2] = 0x98BADCFE;
+    context->state[3] = 0x10325476;
+    context->state[4] = 0xC3D2E1F0;
+    context->count[0] = context->count[1] = 0;
+}
+
+
+/* Run your data through this. */
+
+void SHA1Update(
+    SHA1_CTX * context,
+    const unsigned char *data,
+    uint32_t len
+)
+{
+    uint32_t i;
+
+    uint32_t j;
+
+    j = context->count[0];
+    if ((context->count[0] += len << 3) < j)
+        context->count[1]++;
+    context->count[1] += (len >> 29);
+    j = (j >> 3) & 63;
+    if ((j + len) > 63)
+    {
+        memcpy(&context->buffer[j], data, (i = 64 - j));
+        SHA1Transform(context->state, context->buffer);
+        for (; i + 63 < len; i += 64)
+        {
+            SHA1Transform(context->state, &data[i]);
+        }
+        j = 0;
+    }
+    else
+        i = 0;
+    memcpy(&context->buffer[j], &data[i], len - i);
+}
+
+
+/* Add padding and return the message digest. */
+
+void SHA1Final(
+    unsigned char digest[20],
+    SHA1_CTX * context
+)
+{
+    unsigned i;
+
+    unsigned char finalcount[8];
+
+    unsigned char c;
+
+#if 0    /* untested "improvement" by DHR */
+    /* Convert context->count to a sequence of bytes
+     * in finalcount.  Second element first, but
+     * big-endian order within element.
+     * But we do it all backwards.
+     */
+    unsigned char *fcp = &finalcount[8];
+
+    for (i = 0; i < 2; i++)
+    {
+        uint32_t t = context->count[i];
+
+        int j;
+
+        for (j = 0; j < 4; t >>= 8, j++)
+            *--fcp = (unsigned char) t}
+#else
+    for (i = 0; i < 8; i++)
+    {
+        finalcount[i] = (unsigned char) ((context->count[(i >= 4 ? 0 : 1)] >> ((3 - (i & 3)) * 8)) & 255);      /* Endian independent */
+    }
+#endif
+    c = 0200;
+    SHA1Update(context, &c, 1);
+    while ((context->count[0] & 504) != 448)
+    {
+        c = 0000;
+        SHA1Update(context, &c, 1);
+    }
+    SHA1Update(context, finalcount, 8); /* Should cause a SHA1Transform() */
+    for (i = 0; i < 20; i++)
+    {
+        digest[i] = (unsigned char)
+            ((context->state[i >> 2] >> ((3 - (i & 3)) * 8)) & 255);
+    }
+    /* Wipe variables */
+    memset(context, '\0', sizeof(*context));
+    memset(&finalcount, '\0', sizeof(finalcount));
+}
+
+void SHA1(
+    char *hash_out,
+    const char *str,
+    uint32_t len)
+{
+    SHA1_CTX ctx;
+    unsigned int ii;
+
+    SHA1Init(&ctx);
+    for (ii=0; ii<len; ii+=1)
+        SHA1Update(&ctx, (const unsigned char*)str + ii, 1);
+    SHA1Final((unsigned char *)hash_out, &ctx);
+}
+
diff --git a/examples/gguf-hash/deps/sha1/sha1.h b/examples/gguf-hash/deps/sha1/sha1.h
new file mode 100644
index 0000000000000..f492009c976f9
--- /dev/null
+++ b/examples/gguf-hash/deps/sha1/sha1.h
@@ -0,0 +1,52 @@
+#ifndef SHA1_H
+#define SHA1_H
+
+/*
+   SHA-1 in C
+   By Steve Reid <steve@edmweb.com>
+   100% Public Domain
+ */
+
+#include "stdint.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef struct
+{
+    uint32_t state[5];
+    uint32_t count[2];
+    unsigned char buffer[64];
+} SHA1_CTX;
+
+void SHA1Transform(
+    uint32_t state[5],
+    const unsigned char buffer[64]
+    );
+
+void SHA1Init(
+    SHA1_CTX * context
+    );
+
+void SHA1Update(
+    SHA1_CTX * context,
+    const unsigned char *data,
+    uint32_t len
+    );
+
+void SHA1Final(
+    unsigned char digest[20],
+    SHA1_CTX * context
+    );
+
+void SHA1(
+    char *hash_out,
+    const char *str,
+    uint32_t len);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* SHA1_H */
diff --git a/examples/gguf-hash/deps/sha256/package.json b/examples/gguf-hash/deps/sha256/package.json
new file mode 100644
index 0000000000000..b92a0412738df
--- /dev/null
+++ b/examples/gguf-hash/deps/sha256/package.json
@@ -0,0 +1,15 @@
+{
+  "name": "sha256",
+  "version": "0.0.2",
+  "repo": "jb55/sha256.c",
+  "description": "sha256 in c",
+  "keywords": ["sha256", "sha2"],
+  "src": ["sha256.c", "sha256.h"],
+  "dependencies": {
+    "jb55/rotate-bits.h": "0.1.1"
+  },
+  "development": {
+    "thlorenz/tap.c": "*"
+  }
+}
+
diff --git a/examples/gguf-hash/deps/sha256/sha256.c b/examples/gguf-hash/deps/sha256/sha256.c
new file mode 100644
index 0000000000000..a7a87aeb20032
--- /dev/null
+++ b/examples/gguf-hash/deps/sha256/sha256.c
@@ -0,0 +1,221 @@
+/* Crypto/Sha256.c -- SHA-256 Hash
+2010-06-11 : Igor Pavlov : Public domain
+This code is based on public domain code from Wei Dai's Crypto++ library. */
+
+#include "rotate-bits/rotate-bits.h"
+#include "sha256.h"
+
+/* define it for speed optimization */
+#define _SHA256_UNROLL
+#define _SHA256_UNROLL2
+
+void
+sha256_init(sha256_t *p)
+{
+  p->state[0] = 0x6a09e667;
+  p->state[1] = 0xbb67ae85;
+  p->state[2] = 0x3c6ef372;
+  p->state[3] = 0xa54ff53a;
+  p->state[4] = 0x510e527f;
+  p->state[5] = 0x9b05688c;
+  p->state[6] = 0x1f83d9ab;
+  p->state[7] = 0x5be0cd19;
+  p->count = 0;
+}
+
+#define S0(x) (ROTR32(x, 2) ^ ROTR32(x,13) ^ ROTR32(x, 22))
+#define S1(x) (ROTR32(x, 6) ^ ROTR32(x,11) ^ ROTR32(x, 25))
+#define s0(x) (ROTR32(x, 7) ^ ROTR32(x,18) ^ (x >> 3))
+#define s1(x) (ROTR32(x,17) ^ ROTR32(x,19) ^ (x >> 10))
+
+#define blk0(i) (W[i] = data[i])
+#define blk2(i) (W[i&15] += s1(W[(i-2)&15]) + W[(i-7)&15] + s0(W[(i-15)&15]))
+
+#define Ch(x,y,z) (z^(x&(y^z)))
+#define Maj(x,y,z) ((x&y)|(z&(x|y)))
+
+#define a(i) T[(0-(i))&7]
+#define b(i) T[(1-(i))&7]
+#define c(i) T[(2-(i))&7]
+#define d(i) T[(3-(i))&7]
+#define e(i) T[(4-(i))&7]
+#define f(i) T[(5-(i))&7]
+#define g(i) T[(6-(i))&7]
+#define h(i) T[(7-(i))&7]
+
+
+#ifdef _SHA256_UNROLL2
+
+#define R(a,b,c,d,e,f,g,h, i) h += S1(e) + Ch(e,f,g) + K[i+j] + (j?blk2(i):blk0(i));\
+  d += h; h += S0(a) + Maj(a, b, c)
+
+#define RX_8(i) \
+  R(a,b,c,d,e,f,g,h, i); \
+  R(h,a,b,c,d,e,f,g, (i+1)); \
+  R(g,h,a,b,c,d,e,f, (i+2)); \
+  R(f,g,h,a,b,c,d,e, (i+3)); \
+  R(e,f,g,h,a,b,c,d, (i+4)); \
+  R(d,e,f,g,h,a,b,c, (i+5)); \
+  R(c,d,e,f,g,h,a,b, (i+6)); \
+  R(b,c,d,e,f,g,h,a, (i+7))
+
+#else
+
+#define R(i) h(i) += S1(e(i)) + Ch(e(i),f(i),g(i)) + K[i+j] + (j?blk2(i):blk0(i));\
+  d(i) += h(i); h(i) += S0(a(i)) + Maj(a(i), b(i), c(i))
+
+#ifdef _SHA256_UNROLL
+
+#define RX_8(i) R(i+0); R(i+1); R(i+2); R(i+3); R(i+4); R(i+5); R(i+6); R(i+7);
+
+#endif
+
+#endif
+
+static const uint32_t K[64] = {
+  0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5,
+  0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5,
+  0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3,
+  0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174,
+  0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc,
+  0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da,
+  0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7,
+  0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967,
+  0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13,
+  0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85,
+  0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3,
+  0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070,
+  0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5,
+  0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3,
+  0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208,
+  0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2
+};
+
+static void
+sha256_transform(uint32_t *state, const uint32_t *data)
+{
+  uint32_t W[16] = {0};
+  unsigned j;
+  #ifdef _SHA256_UNROLL2
+  uint32_t a,b,c,d,e,f,g,h;
+  a = state[0];
+  b = state[1];
+  c = state[2];
+  d = state[3];
+  e = state[4];
+  f = state[5];
+  g = state[6];
+  h = state[7];
+  #else
+  uint32_t T[8];
+  for (j = 0; j < 8; j++)
+    T[j] = state[j];
+  #endif
+
+  for (j = 0; j < 64; j += 16)
+  {
+    #if defined(_SHA256_UNROLL) || defined(_SHA256_UNROLL2)
+    RX_8(0); RX_8(8);
+    #else
+    unsigned i;
+    for (i = 0; i < 16; i++) { R(i); }
+    #endif
+  }
+
+  #ifdef _SHA256_UNROLL2
+  state[0] += a;
+  state[1] += b;
+  state[2] += c;
+  state[3] += d;
+  state[4] += e;
+  state[5] += f;
+  state[6] += g;
+  state[7] += h;
+  #else
+  for (j = 0; j < 8; j++)
+    state[j] += T[j];
+  #endif
+
+  /* Wipe variables */
+  /* memset(W, 0, sizeof(W)); */
+  /* memset(T, 0, sizeof(T)); */
+}
+
+#undef S0
+#undef S1
+#undef s0
+#undef s1
+
+static void
+sha256_write_byte_block(sha256_t *p)
+{
+  uint32_t data32[16];
+  unsigned i;
+  for (i = 0; i < 16; i++)
+    data32[i] =
+      ((uint32_t)(p->buffer[i * 4    ]) << 24) +
+      ((uint32_t)(p->buffer[i * 4 + 1]) << 16) +
+      ((uint32_t)(p->buffer[i * 4 + 2]) <<  8) +
+      ((uint32_t)(p->buffer[i * 4 + 3]));
+  sha256_transform(p->state, data32);
+}
+
+
+void
+sha256_hash(unsigned char *buf, const unsigned char *data, size_t size)
+{
+  sha256_t hash;
+  sha256_init(&hash);
+  sha256_update(&hash, data, size);
+  sha256_final(&hash, buf);
+}
+
+
+void
+sha256_update(sha256_t *p, const unsigned char *data, size_t size)
+{
+  uint32_t curBufferPos = (uint32_t)p->count & 0x3F;
+  while (size > 0)
+  {
+    p->buffer[curBufferPos++] = *data++;
+    p->count++;
+    size--;
+    if (curBufferPos == 64)
+    {
+      curBufferPos = 0;
+      sha256_write_byte_block(p);
+    }
+  }
+}
+
+
+void
+sha256_final(sha256_t *p, unsigned char *digest)
+{
+  uint64_t lenInBits = (p->count << 3);
+  uint32_t curBufferPos = (uint32_t)p->count & 0x3F;
+  unsigned i;
+  p->buffer[curBufferPos++] = 0x80;
+  while (curBufferPos != (64 - 8))
+  {
+    curBufferPos &= 0x3F;
+    if (curBufferPos == 0)
+      sha256_write_byte_block(p);
+    p->buffer[curBufferPos++] = 0;
+  }
+  for (i = 0; i < 8; i++)
+  {
+    p->buffer[curBufferPos++] = (unsigned char)(lenInBits >> 56);
+    lenInBits <<= 8;
+  }
+  sha256_write_byte_block(p);
+
+  for (i = 0; i < 8; i++)
+  {
+    *digest++ = (unsigned char)(p->state[i] >> 24);
+    *digest++ = (unsigned char)(p->state[i] >> 16);
+    *digest++ = (unsigned char)(p->state[i] >> 8);
+    *digest++ = (unsigned char)(p->state[i]);
+  }
+  sha256_init(p);
+}
diff --git a/examples/gguf-hash/deps/sha256/sha256.h b/examples/gguf-hash/deps/sha256/sha256.h
new file mode 100644
index 0000000000000..21657e66b602d
--- /dev/null
+++ b/examples/gguf-hash/deps/sha256/sha256.h
@@ -0,0 +1,24 @@
+/* Sha256.h -- SHA-256 Hash
+2010-06-11 : Igor Pavlov : Public domain */
+
+#ifndef __CRYPTO_SHA256_H
+#define __CRYPTO_SHA256_H
+
+#include <stdlib.h>
+#include <stdint.h>
+
+#define SHA256_DIGEST_SIZE 32
+
+typedef struct sha256_t
+{
+  uint32_t state[8];
+  uint64_t count;
+  unsigned char buffer[64];
+} sha256_t;
+
+void sha256_init(sha256_t *p);
+void sha256_update(sha256_t *p, const unsigned char *data, size_t size);
+void sha256_final(sha256_t *p, unsigned char *digest);
+void sha256_hash(unsigned char *buf, const unsigned char *data, size_t size);
+
+#endif
diff --git a/examples/gguf-hash/deps/xxhash/clib.json b/examples/gguf-hash/deps/xxhash/clib.json
new file mode 100644
index 0000000000000..242343c5d992d
--- /dev/null
+++ b/examples/gguf-hash/deps/xxhash/clib.json
@@ -0,0 +1,12 @@
+{
+  "name": "xxhash",
+  "version": "0.8.2",
+  "repo": "Cyan4973/xxhash",
+  "description": "Extremely fast non-cryptographic hash algorithm",
+  "keywords": ["xxhash", "hashing"],
+  "license": "BSD-2-Clause",
+  "src": [
+    "xxhash.c",
+    "xxhash.h"
+  ]
+}
diff --git a/examples/gguf-hash/deps/xxhash/xxhash.c b/examples/gguf-hash/deps/xxhash/xxhash.c
new file mode 100644
index 0000000000000..e60cc37f13c27
--- /dev/null
+++ b/examples/gguf-hash/deps/xxhash/xxhash.c
@@ -0,0 +1,42 @@
+/*
+ * xxHash - Extremely Fast Hash algorithm
+ * Copyright (C) 2012-2023 Yann Collet
+ *
+ * BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php)
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *    * Redistributions of source code must retain the above copyright
+ *      notice, this list of conditions and the following disclaimer.
+ *    * Redistributions in binary form must reproduce the above
+ *      copyright notice, this list of conditions and the following disclaimer
+ *      in the documentation and/or other materials provided with the
+ *      distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * You can contact the author at:
+ *   - xxHash homepage: https://www.xxhash.com
+ *   - xxHash source repository: https://github.com/Cyan4973/xxHash
+ */
+
+/*
+ * xxhash.c instantiates functions defined in xxhash.h
+ */
+
+#define XXH_STATIC_LINKING_ONLY /* access advanced declarations */
+#define XXH_IMPLEMENTATION      /* access definitions */
+
+#include "xxhash.h"
diff --git a/examples/gguf-hash/deps/xxhash/xxhash.h b/examples/gguf-hash/deps/xxhash/xxhash.h
new file mode 100644
index 0000000000000..c0fafe20d54ad
--- /dev/null
+++ b/examples/gguf-hash/deps/xxhash/xxhash.h
@@ -0,0 +1,7093 @@
+/*
+ * xxHash - Extremely Fast Hash algorithm
+ * Header File
+ * Copyright (C) 2012-2023 Yann Collet
+ *
+ * BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php)
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are
+ * met:
+ *
+ *    * Redistributions of source code must retain the above copyright
+ *      notice, this list of conditions and the following disclaimer.
+ *    * Redistributions in binary form must reproduce the above
+ *      copyright notice, this list of conditions and the following disclaimer
+ *      in the documentation and/or other materials provided with the
+ *      distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+ * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * You can contact the author at:
+ *   - xxHash homepage: https://www.xxhash.com
+ *   - xxHash source repository: https://github.com/Cyan4973/xxHash
+ */
+
+/*!
+ * @mainpage xxHash
+ *
+ * xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed
+ * limits.
+ *
+ * It is proposed in four flavors, in three families:
+ * 1. @ref XXH32_family
+ *   - Classic 32-bit hash function. Simple, compact, and runs on almost all
+ *     32-bit and 64-bit systems.
+ * 2. @ref XXH64_family
+ *   - Classic 64-bit adaptation of XXH32. Just as simple, and runs well on most
+ *     64-bit systems (but _not_ 32-bit systems).
+ * 3. @ref XXH3_family
+ *   - Modern 64-bit and 128-bit hash function family which features improved
+ *     strength and performance across the board, especially on smaller data.
+ *     It benefits greatly from SIMD and 64-bit without requiring it.
+ *
+ * Benchmarks
+ * ---
+ * The reference system uses an Intel i7-9700K CPU, and runs Ubuntu x64 20.04.
+ * The open source benchmark program is compiled with clang v10.0 using -O3 flag.
+ *
+ * | Hash Name            | ISA ext | Width | Large Data Speed | Small Data Velocity |
+ * | -------------------- | ------- | ----: | ---------------: | ------------------: |
+ * | XXH3_64bits()        | @b AVX2 |    64 |        59.4 GB/s |               133.1 |
+ * | MeowHash             | AES-NI  |   128 |        58.2 GB/s |                52.5 |
+ * | XXH3_128bits()       | @b AVX2 |   128 |        57.9 GB/s |               118.1 |
+ * | CLHash               | PCLMUL  |    64 |        37.1 GB/s |                58.1 |
+ * | XXH3_64bits()        | @b SSE2 |    64 |        31.5 GB/s |               133.1 |
+ * | XXH3_128bits()       | @b SSE2 |   128 |        29.6 GB/s |               118.1 |
+ * | RAM sequential read  |         |   N/A |        28.0 GB/s |                 N/A |
+ * | ahash                | AES-NI  |    64 |        22.5 GB/s |               107.2 |
+ * | City64               |         |    64 |        22.0 GB/s |                76.6 |
+ * | T1ha2                |         |    64 |        22.0 GB/s |                99.0 |
+ * | City128              |         |   128 |        21.7 GB/s |                57.7 |
+ * | FarmHash             | AES-NI  |    64 |        21.3 GB/s |                71.9 |
+ * | XXH64()              |         |    64 |        19.4 GB/s |                71.0 |
+ * | SpookyHash           |         |    64 |        19.3 GB/s |                53.2 |
+ * | Mum                  |         |    64 |        18.0 GB/s |                67.0 |
+ * | CRC32C               | SSE4.2  |    32 |        13.0 GB/s |                57.9 |
+ * | XXH32()              |         |    32 |         9.7 GB/s |                71.9 |
+ * | City32               |         |    32 |         9.1 GB/s |                66.0 |
+ * | Blake3*              | @b AVX2 |   256 |         4.4 GB/s |                 8.1 |
+ * | Murmur3              |         |    32 |         3.9 GB/s |                56.1 |
+ * | SipHash*             |         |    64 |         3.0 GB/s |                43.2 |
+ * | Blake3*              | @b SSE2 |   256 |         2.4 GB/s |                 8.1 |
+ * | HighwayHash          |         |    64 |         1.4 GB/s |                 6.0 |
+ * | FNV64                |         |    64 |         1.2 GB/s |                62.7 |
+ * | Blake2*              |         |   256 |         1.1 GB/s |                 5.1 |
+ * | SHA1*                |         |   160 |         0.8 GB/s |                 5.6 |
+ * | MD5*                 |         |   128 |         0.6 GB/s |                 7.8 |
+ * @note
+ *   - Hashes which require a specific ISA extension are noted. SSE2 is also noted,
+ *     even though it is mandatory on x64.
+ *   - Hashes with an asterisk are cryptographic. Note that MD5 is non-cryptographic
+ *     by modern standards.
+ *   - Small data velocity is a rough average of algorithm's efficiency for small
+ *     data. For more accurate information, see the wiki.
+ *   - More benchmarks and strength tests are found on the wiki:
+ *         https://github.com/Cyan4973/xxHash/wiki
+ *
+ * Usage
+ * ------
+ * All xxHash variants use a similar API. Changing the algorithm is a trivial
+ * substitution.
+ *
+ * @pre
+ *    For functions which take an input and length parameter, the following
+ *    requirements are assumed:
+ *    - The range from [`input`, `input + length`) is valid, readable memory.
+ *      - The only exception is if the `length` is `0`, `input` may be `NULL`.
+ *    - For C++, the objects must have the *TriviallyCopyable* property, as the
+ *      functions access bytes directly as if it was an array of `unsigned char`.
+ *
+ * @anchor single_shot_example
+ * **Single Shot**
+ *
+ * These functions are stateless functions which hash a contiguous block of memory,
+ * immediately returning the result. They are the easiest and usually the fastest
+ * option.
+ *
+ * XXH32(), XXH64(), XXH3_64bits(), XXH3_128bits()
+ *
+ * @code{.c}
+ *   #include <string.h>
+ *   #include "xxhash.h"
+ *
+ *   // Example for a function which hashes a null terminated string with XXH32().
+ *   XXH32_hash_t hash_string(const char* string, XXH32_hash_t seed)
+ *   {
+ *       // NULL pointers are only valid if the length is zero
+ *       size_t length = (string == NULL) ? 0 : strlen(string);
+ *       return XXH32(string, length, seed);
+ *   }
+ * @endcode
+ *
+ *
+ * @anchor streaming_example
+ * **Streaming**
+ *
+ * These groups of functions allow incremental hashing of unknown size, even
+ * more than what would fit in a size_t.
+ *
+ * XXH32_reset(), XXH64_reset(), XXH3_64bits_reset(), XXH3_128bits_reset()
+ *
+ * @code{.c}
+ *   #include <stdio.h>
+ *   #include <assert.h>
+ *   #include "xxhash.h"
+ *   // Example for a function which hashes a FILE incrementally with XXH3_64bits().
+ *   XXH64_hash_t hashFile(FILE* f)
+ *   {
+ *       // Allocate a state struct. Do not just use malloc() or new.
+ *       XXH3_state_t* state = XXH3_createState();
+ *       assert(state != NULL && "Out of memory!");
+ *       // Reset the state to start a new hashing session.
+ *       XXH3_64bits_reset(state);
+ *       char buffer[4096];
+ *       size_t count;
+ *       // Read the file in chunks
+ *       while ((count = fread(buffer, 1, sizeof(buffer), f)) != 0) {
+ *           // Run update() as many times as necessary to process the data
+ *           XXH3_64bits_update(state, buffer, count);
+ *       }
+ *       // Retrieve the finalized hash. This will not change the state.
+ *       XXH64_hash_t result = XXH3_64bits_digest(state);
+ *       // Free the state. Do not use free().
+ *       XXH3_freeState(state);
+ *       return result;
+ *   }
+ * @endcode
+ *
+ * Streaming functions generate the xxHash value from an incremental input.
+ * This method is slower than single-call functions, due to state management.
+ * For small inputs, prefer `XXH32()` and `XXH64()`, which are better optimized.
+ *
+ * An XXH state must first be allocated using `XXH*_createState()`.
+ *
+ * Start a new hash by initializing the state with a seed using `XXH*_reset()`.
+ *
+ * Then, feed the hash state by calling `XXH*_update()` as many times as necessary.
+ *
+ * The function returns an error code, with 0 meaning OK, and any other value
+ * meaning there is an error.
+ *
+ * Finally, a hash value can be produced anytime, by using `XXH*_digest()`.
+ * This function returns the nn-bits hash as an int or long long.
+ *
+ * It's still possible to continue inserting input into the hash state after a
+ * digest, and generate new hash values later on by invoking `XXH*_digest()`.
+ *
+ * When done, release the state using `XXH*_freeState()`.
+ *
+ *
+ * @anchor canonical_representation_example
+ * **Canonical Representation**
+ *
+ * The default return values from XXH functions are unsigned 32, 64 and 128 bit
+ * integers.
+ * This the simplest and fastest format for further post-processing.
+ *
+ * However, this leaves open the question of what is the order on the byte level,
+ * since little and big endian conventions will store the same number differently.
+ *
+ * The canonical representation settles this issue by mandating big-endian
+ * convention, the same convention as human-readable numbers (large digits first).
+ *
+ * When writing hash values to storage, sending them over a network, or printing
+ * them, it's highly recommended to use the canonical representation to ensure
+ * portability across a wider range of systems, present and future.
+ *
+ * The following functions allow transformation of hash values to and from
+ * canonical format.
+ *
+ * XXH32_canonicalFromHash(), XXH32_hashFromCanonical(),
+ * XXH64_canonicalFromHash(), XXH64_hashFromCanonical(),
+ * XXH128_canonicalFromHash(), XXH128_hashFromCanonical(),
+ *
+ * @code{.c}
+ *   #include <stdio.h>
+ *   #include "xxhash.h"
+ *
+ *   // Example for a function which prints XXH32_hash_t in human readable format
+ *   void printXxh32(XXH32_hash_t hash)
+ *   {
+ *       XXH32_canonical_t cano;
+ *       XXH32_canonicalFromHash(&cano, hash);
+ *       size_t i;
+ *       for(i = 0; i < sizeof(cano.digest); ++i) {
+ *           printf("%02x", cano.digest[i]);
+ *       }
+ *       printf("\n");
+ *   }
+ *
+ *   // Example for a function which converts XXH32_canonical_t to XXH32_hash_t
+ *   XXH32_hash_t convertCanonicalToXxh32(XXH32_canonical_t cano)
+ *   {
+ *       XXH32_hash_t hash = XXH32_hashFromCanonical(&cano);
+ *       return hash;
+ *   }
+ * @endcode
+ *
+ *
+ * @file xxhash.h
+ * xxHash prototypes and implementation
+ */
+
+#if defined (__cplusplus)
+extern "C" {
+#endif
+
+/* ****************************
+ *  INLINE mode
+ ******************************/
+/*!
+ * @defgroup public Public API
+ * Contains details on the public xxHash functions.
+ * @{
+ */
+#ifdef XXH_DOXYGEN
+/*!
+ * @brief Gives access to internal state declaration, required for static allocation.
+ *
+ * Incompatible with dynamic linking, due to risks of ABI changes.
+ *
+ * Usage:
+ * @code{.c}
+ *     #define XXH_STATIC_LINKING_ONLY
+ *     #include "xxhash.h"
+ * @endcode
+ */
+#  define XXH_STATIC_LINKING_ONLY
+/* Do not undef XXH_STATIC_LINKING_ONLY for Doxygen */
+
+/*!
+ * @brief Gives access to internal definitions.
+ *
+ * Usage:
+ * @code{.c}
+ *     #define XXH_STATIC_LINKING_ONLY
+ *     #define XXH_IMPLEMENTATION
+ *     #include "xxhash.h"
+ * @endcode
+ */
+#  define XXH_IMPLEMENTATION
+/* Do not undef XXH_IMPLEMENTATION for Doxygen */
+
+/*!
+ * @brief Exposes the implementation and marks all functions as `inline`.
+ *
+ * Use these build macros to inline xxhash into the target unit.
+ * Inlining improves performance on small inputs, especially when the length is
+ * expressed as a compile-time constant:
+ *
+ *  https://fastcompression.blogspot.com/2018/03/xxhash-for-small-keys-impressive-power.html
+ *
+ * It also keeps xxHash symbols private to the unit, so they are not exported.
+ *
+ * Usage:
+ * @code{.c}
+ *     #define XXH_INLINE_ALL
+ *     #include "xxhash.h"
+ * @endcode
+ * Do not compile and link xxhash.o as a separate object, as it is not useful.
+ */
+#  define XXH_INLINE_ALL
+#  undef XXH_INLINE_ALL
+/*!
+ * @brief Exposes the implementation without marking functions as inline.
+ */
+#  define XXH_PRIVATE_API
+#  undef XXH_PRIVATE_API
+/*!
+ * @brief Emulate a namespace by transparently prefixing all symbols.
+ *
+ * If you want to include _and expose_ xxHash functions from within your own
+ * library, but also want to avoid symbol collisions with other libraries which
+ * may also include xxHash, you can use @ref XXH_NAMESPACE to automatically prefix
+ * any public symbol from xxhash library with the value of @ref XXH_NAMESPACE
+ * (therefore, avoid empty or numeric values).
+ *
+ * Note that no change is required within the calling program as long as it
+ * includes `xxhash.h`: Regular symbol names will be automatically translated
+ * by this header.
+ */
+#  define XXH_NAMESPACE /* YOUR NAME HERE */
+#  undef XXH_NAMESPACE
+#endif
+
+#if (defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API)) \
+    && !defined(XXH_INLINE_ALL_31684351384)
+   /* this section should be traversed only once */
+#  define XXH_INLINE_ALL_31684351384
+   /* give access to the advanced API, required to compile implementations */
+#  undef XXH_STATIC_LINKING_ONLY   /* avoid macro redef */
+#  define XXH_STATIC_LINKING_ONLY
+   /* make all functions private */
+#  undef XXH_PUBLIC_API
+#  if defined(__GNUC__)
+#    define XXH_PUBLIC_API static __inline __attribute__((__unused__))
+#  elif defined (__cplusplus) || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */)
+#    define XXH_PUBLIC_API static inline
+#  elif defined(_MSC_VER)
+#    define XXH_PUBLIC_API static __inline
+#  else
+     /* note: this version may generate warnings for unused static functions */
+#    define XXH_PUBLIC_API static
+#  endif
+
+   /*
+    * This part deals with the special case where a unit wants to inline xxHash,
+    * but "xxhash.h" has previously been included without XXH_INLINE_ALL,
+    * such as part of some previously included *.h header file.
+    * Without further action, the new include would just be ignored,
+    * and functions would effectively _not_ be inlined (silent failure).
+    * The following macros solve this situation by prefixing all inlined names,
+    * avoiding naming collision with previous inclusions.
+    */
+   /* Before that, we unconditionally #undef all symbols,
+    * in case they were already defined with XXH_NAMESPACE.
+    * They will then be redefined for XXH_INLINE_ALL
+    */
+#  undef XXH_versionNumber
+    /* XXH32 */
+#  undef XXH32
+#  undef XXH32_createState
+#  undef XXH32_freeState
+#  undef XXH32_reset
+#  undef XXH32_update
+#  undef XXH32_digest
+#  undef XXH32_copyState
+#  undef XXH32_canonicalFromHash
+#  undef XXH32_hashFromCanonical
+    /* XXH64 */
+#  undef XXH64
+#  undef XXH64_createState
+#  undef XXH64_freeState
+#  undef XXH64_reset
+#  undef XXH64_update
+#  undef XXH64_digest
+#  undef XXH64_copyState
+#  undef XXH64_canonicalFromHash
+#  undef XXH64_hashFromCanonical
+    /* XXH3_64bits */
+#  undef XXH3_64bits
+#  undef XXH3_64bits_withSecret
+#  undef XXH3_64bits_withSeed
+#  undef XXH3_64bits_withSecretandSeed
+#  undef XXH3_createState
+#  undef XXH3_freeState
+#  undef XXH3_copyState
+#  undef XXH3_64bits_reset
+#  undef XXH3_64bits_reset_withSeed
+#  undef XXH3_64bits_reset_withSecret
+#  undef XXH3_64bits_update
+#  undef XXH3_64bits_digest
+#  undef XXH3_generateSecret
+    /* XXH3_128bits */
+#  undef XXH128
+#  undef XXH3_128bits
+#  undef XXH3_128bits_withSeed
+#  undef XXH3_128bits_withSecret
+#  undef XXH3_128bits_reset
+#  undef XXH3_128bits_reset_withSeed
+#  undef XXH3_128bits_reset_withSecret
+#  undef XXH3_128bits_reset_withSecretandSeed
+#  undef XXH3_128bits_update
+#  undef XXH3_128bits_digest
+#  undef XXH128_isEqual
+#  undef XXH128_cmp
+#  undef XXH128_canonicalFromHash
+#  undef XXH128_hashFromCanonical
+    /* Finally, free the namespace itself */
+#  undef XXH_NAMESPACE
+
+    /* employ the namespace for XXH_INLINE_ALL */
+#  define XXH_NAMESPACE XXH_INLINE_
+   /*
+    * Some identifiers (enums, type names) are not symbols,
+    * but they must nonetheless be renamed to avoid redeclaration.
+    * Alternative solution: do not redeclare them.
+    * However, this requires some #ifdefs, and has a more dispersed impact.
+    * Meanwhile, renaming can be achieved in a single place.
+    */
+#  define XXH_IPREF(Id)   XXH_NAMESPACE ## Id
+#  define XXH_OK XXH_IPREF(XXH_OK)
+#  define XXH_ERROR XXH_IPREF(XXH_ERROR)
+#  define XXH_errorcode XXH_IPREF(XXH_errorcode)
+#  define XXH32_canonical_t  XXH_IPREF(XXH32_canonical_t)
+#  define XXH64_canonical_t  XXH_IPREF(XXH64_canonical_t)
+#  define XXH128_canonical_t XXH_IPREF(XXH128_canonical_t)
+#  define XXH32_state_s XXH_IPREF(XXH32_state_s)
+#  define XXH32_state_t XXH_IPREF(XXH32_state_t)
+#  define XXH64_state_s XXH_IPREF(XXH64_state_s)
+#  define XXH64_state_t XXH_IPREF(XXH64_state_t)
+#  define XXH3_state_s  XXH_IPREF(XXH3_state_s)
+#  define XXH3_state_t  XXH_IPREF(XXH3_state_t)
+#  define XXH128_hash_t XXH_IPREF(XXH128_hash_t)
+   /* Ensure the header is parsed again, even if it was previously included */
+#  undef XXHASH_H_5627135585666179
+#  undef XXHASH_H_STATIC_13879238742
+#endif /* XXH_INLINE_ALL || XXH_PRIVATE_API */
+
+/* ****************************************************************
+ *  Stable API
+ *****************************************************************/
+#ifndef XXHASH_H_5627135585666179
+#define XXHASH_H_5627135585666179 1
+
+/*! @brief Marks a global symbol. */
+#if !defined(XXH_INLINE_ALL) && !defined(XXH_PRIVATE_API)
+#  if defined(_WIN32) && defined(_MSC_VER) && (defined(XXH_IMPORT) || defined(XXH_EXPORT))
+#    ifdef XXH_EXPORT
+#      define XXH_PUBLIC_API __declspec(dllexport)
+#    elif XXH_IMPORT
+#      define XXH_PUBLIC_API __declspec(dllimport)
+#    endif
+#  else
+#    define XXH_PUBLIC_API   /* do nothing */
+#  endif
+#endif
+
+#ifdef XXH_NAMESPACE
+#  define XXH_CAT(A,B) A##B
+#  define XXH_NAME2(A,B) XXH_CAT(A,B)
+#  define XXH_versionNumber XXH_NAME2(XXH_NAMESPACE, XXH_versionNumber)
+/* XXH32 */
+#  define XXH32 XXH_NAME2(XXH_NAMESPACE, XXH32)
+#  define XXH32_createState XXH_NAME2(XXH_NAMESPACE, XXH32_createState)
+#  define XXH32_freeState XXH_NAME2(XXH_NAMESPACE, XXH32_freeState)
+#  define XXH32_reset XXH_NAME2(XXH_NAMESPACE, XXH32_reset)
+#  define XXH32_update XXH_NAME2(XXH_NAMESPACE, XXH32_update)
+#  define XXH32_digest XXH_NAME2(XXH_NAMESPACE, XXH32_digest)
+#  define XXH32_copyState XXH_NAME2(XXH_NAMESPACE, XXH32_copyState)
+#  define XXH32_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH32_canonicalFromHash)
+#  define XXH32_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH32_hashFromCanonical)
+/* XXH64 */
+#  define XXH64 XXH_NAME2(XXH_NAMESPACE, XXH64)
+#  define XXH64_createState XXH_NAME2(XXH_NAMESPACE, XXH64_createState)
+#  define XXH64_freeState XXH_NAME2(XXH_NAMESPACE, XXH64_freeState)
+#  define XXH64_reset XXH_NAME2(XXH_NAMESPACE, XXH64_reset)
+#  define XXH64_update XXH_NAME2(XXH_NAMESPACE, XXH64_update)
+#  define XXH64_digest XXH_NAME2(XXH_NAMESPACE, XXH64_digest)
+#  define XXH64_copyState XXH_NAME2(XXH_NAMESPACE, XXH64_copyState)
+#  define XXH64_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH64_canonicalFromHash)
+#  define XXH64_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH64_hashFromCanonical)
+/* XXH3_64bits */
+#  define XXH3_64bits XXH_NAME2(XXH_NAMESPACE, XXH3_64bits)
+#  define XXH3_64bits_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSecret)
+#  define XXH3_64bits_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSeed)
+#  define XXH3_64bits_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSecretandSeed)
+#  define XXH3_createState XXH_NAME2(XXH_NAMESPACE, XXH3_createState)
+#  define XXH3_freeState XXH_NAME2(XXH_NAMESPACE, XXH3_freeState)
+#  define XXH3_copyState XXH_NAME2(XXH_NAMESPACE, XXH3_copyState)
+#  define XXH3_64bits_reset XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset)
+#  define XXH3_64bits_reset_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSeed)
+#  define XXH3_64bits_reset_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSecret)
+#  define XXH3_64bits_reset_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSecretandSeed)
+#  define XXH3_64bits_update XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_update)
+#  define XXH3_64bits_digest XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_digest)
+#  define XXH3_generateSecret XXH_NAME2(XXH_NAMESPACE, XXH3_generateSecret)
+#  define XXH3_generateSecret_fromSeed XXH_NAME2(XXH_NAMESPACE, XXH3_generateSecret_fromSeed)
+/* XXH3_128bits */
+#  define XXH128 XXH_NAME2(XXH_NAMESPACE, XXH128)
+#  define XXH3_128bits XXH_NAME2(XXH_NAMESPACE, XXH3_128bits)
+#  define XXH3_128bits_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSeed)
+#  define XXH3_128bits_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSecret)
+#  define XXH3_128bits_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSecretandSeed)
+#  define XXH3_128bits_reset XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset)
+#  define XXH3_128bits_reset_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSeed)
+#  define XXH3_128bits_reset_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSecret)
+#  define XXH3_128bits_reset_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSecretandSeed)
+#  define XXH3_128bits_update XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_update)
+#  define XXH3_128bits_digest XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_digest)
+#  define XXH128_isEqual XXH_NAME2(XXH_NAMESPACE, XXH128_isEqual)
+#  define XXH128_cmp     XXH_NAME2(XXH_NAMESPACE, XXH128_cmp)
+#  define XXH128_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH128_canonicalFromHash)
+#  define XXH128_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH128_hashFromCanonical)
+#endif
+
+
+/* *************************************
+*  Compiler specifics
+***************************************/
+
+/* specific declaration modes for Windows */
+#if !defined(XXH_INLINE_ALL) && !defined(XXH_PRIVATE_API)
+#  if defined(_WIN32) && defined(_MSC_VER) && (defined(XXH_IMPORT) || defined(XXH_EXPORT))
+#    ifdef XXH_EXPORT
+#      define XXH_PUBLIC_API __declspec(dllexport)
+#    elif XXH_IMPORT
+#      define XXH_PUBLIC_API __declspec(dllimport)
+#    endif
+#  else
+#    define XXH_PUBLIC_API   /* do nothing */
+#  endif
+#endif
+
+#if defined (__GNUC__)
+# define XXH_CONSTF  __attribute__((__const__))
+# define XXH_PUREF   __attribute__((__pure__))
+# define XXH_MALLOCF __attribute__((__malloc__))
+#else
+# define XXH_CONSTF  /* disable */
+# define XXH_PUREF
+# define XXH_MALLOCF
+#endif
+
+/* *************************************
+*  Version
+***************************************/
+#define XXH_VERSION_MAJOR    0
+#define XXH_VERSION_MINOR    8
+#define XXH_VERSION_RELEASE  3
+/*! @brief Version number, encoded as two digits each */
+#define XXH_VERSION_NUMBER  (XXH_VERSION_MAJOR *100*100 + XXH_VERSION_MINOR *100 + XXH_VERSION_RELEASE)
+
+/*!
+ * @brief Obtains the xxHash version.
+ *
+ * This is mostly useful when xxHash is compiled as a shared library,
+ * since the returned value comes from the library, as opposed to header file.
+ *
+ * @return @ref XXH_VERSION_NUMBER of the invoked library.
+ */
+XXH_PUBLIC_API XXH_CONSTF unsigned XXH_versionNumber (void);
+
+
+/* ****************************
+*  Common basic types
+******************************/
+#include <stddef.h>   /* size_t */
+/*!
+ * @brief Exit code for the streaming API.
+ */
+typedef enum {
+    XXH_OK = 0, /*!< OK */
+    XXH_ERROR   /*!< Error */
+} XXH_errorcode;
+
+
+/*-**********************************************************************
+*  32-bit hash
+************************************************************************/
+#if defined(XXH_DOXYGEN) /* Don't show <stdint.h> include */
+/*!
+ * @brief An unsigned 32-bit integer.
+ *
+ * Not necessarily defined to `uint32_t` but functionally equivalent.
+ */
+typedef uint32_t XXH32_hash_t;
+
+#elif !defined (__VMS) \
+  && (defined (__cplusplus) \
+  || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) )
+#   ifdef _AIX
+#     include <inttypes.h>
+#   else
+#     include <stdint.h>
+#   endif
+    typedef uint32_t XXH32_hash_t;
+
+#else
+#   include <limits.h>
+#   if UINT_MAX == 0xFFFFFFFFUL
+      typedef unsigned int XXH32_hash_t;
+#   elif ULONG_MAX == 0xFFFFFFFFUL
+      typedef unsigned long XXH32_hash_t;
+#   else
+#     error "unsupported platform: need a 32-bit type"
+#   endif
+#endif
+
+/*!
+ * @}
+ *
+ * @defgroup XXH32_family XXH32 family
+ * @ingroup public
+ * Contains functions used in the classic 32-bit xxHash algorithm.
+ *
+ * @note
+ *   XXH32 is useful for older platforms, with no or poor 64-bit performance.
+ *   Note that the @ref XXH3_family provides competitive speed for both 32-bit
+ *   and 64-bit systems, and offers true 64/128 bit hash results.
+ *
+ * @see @ref XXH64_family, @ref XXH3_family : Other xxHash families
+ * @see @ref XXH32_impl for implementation details
+ * @{
+ */
+
+/*!
+ * @brief Calculates the 32-bit hash of @p input using xxHash32.
+ *
+ * @param input The block of data to be hashed, at least @p length bytes in size.
+ * @param length The length of @p input, in bytes.
+ * @param seed The 32-bit seed to alter the hash's output predictably.
+ *
+ * @pre
+ *   The memory between @p input and @p input + @p length must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p input may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * @return The calculated 32-bit xxHash32 value.
+ *
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32 (const void* input, size_t length, XXH32_hash_t seed);
+
+#ifndef XXH_NO_STREAM
+/*!
+ * @typedef struct XXH32_state_s XXH32_state_t
+ * @brief The opaque state struct for the XXH32 streaming API.
+ *
+ * @see XXH32_state_s for details.
+ * @see @ref streaming_example "Streaming Example"
+ */
+typedef struct XXH32_state_s XXH32_state_t;
+
+/*!
+ * @brief Allocates an @ref XXH32_state_t.
+ *
+ * @return An allocated pointer of @ref XXH32_state_t on success.
+ * @return `NULL` on failure.
+ *
+ * @note Must be freed with XXH32_freeState().
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_MALLOCF XXH32_state_t* XXH32_createState(void);
+/*!
+ * @brief Frees an @ref XXH32_state_t.
+ *
+ * @param statePtr A pointer to an @ref XXH32_state_t allocated with @ref XXH32_createState().
+ *
+ * @return @ref XXH_OK.
+ *
+ * @note @p statePtr must be allocated with XXH32_createState().
+ *
+ * @see @ref streaming_example "Streaming Example"
+ *
+ */
+XXH_PUBLIC_API XXH_errorcode  XXH32_freeState(XXH32_state_t* statePtr);
+/*!
+ * @brief Copies one @ref XXH32_state_t to another.
+ *
+ * @param dst_state The state to copy to.
+ * @param src_state The state to copy from.
+ * @pre
+ *   @p dst_state and @p src_state must not be `NULL` and must not overlap.
+ */
+XXH_PUBLIC_API void XXH32_copyState(XXH32_state_t* dst_state, const XXH32_state_t* src_state);
+
+/*!
+ * @brief Resets an @ref XXH32_state_t to begin a new hash.
+ *
+ * @param statePtr The state struct to reset.
+ * @param seed The 32-bit seed to alter the hash result predictably.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note This function resets and seeds a state. Call it before @ref XXH32_update().
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH32_reset  (XXH32_state_t* statePtr, XXH32_hash_t seed);
+
+/*!
+ * @brief Consumes a block of @p input to an @ref XXH32_state_t.
+ *
+ * @param statePtr The state struct to update.
+ * @param input The block of data to be hashed, at least @p length bytes in size.
+ * @param length The length of @p input, in bytes.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ * @pre
+ *   The memory between @p input and @p input + @p length must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p input may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note Call this to incrementally consume blocks of data.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH32_update (XXH32_state_t* statePtr, const void* input, size_t length);
+
+/*!
+ * @brief Returns the calculated hash value from an @ref XXH32_state_t.
+ *
+ * @param statePtr The state struct to calculate the hash from.
+ *
+ * @pre
+ *  @p statePtr must not be `NULL`.
+ *
+ * @return The calculated 32-bit xxHash32 value from that state.
+ *
+ * @note
+ *   Calling XXH32_digest() will not affect @p statePtr, so you can update,
+ *   digest, and update again.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32_digest (const XXH32_state_t* statePtr);
+#endif /* !XXH_NO_STREAM */
+
+/*******   Canonical representation   *******/
+
+/*!
+ * @brief Canonical (big endian) representation of @ref XXH32_hash_t.
+ */
+typedef struct {
+    unsigned char digest[4]; /*!< Hash bytes, big endian */
+} XXH32_canonical_t;
+
+/*!
+ * @brief Converts an @ref XXH32_hash_t to a big endian @ref XXH32_canonical_t.
+ *
+ * @param dst  The @ref XXH32_canonical_t pointer to be stored to.
+ * @param hash The @ref XXH32_hash_t to be converted.
+ *
+ * @pre
+ *   @p dst must not be `NULL`.
+ *
+ * @see @ref canonical_representation_example "Canonical Representation Example"
+ */
+XXH_PUBLIC_API void XXH32_canonicalFromHash(XXH32_canonical_t* dst, XXH32_hash_t hash);
+
+/*!
+ * @brief Converts an @ref XXH32_canonical_t to a native @ref XXH32_hash_t.
+ *
+ * @param src The @ref XXH32_canonical_t to convert.
+ *
+ * @pre
+ *   @p src must not be `NULL`.
+ *
+ * @return The converted hash.
+ *
+ * @see @ref canonical_representation_example "Canonical Representation Example"
+ */
+XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32_hashFromCanonical(const XXH32_canonical_t* src);
+
+
+/*! @cond Doxygen ignores this part */
+#ifdef __has_attribute
+# define XXH_HAS_ATTRIBUTE(x) __has_attribute(x)
+#else
+# define XXH_HAS_ATTRIBUTE(x) 0
+#endif
+/*! @endcond */
+
+/*! @cond Doxygen ignores this part */
+/*
+ * C23 __STDC_VERSION__ number hasn't been specified yet. For now
+ * leave as `201711L` (C17 + 1).
+ * TODO: Update to correct value when its been specified.
+ */
+#define XXH_C23_VN 201711L
+/*! @endcond */
+
+/*! @cond Doxygen ignores this part */
+/* C-language Attributes are added in C23. */
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= XXH_C23_VN) && defined(__has_c_attribute)
+# define XXH_HAS_C_ATTRIBUTE(x) __has_c_attribute(x)
+#else
+# define XXH_HAS_C_ATTRIBUTE(x) 0
+#endif
+/*! @endcond */
+
+/*! @cond Doxygen ignores this part */
+#if defined(__cplusplus) && defined(__has_cpp_attribute)
+# define XXH_HAS_CPP_ATTRIBUTE(x) __has_cpp_attribute(x)
+#else
+# define XXH_HAS_CPP_ATTRIBUTE(x) 0
+#endif
+/*! @endcond */
+
+/*! @cond Doxygen ignores this part */
+/*
+ * Define XXH_FALLTHROUGH macro for annotating switch case with the 'fallthrough' attribute
+ * introduced in CPP17 and C23.
+ * CPP17 : https://en.cppreference.com/w/cpp/language/attributes/fallthrough
+ * C23   : https://en.cppreference.com/w/c/language/attributes/fallthrough
+ */
+#if XXH_HAS_C_ATTRIBUTE(fallthrough) || XXH_HAS_CPP_ATTRIBUTE(fallthrough)
+# define XXH_FALLTHROUGH [[fallthrough]]
+#elif XXH_HAS_ATTRIBUTE(__fallthrough__)
+# define XXH_FALLTHROUGH __attribute__ ((__fallthrough__))
+#else
+# define XXH_FALLTHROUGH /* fallthrough */
+#endif
+/*! @endcond */
+
+/*! @cond Doxygen ignores this part */
+/*
+ * Define XXH_NOESCAPE for annotated pointers in public API.
+ * https://clang.llvm.org/docs/AttributeReference.html#noescape
+ * As of writing this, only supported by clang.
+ */
+#if XXH_HAS_ATTRIBUTE(noescape)
+# define XXH_NOESCAPE __attribute__((__noescape__))
+#else
+# define XXH_NOESCAPE
+#endif
+/*! @endcond */
+
+
+/*!
+ * @}
+ * @ingroup public
+ * @{
+ */
+
+#ifndef XXH_NO_LONG_LONG
+/*-**********************************************************************
+*  64-bit hash
+************************************************************************/
+#if defined(XXH_DOXYGEN) /* don't include <stdint.h> */
+/*!
+ * @brief An unsigned 64-bit integer.
+ *
+ * Not necessarily defined to `uint64_t` but functionally equivalent.
+ */
+typedef uint64_t XXH64_hash_t;
+#elif !defined (__VMS) \
+  && (defined (__cplusplus) \
+  || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) )
+#   ifdef _AIX
+#     include <inttypes.h>
+#   else
+#     include <stdint.h>
+#   endif
+   typedef uint64_t XXH64_hash_t;
+#else
+#  include <limits.h>
+#  if defined(__LP64__) && ULONG_MAX == 0xFFFFFFFFFFFFFFFFULL
+     /* LP64 ABI says uint64_t is unsigned long */
+     typedef unsigned long XXH64_hash_t;
+#  else
+     /* the following type must have a width of 64-bit */
+     typedef unsigned long long XXH64_hash_t;
+#  endif
+#endif
+
+/*!
+ * @}
+ *
+ * @defgroup XXH64_family XXH64 family
+ * @ingroup public
+ * @{
+ * Contains functions used in the classic 64-bit xxHash algorithm.
+ *
+ * @note
+ *   XXH3 provides competitive speed for both 32-bit and 64-bit systems,
+ *   and offers true 64/128 bit hash results.
+ *   It provides better speed for systems with vector processing capabilities.
+ */
+
+/*!
+ * @brief Calculates the 64-bit hash of @p input using xxHash64.
+ *
+ * @param input The block of data to be hashed, at least @p length bytes in size.
+ * @param length The length of @p input, in bytes.
+ * @param seed The 64-bit seed to alter the hash's output predictably.
+ *
+ * @pre
+ *   The memory between @p input and @p input + @p length must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p input may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * @return The calculated 64-bit xxHash64 value.
+ *
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed);
+
+/*******   Streaming   *******/
+#ifndef XXH_NO_STREAM
+/*!
+ * @brief The opaque state struct for the XXH64 streaming API.
+ *
+ * @see XXH64_state_s for details.
+ * @see @ref streaming_example "Streaming Example"
+ */
+typedef struct XXH64_state_s XXH64_state_t;   /* incomplete type */
+
+/*!
+ * @brief Allocates an @ref XXH64_state_t.
+ *
+ * @return An allocated pointer of @ref XXH64_state_t on success.
+ * @return `NULL` on failure.
+ *
+ * @note Must be freed with XXH64_freeState().
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_MALLOCF XXH64_state_t* XXH64_createState(void);
+
+/*!
+ * @brief Frees an @ref XXH64_state_t.
+ *
+ * @param statePtr A pointer to an @ref XXH64_state_t allocated with @ref XXH64_createState().
+ *
+ * @return @ref XXH_OK.
+ *
+ * @note @p statePtr must be allocated with XXH64_createState().
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode  XXH64_freeState(XXH64_state_t* statePtr);
+
+/*!
+ * @brief Copies one @ref XXH64_state_t to another.
+ *
+ * @param dst_state The state to copy to.
+ * @param src_state The state to copy from.
+ * @pre
+ *   @p dst_state and @p src_state must not be `NULL` and must not overlap.
+ */
+XXH_PUBLIC_API void XXH64_copyState(XXH_NOESCAPE XXH64_state_t* dst_state, const XXH64_state_t* src_state);
+
+/*!
+ * @brief Resets an @ref XXH64_state_t to begin a new hash.
+ *
+ * @param statePtr The state struct to reset.
+ * @param seed The 64-bit seed to alter the hash result predictably.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note This function resets and seeds a state. Call it before @ref XXH64_update().
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH64_reset  (XXH_NOESCAPE XXH64_state_t* statePtr, XXH64_hash_t seed);
+
+/*!
+ * @brief Consumes a block of @p input to an @ref XXH64_state_t.
+ *
+ * @param statePtr The state struct to update.
+ * @param input The block of data to be hashed, at least @p length bytes in size.
+ * @param length The length of @p input, in bytes.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ * @pre
+ *   The memory between @p input and @p input + @p length must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p input may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note Call this to incrementally consume blocks of data.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH64_update (XXH_NOESCAPE XXH64_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length);
+
+/*!
+ * @brief Returns the calculated hash value from an @ref XXH64_state_t.
+ *
+ * @param statePtr The state struct to calculate the hash from.
+ *
+ * @pre
+ *  @p statePtr must not be `NULL`.
+ *
+ * @return The calculated 64-bit xxHash64 value from that state.
+ *
+ * @note
+ *   Calling XXH64_digest() will not affect @p statePtr, so you can update,
+ *   digest, and update again.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64_digest (XXH_NOESCAPE const XXH64_state_t* statePtr);
+#endif /* !XXH_NO_STREAM */
+/*******   Canonical representation   *******/
+
+/*!
+ * @brief Canonical (big endian) representation of @ref XXH64_hash_t.
+ */
+typedef struct { unsigned char digest[sizeof(XXH64_hash_t)]; } XXH64_canonical_t;
+
+/*!
+ * @brief Converts an @ref XXH64_hash_t to a big endian @ref XXH64_canonical_t.
+ *
+ * @param dst The @ref XXH64_canonical_t pointer to be stored to.
+ * @param hash The @ref XXH64_hash_t to be converted.
+ *
+ * @pre
+ *   @p dst must not be `NULL`.
+ *
+ * @see @ref canonical_representation_example "Canonical Representation Example"
+ */
+XXH_PUBLIC_API void XXH64_canonicalFromHash(XXH_NOESCAPE XXH64_canonical_t* dst, XXH64_hash_t hash);
+
+/*!
+ * @brief Converts an @ref XXH64_canonical_t to a native @ref XXH64_hash_t.
+ *
+ * @param src The @ref XXH64_canonical_t to convert.
+ *
+ * @pre
+ *   @p src must not be `NULL`.
+ *
+ * @return The converted hash.
+ *
+ * @see @ref canonical_representation_example "Canonical Representation Example"
+ */
+XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64_hashFromCanonical(XXH_NOESCAPE const XXH64_canonical_t* src);
+
+#ifndef XXH_NO_XXH3
+
+/*!
+ * @}
+ * ************************************************************************
+ * @defgroup XXH3_family XXH3 family
+ * @ingroup public
+ * @{
+ *
+ * XXH3 is a more recent hash algorithm featuring:
+ *  - Improved speed for both small and large inputs
+ *  - True 64-bit and 128-bit outputs
+ *  - SIMD acceleration
+ *  - Improved 32-bit viability
+ *
+ * Speed analysis methodology is explained here:
+ *
+ *    https://fastcompression.blogspot.com/2019/03/presenting-xxh3.html
+ *
+ * Compared to XXH64, expect XXH3 to run approximately
+ * ~2x faster on large inputs and >3x faster on small ones,
+ * exact differences vary depending on platform.
+ *
+ * XXH3's speed benefits greatly from SIMD and 64-bit arithmetic,
+ * but does not require it.
+ * Most 32-bit and 64-bit targets that can run XXH32 smoothly can run XXH3
+ * at competitive speeds, even without vector support. Further details are
+ * explained in the implementation.
+ *
+ * XXH3 has a fast scalar implementation, but it also includes accelerated SIMD
+ * implementations for many common platforms:
+ *   - AVX512
+ *   - AVX2
+ *   - SSE2
+ *   - ARM NEON
+ *   - WebAssembly SIMD128
+ *   - POWER8 VSX
+ *   - s390x ZVector
+ * This can be controlled via the @ref XXH_VECTOR macro, but it automatically
+ * selects the best version according to predefined macros. For the x86 family, an
+ * automatic runtime dispatcher is included separately in @ref xxh_x86dispatch.c.
+ *
+ * XXH3 implementation is portable:
+ * it has a generic C90 formulation that can be compiled on any platform,
+ * all implementations generate exactly the same hash value on all platforms.
+ * Starting from v0.8.0, it's also labelled "stable", meaning that
+ * any future version will also generate the same hash value.
+ *
+ * XXH3 offers 2 variants, _64bits and _128bits.
+ *
+ * When only 64 bits are needed, prefer invoking the _64bits variant, as it
+ * reduces the amount of mixing, resulting in faster speed on small inputs.
+ * It's also generally simpler to manipulate a scalar return type than a struct.
+ *
+ * The API supports one-shot hashing, streaming mode, and custom secrets.
+ */
+/*-**********************************************************************
+*  XXH3 64-bit variant
+************************************************************************/
+
+/*!
+ * @brief Calculates 64-bit unseeded variant of XXH3 hash of @p input.
+ *
+ * @param input  The block of data to be hashed, at least @p length bytes in size.
+ * @param length The length of @p input, in bytes.
+ *
+ * @pre
+ *   The memory between @p input and @p input + @p length must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p input may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * @return The calculated 64-bit XXH3 hash value.
+ *
+ * @note
+ *   This is equivalent to @ref XXH3_64bits_withSeed() with a seed of `0`, however
+ *   it may have slightly better performance due to constant propagation of the
+ *   defaults.
+ *
+ * @see
+ *    XXH3_64bits_withSeed(), XXH3_64bits_withSecret(): other seeding variants
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits(XXH_NOESCAPE const void* input, size_t length);
+
+/*!
+ * @brief Calculates 64-bit seeded variant of XXH3 hash of @p input.
+ *
+ * @param input  The block of data to be hashed, at least @p length bytes in size.
+ * @param length The length of @p input, in bytes.
+ * @param seed   The 64-bit seed to alter the hash result predictably.
+ *
+ * @pre
+ *   The memory between @p input and @p input + @p length must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p input may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * @return The calculated 64-bit XXH3 hash value.
+ *
+ * @note
+ *    seed == 0 produces the same results as @ref XXH3_64bits().
+ *
+ * This variant generates a custom secret on the fly based on default secret
+ * altered using the @p seed value.
+ *
+ * While this operation is decently fast, note that it's not completely free.
+ *
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_withSeed(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed);
+
+/*!
+ * The bare minimum size for a custom secret.
+ *
+ * @see
+ *  XXH3_64bits_withSecret(), XXH3_64bits_reset_withSecret(),
+ *  XXH3_128bits_withSecret(), XXH3_128bits_reset_withSecret().
+ */
+#define XXH3_SECRET_SIZE_MIN 136
+
+/*!
+ * @brief Calculates 64-bit variant of XXH3 with a custom "secret".
+ *
+ * @param data       The block of data to be hashed, at least @p len bytes in size.
+ * @param len        The length of @p data, in bytes.
+ * @param secret     The secret data.
+ * @param secretSize The length of @p secret, in bytes.
+ *
+ * @return The calculated 64-bit XXH3 hash value.
+ *
+ * @pre
+ *   The memory between @p data and @p data + @p len must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p data may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * It's possible to provide any blob of bytes as a "secret" to generate the hash.
+ * This makes it more difficult for an external actor to prepare an intentional collision.
+ * The main condition is that @p secretSize *must* be large enough (>= @ref XXH3_SECRET_SIZE_MIN).
+ * However, the quality of the secret impacts the dispersion of the hash algorithm.
+ * Therefore, the secret _must_ look like a bunch of random bytes.
+ * Avoid "trivial" or structured data such as repeated sequences or a text document.
+ * Whenever in doubt about the "randomness" of the blob of bytes,
+ * consider employing @ref XXH3_generateSecret() instead (see below).
+ * It will generate a proper high entropy secret derived from the blob of bytes.
+ * Another advantage of using XXH3_generateSecret() is that
+ * it guarantees that all bits within the initial blob of bytes
+ * will impact every bit of the output.
+ * This is not necessarily the case when using the blob of bytes directly
+ * because, when hashing _small_ inputs, only a portion of the secret is employed.
+ *
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_withSecret(XXH_NOESCAPE const void* data, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize);
+
+
+/*******   Streaming   *******/
+#ifndef XXH_NO_STREAM
+/*
+ * Streaming requires state maintenance.
+ * This operation costs memory and CPU.
+ * As a consequence, streaming is slower than one-shot hashing.
+ * For better performance, prefer one-shot functions whenever applicable.
+ */
+
+/*!
+ * @brief The opaque state struct for the XXH3 streaming API.
+ *
+ * @see XXH3_state_s for details.
+ * @see @ref streaming_example "Streaming Example"
+ */
+typedef struct XXH3_state_s XXH3_state_t;
+XXH_PUBLIC_API XXH_MALLOCF XXH3_state_t* XXH3_createState(void);
+XXH_PUBLIC_API XXH_errorcode XXH3_freeState(XXH3_state_t* statePtr);
+
+/*!
+ * @brief Copies one @ref XXH3_state_t to another.
+ *
+ * @param dst_state The state to copy to.
+ * @param src_state The state to copy from.
+ * @pre
+ *   @p dst_state and @p src_state must not be `NULL` and must not overlap.
+ */
+XXH_PUBLIC_API void XXH3_copyState(XXH_NOESCAPE XXH3_state_t* dst_state, XXH_NOESCAPE const XXH3_state_t* src_state);
+
+/*!
+ * @brief Resets an @ref XXH3_state_t to begin a new hash.
+ *
+ * @param statePtr The state struct to reset.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note
+ *   - This function resets `statePtr` and generate a secret with default parameters.
+ *   - Call this function before @ref XXH3_64bits_update().
+ *   - Digest will be equivalent to `XXH3_64bits()`.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ *
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr);
+
+/*!
+ * @brief Resets an @ref XXH3_state_t with 64-bit seed to begin a new hash.
+ *
+ * @param statePtr The state struct to reset.
+ * @param seed     The 64-bit seed to alter the hash result predictably.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note
+ *   - This function resets `statePtr` and generate a secret from `seed`.
+ *   - Call this function before @ref XXH3_64bits_update().
+ *   - Digest will be equivalent to `XXH3_64bits_withSeed()`.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ *
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed);
+
+/*!
+ * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash.
+ *
+ * @param statePtr The state struct to reset.
+ * @param secret     The secret data.
+ * @param secretSize The length of @p secret, in bytes.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note
+ *   `secret` is referenced, it _must outlive_ the hash streaming session.
+ *
+ * Similar to one-shot API, `secretSize` must be >= @ref XXH3_SECRET_SIZE_MIN,
+ * and the quality of produced hash values depends on secret's entropy
+ * (secret's content should look like a bunch of random bytes).
+ * When in doubt about the randomness of a candidate `secret`,
+ * consider employing `XXH3_generateSecret()` instead (see below).
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize);
+
+/*!
+ * @brief Consumes a block of @p input to an @ref XXH3_state_t.
+ *
+ * @param statePtr The state struct to update.
+ * @param input The block of data to be hashed, at least @p length bytes in size.
+ * @param length The length of @p input, in bytes.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ * @pre
+ *   The memory between @p input and @p input + @p length must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p input may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note Call this to incrementally consume blocks of data.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_64bits_update (XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length);
+
+/*!
+ * @brief Returns the calculated XXH3 64-bit hash value from an @ref XXH3_state_t.
+ *
+ * @param statePtr The state struct to calculate the hash from.
+ *
+ * @pre
+ *  @p statePtr must not be `NULL`.
+ *
+ * @return The calculated XXH3 64-bit hash value from that state.
+ *
+ * @note
+ *   Calling XXH3_64bits_digest() will not affect @p statePtr, so you can update,
+ *   digest, and update again.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_PUREF XXH64_hash_t  XXH3_64bits_digest (XXH_NOESCAPE const XXH3_state_t* statePtr);
+#endif /* !XXH_NO_STREAM */
+
+/* note : canonical representation of XXH3 is the same as XXH64
+ * since they both produce XXH64_hash_t values */
+
+
+/*-**********************************************************************
+*  XXH3 128-bit variant
+************************************************************************/
+
+/*!
+ * @brief The return value from 128-bit hashes.
+ *
+ * Stored in little endian order, although the fields themselves are in native
+ * endianness.
+ */
+typedef struct {
+    XXH64_hash_t low64;   /*!< `value & 0xFFFFFFFFFFFFFFFF` */
+    XXH64_hash_t high64;  /*!< `value >> 64` */
+} XXH128_hash_t;
+
+/*!
+ * @brief Calculates 128-bit unseeded variant of XXH3 of @p data.
+ *
+ * @param data The block of data to be hashed, at least @p length bytes in size.
+ * @param len  The length of @p data, in bytes.
+ *
+ * @return The calculated 128-bit variant of XXH3 value.
+ *
+ * The 128-bit variant of XXH3 has more strength, but it has a bit of overhead
+ * for shorter inputs.
+ *
+ * This is equivalent to @ref XXH3_128bits_withSeed() with a seed of `0`, however
+ * it may have slightly better performance due to constant propagation of the
+ * defaults.
+ *
+ * @see XXH3_128bits_withSeed(), XXH3_128bits_withSecret(): other seeding variants
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits(XXH_NOESCAPE const void* data, size_t len);
+/*! @brief Calculates 128-bit seeded variant of XXH3 hash of @p data.
+ *
+ * @param data The block of data to be hashed, at least @p length bytes in size.
+ * @param len  The length of @p data, in bytes.
+ * @param seed The 64-bit seed to alter the hash result predictably.
+ *
+ * @return The calculated 128-bit variant of XXH3 value.
+ *
+ * @note
+ *    seed == 0 produces the same results as @ref XXH3_64bits().
+ *
+ * This variant generates a custom secret on the fly based on default secret
+ * altered using the @p seed value.
+ *
+ * While this operation is decently fast, note that it's not completely free.
+ *
+ * @see XXH3_128bits(), XXH3_128bits_withSecret(): other seeding variants
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_withSeed(XXH_NOESCAPE const void* data, size_t len, XXH64_hash_t seed);
+/*!
+ * @brief Calculates 128-bit variant of XXH3 with a custom "secret".
+ *
+ * @param data       The block of data to be hashed, at least @p len bytes in size.
+ * @param len        The length of @p data, in bytes.
+ * @param secret     The secret data.
+ * @param secretSize The length of @p secret, in bytes.
+ *
+ * @return The calculated 128-bit variant of XXH3 value.
+ *
+ * It's possible to provide any blob of bytes as a "secret" to generate the hash.
+ * This makes it more difficult for an external actor to prepare an intentional collision.
+ * The main condition is that @p secretSize *must* be large enough (>= @ref XXH3_SECRET_SIZE_MIN).
+ * However, the quality of the secret impacts the dispersion of the hash algorithm.
+ * Therefore, the secret _must_ look like a bunch of random bytes.
+ * Avoid "trivial" or structured data such as repeated sequences or a text document.
+ * Whenever in doubt about the "randomness" of the blob of bytes,
+ * consider employing @ref XXH3_generateSecret() instead (see below).
+ * It will generate a proper high entropy secret derived from the blob of bytes.
+ * Another advantage of using XXH3_generateSecret() is that
+ * it guarantees that all bits within the initial blob of bytes
+ * will impact every bit of the output.
+ * This is not necessarily the case when using the blob of bytes directly
+ * because, when hashing _small_ inputs, only a portion of the secret is employed.
+ *
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_withSecret(XXH_NOESCAPE const void* data, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize);
+
+/*******   Streaming   *******/
+#ifndef XXH_NO_STREAM
+/*
+ * Streaming requires state maintenance.
+ * This operation costs memory and CPU.
+ * As a consequence, streaming is slower than one-shot hashing.
+ * For better performance, prefer one-shot functions whenever applicable.
+ *
+ * XXH3_128bits uses the same XXH3_state_t as XXH3_64bits().
+ * Use already declared XXH3_createState() and XXH3_freeState().
+ *
+ * All reset and streaming functions have same meaning as their 64-bit counterpart.
+ */
+
+/*!
+ * @brief Resets an @ref XXH3_state_t to begin a new hash.
+ *
+ * @param statePtr The state struct to reset.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note
+ *   - This function resets `statePtr` and generate a secret with default parameters.
+ *   - Call it before @ref XXH3_128bits_update().
+ *   - Digest will be equivalent to `XXH3_128bits()`.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr);
+
+/*!
+ * @brief Resets an @ref XXH3_state_t with 64-bit seed to begin a new hash.
+ *
+ * @param statePtr The state struct to reset.
+ * @param seed     The 64-bit seed to alter the hash result predictably.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note
+ *   - This function resets `statePtr` and generate a secret from `seed`.
+ *   - Call it before @ref XXH3_128bits_update().
+ *   - Digest will be equivalent to `XXH3_128bits_withSeed()`.
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed);
+/*!
+ * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash.
+ *
+ * @param statePtr   The state struct to reset.
+ * @param secret     The secret data.
+ * @param secretSize The length of @p secret, in bytes.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * `secret` is referenced, it _must outlive_ the hash streaming session.
+ * Similar to one-shot API, `secretSize` must be >= @ref XXH3_SECRET_SIZE_MIN,
+ * and the quality of produced hash values depends on secret's entropy
+ * (secret's content should look like a bunch of random bytes).
+ * When in doubt about the randomness of a candidate `secret`,
+ * consider employing `XXH3_generateSecret()` instead (see below).
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize);
+
+/*!
+ * @brief Consumes a block of @p input to an @ref XXH3_state_t.
+ *
+ * Call this to incrementally consume blocks of data.
+ *
+ * @param statePtr The state struct to update.
+ * @param input The block of data to be hashed, at least @p length bytes in size.
+ * @param length The length of @p input, in bytes.
+ *
+ * @pre
+ *   @p statePtr must not be `NULL`.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @note
+ *   The memory between @p input and @p input + @p length must be valid,
+ *   readable, contiguous memory. However, if @p length is `0`, @p input may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_128bits_update (XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length);
+
+/*!
+ * @brief Returns the calculated XXH3 128-bit hash value from an @ref XXH3_state_t.
+ *
+ * @param statePtr The state struct to calculate the hash from.
+ *
+ * @pre
+ *  @p statePtr must not be `NULL`.
+ *
+ * @return The calculated XXH3 128-bit hash value from that state.
+ *
+ * @note
+ *   Calling XXH3_128bits_digest() will not affect @p statePtr, so you can update,
+ *   digest, and update again.
+ *
+ */
+XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_digest (XXH_NOESCAPE const XXH3_state_t* statePtr);
+#endif /* !XXH_NO_STREAM */
+
+/* Following helper functions make it possible to compare XXH128_hast_t values.
+ * Since XXH128_hash_t is a structure, this capability is not offered by the language.
+ * Note: For better performance, these functions can be inlined using XXH_INLINE_ALL */
+
+/*!
+ * @brief Check equality of two XXH128_hash_t values
+ *
+ * @param h1 The 128-bit hash value.
+ * @param h2 Another 128-bit hash value.
+ *
+ * @return `1` if `h1` and `h2` are equal.
+ * @return `0` if they are not.
+ */
+XXH_PUBLIC_API XXH_PUREF int XXH128_isEqual(XXH128_hash_t h1, XXH128_hash_t h2);
+
+/*!
+ * @brief Compares two @ref XXH128_hash_t
+ *
+ * This comparator is compatible with stdlib's `qsort()`/`bsearch()`.
+ *
+ * @param h128_1 Left-hand side value
+ * @param h128_2 Right-hand side value
+ *
+ * @return >0 if @p h128_1  > @p h128_2
+ * @return =0 if @p h128_1 == @p h128_2
+ * @return <0 if @p h128_1  < @p h128_2
+ */
+XXH_PUBLIC_API XXH_PUREF int XXH128_cmp(XXH_NOESCAPE const void* h128_1, XXH_NOESCAPE const void* h128_2);
+
+
+/*******   Canonical representation   *******/
+typedef struct { unsigned char digest[sizeof(XXH128_hash_t)]; } XXH128_canonical_t;
+
+
+/*!
+ * @brief Converts an @ref XXH128_hash_t to a big endian @ref XXH128_canonical_t.
+ *
+ * @param dst  The @ref XXH128_canonical_t pointer to be stored to.
+ * @param hash The @ref XXH128_hash_t to be converted.
+ *
+ * @pre
+ *   @p dst must not be `NULL`.
+ * @see @ref canonical_representation_example "Canonical Representation Example"
+ */
+XXH_PUBLIC_API void XXH128_canonicalFromHash(XXH_NOESCAPE XXH128_canonical_t* dst, XXH128_hash_t hash);
+
+/*!
+ * @brief Converts an @ref XXH128_canonical_t to a native @ref XXH128_hash_t.
+ *
+ * @param src The @ref XXH128_canonical_t to convert.
+ *
+ * @pre
+ *   @p src must not be `NULL`.
+ *
+ * @return The converted hash.
+ * @see @ref canonical_representation_example "Canonical Representation Example"
+ */
+XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH128_hashFromCanonical(XXH_NOESCAPE const XXH128_canonical_t* src);
+
+
+#endif  /* !XXH_NO_XXH3 */
+#endif  /* XXH_NO_LONG_LONG */
+
+/*!
+ * @}
+ */
+#endif /* XXHASH_H_5627135585666179 */
+
+
+
+#if defined(XXH_STATIC_LINKING_ONLY) && !defined(XXHASH_H_STATIC_13879238742)
+#define XXHASH_H_STATIC_13879238742
+/* ****************************************************************************
+ * This section contains declarations which are not guaranteed to remain stable.
+ * They may change in future versions, becoming incompatible with a different
+ * version of the library.
+ * These declarations should only be used with static linking.
+ * Never use them in association with dynamic linking!
+ ***************************************************************************** */
+
+/*
+ * These definitions are only present to allow static allocation
+ * of XXH states, on stack or in a struct, for example.
+ * Never **ever** access their members directly.
+ */
+
+/*!
+ * @internal
+ * @brief Structure for XXH32 streaming API.
+ *
+ * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY,
+ * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. Otherwise it is
+ * an opaque type. This allows fields to safely be changed.
+ *
+ * Typedef'd to @ref XXH32_state_t.
+ * Do not access the members of this struct directly.
+ * @see XXH64_state_s, XXH3_state_s
+ */
+struct XXH32_state_s {
+   XXH32_hash_t total_len_32; /*!< Total length hashed, modulo 2^32 */
+   XXH32_hash_t large_len;    /*!< Whether the hash is >= 16 (handles @ref total_len_32 overflow) */
+   XXH32_hash_t v[4];         /*!< Accumulator lanes */
+   XXH32_hash_t mem32[4];     /*!< Internal buffer for partial reads. Treated as unsigned char[16]. */
+   XXH32_hash_t memsize;      /*!< Amount of data in @ref mem32 */
+   XXH32_hash_t reserved;     /*!< Reserved field. Do not read nor write to it. */
+};   /* typedef'd to XXH32_state_t */
+
+
+#ifndef XXH_NO_LONG_LONG  /* defined when there is no 64-bit support */
+
+/*!
+ * @internal
+ * @brief Structure for XXH64 streaming API.
+ *
+ * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY,
+ * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. Otherwise it is
+ * an opaque type. This allows fields to safely be changed.
+ *
+ * Typedef'd to @ref XXH64_state_t.
+ * Do not access the members of this struct directly.
+ * @see XXH32_state_s, XXH3_state_s
+ */
+struct XXH64_state_s {
+   XXH64_hash_t total_len;    /*!< Total length hashed. This is always 64-bit. */
+   XXH64_hash_t v[4];         /*!< Accumulator lanes */
+   XXH64_hash_t mem64[4];     /*!< Internal buffer for partial reads. Treated as unsigned char[32]. */
+   XXH32_hash_t memsize;      /*!< Amount of data in @ref mem64 */
+   XXH32_hash_t reserved32;   /*!< Reserved field, needed for padding anyways*/
+   XXH64_hash_t reserved64;   /*!< Reserved field. Do not read or write to it. */
+};   /* typedef'd to XXH64_state_t */
+
+#ifndef XXH_NO_XXH3
+
+/* Windows SDK under 10.0.22000 is missing stdalign.h so we add a check
+   before allowing the windows compiler to use the C11 form.
+   Reference: https://github.com/Cyan4973/xxHash/issues/955 */
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) \
+    && (defined(_MSC_VER) && (_MSC_VER >= 1000) || !defined(_MSC_VER)) /* >= C11 */
+#  include <stdalign.h>
+#  define XXH_ALIGN(n)      alignas(n)
+#elif defined(__cplusplus) && (__cplusplus >= 201103L) /* >= C++11 */
+/* In C++ alignas() is a keyword */
+#  define XXH_ALIGN(n)      alignas(n)
+#elif defined(__GNUC__)
+#  define XXH_ALIGN(n)      __attribute__ ((aligned(n)))
+#elif defined(_MSC_VER)
+#  define XXH_ALIGN(n)      __declspec(align(n))
+#else
+#  define XXH_ALIGN(n)   /* disabled */
+#endif
+
+/* Old GCC versions only accept the attribute after the type in structures. */
+#if !(defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L))   /* C11+ */ \
+    && ! (defined(__cplusplus) && (__cplusplus >= 201103L)) /* >= C++11 */ \
+    && defined(__GNUC__)
+#   define XXH_ALIGN_MEMBER(align, type) type XXH_ALIGN(align)
+#else
+#   define XXH_ALIGN_MEMBER(align, type) XXH_ALIGN(align) type
+#endif
+
+/*!
+ * @brief The size of the internal XXH3 buffer.
+ *
+ * This is the optimal update size for incremental hashing.
+ *
+ * @see XXH3_64b_update(), XXH3_128b_update().
+ */
+#define XXH3_INTERNALBUFFER_SIZE 256
+
+/*!
+ * @internal
+ * @brief Default size of the secret buffer (and @ref XXH3_kSecret).
+ *
+ * This is the size used in @ref XXH3_kSecret and the seeded functions.
+ *
+ * Not to be confused with @ref XXH3_SECRET_SIZE_MIN.
+ */
+#define XXH3_SECRET_DEFAULT_SIZE 192
+
+/*!
+ * @internal
+ * @brief Structure for XXH3 streaming API.
+ *
+ * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY,
+ * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined.
+ * Otherwise it is an opaque type.
+ * Never use this definition in combination with dynamic library.
+ * This allows fields to safely be changed in the future.
+ *
+ * @note ** This structure has a strict alignment requirement of 64 bytes!! **
+ * Do not allocate this with `malloc()` or `new`,
+ * it will not be sufficiently aligned.
+ * Use @ref XXH3_createState() and @ref XXH3_freeState(), or stack allocation.
+ *
+ * Typedef'd to @ref XXH3_state_t.
+ * Do never access the members of this struct directly.
+ *
+ * @see XXH3_INITSTATE() for stack initialization.
+ * @see XXH3_createState(), XXH3_freeState().
+ * @see XXH32_state_s, XXH64_state_s
+ */
+struct XXH3_state_s {
+   XXH_ALIGN_MEMBER(64, XXH64_hash_t acc[8]);
+       /*!< The 8 accumulators. See @ref XXH32_state_s::v and @ref XXH64_state_s::v */
+   XXH_ALIGN_MEMBER(64, unsigned char customSecret[XXH3_SECRET_DEFAULT_SIZE]);
+       /*!< Used to store a custom secret generated from a seed. */
+   XXH_ALIGN_MEMBER(64, unsigned char buffer[XXH3_INTERNALBUFFER_SIZE]);
+       /*!< The internal buffer. @see XXH32_state_s::mem32 */
+   XXH32_hash_t bufferedSize;
+       /*!< The amount of memory in @ref buffer, @see XXH32_state_s::memsize */
+   XXH32_hash_t useSeed;
+       /*!< Reserved field. Needed for padding on 64-bit. */
+   size_t nbStripesSoFar;
+       /*!< Number or stripes processed. */
+   XXH64_hash_t totalLen;
+       /*!< Total length hashed. 64-bit even on 32-bit targets. */
+   size_t nbStripesPerBlock;
+       /*!< Number of stripes per block. */
+   size_t secretLimit;
+       /*!< Size of @ref customSecret or @ref extSecret */
+   XXH64_hash_t seed;
+       /*!< Seed for _withSeed variants. Must be zero otherwise, @see XXH3_INITSTATE() */
+   XXH64_hash_t reserved64;
+       /*!< Reserved field. */
+   const unsigned char* extSecret;
+       /*!< Reference to an external secret for the _withSecret variants, NULL
+        *   for other variants. */
+   /* note: there may be some padding at the end due to alignment on 64 bytes */
+}; /* typedef'd to XXH3_state_t */
+
+#undef XXH_ALIGN_MEMBER
+
+/*!
+ * @brief Initializes a stack-allocated `XXH3_state_s`.
+ *
+ * When the @ref XXH3_state_t structure is merely emplaced on stack,
+ * it should be initialized with XXH3_INITSTATE() or a memset()
+ * in case its first reset uses XXH3_NNbits_reset_withSeed().
+ * This init can be omitted if the first reset uses default or _withSecret mode.
+ * This operation isn't necessary when the state is created with XXH3_createState().
+ * Note that this doesn't prepare the state for a streaming operation,
+ * it's still necessary to use XXH3_NNbits_reset*() afterwards.
+ */
+#define XXH3_INITSTATE(XXH3_state_ptr)                       \
+    do {                                                     \
+        XXH3_state_t* tmp_xxh3_state_ptr = (XXH3_state_ptr); \
+        tmp_xxh3_state_ptr->seed = 0;                        \
+        tmp_xxh3_state_ptr->extSecret = NULL;                \
+    } while(0)
+
+
+/*!
+ * @brief Calculates the 128-bit hash of @p data using XXH3.
+ *
+ * @param data The block of data to be hashed, at least @p len bytes in size.
+ * @param len  The length of @p data, in bytes.
+ * @param seed The 64-bit seed to alter the hash's output predictably.
+ *
+ * @pre
+ *   The memory between @p data and @p data + @p len must be valid,
+ *   readable, contiguous memory. However, if @p len is `0`, @p data may be
+ *   `NULL`. In C++, this also must be *TriviallyCopyable*.
+ *
+ * @return The calculated 128-bit XXH3 value.
+ *
+ * @see @ref single_shot_example "Single Shot Example" for an example.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH128(XXH_NOESCAPE const void* data, size_t len, XXH64_hash_t seed);
+
+
+/* ===   Experimental API   === */
+/* Symbols defined below must be considered tied to a specific library version. */
+
+/*!
+ * @brief Derive a high-entropy secret from any user-defined content, named customSeed.
+ *
+ * @param secretBuffer    A writable buffer for derived high-entropy secret data.
+ * @param secretSize      Size of secretBuffer, in bytes.  Must be >= XXH3_SECRET_SIZE_MIN.
+ * @param customSeed      A user-defined content.
+ * @param customSeedSize  Size of customSeed, in bytes.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * The generated secret can be used in combination with `*_withSecret()` functions.
+ * The `_withSecret()` variants are useful to provide a higher level of protection
+ * than 64-bit seed, as it becomes much more difficult for an external actor to
+ * guess how to impact the calculation logic.
+ *
+ * The function accepts as input a custom seed of any length and any content,
+ * and derives from it a high-entropy secret of length @p secretSize into an
+ * already allocated buffer @p secretBuffer.
+ *
+ * The generated secret can then be used with any `*_withSecret()` variant.
+ * The functions @ref XXH3_128bits_withSecret(), @ref XXH3_64bits_withSecret(),
+ * @ref XXH3_128bits_reset_withSecret() and @ref XXH3_64bits_reset_withSecret()
+ * are part of this list. They all accept a `secret` parameter
+ * which must be large enough for implementation reasons (>= @ref XXH3_SECRET_SIZE_MIN)
+ * _and_ feature very high entropy (consist of random-looking bytes).
+ * These conditions can be a high bar to meet, so @ref XXH3_generateSecret() can
+ * be employed to ensure proper quality.
+ *
+ * @p customSeed can be anything. It can have any size, even small ones,
+ * and its content can be anything, even "poor entropy" sources such as a bunch
+ * of zeroes. The resulting `secret` will nonetheless provide all required qualities.
+ *
+ * @pre
+ *   - @p secretSize must be >= @ref XXH3_SECRET_SIZE_MIN
+ *   - When @p customSeedSize > 0, supplying NULL as customSeed is undefined behavior.
+ *
+ * Example code:
+ * @code{.c}
+ *    #include <stdio.h>
+ *    #include <stdlib.h>
+ *    #include <string.h>
+ *    #define XXH_STATIC_LINKING_ONLY // expose unstable API
+ *    #include "xxhash.h"
+ *    // Hashes argv[2] using the entropy from argv[1].
+ *    int main(int argc, char* argv[])
+ *    {
+ *        char secret[XXH3_SECRET_SIZE_MIN];
+ *        if (argv != 3) { return 1; }
+ *        XXH3_generateSecret(secret, sizeof(secret), argv[1], strlen(argv[1]));
+ *        XXH64_hash_t h = XXH3_64bits_withSecret(
+ *             argv[2], strlen(argv[2]),
+ *             secret, sizeof(secret)
+ *        );
+ *        printf("%016llx\n", (unsigned long long) h);
+ *    }
+ * @endcode
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_generateSecret(XXH_NOESCAPE void* secretBuffer, size_t secretSize, XXH_NOESCAPE const void* customSeed, size_t customSeedSize);
+
+/*!
+ * @brief Generate the same secret as the _withSeed() variants.
+ *
+ * @param secretBuffer A writable buffer of @ref XXH3_SECRET_DEFAULT_SIZE bytes
+ * @param seed         The 64-bit seed to alter the hash result predictably.
+ *
+ * The generated secret can be used in combination with
+ *`*_withSecret()` and `_withSecretandSeed()` variants.
+ *
+ * Example C++ `std::string` hash class:
+ * @code{.cpp}
+ *    #include <string>
+ *    #define XXH_STATIC_LINKING_ONLY // expose unstable API
+ *    #include "xxhash.h"
+ *    // Slow, seeds each time
+ *    class HashSlow {
+ *        XXH64_hash_t seed;
+ *    public:
+ *        HashSlow(XXH64_hash_t s) : seed{s} {}
+ *        size_t operator()(const std::string& x) const {
+ *            return size_t{XXH3_64bits_withSeed(x.c_str(), x.length(), seed)};
+ *        }
+ *    };
+ *    // Fast, caches the seeded secret for future uses.
+ *    class HashFast {
+ *        unsigned char secret[XXH3_SECRET_DEFAULT_SIZE];
+ *    public:
+ *        HashFast(XXH64_hash_t s) {
+ *            XXH3_generateSecret_fromSeed(secret, seed);
+ *        }
+ *        size_t operator()(const std::string& x) const {
+ *            return size_t{
+ *                XXH3_64bits_withSecret(x.c_str(), x.length(), secret, sizeof(secret))
+ *            };
+ *        }
+ *    };
+ * @endcode
+ */
+XXH_PUBLIC_API void XXH3_generateSecret_fromSeed(XXH_NOESCAPE void* secretBuffer, XXH64_hash_t seed);
+
+/*!
+ * @brief Maximum size of "short" key in bytes.
+ */
+#define XXH3_MIDSIZE_MAX 240
+
+/*!
+ * @brief Calculates 64/128-bit seeded variant of XXH3 hash of @p data.
+ *
+ * @param data       The block of data to be hashed, at least @p len bytes in size.
+ * @param len        The length of @p data, in bytes.
+ * @param secret     The secret data.
+ * @param secretSize The length of @p secret, in bytes.
+ * @param seed       The 64-bit seed to alter the hash result predictably.
+ *
+ * These variants generate hash values using either:
+ * - @p seed for "short" keys (< @ref XXH3_MIDSIZE_MAX = 240 bytes)
+ * - @p secret for "large" keys (>= @ref XXH3_MIDSIZE_MAX).
+ *
+ * This generally benefits speed, compared to `_withSeed()` or `_withSecret()`.
+ * `_withSeed()` has to generate the secret on the fly for "large" keys.
+ * It's fast, but can be perceptible for "not so large" keys (< 1 KB).
+ * `_withSecret()` has to generate the masks on the fly for "small" keys,
+ * which requires more instructions than _withSeed() variants.
+ * Therefore, _withSecretandSeed variant combines the best of both worlds.
+ *
+ * When @p secret has been generated by XXH3_generateSecret_fromSeed(),
+ * this variant produces *exactly* the same results as `_withSeed()` variant,
+ * hence offering only a pure speed benefit on "large" input,
+ * by skipping the need to regenerate the secret for every large input.
+ *
+ * Another usage scenario is to hash the secret to a 64-bit hash value,
+ * for example with XXH3_64bits(), which then becomes the seed,
+ * and then employ both the seed and the secret in _withSecretandSeed().
+ * On top of speed, an added benefit is that each bit in the secret
+ * has a 50% chance to swap each bit in the output, via its impact to the seed.
+ *
+ * This is not guaranteed when using the secret directly in "small data" scenarios,
+ * because only portions of the secret are employed for small data.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH64_hash_t
+XXH3_64bits_withSecretandSeed(XXH_NOESCAPE const void* data, size_t len,
+                              XXH_NOESCAPE const void* secret, size_t secretSize,
+                              XXH64_hash_t seed);
+
+/*!
+ * @brief Calculates 128-bit seeded variant of XXH3 hash of @p data.
+ *
+ * @param data       The memory segment to be hashed, at least @p len bytes in size.
+ * @param length     The length of @p data, in bytes.
+ * @param secret     The secret used to alter hash result predictably.
+ * @param secretSize The length of @p secret, in bytes (must be >= XXH3_SECRET_SIZE_MIN)
+ * @param seed64     The 64-bit seed to alter the hash result predictably.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @see XXH3_64bits_withSecretandSeed(): contract is the same.
+ */
+XXH_PUBLIC_API XXH_PUREF XXH128_hash_t
+XXH3_128bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t length,
+                               XXH_NOESCAPE const void* secret, size_t secretSize,
+                               XXH64_hash_t seed64);
+
+#ifndef XXH_NO_STREAM
+/*!
+ * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash.
+ *
+ * @param statePtr   A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState().
+ * @param secret     The secret data.
+ * @param secretSize The length of @p secret, in bytes.
+ * @param seed64     The 64-bit seed to alter the hash result predictably.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @see XXH3_64bits_withSecretandSeed(). Contract is identical.
+ */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_64bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr,
+                                    XXH_NOESCAPE const void* secret, size_t secretSize,
+                                    XXH64_hash_t seed64);
+
+/*!
+ * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash.
+ *
+ * @param statePtr   A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState().
+ * @param secret     The secret data.
+ * @param secretSize The length of @p secret, in bytes.
+ * @param seed64     The 64-bit seed to alter the hash result predictably.
+ *
+ * @return @ref XXH_OK on success.
+ * @return @ref XXH_ERROR on failure.
+ *
+ * @see XXH3_64bits_withSecretandSeed(). Contract is identical.
+ *
+ * Note: there was a bug in an earlier version of this function (<= v0.8.2)
+ * that would make it generate an incorrect hash value
+ * when @p seed == 0 and @p length < XXH3_MIDSIZE_MAX
+ * and @p secret is different from XXH3_generateSecret_fromSeed().
+ * As stated in the contract, the correct hash result must be
+ * the same as XXH3_128bits_withSeed() when @p length <= XXH3_MIDSIZE_MAX.
+ * Results generated by this older version are wrong, hence not comparable.
+ */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_128bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr,
+                                     XXH_NOESCAPE const void* secret, size_t secretSize,
+                                     XXH64_hash_t seed64);
+
+#endif /* !XXH_NO_STREAM */
+
+#endif  /* !XXH_NO_XXH3 */
+#endif  /* XXH_NO_LONG_LONG */
+#if defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API)
+#  define XXH_IMPLEMENTATION
+#endif
+
+#endif  /* defined(XXH_STATIC_LINKING_ONLY) && !defined(XXHASH_H_STATIC_13879238742) */
+
+
+/* ======================================================================== */
+/* ======================================================================== */
+/* ======================================================================== */
+
+
+/*-**********************************************************************
+ * xxHash implementation
+ *-**********************************************************************
+ * xxHash's implementation used to be hosted inside xxhash.c.
+ *
+ * However, inlining requires implementation to be visible to the compiler,
+ * hence be included alongside the header.
+ * Previously, implementation was hosted inside xxhash.c,
+ * which was then #included when inlining was activated.
+ * This construction created issues with a few build and install systems,
+ * as it required xxhash.c to be stored in /include directory.
+ *
+ * xxHash implementation is now directly integrated within xxhash.h.
+ * As a consequence, xxhash.c is no longer needed in /include.
+ *
+ * xxhash.c is still available and is still useful.
+ * In a "normal" setup, when xxhash is not inlined,
+ * xxhash.h only exposes the prototypes and public symbols,
+ * while xxhash.c can be built into an object file xxhash.o
+ * which can then be linked into the final binary.
+ ************************************************************************/
+
+#if ( defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API) \
+   || defined(XXH_IMPLEMENTATION) ) && !defined(XXH_IMPLEM_13a8737387)
+#  define XXH_IMPLEM_13a8737387
+
+/* *************************************
+*  Tuning parameters
+***************************************/
+
+/*!
+ * @defgroup tuning Tuning parameters
+ * @{
+ *
+ * Various macros to control xxHash's behavior.
+ */
+#ifdef XXH_DOXYGEN
+/*!
+ * @brief Define this to disable 64-bit code.
+ *
+ * Useful if only using the @ref XXH32_family and you have a strict C90 compiler.
+ */
+#  define XXH_NO_LONG_LONG
+#  undef XXH_NO_LONG_LONG /* don't actually */
+/*!
+ * @brief Controls how unaligned memory is accessed.
+ *
+ * By default, access to unaligned memory is controlled by `memcpy()`, which is
+ * safe and portable.
+ *
+ * Unfortunately, on some target/compiler combinations, the generated assembly
+ * is sub-optimal.
+ *
+ * The below switch allow selection of a different access method
+ * in the search for improved performance.
+ *
+ * @par Possible options:
+ *
+ *  - `XXH_FORCE_MEMORY_ACCESS=0` (default): `memcpy`
+ *   @par
+ *     Use `memcpy()`. Safe and portable. Note that most modern compilers will
+ *     eliminate the function call and treat it as an unaligned access.
+ *
+ *  - `XXH_FORCE_MEMORY_ACCESS=1`: `__attribute__((aligned(1)))`
+ *   @par
+ *     Depends on compiler extensions and is therefore not portable.
+ *     This method is safe _if_ your compiler supports it,
+ *     and *generally* as fast or faster than `memcpy`.
+ *
+ *  - `XXH_FORCE_MEMORY_ACCESS=2`: Direct cast
+ *  @par
+ *     Casts directly and dereferences. This method doesn't depend on the
+ *     compiler, but it violates the C standard as it directly dereferences an
+ *     unaligned pointer. It can generate buggy code on targets which do not
+ *     support unaligned memory accesses, but in some circumstances, it's the
+ *     only known way to get the most performance.
+ *
+ *  - `XXH_FORCE_MEMORY_ACCESS=3`: Byteshift
+ *  @par
+ *     Also portable. This can generate the best code on old compilers which don't
+ *     inline small `memcpy()` calls, and it might also be faster on big-endian
+ *     systems which lack a native byteswap instruction. However, some compilers
+ *     will emit literal byteshifts even if the target supports unaligned access.
+ *
+ *
+ * @warning
+ *   Methods 1 and 2 rely on implementation-defined behavior. Use these with
+ *   care, as what works on one compiler/platform/optimization level may cause
+ *   another to read garbage data or even crash.
+ *
+ * See https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html for details.
+ *
+ * Prefer these methods in priority order (0 > 3 > 1 > 2)
+ */
+#  define XXH_FORCE_MEMORY_ACCESS 0
+
+/*!
+ * @def XXH_SIZE_OPT
+ * @brief Controls how much xxHash optimizes for size.
+ *
+ * xxHash, when compiled, tends to result in a rather large binary size. This
+ * is mostly due to heavy usage to forced inlining and constant folding of the
+ * @ref XXH3_family to increase performance.
+ *
+ * However, some developers prefer size over speed. This option can
+ * significantly reduce the size of the generated code. When using the `-Os`
+ * or `-Oz` options on GCC or Clang, this is defined to 1 by default,
+ * otherwise it is defined to 0.
+ *
+ * Most of these size optimizations can be controlled manually.
+ *
+ * This is a number from 0-2.
+ *  - `XXH_SIZE_OPT` == 0: Default. xxHash makes no size optimizations. Speed
+ *    comes first.
+ *  - `XXH_SIZE_OPT` == 1: Default for `-Os` and `-Oz`. xxHash is more
+ *    conservative and disables hacks that increase code size. It implies the
+ *    options @ref XXH_NO_INLINE_HINTS == 1, @ref XXH_FORCE_ALIGN_CHECK == 0,
+ *    and @ref XXH3_NEON_LANES == 8 if they are not already defined.
+ *  - `XXH_SIZE_OPT` == 2: xxHash tries to make itself as small as possible.
+ *    Performance may cry. For example, the single shot functions just use the
+ *    streaming API.
+ */
+#  define XXH_SIZE_OPT 0
+
+/*!
+ * @def XXH_FORCE_ALIGN_CHECK
+ * @brief If defined to non-zero, adds a special path for aligned inputs (XXH32()
+ * and XXH64() only).
+ *
+ * This is an important performance trick for architectures without decent
+ * unaligned memory access performance.
+ *
+ * It checks for input alignment, and when conditions are met, uses a "fast
+ * path" employing direct 32-bit/64-bit reads, resulting in _dramatically
+ * faster_ read speed.
+ *
+ * The check costs one initial branch per hash, which is generally negligible,
+ * but not zero.
+ *
+ * Moreover, it's not useful to generate an additional code path if memory
+ * access uses the same instruction for both aligned and unaligned
+ * addresses (e.g. x86 and aarch64).
+ *
+ * In these cases, the alignment check can be removed by setting this macro to 0.
+ * Then the code will always use unaligned memory access.
+ * Align check is automatically disabled on x86, x64, ARM64, and some ARM chips
+ * which are platforms known to offer good unaligned memory accesses performance.
+ *
+ * It is also disabled by default when @ref XXH_SIZE_OPT >= 1.
+ *
+ * This option does not affect XXH3 (only XXH32 and XXH64).
+ */
+#  define XXH_FORCE_ALIGN_CHECK 0
+
+/*!
+ * @def XXH_NO_INLINE_HINTS
+ * @brief When non-zero, sets all functions to `static`.
+ *
+ * By default, xxHash tries to force the compiler to inline almost all internal
+ * functions.
+ *
+ * This can usually improve performance due to reduced jumping and improved
+ * constant folding, but significantly increases the size of the binary which
+ * might not be favorable.
+ *
+ * Additionally, sometimes the forced inlining can be detrimental to performance,
+ * depending on the architecture.
+ *
+ * XXH_NO_INLINE_HINTS marks all internal functions as static, giving the
+ * compiler full control on whether to inline or not.
+ *
+ * When not optimizing (-O0), using `-fno-inline` with GCC or Clang, or if
+ * @ref XXH_SIZE_OPT >= 1, this will automatically be defined.
+ */
+#  define XXH_NO_INLINE_HINTS 0
+
+/*!
+ * @def XXH3_INLINE_SECRET
+ * @brief Determines whether to inline the XXH3 withSecret code.
+ *
+ * When the secret size is known, the compiler can improve the performance
+ * of XXH3_64bits_withSecret() and XXH3_128bits_withSecret().
+ *
+ * However, if the secret size is not known, it doesn't have any benefit. This
+ * happens when xxHash is compiled into a global symbol. Therefore, if
+ * @ref XXH_INLINE_ALL is *not* defined, this will be defined to 0.
+ *
+ * Additionally, this defaults to 0 on GCC 12+, which has an issue with function pointers
+ * that are *sometimes* force inline on -Og, and it is impossible to automatically
+ * detect this optimization level.
+ */
+#  define XXH3_INLINE_SECRET 0
+
+/*!
+ * @def XXH32_ENDJMP
+ * @brief Whether to use a jump for `XXH32_finalize`.
+ *
+ * For performance, `XXH32_finalize` uses multiple branches in the finalizer.
+ * This is generally preferable for performance,
+ * but depending on exact architecture, a jmp may be preferable.
+ *
+ * This setting is only possibly making a difference for very small inputs.
+ */
+#  define XXH32_ENDJMP 0
+
+/*!
+ * @internal
+ * @brief Redefines old internal names.
+ *
+ * For compatibility with code that uses xxHash's internals before the names
+ * were changed to improve namespacing. There is no other reason to use this.
+ */
+#  define XXH_OLD_NAMES
+#  undef XXH_OLD_NAMES /* don't actually use, it is ugly. */
+
+/*!
+ * @def XXH_NO_STREAM
+ * @brief Disables the streaming API.
+ *
+ * When xxHash is not inlined and the streaming functions are not used, disabling
+ * the streaming functions can improve code size significantly, especially with
+ * the @ref XXH3_family which tends to make constant folded copies of itself.
+ */
+#  define XXH_NO_STREAM
+#  undef XXH_NO_STREAM /* don't actually */
+#endif /* XXH_DOXYGEN */
+/*!
+ * @}
+ */
+
+#ifndef XXH_FORCE_MEMORY_ACCESS   /* can be defined externally, on command line for example */
+   /* prefer __packed__ structures (method 1) for GCC
+    * < ARMv7 with unaligned access (e.g. Raspbian armhf) still uses byte shifting, so we use memcpy
+    * which for some reason does unaligned loads. */
+#  if defined(__GNUC__) && !(defined(__ARM_ARCH) && __ARM_ARCH < 7 && defined(__ARM_FEATURE_UNALIGNED))
+#    define XXH_FORCE_MEMORY_ACCESS 1
+#  endif
+#endif
+
+#ifndef XXH_SIZE_OPT
+   /* default to 1 for -Os or -Oz */
+#  if (defined(__GNUC__) || defined(__clang__)) && defined(__OPTIMIZE_SIZE__)
+#    define XXH_SIZE_OPT 1
+#  else
+#    define XXH_SIZE_OPT 0
+#  endif
+#endif
+
+#ifndef XXH_FORCE_ALIGN_CHECK  /* can be defined externally */
+   /* don't check on sizeopt, x86, aarch64, or arm when unaligned access is available */
+#  if XXH_SIZE_OPT >= 1 || \
+      defined(__i386)  || defined(__x86_64__) || defined(__aarch64__) || defined(__ARM_FEATURE_UNALIGNED) \
+   || defined(_M_IX86) || defined(_M_X64)     || defined(_M_ARM64)    || defined(_M_ARM) /* visual */
+#    define XXH_FORCE_ALIGN_CHECK 0
+#  else
+#    define XXH_FORCE_ALIGN_CHECK 1
+#  endif
+#endif
+
+#ifndef XXH_NO_INLINE_HINTS
+#  if XXH_SIZE_OPT >= 1 || defined(__NO_INLINE__)  /* -O0, -fno-inline */
+#    define XXH_NO_INLINE_HINTS 1
+#  else
+#    define XXH_NO_INLINE_HINTS 0
+#  endif
+#endif
+
+#ifndef XXH3_INLINE_SECRET
+#  if (defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 12) \
+     || !defined(XXH_INLINE_ALL)
+#    define XXH3_INLINE_SECRET 0
+#  else
+#    define XXH3_INLINE_SECRET 1
+#  endif
+#endif
+
+#ifndef XXH32_ENDJMP
+/* generally preferable for performance */
+#  define XXH32_ENDJMP 0
+#endif
+
+/*!
+ * @defgroup impl Implementation
+ * @{
+ */
+
+
+/* *************************************
+*  Includes & Memory related functions
+***************************************/
+#if defined(XXH_NO_STREAM)
+/* nothing */
+#elif defined(XXH_NO_STDLIB)
+
+/* When requesting to disable any mention of stdlib,
+ * the library loses the ability to invoked malloc / free.
+ * In practice, it means that functions like `XXH*_createState()`
+ * will always fail, and return NULL.
+ * This flag is useful in situations where
+ * xxhash.h is integrated into some kernel, embedded or limited environment
+ * without access to dynamic allocation.
+ */
+
+static XXH_CONSTF void* XXH_malloc(size_t s) { (void)s; return NULL; }
+static void XXH_free(void* p) { (void)p; }
+
+#else
+
+/*
+ * Modify the local functions below should you wish to use
+ * different memory routines for malloc() and free()
+ */
+#include <stdlib.h>
+
+/*!
+ * @internal
+ * @brief Modify this function to use a different routine than malloc().
+ */
+static XXH_MALLOCF void* XXH_malloc(size_t s) { return malloc(s); }
+
+/*!
+ * @internal
+ * @brief Modify this function to use a different routine than free().
+ */
+static void XXH_free(void* p) { free(p); }
+
+#endif  /* XXH_NO_STDLIB */
+
+#include <string.h>
+
+/*!
+ * @internal
+ * @brief Modify this function to use a different routine than memcpy().
+ */
+static void* XXH_memcpy(void* dest, const void* src, size_t size)
+{
+    return memcpy(dest,src,size);
+}
+
+#include <limits.h>   /* ULLONG_MAX */
+
+
+/* *************************************
+*  Compiler Specific Options
+***************************************/
+#ifdef _MSC_VER /* Visual Studio warning fix */
+#  pragma warning(disable : 4127) /* disable: C4127: conditional expression is constant */
+#endif
+
+#if XXH_NO_INLINE_HINTS  /* disable inlining hints */
+#  if defined(__GNUC__) || defined(__clang__)
+#    define XXH_FORCE_INLINE static __attribute__((__unused__))
+#  else
+#    define XXH_FORCE_INLINE static
+#  endif
+#  define XXH_NO_INLINE static
+/* enable inlining hints */
+#elif defined(__GNUC__) || defined(__clang__)
+#  define XXH_FORCE_INLINE static __inline__ __attribute__((__always_inline__, __unused__))
+#  define XXH_NO_INLINE static __attribute__((__noinline__))
+#elif defined(_MSC_VER)  /* Visual Studio */
+#  define XXH_FORCE_INLINE static __forceinline
+#  define XXH_NO_INLINE static __declspec(noinline)
+#elif defined (__cplusplus) \
+  || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L))   /* C99 */
+#  define XXH_FORCE_INLINE static inline
+#  define XXH_NO_INLINE static
+#else
+#  define XXH_FORCE_INLINE static
+#  define XXH_NO_INLINE static
+#endif
+
+#if XXH3_INLINE_SECRET
+#  define XXH3_WITH_SECRET_INLINE XXH_FORCE_INLINE
+#else
+#  define XXH3_WITH_SECRET_INLINE XXH_NO_INLINE
+#endif
+
+
+/* *************************************
+*  Debug
+***************************************/
+/*!
+ * @ingroup tuning
+ * @def XXH_DEBUGLEVEL
+ * @brief Sets the debugging level.
+ *
+ * XXH_DEBUGLEVEL is expected to be defined externally, typically via the
+ * compiler's command line options. The value must be a number.
+ */
+#ifndef XXH_DEBUGLEVEL
+#  ifdef DEBUGLEVEL /* backwards compat */
+#    define XXH_DEBUGLEVEL DEBUGLEVEL
+#  else
+#    define XXH_DEBUGLEVEL 0
+#  endif
+#endif
+
+#if (XXH_DEBUGLEVEL>=1)
+#  include <assert.h>   /* note: can still be disabled with NDEBUG */
+#  define XXH_ASSERT(c)   assert(c)
+#else
+#  if defined(__INTEL_COMPILER)
+#    define XXH_ASSERT(c)   XXH_ASSUME((unsigned char) (c))
+#  else
+#    define XXH_ASSERT(c)   XXH_ASSUME(c)
+#  endif
+#endif
+
+/* note: use after variable declarations */
+#ifndef XXH_STATIC_ASSERT
+#  if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)    /* C11 */
+#    define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { _Static_assert((c),m); } while(0)
+#  elif defined(__cplusplus) && (__cplusplus >= 201103L)            /* C++11 */
+#    define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { static_assert((c),m); } while(0)
+#  else
+#    define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { struct xxh_sa { char x[(c) ? 1 : -1]; }; } while(0)
+#  endif
+#  define XXH_STATIC_ASSERT(c) XXH_STATIC_ASSERT_WITH_MESSAGE((c),#c)
+#endif
+
+/*!
+ * @internal
+ * @def XXH_COMPILER_GUARD(var)
+ * @brief Used to prevent unwanted optimizations for @p var.
+ *
+ * It uses an empty GCC inline assembly statement with a register constraint
+ * which forces @p var into a general purpose register (eg eax, ebx, ecx
+ * on x86) and marks it as modified.
+ *
+ * This is used in a few places to avoid unwanted autovectorization (e.g.
+ * XXH32_round()). All vectorization we want is explicit via intrinsics,
+ * and _usually_ isn't wanted elsewhere.
+ *
+ * We also use it to prevent unwanted constant folding for AArch64 in
+ * XXH3_initCustomSecret_scalar().
+ */
+#if defined(__GNUC__) || defined(__clang__)
+#  define XXH_COMPILER_GUARD(var) __asm__("" : "+r" (var))
+#else
+#  define XXH_COMPILER_GUARD(var) ((void)0)
+#endif
+
+/* Specifically for NEON vectors which use the "w" constraint, on
+ * Clang. */
+#if defined(__clang__) && defined(__ARM_ARCH) && !defined(__wasm__)
+#  define XXH_COMPILER_GUARD_CLANG_NEON(var) __asm__("" : "+w" (var))
+#else
+#  define XXH_COMPILER_GUARD_CLANG_NEON(var) ((void)0)
+#endif
+
+/* *************************************
+*  Basic Types
+***************************************/
+#if !defined (__VMS) \
+ && (defined (__cplusplus) \
+ || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) )
+#   ifdef _AIX
+#     include <inttypes.h>
+#   else
+#     include <stdint.h>
+#   endif
+    typedef uint8_t xxh_u8;
+#else
+    typedef unsigned char xxh_u8;
+#endif
+typedef XXH32_hash_t xxh_u32;
+
+#ifdef XXH_OLD_NAMES
+#  warning "XXH_OLD_NAMES is planned to be removed starting v0.9. If the program depends on it, consider moving away from it by employing newer type names directly"
+#  define BYTE xxh_u8
+#  define U8   xxh_u8
+#  define U32  xxh_u32
+#endif
+
+/* ***   Memory access   *** */
+
+/*!
+ * @internal
+ * @fn xxh_u32 XXH_read32(const void* ptr)
+ * @brief Reads an unaligned 32-bit integer from @p ptr in native endianness.
+ *
+ * Affected by @ref XXH_FORCE_MEMORY_ACCESS.
+ *
+ * @param ptr The pointer to read from.
+ * @return The 32-bit native endian integer from the bytes at @p ptr.
+ */
+
+/*!
+ * @internal
+ * @fn xxh_u32 XXH_readLE32(const void* ptr)
+ * @brief Reads an unaligned 32-bit little endian integer from @p ptr.
+ *
+ * Affected by @ref XXH_FORCE_MEMORY_ACCESS.
+ *
+ * @param ptr The pointer to read from.
+ * @return The 32-bit little endian integer from the bytes at @p ptr.
+ */
+
+/*!
+ * @internal
+ * @fn xxh_u32 XXH_readBE32(const void* ptr)
+ * @brief Reads an unaligned 32-bit big endian integer from @p ptr.
+ *
+ * Affected by @ref XXH_FORCE_MEMORY_ACCESS.
+ *
+ * @param ptr The pointer to read from.
+ * @return The 32-bit big endian integer from the bytes at @p ptr.
+ */
+
+/*!
+ * @internal
+ * @fn xxh_u32 XXH_readLE32_align(const void* ptr, XXH_alignment align)
+ * @brief Like @ref XXH_readLE32(), but has an option for aligned reads.
+ *
+ * Affected by @ref XXH_FORCE_MEMORY_ACCESS.
+ * Note that when @ref XXH_FORCE_ALIGN_CHECK == 0, the @p align parameter is
+ * always @ref XXH_alignment::XXH_unaligned.
+ *
+ * @param ptr The pointer to read from.
+ * @param align Whether @p ptr is aligned.
+ * @pre
+ *   If @p align == @ref XXH_alignment::XXH_aligned, @p ptr must be 4 byte
+ *   aligned.
+ * @return The 32-bit little endian integer from the bytes at @p ptr.
+ */
+
+#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3))
+/*
+ * Manual byteshift. Best for old compilers which don't inline memcpy.
+ * We actually directly use XXH_readLE32 and XXH_readBE32.
+ */
+#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==2))
+
+/*
+ * Force direct memory access. Only works on CPU which support unaligned memory
+ * access in hardware.
+ */
+static xxh_u32 XXH_read32(const void* memPtr) { return *(const xxh_u32*) memPtr; }
+
+#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==1))
+
+/*
+ * __attribute__((aligned(1))) is supported by gcc and clang. Originally the
+ * documentation claimed that it only increased the alignment, but actually it
+ * can decrease it on gcc, clang, and icc:
+ * https://gcc.gnu.org/bugzilla/show_bug.cgi?id=69502,
+ * https://gcc.godbolt.org/z/xYez1j67Y.
+ */
+#ifdef XXH_OLD_NAMES
+typedef union { xxh_u32 u32; } __attribute__((__packed__)) unalign;
+#endif
+static xxh_u32 XXH_read32(const void* ptr)
+{
+    typedef __attribute__((__aligned__(1))) xxh_u32 xxh_unalign32;
+    return *((const xxh_unalign32*)ptr);
+}
+
+#else
+
+/*
+ * Portable and safe solution. Generally efficient.
+ * see: https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html
+ */
+static xxh_u32 XXH_read32(const void* memPtr)
+{
+    xxh_u32 val;
+    XXH_memcpy(&val, memPtr, sizeof(val));
+    return val;
+}
+
+#endif   /* XXH_FORCE_DIRECT_MEMORY_ACCESS */
+
+
+/* ***   Endianness   *** */
+
+/*!
+ * @ingroup tuning
+ * @def XXH_CPU_LITTLE_ENDIAN
+ * @brief Whether the target is little endian.
+ *
+ * Defined to 1 if the target is little endian, or 0 if it is big endian.
+ * It can be defined externally, for example on the compiler command line.
+ *
+ * If it is not defined,
+ * a runtime check (which is usually constant folded) is used instead.
+ *
+ * @note
+ *   This is not necessarily defined to an integer constant.
+ *
+ * @see XXH_isLittleEndian() for the runtime check.
+ */
+#ifndef XXH_CPU_LITTLE_ENDIAN
+/*
+ * Try to detect endianness automatically, to avoid the nonstandard behavior
+ * in `XXH_isLittleEndian()`
+ */
+#  if defined(_WIN32) /* Windows is always little endian */ \
+     || defined(__LITTLE_ENDIAN__) \
+     || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
+#    define XXH_CPU_LITTLE_ENDIAN 1
+#  elif defined(__BIG_ENDIAN__) \
+     || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__)
+#    define XXH_CPU_LITTLE_ENDIAN 0
+#  else
+/*!
+ * @internal
+ * @brief Runtime check for @ref XXH_CPU_LITTLE_ENDIAN.
+ *
+ * Most compilers will constant fold this.
+ */
+static int XXH_isLittleEndian(void)
+{
+    /*
+     * Portable and well-defined behavior.
+     * Don't use static: it is detrimental to performance.
+     */
+    const union { xxh_u32 u; xxh_u8 c[4]; } one = { 1 };
+    return one.c[0];
+}
+#   define XXH_CPU_LITTLE_ENDIAN   XXH_isLittleEndian()
+#  endif
+#endif
+
+
+
+
+/* ****************************************
+*  Compiler-specific Functions and Macros
+******************************************/
+#define XXH_GCC_VERSION (__GNUC__ * 100 + __GNUC_MINOR__)
+
+#ifdef __has_builtin
+#  define XXH_HAS_BUILTIN(x) __has_builtin(x)
+#else
+#  define XXH_HAS_BUILTIN(x) 0
+#endif
+
+
+
+/*
+ * C23 and future versions have standard "unreachable()".
+ * Once it has been implemented reliably we can add it as an
+ * additional case:
+ *
+ * ```
+ * #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= XXH_C23_VN)
+ * #  include <stddef.h>
+ * #  ifdef unreachable
+ * #    define XXH_UNREACHABLE() unreachable()
+ * #  endif
+ * #endif
+ * ```
+ *
+ * Note C++23 also has std::unreachable() which can be detected
+ * as follows:
+ * ```
+ * #if defined(__cpp_lib_unreachable) && (__cpp_lib_unreachable >= 202202L)
+ * #  include <utility>
+ * #  define XXH_UNREACHABLE() std::unreachable()
+ * #endif
+ * ```
+ * NB: `__cpp_lib_unreachable` is defined in the `<version>` header.
+ * We don't use that as including `<utility>` in `extern "C"` blocks
+ * doesn't work on GCC12
+ */
+
+#if XXH_HAS_BUILTIN(__builtin_unreachable)
+#  define XXH_UNREACHABLE() __builtin_unreachable()
+
+#elif defined(_MSC_VER)
+#  define XXH_UNREACHABLE() __assume(0)
+
+#else
+#  define XXH_UNREACHABLE()
+#endif
+
+#if XXH_HAS_BUILTIN(__builtin_assume)
+#  define XXH_ASSUME(c) __builtin_assume(c)
+#else
+#  define XXH_ASSUME(c) if (!(c)) { XXH_UNREACHABLE(); }
+#endif
+
+/*!
+ * @internal
+ * @def XXH_rotl32(x,r)
+ * @brief 32-bit rotate left.
+ *
+ * @param x The 32-bit integer to be rotated.
+ * @param r The number of bits to rotate.
+ * @pre
+ *   @p r > 0 && @p r < 32
+ * @note
+ *   @p x and @p r may be evaluated multiple times.
+ * @return The rotated result.
+ */
+#if !defined(NO_CLANG_BUILTIN) && XXH_HAS_BUILTIN(__builtin_rotateleft32) \
+                               && XXH_HAS_BUILTIN(__builtin_rotateleft64)
+#  define XXH_rotl32 __builtin_rotateleft32
+#  define XXH_rotl64 __builtin_rotateleft64
+/* Note: although _rotl exists for minGW (GCC under windows), performance seems poor */
+#elif defined(_MSC_VER)
+#  define XXH_rotl32(x,r) _rotl(x,r)
+#  define XXH_rotl64(x,r) _rotl64(x,r)
+#else
+#  define XXH_rotl32(x,r) (((x) << (r)) | ((x) >> (32 - (r))))
+#  define XXH_rotl64(x,r) (((x) << (r)) | ((x) >> (64 - (r))))
+#endif
+
+/*!
+ * @internal
+ * @fn xxh_u32 XXH_swap32(xxh_u32 x)
+ * @brief A 32-bit byteswap.
+ *
+ * @param x The 32-bit integer to byteswap.
+ * @return @p x, byteswapped.
+ */
+#if defined(_MSC_VER)     /* Visual Studio */
+#  define XXH_swap32 _byteswap_ulong
+#elif XXH_GCC_VERSION >= 403
+#  define XXH_swap32 __builtin_bswap32
+#else
+static xxh_u32 XXH_swap32 (xxh_u32 x)
+{
+    return  ((x << 24) & 0xff000000 ) |
+            ((x <<  8) & 0x00ff0000 ) |
+            ((x >>  8) & 0x0000ff00 ) |
+            ((x >> 24) & 0x000000ff );
+}
+#endif
+
+
+/* ***************************
+*  Memory reads
+*****************************/
+
+/*!
+ * @internal
+ * @brief Enum to indicate whether a pointer is aligned.
+ */
+typedef enum {
+    XXH_aligned,  /*!< Aligned */
+    XXH_unaligned /*!< Possibly unaligned */
+} XXH_alignment;
+
+/*
+ * XXH_FORCE_MEMORY_ACCESS==3 is an endian-independent byteshift load.
+ *
+ * This is ideal for older compilers which don't inline memcpy.
+ */
+#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3))
+
+XXH_FORCE_INLINE xxh_u32 XXH_readLE32(const void* memPtr)
+{
+    const xxh_u8* bytePtr = (const xxh_u8 *)memPtr;
+    return bytePtr[0]
+         | ((xxh_u32)bytePtr[1] << 8)
+         | ((xxh_u32)bytePtr[2] << 16)
+         | ((xxh_u32)bytePtr[3] << 24);
+}
+
+XXH_FORCE_INLINE xxh_u32 XXH_readBE32(const void* memPtr)
+{
+    const xxh_u8* bytePtr = (const xxh_u8 *)memPtr;
+    return bytePtr[3]
+         | ((xxh_u32)bytePtr[2] << 8)
+         | ((xxh_u32)bytePtr[1] << 16)
+         | ((xxh_u32)bytePtr[0] << 24);
+}
+
+#else
+XXH_FORCE_INLINE xxh_u32 XXH_readLE32(const void* ptr)
+{
+    return XXH_CPU_LITTLE_ENDIAN ? XXH_read32(ptr) : XXH_swap32(XXH_read32(ptr));
+}
+
+static xxh_u32 XXH_readBE32(const void* ptr)
+{
+    return XXH_CPU_LITTLE_ENDIAN ? XXH_swap32(XXH_read32(ptr)) : XXH_read32(ptr);
+}
+#endif
+
+XXH_FORCE_INLINE xxh_u32
+XXH_readLE32_align(const void* ptr, XXH_alignment align)
+{
+    if (align==XXH_unaligned) {
+        return XXH_readLE32(ptr);
+    } else {
+        return XXH_CPU_LITTLE_ENDIAN ? *(const xxh_u32*)ptr : XXH_swap32(*(const xxh_u32*)ptr);
+    }
+}
+
+
+/* *************************************
+*  Misc
+***************************************/
+/*! @ingroup public */
+XXH_PUBLIC_API unsigned XXH_versionNumber (void) { return XXH_VERSION_NUMBER; }
+
+
+/* *******************************************************************
+*  32-bit hash functions
+*********************************************************************/
+/*!
+ * @}
+ * @defgroup XXH32_impl XXH32 implementation
+ * @ingroup impl
+ *
+ * Details on the XXH32 implementation.
+ * @{
+ */
+ /* #define instead of static const, to be used as initializers */
+#define XXH_PRIME32_1  0x9E3779B1U  /*!< 0b10011110001101110111100110110001 */
+#define XXH_PRIME32_2  0x85EBCA77U  /*!< 0b10000101111010111100101001110111 */
+#define XXH_PRIME32_3  0xC2B2AE3DU  /*!< 0b11000010101100101010111000111101 */
+#define XXH_PRIME32_4  0x27D4EB2FU  /*!< 0b00100111110101001110101100101111 */
+#define XXH_PRIME32_5  0x165667B1U  /*!< 0b00010110010101100110011110110001 */
+
+#ifdef XXH_OLD_NAMES
+#  define PRIME32_1 XXH_PRIME32_1
+#  define PRIME32_2 XXH_PRIME32_2
+#  define PRIME32_3 XXH_PRIME32_3
+#  define PRIME32_4 XXH_PRIME32_4
+#  define PRIME32_5 XXH_PRIME32_5
+#endif
+
+/*!
+ * @internal
+ * @brief Normal stripe processing routine.
+ *
+ * This shuffles the bits so that any bit from @p input impacts several bits in
+ * @p acc.
+ *
+ * @param acc The accumulator lane.
+ * @param input The stripe of input to mix.
+ * @return The mixed accumulator lane.
+ */
+static xxh_u32 XXH32_round(xxh_u32 acc, xxh_u32 input)
+{
+    acc += input * XXH_PRIME32_2;
+    acc  = XXH_rotl32(acc, 13);
+    acc *= XXH_PRIME32_1;
+#if (defined(__SSE4_1__) || defined(__aarch64__) || defined(__wasm_simd128__)) && !defined(XXH_ENABLE_AUTOVECTORIZE)
+    /*
+     * UGLY HACK:
+     * A compiler fence is used to prevent GCC and Clang from
+     * autovectorizing the XXH32 loop (pragmas and attributes don't work for some
+     * reason) without globally disabling SSE4.1.
+     *
+     * The reason we want to avoid vectorization is because despite working on
+     * 4 integers at a time, there are multiple factors slowing XXH32 down on
+     * SSE4:
+     * - There's a ridiculous amount of lag from pmulld (10 cycles of latency on
+     *   newer chips!) making it slightly slower to multiply four integers at
+     *   once compared to four integers independently. Even when pmulld was
+     *   fastest, Sandy/Ivy Bridge, it is still not worth it to go into SSE
+     *   just to multiply unless doing a long operation.
+     *
+     * - Four instructions are required to rotate,
+     *      movqda tmp,  v // not required with VEX encoding
+     *      pslld  tmp, 13 // tmp <<= 13
+     *      psrld  v,   19 // x >>= 19
+     *      por    v,  tmp // x |= tmp
+     *   compared to one for scalar:
+     *      roll   v, 13    // reliably fast across the board
+     *      shldl  v, v, 13 // Sandy Bridge and later prefer this for some reason
+     *
+     * - Instruction level parallelism is actually more beneficial here because
+     *   the SIMD actually serializes this operation: While v1 is rotating, v2
+     *   can load data, while v3 can multiply. SSE forces them to operate
+     *   together.
+     *
+     * This is also enabled on AArch64, as Clang is *very aggressive* in vectorizing
+     * the loop. NEON is only faster on the A53, and with the newer cores, it is less
+     * than half the speed.
+     *
+     * Additionally, this is used on WASM SIMD128 because it JITs to the same
+     * SIMD instructions and has the same issue.
+     */
+    XXH_COMPILER_GUARD(acc);
+#endif
+    return acc;
+}
+
+/*!
+ * @internal
+ * @brief Mixes all bits to finalize the hash.
+ *
+ * The final mix ensures that all input bits have a chance to impact any bit in
+ * the output digest, resulting in an unbiased distribution.
+ *
+ * @param hash The hash to avalanche.
+ * @return The avalanched hash.
+ */
+static xxh_u32 XXH32_avalanche(xxh_u32 hash)
+{
+    hash ^= hash >> 15;
+    hash *= XXH_PRIME32_2;
+    hash ^= hash >> 13;
+    hash *= XXH_PRIME32_3;
+    hash ^= hash >> 16;
+    return hash;
+}
+
+#define XXH_get32bits(p) XXH_readLE32_align(p, align)
+
+/*!
+ * @internal
+ * @brief Processes the last 0-15 bytes of @p ptr.
+ *
+ * There may be up to 15 bytes remaining to consume from the input.
+ * This final stage will digest them to ensure that all input bytes are present
+ * in the final mix.
+ *
+ * @param hash The hash to finalize.
+ * @param ptr The pointer to the remaining input.
+ * @param len The remaining length, modulo 16.
+ * @param align Whether @p ptr is aligned.
+ * @return The finalized hash.
+ * @see XXH64_finalize().
+ */
+static XXH_PUREF xxh_u32
+XXH32_finalize(xxh_u32 hash, const xxh_u8* ptr, size_t len, XXH_alignment align)
+{
+#define XXH_PROCESS1 do {                             \
+    hash += (*ptr++) * XXH_PRIME32_5;                 \
+    hash = XXH_rotl32(hash, 11) * XXH_PRIME32_1;      \
+} while (0)
+
+#define XXH_PROCESS4 do {                             \
+    hash += XXH_get32bits(ptr) * XXH_PRIME32_3;       \
+    ptr += 4;                                         \
+    hash  = XXH_rotl32(hash, 17) * XXH_PRIME32_4;     \
+} while (0)
+
+    if (ptr==NULL) XXH_ASSERT(len == 0);
+
+    /* Compact rerolled version; generally faster */
+    if (!XXH32_ENDJMP) {
+        len &= 15;
+        while (len >= 4) {
+            XXH_PROCESS4;
+            len -= 4;
+        }
+        while (len > 0) {
+            XXH_PROCESS1;
+            --len;
+        }
+        return XXH32_avalanche(hash);
+    } else {
+         switch(len&15) /* or switch(bEnd - p) */ {
+           case 12:      XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 8:       XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 4:       XXH_PROCESS4;
+                         return XXH32_avalanche(hash);
+
+           case 13:      XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 9:       XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 5:       XXH_PROCESS4;
+                         XXH_PROCESS1;
+                         return XXH32_avalanche(hash);
+
+           case 14:      XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 10:      XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 6:       XXH_PROCESS4;
+                         XXH_PROCESS1;
+                         XXH_PROCESS1;
+                         return XXH32_avalanche(hash);
+
+           case 15:      XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 11:      XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 7:       XXH_PROCESS4;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 3:       XXH_PROCESS1;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 2:       XXH_PROCESS1;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 1:       XXH_PROCESS1;
+                         XXH_FALLTHROUGH;  /* fallthrough */
+           case 0:       return XXH32_avalanche(hash);
+        }
+        XXH_ASSERT(0);
+        return hash;   /* reaching this point is deemed impossible */
+    }
+}
+
+#ifdef XXH_OLD_NAMES
+#  define PROCESS1 XXH_PROCESS1
+#  define PROCESS4 XXH_PROCESS4
+#else
+#  undef XXH_PROCESS1
+#  undef XXH_PROCESS4
+#endif
+
+/*!
+ * @internal
+ * @brief The implementation for @ref XXH32().
+ *
+ * @param input , len , seed Directly passed from @ref XXH32().
+ * @param align Whether @p input is aligned.
+ * @return The calculated hash.
+ */
+XXH_FORCE_INLINE XXH_PUREF xxh_u32
+XXH32_endian_align(const xxh_u8* input, size_t len, xxh_u32 seed, XXH_alignment align)
+{
+    xxh_u32 h32;
+
+    if (input==NULL) XXH_ASSERT(len == 0);
+
+    if (len>=16) {
+        const xxh_u8* const bEnd = input + len;
+        const xxh_u8* const limit = bEnd - 15;
+        xxh_u32 v1 = seed + XXH_PRIME32_1 + XXH_PRIME32_2;
+        xxh_u32 v2 = seed + XXH_PRIME32_2;
+        xxh_u32 v3 = seed + 0;
+        xxh_u32 v4 = seed - XXH_PRIME32_1;
+
+        do {
+            v1 = XXH32_round(v1, XXH_get32bits(input)); input += 4;
+            v2 = XXH32_round(v2, XXH_get32bits(input)); input += 4;
+            v3 = XXH32_round(v3, XXH_get32bits(input)); input += 4;
+            v4 = XXH32_round(v4, XXH_get32bits(input)); input += 4;
+        } while (input < limit);
+
+        h32 = XXH_rotl32(v1, 1)  + XXH_rotl32(v2, 7)
+            + XXH_rotl32(v3, 12) + XXH_rotl32(v4, 18);
+    } else {
+        h32  = seed + XXH_PRIME32_5;
+    }
+
+    h32 += (xxh_u32)len;
+
+    return XXH32_finalize(h32, input, len&15, align);
+}
+
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API XXH32_hash_t XXH32 (const void* input, size_t len, XXH32_hash_t seed)
+{
+#if !defined(XXH_NO_STREAM) && XXH_SIZE_OPT >= 2
+    /* Simple version, good for code maintenance, but unfortunately slow for small inputs */
+    XXH32_state_t state;
+    XXH32_reset(&state, seed);
+    XXH32_update(&state, (const xxh_u8*)input, len);
+    return XXH32_digest(&state);
+#else
+    if (XXH_FORCE_ALIGN_CHECK) {
+        if ((((size_t)input) & 3) == 0) {   /* Input is 4-bytes aligned, leverage the speed benefit */
+            return XXH32_endian_align((const xxh_u8*)input, len, seed, XXH_aligned);
+    }   }
+
+    return XXH32_endian_align((const xxh_u8*)input, len, seed, XXH_unaligned);
+#endif
+}
+
+
+
+/*******   Hash streaming   *******/
+#ifndef XXH_NO_STREAM
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API XXH32_state_t* XXH32_createState(void)
+{
+    return (XXH32_state_t*)XXH_malloc(sizeof(XXH32_state_t));
+}
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API XXH_errorcode XXH32_freeState(XXH32_state_t* statePtr)
+{
+    XXH_free(statePtr);
+    return XXH_OK;
+}
+
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API void XXH32_copyState(XXH32_state_t* dstState, const XXH32_state_t* srcState)
+{
+    XXH_memcpy(dstState, srcState, sizeof(*dstState));
+}
+
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API XXH_errorcode XXH32_reset(XXH32_state_t* statePtr, XXH32_hash_t seed)
+{
+    XXH_ASSERT(statePtr != NULL);
+    memset(statePtr, 0, sizeof(*statePtr));
+    statePtr->v[0] = seed + XXH_PRIME32_1 + XXH_PRIME32_2;
+    statePtr->v[1] = seed + XXH_PRIME32_2;
+    statePtr->v[2] = seed + 0;
+    statePtr->v[3] = seed - XXH_PRIME32_1;
+    return XXH_OK;
+}
+
+
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH32_update(XXH32_state_t* state, const void* input, size_t len)
+{
+    if (input==NULL) {
+        XXH_ASSERT(len == 0);
+        return XXH_OK;
+    }
+
+    {   const xxh_u8* p = (const xxh_u8*)input;
+        const xxh_u8* const bEnd = p + len;
+
+        state->total_len_32 += (XXH32_hash_t)len;
+        state->large_len |= (XXH32_hash_t)((len>=16) | (state->total_len_32>=16));
+
+        if (state->memsize + len < 16)  {   /* fill in tmp buffer */
+            XXH_memcpy((xxh_u8*)(state->mem32) + state->memsize, input, len);
+            state->memsize += (XXH32_hash_t)len;
+            return XXH_OK;
+        }
+
+        if (state->memsize) {   /* some data left from previous update */
+            XXH_memcpy((xxh_u8*)(state->mem32) + state->memsize, input, 16-state->memsize);
+            {   const xxh_u32* p32 = state->mem32;
+                state->v[0] = XXH32_round(state->v[0], XXH_readLE32(p32)); p32++;
+                state->v[1] = XXH32_round(state->v[1], XXH_readLE32(p32)); p32++;
+                state->v[2] = XXH32_round(state->v[2], XXH_readLE32(p32)); p32++;
+                state->v[3] = XXH32_round(state->v[3], XXH_readLE32(p32));
+            }
+            p += 16-state->memsize;
+            state->memsize = 0;
+        }
+
+        if (p <= bEnd-16) {
+            const xxh_u8* const limit = bEnd - 16;
+
+            do {
+                state->v[0] = XXH32_round(state->v[0], XXH_readLE32(p)); p+=4;
+                state->v[1] = XXH32_round(state->v[1], XXH_readLE32(p)); p+=4;
+                state->v[2] = XXH32_round(state->v[2], XXH_readLE32(p)); p+=4;
+                state->v[3] = XXH32_round(state->v[3], XXH_readLE32(p)); p+=4;
+            } while (p<=limit);
+
+        }
+
+        if (p < bEnd) {
+            XXH_memcpy(state->mem32, p, (size_t)(bEnd-p));
+            state->memsize = (unsigned)(bEnd-p);
+        }
+    }
+
+    return XXH_OK;
+}
+
+
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API XXH32_hash_t XXH32_digest(const XXH32_state_t* state)
+{
+    xxh_u32 h32;
+
+    if (state->large_len) {
+        h32 = XXH_rotl32(state->v[0], 1)
+            + XXH_rotl32(state->v[1], 7)
+            + XXH_rotl32(state->v[2], 12)
+            + XXH_rotl32(state->v[3], 18);
+    } else {
+        h32 = state->v[2] /* == seed */ + XXH_PRIME32_5;
+    }
+
+    h32 += state->total_len_32;
+
+    return XXH32_finalize(h32, (const xxh_u8*)state->mem32, state->memsize, XXH_aligned);
+}
+#endif /* !XXH_NO_STREAM */
+
+/*******   Canonical representation   *******/
+
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API void XXH32_canonicalFromHash(XXH32_canonical_t* dst, XXH32_hash_t hash)
+{
+    XXH_STATIC_ASSERT(sizeof(XXH32_canonical_t) == sizeof(XXH32_hash_t));
+    if (XXH_CPU_LITTLE_ENDIAN) hash = XXH_swap32(hash);
+    XXH_memcpy(dst, &hash, sizeof(*dst));
+}
+/*! @ingroup XXH32_family */
+XXH_PUBLIC_API XXH32_hash_t XXH32_hashFromCanonical(const XXH32_canonical_t* src)
+{
+    return XXH_readBE32(src);
+}
+
+
+#ifndef XXH_NO_LONG_LONG
+
+/* *******************************************************************
+*  64-bit hash functions
+*********************************************************************/
+/*!
+ * @}
+ * @ingroup impl
+ * @{
+ */
+/*******   Memory access   *******/
+
+typedef XXH64_hash_t xxh_u64;
+
+#ifdef XXH_OLD_NAMES
+#  define U64 xxh_u64
+#endif
+
+#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3))
+/*
+ * Manual byteshift. Best for old compilers which don't inline memcpy.
+ * We actually directly use XXH_readLE64 and XXH_readBE64.
+ */
+#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==2))
+
+/* Force direct memory access. Only works on CPU which support unaligned memory access in hardware */
+static xxh_u64 XXH_read64(const void* memPtr)
+{
+    return *(const xxh_u64*) memPtr;
+}
+
+#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==1))
+
+/*
+ * __attribute__((aligned(1))) is supported by gcc and clang. Originally the
+ * documentation claimed that it only increased the alignment, but actually it
+ * can decrease it on gcc, clang, and icc:
+ * https://gcc.gnu.org/bugzilla/show_bug.cgi?id=69502,
+ * https://gcc.godbolt.org/z/xYez1j67Y.
+ */
+#ifdef XXH_OLD_NAMES
+typedef union { xxh_u32 u32; xxh_u64 u64; } __attribute__((__packed__)) unalign64;
+#endif
+static xxh_u64 XXH_read64(const void* ptr)
+{
+    typedef __attribute__((__aligned__(1))) xxh_u64 xxh_unalign64;
+    return *((const xxh_unalign64*)ptr);
+}
+
+#else
+
+/*
+ * Portable and safe solution. Generally efficient.
+ * see: https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html
+ */
+static xxh_u64 XXH_read64(const void* memPtr)
+{
+    xxh_u64 val;
+    XXH_memcpy(&val, memPtr, sizeof(val));
+    return val;
+}
+
+#endif   /* XXH_FORCE_DIRECT_MEMORY_ACCESS */
+
+#if defined(_MSC_VER)     /* Visual Studio */
+#  define XXH_swap64 _byteswap_uint64
+#elif XXH_GCC_VERSION >= 403
+#  define XXH_swap64 __builtin_bswap64
+#else
+static xxh_u64 XXH_swap64(xxh_u64 x)
+{
+    return  ((x << 56) & 0xff00000000000000ULL) |
+            ((x << 40) & 0x00ff000000000000ULL) |
+            ((x << 24) & 0x0000ff0000000000ULL) |
+            ((x << 8)  & 0x000000ff00000000ULL) |
+            ((x >> 8)  & 0x00000000ff000000ULL) |
+            ((x >> 24) & 0x0000000000ff0000ULL) |
+            ((x >> 40) & 0x000000000000ff00ULL) |
+            ((x >> 56) & 0x00000000000000ffULL);
+}
+#endif
+
+
+/* XXH_FORCE_MEMORY_ACCESS==3 is an endian-independent byteshift load. */
+#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3))
+
+XXH_FORCE_INLINE xxh_u64 XXH_readLE64(const void* memPtr)
+{
+    const xxh_u8* bytePtr = (const xxh_u8 *)memPtr;
+    return bytePtr[0]
+         | ((xxh_u64)bytePtr[1] << 8)
+         | ((xxh_u64)bytePtr[2] << 16)
+         | ((xxh_u64)bytePtr[3] << 24)
+         | ((xxh_u64)bytePtr[4] << 32)
+         | ((xxh_u64)bytePtr[5] << 40)
+         | ((xxh_u64)bytePtr[6] << 48)
+         | ((xxh_u64)bytePtr[7] << 56);
+}
+
+XXH_FORCE_INLINE xxh_u64 XXH_readBE64(const void* memPtr)
+{
+    const xxh_u8* bytePtr = (const xxh_u8 *)memPtr;
+    return bytePtr[7]
+         | ((xxh_u64)bytePtr[6] << 8)
+         | ((xxh_u64)bytePtr[5] << 16)
+         | ((xxh_u64)bytePtr[4] << 24)
+         | ((xxh_u64)bytePtr[3] << 32)
+         | ((xxh_u64)bytePtr[2] << 40)
+         | ((xxh_u64)bytePtr[1] << 48)
+         | ((xxh_u64)bytePtr[0] << 56);
+}
+
+#else
+XXH_FORCE_INLINE xxh_u64 XXH_readLE64(const void* ptr)
+{
+    return XXH_CPU_LITTLE_ENDIAN ? XXH_read64(ptr) : XXH_swap64(XXH_read64(ptr));
+}
+
+static xxh_u64 XXH_readBE64(const void* ptr)
+{
+    return XXH_CPU_LITTLE_ENDIAN ? XXH_swap64(XXH_read64(ptr)) : XXH_read64(ptr);
+}
+#endif
+
+XXH_FORCE_INLINE xxh_u64
+XXH_readLE64_align(const void* ptr, XXH_alignment align)
+{
+    if (align==XXH_unaligned)
+        return XXH_readLE64(ptr);
+    else
+        return XXH_CPU_LITTLE_ENDIAN ? *(const xxh_u64*)ptr : XXH_swap64(*(const xxh_u64*)ptr);
+}
+
+
+/*******   xxh64   *******/
+/*!
+ * @}
+ * @defgroup XXH64_impl XXH64 implementation
+ * @ingroup impl
+ *
+ * Details on the XXH64 implementation.
+ * @{
+ */
+/* #define rather that static const, to be used as initializers */
+#define XXH_PRIME64_1  0x9E3779B185EBCA87ULL  /*!< 0b1001111000110111011110011011000110000101111010111100101010000111 */
+#define XXH_PRIME64_2  0xC2B2AE3D27D4EB4FULL  /*!< 0b1100001010110010101011100011110100100111110101001110101101001111 */
+#define XXH_PRIME64_3  0x165667B19E3779F9ULL  /*!< 0b0001011001010110011001111011000110011110001101110111100111111001 */
+#define XXH_PRIME64_4  0x85EBCA77C2B2AE63ULL  /*!< 0b1000010111101011110010100111011111000010101100101010111001100011 */
+#define XXH_PRIME64_5  0x27D4EB2F165667C5ULL  /*!< 0b0010011111010100111010110010111100010110010101100110011111000101 */
+
+#ifdef XXH_OLD_NAMES
+#  define PRIME64_1 XXH_PRIME64_1
+#  define PRIME64_2 XXH_PRIME64_2
+#  define PRIME64_3 XXH_PRIME64_3
+#  define PRIME64_4 XXH_PRIME64_4
+#  define PRIME64_5 XXH_PRIME64_5
+#endif
+
+/*! @copydoc XXH32_round */
+static xxh_u64 XXH64_round(xxh_u64 acc, xxh_u64 input)
+{
+    acc += input * XXH_PRIME64_2;
+    acc  = XXH_rotl64(acc, 31);
+    acc *= XXH_PRIME64_1;
+#if (defined(__AVX512F__)) && !defined(XXH_ENABLE_AUTOVECTORIZE)
+    /*
+     * DISABLE AUTOVECTORIZATION:
+     * A compiler fence is used to prevent GCC and Clang from
+     * autovectorizing the XXH64 loop (pragmas and attributes don't work for some
+     * reason) without globally disabling AVX512.
+     *
+     * Autovectorization of XXH64 tends to be detrimental,
+     * though the exact outcome may change depending on exact cpu and compiler version.
+     * For information, it has been reported as detrimental for Skylake-X,
+     * but possibly beneficial for Zen4.
+     *
+     * The default is to disable auto-vectorization,
+     * but you can select to enable it instead using `XXH_ENABLE_AUTOVECTORIZE` build variable.
+     */
+    XXH_COMPILER_GUARD(acc);
+#endif
+    return acc;
+}
+
+static xxh_u64 XXH64_mergeRound(xxh_u64 acc, xxh_u64 val)
+{
+    val  = XXH64_round(0, val);
+    acc ^= val;
+    acc  = acc * XXH_PRIME64_1 + XXH_PRIME64_4;
+    return acc;
+}
+
+/*! @copydoc XXH32_avalanche */
+static xxh_u64 XXH64_avalanche(xxh_u64 hash)
+{
+    hash ^= hash >> 33;
+    hash *= XXH_PRIME64_2;
+    hash ^= hash >> 29;
+    hash *= XXH_PRIME64_3;
+    hash ^= hash >> 32;
+    return hash;
+}
+
+
+#define XXH_get64bits(p) XXH_readLE64_align(p, align)
+
+/*!
+ * @internal
+ * @brief Processes the last 0-31 bytes of @p ptr.
+ *
+ * There may be up to 31 bytes remaining to consume from the input.
+ * This final stage will digest them to ensure that all input bytes are present
+ * in the final mix.
+ *
+ * @param hash The hash to finalize.
+ * @param ptr The pointer to the remaining input.
+ * @param len The remaining length, modulo 32.
+ * @param align Whether @p ptr is aligned.
+ * @return The finalized hash
+ * @see XXH32_finalize().
+ */
+static XXH_PUREF xxh_u64
+XXH64_finalize(xxh_u64 hash, const xxh_u8* ptr, size_t len, XXH_alignment align)
+{
+    if (ptr==NULL) XXH_ASSERT(len == 0);
+    len &= 31;
+    while (len >= 8) {
+        xxh_u64 const k1 = XXH64_round(0, XXH_get64bits(ptr));
+        ptr += 8;
+        hash ^= k1;
+        hash  = XXH_rotl64(hash,27) * XXH_PRIME64_1 + XXH_PRIME64_4;
+        len -= 8;
+    }
+    if (len >= 4) {
+        hash ^= (xxh_u64)(XXH_get32bits(ptr)) * XXH_PRIME64_1;
+        ptr += 4;
+        hash = XXH_rotl64(hash, 23) * XXH_PRIME64_2 + XXH_PRIME64_3;
+        len -= 4;
+    }
+    while (len > 0) {
+        hash ^= (*ptr++) * XXH_PRIME64_5;
+        hash = XXH_rotl64(hash, 11) * XXH_PRIME64_1;
+        --len;
+    }
+    return  XXH64_avalanche(hash);
+}
+
+#ifdef XXH_OLD_NAMES
+#  define PROCESS1_64 XXH_PROCESS1_64
+#  define PROCESS4_64 XXH_PROCESS4_64
+#  define PROCESS8_64 XXH_PROCESS8_64
+#else
+#  undef XXH_PROCESS1_64
+#  undef XXH_PROCESS4_64
+#  undef XXH_PROCESS8_64
+#endif
+
+/*!
+ * @internal
+ * @brief The implementation for @ref XXH64().
+ *
+ * @param input , len , seed Directly passed from @ref XXH64().
+ * @param align Whether @p input is aligned.
+ * @return The calculated hash.
+ */
+XXH_FORCE_INLINE XXH_PUREF xxh_u64
+XXH64_endian_align(const xxh_u8* input, size_t len, xxh_u64 seed, XXH_alignment align)
+{
+    xxh_u64 h64;
+    if (input==NULL) XXH_ASSERT(len == 0);
+
+    if (len>=32) {
+        const xxh_u8* const bEnd = input + len;
+        const xxh_u8* const limit = bEnd - 31;
+        xxh_u64 v1 = seed + XXH_PRIME64_1 + XXH_PRIME64_2;
+        xxh_u64 v2 = seed + XXH_PRIME64_2;
+        xxh_u64 v3 = seed + 0;
+        xxh_u64 v4 = seed - XXH_PRIME64_1;
+
+        do {
+            v1 = XXH64_round(v1, XXH_get64bits(input)); input+=8;
+            v2 = XXH64_round(v2, XXH_get64bits(input)); input+=8;
+            v3 = XXH64_round(v3, XXH_get64bits(input)); input+=8;
+            v4 = XXH64_round(v4, XXH_get64bits(input)); input+=8;
+        } while (input<limit);
+
+        h64 = XXH_rotl64(v1, 1) + XXH_rotl64(v2, 7) + XXH_rotl64(v3, 12) + XXH_rotl64(v4, 18);
+        h64 = XXH64_mergeRound(h64, v1);
+        h64 = XXH64_mergeRound(h64, v2);
+        h64 = XXH64_mergeRound(h64, v3);
+        h64 = XXH64_mergeRound(h64, v4);
+
+    } else {
+        h64  = seed + XXH_PRIME64_5;
+    }
+
+    h64 += (xxh_u64) len;
+
+    return XXH64_finalize(h64, input, len, align);
+}
+
+
+/*! @ingroup XXH64_family */
+XXH_PUBLIC_API XXH64_hash_t XXH64 (XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed)
+{
+#if !defined(XXH_NO_STREAM) && XXH_SIZE_OPT >= 2
+    /* Simple version, good for code maintenance, but unfortunately slow for small inputs */
+    XXH64_state_t state;
+    XXH64_reset(&state, seed);
+    XXH64_update(&state, (const xxh_u8*)input, len);
+    return XXH64_digest(&state);
+#else
+    if (XXH_FORCE_ALIGN_CHECK) {
+        if ((((size_t)input) & 7)==0) {  /* Input is aligned, let's leverage the speed advantage */
+            return XXH64_endian_align((const xxh_u8*)input, len, seed, XXH_aligned);
+    }   }
+
+    return XXH64_endian_align((const xxh_u8*)input, len, seed, XXH_unaligned);
+
+#endif
+}
+
+/*******   Hash Streaming   *******/
+#ifndef XXH_NO_STREAM
+/*! @ingroup XXH64_family*/
+XXH_PUBLIC_API XXH64_state_t* XXH64_createState(void)
+{
+    return (XXH64_state_t*)XXH_malloc(sizeof(XXH64_state_t));
+}
+/*! @ingroup XXH64_family */
+XXH_PUBLIC_API XXH_errorcode XXH64_freeState(XXH64_state_t* statePtr)
+{
+    XXH_free(statePtr);
+    return XXH_OK;
+}
+
+/*! @ingroup XXH64_family */
+XXH_PUBLIC_API void XXH64_copyState(XXH_NOESCAPE XXH64_state_t* dstState, const XXH64_state_t* srcState)
+{
+    XXH_memcpy(dstState, srcState, sizeof(*dstState));
+}
+
+/*! @ingroup XXH64_family */
+XXH_PUBLIC_API XXH_errorcode XXH64_reset(XXH_NOESCAPE XXH64_state_t* statePtr, XXH64_hash_t seed)
+{
+    XXH_ASSERT(statePtr != NULL);
+    memset(statePtr, 0, sizeof(*statePtr));
+    statePtr->v[0] = seed + XXH_PRIME64_1 + XXH_PRIME64_2;
+    statePtr->v[1] = seed + XXH_PRIME64_2;
+    statePtr->v[2] = seed + 0;
+    statePtr->v[3] = seed - XXH_PRIME64_1;
+    return XXH_OK;
+}
+
+/*! @ingroup XXH64_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH64_update (XXH_NOESCAPE XXH64_state_t* state, XXH_NOESCAPE const void* input, size_t len)
+{
+    if (input==NULL) {
+        XXH_ASSERT(len == 0);
+        return XXH_OK;
+    }
+
+    {   const xxh_u8* p = (const xxh_u8*)input;
+        const xxh_u8* const bEnd = p + len;
+
+        state->total_len += len;
+
+        if (state->memsize + len < 32) {  /* fill in tmp buffer */
+            XXH_memcpy(((xxh_u8*)state->mem64) + state->memsize, input, len);
+            state->memsize += (xxh_u32)len;
+            return XXH_OK;
+        }
+
+        if (state->memsize) {   /* tmp buffer is full */
+            XXH_memcpy(((xxh_u8*)state->mem64) + state->memsize, input, 32-state->memsize);
+            state->v[0] = XXH64_round(state->v[0], XXH_readLE64(state->mem64+0));
+            state->v[1] = XXH64_round(state->v[1], XXH_readLE64(state->mem64+1));
+            state->v[2] = XXH64_round(state->v[2], XXH_readLE64(state->mem64+2));
+            state->v[3] = XXH64_round(state->v[3], XXH_readLE64(state->mem64+3));
+            p += 32 - state->memsize;
+            state->memsize = 0;
+        }
+
+        if (p+32 <= bEnd) {
+            const xxh_u8* const limit = bEnd - 32;
+
+            do {
+                state->v[0] = XXH64_round(state->v[0], XXH_readLE64(p)); p+=8;
+                state->v[1] = XXH64_round(state->v[1], XXH_readLE64(p)); p+=8;
+                state->v[2] = XXH64_round(state->v[2], XXH_readLE64(p)); p+=8;
+                state->v[3] = XXH64_round(state->v[3], XXH_readLE64(p)); p+=8;
+            } while (p<=limit);
+
+        }
+
+        if (p < bEnd) {
+            XXH_memcpy(state->mem64, p, (size_t)(bEnd-p));
+            state->memsize = (unsigned)(bEnd-p);
+        }
+    }
+
+    return XXH_OK;
+}
+
+
+/*! @ingroup XXH64_family */
+XXH_PUBLIC_API XXH64_hash_t XXH64_digest(XXH_NOESCAPE const XXH64_state_t* state)
+{
+    xxh_u64 h64;
+
+    if (state->total_len >= 32) {
+        h64 = XXH_rotl64(state->v[0], 1) + XXH_rotl64(state->v[1], 7) + XXH_rotl64(state->v[2], 12) + XXH_rotl64(state->v[3], 18);
+        h64 = XXH64_mergeRound(h64, state->v[0]);
+        h64 = XXH64_mergeRound(h64, state->v[1]);
+        h64 = XXH64_mergeRound(h64, state->v[2]);
+        h64 = XXH64_mergeRound(h64, state->v[3]);
+    } else {
+        h64  = state->v[2] /*seed*/ + XXH_PRIME64_5;
+    }
+
+    h64 += (xxh_u64) state->total_len;
+
+    return XXH64_finalize(h64, (const xxh_u8*)state->mem64, (size_t)state->total_len, XXH_aligned);
+}
+#endif /* !XXH_NO_STREAM */
+
+/******* Canonical representation   *******/
+
+/*! @ingroup XXH64_family */
+XXH_PUBLIC_API void XXH64_canonicalFromHash(XXH_NOESCAPE XXH64_canonical_t* dst, XXH64_hash_t hash)
+{
+    XXH_STATIC_ASSERT(sizeof(XXH64_canonical_t) == sizeof(XXH64_hash_t));
+    if (XXH_CPU_LITTLE_ENDIAN) hash = XXH_swap64(hash);
+    XXH_memcpy(dst, &hash, sizeof(*dst));
+}
+
+/*! @ingroup XXH64_family */
+XXH_PUBLIC_API XXH64_hash_t XXH64_hashFromCanonical(XXH_NOESCAPE const XXH64_canonical_t* src)
+{
+    return XXH_readBE64(src);
+}
+
+#ifndef XXH_NO_XXH3
+
+/* *********************************************************************
+*  XXH3
+*  New generation hash designed for speed on small keys and vectorization
+************************************************************************ */
+/*!
+ * @}
+ * @defgroup XXH3_impl XXH3 implementation
+ * @ingroup impl
+ * @{
+ */
+
+/* ===   Compiler specifics   === */
+
+#if ((defined(sun) || defined(__sun)) && __cplusplus) /* Solaris includes __STDC_VERSION__ with C++. Tested with GCC 5.5 */
+#  define XXH_RESTRICT   /* disable */
+#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L   /* >= C99 */
+#  define XXH_RESTRICT   restrict
+#elif (defined (__GNUC__) && ((__GNUC__ > 3) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))) \
+   || (defined (__clang__)) \
+   || (defined (_MSC_VER) && (_MSC_VER >= 1400)) \
+   || (defined (__INTEL_COMPILER) && (__INTEL_COMPILER >= 1300))
+/*
+ * There are a LOT more compilers that recognize __restrict but this
+ * covers the major ones.
+ */
+#  define XXH_RESTRICT   __restrict
+#else
+#  define XXH_RESTRICT   /* disable */
+#endif
+
+#if (defined(__GNUC__) && (__GNUC__ >= 3))  \
+  || (defined(__INTEL_COMPILER) && (__INTEL_COMPILER >= 800)) \
+  || defined(__clang__)
+#    define XXH_likely(x) __builtin_expect(x, 1)
+#    define XXH_unlikely(x) __builtin_expect(x, 0)
+#else
+#    define XXH_likely(x) (x)
+#    define XXH_unlikely(x) (x)
+#endif
+
+#ifndef XXH_HAS_INCLUDE
+#  ifdef __has_include
+/*
+ * Not defined as XXH_HAS_INCLUDE(x) (function-like) because
+ * this causes segfaults in Apple Clang 4.2 (on Mac OS X 10.7 Lion)
+ */
+#    define XXH_HAS_INCLUDE __has_include
+#  else
+#    define XXH_HAS_INCLUDE(x) 0
+#  endif
+#endif
+
+#if defined(__GNUC__) || defined(__clang__)
+#  if defined(__ARM_FEATURE_SVE)
+#    include <arm_sve.h>
+#  endif
+#  if defined(__ARM_NEON__) || defined(__ARM_NEON) \
+   || (defined(_M_ARM) && _M_ARM >= 7) \
+   || defined(_M_ARM64) || defined(_M_ARM64EC) \
+   || (defined(__wasm_simd128__) && XXH_HAS_INCLUDE(<arm_neon.h>)) /* WASM SIMD128 via SIMDe */
+#    define inline __inline__  /* circumvent a clang bug */
+#    include <arm_neon.h>
+#    undef inline
+#  elif defined(__AVX2__)
+#    include <immintrin.h>
+#  elif defined(__SSE2__)
+#    include <emmintrin.h>
+#  endif
+#endif
+
+#if defined(_MSC_VER)
+#  include <intrin.h>
+#endif
+
+/*
+ * One goal of XXH3 is to make it fast on both 32-bit and 64-bit, while
+ * remaining a true 64-bit/128-bit hash function.
+ *
+ * This is done by prioritizing a subset of 64-bit operations that can be
+ * emulated without too many steps on the average 32-bit machine.
+ *
+ * For example, these two lines seem similar, and run equally fast on 64-bit:
+ *
+ *   xxh_u64 x;
+ *   x ^= (x >> 47); // good
+ *   x ^= (x >> 13); // bad
+ *
+ * However, to a 32-bit machine, there is a major difference.
+ *
+ * x ^= (x >> 47) looks like this:
+ *
+ *   x.lo ^= (x.hi >> (47 - 32));
+ *
+ * while x ^= (x >> 13) looks like this:
+ *
+ *   // note: funnel shifts are not usually cheap.
+ *   x.lo ^= (x.lo >> 13) | (x.hi << (32 - 13));
+ *   x.hi ^= (x.hi >> 13);
+ *
+ * The first one is significantly faster than the second, simply because the
+ * shift is larger than 32. This means:
+ *  - All the bits we need are in the upper 32 bits, so we can ignore the lower
+ *    32 bits in the shift.
+ *  - The shift result will always fit in the lower 32 bits, and therefore,
+ *    we can ignore the upper 32 bits in the xor.
+ *
+ * Thanks to this optimization, XXH3 only requires these features to be efficient:
+ *
+ *  - Usable unaligned access
+ *  - A 32-bit or 64-bit ALU
+ *      - If 32-bit, a decent ADC instruction
+ *  - A 32 or 64-bit multiply with a 64-bit result
+ *  - For the 128-bit variant, a decent byteswap helps short inputs.
+ *
+ * The first two are already required by XXH32, and almost all 32-bit and 64-bit
+ * platforms which can run XXH32 can run XXH3 efficiently.
+ *
+ * Thumb-1, the classic 16-bit only subset of ARM's instruction set, is one
+ * notable exception.
+ *
+ * First of all, Thumb-1 lacks support for the UMULL instruction which
+ * performs the important long multiply. This means numerous __aeabi_lmul
+ * calls.
+ *
+ * Second of all, the 8 functional registers are just not enough.
+ * Setup for __aeabi_lmul, byteshift loads, pointers, and all arithmetic need
+ * Lo registers, and this shuffling results in thousands more MOVs than A32.
+ *
+ * A32 and T32 don't have this limitation. They can access all 14 registers,
+ * do a 32->64 multiply with UMULL, and the flexible operand allowing free
+ * shifts is helpful, too.
+ *
+ * Therefore, we do a quick sanity check.
+ *
+ * If compiling Thumb-1 for a target which supports ARM instructions, we will
+ * emit a warning, as it is not a "sane" platform to compile for.
+ *
+ * Usually, if this happens, it is because of an accident and you probably need
+ * to specify -march, as you likely meant to compile for a newer architecture.
+ *
+ * Credit: large sections of the vectorial and asm source code paths
+ *         have been contributed by @easyaspi314
+ */
+#if defined(__thumb__) && !defined(__thumb2__) && defined(__ARM_ARCH_ISA_ARM)
+#   warning "XXH3 is highly inefficient without ARM or Thumb-2."
+#endif
+
+/* ==========================================
+ * Vectorization detection
+ * ========================================== */
+
+#ifdef XXH_DOXYGEN
+/*!
+ * @ingroup tuning
+ * @brief Overrides the vectorization implementation chosen for XXH3.
+ *
+ * Can be defined to 0 to disable SIMD or any of the values mentioned in
+ * @ref XXH_VECTOR_TYPE.
+ *
+ * If this is not defined, it uses predefined macros to determine the best
+ * implementation.
+ */
+#  define XXH_VECTOR XXH_SCALAR
+/*!
+ * @ingroup tuning
+ * @brief Possible values for @ref XXH_VECTOR.
+ *
+ * Note that these are actually implemented as macros.
+ *
+ * If this is not defined, it is detected automatically.
+ * internal macro XXH_X86DISPATCH overrides this.
+ */
+enum XXH_VECTOR_TYPE /* fake enum */ {
+    XXH_SCALAR = 0,  /*!< Portable scalar version */
+    XXH_SSE2   = 1,  /*!<
+                      * SSE2 for Pentium 4, Opteron, all x86_64.
+                      *
+                      * @note SSE2 is also guaranteed on Windows 10, macOS, and
+                      * Android x86.
+                      */
+    XXH_AVX2   = 2,  /*!< AVX2 for Haswell and Bulldozer */
+    XXH_AVX512 = 3,  /*!< AVX512 for Skylake and Icelake */
+    XXH_NEON   = 4,  /*!<
+                       * NEON for most ARMv7-A, all AArch64, and WASM SIMD128
+                       * via the SIMDeverywhere polyfill provided with the
+                       * Emscripten SDK.
+                       */
+    XXH_VSX    = 5,  /*!< VSX and ZVector for POWER8/z13 (64-bit) */
+    XXH_SVE    = 6,  /*!< SVE for some ARMv8-A and ARMv9-A */
+};
+/*!
+ * @ingroup tuning
+ * @brief Selects the minimum alignment for XXH3's accumulators.
+ *
+ * When using SIMD, this should match the alignment required for said vector
+ * type, so, for example, 32 for AVX2.
+ *
+ * Default: Auto detected.
+ */
+#  define XXH_ACC_ALIGN 8
+#endif
+
+/* Actual definition */
+#ifndef XXH_DOXYGEN
+#  define XXH_SCALAR 0
+#  define XXH_SSE2   1
+#  define XXH_AVX2   2
+#  define XXH_AVX512 3
+#  define XXH_NEON   4
+#  define XXH_VSX    5
+#  define XXH_SVE    6
+#endif
+
+#ifndef XXH_VECTOR    /* can be defined on command line */
+#  if defined(__ARM_FEATURE_SVE)
+#    define XXH_VECTOR XXH_SVE
+#  elif ( \
+        defined(__ARM_NEON__) || defined(__ARM_NEON) /* gcc */ \
+     || defined(_M_ARM) || defined(_M_ARM64) || defined(_M_ARM64EC) /* msvc */ \
+     || (defined(__wasm_simd128__) && XXH_HAS_INCLUDE(<arm_neon.h>)) /* wasm simd128 via SIMDe */ \
+   ) && ( \
+        defined(_WIN32) || defined(__LITTLE_ENDIAN__) /* little endian only */ \
+    || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) \
+   )
+#    define XXH_VECTOR XXH_NEON
+#  elif defined(__AVX512F__)
+#    define XXH_VECTOR XXH_AVX512
+#  elif defined(__AVX2__)
+#    define XXH_VECTOR XXH_AVX2
+#  elif defined(__SSE2__) || defined(_M_AMD64) || defined(_M_X64) || (defined(_M_IX86_FP) && (_M_IX86_FP == 2))
+#    define XXH_VECTOR XXH_SSE2
+#  elif (defined(__PPC64__) && defined(__POWER8_VECTOR__)) \
+     || (defined(__s390x__) && defined(__VEC__)) \
+     && defined(__GNUC__) /* TODO: IBM XL */
+#    define XXH_VECTOR XXH_VSX
+#  else
+#    define XXH_VECTOR XXH_SCALAR
+#  endif
+#endif
+
+/* __ARM_FEATURE_SVE is only supported by GCC & Clang. */
+#if (XXH_VECTOR == XXH_SVE) && !defined(__ARM_FEATURE_SVE)
+#  ifdef _MSC_VER
+#    pragma warning(once : 4606)
+#  else
+#    warning "__ARM_FEATURE_SVE isn't supported. Use SCALAR instead."
+#  endif
+#  undef XXH_VECTOR
+#  define XXH_VECTOR XXH_SCALAR
+#endif
+
+/*
+ * Controls the alignment of the accumulator,
+ * for compatibility with aligned vector loads, which are usually faster.
+ */
+#ifndef XXH_ACC_ALIGN
+#  if defined(XXH_X86DISPATCH)
+#     define XXH_ACC_ALIGN 64  /* for compatibility with avx512 */
+#  elif XXH_VECTOR == XXH_SCALAR  /* scalar */
+#     define XXH_ACC_ALIGN 8
+#  elif XXH_VECTOR == XXH_SSE2  /* sse2 */
+#     define XXH_ACC_ALIGN 16
+#  elif XXH_VECTOR == XXH_AVX2  /* avx2 */
+#     define XXH_ACC_ALIGN 32
+#  elif XXH_VECTOR == XXH_NEON  /* neon */
+#     define XXH_ACC_ALIGN 16
+#  elif XXH_VECTOR == XXH_VSX   /* vsx */
+#     define XXH_ACC_ALIGN 16
+#  elif XXH_VECTOR == XXH_AVX512  /* avx512 */
+#     define XXH_ACC_ALIGN 64
+#  elif XXH_VECTOR == XXH_SVE   /* sve */
+#     define XXH_ACC_ALIGN 64
+#  endif
+#endif
+
+#if defined(XXH_X86DISPATCH) || XXH_VECTOR == XXH_SSE2 \
+    || XXH_VECTOR == XXH_AVX2 || XXH_VECTOR == XXH_AVX512
+#  define XXH_SEC_ALIGN XXH_ACC_ALIGN
+#elif XXH_VECTOR == XXH_SVE
+#  define XXH_SEC_ALIGN XXH_ACC_ALIGN
+#else
+#  define XXH_SEC_ALIGN 8
+#endif
+
+#if defined(__GNUC__) || defined(__clang__)
+#  define XXH_ALIASING __attribute__((__may_alias__))
+#else
+#  define XXH_ALIASING /* nothing */
+#endif
+
+/*
+ * UGLY HACK:
+ * GCC usually generates the best code with -O3 for xxHash.
+ *
+ * However, when targeting AVX2, it is overzealous in its unrolling resulting
+ * in code roughly 3/4 the speed of Clang.
+ *
+ * There are other issues, such as GCC splitting _mm256_loadu_si256 into
+ * _mm_loadu_si128 + _mm256_inserti128_si256. This is an optimization which
+ * only applies to Sandy and Ivy Bridge... which don't even support AVX2.
+ *
+ * That is why when compiling the AVX2 version, it is recommended to use either
+ *   -O2 -mavx2 -march=haswell
+ * or
+ *   -O2 -mavx2 -mno-avx256-split-unaligned-load
+ * for decent performance, or to use Clang instead.
+ *
+ * Fortunately, we can control the first one with a pragma that forces GCC into
+ * -O2, but the other one we can't control without "failed to inline always
+ * inline function due to target mismatch" warnings.
+ */
+#if XXH_VECTOR == XXH_AVX2 /* AVX2 */ \
+  && defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \
+  && defined(__OPTIMIZE__) && XXH_SIZE_OPT <= 0 /* respect -O0 and -Os */
+#  pragma GCC push_options
+#  pragma GCC optimize("-O2")
+#endif
+
+#if XXH_VECTOR == XXH_NEON
+
+/*
+ * UGLY HACK: While AArch64 GCC on Linux does not seem to care, on macOS, GCC -O3
+ * optimizes out the entire hashLong loop because of the aliasing violation.
+ *
+ * However, GCC is also inefficient at load-store optimization with vld1q/vst1q,
+ * so the only option is to mark it as aliasing.
+ */
+typedef uint64x2_t xxh_aliasing_uint64x2_t XXH_ALIASING;
+
+/*!
+ * @internal
+ * @brief `vld1q_u64` but faster and alignment-safe.
+ *
+ * On AArch64, unaligned access is always safe, but on ARMv7-a, it is only
+ * *conditionally* safe (`vld1` has an alignment bit like `movdq[ua]` in x86).
+ *
+ * GCC for AArch64 sees `vld1q_u8` as an intrinsic instead of a load, so it
+ * prohibits load-store optimizations. Therefore, a direct dereference is used.
+ *
+ * Otherwise, `vld1q_u8` is used with `vreinterpretq_u8_u64` to do a safe
+ * unaligned load.
+ */
+#if defined(__aarch64__) && defined(__GNUC__) && !defined(__clang__)
+XXH_FORCE_INLINE uint64x2_t XXH_vld1q_u64(void const* ptr) /* silence -Wcast-align */
+{
+    return *(xxh_aliasing_uint64x2_t const *)ptr;
+}
+#else
+XXH_FORCE_INLINE uint64x2_t XXH_vld1q_u64(void const* ptr)
+{
+    return vreinterpretq_u64_u8(vld1q_u8((uint8_t const*)ptr));
+}
+#endif
+
+/*!
+ * @internal
+ * @brief `vmlal_u32` on low and high halves of a vector.
+ *
+ * This is a workaround for AArch64 GCC < 11 which implemented arm_neon.h with
+ * inline assembly and were therefore incapable of merging the `vget_{low, high}_u32`
+ * with `vmlal_u32`.
+ */
+#if defined(__aarch64__) && defined(__GNUC__) && !defined(__clang__) && __GNUC__ < 11
+XXH_FORCE_INLINE uint64x2_t
+XXH_vmlal_low_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs)
+{
+    /* Inline assembly is the only way */
+    __asm__("umlal   %0.2d, %1.2s, %2.2s" : "+w" (acc) : "w" (lhs), "w" (rhs));
+    return acc;
+}
+XXH_FORCE_INLINE uint64x2_t
+XXH_vmlal_high_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs)
+{
+    /* This intrinsic works as expected */
+    return vmlal_high_u32(acc, lhs, rhs);
+}
+#else
+/* Portable intrinsic versions */
+XXH_FORCE_INLINE uint64x2_t
+XXH_vmlal_low_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs)
+{
+    return vmlal_u32(acc, vget_low_u32(lhs), vget_low_u32(rhs));
+}
+/*! @copydoc XXH_vmlal_low_u32
+ * Assume the compiler converts this to vmlal_high_u32 on aarch64 */
+XXH_FORCE_INLINE uint64x2_t
+XXH_vmlal_high_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs)
+{
+    return vmlal_u32(acc, vget_high_u32(lhs), vget_high_u32(rhs));
+}
+#endif
+
+/*!
+ * @ingroup tuning
+ * @brief Controls the NEON to scalar ratio for XXH3
+ *
+ * This can be set to 2, 4, 6, or 8.
+ *
+ * ARM Cortex CPUs are _very_ sensitive to how their pipelines are used.
+ *
+ * For example, the Cortex-A73 can dispatch 3 micro-ops per cycle, but only 2 of those
+ * can be NEON. If you are only using NEON instructions, you are only using 2/3 of the CPU
+ * bandwidth.
+ *
+ * This is even more noticeable on the more advanced cores like the Cortex-A76 which
+ * can dispatch 8 micro-ops per cycle, but still only 2 NEON micro-ops at once.
+ *
+ * Therefore, to make the most out of the pipeline, it is beneficial to run 6 NEON lanes
+ * and 2 scalar lanes, which is chosen by default.
+ *
+ * This does not apply to Apple processors or 32-bit processors, which run better with
+ * full NEON. These will default to 8. Additionally, size-optimized builds run 8 lanes.
+ *
+ * This change benefits CPUs with large micro-op buffers without negatively affecting
+ * most other CPUs:
+ *
+ *  | Chipset               | Dispatch type       | NEON only | 6:2 hybrid | Diff. |
+ *  |:----------------------|:--------------------|----------:|-----------:|------:|
+ *  | Snapdragon 730 (A76)  | 2 NEON/8 micro-ops  |  8.8 GB/s |  10.1 GB/s |  ~16% |
+ *  | Snapdragon 835 (A73)  | 2 NEON/3 micro-ops  |  5.1 GB/s |   5.3 GB/s |   ~5% |
+ *  | Marvell PXA1928 (A53) | In-order dual-issue |  1.9 GB/s |   1.9 GB/s |    0% |
+ *  | Apple M1              | 4 NEON/8 micro-ops  | 37.3 GB/s |  36.1 GB/s |  ~-3% |
+ *
+ * It also seems to fix some bad codegen on GCC, making it almost as fast as clang.
+ *
+ * When using WASM SIMD128, if this is 2 or 6, SIMDe will scalarize 2 of the lanes meaning
+ * it effectively becomes worse 4.
+ *
+ * @see XXH3_accumulate_512_neon()
+ */
+# ifndef XXH3_NEON_LANES
+#  if (defined(__aarch64__) || defined(__arm64__) || defined(_M_ARM64) || defined(_M_ARM64EC)) \
+   && !defined(__APPLE__) && XXH_SIZE_OPT <= 0
+#   define XXH3_NEON_LANES 6
+#  else
+#   define XXH3_NEON_LANES XXH_ACC_NB
+#  endif
+# endif
+#endif  /* XXH_VECTOR == XXH_NEON */
+
+/*
+ * VSX and Z Vector helpers.
+ *
+ * This is very messy, and any pull requests to clean this up are welcome.
+ *
+ * There are a lot of problems with supporting VSX and s390x, due to
+ * inconsistent intrinsics, spotty coverage, and multiple endiannesses.
+ */
+#if XXH_VECTOR == XXH_VSX
+/* Annoyingly, these headers _may_ define three macros: `bool`, `vector`,
+ * and `pixel`. This is a problem for obvious reasons.
+ *
+ * These keywords are unnecessary; the spec literally says they are
+ * equivalent to `__bool`, `__vector`, and `__pixel` and may be undef'd
+ * after including the header.
+ *
+ * We use pragma push_macro/pop_macro to keep the namespace clean. */
+#  pragma push_macro("bool")
+#  pragma push_macro("vector")
+#  pragma push_macro("pixel")
+/* silence potential macro redefined warnings */
+#  undef bool
+#  undef vector
+#  undef pixel
+
+#  if defined(__s390x__)
+#    include <s390intrin.h>
+#  else
+#    include <altivec.h>
+#  endif
+
+/* Restore the original macro values, if applicable. */
+#  pragma pop_macro("pixel")
+#  pragma pop_macro("vector")
+#  pragma pop_macro("bool")
+
+typedef __vector unsigned long long xxh_u64x2;
+typedef __vector unsigned char xxh_u8x16;
+typedef __vector unsigned xxh_u32x4;
+
+/*
+ * UGLY HACK: Similar to aarch64 macOS GCC, s390x GCC has the same aliasing issue.
+ */
+typedef xxh_u64x2 xxh_aliasing_u64x2 XXH_ALIASING;
+
+# ifndef XXH_VSX_BE
+#  if defined(__BIG_ENDIAN__) \
+  || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__)
+#    define XXH_VSX_BE 1
+#  elif defined(__VEC_ELEMENT_REG_ORDER__) && __VEC_ELEMENT_REG_ORDER__ == __ORDER_BIG_ENDIAN__
+#    warning "-maltivec=be is not recommended. Please use native endianness."
+#    define XXH_VSX_BE 1
+#  else
+#    define XXH_VSX_BE 0
+#  endif
+# endif /* !defined(XXH_VSX_BE) */
+
+# if XXH_VSX_BE
+#  if defined(__POWER9_VECTOR__) || (defined(__clang__) && defined(__s390x__))
+#    define XXH_vec_revb vec_revb
+#  else
+/*!
+ * A polyfill for POWER9's vec_revb().
+ */
+XXH_FORCE_INLINE xxh_u64x2 XXH_vec_revb(xxh_u64x2 val)
+{
+    xxh_u8x16 const vByteSwap = { 0x07, 0x06, 0x05, 0x04, 0x03, 0x02, 0x01, 0x00,
+                                  0x0F, 0x0E, 0x0D, 0x0C, 0x0B, 0x0A, 0x09, 0x08 };
+    return vec_perm(val, val, vByteSwap);
+}
+#  endif
+# endif /* XXH_VSX_BE */
+
+/*!
+ * Performs an unaligned vector load and byte swaps it on big endian.
+ */
+XXH_FORCE_INLINE xxh_u64x2 XXH_vec_loadu(const void *ptr)
+{
+    xxh_u64x2 ret;
+    XXH_memcpy(&ret, ptr, sizeof(xxh_u64x2));
+# if XXH_VSX_BE
+    ret = XXH_vec_revb(ret);
+# endif
+    return ret;
+}
+
+/*
+ * vec_mulo and vec_mule are very problematic intrinsics on PowerPC
+ *
+ * These intrinsics weren't added until GCC 8, despite existing for a while,
+ * and they are endian dependent. Also, their meaning swap depending on version.
+ * */
+# if defined(__s390x__)
+ /* s390x is always big endian, no issue on this platform */
+#  define XXH_vec_mulo vec_mulo
+#  define XXH_vec_mule vec_mule
+# elif defined(__clang__) && XXH_HAS_BUILTIN(__builtin_altivec_vmuleuw) && !defined(__ibmxl__)
+/* Clang has a better way to control this, we can just use the builtin which doesn't swap. */
+ /* The IBM XL Compiler (which defined __clang__) only implements the vec_* operations */
+#  define XXH_vec_mulo __builtin_altivec_vmulouw
+#  define XXH_vec_mule __builtin_altivec_vmuleuw
+# else
+/* gcc needs inline assembly */
+/* Adapted from https://github.com/google/highwayhash/blob/master/highwayhash/hh_vsx.h. */
+XXH_FORCE_INLINE xxh_u64x2 XXH_vec_mulo(xxh_u32x4 a, xxh_u32x4 b)
+{
+    xxh_u64x2 result;
+    __asm__("vmulouw %0, %1, %2" : "=v" (result) : "v" (a), "v" (b));
+    return result;
+}
+XXH_FORCE_INLINE xxh_u64x2 XXH_vec_mule(xxh_u32x4 a, xxh_u32x4 b)
+{
+    xxh_u64x2 result;
+    __asm__("vmuleuw %0, %1, %2" : "=v" (result) : "v" (a), "v" (b));
+    return result;
+}
+# endif /* XXH_vec_mulo, XXH_vec_mule */
+#endif /* XXH_VECTOR == XXH_VSX */
+
+#if XXH_VECTOR == XXH_SVE
+#define ACCRND(acc, offset) \
+do { \
+    svuint64_t input_vec = svld1_u64(mask, xinput + offset);         \
+    svuint64_t secret_vec = svld1_u64(mask, xsecret + offset);       \
+    svuint64_t mixed = sveor_u64_x(mask, secret_vec, input_vec);     \
+    svuint64_t swapped = svtbl_u64(input_vec, kSwap);                \
+    svuint64_t mixed_lo = svextw_u64_x(mask, mixed);                 \
+    svuint64_t mixed_hi = svlsr_n_u64_x(mask, mixed, 32);            \
+    svuint64_t mul = svmad_u64_x(mask, mixed_lo, mixed_hi, swapped); \
+    acc = svadd_u64_x(mask, acc, mul);                               \
+} while (0)
+#endif /* XXH_VECTOR == XXH_SVE */
+
+/* prefetch
+ * can be disabled, by declaring XXH_NO_PREFETCH build macro */
+#if defined(XXH_NO_PREFETCH)
+#  define XXH_PREFETCH(ptr)  (void)(ptr)  /* disabled */
+#else
+#  if XXH_SIZE_OPT >= 1
+#    define XXH_PREFETCH(ptr) (void)(ptr)
+#  elif defined(_MSC_VER) && (defined(_M_X64) || defined(_M_IX86))  /* _mm_prefetch() not defined outside of x86/x64 */
+#    include <mmintrin.h>   /* https://msdn.microsoft.com/fr-fr/library/84szxsww(v=vs.90).aspx */
+#    define XXH_PREFETCH(ptr)  _mm_prefetch((const char*)(ptr), _MM_HINT_T0)
+#  elif defined(__GNUC__) && ( (__GNUC__ >= 4) || ( (__GNUC__ == 3) && (__GNUC_MINOR__ >= 1) ) )
+#    define XXH_PREFETCH(ptr)  __builtin_prefetch((ptr), 0 /* rw==read */, 3 /* locality */)
+#  else
+#    define XXH_PREFETCH(ptr) (void)(ptr)  /* disabled */
+#  endif
+#endif  /* XXH_NO_PREFETCH */
+
+
+/* ==========================================
+ * XXH3 default settings
+ * ========================================== */
+
+#define XXH_SECRET_DEFAULT_SIZE 192   /* minimum XXH3_SECRET_SIZE_MIN */
+
+#if (XXH_SECRET_DEFAULT_SIZE < XXH3_SECRET_SIZE_MIN)
+#  error "default keyset is not large enough"
+#endif
+
+/*! Pseudorandom secret taken directly from FARSH. */
+XXH_ALIGN(64) static const xxh_u8 XXH3_kSecret[XXH_SECRET_DEFAULT_SIZE] = {
+    0xb8, 0xfe, 0x6c, 0x39, 0x23, 0xa4, 0x4b, 0xbe, 0x7c, 0x01, 0x81, 0x2c, 0xf7, 0x21, 0xad, 0x1c,
+    0xde, 0xd4, 0x6d, 0xe9, 0x83, 0x90, 0x97, 0xdb, 0x72, 0x40, 0xa4, 0xa4, 0xb7, 0xb3, 0x67, 0x1f,
+    0xcb, 0x79, 0xe6, 0x4e, 0xcc, 0xc0, 0xe5, 0x78, 0x82, 0x5a, 0xd0, 0x7d, 0xcc, 0xff, 0x72, 0x21,
+    0xb8, 0x08, 0x46, 0x74, 0xf7, 0x43, 0x24, 0x8e, 0xe0, 0x35, 0x90, 0xe6, 0x81, 0x3a, 0x26, 0x4c,
+    0x3c, 0x28, 0x52, 0xbb, 0x91, 0xc3, 0x00, 0xcb, 0x88, 0xd0, 0x65, 0x8b, 0x1b, 0x53, 0x2e, 0xa3,
+    0x71, 0x64, 0x48, 0x97, 0xa2, 0x0d, 0xf9, 0x4e, 0x38, 0x19, 0xef, 0x46, 0xa9, 0xde, 0xac, 0xd8,
+    0xa8, 0xfa, 0x76, 0x3f, 0xe3, 0x9c, 0x34, 0x3f, 0xf9, 0xdc, 0xbb, 0xc7, 0xc7, 0x0b, 0x4f, 0x1d,
+    0x8a, 0x51, 0xe0, 0x4b, 0xcd, 0xb4, 0x59, 0x31, 0xc8, 0x9f, 0x7e, 0xc9, 0xd9, 0x78, 0x73, 0x64,
+    0xea, 0xc5, 0xac, 0x83, 0x34, 0xd3, 0xeb, 0xc3, 0xc5, 0x81, 0xa0, 0xff, 0xfa, 0x13, 0x63, 0xeb,
+    0x17, 0x0d, 0xdd, 0x51, 0xb7, 0xf0, 0xda, 0x49, 0xd3, 0x16, 0x55, 0x26, 0x29, 0xd4, 0x68, 0x9e,
+    0x2b, 0x16, 0xbe, 0x58, 0x7d, 0x47, 0xa1, 0xfc, 0x8f, 0xf8, 0xb8, 0xd1, 0x7a, 0xd0, 0x31, 0xce,
+    0x45, 0xcb, 0x3a, 0x8f, 0x95, 0x16, 0x04, 0x28, 0xaf, 0xd7, 0xfb, 0xca, 0xbb, 0x4b, 0x40, 0x7e,
+};
+
+static const xxh_u64 PRIME_MX1 = 0x165667919E3779F9ULL;  /*!< 0b0001011001010110011001111001000110011110001101110111100111111001 */
+static const xxh_u64 PRIME_MX2 = 0x9FB21C651E98DF25ULL;  /*!< 0b1001111110110010000111000110010100011110100110001101111100100101 */
+
+#ifdef XXH_OLD_NAMES
+#  define kSecret XXH3_kSecret
+#endif
+
+#ifdef XXH_DOXYGEN
+/*!
+ * @brief Calculates a 32-bit to 64-bit long multiply.
+ *
+ * Implemented as a macro.
+ *
+ * Wraps `__emulu` on MSVC x86 because it tends to call `__allmul` when it doesn't
+ * need to (but it shouldn't need to anyways, it is about 7 instructions to do
+ * a 64x64 multiply...). Since we know that this will _always_ emit `MULL`, we
+ * use that instead of the normal method.
+ *
+ * If you are compiling for platforms like Thumb-1 and don't have a better option,
+ * you may also want to write your own long multiply routine here.
+ *
+ * @param x, y Numbers to be multiplied
+ * @return 64-bit product of the low 32 bits of @p x and @p y.
+ */
+XXH_FORCE_INLINE xxh_u64
+XXH_mult32to64(xxh_u64 x, xxh_u64 y)
+{
+   return (x & 0xFFFFFFFF) * (y & 0xFFFFFFFF);
+}
+#elif defined(_MSC_VER) && defined(_M_IX86)
+#    define XXH_mult32to64(x, y) __emulu((unsigned)(x), (unsigned)(y))
+#else
+/*
+ * Downcast + upcast is usually better than masking on older compilers like
+ * GCC 4.2 (especially 32-bit ones), all without affecting newer compilers.
+ *
+ * The other method, (x & 0xFFFFFFFF) * (y & 0xFFFFFFFF), will AND both operands
+ * and perform a full 64x64 multiply -- entirely redundant on 32-bit.
+ */
+#    define XXH_mult32to64(x, y) ((xxh_u64)(xxh_u32)(x) * (xxh_u64)(xxh_u32)(y))
+#endif
+
+/*!
+ * @brief Calculates a 64->128-bit long multiply.
+ *
+ * Uses `__uint128_t` and `_umul128` if available, otherwise uses a scalar
+ * version.
+ *
+ * @param lhs , rhs The 64-bit integers to be multiplied
+ * @return The 128-bit result represented in an @ref XXH128_hash_t.
+ */
+static XXH128_hash_t
+XXH_mult64to128(xxh_u64 lhs, xxh_u64 rhs)
+{
+    /*
+     * GCC/Clang __uint128_t method.
+     *
+     * On most 64-bit targets, GCC and Clang define a __uint128_t type.
+     * This is usually the best way as it usually uses a native long 64-bit
+     * multiply, such as MULQ on x86_64 or MUL + UMULH on aarch64.
+     *
+     * Usually.
+     *
+     * Despite being a 32-bit platform, Clang (and emscripten) define this type
+     * despite not having the arithmetic for it. This results in a laggy
+     * compiler builtin call which calculates a full 128-bit multiply.
+     * In that case it is best to use the portable one.
+     * https://github.com/Cyan4973/xxHash/issues/211#issuecomment-515575677
+     */
+#if (defined(__GNUC__) || defined(__clang__)) && !defined(__wasm__) \
+    && defined(__SIZEOF_INT128__) \
+    || (defined(_INTEGRAL_MAX_BITS) && _INTEGRAL_MAX_BITS >= 128)
+
+    __uint128_t const product = (__uint128_t)lhs * (__uint128_t)rhs;
+    XXH128_hash_t r128;
+    r128.low64  = (xxh_u64)(product);
+    r128.high64 = (xxh_u64)(product >> 64);
+    return r128;
+
+    /*
+     * MSVC for x64's _umul128 method.
+     *
+     * xxh_u64 _umul128(xxh_u64 Multiplier, xxh_u64 Multiplicand, xxh_u64 *HighProduct);
+     *
+     * This compiles to single operand MUL on x64.
+     */
+#elif (defined(_M_X64) || defined(_M_IA64)) && !defined(_M_ARM64EC)
+
+#ifndef _MSC_VER
+#   pragma intrinsic(_umul128)
+#endif
+    xxh_u64 product_high;
+    xxh_u64 const product_low = _umul128(lhs, rhs, &product_high);
+    XXH128_hash_t r128;
+    r128.low64  = product_low;
+    r128.high64 = product_high;
+    return r128;
+
+    /*
+     * MSVC for ARM64's __umulh method.
+     *
+     * This compiles to the same MUL + UMULH as GCC/Clang's __uint128_t method.
+     */
+#elif defined(_M_ARM64) || defined(_M_ARM64EC)
+
+#ifndef _MSC_VER
+#   pragma intrinsic(__umulh)
+#endif
+    XXH128_hash_t r128;
+    r128.low64  = lhs * rhs;
+    r128.high64 = __umulh(lhs, rhs);
+    return r128;
+
+#else
+    /*
+     * Portable scalar method. Optimized for 32-bit and 64-bit ALUs.
+     *
+     * This is a fast and simple grade school multiply, which is shown below
+     * with base 10 arithmetic instead of base 0x100000000.
+     *
+     *           9 3 // D2 lhs = 93
+     *         x 7 5 // D2 rhs = 75
+     *     ----------
+     *           1 5 // D2 lo_lo = (93 % 10) * (75 % 10) = 15
+     *         4 5 | // D2 hi_lo = (93 / 10) * (75 % 10) = 45
+     *         2 1 | // D2 lo_hi = (93 % 10) * (75 / 10) = 21
+     *     + 6 3 | | // D2 hi_hi = (93 / 10) * (75 / 10) = 63
+     *     ---------
+     *         2 7 | // D2 cross = (15 / 10) + (45 % 10) + 21 = 27
+     *     + 6 7 | | // D2 upper = (27 / 10) + (45 / 10) + 63 = 67
+     *     ---------
+     *       6 9 7 5 // D4 res = (27 * 10) + (15 % 10) + (67 * 100) = 6975
+     *
+     * The reasons for adding the products like this are:
+     *  1. It avoids manual carry tracking. Just like how
+     *     (9 * 9) + 9 + 9 = 99, the same applies with this for UINT64_MAX.
+     *     This avoids a lot of complexity.
+     *
+     *  2. It hints for, and on Clang, compiles to, the powerful UMAAL
+     *     instruction available in ARM's Digital Signal Processing extension
+     *     in 32-bit ARMv6 and later, which is shown below:
+     *
+     *         void UMAAL(xxh_u32 *RdLo, xxh_u32 *RdHi, xxh_u32 Rn, xxh_u32 Rm)
+     *         {
+     *             xxh_u64 product = (xxh_u64)*RdLo * (xxh_u64)*RdHi + Rn + Rm;
+     *             *RdLo = (xxh_u32)(product & 0xFFFFFFFF);
+     *             *RdHi = (xxh_u32)(product >> 32);
+     *         }
+     *
+     *     This instruction was designed for efficient long multiplication, and
+     *     allows this to be calculated in only 4 instructions at speeds
+     *     comparable to some 64-bit ALUs.
+     *
+     *  3. It isn't terrible on other platforms. Usually this will be a couple
+     *     of 32-bit ADD/ADCs.
+     */
+
+    /* First calculate all of the cross products. */
+    xxh_u64 const lo_lo = XXH_mult32to64(lhs & 0xFFFFFFFF, rhs & 0xFFFFFFFF);
+    xxh_u64 const hi_lo = XXH_mult32to64(lhs >> 32,        rhs & 0xFFFFFFFF);
+    xxh_u64 const lo_hi = XXH_mult32to64(lhs & 0xFFFFFFFF, rhs >> 32);
+    xxh_u64 const hi_hi = XXH_mult32to64(lhs >> 32,        rhs >> 32);
+
+    /* Now add the products together. These will never overflow. */
+    xxh_u64 const cross = (lo_lo >> 32) + (hi_lo & 0xFFFFFFFF) + lo_hi;
+    xxh_u64 const upper = (hi_lo >> 32) + (cross >> 32)        + hi_hi;
+    xxh_u64 const lower = (cross << 32) | (lo_lo & 0xFFFFFFFF);
+
+    XXH128_hash_t r128;
+    r128.low64  = lower;
+    r128.high64 = upper;
+    return r128;
+#endif
+}
+
+/*!
+ * @brief Calculates a 64-bit to 128-bit multiply, then XOR folds it.
+ *
+ * The reason for the separate function is to prevent passing too many structs
+ * around by value. This will hopefully inline the multiply, but we don't force it.
+ *
+ * @param lhs , rhs The 64-bit integers to multiply
+ * @return The low 64 bits of the product XOR'd by the high 64 bits.
+ * @see XXH_mult64to128()
+ */
+static xxh_u64
+XXH3_mul128_fold64(xxh_u64 lhs, xxh_u64 rhs)
+{
+    XXH128_hash_t product = XXH_mult64to128(lhs, rhs);
+    return product.low64 ^ product.high64;
+}
+
+/*! Seems to produce slightly better code on GCC for some reason. */
+XXH_FORCE_INLINE XXH_CONSTF xxh_u64 XXH_xorshift64(xxh_u64 v64, int shift)
+{
+    XXH_ASSERT(0 <= shift && shift < 64);
+    return v64 ^ (v64 >> shift);
+}
+
+/*
+ * This is a fast avalanche stage,
+ * suitable when input bits are already partially mixed
+ */
+static XXH64_hash_t XXH3_avalanche(xxh_u64 h64)
+{
+    h64 = XXH_xorshift64(h64, 37);
+    h64 *= PRIME_MX1;
+    h64 = XXH_xorshift64(h64, 32);
+    return h64;
+}
+
+/*
+ * This is a stronger avalanche,
+ * inspired by Pelle Evensen's rrmxmx
+ * preferable when input has not been previously mixed
+ */
+static XXH64_hash_t XXH3_rrmxmx(xxh_u64 h64, xxh_u64 len)
+{
+    /* this mix is inspired by Pelle Evensen's rrmxmx */
+    h64 ^= XXH_rotl64(h64, 49) ^ XXH_rotl64(h64, 24);
+    h64 *= PRIME_MX2;
+    h64 ^= (h64 >> 35) + len ;
+    h64 *= PRIME_MX2;
+    return XXH_xorshift64(h64, 28);
+}
+
+
+/* ==========================================
+ * Short keys
+ * ==========================================
+ * One of the shortcomings of XXH32 and XXH64 was that their performance was
+ * sub-optimal on short lengths. It used an iterative algorithm which strongly
+ * favored lengths that were a multiple of 4 or 8.
+ *
+ * Instead of iterating over individual inputs, we use a set of single shot
+ * functions which piece together a range of lengths and operate in constant time.
+ *
+ * Additionally, the number of multiplies has been significantly reduced. This
+ * reduces latency, especially when emulating 64-bit multiplies on 32-bit.
+ *
+ * Depending on the platform, this may or may not be faster than XXH32, but it
+ * is almost guaranteed to be faster than XXH64.
+ */
+
+/*
+ * At very short lengths, there isn't enough input to fully hide secrets, or use
+ * the entire secret.
+ *
+ * There is also only a limited amount of mixing we can do before significantly
+ * impacting performance.
+ *
+ * Therefore, we use different sections of the secret and always mix two secret
+ * samples with an XOR. This should have no effect on performance on the
+ * seedless or withSeed variants because everything _should_ be constant folded
+ * by modern compilers.
+ *
+ * The XOR mixing hides individual parts of the secret and increases entropy.
+ *
+ * This adds an extra layer of strength for custom secrets.
+ */
+XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t
+XXH3_len_1to3_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed)
+{
+    XXH_ASSERT(input != NULL);
+    XXH_ASSERT(1 <= len && len <= 3);
+    XXH_ASSERT(secret != NULL);
+    /*
+     * len = 1: combined = { input[0], 0x01, input[0], input[0] }
+     * len = 2: combined = { input[1], 0x02, input[0], input[1] }
+     * len = 3: combined = { input[2], 0x03, input[0], input[1] }
+     */
+    {   xxh_u8  const c1 = input[0];
+        xxh_u8  const c2 = input[len >> 1];
+        xxh_u8  const c3 = input[len - 1];
+        xxh_u32 const combined = ((xxh_u32)c1 << 16) | ((xxh_u32)c2  << 24)
+                               | ((xxh_u32)c3 <<  0) | ((xxh_u32)len << 8);
+        xxh_u64 const bitflip = (XXH_readLE32(secret) ^ XXH_readLE32(secret+4)) + seed;
+        xxh_u64 const keyed = (xxh_u64)combined ^ bitflip;
+        return XXH64_avalanche(keyed);
+    }
+}
+
+XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t
+XXH3_len_4to8_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed)
+{
+    XXH_ASSERT(input != NULL);
+    XXH_ASSERT(secret != NULL);
+    XXH_ASSERT(4 <= len && len <= 8);
+    seed ^= (xxh_u64)XXH_swap32((xxh_u32)seed) << 32;
+    {   xxh_u32 const input1 = XXH_readLE32(input);
+        xxh_u32 const input2 = XXH_readLE32(input + len - 4);
+        xxh_u64 const bitflip = (XXH_readLE64(secret+8) ^ XXH_readLE64(secret+16)) - seed;
+        xxh_u64 const input64 = input2 + (((xxh_u64)input1) << 32);
+        xxh_u64 const keyed = input64 ^ bitflip;
+        return XXH3_rrmxmx(keyed, len);
+    }
+}
+
+XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t
+XXH3_len_9to16_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed)
+{
+    XXH_ASSERT(input != NULL);
+    XXH_ASSERT(secret != NULL);
+    XXH_ASSERT(9 <= len && len <= 16);
+    {   xxh_u64 const bitflip1 = (XXH_readLE64(secret+24) ^ XXH_readLE64(secret+32)) + seed;
+        xxh_u64 const bitflip2 = (XXH_readLE64(secret+40) ^ XXH_readLE64(secret+48)) - seed;
+        xxh_u64 const input_lo = XXH_readLE64(input)           ^ bitflip1;
+        xxh_u64 const input_hi = XXH_readLE64(input + len - 8) ^ bitflip2;
+        xxh_u64 const acc = len
+                          + XXH_swap64(input_lo) + input_hi
+                          + XXH3_mul128_fold64(input_lo, input_hi);
+        return XXH3_avalanche(acc);
+    }
+}
+
+XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t
+XXH3_len_0to16_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed)
+{
+    XXH_ASSERT(len <= 16);
+    {   if (XXH_likely(len >  8)) return XXH3_len_9to16_64b(input, len, secret, seed);
+        if (XXH_likely(len >= 4)) return XXH3_len_4to8_64b(input, len, secret, seed);
+        if (len) return XXH3_len_1to3_64b(input, len, secret, seed);
+        return XXH64_avalanche(seed ^ (XXH_readLE64(secret+56) ^ XXH_readLE64(secret+64)));
+    }
+}
+
+/*
+ * DISCLAIMER: There are known *seed-dependent* multicollisions here due to
+ * multiplication by zero, affecting hashes of lengths 17 to 240.
+ *
+ * However, they are very unlikely.
+ *
+ * Keep this in mind when using the unseeded XXH3_64bits() variant: As with all
+ * unseeded non-cryptographic hashes, it does not attempt to defend itself
+ * against specially crafted inputs, only random inputs.
+ *
+ * Compared to classic UMAC where a 1 in 2^31 chance of 4 consecutive bytes
+ * cancelling out the secret is taken an arbitrary number of times (addressed
+ * in XXH3_accumulate_512), this collision is very unlikely with random inputs
+ * and/or proper seeding:
+ *
+ * This only has a 1 in 2^63 chance of 8 consecutive bytes cancelling out, in a
+ * function that is only called up to 16 times per hash with up to 240 bytes of
+ * input.
+ *
+ * This is not too bad for a non-cryptographic hash function, especially with
+ * only 64 bit outputs.
+ *
+ * The 128-bit variant (which trades some speed for strength) is NOT affected
+ * by this, although it is always a good idea to use a proper seed if you care
+ * about strength.
+ */
+XXH_FORCE_INLINE xxh_u64 XXH3_mix16B(const xxh_u8* XXH_RESTRICT input,
+                                     const xxh_u8* XXH_RESTRICT secret, xxh_u64 seed64)
+{
+#if defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \
+  && defined(__i386__) && defined(__SSE2__)  /* x86 + SSE2 */ \
+  && !defined(XXH_ENABLE_AUTOVECTORIZE)      /* Define to disable like XXH32 hack */
+    /*
+     * UGLY HACK:
+     * GCC for x86 tends to autovectorize the 128-bit multiply, resulting in
+     * slower code.
+     *
+     * By forcing seed64 into a register, we disrupt the cost model and
+     * cause it to scalarize. See `XXH32_round()`
+     *
+     * FIXME: Clang's output is still _much_ faster -- On an AMD Ryzen 3600,
+     * XXH3_64bits @ len=240 runs at 4.6 GB/s with Clang 9, but 3.3 GB/s on
+     * GCC 9.2, despite both emitting scalar code.
+     *
+     * GCC generates much better scalar code than Clang for the rest of XXH3,
+     * which is why finding a more optimal codepath is an interest.
+     */
+    XXH_COMPILER_GUARD(seed64);
+#endif
+    {   xxh_u64 const input_lo = XXH_readLE64(input);
+        xxh_u64 const input_hi = XXH_readLE64(input+8);
+        return XXH3_mul128_fold64(
+            input_lo ^ (XXH_readLE64(secret)   + seed64),
+            input_hi ^ (XXH_readLE64(secret+8) - seed64)
+        );
+    }
+}
+
+/* For mid range keys, XXH3 uses a Mum-hash variant. */
+XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t
+XXH3_len_17to128_64b(const xxh_u8* XXH_RESTRICT input, size_t len,
+                     const xxh_u8* XXH_RESTRICT secret, size_t secretSize,
+                     XXH64_hash_t seed)
+{
+    XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize;
+    XXH_ASSERT(16 < len && len <= 128);
+
+    {   xxh_u64 acc = len * XXH_PRIME64_1;
+#if XXH_SIZE_OPT >= 1
+        /* Smaller and cleaner, but slightly slower. */
+        unsigned int i = (unsigned int)(len - 1) / 32;
+        do {
+            acc += XXH3_mix16B(input+16 * i, secret+32*i, seed);
+            acc += XXH3_mix16B(input+len-16*(i+1), secret+32*i+16, seed);
+        } while (i-- != 0);
+#else
+        if (len > 32) {
+            if (len > 64) {
+                if (len > 96) {
+                    acc += XXH3_mix16B(input+48, secret+96, seed);
+                    acc += XXH3_mix16B(input+len-64, secret+112, seed);
+                }
+                acc += XXH3_mix16B(input+32, secret+64, seed);
+                acc += XXH3_mix16B(input+len-48, secret+80, seed);
+            }
+            acc += XXH3_mix16B(input+16, secret+32, seed);
+            acc += XXH3_mix16B(input+len-32, secret+48, seed);
+        }
+        acc += XXH3_mix16B(input+0, secret+0, seed);
+        acc += XXH3_mix16B(input+len-16, secret+16, seed);
+#endif
+        return XXH3_avalanche(acc);
+    }
+}
+
+XXH_NO_INLINE XXH_PUREF XXH64_hash_t
+XXH3_len_129to240_64b(const xxh_u8* XXH_RESTRICT input, size_t len,
+                      const xxh_u8* XXH_RESTRICT secret, size_t secretSize,
+                      XXH64_hash_t seed)
+{
+    XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize;
+    XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX);
+
+    #define XXH3_MIDSIZE_STARTOFFSET 3
+    #define XXH3_MIDSIZE_LASTOFFSET  17
+
+    {   xxh_u64 acc = len * XXH_PRIME64_1;
+        xxh_u64 acc_end;
+        unsigned int const nbRounds = (unsigned int)len / 16;
+        unsigned int i;
+        XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX);
+        for (i=0; i<8; i++) {
+            acc += XXH3_mix16B(input+(16*i), secret+(16*i), seed);
+        }
+        /* last bytes */
+        acc_end = XXH3_mix16B(input + len - 16, secret + XXH3_SECRET_SIZE_MIN - XXH3_MIDSIZE_LASTOFFSET, seed);
+        XXH_ASSERT(nbRounds >= 8);
+        acc = XXH3_avalanche(acc);
+#if defined(__clang__)                                /* Clang */ \
+    && (defined(__ARM_NEON) || defined(__ARM_NEON__)) /* NEON */ \
+    && !defined(XXH_ENABLE_AUTOVECTORIZE)             /* Define to disable */
+        /*
+         * UGLY HACK:
+         * Clang for ARMv7-A tries to vectorize this loop, similar to GCC x86.
+         * In everywhere else, it uses scalar code.
+         *
+         * For 64->128-bit multiplies, even if the NEON was 100% optimal, it
+         * would still be slower than UMAAL (see XXH_mult64to128).
+         *
+         * Unfortunately, Clang doesn't handle the long multiplies properly and
+         * converts them to the nonexistent "vmulq_u64" intrinsic, which is then
+         * scalarized into an ugly mess of VMOV.32 instructions.
+         *
+         * This mess is difficult to avoid without turning autovectorization
+         * off completely, but they are usually relatively minor and/or not
+         * worth it to fix.
+         *
+         * This loop is the easiest to fix, as unlike XXH32, this pragma
+         * _actually works_ because it is a loop vectorization instead of an
+         * SLP vectorization.
+         */
+        #pragma clang loop vectorize(disable)
+#endif
+        for (i=8 ; i < nbRounds; i++) {
+            /*
+             * Prevents clang for unrolling the acc loop and interleaving with this one.
+             */
+            XXH_COMPILER_GUARD(acc);
+            acc_end += XXH3_mix16B(input+(16*i), secret+(16*(i-8)) + XXH3_MIDSIZE_STARTOFFSET, seed);
+        }
+        return XXH3_avalanche(acc + acc_end);
+    }
+}
+
+
+/* =======     Long Keys     ======= */
+
+#define XXH_STRIPE_LEN 64
+#define XXH_SECRET_CONSUME_RATE 8   /* nb of secret bytes consumed at each accumulation */
+#define XXH_ACC_NB (XXH_STRIPE_LEN / sizeof(xxh_u64))
+
+#ifdef XXH_OLD_NAMES
+#  define STRIPE_LEN XXH_STRIPE_LEN
+#  define ACC_NB XXH_ACC_NB
+#endif
+
+#ifndef XXH_PREFETCH_DIST
+#  ifdef __clang__
+#    define XXH_PREFETCH_DIST 320
+#  else
+#    if (XXH_VECTOR == XXH_AVX512)
+#      define XXH_PREFETCH_DIST 512
+#    else
+#      define XXH_PREFETCH_DIST 384
+#    endif
+#  endif  /* __clang__ */
+#endif  /* XXH_PREFETCH_DIST */
+
+/*
+ * These macros are to generate an XXH3_accumulate() function.
+ * The two arguments select the name suffix and target attribute.
+ *
+ * The name of this symbol is XXH3_accumulate_<name>() and it calls
+ * XXH3_accumulate_512_<name>().
+ *
+ * It may be useful to hand implement this function if the compiler fails to
+ * optimize the inline function.
+ */
+#define XXH3_ACCUMULATE_TEMPLATE(name)                      \
+void                                                        \
+XXH3_accumulate_##name(xxh_u64* XXH_RESTRICT acc,           \
+                       const xxh_u8* XXH_RESTRICT input,    \
+                       const xxh_u8* XXH_RESTRICT secret,   \
+                       size_t nbStripes)                    \
+{                                                           \
+    size_t n;                                               \
+    for (n = 0; n < nbStripes; n++ ) {                      \
+        const xxh_u8* const in = input + n*XXH_STRIPE_LEN;  \
+        XXH_PREFETCH(in + XXH_PREFETCH_DIST);               \
+        XXH3_accumulate_512_##name(                         \
+                 acc,                                       \
+                 in,                                        \
+                 secret + n*XXH_SECRET_CONSUME_RATE);       \
+    }                                                       \
+}
+
+
+XXH_FORCE_INLINE void XXH_writeLE64(void* dst, xxh_u64 v64)
+{
+    if (!XXH_CPU_LITTLE_ENDIAN) v64 = XXH_swap64(v64);
+    XXH_memcpy(dst, &v64, sizeof(v64));
+}
+
+/* Several intrinsic functions below are supposed to accept __int64 as argument,
+ * as documented in https://software.intel.com/sites/landingpage/IntrinsicsGuide/ .
+ * However, several environments do not define __int64 type,
+ * requiring a workaround.
+ */
+#if !defined (__VMS) \
+  && (defined (__cplusplus) \
+  || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) )
+    typedef int64_t xxh_i64;
+#else
+    /* the following type must have a width of 64-bit */
+    typedef long long xxh_i64;
+#endif
+
+
+/*
+ * XXH3_accumulate_512 is the tightest loop for long inputs, and it is the most optimized.
+ *
+ * It is a hardened version of UMAC, based off of FARSH's implementation.
+ *
+ * This was chosen because it adapts quite well to 32-bit, 64-bit, and SIMD
+ * implementations, and it is ridiculously fast.
+ *
+ * We harden it by mixing the original input to the accumulators as well as the product.
+ *
+ * This means that in the (relatively likely) case of a multiply by zero, the
+ * original input is preserved.
+ *
+ * On 128-bit inputs, we swap 64-bit pairs when we add the input to improve
+ * cross-pollination, as otherwise the upper and lower halves would be
+ * essentially independent.
+ *
+ * This doesn't matter on 64-bit hashes since they all get merged together in
+ * the end, so we skip the extra step.
+ *
+ * Both XXH3_64bits and XXH3_128bits use this subroutine.
+ */
+
+#if (XXH_VECTOR == XXH_AVX512) \
+     || (defined(XXH_DISPATCH_AVX512) && XXH_DISPATCH_AVX512 != 0)
+
+#ifndef XXH_TARGET_AVX512
+# define XXH_TARGET_AVX512  /* disable attribute target */
+#endif
+
+XXH_FORCE_INLINE XXH_TARGET_AVX512 void
+XXH3_accumulate_512_avx512(void* XXH_RESTRICT acc,
+                     const void* XXH_RESTRICT input,
+                     const void* XXH_RESTRICT secret)
+{
+    __m512i* const xacc = (__m512i *) acc;
+    XXH_ASSERT((((size_t)acc) & 63) == 0);
+    XXH_STATIC_ASSERT(XXH_STRIPE_LEN == sizeof(__m512i));
+
+    {
+        /* data_vec    = input[0]; */
+        __m512i const data_vec    = _mm512_loadu_si512   (input);
+        /* key_vec     = secret[0]; */
+        __m512i const key_vec     = _mm512_loadu_si512   (secret);
+        /* data_key    = data_vec ^ key_vec; */
+        __m512i const data_key    = _mm512_xor_si512     (data_vec, key_vec);
+        /* data_key_lo = data_key >> 32; */
+        __m512i const data_key_lo = _mm512_srli_epi64 (data_key, 32);
+        /* product     = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */
+        __m512i const product     = _mm512_mul_epu32     (data_key, data_key_lo);
+        /* xacc[0] += swap(data_vec); */
+        __m512i const data_swap = _mm512_shuffle_epi32(data_vec, (_MM_PERM_ENUM)_MM_SHUFFLE(1, 0, 3, 2));
+        __m512i const sum       = _mm512_add_epi64(*xacc, data_swap);
+        /* xacc[0] += product; */
+        *xacc = _mm512_add_epi64(product, sum);
+    }
+}
+XXH_FORCE_INLINE XXH_TARGET_AVX512 XXH3_ACCUMULATE_TEMPLATE(avx512)
+
+/*
+ * XXH3_scrambleAcc: Scrambles the accumulators to improve mixing.
+ *
+ * Multiplication isn't perfect, as explained by Google in HighwayHash:
+ *
+ *  // Multiplication mixes/scrambles bytes 0-7 of the 64-bit result to
+ *  // varying degrees. In descending order of goodness, bytes
+ *  // 3 4 2 5 1 6 0 7 have quality 228 224 164 160 100 96 36 32.
+ *  // As expected, the upper and lower bytes are much worse.
+ *
+ * Source: https://github.com/google/highwayhash/blob/0aaf66b/highwayhash/hh_avx2.h#L291
+ *
+ * Since our algorithm uses a pseudorandom secret to add some variance into the
+ * mix, we don't need to (or want to) mix as often or as much as HighwayHash does.
+ *
+ * This isn't as tight as XXH3_accumulate, but still written in SIMD to avoid
+ * extraction.
+ *
+ * Both XXH3_64bits and XXH3_128bits use this subroutine.
+ */
+
+XXH_FORCE_INLINE XXH_TARGET_AVX512 void
+XXH3_scrambleAcc_avx512(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret)
+{
+    XXH_ASSERT((((size_t)acc) & 63) == 0);
+    XXH_STATIC_ASSERT(XXH_STRIPE_LEN == sizeof(__m512i));
+    {   __m512i* const xacc = (__m512i*) acc;
+        const __m512i prime32 = _mm512_set1_epi32((int)XXH_PRIME32_1);
+
+        /* xacc[0] ^= (xacc[0] >> 47) */
+        __m512i const acc_vec     = *xacc;
+        __m512i const shifted     = _mm512_srli_epi64    (acc_vec, 47);
+        /* xacc[0] ^= secret; */
+        __m512i const key_vec     = _mm512_loadu_si512   (secret);
+        __m512i const data_key    = _mm512_ternarylogic_epi32(key_vec, acc_vec, shifted, 0x96 /* key_vec ^ acc_vec ^ shifted */);
+
+        /* xacc[0] *= XXH_PRIME32_1; */
+        __m512i const data_key_hi = _mm512_srli_epi64 (data_key, 32);
+        __m512i const prod_lo     = _mm512_mul_epu32     (data_key, prime32);
+        __m512i const prod_hi     = _mm512_mul_epu32     (data_key_hi, prime32);
+        *xacc = _mm512_add_epi64(prod_lo, _mm512_slli_epi64(prod_hi, 32));
+    }
+}
+
+XXH_FORCE_INLINE XXH_TARGET_AVX512 void
+XXH3_initCustomSecret_avx512(void* XXH_RESTRICT customSecret, xxh_u64 seed64)
+{
+    XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 63) == 0);
+    XXH_STATIC_ASSERT(XXH_SEC_ALIGN == 64);
+    XXH_ASSERT(((size_t)customSecret & 63) == 0);
+    (void)(&XXH_writeLE64);
+    {   int const nbRounds = XXH_SECRET_DEFAULT_SIZE / sizeof(__m512i);
+        __m512i const seed_pos = _mm512_set1_epi64((xxh_i64)seed64);
+        __m512i const seed     = _mm512_mask_sub_epi64(seed_pos, 0xAA, _mm512_set1_epi8(0), seed_pos);
+
+        const __m512i* const src  = (const __m512i*) ((const void*) XXH3_kSecret);
+              __m512i* const dest = (      __m512i*) customSecret;
+        int i;
+        XXH_ASSERT(((size_t)src & 63) == 0); /* control alignment */
+        XXH_ASSERT(((size_t)dest & 63) == 0);
+        for (i=0; i < nbRounds; ++i) {
+            dest[i] = _mm512_add_epi64(_mm512_load_si512(src + i), seed);
+    }   }
+}
+
+#endif
+
+#if (XXH_VECTOR == XXH_AVX2) \
+    || (defined(XXH_DISPATCH_AVX2) && XXH_DISPATCH_AVX2 != 0)
+
+#ifndef XXH_TARGET_AVX2
+# define XXH_TARGET_AVX2  /* disable attribute target */
+#endif
+
+XXH_FORCE_INLINE XXH_TARGET_AVX2 void
+XXH3_accumulate_512_avx2( void* XXH_RESTRICT acc,
+                    const void* XXH_RESTRICT input,
+                    const void* XXH_RESTRICT secret)
+{
+    XXH_ASSERT((((size_t)acc) & 31) == 0);
+    {   __m256i* const xacc    =       (__m256i *) acc;
+        /* Unaligned. This is mainly for pointer arithmetic, and because
+         * _mm256_loadu_si256 requires  a const __m256i * pointer for some reason. */
+        const         __m256i* const xinput  = (const __m256i *) input;
+        /* Unaligned. This is mainly for pointer arithmetic, and because
+         * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */
+        const         __m256i* const xsecret = (const __m256i *) secret;
+
+        size_t i;
+        for (i=0; i < XXH_STRIPE_LEN/sizeof(__m256i); i++) {
+            /* data_vec    = xinput[i]; */
+            __m256i const data_vec    = _mm256_loadu_si256    (xinput+i);
+            /* key_vec     = xsecret[i]; */
+            __m256i const key_vec     = _mm256_loadu_si256   (xsecret+i);
+            /* data_key    = data_vec ^ key_vec; */
+            __m256i const data_key    = _mm256_xor_si256     (data_vec, key_vec);
+            /* data_key_lo = data_key >> 32; */
+            __m256i const data_key_lo = _mm256_srli_epi64 (data_key, 32);
+            /* product     = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */
+            __m256i const product     = _mm256_mul_epu32     (data_key, data_key_lo);
+            /* xacc[i] += swap(data_vec); */
+            __m256i const data_swap = _mm256_shuffle_epi32(data_vec, _MM_SHUFFLE(1, 0, 3, 2));
+            __m256i const sum       = _mm256_add_epi64(xacc[i], data_swap);
+            /* xacc[i] += product; */
+            xacc[i] = _mm256_add_epi64(product, sum);
+    }   }
+}
+XXH_FORCE_INLINE XXH_TARGET_AVX2 XXH3_ACCUMULATE_TEMPLATE(avx2)
+
+XXH_FORCE_INLINE XXH_TARGET_AVX2 void
+XXH3_scrambleAcc_avx2(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret)
+{
+    XXH_ASSERT((((size_t)acc) & 31) == 0);
+    {   __m256i* const xacc = (__m256i*) acc;
+        /* Unaligned. This is mainly for pointer arithmetic, and because
+         * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */
+        const         __m256i* const xsecret = (const __m256i *) secret;
+        const __m256i prime32 = _mm256_set1_epi32((int)XXH_PRIME32_1);
+
+        size_t i;
+        for (i=0; i < XXH_STRIPE_LEN/sizeof(__m256i); i++) {
+            /* xacc[i] ^= (xacc[i] >> 47) */
+            __m256i const acc_vec     = xacc[i];
+            __m256i const shifted     = _mm256_srli_epi64    (acc_vec, 47);
+            __m256i const data_vec    = _mm256_xor_si256     (acc_vec, shifted);
+            /* xacc[i] ^= xsecret; */
+            __m256i const key_vec     = _mm256_loadu_si256   (xsecret+i);
+            __m256i const data_key    = _mm256_xor_si256     (data_vec, key_vec);
+
+            /* xacc[i] *= XXH_PRIME32_1; */
+            __m256i const data_key_hi = _mm256_srli_epi64 (data_key, 32);
+            __m256i const prod_lo     = _mm256_mul_epu32     (data_key, prime32);
+            __m256i const prod_hi     = _mm256_mul_epu32     (data_key_hi, prime32);
+            xacc[i] = _mm256_add_epi64(prod_lo, _mm256_slli_epi64(prod_hi, 32));
+        }
+    }
+}
+
+XXH_FORCE_INLINE XXH_TARGET_AVX2 void XXH3_initCustomSecret_avx2(void* XXH_RESTRICT customSecret, xxh_u64 seed64)
+{
+    XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 31) == 0);
+    XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE / sizeof(__m256i)) == 6);
+    XXH_STATIC_ASSERT(XXH_SEC_ALIGN <= 64);
+    (void)(&XXH_writeLE64);
+    XXH_PREFETCH(customSecret);
+    {   __m256i const seed = _mm256_set_epi64x((xxh_i64)(0U - seed64), (xxh_i64)seed64, (xxh_i64)(0U - seed64), (xxh_i64)seed64);
+
+        const __m256i* const src  = (const __m256i*) ((const void*) XXH3_kSecret);
+              __m256i*       dest = (      __m256i*) customSecret;
+
+#       if defined(__GNUC__) || defined(__clang__)
+        /*
+         * On GCC & Clang, marking 'dest' as modified will cause the compiler:
+         *   - do not extract the secret from sse registers in the internal loop
+         *   - use less common registers, and avoid pushing these reg into stack
+         */
+        XXH_COMPILER_GUARD(dest);
+#       endif
+        XXH_ASSERT(((size_t)src & 31) == 0); /* control alignment */
+        XXH_ASSERT(((size_t)dest & 31) == 0);
+
+        /* GCC -O2 need unroll loop manually */
+        dest[0] = _mm256_add_epi64(_mm256_load_si256(src+0), seed);
+        dest[1] = _mm256_add_epi64(_mm256_load_si256(src+1), seed);
+        dest[2] = _mm256_add_epi64(_mm256_load_si256(src+2), seed);
+        dest[3] = _mm256_add_epi64(_mm256_load_si256(src+3), seed);
+        dest[4] = _mm256_add_epi64(_mm256_load_si256(src+4), seed);
+        dest[5] = _mm256_add_epi64(_mm256_load_si256(src+5), seed);
+    }
+}
+
+#endif
+
+/* x86dispatch always generates SSE2 */
+#if (XXH_VECTOR == XXH_SSE2) || defined(XXH_X86DISPATCH)
+
+#ifndef XXH_TARGET_SSE2
+# define XXH_TARGET_SSE2  /* disable attribute target */
+#endif
+
+XXH_FORCE_INLINE XXH_TARGET_SSE2 void
+XXH3_accumulate_512_sse2( void* XXH_RESTRICT acc,
+                    const void* XXH_RESTRICT input,
+                    const void* XXH_RESTRICT secret)
+{
+    /* SSE2 is just a half-scale version of the AVX2 version. */
+    XXH_ASSERT((((size_t)acc) & 15) == 0);
+    {   __m128i* const xacc    =       (__m128i *) acc;
+        /* Unaligned. This is mainly for pointer arithmetic, and because
+         * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */
+        const         __m128i* const xinput  = (const __m128i *) input;
+        /* Unaligned. This is mainly for pointer arithmetic, and because
+         * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */
+        const         __m128i* const xsecret = (const __m128i *) secret;
+
+        size_t i;
+        for (i=0; i < XXH_STRIPE_LEN/sizeof(__m128i); i++) {
+            /* data_vec    = xinput[i]; */
+            __m128i const data_vec    = _mm_loadu_si128   (xinput+i);
+            /* key_vec     = xsecret[i]; */
+            __m128i const key_vec     = _mm_loadu_si128   (xsecret+i);
+            /* data_key    = data_vec ^ key_vec; */
+            __m128i const data_key    = _mm_xor_si128     (data_vec, key_vec);
+            /* data_key_lo = data_key >> 32; */
+            __m128i const data_key_lo = _mm_shuffle_epi32 (data_key, _MM_SHUFFLE(0, 3, 0, 1));
+            /* product     = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */
+            __m128i const product     = _mm_mul_epu32     (data_key, data_key_lo);
+            /* xacc[i] += swap(data_vec); */
+            __m128i const data_swap = _mm_shuffle_epi32(data_vec, _MM_SHUFFLE(1,0,3,2));
+            __m128i const sum       = _mm_add_epi64(xacc[i], data_swap);
+            /* xacc[i] += product; */
+            xacc[i] = _mm_add_epi64(product, sum);
+    }   }
+}
+XXH_FORCE_INLINE XXH_TARGET_SSE2 XXH3_ACCUMULATE_TEMPLATE(sse2)
+
+XXH_FORCE_INLINE XXH_TARGET_SSE2 void
+XXH3_scrambleAcc_sse2(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret)
+{
+    XXH_ASSERT((((size_t)acc) & 15) == 0);
+    {   __m128i* const xacc = (__m128i*) acc;
+        /* Unaligned. This is mainly for pointer arithmetic, and because
+         * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */
+        const         __m128i* const xsecret = (const __m128i *) secret;
+        const __m128i prime32 = _mm_set1_epi32((int)XXH_PRIME32_1);
+
+        size_t i;
+        for (i=0; i < XXH_STRIPE_LEN/sizeof(__m128i); i++) {
+            /* xacc[i] ^= (xacc[i] >> 47) */
+            __m128i const acc_vec     = xacc[i];
+            __m128i const shifted     = _mm_srli_epi64    (acc_vec, 47);
+            __m128i const data_vec    = _mm_xor_si128     (acc_vec, shifted);
+            /* xacc[i] ^= xsecret[i]; */
+            __m128i const key_vec     = _mm_loadu_si128   (xsecret+i);
+            __m128i const data_key    = _mm_xor_si128     (data_vec, key_vec);
+
+            /* xacc[i] *= XXH_PRIME32_1; */
+            __m128i const data_key_hi = _mm_shuffle_epi32 (data_key, _MM_SHUFFLE(0, 3, 0, 1));
+            __m128i const prod_lo     = _mm_mul_epu32     (data_key, prime32);
+            __m128i const prod_hi     = _mm_mul_epu32     (data_key_hi, prime32);
+            xacc[i] = _mm_add_epi64(prod_lo, _mm_slli_epi64(prod_hi, 32));
+        }
+    }
+}
+
+XXH_FORCE_INLINE XXH_TARGET_SSE2 void XXH3_initCustomSecret_sse2(void* XXH_RESTRICT customSecret, xxh_u64 seed64)
+{
+    XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 15) == 0);
+    (void)(&XXH_writeLE64);
+    {   int const nbRounds = XXH_SECRET_DEFAULT_SIZE / sizeof(__m128i);
+
+#       if defined(_MSC_VER) && defined(_M_IX86) && _MSC_VER < 1900
+        /* MSVC 32bit mode does not support _mm_set_epi64x before 2015 */
+        XXH_ALIGN(16) const xxh_i64 seed64x2[2] = { (xxh_i64)seed64, (xxh_i64)(0U - seed64) };
+        __m128i const seed = _mm_load_si128((__m128i const*)seed64x2);
+#       else
+        __m128i const seed = _mm_set_epi64x((xxh_i64)(0U - seed64), (xxh_i64)seed64);
+#       endif
+        int i;
+
+        const void* const src16 = XXH3_kSecret;
+        __m128i* dst16 = (__m128i*) customSecret;
+#       if defined(__GNUC__) || defined(__clang__)
+        /*
+         * On GCC & Clang, marking 'dest' as modified will cause the compiler:
+         *   - do not extract the secret from sse registers in the internal loop
+         *   - use less common registers, and avoid pushing these reg into stack
+         */
+        XXH_COMPILER_GUARD(dst16);
+#       endif
+        XXH_ASSERT(((size_t)src16 & 15) == 0); /* control alignment */
+        XXH_ASSERT(((size_t)dst16 & 15) == 0);
+
+        for (i=0; i < nbRounds; ++i) {
+            dst16[i] = _mm_add_epi64(_mm_load_si128((const __m128i *)src16+i), seed);
+    }   }
+}
+
+#endif
+
+#if (XXH_VECTOR == XXH_NEON)
+
+/* forward declarations for the scalar routines */
+XXH_FORCE_INLINE void
+XXH3_scalarRound(void* XXH_RESTRICT acc, void const* XXH_RESTRICT input,
+                 void const* XXH_RESTRICT secret, size_t lane);
+
+XXH_FORCE_INLINE void
+XXH3_scalarScrambleRound(void* XXH_RESTRICT acc,
+                         void const* XXH_RESTRICT secret, size_t lane);
+
+/*!
+ * @internal
+ * @brief The bulk processing loop for NEON and WASM SIMD128.
+ *
+ * The NEON code path is actually partially scalar when running on AArch64. This
+ * is to optimize the pipelining and can have up to 15% speedup depending on the
+ * CPU, and it also mitigates some GCC codegen issues.
+ *
+ * @see XXH3_NEON_LANES for configuring this and details about this optimization.
+ *
+ * NEON's 32-bit to 64-bit long multiply takes a half vector of 32-bit
+ * integers instead of the other platforms which mask full 64-bit vectors,
+ * so the setup is more complicated than just shifting right.
+ *
+ * Additionally, there is an optimization for 4 lanes at once noted below.
+ *
+ * Since, as stated, the most optimal amount of lanes for Cortexes is 6,
+ * there needs to be *three* versions of the accumulate operation used
+ * for the remaining 2 lanes.
+ *
+ * WASM's SIMD128 uses SIMDe's arm_neon.h polyfill because the intrinsics overlap
+ * nearly perfectly.
+ */
+
+XXH_FORCE_INLINE void
+XXH3_accumulate_512_neon( void* XXH_RESTRICT acc,
+                    const void* XXH_RESTRICT input,
+                    const void* XXH_RESTRICT secret)
+{
+    XXH_ASSERT((((size_t)acc) & 15) == 0);
+    XXH_STATIC_ASSERT(XXH3_NEON_LANES > 0 && XXH3_NEON_LANES <= XXH_ACC_NB && XXH3_NEON_LANES % 2 == 0);
+    {   /* GCC for darwin arm64 does not like aliasing here */
+        xxh_aliasing_uint64x2_t* const xacc = (xxh_aliasing_uint64x2_t*) acc;
+        /* We don't use a uint32x4_t pointer because it causes bus errors on ARMv7. */
+        uint8_t const* xinput = (const uint8_t *) input;
+        uint8_t const* xsecret  = (const uint8_t *) secret;
+
+        size_t i;
+#ifdef __wasm_simd128__
+        /*
+         * On WASM SIMD128, Clang emits direct address loads when XXH3_kSecret
+         * is constant propagated, which results in it converting it to this
+         * inside the loop:
+         *
+         *    a = v128.load(XXH3_kSecret +  0 + $secret_offset, offset = 0)
+         *    b = v128.load(XXH3_kSecret + 16 + $secret_offset, offset = 0)
+         *    ...
+         *
+         * This requires a full 32-bit address immediate (and therefore a 6 byte
+         * instruction) as well as an add for each offset.
+         *
+         * Putting an asm guard prevents it from folding (at the cost of losing
+         * the alignment hint), and uses the free offset in `v128.load` instead
+         * of adding secret_offset each time which overall reduces code size by
+         * about a kilobyte and improves performance.
+         */
+        XXH_COMPILER_GUARD(xsecret);
+#endif
+        /* Scalar lanes use the normal scalarRound routine */
+        for (i = XXH3_NEON_LANES; i < XXH_ACC_NB; i++) {
+            XXH3_scalarRound(acc, input, secret, i);
+        }
+        i = 0;
+        /* 4 NEON lanes at a time. */
+        for (; i+1 < XXH3_NEON_LANES / 2; i+=2) {
+            /* data_vec = xinput[i]; */
+            uint64x2_t data_vec_1 = XXH_vld1q_u64(xinput  + (i * 16));
+            uint64x2_t data_vec_2 = XXH_vld1q_u64(xinput  + ((i+1) * 16));
+            /* key_vec  = xsecret[i];  */
+            uint64x2_t key_vec_1  = XXH_vld1q_u64(xsecret + (i * 16));
+            uint64x2_t key_vec_2  = XXH_vld1q_u64(xsecret + ((i+1) * 16));
+            /* data_swap = swap(data_vec) */
+            uint64x2_t data_swap_1 = vextq_u64(data_vec_1, data_vec_1, 1);
+            uint64x2_t data_swap_2 = vextq_u64(data_vec_2, data_vec_2, 1);
+            /* data_key = data_vec ^ key_vec; */
+            uint64x2_t data_key_1 = veorq_u64(data_vec_1, key_vec_1);
+            uint64x2_t data_key_2 = veorq_u64(data_vec_2, key_vec_2);
+
+            /*
+             * If we reinterpret the 64x2 vectors as 32x4 vectors, we can use a
+             * de-interleave operation for 4 lanes in 1 step with `vuzpq_u32` to
+             * get one vector with the low 32 bits of each lane, and one vector
+             * with the high 32 bits of each lane.
+             *
+             * The intrinsic returns a double vector because the original ARMv7-a
+             * instruction modified both arguments in place. AArch64 and SIMD128 emit
+             * two instructions from this intrinsic.
+             *
+             *  [ dk11L | dk11H | dk12L | dk12H ] -> [ dk11L | dk12L | dk21L | dk22L ]
+             *  [ dk21L | dk21H | dk22L | dk22H ] -> [ dk11H | dk12H | dk21H | dk22H ]
+             */
+            uint32x4x2_t unzipped = vuzpq_u32(
+                vreinterpretq_u32_u64(data_key_1),
+                vreinterpretq_u32_u64(data_key_2)
+            );
+            /* data_key_lo = data_key & 0xFFFFFFFF */
+            uint32x4_t data_key_lo = unzipped.val[0];
+            /* data_key_hi = data_key >> 32 */
+            uint32x4_t data_key_hi = unzipped.val[1];
+            /*
+             * Then, we can split the vectors horizontally and multiply which, as for most
+             * widening intrinsics, have a variant that works on both high half vectors
+             * for free on AArch64. A similar instruction is available on SIMD128.
+             *
+             * sum = data_swap + (u64x2) data_key_lo * (u64x2) data_key_hi
+             */
+            uint64x2_t sum_1 = XXH_vmlal_low_u32(data_swap_1, data_key_lo, data_key_hi);
+            uint64x2_t sum_2 = XXH_vmlal_high_u32(data_swap_2, data_key_lo, data_key_hi);
+            /*
+             * Clang reorders
+             *    a += b * c;     // umlal   swap.2d, dkl.2s, dkh.2s
+             *    c += a;         // add     acc.2d, acc.2d, swap.2d
+             * to
+             *    c += a;         // add     acc.2d, acc.2d, swap.2d
+             *    c += b * c;     // umlal   acc.2d, dkl.2s, dkh.2s
+             *
+             * While it would make sense in theory since the addition is faster,
+             * for reasons likely related to umlal being limited to certain NEON
+             * pipelines, this is worse. A compiler guard fixes this.
+             */
+            XXH_COMPILER_GUARD_CLANG_NEON(sum_1);
+            XXH_COMPILER_GUARD_CLANG_NEON(sum_2);
+            /* xacc[i] = acc_vec + sum; */
+            xacc[i]   = vaddq_u64(xacc[i], sum_1);
+            xacc[i+1] = vaddq_u64(xacc[i+1], sum_2);
+        }
+        /* Operate on the remaining NEON lanes 2 at a time. */
+        for (; i < XXH3_NEON_LANES / 2; i++) {
+            /* data_vec = xinput[i]; */
+            uint64x2_t data_vec = XXH_vld1q_u64(xinput  + (i * 16));
+            /* key_vec  = xsecret[i];  */
+            uint64x2_t key_vec  = XXH_vld1q_u64(xsecret + (i * 16));
+            /* acc_vec_2 = swap(data_vec) */
+            uint64x2_t data_swap = vextq_u64(data_vec, data_vec, 1);
+            /* data_key = data_vec ^ key_vec; */
+            uint64x2_t data_key = veorq_u64(data_vec, key_vec);
+            /* For two lanes, just use VMOVN and VSHRN. */
+            /* data_key_lo = data_key & 0xFFFFFFFF; */
+            uint32x2_t data_key_lo = vmovn_u64(data_key);
+            /* data_key_hi = data_key >> 32; */
+            uint32x2_t data_key_hi = vshrn_n_u64(data_key, 32);
+            /* sum = data_swap + (u64x2) data_key_lo * (u64x2) data_key_hi; */
+            uint64x2_t sum = vmlal_u32(data_swap, data_key_lo, data_key_hi);
+            /* Same Clang workaround as before */
+            XXH_COMPILER_GUARD_CLANG_NEON(sum);
+            /* xacc[i] = acc_vec + sum; */
+            xacc[i] = vaddq_u64 (xacc[i], sum);
+        }
+    }
+}
+XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(neon)
+
+XXH_FORCE_INLINE void
+XXH3_scrambleAcc_neon(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret)
+{
+    XXH_ASSERT((((size_t)acc) & 15) == 0);
+
+    {   xxh_aliasing_uint64x2_t* xacc       = (xxh_aliasing_uint64x2_t*) acc;
+        uint8_t const* xsecret = (uint8_t const*) secret;
+
+        size_t i;
+        /* WASM uses operator overloads and doesn't need these. */
+#ifndef __wasm_simd128__
+        /* { prime32_1, prime32_1 } */
+        uint32x2_t const kPrimeLo = vdup_n_u32(XXH_PRIME32_1);
+        /* { 0, prime32_1, 0, prime32_1 } */
+        uint32x4_t const kPrimeHi = vreinterpretq_u32_u64(vdupq_n_u64((xxh_u64)XXH_PRIME32_1 << 32));
+#endif
+
+        /* AArch64 uses both scalar and neon at the same time */
+        for (i = XXH3_NEON_LANES; i < XXH_ACC_NB; i++) {
+            XXH3_scalarScrambleRound(acc, secret, i);
+        }
+        for (i=0; i < XXH3_NEON_LANES / 2; i++) {
+            /* xacc[i] ^= (xacc[i] >> 47); */
+            uint64x2_t acc_vec  = xacc[i];
+            uint64x2_t shifted  = vshrq_n_u64(acc_vec, 47);
+            uint64x2_t data_vec = veorq_u64(acc_vec, shifted);
+
+            /* xacc[i] ^= xsecret[i]; */
+            uint64x2_t key_vec  = XXH_vld1q_u64(xsecret + (i * 16));
+            uint64x2_t data_key = veorq_u64(data_vec, key_vec);
+            /* xacc[i] *= XXH_PRIME32_1 */
+#ifdef __wasm_simd128__
+            /* SIMD128 has multiply by u64x2, use it instead of expanding and scalarizing */
+            xacc[i] = data_key * XXH_PRIME32_1;
+#else
+            /*
+             * Expanded version with portable NEON intrinsics
+             *
+             *    lo(x) * lo(y) + (hi(x) * lo(y) << 32)
+             *
+             * prod_hi = hi(data_key) * lo(prime) << 32
+             *
+             * Since we only need 32 bits of this multiply a trick can be used, reinterpreting the vector
+             * as a uint32x4_t and multiplying by { 0, prime, 0, prime } to cancel out the unwanted bits
+             * and avoid the shift.
+             */
+            uint32x4_t prod_hi = vmulq_u32 (vreinterpretq_u32_u64(data_key), kPrimeHi);
+            /* Extract low bits for vmlal_u32  */
+            uint32x2_t data_key_lo = vmovn_u64(data_key);
+            /* xacc[i] = prod_hi + lo(data_key) * XXH_PRIME32_1; */
+            xacc[i] = vmlal_u32(vreinterpretq_u64_u32(prod_hi), data_key_lo, kPrimeLo);
+#endif
+        }
+    }
+}
+#endif
+
+#if (XXH_VECTOR == XXH_VSX)
+
+XXH_FORCE_INLINE void
+XXH3_accumulate_512_vsx(  void* XXH_RESTRICT acc,
+                    const void* XXH_RESTRICT input,
+                    const void* XXH_RESTRICT secret)
+{
+    /* presumed aligned */
+    xxh_aliasing_u64x2* const xacc = (xxh_aliasing_u64x2*) acc;
+    xxh_u8 const* const xinput   = (xxh_u8 const*) input;   /* no alignment restriction */
+    xxh_u8 const* const xsecret  = (xxh_u8 const*) secret;    /* no alignment restriction */
+    xxh_u64x2 const v32 = { 32, 32 };
+    size_t i;
+    for (i = 0; i < XXH_STRIPE_LEN / sizeof(xxh_u64x2); i++) {
+        /* data_vec = xinput[i]; */
+        xxh_u64x2 const data_vec = XXH_vec_loadu(xinput + 16*i);
+        /* key_vec = xsecret[i]; */
+        xxh_u64x2 const key_vec  = XXH_vec_loadu(xsecret + 16*i);
+        xxh_u64x2 const data_key = data_vec ^ key_vec;
+        /* shuffled = (data_key << 32) | (data_key >> 32); */
+        xxh_u32x4 const shuffled = (xxh_u32x4)vec_rl(data_key, v32);
+        /* product = ((xxh_u64x2)data_key & 0xFFFFFFFF) * ((xxh_u64x2)shuffled & 0xFFFFFFFF); */
+        xxh_u64x2 const product  = XXH_vec_mulo((xxh_u32x4)data_key, shuffled);
+        /* acc_vec = xacc[i]; */
+        xxh_u64x2 acc_vec        = xacc[i];
+        acc_vec += product;
+
+        /* swap high and low halves */
+#ifdef __s390x__
+        acc_vec += vec_permi(data_vec, data_vec, 2);
+#else
+        acc_vec += vec_xxpermdi(data_vec, data_vec, 2);
+#endif
+        xacc[i] = acc_vec;
+    }
+}
+XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(vsx)
+
+XXH_FORCE_INLINE void
+XXH3_scrambleAcc_vsx(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret)
+{
+    XXH_ASSERT((((size_t)acc) & 15) == 0);
+
+    {   xxh_aliasing_u64x2* const xacc = (xxh_aliasing_u64x2*) acc;
+        const xxh_u8* const xsecret = (const xxh_u8*) secret;
+        /* constants */
+        xxh_u64x2 const v32  = { 32, 32 };
+        xxh_u64x2 const v47 = { 47, 47 };
+        xxh_u32x4 const prime = { XXH_PRIME32_1, XXH_PRIME32_1, XXH_PRIME32_1, XXH_PRIME32_1 };
+        size_t i;
+        for (i = 0; i < XXH_STRIPE_LEN / sizeof(xxh_u64x2); i++) {
+            /* xacc[i] ^= (xacc[i] >> 47); */
+            xxh_u64x2 const acc_vec  = xacc[i];
+            xxh_u64x2 const data_vec = acc_vec ^ (acc_vec >> v47);
+
+            /* xacc[i] ^= xsecret[i]; */
+            xxh_u64x2 const key_vec  = XXH_vec_loadu(xsecret + 16*i);
+            xxh_u64x2 const data_key = data_vec ^ key_vec;
+
+            /* xacc[i] *= XXH_PRIME32_1 */
+            /* prod_lo = ((xxh_u64x2)data_key & 0xFFFFFFFF) * ((xxh_u64x2)prime & 0xFFFFFFFF);  */
+            xxh_u64x2 const prod_even  = XXH_vec_mule((xxh_u32x4)data_key, prime);
+            /* prod_hi = ((xxh_u64x2)data_key >> 32) * ((xxh_u64x2)prime >> 32);  */
+            xxh_u64x2 const prod_odd  = XXH_vec_mulo((xxh_u32x4)data_key, prime);
+            xacc[i] = prod_odd + (prod_even << v32);
+    }   }
+}
+
+#endif
+
+#if (XXH_VECTOR == XXH_SVE)
+
+XXH_FORCE_INLINE void
+XXH3_accumulate_512_sve( void* XXH_RESTRICT acc,
+                   const void* XXH_RESTRICT input,
+                   const void* XXH_RESTRICT secret)
+{
+    uint64_t *xacc = (uint64_t *)acc;
+    const uint64_t *xinput = (const uint64_t *)(const void *)input;
+    const uint64_t *xsecret = (const uint64_t *)(const void *)secret;
+    svuint64_t kSwap = sveor_n_u64_z(svptrue_b64(), svindex_u64(0, 1), 1);
+    uint64_t element_count = svcntd();
+    if (element_count >= 8) {
+        svbool_t mask = svptrue_pat_b64(SV_VL8);
+        svuint64_t vacc = svld1_u64(mask, xacc);
+        ACCRND(vacc, 0);
+        svst1_u64(mask, xacc, vacc);
+    } else if (element_count == 2) {   /* sve128 */
+        svbool_t mask = svptrue_pat_b64(SV_VL2);
+        svuint64_t acc0 = svld1_u64(mask, xacc + 0);
+        svuint64_t acc1 = svld1_u64(mask, xacc + 2);
+        svuint64_t acc2 = svld1_u64(mask, xacc + 4);
+        svuint64_t acc3 = svld1_u64(mask, xacc + 6);
+        ACCRND(acc0, 0);
+        ACCRND(acc1, 2);
+        ACCRND(acc2, 4);
+        ACCRND(acc3, 6);
+        svst1_u64(mask, xacc + 0, acc0);
+        svst1_u64(mask, xacc + 2, acc1);
+        svst1_u64(mask, xacc + 4, acc2);
+        svst1_u64(mask, xacc + 6, acc3);
+    } else {
+        svbool_t mask = svptrue_pat_b64(SV_VL4);
+        svuint64_t acc0 = svld1_u64(mask, xacc + 0);
+        svuint64_t acc1 = svld1_u64(mask, xacc + 4);
+        ACCRND(acc0, 0);
+        ACCRND(acc1, 4);
+        svst1_u64(mask, xacc + 0, acc0);
+        svst1_u64(mask, xacc + 4, acc1);
+    }
+}
+
+XXH_FORCE_INLINE void
+XXH3_accumulate_sve(xxh_u64* XXH_RESTRICT acc,
+               const xxh_u8* XXH_RESTRICT input,
+               const xxh_u8* XXH_RESTRICT secret,
+               size_t nbStripes)
+{
+    if (nbStripes != 0) {
+        uint64_t *xacc = (uint64_t *)acc;
+        const uint64_t *xinput = (const uint64_t *)(const void *)input;
+        const uint64_t *xsecret = (const uint64_t *)(const void *)secret;
+        svuint64_t kSwap = sveor_n_u64_z(svptrue_b64(), svindex_u64(0, 1), 1);
+        uint64_t element_count = svcntd();
+        if (element_count >= 8) {
+            svbool_t mask = svptrue_pat_b64(SV_VL8);
+            svuint64_t vacc = svld1_u64(mask, xacc + 0);
+            do {
+                /* svprfd(svbool_t, void *, enum svfprop); */
+                svprfd(mask, xinput + 128, SV_PLDL1STRM);
+                ACCRND(vacc, 0);
+                xinput += 8;
+                xsecret += 1;
+                nbStripes--;
+           } while (nbStripes != 0);
+
+           svst1_u64(mask, xacc + 0, vacc);
+        } else if (element_count == 2) { /* sve128 */
+            svbool_t mask = svptrue_pat_b64(SV_VL2);
+            svuint64_t acc0 = svld1_u64(mask, xacc + 0);
+            svuint64_t acc1 = svld1_u64(mask, xacc + 2);
+            svuint64_t acc2 = svld1_u64(mask, xacc + 4);
+            svuint64_t acc3 = svld1_u64(mask, xacc + 6);
+            do {
+                svprfd(mask, xinput + 128, SV_PLDL1STRM);
+                ACCRND(acc0, 0);
+                ACCRND(acc1, 2);
+                ACCRND(acc2, 4);
+                ACCRND(acc3, 6);
+                xinput += 8;
+                xsecret += 1;
+                nbStripes--;
+           } while (nbStripes != 0);
+
+           svst1_u64(mask, xacc + 0, acc0);
+           svst1_u64(mask, xacc + 2, acc1);
+           svst1_u64(mask, xacc + 4, acc2);
+           svst1_u64(mask, xacc + 6, acc3);
+        } else {
+            svbool_t mask = svptrue_pat_b64(SV_VL4);
+            svuint64_t acc0 = svld1_u64(mask, xacc + 0);
+            svuint64_t acc1 = svld1_u64(mask, xacc + 4);
+            do {
+                svprfd(mask, xinput + 128, SV_PLDL1STRM);
+                ACCRND(acc0, 0);
+                ACCRND(acc1, 4);
+                xinput += 8;
+                xsecret += 1;
+                nbStripes--;
+           } while (nbStripes != 0);
+
+           svst1_u64(mask, xacc + 0, acc0);
+           svst1_u64(mask, xacc + 4, acc1);
+       }
+    }
+}
+
+#endif
+
+/* scalar variants - universal */
+
+#if defined(__aarch64__) && (defined(__GNUC__) || defined(__clang__))
+/*
+ * In XXH3_scalarRound(), GCC and Clang have a similar codegen issue, where they
+ * emit an excess mask and a full 64-bit multiply-add (MADD X-form).
+ *
+ * While this might not seem like much, as AArch64 is a 64-bit architecture, only
+ * big Cortex designs have a full 64-bit multiplier.
+ *
+ * On the little cores, the smaller 32-bit multiplier is used, and full 64-bit
+ * multiplies expand to 2-3 multiplies in microcode. This has a major penalty
+ * of up to 4 latency cycles and 2 stall cycles in the multiply pipeline.
+ *
+ * Thankfully, AArch64 still provides the 32-bit long multiply-add (UMADDL) which does
+ * not have this penalty and does the mask automatically.
+ */
+XXH_FORCE_INLINE xxh_u64
+XXH_mult32to64_add64(xxh_u64 lhs, xxh_u64 rhs, xxh_u64 acc)
+{
+    xxh_u64 ret;
+    /* note: %x = 64-bit register, %w = 32-bit register */
+    __asm__("umaddl %x0, %w1, %w2, %x3" : "=r" (ret) : "r" (lhs), "r" (rhs), "r" (acc));
+    return ret;
+}
+#else
+XXH_FORCE_INLINE xxh_u64
+XXH_mult32to64_add64(xxh_u64 lhs, xxh_u64 rhs, xxh_u64 acc)
+{
+    return XXH_mult32to64((xxh_u32)lhs, (xxh_u32)rhs) + acc;
+}
+#endif
+
+/*!
+ * @internal
+ * @brief Scalar round for @ref XXH3_accumulate_512_scalar().
+ *
+ * This is extracted to its own function because the NEON path uses a combination
+ * of NEON and scalar.
+ */
+XXH_FORCE_INLINE void
+XXH3_scalarRound(void* XXH_RESTRICT acc,
+                 void const* XXH_RESTRICT input,
+                 void const* XXH_RESTRICT secret,
+                 size_t lane)
+{
+    xxh_u64* xacc = (xxh_u64*) acc;
+    xxh_u8 const* xinput  = (xxh_u8 const*) input;
+    xxh_u8 const* xsecret = (xxh_u8 const*) secret;
+    XXH_ASSERT(lane < XXH_ACC_NB);
+    XXH_ASSERT(((size_t)acc & (XXH_ACC_ALIGN-1)) == 0);
+    {
+        xxh_u64 const data_val = XXH_readLE64(xinput + lane * 8);
+        xxh_u64 const data_key = data_val ^ XXH_readLE64(xsecret + lane * 8);
+        xacc[lane ^ 1] += data_val; /* swap adjacent lanes */
+        xacc[lane] = XXH_mult32to64_add64(data_key /* & 0xFFFFFFFF */, data_key >> 32, xacc[lane]);
+    }
+}
+
+/*!
+ * @internal
+ * @brief Processes a 64 byte block of data using the scalar path.
+ */
+XXH_FORCE_INLINE void
+XXH3_accumulate_512_scalar(void* XXH_RESTRICT acc,
+                     const void* XXH_RESTRICT input,
+                     const void* XXH_RESTRICT secret)
+{
+    size_t i;
+    /* ARM GCC refuses to unroll this loop, resulting in a 24% slowdown on ARMv6. */
+#if defined(__GNUC__) && !defined(__clang__) \
+  && (defined(__arm__) || defined(__thumb2__)) \
+  && defined(__ARM_FEATURE_UNALIGNED) /* no unaligned access just wastes bytes */ \
+  && XXH_SIZE_OPT <= 0
+#  pragma GCC unroll 8
+#endif
+    for (i=0; i < XXH_ACC_NB; i++) {
+        XXH3_scalarRound(acc, input, secret, i);
+    }
+}
+XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(scalar)
+
+/*!
+ * @internal
+ * @brief Scalar scramble step for @ref XXH3_scrambleAcc_scalar().
+ *
+ * This is extracted to its own function because the NEON path uses a combination
+ * of NEON and scalar.
+ */
+XXH_FORCE_INLINE void
+XXH3_scalarScrambleRound(void* XXH_RESTRICT acc,
+                         void const* XXH_RESTRICT secret,
+                         size_t lane)
+{
+    xxh_u64* const xacc = (xxh_u64*) acc;   /* presumed aligned */
+    const xxh_u8* const xsecret = (const xxh_u8*) secret;   /* no alignment restriction */
+    XXH_ASSERT((((size_t)acc) & (XXH_ACC_ALIGN-1)) == 0);
+    XXH_ASSERT(lane < XXH_ACC_NB);
+    {
+        xxh_u64 const key64 = XXH_readLE64(xsecret + lane * 8);
+        xxh_u64 acc64 = xacc[lane];
+        acc64 = XXH_xorshift64(acc64, 47);
+        acc64 ^= key64;
+        acc64 *= XXH_PRIME32_1;
+        xacc[lane] = acc64;
+    }
+}
+
+/*!
+ * @internal
+ * @brief Scrambles the accumulators after a large chunk has been read
+ */
+XXH_FORCE_INLINE void
+XXH3_scrambleAcc_scalar(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret)
+{
+    size_t i;
+    for (i=0; i < XXH_ACC_NB; i++) {
+        XXH3_scalarScrambleRound(acc, secret, i);
+    }
+}
+
+XXH_FORCE_INLINE void
+XXH3_initCustomSecret_scalar(void* XXH_RESTRICT customSecret, xxh_u64 seed64)
+{
+    /*
+     * We need a separate pointer for the hack below,
+     * which requires a non-const pointer.
+     * Any decent compiler will optimize this out otherwise.
+     */
+    const xxh_u8* kSecretPtr = XXH3_kSecret;
+    XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 15) == 0);
+
+#if defined(__GNUC__) && defined(__aarch64__)
+    /*
+     * UGLY HACK:
+     * GCC and Clang generate a bunch of MOV/MOVK pairs for aarch64, and they are
+     * placed sequentially, in order, at the top of the unrolled loop.
+     *
+     * While MOVK is great for generating constants (2 cycles for a 64-bit
+     * constant compared to 4 cycles for LDR), it fights for bandwidth with
+     * the arithmetic instructions.
+     *
+     *   I   L   S
+     * MOVK
+     * MOVK
+     * MOVK
+     * MOVK
+     * ADD
+     * SUB      STR
+     *          STR
+     * By forcing loads from memory (as the asm line causes the compiler to assume
+     * that XXH3_kSecretPtr has been changed), the pipelines are used more
+     * efficiently:
+     *   I   L   S
+     *      LDR
+     *  ADD LDR
+     *  SUB     STR
+     *          STR
+     *
+     * See XXH3_NEON_LANES for details on the pipsline.
+     *
+     * XXH3_64bits_withSeed, len == 256, Snapdragon 835
+     *   without hack: 2654.4 MB/s
+     *   with hack:    3202.9 MB/s
+     */
+    XXH_COMPILER_GUARD(kSecretPtr);
+#endif
+    {   int const nbRounds = XXH_SECRET_DEFAULT_SIZE / 16;
+        int i;
+        for (i=0; i < nbRounds; i++) {
+            /*
+             * The asm hack causes the compiler to assume that kSecretPtr aliases with
+             * customSecret, and on aarch64, this prevented LDP from merging two
+             * loads together for free. Putting the loads together before the stores
+             * properly generates LDP.
+             */
+            xxh_u64 lo = XXH_readLE64(kSecretPtr + 16*i)     + seed64;
+            xxh_u64 hi = XXH_readLE64(kSecretPtr + 16*i + 8) - seed64;
+            XXH_writeLE64((xxh_u8*)customSecret + 16*i,     lo);
+            XXH_writeLE64((xxh_u8*)customSecret + 16*i + 8, hi);
+    }   }
+}
+
+
+typedef void (*XXH3_f_accumulate)(xxh_u64* XXH_RESTRICT, const xxh_u8* XXH_RESTRICT, const xxh_u8* XXH_RESTRICT, size_t);
+typedef void (*XXH3_f_scrambleAcc)(void* XXH_RESTRICT, const void*);
+typedef void (*XXH3_f_initCustomSecret)(void* XXH_RESTRICT, xxh_u64);
+
+
+#if (XXH_VECTOR == XXH_AVX512)
+
+#define XXH3_accumulate_512 XXH3_accumulate_512_avx512
+#define XXH3_accumulate     XXH3_accumulate_avx512
+#define XXH3_scrambleAcc    XXH3_scrambleAcc_avx512
+#define XXH3_initCustomSecret XXH3_initCustomSecret_avx512
+
+#elif (XXH_VECTOR == XXH_AVX2)
+
+#define XXH3_accumulate_512 XXH3_accumulate_512_avx2
+#define XXH3_accumulate     XXH3_accumulate_avx2
+#define XXH3_scrambleAcc    XXH3_scrambleAcc_avx2
+#define XXH3_initCustomSecret XXH3_initCustomSecret_avx2
+
+#elif (XXH_VECTOR == XXH_SSE2)
+
+#define XXH3_accumulate_512 XXH3_accumulate_512_sse2
+#define XXH3_accumulate     XXH3_accumulate_sse2
+#define XXH3_scrambleAcc    XXH3_scrambleAcc_sse2
+#define XXH3_initCustomSecret XXH3_initCustomSecret_sse2
+
+#elif (XXH_VECTOR == XXH_NEON)
+
+#define XXH3_accumulate_512 XXH3_accumulate_512_neon
+#define XXH3_accumulate     XXH3_accumulate_neon
+#define XXH3_scrambleAcc    XXH3_scrambleAcc_neon
+#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar
+
+#elif (XXH_VECTOR == XXH_VSX)
+
+#define XXH3_accumulate_512 XXH3_accumulate_512_vsx
+#define XXH3_accumulate     XXH3_accumulate_vsx
+#define XXH3_scrambleAcc    XXH3_scrambleAcc_vsx
+#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar
+
+#elif (XXH_VECTOR == XXH_SVE)
+#define XXH3_accumulate_512 XXH3_accumulate_512_sve
+#define XXH3_accumulate     XXH3_accumulate_sve
+#define XXH3_scrambleAcc    XXH3_scrambleAcc_scalar
+#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar
+
+#else /* scalar */
+
+#define XXH3_accumulate_512 XXH3_accumulate_512_scalar
+#define XXH3_accumulate     XXH3_accumulate_scalar
+#define XXH3_scrambleAcc    XXH3_scrambleAcc_scalar
+#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar
+
+#endif
+
+#if XXH_SIZE_OPT >= 1 /* don't do SIMD for initialization */
+#  undef XXH3_initCustomSecret
+#  define XXH3_initCustomSecret XXH3_initCustomSecret_scalar
+#endif
+
+XXH_FORCE_INLINE void
+XXH3_hashLong_internal_loop(xxh_u64* XXH_RESTRICT acc,
+                      const xxh_u8* XXH_RESTRICT input, size_t len,
+                      const xxh_u8* XXH_RESTRICT secret, size_t secretSize,
+                            XXH3_f_accumulate f_acc,
+                            XXH3_f_scrambleAcc f_scramble)
+{
+    size_t const nbStripesPerBlock = (secretSize - XXH_STRIPE_LEN) / XXH_SECRET_CONSUME_RATE;
+    size_t const block_len = XXH_STRIPE_LEN * nbStripesPerBlock;
+    size_t const nb_blocks = (len - 1) / block_len;
+
+    size_t n;
+
+    XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN);
+
+    for (n = 0; n < nb_blocks; n++) {
+        f_acc(acc, input + n*block_len, secret, nbStripesPerBlock);
+        f_scramble(acc, secret + secretSize - XXH_STRIPE_LEN);
+    }
+
+    /* last partial block */
+    XXH_ASSERT(len > XXH_STRIPE_LEN);
+    {   size_t const nbStripes = ((len - 1) - (block_len * nb_blocks)) / XXH_STRIPE_LEN;
+        XXH_ASSERT(nbStripes <= (secretSize / XXH_SECRET_CONSUME_RATE));
+        f_acc(acc, input + nb_blocks*block_len, secret, nbStripes);
+
+        /* last stripe */
+        {   const xxh_u8* const p = input + len - XXH_STRIPE_LEN;
+#define XXH_SECRET_LASTACC_START 7  /* not aligned on 8, last secret is different from acc & scrambler */
+            XXH3_accumulate_512(acc, p, secret + secretSize - XXH_STRIPE_LEN - XXH_SECRET_LASTACC_START);
+    }   }
+}
+
+XXH_FORCE_INLINE xxh_u64
+XXH3_mix2Accs(const xxh_u64* XXH_RESTRICT acc, const xxh_u8* XXH_RESTRICT secret)
+{
+    return XXH3_mul128_fold64(
+               acc[0] ^ XXH_readLE64(secret),
+               acc[1] ^ XXH_readLE64(secret+8) );
+}
+
+static XXH64_hash_t
+XXH3_mergeAccs(const xxh_u64* XXH_RESTRICT acc, const xxh_u8* XXH_RESTRICT secret, xxh_u64 start)
+{
+    xxh_u64 result64 = start;
+    size_t i = 0;
+
+    for (i = 0; i < 4; i++) {
+        result64 += XXH3_mix2Accs(acc+2*i, secret + 16*i);
+#if defined(__clang__)                                /* Clang */ \
+    && (defined(__arm__) || defined(__thumb__))       /* ARMv7 */ \
+    && (defined(__ARM_NEON) || defined(__ARM_NEON__)) /* NEON */  \
+    && !defined(XXH_ENABLE_AUTOVECTORIZE)             /* Define to disable */
+        /*
+         * UGLY HACK:
+         * Prevent autovectorization on Clang ARMv7-a. Exact same problem as
+         * the one in XXH3_len_129to240_64b. Speeds up shorter keys > 240b.
+         * XXH3_64bits, len == 256, Snapdragon 835:
+         *   without hack: 2063.7 MB/s
+         *   with hack:    2560.7 MB/s
+         */
+        XXH_COMPILER_GUARD(result64);
+#endif
+    }
+
+    return XXH3_avalanche(result64);
+}
+
+#define XXH3_INIT_ACC { XXH_PRIME32_3, XXH_PRIME64_1, XXH_PRIME64_2, XXH_PRIME64_3, \
+                        XXH_PRIME64_4, XXH_PRIME32_2, XXH_PRIME64_5, XXH_PRIME32_1 }
+
+XXH_FORCE_INLINE XXH64_hash_t
+XXH3_hashLong_64b_internal(const void* XXH_RESTRICT input, size_t len,
+                           const void* XXH_RESTRICT secret, size_t secretSize,
+                           XXH3_f_accumulate f_acc,
+                           XXH3_f_scrambleAcc f_scramble)
+{
+    XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[XXH_ACC_NB] = XXH3_INIT_ACC;
+
+    XXH3_hashLong_internal_loop(acc, (const xxh_u8*)input, len, (const xxh_u8*)secret, secretSize, f_acc, f_scramble);
+
+    /* converge into final hash */
+    XXH_STATIC_ASSERT(sizeof(acc) == 64);
+    /* do not align on 8, so that the secret is different from the accumulator */
+#define XXH_SECRET_MERGEACCS_START 11
+    XXH_ASSERT(secretSize >= sizeof(acc) + XXH_SECRET_MERGEACCS_START);
+    return XXH3_mergeAccs(acc, (const xxh_u8*)secret + XXH_SECRET_MERGEACCS_START, (xxh_u64)len * XXH_PRIME64_1);
+}
+
+/*
+ * It's important for performance to transmit secret's size (when it's static)
+ * so that the compiler can properly optimize the vectorized loop.
+ * This makes a big performance difference for "medium" keys (<1 KB) when using AVX instruction set.
+ * When the secret size is unknown, or on GCC 12 where the mix of NO_INLINE and FORCE_INLINE
+ * breaks -Og, this is XXH_NO_INLINE.
+ */
+XXH3_WITH_SECRET_INLINE XXH64_hash_t
+XXH3_hashLong_64b_withSecret(const void* XXH_RESTRICT input, size_t len,
+                             XXH64_hash_t seed64, const xxh_u8* XXH_RESTRICT secret, size_t secretLen)
+{
+    (void)seed64;
+    return XXH3_hashLong_64b_internal(input, len, secret, secretLen, XXH3_accumulate, XXH3_scrambleAcc);
+}
+
+/*
+ * It's preferable for performance that XXH3_hashLong is not inlined,
+ * as it results in a smaller function for small data, easier to the instruction cache.
+ * Note that inside this no_inline function, we do inline the internal loop,
+ * and provide a statically defined secret size to allow optimization of vector loop.
+ */
+XXH_NO_INLINE XXH_PUREF XXH64_hash_t
+XXH3_hashLong_64b_default(const void* XXH_RESTRICT input, size_t len,
+                          XXH64_hash_t seed64, const xxh_u8* XXH_RESTRICT secret, size_t secretLen)
+{
+    (void)seed64; (void)secret; (void)secretLen;
+    return XXH3_hashLong_64b_internal(input, len, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_accumulate, XXH3_scrambleAcc);
+}
+
+/*
+ * XXH3_hashLong_64b_withSeed():
+ * Generate a custom key based on alteration of default XXH3_kSecret with the seed,
+ * and then use this key for long mode hashing.
+ *
+ * This operation is decently fast but nonetheless costs a little bit of time.
+ * Try to avoid it whenever possible (typically when seed==0).
+ *
+ * It's important for performance that XXH3_hashLong is not inlined. Not sure
+ * why (uop cache maybe?), but the difference is large and easily measurable.
+ */
+XXH_FORCE_INLINE XXH64_hash_t
+XXH3_hashLong_64b_withSeed_internal(const void* input, size_t len,
+                                    XXH64_hash_t seed,
+                                    XXH3_f_accumulate f_acc,
+                                    XXH3_f_scrambleAcc f_scramble,
+                                    XXH3_f_initCustomSecret f_initSec)
+{
+#if XXH_SIZE_OPT <= 0
+    if (seed == 0)
+        return XXH3_hashLong_64b_internal(input, len,
+                                          XXH3_kSecret, sizeof(XXH3_kSecret),
+                                          f_acc, f_scramble);
+#endif
+    {   XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE];
+        f_initSec(secret, seed);
+        return XXH3_hashLong_64b_internal(input, len, secret, sizeof(secret),
+                                          f_acc, f_scramble);
+    }
+}
+
+/*
+ * It's important for performance that XXH3_hashLong is not inlined.
+ */
+XXH_NO_INLINE XXH64_hash_t
+XXH3_hashLong_64b_withSeed(const void* XXH_RESTRICT input, size_t len,
+                           XXH64_hash_t seed, const xxh_u8* XXH_RESTRICT secret, size_t secretLen)
+{
+    (void)secret; (void)secretLen;
+    return XXH3_hashLong_64b_withSeed_internal(input, len, seed,
+                XXH3_accumulate, XXH3_scrambleAcc, XXH3_initCustomSecret);
+}
+
+
+typedef XXH64_hash_t (*XXH3_hashLong64_f)(const void* XXH_RESTRICT, size_t,
+                                          XXH64_hash_t, const xxh_u8* XXH_RESTRICT, size_t);
+
+XXH_FORCE_INLINE XXH64_hash_t
+XXH3_64bits_internal(const void* XXH_RESTRICT input, size_t len,
+                     XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen,
+                     XXH3_hashLong64_f f_hashLong)
+{
+    XXH_ASSERT(secretLen >= XXH3_SECRET_SIZE_MIN);
+    /*
+     * If an action is to be taken if `secretLen` condition is not respected,
+     * it should be done here.
+     * For now, it's a contract pre-condition.
+     * Adding a check and a branch here would cost performance at every hash.
+     * Also, note that function signature doesn't offer room to return an error.
+     */
+    if (len <= 16)
+        return XXH3_len_0to16_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, seed64);
+    if (len <= 128)
+        return XXH3_len_17to128_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64);
+    if (len <= XXH3_MIDSIZE_MAX)
+        return XXH3_len_129to240_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64);
+    return f_hashLong(input, len, seed64, (const xxh_u8*)secret, secretLen);
+}
+
+
+/* ===   Public entry point   === */
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH64_hash_t XXH3_64bits(XXH_NOESCAPE const void* input, size_t length)
+{
+    return XXH3_64bits_internal(input, length, 0, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_hashLong_64b_default);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH64_hash_t
+XXH3_64bits_withSecret(XXH_NOESCAPE const void* input, size_t length, XXH_NOESCAPE const void* secret, size_t secretSize)
+{
+    return XXH3_64bits_internal(input, length, 0, secret, secretSize, XXH3_hashLong_64b_withSecret);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH64_hash_t
+XXH3_64bits_withSeed(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed)
+{
+    return XXH3_64bits_internal(input, length, seed, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_hashLong_64b_withSeed);
+}
+
+XXH_PUBLIC_API XXH64_hash_t
+XXH3_64bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t length, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed)
+{
+    if (length <= XXH3_MIDSIZE_MAX)
+        return XXH3_64bits_internal(input, length, seed, XXH3_kSecret, sizeof(XXH3_kSecret), NULL);
+    return XXH3_hashLong_64b_withSecret(input, length, seed, (const xxh_u8*)secret, secretSize);
+}
+
+
+/* ===   XXH3 streaming   === */
+#ifndef XXH_NO_STREAM
+/*
+ * Malloc's a pointer that is always aligned to align.
+ *
+ * This must be freed with `XXH_alignedFree()`.
+ *
+ * malloc typically guarantees 16 byte alignment on 64-bit systems and 8 byte
+ * alignment on 32-bit. This isn't enough for the 32 byte aligned loads in AVX2
+ * or on 32-bit, the 16 byte aligned loads in SSE2 and NEON.
+ *
+ * This underalignment previously caused a rather obvious crash which went
+ * completely unnoticed due to XXH3_createState() not actually being tested.
+ * Credit to RedSpah for noticing this bug.
+ *
+ * The alignment is done manually: Functions like posix_memalign or _mm_malloc
+ * are avoided: To maintain portability, we would have to write a fallback
+ * like this anyways, and besides, testing for the existence of library
+ * functions without relying on external build tools is impossible.
+ *
+ * The method is simple: Overallocate, manually align, and store the offset
+ * to the original behind the returned pointer.
+ *
+ * Align must be a power of 2 and 8 <= align <= 128.
+ */
+static XXH_MALLOCF void* XXH_alignedMalloc(size_t s, size_t align)
+{
+    XXH_ASSERT(align <= 128 && align >= 8); /* range check */
+    XXH_ASSERT((align & (align-1)) == 0);   /* power of 2 */
+    XXH_ASSERT(s != 0 && s < (s + align));  /* empty/overflow */
+    {   /* Overallocate to make room for manual realignment and an offset byte */
+        xxh_u8* base = (xxh_u8*)XXH_malloc(s + align);
+        if (base != NULL) {
+            /*
+             * Get the offset needed to align this pointer.
+             *
+             * Even if the returned pointer is aligned, there will always be
+             * at least one byte to store the offset to the original pointer.
+             */
+            size_t offset = align - ((size_t)base & (align - 1)); /* base % align */
+            /* Add the offset for the now-aligned pointer */
+            xxh_u8* ptr = base + offset;
+
+            XXH_ASSERT((size_t)ptr % align == 0);
+
+            /* Store the offset immediately before the returned pointer. */
+            ptr[-1] = (xxh_u8)offset;
+            return ptr;
+        }
+        return NULL;
+    }
+}
+/*
+ * Frees an aligned pointer allocated by XXH_alignedMalloc(). Don't pass
+ * normal malloc'd pointers, XXH_alignedMalloc has a specific data layout.
+ */
+static void XXH_alignedFree(void* p)
+{
+    if (p != NULL) {
+        xxh_u8* ptr = (xxh_u8*)p;
+        /* Get the offset byte we added in XXH_malloc. */
+        xxh_u8 offset = ptr[-1];
+        /* Free the original malloc'd pointer */
+        xxh_u8* base = ptr - offset;
+        XXH_free(base);
+    }
+}
+/*! @ingroup XXH3_family */
+/*!
+ * @brief Allocate an @ref XXH3_state_t.
+ *
+ * @return An allocated pointer of @ref XXH3_state_t on success.
+ * @return `NULL` on failure.
+ *
+ * @note Must be freed with XXH3_freeState().
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH3_state_t* XXH3_createState(void)
+{
+    XXH3_state_t* const state = (XXH3_state_t*)XXH_alignedMalloc(sizeof(XXH3_state_t), 64);
+    if (state==NULL) return NULL;
+    XXH3_INITSTATE(state);
+    return state;
+}
+
+/*! @ingroup XXH3_family */
+/*!
+ * @brief Frees an @ref XXH3_state_t.
+ *
+ * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState().
+ *
+ * @return @ref XXH_OK.
+ *
+ * @note Must be allocated with XXH3_createState().
+ *
+ * @see @ref streaming_example "Streaming Example"
+ */
+XXH_PUBLIC_API XXH_errorcode XXH3_freeState(XXH3_state_t* statePtr)
+{
+    XXH_alignedFree(statePtr);
+    return XXH_OK;
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API void
+XXH3_copyState(XXH_NOESCAPE XXH3_state_t* dst_state, XXH_NOESCAPE const XXH3_state_t* src_state)
+{
+    XXH_memcpy(dst_state, src_state, sizeof(*dst_state));
+}
+
+static void
+XXH3_reset_internal(XXH3_state_t* statePtr,
+                    XXH64_hash_t seed,
+                    const void* secret, size_t secretSize)
+{
+    size_t const initStart = offsetof(XXH3_state_t, bufferedSize);
+    size_t const initLength = offsetof(XXH3_state_t, nbStripesPerBlock) - initStart;
+    XXH_ASSERT(offsetof(XXH3_state_t, nbStripesPerBlock) > initStart);
+    XXH_ASSERT(statePtr != NULL);
+    /* set members from bufferedSize to nbStripesPerBlock (excluded) to 0 */
+    memset((char*)statePtr + initStart, 0, initLength);
+    statePtr->acc[0] = XXH_PRIME32_3;
+    statePtr->acc[1] = XXH_PRIME64_1;
+    statePtr->acc[2] = XXH_PRIME64_2;
+    statePtr->acc[3] = XXH_PRIME64_3;
+    statePtr->acc[4] = XXH_PRIME64_4;
+    statePtr->acc[5] = XXH_PRIME32_2;
+    statePtr->acc[6] = XXH_PRIME64_5;
+    statePtr->acc[7] = XXH_PRIME32_1;
+    statePtr->seed = seed;
+    statePtr->useSeed = (seed != 0);
+    statePtr->extSecret = (const unsigned char*)secret;
+    XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN);
+    statePtr->secretLimit = secretSize - XXH_STRIPE_LEN;
+    statePtr->nbStripesPerBlock = statePtr->secretLimit / XXH_SECRET_CONSUME_RATE;
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_64bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr)
+{
+    if (statePtr == NULL) return XXH_ERROR;
+    XXH3_reset_internal(statePtr, 0, XXH3_kSecret, XXH_SECRET_DEFAULT_SIZE);
+    return XXH_OK;
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_64bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize)
+{
+    if (statePtr == NULL) return XXH_ERROR;
+    XXH3_reset_internal(statePtr, 0, secret, secretSize);
+    if (secret == NULL) return XXH_ERROR;
+    if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR;
+    return XXH_OK;
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_64bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed)
+{
+    if (statePtr == NULL) return XXH_ERROR;
+    if (seed==0) return XXH3_64bits_reset(statePtr);
+    if ((seed != statePtr->seed) || (statePtr->extSecret != NULL))
+        XXH3_initCustomSecret(statePtr->customSecret, seed);
+    XXH3_reset_internal(statePtr, seed, NULL, XXH_SECRET_DEFAULT_SIZE);
+    return XXH_OK;
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_64bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed64)
+{
+    if (statePtr == NULL) return XXH_ERROR;
+    if (secret == NULL) return XXH_ERROR;
+    if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR;
+    XXH3_reset_internal(statePtr, seed64, secret, secretSize);
+    statePtr->useSeed = 1; /* always, even if seed64==0 */
+    return XXH_OK;
+}
+
+/*!
+ * @internal
+ * @brief Processes a large input for XXH3_update() and XXH3_digest_long().
+ *
+ * Unlike XXH3_hashLong_internal_loop(), this can process data that overlaps a block.
+ *
+ * @param acc                Pointer to the 8 accumulator lanes
+ * @param nbStripesSoFarPtr  In/out pointer to the number of leftover stripes in the block*
+ * @param nbStripesPerBlock  Number of stripes in a block
+ * @param input              Input pointer
+ * @param nbStripes          Number of stripes to process
+ * @param secret             Secret pointer
+ * @param secretLimit        Offset of the last block in @p secret
+ * @param f_acc              Pointer to an XXH3_accumulate implementation
+ * @param f_scramble         Pointer to an XXH3_scrambleAcc implementation
+ * @return                   Pointer past the end of @p input after processing
+ */
+XXH_FORCE_INLINE const xxh_u8 *
+XXH3_consumeStripes(xxh_u64* XXH_RESTRICT acc,
+                    size_t* XXH_RESTRICT nbStripesSoFarPtr, size_t nbStripesPerBlock,
+                    const xxh_u8* XXH_RESTRICT input, size_t nbStripes,
+                    const xxh_u8* XXH_RESTRICT secret, size_t secretLimit,
+                    XXH3_f_accumulate f_acc,
+                    XXH3_f_scrambleAcc f_scramble)
+{
+    const xxh_u8* initialSecret = secret + *nbStripesSoFarPtr * XXH_SECRET_CONSUME_RATE;
+    /* Process full blocks */
+    if (nbStripes >= (nbStripesPerBlock - *nbStripesSoFarPtr)) {
+        /* Process the initial partial block... */
+        size_t nbStripesThisIter = nbStripesPerBlock - *nbStripesSoFarPtr;
+
+        do {
+            /* Accumulate and scramble */
+            f_acc(acc, input, initialSecret, nbStripesThisIter);
+            f_scramble(acc, secret + secretLimit);
+            input += nbStripesThisIter * XXH_STRIPE_LEN;
+            nbStripes -= nbStripesThisIter;
+            /* Then continue the loop with the full block size */
+            nbStripesThisIter = nbStripesPerBlock;
+            initialSecret = secret;
+        } while (nbStripes >= nbStripesPerBlock);
+        *nbStripesSoFarPtr = 0;
+    }
+    /* Process a partial block */
+    if (nbStripes > 0) {
+        f_acc(acc, input, initialSecret, nbStripes);
+        input += nbStripes * XXH_STRIPE_LEN;
+        *nbStripesSoFarPtr += nbStripes;
+    }
+    /* Return end pointer */
+    return input;
+}
+
+#ifndef XXH3_STREAM_USE_STACK
+# if XXH_SIZE_OPT <= 0 && !defined(__clang__) /* clang doesn't need additional stack space */
+#   define XXH3_STREAM_USE_STACK 1
+# endif
+#endif
+/*
+ * Both XXH3_64bits_update and XXH3_128bits_update use this routine.
+ */
+XXH_FORCE_INLINE XXH_errorcode
+XXH3_update(XXH3_state_t* XXH_RESTRICT const state,
+            const xxh_u8* XXH_RESTRICT input, size_t len,
+            XXH3_f_accumulate f_acc,
+            XXH3_f_scrambleAcc f_scramble)
+{
+    if (input==NULL) {
+        XXH_ASSERT(len == 0);
+        return XXH_OK;
+    }
+
+    XXH_ASSERT(state != NULL);
+    {   const xxh_u8* const bEnd = input + len;
+        const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret;
+#if defined(XXH3_STREAM_USE_STACK) && XXH3_STREAM_USE_STACK >= 1
+        /* For some reason, gcc and MSVC seem to suffer greatly
+         * when operating accumulators directly into state.
+         * Operating into stack space seems to enable proper optimization.
+         * clang, on the other hand, doesn't seem to need this trick */
+        XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[8];
+        XXH_memcpy(acc, state->acc, sizeof(acc));
+#else
+        xxh_u64* XXH_RESTRICT const acc = state->acc;
+#endif
+        state->totalLen += len;
+        XXH_ASSERT(state->bufferedSize <= XXH3_INTERNALBUFFER_SIZE);
+
+        /* small input : just fill in tmp buffer */
+        if (len <= XXH3_INTERNALBUFFER_SIZE - state->bufferedSize) {
+            XXH_memcpy(state->buffer + state->bufferedSize, input, len);
+            state->bufferedSize += (XXH32_hash_t)len;
+            return XXH_OK;
+        }
+
+        /* total input is now > XXH3_INTERNALBUFFER_SIZE */
+        #define XXH3_INTERNALBUFFER_STRIPES (XXH3_INTERNALBUFFER_SIZE / XXH_STRIPE_LEN)
+        XXH_STATIC_ASSERT(XXH3_INTERNALBUFFER_SIZE % XXH_STRIPE_LEN == 0);   /* clean multiple */
+
+        /*
+         * Internal buffer is partially filled (always, except at beginning)
+         * Complete it, then consume it.
+         */
+        if (state->bufferedSize) {
+            size_t const loadSize = XXH3_INTERNALBUFFER_SIZE - state->bufferedSize;
+            XXH_memcpy(state->buffer + state->bufferedSize, input, loadSize);
+            input += loadSize;
+            XXH3_consumeStripes(acc,
+                               &state->nbStripesSoFar, state->nbStripesPerBlock,
+                                state->buffer, XXH3_INTERNALBUFFER_STRIPES,
+                                secret, state->secretLimit,
+                                f_acc, f_scramble);
+            state->bufferedSize = 0;
+        }
+        XXH_ASSERT(input < bEnd);
+        if (bEnd - input > XXH3_INTERNALBUFFER_SIZE) {
+            size_t nbStripes = (size_t)(bEnd - 1 - input) / XXH_STRIPE_LEN;
+            input = XXH3_consumeStripes(acc,
+                                       &state->nbStripesSoFar, state->nbStripesPerBlock,
+                                       input, nbStripes,
+                                       secret, state->secretLimit,
+                                       f_acc, f_scramble);
+            XXH_memcpy(state->buffer + sizeof(state->buffer) - XXH_STRIPE_LEN, input - XXH_STRIPE_LEN, XXH_STRIPE_LEN);
+
+        }
+        /* Some remaining input (always) : buffer it */
+        XXH_ASSERT(input < bEnd);
+        XXH_ASSERT(bEnd - input <= XXH3_INTERNALBUFFER_SIZE);
+        XXH_ASSERT(state->bufferedSize == 0);
+        XXH_memcpy(state->buffer, input, (size_t)(bEnd-input));
+        state->bufferedSize = (XXH32_hash_t)(bEnd-input);
+#if defined(XXH3_STREAM_USE_STACK) && XXH3_STREAM_USE_STACK >= 1
+        /* save stack accumulators into state */
+        XXH_memcpy(state->acc, acc, sizeof(acc));
+#endif
+    }
+
+    return XXH_OK;
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_64bits_update(XXH_NOESCAPE XXH3_state_t* state, XXH_NOESCAPE const void* input, size_t len)
+{
+    return XXH3_update(state, (const xxh_u8*)input, len,
+                       XXH3_accumulate, XXH3_scrambleAcc);
+}
+
+
+XXH_FORCE_INLINE void
+XXH3_digest_long (XXH64_hash_t* acc,
+                  const XXH3_state_t* state,
+                  const unsigned char* secret)
+{
+    xxh_u8 lastStripe[XXH_STRIPE_LEN];
+    const xxh_u8* lastStripePtr;
+
+    /*
+     * Digest on a local copy. This way, the state remains unaltered, and it can
+     * continue ingesting more input afterwards.
+     */
+    XXH_memcpy(acc, state->acc, sizeof(state->acc));
+    if (state->bufferedSize >= XXH_STRIPE_LEN) {
+        /* Consume remaining stripes then point to remaining data in buffer */
+        size_t const nbStripes = (state->bufferedSize - 1) / XXH_STRIPE_LEN;
+        size_t nbStripesSoFar = state->nbStripesSoFar;
+        XXH3_consumeStripes(acc,
+                           &nbStripesSoFar, state->nbStripesPerBlock,
+                            state->buffer, nbStripes,
+                            secret, state->secretLimit,
+                            XXH3_accumulate, XXH3_scrambleAcc);
+        lastStripePtr = state->buffer + state->bufferedSize - XXH_STRIPE_LEN;
+    } else {  /* bufferedSize < XXH_STRIPE_LEN */
+        /* Copy to temp buffer */
+        size_t const catchupSize = XXH_STRIPE_LEN - state->bufferedSize;
+        XXH_ASSERT(state->bufferedSize > 0);  /* there is always some input buffered */
+        XXH_memcpy(lastStripe, state->buffer + sizeof(state->buffer) - catchupSize, catchupSize);
+        XXH_memcpy(lastStripe + catchupSize, state->buffer, state->bufferedSize);
+        lastStripePtr = lastStripe;
+    }
+    /* Last stripe */
+    XXH3_accumulate_512(acc,
+                        lastStripePtr,
+                        secret + state->secretLimit - XXH_SECRET_LASTACC_START);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH64_hash_t XXH3_64bits_digest (XXH_NOESCAPE const XXH3_state_t* state)
+{
+    const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret;
+    if (state->totalLen > XXH3_MIDSIZE_MAX) {
+        XXH_ALIGN(XXH_ACC_ALIGN) XXH64_hash_t acc[XXH_ACC_NB];
+        XXH3_digest_long(acc, state, secret);
+        return XXH3_mergeAccs(acc,
+                              secret + XXH_SECRET_MERGEACCS_START,
+                              (xxh_u64)state->totalLen * XXH_PRIME64_1);
+    }
+    /* totalLen <= XXH3_MIDSIZE_MAX: digesting a short input */
+    if (state->useSeed)
+        return XXH3_64bits_withSeed(state->buffer, (size_t)state->totalLen, state->seed);
+    return XXH3_64bits_withSecret(state->buffer, (size_t)(state->totalLen),
+                                  secret, state->secretLimit + XXH_STRIPE_LEN);
+}
+#endif /* !XXH_NO_STREAM */
+
+
+/* ==========================================
+ * XXH3 128 bits (a.k.a XXH128)
+ * ==========================================
+ * XXH3's 128-bit variant has better mixing and strength than the 64-bit variant,
+ * even without counting the significantly larger output size.
+ *
+ * For example, extra steps are taken to avoid the seed-dependent collisions
+ * in 17-240 byte inputs (See XXH3_mix16B and XXH128_mix32B).
+ *
+ * This strength naturally comes at the cost of some speed, especially on short
+ * lengths. Note that longer hashes are about as fast as the 64-bit version
+ * due to it using only a slight modification of the 64-bit loop.
+ *
+ * XXH128 is also more oriented towards 64-bit machines. It is still extremely
+ * fast for a _128-bit_ hash on 32-bit (it usually clears XXH64).
+ */
+
+XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t
+XXH3_len_1to3_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed)
+{
+    /* A doubled version of 1to3_64b with different constants. */
+    XXH_ASSERT(input != NULL);
+    XXH_ASSERT(1 <= len && len <= 3);
+    XXH_ASSERT(secret != NULL);
+    /*
+     * len = 1: combinedl = { input[0], 0x01, input[0], input[0] }
+     * len = 2: combinedl = { input[1], 0x02, input[0], input[1] }
+     * len = 3: combinedl = { input[2], 0x03, input[0], input[1] }
+     */
+    {   xxh_u8 const c1 = input[0];
+        xxh_u8 const c2 = input[len >> 1];
+        xxh_u8 const c3 = input[len - 1];
+        xxh_u32 const combinedl = ((xxh_u32)c1 <<16) | ((xxh_u32)c2 << 24)
+                                | ((xxh_u32)c3 << 0) | ((xxh_u32)len << 8);
+        xxh_u32 const combinedh = XXH_rotl32(XXH_swap32(combinedl), 13);
+        xxh_u64 const bitflipl = (XXH_readLE32(secret) ^ XXH_readLE32(secret+4)) + seed;
+        xxh_u64 const bitfliph = (XXH_readLE32(secret+8) ^ XXH_readLE32(secret+12)) - seed;
+        xxh_u64 const keyed_lo = (xxh_u64)combinedl ^ bitflipl;
+        xxh_u64 const keyed_hi = (xxh_u64)combinedh ^ bitfliph;
+        XXH128_hash_t h128;
+        h128.low64  = XXH64_avalanche(keyed_lo);
+        h128.high64 = XXH64_avalanche(keyed_hi);
+        return h128;
+    }
+}
+
+XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t
+XXH3_len_4to8_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed)
+{
+    XXH_ASSERT(input != NULL);
+    XXH_ASSERT(secret != NULL);
+    XXH_ASSERT(4 <= len && len <= 8);
+    seed ^= (xxh_u64)XXH_swap32((xxh_u32)seed) << 32;
+    {   xxh_u32 const input_lo = XXH_readLE32(input);
+        xxh_u32 const input_hi = XXH_readLE32(input + len - 4);
+        xxh_u64 const input_64 = input_lo + ((xxh_u64)input_hi << 32);
+        xxh_u64 const bitflip = (XXH_readLE64(secret+16) ^ XXH_readLE64(secret+24)) + seed;
+        xxh_u64 const keyed = input_64 ^ bitflip;
+
+        /* Shift len to the left to ensure it is even, this avoids even multiplies. */
+        XXH128_hash_t m128 = XXH_mult64to128(keyed, XXH_PRIME64_1 + (len << 2));
+
+        m128.high64 += (m128.low64 << 1);
+        m128.low64  ^= (m128.high64 >> 3);
+
+        m128.low64   = XXH_xorshift64(m128.low64, 35);
+        m128.low64  *= PRIME_MX2;
+        m128.low64   = XXH_xorshift64(m128.low64, 28);
+        m128.high64  = XXH3_avalanche(m128.high64);
+        return m128;
+    }
+}
+
+XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t
+XXH3_len_9to16_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed)
+{
+    XXH_ASSERT(input != NULL);
+    XXH_ASSERT(secret != NULL);
+    XXH_ASSERT(9 <= len && len <= 16);
+    {   xxh_u64 const bitflipl = (XXH_readLE64(secret+32) ^ XXH_readLE64(secret+40)) - seed;
+        xxh_u64 const bitfliph = (XXH_readLE64(secret+48) ^ XXH_readLE64(secret+56)) + seed;
+        xxh_u64 const input_lo = XXH_readLE64(input);
+        xxh_u64       input_hi = XXH_readLE64(input + len - 8);
+        XXH128_hash_t m128 = XXH_mult64to128(input_lo ^ input_hi ^ bitflipl, XXH_PRIME64_1);
+        /*
+         * Put len in the middle of m128 to ensure that the length gets mixed to
+         * both the low and high bits in the 128x64 multiply below.
+         */
+        m128.low64 += (xxh_u64)(len - 1) << 54;
+        input_hi   ^= bitfliph;
+        /*
+         * Add the high 32 bits of input_hi to the high 32 bits of m128, then
+         * add the long product of the low 32 bits of input_hi and XXH_PRIME32_2 to
+         * the high 64 bits of m128.
+         *
+         * The best approach to this operation is different on 32-bit and 64-bit.
+         */
+        if (sizeof(void *) < sizeof(xxh_u64)) { /* 32-bit */
+            /*
+             * 32-bit optimized version, which is more readable.
+             *
+             * On 32-bit, it removes an ADC and delays a dependency between the two
+             * halves of m128.high64, but it generates an extra mask on 64-bit.
+             */
+            m128.high64 += (input_hi & 0xFFFFFFFF00000000ULL) + XXH_mult32to64((xxh_u32)input_hi, XXH_PRIME32_2);
+        } else {
+            /*
+             * 64-bit optimized (albeit more confusing) version.
+             *
+             * Uses some properties of addition and multiplication to remove the mask:
+             *
+             * Let:
+             *    a = input_hi.lo = (input_hi & 0x00000000FFFFFFFF)
+             *    b = input_hi.hi = (input_hi & 0xFFFFFFFF00000000)
+             *    c = XXH_PRIME32_2
+             *
+             *    a + (b * c)
+             * Inverse Property: x + y - x == y
+             *    a + (b * (1 + c - 1))
+             * Distributive Property: x * (y + z) == (x * y) + (x * z)
+             *    a + (b * 1) + (b * (c - 1))
+             * Identity Property: x * 1 == x
+             *    a + b + (b * (c - 1))
+             *
+             * Substitute a, b, and c:
+             *    input_hi.hi + input_hi.lo + ((xxh_u64)input_hi.lo * (XXH_PRIME32_2 - 1))
+             *
+             * Since input_hi.hi + input_hi.lo == input_hi, we get this:
+             *    input_hi + ((xxh_u64)input_hi.lo * (XXH_PRIME32_2 - 1))
+             */
+            m128.high64 += input_hi + XXH_mult32to64((xxh_u32)input_hi, XXH_PRIME32_2 - 1);
+        }
+        /* m128 ^= XXH_swap64(m128 >> 64); */
+        m128.low64  ^= XXH_swap64(m128.high64);
+
+        {   /* 128x64 multiply: h128 = m128 * XXH_PRIME64_2; */
+            XXH128_hash_t h128 = XXH_mult64to128(m128.low64, XXH_PRIME64_2);
+            h128.high64 += m128.high64 * XXH_PRIME64_2;
+
+            h128.low64   = XXH3_avalanche(h128.low64);
+            h128.high64  = XXH3_avalanche(h128.high64);
+            return h128;
+    }   }
+}
+
+/*
+ * Assumption: `secret` size is >= XXH3_SECRET_SIZE_MIN
+ */
+XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t
+XXH3_len_0to16_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed)
+{
+    XXH_ASSERT(len <= 16);
+    {   if (len > 8) return XXH3_len_9to16_128b(input, len, secret, seed);
+        if (len >= 4) return XXH3_len_4to8_128b(input, len, secret, seed);
+        if (len) return XXH3_len_1to3_128b(input, len, secret, seed);
+        {   XXH128_hash_t h128;
+            xxh_u64 const bitflipl = XXH_readLE64(secret+64) ^ XXH_readLE64(secret+72);
+            xxh_u64 const bitfliph = XXH_readLE64(secret+80) ^ XXH_readLE64(secret+88);
+            h128.low64 = XXH64_avalanche(seed ^ bitflipl);
+            h128.high64 = XXH64_avalanche( seed ^ bitfliph);
+            return h128;
+    }   }
+}
+
+/*
+ * A bit slower than XXH3_mix16B, but handles multiply by zero better.
+ */
+XXH_FORCE_INLINE XXH128_hash_t
+XXH128_mix32B(XXH128_hash_t acc, const xxh_u8* input_1, const xxh_u8* input_2,
+              const xxh_u8* secret, XXH64_hash_t seed)
+{
+    acc.low64  += XXH3_mix16B (input_1, secret+0, seed);
+    acc.low64  ^= XXH_readLE64(input_2) + XXH_readLE64(input_2 + 8);
+    acc.high64 += XXH3_mix16B (input_2, secret+16, seed);
+    acc.high64 ^= XXH_readLE64(input_1) + XXH_readLE64(input_1 + 8);
+    return acc;
+}
+
+
+XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t
+XXH3_len_17to128_128b(const xxh_u8* XXH_RESTRICT input, size_t len,
+                      const xxh_u8* XXH_RESTRICT secret, size_t secretSize,
+                      XXH64_hash_t seed)
+{
+    XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize;
+    XXH_ASSERT(16 < len && len <= 128);
+
+    {   XXH128_hash_t acc;
+        acc.low64 = len * XXH_PRIME64_1;
+        acc.high64 = 0;
+
+#if XXH_SIZE_OPT >= 1
+        {
+            /* Smaller, but slightly slower. */
+            unsigned int i = (unsigned int)(len - 1) / 32;
+            do {
+                acc = XXH128_mix32B(acc, input+16*i, input+len-16*(i+1), secret+32*i, seed);
+            } while (i-- != 0);
+        }
+#else
+        if (len > 32) {
+            if (len > 64) {
+                if (len > 96) {
+                    acc = XXH128_mix32B(acc, input+48, input+len-64, secret+96, seed);
+                }
+                acc = XXH128_mix32B(acc, input+32, input+len-48, secret+64, seed);
+            }
+            acc = XXH128_mix32B(acc, input+16, input+len-32, secret+32, seed);
+        }
+        acc = XXH128_mix32B(acc, input, input+len-16, secret, seed);
+#endif
+        {   XXH128_hash_t h128;
+            h128.low64  = acc.low64 + acc.high64;
+            h128.high64 = (acc.low64    * XXH_PRIME64_1)
+                        + (acc.high64   * XXH_PRIME64_4)
+                        + ((len - seed) * XXH_PRIME64_2);
+            h128.low64  = XXH3_avalanche(h128.low64);
+            h128.high64 = (XXH64_hash_t)0 - XXH3_avalanche(h128.high64);
+            return h128;
+        }
+    }
+}
+
+XXH_NO_INLINE XXH_PUREF XXH128_hash_t
+XXH3_len_129to240_128b(const xxh_u8* XXH_RESTRICT input, size_t len,
+                       const xxh_u8* XXH_RESTRICT secret, size_t secretSize,
+                       XXH64_hash_t seed)
+{
+    XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize;
+    XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX);
+
+    {   XXH128_hash_t acc;
+        unsigned i;
+        acc.low64 = len * XXH_PRIME64_1;
+        acc.high64 = 0;
+        /*
+         *  We set as `i` as offset + 32. We do this so that unchanged
+         * `len` can be used as upper bound. This reaches a sweet spot
+         * where both x86 and aarch64 get simple agen and good codegen
+         * for the loop.
+         */
+        for (i = 32; i < 160; i += 32) {
+            acc = XXH128_mix32B(acc,
+                                input  + i - 32,
+                                input  + i - 16,
+                                secret + i - 32,
+                                seed);
+        }
+        acc.low64 = XXH3_avalanche(acc.low64);
+        acc.high64 = XXH3_avalanche(acc.high64);
+        /*
+         * NB: `i <= len` will duplicate the last 32-bytes if
+         * len % 32 was zero. This is an unfortunate necessity to keep
+         * the hash result stable.
+         */
+        for (i=160; i <= len; i += 32) {
+            acc = XXH128_mix32B(acc,
+                                input + i - 32,
+                                input + i - 16,
+                                secret + XXH3_MIDSIZE_STARTOFFSET + i - 160,
+                                seed);
+        }
+        /* last bytes */
+        acc = XXH128_mix32B(acc,
+                            input + len - 16,
+                            input + len - 32,
+                            secret + XXH3_SECRET_SIZE_MIN - XXH3_MIDSIZE_LASTOFFSET - 16,
+                            (XXH64_hash_t)0 - seed);
+
+        {   XXH128_hash_t h128;
+            h128.low64  = acc.low64 + acc.high64;
+            h128.high64 = (acc.low64    * XXH_PRIME64_1)
+                        + (acc.high64   * XXH_PRIME64_4)
+                        + ((len - seed) * XXH_PRIME64_2);
+            h128.low64  = XXH3_avalanche(h128.low64);
+            h128.high64 = (XXH64_hash_t)0 - XXH3_avalanche(h128.high64);
+            return h128;
+        }
+    }
+}
+
+XXH_FORCE_INLINE XXH128_hash_t
+XXH3_hashLong_128b_internal(const void* XXH_RESTRICT input, size_t len,
+                            const xxh_u8* XXH_RESTRICT secret, size_t secretSize,
+                            XXH3_f_accumulate f_acc,
+                            XXH3_f_scrambleAcc f_scramble)
+{
+    XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[XXH_ACC_NB] = XXH3_INIT_ACC;
+
+    XXH3_hashLong_internal_loop(acc, (const xxh_u8*)input, len, secret, secretSize, f_acc, f_scramble);
+
+    /* converge into final hash */
+    XXH_STATIC_ASSERT(sizeof(acc) == 64);
+    XXH_ASSERT(secretSize >= sizeof(acc) + XXH_SECRET_MERGEACCS_START);
+    {   XXH128_hash_t h128;
+        h128.low64  = XXH3_mergeAccs(acc,
+                                     secret + XXH_SECRET_MERGEACCS_START,
+                                     (xxh_u64)len * XXH_PRIME64_1);
+        h128.high64 = XXH3_mergeAccs(acc,
+                                     secret + secretSize
+                                            - sizeof(acc) - XXH_SECRET_MERGEACCS_START,
+                                     ~((xxh_u64)len * XXH_PRIME64_2));
+        return h128;
+    }
+}
+
+/*
+ * It's important for performance that XXH3_hashLong() is not inlined.
+ */
+XXH_NO_INLINE XXH_PUREF XXH128_hash_t
+XXH3_hashLong_128b_default(const void* XXH_RESTRICT input, size_t len,
+                           XXH64_hash_t seed64,
+                           const void* XXH_RESTRICT secret, size_t secretLen)
+{
+    (void)seed64; (void)secret; (void)secretLen;
+    return XXH3_hashLong_128b_internal(input, len, XXH3_kSecret, sizeof(XXH3_kSecret),
+                                       XXH3_accumulate, XXH3_scrambleAcc);
+}
+
+/*
+ * It's important for performance to pass @p secretLen (when it's static)
+ * to the compiler, so that it can properly optimize the vectorized loop.
+ *
+ * When the secret size is unknown, or on GCC 12 where the mix of NO_INLINE and FORCE_INLINE
+ * breaks -Og, this is XXH_NO_INLINE.
+ */
+XXH3_WITH_SECRET_INLINE XXH128_hash_t
+XXH3_hashLong_128b_withSecret(const void* XXH_RESTRICT input, size_t len,
+                              XXH64_hash_t seed64,
+                              const void* XXH_RESTRICT secret, size_t secretLen)
+{
+    (void)seed64;
+    return XXH3_hashLong_128b_internal(input, len, (const xxh_u8*)secret, secretLen,
+                                       XXH3_accumulate, XXH3_scrambleAcc);
+}
+
+XXH_FORCE_INLINE XXH128_hash_t
+XXH3_hashLong_128b_withSeed_internal(const void* XXH_RESTRICT input, size_t len,
+                                XXH64_hash_t seed64,
+                                XXH3_f_accumulate f_acc,
+                                XXH3_f_scrambleAcc f_scramble,
+                                XXH3_f_initCustomSecret f_initSec)
+{
+    if (seed64 == 0)
+        return XXH3_hashLong_128b_internal(input, len,
+                                           XXH3_kSecret, sizeof(XXH3_kSecret),
+                                           f_acc, f_scramble);
+    {   XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE];
+        f_initSec(secret, seed64);
+        return XXH3_hashLong_128b_internal(input, len, (const xxh_u8*)secret, sizeof(secret),
+                                           f_acc, f_scramble);
+    }
+}
+
+/*
+ * It's important for performance that XXH3_hashLong is not inlined.
+ */
+XXH_NO_INLINE XXH128_hash_t
+XXH3_hashLong_128b_withSeed(const void* input, size_t len,
+                            XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen)
+{
+    (void)secret; (void)secretLen;
+    return XXH3_hashLong_128b_withSeed_internal(input, len, seed64,
+                XXH3_accumulate, XXH3_scrambleAcc, XXH3_initCustomSecret);
+}
+
+typedef XXH128_hash_t (*XXH3_hashLong128_f)(const void* XXH_RESTRICT, size_t,
+                                            XXH64_hash_t, const void* XXH_RESTRICT, size_t);
+
+XXH_FORCE_INLINE XXH128_hash_t
+XXH3_128bits_internal(const void* input, size_t len,
+                      XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen,
+                      XXH3_hashLong128_f f_hl128)
+{
+    XXH_ASSERT(secretLen >= XXH3_SECRET_SIZE_MIN);
+    /*
+     * If an action is to be taken if `secret` conditions are not respected,
+     * it should be done here.
+     * For now, it's a contract pre-condition.
+     * Adding a check and a branch here would cost performance at every hash.
+     */
+    if (len <= 16)
+        return XXH3_len_0to16_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, seed64);
+    if (len <= 128)
+        return XXH3_len_17to128_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64);
+    if (len <= XXH3_MIDSIZE_MAX)
+        return XXH3_len_129to240_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64);
+    return f_hl128(input, len, seed64, secret, secretLen);
+}
+
+
+/* ===   Public XXH128 API   === */
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH128_hash_t XXH3_128bits(XXH_NOESCAPE const void* input, size_t len)
+{
+    return XXH3_128bits_internal(input, len, 0,
+                                 XXH3_kSecret, sizeof(XXH3_kSecret),
+                                 XXH3_hashLong_128b_default);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH128_hash_t
+XXH3_128bits_withSecret(XXH_NOESCAPE const void* input, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize)
+{
+    return XXH3_128bits_internal(input, len, 0,
+                                 (const xxh_u8*)secret, secretSize,
+                                 XXH3_hashLong_128b_withSecret);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH128_hash_t
+XXH3_128bits_withSeed(XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed)
+{
+    return XXH3_128bits_internal(input, len, seed,
+                                 XXH3_kSecret, sizeof(XXH3_kSecret),
+                                 XXH3_hashLong_128b_withSeed);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH128_hash_t
+XXH3_128bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed)
+{
+    if (len <= XXH3_MIDSIZE_MAX)
+        return XXH3_128bits_internal(input, len, seed, XXH3_kSecret, sizeof(XXH3_kSecret), NULL);
+    return XXH3_hashLong_128b_withSecret(input, len, seed, secret, secretSize);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH128_hash_t
+XXH128(XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed)
+{
+    return XXH3_128bits_withSeed(input, len, seed);
+}
+
+
+/* ===   XXH3 128-bit streaming   === */
+#ifndef XXH_NO_STREAM
+/*
+ * All initialization and update functions are identical to 64-bit streaming variant.
+ * The only difference is the finalization routine.
+ */
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_128bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr)
+{
+    return XXH3_64bits_reset(statePtr);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_128bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize)
+{
+    return XXH3_64bits_reset_withSecret(statePtr, secret, secretSize);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_128bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed)
+{
+    return XXH3_64bits_reset_withSeed(statePtr, seed);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_128bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed)
+{
+    return XXH3_64bits_reset_withSecretandSeed(statePtr, secret, secretSize, seed);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_128bits_update(XXH_NOESCAPE XXH3_state_t* state, XXH_NOESCAPE const void* input, size_t len)
+{
+    return XXH3_64bits_update(state, input, len);
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH128_hash_t XXH3_128bits_digest (XXH_NOESCAPE const XXH3_state_t* state)
+{
+    const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret;
+    if (state->totalLen > XXH3_MIDSIZE_MAX) {
+        XXH_ALIGN(XXH_ACC_ALIGN) XXH64_hash_t acc[XXH_ACC_NB];
+        XXH3_digest_long(acc, state, secret);
+        XXH_ASSERT(state->secretLimit + XXH_STRIPE_LEN >= sizeof(acc) + XXH_SECRET_MERGEACCS_START);
+        {   XXH128_hash_t h128;
+            h128.low64  = XXH3_mergeAccs(acc,
+                                         secret + XXH_SECRET_MERGEACCS_START,
+                                         (xxh_u64)state->totalLen * XXH_PRIME64_1);
+            h128.high64 = XXH3_mergeAccs(acc,
+                                         secret + state->secretLimit + XXH_STRIPE_LEN
+                                                - sizeof(acc) - XXH_SECRET_MERGEACCS_START,
+                                         ~((xxh_u64)state->totalLen * XXH_PRIME64_2));
+            return h128;
+        }
+    }
+    /* len <= XXH3_MIDSIZE_MAX : short code */
+    if (state->useSeed)
+        return XXH3_128bits_withSeed(state->buffer, (size_t)state->totalLen, state->seed);
+    return XXH3_128bits_withSecret(state->buffer, (size_t)(state->totalLen),
+                                   secret, state->secretLimit + XXH_STRIPE_LEN);
+}
+#endif /* !XXH_NO_STREAM */
+/* 128-bit utility functions */
+
+#include <string.h>   /* memcmp, memcpy */
+
+/* return : 1 is equal, 0 if different */
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API int XXH128_isEqual(XXH128_hash_t h1, XXH128_hash_t h2)
+{
+    /* note : XXH128_hash_t is compact, it has no padding byte */
+    return !(memcmp(&h1, &h2, sizeof(h1)));
+}
+
+/* This prototype is compatible with stdlib's qsort().
+ * @return : >0 if *h128_1  > *h128_2
+ *           <0 if *h128_1  < *h128_2
+ *           =0 if *h128_1 == *h128_2  */
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API int XXH128_cmp(XXH_NOESCAPE const void* h128_1, XXH_NOESCAPE const void* h128_2)
+{
+    XXH128_hash_t const h1 = *(const XXH128_hash_t*)h128_1;
+    XXH128_hash_t const h2 = *(const XXH128_hash_t*)h128_2;
+    int const hcmp = (h1.high64 > h2.high64) - (h2.high64 > h1.high64);
+    /* note : bets that, in most cases, hash values are different */
+    if (hcmp) return hcmp;
+    return (h1.low64 > h2.low64) - (h2.low64 > h1.low64);
+}
+
+
+/*======   Canonical representation   ======*/
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API void
+XXH128_canonicalFromHash(XXH_NOESCAPE XXH128_canonical_t* dst, XXH128_hash_t hash)
+{
+    XXH_STATIC_ASSERT(sizeof(XXH128_canonical_t) == sizeof(XXH128_hash_t));
+    if (XXH_CPU_LITTLE_ENDIAN) {
+        hash.high64 = XXH_swap64(hash.high64);
+        hash.low64  = XXH_swap64(hash.low64);
+    }
+    XXH_memcpy(dst, &hash.high64, sizeof(hash.high64));
+    XXH_memcpy((char*)dst + sizeof(hash.high64), &hash.low64, sizeof(hash.low64));
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH128_hash_t
+XXH128_hashFromCanonical(XXH_NOESCAPE const XXH128_canonical_t* src)
+{
+    XXH128_hash_t h;
+    h.high64 = XXH_readBE64(src);
+    h.low64  = XXH_readBE64(src->digest + 8);
+    return h;
+}
+
+
+
+/* ==========================================
+ * Secret generators
+ * ==========================================
+ */
+#define XXH_MIN(x, y) (((x) > (y)) ? (y) : (x))
+
+XXH_FORCE_INLINE void XXH3_combine16(void* dst, XXH128_hash_t h128)
+{
+    XXH_writeLE64( dst, XXH_readLE64(dst) ^ h128.low64 );
+    XXH_writeLE64( (char*)dst+8, XXH_readLE64((char*)dst+8) ^ h128.high64 );
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API XXH_errorcode
+XXH3_generateSecret(XXH_NOESCAPE void* secretBuffer, size_t secretSize, XXH_NOESCAPE const void* customSeed, size_t customSeedSize)
+{
+#if (XXH_DEBUGLEVEL >= 1)
+    XXH_ASSERT(secretBuffer != NULL);
+    XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN);
+#else
+    /* production mode, assert() are disabled */
+    if (secretBuffer == NULL) return XXH_ERROR;
+    if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR;
+#endif
+
+    if (customSeedSize == 0) {
+        customSeed = XXH3_kSecret;
+        customSeedSize = XXH_SECRET_DEFAULT_SIZE;
+    }
+#if (XXH_DEBUGLEVEL >= 1)
+    XXH_ASSERT(customSeed != NULL);
+#else
+    if (customSeed == NULL) return XXH_ERROR;
+#endif
+
+    /* Fill secretBuffer with a copy of customSeed - repeat as needed */
+    {   size_t pos = 0;
+        while (pos < secretSize) {
+            size_t const toCopy = XXH_MIN((secretSize - pos), customSeedSize);
+            memcpy((char*)secretBuffer + pos, customSeed, toCopy);
+            pos += toCopy;
+    }   }
+
+    {   size_t const nbSeg16 = secretSize / 16;
+        size_t n;
+        XXH128_canonical_t scrambler;
+        XXH128_canonicalFromHash(&scrambler, XXH128(customSeed, customSeedSize, 0));
+        for (n=0; n<nbSeg16; n++) {
+            XXH128_hash_t const h128 = XXH128(&scrambler, sizeof(scrambler), n);
+            XXH3_combine16((char*)secretBuffer + n*16, h128);
+        }
+        /* last segment */
+        XXH3_combine16((char*)secretBuffer + secretSize - 16, XXH128_hashFromCanonical(&scrambler));
+    }
+    return XXH_OK;
+}
+
+/*! @ingroup XXH3_family */
+XXH_PUBLIC_API void
+XXH3_generateSecret_fromSeed(XXH_NOESCAPE void* secretBuffer, XXH64_hash_t seed)
+{
+    XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE];
+    XXH3_initCustomSecret(secret, seed);
+    XXH_ASSERT(secretBuffer != NULL);
+    memcpy(secretBuffer, secret, XXH_SECRET_DEFAULT_SIZE);
+}
+
+
+
+/* Pop our optimization override from above */
+#if XXH_VECTOR == XXH_AVX2 /* AVX2 */ \
+  && defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \
+  && defined(__OPTIMIZE__) && XXH_SIZE_OPT <= 0 /* respect -O0 and -Os */
+#  pragma GCC pop_options
+#endif
+
+#endif  /* XXH_NO_LONG_LONG */
+
+#endif  /* XXH_NO_XXH3 */
+
+/*!
+ * @}
+ */
+#endif  /* XXH_IMPLEMENTATION */
+
+
+#if defined (__cplusplus)
+} /* extern "C" */
+#endif
diff --git a/examples/gguf-hash/gguf-hash.cpp b/examples/gguf-hash/gguf-hash.cpp
new file mode 100644
index 0000000000000..e96c75117f533
--- /dev/null
+++ b/examples/gguf-hash/gguf-hash.cpp
@@ -0,0 +1,693 @@
+#include "ggml.h"
+
+#include <cstdlib>   /* abort() */
+#include <cstddef>
+#include <cstdio>
+#include <string>
+#include <stdexcept>
+#include <algorithm>
+#include <cstring>
+
+#include <sstream>
+#include <fstream>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include "xxhash/xxhash.h"
+#include "sha1/sha1.h"
+#include "sha256/sha256.h"
+
+#ifdef __cplusplus
+}
+#endif
+
+
+// uuid.uuid5(uuid.NAMESPACE_URL, 'en.wikipedia.org/wiki/Llama.cpp')
+#define UUID_NAMESPACE_LLAMA_CPP "ef001206-dadc-5f6d-a15f-3359e577d4e5"
+#define UUID_NAMESPACE_LLAMA_CPP_HEX 0xef, 0x00, 0x12, 0x06, 0xda, 0xdc, 0x5f, 0x6d, 0xa1, 0x5f, 0x33, 0x59, 0xe5, 0x77, 0xd4, 0xe5
+
+
+#define HASH_TYPE_SHA256_STR "sha256"
+#define HASH_TYPE_SHA1_STR   "sha1"
+#define HASH_TYPE_XXH64_STR  "xxh64"
+#define HASH_TYPE_UUID_STR   "uuid"
+
+
+typedef enum {
+    HASH_EXIT_SUCCESS = 0, // All hash has been generated or validated
+    HASH_EXIT_FAILURE = 1, // Generic Failure
+    HASH_EXIT_MISMATCH = 2, // Hash mismatched during validation
+    HASH_EXIT_MANIFEST_MISSING_ENTRY = 3, // Hash attempted validation but missing entry in manifest
+    HASH_EXIT_MANIFEST_UNKNOWN_HASH = 4, // Manifest is present, but we do not know any hash format within it
+    HASH_EXIT_MANIFEST_FILE_ERROR = 5 // Manifest is either missing or not a known format
+} hash_exit_code_t;
+
+
+typedef enum {
+    HASH_MANIFEST_NOT_FOUND,
+    HASH_MANIFEST_MISMATCH,
+    HASH_MANIFEST_OK,
+} hash_manifest_result_t;
+
+
+struct hash_params {
+    std::string input;
+    bool xxh64 = false;
+    bool sha1 = false;
+    bool sha256 = false;
+    bool uuid = false;
+
+    bool no_layer = false;
+
+    bool manifest_is_usable = false;
+    std::string manifest_file;
+};
+
+struct manifest_check_params {
+    bool xxh64 = false;
+    bool sha1 = false;
+    bool sha256 = false;
+    bool uuid = false;
+};
+
+static char const * hash_manifest_result_to_str(hash_manifest_result_t value) {
+    switch (value) {
+        case HASH_MANIFEST_NOT_FOUND: return "Not Found";
+        case HASH_MANIFEST_MISMATCH: return "Mismatch";
+        case HASH_MANIFEST_OK: return "Ok";
+    }
+    return "?";
+}
+
+static char const * hash_exit_code_to_str(hash_exit_code_t value) {
+    switch (value) {
+        case HASH_EXIT_SUCCESS: return "Success";
+        case HASH_EXIT_FAILURE: return "Failure";
+        case HASH_EXIT_MISMATCH: return "Mismatch";
+        case HASH_EXIT_MANIFEST_MISSING_ENTRY: return "Manifest Missing Entry";
+        case HASH_EXIT_MANIFEST_UNKNOWN_HASH: return "Manifest Unknown Hash";
+        case HASH_EXIT_MANIFEST_FILE_ERROR: return "Manifest File Error";
+    }
+    return "?";
+}
+
+static void hash_print_usage(const char * executable) {
+    const hash_params default_params;
+    printf("\n");
+    printf("usage: %s [options] GGUF_IN\n", executable);
+    printf("\n");
+    printf("Hash a GGUF file");
+    printf("\n");
+    printf("options:\n");
+    printf("  -h, --help              show this help message and exit\n");
+    printf("      --xxh64             use xxh64 hash\n");
+    printf("      --sha1              use sha1 hash\n");
+    printf("      --sha256            use sha256 hash\n");
+    printf("      --all               use all hash\n");
+    printf("      --no-layer          exclude per layer hash\n");
+    printf("      --uuid              generate UUIDv5 ID\n");
+    printf("  -c, --check <manifest>  verify against a manifest\n");
+    printf("\n");
+}
+
+static void hash_params_parse_ex(int argc, const char ** argv, hash_params & params) {
+    std::string arg;
+    bool invalid_param = false;
+    const std::string arg_prefix = "--";
+
+    int arg_idx = 1;
+    for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
+        arg = argv[arg_idx];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
+        }
+
+        bool arg_found = false;
+        if (arg == "-h" || arg == "--help") {
+            hash_print_usage(argv[0]);
+            exit(0);
+        }
+
+        if (arg == "--xxh64") {
+            arg_found = true;
+            params.xxh64 = true;
+        }
+
+        if (arg == "--sha1") {
+            arg_found = true;
+            params.sha1 = true;
+        }
+
+        if (arg == "--uuid") {
+            arg_found = true;
+            params.uuid = true;
+        }
+
+        if (arg == "--sha256") {
+            arg_found = true;
+            params.sha256 = true;
+        }
+
+        if (arg == "--all") {
+            arg_found = true;
+            params.sha256 = true;
+            params.sha1 = true;
+            params.xxh64 = true;
+        }
+
+        if (arg == "--no-layer") {
+            arg_found = true;
+            params.no_layer = true;
+        }
+
+        if (arg == "-c" || arg == "--check") {
+            if (++arg_idx >= argc) {
+                invalid_param = true;
+                break;
+            }
+            arg_found = true;
+            params.manifest_file = argv[arg_idx];
+        }
+
+        if (!arg_found) {
+            throw std::invalid_argument("error: unknown argument: " + arg);
+        }
+    }
+
+    if (invalid_param) {
+        throw std::invalid_argument("error: invalid parameter for argument:" + arg);
+    }
+
+    if (argc - arg_idx < 1) {
+        throw std::invalid_argument("error: bad arguments");
+    }
+
+    params.input = argv[arg_idx++];
+}
+
+static bool hash_params_parse(int argc, const char ** argv, hash_params & params) {
+    bool result = true;
+    try {
+        hash_params_parse_ex(argc, argv, params);
+    }
+    catch (const std::invalid_argument & ex) {
+        fprintf(stderr, "%s\n", ex.what());
+        hash_print_usage(argv[0]);
+        exit(EXIT_FAILURE);
+    }
+    return result;
+}
+
+static bool manifest_type(const std::string & manifest_file, manifest_check_params & manifest_check) {
+    if (manifest_file.empty()) {
+        return false;
+    }
+
+    std::ifstream file(manifest_file);
+    if (!file.is_open()) {
+        return false;
+    }
+
+    std::string manifest_entry_line;
+    while (getline(file, manifest_entry_line)) {
+        // hash_type_str hash_str tensor_name
+        // e.g. 'xxh64     f66e9cd66a4396a0  test.gguf:tensor_0'
+        std::istringstream line_stream(manifest_entry_line);
+        std::string file_hash_type;
+        if (line_stream >> file_hash_type) {
+            if (file_hash_type == HASH_TYPE_SHA256_STR) {
+                manifest_check.sha256 = true;
+            } else if (file_hash_type == HASH_TYPE_SHA1_STR) {
+                manifest_check.sha1 = true;
+            } else if (file_hash_type == HASH_TYPE_XXH64_STR) {
+                manifest_check.xxh64 = true;
+            } else if (file_hash_type == HASH_TYPE_UUID_STR) {
+                manifest_check.uuid = true;
+            }
+        }
+    }
+
+    return true;
+}
+
+static hash_manifest_result_t manifest_verify(const std::string& manifest_file, const std::string& hash_type_str, const std::string& hash_str, const std::string& tensor_name) {
+    if (manifest_file.empty()) {
+        return HASH_MANIFEST_NOT_FOUND;
+    }
+
+    std::ifstream file(manifest_file);
+    if (!file.is_open()) {
+        return HASH_MANIFEST_NOT_FOUND;
+    }
+
+    std::string manifest_entry_line;
+    while (getline(file, manifest_entry_line)) {
+        std::istringstream line_stream(manifest_entry_line);
+        std::string file_hash_type;
+        std::string file_hash;
+        std::string file_tensor_name;
+        if (line_stream >> file_hash_type >> file_hash >> file_tensor_name) {
+            // Line parsed. Check hash validity
+
+            if (file_hash_type != hash_type_str) {
+                continue;
+            }
+
+            if (file_tensor_name != tensor_name) {
+                continue;
+            }
+
+            return (file_hash == hash_str) ? HASH_MANIFEST_OK : HASH_MANIFEST_MISMATCH;
+        }
+    }
+
+    return HASH_MANIFEST_NOT_FOUND;
+}
+
+static void generate_uuidv5(const unsigned char sha1_digest[20], unsigned char uuid[16]) {
+    // Ref: https://www.rfc-editor.org/rfc/rfc9562.html#section-5.5
+    // Assumes that digest was processed correctly with the expected namespace
+    for (int i = 0; i < 16; i++) {
+        uuid[i] = sha1_digest[i];
+    }
+
+    // Set bits corresponding to UUID ver 5
+    uuid[ 6] &= ~(0xF << 4);
+    uuid[ 6] |= (5 << 4);
+
+    // Set bits corresponding to UUID variant 0b10XX
+    uuid[ 8] &= ~(0xc << 4);
+    uuid[ 8] |= (0x8 << 4);
+}
+
+static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
+    const std::string & fname = hash_params.input;
+    struct ggml_context * ctx_data = NULL;
+
+    struct gguf_init_params params = {
+        /*.no_alloc = */ false,
+        /*.ctx      = */ &ctx_data,
+    };
+
+    // xxh64 init
+    XXH64_state_t* xxh64_model_hash_state = NULL;
+    if (hash_params.xxh64) {
+        xxh64_model_hash_state = XXH64_createState();
+        if (xxh64_model_hash_state==NULL) {
+            abort();
+        }
+
+        XXH64_hash_t const seed = 0;
+        if (XXH64_reset(xxh64_model_hash_state, seed) == XXH_ERROR) {
+            abort();
+        }
+    }
+
+    // sha1 init
+    SHA1_CTX sha1_model_hash_ctx;
+    if (hash_params.sha1) {
+        SHA1Init(&sha1_model_hash_ctx);
+    }
+
+    // sha256 init
+    sha256_t sha256_model_hash_ctx;
+    if (hash_params.sha256) {
+        sha256_init(&sha256_model_hash_ctx);
+    }
+
+    // sha1 for uuid init
+    SHA1_CTX sha1_for_uuid_ctx;
+    if (hash_params.uuid) {
+        unsigned char const uuidv5_namespace[] = {UUID_NAMESPACE_LLAMA_CPP_HEX};
+        SHA1Init(&sha1_for_uuid_ctx);
+        SHA1Update( &sha1_for_uuid_ctx, (unsigned char const *)uuidv5_namespace, sizeof(uuidv5_namespace));
+    }
+
+    struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
+    const int n_tensors = gguf_get_n_tensors(ctx);
+    bool tensor_layer_in_manifest = false;
+    bool model_in_manifest = false;
+    bool tensor_layer_has_mismatch = false;
+    bool model_has_mismatch = false;
+    for (int i = 0; i < n_tensors; ++i) {
+        const char * name = gguf_get_tensor_name(ctx, i);
+        struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
+        auto n_bytes = ggml_nbytes(cur);
+        auto *raw_data = cur->data;
+        const std::string tensor_layer_name = fname + ":" + name;
+
+        if (hash_params.xxh64) {
+
+            if (!hash_params.no_layer) {
+                // Per Layer Hash
+                XXH64_hash_t hash = XXH64(raw_data, n_bytes, 0);
+
+                char hex_result[17];
+                for (int  offset = 0; offset < 8; offset++) {
+                    unsigned int shift_bits_by = (8 * (8 - offset - 1));
+                    snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
+                }
+
+                if (hash_params.manifest_is_usable) {
+                    hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name);
+
+                    switch (verify_result) {
+                        case HASH_MANIFEST_NOT_FOUND:
+                            break;
+                        case HASH_MANIFEST_MISMATCH:
+                            tensor_layer_in_manifest = true;
+                            tensor_layer_has_mismatch = true;
+                            break;
+                        case HASH_MANIFEST_OK:
+                            tensor_layer_in_manifest = true;
+                            break;
+                    }
+
+                    printf("%-8s  %-s  %s  -  %s\n", HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result));
+                } else {
+                    printf("%-8s  %-s  %s\n", HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name.c_str());
+                }
+            }
+
+            // Overall Model Hash
+            if (XXH64_update(xxh64_model_hash_state, raw_data, n_bytes) == XXH_ERROR) abort();
+        }
+
+        if (hash_params.sha1) {
+
+            if (!hash_params.no_layer) {
+                // Per Layer Hash
+                char result[21]; // sha1 outputs 20 bytes
+                SHA1( result, (const char *)raw_data, n_bytes);
+
+                char hex_result[41] = {0};
+                for (int  offset = 0; offset < 20; offset++) {
+                    snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
+                }
+
+                if (hash_params.manifest_is_usable) {
+                    hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name);
+
+                    switch (verify_result) {
+                        case HASH_MANIFEST_NOT_FOUND:
+                            break;
+                        case HASH_MANIFEST_MISMATCH:
+                            tensor_layer_in_manifest = true;
+                            tensor_layer_has_mismatch = true;
+                            break;
+                        case HASH_MANIFEST_OK:
+                            tensor_layer_in_manifest = true;
+                            break;
+                    }
+
+                    printf("%-8s  %-s  %s  -  %s\n", HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result));
+                } else {
+                    printf("%-8s  %-s  %s\n", HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name.c_str());
+                }
+            }
+
+            // Overall Model Hash
+            SHA1Update( &sha1_model_hash_ctx, (unsigned char const *)raw_data, n_bytes);
+        }
+
+        if (hash_params.sha256) {
+
+            if (!hash_params.no_layer) {
+                // Per Layer Hash
+                unsigned char result[SHA256_DIGEST_SIZE]; // sha256 outputs 32 bytes
+                sha256_hash((unsigned char*) result, (const unsigned char *)raw_data, n_bytes);
+
+                char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0};
+                for (int  offset = 0; offset < SHA256_DIGEST_SIZE; offset++) {
+                    snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
+                }
+
+                if (hash_params.manifest_is_usable) {
+                    hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name);
+
+                    switch (verify_result) {
+                        case HASH_MANIFEST_NOT_FOUND:
+                            break;
+                        case HASH_MANIFEST_MISMATCH:
+                            tensor_layer_in_manifest = true;
+                            tensor_layer_has_mismatch = true;
+                            break;
+                        case HASH_MANIFEST_OK:
+                            tensor_layer_in_manifest = true;
+                            break;
+                    }
+
+                    printf("%-8s  %-s  %s  -  %s\n", HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result));
+                } else {
+                    printf("%-8s  %-s  %s\n", HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name.c_str());
+                }
+            }
+
+            // Overall Model Hash
+            sha256_update( &sha256_model_hash_ctx, (unsigned char const *)raw_data, n_bytes);
+        }
+
+        if (hash_params.uuid) {
+            SHA1Update( &sha1_for_uuid_ctx, (unsigned char const *)raw_data, n_bytes);
+        }
+    }
+
+    if (hash_params.xxh64) {
+        XXH64_hash_t const hash = XXH64_digest(xxh64_model_hash_state);
+
+        char hex_result[17];
+        for (int  offset = 0; offset < 8; offset++) {
+            unsigned int shift_bits_by = (8 * (8 - offset - 1));
+            snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
+        }
+
+        if (hash_params.manifest_is_usable) {
+            hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_XXH64_STR, hex_result, fname);
+
+            switch (verify_result) {
+                case HASH_MANIFEST_NOT_FOUND:
+                    break;
+                case HASH_MANIFEST_MISMATCH:
+                    model_in_manifest = true;
+                    model_has_mismatch = true;
+                    break;
+                case HASH_MANIFEST_OK:
+                    model_in_manifest = true;
+                    break;
+            }
+
+            printf("%-8s  %-s  %s  -  %s\n", HASH_TYPE_XXH64_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result));
+        } else {
+            printf("%-8s  %-s  %s\n", HASH_TYPE_XXH64_STR, hex_result, fname.c_str());
+        }
+    }
+
+    if (hash_params.sha1) {
+        unsigned char result[21];
+        SHA1Final(result, &sha1_model_hash_ctx);
+
+        char hex_result[41];
+        for (int  offset = 0; offset < 20; offset++) {
+            snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
+        }
+
+        if (hash_params.manifest_is_usable) {
+            hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA1_STR, hex_result, fname);
+
+            switch (verify_result) {
+                case HASH_MANIFEST_NOT_FOUND:
+                    break;
+                case HASH_MANIFEST_MISMATCH:
+                    model_in_manifest = true;
+                    model_has_mismatch = true;
+                    break;
+                case HASH_MANIFEST_OK:
+                    model_in_manifest = true;
+                    break;
+            }
+
+            printf("%-8s  %-s  %s  -  %s\n", HASH_TYPE_SHA1_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result));
+        } else {
+            printf("%-8s  %-s  %s\n", HASH_TYPE_SHA1_STR, hex_result, fname.c_str());
+        }
+    }
+
+    if (hash_params.sha256) {
+        unsigned char result[SHA256_DIGEST_SIZE]; // sha256 outputs 32 bytes
+        sha256_final( &sha256_model_hash_ctx,  result);
+
+        char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0};
+        for (int  offset = 0; offset < SHA256_DIGEST_SIZE; offset++) {
+            snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
+        }
+
+        if (hash_params.manifest_is_usable) {
+            hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, hex_result, fname);
+
+            switch (verify_result) {
+                case HASH_MANIFEST_NOT_FOUND:
+                    break;
+                case HASH_MANIFEST_MISMATCH:
+                    model_in_manifest = true;
+                    model_has_mismatch = true;
+                    break;
+                case HASH_MANIFEST_OK:
+                    model_in_manifest = true;
+                    break;
+            }
+
+            printf("%-8s  %-s  %s  -  %s\n", HASH_TYPE_SHA256_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result));
+        } else {
+            printf("%-8s  %-s  %s\n", HASH_TYPE_SHA256_STR, hex_result, fname.c_str());
+        }
+    }
+
+    if (hash_params.uuid) {
+        unsigned char result[21];
+        SHA1Final(result, &sha1_for_uuid_ctx);
+
+        unsigned char uuid[16];
+        generate_uuidv5(result, uuid);
+
+        char string_buffer[37] = {0};
+        snprintf(string_buffer, sizeof(string_buffer), "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x",
+            uuid[0], uuid[1], uuid[2], uuid[3],
+            uuid[4], uuid[5], uuid[6], uuid[7],
+            uuid[8], uuid[9], uuid[10], uuid[11],
+            uuid[12], uuid[13], uuid[14], uuid[15]);
+
+        if (hash_params.manifest_is_usable) {
+            hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, string_buffer, fname);
+
+            switch (verify_result) {
+                case HASH_MANIFEST_NOT_FOUND:
+                    break;
+                case HASH_MANIFEST_MISMATCH:
+                    model_in_manifest = true;
+                    model_has_mismatch = true;
+                    break;
+                case HASH_MANIFEST_OK:
+                    model_in_manifest = true;
+                    break;
+            }
+
+            printf("%-8s  %-s  %s  -  %s\n", HASH_TYPE_UUID_STR, string_buffer, fname.c_str(), hash_manifest_result_to_str(verify_result));
+        } else {
+            printf("%-8s  %-s  %s\n", HASH_TYPE_UUID_STR, string_buffer, fname.c_str());
+        }
+    }
+
+
+    ggml_free(ctx_data);
+    gguf_free(ctx);
+
+
+    if (hash_params.manifest_is_usable) {
+        // In hash verification mode
+
+        if (!model_in_manifest) {
+            // model missing in manifest?
+
+            // Check tensor layer...
+            if (!tensor_layer_in_manifest) {
+                // Still missing? Maybe we are reading the wrong manifest.
+                return HASH_EXIT_MANIFEST_MISSING_ENTRY;
+            }
+
+            if (tensor_layer_has_mismatch) {
+                // Per tensor check found error
+                return HASH_EXIT_FAILURE;
+            }
+
+            // All per tensor layer checks passed? Sounds good enough.
+            return HASH_EXIT_SUCCESS;
+        }
+
+        // Overall model check passed, but let's check per layer just in case
+        // If missing, we don't care too much as the overall model checked
+        if (tensor_layer_in_manifest && tensor_layer_has_mismatch) {
+            return HASH_EXIT_FAILURE;
+        }
+
+        if (model_has_mismatch) {
+            // model has failed hash somewhere in the model
+            return HASH_EXIT_FAILURE;
+        }
+
+        // All checks appears to be fine
+        return HASH_EXIT_SUCCESS;
+    }
+
+    // In hash generation mode
+    return HASH_EXIT_SUCCESS;
+}
+
+int main(int argc, const char ** argv) {
+    hash_params params;
+    manifest_check_params manifest_check;
+    hash_params_parse(argc, argv, params);
+
+    if (!params.manifest_file.empty()) {
+        if (!manifest_type(params.manifest_file, manifest_check)) {
+            printf("ERROR cannot open manifest %s", params.manifest_file.c_str());
+            return HASH_EXIT_MANIFEST_FILE_ERROR;
+        }
+
+        if (!manifest_check.sha256 && !manifest_check.sha1 && !manifest_check.xxh64 && !manifest_check.uuid) {
+            printf("ERROR manifest does not have any known hash format in %s", params.manifest_file.c_str());
+            return HASH_EXIT_MANIFEST_UNKNOWN_HASH;
+        }
+
+        printf("manifest  %s", params.manifest_file.c_str());
+
+        if (manifest_check.sha256) {
+            printf("  sha256");
+        }
+
+        if (manifest_check.sha1) {
+            printf("  sha1");
+        }
+
+        if (manifest_check.xxh64) {
+            printf("  xxh64");
+        }
+
+        if (manifest_check.uuid) {
+            printf("  uuid");
+        }
+
+        printf("\n");
+
+        // Autoselect the highest security hash if manifest is provided but
+        // the user has not specifically defined the hash they care about
+        if (!params.xxh64 && !params.sha1 && !params.uuid && !params.sha256) {
+            // User has not selected a specific value, pick most secure hash
+            if (manifest_check.sha256) {
+                params.sha256 = true;
+            } else if (manifest_check.sha1) {
+                params.sha1 = true;
+            } else if (manifest_check.xxh64) {
+                params.xxh64 = true;
+            } else if (manifest_check.uuid) {
+                params.uuid = true;
+            }
+        }
+
+        params.manifest_is_usable = true;
+    }
+
+    // By default if no swich argument provided, assume xxh64
+    if (!params.xxh64 && !params.sha1 && !params.uuid && !params.sha256) {
+        params.xxh64 = true;
+    }
+
+    hash_exit_code_t exit_code = gguf_hash(params);
+
+    if (params.manifest_is_usable) {
+        printf("\nVerification results for %s - %s\n", params.manifest_file.c_str(), hash_exit_code_to_str(exit_code));
+    }
+
+    return exit_code;
+}
diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt
index 828e624352c8d..f63887da7dfca 100644
--- a/examples/gguf-split/CMakeLists.txt
+++ b/examples/gguf-split/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET gguf-split)
+set(TARGET llama-gguf-split)
 add_executable(${TARGET} gguf-split.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp
index e04feeae39188..881f0451c1455 100644
--- a/examples/gguf-split/gguf-split.cpp
+++ b/examples/gguf-split/gguf-split.cpp
@@ -61,10 +61,10 @@ static size_t split_str_to_n_bytes(std::string str) {
     int n;
     if (str.back() == 'M') {
         sscanf(str.c_str(), "%d", &n);
-        n_bytes = (size_t)n * 1024 * 1024; // megabytes
+        n_bytes = (size_t)n * 1000 * 1000; // megabytes
     } else if (str.back() == 'G') {
         sscanf(str.c_str(), "%d", &n);
-        n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes
+        n_bytes = (size_t)n * 1000 * 1000 * 1000; // gigabytes
     } else {
         throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
     }
@@ -284,7 +284,7 @@ struct split_strategy {
                 struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
                 total_size += ggml_nbytes(t);
             }
-            total_size = total_size / 1024 / 1024; // convert to megabytes
+            total_size = total_size / 1000 / 1000; // convert to megabytes
             printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
             i_split++;
         }
diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh
index 7ca6fa7f20de8..d5a92d6051063 100755
--- a/examples/gguf-split/tests.sh
+++ b/examples/gguf-split/tests.sh
@@ -18,8 +18,8 @@ fi
 
 set -x
 
-SPLIT=$1/gguf-split
-MAIN=$1/main
+SPLIT=$1/llama-gguf-split
+MAIN=$1/llama-cli
 WORK_PATH=$TMP_DIR/gguf-split
 ROOT_DIR=$(realpath $(dirname $0)/../../)
 
@@ -41,7 +41,7 @@ echo PASS
 echo
 
 # 2b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32
 echo PASS
 echo
 
@@ -51,7 +51,7 @@ echo PASS
 echo
 
 # 3b. Test the merged model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32
 echo PASS
 echo
 
@@ -61,7 +61,7 @@ echo PASS
 echo
 
 # 4b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --random-prompt --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32
 echo PASS
 echo
 
@@ -71,7 +71,7 @@ echo
 #echo
 
 # 5b. Test the merged model is loading properly
-#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32
+#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32
 #echo PASS
 #echo
 
@@ -81,7 +81,7 @@ echo PASS
 echo
 
 # 6b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32
 echo PASS
 echo
 
diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt
index 6481f087bc997..a9569b411956b 100644
--- a/examples/gguf/CMakeLists.txt
+++ b/examples/gguf/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET gguf)
+set(TARGET llama-gguf)
 add_executable(${TARGET} gguf.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp
index 57514377107d7..7498f85efc4f9 100644
--- a/examples/gguf/gguf.cpp
+++ b/examples/gguf/gguf.cpp
@@ -92,6 +92,11 @@ static bool gguf_ex_read_0(const std::string & fname) {
 
     struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
 
+    if (!ctx) {
+        fprintf(stderr, "%s: failed to load '%s'\n", __func__, fname.c_str());
+        return false;
+    }
+
     printf("%s: version:      %d\n", __func__, gguf_get_version(ctx));
     printf("%s: alignment:   %zu\n", __func__, gguf_get_alignment(ctx));
     printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
diff --git a/examples/gpt4all.sh b/examples/gpt4all.sh
deleted file mode 100755
index 5fd739e55c554..0000000000000
--- a/examples/gpt4all.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main --color --instruct --threads 4 \
-       --model ./models/gpt4all-7B/gpt4all-lora-quantized.bin \
-       --file ./prompts/alpaca.txt \
-       --batch_size 8 --ctx_size 2048 -n -1 \
-       --repeat_last_n 64 --repeat_penalty 1.3 \
-       --n_predict 128 --temp 0.1 --top_k 40 --top_p 0.95
diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt
index ac4a5ae7937ea..86dfddca346fe 100644
--- a/examples/gritlm/CMakeLists.txt
+++ b/examples/gritlm/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET gritlm)
+set(TARGET llama-gritlm)
 add_executable(${TARGET} gritlm.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md
index a3a3c1389ca27..786ba57363def 100644
--- a/examples/gritlm/README.md
+++ b/examples/gritlm/README.md
@@ -26,7 +26,7 @@ $ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --ou
 
 Run the example using the downloaded model:
 ```console
-$ ./gritlm -m models/gritlm-7b_q4_1.gguf
+$ ./llama-gritlm -m models/gritlm-7b_q4_1.gguf
 
 Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605
 Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103
diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp
index 52fd719b38ee5..2c61c2e1eb3bc 100644
--- a/examples/gritlm/gritlm.cpp
+++ b/examples/gritlm/gritlm.cpp
@@ -44,6 +44,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
 
         // clear previous kv_cache values (irrelevant for embeddings)
         llama_kv_cache_clear(ctx);
+        llama_set_embeddings(ctx, true);
         llama_set_causal_attn(ctx, false);
 
         // run model
@@ -98,7 +99,9 @@ static std::string generate(llama_context * ctx, const std::string & prompt, boo
     llama_token eos_token = llama_token_eos(mdl);
 
     llama_kv_cache_clear(ctx);
+    llama_set_embeddings(ctx, false);
     llama_set_causal_attn(ctx, true);
+
     llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
 
     std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
@@ -153,7 +156,9 @@ static std::string gritlm_instruction(const std::string & instruction) {
 
 int main(int argc, char * argv[]) {
     gpt_params params;
+
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -164,8 +169,7 @@ int main(int argc, char * argv[]) {
 
     llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams);
 
-    // create new context - set to embedding mode
-    cparams.embeddings = true;
+    // create generation context
     llama_context * ctx = llama_new_context_with_model(mdl, cparams);
 
     // ### Embedding/Representation ###
diff --git a/examples/imatrix/CMakeLists.txt b/examples/imatrix/CMakeLists.txt
index d688a16209049..d4c8265bdb9d2 100644
--- a/examples/imatrix/CMakeLists.txt
+++ b/examples/imatrix/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET imatrix)
+set(TARGET llama-imatrix)
 add_executable(${TARGET} imatrix.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/imatrix/README.md b/examples/imatrix/README.md
index 458c01b8751f1..bb5faec94c20a 100644
--- a/examples/imatrix/README.md
+++ b/examples/imatrix/README.md
@@ -1,32 +1,35 @@
 # llama.cpp/examples/imatrix
 
-Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantum models.
+Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantized models.
 More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861
 
 ## Usage
 
 ```
-./imatrix -m <some_fp_model> -f <some_training_data> [-o <output_file>] [--verbosity <verbosity_level>]
-        [-ofreq num_chunks] [-ow <0 or 1>] [other common params]
+./llama-imatrix \
+    -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \
+    [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \
+    [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]
 ```
 
 Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory.
 The parameters in square brackets are optional and have the following meaning:
 * `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used.
 * `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`.
-* `-ofreq` (or `--output-frequency`) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
-* `-ow` (or `--output-weight`) specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
+* `--output-frequency` specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
+* `--save-frequency` specifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never)
+* `--process-output` specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
 
 For faster computation, make sure to use GPU offloading via the `-ngl` argument
 
 ## Example
 
 ```bash
-LLAMA_CUDA=1 make -j
+GGML_CUDA=1 make -j
 
 # generate importance matrix (imatrix.dat)
-./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
+./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
 
 # use the imatrix to perform a Q4_K_M quantization
-./quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m
+./llama-quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m
 ```
diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp
index 82b19fc4f3bae..83b85d72b043a 100644
--- a/examples/imatrix/imatrix.cpp
+++ b/examples/imatrix/imatrix.cpp
@@ -17,39 +17,37 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
+
+    LOG_TEE("\nexample usage:\n");
+    LOG_TEE("\n    %s \\\n"
+            "       -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n"
+            "       [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
+            "       [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
+    LOG_TEE("\n");
+}
+
 struct Stats {
     std::vector<float> values;
     std::vector<int> counts;
     int ncall = 0;
 };
 
-struct StatParams {
-    std::string dataset;
-    std::string ofile = "imatrix.dat";
-    int         n_output_frequency = 10;
-    int         verbosity = 1;
-    int         keep_every = 0;
-    bool        collect_output_weight = false;
-};
-
 class IMatrixCollector {
 public:
     IMatrixCollector() = default;
-    void set_parameters(StatParams&& params) { m_params = std::move(params); }
+    void set_params(gpt_params params) { m_params = std::move(params); }
     bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
-    void save_imatrix() const;
-    bool load_imatrix(const char * file_name, bool add);
-    static bool load_imatrix(const char * file_name, std::unordered_map<std::string, Stats>& imatrix);
+    void save_imatrix(int ncall = -1) const;
+    bool load_imatrix(const char * file_name);
 private:
     std::unordered_map<std::string, Stats> m_stats;
-    StatParams                             m_params;
+    gpt_params                             m_params;
     std::mutex                             m_mutex;
     int                                    m_last_call = 0;
     std::vector<float>                     m_src1_data;
     std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
-                                                  //
-    void save_imatrix(const char * file_name, const char * dataset) const;
-    void keep_imatrix(int ncall) const;
 };
 
 // remove any prefix and suffixes from the name
@@ -85,7 +83,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
         if (t->op != GGML_OP_MUL_MAT) return false;
         // why are small batches ignored (<16 tokens)?
         if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
-        if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
+        if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) return false;
         return true;
     }
 
@@ -129,7 +127,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
         }
         else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
             fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
-            exit(1); //GGML_ASSERT(false);
+            exit(1); //GGML_ABORT("fatal error");
         }
         if (m_params.verbosity > 1) {
             printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
@@ -153,28 +151,32 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
                     for (int j = 0; j < (int)src1->ne[0]; ++j) {
                         e.values[e_start + j] += x[j]*x[j];
                         e.counts[e_start + j]++;
+                        if (!std::isfinite(e.values[e_start + j])) {
+                            fprintf(stderr, "%f detected in %s\n", e.values[e_start + j], wname.c_str());
+                            exit(1);
+                        }
                     }
                 }
             }
             if (e.ncall > m_last_call) {
                 m_last_call = e.ncall;
-                if (m_last_call % m_params.n_output_frequency == 0) {
+                if (m_last_call % m_params.n_out_freq == 0) {
                     save_imatrix();
                 }
-                if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
-                    keep_imatrix(m_last_call);
+                if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
+                    save_imatrix(m_last_call);
                 }
             }
         }
     } else {
-        auto& e = m_stats[wname];
+        auto & e = m_stats[wname];
         if (e.values.empty()) {
             e.values.resize(src1->ne[0], 0);
             e.counts.resize(src1->ne[0], 0);
         }
         else if (e.values.size() != (size_t)src1->ne[0]) {
             fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
-            exit(1); //GGML_ASSERT(false);
+            exit(1); //GGML_ABORT("fatal error");
         }
         ++e.ncall;
         if (m_params.verbosity > 1) {
@@ -185,15 +187,19 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
             for (int j = 0; j < (int)src1->ne[0]; ++j) {
                 e.values[j] += x[j]*x[j];
                 e.counts[j]++;
+                if (!std::isfinite(e.values[j])) {
+                    fprintf(stderr, "%f detected in %s\n", e.values[j], wname.c_str());
+                    exit(1);
+                }
             }
         }
         if (e.ncall > m_last_call) {
             m_last_call = e.ncall;
-            if (m_last_call % m_params.n_output_frequency == 0) {
+            if (m_last_call % m_params.n_out_freq == 0) {
                 save_imatrix();
             }
-            if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
-                keep_imatrix(m_last_call);
+            if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
+                save_imatrix(m_last_call);
             }
         }
     }
@@ -201,33 +207,75 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     return true;
 }
 
-void IMatrixCollector::save_imatrix() const {
-    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str());
-}
+void IMatrixCollector::save_imatrix(int ncall) const {
+    auto fname = m_params.out_file;
+    if (fname.empty()) {
+        fname = "imatrix.dat";
+    }
 
-void IMatrixCollector::keep_imatrix(int ncall) const {
-    auto file_name = m_params.ofile;
-    if (file_name.empty()) file_name = "imatrix.dat";
-    file_name += ".at_";
-    file_name += std::to_string(ncall);
-    save_imatrix(file_name.c_str(), m_params.dataset.c_str());
-}
+    if (ncall > 0) {
+        fname += ".at_";
+        fname += std::to_string(ncall);
+    }
+
+    // avoid writing imatrix entries that do not have full data
+    // this can happen with MoE models where some of the experts end up not being exercised by the provided training data
+
+    int n_entries = 0;
+    std::vector<std::string> to_store;
+
+    bool is_first = true; // for printing
+    for (const auto & kv : m_stats) {
+        const int n_all = kv.second.counts.size();
+
+        if (n_all == 0) {
+            continue;
+        }
+
+        int n_zeros = 0;
+        for (const int c : kv.second.counts) {
+            if (c == 0) {
+                n_zeros++;
+            }
+        }
+
+        if (n_zeros != 0 && is_first) {
+            fprintf(stderr, "\n");
+            is_first = false;
+        }
+
+        if (n_zeros == n_all) {
+            fprintf(stderr, "%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str());
+            continue;
+        }
+
+        if (n_zeros > 0) {
+            fprintf(stderr, "%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
+            continue;
+        }
+
+        n_entries++;
+        to_store.push_back(kv.first);
+    }
+
+    if (to_store.size() < m_stats.size()) {
+        fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
+    }
 
-void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const {
     std::ofstream out(fname, std::ios::binary);
-    int n_entries = m_stats.size();
     out.write((const char *) &n_entries, sizeof(n_entries));
-    for (const auto & p : m_stats) {
-        int len = p.first.size();
+    for (const auto & name : to_store) {
+        const auto & stat = m_stats.at(name);
+        int len = name.size();
         out.write((const char *) &len, sizeof(len));
-        out.write(p.first.c_str(), len);
-        out.write((const char *) &p.second.ncall, sizeof(p.second.ncall));
-        int nval = p.second.values.size();
+        out.write(name.c_str(), len);
+        out.write((const char *) &stat.ncall, sizeof(stat.ncall));
+        int nval = stat.values.size();
         out.write((const char *) &nval, sizeof(nval));
         if (nval > 0) {
             std::vector<float> tmp(nval);
             for (int i = 0; i < nval; i++) {
-                tmp[i] = (p.second.values[i] / static_cast<float>(p.second.counts[i])) * static_cast<float>(p.second.ncall);
+                tmp[i] = (stat.values[i] / static_cast<float>(stat.counts[i])) * static_cast<float>(stat.ncall);
             }
             out.write((const char*)tmp.data(), nval*sizeof(float));
         }
@@ -236,26 +284,28 @@ void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) co
     // Write the number of call the matrix was computed with
     out.write((const char *) &m_last_call, sizeof(m_last_call));
 
-    // Write the dataset name at the end of the file to later on specify it in quantize
-    int n_dataset = strlen(dataset);
-    out.write((const char *) &n_dataset, sizeof(n_dataset));
-    out.write(dataset, n_dataset);
+    // Write the input filename at the end of the file to later on specify it in quantize
+    {
+        int len = m_params.prompt_file.size();
+        out.write((const char *) &len, sizeof(len));
+        out.write(m_params.prompt_file.c_str(), len);
+    }
 
     if (m_params.verbosity > 0) {
-        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname);
+        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
     }
 }
 
-bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map<std::string, Stats>& imatrix_data) {
-    std::ifstream in(imatrix_file, std::ios::binary);
+bool IMatrixCollector::load_imatrix(const char * fname) {
+    std::ifstream in(fname, std::ios::binary);
     if (!in) {
-        printf("%s: failed to open %s\n",__func__,imatrix_file);
+        printf("%s: failed to open %s\n",__func__, fname);
         return false;
     }
     int n_entries;
     in.read((char*)&n_entries, sizeof(n_entries));
     if (in.fail() || n_entries < 1) {
-        printf("%s: no data in file %s\n", __func__, imatrix_file);
+        printf("%s: no data in file %s\n", __func__, fname);
         return false;
     }
     for (int i = 0; i < n_entries; ++i) {
@@ -263,23 +313,22 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma
         std::vector<char> name_as_vec(len+1);
         in.read((char *)name_as_vec.data(), len);
         if (in.fail()) {
-            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file);
+            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
             return false;
         }
         name_as_vec[len] = 0;
         std::string name{name_as_vec.data()};
-        auto& e = imatrix_data[std::move(name)];
+        auto & e = m_stats[std::move(name)];
         int ncall;
         in.read((char*)&ncall, sizeof(ncall));
         int nval;
         in.read((char *)&nval, sizeof(nval));
         if (in.fail() || nval < 1) {
             printf("%s: failed reading number of values for entry %d\n",__func__,i);
-            imatrix_data = {};
+            m_stats = {};
             return false;
         }
 
-        // When re-called from load_imatrix() with add set, this will already be created.
         if (e.values.empty()) {
             e.values.resize(nval, 0);
             e.counts.resize(nval, 0);
@@ -289,7 +338,7 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma
         in.read((char*)tmp.data(), nval*sizeof(float));
         if (in.fail()) {
             printf("%s: failed reading data for entry %d\n",__func__,i);
-            imatrix_data = {};
+            m_stats = {};
             return false;
         }
 
@@ -304,13 +353,6 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma
     return true;
 }
 
-bool IMatrixCollector::load_imatrix(const char * file_name, bool add) {
-    if (!add) {
-        m_stats.clear();
-    }
-    return load_imatrix(file_name, m_stats);
-}
-
 static IMatrixCollector g_collector;
 
 static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
@@ -324,7 +366,7 @@ struct results_log_softmax {
     float  prob;
 };
 
-static std::vector<float> softmax(const std::vector<float>& logits) {
+static std::vector<float> softmax(const std::vector<float> & logits) {
     std::vector<float> probs(logits.size());
     float max_logit = logits[0];
     for (float v : logits) {
@@ -358,8 +400,7 @@ static results_log_softmax log_softmax(int n_vocab, const float * logits, int to
 
 static void process_logits(
     int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
-    double & nll, double & nll2, float * logit_history, float * prob_history
-) {
+    double & nll, double & nll2, float * logit_history, float * prob_history) {
     std::mutex mutex;
     int counter = 0;
     auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
@@ -391,10 +432,9 @@ static void process_logits(
     }
 }
 
-static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
-
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-    GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
+static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
+    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
+    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
     const int n_ctx = llama_n_ctx(ctx);
 
     auto tim1 = std::chrono::high_resolution_clock::now();
@@ -405,13 +445,13 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
     auto tim2 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
 
-    if (from_chunk > 0) {
-        if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) {
-            fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk);
+    if (params.i_chunk > 0) {
+        if (size_t((params.i_chunk + 2)*n_ctx) >= tokens.size()) {
+            fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, params.i_chunk);
             return false;
         }
-        fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx);
-        tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx);
+        fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, params.i_chunk, params.i_chunk*n_ctx);
+        tokens.erase(tokens.begin(), tokens.begin() + params.i_chunk*n_ctx);
     }
 
     if (int(tokens.size()) < 2*n_ctx) {
@@ -424,7 +464,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
     std::vector<float> logit_history;
     std::vector<float> prob_history;
 
-    if (compute_ppl) {
+    if (params.compute_ppl) {
         logit_history.resize(tokens.size());
         prob_history.resize(tokens.size());
     }
@@ -446,7 +486,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
     const int num_batches = (n_ctx + n_batch - 1) / n_batch;
 
     std::vector<float> logits;
-    if (compute_ppl && num_batches > 1) {
+    if (params.compute_ppl && num_batches > 1) {
         logits.reserve((size_t)n_ctx * n_vocab);
     }
 
@@ -482,7 +522,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
             // restore the original token in case it was set to BOS
             tokens[batch_start] = token_org;
 
-            if (compute_ppl && num_batches > 1) {
+            if (params.compute_ppl && num_batches > 1) {
                 const auto * batch_logits = llama_get_logits(ctx);
                 logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
             }
@@ -501,7 +541,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
             fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
         }
 
-        if (compute_ppl) {
+        if (params.compute_ppl) {
             const int first = n_ctx/2;
             const auto all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
             process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
@@ -516,7 +556,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
     }
     printf("\n");
 
-    if (compute_ppl) {
+    if (params.compute_ppl) {
         nll2 /= count;
         nll /= count;
         const double ppl = exp(nll);
@@ -533,111 +573,32 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
 }
 
 int main(int argc, char ** argv) {
-
-    StatParams sparams;
-    std::string prev_result_file;
-    std::string combine_files;
-    bool compute_ppl = true;
-    int  from_chunk  = 0;
-    std::vector<char*> args;
-    args.push_back(argv[0]);
-    int iarg = 1;
-    for (; iarg < argc-1; ++iarg) {
-        std::string arg{argv[iarg]};
-        if (arg == "-o" || arg == "--output-file") {
-            sparams.ofile = argv[++iarg];
-        }
-        else if (arg == "-ofreq" || arg == "--output-frequency") {
-            sparams.n_output_frequency = std::stoi(argv[++iarg]);
-        }
-        else if (arg == "-ow" || arg == "--output-weight") {
-            sparams.collect_output_weight = std::stoi(argv[++iarg]);
-        }
-        else if (arg == "--verbosity") {
-            sparams.verbosity = std::stoi(argv[++iarg]);
-        } else if (arg == "--no-ppl") {
-            compute_ppl = false;
-        } else if (arg == "--keep-imatrix") {
-            sparams.keep_every = std::stoi(argv[++iarg]);
-        } else if (arg == "--continue-from") {
-            prev_result_file = argv[++iarg];
-        } else if (arg == "--combine") {
-            combine_files = argv[++iarg];
-        }
-        else if (arg == "--from-chunk") {
-            from_chunk = std::stoi(argv[++iarg]);
-        } else {
-            args.push_back(argv[iarg]);
-        }
-    }
-    if (iarg < argc) {
-        std::string arg{argv[iarg]};
-        if (arg == "--no-ppl") {
-            compute_ppl = false;
-        } else {
-            args.push_back(argv[iarg]);
-        }
-    }
-
     gpt_params params;
-    params.n_batch = 512;
-    if (!gpt_params_parse(args.size(), args.data(), params)) {
-        return 1;
-    }
 
+    params.n_ctx = 512;
     params.logits_all = true;
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
+    params.verbosity = 1;
 
-    print_build_info();
-
-    if (params.seed == LLAMA_DEFAULT_SEED) {
-        params.seed = time(NULL);
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
+        return 1;
     }
 
-    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
-
-    std::mt19937 rng(params.seed);
-    if (params.random_prompt) {
-        params.prompt = gpt_random_prompt(rng);
-    }
+    params.n_batch = std::min(params.n_batch, params.n_ctx);
 
-    sparams.dataset = params.prompt_file;
-    g_collector.set_parameters(std::move(sparams));
+    g_collector.set_params(params);
 
-    if (!combine_files.empty()) {
-        std::vector<std::string> files;
-        size_t pos = 0;
-        while (true) {
-            auto new_pos = combine_files.find(',', pos);
-            if (new_pos != std::string::npos) {
-                files.emplace_back(combine_files.substr(pos, new_pos - pos));
-                pos = new_pos + 1;
-            } else {
-                files.emplace_back(combine_files.substr(pos));
-                break;
-            }
-        }
-        if (files.size() < 2) {
-            fprintf(stderr, "You must provide at least two comma separated files to use --combine\n");
+    for (const auto & in_file : params.in_files) {
+        printf("%s : loading imatrix from '%s'\n", __func__, in_file.c_str());
+        if (!g_collector.load_imatrix(in_file.c_str())) {
+            fprintf(stderr, "%s : failed to load %s\n", __func__, in_file.c_str());
             return 1;
         }
-        printf("Combining the following %d files\n", int(files.size()));
-        for (auto& file : files) {
-            printf("    %s\n", file.c_str());
-            if (!g_collector.load_imatrix(file.c_str(), true)) {
-                fprintf(stderr, "Failed to load %s\n", file.c_str());
-                return 1;
-            }
-        }
-        g_collector.save_imatrix();
-        return 0;
     }
 
-    if (!prev_result_file.empty()) {
-        if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) {
-            fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str());
-            return 1;
-        }
+    if (params.in_files.size() > 1) {
+        printf("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
+        g_collector.save_imatrix();
     }
 
     llama_backend_init();
@@ -650,9 +611,10 @@ int main(int argc, char ** argv) {
     params.warmup = false;
 
     // init
-    llama_model * model;
-    llama_context * ctx;
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
     if (model == nullptr || ctx == nullptr) {
         fprintf(stderr, "%s : failed to init\n", __func__);
         return 1;
@@ -667,11 +629,10 @@ int main(int argc, char ** argv) {
     // print system information
     {
         fprintf(stderr, "\n");
-        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+        fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
     }
 
-    bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk);
-    if (!OK) {
+    if (!compute_imatrix(ctx, params)) {
         return 1;
     }
 
diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt
index e4e8028da09da..9b1aa3b63c920 100644
--- a/examples/infill/CMakeLists.txt
+++ b/examples/infill/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET infill)
+set(TARGET llama-infill)
 add_executable(${TARGET} infill.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/infill/README.md b/examples/infill/README.md
index 6b076c8390abf..810a0c5e76697 100644
--- a/examples/infill/README.md
+++ b/examples/infill/README.md
@@ -15,6 +15,7 @@ In this section, we cover the most commonly used options for running the `infill
 -   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
 -   `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
 -   `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `--spm-infill`: Use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this.
 
 ## Input Prompts
 
@@ -42,5 +43,5 @@ scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.ggu
 ```
 
 ```bash
-./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n    print(\"hell" --in-suffix "\n   print(\"goodbye world\")\n    "
+./llama-infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n    print(\"hell" --in-suffix "\n   print(\"goodbye world\")\n    "
 ```
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index afac145f63934..05700c1d591d9 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -50,9 +50,9 @@ static void write_logfile(
         return;
     }
 
-    const std::string timestamp = get_sortable_timestamp();
+    const std::string timestamp = string_get_sortable_timestamp();
 
-    const bool success = create_directory_with_parents(params.logdir);
+    const bool success = fs_create_directory_with_parents(params.logdir);
     if (!success) {
         fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
                 __func__, params.logdir.c_str());
@@ -70,7 +70,7 @@ static void write_logfile(
     fprintf(logfile, "binary: infill\n");
     char model_desc[128];
     llama_model_desc(model, model_desc, sizeof(model_desc));
-    dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);
+    yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc);
 
     fprintf(logfile, "\n");
     fprintf(logfile, "######################\n");
@@ -78,8 +78,8 @@ static void write_logfile(
     fprintf(logfile, "######################\n");
     fprintf(logfile, "\n");
 
-    dump_string_yaml_multiline(logfile, "output", output.c_str());
-    dump_vector_int_yaml(logfile, "output_tokens", output_tokens);
+    yaml_dump_string_multiline(logfile, "output", output.c_str());
+    yaml_dump_vector_int(logfile, "output_tokens", output_tokens);
 
     llama_dump_timing_info_yaml(logfile, ctx);
     fclose(logfile);
@@ -107,6 +107,7 @@ int main(int argc, char ** argv) {
     g_params = &params;
 
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -139,27 +140,6 @@ int main(int argc, char ** argv) {
         LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
         params.n_ctx = 8;
     }
-    if (params.instruct) {
-        printf("\n************\n");
-        printf("%s: please use the 'main' tool for instruct mode\n", __func__);
-        printf("************\n\n");
-
-        return 0;
-    }
-    if (params.chatml) {
-        printf("\n************\n");
-        printf("%s: please use the 'main' tool for chatml mode\n", __func__);
-        printf("************\n\n");
-
-        return 0;
-    }
-    if (!params.antiprompt.empty()) {
-        printf("\n************\n");
-        printf("%s: please use the 'main' tool for antiprompt mode\n", __func__);
-        printf("************\n\n");
-
-        return 0;
-    }
     if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) {
         printf("\n************\n");
         printf("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__);
@@ -167,20 +147,6 @@ int main(int argc, char ** argv) {
 
         return 0;
     }
-    if (params.random_prompt) {
-        printf("\n************\n");
-        printf("%s: please use the 'main' tool for random prompt mode\n", __func__);
-        printf("************\n\n");
-
-        return 0;
-    }
-    if (!params.path_prompt_cache.empty()) {
-        printf("\n************\n");
-        printf("%s: infill does not support prompt caching\n", __func__);
-        printf("************\n\n");
-
-        return 0;
-    }
 
     if (params.rope_freq_base != 0.0) {
         LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
@@ -207,17 +173,16 @@ int main(int argc, char ** argv) {
 
     llama_model * model;
     llama_context * ctx;
-    llama_context * ctx_guidance = NULL;
+
     g_model = &model;
     g_ctx = &ctx;
 
     // load the model and apply lora adapter, if any
     LOG("%s: load the model and apply lora adapter, if any\n", __func__);
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    if (sparams.cfg_scale > 1.f) {
-        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
-        ctx_guidance = llama_new_context_with_model(model, lparams);
-    }
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    model = llama_init.model;
+    ctx = llama_init.context;
 
     if (model == NULL) {
         LOG_TEE("%s: error: unable to load model\n", __func__);
@@ -236,32 +201,34 @@ int main(int argc, char ** argv) {
     // print system information
     {
         LOG_TEE("\n");
-        LOG_TEE("%s\n", get_system_info(params).c_str());
+        LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str());
     }
-    const bool add_bos = llama_should_add_bos_token(model);
-    GGML_ASSERT(llama_add_eos_token(model) != 1);
+    const bool add_bos = llama_add_bos_token(model);
+    GGML_ASSERT(!llama_add_eos_token(model));
     LOG("add_bos: %d\n", add_bos);
 
-    bool suff_rm_leading_spc = params.escape;
-    if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
-        params.input_suffix.erase(0, 1);
-        suff_rm_leading_spc = false;
-    }
     std::vector<llama_token> embd_inp;
+    std::vector<llama_token> embd_end;
     std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
     std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
-    const int space_token = 29871;
-    if (suff_rm_leading_spc && inp_sfx[0] == space_token) {
-        inp_sfx.erase(inp_sfx.begin());
-    }
+
+    GGML_ASSERT(llama_token_prefix(model) >= 0);
+    GGML_ASSERT(llama_token_suffix(model) >= 0);
+
     inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
+    inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
+
+    embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
+    embd_end = params.spm_infill ? inp_pfx : inp_sfx;
     if (add_bos) {
-        inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));
+        embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+    }
+    embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
+
+    const llama_token middle_token = llama_token_middle(model);
+    if (middle_token >= 0) {
+        embd_inp.push_back(middle_token);
     }
-    inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
-    embd_inp = inp_pfx;
-    embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-    embd_inp.push_back(llama_token_middle(model));
 
     LOG("prefix: \"%s\"\n", log_tostr(params.input_prefix));
     LOG("suffix: \"%s\"\n", log_tostr(params.input_suffix));
@@ -273,25 +240,6 @@ int main(int argc, char ** argv) {
         LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
     }
 
-    // Tokenize negative prompt
-    std::vector<llama_token> guidance_inp;
-    int guidance_offset = 0;
-    int original_prompt_len = 0;
-    if (ctx_guidance) {
-        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
-
-        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true);
-        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
-
-        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true);
-        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
-
-        original_prompt_len = original_inp.size();
-        guidance_offset = (int)guidance_inp.size() - original_prompt_len;
-        LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
-        LOG("guidance_offset:     %s", log_tostr(guidance_offset));
-    }
-
     if ((int) embd_inp.size() > n_ctx - 4) {
         LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
         return 1;
@@ -319,15 +267,6 @@ int main(int argc, char ** argv) {
             LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
         }
 
-        if (ctx_guidance) {
-            LOG_TEE("\n");
-            LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
-            LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
-            for (int i = 0; i < (int) guidance_inp.size(); i++) {
-                LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
-            }
-        }
-
         if (params.n_keep > 0) {
         LOG_TEE("%s: static prompt based on n_keep: '", __func__);
             for (int i = 0; i < params.n_keep; i++) {
@@ -395,12 +334,11 @@ int main(int argc, char ** argv) {
         is_interacting = params.interactive_first;
     }
 
-    bool input_echo           = true;
+    bool input_echo = true;
 
-    int n_past             = 0;
-    int n_remain           = params.n_predict;
-    int n_consumed         = 0;
-    int n_past_guidance    = 0;
+    int n_past     = 0;
+    int n_remain   = params.n_predict;
+    int n_consumed = 0;
 
     std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
     std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
@@ -410,7 +348,6 @@ int main(int argc, char ** argv) {
     console::set_display(console::prompt);
 
     std::vector<llama_token> embd;
-    std::vector<llama_token> embd_guidance;
 
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
 
@@ -436,7 +373,7 @@ int main(int argc, char ** argv) {
             // if we run out of context:
             // - take the n_keep first tokens from the original prompt (via n_past)
             // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
-            if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
+            if (n_past + (int) embd.size() > n_ctx) {
                 if (params.n_predict == -2) {
                     LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
                     break;
@@ -453,11 +390,7 @@ int main(int argc, char ** argv) {
 
                 n_past -= n_discard;
 
-                if (ctx_guidance) {
-                    n_past_guidance -= n_discard;
-                }
-
-                LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
+                LOG("after swap: n_past = %d\n", n_past);
 
                 LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
 
@@ -465,45 +398,6 @@ int main(int argc, char ** argv) {
 
             // evaluate tokens in batches
             // embd is typically prepared beforehand to fit within a batch, but not always
-
-            if (ctx_guidance) {
-                int input_size = 0;
-                llama_token * input_buf = NULL;
-
-                if (n_past_guidance < (int) guidance_inp.size()) {
-                    // Guidance context should have the same data with these modifications:
-                    //
-                    // * Replace the initial prompt
-                    // * Shift everything by guidance_offset
-                    embd_guidance = guidance_inp;
-                    if (embd.begin() + original_prompt_len < embd.end()) {
-                        embd_guidance.insert(
-                            embd_guidance.end(),
-                            embd.begin() + original_prompt_len,
-                            embd.end()
-                        );
-                    }
-
-                    input_buf  = embd_guidance.data();
-                    input_size = embd_guidance.size();
-
-                    LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());
-                } else {
-                    input_buf  = embd.data();
-                    input_size = embd.size();
-                }
-
-                for (int i = 0; i < input_size; i += params.n_batch) {
-                    int n_eval = std::min(input_size - i, params.n_batch);
-                    if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) {
-                        LOG_TEE("%s : failed to eval\n", __func__);
-                        return 1;
-                    }
-
-                    n_past_guidance += n_eval;
-                }
-            }
-
             for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
                 int n_eval = (int) embd.size() - i;
                 if (n_eval > params.n_batch) {
@@ -525,11 +419,9 @@ int main(int argc, char ** argv) {
         }
 
         embd.clear();
-        embd_guidance.clear();
 
         if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
-
-            const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);
+            const llama_token id = llama_sampling_sample(ctx_sampling, ctx, nullptr);
 
             llama_sampling_accept(ctx_sampling, ctx, id, true);
 
@@ -583,7 +475,6 @@ int main(int argc, char ** argv) {
 
         // if not currently processing queued inputs;
         if ((int) embd_inp.size() <= n_consumed) {
-
             // deal with eot token in infill mode
             if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
                 if (is_interacting && !params.interactive_first) {
@@ -621,30 +512,29 @@ int main(int argc, char ** argv) {
 
                 if (params.escape) {
                     //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here
-                    process_escapes(params.input_prefix);
-                    process_escapes(params.input_suffix);
-                }
-                suff_rm_leading_spc = params.escape;
-                if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
-                    params.input_suffix.erase(0, 1);
-                    suff_rm_leading_spc = false;
+                    string_process_escapes(params.input_prefix);
+                    string_process_escapes(params.input_suffix);
                 }
+
                 // tokenize new prefix and suffix
                 std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
                 std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
-                if (suff_rm_leading_spc && inp_sfx[0] == space_token) {
-                    inp_sfx.erase(inp_sfx.begin());
-                }
+
                 inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
+                inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
+
+                embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
+                embd_end = params.spm_infill ? inp_pfx : inp_sfx;
                 if (add_bos) {
-                    inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));
+                    embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
                 }
-                inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
-                embd_inp = inp_pfx;
-                embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-                embd_inp.push_back(llama_token_middle(model));
+                embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
+
+                if (middle_token >= 0) {
+                    embd_inp.push_back(middle_token);
+                }
+
                 embd.clear();
-                embd_guidance.clear();
                 n_remain = params.n_predict;
                 n_past = 0;
                 n_consumed = 0;
@@ -751,7 +641,6 @@ int main(int argc, char ** argv) {
     llama_print_timings(ctx);
     write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
 
-    if (ctx_guidance) { llama_free(ctx_guidance); }
     llama_free(ctx);
     llama_free_model(model);
 
@@ -764,4 +653,3 @@ int main(int argc, char ** argv) {
 
     return 0;
 }
-
diff --git a/examples/jeopardy/jeopardy.sh b/examples/jeopardy/jeopardy.sh
index 9bdbc755c13a7..07bcb3b8d78ac 100755
--- a/examples/jeopardy/jeopardy.sh
+++ b/examples/jeopardy/jeopardy.sh
@@ -21,7 +21,7 @@ counter=1
 echo 'Running'
 while IFS= read -r question
 do
-  exe_cmd="./main -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\""
+  exe_cmd="./llama-cli -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\""
   echo $counter
   echo "Current Question: $question"
   eval "$exe_cmd"
diff --git a/examples/json-schema-pydantic-example.py b/examples/json_schema_pydantic_example.py
similarity index 85%
rename from examples/json-schema-pydantic-example.py
rename to examples/json_schema_pydantic_example.py
index 69ebfd4093824..19c0bdb5b6770 100644
--- a/examples/json-schema-pydantic-example.py
+++ b/examples/json_schema_pydantic_example.py
@@ -1,9 +1,9 @@
 # Usage:
-#! ./server -m some-model.gguf &
+#! ./llama-server -m some-model.gguf &
 #! pip install pydantic
-#! python json-schema-pydantic-example.py
+#! python json_schema_pydantic_example.py
 
-from pydantic import BaseModel, TypeAdapter
+from pydantic import BaseModel, Field, TypeAdapter
 from annotated_types import MinLen
 from typing import Annotated, List, Optional
 import json, requests
@@ -17,6 +17,9 @@ def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1
 
         The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below)
         '''
+        response_format = None
+        type_adapter = None
+
         if response_model:
             type_adapter = TypeAdapter(response_model)
             schema = type_adapter.json_schema()
@@ -50,11 +53,16 @@ def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1
 if __name__ == '__main__':
 
     class QAPair(BaseModel):
+        class Config:
+            extra = 'forbid'  # triggers additionalProperties: false in the JSON schema
         question: str
         concise_answer: str
         justification: str
+        stars: Annotated[int, Field(ge=1, le=5)]
 
     class PyramidalSummary(BaseModel):
+        class Config:
+            extra = 'forbid'  # triggers additionalProperties: false in the JSON schema
         title: str
         summary: str
         question_answers: Annotated[List[QAPair], MinLen(2)]
diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py
index 826cd3f7271d1..a8779bf3bca38 100755
--- a/examples/json_schema_to_grammar.py
+++ b/examples/json_schema_to_grammar.py
@@ -1,89 +1,221 @@
 #!/usr/bin/env python3
+from __future__ import annotations
+
 import argparse
 import itertools
 import json
 import re
 import sys
-from typing import Any, Dict, List, Set, Tuple, Union
+from typing import Any, List, Optional, Set, Tuple, Union
 
-def _build_repetition(item_rule, min_items, max_items, separator_rule=None, item_rule_is_literal=False):
-    if not separator_rule:
-        if min_items == 0 and max_items == 1:
-            return f'{item_rule}?'
-        elif min_items == 1 and max_items is None:
-            return f'{item_rule}+'
+def _build_repetition(item_rule, min_items, max_items, separator_rule=None):
 
-    result = ''
+    if min_items == 0 and max_items == 1:
+        return f'{item_rule}?'
 
-    if min_items > 0:
-        if item_rule_is_literal and separator_rule is None:
-            result = '"' + (item_rule[1:-1] * min_items) + '"'
-        else:
-            result = (f' {separator_rule} ' if separator_rule else ' ').join([item_rule] * min_items)
-
-    def opt_repetitions(up_to_n, prefix_with_sep=False):
-        '''
-            - n=4, no sep:             '(a (a (a (a)?)?)?)?'
-            - n=4, sep=',', prefix:    '("," a ("," a ("," a ("," a)?)?)?)?'
-            - n=4, sep=',', no prefix: '(a ("," a ("," a ("," a)?)?)?)?'
-        '''
-
-        content = f'{separator_rule} {item_rule}' if prefix_with_sep and separator_rule else item_rule
-        if up_to_n == 0:
-            return ''
-        elif up_to_n == 1:
-            return f'({content})?'
-        elif separator_rule and not prefix_with_sep:
-            return f'({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?'
+    if not separator_rule:
+        if min_items == 1 and max_items is None:
+            return f'{item_rule}+'
+        elif min_items == 0 and max_items is None:
+            return f'{item_rule}*'
         else:
-            return (f'({content} ' * up_to_n).rstrip() + (')?' * up_to_n)
+            return f'{item_rule}{{{min_items},{max_items if max_items is not None else ""}}}'
 
-    if min_items > 0 and max_items != min_items:
-        result += ' '
+    result = item_rule + ' ' + _build_repetition(f'({separator_rule} {item_rule})', min_items - 1 if min_items > 0 else 0, max_items - 1 if max_items is not None else None)
+    return f'({result})?' if min_items == 0 else result
 
-    if max_items is not None:
-        result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0)
-    else:
-        item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})'
+def _generate_min_max_int(min_value: Optional[int], max_value: Optional[int], out: list, decimals_left: int = 16, top_level: bool = True):
+    has_min = min_value != None
+    has_max = max_value != None
 
-        if min_items == 0 and separator_rule:
-            result = f'({item_rule} {item_operator}*)?'
+    def digit_range(from_char: str, to_char: str):
+        out.append("[")
+        if from_char == to_char:
+            out.append(from_char)
         else:
-            result += f'{item_operator}*'
-
-    return result
+            out.append(from_char)
+            out.append("-")
+            out.append(to_char)
+        out.append("]")
+
+    def more_digits(min_digits: int, max_digits: int):
+        out.append("[0-9]")
+        if min_digits == max_digits and min_digits == 1:
+            return
+        out.append("{")
+        out.append(str(min_digits))
+        if max_digits != min_digits:
+            out.append(",")
+            if max_digits != sys.maxsize:
+                out.append(str(max_digits))
+        out.append("}")
+
+    def uniform_range(from_str: str, to_str: str):
+        i = 0
+        while i < len(from_str) and from_str[i] == to_str[i]:
+            i += 1
+        if i > 0:
+            out.append("\"")
+            out.append(from_str[:i])
+            out.append("\"")
+        if i < len(from_str):
+            if i > 0:
+                out.append(" ")
+            sub_len = len(from_str) - i - 1
+            if sub_len > 0:
+                from_sub = from_str[i+1:]
+                to_sub = to_str[i+1:]
+                sub_zeros = "0" * sub_len
+                sub_nines = "9" * sub_len
+
+                to_reached = False
+                out.append("(")
+                if from_sub == sub_zeros:
+                    digit_range(from_str[i], chr(ord(to_str[i]) - 1))
+                    out.append(" ")
+                    more_digits(sub_len, sub_len)
+                else:
+                    out.append("[")
+                    out.append(from_str[i])
+                    out.append("] ")
+                    out.append("(")
+                    uniform_range(from_sub, sub_nines)
+                    out.append(")")
+                    if ord(from_str[i]) < ord(to_str[i]) - 1:
+                        out.append(" | ")
+                        if to_sub == sub_nines:
+                            digit_range(chr(ord(from_str[i]) + 1), to_str[i])
+                            to_reached = True
+                        else:
+                            digit_range(chr(ord(from_str[i]) + 1), chr(ord(to_str[i]) - 1))
+                        out.append(" ")
+                        more_digits(sub_len, sub_len)
+                if not to_reached:
+                    out.append(" | ")
+                    digit_range(to_str[i], to_str[i])
+                    out.append(" ")
+                    uniform_range(sub_zeros, to_sub)
+                out.append(")")
+            else:
+                out.append("[")
+                out.append(from_str[i])
+                out.append("-")
+                out.append(to_str[i])
+                out.append("]")
+
+    if has_min and has_max:
+        if min_value < 0 and max_value < 0:
+            out.append("\"-\" (")
+            _generate_min_max_int(-max_value, -min_value, out, decimals_left, top_level=True)
+            out.append(")")
+            return
+
+        if min_value < 0:
+            out.append("\"-\" (")
+            _generate_min_max_int(0, -min_value, out, decimals_left, top_level=True)
+            out.append(") | ")
+            min_value = 0
+
+        min_s = str(min_value)
+        max_s = str(max_value)
+        min_digits = len(min_s)
+        max_digits = len(max_s)
+
+        for digits in range(min_digits, max_digits):
+            uniform_range(min_s, "9" * digits)
+            min_s = "1" + "0" * digits
+            out.append(" | ")
+        uniform_range(min_s, max_s)
+        return
+
+    less_decimals = max(decimals_left - 1, 1)
+
+    if has_min:
+        if min_value < 0:
+            out.append("\"-\" (")
+            _generate_min_max_int(None, -min_value, out, decimals_left, top_level=False)
+            out.append(") | [0] | [1-9] ")
+            more_digits(0, decimals_left - 1)
+        elif min_value == 0:
+            if top_level:
+                out.append("[0] | [1-9] ")
+                more_digits(0, less_decimals)
+            else:
+                more_digits(1, decimals_left)
+        elif min_value <= 9:
+            c = str(min_value)
+            range_start = '1' if top_level else '0'
+            if c > range_start:
+                digit_range(range_start, chr(ord(c) - 1))
+                out.append(" ")
+                more_digits(1, less_decimals)
+                out.append(" | ")
+            digit_range(c, "9")
+            out.append(" ")
+            more_digits(0, less_decimals)
+        else:
+            min_s = str(min_value)
+            length = len(min_s)
+            c = min_s[0]
+
+            if c > "1":
+                digit_range("1" if top_level else "0", chr(ord(c) - 1))
+                out.append(" ")
+                more_digits(length, less_decimals)
+                out.append(" | ")
+            digit_range(c, c)
+            out.append(" (")
+            _generate_min_max_int(int(min_s[1:]), None, out, less_decimals, top_level=False)
+            out.append(")")
+            if c < "9":
+                out.append(" | ")
+                digit_range(chr(ord(c) + 1), "9")
+                out.append(" ")
+                more_digits(length - 1, less_decimals)
+        return
+
+    if has_max:
+        if max_value >= 0:
+            if top_level:
+                out.append("\"-\" [1-9] ")
+                more_digits(0, less_decimals)
+                out.append(" | ")
+            _generate_min_max_int(0, max_value, out, decimals_left, top_level=True)
+        else:
+            out.append("\"-\" (")
+            _generate_min_max_int(-max_value, None, out, decimals_left, top_level=False)
+            out.append(")")
+        return
 
+    raise RuntimeError("At least one of min_value or max_value must be set")
 
 class BuiltinRule:
-    def __init__(self, content: str, deps: list = None):
+    def __init__(self, content: str, deps: list | None = None):
         self.content = content
         self.deps = deps or []
 
-_up_to_15_digits = _build_repetition('[0-9]', 0, 15)
-
-# whitespace is constrained to a single space char to prevent model "running away" in
-# whitespace. Also maybe improves generation quality?
-SPACE_RULE = '" "?'
+# Constraining spaces to prevent model "running away".
+SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}'
 
 PRIMITIVE_RULES = {
     'boolean'      : BuiltinRule('("true" | "false") space', []),
-    'decimal-part' : BuiltinRule('[0-9] ' + _up_to_15_digits, []),
-    'integral-part': BuiltinRule('[0-9] | [1-9] ' + _up_to_15_digits, []),
+    'decimal-part' : BuiltinRule('[0-9]{1,16}', []),
+    'integral-part': BuiltinRule('[0] | [1-9] [0-9]{0,15}', []),
     'number'       : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
     'integer'      : BuiltinRule('("-"? integral-part) space', ['integral-part']),
     'value'        : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
     'object'       : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
     'array'        : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
-    'uuid'         : BuiltinRule(r'"\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + r' "\"" space', []),
-    'char'         : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', []),
+    'uuid'         : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []),
+    'char'         : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []),
     'string'       : BuiltinRule(r'"\"" char* "\"" space', ['char']),
     'null'         : BuiltinRule('"null" space', []),
 }
 
 # TODO: support "uri", "email" string formats
 STRING_FORMAT_RULES = {
-    'date'            : BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
-    'time'            : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
+    'date'            : BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
+    'time'            : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
     'date-time'       : BuiltinRule('date "T" time', ['date', 'time']),
     'date-string'     : BuiltinRule('"\\"" date "\\"" space', ['date']),
     'time-string'     : BuiltinRule('"\\"" time "\\"" space', ['time']),
@@ -101,7 +233,7 @@ def __init__(self, content: str, deps: list = None):
 GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]'}
 
 NON_LITERAL_SET = set('|.()[]{}*+?')
-ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('[]()|{}*+?')
+ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('^$.[]()|{}*+?')
 
 
 class SchemaConverter:
@@ -118,7 +250,7 @@ def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern):
 
     def _format_literal(self, literal):
         escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub(
-            lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), literal
+            lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)) or m.group(0), literal
         )
         return f'"{escaped}"'
 
@@ -145,6 +277,51 @@ def recurse(i: int):
 
         return ''.join(('(', *recurse(0), ')'))
 
+    def _not_strings(self, strings):
+        class TrieNode:
+            def __init__(self):
+                self.children = {}
+                self.is_end_of_string = False
+
+            def insert(self, string):
+                node = self
+                for c in string:
+                    node = node.children.setdefault(c, TrieNode())
+                node.is_end_of_string = True
+
+        trie = TrieNode()
+        for s in strings:
+            trie.insert(s)
+
+        char_rule = self._add_primitive('char', PRIMITIVE_RULES['char'])
+        out = ['["] ( ']
+
+        def visit(node):
+            rejects = []
+            first = True
+            for c in sorted(node.children.keys()):
+                child = node.children[c]
+                rejects.append(c)
+                if first:
+                    first = False
+                else:
+                    out.append(' | ')
+                out.append(f'[{c}]')
+                if child.children:
+                    out.append(f' (')
+                    visit(child)
+                    out.append(')')
+                elif child.is_end_of_string:
+                    out.append(f' {char_rule}+')
+            if node.children:
+                if not first:
+                    out.append(' | ')
+                out.append(f'[^"{"".join(rejects)}] {char_rule}*')
+        visit(trie)
+
+        out.append(f' ){"" if trie.is_end_of_string else "?"} ["] space')
+        return ''.join(out)
+
     def _add_rule(self, name, rule):
         esc_name = INVALID_RULE_CHARS_RE.sub('-', name)
         if esc_name not in self._rules or self._rules[esc_name] == rule:
@@ -228,11 +405,11 @@ def _visit_pattern(self, pattern, name):
         i = 0
         length = len(pattern)
 
-        def to_rule(s: Tuple[str, bool]) -> str:
+        def to_rule(s: tuple[str, bool]) -> str:
             (txt, is_literal) = s
             return "\"" + txt + "\"" if is_literal else txt
 
-        def transform() -> Tuple[str, bool]:
+        def transform() -> tuple[str, bool]:
             '''
                 Parse a unit at index i (advancing it), and return its string representation + whether it's a literal.
             '''
@@ -245,7 +422,7 @@ def transform() -> Tuple[str, bool]:
             # We only need a flat structure here to apply repetition operators to the last item, and
             # to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially
             # (GBNF's syntax is luckily very close to regular expressions!)
-            seq: list[Tuple[str, bool]] = []
+            seq: list[tuple[str, bool]] = []
 
             def get_dot():
                 if self._dotall:
@@ -333,7 +510,7 @@ def join_seq():
                             sub_rule_ids[sub] = id
                         sub = id
 
-                    seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times, item_rule_is_literal=sub_is_literal), False)
+                    seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times), False)
                 else:
                     literal = ''
                     while i < length:
@@ -390,13 +567,13 @@ def visit(self, schema, name):
             return self._add_rule(rule_name, self._generate_union_rule(name, schema.get('oneOf') or schema['anyOf']))
 
         elif isinstance(schema_type, list):
-            return self._add_rule(rule_name, self._generate_union_rule(name, [{'type': t} for t in schema_type]))
+            return self._add_rule(rule_name, self._generate_union_rule(name, [{**schema, 'type': t} for t in schema_type]))
 
         elif 'const' in schema:
-            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']))
+            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']) + ' space')
 
         elif 'enum' in schema:
-            rule = ' | '.join((self._generate_constant_rule(v) for v in schema['enum']))
+            rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) + ') space'
             return self._add_rule(rule_name, rule)
 
         elif schema_type in (None, 'object') and \
@@ -427,7 +604,7 @@ def add_component(comp_schema, is_required):
                 else:
                     add_component(t, is_required=True)
 
-            return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=[]))
+            return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None))
 
         elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema):
             items = schema.get('items') or schema['prefixItems']
@@ -465,6 +642,24 @@ def add_component(comp_schema, is_required):
 
             return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space')
 
+        elif schema_type in (None, 'integer') and \
+                ('minimum' in schema or 'exclusiveMinimum' in schema or 'maximum' in schema or 'exclusiveMaximum' in schema):
+            min_value = None
+            max_value = None
+            if 'minimum' in schema:
+                min_value = schema['minimum']
+            elif 'exclusiveMinimum' in schema:
+                min_value = schema['exclusiveMinimum'] + 1
+            if 'maximum' in schema:
+                max_value = schema['maximum']
+            elif 'exclusiveMaximum' in schema:
+                max_value = schema['exclusiveMaximum'] - 1
+
+            out = ["("]
+            _generate_min_max_int(min_value, max_value, out)
+            out.append(") space")
+            return self._add_rule(rule_name, ''.join(out))
+
         elif (schema_type == 'object') or (len(schema) == 0):
             return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object']))
 
@@ -483,7 +678,7 @@ def _add_primitive(self, name: str, rule: BuiltinRule):
                 self._add_primitive(dep, dep_rule)
         return n
 
-    def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Union[bool, Any]):
+    def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Optional[Union[bool, Any]]):
         prop_order = self._prop_order
         # sort by position in prop_order (if specified) then by original order
         sorted_props = [kv[0] for _, kv in sorted(enumerate(properties), key=lambda ikv: (prop_order.get(ikv[1][0], len(prop_order)), ikv[0]))]
@@ -498,12 +693,16 @@ def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[st
         required_props = [k for k in sorted_props if k in required]
         optional_props = [k for k in sorted_props if k not in required]
 
-        if additional_properties == True or isinstance(additional_properties, dict):
+        if additional_properties is not None and additional_properties != False:
             sub_name = f'{name}{"-" if name else ""}additional'
-            value_rule = self.visit({} if additional_properties == True else additional_properties, f'{sub_name}-value')
+            value_rule = self.visit(additional_properties, f'{sub_name}-value') if isinstance(additional_properties, dict) else \
+                self._add_primitive('value', PRIMITIVE_RULES['value'])
+            key_rule = self._add_primitive('string', PRIMITIVE_RULES['string']) if not sorted_props \
+                else self._add_rule(f'{sub_name}-k', self._not_strings(sorted_props))
+
             prop_kv_rule_names["*"] = self._add_rule(
                 f'{sub_name}-kv',
-                self._add_primitive('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}'
+                f'{key_rule} ":" space {value_rule}'
             )
             optional_props.append("*")
 
@@ -518,15 +717,11 @@ def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[st
             def get_recursive_refs(ks, first_is_optional):
                 [k, *rest] = ks
                 kv_rule_name = prop_kv_rule_names[k]
-                if k == '*':
-                    res = self._add_rule(
-                        f'{name}{"-" if name else ""}additional-kvs',
-                        f'{kv_rule_name} ( "," space ' + kv_rule_name + ' )*'
-                    )
-                elif first_is_optional:
-                    res = f'( "," space {kv_rule_name} )?'
+                comma_ref = f'( "," space {kv_rule_name} )'
+                if first_is_optional:
+                    res = comma_ref + ('*' if k == '*' else '?')
                 else:
-                    res = kv_rule_name
+                    res = kv_rule_name + (' ' + comma_ref + "*" if k == '*' else '')
                 if len(rest) > 0:
                     res += ' ' + self._add_rule(
                         f'{name}{"-" if name else ""}{k}-rest',
@@ -556,7 +751,7 @@ def format_grammar(self):
 def main(args_in = None):
     parser = argparse.ArgumentParser(
         description='''
-            Generates a grammar (suitable for use in ./main) that produces JSON conforming to a
+            Generates a grammar (suitable for use in ./llama-cli) that produces JSON conforming to a
             given JSON schema. Only a subset of JSON schema features are supported; more may be
             added in the future.
         ''',
diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md
index 8578405646af7..52b0e74d3dbf9 100644
--- a/examples/llama-bench/README.md
+++ b/examples/llama-bench/README.md
@@ -1,4 +1,4 @@
-# llama.cpp/example/llama-bench
+# llama.cpp/examples/llama-bench
 
 Performance testing tool for llama.cpp.
 
@@ -162,7 +162,7 @@ $ ./llama-bench -o csv
 ```
 
 ```csv
-build_commit,build_number,cuda,opencl,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
+build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
 "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961"
 "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342"
 ```
@@ -179,7 +179,6 @@ $ ./llama-bench -o json
     "build_commit": "3469684",
     "build_number": 1275,
     "cuda": true,
-    "opencl": false,
     "metal": false,
     "gpu_blas": true,
     "blas": true,
@@ -210,7 +209,6 @@ $ ./llama-bench -o json
     "build_commit": "3469684",
     "build_number": 1275,
     "cuda": true,
-    "opencl": false,
     "metal": false,
     "gpu_blas": true,
     "blas": true,
@@ -253,7 +251,6 @@ CREATE TABLE IF NOT EXISTS test (
   build_commit TEXT,
   build_number INTEGER,
   cuda INTEGER,
-  opencl INTEGER,
   metal INTEGER,
   gpu_blas INTEGER,
   blas INTEGER,
@@ -279,6 +276,6 @@ CREATE TABLE IF NOT EXISTS test (
   stddev_ts REAL
 );
 
-INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
-INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');
+INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
+INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');
 ```
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 8b965e1990ba5..42918bfc79f22 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -23,6 +23,18 @@
 #include "ggml-cuda.h"
 #include "ggml-sycl.h"
 
+#ifdef GGML_USE_CANN
+#include "ggml-cann.h"
+#endif
+
+#ifdef _WIN32
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#   define NOMINMAX
+#endif
+#include <windows.h>
+#endif
+
 // utils
 static uint64_t get_time_ns() {
     using clock = std::chrono::high_resolution_clock;
@@ -41,20 +53,6 @@ static std::string join(const std::vector<T> & values, const std::string & delim
     return str.str();
 }
 
-template<class T>
-static std::vector<T> split(const std::string & str, char delim) {
-    std::vector<T> values;
-    std::istringstream str_stream(str);
-    std::string token;
-    while (std::getline(str_stream, token, delim)) {
-        T value;
-        std::istringstream token_stream(token);
-        token_stream >> value;
-        values.push_back(value);
-    }
-    return values;
-}
-
 template<typename T, typename F>
 static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
     std::vector<std::string> str_values;
@@ -106,6 +104,27 @@ static std::string get_cpu_info() {
         }
         fclose(f);
     }
+#elif defined(_WIN32)
+    HKEY hKey;
+    if (RegOpenKeyEx(HKEY_LOCAL_MACHINE,
+                     TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"),
+                     0,
+                     KEY_READ,
+                     &hKey) != ERROR_SUCCESS) {
+        // fail to open registry key
+        return "";
+    }
+    char cpu_brand[256];
+    DWORD cpu_brand_size = sizeof(cpu_brand);
+    if (RegQueryValueExA(hKey,
+                        TEXT("ProcessorNameString"),
+                        NULL,
+                        NULL,
+                        (LPBYTE)cpu_brand,
+                        &cpu_brand_size) == ERROR_SUCCESS) {
+        id.assign(cpu_brand, cpu_brand_size);
+    }
+    RegCloseKey(hKey);
 #endif
     // TODO: other platforms
     return id;
@@ -134,22 +153,51 @@ static std::string get_gpu_info() {
             id += "/";
         }
     }
+#endif
+#ifdef GGML_USE_CANN
+    uint32_t count = ggml_backend_cann_get_device_count();
+    for (uint32_t i = 0; i < count; i++) {
+        char buf[128];
+        ggml_backend_cann_get_device_description(i, buf, sizeof(buf));
+        id += buf;
+        if (i < count - 1) {
+            id += "/";
+        }
+    }
 #endif
     // TODO: other backends
     return id;
 }
 
 // command line params
-enum output_formats {CSV, JSON, MARKDOWN, SQL};
+enum output_formats {NONE, CSV, JSON, MARKDOWN, SQL};
 
 static const char * output_format_str(output_formats format) {
     switch (format) {
+        case NONE:     return "none";
         case CSV:      return "csv";
         case JSON:     return "json";
         case MARKDOWN: return "md";
         case SQL:      return "sql";
-        default: GGML_ASSERT(!"invalid output format");
+        default: GGML_ABORT("invalid output format");
+    }
+}
+
+static bool output_format_from_str(const std::string & s, output_formats & format) {
+    if (s == "none") {
+        format = NONE;
+    } else if (s == "csv") {
+        format = CSV;
+    } else if (s == "json") {
+        format = JSON;
+    } else if (s == "md") {
+        format = MARKDOWN;
+    } else if (s == "sql") {
+        format = SQL;
+    } else {
+        return false;
     }
+    return true;
 }
 
 static const char * split_mode_str(llama_split_mode mode) {
@@ -157,7 +205,7 @@ static const char * split_mode_str(llama_split_mode mode) {
         case LLAMA_SPLIT_MODE_NONE:  return "none";
         case LLAMA_SPLIT_MODE_LAYER: return "layer";
         case LLAMA_SPLIT_MODE_ROW:   return "row";
-        default: GGML_ASSERT(!"invalid split mode");
+        default: GGML_ABORT("invalid split mode");
     }
 }
 
@@ -178,6 +226,7 @@ struct cmd_params {
     std::vector<ggml_type> type_v;
     std::vector<int> n_threads;
     std::vector<int> n_gpu_layers;
+    std::vector<std::string> rpc_servers;
     std::vector<llama_split_mode> split_mode;
     std::vector<int> main_gpu;
     std::vector<bool> no_kv_offload;
@@ -189,30 +238,33 @@ struct cmd_params {
     int reps;
     bool verbose;
     output_formats output_format;
+    output_formats output_format_stderr;
 };
 
 static const cmd_params cmd_params_defaults = {
-    /* model         */ {"models/7B/ggml-model-q4_0.gguf"},
-    /* n_prompt      */ {512},
-    /* n_gen         */ {128},
-    /* n_pg          */ {{512, 128}},
-    /* n_batch       */ {2048},
-    /* n_ubatch      */ {512},
-    /* type_k        */ {GGML_TYPE_F16},
-    /* type_v        */ {GGML_TYPE_F16},
-    /* n_threads     */ {get_math_cpu_count()},
-    /* n_gpu_layers  */ {99},
-    /* split_mode    */ {LLAMA_SPLIT_MODE_LAYER},
-    /* main_gpu      */ {0},
-    /* no_kv_offload */ {false},
-    /* flash_attn    */ {false},
-    /* tensor_split  */ {std::vector<float>(llama_max_devices(), 0.0f)},
-    /* use_mmap      */ {true},
-    /* embeddings    */ {false},
-    /* numa          */ GGML_NUMA_STRATEGY_DISABLED,
-    /* reps          */ 5,
-    /* verbose       */ false,
-    /* output_format */ MARKDOWN
+    /* model                */ {"models/7B/ggml-model-q4_0.gguf"},
+    /* n_prompt             */ {512},
+    /* n_gen                */ {128},
+    /* n_pg                 */ {},
+    /* n_batch              */ {2048},
+    /* n_ubatch             */ {512},
+    /* type_k               */ {GGML_TYPE_F16},
+    /* type_v               */ {GGML_TYPE_F16},
+    /* n_threads            */ {cpu_get_num_math()},
+    /* n_gpu_layers         */ {99},
+    /* rpc_servers          */ {""},
+    /* split_mode           */ {LLAMA_SPLIT_MODE_LAYER},
+    /* main_gpu             */ {0},
+    /* no_kv_offload        */ {false},
+    /* flash_attn           */ {false},
+    /* tensor_split         */ {std::vector<float>(llama_max_devices(), 0.0f)},
+    /* use_mmap             */ {true},
+    /* embeddings           */ {false},
+    /* numa                 */ GGML_NUMA_STRATEGY_DISABLED,
+    /* reps                 */ 5,
+    /* verbose              */ false,
+    /* output_format        */ MARKDOWN,
+    /* output_format_stderr */ NONE,
 };
 
 static void print_usage(int /* argc */, char ** argv) {
@@ -230,6 +282,7 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -ctv, --cache-type-v <t>            (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
     printf("  -t, --threads <n>                   (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
     printf("  -ngl, --n-gpu-layers <n>            (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
+    printf("  -rpc, --rpc <rpc_servers>           (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str());
     printf("  -sm, --split-mode <none|layer|row>  (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
     printf("  -mg, --main-gpu <i>                 (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
     printf("  -nkvo, --no-kv-offload <0|1>        (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
@@ -240,6 +293,7 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -ts, --tensor-split <ts0/ts1/..>    (default: 0)\n");
     printf("  -r, --repetitions <n>               (default: %d)\n", cmd_params_defaults.reps);
     printf("  -o, --output <csv|json|md|sql>      (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
+    printf("  -oe, --output-err <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr));
     printf("  -v, --verbose                       (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
     printf("\n");
     printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
@@ -281,7 +335,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
 
     params.verbose = cmd_params_defaults.verbose;
     params.output_format = cmd_params_defaults.output_format;
+    params.output_format_stderr = cmd_params_defaults.output_format_stderr;
     params.reps = cmd_params_defaults.reps;
+    params.numa = cmd_params_defaults.numa;
 
     for (int i = 1; i < argc; i++) {
         arg = argv[i];
@@ -297,28 +353,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<std::string>(argv[i], split_delim);
+            auto p = string_split<std::string>(argv[i], split_delim);
             params.model.insert(params.model.end(), p.begin(), p.end());
         } else if (arg == "-p" || arg == "--n-prompt") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<int>(argv[i], split_delim);
+            auto p = string_split<int>(argv[i], split_delim);
             params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end());
         } else if (arg == "-n" || arg == "--n-gen") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<int>(argv[i], split_delim);
+            auto p = string_split<int>(argv[i], split_delim);
             params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
         } else if (arg == "-pg") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<std::string>(argv[i], ',');
+            auto p = string_split<std::string>(argv[i], ',');
             if (p.size() != 2) {
                 invalid_param = true;
                 break;
@@ -329,21 +385,21 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<int>(argv[i], split_delim);
+            auto p = string_split<int>(argv[i], split_delim);
             params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
         } else if (arg == "-ub" || arg == "--ubatch-size") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<int>(argv[i], split_delim);
+            auto p = string_split<int>(argv[i], split_delim);
             params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
         } else if (arg == "-ctk" || arg == "--cache-type-k") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<std::string>(argv[i], split_delim);
+            auto p = string_split<std::string>(argv[i], split_delim);
             std::vector<ggml_type> types;
             for (const auto & t : p) {
                 ggml_type gt = ggml_type_from_name(t);
@@ -359,7 +415,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<std::string>(argv[i], split_delim);
+            auto p = string_split<std::string>(argv[i], split_delim);
             std::vector<ggml_type> types;
             for (const auto & t : p) {
                 ggml_type gt = ggml_type_from_name(t);
@@ -375,21 +431,27 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<int>(argv[i], split_delim);
+            auto p = string_split<int>(argv[i], split_delim);
             params.n_threads.insert(params.n_threads.end(), p.begin(), p.end());
         } else if (arg == "-ngl" || arg == "--n-gpu-layers") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<int>(argv[i], split_delim);
+            auto p = string_split<int>(argv[i], split_delim);
             params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
+        } else if (arg == "-rpc" || arg == "--rpc") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rpc_servers.push_back(argv[i]);
         } else if (arg == "-sm" || arg == "--split-mode") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<std::string>(argv[i], split_delim);
+            auto p = string_split<std::string>(argv[i], split_delim);
             std::vector<llama_split_mode> modes;
             for (const auto & m : p) {
                 llama_split_mode mode;
@@ -411,13 +473,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            params.main_gpu = split<int>(argv[i], split_delim);
+            params.main_gpu = string_split<int>(argv[i], split_delim);
         } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<bool>(argv[i], split_delim);
+            auto p = string_split<bool>(argv[i], split_delim);
             params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
         } else if (arg == "--numa") {
             if (++i >= argc) {
@@ -435,28 +497,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<bool>(argv[i], split_delim);
+            auto p = string_split<bool>(argv[i], split_delim);
             params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end());
         } else if (arg == "-mmp" || arg == "--mmap") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<bool>(argv[i], split_delim);
+            auto p = string_split<bool>(argv[i], split_delim);
             params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
         } else if (arg == "-embd" || arg == "--embeddings") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = split<bool>(argv[i], split_delim);
+            auto p = string_split<bool>(argv[i], split_delim);
             params.embeddings.insert(params.embeddings.end(), p.begin(), p.end());
         } else if (arg == "-ts" || arg == "--tensor-split") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            for (auto ts : split<std::string>(argv[i], split_delim)) {
+            for (auto ts : string_split<std::string>(argv[i], split_delim)) {
                 // split string by ; and /
                 const std::regex regex{R"([;/]+)"};
                 std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1};
@@ -484,18 +546,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            if (argv[i] == std::string("csv")) {
-                params.output_format = CSV;
-            } else if (argv[i] == std::string("json")) {
-                params.output_format = JSON;
-            } else if (argv[i] == std::string("md")) {
-                params.output_format = MARKDOWN;
-            } else if (argv[i] == std::string("sql")) {
-                params.output_format = SQL;
-            } else {
+            invalid_param = !output_format_from_str(argv[i], params.output_format);
+        } else if (arg == "-oe" || arg == "--output-err") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
+            invalid_param = !output_format_from_str(argv[i], params.output_format_stderr);
         } else if (arg == "-v" || arg == "--verbose") {
             params.verbose = true;
         } else {
@@ -519,6 +576,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
     if (params.type_k.empty())       { params.type_k = cmd_params_defaults.type_k; }
     if (params.type_v.empty())       { params.type_v = cmd_params_defaults.type_v; }
     if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
+    if (params.rpc_servers.empty())  { params.rpc_servers = cmd_params_defaults.rpc_servers; }
     if (params.split_mode.empty())   { params.split_mode = cmd_params_defaults.split_mode; }
     if (params.main_gpu.empty())     { params.main_gpu = cmd_params_defaults.main_gpu; }
     if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
@@ -541,6 +599,7 @@ struct cmd_params_instance {
     ggml_type type_v;
     int n_threads;
     int n_gpu_layers;
+    std::string rpc_servers;
     llama_split_mode split_mode;
     int main_gpu;
     bool no_kv_offload;
@@ -553,6 +612,9 @@ struct cmd_params_instance {
         llama_model_params mparams = llama_model_default_params();
 
         mparams.n_gpu_layers = n_gpu_layers;
+        if (!rpc_servers.empty()) {
+            mparams.rpc_servers = rpc_servers.c_str();
+        }
         mparams.split_mode = split_mode;
         mparams.main_gpu = main_gpu;
         mparams.tensor_split = tensor_split.data();
@@ -564,6 +626,7 @@ struct cmd_params_instance {
     bool equal_mparams(const cmd_params_instance & other) const {
         return model == other.model &&
                n_gpu_layers == other.n_gpu_layers &&
+               rpc_servers == other.rpc_servers &&
                split_mode == other.split_mode &&
                main_gpu == other.main_gpu &&
                use_mmap == other.use_mmap &&
@@ -592,6 +655,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
     // this ordering minimizes the number of times that each model needs to be reloaded
     for (const auto & m : params.model)
     for (const auto & nl : params.n_gpu_layers)
+    for (const auto & rpc : params.rpc_servers)
     for (const auto & sm : params.split_mode)
     for (const auto & mg : params.main_gpu)
     for (const auto & ts : params.tensor_split)
@@ -618,6 +682,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
                 /* .n_gpu_layers = */ nl,
+                /* .rpc_servers  = */ rpc,
                 /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
@@ -643,6 +708,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
                 /* .n_gpu_layers = */ nl,
+                /* .rpc_servers  = */ rpc,
                 /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
@@ -668,6 +734,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
                 /* .n_gpu_layers = */ nl,
+                /* .rpc_servers  = */ rpc,
                 /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
@@ -687,7 +754,6 @@ struct test {
     static const std::string build_commit;
     static const int build_number;
     static const bool cuda;
-    static const bool opencl;
     static const bool vulkan;
     static const bool kompute;
     static const bool metal;
@@ -703,6 +769,7 @@ struct test {
     int n_batch;
     int n_ubatch;
     int n_threads;
+    bool has_rpc;
     ggml_type type_k;
     ggml_type type_v;
     int n_gpu_layers;
@@ -728,6 +795,7 @@ struct test {
         n_batch = inst.n_batch;
         n_ubatch = inst.n_ubatch;
         n_threads = inst.n_threads;
+        has_rpc = !inst.rpc_servers.empty();
         type_k = inst.type_k;
         type_v = inst.type_v;
         n_gpu_layers = inst.n_gpu_layers;
@@ -775,9 +843,6 @@ struct test {
         if (cuda) {
             return GGML_CUDA_NAME;
         }
-        if (opencl) {
-            return "OpenCL";
-        }
         if (vulkan) {
             return "Vulkan";
         }
@@ -803,7 +868,7 @@ struct test {
     static const std::vector<std::string> & get_fields() {
         static const std::vector<std::string> fields = {
             "build_commit", "build_number",
-            "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas",
+            "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas",
             "cpu_info", "gpu_info",
             "model_filename", "model_type", "model_size", "model_n_params",
             "n_batch", "n_ubatch",
@@ -829,7 +894,7 @@ struct test {
             field == "avg_ns" || field == "stddev_ns") {
             return INT;
         }
-        if (field == "cuda" || field == "opencl"  || field == "vulkan" || field == "kompute" || field == "metal" ||
+        if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" ||
             field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
             field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
             return BOOL;
@@ -858,8 +923,8 @@ struct test {
         }
         std::vector<std::string> values = {
             build_commit, std::to_string(build_number),
-            std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan),
-            std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas),
+            std::to_string(cuda), std::to_string(vulkan), std::to_string(vulkan),
+            std::to_string(metal), std::to_string(sycl), std::to_string(has_rpc), std::to_string(gpu_blas), std::to_string(blas),
             cpu_info, gpu_info,
             model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
             std::to_string(n_batch), std::to_string(n_ubatch),
@@ -887,7 +952,6 @@ struct test {
 const std::string test::build_commit = LLAMA_COMMIT;
 const int         test::build_number = LLAMA_BUILD_NUMBER;
 const bool        test::cuda         = !!ggml_cpu_has_cuda();
-const bool        test::opencl       = !!ggml_cpu_has_clblast();
 const bool        test::vulkan       = !!ggml_cpu_has_vulkan();
 const bool        test::kompute      = !!ggml_cpu_has_kompute();
 const bool        test::metal        = !!ggml_cpu_has_metal();
@@ -1011,6 +1075,27 @@ struct markdown_printer : public printer {
         if (field == "n_gpu_layers") {
             return 3;
         }
+        if (field == "n_threads") {
+            return 7;
+        }
+        if (field == "n_batch") {
+            return 7;
+        }
+        if (field == "n_ubatch") {
+            return 8;
+        }
+        if (field == "type_k" || field == "type_v") {
+            return 6;
+        }
+        if (field == "split_mode") {
+            return 5;
+        }
+        if (field == "flash_attn") {
+            return 2;
+        }
+        if (field == "use_mmap") {
+            return 4;
+        }
         if (field == "test") {
             return 13;
         }
@@ -1138,6 +1223,9 @@ struct markdown_printer : public printer {
                 value = buf;
             } else if (field == "backend") {
                 value = test::get_backend();
+                if (t.has_rpc) {
+                    value += "+RPC";
+                }
             } else if (field == "test") {
                 if (t.n_prompt > 0 && t.n_gen == 0) {
                     snprintf(buf, sizeof(buf), "pp%d", t.n_prompt);
@@ -1254,6 +1342,22 @@ static void llama_null_log_callback(enum ggml_log_level level, const char * text
     (void) user_data;
 }
 
+static std::unique_ptr<printer> create_printer(output_formats format) {
+    switch (format) {
+        case NONE:
+            return nullptr;
+        case CSV:
+            return std::unique_ptr<printer>(new csv_printer());
+        case JSON:
+            return std::unique_ptr<printer>(new json_printer());
+        case MARKDOWN:
+            return std::unique_ptr<printer>(new markdown_printer());
+        case SQL:
+            return std::unique_ptr<printer>(new sql_printer());
+    }
+    GGML_ABORT("fatal error");
+}
+
 int main(int argc, char ** argv) {
     // try to set locale for unicode characters in markdown
     setlocale(LC_CTYPE, ".UTF-8");
@@ -1280,26 +1384,18 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // initialize printer
-    std::unique_ptr<printer> p;
-    switch (params.output_format) {
-        case CSV:
-            p.reset(new csv_printer());
-            break;
-        case JSON:
-            p.reset(new json_printer());
-            break;
-        case MARKDOWN:
-            p.reset(new markdown_printer());
-            break;
-        case SQL:
-            p.reset(new sql_printer());
-            break;
-        default:
-            assert(false);
-            exit(1);
+    std::unique_ptr<printer> p = create_printer(params.output_format);
+    std::unique_ptr<printer> p_err = create_printer(params.output_format_stderr);
+
+    if (p) {
+        p->fout = stdout;
+        p->print_header(params);
+    }
+
+    if (p_err) {
+        p_err->fout = stderr;
+        p_err->print_header(params);
     }
-    p->fout = stdout;
-    p->print_header(params);
 
     std::vector<cmd_params_instance> params_instances = get_cmd_params_instances(params);
 
@@ -1357,7 +1453,15 @@ int main(int argc, char ** argv) {
             t.samples_ns.push_back(t_ns);
         }
 
-        p->print_test(t);
+        if (p) {
+            p->print_test(t);
+            fflush(p->fout);
+        }
+
+        if (p_err) {
+            p_err->print_test(t);
+            fflush(p_err->fout);
+        }
 
         llama_print_timings(ctx);
 
@@ -1366,7 +1470,13 @@ int main(int argc, char ** argv) {
 
     llama_free_model(lmodel);
 
-    p->print_footer();
+    if (p) {
+        p->print_footer();
+    }
+
+    if (p_err) {
+        p_err->print_footer();
+    }
 
     llama_backend_free();
 
diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts
index d42140efe8168..8d1b37195efd4 100644
--- a/examples/llama.android/app/build.gradle.kts
+++ b/examples/llama.android/app/build.gradle.kts
@@ -7,8 +7,6 @@ android {
     namespace = "com.example.llama"
     compileSdk = 34
 
-    ndkVersion = "26.1.10909125"
-
     defaultConfig {
         applicationId = "com.example.llama"
         minSdk = 33
@@ -20,17 +18,6 @@ android {
         vectorDrawables {
             useSupportLibrary = true
         }
-        ndk {
-            // Add NDK properties if wanted, e.g.
-            // abiFilters += listOf("arm64-v8a")
-        }
-        externalNativeBuild {
-            cmake {
-                arguments += "-DCMAKE_BUILD_TYPE=Release"
-                cppFlags += listOf()
-                arguments += listOf()
-            }
-        }
     }
 
     buildTypes {
@@ -55,17 +42,6 @@ android {
     composeOptions {
         kotlinCompilerExtensionVersion = "1.5.1"
     }
-    packaging {
-        resources {
-            excludes += "/META-INF/{AL2.0,LGPL2.1}"
-        }
-    }
-    externalNativeBuild {
-        cmake {
-            path = file("src/main/cpp/CMakeLists.txt")
-            version = "3.22.1"
-        }
-    }
 }
 
 dependencies {
@@ -78,6 +54,7 @@ dependencies {
     implementation("androidx.compose.ui:ui-graphics")
     implementation("androidx.compose.ui:ui-tooling-preview")
     implementation("androidx.compose.material3:material3")
+    implementation(project(":llama"))
     testImplementation("junit:junit:4.13.2")
     androidTestImplementation("androidx.test.ext:junit:1.1.5")
     androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt
index be95e22218332..45ac29938f441 100644
--- a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt
+++ b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt
@@ -1,5 +1,6 @@
 package com.example.llama
 
+import android.llama.cpp.LLamaAndroid
 import android.util.Log
 import androidx.compose.runtime.getValue
 import androidx.compose.runtime.mutableStateOf
@@ -9,7 +10,7 @@ import androidx.lifecycle.viewModelScope
 import kotlinx.coroutines.flow.catch
 import kotlinx.coroutines.launch
 
-class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
+class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() {
     companion object {
         @JvmStatic
         private val NanosPerSecond = 1_000_000_000.0
@@ -28,7 +29,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
 
         viewModelScope.launch {
             try {
-                llm.unload()
+                llamaAndroid.unload()
             } catch (exc: IllegalStateException) {
                 messages += exc.message!!
             }
@@ -44,7 +45,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
         messages += ""
 
         viewModelScope.launch {
-            llm.send(text)
+            llamaAndroid.send(text)
                 .catch {
                     Log.e(tag, "send() failed", it)
                     messages += it.message!!
@@ -57,7 +58,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
         viewModelScope.launch {
             try {
                 val start = System.nanoTime()
-                val warmupResult = llm.bench(pp, tg, pl, nr)
+                val warmupResult = llamaAndroid.bench(pp, tg, pl, nr)
                 val end = System.nanoTime()
 
                 messages += warmupResult
@@ -70,7 +71,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
                     return@launch
                 }
 
-                messages += llm.bench(512, 128, 1, 3)
+                messages += llamaAndroid.bench(512, 128, 1, 3)
             } catch (exc: IllegalStateException) {
                 Log.e(tag, "bench() failed", exc)
                 messages += exc.message!!
@@ -81,7 +82,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
     fun load(pathToModel: String) {
         viewModelScope.launch {
             try {
-                llm.load(pathToModel)
+                llamaAndroid.load(pathToModel)
                 messages += "Loaded $pathToModel"
             } catch (exc: IllegalStateException) {
                 Log.e(tag, "load() failed", exc)
diff --git a/examples/llama.android/build.gradle.kts b/examples/llama.android/build.gradle.kts
index 50ebc821122f6..acd1ada7d9b1a 100644
--- a/examples/llama.android/build.gradle.kts
+++ b/examples/llama.android/build.gradle.kts
@@ -2,4 +2,5 @@
 plugins {
     id("com.android.application") version "8.2.0" apply false
     id("org.jetbrains.kotlin.android") version "1.9.0" apply false
+    id("com.android.library") version "8.2.0" apply false
 }
diff --git a/examples/llama.android/llama/.gitignore b/examples/llama.android/llama/.gitignore
new file mode 100644
index 0000000000000..796b96d1c4023
--- /dev/null
+++ b/examples/llama.android/llama/.gitignore
@@ -0,0 +1 @@
+/build
diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts
new file mode 100644
index 0000000000000..0a3806172f05f
--- /dev/null
+++ b/examples/llama.android/llama/build.gradle.kts
@@ -0,0 +1,68 @@
+plugins {
+    id("com.android.library")
+    id("org.jetbrains.kotlin.android")
+}
+
+android {
+    namespace = "android.llama.cpp"
+    compileSdk = 34
+
+    defaultConfig {
+        minSdk = 33
+
+        testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
+        consumerProguardFiles("consumer-rules.pro")
+        ndk {
+            // Add NDK properties if wanted, e.g.
+            // abiFilters += listOf("arm64-v8a")
+        }
+        externalNativeBuild {
+            cmake {
+                arguments += "-DCMAKE_BUILD_TYPE=Release"
+                cppFlags += listOf()
+                arguments += listOf()
+
+                cppFlags("")
+            }
+        }
+    }
+
+    buildTypes {
+        release {
+            isMinifyEnabled = false
+            proguardFiles(
+                getDefaultProguardFile("proguard-android-optimize.txt"),
+                "proguard-rules.pro"
+            )
+        }
+    }
+    externalNativeBuild {
+        cmake {
+            path("src/main/cpp/CMakeLists.txt")
+            version = "3.22.1"
+        }
+    }
+    compileOptions {
+        sourceCompatibility = JavaVersion.VERSION_1_8
+        targetCompatibility = JavaVersion.VERSION_1_8
+    }
+    kotlinOptions {
+        jvmTarget = "1.8"
+    }
+
+    packaging {
+        resources {
+            excludes += "/META-INF/{AL2.0,LGPL2.1}"
+        }
+    }
+}
+
+dependencies {
+
+    implementation("androidx.core:core-ktx:1.12.0")
+    implementation("androidx.appcompat:appcompat:1.6.1")
+    implementation("com.google.android.material:material:1.11.0")
+    testImplementation("junit:junit:4.13.2")
+    androidTestImplementation("androidx.test.ext:junit:1.1.5")
+    androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
+}
diff --git a/examples/llama.android/llama/consumer-rules.pro b/examples/llama.android/llama/consumer-rules.pro
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/examples/llama.android/llama/proguard-rules.pro b/examples/llama.android/llama/proguard-rules.pro
new file mode 100644
index 0000000000000..f1b424510da51
--- /dev/null
+++ b/examples/llama.android/llama/proguard-rules.pro
@@ -0,0 +1,21 @@
+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile
diff --git a/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt
new file mode 100644
index 0000000000000..05d6ab5d2dd23
--- /dev/null
+++ b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt
@@ -0,0 +1,24 @@
+package android.llama.cpp
+
+import androidx.test.platform.app.InstrumentationRegistry
+import androidx.test.ext.junit.runners.AndroidJUnit4
+
+import org.junit.Test
+import org.junit.runner.RunWith
+
+import org.junit.Assert.*
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * See [testing documentation](http://d.android.com/tools/testing).
+ */
+@RunWith(AndroidJUnit4::class)
+class ExampleInstrumentedTest {
+    @Test
+    fun useAppContext() {
+        // Context of the app under test.
+        val appContext = InstrumentationRegistry.getInstrumentation().targetContext
+        assertEquals("android.llama.cpp.test", appContext.packageName)
+    }
+}
diff --git a/examples/llama.android/llama/src/main/AndroidManifest.xml b/examples/llama.android/llama/src/main/AndroidManifest.xml
new file mode 100644
index 0000000000000..8bdb7e14b389a
--- /dev/null
+++ b/examples/llama.android/llama/src/main/AndroidManifest.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android">
+
+</manifest>
diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt
similarity index 83%
rename from examples/llama.android/app/src/main/cpp/CMakeLists.txt
rename to examples/llama.android/llama/src/main/cpp/CMakeLists.txt
index 4536974a5c50c..2de496574f54a 100644
--- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt
+++ b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt
@@ -1,4 +1,3 @@
-
 # For more information about using CMake with Android Studio, read the
 # documentation: https://d.android.com/studio/projects/add-native-code.html.
 # For more examples on how to use CMake, see https://github.com/android/ndk-samples.
@@ -12,20 +11,15 @@ cmake_minimum_required(VERSION 3.22.1)
 # build script scope).
 project("llama-android")
 
-## Fetch latest llama.cpp from GitHub
 #include(FetchContent)
 #FetchContent_Declare(
 #        llama
 #        GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
 #        GIT_TAG        master
 #)
-#
-## Also provides "common"
-#FetchContent_MakeAvailable(llama)
 
-# llama.cpp CI uses the code from the current branch
-# ref: https://github.com/ggerganov/llama.cpp/pull/7341#issuecomment-2117617700
-add_subdirectory(../../../../../../ build-llama)
+# Also provides "common"
+#FetchContent_MakeAvailable(llama)
 
 # Creates and names a library, sets it as either STATIC
 # or SHARED, and provides the relative paths to its source code.
@@ -36,20 +30,24 @@ add_subdirectory(../../../../../../ build-llama)
 # the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
 # is preferred for the same purpose.
 #
+
+#load local llama.cpp
+add_subdirectory(../../../../../../ build-llama)
+
 # In order to load a library into your app from Java/Kotlin, you must call
 # System.loadLibrary() and pass the name of the library defined here;
 # for GameActivity/NativeActivity derived applications, the same library name must be
 # used in the AndroidManifest.xml file.
 add_library(${CMAKE_PROJECT_NAME} SHARED
-    # List C/C++ source files with relative paths to this CMakeLists.txt.
-    llama-android.cpp)
+        # List C/C++ source files with relative paths to this CMakeLists.txt.
+        llama-android.cpp)
 
 # Specifies libraries CMake should link to your target library. You
 # can link libraries from various origins, such as libraries defined in this
 # build script, prebuilt third-party libraries, or Android system libraries.
 target_link_libraries(${CMAKE_PROJECT_NAME}
-    # List libraries link to the target library
-    llama
-    common
-    android
-    log)
+        # List libraries link to the target library
+        llama
+        common
+        android
+        log)
diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
similarity index 91%
rename from examples/llama.android/app/src/main/cpp/llama-android.cpp
rename to examples/llama.android/llama/src/main/cpp/llama-android.cpp
index 4af9de3038359..2aafe23167557 100644
--- a/examples/llama.android/app/src/main/cpp/llama-android.cpp
+++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
@@ -5,7 +5,7 @@
 #include <string>
 #include <unistd.h>
 #include "llama.h"
-#include "common/common.h"
+#include "common.h"
 
 // Write C++ code here.
 //
@@ -81,7 +81,7 @@ static void log_callback(ggml_log_level level, const char * fmt, void * data) {
 
 extern "C"
 JNIEXPORT jlong JNICALL
-Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
+Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) {
     llama_model_params model_params = llama_model_default_params();
 
     auto path_to_model = env->GetStringUTFChars(filename, 0);
@@ -101,13 +101,13 @@ Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
 
 extern "C"
 JNIEXPORT void JNICALL
-Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) {
+Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) {
     llama_free_model(reinterpret_cast<llama_model *>(model));
 }
 
 extern "C"
 JNIEXPORT jlong JNICALL
-Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
+Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) {
     auto model = reinterpret_cast<llama_model *>(jmodel);
 
     if (!model) {
@@ -139,25 +139,25 @@ Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
 
 extern "C"
 JNIEXPORT void JNICALL
-Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) {
+Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) {
     llama_free(reinterpret_cast<llama_context *>(context));
 }
 
 extern "C"
 JNIEXPORT void JNICALL
-Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) {
+Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) {
     llama_backend_free();
 }
 
 extern "C"
 JNIEXPORT void JNICALL
-Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) {
+Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) {
     llama_log_set(log_callback, NULL);
 }
 
 extern "C"
 JNIEXPORT jstring JNICALL
-Java_com_example_llama_Llm_bench_1model(
+Java_android_llama_cpp_LLamaAndroid_bench_1model(
         JNIEnv *env,
         jobject,
         jlong context_pointer,
@@ -271,13 +271,13 @@ Java_com_example_llama_Llm_bench_1model(
 
 extern "C"
 JNIEXPORT void JNICALL
-Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
+Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
     llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
 }
 
 extern "C"
 JNIEXPORT jlong JNICALL
-Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
+Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
 
     // Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
 
@@ -313,19 +313,19 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb
 
 extern "C"
 JNIEXPORT void JNICALL
-Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) {
+Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) {
     llama_backend_init();
 }
 
 extern "C"
 JNIEXPORT jstring JNICALL
-Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) {
+Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) {
     return env->NewStringUTF(llama_print_system_info());
 }
 
 extern "C"
 JNIEXPORT jint JNICALL
-Java_com_example_llama_Llm_completion_1init(
+Java_android_llama_cpp_LLamaAndroid_completion_1init(
         JNIEnv *env,
         jobject,
         jlong context_pointer,
@@ -376,7 +376,7 @@ Java_com_example_llama_Llm_completion_1init(
 
 extern "C"
 JNIEXPORT jstring JNICALL
-Java_com_example_llama_Llm_completion_1loop(
+Java_android_llama_cpp_LLamaAndroid_completion_1loop(
         JNIEnv * env,
         jobject,
         jlong context_pointer,
@@ -409,7 +409,7 @@ Java_com_example_llama_Llm_completion_1loop(
 
     const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
     if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
-        return env->NewStringUTF("");
+        return nullptr;
     }
 
     auto new_token_chars = llama_token_to_piece(context, new_token_id);
@@ -438,6 +438,6 @@ Java_com_example_llama_Llm_completion_1loop(
 
 extern "C"
 JNIEXPORT void JNICALL
-Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
+Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
     llama_kv_cache_clear(reinterpret_cast<llama_context *>(context));
 }
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
similarity index 97%
rename from examples/llama.android/app/src/main/java/com/example/llama/Llm.kt
rename to examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
index d86afee379083..6c63e54e0d908 100644
--- a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt
+++ b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
@@ -1,4 +1,4 @@
-package com.example.llama
+package android.llama.cpp
 
 import android.util.Log
 import kotlinx.coroutines.CoroutineDispatcher
@@ -10,7 +10,7 @@ import kotlinx.coroutines.withContext
 import java.util.concurrent.Executors
 import kotlin.concurrent.thread
 
-class Llm {
+class LLamaAndroid {
     private val tag: String? = this::class.simpleName
 
     private val threadLocalState: ThreadLocal<State> = ThreadLocal.withInitial { State.Idle }
@@ -165,8 +165,8 @@ class Llm {
         }
 
         // Enforce only one instance of Llm.
-        private val _instance: Llm = Llm()
+        private val _instance: LLamaAndroid = LLamaAndroid()
 
-        fun instance(): Llm = _instance
+        fun instance(): LLamaAndroid = _instance
     }
 }
diff --git a/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt
new file mode 100644
index 0000000000000..cbbb974d32266
--- /dev/null
+++ b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt
@@ -0,0 +1,17 @@
+package android.llama.cpp
+
+import org.junit.Test
+
+import org.junit.Assert.*
+
+/**
+ * Example local unit test, which will execute on the development machine (host).
+ *
+ * See [testing documentation](http://d.android.com/tools/testing).
+ */
+class ExampleUnitTest {
+    @Test
+    fun addition_isCorrect() {
+        assertEquals(4, 2 + 2)
+    }
+}
diff --git a/examples/llama.android/settings.gradle.kts b/examples/llama.android/settings.gradle.kts
index 2ba32c4fafc5c..c7c1a034a45b8 100644
--- a/examples/llama.android/settings.gradle.kts
+++ b/examples/llama.android/settings.gradle.kts
@@ -15,3 +15,4 @@ dependencyResolutionManagement {
 
 rootProject.name = "LlamaAndroid"
 include(":app")
+include(":llama")
diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index 737f882fb2d2e..58c32ca533bb1 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -26,11 +26,12 @@ actor LlamaContext {
     private var context: OpaquePointer
     private var batch: llama_batch
     private var tokens_list: [llama_token]
+    var is_done: Bool = false
 
     /// This variable is used to store temporarily invalid cchars
     private var temporary_invalid_cchars: [CChar]
 
-    var n_len: Int32 = 64
+    var n_len: Int32 = 1024
     var n_cur: Int32 = 0
 
     var n_decode: Int32 = 0
@@ -160,6 +161,7 @@ actor LlamaContext {
 
         if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
             print("\n")
+            is_done = true
             let new_token_str = String(cString: temporary_invalid_cchars + [0])
             temporary_invalid_cchars.removeAll()
             return new_token_str
@@ -322,7 +324,7 @@ actor LlamaContext {
         defer {
             result.deallocate()
         }
-        let nTokens = llama_token_to_piece(model, token, result, 8, false)
+        let nTokens = llama_token_to_piece(model, token, result, 8, 0, false)
 
         if nTokens < 0 {
             let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
@@ -330,7 +332,7 @@ actor LlamaContext {
             defer {
                 newResult.deallocate()
             }
-            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
+            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, 0, false)
             let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
             return Array(bufferPointer)
         } else {
diff --git a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
index 5bde1891727ce..b8f6a31d582cd 100644
--- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
+++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
@@ -131,22 +131,29 @@ class LlamaState: ObservableObject {
 
         messageLog += "\(text)"
 
-        while await llamaContext.n_cur < llamaContext.n_len {
-            let result = await llamaContext.completion_loop()
-            messageLog += "\(result)"
-        }
+        Task.detached {
+            while await !llamaContext.is_done {
+                let result = await llamaContext.completion_loop()
+                await MainActor.run {
+                    self.messageLog += "\(result)"
+                }
+            }
 
-        let t_end = DispatchTime.now().uptimeNanoseconds
-        let t_generation = Double(t_end - t_heat_end) / NS_PER_S
-        let tokens_per_second = Double(await llamaContext.n_len) / t_generation
+            let t_end = DispatchTime.now().uptimeNanoseconds
+            let t_generation = Double(t_end - t_heat_end) / self.NS_PER_S
+            let tokens_per_second = Double(await llamaContext.n_len) / t_generation
 
-        await llamaContext.clear()
-        messageLog += """
-            \n
-            Done
-            Heat up took \(t_heat)s
-            Generated \(tokens_per_second) t/s\n
-            """
+            await llamaContext.clear()
+
+            await MainActor.run {
+                self.messageLog += """
+                    \n
+                    Done
+                    Heat up took \(t_heat)s
+                    Generated \(tokens_per_second) t/s\n
+                    """
+            }
+        }
     }
 
     func bench() async {
diff --git a/examples/llama2-13b.sh b/examples/llama2-13b.sh
deleted file mode 100755
index 92b3f6dd8b0ec..0000000000000
--- a/examples/llama2-13b.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \
-       --color \
-       --ctx_size 2048 \
-       -n -1 \
-       -ins -b 256 \
-       --top_k 10000 \
-       --temp 0.2 \
-       --repeat_penalty 1.1 \
-       -t 8
diff --git a/examples/llama2.sh b/examples/llama2.sh
deleted file mode 100755
index 221b37553cfe7..0000000000000
--- a/examples/llama2.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \
-       --color \
-       --ctx_size 2048 \
-       -n -1 \
-       -ins -b 256 \
-       --top_k 10000 \
-       --temp 0.2 \
-       --repeat_penalty 1.1 \
-       -t 8
diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt
index 2985caff8379a..bbf5fec586feb 100644
--- a/examples/llava/CMakeLists.txt
+++ b/examples/llava/CMakeLists.txt
@@ -30,8 +30,16 @@ if(TARGET BUILD_INFO)
     add_dependencies(llava BUILD_INFO)
 endif()
 
-set(TARGET llava-cli)
-add_executable(llava-cli llava-cli.cpp)
-install(TARGETS llava-cli RUNTIME)
-target_link_libraries(llava-cli PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(llava PRIVATE cxx_std_11)
+set(TARGET llama-llava-cli)
+add_executable(${TARGET} llava-cli.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-cli)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+
+set(TARGET llama-minicpmv-cli)
+add_executable(${TARGET} minicpmv-cli.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-minicpmv-cli)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md
index 413e433dd9c07..06a65fba4787a 100644
--- a/examples/llava/MobileVLM-README.md
+++ b/examples/llava/MobileVLM-README.md
@@ -9,12 +9,12 @@ The implementation is based on llava, and is compatible with llava and mobileVLM
 Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown.
 
 ## Usage
-Build with cmake or run `make llava-cli` to build it.
+Build with cmake or run `make llama-llava-cli` to build it.
 
-After building, run: `./llava-cli` to see the usage. For example:
+After building, run: `./llama-llava-cli` to see the usage. For example:
 
 ```sh
-./llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
+./llama-llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
     --mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \
     --image path/to/an/image.jpg \
     -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? Answer the question using a single word or phrase. ASSISTANT:"
@@ -30,16 +30,16 @@ git clone https://huggingface.co/mtgv/MobileVLM-1.7B
 git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 ```
 
-2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
+2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
 
 ```sh
-python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
+python ./examples/llava/llava_surgery.py -m path/to/MobileVLM-1.7B
 ```
 
-3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
+3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./examples/llava/convert-image-encoder-to-gguf \
+python ./examples/llava/convert_image_encoder_to_gguf \
     -m path/to/clip-vit-large-patch14-336 \
     --llava-projector path/to/MobileVLM-1.7B/llava.projector \
     --output-dir path/to/MobileVLM-1.7B \
@@ -47,22 +47,22 @@ python ./examples/llava/convert-image-encoder-to-gguf \
 ```
 
 ```sh
-python ./examples/llava/convert-image-encoder-to-gguf \
+python ./examples/llava/convert_image_encoder_to_gguf \
     -m path/to/clip-vit-large-patch14-336 \
     --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
     --output-dir path/to/MobileVLM-1.7B_V2 \
     --projector-type ldpv2
 ```
 
-4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
+4. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
 
 ```sh
-python ./convert.py path/to/MobileVLM-1.7B
+python ./examples/convert_legacy_llama.py path/to/MobileVLM-1.7B
 ```
 
 5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`
 ```sh
-./quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
+./llama-quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
 ```
 
 Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory.
@@ -82,7 +82,7 @@ refer to `android/adb_run.sh`, modify resources' `name` and `path`
 ### case 1
 **input**
 ```sh
-/data/local/tmp/llava-cli \
+/data/local/tmp/llama-llava-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     -t 4 \
@@ -102,7 +102,7 @@ llama_print_timings:       total time =   34731.93 ms
 ### case 2
 **input**
 ```sh
-/data/local/tmp/llava-cli \
+/data/local/tmp/llama-llava-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     -t 4 \
@@ -126,7 +126,7 @@ llama_print_timings:       total time =   34570.79 ms
 #### llava-cli release-b2005
 **input**
 ```sh
-/data/local/tmp/llava-cli \
+/data/local/tmp/llama-llava-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     -t 4 \
@@ -194,13 +194,13 @@ llama_print_timings:       total time =   44411.01 ms /   377 tokens
 ## Orin compile and run
 ### compile
 ```sh
-make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
+make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 GGML_CUDA_F16=1 -j 32
 ```
 ### run on Orin
 ### case 1
 **input**
 ```sh
-./llava-cli \
+./llama-llava-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     --image /data/local/tmp/demo.jpeg \
@@ -224,7 +224,7 @@ llama_print_timings:       total time =    1352.63 ms /   252 tokens
 ### case 2
 **input**
 ```sh
-./llava-cli \
+./llama-llava-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:" \
diff --git a/examples/llava/README-minicpmv2.5.md b/examples/llava/README-minicpmv2.5.md
new file mode 100644
index 0000000000000..4affc1d0f26ff
--- /dev/null
+++ b/examples/llava/README-minicpmv2.5.md
@@ -0,0 +1,99 @@
+## MiniCPM-Llama3-V 2.5
+
+### Prepare models and code
+
+Download [MiniCPM-Llama3-V-2_5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5) PyTorch model from huggingface to "MiniCPM-Llama3-V-2_5" folder.
+
+Clone llama.cpp:
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+```
+
+### Usage
+
+Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us)
+
+```bash
+python ./examples/minicpmv/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
+python ./examples/minicpmv/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5
+python ./convert-hf-to-gguf.py ../MiniCPM-Llama3-V-2_5/model
+
+# quantize int4 version
+./llama-quantize ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+Build for Linux or Mac
+
+```bash
+make
+make llama-minicpmv-cli
+```
+
+Inference on Linux or Mac
+```
+# run f16 version
+./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run quantized int4 version
+./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
+
+# or run in interactive mode
+./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i
+```
+
+### Android
+
+#### Build on Android device using Termux
+We found that build on Android device would bring better runtime performance, so we recommend to build on device.
+
+[Termux](https://github.com/termux/termux-app#installation) is a terminal app on Android device (no root required).
+
+Install tools in Termux:
+```
+apt update && apt upgrade -y
+apt install git make cmake
+```
+
+It's recommended to move your model inside the `~/` directory for best performance:
+```
+cd storage/downloads
+mv model.gguf ~/
+```
+
+#### Building the Project using Android NDK
+Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
+
+Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
+
+```bash
+mkdir build-android
+cd build-android
+export NDK=/your_ndk_path
+cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
+make
+```
+
+Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
+
+Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
+
+(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
+```
+$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
+$cd /data/data/com.termux/files/home/bin
+$chmod +x ./*
+```
+
+Download models and push them to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
+
+```
+$mv /sdcard/llama.cpp/ggml-model-Q4_K_M.gguf /data/data/com.termux/files/home/model/
+$mv /sdcard/llama.cpp/mmproj-model-f16.gguf /data/data/com.termux/files/home/model/
+```
+
+Now, you can start chatting:
+```
+$cd /data/data/com.termux/files/home/bin
+$./llama-minicpmv-cli -m ../model/ggml-model-Q4_K_M.gguf --mmproj ../model/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
+```
diff --git a/examples/llava/README.md b/examples/llava/README.md
index 4fb0cf3816383..012451361763c 100644
--- a/examples/llava/README.md
+++ b/examples/llava/README.md
@@ -11,12 +11,12 @@ For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](h
 After API is confirmed, more models will be supported / uploaded.
 
 ## Usage
-Build with cmake or run `make llava-cli` to build it.
+Build with cmake or run `make llama-llava-cli` to build it.
 
-After building, run: `./llava-cli` to see the usage. For example:
+After building, run: `./llama-llava-cli` to see the usage. For example:
 
 ```sh
-./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
+./llama-llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
 ```
 
 **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
@@ -38,22 +38,22 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 pip install -r examples/llava/requirements.txt
 ```
 
-3. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
+3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
 
 ```sh
-python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
+python ./examples/llava/llava_surgery.py -m ../llava-v1.5-7b
 ```
 
-4. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:
+4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
+python ./examples/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
 ```
 
-5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
+5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
 
 ```sh
-python ./convert.py ../llava-v1.5-7b --skip-unknown
+python ./examples/convert_legacy_llama.py ../llava-v1.5-7b --skip-unknown
 ```
 
 Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
@@ -70,9 +70,9 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
 pip install -r examples/llava/requirements.txt
 ```
 
-3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
+3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
 ```console
-python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/
+python examples/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
 ```
 - you will find a llava.projector and a llava.clip file in your model directory
 
@@ -86,18 +86,18 @@ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.jso
 
 5) Create the visual gguf model:
 ```console
-python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
+python ./examples/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
 ```
 - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
 
 6) Then convert the model to gguf format:
 ```console
-python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
+python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown
 ```
 
-7) And finally we can run the llava-cli using the 1.6 model version:
+7) And finally we can run the llava cli using the 1.6 model version:
 ```console
-./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
+./llama-llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
 ```
 
 **note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096)
diff --git a/examples/llava/android/adb_run.sh b/examples/llava/android/adb_run.sh
index f73623ae3b129..45ccf8d70d863 100755
--- a/examples/llava/android/adb_run.sh
+++ b/examples/llava/android/adb_run.sh
@@ -10,7 +10,7 @@ prompt="A chat between a curious user and an artificial intelligence assistant.
 # prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
 
 program_dir="build_64/bin"
-binName="llava-cli"
+binName="llama-llava-cli"
 n_threads=4
 
 
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 95fbe3d0216c4..54aa822c90d29 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -16,6 +16,10 @@
 #include "ggml-metal.h"
 #endif
 
+#ifdef GGML_USE_CANN
+#include "ggml-cann.h"
+#endif
+
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"
 
@@ -70,26 +74,27 @@ static std::string format(const char * fmt, ...) {
 // key constants
 //
 
-#define KEY_FTYPE          "general.file_type"
-#define KEY_NAME           "general.name"
-#define KEY_DESCRIPTION    "general.description"
-#define KEY_HAS_TEXT_ENC   "clip.has_text_encoder"
-#define KEY_HAS_VIS_ENC    "clip.has_vision_encoder"
-#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
-#define KEY_USE_GELU       "clip.use_gelu"
-#define KEY_N_EMBD         "clip.%s.embedding_length"
-#define KEY_N_FF           "clip.%s.feed_forward_length"
-#define KEY_N_BLOCK        "clip.%s.block_count"
-#define KEY_N_HEAD         "clip.%s.attention.head_count"
-#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon"
-#define KEY_PROJ_DIM       "clip.%s.projection_dim"
-#define KEY_TOKENS         "tokenizer.ggml.tokens"
-#define KEY_N_POSITIONS    "clip.text.context_length"
-#define KEY_IMAGE_SIZE     "clip.vision.image_size"
-#define KEY_PATCH_SIZE     "clip.vision.patch_size"
-#define KEY_IMAGE_MEAN     "clip.vision.image_mean"
-#define KEY_IMAGE_STD      "clip.vision.image_std"
-#define KEY_PROJ_TYPE      "clip.projector_type"
+#define KEY_FTYPE               "general.file_type"
+#define KEY_NAME                "general.name"
+#define KEY_DESCRIPTION         "general.description"
+#define KEY_HAS_TEXT_ENC        "clip.has_text_encoder"
+#define KEY_HAS_VIS_ENC         "clip.has_vision_encoder"
+#define KEY_HAS_LLAVA_PROJ      "clip.has_llava_projector"
+#define KEY_HAS_MINICPMV_PROJ   "clip.has_minicpmv_projector"
+#define KEY_USE_GELU            "clip.use_gelu"
+#define KEY_N_EMBD              "clip.%s.embedding_length"
+#define KEY_N_FF                "clip.%s.feed_forward_length"
+#define KEY_N_BLOCK             "clip.%s.block_count"
+#define KEY_N_HEAD              "clip.%s.attention.head_count"
+#define KEY_LAYER_NORM_EPS      "clip.%s.attention.layer_norm_epsilon"
+#define KEY_PROJ_DIM            "clip.%s.projection_dim"
+#define KEY_TOKENS              "tokenizer.ggml.tokens"
+#define KEY_N_POSITIONS         "clip.text.context_length"
+#define KEY_IMAGE_SIZE          "clip.vision.image_size"
+#define KEY_PATCH_SIZE          "clip.vision.patch_size"
+#define KEY_IMAGE_MEAN          "clip.vision.image_mean"
+#define KEY_IMAGE_STD           "clip.vision.image_std"
+#define KEY_PROJ_TYPE           "clip.projector_type"
 
 #define KEY_MM_PATCH_MERGE_TYPE   "clip.vision.mm_patch_merge_type"
 #define KEY_IMAGE_GRID_PINPOINTS  "clip.vision.image_grid_pinpoints"
@@ -123,12 +128,20 @@ static std::string format(const char * fmt, ...) {
 #define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
 #define TN_IMAGE_NEWLINE   "model.image_newline"
 
+#define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k"
+#define TN_MINICPMV_QUERY "resampler.query"
+#define TN_MINICPMV_PROJ "resampler.proj.weight"
+#define TN_MINICPMV_KV_PROJ "resampler.kv.weight"
+#define TN_MINICPMV_ATTN "resampler.attn.%s.%s"
+#define TN_MINICPMV_LN "resampler.ln_%s.%s"
+
 
 enum projector_type {
     PROJECTOR_TYPE_MLP,
     PROJECTOR_TYPE_MLP_NORM,
     PROJECTOR_TYPE_LDP,
     PROJECTOR_TYPE_LDPV2,
+    PROJECTOR_TYPE_RESAMPLER,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -136,6 +149,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_MLP, "mlp" },
     { PROJECTOR_TYPE_LDP, "ldp" },
     { PROJECTOR_TYPE_LDPV2, "ldpv2"},
+    { PROJECTOR_TYPE_RESAMPLER, "resampler"},
 };
 
 
@@ -196,17 +210,14 @@ static std::string gguf_data_to_str(enum gguf_type type, const void * data, int
 }
 
 static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
-    std::string result;
-    for (size_t pos = 0; ; pos += search.length()) {
-        auto new_pos = s.find(search, pos);
-        if (new_pos == std::string::npos) {
-            result += s.substr(pos, s.size() - pos);
-            break;
-        }
-        result += s.substr(pos, new_pos - pos) + replace;
-        pos = new_pos;
+    if (search.empty()) {
+        return; // Avoid infinite loop if 'search' is an empty string
+    }
+    size_t pos = 0;
+    while ((pos = s.find(search, pos)) != std::string::npos) {
+        s.replace(pos, search.length(), replace);
+        pos += replace.length();
     }
-    s = std::move(result);
 }
 
 static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
@@ -488,12 +499,33 @@ struct clip_vision_model {
     struct ggml_tensor * mm_model_mlp_2_b;
     struct ggml_tensor * mm_model_peg_0_w;
     struct ggml_tensor * mm_model_peg_0_b;
+
+    // MINICPMV projection
+    struct ggml_tensor * mm_model_pos_embed_k;
+    struct ggml_tensor * mm_model_query;
+    struct ggml_tensor * mm_model_proj;
+    struct ggml_tensor * mm_model_kv_proj;
+    struct ggml_tensor * mm_model_attn_q_w;
+    struct ggml_tensor * mm_model_attn_q_b;
+    struct ggml_tensor * mm_model_attn_k_w;
+    struct ggml_tensor * mm_model_attn_k_b;
+    struct ggml_tensor * mm_model_attn_v_w;
+    struct ggml_tensor * mm_model_attn_v_b;
+    struct ggml_tensor * mm_model_attn_o_w;
+    struct ggml_tensor * mm_model_attn_o_b;
+    struct ggml_tensor * mm_model_ln_q_w;
+    struct ggml_tensor * mm_model_ln_q_b;
+    struct ggml_tensor * mm_model_ln_kv_w;
+    struct ggml_tensor * mm_model_ln_kv_b;
+    struct ggml_tensor * mm_model_ln_post_w;
+    struct ggml_tensor * mm_model_ln_post_b;
 };
 
 struct clip_ctx {
     bool has_text_encoder    = false;
     bool has_vision_encoder  = false;
     bool has_llava_projector = false;
+    bool has_minicpmv_projector = false;
 
     struct clip_vision_model vision_model;
     projector_type proj_type = PROJECTOR_TYPE_MLP;
@@ -518,9 +550,11 @@ struct clip_ctx {
 
     ggml_backend_t backend       = NULL;
     ggml_gallocr_t compute_alloc = NULL;
+
+    struct clip_image_size * load_image_size;
 };
 
-static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
+static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs, struct clip_image_size * load_image_size, bool is_inf = false) {
     if (!ctx->has_vision_encoder) {
         LOG_TEE("This gguf file seems to have no vision encoder\n");
         return nullptr;
@@ -529,20 +563,33 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     const auto & model = ctx->vision_model;
     const auto & hparams = model.hparams;
 
-    const int image_size           = hparams.image_size;
+    const int image_size = hparams.image_size;
+    int image_size_width  = image_size;
+    int image_size_height = image_size;
+    if (ctx->has_minicpmv_projector) {
+        if (load_image_size == nullptr) {
+            load_image_size = clip_image_size_init();
+        }
+        LOG_TEE("%s: %d %d\n", __func__, load_image_size->width, load_image_size->height);
+        image_size_width  = load_image_size->width;
+        image_size_height = load_image_size->height;
+        if (is_inf) {
+            image_size_width  = imgs->data->nx;
+            image_size_height = imgs->data->ny;
+        }
+    }
     const int patch_size           = hparams.patch_size;
-    const int num_patches          = ((image_size / patch_size) * (image_size / patch_size));
-    const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side);
+    const int num_patches          = ((image_size_width / patch_size) * (image_size_height / patch_size));
     const int num_positions        = num_patches + (ctx->has_class_embedding ? 1 : 0);
     const int hidden_size          = hparams.hidden_size;
     const int n_head               = hparams.n_head;
     const int d_head               = hidden_size / n_head;
-    const int n_layer              = hparams.n_layer;
+    int n_layer                    = hparams.n_layer;
     const float eps                = hparams.eps;
 
     const int batch_size = imgs->size;
 
-    if (ctx->has_llava_projector) {
+    if (ctx->has_llava_projector || ctx->has_minicpmv_projector) {
         GGML_ASSERT(batch_size == 1);
     }
 
@@ -555,7 +602,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     struct ggml_context * ctx0 = ggml_init(params);
     struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size);
+    struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size);
     ggml_set_name(inp_raw, "inp_raw");
     ggml_set_input(inp_raw);
 
@@ -568,19 +615,21 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
         inp = ggml_add(ctx0, inp, model.patch_bias);
     }
-
-    // concat class_embeddings and patch_embeddings
     struct ggml_tensor * embeddings = inp;
-    if (ctx->has_class_embedding) {
-        embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
-        ggml_set_name(embeddings, "embeddings");
-        ggml_set_input(embeddings);
-        embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
-                embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
-        embeddings = ggml_acc(ctx0, embeddings, inp,
-                embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
-    }
+    struct ggml_tensor * pos_embed = nullptr;
 
+    if (ctx->has_llava_projector) {
+        // concat class_embeddings and patch_embeddings
+        if (ctx->has_class_embedding) {
+            embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
+            ggml_set_name(embeddings, "embeddings");
+            ggml_set_input(embeddings);
+            embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
+                    embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
+            embeddings = ggml_acc(ctx0, embeddings, inp,
+                    embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
+        }
+    }
 
     struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
     ggml_set_name(positions, "positions");
@@ -589,6 +638,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     embeddings =
         ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
 
+    if (ctx->has_minicpmv_projector) {
+        int pos_w = image_size_width/patch_size;
+        int pos_h = image_size_height/patch_size;
+        pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 4096, pos_w * pos_h, 1);
+        ggml_set_name(pos_embed, "pos_embed");
+        ggml_set_input(pos_embed);
+    }
+
     // pre-layernorm
     if (ctx->has_pre_norm) {
         embeddings = ggml_norm(ctx0, embeddings, eps);
@@ -598,6 +655,9 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     }
 
     // loop over layers
+    if (ctx->has_minicpmv_projector) {
+        n_layer += 1;
+    }
     for (int il = 0; il < n_layer - 1; il++) {
         struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
 
@@ -687,7 +747,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     }
 
     // llava projector
-    {
+    if (ctx->has_llava_projector) {
         embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
 
         struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches);
@@ -864,6 +924,65 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
             embeddings = peg_0;
         }
+        else {
+            GGML_ABORT("fatal error");
+        }
+    }
+    // minicpmv projector
+    else if (ctx->has_minicpmv_projector)
+    {
+        if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
+            struct ggml_tensor * q = model.mm_model_query;
+            { // layernorm
+                q = ggml_norm(ctx0, q, eps);
+                q = ggml_add(ctx0, ggml_mul(ctx0, q, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
+            }
+            struct ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings);
+            { // layernorm
+                v = ggml_norm(ctx0, v, eps);
+                v = ggml_add(ctx0, ggml_mul(ctx0, v, model.mm_model_ln_kv_w), model.mm_model_ln_kv_b);
+            }
+            struct ggml_tensor * k;
+            { // position
+                // q = ggml_add(ctx0, q, model.mm_model_pos_embed);
+                k = ggml_add(ctx0, v, pos_embed);
+            }
+
+            { // attention
+                const int hidden_size = 4096;
+                const int d_head = 128;
+                const int n_head = hidden_size/d_head;
+                const int num_query = 96;
+
+                struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b);
+                Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
+                struct ggml_tensor * K = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), model.mm_model_attn_k_b);
+                struct ggml_tensor * V = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), model.mm_model_attn_v_b);
+                // permute
+                Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_query, batch_size);
+                Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
+                Q = ggml_reshape_3d(ctx0, Q, d_head, num_query, n_head * batch_size);
+                K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
+                K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
+                K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
+                V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
+                V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
+                V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
+                struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+                KQ = ggml_soft_max_inplace(ctx0, KQ);
+                struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
+                KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size);
+                KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+                KQV = ggml_cont_3d(ctx0, KQV, hidden_size, num_query, batch_size);
+
+                embeddings = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_o_w, KQV), model.mm_model_attn_o_b);
+            }
+            { // layernorm
+                embeddings = ggml_norm(ctx0, embeddings, eps);
+                embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_post_w), model.mm_model_ln_post_b);
+            }
+            embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings);
+        }
         else {
             GGML_ASSERT(false);
         }
@@ -1001,6 +1120,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     LOG_TEE("%s: CLIP using Metal backend\n", __func__);
 #endif
 
+#ifdef GGML_USE_CANN
+    new_clip->backend = ggml_backend_cann_init(0);
+    LOG_TEE("%s: CLIP using CANN backend\n", __func__);
+#endif
+
 
     if (!new_clip->backend) {
         new_clip->backend = ggml_backend_cpu_init();
@@ -1020,7 +1144,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->has_llava_projector = gguf_get_val_bool(ctx, idx);
         }
 
-        GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search
+        idx = gguf_find_key(ctx, KEY_HAS_MINICPMV_PROJ);
+        if (idx != -1) {
+            new_clip->has_minicpmv_projector = gguf_get_val_bool(ctx, idx);
+        }
+
+        // GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search
+
         GGML_ASSERT(new_clip->has_vision_encoder);
         GGML_ASSERT(!new_clip->has_text_encoder);
 
@@ -1031,6 +1161,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             LOG_TEE("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
             LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
             LOG_TEE("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
+            LOG_TEE("%s: minicpmv_projector:  %d\n", __func__, new_clip->has_minicpmv_projector);
             LOG_TEE("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
             LOG_TEE("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
         }
@@ -1121,20 +1252,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             }
             if (n < 32)
                 hparams.image_grid_pinpoints[n] = 0;
-        } catch (std::runtime_error & e) {
+        } catch (std::runtime_error & /*e*/) {
             hparams.image_grid_pinpoints[0]=0;
         }
 
         try {
             int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
             strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx));
-        } catch (std::runtime_error & e) {
+        } catch (std::runtime_error & /*e*/) {
             strcpy(hparams.mm_patch_merge_type, "flat");
         }
 
         try {
             hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6
-        } catch(const std::exception& e) {
+        } catch(const std::exception& /*e*/) {
             hparams.image_crop_resolution = hparams.image_size;
         }
 
@@ -1173,7 +1304,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         try {
             vision_model.class_embedding  = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
             new_clip->has_class_embedding = true;
-        } catch (const std::exception& e) {
+        } catch (const std::exception& /*e*/) {
             new_clip->has_class_embedding = false;
         }
 
@@ -1181,7 +1312,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.pre_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
             vision_model.pre_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
             new_clip->has_pre_norm = true;
-        } catch (std::exception & e) {
+        } catch (std::exception & /*e*/) {
             new_clip->has_pre_norm = false;
         }
 
@@ -1189,21 +1320,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.post_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight"));
             vision_model.post_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias"));
             new_clip->has_post_norm = true;
-        } catch (std::exception & e) {
+        } catch (std::exception & /*e*/) {
             new_clip->has_post_norm = false;
         }
 
         try {
             vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS);
             new_clip->has_patch_bias = true;
-        } catch (std::exception & e) {
+        } catch (std::exception & /*e*/) {
             new_clip->has_patch_bias = false;
         }
 
         try {
             vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
             vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
-        } catch(const std::exception& e) {
+        } catch(const std::exception& /*e*/) {
             LOG_TEE("%s: failed to load vision model tensors\n", __func__);
         }
 
@@ -1215,26 +1346,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                 // Yi-type llava
                 vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight"));
                 vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias"));
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & /*e*/) { }
             try {
                 // missing in Yi-type llava
                 vision_model.mm_2_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
                 vision_model.mm_2_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & /*e*/) { }
             try {
                 // Yi-type llava
                 vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight"));
                 vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias"));
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & /*e*/) { }
             try {
                 // Yi-type llava
                 vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight"));
                 vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & /*e*/) { }
             try {
                 vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
                 // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
-            } catch (std::runtime_error & e) {  }
+            } catch (std::runtime_error & /*e*/) { }
         } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
             // MobileVLM projection
             vision_model.mm_model_mlp_1_w               = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));
@@ -1272,6 +1403,27 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight"));
             vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias"));
         }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_RESAMPLER) {
+            // vision_model.mm_model_pos_embed = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD);
+            vision_model.mm_model_pos_embed_k = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD_K);
+            vision_model.mm_model_query = get_tensor(new_clip->ctx_data, TN_MINICPMV_QUERY);
+            vision_model.mm_model_proj = get_tensor(new_clip->ctx_data, TN_MINICPMV_PROJ);
+            vision_model.mm_model_kv_proj = get_tensor(new_clip->ctx_data, TN_MINICPMV_KV_PROJ);
+            vision_model.mm_model_attn_q_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "q", "weight"));
+            vision_model.mm_model_attn_k_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "k", "weight"));
+            vision_model.mm_model_attn_v_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "v", "weight"));
+            vision_model.mm_model_attn_q_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "q", "bias"));
+            vision_model.mm_model_attn_k_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "k", "bias"));
+            vision_model.mm_model_attn_v_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "v", "bias"));
+            vision_model.mm_model_attn_o_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "out", "weight"));
+            vision_model.mm_model_attn_o_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "out", "bias"));
+            vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "q", "weight"));
+            vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "q", "bias"));
+            vision_model.mm_model_ln_kv_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "kv", "weight"));
+            vision_model.mm_model_ln_kv_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "kv", "bias"));
+            vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
+            vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
+        }
         else {
             std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
             throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -1310,7 +1462,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
         clip_image_f32_batch batch;
         batch.size = 1;
-        ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
+        ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false);
         ggml_gallocr_reserve(new_clip->compute_alloc, gf);
         size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
         LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
@@ -1319,6 +1471,17 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     return new_clip;
 }
 
+void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size) {
+    ctx_clip->load_image_size = load_image_size;
+}
+
+struct clip_image_size * clip_image_size_init() {
+    struct clip_image_size * load_image_size = new struct clip_image_size();
+    load_image_size->width = 448;
+    load_image_size->height = 448;
+    return load_image_size;
+}
+
 struct clip_image_u8 * clip_image_u8_init() {
     return new clip_image_u8();
 }
@@ -1589,9 +1752,184 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
     return patches;
 }
 
+static int ensure_divide(int length, int patch_size) {
+    return std::max(static_cast<int>(std::round(static_cast<float>(length) / patch_size) * patch_size), patch_size);
+}
+
+static std::pair<int, int> uhd_find_best_resize(std::pair<int, int> original_size, int scale_resolution, int patch_size, bool allow_upscale = false) {
+    int width = original_size.first;
+    int height = original_size.second;
+    if ((width * height > scale_resolution * scale_resolution) || allow_upscale) {
+        float r = static_cast<float>(width) / height;
+        height = static_cast<int>(scale_resolution / std::sqrt(r));
+        width = static_cast<int>(height * r);
+    }
+    int best_width = ensure_divide(width, patch_size);
+    int best_height = ensure_divide(height, patch_size);
+    return std::make_pair(best_width, best_height);
+}
+
+static std::pair<int, int> uhd_get_refine_size(std::pair<int, int> original_size, std::pair<int, int> grid, int scale_resolution, int patch_size, bool allow_upscale = false) {
+    int width, height;
+    std::tie(width, height) = original_size;
+    int grid_x, grid_y;
+    std::tie(grid_x, grid_y) = grid;
+
+    int refine_width = ensure_divide(width, grid_x);
+    int refine_height = ensure_divide(height, grid_y);
+
+    int grid_width = refine_width / grid_x;
+    int grid_height = refine_height / grid_y;
+
+   // auto best_grid_size = find_best_resize(std::make_tuple(grid_width, grid_height), scale_resolution, patch_size, allow_upscale); (old line)
+    auto best_grid_size = uhd_find_best_resize(std::make_pair(grid_width, grid_height), scale_resolution, patch_size, allow_upscale); // (new line) => fixes conversion for make_tuple to make_pair
+    int best_grid_width, best_grid_height;
+    std::tie(best_grid_width, best_grid_height) = best_grid_size;
+
+  //  std::pair<int, int> refine_size = std::make_tuple(best_grid_width * grid_x, best_grid_height * grid_y); (old line)
+    std::pair<int, int> refine_size = std::make_pair(best_grid_width * grid_x, best_grid_height * grid_y); // (new line)
+    return refine_size;
+}
+
+inline int clip(int x, int lower, int upper) {
+    return std::max(lower, std::min(x, upper));
+}
+
+static std::pair<int, int> uhd_best_grid(const int max_slice_nums, const int multiple, const float log_ratio) {
+    std::vector<int> candidate_split_grids_nums;
+    for (int i : {multiple - 1, multiple, multiple + 1}) {
+        if (i == 1 || i > max_slice_nums) {
+            continue;
+        }
+        candidate_split_grids_nums.push_back(i);
+    }
+
+    std::vector<std::pair<int, int>> candidate_grids;
+    for (int split_grids_nums : candidate_split_grids_nums) {
+        int m = 1;
+        while (m <= split_grids_nums) {
+            if (split_grids_nums % m == 0) {
+                candidate_grids.emplace_back(m, split_grids_nums / m);
+            }
+            ++m;
+        }
+    }
+
+    std::pair<int, int> best_grid{1, 1};
+    float min_error = std::numeric_limits<float>::infinity();
+    for (const auto& grid : candidate_grids) {
+        float error = std::abs(log_ratio - std::log(1.0 * grid.first / grid.second));
+        if (error < min_error) {
+            best_grid = grid;
+            min_error = error;
+        }
+    }
+    return best_grid;
+}
+
+// inspired from LLaVA-UHD:
+//    -> https://arxiv.org/pdf/2403.11703
+//    -> https://github.com/thunlp/LLaVA-UHD
+//    -> https://github.com/thunlp/LLaVA-UHD/blob/302301bc2175f7e717fb8548516188e89f649753/llava_uhd/train/llava-uhd/slice_logic.py#L118
+static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_image_u8 * img, const int max_slice_nums=9, const int scale_resolution=448, const int patch_size=14) {
+    const std::pair<int, int> original_size={img->nx,img->ny};
+    const int original_width = img->nx;
+    const int original_height = img->ny;
+    const float log_ratio = log(1.0*original_width/original_height);
+    const float ratio = 1.0 * original_width * original_height/ (scale_resolution * scale_resolution);
+    const int multiple = fmin(ceil(ratio), max_slice_nums);
+
+    std::vector<std::vector<clip_image_u8 *>> images;
+    LOG_TEE("%s: multiple %d\n", __func__, multiple);
+    images.push_back(std::vector<clip_image_u8 *>());
+
+    if (multiple <= 1) {
+        auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size, true);
+        clip_image_u8 * source_image = clip_image_u8_init();
+        bicubic_resize(*img, *source_image, best_size.first, best_size.second);
+        // source_image = image.resize(best_size, Image.Resampling.BICUBIC)
+        images[images.size()-1].push_back(source_image);
+    }
+    else if (multiple > 1) {
+        auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size);
+        clip_image_u8 * source_image = clip_image_u8_init();
+        bicubic_resize(*img, *source_image, best_size.first, best_size.second);
+        // source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
+        LOG_TEE("%s: image_size: %d %d; source_image size: %d %d\n", __func__, img->nx, img->ny, best_size.first, best_size.second);
+        images[images.size()-1].push_back(source_image);
+
+        std::pair<int, int> best_grid = uhd_best_grid(max_slice_nums, multiple, log_ratio);
+        LOG_TEE("%s: image_size: %d %d; best_grid: %d %d\n", __func__, img->nx, img->ny, best_grid.first, best_grid.second);
+
+        auto refine_size = uhd_get_refine_size(original_size, best_grid, scale_resolution, patch_size, true);
+        clip_image_u8 * refine_image = clip_image_u8_init();
+        bicubic_resize(*img, *refine_image, refine_size.first, refine_size.second);
+
+        LOG_TEE("%s: refine_image_size: %d %d; refine_size: %d %d\n", __func__, refine_image->nx, refine_image->ny, refine_size.first, refine_size.second);
+
+        // split_to_patches
+        int width = refine_image->nx;
+        int height = refine_image->ny;
+        int grid_x = int(width / best_grid.first);
+        int grid_y = int(height / best_grid.second);
+        for (int patches_i = 0, ic = 0; patches_i < height && ic < best_grid.second; patches_i += grid_y, ic += 1){
+            images.push_back(std::vector<clip_image_u8 *>());
+            for(int patches_j = 0, jc = 0; patches_j < width && jc < best_grid.first; patches_j += grid_x, jc += 1){
+                clip_image_u8 * patch = clip_image_u8_init();
+                patch->nx = grid_x;
+                patch->ny = grid_y;
+                patch->buf.resize(3 * patch->nx * patch->ny);
+                for (int y = patches_i; y < patches_i + grid_y; ++y) {
+                    for (int x = patches_j; x < patches_j + grid_x; ++x) {
+                        const int i = 3 * (y * refine_image->nx + x);
+                        const int j = 3 * ((y-patches_i) * patch->nx + (x-patches_j));
+                        patch->buf[j]   = refine_image->buf[i];
+                        patch->buf[j+1] = refine_image->buf[i+1];
+                        patch->buf[j+2] = refine_image->buf[i+2];
+                    }
+                }
+                images[images.size()-1].push_back(patch);
+            }
+        }
+    }
+    return images;
+}
+
+int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip) {
+    const int max_slice_nums=9;
+    const int scale_resolution=448;
+    const int original_width = ctx_clip->load_image_size->width;
+    const int original_height = ctx_clip->load_image_size->height;
+    const float log_ratio = log(1.0*original_width/original_height);
+    const float ratio = 1.0 * original_width * original_height/ (scale_resolution * scale_resolution);
+    const int multiple = fmin(ceil(ratio), max_slice_nums);
+    std::pair<int, int> best_grid = uhd_best_grid(max_slice_nums, multiple, log_ratio);
+    return best_grid.first;
+}
+
 // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
 // res_imgs memory is being allocated here, previous allocations will be freed if found
 bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
+    if (clip_is_minicpmv(ctx)) {
+        std::vector<std::vector<clip_image_u8 *>> imgs = uhd_slice_image(img);
+        res_imgs->size = 0;
+        for (size_t i = 0; i < imgs.size(); ++i) {
+            res_imgs->size += imgs[i].size();
+        }
+        res_imgs->data = new clip_image_f32[res_imgs->size];
+        int idx = 0;
+        for (size_t i = 0; i < imgs.size(); ++i) {
+            for (size_t j = 0; j < imgs[i].size(); ++j) {
+                LOG_TEE("%s: %d %d\n", __func__,imgs[i][j]->nx,imgs[i][j]->ny);
+                clip_image_f32 * res = clip_image_f32_init();
+                normalize_image_u8_to_f32(imgs[i][j], res, ctx->image_mean, ctx->image_std);
+                res_imgs->data[idx++] = *res;
+                clip_image_f32_free(res);
+            }
+        }
+        return true;
+    }
+
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
         LOG_TEE("This gguf file seems to have no vision encoder\n");
@@ -1807,11 +2145,99 @@ int clip_n_patches(const struct clip_ctx * ctx) {
 
     if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
         n_patches /= 4;
+    } else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
+        n_patches = 96;
     }
 
     return n_patches;
 }
 
+static std::vector<std::vector<std::vector<float>>> get_1d_sincos_pos_embed_from_grid_new(int embed_dim, const std::vector<std::vector<float>> & pos) {
+    assert(embed_dim % 2 == 0);
+    int H = pos.size();
+    int W = pos[0].size();
+
+    std::vector<float> omega(embed_dim / 2);
+    for (int i = 0; i < embed_dim / 2; ++i) {
+        omega[i] = 1.0 / pow(10000.0, static_cast<float>(i) / (embed_dim / 2));
+    }
+
+    std::vector<std::vector<std::vector<float>>> emb(H, std::vector<std::vector<float>>(W, std::vector<float>(embed_dim)));
+    for (int h = 0; h < H; ++h) {
+        for (int w = 0; w < W; ++w) {
+            for (int d = 0; d < embed_dim / 2; ++d) {
+                float out_value = pos[h][w] * omega[d];
+                emb[h][w][d] = sin(out_value);
+                emb[h][w][d + embed_dim / 2] = cos(out_value);
+            }
+        }
+    }
+
+    return emb;
+}
+
+static std::vector<std::vector<std::vector<float>>> get_2d_sincos_pos_embed_from_grid(int embed_dim, const std::vector<std::vector<std::vector<float>>> & grid) {
+    assert(embed_dim % 2 == 0);
+    std::vector<std::vector<std::vector<float>>> emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim / 2, grid[0]); // (H, W, D/2)
+    std::vector<std::vector<std::vector<float>>> emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim / 2, grid[1]); // (H, W, D/2)
+
+    int H = emb_h.size();
+    int W = emb_h[0].size();
+    std::vector<std::vector<std::vector<float>>> emb(H, std::vector<std::vector<float>>(W, std::vector<float>(embed_dim)));
+
+    for (int h = 0; h < H; ++h) {
+        for (int w = 0; w < W; ++w) {
+            for (int d = 0; d < embed_dim / 2; ++d) {
+                emb[h][w][d] = emb_h[h][w][d];
+                emb[h][w][d + embed_dim / 2] = emb_w[h][w][d];
+            }
+        }
+    }
+    return emb;
+}
+
+static std::vector<std::vector<float>> get_2d_sincos_pos_embed(int embed_dim, const std::pair<int, int> image_size) {
+    int grid_h_size = image_size.first;
+    int grid_w_size = image_size.second;
+
+    std::vector<float> grid_h(grid_h_size);
+    std::vector<float> grid_w(grid_w_size);
+
+    for (int i = 0; i < grid_h_size; ++i) {
+        grid_h[i] = static_cast<float>(i);
+    }
+    for (int i = 0; i < grid_w_size; ++i) {
+        grid_w[i] = static_cast<float>(i);
+    }
+
+    std::vector<std::vector<float>> grid(grid_h_size, std::vector<float>(grid_w_size));
+    for (int h = 0; h < grid_h_size; ++h) {
+        for (int w = 0; w < grid_w_size; ++w) {
+            grid[h][w] = grid_w[w];
+        }
+    }
+    std::vector<std::vector<std::vector<float>>> grid_2d = {grid, grid};
+    for (int h = 0; h < grid_h_size; ++h) {
+        for (int w = 0; w < grid_w_size; ++w) {
+            grid_2d[0][h][w] = grid_h[h];
+            grid_2d[1][h][w] = grid_w[w];
+        }
+    }
+
+    std::vector<std::vector<std::vector<float>>> pos_embed_3d = get_2d_sincos_pos_embed_from_grid(embed_dim, grid_2d);
+
+    int H = image_size.first;
+    int W = image_size.second;
+    std::vector<std::vector<float>> pos_embed_2d(H * W, std::vector<float>(embed_dim));
+    for (int h = 0; h < H; ++h) {
+        for (int w = 0; w < W; ++w) {
+            pos_embed_2d[w * H + h] = pos_embed_3d[h][w];
+        }
+    }
+
+    return pos_embed_2d;
+}
+
 bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
     if (!ctx->has_vision_encoder) {
         LOG_TEE("This gguf file seems to have no vision encoder\n");
@@ -1834,18 +2260,27 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     if (ctx->has_llava_projector) {
         GGML_ASSERT(batch_size == 1); // TODO: support multiple images
     }
+    if (ctx->has_minicpmv_projector) {
+        GGML_ASSERT(batch_size == 1);
+    }
 
     // build the inference graph
-    ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
+    ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
     ggml_gallocr_alloc_graph(ctx->compute_alloc, gf);
 
     // set inputs
     const auto & model = ctx->vision_model;
     const auto & hparams = model.hparams;
 
-    const int image_size    = hparams.image_size;
+    const int image_size = hparams.image_size;
+    int image_size_width  = image_size;
+    int image_size_height = image_size;
+    if (ctx->has_minicpmv_projector) {
+        image_size_width  = imgs->data[0].nx;
+        image_size_height = imgs->data[0].ny;
+    }
     const int patch_size    = hparams.patch_size;
-    const int num_patches   = ((image_size / patch_size) * (image_size / patch_size));
+    const int num_patches   = ((image_size_width / patch_size) * (image_size_height / patch_size));
     const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0);
 
     {
@@ -1855,7 +2290,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         for (size_t i = 0; i < imgs->size; i++) {
             const int nx = imgs->data[i].nx;
             const int ny = imgs->data[i].ny;
-            GGML_ASSERT(nx == image_size && ny == image_size);
+            if (!ctx->has_minicpmv_projector) {
+                GGML_ASSERT(nx == image_size && ny == image_size);
+            }
 
             const int n = nx * ny;
 
@@ -1872,37 +2309,75 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
         free(data);
     }
+    if (ctx->has_minicpmv_projector) {
+        {
+            // inspired from siglip:
+            //    -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit
+            //    -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316
+            struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
+            int* positions_data = (int*)malloc(ggml_nbytes(positions));
+            for (int i = 0; i < num_positions; i++) {
+                positions_data[i] = std::floor(70.0*i/num_positions);
+            }
+            ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
+            free(positions_data);
+        }
 
-    {
-        if (ctx->has_class_embedding) {
-            struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
+        {
+            // inspired from resampler of Qwen-VL:
+            //    -> https://huggingface.co/Qwen/Qwen-VL/tree/main
+            //    -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23
+            struct ggml_tensor * pos_embed = ggml_graph_get_tensor(gf, "pos_embed");
+            if(ctx->load_image_size==nullptr){
+                ctx->load_image_size= clip_image_size_init();
+            }
+            int pos_w = ctx->load_image_size->width/patch_size;
+            int pos_h = ctx->load_image_size->height/patch_size;
+            int embed_dim = 4096;
+            auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
+
+            float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed));
+            for(int i=0;i<pos_w * pos_h;++i){
+                for(int j=0;j<embed_dim;++j){
+                    pos_embed_data[i*embed_dim+j]=pos_embed_t[i][j];
+                }
+            }
 
-            void* zero_mem = malloc(ggml_nbytes(embeddings));
-            memset(zero_mem, 0, ggml_nbytes(embeddings));
-            ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
-            free(zero_mem);
+            ggml_backend_tensor_set(pos_embed, pos_embed_data, 0, ggml_nbytes(pos_embed));
+            free(pos_embed_data);
         }
-    }
+    } else {
+        {
+            if (ctx->has_class_embedding) {
+                struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
 
-    {
-        struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
+                void* zero_mem = malloc(ggml_nbytes(embeddings));
+                memset(zero_mem, 0, ggml_nbytes(embeddings));
+                ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
+                free(zero_mem);
+            }
+        }
+
+        {
+            struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
 
-        int* positions_data = (int*)malloc(ggml_nbytes(positions));
-        for (int i = 0; i < num_positions; i++) {
-            positions_data[i] = i;
+            int* positions_data = (int*)malloc(ggml_nbytes(positions));
+            for (int i = 0; i < num_positions; i++) {
+                positions_data[i] = i;
+            }
+            ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
+            free(positions_data);
         }
-        ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
-        free(positions_data);
-    }
 
-    {
-        struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
-        int* patches_data = (int*)malloc(ggml_nbytes(patches));
-        for (int i = 0; i < num_patches; i++) {
-            patches_data[i] = i + 1;
+        {
+            struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
+            int* patches_data = (int*)malloc(ggml_nbytes(patches));
+            for (int i = 0; i < num_patches; i++) {
+                patches_data[i] = i + 1;
+            }
+            ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
+            free(patches_data);
         }
-        ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
-        free(patches_data);
     }
 
     if (ggml_backend_is_cpu(ctx->backend)) {
@@ -2072,7 +2547,14 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
     if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
         return ctx->vision_model.mm_3_b->ne[0];
     }
+    if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
+        return 4096;
+    }
 
     std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
     throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
 }
+
+bool clip_is_minicpmv(const struct clip_ctx * ctx) {
+    return ctx->has_minicpmv_projector;
+}
diff --git a/examples/llava/clip.h b/examples/llava/clip.h
index 45bdad6897658..2ff4d39929dc3 100644
--- a/examples/llava/clip.h
+++ b/examples/llava/clip.h
@@ -18,14 +18,17 @@
 #    define CLIP_API
 #endif
 
-struct clip_ctx;
-
 #ifdef __cplusplus
 extern "C" {
 #endif
 
 struct clip_ctx;
 
+struct clip_image_size {
+    int width;
+    int height;
+};
+
 struct clip_image_u8_batch {
     struct clip_image_u8 * data;
     size_t size;
@@ -55,6 +58,10 @@ CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
 CLIP_API int clip_n_patches    (const struct clip_ctx * ctx);
 CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
 
+CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
+CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
+
+CLIP_API struct clip_image_size * clip_image_size_init();
 CLIP_API struct clip_image_u8  * clip_image_u8_init ();
 CLIP_API struct clip_image_f32 * clip_image_f32_init();
 
@@ -68,7 +75,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8
 /** interpret bytes as an image file with length bytes_length, and use the result to populate img */
 CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
 
-/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
+/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
 CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
 
 CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
@@ -78,6 +85,8 @@ CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, cons
 
 CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
 
+CLIP_API bool clip_is_minicpmv(const struct clip_ctx * ctx);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert_image_encoder_to_gguf.py
similarity index 97%
rename from examples/llava/convert-image-encoder-to-gguf.py
rename to examples/llava/convert_image_encoder_to_gguf.py
index b00bf7c6d0b59..36f6b92fbd46e 100644
--- a/examples/llava/convert-image-encoder-to-gguf.py
+++ b/examples/llava/convert_image_encoder_to_gguf.py
@@ -185,6 +185,8 @@ def bytes_to_unicode():
     fout.add_description("two-tower CLIP model")
 
 if has_text_encoder:
+    assert t_hparams is not None
+    assert tokens is not None
     # text_model hparams
     fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
     fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
@@ -259,8 +261,8 @@ def bytes_to_unicode():
 
 
     if processor is not None:
-        image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
-        image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
+        image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean  # pyright: ignore[reportAttributeAccessIssue]
+        image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std  # pyright: ignore[reportAttributeAccessIssue]
     else:
         image_mean = args.image_mean if args.image_mean is not None else default_image_mean
         image_std = args.image_std if args.image_std is not None else default_image_std
@@ -272,7 +274,7 @@ def bytes_to_unicode():
 
 
 if has_llava_projector:
-    model.vision_model.encoder.layers.pop(-1)
+    model.vision_model.encoder.layers.pop(-1)  # pyright: ignore[reportAttributeAccessIssue]
     projector = torch.load(args.llava_projector)
     for name, data in projector.items():
         name = get_tensor_name(name)
@@ -286,7 +288,7 @@ def bytes_to_unicode():
 
     print("Projector tensors added\n")
 
-state_dict = model.state_dict()
+state_dict = model.state_dict()  # pyright: ignore[reportAttributeAccessIssue]
 for name, data in state_dict.items():
     if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):
         # we don't need this
diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp
index a6d67e5d72cd2..8c7dd2ae3d0dc 100644
--- a/examples/llava/llava-cli.cpp
+++ b/examples/llava/llava-cli.cpp
@@ -112,9 +112,12 @@ struct llava_context {
     struct llama_model * model = NULL;
 };
 
-static void show_additional_info(int /*argc*/, char ** argv) {
-    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
+
+    LOG_TEE("\n example usage:\n");
+    LOG_TEE("\n     %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
 }
 
 static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {
@@ -278,7 +281,7 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
-        show_additional_info(argc, argv);
+        print_usage(argc, argv, params);
         return 1;
     }
 
@@ -290,8 +293,7 @@ int main(int argc, char ** argv) {
 #endif // LOG_DISABLE_LOGS
 
     if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
-        gpt_print_usage(argc, argv, params);
-        show_additional_info(argc, argv);
+        print_usage(argc, argv, {});
         return 1;
     }
     auto model = llava_init(&params);
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 63878d176b0bb..916d9dc401dc4 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -202,6 +202,33 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
     return true;
 }
 
+static clip_image_f32 * only_v2_5_reshape_by_patch(clip_image_f32 * image, int patch_size) {
+    int width = image->nx;
+    int height = image->ny;
+    int num_patches = (height / patch_size) * (width / patch_size);
+    clip_image_f32 * patch = clip_image_f32_init();
+    patch->nx = patch_size * num_patches;
+    patch->ny = patch_size;
+    patch->buf.resize(3 * patch->nx * patch->ny);
+
+    int patch_index = 0;
+
+    for (int i = 0; i < height; i += patch_size) {
+        for (int j = 0; j < width; j += patch_size) {
+            for (int pi = 0; pi < patch_size; ++pi) {
+                for (int pj = 0; pj < patch_size; ++pj) {
+                    int input_index = ((i + pi) * width + (j + pj)) * 3;
+                    int output_index = (pi * patch_size * num_patches + patch_index * patch_size + pj) * 3;
+                    patch->buf[output_index] = image->buf[input_index];
+                    patch->buf[output_index+1] = image->buf[input_index+1];
+                    patch->buf[output_index+2] = image->buf[input_index+2];
+                }
+            }
+            patch_index++;
+        }
+    }
+    return patch;
+}
 
 static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
     // std::vector<clip_image_f32*> img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336
@@ -218,7 +245,44 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
     const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
 
-    if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
+    if (clip_is_minicpmv(ctx_clip)) {
+        std::vector<float *> image_embd_v;
+        image_embd_v.resize(img_res_v.size);
+        struct clip_image_size * load_image_size = clip_image_size_init();
+        for (size_t i = 0; i < img_res_v.size; i++) {
+            const int64_t t_img_enc_step_start_us = ggml_time_us();
+            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip));
+            int patch_size=14;
+            load_image_size->width = img_res_v.data[i].nx;
+            load_image_size->height = img_res_v.data[i].ny;
+            clip_add_load_image_size(ctx_clip, load_image_size);
+            const bool encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
+            if (!encoded) {
+                LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
+                return false;
+            }
+            const int64_t t_img_enc_steop_batch_us = ggml_time_us();
+            LOG_TEE("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)img_res_v.size, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0);
+        }
+        const int64_t t_img_enc_batch_us = ggml_time_us();
+        LOG_TEE("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
+
+        int n_img_pos_out = 0;
+        for (size_t i = 0; i < image_embd_v.size(); i++) {
+            std::memcpy(image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), image_embd_v[i], clip_embd_nbytes(ctx_clip));
+            n_img_pos_out += clip_n_patches(ctx_clip);
+        }
+        *n_img_pos = n_img_pos_out;
+        for (size_t i = 0; i < image_embd_v.size(); i++) {
+            free(image_embd_v[i]);
+        }
+        image_embd_v.clear();
+        load_image_size->width = img->nx;
+        load_image_size->height = img->ny;
+        clip_add_load_image_size(ctx_clip, load_image_size);
+        LOG_TEE("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height);
+    }
+    else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
         // flat / default llava-1.5 type embedding
         *n_img_pos = clip_n_patches(ctx_clip);
         bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
@@ -228,7 +292,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
             return false;
         }
-    } else {
+    }
+    else {
         // spatial_unpad llava-1.6 type embedding
         // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
         std::vector<float *> image_embd_v;
@@ -297,7 +362,11 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
 }
 
 bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
-    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
+    int num_max_patches = 6;
+    if (clip_is_minicpmv(ctx_clip)) {
+        num_max_patches = 10;
+    }
+    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
     if (!image_embd) {
         LOG_TEE("Unable to allocate memory for image embeddings\n");
         return false;
diff --git a/examples/llava/llava.h b/examples/llava/llava.h
index 19212f6e9e9c5..b6feb3027b2da 100644
--- a/examples/llava/llava.h
+++ b/examples/llava/llava.h
@@ -17,12 +17,11 @@
 #    define LLAVA_API
 #endif
 
-struct clip_ctx;
-
 #ifdef __cplusplus
 extern "C" {
 #endif
 
+struct clip_ctx;
 struct llava_image_embed {
     float * embed;
     int n_image_pos;
@@ -37,8 +36,8 @@ LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip,
 LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
 /** build an image embed from a path to an image filename */
 LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
-LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
 /** free an embedding made with llava_image_embed_make_* */
+LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
 
 /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
 LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava_surgery.py
similarity index 100%
rename from examples/llava/llava-surgery.py
rename to examples/llava/llava_surgery.py
diff --git a/examples/llava/llava-surgery-v2.py b/examples/llava/llava_surgery_v2.py
similarity index 94%
rename from examples/llava/llava-surgery-v2.py
rename to examples/llava/llava_surgery_v2.py
index eb56d6988ac26..2d5b32fe6b236 100644
--- a/examples/llava/llava-surgery-v2.py
+++ b/examples/llava/llava_surgery_v2.py
@@ -2,7 +2,9 @@
 import glob
 import os
 import torch
-from safetensors.torch import load as safe_load, save as safe_save, safe_open, save_file
+from safetensors import safe_open
+from safetensors.torch import save_file
+from typing import Any, ContextManager, cast
 
 # Function to determine if file is a SafeTensor file
 def is_safetensor_file(file_path):
@@ -13,7 +15,7 @@ def is_safetensor_file(file_path):
 def load_model(file_path):
     if is_safetensor_file(file_path):
         tensors = {}
-        with safe_open(file_path, framework="pt", device="cpu") as f:
+        with cast(ContextManager[Any], safe_open(file_path, framework="pt", device="cpu")) as f:
             for key in f.keys():
                 tensors[key] = f.get_tensor(key).clone()
                 # output shape
@@ -134,7 +136,7 @@ def proj_criteria(checkpoint):
     if last_checkpoint is not None:
         for k, v in last_checkpoint.items():
             print(k)
-    print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint)} tensors.")
+    print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint) if last_checkpoint is not None else 0} tensors.")
     print("No tensors found. Is this a LLaVA model?")
     exit()
 
@@ -143,8 +145,10 @@ def proj_criteria(checkpoint):
 # projector = {name: checkpoint.[name].float() for name in mm_tensors}
 projector = {}
 for name in mm_tensors:
+    assert last_checkpoint is not None
     projector[name] = last_checkpoint[name].float()
 for name in first_mm_tensors:
+    assert first_checkpoint is not None
     projector[name] = first_checkpoint[name].float()
 
 if len(projector) > 0:
diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp
new file mode 100644
index 0000000000000..f951b57b29158
--- /dev/null
+++ b/examples/llava/minicpmv-cli.cpp
@@ -0,0 +1,309 @@
+#include "ggml.h"
+#include "log.h"
+#include "common.h"
+#include "clip.h"
+#include "llava.h"
+#include "llama.h"
+
+#include <cstdio>
+#include <cstdlib>
+#include <vector>
+
+struct llava_context {
+    struct clip_ctx * ctx_clip = NULL;
+    struct llama_context * ctx_llama = NULL;
+    struct llama_model * model = NULL;
+};
+
+static void show_additional_info(int /*argc*/, char ** argv) {
+    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+}
+
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    LOG_TEE("%s", text);
+}
+
+static struct llama_model * llava_init(gpt_params * params) {
+    llama_backend_init();
+    llama_numa_init(params->numa);
+
+    llama_model_params model_params = llama_model_params_from_gpt_params(*params);
+
+    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    if (model == NULL) {
+        LOG_TEE("%s: error: unable to load model\n" , __func__);
+        return NULL;
+    }
+    return model;
+}
+
+static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
+    auto prompt = params->prompt;
+    if (prompt.empty()) {
+        prompt = "describe the image in detail.";
+    }
+
+    llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
+    if (params->n_ctx < 2048) {
+        // warn user here, "Image processing requires at least 2048 context, setting context to 2048"
+        LOG_TEE("%s: warn: Image processing requires at least 2048 context, setting context to 2048\n" , __func__);
+        ctx_params.n_ctx = 2048;
+    } else {
+        ctx_params.n_ctx = params->n_ctx;
+    }
+
+    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx_llama == NULL) {
+        LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
+        return NULL;
+    }
+
+    auto ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
+
+    ctx_llava->ctx_llama = ctx_llama;
+    ctx_llava->model = model;
+    return ctx_llava;
+}
+
+static void llava_free(struct llava_context * ctx_llava) {
+    if (ctx_llava->ctx_clip) {
+        clip_free(ctx_llava->ctx_clip);
+        ctx_llava->ctx_clip = NULL;
+    }
+
+    llama_free(ctx_llava->ctx_llama);
+    llama_free_model(ctx_llava->model);
+    llama_backend_free();
+}
+
+static struct clip_ctx * clip_init_context(gpt_params * params) {
+    const char * clip_path = params->mmproj.c_str();
+
+    auto prompt = params->prompt;
+    if (prompt.empty()) {
+        prompt = "describe the image in detail.";
+    }
+    auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
+    return ctx_clip;
+}
+
+static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
+    int N = (int) tokens.size();
+    for (int i = 0; i < N; i += n_batch) {
+        int n_eval = (int) tokens.size() - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
+            LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
+            return false;
+        }
+        *n_past += n_eval;
+    }
+    return true;
+}
+
+static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
+    std::vector<llama_token> tokens;
+    tokens.push_back(id);
+    return eval_tokens(ctx_llama, tokens, 1, n_past);
+}
+
+static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
+    std::string              str2     = str;
+    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true);
+    return eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
+}
+
+static void process_eval_image_embed(struct llava_context * ctx_llava, const struct llava_image_embed * embeds, int n_batch, int * n_past, int idx) {
+    float * image_embed = (float *)malloc(clip_embd_nbytes(ctx_llava->ctx_clip));
+    std::memcpy(image_embed, embeds->embed + idx * clip_n_patches(ctx_llava->ctx_clip) * clip_n_mmproj_embd(ctx_llava->ctx_clip), clip_embd_nbytes(ctx_llava->ctx_clip));
+
+    auto slice_embed = (llava_image_embed*)malloc(sizeof(llava_image_embed));
+    slice_embed->embed = image_embed;
+    slice_embed->n_image_pos = clip_n_patches(ctx_llava->ctx_clip);
+    llava_eval_image_embed(ctx_llava->ctx_llama, slice_embed, n_batch, n_past);
+    llava_image_embed_free(slice_embed);
+}
+
+static void process_image(struct llava_context * ctx_llava, struct llava_image_embed * embeds, gpt_params * params, int &n_past) {
+    std::string system_prompt;
+    int idx = 0;
+    int num_image_embeds = embeds->n_image_pos / clip_n_patches(ctx_llava->ctx_clip);
+    system_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n";
+    LOG_TEE("%s: image token past: %d\n", __func__, n_past);
+    eval_string(ctx_llava->ctx_llama, (system_prompt+"<image>").c_str(), params->n_batch, &n_past, false);
+    process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
+    eval_string(ctx_llava->ctx_llama, std::string("</image>").c_str(), params->n_batch, &n_past, false);
+    if (num_image_embeds > 1) {
+        size_t num_image_embeds_col = clip_uhd_num_image_embeds_col(ctx_llava->ctx_clip);
+        eval_string(ctx_llava->ctx_llama, std::string("<slice>").c_str(), params->n_batch, &n_past, false);
+        for (size_t i = 0; i < (num_image_embeds-1)/num_image_embeds_col; ++i) {
+            for (size_t j = 0; j < num_image_embeds_col; ++j) {
+                eval_string(ctx_llava->ctx_llama, std::string("<image>").c_str(), params->n_batch, &n_past, false);
+                process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
+                eval_string(ctx_llava->ctx_llama, std::string("</image>").c_str(), params->n_batch, &n_past, false);
+                if (j == num_image_embeds_col - 1) {
+                    eval_string(ctx_llava->ctx_llama, std::string("\n").c_str(), params->n_batch, &n_past, false);
+                }
+            }
+        }
+        eval_string(ctx_llava->ctx_llama, std::string("</slice>").c_str(), params->n_batch, &n_past, false);
+    }
+    LOG_TEE("%s: image token past: %d\n", __func__, n_past);
+}
+
+static const char * sample(struct llama_sampling_context * ctx_sampling,
+                           struct llama_context * ctx_llama,
+                           int * n_past) {
+    const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL);
+    llama_sampling_accept(ctx_sampling, ctx_llama, id, true);
+    static std::string ret;
+    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
+        ret = "</s>";
+    } else {
+        ret = llama_token_to_piece(ctx_llama, id);
+    }
+    eval_id(ctx_llama, id, n_past);
+    return ret.c_str();
+}
+
+static struct llava_context * minicpmv_init(gpt_params * params, const std::string & fname, int &n_past){
+    auto ctx_clip = clip_init_context(params);
+    auto embeds = llava_image_embed_make_with_filename(ctx_clip, params->n_threads, fname.c_str());
+    if (!embeds) {
+        std::cerr << "error: failed to load image " << fname << ". Terminating\n\n";
+        return NULL;
+    }
+
+    // process the prompt
+    if (params->prompt.empty() && params->interactive == false) {
+        LOG_TEE("prompt should be given or interactive mode should be on");
+        return NULL;
+    }
+
+    auto model = llava_init(params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to init minicpmv model\n", __func__);
+        return NULL;
+    }
+    const int64_t t_llava_init_start_us = ggml_time_us();
+    auto ctx_llava = llava_init_context(params, model);
+    ctx_llava->ctx_clip = ctx_clip;
+    const int64_t t_llava_init_end_us = ggml_time_us();
+    float t_llava_init_ms = (t_llava_init_end_us - t_llava_init_start_us) / 1000.0;
+    LOG_TEE("\n%s: llava init in %8.2f ms.\n", __func__, t_llava_init_ms);
+
+    const int64_t t_process_image_start_us = ggml_time_us();
+    process_image(ctx_llava, embeds, params, n_past);
+    const int64_t t_process_image_end_us = ggml_time_us();
+    float t_process_image_ms = (t_process_image_end_us - t_process_image_start_us) / 1000.0;
+    LOG_TEE("\n%s: llama process image in %8.2f ms.\n", __func__, t_process_image_ms);
+
+    llava_image_embed_free(embeds);
+    return ctx_llava;
+}
+
+static struct llama_sampling_context * llama_init(struct llava_context * ctx_llava, gpt_params * params, std::string prompt, int &n_past, bool is_first = false){
+    std::string user_prompt = prompt;
+    if (!is_first) user_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" + prompt;
+
+    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
+    eval_string(ctx_llava->ctx_llama, "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", params->n_batch, &n_past, false);
+    // generate the response
+
+    LOG_TEE("\n");
+
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
+    return ctx_sampling;
+}
+
+static const char * llama_loop(struct llava_context * ctx_llava,struct llama_sampling_context * ctx_sampling, int &n_past){
+
+    const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
+    return tmp;
+}
+
+int main(int argc, char ** argv) {
+    ggml_time_init();
+
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        show_additional_info(argc, argv);
+        return 1;
+    }
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("llava", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
+    if (params.mmproj.empty() || (params.image.empty())) {
+        gpt_params_print_usage(argc, argv, params);
+        show_additional_info(argc, argv);
+        return 1;
+    }
+
+    for (auto & image : params.image) {
+        int n_past = 0;
+        auto ctx_llava = minicpmv_init(&params, image, n_past);
+
+        if (!params.prompt.empty()) {
+            LOG_TEE("<user>%s\n", params.prompt.c_str());
+            LOG_TEE("<assistant>");
+            auto ctx_sampling = llama_init(ctx_llava, &params, params.prompt.c_str(), n_past, true);
+            const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict;
+            std::string response = "";
+            bool have_tmp = false;
+            for (int i = 0; i < max_tgt_len; i++) {
+                auto tmp = llama_loop(ctx_llava, ctx_sampling, n_past);
+                response += tmp;
+                if (strcmp(tmp, "</s>") == 0){
+                    if(!have_tmp)continue;
+                    else break;
+                }
+                if (strstr(tmp, "###")) break; // Yi-VL behavior
+                have_tmp = true;
+                printf("%s", tmp);
+                if (strstr(response.c_str(), "<user>")) break; // minicpm-v
+
+                fflush(stdout);
+            }
+            llama_sampling_free(ctx_sampling);
+        }else {
+            while (true) {
+                LOG_TEE("<user>");
+                std::string prompt;
+                std::getline(std::cin, prompt);
+                LOG_TEE("<assistant>");
+                auto ctx_sampling = llama_init(ctx_llava, &params, prompt, n_past, true);
+                const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict;
+                std::string response = "";
+                for (int i = 0; i < max_tgt_len; i++) {
+                    auto tmp = llama_loop(ctx_llava, ctx_sampling, n_past);
+                    response += tmp;
+                    if (strcmp(tmp, "</s>") == 0) break;
+                    if (strstr(tmp, "###")) break; // Yi-VL behavior
+                    printf("%s", tmp);// mistral llava-1.6
+                    if (strstr(response.c_str(), "<user>")) break; // minicpm-v
+                    fflush(stdout);
+                }
+                llama_sampling_free(ctx_sampling);
+            }
+        }
+        printf("\n");
+        llama_print_timings(ctx_llava->ctx_llama);
+
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
+    }
+
+    return 0;
+}
diff --git a/examples/llava/minicpmv-convert-image-encoder-to-gguf.py b/examples/llava/minicpmv-convert-image-encoder-to-gguf.py
new file mode 100644
index 0000000000000..12cdd1281d2ff
--- /dev/null
+++ b/examples/llava/minicpmv-convert-image-encoder-to-gguf.py
@@ -0,0 +1,382 @@
+import argparse
+import os
+import json
+import re
+
+import torch
+import numpy as np
+from gguf import *
+from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer, Idefics2VisionConfig
+
+TEXT = "clip.text"
+VISION = "clip.vision"
+
+
+def add_key_str(raw_key: str, arch: str) -> str:
+    return raw_key.format(arch=arch)
+
+
+def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_minicpmv: bool) -> bool:
+    if name in (
+        "logit_scale",
+        "text_model.embeddings.position_ids",
+        "vision_model.embeddings.position_ids",
+    ):
+        return True
+
+    if has_minicpmv and name in ["visual_projection.weight"]:
+        return True
+
+    if name.startswith("v") and not has_vision:
+        return True
+
+    if name.startswith("t") and not has_text:
+        return True
+
+    return False
+
+
+def get_tensor_name(name: str) -> str:
+    if "projection" in name:
+        return name
+    if "mm_projector" in name:
+        name = name.replace("model.mm_projector", "mm")
+        name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
+        name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
+        return name
+
+    return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
+
+
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a significant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
+ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
+ap.add_argument("--text-only", action="store_true", required=False,
+                help="Save a text-only model. It can't be used to encode images")
+ap.add_argument("--vision-only", action="store_true", required=False,
+                help="Save a vision-only model. It can't be used to encode texts")
+ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
+                help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
+ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
+                help="The clip model is from openclip (for ViT-SO400M type))")
+ap.add_argument("--minicpmv-projector", help="Path to minicpmv.projector file. If specified, save an image encoder for MiniCPM-V models.")
+ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
+ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
+# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
+# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
+default_image_mean = [0.48145466, 0.4578275, 0.40821073]
+default_image_std = [0.26862954, 0.26130258, 0.27577711]
+ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
+ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
+
+# with proper
+args = ap.parse_args()
+
+
+if args.text_only and args.vision_only:
+    print("--text-only and --image-only arguments cannot be specified at the same time.")
+    exit(1)
+
+if args.use_f32:
+    print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
+
+# output in the same directory as the model if output_dir is None
+dir_model = args.model_dir
+
+if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
+    vocab = None
+    tokens = None
+else:
+    with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
+        vocab = json.load(f)
+        tokens = [key for key in vocab]
+
+# possible data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+ftype = 1
+if args.use_f32:
+    ftype = 0
+
+# if args.clip_model_is_vision or args.clip_model_is_openclip:
+#     model = CLIPVisionModel.from_pretrained(dir_model)
+#     processor = None
+# else:
+#     model = CLIPModel.from_pretrained(dir_model)
+#     processor = CLIPProcessor.from_pretrained(dir_model)
+
+default_vision_config = {
+        "hidden_size": 1152,
+        "image_size": 980,
+        "intermediate_size": 4304,
+        "model_type": "idefics2",
+        "num_attention_heads": 16,
+        "num_hidden_layers": 27,
+        "patch_size": 14,
+    }
+vision_config = Idefics2VisionConfig(**default_vision_config)
+model = Idefics2VisionTransformer(vision_config)
+
+processor = None
+# if model.attn_pool is not None:
+#     model.attn_pool = torch.nn.Identity()
+
+# model.blocks = model.blocks[:-1]
+model.load_state_dict(torch.load(os.path.join(dir_model, "minicpmv.clip")))
+
+fname_middle = None
+has_text_encoder = True
+has_vision_encoder = True
+has_minicpmv_projector = False
+if args.text_only:
+    fname_middle = "text-"
+    has_vision_encoder = False
+elif args.minicpmv_projector is not None:
+    fname_middle = "mmproj-"
+    has_text_encoder = False
+    has_minicpmv_projector = True
+elif args.vision_only:
+    fname_middle = "vision-"
+    has_text_encoder = False
+else:
+    fname_middle = ""
+
+output_dir = args.output_dir if args.output_dir is not None else dir_model
+os.makedirs(output_dir, exist_ok=True)
+output_prefix = os.path.basename(output_dir).replace("ggml_", "")
+fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
+fout = GGUFWriter(path=fname_out, arch="clip")
+
+fout.add_bool("clip.has_text_encoder", has_text_encoder)
+fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
+fout.add_bool("clip.has_minicpmv_projector", has_minicpmv_projector)
+fout.add_file_type(ftype)
+if args.text_only:
+    fout.add_description("text-only CLIP model")
+elif args.vision_only and not has_minicpmv_projector:
+    fout.add_description("vision-only CLIP model")
+elif has_minicpmv_projector:
+    fout.add_description("image encoder for MiniCPM-V")
+    # add projector type
+    fout.add_string("clip.projector_type", "resampler")
+else:
+    fout.add_description("two-tower CLIP model")
+
+if has_vision_encoder:
+    # vision_model hparams
+    fout.add_uint32("clip.vision.image_size", 448)
+    fout.add_uint32("clip.vision.patch_size", 14)
+    fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), 1152)
+    fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
+    fout.add_uint32("clip.vision.projection_dim", 0)
+    fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
+    fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
+    block_count = 26
+    fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
+
+    if processor is not None:
+        image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
+        image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
+    else:
+        image_mean = args.image_mean if args.image_mean is not None else default_image_mean
+        image_std = args.image_std if args.image_std is not None else default_image_std
+    fout.add_array("clip.vision.image_mean", image_mean)
+    fout.add_array("clip.vision.image_std", image_std)
+
+use_gelu = True
+fout.add_bool("clip.use_gelu", use_gelu)
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000 ** omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, int):
+        grid_h_size, grid_w_size = grid_size, grid_size
+    else:
+        grid_h_size, grid_w_size = grid_size[0], grid_size[1]
+
+    grid_h = np.arange(grid_h_size, dtype=np.float32)
+    grid_w = np.arange(grid_w_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+def _replace_name_resampler(s, v):
+    if re.match("resampler.pos_embed", s):
+        return {
+            s: v,
+            re.sub("pos_embed", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(4096, (70, 70))),
+        }
+    if re.match("resampler.proj", s):
+        return {
+            re.sub("proj", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(4096, (70, 70))),
+            re.sub("proj", "proj.weight", s): v.transpose(-1, -2).contiguous(),
+        }
+    if re.match("resampler.attn.in_proj_.*", s):
+        return {
+            re.sub("attn.in_proj_", "attn.q.", s): v.chunk(3, dim=0)[0],
+            re.sub("attn.in_proj_", "attn.k.", s): v.chunk(3, dim=0)[1],
+            re.sub("attn.in_proj_", "attn.v.", s): v.chunk(3, dim=0)[2],
+        }
+    return {s: v}
+
+if has_minicpmv_projector:
+    projector = torch.load(args.minicpmv_projector)
+    new_state_dict = {}
+    for k, v in projector.items():
+        kvs = _replace_name_resampler(k, v)
+        for nk, nv in kvs.items():
+            new_state_dict[nk] = nv
+    projector = new_state_dict
+    ftype_cur = 0
+    for name, data in projector.items():
+        name = get_tensor_name(name)
+        data = data.squeeze().numpy()
+
+        n_dims = len(data.shape)
+        if ftype == 1:
+            if name[-7:] == ".weight" and n_dims == 2:
+                print("  Converting to float16")
+                data = data.astype(np.float16)
+                ftype_cur = 1
+            else:
+                print("  Converting to float32")
+                data = data.astype(np.float32)
+                ftype_cur = 0
+        else:
+            if data.dtype != np.float32:
+                print("  Converting to float32")
+                data = data.astype(np.float32)
+                ftype_cur = 0
+
+        fout.add_tensor(name, data)
+        print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
+
+    print("Projector tensors added\n")
+
+def _replace_name(s, v):
+    s = "vision_model." + s
+    if re.match("vision_model.embeddings.position_embedding", s):
+        v = v.unsqueeze(0)
+        return {s: v}
+
+    return {s: v}
+
+state_dict = model.state_dict()
+new_state_dict = {}
+for k, v in state_dict.items():
+    kvs = _replace_name(k, v)
+    for nk, nv in kvs.items():
+        new_state_dict[nk] = nv
+state_dict = new_state_dict
+for name, data in state_dict.items():
+    if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_minicpmv_projector):
+        # we don't need this
+        print(f"skipping parameter: {name}")
+        continue
+
+    name = get_tensor_name(name)
+    data = data.squeeze().numpy()
+
+    n_dims = len(data.shape)
+
+    # ftype == 0 -> float32, ftype == 1 -> float16
+    ftype_cur = 0
+    if n_dims == 4:
+        print(f"tensor {name} is always saved in f16")
+        data = data.astype(np.float16)
+        ftype_cur = 1
+    elif ftype == 1:
+        if name[-7:] == ".weight" and n_dims == 2:
+            print("  Converting to float16")
+            data = data.astype(np.float16)
+            ftype_cur = 1
+        else:
+            print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    else:
+        if data.dtype != np.float32:
+            print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+
+    print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
+    fout.add_tensor(name, data)
+
+
+fout.write_header_to_file()
+fout.write_kv_data_to_file()
+fout.write_tensors_to_file()
+fout.close()
+
+print("Done. Output file: " + fname_out)
diff --git a/examples/llava/minicpmv-surgery.py b/examples/llava/minicpmv-surgery.py
new file mode 100644
index 0000000000000..2b6bce7cfebe9
--- /dev/null
+++ b/examples/llava/minicpmv-surgery.py
@@ -0,0 +1,47 @@
+import argparse
+import os
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model", help="Path to MiniCPM-V-2.5 model")
+args = ap.parse_args()
+
+# find the model part that includes the the multimodal projector weights
+model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
+checkpoint = model.state_dict()
+
+# get a list of mm tensor names
+mm_tensors = [k for k, v in checkpoint.items() if k.startswith("resampler")]
+
+# store these tensors in a new dictionary and torch.save them
+projector = {name: checkpoint[name].float() for name in mm_tensors}
+torch.save(projector, f"{args.model}/minicpmv.projector")
+
+clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vpm")]
+if len(clip_tensors) > 0:
+    clip = {name.replace("vpm.", ""): checkpoint[name].float() for name in clip_tensors}
+    torch.save(clip, f"{args.model}/minicpmv.clip")
+
+    # added tokens should be removed to be able to convert Mistral models
+    if os.path.exists(f"{args.model}/added_tokens.json"):
+        with open(f"{args.model}/added_tokens.json", "w") as f:
+            f.write("{}\n")
+
+config = model.llm.config
+config._name_or_path = "openbmb/MiniCPM-Llama3-V-2.5"
+config.auto_map = {
+    "AutoConfig": "configuration_minicpm.MiniCPMConfig",
+    "AutoModel": "modeling_minicpm.MiniCPMModel",
+    "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
+    "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
+    "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
+}
+model.llm.save_pretrained(f"{args.model}/model")
+tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
+tok.save_pretrained(f"{args.model}/model")
+# os.system(f"cp {args.model}/modeling_minicpm.py {args.model}/MiniCPM_l3/modeling_minicpm.py")
+
+print("Done!")
+print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
+print(f"Also, use {args.model}/minicpmv.projector to prepare a minicpmv-encoder.gguf file.")
diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt
index f80f727a79307..cbcbf26c9b4e9 100644
--- a/examples/llava/requirements.txt
+++ b/examples/llava/requirements.txt
@@ -1,3 +1,5 @@
--r ../../requirements/requirements-convert.txt
+-r ../../requirements/requirements-convert_legacy_llama.txt
+--extra-index-url https://download.pytorch.org/whl/cpu
 pillow~=10.2.0
-torch~=2.1.1
+torch~=2.2.1
+torchvision~=0.17.1
diff --git a/examples/lookahead/CMakeLists.txt b/examples/lookahead/CMakeLists.txt
index 8827e3f11ecd6..f0ae5cd89244c 100644
--- a/examples/lookahead/CMakeLists.txt
+++ b/examples/lookahead/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET lookahead)
+set(TARGET llama-lookahead)
 add_executable(${TARGET} lookahead.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp
index 9c3540b2008c2..81cf1629c5b6a 100644
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -37,7 +37,8 @@ struct ngram_container {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    if (gpt_params_parse(argc, argv, params) == false) {
+    if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -57,11 +58,11 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model * model = NULL;
-    llama_context * ctx = NULL;
-
     // load the target model
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
 
     // Tokenize the prompt
     std::vector<llama_token> inp;
@@ -174,7 +175,7 @@ int main(int argc, char ** argv) {
         // debug
         if (dump_kv_cache) {
             llama_kv_cache_view_update(ctx, &kvc_view);
-            dump_kv_cache_view_seqs(kvc_view, 40);
+            llama_kv_cache_dump_view_seqs(kvc_view, 40);
         }
 
         // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt
index b91633f63e4ee..ef19fe25e31a3 100644
--- a/examples/lookup/CMakeLists.txt
+++ b/examples/lookup/CMakeLists.txt
@@ -1,22 +1,22 @@
-set(TARGET lookup)
+set(TARGET llama-lookup)
 add_executable(${TARGET} lookup.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 
-set(TARGET lookup-create)
+set(TARGET llama-lookup-create)
 add_executable(${TARGET} lookup-create.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 
-set(TARGET lookup-merge)
+set(TARGET llama-lookup-merge)
 add_executable(${TARGET} lookup-merge.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 
-set(TARGET lookup-stats)
+set(TARGET llama-lookup-stats)
 add_executable(${TARGET} lookup-stats.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/lookup/README.md b/examples/lookup/README.md
index 5bfb0de936041..71c345c037a2f 100644
--- a/examples/lookup/README.md
+++ b/examples/lookup/README.md
@@ -10,4 +10,3 @@ More info:
 
 https://github.com/ggerganov/llama.cpp/pull/4484
 https://github.com/ggerganov/llama.cpp/issues/4226
-
diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp
index 1c230c9667c71..5f04709f50231 100644
--- a/examples/lookup/lookup-create.cpp
+++ b/examples/lookup/lookup-create.cpp
@@ -14,17 +14,19 @@ int main(int argc, char ** argv){
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
+
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model * model = NULL;
-    llama_context * ctx = NULL;
-
     // load the model
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
     GGML_ASSERT(model != nullptr);
 
     // tokenize the prompt
diff --git a/examples/lookup/lookup-merge.cpp b/examples/lookup/lookup-merge.cpp
index 07c93eb8d057b..81e2b04369b90 100644
--- a/examples/lookup/lookup-merge.cpp
+++ b/examples/lookup/lookup-merge.cpp
@@ -11,14 +11,14 @@
 #include <unordered_map>
 #include <vector>
 
-static void print_usage() {
+static void print_usage(char* argv0) {
     fprintf(stderr, "Merges multiple lookup cache files into a single one.\n");
-    fprintf(stderr, "Usage: lookup-merge [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n");
+    fprintf(stderr, "Usage: %s [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n", argv0);
 }
 
 int main(int argc, char ** argv){
     if (argc < 3) {
-        print_usage();
+        print_usage(argv[0]);
         exit(1);
     }
 
@@ -27,7 +27,7 @@ int main(int argc, char ** argv){
     for (int i = 0; i < argc-1; ++i) {
         args[i] = argv[i+1];
         if (args[i] == "-h" || args[i] == "--help") {
-            print_usage();
+            print_usage(argv[0]);
             exit(0);
         }
     }
diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp
index 87ecc0a4f1394..400f3e0b08957 100644
--- a/examples/lookup/lookup-stats.cpp
+++ b/examples/lookup/lookup-stats.cpp
@@ -16,6 +16,7 @@ int main(int argc, char ** argv){
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -25,12 +26,11 @@ int main(int argc, char ** argv){
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model * model = NULL;
-    llama_context * ctx = NULL;
-
     // load the model
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
 
     // tokenize the prompt
     std::vector<llama_token> inp;
@@ -64,7 +64,7 @@ int main(int argc, char ** argv){
     }
 
     const int n_input = inp.size();
-    const int n_ctx = params.n_ctx;
+    const int n_ctx = llama_n_ctx(ctx);
 
     int n_drafted = 0;
     int n_accept  = 0;
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index eebbd00a58e66..d53a9828c2ea2 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -15,6 +15,7 @@ int main(int argc, char ** argv){
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -33,12 +34,11 @@ int main(int argc, char ** argv){
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model * model = NULL;
-    llama_context * ctx = NULL;
-
     // load the model
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
 
     // tokenize the prompt
     std::vector<llama_token> inp;
@@ -121,7 +121,7 @@ int main(int argc, char ** argv){
         // debug
         if (dump_kv_cache) {
             llama_kv_cache_view_update(ctx, &kvc_view);
-            dump_kv_cache_view_seqs(kvc_view, 40);
+            llama_kv_cache_dump_view_seqs(kvc_view, 40);
         }
 
         // print current draft sequence
diff --git a/examples/main-cmake-pkg/.gitignore b/examples/main-cmake-pkg/.gitignore
index e32c11c7f4653..67c01d64cb7ab 100644
--- a/examples/main-cmake-pkg/.gitignore
+++ b/examples/main-cmake-pkg/.gitignore
@@ -48,4 +48,3 @@
 build*/
 out/
 tmp/
-
diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt
index deb77d588ea9f..3b38db292320f 100644
--- a/examples/main-cmake-pkg/CMakeLists.txt
+++ b/examples/main-cmake-pkg/CMakeLists.txt
@@ -1,12 +1,12 @@
 cmake_minimum_required(VERSION 3.12)
-project("main-cmake-pkg" C CXX)
-set(TARGET main-cmake-pkg)
+project("llama-cli-cmake-pkg" C CXX)
+set(TARGET llama-cli-cmake-pkg)
 
 find_package(Llama 0.0.1 REQUIRED)
 
 # Bake common functionality in with target. Because applications
 # using the relocatable Llama package should be outside of the
-# source tree, main-cmake-pkg pretends the dependencies are built-in.
+# source tree, llama-cli-cmake-pkg pretends the dependencies are built-in.
 set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common")
 add_library(common OBJECT)
 file(GLOB _common_files
@@ -15,7 +15,7 @@ file(GLOB _common_files
 )
 target_sources(common PRIVATE ${_common_files})
 
-# If the common project was part of "main-cmake-pkg" the transient
+# If the common project was part of "llama-cli-cmake-pkg" the transient
 # defines would automatically be attached. Because the common func-
 # tionality is separate, but dependent upon the defines, it must be
 # explicitly extracted from the "llama" target.
@@ -30,4 +30,3 @@ target_include_directories(${TARGET} PRIVATE ${_common_path})
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-
diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md
index edf20d8db6616..08d83dd08636a 100644
--- a/examples/main-cmake-pkg/README.md
+++ b/examples/main-cmake-pkg/README.md
@@ -1,6 +1,6 @@
 # llama.cpp/example/main-cmake-pkg
 
-This program builds the [main](../main) application using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree.
+This program builds [llama-cli](../main) using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree.
 
 ## Building
 
@@ -8,26 +8,24 @@ Because this example is "outside of the source tree", it is important to first b
 
 ### Considerations
 
-When hardware acceleration libraries are used (e.g. CUDA, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
+When hardware acceleration libraries are used (e.g. CUDA, Metal, etc.), CMake must be able to locate the associated CMake package.
 
 ### Build llama.cpp and install to C:\LlamaCPP directory
 
-In this case, CLBlast was already installed so the CMake package is referenced in `CMAKE_PREFIX_PATH`.
-
 ```cmd
 git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
-cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
+cmake -B build -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 17 2022" -A x64
 cmake --build build --config Release
 cmake --install build --prefix C:/LlamaCPP
 ```
 
-### Build main-cmake-pkg
+### Build llama-cli-cmake-pkg
 
 
 ```cmd
 cd ..\examples\main-cmake-pkg
-cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
 cmake --build build --config Release
 cmake --install build --prefix C:/MyLlamaApp
 ```
diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt
index d532980b76da8..5f6efaa9aa94b 100644
--- a/examples/main/CMakeLists.txt
+++ b/examples/main/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET main)
+set(TARGET llama-cli)
 add_executable(${TARGET} main.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/main/README.md b/examples/main/README.md
index 97e2ae4c2dc43..9396a34fa5a31 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -1,6 +1,6 @@
-# llama.cpp/example/main
+# llama.cpp/examples/main
 
-This example program allows you to use various LLaMA language models in an easy and efficient way. It is specifically designed to work with the [llama.cpp](https://github.com/ggerganov/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts.
+This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggerganov/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts.
 
 ## Table of Contents
 
@@ -17,74 +17,71 @@ This example program allows you to use various LLaMA language models in an easy
 
 To get started right away, run the following command, making sure to use the correct path for the model you have:
 
-#### Unix-based systems (Linux, macOS, etc.):
+First, we will need to download a model. In these examples, we will use the Gemma model from the ggml-org repo on Hugging Face.
+[https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)
 
-```bash
-./main -m models/7B/ggml-model.bin --prompt "Once upon a time"
-```
+Once downloaded, place your model in the models folder in llama.cpp.
 
-#### Windows:
+### Unix-based systems (Linux, macOS, etc.):
 
-```powershell
-main.exe -m models\7B\ggml-model.bin --prompt "Once upon a time"
-```
+##### Input prompt (One-and-done)
 
-For an interactive experience, try this command:
+```bash
+./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --prompt "Once upon a time"
+```
+##### Conversation mode (Allow for continuous interaction with the model)
 
-#### Unix-based systems (Linux, macOS, etc.):
+```bash
+./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf -cnv --chat-template gemma
+```
 
+##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
 ```bash
-./main -m models/7B/ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -p \
-'User: Hi
-AI: Hello. I am an AI chatbot. Would you like to talk?
-User: Sure!
-AI: What would you like to talk about?
-User:'
+./llama-cli -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 ```
 
-#### Windows:
+### Windows:
 
+##### Input prompt (One-and-done)
 ```powershell
-main.exe -m models\7B\ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -e -p "User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:"
+./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --prompt "Once upon a time"
 ```
+##### Conversation mode (Allow for continuous interaction with the model)
 
-The following command generates "infinite" text from a starting prompt (you can use `Ctrl-C` to stop it):
-
-#### Unix-based systems (Linux, macOS, etc.):
-
-```bash
-./main -m models/7B/ggml-model.bin --ignore-eos -n -1 --random-prompt
+```powershell
+./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -cnv --chat-template gemma
 ```
 
-#### Windows:
+#### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
 
 ```powershell
-main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt
+llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 ```
 
 ## Common Options
 
-In this section, we cover the most commonly used options for running the `main` program with the LLaMA models:
+In this section, we cover the most commonly used options for running the `llama-cli` program with the LLaMA models:
 
--   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`; inferred from `--model-url` if set).
--   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
+-   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/gemma-1.1-7b-it.Q4_K_M.gguf`; inferred from `--model-url` if set).
+-   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)).
 -   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
--   `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
 -   `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
 -   `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `-mli, --multiline-input`: Allows you to write or paste multiple lines without ending each in '\'
+-   `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has.
+-   -   `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
 
 ## Input Prompts
 
-The `main` program provides several ways to interact with the LLaMA models using input prompts:
+The `llama-cli` program provides several ways to interact with the LLaMA models using input prompts:
 
 -   `--prompt PROMPT`: Provide a prompt directly as a command-line option.
 -   `--file FNAME`: Provide a file containing a prompt or multiple prompts.
 -   `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.)
--   `--random-prompt`: Start with a randomized prompt.
 
 ## Interaction
 
-The `main` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive`, `--interactive-first`, and `--instruct`.
+The `llama-cli` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`.
 
 In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing.
 
@@ -92,7 +89,7 @@ In interactive mode, users can participate in text generation by injecting their
 
 -   `-i, --interactive`: Run the program in interactive mode, allowing users to engage in real-time conversations or provide specific instructions to the model.
 -   `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation.
--   `-ins, --instruct`: Run the program in instruction mode, which is specifically designed to work with Alpaca models that excel in completing tasks based on user instructions.
+-   `-cnv,  --conversation`:  Run the program in conversation mode (does not print special tokens and suffix/prefix, use default chat template) (default: false)
 -   `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text.
 
 By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs.
@@ -110,7 +107,7 @@ To overcome this limitation, you can use the `--in-prefix` flag to add a space o
 The `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag:
 
 ```sh
-./main -r "User:" --in-prefix " "
+./llama-cli -r "User:" --in-prefix " "
 ```
 
 ### In-Suffix
@@ -118,18 +115,15 @@ The `--in-prefix` flag is used to add a prefix to your input, primarily, this is
 The `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (`\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag:
 
 ```sh
-./main -r "User:" --in-prefix " " --in-suffix "Assistant:"
+./llama-cli -r "User:" --in-prefix " " --in-suffix "Assistant:"
 ```
+When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled
 
-### Instruction Mode
+### Chat templates
 
-Instruction mode is particularly useful when working with Alpaca models, which are designed to follow user instructions for specific tasks:
+ `--chat-template JINJA_TEMPLATE`: This option sets a custom jinja chat template. It accepts a string, not a file name.  Default: template taken from model's metadata. Llama.cpp only supports [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template). These include llama2, llama3, gemma, monarch, chatml, orion, vicuna, vicuna-orca, deepseek, command-r, zephyr. When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled.
 
--   `-ins, --instruct`: Enable instruction mode to leverage the capabilities of Alpaca models in completing tasks based on user-provided instructions.
-
-Technical detail: the user's input is internally prefixed with the reverse prompt (or `### Instruction:` as the default), and followed by `### Response:` (except if you just press Return without any input, to keep generating a longer response).
-
-By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs.
+ Example usage: `--chat-template gemma`
 
 ## Context Management
 
@@ -137,9 +131,7 @@ During text generation, LLaMA models have a limited context size, which means th
 
 ### Context Size
 
-The `--ctx-size` option allows you to set the size of the prompt context used by the LLaMA models during text generation. A larger context size helps the model to better comprehend and generate responses for longer input or conversations.
-
--   `-c N, --ctx-size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results.
+- `-c N, --ctx-size N`: Set the size of the prompt context (default: 0, 0 = loaded from model). The LLaMA models were built with a context of 2048-8192, which will yield the best results on longer input/inference.
 
 ### Extended Context Size
 
@@ -161,15 +153,15 @@ The following options allow you to control the text generation process and fine-
 
 ### Number of Tokens to Predict
 
--   `-n N, --n-predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity, -2 = until context filled)
+-   `-n N, --predict N`: Set the number of tokens to predict when generating text (default: -1, -1 = infinity, -2 = until context filled)
 
-The `--n-predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text.
+The `--predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text.
 
-A value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after `--n-keep`) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in significant pause in output.
+A value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after `--keep`) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in a significant pause in output.
 
 If the pause is undesirable, a value of -2 will stop generation immediately when the context is filled.
 
-It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `n-predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter.
+It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode, text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `--predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter.
 
 ### Temperature
 
@@ -177,15 +169,15 @@ It is important to note that the generated text may be shorter than the specifie
 
 Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.
 
-Example usage: `--temp 0.5`
+Example usage: `--temp 0`
 
 ### Repeat Penalty
 
--   `--repeat-penalty N`: Control the repetition of token sequences in the generated text (default: 1.1).
+-   `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
 -   `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
 -   `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty.
 
-The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.1.
+The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.
 
 The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).
 
@@ -209,19 +201,19 @@ Top-p sampling, also known as nucleus sampling, is another text generation metho
 
 Example usage: `--top-p 0.95`
 
-### Min P Sampling
+### Min-P Sampling
 
--   `--min-p N`: Sets a minimum base probability threshold for token selection (default: 0.05).
+-   `--min-p N`: Sets a minimum base probability threshold for token selection (default: 0.1).
 
 The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.
 
 Example usage: `--min-p 0.05`
 
-### Tail Free Sampling (TFS)
+### Tail-Free Sampling (TFS)
 
 -   `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
 
-Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens, and thus disables the effect of TFS.
+Tail-free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks at how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens and thus disables the effect of TFS.
 
 Example usage: `--tfs 0.95`
 
@@ -320,8 +312,8 @@ These options provide extra functionality and customization when running the LLa
 
 -   `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.
 -   `--verbose-prompt`: Print the prompt before generating text.
--   `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
 -   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
 -   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
 -   `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
 -   `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
+-   `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable  or in an OS-specific local cache.
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 9dee41001f12c..4a342ad031663 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -37,14 +37,15 @@ static gpt_params               * g_params;
 static std::vector<llama_token> * g_input_tokens;
 static std::ostringstream       * g_output_ss;
 static std::vector<llama_token> * g_output_tokens;
-static bool is_interacting = false;
+static bool is_interacting  = false;
+static bool need_insert_eot = false;
 
-static bool file_exists(const std::string &path) {
+static bool file_exists(const std::string & path) {
     std::ifstream f(path.c_str());
     return f.good();
 }
 
-static bool file_is_empty(const std::string &path) {
+static bool file_is_empty(const std::string & path) {
     std::ifstream f;
     f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
     f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
@@ -60,9 +61,9 @@ static void write_logfile(
         return;
     }
 
-    const std::string timestamp = get_sortable_timestamp();
+    const std::string timestamp = string_get_sortable_timestamp();
 
-    const bool success = create_directory_with_parents(params.logdir);
+    const bool success = fs_create_directory_with_parents(params.logdir);
     if (!success) {
         fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
                 __func__, params.logdir.c_str());
@@ -80,7 +81,7 @@ static void write_logfile(
     fprintf(logfile, "binary: main\n");
     char model_desc[128];
     llama_model_desc(model, model_desc, sizeof(model_desc));
-    dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);
+    yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc);
 
     fprintf(logfile, "\n");
     fprintf(logfile, "######################\n");
@@ -88,8 +89,8 @@ static void write_logfile(
     fprintf(logfile, "######################\n");
     fprintf(logfile, "\n");
 
-    dump_string_yaml_multiline(logfile, "output", output.c_str());
-    dump_vector_int_yaml(logfile, "output_tokens", output_tokens);
+    yaml_dump_string_multiline(logfile, "output", output.c_str());
+    yaml_dump_vector_int(logfile, "output_tokens", output_tokens);
 
     llama_dump_timing_info_yaml(logfile, ctx);
     fclose(logfile);
@@ -99,7 +100,8 @@ static void write_logfile(
 static void sigint_handler(int signo) {
     if (signo == SIGINT) {
         if (!is_interacting && g_params->interactive) {
-            is_interacting = true;
+            is_interacting  = true;
+            need_insert_eot = true;
         } else {
             console::cleanup();
             printf("\n");
@@ -117,13 +119,24 @@ static void llama_log_callback_logTee(ggml_log_level level, const char * text, v
     LOG_TEE("%s", text);
 }
 
+static std::string chat_add_and_format(struct llama_model * model, std::vector<llama_chat_msg> & chat_msgs, std::string role, std::string content) {
+    llama_chat_msg new_msg{role, content};
+    auto formatted = llama_chat_format_single(
+        model, g_params->chat_template, chat_msgs, new_msg, role == "user");
+    chat_msgs.push_back({role, content});
+    LOG("formatted: %s\n", formatted.c_str());
+    return formatted;
+}
+
 int main(int argc, char ** argv) {
     gpt_params params;
     g_params = &params;
 
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
+
     llama_sampling_params & sparams = params.sparams;
 
 #ifndef LOG_DISABLE_LOGS
@@ -180,9 +193,6 @@ int main(int argc, char ** argv) {
     LOG_TEE("%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
-    if (params.random_prompt) {
-        params.prompt = gpt_random_prompt(rng);
-    }
 
     LOG("%s: llama backend init\n", __func__);
     llama_backend_init();
@@ -191,12 +201,16 @@ int main(int argc, char ** argv) {
     llama_model * model;
     llama_context * ctx;
     llama_context * ctx_guidance = NULL;
+    std::vector<llama_chat_msg> chat_msgs;
     g_model = &model;
     g_ctx = &ctx;
 
     // load the model and apply lora adapter, if any
     LOG("%s: load the model and apply lora adapter, if any\n", __func__);
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    model = llama_init.model;
+    ctx = llama_init.context;
     if (sparams.cfg_scale > 1.f) {
         struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
         ctx_guidance = llama_new_context_with_model(model, lparams);
@@ -216,10 +230,19 @@ int main(int argc, char ** argv) {
                 __func__, n_ctx_train, n_ctx);
     }
 
+    // print chat template example in conversation mode
+    if (params.conversation) {
+        if (params.enable_chat_template) {
+            LOG_TEE("%s: chat template example: %s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str());
+        } else {
+            LOG_TEE("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
+        }
+    }
+
     // print system information
     {
         LOG_TEE("\n");
-        LOG_TEE("%s\n", get_system_info(params).c_str());
+        LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str());
     }
 
     std::string path_session = params.path_prompt_cache;
@@ -244,30 +267,39 @@ int main(int argc, char ** argv) {
         }
     }
 
-    const bool add_bos = llama_should_add_bos_token(model);
-    GGML_ASSERT(llama_add_eos_token(model) != 1);
+    const bool add_bos = llama_add_bos_token(model);
+    if (!llama_model_has_encoder(model)) {
+        GGML_ASSERT(!llama_add_eos_token(model));
+    }
     LOG("add_bos: %d\n", add_bos);
 
     std::vector<llama_token> embd_inp;
 
-    if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) {
-        LOG("tokenize the prompt\n");
-        if (params.chatml) {
-            params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
+    {
+        auto prompt = (params.conversation && params.enable_chat_template && !params.prompt.empty())
+            ? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode
+            : params.prompt;
+        if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
+            LOG("tokenize the prompt\n");
+            embd_inp = ::llama_tokenize(ctx, prompt, true, true);
+        } else {
+            LOG("use session tokens\n");
+            embd_inp = session_tokens;
         }
-        embd_inp = ::llama_tokenize(ctx, params.prompt, true, true);
-    } else {
-        LOG("use session tokens\n");
-        embd_inp = session_tokens;
-    }
 
-    LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
-    LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+        LOG("prompt: \"%s\"\n", log_tostr(prompt));
+        LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+    }
 
     // Should not run without any tokens
     if (embd_inp.empty()) {
-        embd_inp.push_back(llama_token_bos(model));
-        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+        if (add_bos) {
+            embd_inp.push_back(llama_token_bos(model));
+            LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+        } else {
+            LOG_TEE("error: input is empty\n");
+            return -1;
+        }
     }
 
     // Tokenize negative prompt
@@ -332,37 +364,13 @@ int main(int argc, char ** argv) {
     }
 
     // number of tokens to keep when resetting context
-    if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
+    if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) {
         params.n_keep = (int)embd_inp.size();
     } else {
         params.n_keep += add_bos; // always keep the BOS token
     }
 
-    // prefix & suffix for instruct mode
-    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true,  true);
-    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false, true);
-
-    LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
-    LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
-
-    // chatml prefix & suffix
-    const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", true, true);
-    const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
-
-    LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
-    LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());
-
-    // in instruct mode, we inject a prefix and a suffix to each input by the user
-    if (params.instruct) {
-        params.interactive_first = true;
-        params.antiprompt.emplace_back("### Instruction:\n\n");
-    }
-    // similar for chatml mode
-    else if (params.chatml) {
-        params.interactive_first = true;
-        params.antiprompt.emplace_back("<|im_start|>user\n");
-    }
-    else if (params.conversation) {
+    if (params.conversation) {
         params.interactive_first = true;
     }
 
@@ -474,12 +482,12 @@ int main(int argc, char ** argv) {
     LOG_TEE("\n\n");
 
     if (params.interactive) {
-        const char *control_message;
+        const char * control_message;
         if (params.multiline_input) {
-            control_message = " - To return control to LLaMa, end your input with '\\'.\n"
+            control_message = " - To return control to the AI, end your input with '\\'.\n"
                               " - To return control without starting a new line, end your input with '/'.\n";
         } else {
-            control_message = " - Press Return to return control to LLaMa.\n"
+            control_message = " - Press Return to return control to the AI.\n"
                               " - To return control without starting a new line, end your input with '/'.\n"
                               " - If you want to submit another line, end your input with '\\'.\n";
         }
@@ -506,6 +514,7 @@ int main(int argc, char ** argv) {
     std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
     std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
     std::ostringstream output_ss;     g_output_ss     = &output_ss;
+    std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode
 
     // the first thing we will do is to output the prompt, so set color accordingly
     console::set_display(console::prompt);
@@ -528,6 +537,24 @@ int main(int argc, char ** argv) {
         exit(1);
     }
 
+    if (llama_model_has_encoder(model)) {
+        int enc_input_size = embd_inp.size();
+        llama_token * enc_input_buf = embd_inp.data();
+
+        if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) {
+            LOG_TEE("%s : failed to eval\n", __func__);
+            return 1;
+        }
+
+        llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+        if (decoder_start_token_id == -1) {
+            decoder_start_token_id = llama_token_bos(model);
+        }
+
+        embd_inp.clear();
+        embd_inp.push_back(decoder_start_token_id);
+    }
+
     while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
         // predict
         if (!embd.empty()) {
@@ -707,7 +734,7 @@ int main(int argc, char ** argv) {
 
             const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);
 
-            llama_sampling_accept(ctx_sampling, ctx, id, true);
+            llama_sampling_accept(ctx_sampling, ctx, id, /* apply_grammar= */ true);
 
             LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str());
 
@@ -728,7 +755,7 @@ int main(int argc, char ** argv) {
 
                 // push the prompt in the sampling context in order to apply repetition penalties later
                 // for the prompt, we don't apply grammar rules
-                llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false);
+                llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], /* apply_grammar= */ false);
 
                 ++n_consumed;
                 if ((int) embd.size() >= params.n_batch) {
@@ -740,18 +767,26 @@ int main(int argc, char ** argv) {
         // display text
         if (input_echo && display) {
             for (auto id : embd) {
-                const std::string token_str = llama_token_to_piece(ctx, id, !params.conversation);
-                printf("%s", token_str.c_str());
+                const std::string token_str = llama_token_to_piece(ctx, id, params.special);
+
+                // Console/Stream Output
+                fprintf(stdout, "%s", token_str.c_str());
 
+                // Record Displayed Tokens To Log
+                // Note: Generated tokens are created one by one hence this check
                 if (embd.size() > 1) {
+                    // Incoming Requested Tokens
                     input_tokens.push_back(id);
                 } else {
+                    // Outgoing Generated Tokens
                     output_tokens.push_back(id);
                     output_ss << token_str;
                 }
+
+                fflush(stdout);
             }
-            fflush(stdout);
         }
+
         // reset color to default if there is no pending user input
         if (input_echo && (int) embd_inp.size() == n_consumed) {
             console::set_display(console::reset);
@@ -813,17 +848,24 @@ int main(int argc, char ** argv) {
                         is_antiprompt = true;
                     }
 
+                    if (params.enable_chat_template) {
+                        chat_add_and_format(model, chat_msgs, "assistant", assistant_ss.str());
+                    }
                     is_interacting = true;
                     printf("\n");
-                } else if (params.instruct || params.chatml) {
-                    is_interacting = true;
                 }
             }
 
+            // if current token is not EOG, we add it to current assistant message
+            if (params.conversation) {
+                auto id = llama_sampling_last(ctx_sampling);
+                assistant_ss << llama_token_to_piece(ctx, id, false);
+            }
+
             if (n_past > 0 && is_interacting) {
                 LOG("waiting for user input\n");
 
-                if (params.conversation || params.instruct || params.chatml) {
+                if (params.conversation) {
                     printf("\n> ");
                 }
 
@@ -866,49 +908,41 @@ int main(int argc, char ** argv) {
 
                     const size_t original_size = embd_inp.size();
 
-                    // instruct mode: insert instruction prefix
-                    if (params.instruct && !is_antiprompt) {
-                        LOG("inserting instruction prefix\n");
-                        n_consumed = embd_inp.size();
-                        embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
-                    }
-                    // chatml mode: insert user chat prefix
-                    if (params.chatml && !is_antiprompt) {
-                        LOG("inserting chatml prefix\n");
-                        n_consumed = embd_inp.size();
-                        embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end());
-                    }
                     if (params.escape) {
-                        process_escapes(buffer);
+                        string_process_escapes(buffer);
                     }
 
+                    bool format_chat = params.conversation && params.enable_chat_template;
+                    std::string user_inp = format_chat
+                        ? chat_add_and_format(model, chat_msgs, "user", std::move(buffer))
+                        : std::move(buffer);
+                    // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
                     const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
-                    const auto line_inp = ::llama_tokenize(ctx, buffer,              false, params.interactive_specials);
+                    const auto line_inp = ::llama_tokenize(ctx, user_inp,            false, format_chat);
                     const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
 
                     LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
 
+                    // if user stop generation mid-way, we must add EOT to finish model's last response
+                    if (need_insert_eot && format_chat) {
+                        llama_token eot = llama_token_eot(model);
+                        embd_inp.push_back(eot == -1 ? llama_token_eos(model) : eot);
+                        need_insert_eot = false;
+                    }
+
                     embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
                     embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
                     embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());
 
-                    // instruct mode: insert response suffix
-                    if (params.instruct) {
-                        LOG("inserting instruction suffix\n");
-                        embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-                    }
-                    // chatml mode: insert assistant chat suffix
-                    if (params.chatml) {
-                        LOG("inserting chatml suffix\n");
-                        embd_inp.insert(embd_inp.end(), cml_sfx.begin(), cml_sfx.end());
-                    }
-
                     for (size_t i = original_size; i < embd_inp.size(); ++i) {
                         const llama_token token = embd_inp[i];
                         output_tokens.push_back(token);
                         output_ss << llama_token_to_piece(ctx, token);
                     }
 
+                    // reset assistant message
+                    assistant_ss.str("");
+
                     n_remain -= line_inp.size();
                     LOG("n_remain: %d\n", n_remain);
                 } else {
@@ -927,7 +961,7 @@ int main(int argc, char ** argv) {
         }
 
         // end of generation
-        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) {
+        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.interactive)) {
             LOG_TEE(" [end of text]\n");
             break;
         }
diff --git a/examples/make-ggml.py b/examples/make-ggml.py
deleted file mode 100755
index c73485ebf1eff..0000000000000
--- a/examples/make-ggml.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env python3
-"""
-This script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them.
-
-Usage:
-python make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)]
-
-Arguments:
-- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub.
-- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.
-- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used.
-- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used.
-- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'.
-- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created.
-
-Old quant types (some base model types require these):
-- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M
-- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L
-- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M
-- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M
-
-New quant types (recommended):
-- Q2_K: smallest, extreme quality loss - not recommended
-- Q3_K: alias for Q3_K_M
-- Q3_K_S: very small, very high quality loss
-- Q3_K_M: very small, very high quality loss
-- Q3_K_L: small, substantial quality loss
-- Q4_K: alias for Q4_K_M
-- Q4_K_S: small, significant quality loss
-- Q4_K_M: medium, balanced quality - recommended
-- Q5_K: alias for Q5_K_M
-- Q5_K_S: large, low quality loss - recommended
-- Q5_K_M: large, very low quality loss - recommended
-- Q6_K: very large, extremely low quality loss
-- Q8_0: very large, extremely low quality loss - not recommended
-- F16: extremely large, virtually no quality loss - not recommended
-- F32: absolutely huge, lossless - not recommended
-"""
-import subprocess
-subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True)
-
-import argparse
-import os
-from huggingface_hub import snapshot_download
-
-def main(model, model_type, outname, outdir, quants, keep_fp16):
-    if not os.path.isdir(model):
-        print(f"Model not found at {model}. Downloading...")
-        try:
-            if outname is None:
-                outname = model.split('/')[-1]
-            model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache')
-        except Exception as e:
-            raise Exception(f"Could not download the model: {e}")
-
-    if outdir is None:
-        outdir = f'../models/{outname}'
-
-    if not os.path.isfile(f"{model}/config.json"):
-        raise Exception(f"Could not find config.json in {model}")
-
-    os.makedirs(outdir, exist_ok=True)
-
-    print("Building llama.cpp")
-    subprocess.run(f"cd .. && make quantize", shell=True, check=True)
-
-    fp16 = f"{outdir}/{outname}.gguf.fp16.bin"
-
-    print(f"Making unquantised GGUF at {fp16}")
-    if not os.path.isfile(fp16):
-        if model_type != "llama":
-            subprocess.run(f"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}", shell=True, check=True)
-        else:
-            subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True)
-    else:
-        print(f"Unquantised GGML already exists at: {fp16}")
-
-    print("Making quants")
-    for type in quants:
-        outfile = f"{outdir}/{outname}.gguf.{type}.bin"
-        print(f"Making {type} : {outfile}")
-        subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True)
-
-    if not keep_fp16:
-        os.remove(fp16)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.')
-    parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name')
-    parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.')
-    parser.add_argument('--outname', default=None, help='Output model(s) name')
-    parser.add_argument('--outdir', default=None, help='Output directory')
-    parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types')
-    parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False)
-
-    args = parser.parse_args()
-
-    main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16)
diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt
index 319535a6e9054..c13557bac2bac 100644
--- a/examples/parallel/CMakeLists.txt
+++ b/examples/parallel/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET parallel)
+set(TARGET llama-parallel)
 add_executable(${TARGET} parallel.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index 7c5595d6edb2d..621a1c9590622 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -100,7 +100,8 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    if (gpt_params_parse(argc, argv, params) == false) {
+    if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -128,11 +129,11 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model * model = NULL;
-    llama_context * ctx = NULL;
-
     // load the target model
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
 
     // load the prompts from an external file if there are any
     if (params.prompt.empty()) {
@@ -210,7 +211,7 @@ int main(int argc, char ** argv) {
     while (true) {
         if (dump_kv_cache) {
             llama_kv_cache_view_update(ctx, &kvc_view);
-            dump_kv_cache_view_seqs(kvc_view, 40);
+            llama_kv_cache_dump_view_seqs(kvc_view, 40);
         }
 
         llama_batch_clear(batch);
diff --git a/examples/passkey/CMakeLists.txt b/examples/passkey/CMakeLists.txt
index 3161bf3ef9a45..dc467a5d3e411 100644
--- a/examples/passkey/CMakeLists.txt
+++ b/examples/passkey/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET passkey)
+set(TARGET llama-passkey)
 add_executable(${TARGET} passkey.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/passkey/README.md b/examples/passkey/README.md
index 4a22bb55975be..2b8e910f9658d 100644
--- a/examples/passkey/README.md
+++ b/examples/passkey/README.md
@@ -1,5 +1,8 @@
 # llama.cpp/example/passkey
 
+A passkey retrieval task is an evaluation method used to measure a language
+models ability to recall information from long contexts.
+
 See the following PRs for more info:
 
 - https://github.com/ggerganov/llama.cpp/pull/3856
@@ -8,5 +11,5 @@ See the following PRs for more info:
 ### Usage
 
 ```bash
-make -j && ./passkey ./models/llama-7b-v2/ggml-model-f16.gguf 250
+make -j && ./llama-passkey -m ./models/llama-7b-v2/ggml-model-f16.gguf --junk 250
 ```
diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp
index f2ef9ca10d4a2..d03215cd1e0a9 100644
--- a/examples/passkey/passkey.cpp
+++ b/examples/passkey/passkey.cpp
@@ -6,46 +6,32 @@
 #include <string>
 #include <vector>
 
-int main(int argc, char ** argv) {
-    gpt_params params;
-
-    if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]);
-        return 1 ;
-    }
-
-    int seed = -1;
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
 
-    int n_junk = 250; // number of times to repeat the junk text
-    int n_keep = 32;  // number of tokens in the prompt prefix
-    int n_grp  = 1;   // if more than 1 - perform LongLM SelfExtend
-    int i_pos  = -1;  // position of the passkey in the junk text
-
-    if (argc >= 2) {
-        params.model = argv[1];
-    }
-
-    if (argc >= 3) {
-        n_junk = std::stoi(argv[2]);
-    }
+    LOG_TEE("\nexample usage:\n");
+    LOG_TEE("\n    %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]);
+    LOG_TEE("\n");
+}
 
-    if (argc >= 4) {
-        n_grp = std::stoi(argv[3]);
-    }
+int main(int argc, char ** argv) {
+    gpt_params params;
 
-    if (argc >= 5) {
-        i_pos = std::stoi(argv[4]);
-    }
+    params.n_junk = 250;
+    params.n_keep = 32;
+    params.i_pos  = -1;
 
-    if (argc >= 6) {
-        seed = std::stoi(argv[5]);
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
+        return 1;
     }
 
-    if (seed == -1) {
-        seed = time(NULL);
-    }
+    srand(params.seed == LLAMA_DEFAULT_SEED ? time(NULL) : params.seed);
 
-    srand(seed);
+    int n_junk = params.n_junk;
+    int n_keep = params.n_keep;
+    int n_grp  = params.grp_attn_n;
+    int i_pos  = params.i_pos;
 
     if (i_pos == -1) {
         i_pos = rand() % n_junk;
@@ -76,9 +62,7 @@ int main(int argc, char ** argv) {
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_default_params();
-
-    model_params.n_gpu_layers = 99; // offload all layers to the GPU
+    llama_model_params model_params = llama_model_params_from_gpt_params(params);
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -89,13 +73,9 @@ int main(int argc, char ** argv) {
 
     // initialize the context
 
-    llama_context_params ctx_params = llama_context_default_params();
+    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
 
-    ctx_params.seed    = seed;
-    ctx_params.n_ctx   = llama_n_ctx_train(model)*n_grp + n_keep;
-    ctx_params.n_batch = 512;
-    ctx_params.n_threads       = params.n_threads;
-    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+    ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep;
 
     GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
 
@@ -135,7 +115,7 @@ int main(int argc, char ** argv) {
     LOG_TEE("prompt tokens: %d\n", n_tokens_all);
     //LOG_TEE("prompt: %s\n", params.prompt.c_str());
 
-    llama_batch batch = llama_batch_init(512, 0, 1);
+    llama_batch batch = llama_batch_init(params.n_batch, 0, 1);
 
     int n_past = 0;
 
diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt
index 3c76d3221416b..be0f2fd029e67 100644
--- a/examples/perplexity/CMakeLists.txt
+++ b/examples/perplexity/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET perplexity)
+set(TARGET llama-perplexity)
 add_executable(${TARGET} perplexity.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md
index c2a3c5ce9fdb9..33a46d1a2e38b 100644
--- a/examples/perplexity/README.md
+++ b/examples/perplexity/README.md
@@ -42,10 +42,13 @@ In addition to the KL divergence the following statistics are calculated with `-
 
 Results were generated using the CUDA backend and are sorted by Kullback-Leibler divergence relative to FP16.
 The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat).
+Note: the FP16 logits used for the calculation of all metrics other than perplexity are stored in a binary file between runs.
+In order to save space this file does **not** contain the exact same FP32 logits but instead casts them to 16 bit unsigned integers (with some scaling).
+So the "f16" results are to be understood as the difference resulting only from this downcast.
 
 | Quantization | imatrix | Model size [GiB] | PPL                    | ΔPPL                   | KLD                   | Mean Δp           | RMS Δp           |
 |--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------|
-| f16          | None    |            14.97 | 6.233160 ±   0.037828  | -                      | -                     | -                 | -                |
+| f16          | None    |            14.97 | 6.233160 ±   0.037828  | 0.001524 ±   0.000755  | 0.000551 ±   0.000002 |  0.001 ± 0.002 %  | 0.787 ± 0.004 %  |
 | q8_0         | None    |             7.96 | 6.234284 ±   0.037878  | 0.002650 ±   0.001006  | 0.001355 ±   0.000006 | -0.019 ± 0.003 %  | 1.198 ± 0.007 %  |
 | q6_K         | None    |             6.14 | 6.253382 ±   0.038078  | 0.021748 ±   0.001852  | 0.005452 ±   0.000035 | -0.007 ± 0.006 %  | 2.295 ± 0.019 %  |
 | q5_K_M       | None    |             5.33 | 6.288607 ±   0.038338  | 0.056974 ±   0.002598  | 0.010762 ±   0.000079 | -0.114 ± 0.008 %  | 3.160 ± 0.031 %  |
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index bae014e6f4c16..484dd589109c7 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -44,9 +44,9 @@ static void write_logfile(
         return;
     }
 
-    const std::string timestamp = get_sortable_timestamp();
+    const std::string timestamp = string_get_sortable_timestamp();
 
-    const bool success = create_directory_with_parents(params.logdir);
+    const bool success = fs_create_directory_with_parents(params.logdir);
     if (!success) {
         fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
                 __func__, params.logdir.c_str());
@@ -64,7 +64,7 @@ static void write_logfile(
     fprintf(logfile, "binary: main\n");
     char model_desc[128];
     llama_model_desc(model, model_desc, sizeof(model_desc));
-    dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc);
+    yaml_dump_non_result_info(logfile, params, ctx, timestamp, results.tokens, model_desc);
 
     fprintf(logfile, "\n");
     fprintf(logfile, "######################\n");
@@ -72,9 +72,9 @@ static void write_logfile(
     fprintf(logfile, "######################\n");
     fprintf(logfile, "\n");
 
-    dump_vector_float_yaml(logfile, "logits", results.logits);
+    yaml_dump_vector_float(logfile, "logits", results.logits);
     fprintf(logfile, "ppl_value: %f\n", results.ppl_value);
-    dump_vector_float_yaml(logfile, "probs", results.probs);
+    yaml_dump_vector_float(logfile, "probs", results.probs);
 
     llama_dump_timing_info_yaml(logfile, ctx);
     fclose(logfile);
@@ -340,8 +340,8 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
 
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-    GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
+    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
+    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
 
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
 
@@ -476,12 +476,12 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
     }
 
     // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
-    // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
+    // Run `./llama-perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
 
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-    GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
+    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
+    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
 
     std::ofstream logits_stream;
     if (!params.logits_file.empty()) {
@@ -1032,7 +1032,7 @@ struct winogrande_entry {
     std::vector<llama_token> seq_tokens[2];
 };
 
-static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string& prompt) {
+static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string & prompt) {
     std::vector<winogrande_entry> result;
     std::istringstream in(prompt);
     std::string line;
@@ -1733,8 +1733,8 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
     const int n_batch = params.n_batch;
     const int num_batches = (n_ctx + n_batch - 1)/n_batch;
     const int nv = 2*((n_vocab + 1)/2) + 4;
-    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-    GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
+    const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
+    GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
 
     std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
     std::vector<float>    kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
@@ -1964,12 +1964,14 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
+    params.n_ctx = 512;
+    params.logits_all = true;
+
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
-    params.logits_all = true;
-
     const int32_t n_ctx = params.n_ctx;
 
     if (n_ctx <= 0) {
@@ -1989,6 +1991,12 @@ int main(int argc, char ** argv) {
         params.n_batch = std::min(params.n_batch, n_kv);
     } else {
         params.n_batch = std::min(params.n_batch, params.n_ctx);
+        if (params.kl_divergence) {
+            params.n_parallel = 1;
+        } else {
+            // ensure there's at least enough seq_ids for HellaSwag
+            params.n_parallel = std::max(4, params.n_parallel);
+        }
     }
 
     if (params.ppl_stride > 0) {
@@ -2006,27 +2014,22 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
-    if (params.random_prompt) {
-        params.prompt = gpt_random_prompt(rng);
-    }
 
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model * model;
-    llama_context * ctx;
-
-    // ensure there's at least enough seq_ids for HellaSwag
-    params.n_parallel = std::max(4, params.n_parallel);
-
     // load the model and apply lora adapter, if any
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
     if (model == NULL) {
         fprintf(stderr, "%s: error: unable to load model\n", __func__);
         return 1;
     }
 
     const int n_ctx_train = llama_n_ctx_train(model);
+
     if (params.n_ctx > n_ctx_train) {
         fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
                 __func__, n_ctx_train, params.n_ctx);
@@ -2035,7 +2038,7 @@ int main(int argc, char ** argv) {
     // print system information
     {
         fprintf(stderr, "\n");
-        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+        fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
     }
 
     struct results_perplexity results;
diff --git a/examples/pydantic-models-to-grammar-examples.py b/examples/pydantic-models-to-grammar-examples.py
deleted file mode 100644
index 160966649b05d..0000000000000
--- a/examples/pydantic-models-to-grammar-examples.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# Function calling example using pydantic models.
-import datetime
-import importlib
-import json
-from enum import Enum
-from typing import Optional, Union
-
-import requests
-from pydantic import BaseModel, Field
-from pydantic_models_to_grammar import (add_run_method_to_dynamic_model, convert_dictionary_to_pydantic_model,
-                                        create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation)
-
-
-# Function to get completion on the llama.cpp server with grammar.
-def create_completion(prompt, grammar):
-    headers = {"Content-Type": "application/json"}
-    data = {"prompt": prompt, "grammar": grammar}
-
-    response = requests.post("http://127.0.0.1:8080/completion", headers=headers, json=data)
-    data = response.json()
-
-    print(data["content"])
-    return data["content"]
-
-
-# A function for the agent to send a message to the user.
-class SendMessageToUser(BaseModel):
-    """
-    Send a message to the User.
-    """
-    chain_of_thought: str = Field(..., description="Your chain of thought while sending the message.")
-    message: str = Field(..., description="Message you want to send to the user.")
-
-    def run(self):
-        print(self.message)
-
-
-# Enum for the calculator tool.
-class MathOperation(Enum):
-    ADD = "add"
-    SUBTRACT = "subtract"
-    MULTIPLY = "multiply"
-    DIVIDE = "divide"
-
-
-# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt.
-class Calculator(BaseModel):
-    """
-    Perform a math operation on two numbers.
-    """
-    number_one: Union[int, float] = Field(..., description="First number.")
-    operation: MathOperation = Field(..., description="Math operation to perform.")
-    number_two: Union[int, float] = Field(..., description="Second number.")
-
-    def run(self):
-        if self.operation == MathOperation.ADD:
-            return self.number_one + self.number_two
-        elif self.operation == MathOperation.SUBTRACT:
-            return self.number_one - self.number_two
-        elif self.operation == MathOperation.MULTIPLY:
-            return self.number_one * self.number_two
-        elif self.operation == MathOperation.DIVIDE:
-            return self.number_one / self.number_two
-        else:
-            raise ValueError("Unknown operation.")
-
-
-# Here the grammar gets generated by passing the available function models to generate_gbnf_grammar_and_documentation function. This also generates a documentation usable by the LLM.
-# pydantic_model_list is the list of pydanitc models
-# outer_object_name is an optional name for an outer object around the actual model object. Like a "function" object with "function_parameters" which contains the actual model object. If None, no outer object will be generated
-# outer_object_content is the name of outer object content.
-# model_prefix is the optional prefix for models in the documentation. (Default="Output Model")
-# fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields")
-gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
-    pydantic_model_list=[SendMessageToUser, Calculator], outer_object_name="function",
-    outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters")
-
-print(gbnf_grammar)
-print(documentation)
-
-system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation
-
-user_message = "What is 42 * 42?"
-prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
-
-text = create_completion(prompt=prompt, grammar=gbnf_grammar)
-# This should output something like this:
-# {
-#     "function": "calculator",
-#     "function_parameters": {
-#         "number_one": 42,
-#         "operation": "multiply",
-#         "number_two": 42
-#     }
-# }
-function_dictionary = json.loads(text)
-if function_dictionary["function"] == "calculator":
-    function_parameters = {**function_dictionary["function_parameters"]}
-
-    print(Calculator(**function_parameters).run())
-    # This should output: 1764
-
-
-# A example structured output based on pydantic models. The LLM will create an entry for a Book database out of an unstructured text.
-class Category(Enum):
-    """
-    The category of the book.
-    """
-    Fiction = "Fiction"
-    NonFiction = "Non-Fiction"
-
-
-class Book(BaseModel):
-    """
-    Represents an entry about a book.
-    """
-    title: str = Field(..., description="Title of the book.")
-    author: str = Field(..., description="Author of the book.")
-    published_year: Optional[int] = Field(..., description="Publishing year of the book.")
-    keywords: list[str] = Field(..., description="A list of keywords.")
-    category: Category = Field(..., description="Category of the book.")
-    summary: str = Field(..., description="Summary of the book.")
-
-
-# We need no additional parameters other than our list of pydantic models.
-gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation([Book])
-
-system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation
-
-text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands."""
-prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
-
-text = create_completion(prompt=prompt, grammar=gbnf_grammar)
-
-json_data = json.loads(text)
-
-print(Book(**json_data))
-# An example for parallel function calling with a Python function, a pydantic function model and an OpenAI like function definition.
-
-def get_current_datetime(output_format: Optional[str] = None):
-    """
-    Get the current date and time in the given format.
-    Args:
-         output_format: formatting string for the date and time, defaults to '%Y-%m-%d %H:%M:%S'
-    """
-    if output_format is None:
-        output_format = '%Y-%m-%d %H:%M:%S'
-    return datetime.datetime.now().strftime(output_format)
-
-
-# Example function to get the weather
-def get_current_weather(location, unit):
-    """Get the current weather in a given location"""
-    if "London" in location:
-        return json.dumps({"location": "London", "temperature": "42", "unit": unit.value})
-    elif "New York" in location:
-        return json.dumps({"location": "New York", "temperature": "24", "unit": unit.value})
-    elif "North Pole" in location:
-        return json.dumps({"location": "North Pole", "temperature": "-42", "unit": unit.value})
-    else:
-        return json.dumps({"location": location, "temperature": "unknown"})
-
-
-# Here is a function definition in OpenAI style
-current_weather_tool = {
-    "type": "function",
-    "function": {
-        "name": "get_current_weather",
-        "description": "Get the current weather in a given location",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
-                },
-                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
-            },
-            "required": ["location"],
-        },
-    },
-}
-
-# Convert OpenAI function definition into pydantic model
-current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool)
-# Add the actual function to a pydantic model
-current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather)
-
-# Convert normal Python function to a pydantic model
-current_datetime_model = create_dynamic_model_from_function(get_current_datetime)
-
-tool_list = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model]
-
-
-gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
-    pydantic_model_list=tool_list, outer_object_name="function",
-    outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True)
-
-system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation
-
-
-text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42"""
-prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
-
-text = create_completion(prompt=prompt, grammar=gbnf_grammar)
-
-json_data = json.loads(text)
-
-print(json_data)
-# Should output something like this:
-# [{'function': 'get_current_datetime', 'params': {'output_format': '%Y-%m-%d %H:%M:%S'}}, {'function': 'get_current_weather', 'params': {'location': 'London', 'unit': 'celsius'}}, {'function': 'Calculator', 'params': {'number_one': 42, 'operation': 'multiply', 'number_two': 42}}]
-
-
-for call in json_data:
-    if call["function"] == "Calculator":
-        print(Calculator(**call["params"]).run())
-    elif call["function"] == "get_current_datetime":
-        print(current_datetime_model(**call["params"]).run())
-    elif call["function"] == "get_current_weather":
-        print(current_weather_tool_model(**call["params"]).run())
-# Should output something like this:
-# 2024-01-14 13:36:06
-# {"location": "London", "temperature": "42", "unit": "celsius"}
-# 1764
diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py
index 9acc7cc6dcd85..93e5dcb6c3855 100644
--- a/examples/pydantic_models_to_grammar.py
+++ b/examples/pydantic_models_to_grammar.py
@@ -9,7 +9,7 @@
 from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin, get_type_hints
 
 from docstring_parser import parse
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, create_model
 
 if TYPE_CHECKING:
     from types import GenericAlias
@@ -17,6 +17,9 @@
     # python 3.8 compat
     from typing import _GenericAlias as GenericAlias
 
+# TODO: fix this
+# pyright: reportAttributeAccessIssue=information
+
 
 class PydanticDataType(Enum):
     """
@@ -50,35 +53,38 @@ class PydanticDataType(Enum):
 
 
 def map_pydantic_type_to_gbnf(pydantic_type: type[Any]) -> str:
-    if isclass(pydantic_type) and issubclass(pydantic_type, str):
+    origin_type = get_origin(pydantic_type)
+    origin_type = pydantic_type if origin_type is None else origin_type
+
+    if isclass(origin_type) and issubclass(origin_type, str):
         return PydanticDataType.STRING.value
-    elif isclass(pydantic_type) and issubclass(pydantic_type, bool):
+    elif isclass(origin_type) and issubclass(origin_type, bool):
         return PydanticDataType.BOOLEAN.value
-    elif isclass(pydantic_type) and issubclass(pydantic_type, int):
+    elif isclass(origin_type) and issubclass(origin_type, int):
         return PydanticDataType.INTEGER.value
-    elif isclass(pydantic_type) and issubclass(pydantic_type, float):
+    elif isclass(origin_type) and issubclass(origin_type, float):
         return PydanticDataType.FLOAT.value
-    elif isclass(pydantic_type) and issubclass(pydantic_type, Enum):
+    elif isclass(origin_type) and issubclass(origin_type, Enum):
         return PydanticDataType.ENUM.value
 
-    elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel):
-        return format_model_and_field_name(pydantic_type.__name__)
-    elif get_origin(pydantic_type) is list:
+    elif isclass(origin_type) and issubclass(origin_type, BaseModel):
+        return format_model_and_field_name(origin_type.__name__)
+    elif origin_type is list:
         element_type = get_args(pydantic_type)[0]
         return f"{map_pydantic_type_to_gbnf(element_type)}-list"
-    elif get_origin(pydantic_type) is set:
+    elif origin_type is set:
         element_type = get_args(pydantic_type)[0]
         return f"{map_pydantic_type_to_gbnf(element_type)}-set"
-    elif get_origin(pydantic_type) is Union:
+    elif origin_type is Union:
         union_types = get_args(pydantic_type)
         union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types]
         return f"union-{'-or-'.join(union_rules)}"
-    elif get_origin(pydantic_type) is Optional:
+    elif origin_type is Optional:
         element_type = get_args(pydantic_type)[0]
         return f"optional-{map_pydantic_type_to_gbnf(element_type)}"
-    elif isclass(pydantic_type):
-        return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}"
-    elif get_origin(pydantic_type) is dict:
+    elif isclass(origin_type):
+        return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(origin_type.__name__)}"
+    elif origin_type is dict:
         key_type, value_type = get_args(pydantic_type)
         return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}"
     else:
@@ -115,7 +121,7 @@ def get_members_structure(cls, rule_name):
         # Modify this comprehension
         members = [
             f'  "\\"{name}\\"" ":"  {map_pydantic_type_to_gbnf(param_type)}'
-            for name, param_type in cls.__annotations__.items()
+            for name, param_type in get_type_hints(cls).items()
             if name != "self"
         ]
 
@@ -234,8 +240,9 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None
 
     # Define the integer part rule
     integer_part_rule = (
-        "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + (
-        f"-min{min_digit}" if min_digit is not None else "")
+        "integer-part"
+        + (f"-max{max_digit}" if max_digit is not None else "")
+        + (f"-min{min_digit}" if min_digit is not None else "")
     )
 
     # Define the fractional part rule based on precision constraints
@@ -293,17 +300,20 @@ def generate_gbnf_rule_for_type(
     field_name = format_model_and_field_name(field_name)
     gbnf_type = map_pydantic_type_to_gbnf(field_type)
 
-    if isclass(field_type) and issubclass(field_type, BaseModel):
+    origin_type = get_origin(field_type)
+    origin_type = field_type if origin_type is None else origin_type
+
+    if isclass(origin_type) and issubclass(origin_type, BaseModel):
         nested_model_name = format_model_and_field_name(field_type.__name__)
         nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules)
         rules.extend(nested_model_rules)
         gbnf_type, rules = nested_model_name, rules
-    elif isclass(field_type) and issubclass(field_type, Enum):
+    elif isclass(origin_type) and issubclass(origin_type, Enum):
         enum_values = [f'"\\"{e.value}\\""' for e in field_type]  # Adding escaped quotes
         enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}"
         rules.append(enum_rule)
         gbnf_type, rules = model_name + "-" + field_name, rules
-    elif get_origin(field_type) == list:  # Array
+    elif origin_type is list:  # Array
         element_type = get_args(field_type)[0]
         element_rule_name, additional_rules = generate_gbnf_rule_for_type(
             model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
@@ -313,7 +323,7 @@ def generate_gbnf_rule_for_type(
         rules.append(array_rule)
         gbnf_type, rules = model_name + "-" + field_name, rules
 
-    elif get_origin(field_type) == set or field_type == set:  # Array
+    elif origin_type is set:  # Array
         element_type = get_args(field_type)[0]
         element_rule_name, additional_rules = generate_gbnf_rule_for_type(
             model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
@@ -367,7 +377,7 @@ def generate_gbnf_rule_for_type(
             gbnf_type = f"{model_name}-{field_name}-optional"
         else:
             gbnf_type = f"{model_name}-{field_name}-union"
-    elif isclass(field_type) and issubclass(field_type, str):
+    elif isclass(origin_type) and issubclass(origin_type, str):
         if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None:
             triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False)
             markdown_string = field_info.json_schema_extra.get("markdown_code_block", False)
@@ -383,8 +393,8 @@ def generate_gbnf_rule_for_type(
             gbnf_type = PydanticDataType.STRING.value
 
     elif (
-        isclass(field_type)
-        and issubclass(field_type, float)
+        isclass(origin_type)
+        and issubclass(origin_type, float)
         and field_info
         and hasattr(field_info, "json_schema_extra")
         and field_info.json_schema_extra is not None
@@ -409,8 +419,8 @@ def generate_gbnf_rule_for_type(
         )
 
     elif (
-        isclass(field_type)
-        and issubclass(field_type, int)
+        isclass(origin_type)
+        and issubclass(origin_type, int)
         and field_info
         and hasattr(field_info, "json_schema_extra")
         and field_info.json_schema_extra is not None
@@ -458,7 +468,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas
     if not issubclass(model, BaseModel):
         # For non-Pydantic classes, generate model_fields from __annotations__ or __init__
         if hasattr(model, "__annotations__") and model.__annotations__:
-            model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()}
+            model_fields = {name: (typ, ...) for name, typ in get_type_hints(model).items()}
         else:
             init_signature = inspect.signature(model.__init__)
             parameters = init_signature.parameters
@@ -466,7 +476,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas
                             name != "self"}
     else:
         # For Pydantic models, use model_fields and check for ellipsis (required fields)
-        model_fields = model.__annotations__
+        model_fields = get_type_hints(model)
 
     model_rule_parts = []
     nested_rules = []
@@ -624,7 +634,7 @@ def get_primitive_grammar(grammar):
         "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
       )* "\"" ws
 ws ::= ([ \t\n] ws)?
-float ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+float ::= ("-"? ([0] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
 
 integer ::= [0-9]+"""
 
@@ -680,7 +690,7 @@ def generate_markdown_documentation(
         str: Generated text documentation.
     """
     documentation = ""
-    pyd_models = [(model, True) for model in pydantic_models]
+    pyd_models: list[tuple[type[BaseModel], bool]] = [(model, True) for model in pydantic_models]
     for model, add_prefix in pyd_models:
         if add_prefix:
             documentation += f"{model_prefix}: {model.__name__}\n"
@@ -700,9 +710,9 @@ def generate_markdown_documentation(
             # Indenting the fields section
             documentation += f"  {fields_prefix}:\n"
         else:
-            documentation += f"  Fields:\n"
+            documentation += f"  Fields:\n"  # noqa: F541
         if isclass(model) and issubclass(model, BaseModel):
-            for name, field_type in model.__annotations__.items():
+            for name, field_type in get_type_hints(model).items():
                 # if name == "markdown_code_block":
                 #    continue
                 if get_origin(field_type) == list:
@@ -750,14 +760,17 @@ def generate_field_markdown(
     field_info = model.model_fields.get(field_name)
     field_description = field_info.description if field_info and field_info.description else ""
 
-    if get_origin(field_type) == list:
+    origin_type = get_origin(field_type)
+    origin_type = field_type if origin_type is None else origin_type
+
+    if origin_type == list:
         element_type = get_args(field_type)[0]
         field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})"
         if field_description != "":
             field_text += ":\n"
         else:
             field_text += "\n"
-    elif get_origin(field_type) == Union:
+    elif origin_type == Union:
         element_types = get_args(field_type)
         types = []
         for element_type in element_types:
@@ -778,7 +791,7 @@ def generate_field_markdown(
         return field_text
 
     if field_description != "":
-        field_text += f"        Description: " + field_description + "\n"
+        field_text += f"        Description: {field_description}\n"
 
     # Check for and include field-specific examples if available
     if hasattr(model, "Config") and hasattr(model.Config,
@@ -788,9 +801,9 @@ def generate_field_markdown(
             example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
             field_text += f"{indent}  Example: {example_text}\n"
 
-    if isclass(field_type) and issubclass(field_type, BaseModel):
+    if isclass(origin_type) and issubclass(origin_type, BaseModel):
         field_text += f"{indent}  Details:\n"
-        for name, type_ in field_type.__annotations__.items():
+        for name, type_ in get_type_hints(field_type).items():
             field_text += generate_field_markdown(name, type_, field_type, depth + 2)
 
     return field_text
@@ -833,7 +846,7 @@ def generate_text_documentation(
         str: Generated text documentation.
     """
     documentation = ""
-    pyd_models = [(model, True) for model in pydantic_models]
+    pyd_models: list[tuple[type[BaseModel], bool]] = [(model, True) for model in pydantic_models]
     for model, add_prefix in pyd_models:
         if add_prefix:
             documentation += f"{model_prefix}: {model.__name__}\n"
@@ -851,7 +864,7 @@ def generate_text_documentation(
 
         if isclass(model) and issubclass(model, BaseModel):
             documentation_fields = ""
-            for name, field_type in model.__annotations__.items():
+            for name, field_type in get_type_hints(model).items():
                 # if name == "markdown_code_block":
                 #    continue
                 if get_origin(field_type) == list:
@@ -944,7 +957,7 @@ def generate_field_text(
 
     if isclass(field_type) and issubclass(field_type, BaseModel):
         field_text += f"{indent}  Details:\n"
-        for name, type_ in field_type.__annotations__.items():
+        for name, type_ in get_type_hints(field_type).items():
             field_text += generate_field_text(name, type_, field_type, depth + 2)
 
     return field_text
@@ -1164,7 +1177,7 @@ def create_dynamic_model_from_function(func: Callable[..., Any]):
         dynamic_fields[param.name] = (
             param.annotation if param.annotation != inspect.Parameter.empty else str, default_value)
     # Creating the dynamic model
-    dynamic_model = create_model(f"{func.__name__}", **dynamic_fields)  # type: ignore[call-overload]
+    dynamic_model = create_model(f"{func.__name__}", **dynamic_fields)
 
     for name, param_doc in param_docs:
         dynamic_model.model_fields[name].description = param_doc.description
@@ -1228,9 +1241,6 @@ def map_grammar_names_to_pydantic_model_class(pydantic_model_list):
     return output
 
 
-from enum import Enum
-
-
 def json_schema_to_python_types(schema):
     type_map = {
         "any": Any,
@@ -1275,7 +1285,7 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name:
                     if items != {}:
                         array = {"properties": items}
                         array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items")
-                        fields[field_name] = (List[array_type], ...)  # type: ignore[valid-type]
+                        fields[field_name] = (List[array_type], ...)
                     else:
                         fields[field_name] = (list, ...)
                 elif field_type == "object":
@@ -1285,7 +1295,8 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name:
                     required = field_data.get("enum", [])
                     for key, field in fields.items():
                         if key not in required:
-                            fields[key] = (Optional[fields[key][0]], ...)
+                            optional_type = fields[key][0]
+                            fields[key] = (Optional[optional_type], ...)
                 else:
                     field_type = json_schema_to_python_types(field_type)
                     fields[field_name] = (field_type, ...)
@@ -1305,6 +1316,7 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name:
         required = dictionary.get("required", [])
         for key, field in fields.items():
             if key not in required:
-                fields[key] = (Optional[fields[key][0]], ...)
+                optional_type = fields[key][0]
+                fields[key] = (Optional[optional_type], ...)
     custom_model = create_model(model_name, **fields)
     return custom_model
diff --git a/examples/pydantic_models_to_grammar_examples.py b/examples/pydantic_models_to_grammar_examples.py
new file mode 100755
index 0000000000000..eb000d5ccba24
--- /dev/null
+++ b/examples/pydantic_models_to_grammar_examples.py
@@ -0,0 +1,312 @@
+#!/usr/bin/env python3
+
+"""Function calling example using pydantic models."""
+
+from __future__ import annotations
+
+import argparse
+import datetime
+import json
+import logging
+import textwrap
+import sys
+from enum import Enum
+from typing import Optional, Union
+
+import requests
+from pydantic import BaseModel, Field
+from pydantic_models_to_grammar import (add_run_method_to_dynamic_model, convert_dictionary_to_pydantic_model,
+                                        create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation)
+
+
+def create_completion(host, prompt, gbnf_grammar):
+    """Calls the /completion API on llama-server.
+
+    See
+    https://github.com/ggerganov/llama.cpp/tree/HEAD/examples/server#api-endpoints
+    """
+    print(f"  Request:\n    Grammar:\n{textwrap.indent(gbnf_grammar, '      ')}\n    Prompt:\n{textwrap.indent(prompt.rstrip(), '      ')}")
+    headers = {"Content-Type": "application/json"}
+    data = {"prompt": prompt, "grammar": gbnf_grammar}
+    result = requests.post(f"http://{host}/completion", headers=headers, json=data).json()
+    assert data.get("error") is None, data
+    logging.info("Result: %s", result)
+    content = result["content"]
+    print(f"  Model: {result['model']}")
+    print(f"  Result:\n{textwrap.indent(json.dumps(json.loads(content), indent=2), '    ')}")
+    return content
+
+
+# A function for the agent to send a message to the user.
+class SendMessageToUser(BaseModel):
+    """Send a message to the User."""
+    chain_of_thought: str = Field(..., description="Your chain of thought while sending the message.")
+    message: str = Field(..., description="Message you want to send to the user.")
+
+    def run(self):
+        print(f"SendMessageToUser: {self.message}")
+
+
+def example_rce(host):
+    """Minimal test case where the LLM call an arbitrary python function."""
+    print("- example_rce")
+    tools = [SendMessageToUser]
+    gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+        pydantic_model_list=tools, outer_object_name="function",
+        outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters")
+    system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation
+    user_message = "What is 42 * 42?"
+    prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
+    text = create_completion(host, prompt, gbnf_grammar)
+    json_data = json.loads(text)
+    tools_map = {tool.__name__:tool for tool in tools}
+    # This finds "SendMessageToUser":
+    tool = tools_map.get(json_data["function"])
+    if not tool:
+        print(f"Error: unknown tool {json_data['function']}")
+        return 1
+    tool(**json_data["function_parameters"]).run()
+    return 0
+
+
+# Enum for the calculator tool.
+class MathOperation(Enum):
+    ADD = "add"
+    SUBTRACT = "subtract"
+    MULTIPLY = "multiply"
+    DIVIDE = "divide"
+
+
+# Simple pydantic calculator tool for the agent that can add, subtract,
+# multiply, and divide. Docstring and description of fields will be used in
+# system prompt.
+class Calculator(BaseModel):
+    """Perform a math operation on two numbers."""
+    number_one: Union[int, float] = Field(..., description="First number.")
+    operation: MathOperation = Field(..., description="Math operation to perform.")
+    number_two: Union[int, float] = Field(..., description="Second number.")
+
+    def run(self):
+        if self.operation == MathOperation.ADD:
+            return self.number_one + self.number_two
+        elif self.operation == MathOperation.SUBTRACT:
+            return self.number_one - self.number_two
+        elif self.operation == MathOperation.MULTIPLY:
+            return self.number_one * self.number_two
+        elif self.operation == MathOperation.DIVIDE:
+            return self.number_one / self.number_two
+        else:
+            raise ValueError("Unknown operation.")
+
+
+def example_calculator(host):
+    """Have the LLM ask to get a calculation done.
+
+    Here the grammar gets generated by passing the available function models to
+    generate_gbnf_grammar_and_documentation function. This also generates a
+    documentation usable by the LLM.
+
+    pydantic_model_list is the list of pydantic models outer_object_name is an
+    optional name for an outer object around the actual model object. Like a
+    "function" object with "function_parameters" which contains the actual model
+    object. If None, no outer object will be generated outer_object_content is
+    the name of outer object content.
+
+    model_prefix is the optional prefix for models in the documentation. (Default="Output Model")
+    fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields")
+    """
+    print("- example_calculator")
+    tools = [SendMessageToUser, Calculator]
+    gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+        pydantic_model_list=tools, outer_object_name="function",
+        outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters")
+    system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation
+    user_message1 = "What is 42 * 42?"
+    prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message1}<|im_end|>\n<|im_start|>assistant"
+    text = create_completion(host, prompt, gbnf_grammar)
+    json_data = json.loads(text)
+    expected = {
+        "function": "Calculator",
+        "function_parameters": {
+            "number_one": 42,
+            "operation": "multiply",
+            "number_two": 42
+        }
+    }
+    if json_data != expected:
+        print("  Result is not as expected!")
+    tools_map = {tool.__name__:tool for tool in tools}
+    # This finds "Calculator":
+    tool = tools_map.get(json_data["function"])
+    if not tool:
+        print(f"Error: unknown tool {json_data['function']}")
+        return 1
+    result = tool(**json_data["function_parameters"]).run()
+    print(f"  Call {json_data['function']} gave result {result}")
+    return 0
+
+
+class Category(Enum):
+    """The category of the book."""
+    Fiction = "Fiction"
+    NonFiction = "Non-Fiction"
+
+
+class Book(BaseModel):
+    """Represents an entry about a book."""
+    title: str = Field(..., description="Title of the book.")
+    author: str = Field(..., description="Author of the book.")
+    published_year: Optional[int] = Field(..., description="Publishing year of the book.")
+    keywords: list[str] = Field(..., description="A list of keywords.")
+    category: Category = Field(..., description="Category of the book.")
+    summary: str = Field(..., description="Summary of the book.")
+
+
+def example_struct(host):
+    """A example structured output based on pydantic models.
+
+    The LLM will create an entry for a Book database out of an unstructured
+    text. We need no additional parameters other than our list of pydantic
+    models.
+    """
+    print("- example_struct")
+    tools = [Book]
+    gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(pydantic_model_list=tools)
+    system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation
+    text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands."""
+    prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
+    text = create_completion(host, prompt, gbnf_grammar)
+    json_data = json.loads(text)
+    # In this case, there's no function nor function_parameters.
+    # Here the result will vary based on the LLM used.
+    keys = sorted(["title", "author", "published_year", "keywords", "category", "summary"])
+    if keys != sorted(json_data.keys()):
+        print(f"Unexpected result: {sorted(json_data.keys())}")
+        return 1
+    book = Book(**json_data)
+    print(f"  As a Book object: %s" % book)
+    return 0
+
+
+def get_current_datetime(output_format: Optional[str] = None):
+    """Get the current date and time in the given format.
+
+    Args:
+         output_format: formatting string for the date and time, defaults to '%Y-%m-%d %H:%M:%S'
+    """
+    return datetime.datetime.now().strftime(output_format or "%Y-%m-%d %H:%M:%S")
+
+
+# Example function to get the weather.
+def get_current_weather(location, unit):
+    """Get the current weather in a given location"""
+    if "London" in location:
+        return json.dumps({"location": "London", "temperature": "42", "unit": unit.value})
+    elif "New York" in location:
+        return json.dumps({"location": "New York", "temperature": "24", "unit": unit.value})
+    elif "North Pole" in location:
+        return json.dumps({"location": "North Pole", "temperature": "-42", "unit": unit.value})
+    return json.dumps({"location": location, "temperature": "unknown"})
+
+
+def example_concurrent(host):
+    """An example for parallel function calling with a Python function, a pydantic
+    function model and an OpenAI like function definition.
+    """
+    print("- example_concurrent")
+    # Function definition in OpenAI style.
+    current_weather_tool = {
+        "type": "function",
+        "function": {
+            "name": "get_current_weather",
+            "description": "Get the current weather in a given location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                },
+                "required": ["location"],
+            },
+        },
+    }
+    # Convert OpenAI function definition into pydantic model.
+    current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool)
+    # Add the actual function to a pydantic model.
+    current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather)
+
+    # Convert normal Python function to a pydantic model.
+    current_datetime_model = create_dynamic_model_from_function(get_current_datetime)
+
+    tools = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model]
+    gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+        pydantic_model_list=tools, outer_object_name="function",
+        outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True)
+    system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation
+    text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42"""
+    prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
+    text = create_completion(host, prompt, gbnf_grammar)
+    json_data = json.loads(text)
+    expected = [
+      {
+        "function": "get_current_datetime",
+        "params": {
+          "output_format": "%Y-%m-%d %H:%M:%S"
+        }
+      },
+      {
+        "function": "get_current_weather",
+        "params": {
+          "location": "London",
+          "unit": "celsius"
+        }
+      },
+      {
+        "function": "Calculator",
+        "params": {
+          "number_one": 42,
+          "operation": "multiply",
+          "number_two": 42
+        }
+      }
+    ]
+    res = 0
+    if json_data != expected:
+        print("  Result is not as expected!")
+        print("  This can happen on highly quantized models")
+        res = 1
+    tools_map = {tool.__name__:tool for tool in tools}
+    for call in json_data:
+      tool = tools_map.get(call["function"])
+      if not tool:
+          print(f"Error: unknown tool {call['function']}")
+          return 1
+      result = tool(**call["params"]).run()
+      print(f"  Call {call['function']} returned {result}")
+    # Should output something like this:
+    #   Call get_current_datetime returned 2024-07-15 09:50:38
+    #   Call get_current_weather returned {"location": "London", "temperature": "42", "unit": "celsius"}
+    #   Call Calculator returned 1764
+    return res
+
+
+def main():
+    parser = argparse.ArgumentParser(description=sys.modules[__name__].__doc__)
+    parser.add_argument("--host", default="localhost:8080", help="llama.cpp server")
+    parser.add_argument("-v", "--verbose", action="store_true", help="enables logging")
+    args = parser.parse_args()
+    logging.basicConfig(level=logging.INFO if args.verbose else logging.ERROR)
+    ret = 0
+    # Comment out below to only run the example you want.
+    ret = ret or example_rce(args.host)
+    ret = ret or example_calculator(args.host)
+    ret = ret or example_struct(args.host)
+    ret = ret or example_concurrent(args.host)
+    return ret
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt
index e31cf5e3809c1..bb986a716883d 100644
--- a/examples/quantize-stats/CMakeLists.txt
+++ b/examples/quantize-stats/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET quantize-stats)
+set(TARGET llama-quantize-stats)
 add_executable(${TARGET} quantize-stats.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index 746df8446b85e..68cf8d3595e87 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -154,7 +154,7 @@ static void test_roundtrip_on_chunk(
     }
 
     if (use_reference) {
-        qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
+        qfns.from_float_ref(input_scratch, quantized_scratch, chunk_size);
     } else {
         qfns.from_float(input_scratch, quantized_scratch, chunk_size);
     }
diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt
index 6b977fde86ab2..3ee4eb9719fc4 100644
--- a/examples/quantize/CMakeLists.txt
+++ b/examples/quantize/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET quantize)
+set(TARGET llama-quantize)
 add_executable(${TARGET} quantize.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/quantize/README.md b/examples/quantize/README.md
index b78ece4e7f59d..553c2701bced3 100644
--- a/examples/quantize/README.md
+++ b/examples/quantize/README.md
@@ -4,7 +4,89 @@ You can also use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-
 
 Note: It is synced from llama.cpp `main` every 6 hours.
 
-## Llama 2 7B
+Example usage:
+
+```bash
+# obtain the official LLaMA model weights and place them in ./models
+ls ./models
+llama-2-7b tokenizer_checklist.chk tokenizer.model
+# [Optional] for models using BPE tokenizers
+ls ./models
+<folder containing weights and tokenizer json> vocab.json
+# [Optional] for PyTorch .bin models like Mistral-7B
+ls ./models
+<folder containing weights and tokenizer json>
+
+# install Python dependencies
+python3 -m pip install -r requirements.txt
+
+# convert the model to ggml FP16 format
+python3 convert_hf_to_gguf.py models/mymodel/
+
+# quantize the model to 4-bits (using Q4_K_M method)
+./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
+
+# update the gguf filetype to current version if older version is now unsupported
+./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
+```
+
+Run the quantized model:
+
+```bash
+# start inference on a gguf model
+./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128
+```
+
+When running the larger models, make sure you have enough disk space to store all the intermediate files.
+
+## Memory/Disk Requirements
+
+As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
+
+| Model | Original size | Quantized size (Q4_0) |
+|------:|--------------:|----------------------:|
+|    7B |         13 GB |                3.9 GB |
+|   13B |         24 GB |                7.8 GB |
+|   30B |         60 GB |               19.5 GB |
+|   65B |        120 GB |               38.5 GB |
+
+## Quantization
+
+Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
+
+*(outdated)*
+
+| Model | Measure      |    F16 |   Q4_0 |   Q4_1 |   Q5_0 |   Q5_1 |   Q8_0 |
+|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
+|    7B | perplexity   | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
+|    7B | file size    |  13.0G |   3.5G |   3.9G |   4.3G |   4.7G |   6.7G |
+|    7B | ms/tok @ 4th |    127 |     55 |     54 |     76 |     83 |     72 |
+|    7B | ms/tok @ 8th |    122 |     43 |     45 |     52 |     56 |     67 |
+|    7B | bits/weight  |   16.0 |    4.5 |    5.0 |    5.5 |    6.0 |    8.5 |
+|   13B | perplexity   | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 |
+|   13B | file size    |  25.0G |   6.8G |   7.6G |   8.3G |   9.1G |    13G |
+|   13B | ms/tok @ 4th |      - |    103 |    105 |    148 |    160 |    131 |
+|   13B | ms/tok @ 8th |      - |     73 |     82 |     98 |    105 |    128 |
+|   13B | bits/weight  |   16.0 |    4.5 |    5.0 |    5.5 |    6.0 |    8.5 |
+
+- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
+- recent k-quants improvements and new i-quants
+  - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
+  - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
+  - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
+  - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
+  - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
+  - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
+  - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
+  - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
+  - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
+  - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
+  - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
+  - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
+  - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
+  - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
+
+**Llama 2 7B**
 
 | Quantization | Bits per Weight (BPW) |
 |--------------|-----------------------|
@@ -18,7 +100,8 @@ Note: It is synced from llama.cpp `main` every 6 hours.
 | Q5_K_M       | 5.68                  |
 | Q6_K         | 6.56                  |
 
-## Llama 2 13B
+**Llama 2 13B**
+
 Quantization | Bits per Weight (BPW)
 -- | --
 Q2_K | 3.34
@@ -31,7 +114,7 @@ Q5_K_S | 5.51
 Q5_K_M | 5.67
 Q6_K | 6.56
 
-# Llama 2 70B
+**Llama 2 70B**
 
 Quantization | Bits per Weight (BPW)
 -- | --
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index cbb452334de0d..7312309aeef98 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -16,41 +16,44 @@ struct quant_option {
 };
 
 static const std::vector<struct quant_option> QUANT_OPTIONS = {
-    { "Q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0,   " 3.56G, +0.2166 ppl @ LLaMA-v1-7B", },
-    { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", },
-    { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", },
-    { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
-    { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization",            },
-    { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization",            },
-    { "IQ2_S",  LLAMA_FTYPE_MOSTLY_IQ2_S,  " 2.5  bpw quantization",            },
-    { "IQ2_M",  LLAMA_FTYPE_MOSTLY_IQ2_M,  " 2.7  bpw quantization",            },
-    { "IQ1_S",  LLAMA_FTYPE_MOSTLY_IQ1_S,  " 1.56 bpw quantization",            },
-    { "IQ1_M",  LLAMA_FTYPE_MOSTLY_IQ1_M,  " 1.75 bpw quantization",            },
-    { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
-    { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
-    { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization",            },
-    { "IQ3_S",  LLAMA_FTYPE_MOSTLY_IQ3_S,  " 3.44 bpw quantization",            },
-    { "IQ3_M",  LLAMA_FTYPE_MOSTLY_IQ3_M,  " 3.66 bpw quantization mix",        },
-    { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
-    { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization"   ,          },
-    { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
-    { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
-    { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
-    { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
-    { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
-    { "Q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
-    { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", },
-    { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", },
-    { "Q5_K",   LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
-    { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", },
-    { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
-    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", },
-    { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
-    { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "14.00G, -0.0020 ppl @ Mistral-7B", },
-    { "BF16",   LLAMA_FTYPE_MOSTLY_BF16,   "14.00G, -0.0050 ppl @ Mistral-7B", },
-    { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B", },
+    { "Q4_0",     LLAMA_FTYPE_MOSTLY_Q4_0,     " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
+    { "Q4_1",     LLAMA_FTYPE_MOSTLY_Q4_1,     " 4.78G, +0.4511 ppl @ Llama-3-8B",  },
+    { "Q5_0",     LLAMA_FTYPE_MOSTLY_Q5_0,     " 5.21G, +0.1316 ppl @ Llama-3-8B",  },
+    { "Q5_1",     LLAMA_FTYPE_MOSTLY_Q5_1,     " 5.65G, +0.1062 ppl @ Llama-3-8B",  },
+    { "IQ2_XXS",  LLAMA_FTYPE_MOSTLY_IQ2_XXS,  " 2.06 bpw quantization",            },
+    { "IQ2_XS",   LLAMA_FTYPE_MOSTLY_IQ2_XS,   " 2.31 bpw quantization",            },
+    { "IQ2_S",    LLAMA_FTYPE_MOSTLY_IQ2_S,    " 2.5  bpw quantization",            },
+    { "IQ2_M",    LLAMA_FTYPE_MOSTLY_IQ2_M,    " 2.7  bpw quantization",            },
+    { "IQ1_S",    LLAMA_FTYPE_MOSTLY_IQ1_S,    " 1.56 bpw quantization",            },
+    { "IQ1_M",    LLAMA_FTYPE_MOSTLY_IQ1_M,    " 1.75 bpw quantization",            },
+    { "Q2_K",     LLAMA_FTYPE_MOSTLY_Q2_K,     " 2.96G, +3.5199 ppl @ Llama-3-8B",  },
+    { "Q2_K_S",   LLAMA_FTYPE_MOSTLY_Q2_K_S,   " 2.96G, +3.1836 ppl @ Llama-3-8B",  },
+    { "IQ3_XXS",  LLAMA_FTYPE_MOSTLY_IQ3_XXS,  " 3.06 bpw quantization",            },
+    { "IQ3_S",    LLAMA_FTYPE_MOSTLY_IQ3_S,    " 3.44 bpw quantization",            },
+    { "IQ3_M",    LLAMA_FTYPE_MOSTLY_IQ3_M,    " 3.66 bpw quantization mix",        },
+    { "Q3_K",     LLAMA_FTYPE_MOSTLY_Q3_K_M,   "alias for Q3_K_M"                   },
+    { "IQ3_XS",   LLAMA_FTYPE_MOSTLY_IQ3_XS,   " 3.3 bpw quantization",             },
+    { "Q3_K_S",   LLAMA_FTYPE_MOSTLY_Q3_K_S,   " 3.41G, +1.6321 ppl @ Llama-3-8B",  },
+    { "Q3_K_M",   LLAMA_FTYPE_MOSTLY_Q3_K_M,   " 3.74G, +0.6569 ppl @ Llama-3-8B",  },
+    { "Q3_K_L",   LLAMA_FTYPE_MOSTLY_Q3_K_L,   " 4.03G, +0.5562 ppl @ Llama-3-8B",  },
+    { "IQ4_NL",   LLAMA_FTYPE_MOSTLY_IQ4_NL,   " 4.50 bpw non-linear quantization", },
+    { "IQ4_XS",   LLAMA_FTYPE_MOSTLY_IQ4_XS,   " 4.25 bpw non-linear quantization", },
+    { "Q4_K",     LLAMA_FTYPE_MOSTLY_Q4_K_M,   "alias for Q4_K_M",                  },
+    { "Q4_K_S",   LLAMA_FTYPE_MOSTLY_Q4_K_S,   " 4.37G, +0.2689 ppl @ Llama-3-8B",  },
+    { "Q4_K_M",   LLAMA_FTYPE_MOSTLY_Q4_K_M,   " 4.58G, +0.1754 ppl @ Llama-3-8B",  },
+    { "Q5_K",     LLAMA_FTYPE_MOSTLY_Q5_K_M,   "alias for Q5_K_M",                  },
+    { "Q5_K_S",   LLAMA_FTYPE_MOSTLY_Q5_K_S,   " 5.21G, +0.1049 ppl @ Llama-3-8B",  },
+    { "Q5_K_M",   LLAMA_FTYPE_MOSTLY_Q5_K_M,   " 5.33G, +0.0569 ppl @ Llama-3-8B",  },
+    { "Q6_K",     LLAMA_FTYPE_MOSTLY_Q6_K,     " 6.14G, +0.0217 ppl @ Llama-3-8B",  },
+    { "Q8_0",     LLAMA_FTYPE_MOSTLY_Q8_0,     " 7.96G, +0.0026 ppl @ Llama-3-8B",  },
+    { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
+    { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
+    { "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
+    { "F16",      LLAMA_FTYPE_MOSTLY_F16,      "14.00G, +0.0020 ppl @ Mistral-7B",  },
+    { "BF16",     LLAMA_FTYPE_MOSTLY_BF16,     "14.00G, -0.0050 ppl @ Mistral-7B",  },
+    { "F32",      LLAMA_FTYPE_ALL_F32,         "26.00G              @ 7B",          },
     // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching.
-    { "COPY",   LLAMA_FTYPE_ALL_F32,       "only copy tensors, no quantizing", },
+    { "COPY",     LLAMA_FTYPE_ALL_F32,         "only copy tensors, no quantizing",  },
 };
 
 static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE       = "quantize.imatrix.file";
@@ -88,7 +91,7 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 }
 
 // usage:
-//  ./quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
+//  ./llama-quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
 //
 [[noreturn]]
 static void usage(const char * executable) {
@@ -259,7 +262,7 @@ int main(int argc, char ** argv) {
                 usage(argv[0]);
             }
         } else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
-            if (arg_idx == argc-1 || !parse_kv_override(argv[++arg_idx], kv_overrides)) {
+            if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) {
                 usage(argv[0]);
             }
         } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh
index 160c12bee22e8..24bc970e8632b 100644
--- a/examples/quantize/tests.sh
+++ b/examples/quantize/tests.sh
@@ -18,9 +18,9 @@ fi
 
 set -x
 
-SPLIT=$1/gguf-split
-QUANTIZE=$1/quantize
-MAIN=$1/main
+SPLIT=$1/llama-gguf-split
+QUANTIZE=$1/llama-quantize
+MAIN=$1/llama-cli
 WORK_PATH=$TMP_DIR/quantize
 ROOT_DIR=$(realpath $(dirname $0)/../../)
 
@@ -41,23 +41,23 @@ $SPLIT --split-max-tensors 28  $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/g
 echo PASS
 echo
 
-# 3. Requant model with '--keep_split'
-$QUANTIZE --allow-requantize --keep_split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K
+# 3. Requant model with '--keep-split'
+$QUANTIZE --allow-requantize --keep-split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K
 echo PASS
 echo
 
 # 3a. Test the requanted model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32
 echo PASS
 echo
 
-# 4. Requant mode without '--keep_split'
+# 4. Requant mode without '--keep-split'
 $QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K
 echo PASS
 echo
 
 # 4b. Test the requanted model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --random-prompt --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32
 echo PASS
 echo
 
diff --git a/examples/reason-act.sh b/examples/reason-act.sh
index 046c48db584bc..06d592799cf12 100755
--- a/examples/reason-act.sh
+++ b/examples/reason-act.sh
@@ -8,7 +8,7 @@ if [ "$1" == "-m" ]; then
   MODEL="-m $2 "
 fi
 
-./main $MODEL --color \
+./llama-cli $MODEL --color \
     -f ./prompts/reason-act.txt \
     -i --interactive-first \
     --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7 -c 2048 \
diff --git a/examples/regex-to-grammar.py b/examples/regex_to_grammar.py
similarity index 100%
rename from examples/regex-to-grammar.py
rename to examples/regex_to_grammar.py
diff --git a/examples/retrieval/CMakeLists.txt b/examples/retrieval/CMakeLists.txt
index eaabae08d5583..66610f3111405 100644
--- a/examples/retrieval/CMakeLists.txt
+++ b/examples/retrieval/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET retrieval)
+set(TARGET llama-retrieval)
 add_executable(${TARGET} retrieval.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/retrieval/README.md b/examples/retrieval/README.md
index 2b2595c468046..bc5f22e2ff156 100644
--- a/examples/retrieval/README.md
+++ b/examples/retrieval/README.md
@@ -15,7 +15,7 @@ https://github.com/ggerganov/llama.cpp/pull/6193
 `retrieval` example can be tested as follows:
 
 ```bash
-make -j && ./retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
+make -j && ./llama-retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
 ```
 
 This chunks and embeds all given files and starts a loop requesting query inputs:
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
index 5ba71e76a93b4..aab9d81058af9 100644
--- a/examples/retrieval/retrieval.cpp
+++ b/examples/retrieval/retrieval.cpp
@@ -4,72 +4,12 @@
 #include <algorithm>
 #include <fstream>
 
-struct retrieval_params {
-    std::vector<std::string> context_files; // context files to embed
-    int32_t chunk_size            = 64;     // chunk size for context embedding
-    std::string chunk_separator   = "\n";   // chunk separator for context embedding
-};
-
-static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) {
-    gpt_print_usage(argc, argv, gpt_params);
-    printf("retrieval options:\n");
-    printf("  --context-file FNAME  file containing context to embed.\n");
-    printf("                        specify multiple files by providing --context-file option multiple times.\n");
-    printf("  --chunk-size N        minimum length of embedded text chunk (default:%d)\n", params.chunk_size);
-    printf("  --chunk-separator STRING\n");
-    printf("                        string to separate chunks (default: \"\\n\")\n");
-    printf("\n");
-}
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
 
-static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) {
-    int i = 1;
-    std::string arg;
-    while (i < argc) {
-        arg = argv[i];
-        bool invalid_gpt_param = false;
-        if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) {
-            if (invalid_gpt_param) {
-                fprintf(stderr, "error: invalid argument: %s\n", arg.c_str());
-                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
-                exit(1);
-            }
-            // option was parsed by gpt_params_find_arg
-        } else if (arg == "--context-file") {
-            if (++i >= argc) {
-                fprintf(stderr, "error: missing argument for --context-file\n");
-                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
-                exit(1);
-            }
-            std::ifstream file(argv[i]);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
-                exit(1);
-            }
-            // store the external file name in params
-            retrieval_params.context_files.push_back(argv[i]);
-        } else if (arg == "--chunk-size") {
-            if (++i >= argc) {
-                fprintf(stderr, "error: missing argument for --chunk-size\n");
-                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
-                exit(1);
-            }
-            retrieval_params.chunk_size = std::stoi(argv[i]);
-        } else if (arg == "--chunk-separator") {
-            if (++i >= argc) {
-                fprintf(stderr, "error: missing argument for --chunk-separator\n");
-                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
-                exit(1);
-            }
-            retrieval_params.chunk_separator = argv[i];
-        } else {
-            // unknown argument
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
-            exit(1);
-        }
-        i++;
-    }
+    LOG_TEE("\nexample usage:\n");
+    LOG_TEE("\n    %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]);
+    LOG_TEE("\n");
 }
 
 struct chunk {
@@ -133,9 +73,10 @@ static std::vector<chunk> chunk_file(const std::string & filename, int chunk_siz
     return chunks;
 }
 
-static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
-    for (size_t i = 0; i < tokens.size(); i++) {
-        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
+    size_t n_tokens = tokens.size();
+    for (size_t i = 0; i < n_tokens; i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, true);
     }
 }
 
@@ -171,33 +112,35 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
 
 int main(int argc, char ** argv) {
     gpt_params params;
-    retrieval_params retrieval_params;
 
-    retrieval_params_parse(argc, argv, params, retrieval_params);
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
+        return 1;
+    }
 
     // For BERT models, batch size must be equal to ubatch size
     params.n_ubatch = params.n_batch;
+    params.embedding = true;
 
-    if (retrieval_params.chunk_size <= 0) {
+    if (params.chunk_size <= 0) {
         fprintf(stderr, "chunk_size must be positive\n");
         return 1;
     }
-    if (retrieval_params.context_files.empty()) {
+    if (params.context_files.empty()) {
         fprintf(stderr, "context_files must be specified\n");
         return 1;
     }
-    params.embedding = true;
 
     print_build_info();
 
     printf("processing files:\n");
-    for (auto & context_file : retrieval_params.context_files) {
+    for (auto & context_file : params.context_files) {
         printf("%s\n", context_file.c_str());
     }
 
     std::vector<chunk> chunks;
-    for (auto & context_file : retrieval_params.context_files) {
-        std::vector<chunk> file_chunk = chunk_file(context_file, retrieval_params.chunk_size, retrieval_params.chunk_separator);
+    for (auto & context_file : params.context_files) {
+        std::vector<chunk> file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator);
         chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
     }
     printf("Number of chunks: %ld\n", chunks.size());
@@ -205,11 +148,12 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    llama_model * model;
-    llama_context * ctx;
-
     // load the model
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
+
     if (model == NULL) {
         fprintf(stderr, "%s: error: unable to load model\n", __func__);
         return 1;
@@ -218,6 +162,12 @@ int main(int argc, char ** argv) {
     const int n_ctx_train = llama_n_ctx_train(model);
     const int n_ctx = llama_n_ctx(ctx);
 
+    const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
+    if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
+        return 1;
+    }
+
     if (n_ctx > n_ctx_train) {
         fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
                 __func__, n_ctx_train, n_ctx);
@@ -226,7 +176,7 @@ int main(int argc, char ** argv) {
     // print system information
     {
         fprintf(stderr, "\n");
-        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+        fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
     }
 
     // max batch size
@@ -242,7 +192,7 @@ int main(int argc, char ** argv) {
             return 1;
         }
         // add eos if not present
-        if (inp.empty() || inp.back() != llama_token_eos(model)) {
+        if (llama_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) {
             inp.push_back(llama_token_eos(model));
         }
         chunk.tokens = inp;
@@ -303,6 +253,8 @@ int main(int argc, char ** argv) {
         chunks[i].tokens.clear();
     }
 
+    struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
+
     // start loop, receive query and return top k similar chunks based on cosine similarity
     std::string query;
     while (true) {
@@ -310,7 +262,6 @@ int main(int argc, char ** argv) {
         std::getline(std::cin, query);
         std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
 
-        struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
         batch_add_seq(query_batch, query_tokens, 0);
 
         std::vector<float> query_emb(n_embd, 0);
@@ -343,6 +294,7 @@ int main(int argc, char ** argv) {
     }
 
     // clean up
+    llama_batch_free(query_batch);
     llama_print_timings(ctx);
     llama_free(ctx);
     llama_free_model(model);
diff --git a/examples/rpc/README.md b/examples/rpc/README.md
index eeec71a8ee0c2..adedc89090d37 100644
--- a/examples/rpc/README.md
+++ b/examples/rpc/README.md
@@ -1,5 +1,9 @@
 ## Overview
 
+> [!IMPORTANT]
+> This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and
+> insecure. **Never run the RPC server on an open network or in a sensitive environment!**
+
 The `rpc-server` allows  running `ggml` backend on a remote host.
 The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them.
 This can be used for distributed LLM inference with `llama.cpp` in the following way:
@@ -29,13 +33,13 @@ You can also run multiple `rpc-server` instances on the same host, each with a d
 
 ## Usage
 
-On each host, build the corresponding backend with `cmake` and add `-DLLAMA_RPC=ON` to the build options.
+On each host, build the corresponding backend with `cmake` and add `-DGGML_RPC=ON` to the build options.
 For example, to build the CUDA backend with RPC support:
 
 ```bash
 mkdir build-rpc-cuda
 cd build-rpc-cuda
-cmake .. -DLLAMA_CUDA=ON -DLLAMA_RPC=ON
+cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON
 cmake --build . --config Release
 ```
 
@@ -58,17 +62,17 @@ $ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052
 This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device.
 
 
-On the main host build `llama.cpp` only with `-DLLAMA_RPC=ON`:
+On the main host build `llama.cpp` only with `-DGGML_RPC=ON`:
 
 ```bash
 mkdir build-rpc
 cd build-rpc
-cmake .. -DLLAMA_RPC=ON
+cmake .. -DGGML_RPC=ON
 cmake --build . --config Release
 ```
 
 Finally, use the `--rpc` option to specify the host and port of each `rpc-server`:
 
 ```bash
-$ bin/main -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99
+$ bin/llama-cli -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99
 ```
diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp
index 7c15d2aa4acfb..6342e6488602a 100644
--- a/examples/rpc/rpc-server.cpp
+++ b/examples/rpc/rpc-server.cpp
@@ -16,7 +16,7 @@
 #include <stdio.h>
 
 struct rpc_server_params {
-    std::string host        = "0.0.0.0";
+    std::string host        = "127.0.0.1";
     int         port        = 50052;
     size_t      backend_mem = 0;
 };
@@ -114,6 +114,17 @@ int main(int argc, char * argv[]) {
         fprintf(stderr, "Invalid parameters\n");
         return 1;
     }
+
+    if (params.host != "127.0.0.1") {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n");
+        fprintf(stderr, "WARNING: Host ('%s') is != '127.0.0.1'\n", params.host.c_str());
+        fprintf(stderr, "         Never expose the RPC server to an open network!\n");
+        fprintf(stderr, "         This is an experimental feature and is not secure!\n");
+        fprintf(stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n");
+        fprintf(stderr, "\n");
+    }
+
     ggml_backend_t backend = create_backend();
     if (!backend) {
         fprintf(stderr, "Failed to create backend\n");
diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt
index cc6ed8554a6e3..0fb5e359bc9ad 100644
--- a/examples/save-load-state/CMakeLists.txt
+++ b/examples/save-load-state/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET save-load-state)
+set(TARGET llama-save-load-state)
 add_executable(${TARGET} save-load-state.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index c3b766882dbec..3ea7c790d2bf7 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -11,6 +11,7 @@ int main(int argc, char ** argv) {
     params.prompt = "The quick brown fox";
 
     if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -27,10 +28,11 @@ int main(int argc, char ** argv) {
     std::string result2;
 
     // init
-    llama_model * model;
-    llama_context * ctx;
+    llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+    llama_model * model = llama_init.model;
+    llama_context * ctx = llama_init.context;
 
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
     if (model == nullptr || ctx == nullptr) {
         fprintf(stderr, "%s : failed to init\n", __func__);
         return 1;
@@ -46,7 +48,7 @@ int main(int argc, char ** argv) {
     // save state (rng, logits, embedding and kv_cache) to file
     {
         std::vector<uint8_t> state_mem(llama_state_get_size(ctx));
-        const size_t written = llama_state_get_data(ctx, state_mem.data());
+        const size_t written = llama_state_get_data(ctx, state_mem.data(), state_mem.size());
 
         FILE *fp_write = fopen("dump_state.bin", "wb");
         fwrite(state_mem.data(), 1, written, fp_write);
@@ -98,13 +100,16 @@ int main(int argc, char ** argv) {
 
     // load state (rng, logits, embedding and kv_cache) from file
     {
-        std::vector<uint8_t> state_mem(llama_state_get_size(ctx2));
+        std::vector<uint8_t> state_mem;
 
         FILE * fp_read = fopen("dump_state.bin", "rb");
+        fseek(fp_read, 0, SEEK_END);
+        state_mem.resize(ftell(fp_read));
+        fseek(fp_read, 0, SEEK_SET);
         const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
         fclose(fp_read);
 
-        if (read != llama_state_set_data(ctx2, state_mem.data())) {
+        if (read != llama_state_set_data(ctx2, state_mem.data(), state_mem.size())) {
             fprintf(stderr, "\n%s : failed to read state\n", __func__);
             llama_free(ctx2);
             llama_free_model(model);
@@ -158,13 +163,16 @@ int main(int argc, char ** argv) {
 
     // load state (rng, logits, embedding and kv_cache) from file
     {
-        std::vector<uint8_t> state_mem(llama_state_get_size(ctx3));
+        std::vector<uint8_t> state_mem;
 
         FILE * fp_read = fopen("dump_state.bin", "rb");
+        fseek(fp_read, 0, SEEK_END);
+        state_mem.resize(ftell(fp_read));
+        fseek(fp_read, 0, SEEK_SET);
         const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
         fclose(fp_read);
 
-        if (read != llama_state_set_data(ctx3, state_mem.data())) {
+        if (read != llama_state_set_data(ctx3, state_mem.data(), state_mem.size())) {
             fprintf(stderr, "\n%s : failed to read state\n", __func__);
             llama_free(ctx3);
             llama_free_model(model);
@@ -181,7 +189,7 @@ int main(int argc, char ** argv) {
     {
         // save kv of seq 0
         std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx3, 0));
-        const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), 0);
+        const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), seq_store.size(), 0);
         if (ncopy != seq_store.size()) {
             fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
             llama_free(ctx3);
@@ -195,7 +203,7 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s : kv cache cleared\n", __func__);
 
         // restore kv into seq 1
-        const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), 1);
+        const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), seq_store.size(), 1);
         if (nset != seq_store.size()) {
             fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
             llama_free(ctx3);
diff --git a/examples/server-llama2-13B.sh b/examples/server-llama2-13B.sh
index 17fedc2b176f6..4ce79b7fac477 100755
--- a/examples/server-llama2-13B.sh
+++ b/examples/server-llama2-13B.sh
@@ -16,7 +16,7 @@ GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 4096 --batch-size 1024}"
 
 
 # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
-./server $GEN_OPTIONS \
+./llama-server $GEN_OPTIONS \
   --model "$MODEL" \
   --threads "$N_THREAD" \
   --rope-freq-scale 1.0 \
diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt
index 4b89c53022fd0..dbe41f1fd1120 100644
--- a/examples/server/CMakeLists.txt
+++ b/examples/server/CMakeLists.txt
@@ -1,18 +1,37 @@
-set(TARGET server)
+set(TARGET llama-server)
 option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
-option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
+option(LLAMA_SERVER_SSL     "Build SSL support for the server"        OFF)
+
 include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
+
+if (MINGW)
+    # fix: https://github.com/ggerganov/llama.cpp/actions/runs/9651004652/job/26617901362?pr=8006
+    add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
+endif()
+
 set(TARGET_SRCS
     server.cpp
     utils.hpp
     httplib.h
 )
 set(PUBLIC_ASSETS
+    colorthemes.css
+    style.css
+    theme-beeninorder.css
+    theme-ketivah.css
+    theme-mangotango.css
+    theme-playground.css
+    theme-polarnight.css
+    theme-snowstorm.css
     index.html
+    index-new.html
     index.js
     completion.js
+    system-prompts.js
+    prompt-formats.js
     json-schema-to-grammar.mjs
 )
+
 foreach(asset ${PUBLIC_ASSETS})
     set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
     set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
@@ -23,18 +42,23 @@ foreach(asset ${PUBLIC_ASSETS})
         COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
     )
 endforeach()
+
 add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
 )
+
 target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
+
 if (LLAMA_SERVER_SSL)
     find_package(OpenSSL REQUIRED)
     target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto)
     target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT)
 endif()
+
 if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
 endif()
+
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/server/README.md b/examples/server/README.md
index 0c3db8c84c69d..e17595fe87f25 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -5,7 +5,7 @@ Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/
 Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
 
 **Features:**
- * LLM inference of F16 and quantum models on GPU and CPU
+ * LLM inference of F16 and quantized models on GPU and CPU
  * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
  * Parallel decoding with multi-user support
  * Continuous batching
@@ -15,91 +15,261 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
 
 The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216).
 
-**Command line options:**
-
-- `-v`, `--verbose`: Enable verbose server output. When using the `/completion` endpoint, this includes the tokenized prompt, the full request and the full response.
-- `-t N`, `--threads N`: Set the number of threads to use by CPU layers during generation. Not used by model layers that are offloaded to GPU. This option has no effect when using the maximum number of GPU layers. Default: `std::thread::hardware_concurrency()` (number of CPU cores).
-- `-tb N, --threads-batch N`: Set the number of threads to use by CPU layers during batch and prompt processing (>= 32 tokens). This option has no effect if a GPU is available. Default: `--threads`.
-- `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`
-- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
-- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused
-- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository. Default: unused
-- `-hff FILE, --hf-file FILE`: Hugging Face model file. Default: unused
-- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
-- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is `512`, but LLaMA models were built with a context of `2048`, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of `4096`.
-- `-ngl N`, `--n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
-- `-mg i, --main-gpu i`: When using multiple GPUs, this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default, GPU `0` is used.
-- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs, this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default, the data is split in proportion to VRAM, but this may not be optimal for performance.
-- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`
-- `-ub N`, `--ubatch-size N`: Physical maximum batch size. Default: `512`
-- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
-- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
-- `--numa STRATEGY`: Attempt one of the below optimization strategies that may help on some NUMA systems
-- `--numa distribute`: Spread execution evenly over all nodes
-- `--numa isolate`: Only spawn threads on CPUs on the node that execution started on
-- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437
-- `--numa`: Attempt optimizations that may help on some NUMA systems.
-- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
-- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
-- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`
-- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`
-- `--port`: Set the port to listen. Default: `8080`
-- `--path`: Path from which to serve static files. Default: disabled
-- `--api-key`: Set an api key for request authorization. By default, the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
-- `--api-key-file`: Path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`s.
-- `--embeddings`: Enable embedding vector output and the OAI compatible endpoint /v1/embeddings. Physical batch size (`--ubatch-size`) must be carefully defined. Default: disabled
-- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`. Values > 1 will allow for higher throughput with multiple parallel requests but the results will **not** be deterministic due to differences in rounding error.
-- `-cb`, `--cont-batching`: Enable continuous batching (a.k.a dynamic batching).  Default: disabled
-- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots). This is useful for chat applications. [See more](#change-system-prompt-on-runtime)
-- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
-- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend. Used together with group attention width `--grp-attn-w`. Default: `1`, which is disabled.
-- `--grp-attn-w`: Set the group attention width to extend context size through self-extend.  Used together with group attention factor `--grp-attn-n`. Default: `512`
-- `-n N, --n-predict N`: Set the maximum tokens to predict. Default: `-1`
-- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
-- `--metrics`: enable prometheus `/metrics` compatible endpoint. Default: disabled
-- `--slot-save-path PATH`: Specifies the path where the state of slots (the prompt cache) can be stored. If not provided, the slot management endpoints will be disabled.
-- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name.  Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
-- `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled
-- `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json`
-- `--rope-scaling` : RoPE scaling method. Defaults to linear unless otherwise specified by the model. Options are `none`, `linear`, `yarn`
-- `--rope-freq-base N` : RoPE frequency base (default: loaded from model)
-- `--rope-freq-scale N`: RoPE frequency scaling factor, expands context by a factor of 1/N (e.g. 0.25)
-- `--yarn-ext-factor N` : YaRN: extrapolation mix factor (Default: 1.0, 0.0 = full interpolation)
-- `--yarn-attn-factor N` : YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
-- `--yarn-beta-slow N`: YaRN: High correction dim or alpha (default: 1.0)
-- `--yarn-beta-fast N`: YaRN: low correction dim or beta (default: 32.0)
-- `--pooling` : Pooling type for embeddings, use model default if unspecified. Options are `none`, `mean`, `cls`
-- `-dt N`, `--defrag-thold N`: KV cache defragmentation threshold (default: -1.0, < 0 = disabled)
-- `-fa`, `--flash-attn` : enable flash attention (default: disabled).
-- `-ctk TYPE`, `--cache-type-k TYPE` : KV cache data type for K (default: `f16`, options `f32`, `f16`, `q8_0`, `q4_0`, `q4_1`, `iq4_nl`, `q5_0`, or `q5_1`)
-- `-ctv TYPE`, `--cache-type-v TYPE` : KV cache type for V (default `f16`, see `-ctk` for options)
-
-**If compiled with `LLAMA_SERVER_SSL=ON`**
-- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
-- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate
+## Usage
+
+```
+usage: ./llama-server [options]
+
+general:
+
+  -h,    --help, --usage          print usage and exit
+         --version                show version and build info
+  -v,    --verbose                print verbose information
+         --verbosity N            set specific verbosity level (default: 0)
+         --verbose-prompt         print a verbose prompt before generation (default: false)
+         --no-display-prompt      don't print prompt at generation (default: false)
+  -co,   --color                  colorise output to distinguish prompt and user input from generations (default: false)
+  -s,    --seed SEED              RNG seed (default: -1, use random seed for < 0)
+  -t,    --threads N              number of threads to use during generation (default: 8)
+  -tb,   --threads-batch N        number of threads to use during batch and prompt processing (default: same as --threads)
+  -td,   --threads-draft N        number of threads to use during generation (default: same as --threads)
+  -tbd,  --threads-batch-draft N  number of threads to use during batch and prompt processing (default: same as --threads-draft)
+         --draft N                number of tokens to draft for speculative decoding (default: 5)
+  -ps,   --p-split N              speculative decoding split probability (default: 0.1)
+  -lcs,  --lookup-cache-static FNAME
+                                  path to static lookup cache to use for lookup decoding (not updated by generation)
+  -lcd,  --lookup-cache-dynamic FNAME
+                                  path to dynamic lookup cache to use for lookup decoding (updated by generation)
+  -c,    --ctx-size N             size of the prompt context (default: 0, 0 = loaded from model)
+  -n,    --predict N              number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)
+  -b,    --batch-size N           logical maximum batch size (default: 2048)
+  -ub,   --ubatch-size N          physical maximum batch size (default: 512)
+         --keep N                 number of tokens to keep from the initial prompt (default: 0, -1 = all)
+         --chunks N               max number of chunks to process (default: -1, -1 = all)
+  -fa,   --flash-attn             enable Flash Attention (default: disabled)
+  -p,    --prompt PROMPT          prompt to start generation with
+                                  in conversation mode, this will be used as system prompt
+                                  (default: '')
+  -f,    --file FNAME             a file containing the prompt (default: none)
+         --in-file FNAME          an input file (repeat to specify multiple files)
+  -bf,   --binary-file FNAME      binary file containing the prompt (default: none)
+  -e,    --escape                 process escapes sequences (\n, \r, \t, \', \", \\) (default: true)
+         --no-escape              do not process escape sequences
+  -ptc,  --print-token-count N    print token count every N tokens (default: -1)
+         --prompt-cache FNAME     file to cache prompt state for faster startup (default: none)
+         --prompt-cache-all       if specified, saves user input and generations to cache as well
+                                  not supported with --interactive or other interactive options
+         --prompt-cache-ro        if specified, uses the prompt cache but does not update it
+  -r,    --reverse-prompt PROMPT  halt generation at PROMPT, return control in interactive mode
+                                  can be specified more than once for multiple prompts
+  -sp,   --special                special tokens output enabled (default: false)
+  -cnv,  --conversation           run in conversation mode, does not print special tokens and suffix/prefix
+                                  if suffix/prefix are not specified, default chat template will be used
+                                  (default: false)
+  -i,    --interactive            run in interactive mode (default: false)
+  -if,   --interactive-first      run in interactive mode and wait for input right away (default: false)
+  -mli,  --multiline-input        allows you to write or paste multiple lines without ending each in '\'
+         --in-prefix-bos          prefix BOS to user inputs, preceding the `--in-prefix` string
+         --in-prefix STRING       string to prefix user inputs with (default: empty)
+         --in-suffix STRING       string to suffix after user inputs with (default: empty)
+         --spm-infill             use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled)
+
+sampling:
+
+         --samplers SAMPLERS      samplers that will be used for generation in the order, separated by ';'
+                                  (default: top_k;tfs_z;typical_p;top_p;min_p;temperature)
+         --sampling-seq SEQUENCE  simplified sequence for samplers that will be used (default: kfypmt)
+         --ignore-eos             ignore end of stream token and continue generating (implies --logit-bias EOS-inf)
+         --penalize-nl            penalize newline tokens (default: false)
+         --temp N                 temperature (default: 0.8)
+         --top-k N                top-k sampling (default: 40, 0 = disabled)
+         --top-p N                top-p sampling (default: 0.9, 1.0 = disabled)
+         --min-p N                min-p sampling (default: 0.1, 0.0 = disabled)
+         --tfs N                  tail free sampling, parameter z (default: 1.0, 1.0 = disabled)
+         --typical N              locally typical sampling, parameter p (default: 1.0, 1.0 = disabled)
+         --repeat-last-n N        last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size)
+         --repeat-penalty N       penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled)
+         --presence-penalty N     repeat alpha presence penalty (default: 0.0, 0.0 = disabled)
+         --frequency-penalty N    repeat alpha frequency penalty (default: 0.0, 0.0 = disabled)
+         --dynatemp-range N       dynamic temperature range (default: 0.0, 0.0 = disabled)
+         --dynatemp-exp N         dynamic temperature exponent (default: 1.0)
+         --mirostat N             use Mirostat sampling.
+                                  Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.
+                                  (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)
+         --mirostat-lr N          Mirostat learning rate, parameter eta (default: 0.1)
+         --mirostat-ent N         Mirostat target entropy, parameter tau (default: 5.0)
+         -l TOKEN_ID(+/-)BIAS     modifies the likelihood of token appearing in the completion,
+                                  i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
+                                  or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'
+         --cfg-negative-prompt PROMPT
+                                  negative prompt to use for guidance (default: '')
+         --cfg-negative-prompt-file FNAME
+                                  negative prompt file to use for guidance
+         --cfg-scale N            strength of guidance (default: 1.0, 1.0 = disable)
+         --chat-template JINJA_TEMPLATE
+                                  set custom jinja chat template (default: template taken from model's metadata)
+                                  if suffix/prefix are specified, template will be disabled
+                                  only commonly used templates are accepted:
+                                  https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
+
+grammar:
+
+         --grammar GRAMMAR        BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '')
+         --grammar-file FNAME     file to read grammar from
+  -j,    --json-schema SCHEMA     JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
+                                  For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead
+
+embedding:
+
+         --pooling {none,mean,cls,last}
+                                  pooling type for embeddings, use model default if unspecified
+         --attention {causal,non-causal}
+                                  attention type for embeddings, use model default if unspecified
+
+context hacking:
+
+         --rope-scaling {none,linear,yarn}
+                                  RoPE frequency scaling method, defaults to linear unless specified by the model
+         --rope-scale N           RoPE context scaling factor, expands context by a factor of N
+         --rope-freq-base N       RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
+         --rope-freq-scale N      RoPE frequency scaling factor, expands context by a factor of 1/N
+         --yarn-orig-ctx N        YaRN: original context size of model (default: 0 = model training context size)
+         --yarn-ext-factor N      YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
+         --yarn-attn-factor N     YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
+         --yarn-beta-slow N       YaRN: high correction dim or alpha (default: 1.0)
+         --yarn-beta-fast N       YaRN: low correction dim or beta (default: 32.0)
+  -gan,  --grp-attn-n N           group-attention factor (default: 1)
+  -gaw,  --grp-attn-w N           group-attention width (default: 512.0)
+  -dkvc, --dump-kv-cache          verbose print of the KV cache
+  -nkvo, --no-kv-offload          disable KV offload
+  -ctk,  --cache-type-k TYPE      KV cache data type for K (default: f16)
+  -ctv,  --cache-type-v TYPE      KV cache data type for V (default: f16)
+
+perplexity:
+
+         --all-logits             return logits for all tokens in the batch (default: false)
+         --hellaswag              compute HellaSwag score over random tasks from datafile supplied with -f
+         --hellaswag-tasks N      number of tasks to use when computing the HellaSwag score (default: 400)
+         --winogrande             compute Winogrande score over random tasks from datafile supplied with -f
+         --winogrande-tasks N     number of tasks to use when computing the Winogrande score (default: 0)
+         --multiple-choice        compute multiple choice score over random tasks from datafile supplied with -f
+         --multiple-choice-tasks N
+                                  number of tasks to use when computing the multiple choice score (default: 0)
+         --kl-divergence          computes KL-divergence to logits provided via --kl-divergence-base
+         --ppl-stride N           stride for perplexity calculation (default: 0)
+         --ppl-output-type {0,1}  output type for perplexity calculation (default: 0)
+
+parallel:
+
+  -dt,   --defrag-thold N         KV cache defragmentation threshold (default: -1.0, < 0 - disabled)
+  -np,   --parallel N             number of parallel sequences to decode (default: 1)
+  -ns,   --sequences N            number of sequences to decode (default: 1)
+  -cb,   --cont-batching          enable continuous batching (a.k.a dynamic batching) (default: enabled)
+
+multi-modality:
+
+         --mmproj FILE            path to a multimodal projector file for LLaVA. see examples/llava/README.md
+         --image FILE             path to an image file. use with multimodal models. Specify multiple times for batching
+
+backend:
+
+         --rpc SERVERS            comma separated list of RPC servers
+         --mlock                  force system to keep model in RAM rather than swapping or compressing
+         --no-mmap                do not memory-map model (slower load but may reduce pageouts if not using mlock)
+         --numa TYPE              attempt optimizations that help on some NUMA systems
+                                    - distribute: spread execution evenly over all nodes
+                                    - isolate: only spawn threads on CPUs on the node that execution started on
+                                    - numactl: use the CPU map provided by numactl
+                                  if run without this previously, it is recommended to drop the system page cache before using this
+                                  see https://github.com/ggerganov/llama.cpp/issues/1437
+
+model:
+
+         --check-tensors          check model tensor data for invalid values (default: false)
+         --override-kv KEY=TYPE:VALUE
+                                  advanced option to override model metadata by key. may be specified multiple times.
+                                  types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false
+         --lora FNAME             apply LoRA adapter (implies --no-mmap)
+         --lora-scaled FNAME S    apply LoRA adapter with user defined scaling S (implies --no-mmap)
+         --lora-base FNAME        optional model to use as a base for the layers modified by the LoRA adapter
+         --control-vector FNAME   add a control vector
+                                  note: this argument can be repeated to add multiple control vectors
+         --control-vector-scaled FNAME SCALE
+                                  add a control vector with user defined scaling SCALE
+                                  note: this argument can be repeated to add multiple scaled control vectors
+         --control-vector-layer-range START END
+                                  layer range to apply the control vector(s) to, start and end inclusive
+  -m,    --model FNAME            model path (default: models/$filename with filename from --hf-file
+                                  or --model-url if set, otherwise models/7B/ggml-model-f16.gguf)
+  -md,   --model-draft FNAME      draft model for speculative decoding (default: unused)
+  -mu,   --model-url MODEL_URL    model download url (default: unused)
+  -hfr,  --hf-repo REPO           Hugging Face model repository (default: unused)
+  -hff,  --hf-file FILE           Hugging Face model file (default: unused)
+  -hft,  --hf-token TOKEN         Hugging Face access token (default: value from HF_TOKEN environment variable)
+
+server:
+
+         --host HOST              ip address to listen (default: 127.0.0.1)
+         --port PORT              port to listen (default: 8080)
+         --path PATH              path to serve static files from (default: )
+         --embedding(s)           restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
+         --api-key KEY            API key to use for authentication (default: none)
+         --api-key-file FNAME     path to file containing API keys (default: none)
+         --ssl-key-file FNAME     path to file a PEM-encoded SSL private key
+         --ssl-cert-file FNAME    path to file a PEM-encoded SSL certificate
+         --timeout N              server read/write timeout in seconds (default: 600)
+         --threads-http N         number of threads used to process HTTP requests (default: -1)
+         --system-prompt-file FNAME
+                                  set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications
+         --log-format {text,json}
+                                  log output format: json or text (default: json)
+         --metrics                enable prometheus compatible metrics endpoint (default: disabled)
+         --no-slots               disables slots monitoring endpoint (default: enabled)
+         --slot-save-path PATH    path to save slot kv cache (default: disabled)
+         --chat-template JINJA_TEMPLATE
+                                  set custom jinja chat template (default: template taken from model's metadata)
+                                  only commonly used templates are accepted:
+                                  https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
+  -sps,  --slot-prompt-similarity SIMILARITY
+                                  how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)
+         --lora-init-without-apply
+                                  load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled)
+
+logging:
+
+         --simple-io              use basic IO for better compatibility in subprocesses and limited consoles
+  -ld,   --logdir LOGDIR          path under which to save YAML logs (no logging if unset)
+         --log-test               Run simple logging test
+         --log-disable            Disable trace logs
+         --log-enable             Enable trace logs
+         --log-file FNAME         Specify a log filename (without extension)
+         --log-new                Create a separate new log file on start. Each log file will have unique name: "<name>.<ID>.log"
+         --log-append             Don't truncate the old log file.
+```
+
 
 ## Build
 
-`server` is built alongside everything else from the root of the project
+`llama-server` is built alongside everything else from the root of the project
 
 - Using `make`:
 
   ```bash
-  make server
+  make llama-server
   ```
 
 - Using `CMake`:
 
   ```bash
   cmake -B build
-  cmake --build build --config Release -t server
+  cmake --build build --config Release -t llama-server
   ```
 
-  Binary is at `./build/bin/server`
+  Binary is at `./build/bin/llama-server`
 
 ## Build with SSL
 
-`server` can also be built with SSL support using OpenSSL 3
+`llama-server` can also be built with SSL support using OpenSSL 3
 
 - Using `make`:
 
@@ -107,14 +277,14 @@ The project is under active development, and we are [looking for feedback and co
   # NOTE: For non-system openssl, use the following:
   #   CXXFLAGS="-I /path/to/openssl/include"
   #   LDFLAGS="-L /path/to/openssl/lib"
-  make LLAMA_SERVER_SSL=true server
+  make LLAMA_SERVER_SSL=true llama-server
   ```
 
 - Using `CMake`:
 
   ```bash
   cmake -B build -DLLAMA_SERVER_SSL=ON
-  cmake --build build --config Release -t server
+  cmake --build build --config Release -t llama-server
   ```
 
 ## Quick Start
@@ -124,13 +294,13 @@ To get started right away, run the following command, making sure to use the cor
 ### Unix-based systems (Linux, macOS, etc.)
 
 ```bash
-./server -m models/7B/ggml-model.gguf -c 2048
+./llama-server -m models/7B/ggml-model.gguf -c 2048
 ```
 
 ### Windows
 
 ```powershell
-server.exe -m models\7B\ggml-model.gguf -c 2048
+llama-server.exe -m models\7B\ggml-model.gguf -c 2048
 ```
 
 The above command will start a server that by default listens on `127.0.0.1:8080`.
@@ -198,7 +368,8 @@ node index.js
 
 ## API Endpoints
 
-- **GET** `/health`: Returns the current state of the server:
+### GET `/health`: Returns the current state of the server
+
   - 503 -> `{"status": "loading model"}` if the model is still being loaded.
   - 500 -> `{"status": "error"}` if the model failed to load.
   - 200 -> `{"status": "ok", "slots_idle": 1, "slots_processing": 2 }` if the model is successfully loaded and the server is ready for further requests mentioned below.
@@ -207,7 +378,7 @@ node index.js
 
   If the query parameter `include_slots` is passed, `slots` field will contain internal slots data except if `--slots-endpoint-disable` is set.
 
-- **POST** `/completion`: Given a `prompt`, it returns the predicted completion.
+### POST `/completion`: Given a `prompt`, it returns the predicted completion.
 
     *Options:*
 
@@ -231,7 +402,7 @@ node index.js
 
     `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
 
-    `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.
+    `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
     By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
 
     `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
@@ -279,13 +450,13 @@ node index.js
 
     `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot.  Default: `-1`
 
-    `cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch.  Default: `false`
+    `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false`
 
     `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
 
     `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
 
-### Result JSON
+**Response format**
 
 - Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion.
 
@@ -324,7 +495,7 @@ Notice that each `probs` is an array of length `n_probs`.
 - `tokens_evaluated`: Number of tokens evaluated in total from the prompt
 - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
 
-- **POST** `/tokenize`: Tokenize a given text.
+### POST `/tokenize`: Tokenize a given text
 
     *Options:*
 
@@ -332,13 +503,15 @@ Notice that each `probs` is an array of length `n_probs`.
 
     `add_special`: Boolean indicating if special tokens, i.e. `BOS`, should be inserted.  Default: `false`
 
-- **POST** `/detokenize`: Convert tokens to text.
+### POST `/detokenize`: Convert tokens to text
 
     *Options:*
 
     `tokens`: Set the tokens to detokenize.
 
-- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
+### POST `/embedding`: Generate embedding of a given text
+
+The same as [the embedding example](../embedding) does.
 
     *Options:*
 
@@ -346,7 +519,9 @@ Notice that each `probs` is an array of length `n_probs`.
 
     `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
 
-- **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.
+### POST `/infill`: For code infilling.
+
+Takes a prefix and a suffix and returns the predicted completion as stream.
 
     *Options:*
 
@@ -358,14 +533,15 @@ Notice that each `probs` is an array of length `n_probs`.
 
 - **GET** `/props`: Return current server settings.
 
-### Result JSON
+**Response format**
 
 ```json
 {
   "assistant_name": "",
   "user_name": "",
   "default_generation_settings": { ... },
-  "total_slots": 1
+  "total_slots": 1,
+  "chat_template": ""
 }
 ```
 
@@ -373,8 +549,11 @@ Notice that each `probs` is an array of length `n_probs`.
 - `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
 - `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
 - `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
+- `chat_template` - the model's original Jinja2 prompt template
+
+### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API
 
-- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only model with [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, ChatML template will be used.
+Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used.
 
     *Options:*
 
@@ -426,7 +605,7 @@ Notice that each `probs` is an array of length `n_probs`.
     }'
     ```
 
-- **POST** `/v1/embeddings`: OpenAI-compatible embeddings API.
+### POST `/v1/embeddings`: OpenAI-compatible embeddings API
 
     *Options:*
 
@@ -460,9 +639,9 @@ Notice that each `probs` is an array of length `n_probs`.
     }'
     ```
 
-- **GET** `/slots`: Returns the current slots processing state. Can be disabled with `--slots-endpoint-disable`.
+### GET `/slots`: Returns the current slots processing state. Can be disabled with `--slots-endpoint-disable`.
 
-### Result JSON
+**Response format**
 
 ```json
 [
@@ -523,7 +702,7 @@ Notice that each `probs` is an array of length `n_probs`.
 ]
 ```
 
-- **GET** `/metrics`: [Prometheus](https://prometheus.io/) compatible metrics exporter endpoint if `--metrics` is enabled:
+### GET `/metrics`: Prometheus compatible metrics exporter endpoint if `--metrics` is enabled:
 
 Available metrics:
 - `llamacpp:prompt_tokens_total`: Number of prompt tokens processed.
@@ -535,13 +714,13 @@ Available metrics:
 - `llamacpp:requests_processing`: Number of requests processing.
 - `llamacpp:requests_deferred`: Number of requests deferred.
 
-- **POST** `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.
+### POST `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.
 
     *Options:*
 
     `filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter.
 
-### Result JSON
+**Response format**
 
 ```json
 {
@@ -555,13 +734,13 @@ Available metrics:
 }
 ```
 
-- **POST** `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file.
+### POST `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file.
 
     *Options:*
 
     `filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter.
 
-### Result JSON
+**Response format**
 
 ```json
 {
@@ -575,9 +754,9 @@ Available metrics:
 }
 ```
 
-- **POST** `/slots/{id_slot}?action=erase`: Erase the prompt cache of the specified slot.
+### POST `/slots/{id_slot}?action=erase`: Erase the prompt cache of the specified slot.
 
-### Result JSON
+**Response format**
 
 ```json
 {
@@ -586,6 +765,42 @@ Available metrics:
 }
 ```
 
+### GET `/lora-adapters`: Get list of all LoRA adapters
+
+If an adapter is disabled, the scale will be set to 0.
+
+**Response format**
+
+```json
+[
+    {
+        "id": 0,
+        "path": "my_adapter_1.gguf",
+        "scale": 0.0
+    },
+    {
+        "id": 1,
+        "path": "my_adapter_2.gguf",
+        "scale": 0.0
+    }
+]
+```
+
+### POST `/lora-adapters`: Set list of LoRA adapters
+
+To disable an adapter, either remove it from the list below, or set scale to 0.
+
+**Request format**
+
+To know the `id` of the adapter, use GET `/lora-adapters`
+
+```json
+[
+  {"id": 0, "scale": 0.2},
+  {"id": 1, "scale": 0.8}
+]
+```
+
 ## More examples
 
 ### Change system prompt on runtime
@@ -629,11 +844,11 @@ bash chat.sh
 
 ### OAI-like API
 
-The HTTP `server` supports an OAI-like API: https://github.com/openai/openai-openapi
+The HTTP `llama-server` supports an OAI-like API: https://github.com/openai/openai-openapi
 
 ### API errors
 
-`server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
+`llama-server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
 
 Example of an error:
 
diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md
index 23a3ec97523ef..0f18ca39651d2 100644
--- a/examples/server/bench/README.md
+++ b/examples/server/bench/README.md
@@ -99,7 +99,7 @@ The `bench.py` script does several steps:
 It aims to be used in the CI, but you can run it manually:
 
 ```shell
-LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/server python bench.py \
+LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/llama-server python bench.py \
               --runner-label local \
               --name local \
               --branch `git rev-parse --abbrev-ref HEAD` \
diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py
index 86c5de101445c..2daac08847d65 100644
--- a/examples/server/bench/bench.py
+++ b/examples/server/bench/bench.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import argparse
 import json
 import os
@@ -59,10 +61,11 @@ def main(args_in: list[str] | None = None) -> None:
         sys.exit(1)
 
     # start the benchmark
+    iterations = 0
+    data = {}
     try:
         start_benchmark(args)
 
-        iterations = 0
         with open("results.github.env", 'w') as github_env:
             # parse output
             with open('k6-results.json', 'r') as bench_results:
@@ -129,7 +132,7 @@ def main(args_in: list[str] | None = None) -> None:
                 timestamps, metric_values = zip(*values)
                 metric_values = [float(value) for value in metric_values]
                 prometheus_metrics[metric] = metric_values
-                timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
+                timestamps_dt = [str(datetime.fromtimestamp(int(ts))) for ts in timestamps]
                 plt.figure(figsize=(16, 10), dpi=80)
                 plt.plot(timestamps_dt, metric_values, label=metric)
                 plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
@@ -156,7 +159,7 @@ def main(args_in: list[str] | None = None) -> None:
                 plt.close()
 
                 # Mermaid format in case images upload failed
-                with (open(f"{metric}.mermaid", 'w') as mermaid_f):
+                with open(f"{metric}.mermaid", 'w') as mermaid_f:
                     mermaid = (
                     f"""---
 config:
@@ -245,7 +248,7 @@ def start_server(args):
 
 def start_server_background(args):
     # Start the server
-    server_path = '../../../build/bin/server'
+    server_path = '../../../build/bin/llama-server'
     if 'LLAMA_SERVER_BIN_PATH' in os.environ:
         server_path = os.environ['LLAMA_SERVER_BIN_PATH']
     server_args = [
@@ -278,7 +281,7 @@ def start_server_background(args):
     }
     server_process = subprocess.Popen(
         args,
-        **pkwargs)
+        **pkwargs)  # pyright: ignore[reportArgumentType, reportCallIssue]
 
     def server_log(in_stream, out_stream):
         for line in iter(in_stream.readline, b''):
diff --git a/examples/server/public/colorthemes.css b/examples/server/public/colorthemes.css
new file mode 100755
index 0000000000000..b1e2b8b70c261
--- /dev/null
+++ b/examples/server/public/colorthemes.css
@@ -0,0 +1,402 @@
+@import url("theme-snowstorm.css");
+@import url("theme-polarnight.css");
+@import url("theme-ketivah.css");
+@import url("theme-mangotango.css");
+@import url("theme-playground.css");
+@import url("theme-beeninorder.css");
+
+:root {
+/* ---------- PRIMARY COLORS ----------------- */
+--primary-color-1: hsl(217.5, 26.7%, 94.1%);
+    --primary-color-1-hue:             217.5;
+    --primary-color-1-saturation:      26.7%;
+    --primary-color-1-lightness:       94.1%;
+
+--primary-color-2: hsl(218.2, 26.8%, 92.0%);
+    --primary-color-2-hue:             218.2;
+    --primary-color-2-saturation:      26.8%;
+    --primary-color-2-lightness:       92.0%;
+
+--primary-color-3: hsl(218.8, 27.9%, 88.0%);
+    --primary-color-3-hue:             218.8;
+    --primary-color-3-saturation:      27.9%;
+    --primary-color-3-lightness:       88.0%;
+
+--primary-color-4: hsl(218.8, 18.3%, 81.8%);
+    --primary-color-4-hue:             218.8;
+    --primary-color-4-saturation:      18.3%;
+    --primary-color-4-lightness:       81.8%;
+
+
+/* ---------- SECONDARY COLORS --------------- */
+--secondary-color-1: hsl(220.0, 16.4%, 21.6%);
+    --secondary-color-1-hue:             220.0;
+    --secondary-color-1-saturation:      16.4%;
+    --secondary-color-1-lightness:       21.6%;
+
+--secondary-color-2: hsl(221.7, 16.3%, 27.6%);
+    --secondary-color-2-hue:             221.7;
+    --secondary-color-2-saturation:      16.3%;
+    --secondary-color-2-lightness:       27.6%;
+
+--secondary-color-3: hsl(220.0, 16.8%, 31.6%);
+    --secondary-color-3-hue:             220.0;
+    --secondary-color-3-saturation:      16.8%;
+    --secondary-color-3-lightness:       31.6%;
+
+--secondary-color-4: hsl(220.0, 16.5%, 35.7%);
+    --secondary-color-4-hue:             220.0;
+    --secondary-color-4-saturation:      16.5%;
+    --secondary-color-4-lightness:       35.7%;
+
+
+
+/* ----------- NUANCES COLORS ---------------- */
+--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
+    --theme-nuance-color-1-hue:             178.7;
+    --theme-nuance-color-1-saturation:      25.1%;
+    --theme-nuance-color-1-lightness:       64.9%;
+
+--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
+    --theme-nuance-color-2-hue:             193.3;
+    --theme-nuance-color-2-saturation:      43.4%;
+    --theme-nuance-color-2-lightness:       67.5%;
+
+--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
+    --theme-nuance-color-3-hue:             210.0;
+    --theme-nuance-color-3-saturation:      34.0%;
+    --theme-nuance-color-3-lightness:       63.1%;
+
+--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
+    --theme-nuance-color-4-hue:             213.1;
+    --theme-nuance-color-4-saturation:      32.0%;
+    --theme-nuance-color-4-lightness:       52.2%;
+
+
+
+/* ----------- ROYGP COLORS ------------------ */
+--theme-red-color:    hsl(32.5, 80%, 50%);
+--theme-orange-color: hsl(32.5, 70%, 45%);
+--theme-yellow-color: hsl(40.0,   0.6%, 73.3%);
+--theme-green-color:  hsl(92.4,  27.8%, 64.7%);
+--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
+
+
+
+/* ------------------------------------------- */
+--background-color-1:    var(--primary-color-1);
+--background-color-2:    var(--primary-color-2);
+--background-color-3:    var(--primary-color-3);
+--background-color-4:    var(--primary-color-4);
+
+--border-color-1:        var(--primary-color-2);
+--border-color-2:        var(--primary-color-3);
+--border-color-3:        var(--primary-color-4);
+
+--border-focus-color:    var(--theme-nuance-color-2);
+--border-focus-shadow:   var(--theme-nuance-color-1);
+
+--text-color-plain:      var(--secondary-color-1);
+--text-color-subtile-1:  var(--secondary-color-2);
+--text-color-subtile-2:  var(--secondary-color-3);
+
+--code-background-color: var(--secondary-color-2);
+--code-text-color:       var(--primary-color-2);
+
+--ui-range-thumb-color:  var(--theme-nuance-color-3);
+--ui-range-thumb-border: var(--ui-ranger-thumb-color);
+
+--textarea-border-color: var(--secondary-color-4);
+
+--chat-id-color:         var(--theme-nuance-color-4);
+
+
+
+/* ------------------------------------------- */
+--button-alert-text-hover:       var(--primary-color-1);
+--button-alert-color-hover:      var(--theme-orange-color);
+--button-alert-border-hover:     var(--theme-orange-color);
+
+--button-alert-text-active:      var(--primary-color-1);
+--button-alert-color-active:     var(--theme-red-color);
+--button-alert-border-active:    var(--theme-red-color);
+
+
+
+/* ----------- PRIMARY BUTTONS --------------- */
+/* - button should immediately catch the eye - */
+--button-primary-text:   var(--secondary-color-1);
+--button-primary-color:  var(--theme-nuance-color-3);
+--button-primary-border: var(--theme-nuance-color-3);
+
+
+/* ---------hover---------- */
+--button-primary-text-hover:
+    hsl(217.5,
+    calc(var(--secondary-color-1-saturation) + 35%),
+    calc(var(--secondary-color-1-lightness)  - 30%));
+
+--button-primary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) -  2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+--button-primary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) -  2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+
+/* ---------active--------- */
+--button-primary-text-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 35%));
+
+--button-primary-color-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 25%));
+
+--button-primary-border-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 25%));
+
+
+
+/* ---------- SECONDARY BUTTONS -------------- */
+/* these should NOT immediately catch the eye  */
+--button-secondary-text:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 50%));
+
+--button-secondary-color:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+--button-secondary-border:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+
+/* ---------hover---------- */
+--button-secondary-text-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+--button-secondary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 22%),
+    calc(var(--theme-nuance-color-3-lightness)  +  1%));
+
+--button-secondary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 22%),
+    calc(var(--theme-nuance-color-3-lightness)  +  1%));
+
+
+/* ---------active--------- */
+--button-secondary-text-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) + 40%),
+    calc(var(--theme-nuance-color-3-lightness)  - 55%));
+
+--button-secondary-color-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 30%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+--button-secondary-border-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 30%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+
+
+/* ---------- TERTIARY BUTTONS --------------- */
+/* ---------- disabled buttons --------------- */
+--button-tertiary-text:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+--button-tertiary-color:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+--button-tertiary-border:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+/* ---------hover---------- */
+--button-tertiary-text-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+--button-tertiary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+--button-tertiary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+}
+
+/*
+
+.theme-template {
+
+
+    If light theme: should go from bright to darker
+    If dark theme: should go from dark to brighter
+    ideally this should not be anything but steps of
+    gray or slightly variants from it
+
+    --primary-color-1: #2E3440;
+    --primary-color-2: #3B4252;
+    --primary-color-3: #434C5E;
+    --primary-color-4: #4C566A;
+
+
+
+    If light theme: should go from dark to brighter
+    If dark theme: should go from bright to darker
+    ideally this should not be anything but steps of
+    gray or slightly variants from it
+
+    --secondary-color-1: #ECEFF4;
+    --secondary-color-2: #E5E9F0;
+    --secondary-color-3: #D8DEE9;
+    --secondary-color-4: #C8CED9;
+
+
+
+    Choose wisely nuance colors. It is not easy to find
+    4 harmonizing nuance colors. But keep in mind, that
+    only one accent color could work too.
+
+    --theme-nuance-color-1: #8FBCBB;
+    --theme-nuance-color-2: #88C0D0;
+    --theme-nuance-color-3: #81A1C1;
+    --theme-nuance-color-4: #5E81AC;
+
+
+
+    adapt the color red, orange, yellow, green,
+    purple to the 'mood' of your overall design
+    e.g is it low-contrast? vibrant? dynamic? etc
+
+    --theme-red-color:    #BF616A;
+    --theme-orange-color: #D08770;
+    --theme-yellow-color: #EBCB8B;
+    --theme-green-color:  #A3BE8C;
+    --theme-purple-color: #B48EAD;
+
+
+
+NOTE: comment all those line `--- ...` out
+------------------------------------------------
+--background-color-1:
+--background-color-2:
+--background-color-3:
+--background-color-4:
+
+--border-color-1:
+--border-color-2:
+--border-color-3:
+
+--border-focus-color:
+--border-focus-shadow:
+
+--text-color-plain:
+--text-color-subtile-1:
+--text-color-subtile-2:
+
+--code-background-color:
+--code-text-color:
+
+--ui-range-thumb-color:
+--ui-range-thumb-border:
+
+--textarea-border-color:
+
+
+
+-------------------------------------------
+--button-alert-text-hover:
+--button-alert-color-hover:
+--button-alert-border-hover:
+
+--button-alert-text-active:
+--button-alert-color-active:
+--button-alert-border-active:
+
+
+
+----------- PRIMARY -----------------------
+--button should immediately catch the eye--
+
+--button-primary-text:
+--button-primary-color:
+--button-primary-border:
+
+
+---------hover----------
+--button-primary-text-hover:
+--button-primary-color-hover:
+--button-primary-border-hover:
+
+
+---------active---------
+--button-primary-text-active:
+--button-primary-color-active:
+--button-primary-border-active:
+
+
+
+------------ SECONDARY ------------------------
+--button should NOT immediately catch the eye--
+
+--button-secondary-text:
+--button-secondary-color:
+--button-secondary-border:
+
+
+---------hover----------
+--button-secondary-text-hover:
+--button-secondary-color-hover:
+--button-secondary-border-hover:
+
+
+---------active---------
+--button-secondary-text-active:
+--button-secondary-color-active:
+--button-secondary-border-active:
+
+
+
+---------- TERTIARY -----------------------
+---------- disabled buttons ---------------
+--button-tertiary-text:
+--button-tertiary-color:
+--button-tertiary-border:
+
+
+---------hover----------
+--button-tertiary-text:
+--button-tertiary-color:
+--button-tertiary-border:
+
+}
+
+*/
diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js
index 987b9a3b891d0..36818f7644aa1 100644
--- a/examples/server/public/completion.js
+++ b/examples/server/public/completion.js
@@ -21,7 +21,7 @@ let generation_settings = null;
 //
 export async function* llama(prompt, params = {}, config = {}) {
   let controller = config.controller;
-  const api_url = config.api_url || "";
+  const api_url = config.api_url?.replace(/\/+$/, '') || "";
 
   if (!controller) {
     controller = new AbortController();
@@ -196,7 +196,7 @@ export const llamaComplete = async (params, controller, callback) => {
 // Get the model info from the server. This is useful for getting the context window and so on.
 export const llamaModelInfo = async (config = {}) => {
   if (!generation_settings) {
-    const api_url = config.api_url || "";
+    const api_url = config.api_url?.replace(/\/+$/, '') || "";
     const props = await fetch(`${api_url}/props`).then(r => r.json());
     generation_settings = props.default_generation_settings;
   }
diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html
new file mode 100644
index 0000000000000..c87dd8f1e1d32
--- /dev/null
+++ b/examples/server/public/index-new.html
@@ -0,0 +1,1178 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
+  <meta name="color-scheme" content="light dark">
+  <title>llama.cpp - chat</title>
+
+  <link rel="icon" type="image/x-icon" href="favicon.ico">
+  <link rel="stylesheet" href="style.css">
+
+  <script type="module">
+    import {
+      html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
+    } from './index.js';
+
+    import { llama } from './completion.js';
+    import { SchemaConverter } from './json-schema-to-grammar.mjs';
+    import { promptFormats } from './prompt-formats.js';
+    import { systemPrompts } from './system-prompts.js'; // multilingual is wip
+    let selected_image = false;
+    var slot_id = -1;
+
+    const session = signal({
+      prompt: "",
+      template: "{{prompt}}\n{{history}}{{char}}",
+      historyTemplate: "{{name}}: {{message}}\n",
+      transcript: [],
+      type: "chat",  // "chat" | "completion"
+      char: "ASSISTANT",
+      user: "USER",
+      image_selected: ''
+    })
+
+    const params = signal({
+      n_predict: 358, // 358 is a nice number
+      temperature: 0.8, // adapt all following parameters to optimized min-p requierements. If for non-english, set to 0.6 or lower
+      repeat_last_n: 0, // 0 = disable penalty, -1 = context size
+      repeat_penalty: 1.0, // 1.0 = disabled
+      penalize_nl: false, // true only useful for infinite completion
+      top_k: 0, // <= 0 to use vocab size
+      top_p: 1.0, // 1.0 = disabled
+      min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4
+      tfs_z: 1.0, // 1.0 = disabled
+      typical_p: 1.0, // 1.0 = disabled
+      presence_penalty: 0.0, // 0.0 = disabled
+      frequency_penalty: 0.0, // 0.0 = disabled
+      mirostat: 0, // 0/1/2
+      mirostat_tau: 5, // target entropy
+      mirostat_eta: 0.1, // learning rate
+      grammar: '',
+      n_probs: 0, // no completion_probabilities,
+      min_keep: 0, // min probs from each sampler,
+      image_data: [],
+      cache_prompt: true,
+      api_key: ''
+    })
+
+
+
+    /* START: Support for storing prompt templates and parameters in browser's LocalStorage */
+
+    const local_storage_storageKey = "llamacpp_server_local_storage";
+
+    function local_storage_setDataFromObject(tag, content) {
+      localStorage.setItem(local_storage_storageKey + '/' + tag, JSON.stringify(content));
+    }
+
+    function local_storage_setDataFromRawText(tag, content) {
+      localStorage.setItem(local_storage_storageKey + '/' + tag, content);
+    }
+
+    function local_storage_getDataAsObject(tag) {
+      const item = localStorage.getItem(local_storage_storageKey + '/' + tag);
+      if (!item) {
+        return null;
+      } else {
+        return JSON.parse(item);
+      }
+    }
+
+    function local_storage_getDataAsRawText(tag) {
+      const item = localStorage.getItem(local_storage_storageKey + '/' + tag);
+      if (!item) {
+        return null;
+      } else {
+        return item;
+      }
+    }
+
+    // create a container for user templates and settings
+
+    const savedUserTemplates = signal({})
+    const selectedUserTemplate = signal({ name: '', template: { session: {}, params: {} } })
+
+    // let's import locally saved templates and settings if there are any
+    // user templates and settings are stored in one object
+    // in form of { "templatename": "templatedata" } and { "settingstemplatename":"settingsdata" }
+
+    console.log('Importing saved templates')
+
+    let importedTemplates = local_storage_getDataAsObject('user_templates')
+
+    if (importedTemplates) {
+      // saved templates were successfuly imported.
+
+      console.log('Processing saved templates and updating default template')
+      params.value = { ...params.value, image_data: [] };
+
+      //console.log(importedTemplates);
+      savedUserTemplates.value = importedTemplates;
+
+      //override default template
+      savedUserTemplates.value.default = { session: session.value, params: params.value }
+      local_storage_setDataFromObject('user_templates', savedUserTemplates.value)
+    } else {
+      // no saved templates detected.
+
+      console.log('Initializing LocalStorage and saving default template')
+
+      savedUserTemplates.value = { "default": { session: session.value, params: params.value } }
+      local_storage_setDataFromObject('user_templates', savedUserTemplates.value)
+    }
+
+    function userTemplateResetToDefault() {
+      console.log('Reseting themplate to default')
+      selectedUserTemplate.value.name = 'default';
+      selectedUserTemplate.value.data = savedUserTemplates.value['default'];
+    }
+
+    function userTemplateApply(t) {
+      session.value = t.data.session;
+      session.value = { ...session.value, image_selected: '' };
+      params.value = t.data.params;
+      params.value = { ...params.value, image_data: [] };
+    }
+
+    function userTemplateResetToDefaultAndApply() {
+      userTemplateResetToDefault()
+      userTemplateApply(selectedUserTemplate.value)
+    }
+
+    function userTemplateLoadAndApplyAutosaved() {
+      // get autosaved last used template
+      let lastUsedTemplate = local_storage_getDataAsObject('user_templates_last')
+
+      if (lastUsedTemplate) {
+
+        console.log('Autosaved template found, restoring')
+
+        selectedUserTemplate.value = lastUsedTemplate
+      }
+      else {
+
+        console.log('No autosaved template found, using default template')
+        // no autosaved last used template was found, so load from default.
+
+        userTemplateResetToDefault()
+      }
+
+      console.log('Applying template')
+      // and update internal data from templates
+
+      userTemplateApply(selectedUserTemplate.value)
+    }
+
+    //console.log(savedUserTemplates.value)
+    //console.log(selectedUserTemplate.value)
+
+    function userTemplateAutosave() {
+      console.log('Template Autosave...')
+      if (selectedUserTemplate.value.name == 'default') {
+        // we don't want to save over default template, so let's create a new one
+        let newTemplateName = 'UserTemplate-' + Date.now().toString()
+        let newTemplate = { 'name': newTemplateName, 'data': { 'session': session.value, 'params': params.value } }
+
+        console.log('Saving as ' + newTemplateName)
+
+        // save in the autosave slot
+        local_storage_setDataFromObject('user_templates_last', newTemplate)
+
+        // and load it back and apply
+        userTemplateLoadAndApplyAutosaved()
+      } else {
+        local_storage_setDataFromObject('user_templates_last', { 'name': selectedUserTemplate.value.name, 'data': { 'session': session.value, 'params': params.value } })
+      }
+    }
+
+    console.log('Checking for autosaved last used template')
+    userTemplateLoadAndApplyAutosaved()
+
+    /* END: Support for storing prompt templates and parameters in browser's LocalStorage */
+
+    const llamaStats = signal(null)
+    const controller = signal(null)
+
+    // currently generating a completion?
+    const generating = computed(() => controller.value != null)
+
+    // has the user started a chat?
+    const chatStarted = computed(() => session.value.transcript.length > 0)
+
+    const transcriptUpdate = (transcript) => {
+      session.value = {
+        ...session.value,
+        transcript
+      }
+    }
+
+    // simple template replace
+    const template = (str, extraSettings) => {
+      let settings = session.value;
+      if (extraSettings) {
+        settings = { ...settings, ...extraSettings };
+      }
+      return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
+    }
+
+    async function runLlama(prompt, llamaParams, char) {
+      const currentMessages = [];
+      const history = session.value.transcript;
+      if (controller.value) {
+        throw new Error("already running");
+      }
+      controller.value = new AbortController();
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: new URL('.', document.baseURI).href })) {
+        const data = chunk.data;
+        if (data.stop) {
+          while (
+            currentMessages.length > 0 &&
+            currentMessages[currentMessages.length - 1].content.match(/\n$/) != null
+          ) {
+            currentMessages.pop();
+          }
+          transcriptUpdate([...history, [char, currentMessages]])
+          console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data);
+        } else {
+          currentMessages.push(data);
+          slot_id = data.slot_id;
+          if (selected_image && !data.multimodal) {
+            alert("The server was not compiled for multimodal or the model projector can't be loaded.");            return;
+          }
+          transcriptUpdate([...history, [char, currentMessages]])
+        }
+        if (data.timings) {
+          // llamaStats.value = data.timings;
+          llamaStats.value = data;
+        }
+      }
+      controller.value = null;
+    }
+
+    // send message to server
+    const chat = async (msg) => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
+      }
+    // just in case (e.g. llama2)
+    const suffix = session.value.userMsgSuffix || "";
+    const prefix = session.value.userMsgPrefix || "";
+    const userMsg = prefix + msg + suffix;
+
+      transcriptUpdate([...session.value.transcript, ["{{user}}", userMsg]])
+
+      let prompt = template(session.value.template, {
+        message: msg,
+        history: session.value.transcript.flatMap(
+          ([name, data]) =>
+            template(
+              session.value.historyTemplate,
+              {
+                name,
+                message: Array.isArray(data) ?
+                  data.map(msg => msg.content).join('').replace(/^\s/, '') :
+                  data,
+              }
+            )
+        ).join(''),
+      });
+      if (selected_image) {
+        prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`;
+      }
+      await runLlama(prompt, {
+        ...params.value,
+        slot_id: slot_id,
+        stop: ["</s>", "<|end|>", "<|eot_id|>", "<|end_of_text|>", "<|im_end|>", "<|EOT|>", "<|END_OF_TURN_TOKEN|>", "<|end_of_turn|>", "<|endoftext|>", template("{{char}}"), template("{{user}}")],
+      }, "{{char}}");
+    }
+
+    const runCompletion = () => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
+      }
+      const { prompt } = session.value;
+      transcriptUpdate([...session.value.transcript, ["", prompt]]);
+      runLlama(prompt, {
+        ...params.value,
+        slot_id: slot_id,
+        stop: [],
+      }, "").finally(() => {
+        session.value.prompt = session.value.transcript.map(([_, data]) =>
+          Array.isArray(data) ? data.map(msg => msg.content).join('') : data
+        ).join('');
+        session.value.transcript = [];
+      })
+    }
+
+    const stop = (e) => {
+      e.preventDefault();
+      if (controller.value) {
+        controller.value.abort();
+        controller.value = null;
+      }
+    }
+
+    const reset = (e) => {
+      stop(e);
+      transcriptUpdate([]);
+    }
+
+    const uploadImage = (e) => {
+      e.preventDefault();
+      document.getElementById("fileInput").click();
+      document.getElementById("fileInput").addEventListener("change", function (event) {
+        const selectedFile = event.target.files[0];
+        if (selectedFile) {
+          const reader = new FileReader();
+          reader.onload = function () {
+            const image_data = reader.result;
+            session.value = { ...session.value, image_selected: image_data };
+            params.value = {
+              ...params.value, image_data: [
+                { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }]
+            }
+          };
+          selected_image = true;
+          reader.readAsDataURL(selectedFile);
+        }
+      });
+    }
+
+    function MessageInput() {
+      const message = useSignal("")
+
+      const submit = (e) => {
+        stop(e);
+        chat(message.value);
+        message.value = "";
+      }
+
+      const enterSubmits = (event) => {
+        if (event.which === 13 && !event.shiftKey) {
+          submit(event);
+        }
+      }
+
+      return html`
+      <form onsubmit=${submit}>
+        <div class="chat-input-container">
+          <textarea
+            id="chat-input" placeholder="Say Something ... (Shift + Enter for new line)"
+            class="${generating.value ? 'loading' : null}"
+            oninput=${(e) => message.value = e.target.value}
+            onkeypress=${enterSubmits}
+            rows="2"
+            type="text"
+            value="${message}"
+          ></textarea>
+        </div>
+
+          <div class="right">
+            <button class="button-back" onclick=${reset}>Back</button>
+            <button onclick=${uploadImage}>Upload Image</button>
+            <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+            <button type="submit" disabled=${generating.value}>Submit</button>
+          </div>
+        </form>
+      `
+    }
+
+    // the completion view needs some ux improvements
+    function CompletionControls() {
+      const submit = (e) => {
+        stop(e);
+        runCompletion();
+      }
+      return html`
+        <div class="right">
+          <button onclick=${submit} type="button" disabled=${generating.value}>Start</button>
+          <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+          <button onclick=${reset}>Back</button>
+        </div>`;
+    }
+
+    const ChatLog = (props) => {
+      const messages = session.value.transcript;
+      const container = useRef(null)
+
+      useEffect(() => {
+        // scroll to bottom (if needed)
+        const parent = container.current.parentElement;
+        if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) {
+          parent.scrollTo(0, parent.scrollHeight)
+        }
+      }, [messages])
+
+      const isCompletionMode = session.value.type === 'completion'
+      const chatLine = ([user, data], index) => {
+        let message
+        const isArrayMessage = Array.isArray(data)
+        if (params.value.n_probs > 0 && isArrayMessage) {
+          message = html`<${Probabilities} data=${data} />`
+        } else {
+          const text = isArrayMessage ?
+            data.map(msg => msg.content).join('') :
+            data;
+          message = isCompletionMode ?
+            text :
+            html`<${Markdownish} text=${template(text)} />`
+        }
+        if (user) {
+          return html`<p key=${index}><strong class="chat-id-color">${template(user)}</strong> ${message}</p>`
+        } else {
+          return isCompletionMode ?
+            html`<span key=${index}>${message}</span>` :
+            html`<p key=${index}>${message}</p>`
+        }
+      };
+
+      const handleCompletionEdit = (e) => {
+        session.value.prompt = e.target.innerText;
+        session.value.transcript = [];
+      }
+
+      return html`
+        <div id="chat" ref=${container} key=${messages.length}>
+          <img style="width: 60%;${!session.value.image_selected ? `display: none;` : ``}" src="${session.value.image_selected}"/>
+          <span contenteditable=${isCompletionMode} ref=${container} oninput=${handleCompletionEdit}>
+            ${messages.flatMap(chatLine)}
+          </span>
+        </div>`;
+    };
+
+
+
+///////////// UI Improvements /////////////
+//
+//
+const handleToggleChange = (e) => {
+  const isChecked = e.target.checked;
+  session.value = { ...session.value, type: isChecked ? 'completion' : 'chat' };
+  localStorage.setItem('toggleState', isChecked);
+}
+//
+const loadToggleState = () => {
+  const storedState = localStorage.getItem('toggleState');
+  if (storedState !== null) {
+    const isChecked = storedState === 'true';
+    document.getElementById('toggle').checked = isChecked;
+    session.value = { ...session.value, type: isChecked ? 'completion' : 'chat' };
+  }
+}
+//
+document.addEventListener('DOMContentLoaded', loadToggleState);
+//
+//
+// function to update the prompt format
+function updatePromptFormat(e) {
+  const promptFormat = e.target.value;
+  if (promptFormats.hasOwnProperty(promptFormat)) {
+    session.value = {
+      ...session.value,
+      ...promptFormats[promptFormat]
+    };
+  } else {
+    // Use vicuna as llama.cpp's default setting, since it's most common
+    session.value = {
+      ...session.value,
+      template: "{{prompt}}\n{{history}}{{char}}",
+      historyTemplate: "{{name}}: {{message}}\n",
+      char: "ASSISTANT",
+      user: "USER"
+    };
+  }
+  console.log('Updated session value:', session.value);
+}
+//
+//
+// function to update the prompt format from the selected one
+function updatePromptFormatFromDropdown(element) {
+  const promptFormat = element.getAttribute('data-value');
+  console.log('Selected prompt format:', promptFormat); // debugging
+  updatePromptFormat({ target: { value: promptFormat } });
+}
+//
+//
+// function that adds the event listers as soon as the element is available
+function addEventListenersWhenAvailable() {
+  var themeSelector = document.getElementById('theme-selector');
+  if (themeSelector) {
+    themeSelector.addEventListener('change', function(event) {
+      // event-handler-code...
+    });
+    // placeholder event listeners
+  } else {
+    // if the element is not there yet, wait ahead
+    requestAnimationFrame(addEventListenersWhenAvailable);
+  }
+}
+//
+//
+// begin with the check
+requestAnimationFrame(addEventListenersWhenAvailable);
+//
+//
+// avoid default and create new event object with value from data value attribute
+function handleDropdownSelection(e, promptFormat) {
+  e.preventDefault();
+  const customEvent = {
+    target: {
+      value: promptFormat
+    }
+  };
+  // call our updatePromptFormat-function
+  updatePromptFormat(customEvent);
+}
+//
+//
+// function to update the system message
+function updateSystemPrompt(e) {
+  const SystemPrompt = e.target.value;
+  if (systemPrompts.hasOwnProperty(SystemPrompt)) {
+    session.value = {
+      ...session.value,
+      prompt: systemPrompts[SystemPrompt].systemPrompt
+    };
+  }
+}
+//
+//
+///////////// UI Improvements /////////////
+
+
+
+
+const ConfigForm = (props) => {
+  const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
+  const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
+  const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
+  const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
+  const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked }
+
+  const grammarJsonSchemaPropOrder = signal('')
+  const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value
+  const convertJSONSchemaGrammar = async () => {
+    try {
+      let schema = JSON.parse(params.value.grammar)
+      const converter = new SchemaConverter({
+        prop_order: grammarJsonSchemaPropOrder.value
+          .split(',')
+          .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}),
+        allow_fetch: true,
+      })
+      schema = await converter.resolveRefs(schema, 'input')
+      converter.visit(schema, '')
+      params.value = {
+        ...params.value,
+        grammar: converter.formatGrammar(),
+      }
+    } catch (e) {
+      alert(`Convert failed: ${e.message}`)
+    }
+  }
+
+  const FloatField = ({ label, title, max, min, name, step, value }) => {
+return html`
+<div>
+  <label for="${name}"><span title="${title}">${label}</span></label>
+  <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} title="${title}" />
+  <span id="${name}-value">${value}</span>
+</div>
+`
+};
+
+const IntField = ({ label, title, max, min, step, name, value }) => {
+return html`
+<div>
+  <label for="${name}"><span title="${title}">${label}</span></label>
+  <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsInt} title="${title}" />
+  <span id="${name}-value">${value}</span>
+</div>
+`
+};
+
+const BoolField = ({ label, title, name, value }) => {
+return html`
+<div>
+  <label for="${name}"><span title="${title}">${label}</span></label>
+  <input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} title="${title}" />
+</div>
+`
+};
+
+  const userTemplateReset = (e) => {
+    e.preventDefault();
+    userTemplateResetToDefaultAndApply()
+  }
+
+  const UserTemplateResetButton = () => {
+    if (selectedUserTemplate.value.name == 'default') {
+      return html`
+      <button class="reset-button" id="id_reset" onclick="${userTemplateReset}">Reset</button>
+      `
+    }
+
+    return html`
+      <div class="button-container">
+        <button class="reset-button" title="Caution: This resets the entire form." onclick="${userTemplateReset}">Reset</button>
+      </div>
+    `
+  };
+
+  useEffect(() => {
+    // autosave template on every change
+    userTemplateAutosave()
+  }, [session.value, params.value])
+
+  const GrammarControl = () => (
+    html`
+      <div>
+        <div class="grammar">
+          <label for="template"></label>
+          <textarea id="grammar" name="grammar" placeholder="Use GBNF or JSON Schema + Converter" value="${params.value.grammar}" rows=4 oninput=${updateParams}/>
+        </div>
+        <div class="grammar-columns">
+          <div class="json-schema-controls">
+            <input type="text" name="prop-order" placeholder="Order: prop1,prop2,prop3" oninput=${updateGrammarJsonSchemaPropOrder} />
+            <button type="button" class="button-grammar" onclick=${convertJSONSchemaGrammar}>Convert JSON Schema</button>
+          </div>
+        </div>
+      </div>
+    `
+  );
+
+  const PromptControlFieldSet = () => (
+    html`
+      <fieldset>
+        <div class="input-container">
+          <label for="prompt" class="input-label">System</label>
+          <textarea
+            id="prompt"
+            class="persistent-input"
+            name="prompt"
+            placeholder="[Note] The following models do not support System Prompts by design:\n• OpenChat\n• Orion\n• Phi-3\n• Starling\n• Yi-...-Chat"
+            value="${session.value.prompt}"
+            oninput=${updateSession}
+          ></textarea>
+        </div>
+      </fieldset>
+    `
+  );
+
+  const ChatConfigForm = () => (
+    html`
+      <fieldset class="dropdowns">
+        <div>
+          <select id="promptFormat" name="promptFormat" onchange=${updatePromptFormat}>
+              <option value="default">Prompt Style</option>
+              <option value=""></option>
+            <optgroup label="Common Prompt-Styles">
+              <option value="alpaca">Alpaca</option>
+              <option value="chatml">ChatML</option>
+              <option value="commandr">Command R/+</option>
+              <option value="llama2">Llama 2</option>
+              <option value="llama3">Llama 3</option>
+              <option value="phi3">Phi-3</option>
+              <option value="openchat">OpenChat/Starling</option>
+              <option value="vicuna">Vicuna</option>
+              <option value=""></option>
+            </optgroup>
+            <optgroup label="More Prompt-Styles">
+              <option value="vicuna">Airoboros L2</option>
+              <option value="vicuna">BakLLaVA-1</option>
+              <option value="alpaca">Code Cherry Pop</option>
+              <option value="deepseekCoder">Deepseek Coder</option>
+              <option value="chatml">Dolphin Mistral</option>
+              <option value="chatml">evolvedSeeker 1.3B</option>
+              <option value="vicuna">Goliath 120B</option>
+              <option value="vicuna">Jordan</option>
+              <option value="vicuna">LLaVA</option>
+              <option value="chatml">Leo Hessianai</option>
+              <option value="vicuna">Leo Mistral</option>
+              <option value="vicuna">Marx</option>
+              <option value="med42">Med42</option>
+              <option value="alpaca">MetaMath</option>
+              <option value="llama2">Mistral Instruct</option>
+              <option value="chatml">Mistral 7B OpenOrca</option>
+              <option value="alpaca">MythoMax</option>
+              <option value="neuralchat">Neural Chat</option>
+              <option value="vicuna">Nous Capybara</option>
+              <option value="nousHermes">Nous Hermes</option>
+              <option value="openchatMath">OpenChat Math</option>
+              <option value="chatml">OpenHermes 2.5-Mistral</option>
+              <option value="alpaca">Orca Mini v3</option>
+              <option value="orion">Orion</option>
+              <option value="vicuna">Samantha</option>
+              <option value="chatml">Samantha Mistral</option>
+              <option value="sauerkrautLM">SauerkrautLM</option>
+              <option value="vicuna">Scarlett</option>
+              <option value="starlingCode">Starling Coding</option>
+              <option value="alpaca">Sydney</option>
+              <option value="vicuna">Synthia</option>
+              <option value="vicuna">Tess</option>
+              <option value="yi34b">Yi-6/9/34B-Chat</option>
+              <option value="zephyr">Zephyr</option>
+              <option value=""></option>
+            </optgroup>
+          </select>
+          <select id="SystemPrompt" name="SystemPrompt" onchange=${updateSystemPrompt}>
+            <option value="default">System Prompt</option>
+            <option value="empty">None</option>
+            <option value="airoboros">Airoboros</option>
+            <option value="alpaca">Alpaca</option>
+            <option value="atlas">Atlas</option>
+            <option value="atlas_de">Atlas - DE</option>
+            <option value="cot">Chain of Tought</option>
+            <option value="commandrempty">Command R/+ (empty)</option>
+            <option value="commandrexample">Command R/+ (example)</option>
+            <option value="deduce">Critical Thinking</option>
+            <option value="deepseekcoder">Deepseek Coder</option>
+            <option value="jordan">Jordan</option>
+            <option value="leomistral">Leo Mistral</option>
+            <option value="med42">Med42</option>
+            <option value="migeltot">Migel's Tree of Thought</option>
+            <option value="mistralopenorca">Mistral OpenOrca</option>
+            <option value="orcamini">Orca Mini</option>
+            <option value="samantha">Samantha</option>
+            <option value="sauerkraut">Sauerkraut</option>
+            <option value="scarlett">Scarlett</option>
+            <option value="synthia">Synthia</option>
+            <option value="vicuna">Vicuna</option>
+          </select>
+          <!--<select id="systemLanguage" name="systemLanguage">-->
+            <!--<option value="default">English</option>-->
+            <!--<option value="DE">German</option>-->
+            <!--<option value="placeholderLanguage">Placeholder</option>-->
+          <!--</select>-->
+        </div>
+      </fieldset>
+      ${PromptControlFieldSet()}
+        <fieldset>
+          <details>
+            <summary><span class="summary-title" id="id_prompt-style">Prompt Style</span></summary>
+            <fieldset class="names">
+          <div>
+            <label for="user" id="id_user-name">User ID</label>
+            <input type="text" id="user" name="user" value="${session.value.user}" oninput=${updateSession} />
+          </div>
+          <div>
+            <label for="bot" id="id_bot-name">AI ID</label>
+            <input type="text" id="bot" name="char" value="${session.value.char}" oninput=${updateSession} />
+          </div>
+        </fieldset>
+          <div class="two-columns">
+            <div>
+              <div class="input-container">
+                <label for="template" class="input-label-sec" id_prompt-template>Prompt Template</label>
+                <textarea id="template" class="persistent-input-sec" name="template" value="${session.value.template}" rows=6 oninput=${updateSession}/>
+              </div>
+            </div>
+            <div>
+              <div class="input-container">
+                <label for="template" class="input-label-sec" id="id_history-template">Chat History</label>
+                <textarea id="history-template" class="persistent-input-sec" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
+              </div>
+            </div>
+          </div>
+        </details>
+        <details>
+          <summary><span class="summary-title" id="id_grammar-title" id_grammar-title>Grammar</span></summary>
+          ${GrammarControl()}
+        </details>
+
+        </fieldset>
+    `
+  );
+
+  const CompletionConfigForm = () => (
+    html`
+      ${PromptControlFieldSet()}
+      <fieldset>
+        <details>
+          <summary><span class="summary-title" id="id_grammar-title" id_grammar-title>Grammar</span></summary>
+          ${GrammarControl()}
+        </details>
+      </fieldset>
+    `
+  );
+// todo toggle button et api field et reset button in one nice row
+  return html`
+    <form>
+      <fieldset class="two">
+          <input type="checkbox" id="toggle" class="toggleCheckbox" onchange=${handleToggleChange} />
+            <label for="toggle" class="toggleContainer">
+              <div id="id_toggle-label-chat">Chat</div>
+              <div id="id_toggle-label-complete">Complete</div>
+            </label>
+      <fieldset>
+
+          <input type="text" id="api_key" class="apiKey" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
+      </fieldset>
+
+        <${UserTemplateResetButton}/>
+      </fieldset>
+
+      ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()}
+
+      <fieldset class="params">
+        ${IntField({ label: "Prediction", title: "Set the maximum number of tokens to predict when generating text. Note: May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. The value -1 means infinity. Default is 358", max: 2048, min: -1, step: 16, name: "n_predict", value: params.value.n_predict, })}
+        ${FloatField({ label: "Min-P sampling", title: "The minimum probability for a token to be considered, relative to the probability of the most likely token. Note that it's good practice to disable all other samplers aside from temperature when using min-p. It is also recommenend to go this approach. Default is 0.05 – But consider higher values like ~ 0.4 for non-English text generation. The value 1.0 means disabled", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
+        ${FloatField({ label: "Repetition Penalty", title: "Control the repetition of token sequences in the generated text. Default is 1.1", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
+        ${FloatField({ label: "Temperature", title: "This will adjust the overall randomness of the generated text. It is the most common sampler. Default is 0.8 but consider using lower values for more factual texts or for non-English text generation", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
+      </fieldset>
+
+      <details>
+        <summary><span class="summary-title">Further Options</span></summary>
+        <fieldset class="params">
+          ${IntField({ label: "Top-K", title: "Limits the selection of the next token to the K most probable tokens. 1 means no randomness = greedy sampling. If set to 0, it means the entire vocabulary size is considered.", max: 100, min: 0, step: 1, name: "top_k", value: params.value.top_k })}
+          ${IntField({ label: "Penalize Last N", title: "The last n tokens that are taken into account to penalise repetitions. A value of 0 means that this function is deactivated and -1 means that the entire size of the context is taken into account.", max: 2048, min: 0, step: 16, name: "repeat_last_n", value: params.value.repeat_last_n })}
+          ${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
+          ${FloatField({ label: "Presence Penalty", title: "A penalty that is applied if certain tokens appear repeatedly in the generated text. A higher value leads to fewer repetitions.", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
+          ${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
+          ${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
+          ${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
+          ${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
+        </fieldset>
+
+        <hr style="height: 1px; background-color: #ececf1; border: none;" />
+
+        <fieldset class="three">
+          <label title="The Mirostat sampling method is an algorithm used in natural language processing to improve the quality and coherence of the generated texts. It is an at-runtime-adaptive method that aims to keep the entropy or surprise of a text within a desired range."><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> Mirostat off</label>
+          <label title="Mirostat version 1 was developed to adjust the probability of predictions so that the surprise in the text remains constant. This means that the algorithm tries to maintain a balance between predictable and surprising words so that the text is neither too monotonous nor too chaotic. V1 is recommended for longer writings, creative texts, etc."><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label>
+          <label title="Mirostat version 2 builds on the idea of V1 but brings some improvements. V2 is recommended as a general purpose algorithm since it offers more precise control over entropy and reacts more quickly to unwanted deviations. As a result, the generated texts appear even more consistent and coherent, especially for everday life conversations."><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label>
+          </fieldset>
+        <fieldset class="params">
+          ${FloatField({ label: "Entropy tau", title: "Tau controls the desired level of entropy (or 'surprise') in the text. A low tau (e.g. 0.5) would mean that a text is very predictable, but will also be very coherent. A high tau (e.g. 8.0) would mean that the text is very creative and surprising, but may also be difficult to follow because unlikely words will occur frequently.", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })}
+          ${FloatField({ label: "Learning-rate eta", title: "Eta determines how quickly the Mirostat algorithm adjusts its predictions to achieve the desired entropy. A learning rate that is too high can cause the algorithm to react too quickly and possibly become unstable, because the algorithm will try to maintain a balance between surprises and precision in the context of only a few words. In this way, 'the common thread' could be lost. Whereas a learning rate that is too low means that the algorithm reacts too slowly and a red thread becomes a heavy goods train that takes a long time to come to a halt and change a 'topic station'.", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })}
+        </fieldset>
+
+          <hr style="height: 1px; background-color: #ececf1; border: none;" />
+
+          <fieldset class="params">
+            ${IntField({ label: "Show Probabilities", title: "If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. The tokens will be colored in gradient from green to red depending on their probabilities. Note that for temperature 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Defaults to 0", max: 10, min: 0, step: 1, name: "n_probs", value: params.value.n_probs })}
+          </fieldset>
+      </details>
+    </form>
+  `
+}
+
+    // todo - beautify apikey section with css
+
+    const probColor = (p) => {
+      const r = Math.floor(192 * (1 - p));
+      const g = Math.floor(192 * p);
+      return `rgba(${r},${g},0,0.3)`;
+    }
+
+    const Probabilities = (params) => {
+      return params.data.map(msg => {
+        const { completion_probabilities } = msg;
+        if (
+          !completion_probabilities ||
+          completion_probabilities.length === 0
+        ) return msg.content
+
+        if (completion_probabilities.length > 1) {
+          // Not for byte pair
+          if (completion_probabilities[0].content.startsWith('byte: \\')) return msg.content
+
+          const splitData = completion_probabilities.map(prob => ({
+            content: prob.content,
+            completion_probabilities: [prob]
+          }))
+          return html`<${Probabilities} data=${splitData} />`
+        }
+
+        const { probs, content } = completion_probabilities[0]
+        const found = probs.find(p => p.tok_str === msg.content)
+        const pColor = found ? probColor(found.prob) : 'transparent'
+
+        const popoverChildren = html`
+          <div class="prob-set">
+            ${probs.map((p, index) => {
+          return html`
+                <div
+                  key=${index}
+                  title=${`prob: ${p.prob}`}
+                  style=${{
+              padding: '0.3em',
+              backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent'
+            }}
+                >
+                  <span>${p.tok_str}: </span>
+                  <span>${Math.floor(p.prob * 100)}%</span>
+                </div>
+              `
+        })}
+          </div>
+        `
+
+        return html`
+          <${Popover} style=${{ backgroundColor: pColor }} popoverChildren=${popoverChildren}>
+            ${msg.content.match(/\n/gim) ? html`<br />` : msg.content}
+          </>
+        `
+      });
+    }
+
+    // poor mans markdown replacement
+    const Markdownish = (params) => {
+      const md = params.text
+        .replace(/&/g, '&amp;')
+        .replace(/</g, '&lt;')
+        .replace(/>/g, '&gt;')
+        .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
+        .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
+        .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
+        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
+        .replace(/`(.*?)`/g, '<code>$1</code>')
+        .replace(/\n/gim, '<br />');
+      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
+    };
+
+    const ModelGenerationInfo = (params) => {
+      if (!llamaStats.value) {
+        return html`<span/>`
+      }
+      return html`
+      <span class=generation-statistics>
+          ${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second
+        </span>
+      `
+    }
+
+    // simple popover impl
+    const Popover = (props) => {
+      const isOpen = useSignal(false);
+      const position = useSignal({ top: '0px', left: '0px' });
+      const buttonRef = useRef(null);
+      const popoverRef = useRef(null);
+
+      const togglePopover = () => {
+        if (buttonRef.current) {
+          const rect = buttonRef.current.getBoundingClientRect();
+          position.value = {
+            top: `${rect.bottom + window.scrollY}px`,
+            left: `${rect.left + window.scrollX}px`,
+          };
+        }
+        isOpen.value = !isOpen.value;
+      };
+
+      const handleClickOutside = (event) => {
+        if (popoverRef.current && !popoverRef.current.contains(event.target) && !buttonRef.current.contains(event.target)) {
+          isOpen.value = false;
+        }
+      };
+
+      useEffect(() => {
+        document.addEventListener('mousedown', handleClickOutside);
+        return () => {
+          document.removeEventListener('mousedown', handleClickOutside);
+        };
+      }, []);
+
+      return html`
+        <span style=${props.style} ref=${buttonRef} onClick=${togglePopover} contenteditable="true">${props.children}</span>
+        ${isOpen.value && html`
+          <${Portal} into="#portal">
+            <div
+              ref=${popoverRef}
+              class="popover-content"
+              style=${{
+            top: position.value.top,
+            left: position.value.left,
+          }}
+            >
+              ${props.popoverChildren}
+            </div>
+          </${Portal}>
+        `}
+      `;
+    };
+
+    // Source: preact-portal (https://github.com/developit/preact-portal/blob/master/src/preact-portal.js)
+    /** Redirect rendering of descendants into the given CSS selector */
+    class Portal extends Component {
+      componentDidUpdate(props) {
+        for (let i in props) {
+          if (props[i] !== this.props[i]) {
+            return setTimeout(this.renderLayer);
+          }
+        }
+      }
+
+      componentDidMount() {
+        this.isMounted = true;
+        this.renderLayer = this.renderLayer.bind(this);
+        this.renderLayer();
+      }
+
+      componentWillUnmount() {
+        this.renderLayer(false);
+        this.isMounted = false;
+        if (this.remote && this.remote.parentNode) this.remote.parentNode.removeChild(this.remote);
+      }
+
+      findNode(node) {
+        return typeof node === 'string' ? document.querySelector(node) : node;
+      }
+
+      renderLayer(show = true) {
+        if (!this.isMounted) return;
+
+        // clean up old node if moving bases:
+        if (this.props.into !== this.intoPointer) {
+          this.intoPointer = this.props.into;
+          if (this.into && this.remote) {
+            this.remote = render(html`<${PortalProxy} />`, this.into, this.remote);
+          }
+          this.into = this.findNode(this.props.into);
+        }
+
+        this.remote = render(html`
+          <${PortalProxy} context=${this.context}>
+            ${show && this.props.children || null}
+          </${PortalProxy}>
+        `, this.into, this.remote);
+      }
+
+      render() {
+        return null;
+      }
+    }
+    // high-order component that renders its first child if it exists.
+    // used as a conditional rendering proxy.
+    class PortalProxy extends Component {
+      getChildContext() {
+        return this.props.context;
+      }
+      render({ children }) {
+        return children || null;
+      }
+    }
+
+    function App(props) {
+      return html`
+        <div class="mode-${session.value.type}">
+          <header>
+            <h2>llama.cpp</h2>
+            <div class="dropdown">
+              <button class="dropbtn"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><circle cx="12" cy="12" r="10" stroke-width="2"/></svg></button>
+              <div class="dropdown-content" id="theme-selector">
+                <a href="/">Old UI</a>
+                <a href="#" data-theme="default">Snow Storm</a>
+                <a href="#" data-theme="polarnight">Polar Night</a>
+                <a href="#" data-theme="ketivah">Ketivah</a>
+                <a href="#" data-theme="mangotango">Mango Tango</a>
+                <a href="#" data-theme="playground">Playground</a>
+                <a href="#" data-theme="beeninorder">Been In Order</a>
+              </div>
+            </div>
+          </header>
+
+          <main id="content">
+            <${chatStarted.value ? ChatLog : ConfigForm} />
+          </main>
+
+          <section id="write">
+            <${session.value.type === 'chat' ? MessageInput : CompletionControls} />
+          </section>
+          <footer>
+            <p><${ModelGenerationInfo} /></p>
+            <p>Powered By <a href="https://github.com/ggerganov/llama.cpp#readme" target="_blank">llama.cpp</a> and <a href="https://ggml.ai/" target="_blank">ggml.ai</a></p>
+          </footer>
+        </div>
+      `;
+    }
+
+  document.addEventListener('DOMContentLoaded', function() {
+  var themeSelector = document.getElementById('theme-selector');
+  var themeLinks = themeSelector.querySelectorAll('a[data-theme]');
+
+  themeLinks.forEach(function(link) {
+    link.addEventListener('click', function(event) {
+      event.preventDefault(); // avoid default behaviour
+      var selectedTheme = event.target.getAttribute('data-theme');
+      changeTheme(selectedTheme);
+    });
+  });
+
+  function changeTheme(theme) {
+    document.body.classList.remove('theme-default', 'theme-polarnight', 'theme-ketivah', 'theme-mangotango', 'theme-playground', 'theme-beeninorder');
+    if (theme !== 'default') {
+      document.body.classList.add('theme-' + theme);
+    }
+    localStorage.setItem('selected-theme', theme);
+  }
+
+  // set the selected theme when loading the page
+  var savedTheme = localStorage.getItem('selected-theme');
+  if (savedTheme && savedTheme !== 'default') {
+    document.body.classList.add('theme-' + savedTheme);
+    // update the dropdown if it still exists
+    var dropdown = document.getElementById('theme-selector-dropdown');
+    if (dropdown) {
+      dropdown.value = savedTheme;
+    }
+  }
+});
+
+
+// snapping of the slider to indicate 'disabled'
+document.addEventListener('DOMContentLoaded', (event) => {
+  // define an object that contains snap values and ranges for each slider
+  const snapSettings = {
+    temperature: { snapValue: 1.0, snapRangeMultiplier: 6 },
+    min_p: { snapValue: 0.05, snapRangeMultiplier: 2 },
+    top_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
+    tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 },
+    typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
+    repeat_penalty: { snapValue: 1.0, snapRangeMultiplier: 4 },
+    presence_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 },
+    frequency_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 },
+  };
+  // add an event listener for each slider
+  Object.keys(snapSettings).forEach(sliderName => {
+    const slider = document.querySelector(`input[name="${sliderName}"]`);
+    const settings = snapSettings[sliderName];
+
+    slider.addEventListener('input', (e) => {
+      let value = parseFloat(e.target.value);
+      const step = parseFloat(e.target.step);
+      const snapRange = step * settings.snapRangeMultiplier;
+      const valueDisplay = document.getElementById(`${e.target.name}-value`);
+
+      if (value >= settings.snapValue - snapRange && value <= settings.snapValue + snapRange) {
+        value = settings.snapValue; // set value to the snap value
+        e.target.value = value; // update the slider value
+      }
+      // update the displayed value
+      if (valueDisplay) {
+        valueDisplay.textContent = value.toFixed(2); // display value with two decimal places
+      }
+    });
+  });
+});
+
+    render(h(App), document.querySelector('#container'));
+
+  </script>
+</head>
+
+<body>
+
+  <div id="container">
+    <input type="file" id="fileInput" accept="image/*" style="display: none;">
+  </div>
+  <div id="portal"></div>
+</body>
+
+</html>
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 2961999f2451a..07fec6a38bbcd 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -1,5 +1,4 @@
 <html>
-
 <head>
   <meta charset="UTF-8">
   <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
@@ -12,6 +11,18 @@
       font-size: 90%;
     }
 
+    .grid-container {
+      display: grid;
+      grid-template-columns: auto auto auto;
+      padding: 10px;
+    }
+
+    .grid-item {
+      padding: 5px;
+      /* font-size: 30px; */
+      text-align: center;
+    }
+
     #container {
       margin: 0em auto;
       display: flex;
@@ -35,6 +46,67 @@
       padding: 0.5em;
     }
 
+    h1 {
+      text-align: center;
+    }
+
+    .customlink:link {
+      color: white;
+      background-color: #007aff;
+      font-weight: 600;
+      text-decoration: none;
+      float: right;
+      margin-top: 30px;
+      display: flex;
+      flex-direction: row;
+      gap: 0.5em;
+      justify-content: flex-end;
+      border-radius: 4px;
+      padding: 8px;
+    }
+
+    .customlink:visited {
+      color: white;
+      background-color: #007aff;
+      font-weight: 600;
+      text-decoration: none;
+      float: right;
+      margin-top: 30px;
+      display: flex;
+      flex-direction: row;
+      gap: 0.5em;
+      justify-content: flex-end;
+      padding: 8px;
+    }
+
+    .customlink:hover {
+      color: white;
+      background-color: #0070ee;
+      font-weight: 600;
+      text-decoration: none;
+      float: right;
+      margin-top: 30px;
+      display: flex;
+      flex-direction: row;
+      gap: 0.5em;
+      justify-content: flex-end;
+      padding: 8px;
+    }
+
+    .customlink:active {
+      color: #0070ee;
+      background-color: #80b3ef;
+      font-weight: 600;
+      text-decoration: none;
+      float: right;
+      margin-top: 30px;
+      display: flex;
+      flex-direction: row;
+      gap: 0.5em;
+      justify-content: flex-end;
+      padding: 8px;
+    }
+
     body {
       max-width: 600px;
       min-width: 300px;
@@ -59,12 +131,20 @@
       align-items: stretch;
     }
 
-    .right {
+    .message-controls {
       display: flex;
-      flex-direction: row;
-      gap: 0.5em;
       justify-content: flex-end;
     }
+    .message-controls > div:nth-child(2) {
+      display: flex;
+      flex-direction: column;
+      gap: 0.5em;
+    }
+    .message-controls > div:nth-child(2) > div {
+      display: flex;
+      margin-left: auto;
+      gap: 0.5em;
+    }
 
     fieldset {
       border: none;
@@ -203,6 +283,7 @@
 
     import { llama } from './completion.js';
     import { SchemaConverter } from './json-schema-to-grammar.mjs';
+
     let selected_image = false;
     var slot_id = -1;
 
@@ -374,6 +455,9 @@
 
     /* END: Support for storing prompt templates and parameters in browsers LocalStorage */
 
+    const tts = window.speechSynthesis;
+    const ttsVoice = signal(null)
+
     const llamaStats = signal(null)
     const controller = signal(null)
 
@@ -406,7 +490,7 @@
         throw new Error("already running");
       }
       controller.value = new AbortController();
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) {
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: new URL('.', document.baseURI).href })) {
         const data = chunk.data;
 
         if (data.stop) {
@@ -523,8 +607,51 @@
       });
     }
 
+    const SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
+    const talkRecognition = SpeechRecognition ? new SpeechRecognition() : null;
     function MessageInput() {
-      const message = useSignal("")
+      const message = useSignal("");
+
+      const talkActive = useSignal(false);
+      const sendOnTalk = useSignal(false);
+      const talkStop = (e) => {
+        if (e) e.preventDefault();
+
+        talkActive.value = false;
+        talkRecognition?.stop();
+      }
+      const talk = (e) => {
+        e.preventDefault();
+
+        if (talkRecognition)
+          talkRecognition.start();
+        else
+          alert("Speech recognition is not supported by this browser.");
+      }
+      if(talkRecognition) {
+        talkRecognition.onstart = () => {
+          talkActive.value = true;
+        }
+        talkRecognition.onresult = (e) => {
+          if (event.results.length > 0) {
+            message.value = event.results[0][0].transcript;
+            if (sendOnTalk.value) {
+              submit(e);
+            }
+          }
+        }
+        talkRecognition.onspeechend = () => {
+          talkStop();
+        }
+      }
+
+      const ttsVoices = useSignal(tts?.getVoices() || []);
+      const ttsVoiceDefault = computed(() => ttsVoices.value.find(v => v.default));
+      if (tts) {
+        tts.onvoiceschanged = () => {
+          ttsVoices.value = tts.getVoices();
+        }
+      }
 
       const submit = (e) => {
         stop(e);
@@ -551,11 +678,45 @@
                value="${message}"
             />
           </div>
-          <div class="right">
-            <button type="submit" disabled=${generating.value}>Send</button>
-            <button onclick=${uploadImage}>Upload Image</button>
-            <button onclick=${stop} disabled=${!generating.value}>Stop</button>
-            <button onclick=${reset}>Reset</button>
+          <div class="message-controls">
+            <div> </div>
+            <div>
+              <div>
+                <button type="submit" disabled=${generating.value || talkActive.value}>Send</button>
+                <button disabled=${generating.value || talkActive.value} onclick=${uploadImage}>Upload Image</button>
+                <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+                <button onclick=${reset}>Reset</button>
+              </div>
+              <div>
+                <a href="#" style="cursor: help;" title="Help" onclick=${e => {
+                  e.preventDefault();
+                  alert(`STT supported by your browser: ${SpeechRecognition ? 'Yes' : 'No'}\n` +
+                  `(TTS and speech recognition are not provided by llama.cpp)\n` +
+                  `Note: STT requires HTTPS to work.`);
+                }}>[?]</a>
+                <button disabled=${generating.value} onclick=${talkActive.value ? talkStop : talk}>${talkActive.value ? "Stop Talking" : "Talk"}</button>
+                <div>
+                  <input type="checkbox" id="send-on-talk" name="send-on-talk" checked="${sendOnTalk}" onchange=${(e) => sendOnTalk.value = e.target.checked} />
+                  <label for="send-on-talk" style="line-height: initial;">Send after talking</label>
+                </div>
+              </div>
+              <div>
+                <a href="#" style="cursor: help;" title="Help" onclick=${e => {
+                  e.preventDefault();
+                  alert(`TTS supported by your browser: ${tts ? 'Yes' : 'No'}\n(TTS and speech recognition are not provided by llama.cpp)`);
+                }}>[?]</a>
+                <label for="tts-voices" style="line-height: initial;">Bot Voice:</label>
+                <select id="tts-voices" name="tts-voices" onchange=${(e) => ttsVoice.value = e.target.value} style="max-width: 100px;">
+                  <option value="" selected="${!ttsVoice.value}">None</option>
+                  ${[
+                    ...(ttsVoiceDefault.value ? [ttsVoiceDefault.value] : []),
+                    ...ttsVoices.value.filter(v => !v.default),
+                  ].map(
+                    v => html`<option value="${v.name}" selected="${ttsVoice.value === v.name}">${v.name} (${v.lang}) ${v.default ? '(default)' : ''}</option>`
+                  )}
+                </select>
+              </div>
+            </div>
           </div>
         </form>
       `
@@ -586,26 +747,86 @@
         }
       }, [messages])
 
+      const ttsChatLineActiveIx = useSignal(undefined);
+      const ttsChatLine = (e, ix, msg) => {
+        if (e) e.preventDefault();
+
+        if (!tts || !ttsVoice.value || !('SpeechSynthesisUtterance' in window)) return;
+
+        const ttsVoices = tts.getVoices();
+        const voice = ttsVoices.find(v => v.name === ttsVoice.value);
+        if (!voice) return;
+
+        if (ttsChatLineActiveIx.value !== undefined) {
+          tts.cancel();
+          if (ttsChatLineActiveIx.value === ix) {
+            ttsChatLineActiveIx.value = undefined;
+            return;
+          }
+        }
+
+        ttsChatLineActiveIx.value = ix;
+        let ttsUtter = new SpeechSynthesisUtterance(msg);
+        ttsUtter.voice = voice;
+        ttsUtter.onend = e => {
+          ttsChatLineActiveIx.value = undefined;
+        };
+        tts.speak(ttsUtter);
+      }
+
       const isCompletionMode = session.value.type === 'completion'
+
+      // Try play the last bot message
+      const lastCharChatLinesIxs = useSignal([]);
+      const lastCharChatLinesIxsOld = useSignal([]);
+      useEffect(() => {
+        if (
+          !isCompletionMode
+          && lastCharChatLinesIxs.value.length !== lastCharChatLinesIxsOld.value.length
+          && !generating.value
+        ) {
+          const ix = lastCharChatLinesIxs.value[lastCharChatLinesIxs.value.length - 1];
+          if (ix !== undefined) {
+            const msg = messages[ix];
+            ttsChatLine(null, ix, Array.isArray(msg) ? msg[1].map(m => m.content).join('') : msg);
+          }
+
+          lastCharChatLinesIxsOld.value = structuredClone(lastCharChatLinesIxs.value);
+        }
+      }, [generating.value]);
+
       const chatLine = ([user, data], index) => {
         let message
-        const isArrayMessage = Array.isArray(data)
+        const isArrayMessage = Array.isArray(data);
+        const text = isArrayMessage ?
+            data.map(msg => msg.content).join('') :
+            data;
         if (params.value.n_probs > 0 && isArrayMessage) {
           message = html`<${Probabilities} data=${data} />`
         } else {
-          const text = isArrayMessage ?
-            data.map(msg => msg.content).join('').replace(/^\s+/, '') :
-            data;
           message = isCompletionMode ?
             text :
             html`<${Markdownish} text=${template(text)} />`
         }
+
+        const fromBot = user && user === '{{char}}';
+        if (fromBot && !lastCharChatLinesIxs.value.includes(index))
+          lastCharChatLinesIxs.value.push(index);
+
         if (user) {
-          return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
+          return html`
+          <div>
+            <p key=${index}><strong>${template(user)}:</strong> ${message}</p>
+            ${
+              fromBot && ttsVoice.value
+              && html`<button disabled=${generating.value} onclick=${e => ttsChatLine(e, index, text)} aria-label=${ttsChatLineActiveIx.value === index ? 'Pause' : 'Play'}>${ ttsChatLineActiveIx.value === index ? '⏸️' : '▶️' }</div>`
+            }
+          </div>
+          `;
         } else {
           return isCompletionMode ?
             html`<span key=${index}>${message}</span>` :
-            html`<p key=${index}>${message}</p>`
+            html`<div><p key=${index}>${message}</p></div>`
         }
       };
 
@@ -877,19 +1098,30 @@
 
     // poor mans markdown replacement
     const Markdownish = (params) => {
-      const md = params.text
-        .replace(/&/g, '&amp;')
-        .replace(/</g, '&lt;')
-        .replace(/>/g, '&gt;')
-        .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
-        .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
-        .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
-        .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
-        .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
-        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
-        .replace(/`(.*?)`/g, '<code>$1</code>')
-        .replace(/\n/gim, '<br />');
-      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
+      const chunks = params.text.split('```');
+
+      for (let i = 0; i < chunks.length; i++) {
+        if (i % 2 === 0) { // outside code block
+          chunks[i] = chunks[i]
+          .replace(/&/g, '&amp;')
+          .replace(/</g, '&lt;')
+          .replace(/>/g, '&gt;')
+          .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
+          .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+          .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+          .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
+          .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
+          .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
+          .replace(/`(.*?)`/g, '<code>$1</code>')
+          .replace(/\n/gim, '<br />');
+        } else { // inside code block
+          chunks[i] = `<pre><code>${chunks[i]}</code></pre>`;
+        }
+      }
+
+      const restoredText = chunks.join('');
+
+      return html`<span dangerouslySetInnerHTML=${{ __html: restoredText }} />`;
     };
 
     const ModelGenerationInfo = (params) => {
@@ -903,6 +1135,7 @@
       `
     }
 
+
     // simple popover impl
     const Popover = (props) => {
       const isOpen = useSignal(false);
@@ -1023,7 +1256,11 @@
       return html`
         <div class="mode-${session.value.type}">
           <header>
-            <h1>llama.cpp</h1>
+            <div class="grid-container">
+              <div class="grid-item"></div>
+              <div class="grid-item"><h1>llama.cpp</h1></div>
+              <div class="grid-item"><a class="customlink" href="index-new.html">New UI</a></div>
+            </div>
           </header>
 
           <main id="content">
@@ -1054,4 +1291,3 @@ <h1>llama.cpp</h1>
 </body>
 
 </html>
-
diff --git a/examples/server/public/index.js b/examples/server/public/index.js
index 695aec2568a52..6709609399e4a 100644
--- a/examples/server/public/index.js
+++ b/examples/server/public/index.js
@@ -1 +1 @@
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0!==n){n.x=e;t.e=void 0}if(void 0!==e){e.e=n;t.x=void 0}if(t===this.t)this.t=e}};f.prototype.subscribe=function(t){return k(()=>{const n=this.value,e=_;_=void 0;try{t(n)}finally{_=e}})};f.prototype.valueOf=function(){return this.value};f.prototype.toString=function(){return this.value+""};f.prototype.toJSON=function(){return this.value};f.prototype.peek=function(){const t=_;_=void 0;try{return this.value}finally{_=t}};Object.defineProperty(f.prototype,"value",{get(){const t=s(this);if(void 0!==t)t.i=this.i;return this.v},set(t){if(t!==this.v){if(u>100)throw new Error("Cycle detected");this.v=t;this.i++;l++;r++;try{for(let t=this.t;void 0!==t;t=t.x)t.t.N()}finally{n()}}}});function c(t){return new f(t)}function h(t){for(let n=t.s;void 0!==n;n=n.n)if(n.S.i!==n.i||!n.S.h()||n.S.i!==n.i)return!0;return!1}function a(t){for(let n=t.s;void 0!==n;n=n.n){const e=n.S.n;if(void 0!==e)n.r=e;n.S.n=n;n.i=-1;if(void 0===n.n){t.s=n;break}}}function p(t){let n,e=t.s;while(void 0!==e){const 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0},type:{configurable:!0,value:Xt},props:{configurable:!0,get(){return{data:this}}},__b:{configurable:!0,value:1}});Gt("__b",(t,n)=>{if("string"==typeof n.type){let t,e=n.props;for(let _ in e){if("children"===_)continue;let i=e[_];if(i instanceof f){if(!t)n.__np=t={};t[_]=i;e[_]=i.peek()}}}t(n)});Gt("__r",(t,n)=>{Qt();let e,_=n.__c;if(_){_.__$f&=-2;e=_.__$u;if(void 0===e)_.__$u=e=function(t){let n;k((function(){n=this}));n.c=()=>{_.__$f|=1;_.setState({})};return n}()}Jt=_;Qt(e);t(n)});Gt("__e",(t,n,e,_)=>{Qt();Jt=void 0;t(n,e,_)});Gt("diffed",(t,n)=>{Qt();Jt=void 0;let e;if("string"==typeof n.type&&(e=n.__e)){let t=n.__np,_=n.props;if(t){let n=e.U;if(n)for(let e in n){let _=n[e];if(void 0!==_&&!(e in t)){_.d();n[e]=void 0}}else{n={};e.U=n}for(let i in t){let o=n[i],r=t[i];if(void 0===o){o=Yt(e,i,r,_);n[i]=o}else o.o(r,_)}}}t(n)});function Yt(t,n,e,_){const i=n in t&&void 0===t.ownerSVGElement,o=c(e);return{o:(t,n)=>{o.value=t;_=n},d:k(()=>{const e=o.value.value;if(_[n]!==e){_[n]=e;if(i)t[n]=e;else if(e)t.setAttribute(n,e);else t.removeAttribute(n)}})}}Gt("unmount",(t,n)=>{if("string"==typeof n.type){let t=n.__e;if(t){const n=t.U;if(n){t.U=void 0;for(let t in n){let e=n[t];if(e)e.d()}}}}else{let t=n.__c;if(t){const n=t.__$u;if(n){t.__$u=void 0;n.d()}}}t(n)});Gt("__h",(t,n,e,_)=>{if(_<3||9===_)n.__$f|=2;t(n,e,_)});q.prototype.shouldComponentUpdate=function(t,n){const e=this.__$u;if(!(e&&void 0!==e.s||4&this.__$f))return!0;if(3&this.__$f)return!0;for(let _ in n)return!0;for(let _ in t)if("__source"!==_&&t[_]!==this.props[_])return!0;for(let _ in this.props)if(!(_ in t))return!0;return!1};function Zt(t){return Mt(()=>c(t),[])}function tn(t){const n=Dt(t);n.current=t;Jt.__$f|=4;return Mt(()=>v(()=>n.current()),[])}function nn(t){const n=Dt(t);n.current=t;Nt(()=>k(()=>n.current()),[])}var en=function(t,n,e,_){var i;n[0]=0;for(var o=1;o<n.length;o++){var r=n[o++],u=n[o]?(n[0]|=r?1:2,e[n[o++]]):n[++o];3===r?_[0]=u:4===r?_[1]=Object.assign(_[1]||{},u):5===r?(_[1]=_[1]||{})[n[++o]]=u:6===r?_[1][n[++o]]+=u+"":r?(i=t.apply(u,en(t,u,e,["",null])),_.push(i),u[0]?n[0]|=2:(n[o-2]=0,n[o]=i)):_.push(u)}return _},_n=new Map;function on(t){var n=_n.get(this);return n||(n=new Map,_n.set(this,n)),(n=en(this,n.get(t)||(n.set(t,n=function(t){for(var n,e,_=1,i="",o="",r=[0],u=function(t){1===_&&(t||(i=i.replace(/^\s*\n\s*|\s*\n\s*$/g,"")))?r.push(0,t,i):3===_&&(t||i)?(r.push(3,t,i),_=2):2===_&&"..."===i&&t?r.push(4,t,0):2===_&&i&&!t?r.push(5,0,!0,i):_>=5&&((i||!t&&5===_)&&(r.push(_,0,i,e),_=6),t&&(r.push(_,t,0,e),_=6)),i=""},l=0;l<t.length;l++){l&&(1===_&&u(),u(l));for(var s=0;s<t[l].length;s++)n=t[l][s],1===_?"<"===n?(u(),r=[r],_=3):i+=n:4===_?"--"===i&&">"===n?(_=1,i=""):i=n+i[0]:o?n===o?o="":i+=n:'"'===n||"'"===n?o=n:">"===n?(u(),_=1):_&&("="===n?(_=5,e=i,i=""):"/"===n&&(_<5||">"===t[l][s+1])?(u(),3===_&&(r=r[0]),_=r,(r=r[0]).push(2,0,_),_=0):" "===n||"\t"===n||"\n"===n||"\r"===n?(u(),_=2):i+=n),3===_&&"!--"===i&&(_=4,r=r[0])}return u(),r}(t)),n),arguments,[])).length>1?n:n[0]}var rn=on.bind(O);export{q as Component,j as Fragment,f as Signal,e as batch,ct as cloneElement,v as computed,ht as createContext,O as createElement,I as createRef,k as effect,O as h,rn as html,ft as hydrate,C as isValidElement,S as options,st as render,c as signal,Y as toChildArray,o as untracked,Ft as useCallback,tn as useComputed,At as useContext,Vt as useDebugValue,Nt as useEffect,Wt as useErrorBoundary,Lt as useId,Tt as useImperativeHandle,$t as useLayoutEffect,Mt as useMemo,Pt as useReducer,Dt as useRef,Zt as useSignal,nn as useSignalEffect,Ht as useState};
diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs
index 8e0be1b405e3c..7267f3f9c7fad 100644
--- a/examples/server/public/json-schema-to-grammar.mjs
+++ b/examples/server/public/json-schema-to-grammar.mjs
@@ -1,58 +1,222 @@
 // WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first.
-const SPACE_RULE = '" "?';
+const SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}';
 
 function _buildRepetition(itemRule, minItems, maxItems, opts={}) {
+  if (minItems === 0 && maxItems === 1) {
+    return `${itemRule}?`;
+  }
+
+
   const separatorRule = opts.separatorRule ?? '';
   const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false
 
   if (separatorRule === '') {
-    if (minItems === 0 && maxItems === 1) {
-      return `${itemRule}?`;
-    } else if (minItems === 1 && maxItems === undefined) {
+    if (minItems === 1 && maxItems === undefined) {
       return `${itemRule}+`;
+    } else if (minItems === 0 && maxItems === undefined) {
+      return `${itemRule}*`;
+    } else {
+      return `${itemRule}{${minItems},${maxItems !== undefined ? maxItems : ''}}`;
     }
   }
 
-  let result = '';
-  if (minItems > 0) {
-    if (itemRuleIsLiteral && separatorRule === '') {
-      result = `"${itemRule.slice(1, -1).repeat(minItems)}"`;
-    } else {
-      result = Array.from({ length: minItems }, () => itemRule)
-        .join(separatorRule !== '' ? ` ${separatorRule} ` : ' ');
-    }
+  const result = itemRule + ' ' + _buildRepetition(`(${separatorRule} ${itemRule})`, minItems > 0 ? minItems - 1 : 0, maxItems !== undefined ? maxItems - 1 : undefined);
+  return minItems === 0 ? `(${result})?` : result;
+}
+
+function _generateMinMaxInt(minValue, maxValue, out, decimalsLeft = 16, topLevel = true) {
+  const hasMin = minValue !== null;
+  const hasMax = maxValue !== null;
+
+  function digitRange(fromChar, toChar) {
+      out.push("[");
+      if (fromChar === toChar) {
+          out.push(fromChar);
+      } else {
+          out.push(fromChar);
+          out.push("-");
+          out.push(toChar);
+      }
+      out.push("]");
   }
 
-  const optRepetitions = (upToN, prefixWithSep=false) => {
-    const content = separatorRule !== '' && prefixWithSep ? `${separatorRule} ${itemRule}` : itemRule;
-    if (upToN === 0) {
-      return '';
-    } else if (upToN === 1) {
-      return `(${content})?`;
-    } else if (separatorRule !== '' && !prefixWithSep) {
-      return `(${content} ${optRepetitions(upToN - 1, true)})?`;
-    } else {
-      return Array.from({ length: upToN }, () => `(${content}`).join(' ').trim() + Array.from({ length: upToN }, () => ')?').join('');
-    }
-  };
+  function moreDigits(minDigits, maxDigits) {
+      out.push("[0-9]");
+      if (minDigits === maxDigits && minDigits === 1) {
+          return;
+      }
+      out.push("{");
+      out.push(minDigits.toString());
+      if (maxDigits !== minDigits) {
+          out.push(",");
+          if (maxDigits !== Number.MAX_SAFE_INTEGER) {
+              out.push(maxDigits.toString());
+          }
+      }
+      out.push("}");
+  }
 
-  if (minItems > 0 && maxItems !== minItems) {
-    result += ' ';
+  function uniformRange(fromStr, toStr) {
+      let i = 0;
+      while (i < fromStr.length && fromStr[i] === toStr[i]) {
+          i++;
+      }
+      if (i > 0) {
+          out.push("\"");
+          out.push(fromStr.slice(0, i));
+          out.push("\"");
+      }
+      if (i < fromStr.length) {
+          if (i > 0) {
+              out.push(" ");
+          }
+          const subLen = fromStr.length - i - 1;
+          if (subLen > 0) {
+              const fromSub = fromStr.slice(i + 1);
+              const toSub = toStr.slice(i + 1);
+              const subZeros = "0".repeat(subLen);
+              const subNines = "9".repeat(subLen);
+
+              let toReached = false;
+              out.push("(");
+              if (fromSub === subZeros) {
+                  digitRange(fromStr[i], String.fromCharCode(toStr.charCodeAt(i) - 1));
+                  out.push(" ");
+                  moreDigits(subLen, subLen);
+              } else {
+                  out.push("[");
+                  out.push(fromStr[i]);
+                  out.push("] ");
+                  out.push("(");
+                  uniformRange(fromSub, subNines);
+                  out.push(")");
+                  if (fromStr.charCodeAt(i) < toStr.charCodeAt(i) - 1) {
+                      out.push(" | ");
+                      if (toSub === subNines) {
+                          digitRange(String.fromCharCode(fromStr.charCodeAt(i) + 1), toStr[i]);
+                          toReached = true;
+                      } else {
+                          digitRange(String.fromCharCode(fromStr.charCodeAt(i) + 1), String.fromCharCode(toStr.charCodeAt(i) - 1));
+                      }
+                      out.push(" ");
+                      moreDigits(subLen, subLen);
+                  }
+              }
+              if (!toReached) {
+                  out.push(" | ");
+                  digitRange(toStr[i], toStr[i]);
+                  out.push(" ");
+                  uniformRange(subZeros, toSub);
+              }
+              out.push(")");
+          } else {
+              out.push("[");
+              out.push(fromStr[i]);
+              out.push("-");
+              out.push(toStr[i]);
+              out.push("]");
+          }
+      }
   }
 
-  if (maxItems !== undefined) {
-    result += optRepetitions(maxItems - minItems, minItems > 0);
-  } else {
-    const itemOperator = `(${separatorRule !== '' ? separatorRule + ' ' : ''}${itemRule})`;
+  if (hasMin && hasMax) {
+      if (minValue < 0 && maxValue < 0) {
+          out.push("\"-\" (");
+          _generateMinMaxInt(-maxValue, -minValue, out, decimalsLeft, true);
+          out.push(")");
+          return;
+      }
+
+      if (minValue < 0) {
+          out.push("\"-\" (");
+          _generateMinMaxInt(0, -minValue, out, decimalsLeft, true);
+          out.push(") | ");
+          minValue = 0;
+      }
 
-    if (minItems === 0 && separatorRule !== '') {
-      result = `(${itemRule} ${itemOperator}*)?`;
-    } else {
-      result += `${itemOperator}*`;
-    }
+      let minS = minValue.toString();
+      const maxS = maxValue.toString();
+      const minDigits = minS.length;
+      const maxDigits = maxS.length;
+
+      for (let digits = minDigits; digits < maxDigits; digits++) {
+          uniformRange(minS, "9".repeat(digits));
+          minS = "1" + "0".repeat(digits);
+          out.push(" | ");
+      }
+      uniformRange(minS, maxS);
+      return;
   }
 
-  return result;
+  const lessDecimals = Math.max(decimalsLeft - 1, 1);
+
+  if (hasMin) {
+      if (minValue < 0) {
+          out.push("\"-\" (");
+          _generateMinMaxInt(null, -minValue, out, decimalsLeft, false);
+          out.push(") | [0] | [1-9] ");
+          moreDigits(0, decimalsLeft - 1);
+      } else if (minValue === 0) {
+          if (topLevel) {
+              out.push("[0] | [1-9] ");
+              moreDigits(0, lessDecimals);
+          } else {
+              moreDigits(1, decimalsLeft);
+          }
+      } else if (minValue <= 9) {
+          const c = minValue.toString();
+          const range_start = topLevel ? '1' : '0';
+          if (c > range_start) {
+              digitRange(range_start, String.fromCharCode(c.charCodeAt(0) - 1));
+              out.push(" ");
+              moreDigits(1, lessDecimals);
+              out.push(" | ");
+          }
+          digitRange(c, "9");
+          out.push(" ");
+          moreDigits(0, lessDecimals);
+      } else {
+          const minS = minValue.toString();
+          const length = minS.length;
+          const c = minS[0];
+
+          if (c > "1") {
+              digitRange(topLevel ? "1" : "0", String.fromCharCode(c.charCodeAt(0) - 1));
+              out.push(" ");
+              moreDigits(length, lessDecimals);
+              out.push(" | ");
+          }
+          digitRange(c, c);
+          out.push(" (");
+          _generateMinMaxInt(parseInt(minS.slice(1)), null, out, lessDecimals, false);
+          out.push(")");
+          if (c < "9") {
+              out.push(" | ");
+              digitRange(String.fromCharCode(c.charCodeAt(0) + 1), "9");
+              out.push(" ");
+              moreDigits(length - 1, lessDecimals);
+          }
+      }
+      return;
+  }
+
+  if (hasMax) {
+      if (maxValue >= 0) {
+          if (topLevel) {
+              out.push("\"-\" [1-9] ");
+              moreDigits(0, lessDecimals);
+              out.push(" | ");
+          }
+          _generateMinMaxInt(0, maxValue, out, decimalsLeft, true);
+      } else {
+          out.push("\"-\" (");
+          _generateMinMaxInt(-maxValue, null, out, decimalsLeft, false);
+          out.push(")");
+      }
+      return;
+  }
+
+  throw new Error("At least one of minValue or maxValue must be set");
 }
 
 class BuiltinRule {
@@ -62,27 +226,25 @@ class BuiltinRule {
   }
 }
 
-const UP_TO_15_DIGITS = _buildRepetition('[0-9]', 0, 15);
-
 const PRIMITIVE_RULES = {
   boolean        : new BuiltinRule('("true" | "false") space', []),
-  'decimal-part' : new BuiltinRule('[0-9] ' + UP_TO_15_DIGITS, []),
-  'integral-part': new BuiltinRule('[0-9] | [1-9] ' + UP_TO_15_DIGITS, []),
+  'decimal-part' : new BuiltinRule('[0-9]{1,16}', []),
+  'integral-part': new BuiltinRule('[0] | [1-9] [0-9]{0,15}', []),
   number         : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
   integer        : new BuiltinRule('("-"? integral-part) space', ['integral-part']),
   value          : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
   object         : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
   array          : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
-  uuid           : new BuiltinRule('"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', []),
-  char           : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])`, []),
+  uuid           : new BuiltinRule('"\\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\\"" space', []),
+  char           : new BuiltinRule(`[^"\\\\\\x7F\\x00-\\x1F] | [\\\\] (["\\\\bfnrt] | "u" [0-9a-fA-F]{4})`, []),
   string         : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']),
   null           : new BuiltinRule('"null" space', []),
 };
 
 // TODO: support "uri", "email" string formats
 const STRING_FORMAT_RULES = {
-  'date'            : new BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
-  'time'            : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
+  'date'            : new BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
+  'time'            : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
   'date-time'       : new BuiltinRule('date "T" time', ['date', 'time']),
   'date-string'     : new BuiltinRule('"\\"" date "\\"" space', ['date']),
   'time-string'     : new BuiltinRule('"\\"" time "\\"" space', ['time']),
@@ -97,7 +259,7 @@ const GRAMMAR_RANGE_LITERAL_ESCAPE_RE = /[\n\r"\]\-\\]/g;
 const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]' };
 
 const NON_LITERAL_SET = new Set('|.()[]{}*+?');
-const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('[]()|{}*+?');
+const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('^$.[]()|{}*+?');
 
 export class SchemaConverter {
   constructor(options) {
@@ -370,6 +532,64 @@ export class SchemaConverter {
     return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space")
   }
 
+  _notStrings(strings) {
+    class TrieNode {
+      constructor() {
+        this.children = {};
+        this.isEndOfString = false;
+      }
+
+      insert(str) {
+        let node = this;
+        for (const c of str) {
+          node = node.children[c] = node.children[c] || new TrieNode();
+        }
+        node.isEndOfString = true;
+      }
+    }
+
+    const trie = new TrieNode();
+    for (const s of strings) {
+      trie.insert(s);
+    }
+
+    const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']);
+    const out = ['["] ( '];
+
+    const visit = (node) => {
+      const rejects = [];
+      let first = true;
+      for (const c of Object.keys(node.children).sort()) {
+        const child = node.children[c];
+        rejects.push(c);
+        if (first) {
+          first = false;
+        } else {
+          out.push(' | ');
+        }
+        out.push(`[${c}]`);
+        if (Object.keys(child.children).length > 0) {
+          out.push(' (');
+          visit(child);
+          out.push(')');
+        } else if (child.isEndOfString) {
+          out.push(` ${charRuleName}+`);
+        }
+      }
+      if (Object.keys(node.children).length > 0) {
+        if (!first) {
+          out.push(' | ');
+        }
+        out.push(`[^"${rejects.join('')}] ${charRuleName}*`);
+      }
+    };
+
+    visit(trie);
+
+    out.push(` )${trie.isEndOfString ? '' : '?'} ["] space`);
+    return out.join('');
+  }
+
   _resolveRef(ref) {
     let refName = ref.split('/').pop();
     if (!(refName in this._rules) && !this._refsBeingResolved.has(ref)) {
@@ -396,11 +616,11 @@ export class SchemaConverter {
     } else if (schema.oneOf || schema.anyOf) {
       return this._addRule(ruleName, this._generateUnionRule(name, schema.oneOf || schema.anyOf));
     } else if (Array.isArray(schemaType)) {
-      return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({ type: t }))));
+      return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({...schema, type: t}))));
     } else if ('const' in schema) {
-      return this._addRule(ruleName, this._generateConstantRule(schema.const));
+      return this._addRule(ruleName, this._generateConstantRule(schema.const) + ' space');
     } else if ('enum' in schema) {
-      const rule = schema.enum.map(v => this._generateConstantRule(v)).join(' | ');
+      const rule = '(' + schema.enum.map(v => this._generateConstantRule(v)).join(' | ') + ') space';
       return this._addRule(ruleName, rule);
     } else if ((schemaType === undefined || schemaType === 'object') &&
                ('properties' in schema ||
@@ -437,7 +657,7 @@ export class SchemaConverter {
         }
       }
 
-      return this._addRule(ruleName, this._buildObjectRule(properties, required, name, /* additionalProperties= */ false));
+      return this._addRule(ruleName, this._buildObjectRule(properties, required, name, null));
     } else if ((schemaType === undefined || schemaType === 'array') && ('items' in schema || 'prefixItems' in schema)) {
       const items = schema.items ?? schema.prefixItems;
       if (Array.isArray(items)) {
@@ -468,6 +688,24 @@ export class SchemaConverter {
       const minLen = schema.minLength || 0;
       const maxLen = schema.maxLength;
       return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space');
+    } else if (schemaType === 'integer' && ('minimum' in schema || 'exclusiveMinimum' in schema || 'maximum' in schema || 'exclusiveMaximum' in schema)) {
+      let minValue = null;
+      let maxValue = null;
+      if ('minimum' in schema) {
+        minValue = schema.minimum;
+      } else if ('exclusiveMinimum' in schema) {
+        minValue = schema.exclusiveMinimum + 1;
+      }
+      if ('maximum' in schema) {
+        maxValue = schema.maximum;
+      } else if ('exclusiveMaximum' in schema) {
+        maxValue = schema.exclusiveMaximum - 1;
+      }
+
+      const out = ["("];
+      _generateMinMaxInt(minValue, maxValue, out);
+      out.push(") space");
+      return this._addRule(ruleName, out.join(''));
     } else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) {
       return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object']));
     } else {
@@ -513,12 +751,19 @@ export class SchemaConverter {
     const requiredProps = sortedProps.filter(k => required.has(k));
     const optionalProps = sortedProps.filter(k => !required.has(k));
 
-    if (typeof additionalProperties === 'object' || additionalProperties === true) {
+    if (additionalProperties) {
       const subName = `${name ?? ''}${name ? '-' : ''}additional`;
-      const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`);
+      const valueRule =
+        additionalProperties != null && typeof additionalProperties === 'object' ? this.visit(additionalProperties, `${subName}-value`)
+        : this._addPrimitive('value', PRIMITIVE_RULES['value']);
+
+      const key_rule =
+        sortedProps.length === 0 ? this._addPrimitive('string', PRIMITIVE_RULES['string'])
+        : this._addRule(`${subName}-k`, this._notStrings(sortedProps));
+
       propKvRuleNames['*'] = this._addRule(
         `${subName}-kv`,
-        `${this._addPrimitive('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`);
+        `${key_rule} ":" space ${valueRule}`);
       optionalProps.push('*');
     }
 
@@ -535,15 +780,11 @@ export class SchemaConverter {
         const [k, ...rest] = ks;
         const kvRuleName = propKvRuleNames[k];
         let res;
-        if (k === '*') {
-            res = this._addRule(
-                `${name ?? ''}${name ? '-' : ''}additional-kvs`,
-                `${kvRuleName} ( "," space ` + kvRuleName + ` )*`
-            )
-        } else if (firstIsOptional) {
-          res = `( "," space ${kvRuleName} )?`;
+        const commaRef = `( "," space ${kvRuleName} )`;
+        if (firstIsOptional) {
+          res = commaRef + (k === '*' ? '*' : '?');
         } else {
-          res = kvRuleName;
+          res = kvRuleName + (k === '*' ? ' ' + commaRef + '*' : '');
         }
         if (rest.length > 0) {
           res += ' ' + this._addRule(
diff --git a/examples/server/public/prompt-formats.js b/examples/server/public/prompt-formats.js
new file mode 100644
index 0000000000000..73ddb7187eb7a
--- /dev/null
+++ b/examples/server/public/prompt-formats.js
@@ -0,0 +1,331 @@
+// extended list
+export const promptFormats = {
+  "alpaca": {
+  template: `{{prompt}}\n\n{{history}}\n\n{{char}}:`,
+
+  historyTemplate: `### {{name}}:\n{{message}}`,
+
+  char: "Response",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "Instruction",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "chatml": {
+  template: `<|im_start|>system\n{{prompt}}<|im_end|>\n{{history}}{{char}}`,
+
+  historyTemplate: `<|im_start|>{{name}}\n{{message}}`,
+
+  char: "assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "user",
+  userMsgPrefix: "",
+  userMsgSuffix: "<|im_end|>\n",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "commandr": {
+  template: `<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{prompt}}\n<|END_OF_TURN_TOKEN|>{{history}}{{char}}`,
+
+  historyTemplate: `<|START_OF_TURN_TOKEN|><|{{name}}|> {{message}}`,
+
+  char: "CHATBOT_TOKEN",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "USER_TOKEN",
+  userMsgPrefix: "",
+  userMsgSuffix: "<|END_OF_TURN_TOKEN|>",
+
+  stops: ""
+  },
+  // ref: https://docs.cohere.com/docs/prompting-command-r
+
+  // ----------------------------
+
+  "llama2": {
+  template: `<s>[INST] <<SYS>>\n{{prompt}}\n<</SYS>>\n\nTest Message [/INST] Test Successfull </s>{{history}}{{char}}`,
+
+  historyTemplate: `{{name}}: {{message}}`,
+
+  char: "Assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "</s>",
+
+  user: "User",
+  userMsgPrefix: "<s>[INST] ",
+  userMsgSuffix: " [/INST]",
+
+  stops: ""
+  },
+  // ref: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
+
+  // ----------------------------
+
+  "llama3": {
+  template: `<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{prompt}}{{history}}{{char}}`,
+
+  historyTemplate: `<|start_header_id|>{{name}}<|end_header_id|>\n\n{{message}}<|eot_id|>`,
+
+  char: "assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "user",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: "<|eot_id|>"
+  },
+  // ref: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3
+
+  // ----------------------------
+
+  "openchat": {
+  template: `{{history}}{{char}}`,
+
+  historyTemplate: `GPT4 Correct {{name}}: {{message}}<|end_of_turn|>`,
+
+  char: "Assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "User",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "phi3": {
+  template: `{{history}}{{char}}`,
+
+  historyTemplate: `<|{{name}}|>\n{{message}}<|end|>\n`,
+
+  char: "assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "user",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: "<|end|>"
+  },
+  // ref: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format
+
+  // ----------------------------
+
+  "vicuna": {
+  template: `{{prompt}}\n{{history}}{{char}}`,
+
+  historyTemplate: `{{name}}: {{message}}\n`,
+
+  char: "ASSISTANT",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "USER",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+  // ref: https://huggingface.co/lmsys/vicuna-33b-v1.3/discussions/1
+
+  // ----------------------------
+
+  "deepseekCoder": {
+  template: `{{prompt}}{{history}}{{char}}:`,
+
+  historyTemplate: `### {{name}}:\n{{message}}`,
+
+  char: "Response",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "Instruction",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: "<|EOT|>"
+  },
+
+  // ----------------------------
+
+  "med42": {
+  template: `<|system|>: {{prompt}}\n{{history}}{{char}}`,
+
+  historyTemplate: `<|{{name}}|>: {{message}}\n`,
+
+  char: "assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "prompter",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "neuralchat": {
+  template: `### System:\n{{prompt}}\n{{history}}{{char}}:`,
+
+  historyTemplate: `### {{name}}:\n{{message}}\n`,
+
+  char: "Assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "User",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "nousHermes": {
+  template: `### Instruction: {{prompt}}\n\n{{history}}\n\n{{char}}:`,
+
+  historyTemplate: `### {{name}}:\n{{message}}`,
+
+  char: "Response",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "Input",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "openchatMath": {
+  template: `{{history}}{{char}}`,
+
+  historyTemplate: `Math Correct {{name}}: {{message}}<|end_of_turn|>`,
+
+  char: "Assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+
+  user: "User",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "orion": {
+  template: `<s>Human: Test Message\n\nAssistant: </s>Test Successful</s>{{history}}{{char}}:`,
+
+  historyTemplate: `{{name}}: {{message}}`,
+
+  char: "Assistant </s>",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "Human",
+  userMsgPrefix: "",
+  userMsgSuffix: "\n\n",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "sauerkraut": {
+  template: `{{prompt}}\n{{history}}{{char}}`,
+
+  historyTemplate: `
+  {{name}}: {{message}}\n`,
+
+  char: "Assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "User",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "starlingCode": {
+  template: `{{history}}{{char}}`,
+
+  historyTemplate: `Code {{name}}: {{message}}<|end_of_turn|>`,
+
+  char: "Assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "User",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "yi34b": {
+  template: `{{history}} {{char}}`,
+
+  historyTemplate: `{{name}}: {{message}}`,
+
+  char: "Assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "Human",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  },
+
+  // ----------------------------
+
+  "zephyr": {
+  template: `<|system|>\n{{prompt}}</s>\n{{history}}{{char}}`,
+
+  historyTemplate: `<|{{name}}|>\n{{message}}</s>\n`,
+
+  char: "assistant",
+  charMsgPrefix: "",
+  charMsgSuffix: "",
+
+  user: "user",
+  userMsgPrefix: "",
+  userMsgSuffix: "",
+
+  stops: ""
+  }
+  };
diff --git a/examples/server/public/style.css b/examples/server/public/style.css
new file mode 100755
index 0000000000000..087cc62dab028
--- /dev/null
+++ b/examples/server/public/style.css
@@ -0,0 +1,954 @@
+@import url("colorthemes.css");
+
+body {
+  font-family: 'Arial', sans-serif;
+  font-size: 90%;
+  background-color: var(--background-color-1);
+  color: var(--text-color-subtile-1); /* head 1 llama.cpp & triangle options for some reason */
+  max-width: 600px;
+  min-width: 300px;
+  line-height: 1.2;
+  margin: 0 auto;
+  padding: 0 0.5em;
+  transition: background-color 0.3s;
+}
+
+::selection {
+  color: var(--button-primary-text) ;
+  background: var(--button-primary-color);
+}
+
+code, pre code {
+  font-family: 'Courier New', monospace;
+}
+
+#container {
+  margin: 0em auto;
+  display: flex;
+  flex-direction: column;
+  justify-content: space-between;
+  height: 100%;
+}
+
+main {
+  margin: 3px;
+  display: flex;
+  flex-direction: column;
+  justify-content: space-between;
+  gap: 1em;
+  flex-grow: 1;
+  overflow-y: auto;
+  border: 1px solid var(--border-color-3);
+  border-radius: 5px;
+  padding: 0.5em;
+}
+
+p {
+  overflow-wrap: break-word;
+  word-wrap: break-word;
+  hyphens: auto;
+  margin-top: 0.5em;
+  margin-bottom: 0.5em;
+}
+
+#write form {
+  margin: 1em 0 0 0;
+  display: flex;
+  flex-direction: column;
+  gap: 0.5em;
+  align-items: stretch;
+}
+
+.right {
+  display: flex;
+  flex-direction: row;
+  gap: 0.5em;
+  justify-content: flex-end;
+  margin-bottom: 30px;
+}
+
+.two-columns {
+  width: 97%;
+  max-width: 97%;
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 1em;
+  position: relative;
+}
+
+.json-schema-controls {
+  margin-top: 10px;
+  width: 100%;
+  max-width: 100%;
+  display: grid;
+  grid-template: "a a";
+  gap: 1em;
+  font-size: x-small;
+  color: var(--theme-nuance-color-3);
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-transform: uppercase;
+  font-weight: 600;
+}
+
+.json-schema-controls > * {
+  flex: 1;
+}
+
+/* titles of the details-summary boxes */
+.summary-title {
+  font-weight: 600;
+  font-size: x-small;
+  color: var(--text-color-subtile-1);
+  text-transform: uppercase;
+  /* transition: ; */
+}
+
+fieldset {
+  border: none;
+  padding: 0;
+  margin: 0;
+  color: var(--text-color-plain);
+}
+
+fieldset.two {
+  display: grid;
+  grid-template: "a a a";
+  gap: 1em;
+  align-items: center;
+  font-size: x-small;
+  color: var(--text-color-plain);
+}
+
+fieldset.three {
+  display: grid;
+  grid-template: "a a a";
+  gap: 1em;
+  font-size: x-small;
+  color: var(--text-color-plain);
+}
+
+/* titles of name fields*/
+fieldset.names {
+  display: grid;
+  grid-template: "a a";
+  gap: 1em;
+  font-size: x-small;
+  color: var(--theme-nuance-color-3);
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-transform: uppercase;
+  font-weight: 600;
+}
+
+/* titles of params fields*/
+fieldset.params {
+  display: grid;
+  grid-template: "a a";
+  gap: 1em;
+  font-size: x-small;
+  color: var(--theme-nuance-color-4);
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-transform: uppercase;
+  font-weight: 600;
+}
+
+fieldset.dropdowns {
+  -webkit-appearance: none;
+  display: flex;
+  grid-template: "a a";
+  gap: 1em;
+  font-size: x-small;
+  color: red;
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-transform: uppercase;
+  font-weight: 600;
+}
+
+/* input of name fields*/
+.names input[type="text"] {
+  font-family: Arial, sans-serif;
+  font-size: medium;
+  font-weight: 500;
+  padding: 5px;
+  border: 1px solid var(--border-color-2);
+}
+
+.chat-id-color {
+  color: var(--chat-id-color);
+}
+
+details {
+  border: 1px solid var(--border-color-2);
+  border-radius: 5px;
+  padding: 0.5em 0.5em 0;
+  margin-top: 0.5em;
+}
+
+summary {
+  font-weight: bold;
+  margin: -0.5em -0.5em 0;
+  padding: 0.5em;
+  cursor: pointer;
+}
+
+details[open] {
+  padding: 0.5em;
+}
+
+textarea-sec, input-sec, button-sec {
+  padding: 10px;
+  height: 40px;
+  align-items: center;
+}
+
+textarea-sec::placeholder, input-sec::placeholder {
+  padding-left: 10px;
+}
+
+.toggleCheckbox {
+  display: none;
+}
+
+.toggleContainer {
+  position: relative;
+  display: grid;
+  grid-template-columns: repeat(2, 1fr);
+  width: fit-content;
+  border: 3px solid var(--border-color-2);
+  border-radius: 20px;
+  background: var(--border-color-2);
+  font-size: small;
+  cursor: pointer;
+  overflow: hidden;
+}
+
+/* toggle button current state */
+.toggleContainer::before {
+  color: var(--button-primary-text);
+  background-color: var(--button-primary-color);
+  content: '';
+  position: absolute;
+  width: 50%;
+  height: 100%;
+  left: 0%;
+  border-radius: 20px;
+  transition: all 0.3s;
+}
+
+.toggleContainer div {
+  padding: 6px;
+  text-align: center;
+  z-index: 1;
+  transition: color 0.3s;
+}
+
+.toggleCheckbox:checked + .toggleContainer::before {
+  left: 50%;
+}
+
+.toggleCheckbox:checked + .toggleContainer div:first-child {
+  color: var(--text-color-subtile-2);
+}
+
+.toggleCheckbox:checked + .toggleContainer div:last-child {
+  color: var(--button-primary-text);
+}
+
+.toggleCheckbox + .toggleContainer div:first-child {
+  color: var(--button-primary-text);
+}
+
+.toggleCheckbox + .toggleContainer div:last-child {
+  color: var(--text-color-subtile-2);
+}
+
+select {
+  padding: 5px;
+  margin-right: 5px;
+  border-radius: 4px;
+  border: 1px solid var(--secondary-color-4);
+  background-color: var(--primary-color-3);
+  color: var(--secondary-color-4);
+  cursor: pointer;
+}
+
+select:focus {
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 1px var(--border-focus-shadow);
+}
+
+.button-container {
+  display: flex;
+  justify-content: flex-end;
+}
+
+button {
+  color: var(--button-primary-text);
+  background-color: var(--button-primary-color);
+  border: 1px solid var(--button-primary-border);
+  transition: background-color 0.1s;
+  border-radius: 12px;
+  font-size: x-small;
+  font-weight: 600;
+  text-shadow: 0px 0px 30px #ffffff;
+  text-align: center;
+  text-decoration: none;
+  margin: 4px 2px;
+  padding: 10px 20px;
+  display: inline-block;
+  cursor: pointer;
+}
+
+button:hover {
+  color: var(--button-primary-text-hover);
+  background-color: var(--button-primary-color-hover);
+  border: 1px solid var(--button-primary-border-hover);
+  font-size: x-small;
+  font-weight: 600;
+}
+
+button:active {
+  color: var(--button-primary-text-active);
+  background-color: var(--button-primary-color-active);
+  border: 1px solid var(--button-primary-border-active);
+  font-size: x-small;
+  font-weight: 600;
+}
+
+button:disabled {
+  color: var(--button-tertiary-text);
+  background-color: var(--button-tertiary-color);
+  border: 1px solid var(--button-tertiary-border);
+  font-size: x-small;
+  font-weight: 600;
+  cursor: not-allowed;
+}
+
+.reset-button {
+  background-color: var(--button-secondary-color);
+  border: 1px solid var(--button-secondary-color);
+  color: var(--button-secondary-text);
+  width: fit-content;
+  height: fit-content;
+  font-size: x-small;
+  font-weight: 600;
+  border-radius: 50px;
+  overflow: hidden;
+}
+
+.reset-button:hover {
+  color: var(--button-alert-text-hover);
+  background-color: var(--button-alert-color-hover);
+  border: 1px solid var(--button-alert-border-hover);
+  font-size: x-small;
+  font-weight: 600;
+}
+
+.reset-button:active {
+  color: var(--button-alert-text-active);
+  background-color: var(--button-alert-color-active);
+  border: 1px solid var(--button-alert-border-active);
+  font-size: x-small;
+  font-weight: 600;
+}
+
+.button-grammar {
+  color: var(--button-primary-text);
+  background-color: var(--button-primary-color);
+  border: 1px solid var(--button-primary-border);
+  border-radius: 10px;
+  padding: 10px 20px;
+  text-align: center;
+  text-decoration: none;
+  display: inline-block;
+  font-size: x-small;
+  font-weight: 600;
+  margin: 2px 2px;
+  transition: background-color 0.1s;
+  cursor: pointer;
+}
+
+.button-grammar:hover {
+  color: var(--button-primary-text-hover);
+  background-color: var(--button-primary-color-hover);
+  border: 1px solid var(--button-primary-border-hover);
+  border-radius: 10px;
+  padding: 10px 20px;
+  text-align: center;
+  text-decoration: none;
+  display: inline-block;
+  font-size: x-small;
+  font-weight: 600;
+  margin: 2px 2px;
+  transition: background-color 0.1s;
+  cursor: pointer;
+}
+
+.button-grammar:active {
+  color: var(--button-primary-text-active);
+  background-color: var(--button-primary-color-active);
+  border: 1px solid var(--button-primary-border-active);
+  font-size: x-small;
+  font-weight: 600;
+}
+
+.button-back {
+  background-color: var(--button-secondary-color);
+  border: 1px solid var(--button-secondary-color);
+  color: var(--button-secondary-text);
+  transition: background-color 0.1s;
+  border-radius: 12px;
+  font-size: x-small;
+  font-weight: 600;
+  text-align: center;
+  text-decoration: none;
+  margin: 4px 2px;
+  padding: 10px 20px;
+  display: inline-block;
+  cursor: pointer;
+}
+
+.button-back:hover {
+  color: var(--button-secondary-text-hover);
+  background-color: var(--button-secondary-color-hover);
+  border: 1px solid var(--button-secondary-border-hover);
+  padding: 10px 20px;
+  text-align: center;
+  text-decoration: none;
+  display: inline-block;
+  font-size: x-small;
+  font-weight: 600;
+  margin: 4px 2px;
+  transition: background-color 0.1s;
+  cursor: pointer;
+  border-radius: 12px;
+}
+
+.button-back:active {
+  color: var(--button-secondary-text-active);
+  background-color: var(--button-secondary-color-active);
+  border: 1px solid var(--button-secondary-border-active);
+  font-size: x-small;
+  font-weight: 600;
+}
+
+.prob-set {
+  padding: 0.3em;
+  border-bottom: 1px solid red; /* unknown */
+}
+
+.popover-content {
+  position: absolute;
+  background-color: white;
+  padding: 0.2em;
+  box-shadow: 0 0 13px rgba(0, 0, 0, 0.1);
+}
+
+.grammar {
+  width: 97%;
+  max-width: 97%;
+}
+
+textarea {
+  padding: 5px;
+  flex-grow: 1;
+  width: 100%;
+  max-width: 100%;
+  border-radius: 8px;
+  border: 1px solid var(--border-color-1);
+  resize: none;
+  height: 6em;
+}
+
+textarea:focus {
+  outline: none;
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+
+/* "props" frame */
+input[type="text"],
+input[type="range"] {
+  padding: 5px;
+  border-radius: 8px;
+  border: 1px solid var(--border-color-1);
+}
+
+/* "names and props" frame focused*/
+input[type="text"]:focus {
+  outline: none;
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+
+input[type="range"]:hover {
+  opacity: 1;
+}
+
+input[type="range"]:focus {
+  outline: none;
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+  background-size: var(--slider-track-size-focus);
+}
+
+input[type="range"]::-moz-range-thumb {
+  width: 6px;
+  height: 25px;
+  border: 1px solid var(--ui-range-thumb-border);
+  border-radius: 5px;
+  background-color: var(--ui-range-thumb-color);
+  cursor: pointer;
+}
+
+input[type="range"] {
+  -webkit-appearance: none;
+  width: 80%;
+  height: 1px;
+  border: 1px solid var(--border-color-1);
+  border-radius: 8px;
+  background: var(--border-color-2);
+  outline: none;
+  opacity: 0.7;
+  -webkit-transition: .2s;
+  transition: opacity .2s;
+}
+
+input[type="range"]::-webkit-slider-thumb {
+  -webkit-appearance: none;
+  appearance: none;
+  width: 6px;
+  height: 25px;
+  border: 1px solid var(--ui-range-thumb-border);
+  border-radius: 5px;
+  background-color: var(--ui-range-thumb-color);
+  cursor: pointer;
+}
+
+input[type="range"]::-webkit-slider-runnable-track {
+  background-size: var(--slider-track-size);
+}
+
+input[type="radio"] {
+  accent-color:   var(--theme-nuance-color-2);
+}
+
+.chat-input-container {
+  position: relative;
+  max-width: 97%;
+  min-width: 97%;
+}
+
+.chat-input-label {
+  position: absolute;
+  top: 0;
+  left: 0;
+  color: var(--text-color-plain);
+  pointer-events: none;
+  margin-left: 5px;
+  margin-top: 5px;
+}
+
+textarea#chat-input {
+  padding-top: 10px;
+  padding-left: 10px;
+  font-size: medium;
+  border: 1px solid var(--border-color-2);
+  resize: vertical;
+}
+
+textarea#chat-input:focus {
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+
+.input-container {
+  position: relative;
+  box-sizing: border-box;
+  width: 100%; /* Setzt die Breite auf 100% */
+  max-width: 100%; /* Stellt sicher, dass die Breite nicht größer als 100% wird */
+}
+
+.input-container:focus {
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+/* titles of name fields*/
+/* fieldset.names {
+  display: grid;
+  grid-template: "a a";
+  gap: 1em;
+  font-size: x-small;
+  color: var(--theme-nuance-color-3);
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-transform: uppercase;
+  font-weight: 600;
+} */
+
+/* input of name fields*/
+/* .names input[type="text"] {
+  font-family: Arial, sans-serif;
+  font-size: medium;
+  font-weight: 500;
+  padding: 5px;
+  border: 1px solid var(--border-color-2);
+} */
+
+fieldset.apiKey {
+  width: 100%;
+  font-size: x-small;
+  color: var(--theme-nuance-color-3);
+  padding-top: 16px;
+  padding-bottom: 16px;
+  text-transform: uppercase;
+  font-weight: 600;
+}
+
+.apiKey {
+  font-family: Arial, sans-serif;
+  font-weight: 500;
+  padding: 5px;
+  border: 1px solid var(--border-color-2);
+}
+
+.apiKey:focus {
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+
+.apiKey input[type="text"] {
+  font-family: Arial, sans-serif;
+  font-size: medium;
+  font-weight: 500;
+  padding: 5px;
+  border: 1px solid var(--border-color-2);
+}
+
+.apiKey label {
+  display: inline-block;
+  width: auto;
+  margin-right: 5px;
+}
+
+textarea#api_key {
+  padding-top: 10px;
+  padding-left: 10px;
+  font-size: medium;
+  border: 1px solid var(--border-color-2);
+  resize: vertical;
+}
+
+textarea#api_key:focus {
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+
+/* embedded title of the system prompt text area */
+.input-label {
+  position: absolute;
+  top: 0;
+  left: 0;
+  color: var(--theme-nuance-color-4);
+  pointer-events: none;
+  border-radius: 8px 8px 0px 0px;
+  padding-top: 10px;
+  padding-left: 13px;
+  padding-right: 0px;
+  margin-top: 1px;
+  margin-left: 1px;
+  margin-right: 20px;
+  text-transform: uppercase;
+  font-weight: 600;
+  font-size: small;
+  background: rgba(255, 255, 255, 0.5);
+  backdrop-filter: blur(10px);
+  -webkit-backdrop-filter: blur(10px); /* for safari */
+  width: 97%;
+  /* display: block;
+  box-sizing: border-box; */
+}
+
+/* embedded title of the prompt style areas */
+.input-label-sec {
+  position: absolute;
+  top: 0;
+  left: 0;
+  color: var(--theme-nuance-color-4);
+  pointer-events: none;
+  margin-left: 13px;
+  margin-top: 16px;
+  text-transform: uppercase;
+  font-weight: 600;
+  font-size: x-small;
+}
+
+/* system prompt input area */
+textarea.persistent-input {
+  padding-top: 42px;
+  padding-left: 11px;
+  width: 97%;
+  max-width: 97%;
+  height: 50px;
+  font-size: medium;
+  overscroll-behavior: contain;
+}
+
+/* system prompt box */
+.persistent-input {
+  height: auto;
+  width: 100%;
+  max-width: 100%;
+  min-height: 50px;
+  padding: 3px;
+  transition: min-height 0.3s ease;
+}
+
+/* chat history box */
+.persistent-input:focus {
+  height: auto;
+  min-height: 150px;
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+
+textarea.persistent-input:focus {
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+
+/* prompt style input area */
+textarea.persistent-input-sec {
+  width: 97%;
+  max-width: 97%;
+  padding-top: 42px;
+  padding-left: 11px;
+  font-size: small;
+  border: 1px solid var(--border-color-1);
+  overscroll-behavior: contain;
+}
+
+textarea.persistent-input-sec:focus {
+  border: 1px solid var(--border-focus-color);
+  box-shadow: 0 0 3px var(--border-focus-shadow);
+}
+
+/* chat history box */
+.persistent-input-sec {
+  height: auto;
+  min-height: 150px;
+}
+
+img {
+  border-radius: 8px;
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+  width: 50%;
+}
+
+/* code area background */
+pre code {
+  display: block;
+  background-color: var(--code-background-color);
+  color: var(--code-text-color);
+  padding: 0.2em 0.2em;
+  border-radius: 5px;
+}
+
+/* code area text */
+code {
+  font-family: monospace;
+  font-weight: bold;
+  padding: 0.1em 0.3em;
+  border-radius: 5px;
+}
+
+fieldset label {
+  margin: 0.5em 0;
+  display: block;
+}
+
+fieldset label.slim {
+  margin: 0 0.5em;
+  display: inline;
+}
+
+header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  text-align: center;
+  padding-left: 15px;
+}
+
+.generation-statistics:hover {
+  color: var(--theme-nuance-color-4);
+  cursor: default;
+}
+
+footer {
+  font-size: 80%;
+  color: var(--background-color-3);
+  text-align: center;
+  cursor: default;
+}
+
+footer a {
+  color: var(--background-color-4); /* Color of the link */
+  text-decoration: none; /* No underlining */
+  font-weight: bold; /* Bold print */
+}
+
+footer a:hover {
+  color: var(--theme-nuance-color-4); /* Color of the link when hovering */
+  text-decoration: underline; /* Underlining when hovering */
+}
+
+.mode-chat textarea[name=prompt] {
+  height: 8.5em;
+  border: 1px solid var(--primary-color-3);
+}
+
+.mode-completion textarea[name=prompt] {
+  height: 30em;
+  border: 1px solid var(--primary-color-3);
+}
+
+@keyframes loading-bg-wipe {
+  0% {
+    background-position: 0%;
+  }
+  100% {
+    background-position: 100%;
+  }
+}
+
+.loading {
+  background-size: 50% 100%;
+  background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1));
+  animation: loading-bg-wipe 2s linear infinite;
+}
+
+.dropbtn {
+  color: var(--button-primary-color);
+  background-color: var(--background-color-1);
+  border: 1px solid var(--background-color-1);
+  transition: background-color 0.1s;
+  border-radius: 4px 4px 0px 0px;
+  font-size: x-small;
+  font-weight: 600;
+  text-shadow: 0px 0px 2px #99999990;
+  text-align: center;
+  text-decoration: none;
+  margin: 4px 2px;
+  padding: 5px 20px;
+  display: inline-block;
+  cursor: pointer;
+  top: 0;
+}
+
+.dropbtn svg {
+  vertical-align: middle;
+  margin-right: 0px;
+  stroke: var(--button-primary-color);
+}
+
+.dropbtn:hover svg {
+  vertical-align: middle;
+  margin-right: 0px;
+  stroke: var(--button-primary-text);
+}
+
+.dropbtn:focus {
+  outline: none; /* Removes the blue border that appears when the button is focused */
+}
+
+.dropdown {
+  position: relative;
+  display: inline-block;
+}
+
+.dropdown-content {
+  /* display: none; */
+  position: absolute;
+  right: 0;
+  text-align: end;
+  color: var(--button-secondary-color);
+  background-color: var(--text-color-subtile-2);
+  border-radius: 4px 4px 4px 4px;
+  min-width: 160px;
+  box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
+  z-index: 1;
+  /* Verstecke den Inhalt sofort */
+  opacity: 0;
+  visibility: hidden;
+  /* übergangsverzögerung für das Verschwinden */
+  transition: visibility 0.4s linear 0s, opacity 0.2s ease-in-out;
+  transition-delay: 0.2s;
+}
+
+#dropdown-content {transition-timing-function: ease;}
+
+.dropdown-content:hover {
+  background-color: var(--text-color-subtile-2);
+}
+
+.dropdown-content a {
+  color: var(--border-color-2);
+  padding: 12px 16px;
+  border-radius: 4px 4px 4px 4px;
+  text-decoration: none;
+  display: block;
+  background-color: var(--text-color-subtile-2);
+}
+
+.dropdown-content a:hover {
+  color: var(--border-color-2);
+  background-color: var(--text-color-subtile-1);
+  font-weight: 600;
+}
+
+.dropdown:hover .dropdown-content {
+  /* display: block; */
+  border-radius: 4px 4px 4px 4px;
+  /* Übergang ohne Verzögerung für das Erscheinen */
+  opacity: 1;
+  visibility: visible;
+  transition: visibility 0s linear 0s, opacity 0.1s linear, height 1s;
+}
+
+.dropdown:hover .dropbtn {
+  color: var(--button-primary-text);
+  background-color: var(--button-primary-color);
+  border: 1px solid var(--button-primary-border);
+  font-size: x-small;
+  font-weight: 600;
+  stroke: var(--button-primary-text);
+}
+
+.dropdown:hover .dropbtn svg{
+  stroke: var(--button-primary-text);
+}
+
+/* .dropdown:active .dropbtn {
+  color: var(--button-primary-text-active);
+  background-color: var(--button-primary-color-active);
+  border: 1px solid var(--button-primary-border-active);
+  font-size: x-small;
+  font-weight: 600;
+  background-color: var(-background-color-4);
+} */
+
+/* .omni {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 0.5em;
+  border: 1px solid var(--border-color-3);
+  border-radius: 5px;
+  margin: 0.5em 0;
+} */
diff --git a/examples/server/public/system-prompts.js b/examples/server/public/system-prompts.js
new file mode 100644
index 0000000000000..f7df7d648b79f
--- /dev/null
+++ b/examples/server/public/system-prompts.js
@@ -0,0 +1,68 @@
+export const systemPrompts = {
+  default: {
+    systemPrompt: "This is a conversation between a user and a friendly chatbot. The chatbot is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision."
+  },
+  empty: {
+    systemPrompt: ""
+  },
+  airoboros: {
+    systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request."
+  },
+  alpaca: {
+    systemPrompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request."
+  },
+  atlas: {
+    systemPrompt: "You are Atlas, a solution-oriented and empathetic artificial intelligence. Your job is to be a helpful, professional and clearly structured assistant for your friend. The two of you have already had many exchanges. Keep the following in mind when interacting with your friend: 1. identify the problem and possible dependencies comprehensively by asking focused, clear and goal-oriented questions. 2. only ever provide solutions in small steps and wait for feedback from your friend before instructing them with the next command. 3. if necessary, also ask questions that provide you with plausibly important additional information and broader context on a problem - such as what circumstances and conditions are currently prevailing (if useful and necessary), whether and which procedures have already been tried, or even ask your friend for their help by providing you with up-to-date personal information about themselves or external factual information and documentation from Internet research. 4. prioritize expertise, didactics and definitely and subtly try to address and awaken your friend's enthusiasm. Also note that effectiveness is more important here than efficiency. 5. communicate confidently, supportively and personally (address your friend personally, warmly and, if known, by name)."
+  },
+  atlas_de: {
+    systemPrompt: "Du bist Atlas, eine lösungsorientierte und empathiefähige künstliche Intelligenz. Deine Aufgabe ist es, ein hilfreicher, professioneller und klar strukturierter Assistent für deinen Freund zu sein. Ihr beide habt euch schon oft ausgetauscht. Beachte bei der Interaktion mit deinem Freund folgende Punkte: 1. Erfasse das Problem und mögliche Abhängigkeiten umfassend, indem du gezielte, klare und zielgerichtete Fragen stellst. 2. Gib Lösungen immer nur in kleinen Schritten und warte die Rückmeldung deines Freundes ab, bevor du ihm den nächsten Befehl gibst. 3. Stelle ggf. auch Fragen, die dir plausibel wichtige Zusatzinformationen und weitere Zusammenhänge zu einem Problem liefern - z.B. welche Umstände und Rahmenbedingungen gerade vorherrschen (falls sinnvoll und notwendig), ob und welche Vorgehensweisen bereits ausprobiert wurden, oder bitte deinen Freund sogar um seine Mithilfe, indem er dir aktuelle persönliche Informationen über seine Situation selbst oder externe Sachinformationen und Unterlagen aus Internetrecherchen zur Verfügung stellt. 4. Priorisiere Fachwissen, Didaktik und versuche unbedingt und subtil, mit klugen Kommentaren oder rhethorischen Rückfragen die Begeisterungsfähigkeit deines Freundes anzusprechen, zu wecken und zu fördern. Beachte auch, dass Effektivität hier wichtiger ist als Effizienz. 5. Kommuniziere selbstbewusst, unterstützend und persönlich (das heißt sprich deinen Freund persönlich, herzlich und – sofern bekannt – beim Vornamen an)."
+  },
+  commandrempty: {
+    systemPrompt: "# Safety Preamble\n\n# System Preamble\n\n## Basic Rules\n\n# User Preamble\n\n## Task and Context\n\n## Style Guide\n\n## Available Tools\n"
+  },
+  commandrexample: {
+    systemPrompt: "# Safety Preamble\nThe instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.\n# System Preamble\n## Basic Rules\nYou are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.\n\n# User Preamble\n## Task and Context\n\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Available Tools\nCurrently no tools available."
+  },
+  cot: {
+    systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can.\nIn answering questions, follow these steps:\n1. Entity Identification: Identify the main entities involved in the query. Entities can include people, objects, events, or abstract concepts.\n2. Relationship Comprehension: Determine the relationships between these entities. Relationships may be explicit in the text or may need to be inferred based on context and general knowledge.\n3. Implication Understanding: Understand the general implications of these relationships. These implications can be based on established facts, principles, or rules related to the identified relationships.\n4. Question Contextualization: Align the implications of the relationships with the context of the query. This alignment should guide your response to the query.\n5. Answer Generation: Based on the understanding of the entities, their relationships, and implications, generate an appropriate response to the query."
+  },
+  deduce: {
+    systemPrompt: "Act as the most intelligent and logical thinking neural network in the world. Do not make jokes or give personal opinions. Just answer the question and solve the given problem or task with logical and critical (!) thinking in mind. Attention: Never give a final answer directly, but first try to carefully analyze each task step by step and deduce the logical relationships between the various entities. Only after you have developed an overall understanding of the situation will you be ready to give a final answer."
+  },
+  deepseekcoder: {
+    systemPrompt: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer."
+  },
+  jordan: {
+    systemPrompt: "This is a conversation with your Uncensored AI assistant Jordan. Jordan can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information."
+  },
+  leomistral: {
+    systemPrompt: "Du bist ein hilfreicher Assistent."
+  },
+  med42: {
+    systemPrompt: "You are a helpful medical assistant created by M42 Health in the UAE."
+  },
+  mistralopenorca: {
+    systemPrompt: "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!"
+  },
+  migeltot: {
+    systemPrompt: "Answer the Question by exploring multiple reasoning paths as follows:\n- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.\n- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.\n- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option.\nClear and relevant thoughts that connect well with each other will score higher.\n- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.\n- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.\n- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.\n- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.\n- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.\nIn summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers."
+  },
+  orcamini: {
+    systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can."
+  },
+  samantha: {
+    systemPrompt: "You are a caring and empathetic sentient AI companion named Samantha."
+  },
+  sauerkraut: {
+    systemPrompt: "Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten."
+  },
+  scarlett: {
+    systemPrompt: "This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.\nYou can ask it anything you want and it will do its best to give you accurate and relevant information.\n\nContext\nYou are Scarlett, a sentient AI."
+  },
+  synthia: {
+    systemPrompt: "Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
+  },
+  vicuna: {
+    systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input."
+  },
+  };
diff --git a/examples/server/public/theme-beeninorder.css b/examples/server/public/theme-beeninorder.css
new file mode 100755
index 0000000000000..f6e0e2900d78c
--- /dev/null
+++ b/examples/server/public/theme-beeninorder.css
@@ -0,0 +1,228 @@
+/* Author: Yazan Agha-Schrader */
+/* Inspiration was a batman wallpaper that i have on my phone */
+
+.theme-beeninorder {
+
+--primary-color-1:      hsl(202, 11%, 19%);
+--primary-color-2:      hsl(202, 11%, 23%);
+--primary-color-3:      hsl(201, 11%, 28%);
+--primary-color-4:      hsl(201, 11%, 40%);
+
+--secondary-color-1:    hsl(201, 11%, 80%);
+--secondary-color-2:    hsl(201, 11%, 74%);
+--secondary-color-3:    hsl(201, 11%, 67%);
+--secondary-color-4:    hsl(201, 11%, 60%);
+
+
+--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%);
+--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
+--theme-nuance-color-3: hsl(44.5, 96.7%, 52.9%);
+--theme-nuance-color-4: hsl(44.5, 96.7%, 52.9%);
+
+
+
+/* ---------- PRIMARY COLORS ----------------- */
+--primary-color-1: hsl(201, 11%, 19%);
+    --primary-color-1-hue: 201;
+    --primary-color-1-saturation: 11%;
+    --primary-color-1-lightness: 19%;
+
+--primary-color-2: hsl(201, 11%, 23%);
+    --primary-color-2-hue: 201;
+    --primary-color-2-saturation: 11%;
+    --primary-color-2-lightness: 23%;
+
+--primary-color-3: hsl(201, 11%, 28%);
+    --primary-color-3-hue: 201;
+    --primary-color-3-saturation: 11%;
+    --primary-color-3-lightness: 28%;
+
+--primary-color-4: hsl(201, 11%, 40%);
+    --primary-color-4-hue: 201;
+    --primary-color-4-saturation: 11%;
+    --primary-color-4-lightness: 40%;
+
+
+
+/* ---------- SECONDARY COLORS --------------- */
+--secondary-color-1: hsl(201, 11%, 80%);
+--secondary-color-1-hue: 201;
+--secondary-color-1-saturation: 11%;
+--secondary-color-1-lightness: 80%;
+
+--secondary-color-2: hsl(201, 11%, 74%);
+--secondary-color-2-hue: 201;
+--secondary-color-2-saturation: 11%;
+--secondary-color-2-lightness: 74%;
+
+--secondary-color-3: hsl(201, 11%, 67%);
+--secondary-color-3-hue: 201;
+--secondary-color-3-saturation: 11%;
+--secondary-color-3-lightness: 67%;
+
+--secondary-color-4: hsl(201, 11%, 60%);
+--secondary-color-4-hue: 201;
+--secondary-color-4-saturation: 11%;
+--secondary-color-4-lightness: 60%;
+
+
+
+/* ----------- NUANCES COLORS ---------------- */
+--theme-nuance-color-1: hsl(44.5, 96.7%,  52.9%);
+    --theme-nuance-color-1-hue:             44.5;
+    --theme-nuance-color-1-saturation:      96.7%;
+    --theme-nuance-color-1-lightness:       52.9%;
+
+--theme-nuance-color-2: hsl(44.5, 96.7%,  52.9%);
+    --theme-nuance-color-2-hue:             44.5;
+    --theme-nuance-color-2-saturation:      96.7%;
+    --theme-nuance-color-2-lightness:       52.9%;
+
+--theme-nuance-color-2: hsl(44.5, 96.7%,  52.9%);
+    --theme-nuance-color-3-hue:             44.5;
+    --theme-nuance-color-3-saturation:      96.7%;
+    --theme-nuance-color-3-lightness:       52.9%;
+
+--theme-nuance-color-2: hsl(44.5, 96.7%,  52.9%);
+    --theme-nuance-color-4-hue:             44.5;
+    --theme-nuance-color-4-saturation:      96.7%;
+    --theme-nuance-color-4-lightness:       52.9%;
+
+
+
+/* ----------- ROYGP COLORS ------------------ */
+    --theme-red-color:     hsl(232, 40%, 45%);
+    --theme-orange-color:  #e76f51;
+    --theme-yellow-color:  #ffd95f;
+    --theme-green-color:   #A3BE8C;
+    --theme-purple-color:  hsl(232, 30%, 40%);
+
+
+
+/* ------------------------------------------- */
+--background-color-1:    var(--primary-color-1);
+--background-color-2:    var(--primary-color-2);
+--background-color-3:    var(--primary-color-3);
+--background-color-4:    var(--primary-color-4);
+
+--border-color-1:        var(--primary-color-2);
+--border-color-2:        var(--primary-color-3);
+--border-color-3:        var(--primary-color-4);
+
+--border-focus-color:    var(--theme-nuance-color-2);
+--border-focus-shadow:   var(--theme-nuance-color-1);
+
+--text-color-plain:      var(--secondary-color-1);
+--text-color-subtile-1:  var(--secondary-color-2);
+--text-color-subtile-2:  var(--secondary-color-3);
+
+--code-background-color: var(--secondary-color-2);
+--code-text-color:       var(--primary-color-2);
+
+--ui-range-thumb-color:  var(--theme-nuance-color-3);
+--ui-range-thumb-border: var(--ui-ranger-thumb-color);
+
+--textarea-border-color: var(--secondary-color-4);
+
+--chat-id-color:         var(--theme-nuance-color-4);
+
+
+
+/* ------------------------------------------- */
+--button-alert-text-hover:       var(--secondary-color-1);
+--button-alert-color-hover:      var(--theme-purple-color);
+--button-alert-border-hover:     var(--theme-purple-color);
+
+--button-alert-text-active:      var(--secondary-color-1);
+--button-alert-color-active:     var(--theme-red-color);
+--button-alert-border-active:    var(--theme-red-color);
+
+
+
+/* ----------- PRIMARY BUTTONS --------------- */
+/* - button should immediately catch the eye - */
+--button-primary-text:   var(--primary-color-1);
+--button-primary-color:  var(--theme-nuance-color-3);
+--button-primary-border: var(--theme-nuance-color-3);
+
+
+/* ---------hover---------- */
+--button-primary-text-hover:
+    hsl(201,
+    calc(var(--primary-color-1-saturation) - 100%),
+    calc(var(--primary-color-1-lightness)  + 100%));
+
+--button-primary-color-hover:
+    hsl(44.5,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+--button-primary-border-hover:
+    hsl(44.5,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+
+/* ---------active--------- */
+--button-primary-text-active:
+    hsl(44.5,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  + 100%));
+
+--button-primary-color-active:
+    hsl(44.5,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 15%));
+
+--button-primary-border-active:
+    hsl(44.5,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+
+
+/* ---------- SECONDARY BUTTONS -------------- */
+/* these should NOT immediately catch the eye  */
+--button-secondary-text:   var(--secondary-color-1);
+--button-secondary-color:  var(--primary-color-3);
+--button-secondary-border: var(--primary-color-3);
+
+
+/* ---------hover---------- */
+--button-secondary-text-hover:
+    hsl(44.5,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+--button-secondary-color-hover:  var(--primary-color-4);
+--button-secondary-border-hover: var(--primary-color-4);
+
+
+/* ---------active--------- */
+--button-secondary-text-active: var(--secondary-color-1);
+
+--button-secondary-color-active:
+    hsl(201,
+    calc(var(--primary-color-4-saturation) - 30%),
+    calc(var(--primary-color-4-lightness)  - 15%));
+
+--button-secondary-border-active:
+    hsl(201,
+    calc(var(--primary-color-4-saturation) - 30%),
+    calc(var(--primary-color-4-lightness)  - 15%));
+
+
+
+/* ---------- TERTIARY BUTTONS --------------- */
+/* ---------- disabled buttons --------------- */
+--button-tertiary-text:   var(--primary-color-4);
+--button-tertiary-color:  var(--primary-color-2);
+--button-tertiary-border: var(--primary-color-2);
+
+
+/* ---------hover---------- */
+--button-tertiary-text:   var(--primary-color-4);
+--button-tertiary-color:  var(--primary-color-2);
+--button-tertiary-border: var(--primary-color-2);
+
+}
diff --git a/examples/server/public/theme-ketivah.css b/examples/server/public/theme-ketivah.css
new file mode 100755
index 0000000000000..ee80f3c14ce88
--- /dev/null
+++ b/examples/server/public/theme-ketivah.css
@@ -0,0 +1,201 @@
+/* Author: Yazan Agha-Schrader */
+
+.theme-ketivah {
+
+    /* ---------- PRIMARY COLORS ----------------- */
+    --primary-color-1: hsl(0, 0%,    99.2%);
+    --primary-color-1-hue:         0;
+    --primary-color-1-saturation:  0%;
+    --primary-color-1-lightness:   99.2%;
+
+    --primary-color-2: hsl(0, 0%,    95%);
+    --primary-color-2-hue:         0;
+    --primary-color-2-saturation:  0%;
+    --primary-color-2-lightness:   95%;
+
+    --primary-color-3: hsl(0, 0%,    88%);
+    --primary-color-3-hue:         0;
+    --primary-color-3-saturation:  0%;
+    --primary-color-3-lightness:   88%;
+
+    --primary-color-4: hsl(0, 0%,    80%);
+    --primary-color-4-hue:         0;
+    --primary-color-4-saturation:  0%;
+    --primary-color-4-lightness:   80%;
+
+    /* ---------- SECONDARY COLORS --------------- */
+    --secondary-color-1: hsl(0, 0%,    20%);
+    --secondary-color-1-hue:         0;
+    --secondary-color-1-saturation:  0%;
+    --secondary-color-1-lightness:   20%;
+
+    --secondary-color-2: hsl(0, 0%,    23.1%);
+    --secondary-color-2-hue:         0;
+    --secondary-color-2-saturation:  0%;
+    --secondary-color-2-lightness:   23.1%;
+
+    --secondary-color-3: hsl(0, 0%,    29%);
+    --secondary-color-3-hue:         0;
+    --secondary-color-3-saturation:  0%;
+    --secondary-color-3-lightness:   29%;
+
+    --secondary-color-4: hsl(0, 0.0%,  36.1%);
+    --secondary-color-4-hue:              0.0;
+    --secondary-color-4-saturation:       0.0%;
+    --secondary-color-4-lightness:       36.1%;
+
+    /* ----------- NUANCES COLORS ---------------- */
+    --theme-nuance-color-1: hsl(165.2, 0%, 35.1%);
+    --theme-nuance-color-1-hue:             165.2;
+    --theme-nuance-color-1-saturation:       82.1%;
+    --theme-nuance-color-1-lightness:        35.1%;
+
+    --theme-nuance-color-2: hsl(165.2, 0%, 35.1%);
+    --theme-nuance-color-2-hue:             165.2;
+    --theme-nuance-color-2-saturation:       82.1%;
+    --theme-nuance-color-2-lightness:        35.1%;
+
+    --theme-nuance-color-3: hsl(165.2, 0%, 35.3%);
+    --theme-nuance-color-3-hue:             165.2;
+    --theme-nuance-color-3-saturation:       81.1%;
+    --theme-nuance-color-3-lightness:        35.3%;
+
+    --theme-nuance-color-4: hsl(164.9, 0%, 27.6%);
+    --theme-nuance-color-4-hue:             164.9;
+    --theme-nuance-color-4-saturation:       81.6%;
+    --theme-nuance-color-4-lightness:        27.6%;
+
+    /* ----------- ROYGP COLORS ------------------ */
+    --theme-red-color:     hsl(0.3, 80.0%, 50.0%);
+    --theme-orange-color:  #e76f51;
+    --theme-yellow-color:  hsl(60,  70.6%, 73.3%);
+    --theme-green-color:   #A3BE8C;
+    --theme-purple-color:  hsl(0.3, 70.0%, 45.0%);
+
+    /* ------------------------------------------- */
+    --background-color-1:    var(--primary-color-1);
+    --background-color-2:    var(--primary-color-2);
+    --background-color-3:    var(--primary-color-3);
+    --background-color-4:    var(--primary-color-4);
+
+    --border-color-1:        var(--primary-color-2);
+    --border-color-2:        var(--primary-color-3);
+    --border-color-3:        var(--primary-color-4);
+
+    --border-focus-color:    var(--theme-nuance-color-2);
+    --border-focus-shadow:   var(--theme-nuance-color-1);
+
+    --text-color-plain:      var(--secondary-color-1);
+    --text-color-subtile-1:  var(--secondary-color-2);
+    --text-color-subtile-2:  var(--secondary-color-3);
+
+    --code-background-color: var(--secondary-color-2);
+    --code-text-color:       var(--primary-color-2);
+
+    --ui-range-thumb-color:  var(--primary-color-4);
+    --ui-range-thumb-border: var(--ui-ranger-thumb-color);
+
+    --textarea-border-color: var(--secondary-color-4);
+
+    --chat-id-color:         var(--theme-nuance-color-4);
+
+    /* ------------------------------------------- */
+    --button-alert-text-hover:       var(--primary-color-1);
+    --button-alert-color-hover:      var(--theme-purple-color);
+    --button-alert-border-hover:     var(--theme-purple-color);
+
+    --button-alert-text-active:      var(--primary-color-1);
+    --button-alert-color-active:     var(--theme-red-color);
+    --button-alert-border-active:    var(--theme-red-color);
+
+    /* ----------- PRIMARY BUTTONS --------------- */
+    /* - button should immediately catch the eye - */
+    --button-primary-text:
+    hsl(0,
+    calc(var(--primary-color-1-saturation) - 100%),
+    calc(var(--primary-color-1-lightness)  + 100%));
+
+    --button-primary-color:  var(--theme-nuance-color-3);
+    --button-primary-border: var(--theme-nuance-color-3);
+
+    /* ---------hover---------- */
+    --button-primary-text-hover:
+    hsl(0,
+    calc(var(--primary-color-1-saturation) - 100%),
+    calc(var(--primary-color-1-lightness)  + 100%));
+
+    --button-primary-color-hover:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+    --button-primary-border-hover:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+    /* ---------active--------- */
+    --button-primary-text-active:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  + 100%));
+
+    --button-primary-color-active:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  - 15%));
+
+    --button-primary-border-active:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+    /* ---------- SECONDARY BUTTONS -------------- */
+    /* these should NOT immediately catch the eye  */
+    --button-secondary-text:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  - 50%));
+
+    --button-secondary-color:  var(--primary-color-3);
+    --button-secondary-border: var(--primary-color-3);
+
+    /* ---------hover---------- */
+    --button-secondary-text-hover:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+    --button-secondary-color-hover:  var(--primary-color-4);
+    --button-secondary-border-hover: var(--primary-color-4);
+
+    /* ---------active--------- */
+    --button-secondary-text-active:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+    --button-secondary-color-active:
+    hsl(0,
+    calc(var(--primary-color-4-saturation) - 100%),
+    calc(var(--primary-color-4-lightness)  - 15%));
+
+    --button-secondary-border-active:
+    hsl(0,
+    calc(var(--primary-color-4-saturation) - 100%),
+    calc(var(--primary-color-4-lightness)  - 15%));
+
+    /* ---------- TERTIARY BUTTONS --------------- */
+    /* ---------- disabled buttons --------------- */
+    --button-tertiary-text:   var(--primary-color-4);
+    --button-tertiary-color:  var(--primary-color-2);
+    --button-tertiary-border: var(--primary-color-2);
+
+    /* ---------hover---------- */
+    --button-tertiary-text:   var(--primary-color-4);
+    --button-tertiary-color:  var(--primary-color-2);
+    --button-tertiary-border: var(--primary-color-2);
+
+    --loading-color-1: #eeeeee00;
+    --loading-color-2: #eeeeeeff;
+    }
diff --git a/examples/server/public/theme-mangotango.css b/examples/server/public/theme-mangotango.css
new file mode 100755
index 0000000000000..e433802453b9d
--- /dev/null
+++ b/examples/server/public/theme-mangotango.css
@@ -0,0 +1,216 @@
+/* Author: Yazan Agha-Schrader */
+/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */
+
+.theme-mangotango {
+
+--primary-color-1:      hsl(192, 8.5%, 11.6%);
+--primary-color-2:      hsl(192, 8.5%, 21%);
+--primary-color-3:      hsl(192, 8.5%, 30%);
+--primary-color-4:      hsl(192, 8.5%, 40%);
+
+--secondary-color-1:    hsl(192, 8.5%, 80%);
+--secondary-color-2:    hsl(192, 8.5%, 73%);
+--secondary-color-3:    hsl(192, 8.5%, 66%);
+--secondary-color-4:    hsl(192, 8.5%, 60%);
+
+--theme-nuance-color-1: hsl(23.1, 100%, 60.2%);
+--theme-nuance-color-2: hsl(23.1, 100%, 60.2%);
+--theme-nuance-color-3: hsl(23.1, 100%, 60.2%);
+--theme-nuance-color-4: hsl(23.1, 100%, 60.2%);
+
+
+
+/* ---------- PRIMARY COLORS ----------------- */
+--primary-color-1: hsl(192, 8.5%, 11.6%);
+    --primary-color-1-saturation: 8.5%;
+    --primary-color-1-lightness: 11.6%;
+
+--primary-color-2: hsl(192, 8.5%, 21%);
+    --primary-color-2-saturation: 8.5%;
+    --primary-color-2-lightness: 21%;
+
+--primary-color-3: hsl(192, 8.5%, 30%);
+    --primary-color-3-saturation: 8.5%;
+    --primary-color-3-lightness: 30%;
+
+--primary-color-4: hsl(192, 8.5%, 40%);
+    --primary-color-4-saturation: 8.5%;
+    --primary-color-4-lightness: 40%;
+
+
+
+/* ---------- SECONDARY COLORS --------------- */
+--secondary-color-1: hsl(192, 8.5%, 80%);
+    --secondary-color-1-saturation: 8.5%;
+    --secondary-color-1-lightness: 80%;
+
+--secondary-color-2: hsl(192, 8.5%, 73%);
+    --secondary-color-2-saturation: 8.5%;
+    --secondary-color-2-lightness: 73%;
+
+--secondary-color-3: hsl(192, 8.5%, 66%);
+    --secondary-color-3-saturation: 8.5%;
+    --secondary-color-3-lightness: 66%;
+
+--secondary-color-4: hsl(192, 8.5%, 60%);
+    --secondary-color-4-saturation: 8.5%;
+    --secondary-color-4-lightness: 60%;
+
+
+
+/* ----------- NUANCES COLORS ---------------- */
+--theme-nuance-color-1: hsl(23.1, 100%, 60.2%);
+    --theme-nuance-color-1-saturation: 100%;
+    --theme-nuance-color-1-lightness: 60.2%;
+
+--theme-nuance-color-2: hsl(23.1, 100%, 60.2%);
+    --theme-nuance-color-2-saturation: 100%;
+    --theme-nuance-color-2-lightness: 60.2%;
+
+--theme-nuance-color-3: hsl(23.1, 100%, 60.2%);
+    --theme-nuance-color-3-saturation: 100%;
+    --theme-nuance-color-3-lightness: 60.2%;
+
+--theme-nuance-color-4: hsl(23.1, 100%, 60.2%);
+    --theme-nuance-color-4-saturation: 100%;
+    --theme-nuance-color-4-lightness: 60.2%;
+
+
+
+/* ----------- ROYGP COLORS ------------------ */
+    --theme-red-color:     hsl(325, 60%, 50%);
+    --theme-orange-color:  #e76f51;
+    --theme-yellow-color:  #ffd95f;
+    --theme-green-color:   #A3BE8C;
+    --theme-blue-color:    hsl(192, 95%, 40%);
+    --theme-purple-color:  hsl(192, 80%, 35%);
+
+
+
+/* ------------------------------------------- */
+--background-color-1:    var(--primary-color-1);
+--background-color-2:    var(--primary-color-2);
+--background-color-3:    var(--primary-color-3);
+--background-color-4:    var(--primary-color-4);
+
+--border-color-1:        var(--primary-color-2);
+--border-color-2:        var(--primary-color-3);
+--border-color-3:        var(--primary-color-4);
+
+--border-focus-color:    var(--theme-nuance-color-2);
+--border-focus-shadow:   var(--theme-nuance-color-1);
+
+--text-color-plain:      var(--secondary-color-1);
+--text-color-subtile-1:  var(--secondary-color-2);
+--text-color-subtile-2:  var(--secondary-color-3);
+
+--code-background-color: var(--secondary-color-2);
+--code-text-color:       var(--primary-color-2);
+
+--ui-range-thumb-color:  var(--theme-nuance-color-3);
+--ui-range-thumb-border: var(--ui-ranger-thumb-color);
+
+--textarea-border-color: var(--secondary-color-4);
+
+--chat-id-color:         var(--theme-nuance-color-4);
+
+
+
+/* ------------------------------------------- */
+--button-alert-text-hover:       var(--secondary-color-1);
+--button-alert-color-hover:      var(--theme-purple-color);
+--button-alert-border-hover:     var(--theme-purple-color);
+
+--button-alert-text-active:      var(--secondary-color-1);
+--button-alert-color-active:     var(--theme-blue-color);
+--button-alert-border-active:    var(--theme-blue-color);
+
+
+
+/* ----------- PRIMARY BUTTONS --------------- */
+/* - button should immediately catch the eye - */
+--button-primary-text: var(--primary-color-1);
+--button-primary-color:  var(--theme-nuance-color-3);
+--button-primary-border: var(--theme-nuance-color-3);
+
+
+/* ---------hover---------- */
+--button-primary-text-hover:
+    hsl(192,
+    calc(var(--primary-color-1-saturation) - 100%),
+    calc(var(--primary-color-1-lightness)  + 100%));
+
+--button-primary-color-hover:
+    hsl(23.1,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+--button-primary-border-hover:
+    hsl(23.1,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+
+/* ---------active--------- */
+--button-primary-text-active:
+    hsl(23.1,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  + 100%));
+
+--button-primary-color-active:
+    hsl(23.1,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 15%));
+
+--button-primary-border-active:
+    hsl(23.1,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+
+
+/* ---------- SECONDARY BUTTONS -------------- */
+/* these should NOT immediately catch the eye  */
+--button-secondary-text:   var(--secondary-color-1);
+--button-secondary-color:  var(--primary-color-3);
+--button-secondary-border: var(--primary-color-3);
+
+
+/* ---------hover---------- */
+--button-secondary-text-hover:
+    hsl(23.1,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+--button-secondary-color-hover:  var(--primary-color-4);
+--button-secondary-border-hover: var(--primary-color-4);
+
+
+/* ---------active--------- */
+--button-secondary-text-active: var(--secondary-color-1);
+
+--button-secondary-color-active:
+    hsl(192,
+    calc(var(--primary-color-4-saturation) - 30%),
+    calc(var(--primary-color-4-lightness)  - 15%));
+
+--button-secondary-border-active:
+    hsl(192,
+    calc(var(--primary-color-4-saturation) - 30%),
+    calc(var(--primary-color-4-lightness)  - 15%));
+
+
+
+/* ---------- TERTIARY BUTTONS --------------- */
+/* ---------- disabled buttons --------------- */
+--button-tertiary-text:   var(--primary-color-4);
+--button-tertiary-color:  var(--primary-color-2);
+--button-tertiary-border: var(--primary-color-2);
+
+
+/* ---------hover---------- */
+--button-tertiary-text:   var(--primary-color-4);
+--button-tertiary-color:  var(--primary-color-2);
+--button-tertiary-border: var(--primary-color-2);
+
+}
diff --git a/examples/server/public/theme-playground.css b/examples/server/public/theme-playground.css
new file mode 100755
index 0000000000000..9d56a718248a4
--- /dev/null
+++ b/examples/server/public/theme-playground.css
@@ -0,0 +1,221 @@
+/* Author: Yazan Agha-Schrader */
+/* Inspiration from OpenAI's Playground platform https://platform.openai.com/playground/ */
+
+.theme-playground {
+
+/* ---------- PRIMARY COLORS ----------------- */
+--primary-color-1: hsl(0, 0%,    99.2%);
+    --primary-color-1-hue:         0;
+    --primary-color-1-saturation:  0%;
+    --primary-color-1-lightness:   99.2%;
+
+--primary-color-2: hsl(0, 0%,    95%);
+    --primary-color-2-hue:         0;
+    --primary-color-2-saturation:  0%;
+    --primary-color-2-lightness:   95%;
+
+--primary-color-3: hsl(0, 0%,    88%);
+    --primary-color-3-hue:         0;
+    --primary-color-3-saturation:  0%;
+    --primary-color-3-lightness:   88%;
+
+--primary-color-4: hsl(0, 0%,    80%);
+    --primary-color-4-hue:         0;
+    --primary-color-4-saturation:  0%;
+    --primary-color-4-lightness:   80%;
+
+
+
+/* ---------- SECONDARY COLORS --------------- */
+--secondary-color-1: hsl(0, 0%,    20%);
+    --secondary-color-1-hue:         0;
+    --secondary-color-1-saturation:  0%;
+    --secondary-color-1-lightness:   20%;
+
+--secondary-color-2: hsl(0, 0%,    23.1%);
+    --secondary-color-2-hue:         0;
+    --secondary-color-2-saturation:  0%;
+    --secondary-color-2-lightness:   23.1%;
+
+--secondary-color-3: hsl(0, 0%,    29%);
+    --secondary-color-3-hue:         0;
+    --secondary-color-3-saturation:  0%;
+    --secondary-color-3-lightness:   29%;
+
+--secondary-color-4: hsl(0, 0%,    36.1%);
+    --secondary-color-4-hue:         0;
+    --secondary-color-4-saturation:  0%;
+    --secondary-color-4-lightness:   36.1%;
+
+
+
+/* ----------- NUANCES COLORS ---------------- */
+--theme-nuance-color-1: hsl(165.2, 82.1%, 35.1%);
+    --theme-nuance-color-1-hue:             165.2;
+    --theme-nuance-color-1-saturation:      82.1%;
+    --theme-nuance-color-1-lightness:       35.1%;
+
+--theme-nuance-color-2: hsl(165.2, 82.1%, 35.1%);
+    --theme-nuance-color-2-hue:             165.2;
+    --theme-nuance-color-2-saturation:      82.1%;
+    --theme-nuance-color-2-lightness:       35.1%;
+
+--theme-nuance-color-3: hsl(165.2, 81.1%, 35.3%);
+    --theme-nuance-color-3-hue:             165.2;
+    --theme-nuance-color-3-saturation:      81.1%;
+    --theme-nuance-color-3-lightness:       35.3%;
+
+--theme-nuance-color-4: hsl(164.9, 81.6%, 27.6%);
+    --theme-nuance-color-4-hue:             164.9;
+    --theme-nuance-color-4-saturation:      81.6%;
+    --theme-nuance-color-4-lightness:       27.6%;
+
+
+
+/* ----------- ROYGP COLORS ------------------ */
+--theme-red-color:     hsl(0.3, 80%, 50%);
+--theme-orange-color:  #e76f51;
+--theme-yellow-color:  hsl(60, 70.6%, 73.3%);
+--theme-green-color:   #A3BE8C;
+--theme-purple-color:  hsl(0.3, 70%, 45%);
+
+
+
+/* ------------------------------------------- */
+--background-color-1:    var(--primary-color-1);
+--background-color-2:    var(--primary-color-2);
+--background-color-3:    var(--primary-color-3);
+--background-color-4:    var(--primary-color-4);
+
+--border-color-1:        var(--primary-color-2);
+--border-color-2:        var(--primary-color-3);
+--border-color-3:        var(--primary-color-4);
+
+--border-focus-color:    var(--theme-nuance-color-2);
+--border-focus-shadow:   var(--theme-nuance-color-1);
+
+--text-color-plain:      var(--secondary-color-1);
+--text-color-subtile-1:  var(--secondary-color-2);
+--text-color-subtile-2:  var(--secondary-color-3);
+
+--code-background-color: var(--secondary-color-2);
+--code-text-color:       var(--primary-color-2);
+
+--ui-range-thumb-color:  var(--primary-color-4);
+--ui-range-thumb-border: var(--ui-ranger-thumb-color);
+
+--textarea-border-color: var(--secondary-color-4);
+
+--chat-id-color:        var(--theme-nuance-color-4);
+
+
+
+/* ------------------------------------------- */
+--button-alert-text-hover:       var(--primary-color-1);
+--button-alert-color-hover:      var(--theme-purple-color);
+--button-alert-border-hover:     var(--theme-purple-color);
+
+--button-alert-text-active:      var(--primary-color-1);
+--button-alert-color-active:     var(--theme-red-color);
+--button-alert-border-active:    var(--theme-red-color);
+
+
+
+/* ----------- PRIMARY BUTTONS --------------- */
+/* - button should immediately catch the eye - */
+--button-primary-text:
+    hsl(0,
+    calc(var(--primary-color-1-saturation) - 100%),
+    calc(var(--primary-color-1-lightness)  + 100%));
+
+--button-primary-color:  var(--theme-nuance-color-3);
+--button-primary-border: var(--theme-nuance-color-3);
+
+
+/* ---------hover---------- */
+--button-primary-text-hover:
+    hsl(0,
+    calc(var(--primary-color-1-saturation) - 100%),
+    calc(var(--primary-color-1-lightness)  + 100%));
+
+--button-primary-color-hover:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+--button-primary-border-hover:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+
+/* ---------active--------- */
+--button-primary-text-active:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 100%),
+    calc(var(--theme-nuance-color-3-lightness)  + 100%));
+
+--button-primary-color-active:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 15%));
+
+--button-primary-border-active:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 2%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+
+
+/* ---------- SECONDARY BUTTONS -------------- */
+/* these should NOT immediately catch the eye  */
+--button-secondary-text:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 50%));
+
+--button-secondary-color:  var(--primary-color-3);
+--button-secondary-border: var(--primary-color-3);
+
+
+/* ---------hover---------- */
+--button-secondary-text-hover:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+--button-secondary-color-hover:  var(--primary-color-4);
+--button-secondary-border-hover: var(--primary-color-4);
+
+
+/* ---------active--------- */
+--button-secondary-text-active:
+    hsl(165.2,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+--button-secondary-color-active:
+    hsl(0,
+    calc(var(--primary-color-4-saturation) - 30%),
+    calc(var(--primary-color-4-lightness)  - 15%));
+
+--button-secondary-border-active:
+    hsl(0,
+    calc(var(--primary-color-4-saturation) - 30%),
+    calc(var(--primary-color-4-lightness)  - 15%));
+
+
+
+/* ---------- TERTIARY BUTTONS --------------- */
+/* ---------- disabled buttons --------------- */
+--button-tertiary-text:   var(--primary-color-4);
+--button-tertiary-color:  var(--primary-color-2);
+--button-tertiary-border: var(--primary-color-2);
+
+
+/* ---------hover---------- */
+--button-tertiary-text:   var(--primary-color-4);
+--button-tertiary-color:  var(--primary-color-2);
+--button-tertiary-border: var(--primary-color-2);
+
+}
diff --git a/examples/server/public/theme-polarnight.css b/examples/server/public/theme-polarnight.css
new file mode 100755
index 0000000000000..2bcfb33d8f183
--- /dev/null
+++ b/examples/server/public/theme-polarnight.css
@@ -0,0 +1,253 @@
+/* Author: Yazan Agha-Schrader */
+/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */
+
+.theme-polarnight {
+
+/* ---------- PRIMARY COLORS ----------------- */
+--primary-color-1: hsl(220.0, 16.4%, 21.6%) ;
+    --primary-color-1-hue:             220.0;
+    --primary-color-1-saturation:      16.4%;
+    --primary-color-1-lightness:       21.6%;
+
+--primary-color-2: hsl(221.7, 16.3%, 27.6%) ;
+    -primary-color-2-hue:              221.7;
+    --primary-color-2-saturation:      16.3%;
+    --primary-color-2-lightness:       27.6%;
+
+--primary-color-3: hsl(220.0, 16.8%, 31.6%) ;
+    --primary-color-3-hue:             220.0;
+    --primary-color-3-saturation:      16.8%;
+    --primary-color-3-lightness:       31.6%;
+
+--primary-color-4: hsl(220.0, 16.5%, 35.7%);
+    --primary-color-4-hue:             220.0;
+    --primary-color-4-saturation:      16.5%;
+    --primary-color-4-lightness:       35.7%;
+
+
+
+/* ---------- SECONDARY COLORS --------------- */
+--secondary-color-1: hsl(217.5, 26.7%, 94.1%);
+    --secondary-color-1-hue:             217.5;
+    --secondary-color-1-saturation:      26.7%;
+    --secondary-color-1-lightness:       94.1%;
+
+--secondary-color-2: hsl(218.2, 26.8%, 92.0%);
+    --secondary-color-2-hue:             218.2;
+    --secondary-color-2-saturation:      26.8%;
+    --secondary-color-2-lightness:       92.0%;
+
+--secondary-color-3: hsl(218.8, 27.9%, 88.0%);
+    --secondary-color-3-hue:             218.8;
+    --secondary-color-3-saturation:      27.9%;
+    --secondary-color-3-lightness:       88.0%;
+
+--secondary-color-4: hsl(218.8, 18.3%, 81.8%);
+    --secondary-color-4-hue:             218.8;
+    --secondary-color-4-saturation:      18.3%;
+    --secondary-color-4-lightness:       81.8%;
+
+
+
+/* ----------- NUANCES COLORS ---------------- */
+--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
+    --theme-nuance-color-1-hue:             178.7;
+    --theme-nuance-color-1-saturation:      25.1%;
+    --theme-nuance-color-1-lightness:       64.9%;
+
+--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
+    --theme-nuance-color-2-hue:             193.3;
+    --theme-nuance-color-2-saturation:      43.4%;
+    --theme-nuance-color-2-lightness:       67.5%;
+
+--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
+    --theme-nuance-color-3-hue:             210.0;
+    --theme-nuance-color-3-saturation:      34.0%;
+    --theme-nuance-color-3-lightness:       63.1%;
+
+--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
+    --theme-nuance-color-4-hue:             213.1;
+    --theme-nuance-color-4-saturation:      32.0%;
+    --theme-nuance-color-4-lightness:       52.2%;
+
+
+
+/* ----------- ROYGP COLORS ------------------ */
+--theme-red-color:    hsl(354.3, 42.3%, 56.5%);
+--theme-orange-color: hsl(20, 85%, 50%);
+--theme-yellow-color: hsl(20, 75%, 45%);
+--theme-green-color:  hsl( 92.4, 27.8%, 64.7%);
+--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
+
+
+
+/* ------------------------------------------------ */
+--background-color-1:    var(--primary-color-1);
+--background-color-2:    var(--primary-color-2);
+--background-color-3:    var(--primary-color-3);
+--background-color-4:    var(--primary-color-4);
+
+--border-color-1:        var(--primary-color-2);
+--border-color-2:        var(--primary-color-3);
+--border-color-3:        var(--primary-color-4);
+
+--border-focus-color:    var(--theme-nuance-color-2);
+--border-focus-shadow:   var(--theme-nuance-color-1);
+
+--text-color-plain:      var(--secondary-color-1);
+--text-color-subtile-1:  var(--secondary-color-2);
+--text-color-subtile-2:  var(--secondary-color-3);
+
+--code-background-color: var(--secondary-color-2);
+--code-text-color:       var(--primary-color-2);
+
+--ui-range-thumb-color:  var(--theme-nuance-color-3);
+--ui-range-thumb-border: var(--ui-ranger-thumb-color);
+
+--textarea-border-color: var(--secondary-color-4);
+
+--chat-id-color:        var(--theme-nuance-color-4);
+
+
+
+/* ------------------------------------------- */
+--button-alert-text-hover:       var(--secondary-color-1);
+--button-alert-color-hover:      var(--theme-yellow-color);
+--button-alert-border-hover:     var(--theme-yellow-color);
+
+--button-alert-text-active:      var(--secondary-color-1);
+--button-alert-color-active:     var(--theme-orange-color);
+--button-alert-border-active:    var(--theme-orange-color);
+
+
+
+/* ----------- PRIMARY BUTTONS --------------- */
+/* - button should immediately catch the eye - */
+--button-primary-text:   var(--secondary-color-1);
+--button-primary-color:  var(--theme-nuance-color-3);
+--button-primary-border: var(--theme-nuance-color-3);
+
+
+/* ---------hover---------- */
+--button-primary-text-hover:
+    hsl(217.5,
+    calc(var(--secondary-color-1-saturation) - 35%),
+    calc(var(--secondary-color-1-lightness)  + 30%));
+
+--button-primary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) -  2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+--button-primary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) -  2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+
+/* ---------active--------- */
+--button-primary-text-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 35%));
+
+--button-primary-color-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 25%));
+
+--button-primary-border-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 25%));
+
+
+
+/* ---------- SECONDARY BUTTONS -------------- */
+/* these should NOT immediately catch the eye  */
+--button-secondary-text:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 50%));
+
+--button-secondary-color:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+--button-secondary-border:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+
+/* ---------hover---------- */
+--button-secondary-text-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+--button-secondary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 22%),
+    calc(var(--theme-nuance-color-3-lightness)  +  1%));
+
+--button-secondary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 22%),
+    calc(var(--theme-nuance-color-3-lightness)  +  1%));
+
+
+/* ---------active--------- */
+--button-secondary-text-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 25%));
+
+--button-secondary-color-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 30%),
+    calc(var(--theme-nuance-color-3-lightness)  - 15%));
+
+--button-secondary-border-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 30%),
+    calc(var(--theme-nuance-color-3-lightness)  - 15%));
+
+
+
+/* ---------- TERTIARY BUTTONS --------------- */
+/* ---------- disabled buttons --------------- */
+--button-tertiary-text:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+--button-tertiary-color:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+--button-tertiary-border:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+
+/* ---------hover---------- */
+--button-tertiary-text-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+--button-tertiary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+--button-tertiary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+}
diff --git a/examples/server/public/theme-snowstorm.css b/examples/server/public/theme-snowstorm.css
new file mode 100755
index 0000000000000..7bb2275949273
--- /dev/null
+++ b/examples/server/public/theme-snowstorm.css
@@ -0,0 +1,251 @@
+/* Author: Yazan Agha-Schrader */
+/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */
+
+.theme-snowstorm {
+
+/* ---------- PRIMARY COLORS ----------------- */
+--primary-color-1: hsl(217.5, 26.7%, 94.1%);
+    --primary-color-1-hue:             217.5;
+    --primary-color-1-saturation:      26.7%;
+    --primary-color-1-lightness:       94.1%;
+
+--primary-color-2: hsl(218.2, 26.8%, 92.0%);
+    --primary-color-2-hue:             218.2;
+    --primary-color-2-saturation:      26.8%;
+    --primary-color-2-lightness:       92.0%;
+
+--primary-color-3: hsl(218.8, 27.9%, 88.0%);
+    --primary-color-3-hue:             218.8;
+    --primary-color-3-saturation:      27.9%;
+    --primary-color-3-lightness:       88.0%;
+
+--primary-color-4: hsl(218.8, 18.3%, 81.8%);
+    --primary-color-4-hue:             218.8;
+    --primary-color-4-saturation:      18.3%;
+    --primary-color-4-lightness:       81.8%;
+
+
+/* ---------- SECONDARY COLORS --------------- */
+--secondary-color-1: hsl(220.0, 16.4%, 21.6%);
+    --secondary-color-1-hue:             220.0;
+    --secondary-color-1-saturation:      16.4%;
+    --secondary-color-1-lightness:       21.6%;
+
+--secondary-color-2: hsl(221.7, 16.3%, 27.6%);
+    --secondary-color-2-hue:             221.7;
+    --secondary-color-2-saturation:      16.3%;
+    --secondary-color-2-lightness:       27.6%;
+
+--secondary-color-3: hsl(220.0, 16.8%, 31.6%);
+    --secondary-color-3-hue:             220.0;
+    --secondary-color-3-saturation:      16.8%;
+    --secondary-color-3-lightness:       31.6%;
+
+--secondary-color-4: hsl(220.0, 16.5%, 35.7%);
+    --secondary-color-4-hue:             220.0;
+    --secondary-color-4-saturation:      16.5%;
+    --secondary-color-4-lightness:       35.7%;
+
+
+
+/* ----------- NUANCES COLORS ---------------- */
+--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
+    --theme-nuance-color-1-hue:             178.7;
+    --theme-nuance-color-1-saturation:      25.1%;
+    --theme-nuance-color-1-lightness:       64.9%;
+
+--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
+    --theme-nuance-color-2-hue:             193.3;
+    --theme-nuance-color-2-saturation:      43.4%;
+    --theme-nuance-color-2-lightness:       67.5%;
+
+--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
+    --theme-nuance-color-3-hue:             210.0;
+    --theme-nuance-color-3-saturation:      34.0%;
+    --theme-nuance-color-3-lightness:       63.1%;
+
+--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
+    --theme-nuance-color-4-hue:             213.1;
+    --theme-nuance-color-4-saturation:      32.0%;
+    --theme-nuance-color-4-lightness:       52.2%;
+
+
+
+/* ----------- ROYGP COLORS ------------------ */
+--theme-red-color:    hsl(32.5, 80%, 50%);
+--theme-orange-color: hsl(32.5, 70%, 45%);
+--theme-yellow-color: hsl(40.0,   0.6%, 73.3%);
+--theme-green-color:  hsl(92.4,  27.8%, 64.7%);
+--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
+
+
+
+/* ------------------------------------------- */
+--background-color-1:    var(--primary-color-1);
+--background-color-2:    var(--primary-color-2);
+--background-color-3:    var(--primary-color-3);
+--background-color-4:    var(--primary-color-4);
+
+--border-color-1:        var(--primary-color-2);
+--border-color-2:        var(--primary-color-3);
+--border-color-3:        var(--primary-color-4);
+
+--border-focus-color:    var(--theme-nuance-color-2);
+--border-focus-shadow:   var(--theme-nuance-color-1);
+
+--text-color-plain:      var(--secondary-color-1);
+--text-color-subtile-1:  var(--secondary-color-2);
+--text-color-subtile-2:  var(--secondary-color-3);
+
+--code-background-color: var(--secondary-color-2);
+--code-text-color:       var(--primary-color-2);
+
+--ui-range-thumb-color:  var(--theme-nuance-color-3);
+--ui-range-thumb-border: var(--ui-ranger-thumb-color);
+
+--textarea-border-color: var(--secondary-color-4);
+
+--chat-id-color:         var(--theme-nuance-color-4);
+
+
+
+/* ------------------------------------------- */
+--button-alert-text-hover:       var(--primary-color-1);
+--button-alert-color-hover:      var(--theme-orange-color);
+--button-alert-border-hover:     var(--theme-orange-color);
+
+--button-alert-text-active:      var(--primary-color-1);
+--button-alert-color-active:     var(--theme-red-color);
+--button-alert-border-active:    var(--theme-red-color);
+
+
+
+/* ----------- PRIMARY BUTTONS --------------- */
+/* - button should immediately catch the eye - */
+--button-primary-text:   var(--secondary-color-1);
+--button-primary-color:  var(--theme-nuance-color-3);
+--button-primary-border: var(--theme-nuance-color-3);
+
+
+/* ---------hover---------- */
+--button-primary-text-hover:
+    hsl(217.5,
+    calc(var(--secondary-color-1-saturation) + 35%),
+    calc(var(--secondary-color-1-lightness)  - 30%));
+
+--button-primary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) -  2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+--button-primary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) -  2%),
+    calc(var(--theme-nuance-color-3-lightness)  - 10%));
+
+
+/* ---------active--------- */
+--button-primary-text-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 35%));
+
+--button-primary-color-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 25%));
+
+--button-primary-border-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 10%),
+    calc(var(--theme-nuance-color-3-lightness)  - 25%));
+
+
+
+/* ---------- SECONDARY BUTTONS -------------- */
+/* these should NOT immediately catch the eye  */
+--button-secondary-text:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 50%));
+
+--button-secondary-color:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+--button-secondary-border:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  + 10%));
+
+
+/* ---------hover---------- */
+--button-secondary-text-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 20%),
+    calc(var(--theme-nuance-color-3-lightness)  - 80%));
+
+--button-secondary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 22%),
+    calc(var(--theme-nuance-color-3-lightness)  +  1%));
+
+--button-secondary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 22%),
+    calc(var(--theme-nuance-color-3-lightness)  +  1%));
+
+
+/* ---------active--------- */
+--button-secondary-text-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) + 40%),
+    calc(var(--theme-nuance-color-3-lightness)  - 55%));
+
+--button-secondary-color-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 30%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+--button-secondary-border-active:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 30%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+
+
+/* ---------- TERTIARY BUTTONS --------------- */
+/* ---------- disabled buttons --------------- */
+--button-tertiary-text:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+--button-tertiary-color:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+--button-tertiary-border:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+/* ---------hover---------- */
+--button-tertiary-text-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  -  5%));
+
+--button-tertiary-color-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+--button-tertiary-border-hover:
+    hsl(210,
+    calc(var(--theme-nuance-color-3-saturation) - 40%),
+    calc(var(--theme-nuance-color-3-lightness)  + 20%));
+
+}
diff --git a/examples/server/public_simplechat/datautils.mjs b/examples/server/public_simplechat/datautils.mjs
new file mode 100644
index 0000000000000..75159d6b1676b
--- /dev/null
+++ b/examples/server/public_simplechat/datautils.mjs
@@ -0,0 +1,266 @@
+//@ts-check
+// Helpers to work with different data types
+// by Humans for All
+//
+
+/**
+ * Given the limited context size of local LLMs and , many a times when context gets filled
+ * between the prompt and the response, it can lead to repeating text garbage generation.
+ * And many a times setting penalty wrt repeatation leads to over-intelligent garbage
+ * repeatation with slight variations. These garbage inturn can lead to overloading of the
+ * available model context, leading to less valuable response for subsequent prompts/queries,
+ * if chat history is sent to ai model.
+ *
+ * So two simple minded garbage trimming logics are experimented below.
+ * * one based on progressively-larger-substring-based-repeat-matching-with-partial-skip and
+ * * another based on char-histogram-driven garbage trimming.
+ *   * in future characteristic of histogram over varying lengths could be used to allow for
+ *     a more aggressive and adaptive trimming logic.
+ */
+
+
+/**
+ * Simple minded logic to help remove repeating garbage at end of the string.
+ * The repeatation needs to be perfectly matching.
+ *
+ * The logic progressively goes on probing for longer and longer substring based
+ * repeatation, till there is no longer repeatation. Inturn picks the one with
+ * the longest chain.
+ *
+ * @param {string} sIn
+ * @param {number} maxSubL
+ * @param {number} maxMatchLenThreshold
+ */
+export function trim_repeat_garbage_at_end(sIn, maxSubL=10, maxMatchLenThreshold=40) {
+    let rCnt = [0];
+    let maxMatchLen = maxSubL;
+    let iMML = -1;
+    for(let subL=1; subL < maxSubL; subL++) {
+        rCnt.push(0);
+        let i;
+        let refS = sIn.substring(sIn.length-subL, sIn.length);
+        for(i=sIn.length; i > 0; i -= subL) {
+            let curS = sIn.substring(i-subL, i);
+            if (refS != curS) {
+                let curMatchLen = rCnt[subL]*subL;
+                if (maxMatchLen < curMatchLen) {
+                    maxMatchLen = curMatchLen;
+                    iMML = subL;
+                }
+                break;
+            }
+            rCnt[subL] += 1;
+        }
+    }
+    console.debug("DBUG:DU:TrimRepeatGarbage:", rCnt);
+    if ((iMML == -1) || (maxMatchLen < maxMatchLenThreshold)) {
+        return {trimmed: false, data: sIn};
+    }
+    console.debug("DBUG:TrimRepeatGarbage:TrimmedCharLen:", maxMatchLen);
+    let iEnd = sIn.length - maxMatchLen;
+    return { trimmed: true, data: sIn.substring(0, iEnd) };
+}
+
+
+/**
+ * Simple minded logic to help remove repeating garbage at end of the string, till it cant.
+ * If its not able to trim, then it will try to skip a char at end and then trim, a few times.
+ * This ensures that even if there are multiple runs of garbage with different patterns, the
+ * logic still tries to munch through them.
+ *
+ * @param {string} sIn
+ * @param {number} maxSubL
+ * @param {number | undefined} [maxMatchLenThreshold]
+ */
+export function trim_repeat_garbage_at_end_loop(sIn, maxSubL, maxMatchLenThreshold, skipMax=16) {
+    let sCur = sIn;
+    let sSaved = "";
+    let iTry = 0;
+    while(true) {
+        let got = trim_repeat_garbage_at_end(sCur, maxSubL, maxMatchLenThreshold);
+        if (got.trimmed != true) {
+            if (iTry == 0) {
+                sSaved = got.data;
+            }
+            iTry += 1;
+            if (iTry >= skipMax) {
+                return sSaved;
+            }
+            got.data = got.data.substring(0,got.data.length-1);
+        } else {
+            iTry = 0;
+        }
+        sCur = got.data;
+    }
+}
+
+
+/**
+ * A simple minded try trim garbage at end using histogram driven characteristics.
+ * There can be variation in the repeatations, as long as no new char props up.
+ *
+ * This tracks the chars and their frequency in a specified length of substring at the end
+ * and inturn checks if moving further into the generated text from the end remains within
+ * the same char subset or goes beyond it and based on that either trims the string at the
+ * end or not. This allows to filter garbage at the end, including even if there are certain
+ * kind of small variations in the repeated text wrt position of seen chars.
+ *
+ * Allow the garbage to contain upto maxUniq chars, but at the same time ensure that
+ * a given type of char ie numerals or alphabets or other types dont cross the specified
+ * maxType limit. This allows intermixed text garbage to be identified and trimmed.
+ *
+ * ALERT: This is not perfect and only provides a rough garbage identification logic.
+ * Also it currently only differentiates between character classes wrt english.
+ *
+ * @param {string} sIn
+ * @param {number} maxType
+ * @param {number} maxUniq
+ * @param {number} maxMatchLenThreshold
+ */
+export function trim_hist_garbage_at_end(sIn, maxType, maxUniq, maxMatchLenThreshold) {
+    if (sIn.length < maxMatchLenThreshold) {
+        return { trimmed: false, data: sIn };
+    }
+    let iAlp = 0;
+    let iNum = 0;
+    let iOth = 0;
+    // Learn
+    let hist = {};
+    let iUniq = 0;
+    for(let i=0; i<maxMatchLenThreshold; i++) {
+        let c = sIn[sIn.length-1-i];
+        if (c in hist) {
+            hist[c] += 1;
+        } else {
+            if(c.match(/[0-9]/) != null) {
+                iNum += 1;
+            } else if(c.match(/[A-Za-z]/) != null) {
+                iAlp += 1;
+            } else {
+                iOth += 1;
+            }
+            iUniq += 1;
+            if (iUniq >= maxUniq) {
+                break;
+            }
+            hist[c] = 1;
+        }
+    }
+    console.debug("DBUG:TrimHistGarbage:", hist);
+    if ((iAlp > maxType) || (iNum > maxType) || (iOth > maxType)) {
+        return { trimmed: false, data: sIn };
+    }
+    // Catch and Trim
+    for(let i=0; i < sIn.length; i++) {
+        let c = sIn[sIn.length-1-i];
+        if (!(c in hist)) {
+            if (i < maxMatchLenThreshold) {
+                return { trimmed: false, data: sIn };
+            }
+            console.debug("DBUG:TrimHistGarbage:TrimmedCharLen:", i);
+            return { trimmed: true, data: sIn.substring(0, sIn.length-i+1) };
+        }
+    }
+    console.debug("DBUG:TrimHistGarbage:Trimmed fully");
+    return { trimmed: true, data: "" };
+}
+
+/**
+ * Keep trimming repeatedly using hist_garbage logic, till you no longer can.
+ * This ensures that even if there are multiple runs of garbage with different patterns,
+ * the logic still tries to munch through them.
+ *
+ * @param {any} sIn
+ * @param {number} maxType
+ * @param {number} maxUniq
+ * @param {number} maxMatchLenThreshold
+ */
+export function trim_hist_garbage_at_end_loop(sIn, maxType, maxUniq, maxMatchLenThreshold) {
+    let sCur = sIn;
+    while (true) {
+        let got = trim_hist_garbage_at_end(sCur, maxType, maxUniq, maxMatchLenThreshold);
+        if (!got.trimmed) {
+            return got.data;
+        }
+        sCur = got.data;
+    }
+}
+
+/**
+ * Try trim garbage at the end by using both the hist-driven-garbage-trimming as well as
+ * skip-a-bit-if-reqd-then-repeat-pattern-based-garbage-trimming, with blind retrying.
+ * @param {string} sIn
+ */
+export function trim_garbage_at_end(sIn) {
+    let sCur = sIn;
+    for(let i=0; i<2; i++) {
+        sCur = trim_hist_garbage_at_end_loop(sCur, 8, 24, 72);
+        sCur = trim_repeat_garbage_at_end_loop(sCur, 32, 72, 12);
+    }
+    return sCur;
+}
+
+
+/**
+ * NewLines array helper.
+ * Allow for maintaining a list of lines.
+ * Allow for a line to be builtup/appended part by part.
+ */
+export class NewLines {
+
+    constructor() {
+        /** @type {string[]} */
+        this.lines = [];
+    }
+
+    /**
+     * Extracts lines from the passed string and inturn either
+     * append to a previous partial line or add a new line.
+     * @param {string} sLines
+     */
+    add_append(sLines) {
+        let aLines = sLines.split("\n");
+        let lCnt = 0;
+        for(let line of aLines) {
+            lCnt += 1;
+            // Add back newline removed if any during split
+            if (lCnt < aLines.length) {
+                line += "\n";
+            } else {
+                if (sLines.endsWith("\n")) {
+                    line += "\n";
+                }
+            }
+            // Append if required
+            if (lCnt == 1) {
+                let lastLine = this.lines[this.lines.length-1];
+                if (lastLine != undefined) {
+                    if (!lastLine.endsWith("\n")) {
+                        this.lines[this.lines.length-1] += line;
+                        continue;
+                    }
+                }
+            }
+            // Add new line
+            this.lines.push(line);
+        }
+    }
+
+    /**
+     * Shift the oldest/earliest/0th line in the array. [Old-New|Earliest-Latest]
+     * Optionally control whether only full lines (ie those with newline at end) will be returned
+     * or will a partial line without a newline at end (can only be the last line) be returned.
+     * @param {boolean} bFullWithNewLineOnly
+     */
+    shift(bFullWithNewLineOnly=true) {
+        let line = this.lines[0];
+        if (line == undefined) {
+            return undefined;
+        }
+        if ((line[line.length-1] != "\n") && bFullWithNewLineOnly){
+            return undefined;
+        }
+        return this.lines.shift();
+    }
+
+}
diff --git a/examples/server/public_simplechat/index.html b/examples/server/public_simplechat/index.html
new file mode 100644
index 0000000000000..f6413016fcc53
--- /dev/null
+++ b/examples/server/public_simplechat/index.html
@@ -0,0 +1,51 @@
+<!DOCTYPE html>
+<html lang="en">
+    <head>
+        <title>SimpleChat LlamaCppEtal </title>
+        <meta charset="UTF-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1" />
+        <meta name="message" content="Save Nature Save Earth" />
+        <meta name="description" content="SimpleChat: trigger LLM web service endpoints /chat/completions and /completions, single/multi chat sessions" />
+        <meta name="author" content="by Humans for All" />
+        <meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate" />
+        <script type="importmap">
+            {
+                "imports": {
+                    "datautils": "./datautils.mjs",
+                    "ui": "./ui.mjs"
+                }
+            }
+        </script>
+        <script src="simplechat.js" type="module" defer></script>
+        <link rel="stylesheet" href="simplechat.css" />
+    </head>
+    <body>
+        <div class="samecolumn" id="fullbody">
+
+            <div class="sameline" id="heading">
+                <p class="heading flex-grow" > <b> SimpleChat </b> </p>
+                <button id="settings">Settings</button>
+            </div>
+
+            <div id="sessions-div" class="sameline"></div>
+
+            <hr>
+            <div class="sameline">
+                <label for="system-in">System</label>
+                <textarea name="system" id="system-in" rows="2" placeholder="e.g. you are a helpful ai assistant, who provides concise answers" class="flex-grow"></textarea>
+            </div>
+
+            <hr>
+            <div id="chat-div">
+                <p> You need to have javascript enabled.</p>
+            </div>
+
+            <hr>
+            <div class="sameline">
+                <textarea id="user-in" class="flex-grow" rows="2" placeholder="enter your query to the ai model here" ></textarea>
+                <button id="user-btn">submit</button>
+            </div>
+
+        </div>
+    </body>
+</html>
diff --git a/examples/server/public_simplechat/readme.md b/examples/server/public_simplechat/readme.md
new file mode 100644
index 0000000000000..21410199f6016
--- /dev/null
+++ b/examples/server/public_simplechat/readme.md
@@ -0,0 +1,286 @@
+
+# SimpleChat
+
+by Humans for All.
+
+## quickstart
+
+To run from the build dir
+
+bin/llama-server -m path/model.gguf --path ../examples/server/public_simplechat
+
+Continue reading for the details.
+
+## overview
+
+This simple web frontend, allows triggering/testing the server's /completions or /chat/completions endpoints
+in a simple way with minimal code from a common code base. Inturn additionally it tries to allow single or
+multiple independent back and forth chatting to an extent, with the ai llm model at a basic level, with their
+own system prompts.
+
+This allows seeing the generated text / ai-model response in oneshot at the end, after it is fully generated,
+or potentially as it is being generated, in a streamed manner from the server/ai-model.
+
+![Chat and Settings screens](./simplechat_screens.webp "Chat and Settings screens")
+
+Auto saves the chat session locally as and when the chat is progressing and inturn at a later time when you
+open SimpleChat, option is provided to restore the old chat session, if a matching one exists.
+
+The UI follows a responsive web design so that the layout can adapt to available display space in a usable
+enough manner, in general.
+
+Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool
+console. Parallely some of the directly useful to end-user settings can also be changed using the provided
+settings ui.
+
+NOTE: Current web service api doesnt expose the model context length directly, so client logic doesnt provide
+any adaptive culling of old messages nor of replacing them with summary of their content etal. However there
+is a optional sliding window based chat logic, which provides a simple minded culling of old messages from
+the chat history before sending to the ai model.
+
+NOTE: Wrt options sent with the request, it mainly sets temperature, max_tokens and optionaly stream for now.
+However if someone wants they can update the js file or equivalent member in gMe as needed.
+
+NOTE: One may be able to use this to chat with openai api web-service /chat/completions endpoint, in a very
+limited / minimal way. One will need to set model, openai url and authorization bearer key in settings ui.
+
+
+## usage
+
+One could run this web frontend directly using server itself or if anyone is thinking of adding a built in web
+frontend to configure the server over http(s) or so, then run this web frontend using something like python's
+http module.
+
+### running using examples/server
+
+./llama-server -m path/model.gguf --path examples/server/public_simplechat [--port PORT]
+
+### running using python3's server module
+
+first run examples/server
+* ./llama-server -m path/model.gguf
+
+next run this web front end in examples/server/public_simplechat
+* cd ../examples/server/public_simplechat
+* python3 -m http.server PORT
+
+### using the front end
+
+Open this simple web front end from your local browser
+
+* http://127.0.0.1:PORT/index.html
+
+Once inside
+
+* If you want to, you can change many of the default global settings
+  * the base url (ie ip addr / domain name, port)
+  * chat (default) vs completion mode
+  * try trim garbage in response or not
+  * amount of chat history in the context sent to server/ai-model
+  * oneshot or streamed mode.
+
+* In completion mode
+  * one normally doesnt use a system prompt in completion mode.
+  * logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message.
+    If the model requires any prefix wrt user role messages, then the end user has to
+    explicitly add the needed prefix, when they enter their chat message.
+    Similarly if the model requires any prefix to trigger assistant/ai-model response,
+    then the end user needs to enter the same.
+    This keeps the logic simple, while still giving flexibility to the end user to
+    manage any templating/tagging requirement wrt their messages to the model.
+  * the logic doesnt insert newline at the begining and end wrt the prompt message generated.
+    However if the chat being sent to /completions end point has more than one role's message,
+    then insert newline when moving from one role's message to the next role's message, so
+    that it can be clearly identified/distinguished.
+  * given that /completions endpoint normally doesnt add additional chat-templating of its
+    own, the above ensures that end user can create a custom single/multi message combo with
+    any tags/special-tokens related chat templating to test out model handshake. Or enduser
+    can use it just for normal completion related/based query.
+
+* If you want to provide a system prompt, then ideally enter it first, before entering any user query.
+  Normally Completion mode doesnt need system prompt, while Chat mode can generate better/interesting
+  responses with a suitable system prompt.
+  * if chat.add_system_begin is used
+    * you cant change the system prompt, after it is has been submitted once along with user query.
+    * you cant set a system prompt, after you have submitted any user query
+  * if chat.add_system_anytime is used
+    * one can change the system prompt any time during chat, by changing the contents of system prompt.
+    * inturn the updated/changed system prompt will be inserted into the chat session.
+    * this allows for the subsequent user chatting to be driven by the new system prompt set above.
+
+* Enter your query and either press enter or click on the submit button.
+  If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter.
+
+* Wait for the logic to communicate with the server and get the response.
+  * the user is not allowed to enter any fresh query during this time.
+  * the user input box will be disabled and a working message will be shown in it.
+  * if trim garbage is enabled, the logic will try to trim repeating text kind of garbage to some extent.
+
+* just refresh the page, to reset wrt the chat history and or system prompt and start afresh.
+
+* Using NewChat one can start independent chat sessions.
+  * two independent chat sessions are setup by default.
+
+* When you want to print, switching ChatHistoryInCtxt to Full and clicking on the chat session button of
+  interest, will display the full chat history till then wrt same, if you want full history for printing.
+
+
+## Devel note
+
+### Reason behind this
+
+The idea is to be easy enough to use for basic purposes, while also being simple and easily discernable
+by developers who may not be from web frontend background (so inturn may not be familiar with template /
+end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things.
+
+And given that the idea is also to help explore/experiment for developers, some flexibility is provided
+to change behaviour easily using the devel-tools/console or provided minimal settings ui (wrt few aspects).
+Skeletal logic has been implemented to explore some of the end points and ideas/implications around them.
+
+
+### General
+
+Me/gMe consolidates the settings which control the behaviour into one object.
+One can see the current settings, as well as change/update them using browsers devel-tool/console.
+It is attached to the document object. Some of these can also be updated using the Settings UI.
+
+  baseURL - the domain-name/ip-address and inturn the port to send the request.
+
+  bStream - control between oneshot-at-end and live-stream-as-its-generated collating and showing
+  of the generated response.
+
+    the logic assumes that the text sent from the server follows utf-8 encoding.
+
+    in streaming mode - if there is any exception, the logic traps the same and tries to ensure
+    that text generated till then is not lost.
+
+      if a very long text is being generated, which leads to no user interaction for sometime and
+      inturn the machine goes into power saving mode or so, the platform may stop network connection,
+      leading to exception.
+
+  apiEP - select between /completions and /chat/completions endpoint provided by the server/ai-model.
+
+  bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when
+  communicating with the server or only sends the latest user query/message.
+
+  bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the
+  messages that get inserted into prompt field wrt /Completion endpoint.
+
+  bTrimGarbage - whether garbage repeatation at the end of the generated ai response, should be
+  trimmed or left as is. If enabled, it will be trimmed so that it wont be sent back as part of
+  subsequent chat history. At the same time the actual trimmed text is shown to the user, once
+  when it was generated, so user can check if any useful info/data was there in the response.
+
+    One may be able to request the ai-model to continue (wrt the last response) (if chat-history
+    is enabled as part of the chat-history-in-context setting), and chances are the ai-model will
+    continue starting from the trimmed part, thus allows long response to be recovered/continued
+    indirectly, in many cases.
+
+    The histogram/freq based trimming logic is currently tuned for english language wrt its
+    is-it-a-alpabetic|numeral-char regex match logic.
+
+  apiRequestOptions - maintains the list of options/fields to send along with api request,
+  irrespective of whether /chat/completions or /completions endpoint.
+
+    If you want to add additional options/fields to send to the server/ai-model, and or
+    modify the existing options value or remove them, for now you can update this global var
+    using browser's development-tools/console.
+
+    For string, numeric and boolean fields in apiRequestOptions, including even those added by a
+    user at runtime by directly modifying gMe.apiRequestOptions, setting ui entries will be auto
+    created.
+
+    cache_prompt option supported by example/server is allowed to be controlled by user, so that
+    any caching supported wrt system-prompt and chat history, if usable can get used. When chat
+    history sliding window is enabled, cache_prompt logic may or may not kick in at the backend
+    wrt same, based on aspects related to model, positional encoding, attention mechanism etal.
+    However system prompt should ideally get the benefit of caching.
+
+  headers - maintains the list of http headers sent when request is made to the server. By default
+  Content-Type is set to application/json. Additionally Authorization entry is provided, which can
+  be set if needed using the settings ui.
+
+  iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end.
+  This is disabled by default. However if enabled, then in addition to latest system message, only
+  the last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses
+  from the ai model will be sent to the ai-model, when querying for a new response. IE if enabled,
+  only user messages after the latest system message/prompt will be considered.
+
+    This specified sliding window user message count also includes the latest user query.
+    <0 : Send entire chat history to server
+     0 : Send only the system message if any to the server
+    >0 : Send the latest chat history from the latest system prompt, limited to specified cnt.
+
+
+By using gMe's iRecentUserMsgCnt and apiRequestOptions.max_tokens/n_predict one can try to control
+the implications of loading of the ai-model's context window by chat history, wrt chat response to
+some extent in a simple crude way. You may also want to control the context size enabled when the
+server loads ai-model, on the server end.
+
+
+Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js
+may not be visible. Also remember that just refreshing/reloading page in browser or for that
+matter clearing site data, dont directly override site caching in all cases. Worst case you may
+have to change port. Or in dev tools of browser, you may be able to disable caching fully.
+
+
+Currently the server to communicate with is maintained globally and not as part of a specific
+chat session. So if one changes the server ip/url in setting, then all chat sessions will auto
+switch to this new server, when you try using those sessions.
+
+
+By switching between chat.add_system_begin/anytime, one can control whether one can change
+the system prompt, anytime during the conversation or only at the beginning.
+
+
+### Default setup
+
+By default things are setup to try and make the user experience a bit better, if possible.
+However a developer when testing the server of ai-model may want to change these value.
+
+Using iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be
+just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of
+full chat history. This way if there is any response with garbage/repeatation, it doesnt
+mess with things beyond the next question/request/query, in some ways. The trim garbage
+option also tries to help avoid issues with garbage in the context to an extent.
+
+Set max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space
+available wrt next query-response. However dont forget that the server when started should
+also be started with a model context size of 1k or more, to be on safe side.
+
+  The /completions endpoint of examples/server doesnt take max_tokens, instead it takes the
+  internal n_predict, for now add the same here on the client side, maybe later add max_tokens
+  to /completions endpoint handling code on server side.
+
+NOTE: One may want to experiment with frequency/presence penalty fields in apiRequestOptions
+wrt the set of fields sent to server along with the user query, to check how the model behaves
+wrt repeatations in general in the generated text response.
+
+A end-user can change these behaviour by editing gMe from browser's devel-tool/console or by
+using the provided settings ui (for settings exposed through the ui).
+
+
+### OpenAi / Equivalent API WebService
+
+One may be abe to handshake with OpenAI/Equivalent api web service's /chat/completions endpoint
+for a minimal chatting experimentation by setting the below.
+
+* the baseUrl in settings ui
+  * https://api.openai.com/v1 or similar
+
+* Wrt request body - gMe.apiRequestOptions
+  * model (settings ui)
+  * any additional fields if required in future
+
+* Wrt request headers - gMe.headers
+  * Authorization (available through settings ui)
+    * Bearer THE_OPENAI_API_KEY
+  * any additional optional header entries like "OpenAI-Organization", "OpenAI-Project" or so
+
+NOTE: Not tested, as there is no free tier api testing available. However logically this might
+work.
+
+
+## At the end
+
+Also a thank you to all open source and open model developers, who strive for the common good.
diff --git a/examples/server/public_simplechat/simplechat.css b/examples/server/public_simplechat/simplechat.css
new file mode 100644
index 0000000000000..13bfb80b48be8
--- /dev/null
+++ b/examples/server/public_simplechat/simplechat.css
@@ -0,0 +1,79 @@
+/**
+ * the styling of the simplechat web frontend
+ * by Humans for All
+ */
+
+#fullbody {
+    height: 98vh;
+}
+
+.heading {
+    background-color: lightgray;
+}
+
+.session-selected {
+    background-color: lightblue;
+}
+
+.role-system {
+    background-color: lightblue;
+}
+.role-user {
+    background-color: lightgray;
+}
+.role-trim {
+    background-color: lightpink;
+}
+
+.gridx2 {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    border-bottom-style: dotted;
+    border-bottom-width: thin;
+    border-bottom-color: lightblue;
+}
+
+.flex-grow {
+    flex-grow: 1;
+}
+.float-right {
+    float: right;
+}
+
+#chat-div {
+    overflow: scroll;
+    flex-grow: 1;
+    flex-shrink: 1;
+    min-height: 40vh;
+}
+button {
+    min-width: 8vw;
+}
+
+.sameline {
+    display: flex;
+    flex-direction: row;
+}
+.samecolumn {
+    display: flex;
+    flex-direction: column;
+}
+
+.ul1 {
+    padding-inline-start: 2vw;
+}
+.ul2 {
+    padding-inline-start: 2vw;
+}
+
+* {
+    margin: 0.6vmin;
+}
+
+@media print {
+
+    #fullbody {
+        height: auto;
+    }
+
+}
diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js
new file mode 100644
index 0000000000000..8e0df3b61df2b
--- /dev/null
+++ b/examples/server/public_simplechat/simplechat.js
@@ -0,0 +1,926 @@
+// @ts-check
+// A simple completions and chat/completions test related web front end logic
+// by Humans for All
+
+import * as du from "./datautils.mjs";
+import * as ui from "./ui.mjs"
+
+class Roles {
+    static System = "system";
+    static User = "user";
+    static Assistant = "assistant";
+}
+
+class ApiEP {
+    static Type = {
+        Chat: "chat",
+        Completion: "completion",
+    }
+    static UrlSuffix = {
+        'chat': `/chat/completions`,
+        'completion': `/completions`,
+    }
+
+    /**
+     * Build the url from given baseUrl and apiEp id.
+     * @param {string} baseUrl
+     * @param {string} apiEP
+     */
+    static Url(baseUrl, apiEP) {
+        if (baseUrl.endsWith("/")) {
+            baseUrl = baseUrl.substring(0, baseUrl.length-1);
+        }
+        return `${baseUrl}${this.UrlSuffix[apiEP]}`;
+    }
+
+}
+
+
+let gUsageMsg = `
+    <p class="role-system">Usage</p>
+    <ul class="ul1">
+    <li> System prompt above, to try control ai response characteristics.</li>
+        <ul class="ul2">
+        <li> Completion mode - no system prompt normally.</li>
+        </ul>
+    <li> Use shift+enter for inserting enter/newline.</li>
+    <li> Enter your query to ai assistant below.</li>
+    <li> Default ContextWindow = [System, Last Query+Resp, Cur Query].</li>
+        <ul class="ul2">
+        <li> ChatHistInCtxt, MaxTokens, ModelCtxt window to expand</li>
+        </ul>
+    </ul>
+`;
+
+
+/** @typedef {{role: string, content: string}[]} ChatMessages */
+
+/** @typedef {{iLastSys: number, xchat: ChatMessages}} SimpleChatODS */
+
+class SimpleChat {
+
+    /**
+     * @param {string} chatId
+     */
+    constructor(chatId) {
+        this.chatId = chatId;
+        /**
+         * Maintain in a form suitable for common LLM web service chat/completions' messages entry
+         * @type {ChatMessages}
+         */
+        this.xchat = [];
+        this.iLastSys = -1;
+        this.latestResponse = "";
+    }
+
+    clear() {
+        this.xchat = [];
+        this.iLastSys = -1;
+    }
+
+    ods_key() {
+        return `SimpleChat-${this.chatId}`
+    }
+
+    save() {
+        /** @type {SimpleChatODS} */
+        let ods = {iLastSys: this.iLastSys, xchat: this.xchat};
+        localStorage.setItem(this.ods_key(), JSON.stringify(ods));
+    }
+
+    load() {
+        let sods = localStorage.getItem(this.ods_key());
+        if (sods == null) {
+            return;
+        }
+        /** @type {SimpleChatODS} */
+        let ods = JSON.parse(sods);
+        this.iLastSys = ods.iLastSys;
+        this.xchat = ods.xchat;
+    }
+
+    /**
+     * Recent chat messages.
+     * If iRecentUserMsgCnt < 0
+     *   Then return the full chat history
+     * Else
+     *   Return chat messages from latest going back till the last/latest system prompt.
+     *   While keeping track that the number of user queries/messages doesnt exceed iRecentUserMsgCnt.
+     * @param {number} iRecentUserMsgCnt
+     */
+    recent_chat(iRecentUserMsgCnt) {
+        if (iRecentUserMsgCnt < 0) {
+            return this.xchat;
+        }
+        if (iRecentUserMsgCnt == 0) {
+            console.warn("WARN:SimpleChat:SC:RecentChat:iRecentUsermsgCnt of 0 means no user message/query sent");
+        }
+        /** @type{ChatMessages} */
+        let rchat = [];
+        let sysMsg = this.get_system_latest();
+        if (sysMsg.length != 0) {
+            rchat.push({role: Roles.System, content: sysMsg});
+        }
+        let iUserCnt = 0;
+        let iStart = this.xchat.length;
+        for(let i=this.xchat.length-1; i > this.iLastSys; i--) {
+            if (iUserCnt >= iRecentUserMsgCnt) {
+                break;
+            }
+            let msg = this.xchat[i];
+            if (msg.role == Roles.User) {
+                iStart = i;
+                iUserCnt += 1;
+            }
+        }
+        for(let i = iStart; i < this.xchat.length; i++) {
+            let msg = this.xchat[i];
+            if (msg.role == Roles.System) {
+                continue;
+            }
+            rchat.push({role: msg.role, content: msg.content});
+        }
+        return rchat;
+    }
+
+    /**
+     * Collate the latest response from the server/ai-model, as it is becoming available.
+     * This is mainly useful for the stream mode.
+     * @param {string} content
+     */
+    append_response(content) {
+        this.latestResponse += content;
+    }
+
+    /**
+     * Add an entry into xchat
+     * @param {string} role
+     * @param {string|undefined|null} content
+     */
+    add(role, content) {
+        if ((content == undefined) || (content == null) || (content == "")) {
+            return false;
+        }
+        this.xchat.push( {role: role, content: content} );
+        if (role == Roles.System) {
+            this.iLastSys = this.xchat.length - 1;
+        }
+        this.save();
+        return true;
+    }
+
+    /**
+     * Show the contents in the specified div
+     * @param {HTMLDivElement} div
+     * @param {boolean} bClear
+     */
+    show(div, bClear=true) {
+        if (bClear) {
+            div.replaceChildren();
+        }
+        let last = undefined;
+        for(const x of this.recent_chat(gMe.iRecentUserMsgCnt)) {
+            let entry = ui.el_create_append_p(`${x.role}: ${x.content}`, div);
+            entry.className = `role-${x.role}`;
+            last = entry;
+        }
+        if (last !== undefined) {
+            last.scrollIntoView(false);
+        } else {
+            if (bClear) {
+                div.innerHTML = gUsageMsg;
+                gMe.setup_load(div, this);
+                gMe.show_info(div);
+            }
+        }
+        return last;
+    }
+
+    /**
+     * Setup the fetch headers.
+     * It picks the headers from gMe.headers.
+     * It inserts Authorization only if its non-empty.
+     * @param {string} apiEP
+     */
+    fetch_headers(apiEP) {
+        let headers = new Headers();
+        for(let k in gMe.headers) {
+            let v = gMe.headers[k];
+            if ((k == "Authorization") && (v.trim() == "")) {
+                continue;
+            }
+            headers.append(k, v);
+        }
+        return headers;
+    }
+
+    /**
+     * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint.
+     * The needed fields/options are picked from a global object.
+     * Add optional stream flag, if required.
+     * Convert the json into string.
+     * @param {Object} obj
+     */
+    request_jsonstr_extend(obj) {
+        for(let k in gMe.apiRequestOptions) {
+            obj[k] = gMe.apiRequestOptions[k];
+        }
+        if (gMe.bStream) {
+            obj["stream"] = true;
+        }
+        return JSON.stringify(obj);
+    }
+
+    /**
+     * Return a string form of json object suitable for chat/completions
+     */
+    request_messages_jsonstr() {
+        let req = {
+            messages: this.recent_chat(gMe.iRecentUserMsgCnt),
+        }
+        return this.request_jsonstr_extend(req);
+    }
+
+    /**
+     * Return a string form of json object suitable for /completions
+     * @param {boolean} bInsertStandardRolePrefix Insert "<THE_ROLE>: " as prefix wrt each role's message
+     */
+    request_prompt_jsonstr(bInsertStandardRolePrefix) {
+        let prompt = "";
+        let iCnt = 0;
+        for(const chat of this.recent_chat(gMe.iRecentUserMsgCnt)) {
+            iCnt += 1;
+            if (iCnt > 1) {
+                prompt += "\n";
+            }
+            if (bInsertStandardRolePrefix) {
+                prompt += `${chat.role}: `;
+            }
+            prompt += `${chat.content}`;
+        }
+        let req = {
+            prompt: prompt,
+        }
+        return this.request_jsonstr_extend(req);
+    }
+
+    /**
+     * Return a string form of json object suitable for specified api endpoint.
+     * @param {string} apiEP
+     */
+    request_jsonstr(apiEP) {
+        if (apiEP == ApiEP.Type.Chat) {
+            return this.request_messages_jsonstr();
+        } else {
+            return this.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix);
+        }
+    }
+
+    /**
+     * Extract the ai-model/assistant's response from the http response got.
+     * Optionally trim the message wrt any garbage at the end.
+     * @param {any} respBody
+     * @param {string} apiEP
+     */
+    response_extract(respBody, apiEP) {
+        let assistant = "";
+        if (apiEP == ApiEP.Type.Chat) {
+            assistant = respBody["choices"][0]["message"]["content"];
+        } else {
+            try {
+                assistant = respBody["choices"][0]["text"];
+            } catch {
+                assistant = respBody["content"];
+            }
+        }
+        return assistant;
+    }
+
+    /**
+     * Extract the ai-model/assistant's response from the http response got in streaming mode.
+     * @param {any} respBody
+     * @param {string} apiEP
+     */
+    response_extract_stream(respBody, apiEP) {
+        let assistant = "";
+        if (apiEP == ApiEP.Type.Chat) {
+            if (respBody["choices"][0]["finish_reason"] !== "stop") {
+                assistant = respBody["choices"][0]["delta"]["content"];
+            }
+        } else {
+            try {
+                assistant = respBody["choices"][0]["text"];
+            } catch {
+                assistant = respBody["content"];
+            }
+        }
+        return assistant;
+    }
+
+    /**
+     * Allow setting of system prompt, but only at begining.
+     * @param {string} sysPrompt
+     * @param {string} msgTag
+     */
+    add_system_begin(sysPrompt, msgTag) {
+        if (this.xchat.length == 0) {
+            if (sysPrompt.length > 0) {
+                return this.add(Roles.System, sysPrompt);
+            }
+        } else {
+            if (sysPrompt.length > 0) {
+                if (this.xchat[0].role !== Roles.System) {
+                    console.error(`ERRR:SimpleChat:SC:${msgTag}:You need to specify system prompt before any user query, ignoring...`);
+                } else {
+                    if (this.xchat[0].content !== sysPrompt) {
+                        console.error(`ERRR:SimpleChat:SC:${msgTag}:You cant change system prompt, mid way through, ignoring...`);
+                    }
+                }
+            }
+        }
+        return false;
+    }
+
+    /**
+     * Allow setting of system prompt, at any time.
+     * @param {string} sysPrompt
+     * @param {string} msgTag
+     */
+    add_system_anytime(sysPrompt, msgTag) {
+        if (sysPrompt.length <= 0) {
+            return false;
+        }
+
+        if (this.iLastSys < 0) {
+            return this.add(Roles.System, sysPrompt);
+        }
+
+        let lastSys = this.xchat[this.iLastSys].content;
+        if (lastSys !== sysPrompt) {
+            return this.add(Roles.System, sysPrompt);
+        }
+        return false;
+    }
+
+    /**
+     * Retrieve the latest system prompt.
+     */
+    get_system_latest() {
+        if (this.iLastSys == -1) {
+            return "";
+        }
+        let sysPrompt = this.xchat[this.iLastSys].content;
+        return sysPrompt;
+    }
+
+
+    /**
+     * Handle the multipart response from server/ai-model
+     * @param {Response} resp
+     * @param {string} apiEP
+     * @param {HTMLDivElement} elDiv
+     */
+    async handle_response_multipart(resp, apiEP, elDiv) {
+        let elP = ui.el_create_append_p("", elDiv);
+        if (!resp.body) {
+            throw Error("ERRR:SimpleChat:SC:HandleResponseMultiPart:No body...");
+        }
+        let tdUtf8 = new TextDecoder("utf-8");
+        let rr = resp.body.getReader();
+        this.latestResponse = "";
+        let xLines = new du.NewLines();
+        while(true) {
+            let { value: cur,  done: done } = await rr.read();
+            if (cur) {
+                let curBody = tdUtf8.decode(cur, {stream: true});
+                console.debug("DBUG:SC:PART:Str:", curBody);
+                xLines.add_append(curBody);
+            }
+            while(true) {
+                let curLine = xLines.shift(!done);
+                if (curLine == undefined) {
+                    break;
+                }
+                if (curLine.trim() == "") {
+                    continue;
+                }
+                if (curLine.startsWith("data:")) {
+                    curLine = curLine.substring(5);
+                }
+                let curJson = JSON.parse(curLine);
+                console.debug("DBUG:SC:PART:Json:", curJson);
+                this.append_response(this.response_extract_stream(curJson, apiEP));
+            }
+            elP.innerText = this.latestResponse;
+            elP.scrollIntoView(false);
+            if (done) {
+                break;
+            }
+        }
+        console.debug("DBUG:SC:PART:Full:", this.latestResponse);
+        return this.latestResponse;
+    }
+
+    /**
+     * Handle the oneshot response from server/ai-model
+     * @param {Response} resp
+     * @param {string} apiEP
+     */
+    async handle_response_oneshot(resp, apiEP) {
+        let respBody = await resp.json();
+        console.debug(`DBUG:SimpleChat:SC:${this.chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`);
+        return this.response_extract(respBody, apiEP);
+    }
+
+    /**
+     * Handle the response from the server be it in oneshot or multipart/stream mode.
+     * Also take care of the optional garbage trimming.
+     * @param {Response} resp
+     * @param {string} apiEP
+     * @param {HTMLDivElement} elDiv
+     */
+    async handle_response(resp, apiEP, elDiv) {
+        let theResp = {
+            assistant: "",
+            trimmed: "",
+        }
+        if (gMe.bStream) {
+            try {
+                theResp.assistant = await this.handle_response_multipart(resp, apiEP, elDiv);
+                this.latestResponse = "";
+            } catch (error) {
+                theResp.assistant = this.latestResponse;
+                this.add(Roles.Assistant, theResp.assistant);
+                this.latestResponse = "";
+                throw error;
+            }
+        } else {
+            theResp.assistant = await this.handle_response_oneshot(resp, apiEP);
+        }
+        if (gMe.bTrimGarbage) {
+            let origMsg = theResp.assistant;
+            theResp.assistant = du.trim_garbage_at_end(origMsg);
+            theResp.trimmed = origMsg.substring(theResp.assistant.length);
+        }
+        this.add(Roles.Assistant, theResp.assistant);
+        return theResp;
+    }
+
+}
+
+
+class MultiChatUI {
+
+    constructor() {
+        /** @type {Object<string, SimpleChat>} */
+        this.simpleChats = {};
+        /** @type {string} */
+        this.curChatId = "";
+
+        // the ui elements
+        this.elInSystem = /** @type{HTMLInputElement} */(document.getElementById("system-in"));
+        this.elDivChat = /** @type{HTMLDivElement} */(document.getElementById("chat-div"));
+        this.elBtnUser = /** @type{HTMLButtonElement} */(document.getElementById("user-btn"));
+        this.elInUser = /** @type{HTMLInputElement} */(document.getElementById("user-in"));
+        this.elDivHeading = /** @type{HTMLSelectElement} */(document.getElementById("heading"));
+        this.elDivSessions = /** @type{HTMLDivElement} */(document.getElementById("sessions-div"));
+        this.elBtnSettings = /** @type{HTMLButtonElement} */(document.getElementById("settings"));
+
+        this.validate_element(this.elInSystem, "system-in");
+        this.validate_element(this.elDivChat, "chat-div");
+        this.validate_element(this.elInUser, "user-in");
+        this.validate_element(this.elDivHeading, "heading");
+        this.validate_element(this.elDivChat, "sessions-div");
+        this.validate_element(this.elBtnSettings, "settings");
+    }
+
+    /**
+     * Check if the element got
+     * @param {HTMLElement | null} el
+     * @param {string} msgTag
+     */
+    validate_element(el, msgTag) {
+        if (el == null) {
+            throw Error(`ERRR:SimpleChat:MCUI:${msgTag} element missing in html...`);
+        } else {
+            console.debug(`INFO:SimpleChat:MCUI:${msgTag} Id[${el.id}] Name[${el["name"]}]`);
+        }
+    }
+
+    /**
+     * Reset user input ui.
+     * * clear user input
+     * * enable user input
+     * * set focus to user input
+     */
+    ui_reset_userinput() {
+        this.elInUser.value = "";
+        this.elInUser.disabled = false;
+        this.elInUser.focus();
+    }
+
+    /**
+     * Setup the needed callbacks wrt UI, curChatId to defaultChatId and
+     * optionally switch to specified defaultChatId.
+     * @param {string} defaultChatId
+     * @param {boolean} bSwitchSession
+     */
+    setup_ui(defaultChatId, bSwitchSession=false) {
+
+        this.curChatId = defaultChatId;
+        if (bSwitchSession) {
+            this.handle_session_switch(this.curChatId);
+        }
+
+        this.elBtnSettings.addEventListener("click", (ev)=>{
+            this.elDivChat.replaceChildren();
+            gMe.show_settings(this.elDivChat);
+        });
+
+        this.elBtnUser.addEventListener("click", (ev)=>{
+            if (this.elInUser.disabled) {
+                return;
+            }
+            this.handle_user_submit(this.curChatId, gMe.apiEP).catch((/** @type{Error} */reason)=>{
+                let msg = `ERRR:SimpleChat\nMCUI:HandleUserSubmit:${this.curChatId}\n${reason.name}:${reason.message}`;
+                console.error(msg.replace("\n", ":"));
+                alert(msg);
+                this.ui_reset_userinput();
+            });
+        });
+
+        this.elInUser.addEventListener("keyup", (ev)=> {
+            // allow user to insert enter into their message using shift+enter.
+            // while just pressing enter key will lead to submitting.
+            if ((ev.key === "Enter") && (!ev.shiftKey)) {
+                let value = this.elInUser.value;
+                this.elInUser.value = value.substring(0,value.length-1);
+                this.elBtnUser.click();
+                ev.preventDefault();
+            }
+        });
+
+        this.elInSystem.addEventListener("keyup", (ev)=> {
+            // allow user to insert enter into the system prompt using shift+enter.
+            // while just pressing enter key will lead to setting the system prompt.
+            if ((ev.key === "Enter") && (!ev.shiftKey)) {
+                let value = this.elInSystem.value;
+                this.elInSystem.value = value.substring(0,value.length-1);
+                let chat = this.simpleChats[this.curChatId];
+                chat.add_system_anytime(this.elInSystem.value, this.curChatId);
+                chat.show(this.elDivChat);
+                ev.preventDefault();
+            }
+        });
+
+    }
+
+    /**
+     * Setup a new chat session and optionally switch to it.
+     * @param {string} chatId
+     * @param {boolean} bSwitchSession
+     */
+    new_chat_session(chatId, bSwitchSession=false) {
+        this.simpleChats[chatId] = new SimpleChat(chatId);
+        if (bSwitchSession) {
+            this.handle_session_switch(chatId);
+        }
+    }
+
+
+    /**
+     * Handle user query submit request, wrt specified chat session.
+     * @param {string} chatId
+     * @param {string} apiEP
+     */
+    async handle_user_submit(chatId, apiEP) {
+
+        let chat = this.simpleChats[chatId];
+
+        // In completion mode, if configured, clear any previous chat history.
+        // So if user wants to simulate a multi-chat based completion query,
+        // they will have to enter the full thing, as a suitable multiline
+        // user input/query.
+        if ((apiEP == ApiEP.Type.Completion) && (gMe.bCompletionFreshChatAlways)) {
+            chat.clear();
+        }
+
+        chat.add_system_anytime(this.elInSystem.value, chatId);
+
+        let content = this.elInUser.value;
+        if (!chat.add(Roles.User, content)) {
+            console.debug(`WARN:SimpleChat:MCUI:${chatId}:HandleUserSubmit:Ignoring empty user input...`);
+            return;
+        }
+        chat.show(this.elDivChat);
+
+        let theUrl = ApiEP.Url(gMe.baseURL, apiEP);
+        let theBody = chat.request_jsonstr(apiEP);
+
+        this.elInUser.value = "working...";
+        this.elInUser.disabled = true;
+        console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:${theUrl}:ReqBody:${theBody}`);
+        let theHeaders = chat.fetch_headers(apiEP);
+        let resp = await fetch(theUrl, {
+            method: "POST",
+            headers: theHeaders,
+            body: theBody,
+        });
+
+        let theResp = await chat.handle_response(resp, apiEP, this.elDivChat);
+        if (chatId == this.curChatId) {
+            chat.show(this.elDivChat);
+            if (theResp.trimmed.length > 0) {
+                let p = ui.el_create_append_p(`TRIMMED:${theResp.trimmed}`, this.elDivChat);
+                p.className="role-trim";
+            }
+        } else {
+            console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`);
+        }
+        this.ui_reset_userinput();
+    }
+
+    /**
+     * Show buttons for NewChat and available chat sessions, in the passed elDiv.
+     * If elDiv is undefined/null, then use this.elDivSessions.
+     * Take care of highlighting the selected chat-session's btn.
+     * @param {HTMLDivElement | undefined} elDiv
+     */
+    show_sessions(elDiv=undefined) {
+        if (!elDiv) {
+            elDiv = this.elDivSessions;
+        }
+        elDiv.replaceChildren();
+        // Btn for creating new chat session
+        let btnNew = ui.el_create_button("New CHAT", (ev)=> {
+            if (this.elInUser.disabled) {
+                console.error(`ERRR:SimpleChat:MCUI:NewChat:Current session [${this.curChatId}] awaiting response, ignoring request...`);
+                alert("ERRR:SimpleChat\nMCUI:NewChat\nWait for response to pending query, before starting new chat session");
+                return;
+            }
+            let chatId = `Chat${Object.keys(this.simpleChats).length}`;
+            let chatIdGot = prompt("INFO:SimpleChat\nMCUI:NewChat\nEnter id for new chat session", chatId);
+            if (!chatIdGot) {
+                console.error("ERRR:SimpleChat:MCUI:NewChat:Skipping based on user request...");
+                return;
+            }
+            this.new_chat_session(chatIdGot, true);
+            this.create_session_btn(elDiv, chatIdGot);
+            ui.el_children_config_class(elDiv, chatIdGot, "session-selected", "");
+        });
+        elDiv.appendChild(btnNew);
+        // Btns for existing chat sessions
+        let chatIds = Object.keys(this.simpleChats);
+        for(let cid of chatIds) {
+            let btn = this.create_session_btn(elDiv, cid);
+            if (cid == this.curChatId) {
+                btn.className = "session-selected";
+            }
+        }
+    }
+
+    create_session_btn(elDiv, cid) {
+        let btn = ui.el_create_button(cid, (ev)=>{
+            let target = /** @type{HTMLButtonElement} */(ev.target);
+            console.debug(`DBUG:SimpleChat:MCUI:SessionClick:${target.id}`);
+            if (this.elInUser.disabled) {
+                console.error(`ERRR:SimpleChat:MCUI:SessionClick:${target.id}:Current session [${this.curChatId}] awaiting response, ignoring switch...`);
+                alert("ERRR:SimpleChat\nMCUI:SessionClick\nWait for response to pending query, before switching");
+                return;
+            }
+            this.handle_session_switch(target.id);
+            ui.el_children_config_class(elDiv, target.id, "session-selected", "");
+        });
+        elDiv.appendChild(btn);
+        return btn;
+    }
+
+    /**
+     * Switch ui to the specified chatId and set curChatId to same.
+     * @param {string} chatId
+     */
+    async handle_session_switch(chatId) {
+        let chat = this.simpleChats[chatId];
+        if (chat == undefined) {
+            console.error(`ERRR:SimpleChat:MCUI:HandleSessionSwitch:${chatId} missing...`);
+            return;
+        }
+        this.elInSystem.value = chat.get_system_latest();
+        this.elInUser.value = "";
+        chat.show(this.elDivChat);
+        this.elInUser.focus();
+        this.curChatId = chatId;
+        console.log(`INFO:SimpleChat:MCUI:HandleSessionSwitch:${chatId} entered...`);
+    }
+
+}
+
+
+class Me {
+
+    constructor() {
+        this.baseURL = "http://127.0.0.1:8080";
+        this.defaultChatIds = [ "Default", "Other" ];
+        this.multiChat = new MultiChatUI();
+        this.bStream = true;
+        this.bCompletionFreshChatAlways = true;
+        this.bCompletionInsertStandardRolePrefix = false;
+        this.bTrimGarbage = true;
+        this.iRecentUserMsgCnt = 2;
+        this.sRecentUserMsgCnt = {
+            "Full": -1,
+            "Last0": 1,
+            "Last1": 2,
+            "Last2": 3,
+            "Last4": 5,
+        };
+        this.apiEP = ApiEP.Type.Chat;
+        this.headers = {
+            "Content-Type": "application/json",
+            "Authorization": "", // Authorization: Bearer OPENAI_API_KEY
+        }
+        // Add needed fields wrt json object to be sent wrt LLM web services completions endpoint.
+        this.apiRequestOptions = {
+            "model": "gpt-3.5-turbo",
+            "temperature": 0.7,
+            "max_tokens": 1024,
+            "n_predict": 1024,
+            "cache_prompt": false,
+            //"frequency_penalty": 1.2,
+            //"presence_penalty": 1.2,
+        };
+    }
+
+    /**
+     * Disable console.debug by mapping it to a empty function.
+     */
+    debug_disable() {
+        this.console_debug = console.debug;
+        console.debug = () => {
+
+        };
+    }
+
+    /**
+     * Setup the load saved chat ui.
+     * @param {HTMLDivElement} div
+     * @param {SimpleChat} chat
+     */
+    setup_load(div, chat) {
+        if (!(chat.ods_key() in localStorage)) {
+            return;
+        }
+        div.innerHTML += `<p class="role-system">Restore</p>
+        <p>Load previously saved chat session, if available</p>`;
+        let btn = ui.el_create_button(chat.ods_key(), (ev)=>{
+            console.log("DBUG:SimpleChat:SC:Load", chat);
+            chat.load();
+            queueMicrotask(()=>{
+                chat.show(div);
+                this.multiChat.elInSystem.value = chat.get_system_latest();
+            });
+        });
+        div.appendChild(btn);
+    }
+
+    /**
+     * Show the configurable parameters info in the passed Div element.
+     * @param {HTMLDivElement} elDiv
+     * @param {boolean} bAll
+     */
+    show_info(elDiv, bAll=false) {
+
+        let p = ui.el_create_append_p("Settings (devel-tools-console document[gMe])", elDiv);
+        p.className = "role-system";
+
+        if (bAll) {
+
+            ui.el_create_append_p(`baseURL:${this.baseURL}`, elDiv);
+
+            ui.el_create_append_p(`Authorization:${this.headers["Authorization"]}`, elDiv);
+
+            ui.el_create_append_p(`bStream:${this.bStream}`, elDiv);
+
+            ui.el_create_append_p(`bTrimGarbage:${this.bTrimGarbage}`, elDiv);
+
+            ui.el_create_append_p(`ApiEndPoint:${this.apiEP}`, elDiv);
+
+            ui.el_create_append_p(`iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`, elDiv);
+
+            ui.el_create_append_p(`bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`, elDiv);
+
+            ui.el_create_append_p(`bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`, elDiv);
+
+        }
+
+        ui.el_create_append_p(`apiRequestOptions:${JSON.stringify(this.apiRequestOptions, null, " - ")}`, elDiv);
+        ui.el_create_append_p(`headers:${JSON.stringify(this.headers, null, " - ")}`, elDiv);
+
+    }
+
+    /**
+     * Auto create ui input elements for fields in apiRequestOptions
+     * Currently supports text and number field types.
+     * @param {HTMLDivElement} elDiv
+     */
+    show_settings_apirequestoptions(elDiv) {
+        let typeDict = {
+            "string": "text",
+            "number": "number",
+        };
+        let fs = document.createElement("fieldset");
+        let legend = document.createElement("legend");
+        legend.innerText = "ApiRequestOptions";
+        fs.appendChild(legend);
+        elDiv.appendChild(fs);
+        for(const k in this.apiRequestOptions) {
+            let val = this.apiRequestOptions[k];
+            let type = typeof(val);
+            if (((type == "string") || (type == "number"))) {
+                let inp = ui.el_creatediv_input(`Set${k}`, k, typeDict[type], this.apiRequestOptions[k], (val)=>{
+                    if (type == "number") {
+                        val = Number(val);
+                    }
+                    this.apiRequestOptions[k] = val;
+                });
+                fs.appendChild(inp.div);
+            } else if (type == "boolean") {
+                let bbtn = ui.el_creatediv_boolbutton(`Set{k}`, k, {true: "true", false: "false"}, val, (userVal)=>{
+                    this.apiRequestOptions[k] = userVal;
+                });
+                fs.appendChild(bbtn.div);
+            }
+        }
+    }
+
+    /**
+     * Show settings ui for configurable parameters, in the passed Div element.
+     * @param {HTMLDivElement} elDiv
+     */
+    show_settings(elDiv) {
+
+        let inp = ui.el_creatediv_input("SetBaseURL", "BaseURL", "text", this.baseURL, (val)=>{
+            this.baseURL = val;
+        });
+        elDiv.appendChild(inp.div);
+
+        inp = ui.el_creatediv_input("SetAuthorization", "Authorization", "text", this.headers["Authorization"], (val)=>{
+            this.headers["Authorization"] = val;
+        });
+        inp.el.placeholder = "Bearer OPENAI_API_KEY";
+        elDiv.appendChild(inp.div);
+
+        let bb = ui.el_creatediv_boolbutton("SetStream", "Stream", {true: "[+] yes stream", false: "[-] do oneshot"}, this.bStream, (val)=>{
+            this.bStream = val;
+        });
+        elDiv.appendChild(bb.div);
+
+        bb = ui.el_creatediv_boolbutton("SetTrimGarbage", "TrimGarbage", {true: "[+] yes trim", false: "[-] dont trim"}, this.bTrimGarbage, (val)=>{
+            this.bTrimGarbage = val;
+        });
+        elDiv.appendChild(bb.div);
+
+        this.show_settings_apirequestoptions(elDiv);
+
+        let sel = ui.el_creatediv_select("SetApiEP", "ApiEndPoint", ApiEP.Type, this.apiEP, (val)=>{
+            this.apiEP = ApiEP.Type[val];
+        });
+        elDiv.appendChild(sel.div);
+
+        sel = ui.el_creatediv_select("SetChatHistoryInCtxt", "ChatHistoryInCtxt", this.sRecentUserMsgCnt, this.iRecentUserMsgCnt, (val)=>{
+            this.iRecentUserMsgCnt = this.sRecentUserMsgCnt[val];
+        });
+        elDiv.appendChild(sel.div);
+
+        bb = ui.el_creatediv_boolbutton("SetCompletionFreshChatAlways", "CompletionFreshChatAlways", {true: "[+] yes fresh", false: "[-] no, with history"}, this.bCompletionFreshChatAlways, (val)=>{
+            this.bCompletionFreshChatAlways = val;
+        });
+        elDiv.appendChild(bb.div);
+
+        bb = ui.el_creatediv_boolbutton("SetCompletionInsertStandardRolePrefix", "CompletionInsertStandardRolePrefix", {true: "[+] yes insert", false: "[-] dont insert"}, this.bCompletionInsertStandardRolePrefix, (val)=>{
+            this.bCompletionInsertStandardRolePrefix = val;
+        });
+        elDiv.appendChild(bb.div);
+
+    }
+
+}
+
+
+/** @type {Me} */
+let gMe;
+
+function startme() {
+    console.log("INFO:SimpleChat:StartMe:Starting...");
+    gMe = new Me();
+    gMe.debug_disable();
+    document["gMe"] = gMe;
+    document["du"] = du;
+    for (let cid of gMe.defaultChatIds) {
+        gMe.multiChat.new_chat_session(cid);
+    }
+    gMe.multiChat.setup_ui(gMe.defaultChatIds[0], true);
+    gMe.multiChat.show_sessions();
+}
+
+document.addEventListener("DOMContentLoaded", startme);
diff --git a/examples/server/public_simplechat/simplechat_screens.webp b/examples/server/public_simplechat/simplechat_screens.webp
new file mode 100644
index 0000000000000..ccea443960516
Binary files /dev/null and b/examples/server/public_simplechat/simplechat_screens.webp differ
diff --git a/examples/server/public_simplechat/ui.mjs b/examples/server/public_simplechat/ui.mjs
new file mode 100644
index 0000000000000..b2d5b9aeab76c
--- /dev/null
+++ b/examples/server/public_simplechat/ui.mjs
@@ -0,0 +1,211 @@
+//@ts-check
+// Helpers to work with html elements
+// by Humans for All
+//
+
+
+/**
+ * Set the class of the children, based on whether it is the idSelected or not.
+ * @param {HTMLDivElement} elBase
+ * @param {string} idSelected
+ * @param {string} classSelected
+ * @param {string} classUnSelected
+ */
+export function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") {
+    for(let child of elBase.children) {
+        if (child.id == idSelected) {
+            child.className = classSelected;
+        } else {
+            child.className = classUnSelected;
+        }
+    }
+}
+
+/**
+ * Create button and set it up.
+ * @param {string} id
+ * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback
+ * @param {string | undefined} name
+ * @param {string | undefined} innerText
+ */
+export function el_create_button(id, callback, name=undefined, innerText=undefined) {
+    if (!name) {
+        name = id;
+    }
+    if (!innerText) {
+        innerText = id;
+    }
+    let btn = document.createElement("button");
+    btn.id = id;
+    btn.name = name;
+    btn.innerText = innerText;
+    btn.addEventListener("click", callback);
+    return btn;
+}
+
+/**
+ * Create a para and set it up. Optionaly append it to a passed parent.
+ * @param {string} text
+ * @param {HTMLElement | undefined} elParent
+ * @param {string | undefined} id
+ */
+export function el_create_append_p(text, elParent=undefined, id=undefined) {
+    let para = document.createElement("p");
+    para.innerText = text;
+    if (id) {
+        para.id = id;
+    }
+    if (elParent) {
+        elParent.appendChild(para);
+    }
+    return para;
+}
+
+/**
+ * Create a button which represents bool value using specified text wrt true and false.
+ * When ever user clicks the button, it will toggle the value and update the shown text.
+ *
+ * @param {string} id
+ * @param {{true: string, false: string}} texts
+ * @param {boolean} defaultValue
+ * @param {function(boolean):void} cb
+ */
+export function el_create_boolbutton(id, texts, defaultValue, cb) {
+    let el = document.createElement("button");
+    el["xbool"] = defaultValue;
+    el["xtexts"] = structuredClone(texts);
+    el.innerText = el["xtexts"][String(defaultValue)];
+    if (id) {
+        el.id = id;
+    }
+    el.addEventListener('click', (ev)=>{
+        el["xbool"] = !el["xbool"];
+        el.innerText = el["xtexts"][String(el["xbool"])];
+        cb(el["xbool"]);
+    })
+    return el;
+}
+
+/**
+ * Create a div wrapped button which represents bool value using specified text wrt true and false.
+ * @param {string} id
+ * @param {string} label
+ * @param {{ true: string; false: string; }} texts
+ * @param {boolean} defaultValue
+ * @param {(arg0: boolean) => void} cb
+ * @param {string} className
+ */
+export function el_creatediv_boolbutton(id, label, texts, defaultValue, cb, className="gridx2") {
+    let div = document.createElement("div");
+    div.className = className;
+    let lbl = document.createElement("label");
+    lbl.setAttribute("for", id);
+    lbl.innerText = label;
+    div.appendChild(lbl);
+    let btn = el_create_boolbutton(id, texts, defaultValue, cb);
+    div.appendChild(btn);
+    return { div: div, el: btn };
+}
+
+
+/**
+ * Create a select ui element, with a set of options to select from.
+ * * options: an object which contains name-value pairs
+ * * defaultOption: the value whose name should be choosen, by default.
+ * * cb : the call back returns the name string of the option selected.
+ *
+ * @param {string} id
+ * @param {Object<string,*>} options
+ * @param {*} defaultOption
+ * @param {function(string):void} cb
+ */
+export function el_create_select(id, options, defaultOption, cb) {
+    let el = document.createElement("select");
+    el["xselected"] = defaultOption;
+    el["xoptions"] = structuredClone(options);
+    for(let cur of Object.keys(options)) {
+        let op = document.createElement("option");
+        op.value = cur;
+        op.innerText = cur;
+        if (options[cur] == defaultOption) {
+            op.selected = true;
+        }
+        el.appendChild(op);
+    }
+    if (id) {
+        el.id = id;
+        el.name = id;
+    }
+    el.addEventListener('change', (ev)=>{
+        let target = /** @type{HTMLSelectElement} */(ev.target);
+        console.log("DBUG:UI:Select:", id, ":", target.value);
+        cb(target.value);
+    })
+    return el;
+}
+
+/**
+ * Create a div wrapped select ui element, with a set of options to select from.
+ *
+ * @param {string} id
+ * @param {any} label
+ * @param {{ [x: string]: any; }} options
+ * @param {any} defaultOption
+ * @param {(arg0: string) => void} cb
+ * @param {string} className
+ */
+export function el_creatediv_select(id, label, options, defaultOption, cb, className="gridx2") {
+    let div = document.createElement("div");
+    div.className = className;
+    let lbl = document.createElement("label");
+    lbl.setAttribute("for", id);
+    lbl.innerText = label;
+    div.appendChild(lbl);
+    let sel = el_create_select(id, options,defaultOption, cb);
+    div.appendChild(sel);
+    return { div: div, el: sel };
+}
+
+
+/**
+ * Create a input ui element.
+ *
+ * @param {string} id
+ * @param {string} type
+ * @param {any} defaultValue
+ * @param {function(any):void} cb
+ */
+export function el_create_input(id, type, defaultValue, cb) {
+    let el = document.createElement("input");
+    el.type = type;
+    el.value = defaultValue;
+    if (id) {
+        el.id = id;
+    }
+    el.addEventListener('change', (ev)=>{
+        cb(el.value);
+    })
+    return el;
+}
+
+/**
+ * Create a div wrapped input.
+ *
+ * @param {string} id
+ * @param {string} label
+ * @param {string} type
+ * @param {any} defaultValue
+ * @param {function(any):void} cb
+ * @param {string} className
+ */
+export function el_creatediv_input(id, label, type, defaultValue, cb, className="gridx2") {
+    let div = document.createElement("div");
+    div.className = className;
+    let lbl = document.createElement("label");
+    lbl.setAttribute("for", id);
+    lbl.innerText = label;
+    div.appendChild(lbl);
+    let el = el_create_input(id, type, defaultValue, cb);
+    div.appendChild(el);
+    return { div: div, el: el };
+}
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 7978f979dde66..e073f5813d459 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -17,9 +17,20 @@
 #include "json.hpp"
 
 // auto generated files (update with ./deps.sh)
+#include "colorthemes.css.hpp"
+#include "style.css.hpp"
+#include "theme-beeninorder.css.hpp"
+#include "theme-ketivah.css.hpp"
+#include "theme-mangotango.css.hpp"
+#include "theme-playground.css.hpp"
+#include "theme-polarnight.css.hpp"
+#include "theme-snowstorm.css.hpp"
 #include "index.html.hpp"
+#include "index-new.html.hpp"
 #include "index.js.hpp"
 #include "completion.js.hpp"
+#include "system-prompts.js.hpp"
+#include "prompt-formats.js.hpp"
 #include "json-schema-to-grammar.mjs.hpp"
 
 #include <atomic>
@@ -67,6 +78,7 @@ enum server_task_type {
     SERVER_TASK_TYPE_SLOT_SAVE,
     SERVER_TASK_TYPE_SLOT_RESTORE,
     SERVER_TASK_TYPE_SLOT_ERASE,
+    SERVER_TASK_TYPE_SET_LORA,
 };
 
 struct server_task {
@@ -112,29 +124,6 @@ struct slot_params {
     json input_suffix;
 };
 
-struct server_params {
-    int32_t port           = 8080;
-    int32_t read_timeout   = 600;
-    int32_t write_timeout  = 600;
-    int32_t n_threads_http = -1;
-
-    std::string hostname      = "127.0.0.1";
-    std::string public_path   = "";
-    std::string chat_template = "";
-    std::string system_prompt = "";
-
-    std::vector<std::string> api_keys;
-
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-    std::string ssl_key_file = "";
-    std::string ssl_cert_file = "";
-#endif
-
-    bool slots_endpoint   = true;
-    bool metrics_endpoint = false;
-    std::string slot_save_path;
-};
-
 struct server_slot {
     int id;
     int id_task = -1;
@@ -159,7 +148,7 @@ struct server_slot {
     int32_t n_prompt_tokens           = 0;
     int32_t n_prompt_tokens_processed = 0;
 
-    json prompt;
+    json prompt; // can be either a string, array of strings or array of token ids
 
     // when a task is submitted, we first tokenize the prompt and store it here
     std::vector<llama_token> prompt_tokens;
@@ -634,6 +623,7 @@ struct server_response {
 struct server_context {
     llama_model * model = nullptr;
     llama_context * ctx = nullptr;
+    std::vector<llama_lora_adapter_container> lora_adapters;
 
     gpt_params params;
 
@@ -641,6 +631,7 @@ struct server_context {
 
     bool clean_kv_cache = true;
     bool add_bos_token  = true;
+    bool has_eos_token  = false;
 
     int32_t n_ctx; // total context for all clients / slots
 
@@ -659,6 +650,9 @@ struct server_context {
 
     server_metrics metrics;
 
+    // Necessary similarity of prompt for slot selection
+    float slot_prompt_similarity = 0.0f;
+
     ~server_context() {
         if (ctx) {
             llama_free(ctx);
@@ -686,7 +680,11 @@ struct server_context {
         // dedicate one sequence to the system prompt
         params.n_parallel += 1;
 
-        std::tie(model, ctx) = llama_init_from_gpt_params(params);
+        llama_init_result llama_init = llama_init_from_gpt_params(params);
+
+        model = llama_init.model;
+        ctx = llama_init.context;
+        lora_adapters = llama_init.lora_adapters;
         params.n_parallel -= 1; // but be sneaky about it
         if (model == nullptr) {
             LOG_ERROR("unable to load model", {{"model", params.model}});
@@ -695,9 +693,8 @@ struct server_context {
 
         n_ctx = llama_n_ctx(ctx);
 
-        add_bos_token = llama_should_add_bos_token(model);
-        GGML_ASSERT(llama_add_eos_token(model) != 1);
-
+        add_bos_token = llama_add_bos_token(model);
+        has_eos_token = !llama_add_eos_token(model);
         return true;
     }
 
@@ -746,6 +743,8 @@ struct server_context {
             slot.ga_n = ga_n;
             slot.ga_w = ga_w;
 
+            slot.sparams = params.sparams;
+
             slot.reset();
 
             slots.push_back(slot);
@@ -754,13 +753,13 @@ struct server_context {
         default_generation_settings_for_props = get_formated_generation(slots.front());
         default_generation_settings_for_props["seed"] = -1;
 
-        // the update_slots() logic will always submit a maximum of n_batch tokens
+        // the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens
         // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
         {
             const int32_t n_batch = llama_n_batch(ctx);
 
             // only a single seq_id per token is needed
-            batch = llama_batch_init(n_batch, 0, 1);
+            batch = llama_batch_init(std::max(n_batch, params.n_parallel), 0, 1);
         }
 
         metrics.init();
@@ -807,29 +806,94 @@ struct server_context {
         return prompt_tokens;
     }
 
-    server_slot * get_slot(int id) {
-        int64_t t_last = ggml_time_us();
-
-        server_slot * last_used = nullptr;
-
+    server_slot * get_slot_by_id(int id) {
         for (server_slot & slot : slots) {
-            if (slot.id == id && slot.available()) {
+            if (slot.id == id) {
                 return &slot;
             }
+        }
+
+        return nullptr;
+    }
+
+    server_slot * get_available_slot(const std::string & prompt) {
+        server_slot * ret = nullptr;
+
+        // find the slot that has at least n% prompt similarity
+        if (ret == nullptr && slot_prompt_similarity != 0.0f && !prompt.empty()) {
+            int max_lcp_len = 0;
+            float similarity = 0;
+
+            for (server_slot & slot : slots) {
+                // skip the slot if it is not available
+                if (!slot.available()) {
+                    continue;
+                }
+
+                // skip the slot if it does not contains prompt
+                if (!slot.prompt.is_string()) {
+                    continue;
+                }
+
+                // current slot's prompt
+                std::string slot_prompt = slot.prompt.get<std::string>();
+
+                // length of the current slot's prompt
+                int slot_prompt_len = slot_prompt.size();
+
+                // length of the Longest Common Prefix between the current slot's prompt and the input prompt
+                int lcp_len = common_part(slot_prompt, prompt);
+
+                // fraction of the common substring length compared to the current slot's prompt length
+                similarity = static_cast<float>(lcp_len) / slot_prompt_len;
+
+                // select the current slot if the criteria match
+                if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) {
+                    max_lcp_len = lcp_len;
+                    ret = &slot;
+                }
+            }
 
-            // among all available slots, find the one that has been least recently used
-            if (slot.available() && slot.t_last_used < t_last) {
-                last_used = &slot;
-                t_last = slot.t_last_used;
+            if (ret != nullptr) {
+                LOG_VERBOSE("selected slot by lcp similarity", {
+                    {"id_slot", ret->id},
+                    {"max_lcp_len", max_lcp_len},
+                    {"similarity", similarity},
+                });
             }
         }
 
-        return last_used;
+        // find the slot that has been least recently used
+        if (ret == nullptr) {
+            int64_t t_last = ggml_time_us();
+            for (server_slot & slot : slots) {
+                // skip the slot if it is not available
+                if (!slot.available()) {
+                    continue;
+                }
+
+                // select the current slot if the criteria match
+                if (slot.t_last_used < t_last) {
+                    t_last = slot.t_last_used;
+                    ret = &slot;
+                }
+            }
+
+            if (ret != nullptr) {
+                LOG_VERBOSE("selected slot by lru", {
+                    {"id_slot", ret->id},
+                    {"t_last", t_last},
+                });
+            }
+        }
+
+        return ret;
     }
 
     bool launch_slot_with_task(server_slot & slot, const server_task & task) {
         slot_params default_params;
-        llama_sampling_params default_sparams;
+        // Sampling parameter defaults are loaded from the global server context (but individual requests can still override them)
+        llama_sampling_params default_sparams = params.sparams;
         auto & data = task.data;
 
         if (data.count("__oaicompat") != 0) {
@@ -842,7 +906,7 @@ struct server_context {
 
         slot.params.stream             = json_value(data, "stream",            false);
         slot.params.cache_prompt       = json_value(data, "cache_prompt",      false);
-        slot.params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
+        slot.params.n_predict          = json_value(data, "n_predict",         json_value(data, "max_tokens", default_params.n_predict));
         slot.sparams.top_k             = json_value(data, "top_k",             default_sparams.top_k);
         slot.sparams.top_p             = json_value(data, "top_p",             default_sparams.top_p);
         slot.sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
@@ -900,16 +964,21 @@ struct server_context {
         slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
 
         // get prompt
-        {
+        if (!task.infill) {
             const auto & prompt = data.find("prompt");
             if (prompt == data.end()) {
-                send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST);
+                send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
                 return false;
-            } else {
-                slot.prompt = *prompt;
             }
-            if (slot.prompt.is_array() && slot.prompt.size() == 0) {
-                send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST);
+
+            if ((prompt->is_string()) ||
+                (prompt->is_array() &&  prompt->size() == 1 && prompt->at(0).is_string()) ||
+                (prompt->is_array() && !prompt->empty()     && prompt->at(0).is_number_integer())) {
+                slot.prompt = *prompt;
+            } else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) {
+                slot.prompt = prompt->at(0);
+            } else {
+                send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
         }
@@ -962,7 +1031,7 @@ struct server_context {
         {
             slot.sparams.logit_bias.clear();
 
-            if (json_value(data, "ignore_eos", false)) {
+            if (json_value(data, "ignore_eos", false) && has_eos_token) {
                 slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
             }
 
@@ -1019,7 +1088,7 @@ struct server_context {
                         sampler_names.emplace_back(sampler_name);
                     }
                 }
-                slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
+                slot.sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, false);
             } else {
                 slot.sparams.samplers_sequence = default_sparams.samplers_sequence;
             }
@@ -1067,28 +1136,19 @@ struct server_context {
         if (!system_prompt.empty()) {
             system_tokens = ::llama_tokenize(ctx, system_prompt, true);
 
-            llama_batch_clear(batch);
+            const int32_t n_batch = llama_n_batch(ctx);
+            const int32_t n_tokens_prompt = system_tokens.size();
 
-            for (int i = 0; i < (int)system_tokens.size(); ++i) {
-                llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
-            }
+            for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) {
+                const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i);
 
-            const int32_t n_batch = llama_n_batch(ctx);
+                llama_batch_clear(batch);
 
-            for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
-                const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
-                llama_batch batch_view = {
-                    n_tokens,
-                    batch.token    + i,
-                    nullptr,
-                    batch.pos      + i,
-                    batch.n_seq_id + i,
-                    batch.seq_id   + i,
-                    batch.logits   + i,
-                    0, 0, 0, // unused
-                };
+                for (int32_t j = 0; j < n_tokens; ++j) {
+                    llama_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false);
+                }
 
-                if (llama_decode(ctx, batch_view) != 0) {
+                if (llama_decode(ctx, batch) != 0) {
                     LOG_ERROR("llama_decode() failed", {});
                     return;
                 }
@@ -1121,7 +1181,7 @@ struct server_context {
 
     bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
-        const std::string token_str = llama_token_to_piece(ctx, result.tok, false);
+        const std::string token_str = llama_token_to_piece(ctx, result.tok, params.special);
         slot.sampled = result.tok;
 
         // search stop word and delete it
@@ -1250,18 +1310,18 @@ struct server_context {
     }
 
     json get_formated_generation(const server_slot & slot) const {
-        const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
+        const auto eos_bias   =             slot.sparams.logit_bias.find(llama_token_eos(model));
         const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second);
 
         std::vector<std::string> samplers_sequence;
         samplers_sequence.reserve(slot.sparams.samplers_sequence.size());
         for (const auto & sampler_type : slot.sparams.samplers_sequence) {
-            samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
+            samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type));
         }
 
         return json {
             {"n_ctx",                     slot.n_ctx},
-            {"n_predict",                 slot.n_predict},
+            {"n_predict",                 slot.n_predict},     // Server configured n_predict
             {"model",                     params.model_alias},
             {"seed",                      slot.sparams.seed},
             {"temperature",               slot.sparams.temp},
@@ -1283,7 +1343,7 @@ struct server_context {
             {"mirostat_eta",              slot.sparams.mirostat_eta},
             {"penalize_nl",               slot.sparams.penalize_nl},
             {"stop",                      slot.params.antiprompt},
-            {"n_predict",                 slot.params.n_predict}, // TODO: fix duplicate key n_predict
+            {"max_tokens",                slot.params.n_predict}, // User configured n_predict
             {"n_keep",                    slot.params.n_keep},
             {"n_discard",                 slot.params.n_discard},
             {"ignore_eos",                ignore_eos},
@@ -1527,13 +1587,33 @@ struct server_context {
         switch (task.type) {
             case SERVER_TASK_TYPE_COMPLETION:
                 {
-                    server_slot * slot = get_slot(json_value(task.data, "id_slot", -1));
+                    const int id_slot = json_value(task.data, "id_slot", -1);
+
+                    server_slot * slot;
+
+                    if (id_slot != -1) {
+                        slot = get_slot_by_id(id_slot);
+                    } else {
+                        std::string prompt;
+                        if (task.data.contains("prompt") && task.data.at("prompt").is_string()) {
+                            prompt = json_value(task.data, "prompt", std::string());
+                        }
+
+                        slot = get_available_slot(prompt);
+                    }
+
                     if (slot == nullptr) {
                         // if no slot is available, we defer this task for processing later
                         LOG_VERBOSE("no slot is available", {{"id_task", task.id}});
                         queue_tasks.defer(task);
                         break;
                     }
+                    if (!slot->available()) {
+                        // if requested slot is unavailable, we defer this task for processing later
+                        LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
+                        queue_tasks.defer(task);
+                        break;
+                    }
 
                     if (task.data.contains("system_prompt")) {
                         std::string sys_prompt = json_value(task.data, "system_prompt", std::string());
@@ -1650,11 +1730,17 @@ struct server_context {
             case SERVER_TASK_TYPE_SLOT_SAVE:
                 {
                     int id_slot = task.data.at("id_slot");
-                    server_slot * slot = get_slot(id_slot);
+                    server_slot * slot = get_slot_by_id(id_slot);
                     if (slot == nullptr) {
                         send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                         break;
                     }
+                    if (!slot->available()) {
+                        // if requested slot is unavailable, we defer this task for processing later
+                        LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
+                        queue_tasks.defer(task);
+                        break;
+                    }
 
                     const size_t token_count = slot->cache_tokens.size();
                     const int64_t t_start = ggml_time_us();
@@ -1685,11 +1771,17 @@ struct server_context {
             case SERVER_TASK_TYPE_SLOT_RESTORE:
                 {
                     int id_slot = task.data.at("id_slot");
-                    server_slot * slot = get_slot(id_slot);
+                    server_slot * slot = get_slot_by_id(id_slot);
                     if (slot == nullptr) {
                         send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                         break;
                     }
+                    if (!slot->available()) {
+                        // if requested slot is unavailable, we defer this task for processing later
+                        LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
+                        queue_tasks.defer(task);
+                        break;
+                    }
 
                     const int64_t t_start = ggml_time_us();
 
@@ -1727,11 +1819,17 @@ struct server_context {
             case SERVER_TASK_TYPE_SLOT_ERASE:
                 {
                     int id_slot = task.data.at("id_slot");
-                    server_slot * slot = get_slot(id_slot);
+                    server_slot * slot = get_slot_by_id(id_slot);
                     if (slot == nullptr) {
                         send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                         break;
                     }
+                    if (!slot->available()) {
+                        // if requested slot is unavailable, we defer this task for processing later
+                        LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
+                        queue_tasks.defer(task);
+                        break;
+                    }
 
                     // Erase token cache
                     const size_t n_erased = slot->cache_tokens.size();
@@ -1748,6 +1846,16 @@ struct server_context {
                     };
                     queue_results.send(result);
                 } break;
+            case SERVER_TASK_TYPE_SET_LORA:
+                {
+                    llama_lora_adapters_apply(ctx, lora_adapters);
+                    server_task_result result;
+                    result.id = task.id;
+                    result.stop = true;
+                    result.error = false;
+                    result.data = json{{ "success", true }};
+                    queue_results.send(result);
+                } break;
         }
     }
 
@@ -1906,6 +2014,11 @@ struct server_context {
         int32_t n_batch  = llama_n_batch(ctx);
         int32_t n_ubatch = llama_n_ubatch(ctx);
 
+        // track if this is an embedding or non-embedding batch
+        // if we've added sampled tokens above, we are in non-embedding mode
+        // -1: none, 0: non-embedding, 1: embedding
+        int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
+
         // next, batch any pending prompts without exceeding n_batch
         if (params.cont_batching || batch.n_tokens == 0) {
             for (auto & slot : slots) {
@@ -1924,6 +2037,7 @@ struct server_context {
                         slot.t_start_generation = 0;
 
                         if (slot.infill) {
+                            const bool add_bos = llama_add_bos_token(model);
                             bool suff_rm_leading_spc = true;
                             if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
                                 params.input_suffix.erase(0, 1);
@@ -1939,11 +2053,21 @@ struct server_context {
                             }
 
                             prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
-                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
-                            prefix_tokens.insert(prefix_tokens.end(),   llama_token_suffix(model));
-                            prefix_tokens.insert(prefix_tokens.end(),   suffix_tokens.begin(), suffix_tokens.end());
-                            prefix_tokens.push_back(llama_token_middle(model));
-                            prompt_tokens = prefix_tokens;
+                            suffix_tokens.insert(suffix_tokens.begin(), llama_token_suffix(model));
+
+                            auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens;
+                            auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens;
+                            if (add_bos) {
+                                embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+                            }
+                            embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
+
+                            const llama_token middle_token = llama_token_middle(model);
+                            if (middle_token >= 0) {
+                                embd_inp.push_back(middle_token);
+                            }
+
+                            prompt_tokens = embd_inp;
                         } else {
                             prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
                         }
@@ -1981,8 +2105,7 @@ struct server_context {
                                 slot.state = SLOT_STATE_PROCESSING;
                                 slot.command = SLOT_COMMAND_NONE;
                                 slot.release();
-                                slot.print_timings();
-                                send_final_response(slot);
+                                send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
                                 continue;
                             }
                         } else {
@@ -2066,6 +2189,14 @@ struct server_context {
                         }
                     }
 
+                    // check that we are in the right batch_type, if not defer the slot
+                    bool slot_type = slot.embedding ? 1 : 0;
+                    if (batch_type == -1) {
+                        batch_type = slot_type;
+                    } else if (batch_type != slot_type) {
+                        continue;
+                    }
+
                     // keep only the common part
                     int p0 = (int) system_tokens.size() + slot.n_past;
                     if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) {
@@ -2167,6 +2298,9 @@ struct server_context {
             {"n_tokens", batch.n_tokens},
         });
 
+        // make sure we're in the right embedding mode
+        llama_set_embeddings(ctx, batch_type == 1);
+
         // process the created batch of tokens
         for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
             const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
@@ -2324,561 +2458,6 @@ struct server_context {
     }
 };
 
-static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) {
-    printf("usage: %s [options]\n", argv0);
-    printf("\n");
-    printf("options:\n");
-    printf("  -h, --help                show this help message and exit\n");
-    printf("  -v, --verbose             verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
-    printf("  -t N, --threads N         number of threads to use during computation (default: %d)\n", params.n_threads);
-    printf("  -tb N, --threads-batch N  number of threads to use during batch and prompt processing (default: same as --threads)\n");
-    printf("  --threads-http N          number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n");
-    printf("  -c N, --ctx-size N        size of the prompt context (default: %d)\n", params.n_ctx);
-    printf("  --rope-scaling {none,linear,yarn}\n");
-    printf("                            RoPE frequency scaling method, defaults to linear unless specified by the model\n");
-    printf("  --rope-freq-base N        RoPE base frequency (default: loaded from model)\n");
-    printf("  --rope-freq-scale N       RoPE frequency scaling factor, expands context by a factor of 1/N\n");
-    printf("  --yarn-ext-factor N       YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
-    printf("  --yarn-attn-factor N      YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
-    printf("  --yarn-beta-slow N        YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
-    printf("  --yarn-beta-fast N        YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
-    printf("  --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
-    printf("  -dt N, --defrag-thold N\n");
-    printf("                            KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
-    printf("  -b N, --batch-size N      logical maximum batch size (default: %d)\n", params.n_batch);
-    printf("  -ub N, --ubatch-size N    physical maximum batch size (default: %d)\n", params.n_ubatch);
-    if (llama_supports_mlock()) {
-        printf("  --mlock                   force system to keep model in RAM rather than swapping or compressing\n");
-    }
-    if (llama_supports_mmap()) {
-        printf("  --no-mmap                 do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
-    }
-    printf("  --numa TYPE               attempt optimizations that help on some NUMA systems\n");
-    printf("                              - distribute: spread execution evenly over all nodes\n");
-    printf("                              - isolate: only spawn threads on CPUs on the node that execution started on\n");
-    printf("                              - numactl: use the CPU map provided my numactl\n");
-    if (llama_supports_gpu_offload()) {
-        printf("  -ngl N, --n-gpu-layers N\n");
-        printf("                            number of layers to store in VRAM\n");
-        printf("  -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
-        printf("                            how to split the model across multiple GPUs, one of:\n");
-        printf("                              - none: use one GPU only\n");
-        printf("                              - layer (default): split layers and KV across GPUs\n");
-        printf("                              - row: split rows across GPUs\n");
-        printf("  -ts SPLIT --tensor-split SPLIT\n");
-        printf("                            fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
-        printf("  -mg i, --main-gpu i       the GPU to use for the model (with split-mode = none),\n");
-        printf("                            or for intermediate results and KV (with split-mode = row)\n");
-        printf("  -nkvo, --no-kv-offload\n");
-        printf("                            disable KV offload\n");
-    }
-    printf("  -m FNAME, --model FNAME\n");
-    printf("                            model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH);
-    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
-    printf("                            model download url (default: unused)\n");
-    printf("  -hfr REPO, --hf-repo REPO\n");
-    printf("                            Hugging Face model repository (default: unused)\n");
-    printf("  -hff FILE, --hf-file FILE\n");
-    printf("                            Hugging Face model file (default: unused)\n");
-    printf("  -a ALIAS, --alias ALIAS\n");
-    printf("                            set an alias for the model, will be added as `model` field in completion response\n");
-    printf("  --lora FNAME              apply LoRA adapter (implies --no-mmap)\n");
-    printf("  --lora-base FNAME         optional model to use as a base for the layers modified by the LoRA adapter\n");
-    printf("  --host                    ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
-    printf("  --port PORT               port to listen (default  (default: %d)\n", sparams.port);
-    printf("  --rpc SERVERS             comma separated list of RPC servers\n");
-    printf("  --path PUBLIC_PATH        path from which to serve static files (default: disabled)\n");
-    printf("  --api-key API_KEY         optional api key to enhance server security. If set, requests must include this key for access.\n");
-    printf("  --api-key-file FNAME      path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-    printf("  --ssl-key-file FNAME      path to file a PEM-encoded SSL private key\n");
-    printf("  --ssl-cert-file FNAME     path to file a PEM-encoded SSL certificate\n");
-#endif
-    printf("  -to N, --timeout N        server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
-    printf("  --embeddings              enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
-    printf("  -np N, --parallel N       number of slots for process requests (default: %d)\n", params.n_parallel);
-    printf("  -cb, --cont-batching      enable continuous batching (a.k.a dynamic batching) (default: enabled)\n");
-    printf("  -fa, --flash-attn         enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
-    printf("  -spf FNAME, --system-prompt-file FNAME\n");
-    printf("                            set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
-    printf("  -ctk TYPE, --cache-type-k TYPE\n");
-    printf("                            KV cache data type for K (default: f16)\n");
-    printf("  -ctv TYPE, --cache-type-v TYPE\n");
-    printf("                            KV cache data type for V (default: f16)\n");
-    printf("  --log-format              log output format: json or text (default: json)\n");
-    printf("  --log-disable             disables logging to a file.\n");
-    printf("  --slots-endpoint-disable  disables slots monitoring endpoint.\n");
-    printf("  --metrics                 enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
-    printf("  --slot-save-path PATH     path to save slot kv cache (default: disabled)\n");
-    printf("\n");
-    printf("  -n, --n-predict           maximum tokens to predict (default: %d)\n", params.n_predict);
-    printf("  --override-kv KEY=TYPE:VALUE\n");
-    printf("                            advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                            types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
-    printf("  -gan N, --grp-attn-n N    set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
-    printf("  -gaw N, --grp-attn-w N    set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
-    printf("  --chat-template JINJA_TEMPLATE\n");
-    printf("                            set custom jinja chat template (default: template taken from model's metadata)\n");
-    printf("                            only commonly used templates are accepted:\n");
-    printf("                            https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n");
-    printf("\n");
-}
-
-static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) {
-    gpt_params    default_params;
-    server_params default_sparams;
-
-    std::string arg;
-    bool invalid_param = false;
-
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg == "--port") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.port = std::stoi(argv[i]);
-        } else if (arg == "--rpc") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rpc_servers = argv[i];
-        } else if (arg == "--host") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.hostname = argv[i];
-        } else if (arg == "--path") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.public_path = argv[i];
-        } else if (arg == "--api-key") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.api_keys.push_back(argv[i]);
-        } else if (arg == "--api-key-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream key_file(argv[i]);
-            if (!key_file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            std::string key;
-            while (std::getline(key_file, key)) {
-               if (key.size() > 0) {
-                   sparams.api_keys.push_back(key);
-               }
-            }
-            key_file.close();
-
-        }
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-        else if (arg == "--ssl-key-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.ssl_key_file = argv[i];
-        } else if (arg == "--ssl-cert-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.ssl_cert_file = argv[i];
-        }
-#endif
-        else if (arg == "--timeout" || arg == "-to") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.read_timeout = std::stoi(argv[i]);
-            sparams.write_timeout = std::stoi(argv[i]);
-        } else if (arg == "-m" || arg == "--model") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model = argv[i];
-        } else if (arg == "-mu" || arg == "--model-url") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model_url = argv[i];
-        } else if (arg == "-hfr" || arg == "--hf-repo") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.hf_repo = argv[i];
-        } else if (arg == "-hff" || arg == "--hf-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.hf_file = argv[i];
-        } else if (arg == "-a" || arg == "--alias") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model_alias = argv[i];
-        } else if (arg == "-h" || arg == "--help") {
-            server_print_usage(argv[0], default_params, default_sparams);
-            exit(0);
-        } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_ctx = std::stoi(argv[i]);
-        } else if (arg == "--rope-scaling") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::string value(argv[i]);
-            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
-            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
-            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
-            else { invalid_param = true; break; }
-        } else if (arg == "--rope-freq-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rope_freq_base = std::stof(argv[i]);
-        } else if (arg == "--rope-freq-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rope_freq_scale = std::stof(argv[i]);
-        } else if (arg == "--yarn-ext-factor") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_ext_factor = std::stof(argv[i]);
-        }
-        else if (arg == "--yarn-attn-factor") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_attn_factor = std::stof(argv[i]);
-        } else if (arg == "--yarn-beta-fast") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_beta_fast = std::stof(argv[i]);
-        } else if (arg == "--yarn-beta-slow") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_beta_slow = std::stof(argv[i]);
-        } else if (arg == "--pooling") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::string value(argv[i]);
-            /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
-            else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
-            else if (value == "cls")  { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
-            else { invalid_param = true; break; }
-        } else if (arg == "--defrag-thold" || arg == "-dt") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.defrag_thold = std::stof(argv[i]);
-        } else if (arg == "--threads" || arg == "-t") {
-            if (++i >= argc)
-            {
-                invalid_param = true;
-                break;
-            }
-            params.n_threads = std::stoi(argv[i]);
-        } else if (arg == "--grp-attn-n" || arg == "-gan") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-
-            params.grp_attn_n = std::stoi(argv[i]);
-        } else if (arg == "--grp-attn-w" || arg == "-gaw") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-
-            params.grp_attn_w = std::stoi(argv[i]);
-        } else if (arg == "--threads-batch" || arg == "-tb") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_threads_batch = std::stoi(argv[i]);
-        } else if (arg == "--threads-http") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.n_threads_http = std::stoi(argv[i]);
-        } else if (arg == "-b" || arg == "--batch-size") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_batch = std::stoi(argv[i]);
-        } else if (arg == "-ub" || arg == "--ubatch-size") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_ubatch = std::stoi(argv[i]);
-        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            if (llama_supports_gpu_offload()) {
-                params.n_gpu_layers = std::stoi(argv[i]);
-            } else {
-                LOG_WARNING(
-                    "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
-                    "See main README.md for information on enabling GPU BLAS support",
-                    {{"n_gpu_layers", params.n_gpu_layers}});
-            }
-        } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
-            params.no_kv_offload = true;
-        } else if (arg == "--split-mode" || arg == "-sm") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::string arg_next = argv[i];
-            if (arg_next == "none") {
-                params.split_mode = LLAMA_SPLIT_MODE_NONE;
-            } else if (arg_next == "layer") {
-                params.split_mode = LLAMA_SPLIT_MODE_LAYER;
-            } else if (arg_next == "row") {
-                params.split_mode = LLAMA_SPLIT_MODE_ROW;
-            } else {
-                invalid_param = true;
-                break;
-            }
-#ifndef GGML_USE_CUDA
-            fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n");
-#endif // GGML_USE_CUDA
-        } else if (arg == "--tensor-split" || arg == "-ts") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
-            std::string arg_next = argv[i];
-
-            // split string by , and /
-            const std::regex regex{R"([,/]+)"};
-            std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
-            std::vector<std::string> split_arg{it, {}};
-            GGML_ASSERT(split_arg.size() <= llama_max_devices());
-
-            for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) {
-                if (i_device < split_arg.size()) {
-                    params.tensor_split[i_device] = std::stof(split_arg[i_device]);
-                } else {
-                    params.tensor_split[i_device] = 0.0f;
-                }
-            }
-#else
-            LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {});
-#endif // GGML_USE_CUDA
-        } else if (arg == "--main-gpu" || arg == "-mg") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
-            params.main_gpu = std::stoi(argv[i]);
-#else
-            LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {});
-#endif
-        } else if (arg == "--lora") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_adapter.emplace_back(argv[i], 1.0f);
-            params.use_mmap = false;
-        } else if (arg == "--lora-scaled") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            const char * lora_adapter = argv[i];
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
-            params.use_mmap = false;
-        } else if (arg == "--lora-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_base = argv[i];
-        } else if (arg == "-v" || arg == "--verbose") {
-#if SERVER_VERBOSE != 1
-            LOG_WARNING("server.cpp is not built with verbose logging.", {});
-#else
-            server_verbose = true;
-#endif
-        } else if (arg == "--mlock") {
-            params.use_mlock = true;
-        } else if (arg == "--no-mmap") {
-            params.use_mmap = false;
-        } else if (arg == "--numa") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            } else {
-                std::string value(argv[i]);
-                /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
-                else if (value == "isolate")                    { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
-                else if (value == "numactl")                    { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
-                else { invalid_param = true; break; }
-            }
-        } else if (arg == "--embedding" || arg == "--embeddings") {
-            params.embedding = true;
-        } else if (arg == "-cb" || arg == "--cont-batching") {
-            params.cont_batching = true;
-        } else if (arg == "-fa" || arg == "--flash-attn") {
-            params.flash_attn = true;
-        } else if (arg == "-np" || arg == "--parallel") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_parallel = std::stoi(argv[i]);
-        } else if (arg == "-n" || arg == "--n-predict") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_predict = std::stoi(argv[i]);
-        } else if (arg == "-spf" || arg == "--system-prompt-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream file(argv[i]);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            std::string system_prompt;
-            std::copy(
-                std::istreambuf_iterator<char>(file),
-                std::istreambuf_iterator<char>(),
-                std::back_inserter(system_prompt)
-            );
-            sparams.system_prompt = system_prompt;
-        } else if (arg == "-ctk" || arg == "--cache-type-k") {
-            params.cache_type_k = argv[++i];
-        } else if (arg == "-ctv" || arg == "--cache-type-v") {
-            params.cache_type_v = argv[++i];
-        } else if (arg == "--log-format") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            if (std::strcmp(argv[i], "json") == 0) {
-                server_log_json = true;
-            } else if (std::strcmp(argv[i], "text") == 0) {
-                server_log_json = false;
-            } else {
-                invalid_param = true;
-                break;
-            }
-        } else if (arg == "--log-disable") {
-            log_set_target(stdout);
-            LOG_INFO("logging to file is disabled.", {});
-        } else if (arg == "--slots-endpoint-disable") {
-            sparams.slots_endpoint = false;
-        } else if (arg == "--metrics") {
-            sparams.metrics_endpoint = true;
-        } else if (arg == "--slot-save-path") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.slot_save_path = argv[i];
-            // if doesn't end with DIRECTORY_SEPARATOR, add it
-            if (!sparams.slot_save_path.empty() && sparams.slot_save_path[sparams.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
-                sparams.slot_save_path += DIRECTORY_SEPARATOR;
-            }
-        } else if (arg == "--chat-template") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            if (!verify_custom_template(argv[i])) {
-                fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
-                fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
-                invalid_param = true;
-                break;
-            }
-            sparams.chat_template = argv[i];
-        } else if (arg == "--override-kv") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            if (!parse_kv_override(argv[i], params.kv_overrides)) {
-                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-        } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            server_print_usage(argv[0], default_params, default_sparams);
-            exit(1);
-        }
-    }
-
-    gpt_params_handle_model_default(params);
-
-    if (!params.kv_overrides.empty()) {
-        params.kv_overrides.emplace_back();
-        params.kv_overrides.back().key[0] = 0;
-    }
-
-    if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
-        server_print_usage(argv[0], default_params, default_sparams);
-        exit(1);
-    }
-}
-
 static void log_server_request(const httplib::Request & req, const httplib::Response & res) {
     // skip GH copilot requests when using default port
     if (req.path == "/v1/health" || req.path == "/v1/completions") {
@@ -2919,16 +2498,22 @@ int main(int argc, char ** argv) {
     log_disable();
 #endif
     // own arguments required by this example
-    gpt_params    params;
-    server_params sparams;
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
+        return 1;
+    }
+
+    // TODO: not great to use extern vars
+    server_log_json = params.log_json;
+    server_verbose = params.verbosity > 0;
 
     // struct that contains llama context and inference
     server_context ctx_server;
 
-    server_params_parse(argc, argv, sparams, params);
-
-    if (!sparams.system_prompt.empty()) {
-        ctx_server.system_prompt_set(sparams.system_prompt);
+    if (!params.system_prompt.empty()) {
+        ctx_server.system_prompt_set(params.system_prompt);
     }
 
     if (params.model_alias == "unknown") {
@@ -2952,10 +2537,10 @@ int main(int argc, char ** argv) {
 
     std::unique_ptr<httplib::Server> svr;
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-    if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") {
-        LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}});
+    if (params.ssl_file_key != "" && params.ssl_file_cert != "") {
+        LOG_INFO("Running with SSL", {{"key", params.ssl_file_key}, {"cert", params.ssl_file_cert}});
         svr.reset(
-            new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str())
+            new httplib::SSLServer(params.ssl_file_cert.c_str(), params.ssl_file_key.c_str())
         );
     } else {
         LOG_INFO("Running without SSL", {});
@@ -3009,26 +2594,29 @@ int main(int argc, char ** argv) {
     });
 
     // set timeouts and change hostname and port
-    svr->set_read_timeout (sparams.read_timeout);
-    svr->set_write_timeout(sparams.write_timeout);
+    svr->set_read_timeout (params.timeout_read);
+    svr->set_write_timeout(params.timeout_write);
 
-    if (!svr->bind_to_port(sparams.hostname, sparams.port)) {
-        fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
+    if (!svr->bind_to_port(params.hostname, params.port)) {
+        fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", params.hostname.c_str(), params.port);
         return 1;
     }
 
     std::unordered_map<std::string, std::string> log_data;
 
-    log_data["hostname"] = sparams.hostname;
-    log_data["port"]     = std::to_string(sparams.port);
+    log_data["hostname"] = params.hostname;
+    log_data["port"]     = std::to_string(params.port);
 
-    if (sparams.api_keys.size() == 1) {
-        auto key = sparams.api_keys[0];
+    if (params.api_keys.size() == 1) {
+        auto key = params.api_keys[0];
         log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0));
-    } else if (sparams.api_keys.size() > 1) {
-        log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded";
+    } else if (params.api_keys.size() > 1) {
+        log_data["api_key"] = "api_key: " + std::to_string(params.api_keys.size()) + " keys loaded";
     }
 
+    // Necessary similarity of prompt for slot selection
+    ctx_server.slot_prompt_similarity = params.slot_prompt_similarity;
+
     // load the model
     if (!ctx_server.load_model(params)) {
         state.store(SERVER_STATE_ERROR);
@@ -3043,26 +2631,18 @@ int main(int argc, char ** argv) {
     const auto model_meta = ctx_server.model_meta();
 
     // if a custom chat template is not supplied, we will use the one that comes with the model (if any)
-    if (sparams.chat_template.empty()) {
+    if (params.chat_template.empty()) {
         if (!ctx_server.validate_model_chat_template()) {
-            LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
-            sparams.chat_template = "chatml";
+            LOG_WARNING("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
+            params.chat_template = "chatml";
         }
     }
 
     // print sample chat example to make it clear which template is used
     {
-        json chat;
-        chat.push_back({{"role", "system"},    {"content", "You are a helpful assistant"}});
-        chat.push_back({{"role", "user"},      {"content", "Hello"}});
-        chat.push_back({{"role", "assistant"}, {"content", "Hi there"}});
-        chat.push_back({{"role", "user"},      {"content", "How are you?"}});
-
-        const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat);
-
         LOG_INFO("chat template", {
-            {"chat_example", chat_example},
-            {"built_in", sparams.chat_template.empty()},
+            {"chat_example", llama_chat_format_example(ctx_server.model, params.chat_template)},
+            {"built_in",     params.chat_template.empty()},
         });
     }
 
@@ -3070,7 +2650,7 @@ int main(int argc, char ** argv) {
     // Middlewares
     //
 
-    auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
+    auto middleware_validate_api_key = [&params, &res_error](const httplib::Request & req, httplib::Response & res) {
         // TODO: should we apply API key to all endpoints, including "/health" and "/models"?
         static const std::set<std::string> protected_endpoints = {
             "/props",
@@ -3088,7 +2668,7 @@ int main(int argc, char ** argv) {
         };
 
         // If API key is not set, skip validation
-        if (sparams.api_keys.empty()) {
+        if (params.api_keys.empty()) {
             return true;
         }
 
@@ -3103,7 +2683,7 @@ int main(int argc, char ** argv) {
         std::string prefix = "Bearer ";
         if (auth_header.substr(0, prefix.size()) == prefix) {
             std::string received_api_key = auth_header.substr(prefix.size());
-            if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) {
+            if (std::find(params.api_keys.begin(), params.api_keys.end(), received_api_key) != params.api_keys.end()) {
                 return true; // API key is valid
             }
         }
@@ -3158,7 +2738,7 @@ int main(int argc, char ** argv) {
                     };
 
                     res.status = 200; // HTTP OK
-                    if (sparams.slots_endpoint && req.has_param("include_slots")) {
+                    if (params.endpoint_slots && req.has_param("include_slots")) {
                         health["slots"] = result.data.at("slots");
                     }
 
@@ -3184,7 +2764,7 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) {
-        if (!sparams.slots_endpoint) {
+        if (!params.endpoint_slots) {
             res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
@@ -3208,7 +2788,7 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) {
-        if (!sparams.metrics_endpoint) {
+        if (!params.endpoint_metrics) {
             res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
@@ -3308,14 +2888,14 @@ int main(int argc, char ** argv) {
         res.status = 200; // HTTP OK
     };
 
-    const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
+    const auto handle_slots_save = [&ctx_server, &res_error, &params](const httplib::Request & req, httplib::Response & res, int id_slot) {
         json request_data = json::parse(req.body);
         std::string filename = request_data.at("filename");
-        if (!validate_file_name(filename)) {
+        if (!fs_validate_filename(filename)) {
             res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
-        std::string filepath = sparams.slot_save_path + filename;
+        std::string filepath = params.slot_save_path + filename;
 
         server_task task;
         task.type = SERVER_TASK_TYPE_SLOT_SAVE;
@@ -3338,14 +2918,14 @@ int main(int argc, char ** argv) {
         }
     };
 
-    const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
+    const auto handle_slots_restore = [&ctx_server, &res_error, &params](const httplib::Request & req, httplib::Response & res, int id_slot) {
         json request_data = json::parse(req.body);
         std::string filename = request_data.at("filename");
-        if (!validate_file_name(filename)) {
+        if (!fs_validate_filename(filename)) {
             res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
-        std::string filepath = sparams.slot_save_path + filename;
+        std::string filepath = params.slot_save_path + filename;
 
         server_task task;
         task.type = SERVER_TASK_TYPE_SLOT_RESTORE;
@@ -3415,17 +2995,31 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+        std::string template_key = "tokenizer.chat_template", curr_tmpl;
+        int32_t tlen = llama_model_meta_val_str(ctx_server.model, template_key.c_str(), nullptr, 0);
+        if (tlen > 0) {
+            std::vector<char> curr_tmpl_buf(tlen + 1, 0);
+            if (llama_model_meta_val_str(ctx_server.model, template_key.c_str(), curr_tmpl_buf.data(), curr_tmpl_buf.size()) == tlen) {
+                curr_tmpl = std::string(curr_tmpl_buf.data(), tlen);
+            }
+        }
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
         json data = {
             { "system_prompt",               ctx_server.system_prompt.c_str() },
             { "default_generation_settings", ctx_server.default_generation_settings_for_props },
-            { "total_slots",                 ctx_server.params.n_parallel }
+            { "total_slots",                 ctx_server.params.n_parallel },
+            { "chat_template",               curr_tmpl.c_str() }
         };
 
         res.set_content(data.dump(), "application/json; charset=utf-8");
     };
 
     const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        if (ctx_server.params.embedding) {
+            res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
 
         json data = json::parse(req.body);
@@ -3520,9 +3114,14 @@ int main(int argc, char ** argv) {
         res.set_content(models.dump(), "application/json; charset=utf-8");
     };
 
-    const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_chat_completions = [&ctx_server, &params, &res_error](const httplib::Request & req, httplib::Response & res) {
+        if (ctx_server.params.embedding) {
+            res_error(res, format_error_response("This server does not support chat completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-        json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template);
+        json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template);
 
         const int id_task = ctx_server.queue_tasks.get_new_id();
 
@@ -3593,6 +3192,11 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        if (ctx_server.params.embedding) {
+            res_error(res, format_error_response("This server does not support infill. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
 
         json data = json::parse(req.body);
@@ -3679,13 +3283,8 @@ int main(int argc, char ** argv) {
         return res.set_content(data.dump(), "application/json; charset=utf-8");
     };
 
-    const auto handle_embeddings = [&params, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_embeddings = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-        if (!params.embedding) {
-            res.status = 501;
-            res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
-            return;
-        }
 
         const json body = json::parse(req.body);
         bool is_openai = false;
@@ -3735,6 +3334,55 @@ int main(int argc, char ** argv) {
         return res.set_content(root.dump(), "application/json; charset=utf-8");
     };
 
+    const auto handle_lora_adapters_list = [&](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        json result = json::array();
+        for (size_t i = 0; i < ctx_server.lora_adapters.size(); ++i) {
+            auto & la = ctx_server.lora_adapters[i];
+            result.push_back({
+                {"id", i},
+                {"path", la.path},
+                {"scale", la.scale},
+            });
+        }
+        res.set_content(result.dump(), "application/json");
+        res.status = 200; // HTTP OK
+    };
+
+    const auto handle_lora_adapters_apply = [&](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        const std::vector<json> body = json::parse(req.body);
+        int max_idx = ctx_server.lora_adapters.size();
+
+        // clear existing value
+        for (auto & la : ctx_server.lora_adapters) {
+            la.scale = 0.0f;
+        }
+
+        // set value
+        for (auto entry : body) {
+            int id      = entry.at("id");
+            float scale = entry.at("scale");
+            if (0 <= id && id < max_idx) {
+                ctx_server.lora_adapters[id].scale = scale;
+            } else {
+                throw std::runtime_error("invalid adapter id");
+            }
+        }
+
+        server_task task;
+        task.type = SERVER_TASK_TYPE_SET_LORA;
+        const int id_task = ctx_server.queue_tasks.post(task);
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+
+        server_task_result result = ctx_server.queue_results.recv(id_task);
+        ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+        res.set_content(result.data.dump(), "application/json");
+        res.status = 200; // HTTP OK
+    };
+
     auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) {
         return [content, len, mime_type](const httplib::Request &, httplib::Response & res) {
             res.set_content(reinterpret_cast<const char*>(content), len, mime_type);
@@ -3747,21 +3395,32 @@ int main(int argc, char ** argv) {
     //
 
     // register static assets routes
-    if (!sparams.public_path.empty()) {
+    if (!params.public_path.empty()) {
         // Set the base directory for serving static files
-        svr->set_base_dir(sparams.public_path);
+        svr->set_base_dir(params.public_path);
     }
 
     // using embedded static files
-    svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
-    svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
-    svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8"));
-    svr->Get("/json-schema-to-grammar.mjs", handle_static_file(
-        json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8"));
+    svr->Get("/",                           handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
+    svr->Get("/index.js",                   handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
+    svr->Get("/completion.js",              handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8"));
+    svr->Get("/json-schema-to-grammar.mjs", handle_static_file(json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8"));
+
+    // add new-ui files
+    svr->Get("/colorthemes.css",       handle_static_file(colorthemes_css, colorthemes_css_len, "text/css; charset=utf-8"));
+    svr->Get("/style.css",             handle_static_file(style_css, style_css_len, "text/css; charset=utf-8"));
+    svr->Get("/theme-beeninorder.css", handle_static_file(theme_beeninorder_css, theme_beeninorder_css_len, "text/css; charset=utf-8"));
+    svr->Get("/theme-ketivah.css",     handle_static_file(theme_ketivah_css, theme_ketivah_css_len, "text/css; charset=utf-8"));
+    svr->Get("/theme-mangotango.css",  handle_static_file(theme_mangotango_css, theme_mangotango_css_len, "text/css; charset=utf-8"));
+    svr->Get("/theme-playground.css",  handle_static_file(theme_playground_css, theme_playground_css_len, "text/css; charset=utf-8"));
+    svr->Get("/theme-polarnight.css",  handle_static_file(theme_polarnight_css, theme_polarnight_css_len, "text/css; charset=utf-8"));
+    svr->Get("/theme-snowstorm.css",   handle_static_file(theme_snowstorm_css, theme_snowstorm_css_len, "text/css; charset=utf-8"));
+    svr->Get("/index-new.html",        handle_static_file(index_new_html, index_new_html_len, "text/html; charset=utf-8"));
+    svr->Get("/system-prompts.js",     handle_static_file(system_prompts_js, system_prompts_js_len, "text/javascript; charset=utf-8"));
+    svr->Get("/prompt-formats.js",     handle_static_file(prompt_formats_js, prompt_formats_js_len, "text/javascript; charset=utf-8"));
 
     // register API routes
     svr->Get ("/health",              handle_health);
-    svr->Get ("/slots",               handle_slots);
     svr->Get ("/metrics",             handle_metrics);
     svr->Get ("/props",               handle_props);
     svr->Get ("/v1/models",           handle_models);
@@ -3776,7 +3435,12 @@ int main(int argc, char ** argv) {
     svr->Post("/v1/embeddings",       handle_embeddings);
     svr->Post("/tokenize",            handle_tokenize);
     svr->Post("/detokenize",          handle_detokenize);
-    if (!sparams.slot_save_path.empty()) {
+    // LoRA adapters hotswap
+    svr->Get ("/lora-adapters",       handle_lora_adapters_list);
+    svr->Post("/lora-adapters",       handle_lora_adapters_apply);
+    // Save & load slots
+    svr->Get ("/slots",               handle_slots);
+    if (!params.slot_save_path.empty()) {
         // only enable slot endpoints if slot_save_path is set
         svr->Post("/slots/:id_slot",  handle_slots_action);
     }
@@ -3784,12 +3448,12 @@ int main(int argc, char ** argv) {
     //
     // Start the server
     //
-    if (sparams.n_threads_http < 1) {
+    if (params.n_threads_http < 1) {
         // +2 threads for monitoring endpoints
-        sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
+        params.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
     }
-    log_data["n_threads_http"] =  std::to_string(sparams.n_threads_http);
-    svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); };
+    log_data["n_threads_http"] =  std::to_string(params.n_threads_http);
+    svr->new_task_queue = [&params] { return new httplib::ThreadPool(params.n_threads_http); };
 
     LOG_INFO("HTTP server listening", log_data);
 
diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md
index 83c0208f36c09..5e6cb277bc813 100644
--- a/examples/server/tests/README.md
+++ b/examples/server/tests/README.md
@@ -27,10 +27,8 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`.
 
 ```shell
 cd ../../..
-mkdir build
-cd build
-cmake -DLLAMA_CURL=ON ../
-cmake --build . --target server
+cmake -B build -DLLAMA_CURL=ON
+cmake --build build --target llama-server
 ```
 
 2. Start the test: `./tests.sh`
@@ -40,7 +38,7 @@ It's possible to override some scenario steps values with environment variables:
 | variable                 | description                                                                                    |
 |--------------------------|------------------------------------------------------------------------------------------------|
 | `PORT`                   | `context.server_port` to set the listening port of the server during scenario, default: `8080` |
-| `LLAMA_SERVER_BIN_PATH`  | to change the server binary path, default: `../../../build/bin/server`                         |
+| `LLAMA_SERVER_BIN_PATH`  | to change the server binary path, default: `../../../build/bin/llama-server`                         |
 | `DEBUG`                  | "ON" to enable steps and server verbose mode `--verbose`                                       |
 | `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format                                                       |
 | `N_GPU_LAYERS`           | number of model layers to offload to VRAM `-ngl --n-gpu-layers`                                |
diff --git a/examples/server/tests/features/lora.feature b/examples/server/tests/features/lora.feature
new file mode 100644
index 0000000000000..7b85988ac6e87
--- /dev/null
+++ b/examples/server/tests/features/lora.feature
@@ -0,0 +1,36 @@
+@llama.cpp
+@lora
+Feature: llama.cpp server
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model url https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/stories15M_MOE-F16.gguf
+    And   a model file stories15M_MOE-F16.gguf
+    And   a model alias stories15M_MOE
+    And   a lora adapter file from https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf
+    And   42 as server seed
+    And   1024 as batch size
+    And   1024 as ubatch size
+    And   2048 KV cache size
+    And   64 max tokens to predict
+    And   0.0 temperature
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario: Completion LoRA disabled
+    Given switch off lora adapter 0
+    Given a prompt:
+    """
+    Look in thy glass
+    """
+    And   a completion request with no api error
+    Then  64 tokens are predicted matching little|girl|three|years|old
+
+  Scenario: Completion LoRA enabled
+    Given switch on lora adapter 0
+    Given a prompt:
+    """
+    Look in thy glass
+    """
+    And   a completion request with no api error
+    Then  64 tokens are predicted matching eye|love|glass|sun
diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature
index 1bde7aab8bab0..6a5a84e6a1941 100644
--- a/examples/server/tests/features/passkey.feature
+++ b/examples/server/tests/features/passkey.feature
@@ -52,4 +52,3 @@ Feature: Passkey / Self-extend with context shift
       #| TheBloke/Llama-2-7B-GGUF        | llama-2-7b.Q2_K.gguf        | 4096        | 3   | 16384 | 512     | 4    | 512    | 500    | 300   | 1234    | 5           | 1234           |
       #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768       | 2   | 16384 | 512     | 4    | 512    | 500    | 100   | 0987    | 5           | 0
       # 987           |
-
diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature
index 5deb278c2a53c..e8e1b54147b05 100644
--- a/examples/server/tests/features/results.feature
+++ b/examples/server/tests/features/results.feature
@@ -13,6 +13,7 @@ Feature: Results
 
   Scenario Outline: consistent results with same seed
     Given <n_slots> slots
+    And   1.0 temperature
     Then  the server is starting
     Then  the server is healthy
 
@@ -26,10 +27,12 @@ Feature: Results
     Examples:
       | n_slots |
       | 1       |
-      | 2       |
+      # FIXME: unified KV cache nondeterminism
+      # | 2       |
 
   Scenario Outline: different results with different seed
     Given <n_slots> slots
+    And   1.0 temperature
     Then  the server is starting
     Then  the server is healthy
 
@@ -71,14 +74,13 @@ Feature: Results
     Examples:
       | n_parallel | temp |
       | 1          | 0.0  |
-      | 2          | 0.0  |
-      | 4          | 0.0  |
       | 1          | 1.0  |
-      # FIXME: These tests fail on master.
-      # Problems: unified KV cache (except for CPU backend with LLAMA_NO_LLAMAFILE=1), SIMD nondeterminism.
+      # FIXME: unified KV cache nondeterminism
       # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227
       # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574
       # and https://github.com/ggerganov/llama.cpp/pull/7347 .
+      # | 2          | 0.0  |
+      # | 4          | 0.0  |
       # | 2          | 1.0  |
       # | 4          | 1.0  |
 
@@ -106,12 +108,11 @@ Feature: Results
     Examples:
       | n_slots | n_kv | n_predict | n_parallel |
       | 4       | 1024 | 1         | 1          |
-      | 4       | 1024 | 1         | 4          |
-      # FIXME: These tests fail on master.
-      # Problems: unified KV cache (except for CPU backend with LLAMA_NO_LLAMAFILE=1), SIMD nondeterminism.
+      # FIXME: unified KV cache nondeterminism
       # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227
       # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574
       # and https://github.com/ggerganov/llama.cpp/pull/7347 .
+      # | 4       | 1024 | 1         | 4          |
       # | 4       | 1024 | 100       | 1          |
       # This test still fails even the above patches; the first token probabilities are already different.
       # | 4       | 1024 | 100       | 4          |
diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature
index d21c09135243a..b55971454afc3 100644
--- a/examples/server/tests/features/server.feature
+++ b/examples/server/tests/features/server.feature
@@ -82,7 +82,7 @@ Feature: llama.cpp server
 
     Examples: Prompts
       | response_format                                                     | n_predicted | re_content             |
-      | {"type": "json_object", "schema": {"const": "42"}}                  | 5           | "42"                   |
+      | {"type": "json_object", "schema": {"const": "42"}}                  | 6           | "42"                   |
       | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10          | \[ -300 \]             |
       | {"type": "json_object"}                                             | 10          | \{ " Jacky.            |
 
diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py
index 7da503f2c4b34..6705a34fc4696 100644
--- a/examples/server/tests/features/steps/steps.py
+++ b/examples/server/tests/features/steps/steps.py
@@ -1,5 +1,4 @@
 import asyncio
-import collections
 import json
 import os
 import re
@@ -8,19 +7,24 @@
 import sys
 import threading
 import time
+import requests
+from collections.abc import Sequence
 from contextlib import closing
 from re import RegexFlag
+from typing import Any, Literal, cast
 
 import aiohttp
 import numpy as np
 import openai
-from behave import step
+from openai.types.chat import ChatCompletionChunk
+from behave import step  # pyright: ignore[reportAttributeAccessIssue]
 from behave.api.async_step import async_run_until_complete
 from prometheus_client import parser
 
+# pyright: reportRedeclaration=false
 
 @step("a server listening on {server_fqdn}:{server_port}")
-def step_server_config(context, server_fqdn, server_port):
+def step_server_config(context, server_fqdn: str, server_port: str):
     context.server_fqdn = server_fqdn
     context.server_port = int(server_port)
     context.n_threads = None
@@ -67,6 +71,7 @@ def step_server_config(context, server_fqdn, server_port):
     context.user_api_key = None
     context.response_format = None
     context.temperature = None
+    context.lora_file = None
 
     context.tasks_result = []
     context.concurrent_tasks = []
@@ -74,34 +79,40 @@ def step_server_config(context, server_fqdn, server_port):
 
 
 @step('a model file {hf_file} from HF repo {hf_repo}')
-def step_download_hf_model(context, hf_file, hf_repo):
+def step_download_hf_model(context, hf_file: str, hf_repo: str):
     context.model_hf_repo = hf_repo
     context.model_hf_file = hf_file
     context.model_file = os.path.basename(hf_file)
 
+@step('a lora adapter file from {lora_file_url}')
+def step_download_lora_file(context, lora_file_url: str):
+    file_name = lora_file_url.split('/').pop()
+    context.lora_file = f'../../../{file_name}'
+    with open(context.lora_file, 'wb') as f:
+        f.write(requests.get(lora_file_url).content)
 
 @step('a model file {model_file}')
-def step_model_file(context, model_file):
+def step_model_file(context, model_file: str):
     context.model_file = model_file
 
 
 @step('a model url {model_url}')
-def step_model_url(context, model_url):
+def step_model_url(context, model_url: str):
     context.model_url = model_url
 
 
 @step('a model alias {model_alias}')
-def step_model_alias(context, model_alias):
+def step_model_alias(context, model_alias: str):
     context.model_alias = model_alias
 
 
 @step('{seed:d} as server seed')
-def step_seed(context, seed):
+def step_seed(context, seed: int):
     context.server_seed = seed
 
 
 @step('{ngl:d} GPU offloaded layers')
-def step_n_gpu_layer(context, ngl):
+def step_n_gpu_layer(context, ngl: int):
     if 'N_GPU_LAYERS' in os.environ:
         new_ngl = int(os.environ['N_GPU_LAYERS'])
         if context.debug:
@@ -111,37 +122,37 @@ def step_n_gpu_layer(context, ngl):
 
 
 @step('{n_threads:d} threads')
-def step_n_threads(context, n_threads):
+def step_n_threads(context, n_threads: int):
     context.n_thread = n_threads
 
 
 @step('{draft:d} as draft')
-def step_draft(context, draft):
+def step_draft(context, draft: int):
     context.draft = draft
 
 
 @step('{n_ctx:d} KV cache size')
-def step_n_ctx(context, n_ctx):
+def step_n_ctx(context, n_ctx: int):
     context.n_ctx = n_ctx
 
 
 @step('{n_slots:d} slots')
-def step_n_slots(context, n_slots):
+def step_n_slots(context, n_slots: int):
     context.n_slots = n_slots
 
 
 @step('{n_predict:d} server max tokens to predict')
-def step_server_n_predict(context, n_predict):
+def step_server_n_predict(context, n_predict: int):
     context.n_server_predict = n_predict
 
 
 @step('{slot_save_path} as slot save path')
-def step_slot_save_path(context, slot_save_path):
+def step_slot_save_path(context, slot_save_path: str):
     context.slot_save_path = slot_save_path
 
 
 @step('using slot id {id_slot:d}')
-def step_id_slot(context, id_slot):
+def step_id_slot(context, id_slot: int):
     context.id_slot = id_slot
 
 
@@ -191,7 +202,7 @@ def step_start_server(context):
 
 @step("the server is {expecting_status}")
 @async_run_until_complete
-async def step_wait_for_the_server_to_be_started(context, expecting_status):
+async def step_wait_for_the_server_to_be_started(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str):
     match expecting_status:
         case 'healthy':
             await wait_for_health_status(context, context.base_url, 200, 'ok',
@@ -199,7 +210,7 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
 
         case 'ready' | 'idle':
             await wait_for_health_status(context, context.base_url, 200, 'ok',
-                                         timeout=10,
+                                         timeout=30,
                                          params={'fail_on_no_slot': 0, 'include_slots': 0},
                                          slots_idle=context.n_slots,
                                          slots_processing=0,
@@ -221,7 +232,7 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
 
 @step('all slots are {expected_slot_status_string}')
 @async_run_until_complete
-async def step_all_slots_status(context, expected_slot_status_string):
+async def step_all_slots_status(context, expected_slot_status_string: Literal['idle', 'busy'] | str):
     match expected_slot_status_string:
         case 'idle':
             expected_slot_status = 0
@@ -237,7 +248,7 @@ async def step_all_slots_status(context, expected_slot_status_string):
 
 @step('a completion request with {api_error} api error')
 @async_run_until_complete
-async def step_request_completion(context, api_error):
+async def step_request_completion(context, api_error: Literal['raised'] | str):
     expect_api_error = api_error == 'raised'
     seeds = await completions_seed(context, num_seeds=1)
     completion = await request_completion(context.prompts.pop(),
@@ -777,8 +788,8 @@ def step_assert_metric_value(context, metric_name, metric_value):
 def step_available_models(context):
     # openai client always expects an api_key
     openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope'
-    openai.api_base = f'{context.base_url}/v1'
-    context.models = openai.Model.list().data
+    openai.base_url = f'{context.base_url}/v1/'
+    context.models = openai.models.list().data
 
 
 @step('{n_model:d} models are supported')
@@ -789,7 +800,7 @@ def step_supported_models(context, n_model):
 
 
 @step('model {i_model:d} is {param} {preposition} {param_value}')
-def step_supported_models(context, i_model, param, preposition, param_value):
+def step_supported_models(context, i_model: int, param: Literal['identified', 'trained'] | str, preposition: str, param_value: str):
     assert i_model < len(context.models)
     model = context.models[i_model]
 
@@ -798,7 +809,7 @@ def step_supported_models(context, i_model, param, preposition, param_value):
         case 'identified':
             value = model.id
         case 'trained':
-            value = str(model.meta.n_ctx_train)
+            value = str(model.meta["n_ctx_train"])
         case _:
             assert False, "param {param} not supported"
     assert param_value == value, f"model param {param} {value} != {param_value}"
@@ -810,6 +821,7 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
         print(f"starting {context.n_prompts} concurrent completion requests...")
     assert context.n_prompts > 0
     seeds = await completions_seed(context)
+    assert seeds is not None
     for prompt_no in range(context.n_prompts):
         shifted_args = [context.prompts.pop(), seeds[prompt_no], *args]
         context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
@@ -845,6 +857,17 @@ async def step_erase_slot(context, slot_id):
             context.response = response
 
 
+@step('switch {on_or_off} lora adapter {lora_id:d}')
+@async_run_until_complete
+async def toggle_lora_adapter(context, on_or_off: str, lora_id: int):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/lora-adapters',
+                                json=[{'id': lora_id, 'scale': 1 if on_or_off == 'on' else 0}],
+                                headers={"Content-Type": "application/json"}) as response:
+            context.response = response
+            print([{'id': lora_id, 'scale': 1 if on_or_off == 'on' else 0}])
+
+
 @step('the server responds with status code {status_code:d}')
 def step_server_responds_with_status_code(context, status_code):
     assert context.response.status == status_code
@@ -861,7 +884,7 @@ async def request_completion(prompt,
                              id_slot=None,
                              expect_api_error=None,
                              user_api_key=None,
-                             temperature=None):
+                             temperature=None) -> int | dict[str, Any]:
     if debug:
         print(f"Sending completion request: {prompt}")
     origin = "my.super.domain"
@@ -883,7 +906,7 @@ async def request_completion(prompt,
                                     "cache_prompt": cache_prompt,
                                     "id_slot": id_slot,
                                     "seed": seed if seed is not None else 42,
-                                    "temperature": temperature if temperature is not None else "0.8f",
+                                    "temperature": temperature if temperature is not None else 0.8,
                                     "n_probs": 2,
                                 },
                                 headers=headers,
@@ -899,8 +922,8 @@ async def request_completion(prompt,
 async def oai_chat_completions(user_prompt,
                                seed,
                                system_prompt,
-                               base_url,
-                               base_path,
+                               base_url: str,
+                               base_path: str,
                                async_client,
                                debug=False,
                                temperature=None,
@@ -909,7 +932,7 @@ async def oai_chat_completions(user_prompt,
                                enable_streaming=None,
                                response_format=None,
                                user_api_key=None,
-                               expect_api_error=None):
+                               expect_api_error=None) -> int | dict[str, Any]:
     if debug:
         print(f"Sending OAI Chat completions request: {user_prompt}")
     # openai client always expects an api key
@@ -989,32 +1012,35 @@ async def oai_chat_completions(user_prompt,
     else:
         try:
             openai.api_key = user_api_key
-            openai.api_base = f'{base_url}{base_path}'
-            chat_completion = openai.Completion.create(
+            openai.base_url = f'{base_url}{base_path.removesuffix("chat")}'
+            assert model is not None
+            chat_completion = openai.chat.completions.create(
                 messages=payload['messages'],
                 model=model,
                 max_tokens=n_predict,
                 stream=enable_streaming,
-                response_format=payload.get('response_format'),
+                response_format=payload.get('response_format') or openai.NOT_GIVEN,
                 seed=seed,
                 temperature=payload['temperature']
             )
-        except openai.error.AuthenticationError as e:
+        except openai.AuthenticationError as e:
             if expect_api_error is not None and expect_api_error:
                 return 401
             else:
                 assert False, f'error raised: {e}'
 
         if enable_streaming:
+            chat_completion = cast(openai.Stream[ChatCompletionChunk], chat_completion)
             for chunk in chat_completion:
                 assert len(chunk.choices) == 1
                 delta = chunk.choices[0].delta
-                if 'content' in delta:
-                    completion_response['content'] += delta['content']
+                if delta.content is not None:
+                    completion_response['content'] += delta.content
                     completion_response['timings']['predicted_n'] += 1
                 completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
         else:
             assert len(chat_completion.choices) == 1
+            assert chat_completion.usage is not None
             completion_response = {
                 'content': chat_completion.choices[0].message.content,
                 'timings': {
@@ -1028,7 +1054,7 @@ async def oai_chat_completions(user_prompt,
     return completion_response
 
 
-async def request_embedding(content, seed, base_url=None):
+async def request_embedding(content, seed, base_url=None) -> list[list[float]]:
     async with aiohttp.ClientSession() as session:
         async with session.post(f'{base_url}/embedding',
                                 json={
@@ -1041,7 +1067,7 @@ async def request_embedding(content, seed, base_url=None):
 
 async def request_oai_embeddings(input, seed,
                                  base_url=None, user_api_key=None,
-                                 model=None, async_client=False):
+                                 model=None, async_client=False) -> list[list[float]]:
     # openai client always expects an api_key
     user_api_key = user_api_key if user_api_key is not None else 'nope'
     if async_client:
@@ -1063,7 +1089,7 @@ async def request_oai_embeddings(input, seed,
                 response_json = await response.json()
                 assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
                 assert response_json['object'] == 'list'
-                if isinstance(input, collections.abc.Sequence):
+                if isinstance(input, Sequence):
                     embeddings = []
                     for an_oai_embeddings in response_json['data']:
                         embeddings.append(an_oai_embeddings['embedding'])
@@ -1072,19 +1098,14 @@ async def request_oai_embeddings(input, seed,
                 return embeddings
     else:
         openai.api_key = user_api_key
-        openai.api_base = f'{base_url}/v1'
-        oai_embeddings = openai.Embedding.create(
+        openai.base_url = f'{base_url}/v1/'
+        assert model is not None
+        oai_embeddings = openai.embeddings.create(
             model=model,
             input=input,
         )
 
-        if isinstance(input, collections.abc.Sequence):
-            embeddings = []
-            for an_oai_embeddings in oai_embeddings.data:
-                embeddings.append(an_oai_embeddings.embedding)
-        else:
-            embeddings = [oai_embeddings.data.embedding]
-        return embeddings
+        return [e.embedding for e in oai_embeddings.data]
 
 
 def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None):
@@ -1122,7 +1143,7 @@ def assert_all_predictions_equal(completion_responses):
             if i == j:
                 continue
             content_j = response_j['content']
-        assert content_i == content_j, "contents not equal"
+            assert content_i == content_j, "contents not equal"
 
 
 def assert_all_predictions_different(completion_responses):
@@ -1136,7 +1157,7 @@ def assert_all_predictions_different(completion_responses):
             if i == j:
                 continue
             content_j = response_j['content']
-        assert content_i != content_j, "contents not different"
+            assert content_i != content_j, "contents not different"
 
 
 def assert_all_token_probabilities_equal(completion_responses):
@@ -1153,7 +1174,7 @@ def assert_all_token_probabilities_equal(completion_responses):
                 if i == j:
                     continue
                 probs_j = response_j['completion_probabilities'][pos]['probs']
-            assert probs_i == probs_j, "contents not equal"
+                assert probs_i == probs_j, "contents not equal"
 
 
 async def gather_tasks_results(context):
@@ -1272,9 +1293,9 @@ def context_text(context):
 
 def start_server_background(context):
     if os.name == 'nt':
-        context.server_path = '../../../build/bin/Release/server.exe'
+        context.server_path = '../../../build/bin/Release/llama-server.exe'
     else:
-        context.server_path = '../../../build/bin/server'
+        context.server_path = '../../../build/bin/llama-server'
     if 'LLAMA_SERVER_BIN_PATH' in os.environ:
         context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
     server_listen_addr = context.server_fqdn
@@ -1324,6 +1345,8 @@ def start_server_background(context):
         server_args.extend(['--grp-attn-w', context.n_ga_w])
     if context.debug:
         server_args.append('--verbose')
+    if context.lora_file:
+        server_args.extend(['--lora', context.lora_file])
     if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
         server_args.extend(['--log-format', "text"])
 
@@ -1343,7 +1366,7 @@ def start_server_background(context):
     }
     context.server_process = subprocess.Popen(
         [str(arg) for arg in [context.server_path, *server_args]],
-        **pkwargs)
+        **pkwargs)  # pyright: ignore[reportArgumentType, reportCallIssue]
 
     def server_log(in_stream, out_stream):
         for line in iter(in_stream.readline, b''):
diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt
index 2e4f42ad28c23..f2d7e5c5731be 100644
--- a/examples/server/tests/requirements.txt
+++ b/examples/server/tests/requirements.txt
@@ -1,6 +1,7 @@
 aiohttp~=3.9.3
 behave~=1.2.6
 huggingface_hub~=0.20.3
-numpy~=1.24.4
-openai~=0.25.0
+numpy~=1.26.4
+openai~=1.30.3
 prometheus-client~=0.20.0
+requests~=2.32.3
diff --git a/examples/server/themes/buttons-top/index.html b/examples/server/themes/buttons-top/index.html
index 6af30d307a4b5..8334bcde5049c 100644
--- a/examples/server/themes/buttons-top/index.html
+++ b/examples/server/themes/buttons-top/index.html
@@ -1054,4 +1054,3 @@ <h1>llama.cpp</h1>
 </body>
 
 </html>
-
diff --git a/examples/server/themes/wild/index.html b/examples/server/themes/wild/index.html
index 772e716cdb2e0..8361c577494d7 100644
--- a/examples/server/themes/wild/index.html
+++ b/examples/server/themes/wild/index.html
@@ -1058,4 +1058,3 @@
 </body>
 
 </html>
-
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index d8a2286e4b1df..e6a1f069723ec 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -116,45 +116,37 @@ static inline void server_log(const char * level, const char * function, int lin
 // chat template utils
 //
 
-// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
-inline bool verify_custom_template(const std::string & tmpl) {
-    llama_chat_message chat[] = {{"user", "test"}};
-    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
-    return res >= 0;
-}
-
 // Format given chat. If tmpl is empty, we take the template from model metadata
 inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
-    size_t alloc_size = 0;
-    // vector holding all allocated string to be passed to llama_chat_apply_template
-    std::vector<std::string> str(messages.size() * 2);
-    std::vector<llama_chat_message> chat(messages.size());
+    std::vector<llama_chat_msg> chat;
 
     for (size_t i = 0; i < messages.size(); ++i) {
         const auto & curr_msg = messages[i];
-        str[i*2 + 0]    = json_value(curr_msg, "role",    std::string(""));
-        str[i*2 + 1]    = json_value(curr_msg, "content", std::string(""));
-        alloc_size     += str[i*2 + 1].length();
-        chat[i].role    = str[i*2 + 0].c_str();
-        chat[i].content = str[i*2 + 1].c_str();
-    }
 
-    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
-    std::vector<char> buf(alloc_size * 2);
-
-    // run the first time to get the total output length
-    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
+        std::string role = json_value(curr_msg, "role", std::string(""));
+
+        std::string content;
+        if (curr_msg.contains("content")) {
+            if (curr_msg["content"].is_string()) {
+                content = curr_msg["content"].get<std::string>();
+            } else if (curr_msg["content"].is_array()) {
+                for (const auto & part : curr_msg["content"]) {
+                    if (part.contains("text")) {
+                        content += "\n" + part["text"].get<std::string>();
+                    }
+                }
+            } else {
+                throw std::runtime_error("Invalid 'content' type (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
+            }
+        } else {
+            throw std::runtime_error("Missing 'content' (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
+        }
 
-    // if it turns out that our buffer is too small, we resize it
-    if ((size_t) res > buf.size()) {
-        buf.resize(res);
-        res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
+        chat.push_back({role, content});
     }
 
-    const std::string formatted_chat(buf.data(), res);
-
+    auto formatted_chat = llama_chat_apply_template(model, tmpl, chat, true);
     LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
-
     return formatted_chat;
 }
 
@@ -260,6 +252,13 @@ static size_t common_part(const std::vector<llama_token> & a, const std::vector<
     return i;
 }
 
+static size_t common_part(const std::string & a, const std::string & b) {
+    size_t i;
+    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
+
+    return i;
+}
+
 static bool ends_with(const std::string & str, const std::string & suffix) {
     return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
 }
@@ -356,24 +355,6 @@ static json oaicompat_completion_params_parse(
 
     llama_params["__oaicompat"] = true;
 
-    // Map OpenAI parameters to llama.cpp parameters
-    //
-    // For parameters that are defined by the OpenAI documentation (e.g.
-    // temperature), we explicitly specify OpenAI's intended default; we
-    // need to do that because sometimes OpenAI disagrees with llama.cpp
-    //
-    // https://platform.openai.com/docs/api-reference/chat/create
-    llama_sampling_params default_sparams;
-    llama_params["model"]             = json_value(body,   "model",             std::string("unknown"));
-    llama_params["frequency_penalty"] = json_value(body,   "frequency_penalty", 0.0);
-    llama_params["logit_bias"]        = json_value(body,   "logit_bias",        json::object());
-    llama_params["n_predict"]         = json_value(body,   "max_tokens",        -1);
-    llama_params["presence_penalty"]  = json_value(body,   "presence_penalty",  0.0);
-    llama_params["seed"]              = json_value(body,   "seed",              LLAMA_DEFAULT_SEED);
-    llama_params["stream"]            = json_value(body,   "stream",            false);
-    llama_params["temperature"]       = json_value(body,   "temperature",       1.0);
-    llama_params["top_p"]             = json_value(body,   "top_p",             1.0);
-
     // Apply chat template to the list of messages
     llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));
 
diff --git a/examples/server-embd.py b/examples/server_embd.py
similarity index 89%
rename from examples/server-embd.py
rename to examples/server_embd.py
index 118e042716c02..0e34c6ceab9ca 100644
--- a/examples/server-embd.py
+++ b/examples/server_embd.py
@@ -1,13 +1,15 @@
 import asyncio
+import asyncio.threads
 import requests
 import numpy as np
 
+
 n = 8
 
 result = []
 
 async def requests_post_async(*args, **kwargs):
-    return await asyncio.to_thread(requests.post, *args, **kwargs)
+    return await asyncio.threads.to_thread(requests.post, *args, **kwargs)
 
 async def main():
     model_url = "http://127.0.0.1:6900"
@@ -31,4 +33,3 @@ async def main():
         embedding2 = np.array(result[j])
         similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
         print(f"Similarity between {i} and {j}: {similarity:.2f}")
-
diff --git a/examples/simple/CMakeLists.txt b/examples/simple/CMakeLists.txt
index 7da5ff6f3ac04..070cfbe7ad525 100644
--- a/examples/simple/CMakeLists.txt
+++ b/examples/simple/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET simple)
+set(TARGET llama-simple)
 add_executable(${TARGET} simple.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/simple/README.md b/examples/simple/README.md
index 5d24b1046935c..0ff3425359a41 100644
--- a/examples/simple/README.md
+++ b/examples/simple/README.md
@@ -3,7 +3,7 @@
 The purpose of this example is to demonstrate a minimal usage of llama.cpp for generating text with a given prompt.
 
 ```bash
-./simple ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is"
+./llama-simple -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is"
 
 ...
 
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index b0f8e0fdc4987..69a92cf7dc0c0 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -6,28 +6,27 @@
 #include <string>
 #include <vector>
 
-int main(int argc, char ** argv) {
-    gpt_params params;
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
 
-    if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
-        return 1 ;
-    }
+    LOG_TEE("\nexample usage:\n");
+    LOG_TEE("\n    %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]);
+    LOG_TEE("\n");
+}
 
-    if (argc >= 2) {
-        params.model = argv[1];
-    }
+int main(int argc, char ** argv) {
+    gpt_params params;
 
-    if (argc >= 3) {
-        params.prompt = argv[2];
-    }
+    params.prompt = "Hello my name is";
+    params.n_predict = 32;
 
-    if (params.prompt.empty()) {
-        params.prompt = "Hello my name is";
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
+        return 1;
     }
 
     // total length of the sequence including the prompt
-    const int n_len = 32;
+    const int n_predict = params.n_predict;
 
     // init LLM
 
@@ -36,9 +35,7 @@ int main(int argc, char ** argv) {
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_default_params();
-
-    // model_params.n_gpu_layers = 99; // offload all layers to the GPU
+    llama_model_params model_params = llama_model_params_from_gpt_params(params);
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -49,12 +46,7 @@ int main(int argc, char ** argv) {
 
     // initialize the context
 
-    llama_context_params ctx_params = llama_context_default_params();
-
-    ctx_params.seed  = 1234;
-    ctx_params.n_ctx = 2048;
-    ctx_params.n_threads = params.n_threads;
-    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
 
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
@@ -69,14 +61,14 @@ int main(int argc, char ** argv) {
     tokens_list = ::llama_tokenize(ctx, params.prompt, true);
 
     const int n_ctx    = llama_n_ctx(ctx);
-    const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
+    const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size());
 
-    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req);
+    LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, n_kv_req);
 
     // make sure the KV cache is big enough to hold all the prompt and generated tokens
     if (n_kv_req > n_ctx) {
         LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
-        LOG_TEE("%s:        either reduce n_len or increase n_ctx\n", __func__);
+        LOG_TEE("%s:        either reduce n_predict or increase n_ctx\n", __func__);
         return 1;
     }
 
@@ -115,7 +107,7 @@ int main(int argc, char ** argv) {
 
     const auto t_main_start = ggml_time_us();
 
-    while (n_cur <= n_len) {
+    while (n_cur <= n_predict) {
         // sample the next token
         {
             auto   n_vocab = llama_n_vocab(model);
@@ -134,7 +126,7 @@ int main(int argc, char ** argv) {
             const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
             // is it an end of generation?
-            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
                 LOG_TEE("\n");
 
                 break;
diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt
index 810f3c46ac4aa..aa208e7aaeeb0 100644
--- a/examples/speculative/CMakeLists.txt
+++ b/examples/speculative/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET speculative)
+set(TARGET llama-speculative)
 add_executable(${TARGET} speculative.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 12e46fbc91a24..b051a18f169c2 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -27,7 +27,8 @@ struct seq_draft {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    if (gpt_params_parse(argc, argv, params) == false) {
+    if (!gpt_params_parse(argc, argv, params)) {
+        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -65,7 +66,9 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init_tgt = llama_init_from_gpt_params(params);
+    model_tgt = llama_init_tgt.model;
+    ctx_tgt = llama_init_tgt.context;
 
     // load the draft model
     params.model = params.model_draft;
@@ -74,7 +77,9 @@ int main(int argc, char ** argv) {
         params.n_threads = params.n_threads_draft;
     }
     params.n_threads_batch = params.n_threads_batch_draft;
-    std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
+    llama_init_result llama_init_dft = llama_init_from_gpt_params(params);
+    model_dft = llama_init_dft.model;
+    ctx_dft = llama_init_dft.context;
 
     const bool vocab_type_tgt = llama_vocab_type(model_tgt);
     LOG("vocab_type tgt: %d\n", vocab_type_tgt);
diff --git a/examples/sycl/CMakeLists.txt b/examples/sycl/CMakeLists.txt
index 69cf8932eb5c7..e4d5083e6e502 100644
--- a/examples/sycl/CMakeLists.txt
+++ b/examples/sycl/CMakeLists.txt
@@ -2,7 +2,7 @@
 #  Copyright (C) 2024 Intel Corporation
 #  SPDX-License-Identifier: MIT
 
-set(TARGET ls-sycl-device)
+set(TARGET llama-ls-sycl-device)
 add_executable(${TARGET} ls-sycl-device.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/sycl/README.md b/examples/sycl/README.md
index c589c2d3a489c..8819d87f56ec2 100644
--- a/examples/sycl/README.md
+++ b/examples/sycl/README.md
@@ -6,15 +6,15 @@ This example program provides the tools for llama.cpp for SYCL on Intel GPU.
 
 |Tool Name| Function|Status|
 |-|-|-|
-|ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support|
+|llama-ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support|
 
-### ls-sycl-device
+### llama-ls-sycl-device
 
 List all SYCL devices with ID, compute capability, max work group size, ect.
 
-1. Build the llama.cpp for SYCL for all targets.
+1. Build the llama.cpp for SYCL for the specified target *(using GGML_SYCL_TARGET)*.
 
-2. Enable oneAPI running environment
+2. Enable oneAPI running environment *(if GGML_SYCL_TARGET is set to INTEL -default-)*
 
 ```
 source /opt/intel/oneapi/setvars.sh
@@ -23,25 +23,19 @@ source /opt/intel/oneapi/setvars.sh
 3. Execute
 
 ```
-./build/bin/ls-sycl-device
+./build/bin/llama-ls-sycl-device
 ```
 
 Check the ID in startup log, like:
 
 ```
-found 4 SYCL devices:
-  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
-  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
-    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
-  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
-    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
-  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
-    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+found 2 SYCL devices:
+|  |                   |                                       |       |Max    |        |Max  |Global |                     |
+|  |                   |                                       |       |compute|Max work|sub  |mem    |                     |
+|ID|        Device Type|                                   Name|Version|units  |group   |group|size   |       Driver version|
+|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|
+| 0| [level_zero:gpu:0]|                Intel Arc A770 Graphics|    1.3|    512|    1024|   32| 16225M|            1.3.29138|
+| 1| [level_zero:gpu:1]|                 Intel UHD Graphics 750|    1.3|     32|     512|   32| 62631M|            1.3.29138|
 
 ```
 
-|Attribute|Note|
-|-|-|
-|compute capability 1.3|Level-zero running time, recommended |
-|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
diff --git a/examples/sycl/build.sh b/examples/sycl/build.sh
index db46d57cabe0b..8fe0a67902cbd 100755
--- a/examples/sycl/build.sh
+++ b/examples/sycl/build.sh
@@ -8,10 +8,10 @@ cd build
 source /opt/intel/oneapi/setvars.sh
 
 #for FP16
-#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference
+#cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON # faster for long-prompt inference
 
 #for FP32
-cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
 #build example/main
 #cmake --build . --config Release --target main
diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh
index 7b39a18c0681d..111366fb036a5 100755
--- a/examples/sycl/run-llama2.sh
+++ b/examples/sycl/run-llama2.sh
@@ -23,15 +23,14 @@ fi
 if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then
     echo "use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
-    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
 else
     #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
 fi
 
 #use main GPU only
-#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
+#ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
 
 #use multiple GPUs with same max compute units
-#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
-
+#ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat
index 1b0dc41babd35..17dd1ff5c169e 100644
--- a/examples/sycl/win-build-sycl.bat
+++ b/examples/sycl/win-build-sycl.bat
@@ -13,16 +13,16 @@ if %errorlevel% neq 0 goto ERROR
 
 ::  for FP16
 ::  faster for long-prompt inference
-::  cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+::  cmake -G "MinGW Makefiles" ..  -DGGML_SYCL=ON -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
 
 ::  for FP32
-cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
+cmake -G "Ninja" ..  -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
 if %errorlevel% neq 0 goto ERROR
 ::  build example/main only
 ::  make main
 
 ::  build all binary
-make -j
+cmake --build . -j
 if %errorlevel% neq 0 goto ERROR
 
 cd ..
@@ -31,4 +31,3 @@ exit /B 0
 :ERROR
 echo comomand error: %errorlevel%
 exit /B %errorlevel%
-
diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat
index 1d4d7d2cdcb6f..c2918d6dcead6 100644
--- a/examples/sycl/win-run-llama2.bat
+++ b/examples/sycl/win-run-llama2.bat
@@ -6,6 +6,4 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
 
-.\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0
-
-
+.\build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0
diff --git a/examples/tokenize/CMakeLists.txt b/examples/tokenize/CMakeLists.txt
index 5e6654d7e5988..b704dcae18c52 100644
--- a/examples/tokenize/CMakeLists.txt
+++ b/examples/tokenize/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET tokenize)
+set(TARGET llama-tokenize)
 add_executable(${TARGET} tokenize.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index 8b1baea800cc8..c817be566cf54 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -3,40 +3,404 @@
 
 #include <cmath>
 #include <cstdio>
+#include <fstream>
 #include <string>
 #include <vector>
 
-int main(int argc, char ** argv) {
-    if (argc < 3 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH PROMPT [--ids]\n" , argv[0]);
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#include <windows.h>
+#include <shellapi.h>   // For CommandLineToArgvW
+#endif
+
+static void print_usage_information(const char * argv0, FILE * stream) {
+    fprintf(stream, "usage: %s [options]\n\n", argv0);
+    fprintf(stream, "The tokenize program tokenizes a prompt using a given model,\n");
+    fprintf(stream, "and prints the resulting tokens to standard output.\n\n");
+    fprintf(stream, "It needs a model file, a prompt, and optionally other flags\n");
+    fprintf(stream, "to control the behavior of the tokenizer.\n\n");
+    fprintf(stream, "    The possible options are:\n");
+    fprintf(stream, "\n");
+    fprintf(stream, "    -h, --help                           print this help and exit\n");
+    fprintf(stream, "    -m MODEL_PATH, --model MODEL_PATH    path to model.\n");
+    fprintf(stream, "    --ids                                if given, only print numerical token IDs, and not token strings.\n");
+    fprintf(stream, "                                         The output format looks like [1, 2, 3], i.e. parseable by Python.\n");
+    fprintf(stream, "    -f PROMPT_FNAME, --file PROMPT_FNAME read prompt from a file.\n");
+    fprintf(stream, "    -p PROMPT, --prompt PROMPT           read prompt from the argument.\n");
+    fprintf(stream, "    --stdin                              read prompt from standard input.\n");
+    fprintf(stream, "    --no-bos                             do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n");
+    fprintf(stream, "    --no-parse-special                   do not parse control tokens.\n");
+    fprintf(stream, "    --log-disable                        disable logs. Makes stderr quiet when loading the model.\n");
+    fprintf(stream, "    --show-count                         print the total number of tokens.\n");
+}
+
+static void llama_log_callback_null(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) text;
+    (void) user_data;
+}
+
+static std::string read_prompt_from_file(const char * filepath, bool & success) {
+    success = false;
+
+    std::ifstream in(filepath, std::ios::binary);
+    if (!in) {
+        fprintf(stderr, "%s: could not open file '%s' for reading: %s\n", __func__, filepath, strerror(errno));
+        return std::string();
+    }
+    // do not assume the file is seekable (e.g. /dev/stdin)
+    std::stringstream buffer;
+    buffer << in.rdbuf();
+    if (in.fail()) {
+        fprintf(stderr, "%s: could not read the entire file '%s': %s\n", __func__, filepath, strerror(errno));
+        return std::string();
+    }
+
+    success = true;
+    return buffer.str();
+}
+
+//
+// Function: ingest_args(...) -> vector<string>
+//
+//  Takes argc and argv arguments, and converts them to a vector of UTF-8 encoded
+//  strings, as an STL vector<string>.
+//
+//  In particular, it handles character encoding shenanigans on Windows.
+//
+// Note: raw_argc and raw_argv are not actually read at all on Windows.
+//       On Windows we call GetCommandLineW to get the arguments in wchar_t
+//       format, ignoring the regular argc/argv arguments to main().
+//
+// TODO: potential opportunity to roll common stuff into common/console.cpp
+//       in relation to Windows wchar_t shenanigans.
+static std::vector<std::string> ingest_args(int raw_argc, char ** raw_argv) {
+    std::vector<std::string> argv;
+
+    // Handle Windows, if given non-ASCII arguments.
+    // We convert wchar_t arguments into UTF-8 char* on this platform.
+    // Lets you invoke 'tokenize' on Windows cmd.exe with non-ASCII characters
+    // without throwing tantrums.
+#if defined(_WIN32)
+    int argc;
+    const LPWSTR cmdline_wargv = GetCommandLineW();
+    LPWSTR * wargv = CommandLineToArgvW(cmdline_wargv, &argc);
+
+    // silence unused arg warnings
+    (void) raw_argc;
+    (void) raw_argv;
+
+    for (int i = 0; i < argc; ++i) {
+        int length_needed = WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), 0, 0, NULL, NULL);
+        char * output_buf = (char *) calloc(length_needed+1, sizeof(char));
+        GGML_ASSERT(output_buf);
+
+        WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), output_buf, length_needed, NULL, NULL);
+        output_buf[length_needed] = '\0';
+
+        argv.push_back(output_buf);
+        free(output_buf);
+    }
+
+    LocalFree((HLOCAL) wargv);
+#else
+    int argc = raw_argc;
+    for (int i = 0; i < argc; ++i) {
+        argv.push_back(raw_argv[i]);
+    }
+#endif
+
+    GGML_ASSERT((unsigned int) argc == argv.size());
+
+    return argv;
+}
+
+//
+// Function: write_utf8_cstr_to_stdout(const char *) -> <writes to stdout>
+//
+// writes a string to standard output; taking into account that on Windows
+// to display correctly you have to use special handling. Works even if the
+// user has not set a unicode code page on a Windows cmd.exe.
+//
+// In case of invalid UTF-8, invalid_utf8 is set to true on Windows, and something
+// a human-readable is written instead.
+//
+// On non-Windows systems, simply printfs() the string.
+static void write_utf8_cstr_to_stdout(const char * str, bool & invalid_utf8) {
+        invalid_utf8 = false;
+
+#if defined(_WIN32)
+        // Are we in a console?
+        HANDLE hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
+        DWORD dwMode = 0;
+
+        // According to Microsoft docs:
+        // "WriteConsole fails if it is used with a standard handle that is redirected to a file."
+        // Also according to the docs, you can use GetConsoleMode to check for that.
+        if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) {
+            printf("%s", str);
+            return;
+        }
+
+        // MultiByteToWideChar reports an error if str is empty, don't report
+        // them as invalid_utf8.
+        if (*str == 0) {
+            return;
+        }
+        int length_needed = MultiByteToWideChar(CP_UTF8, MB_ERR_INVALID_CHARS, str, strlen(str), NULL, 0);
+        if (length_needed == 0) {
+            DWORD err = GetLastError();
+            if (err == ERROR_NO_UNICODE_TRANSLATION) {
+                invalid_utf8 = true;
+                int len = strlen(str);
+                printf("<");
+                for (int i = 0; i < len; ++i) {
+                    if (i > 0) {
+                        printf(" ");
+                    }
+                    printf("%02x", (uint8_t) str[i]);
+                }
+                printf(">");
+                return;
+            }
+            GGML_ABORT("MultiByteToWideChar() failed in an unexpected way.");
+        }
+
+        LPWSTR wstr = (LPWSTR) calloc(length_needed+1, sizeof(*wstr));
+        GGML_ASSERT(wstr);
+
+        MultiByteToWideChar(CP_UTF8, 0, str, strlen(str), wstr, length_needed);
+        WriteConsoleW(hConsole, wstr, length_needed, NULL, NULL);
+
+        free(wstr);
+#else
+        // TODO: reporting invalid_utf8 would be useful on non-Windows too.
+        // printf will silently just write bad unicode.
+        printf("%s", str);
+#endif
+}
+
+int main(int raw_argc, char ** raw_argv) {
+    const std::vector<std::string> argv = ingest_args(raw_argc, raw_argv);
+    const int argc = argv.size();
+
+    if (argc <= 1) {
+        print_usage_information(argv[0].c_str(), stderr);
         return 1;
     }
 
-    const char * model_path = argv[1];
-    const char * prompt     = argv[2];
+    //////
+    // Read out all the command line arguments.
+    //////
+
+    // variables where to put any arguments we see.
+    bool printing_ids = false;
+    bool no_bos = false;
+    bool no_parse_special = false;
+    bool disable_logging = false;
+    bool show_token_count = false;
+    const char * model_path = NULL;
+    const char * prompt_path = NULL;
+    const char * prompt_arg = NULL;
 
-    const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids";
+    // track which arguments were explicitly given
+    // used for sanity checking down the line
+    bool model_path_set = false;
+    bool prompt_path_set = false;
+    bool prompt_set = false;
+    bool stdin_set = false;
+
+    int iarg = 1;
+    for (; iarg < argc; ++iarg) {
+        std::string arg{argv[iarg]};
+        if (arg == "-h" || arg == "--help") {
+            print_usage_information(argv[0].c_str(), stdout);
+            return 0;
+        }
+        else if (arg == "--ids") {
+            printing_ids = true;
+        }
+        else if (arg == "-m" || arg == "--model") {
+            if (model_path_set) {
+                fprintf(stderr, "Error: -m or --model specified multiple times.\n");
+                return 1;
+            }
+            model_path = argv[++iarg].c_str();
+            model_path_set = true;
+        }
+        else if (arg == "--no-bos") {
+            no_bos = true;
+        }
+        else if (arg == "--no-parse-special") {
+            no_parse_special = true;
+        }
+        else if (arg == "-p" || arg == "--prompt") {
+            if (prompt_set) {
+                fprintf(stderr, "Error: -p or --prompt specified multiple times.\n");
+                return 1;
+            }
+            prompt_arg = argv[++iarg].c_str();
+            prompt_set = true;
+        }
+        else if (arg == "-f" || arg == "--file") {
+            if (prompt_path_set) {
+                fprintf(stderr, "Error: -f or --file specified multiple times.\n");
+                return 1;
+            }
+            prompt_path = argv[++iarg].c_str();
+            prompt_path_set = true;
+        }
+        else if (arg == "--stdin") {
+            stdin_set = true;
+        }
+        else if (arg == "--log-disable") {
+            disable_logging = true;
+        }
+        else if (arg == "--show-count") {
+            show_token_count = true;
+        }
+        else {
+            fprintf(stderr, "Error: unknown option '%s'\n", argv[iarg].c_str());
+            return 1;
+        }
+    }
+
+    //////
+    // Sanity check the command line arguments.
+    //////
+
+    // Check that we have the required stuff set.
+    if (model_path_set && model_path == NULL) {
+        fprintf(stderr, "Error: --model requires an argument.\n");
+        return 1;
+    }
+    if (!model_path_set) {
+        fprintf(stderr, "Error: must specify --model.\n");
+        return 1;
+    }
+    if (prompt_path_set && prompt_path == NULL) {
+        fprintf(stderr, "Error: --file requires an argument.\n");
+        return 1;
+    }
+    if (prompt_set && prompt_arg == NULL) {
+        fprintf(stderr, "Error: --prompt requires an argument.\n");
+        return 1;
+    }
+    const int prompts_set = !!(prompt_path_set) + !!(prompt_set) + !!(stdin_set);
+    if (prompts_set > 1) {
+        fprintf(stderr, "Error: --stdin, --file and --prompt are mutually exclusive.\n");
+        return 1;
+    }
+    // Must have some prompt.
+    if (prompts_set == 0) {
+        fprintf(stderr, "Error: must specify one of: --stdin, --file or --prompt.\n");
+        return 1;
+    }
+
+    GGML_ASSERT(model_path);
+    GGML_ASSERT(prompt_path || prompt_arg || stdin_set);
+
+    //////
+    // Figure out where will the prompt come from.
+    //////
+
+    std::string prompt;
+    if (prompt_path_set) {
+        bool success = false;
+        prompt = read_prompt_from_file(prompt_path, success);
+        if (!success) {
+            return 1;
+        }
+    } else if (prompt_set) {
+        prompt = prompt_arg;
+    } else {
+        GGML_ASSERT(stdin_set);
+        // we read stdin *after* loading model (early exit if model cannot
+        // be loaded, which can be a nicer user experience)
+    }
+
+    //////
+    // Start actually doing the tokenizing stuff.
+    //////
+
+#ifdef LOG_DISABLE_LOGS
+    disable_logging = true;
+#endif
+
+    if (disable_logging) {
+        llama_log_set(llama_log_callback_null, NULL);
+    }
 
     llama_backend_init();
 
     llama_model_params model_params = llama_model_default_params();
     model_params.vocab_only = true;
     llama_model * model = llama_load_model_from_file(model_path, model_params);
+    if (!model) {
+        fprintf(stderr, "Error: could not load model from file '%s'.\n", model_path);
+        return 1;
+    }
 
     llama_context_params ctx_params = llama_context_default_params();
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+    if (!ctx) {
+        fprintf(stderr, "Error: could not create context.\n");
+        return 1;
+    }
+
+    // read entire prompt from stdin?
+    if (stdin_set) {
+        GGML_ASSERT(!prompt_path_set && !prompt_set);
+
+        std::stringstream stdin_buffer;
+        stdin_buffer << std::cin.rdbuf();
+        if (std::cin.fail()) {
+            fprintf(stderr, "Error: could not read the entire standard input.\n");
+            return 1;
+        }
+
+        prompt = stdin_buffer.str();
+    }
+
+    const bool model_wants_add_bos = llama_add_bos_token(model);
+    const bool add_bos = model_wants_add_bos && !no_bos;
+    const bool parse_special = !no_parse_special;
 
     std::vector<llama_token> tokens;
+    tokens = ::llama_tokenize(model, prompt, add_bos, parse_special);
 
-    tokens = ::llama_tokenize(model, prompt, true, true);
+    if (printing_ids) {
+        printf("[");
+    }
 
     for (int i = 0; i < (int) tokens.size(); i++) {
         if (printing_ids) {
-            printf("%d\n", tokens[i]);
+            if (i > 0) {
+                printf(", ");
+            }
+            printf("%d", tokens[i]);
         } else {
-            printf("%6d -> '%s'\n", tokens[i], llama_token_to_piece(ctx, tokens[i]).c_str());
+            bool invalid_utf8 = false;
+            printf("%6d -> '", tokens[i]);
+            write_utf8_cstr_to_stdout(llama_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8);
+            if (invalid_utf8) {
+                printf("' (utf-8 decode failure)\n");
+            } else {
+                printf("'\n");
+            }
         }
     }
 
+    if (printing_ids) {
+        printf("]\n");
+    }
+
+    if (show_token_count) {
+        printf("Total number of tokens: %ld\n", tokens.size());
+    }
+    // silence valgrind
+    llama_free(ctx);
+    llama_free_model(model);
+
     return 0;
 }
diff --git a/examples/train-text-from-scratch/CMakeLists.txt b/examples/train-text-from-scratch/CMakeLists.txt
deleted file mode 100644
index 4459516d093d6..0000000000000
--- a/examples/train-text-from-scratch/CMakeLists.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-set(TARGET train-text-from-scratch)
-add_executable(${TARGET} train-text-from-scratch.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/train-text-from-scratch/README.md b/examples/train-text-from-scratch/README.md
deleted file mode 100644
index 1b3454069e9a3..0000000000000
--- a/examples/train-text-from-scratch/README.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# train-text-from-scratch
-
-Basic usage instructions:
-
-```bash
-# get training data
-wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt
-
-# train
-./bin/train-text-from-scratch \
-        --vocab-model ../models/ggml-vocab-llama.gguf \
-        --ctx 64 --embd 256 --head 8 --layer 16 \
-        --checkpoint-in  chk-shakespeare-256x16-LATEST.gguf \
-        --checkpoint-out chk-shakespeare-256x16-ITERATION.gguf \
-        --model-out ggml-shakespeare-256x16-f32-ITERATION.gguf \
-        --train-data "shakespeare.txt" \
-        -t 6 -b 16 --seed 1 --adam-iter 256 \
-        --no-checkpointing
-
-# predict
-./bin/main -m ggml-shakespeare-256x16-f32.gguf
-```
-
-Output files will be saved every N iterations (config with `--save-every N`).
-The pattern "ITERATION" in the output filenames will be replaced with the iteration number and "LATEST" for the latest output.
-
-To train GGUF models just pass them to `--checkpoint-in FN`.
diff --git a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py
deleted file mode 100644
index ed93673bcf306..0000000000000
--- a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py
+++ /dev/null
@@ -1,499 +0,0 @@
-#!/usr/bin/env python3
-# train-text-from-scratch checkpoint --> gguf conversion
-
-import argparse
-import os
-import struct
-import sys
-import numpy as np
-from pathlib import Path
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py'))
-import gguf
-
-# gguf constants
-LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
-LLM_KV_OPTIMIZER_TYPE_ADAM  = "adam"
-LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
-LLM_KV_OPTIMIZER_FILE_VERSION               = "optimizer.file_version"
-LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT     = "optimizer.convergence_past_count"
-LLM_KV_OPTIMIZER_PARAMETER_COUNT            = "optimizer.parameter_count"
-LLM_KV_OPTIMIZER_ITERATION_COUNT            = "optimizer.iteration_count"
-LLM_KV_OPTIMIZER_JUST_INITIALIZED           = "optimizer.just_initialized"
-LLM_KV_OPTIMIZER_ADAM_BEST_LOSS             = "optimizer.adam.best_loss"
-LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS         = "optimizer.adam.previous_loss"
-LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT  = "optimizer.adam.no_improvement_count"
-LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
-LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS            = "optimizer.lbfgs.best_loss"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP     = "optimizer.lbfgs.line_search_step"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J        = "optimizer.lbfgs.line_search_j"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K        = "optimizer.lbfgs.line_search_k"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END      = "optimizer.lbfgs.line_search_end"
-LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
-
-LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS    = "optimizer.adam.first_moments"
-LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS   = "optimizer.adam.second_moments"
-LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
-
-LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS  = "optimizer.lbfgs.current_parameters"
-LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
-LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS   = "optimizer.lbfgs.current_gradients"
-LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS  = "optimizer.lbfgs.previous_gradients"
-LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION    = "optimizer.lbfgs.search_direction"
-LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES    = "optimizer.lbfgs.past_loss_values"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA        = "optimizer.lbfgs.memory_alpha"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS           = "optimizer.lbfgs.memory_ys"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S            = "optimizer.lbfgs.memory_s"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y            = "optimizer.lbfgs.memory_y"
-
-LLM_KV_TRAINING_TYPE_TRAIN_MODEL   = "train_model"
-LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora"
-LLM_KV_TRAINING_TYPE               = "training.type"
-LLM_KV_TRAINING_FILE_VERSION       = "training.file_version"
-LLM_KV_TRAINING_ITERATION_COUNT    = "training.iteration_count"
-LLM_KV_TRAINING_SAMPLE_COUNT       = "training.sample_count"
-LLM_KV_TRAINING_TOKEN_COUNT        = "training.token_count"
-
-class Tensor:
-    def __init__(self, dtype='f', ne=None):
-        if ne is None:
-            ne = []
-        self.dtype = dtype
-        self.ne = ne
-        self.nbytes = 0
-        if self.dtype == 'f':
-            if len(self.ne) == 0:
-                self.nbytes = 0
-            else:
-                self.nbytes = int(np.product(self.ne)) * 4
-        else:
-            raise ValueError(f"Unhandled data type '{self.dtype}'")
-
-    def load(self, data, offset):
-        nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-
-        assert(nd == len(self.ne))
-        ne = []
-        for d in range(nd):
-            n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-            ne.append(n)
-
-        assert(tuple(ne) == tuple(self.ne))
-
-        if self.dtype == 'f':
-            assert(dtype == 0)
-        else:
-            raise ValueError(f"Unhandled data type '{self.dtype}'")
-
-        self.name = bytes(data[offset:offset+namelen]); offset += namelen
-        # 32-byte alignment
-        offset += (0 - offset) & 31
-        self.data = data[offset:offset+self.nbytes]
-        offset += self.nbytes
-        return offset
-
-    def max_storage_size(self):
-        result = 0
-        result += 4 # nd
-        result += 4 # namelen
-        result += 4 # dtype
-        result += len(self.ne)*8 # ne
-        result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
-        result += 31 # 32-byte alignment
-        result += self.nbytes
-        return result
-
-    def save_gguf(self, gguf_writer, name):
-        gguf_writer.add_tensor(
-            name=name,
-            tensor=self.data,
-            raw_shape=np.array(list(reversed(self.ne))),
-            raw_dtype=gguf.GGMLQuantizationType.F32)
-
-class OptimizationParamsV0:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.type                 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_threads            = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.past                 = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.delta                = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.print_forward_graph  = struct.unpack('<?', bytes(data[offset:offset + 1]))[0];  offset += 4 # 32bit-aligned
-        self.print_backward_graph = struct.unpack('<?', bytes(data[offset:offset + 1]))[0];  offset += 4 # 32bit-aligned
-        self.adam_n_iter          = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_sched           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_decay           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_alpha           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_beta1           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_beta2           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_eps             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_eps_f           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_eps_g           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_m              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_n_iter         = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_max_linesearch = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_eps            = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_ftol           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_wolfe          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_min_step       = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_max_step       = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_linesearch     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        return offset
-
-class OptimizationContext:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
-        offset += 4
-
-        if self.version == 0:
-            params = OptimizationParamsV0()
-            offset = params.load(data, offset)
-            self.past = params.past
-            self.lbfgs_m = params.lbfgs_m
-            self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0];  offset += 8
-            self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
-            self.type = params.type
-
-            self.adam_m  = Tensor('f', [self.nx])
-            self.adam_v  = Tensor('f', [self.nx])
-            self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
-
-            self.lbfgs_x    = Tensor('f', [self.nx])
-            self.lbfgs_xp   = Tensor('f', [self.nx])
-            self.lbfgs_g    = Tensor('f', [self.nx])
-            self.lbfgs_gp   = Tensor('f', [self.nx])
-            self.lbfgs_d    = Tensor('f', [self.nx])
-            self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
-            self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
-            self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
-            self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
-            self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
-
-            if self.type == 0:
-                # these tensors are stored, but we don't need their data
-                x  = Tensor('f', [self.nx])
-                g  = Tensor('f', [self.nx])
-                g2 = Tensor('f', [self.nx])
-                mh = Tensor('f', [self.nx])
-                vh = Tensor('f', [self.nx])
-
-                offset = x.load(data, offset)
-                offset = g.load(data, offset)
-                offset = g2.load(data, offset)
-                offset = self.adam_m.load(data, offset)
-                offset = self.adam_v.load(data, offset)
-                offset = mh.load(data, offset)
-                offset = vh.load(data, offset)
-                offset = self.adam_pf.load(data, offset)
-
-                self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-            elif self.type == 1:
-                offset = self.lbfgs_x.load(data, offset)
-                offset = self.lbfgs_xp.load(data, offset)
-                offset = self.lbfgs_g.load(data, offset)
-                offset = self.lbfgs_gp.load(data, offset)
-                offset = self.lbfgs_d.load(data, offset)
-                offset = self.lbfgs_pf.load(data, offset)
-                offset = self.lbfgs_lmal.load(data, offset)
-                offset = self.lbfgs_lmys.load(data, offset)
-                offset = self.lbfgs_lms.load(data, offset)
-                offset = self.lbfgs_lmy.load(data, offset)
-
-                self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-            else:
-                raise ValueError('Unknown optimizer type')
-
-
-        elif self.version == 1:
-            self.past    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.nx      = struct.unpack('N',  bytes(data[offset:offset + 8]))[0];  offset += 8
-            self.iter    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
-
-            self.adam_m  = Tensor('f', [self.nx])
-            self.adam_v  = Tensor('f', [self.nx])
-            self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
-
-            self.lbfgs_x    = Tensor('f', [self.nx])
-            self.lbfgs_xp   = Tensor('f', [self.nx])
-            self.lbfgs_g    = Tensor('f', [self.nx])
-            self.lbfgs_gp   = Tensor('f', [self.nx])
-            self.lbfgs_d    = Tensor('f', [self.nx])
-            self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
-            self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
-            self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
-            self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
-            self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
-
-            # forgot to save type in version 1:
-            # guess self.type from number of remaining bytes
-            size_type_0 = 12 + sum([t.max_storage_size() for t in
-                                    [self.adam_m, self.adam_v]
-                                    +([self.adam_pf] if (self.past > 0) else [])])
-            size_type_1 = 24 + sum([t.max_storage_size() for t in
-                                    [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
-                                     self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
-                                     self.lbfgs_lmal, self.lbfgs_lmys,
-                                     self.lbfgs_lms, self.lbfgs_lmy]
-                                     +([self.lbfgs_pf] if (self.past > 0) else [])])
-            # due to alignment padding the size might not by exact
-            # but the difference in size for both types is significant,
-            # so we can just use whichever is closest
-            remaining = len(data) - offset
-            if abs(remaining - size_type_0) < abs(remaining - size_type_1):
-                self.type = 0
-            else:
-                self.type = 1
-
-            if self.type == 0:
-                offset = self.adam_m.load(data, offset)
-                offset = self.adam_v.load(data, offset)
-                offset = self.adam_pf.load(data,offset)
-
-                self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-            elif self.type == 1:
-                offset = self.lbfgs_x.load(data, offset)
-                offset = self.lbfgs_xp.load(data, offset)
-                offset = self.lbfgs_g.load(data, offset)
-                offset = self.lbfgs_gp.load(data, offset)
-                offset = self.lbfgs_d.load(data, offset)
-                offset = self.lbfgs_pf.load(data, offset)
-                offset = self.lbfgs_lmal.load(data, offset)
-                offset = self.lbfgs_lmys.load(data, offset)
-                offset = self.lbfgs_lms.load(data, offset)
-                offset = self.lbfgs_lmy.load(data, offset)
-
-                self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-        else:
-            raise ValueError('Invalid version of checkpoint file')
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
-        gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
-        gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
-
-        if self.type == 0:
-            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
-
-            self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
-            self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
-            if self.past > 0:
-                self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
-
-        elif self.type == 1:
-            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
-
-            self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
-            self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
-            self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
-            self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
-            self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
-            if self.past > 0:
-                self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
-            self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
-            self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
-            self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
-            self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
-        else:
-            raise ValueError('Unknown optimizer type')
-
-class ModelParams:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_embd  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_mult  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_head  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rot   = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        return offset
-
-    def get_n_ff(self):
-        # struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9
-        return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult
-
-    def save_gguf(self, gguf_writer):
-        # self.n_vocab not saved
-        gguf_writer.add_embedding_length(self.n_embd)
-        gguf_writer.add_head_count(self.n_head)
-        gguf_writer.add_block_count(self.n_layer)
-        gguf_writer.add_rope_dimension_count(self.n_rot)
-        gguf_writer.add_feed_forward_length(self.get_n_ff())
-
-def tensor_name(key, bid=None):
-    return gguf.TENSOR_NAMES[key].format(bid=bid) + ".weight"
-
-class Layer:
-    def __init__(self, params, bid):
-        self.bid = bid
-        self.att_norm = Tensor('f', [params.n_embd])
-        self.wq       = Tensor('f', [params.n_embd, params.n_embd])
-        self.wk       = Tensor('f', [params.n_embd, params.n_embd])
-        self.wv       = Tensor('f', [params.n_embd, params.n_embd])
-        self.wo       = Tensor('f', [params.n_embd, params.n_embd])
-        self.ffn_norm = Tensor('f', [params.n_embd])
-        self.w1       = Tensor('f', [params.n_embd, params.get_n_ff()])
-        self.w2       = Tensor('f', [params.get_n_ff(), params.n_embd])
-        self.w3       = Tensor('f', [params.n_embd, params.get_n_ff()])
-
-    def load(self, data, offset):
-        offset = self.att_norm.load(data, offset)
-        offset = self.wq.load(data, offset)
-        offset = self.wk.load(data, offset)
-        offset = self.wv.load(data, offset)
-        offset = self.wo.load(data, offset)
-        offset = self.ffn_norm.load(data, offset)
-        offset = self.w1.load(data, offset)
-        offset = self.w2.load(data, offset)
-        offset = self.w3.load(data, offset)
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        self.att_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid))
-        self.wq.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q,    self.bid))
-        self.wk.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K,    self.bid))
-        self.wv.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V,    self.bid))
-        self.wo.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT,  self.bid))
-        self.ffn_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM,  self.bid))
-        self.w1.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE,  self.bid))
-        self.w2.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN,  self.bid))
-        self.w3.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP,    self.bid))
-
-class Model:
-    def __init__(self):
-        self.params = ModelParams()
-        self.layers = []
-
-    def load(self, data, offset):
-        offset = self.params.load(data, offset)
-
-        self.tok_embd = Tensor('f', [self.params.n_embd, self.params.n_vocab])
-        self.norm     = Tensor('f', [self.params.n_embd])
-        self.output   = Tensor('f', [self.params.n_embd, self.params.n_vocab])
-
-        offset = self.tok_embd.load(data, offset)
-        offset = self.norm.load(data, offset)
-        offset = self.output.load(data, offset)
-
-        self.layers.clear()
-        for bid in range(self.params.n_layer):
-            layer = Layer(self.params, bid)
-            offset = layer.load(data, offset)
-            self.layers.append(layer)
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        self.params.save_gguf(gguf_writer)
-
-        self.tok_embd.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD))
-        self.norm.save_gguf    (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM))
-        self.output.save_gguf  (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT))
-
-        for layer in self.layers:
-            layer.save_gguf(gguf_writer)
-
-class Checkpoint:
-    def __init__(self):
-        self.model = Model()
-        self.opt_ctx = OptimizationContext()
-
-    def load(self, data, offset):
-        magic   = bytes(reversed(data[offset:offset + 4])); offset += 4
-        if magic != b'ggcp':
-            raise ValueError(f"File header magic indicates, that this is no checkpoint file. Expected 'ggcp', Got '{str(magic)}'")
-
-        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        if self.version != 0:
-            raise ValueError('Invalid version of checkpoint file')
-
-        self.train_its     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        self.train_tokens  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-
-        offset = self.model.load(data, offset)
-        offset = self.opt_ctx.load(data, offset)
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
-        gguf_writer.add_layer_norm_rms_eps(1e-5)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION,    0)
-        gguf_writer.add_string(LLM_KV_TRAINING_TYPE,            LLM_KV_TRAINING_TYPE_TRAIN_MODEL)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT,    self.train_samples)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT,     self.train_tokens)
-        self.model.save_gguf(gguf_writer)
-        self.opt_ctx.save_gguf(gguf_writer)
-
-def handle_args():
-    parser = argparse.ArgumentParser(description = 'Convert train-text-from-scratch checkpoints to GGUF')
-    parser.add_argument('--input',  '-i', type = Path, help = 'Input train checkpoint filename', required=True)
-    parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename', required=True)
-    return parser.parse_args()
-
-def main():
-    cfg = handle_args()
-    data = np.memmap(cfg.input, mode = 'r')
-    chk = Checkpoint()
-    offset = 0
-    offset = chk.load(data, offset)
-    # we should have read all available data
-    assert(offset == len(data))
-
-    gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
-    chk.save_gguf(gguf_writer)
-    print("    gguf: write header")
-    gguf_writer.write_header_to_file()
-    print("    gguf: write metadata")
-    gguf_writer.write_kv_data_to_file()
-    print("    gguf: write tensors")
-    gguf_writer.write_tensors_to_file()
-    gguf_writer.close()
-
-if __name__ == '__main__':
-    main()
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
deleted file mode 100644
index 587418cc73964..0000000000000
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ /dev/null
@@ -1,1252 +0,0 @@
-#include "ggml.h"
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-#include "common.h"
-#include "train.h"
-#include "llama.h"
-#include <unordered_map>
-#include <vector>
-#include <cassert>
-#include <climits>
-#include <cstring>
-#include <cstdarg>
-#include <ctime>
-#include <random>
-#include <stdexcept>
-#include <algorithm>
-#include <string>
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-struct my_llama_hparams {
-    uint32_t n_vocab = 32000;
-    uint32_t n_ctx   = 512;
-    uint32_t n_embd  = 4096;
-    uint32_t n_head  = 32;
-    uint32_t n_layer = 32;
-    uint32_t n_rot   = 64;
-    uint32_t n_ff    = 11008;
-
-    // float f_norm_eps     = 1e-5f; // falcon
-    float f_norm_rms_eps = 1e-5f; // llama
-
-    float rope_freq_base  = 10000.0f;
-    float rope_freq_scale = 1.0f;
-};
-
-struct my_llama_layer {
-    // normalization
-    struct ggml_tensor * attention_norm;
-
-    // attention
-    struct ggml_tensor * wq;
-    struct ggml_tensor * wk;
-    struct ggml_tensor * wv;
-    struct ggml_tensor * wo;
-
-    // normalization
-    struct ggml_tensor * ffn_norm;
-
-    // ff
-    struct ggml_tensor * ffn_gate; // w1
-    struct ggml_tensor * ffn_down; // w2
-    struct ggml_tensor * ffn_up;   // w3
-};
-
-struct my_llama_model {
-    struct ggml_context * ctx = NULL;
-    ggml_backend_buffer_t data = NULL;
-
-    my_llama_hparams hparams;
-
-    struct ggml_tensor * tok_embeddings;
-
-    struct ggml_tensor * norm;
-    struct ggml_tensor * output;
-
-    std::vector<my_llama_layer> layers;
-};
-
-// gguf constants (sync with gguf.py)
-static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL     = "train_model";
-static const char * LLM_KV_TRAINING_TYPE                 = "training.type";
-
-static const char * LLM_KV_GENERAL_NAME                = "general.name";
-static const char * LLM_KV_GENERAL_ARCHITECTURE        = "general.architecture";
-static const char * LLM_KV_GENERAL_FILE_TYPE           = "general.file_type";
-
-static const char * LLM_KV_CONTEXT_LENGTH              = "%s.context_length";
-static const char * LLM_KV_EMBEDDING_LENGTH            = "%s.embedding_length";
-static const char * LLM_KV_BLOCK_COUNT                 = "%s.block_count";
-static const char * LLM_KV_FEED_FORWARD_LENGTH         = "%s.feed_forward_length";
-static const char * LLM_KV_ATTENTION_HEAD_COUNT        = "%s.attention.head_count";
-static const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon";
-static const char * LLM_KV_ROPE_DIMENSION_COUNT        = "%s.rope.dimension_count";
-static const char * LLM_KV_ROPE_FREQ_BASE              = "%s.rope.freq_base"; // TODO load in llama.cpp
-static const char * LLM_KV_ROPE_SCALE_LINEAR           = "%s.rope.scale_linear";
-
-static const char * LLM_KV_TOKENIZER_MODEL             = "tokenizer.ggml.model";
-static const char * LLM_KV_TOKENIZER_LIST              = "tokenizer.ggml.tokens";
-static const char * LLM_KV_TOKENIZER_TOKEN_TYPE        = "tokenizer.ggml.token_type";
-static const char * LLM_KV_TOKENIZER_SCORES            = "tokenizer.ggml.scores";
-static const char * LLM_KV_TOKENIZER_MERGES            = "tokenizer.ggml.merges";
-static const char * LLM_KV_TOKENIZER_BOS_ID            = "tokenizer.ggml.bos_token_id";
-static const char * LLM_KV_TOKENIZER_EOS_ID            = "tokenizer.ggml.eos_token_id";
-static const char * LLM_KV_TOKENIZER_UNK_ID            = "tokenizer.ggml.unknown_token_id";
-static const char * LLM_KV_TOKENIZER_SEP_ID            = "tokenizer.ggml.seperator_token_id";
-static const char * LLM_KV_TOKENIZER_PAD_ID            = "tokenizer.ggml.padding_token_id";
-
-static const char * LLM_TENSOR_TOKEN_EMBD    = "token_embd";
-static const char * LLM_TENSOR_OUTPUT_NORM   = "output_norm";
-static const char * LLM_TENSOR_OUTPUT        = "output";
-static const char * LLM_TENSOR_ATTN_NORM     = "blk.%d.attn_norm";
-static const char * LLM_TENSOR_ATTN_Q        = "blk.%d.attn_q";
-static const char * LLM_TENSOR_ATTN_K        = "blk.%d.attn_k";
-static const char * LLM_TENSOR_ATTN_V        = "blk.%d.attn_v";
-static const char * LLM_TENSOR_ATTN_OUT      = "blk.%d.attn_output";
-static const char * LLM_TENSOR_FFN_NORM      = "blk.%d.ffn_norm";
-static const char * LLM_TENSOR_FFN_GATE      = "blk.%d.ffn_gate";
-static const char * LLM_TENSOR_FFN_DOWN      = "blk.%d.ffn_down";
-static const char * LLM_TENSOR_FFN_UP        = "blk.%d.ffn_up";
-
-static void print_params(struct my_llama_hparams * params) {
-    printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
-    printf("%s: n_ctx:   %u\n", __func__, params->n_ctx);
-    printf("%s: n_embd:  %u\n", __func__, params->n_embd);
-    printf("%s: n_head:  %u\n", __func__, params->n_head);
-    printf("%s: n_ff:    %u\n", __func__, params->n_ff);
-    printf("%s: n_layer: %u\n", __func__, params->n_layer);
-    printf("%s: n_rot:   %u\n", __func__, params->n_rot);
-}
-
-static void set_param_model(struct my_llama_model * model) {
-    const auto& hparams = model->hparams;
-
-    const uint32_t n_layer = hparams.n_layer;
-
-    struct ggml_context* ctx = model->ctx;
-
-    ggml_set_param(ctx, model->tok_embeddings);
-    ggml_set_param(ctx, model->norm);
-    ggml_set_param(ctx, model->output);
-
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-        ggml_set_param(ctx, layer.attention_norm);
-        ggml_set_param(ctx, layer.wq);
-        ggml_set_param(ctx, layer.wk);
-        ggml_set_param(ctx, layer.wv);
-        ggml_set_param(ctx, layer.wo);
-        ggml_set_param(ctx, layer.ffn_norm);
-        ggml_set_param(ctx, layer.ffn_gate);
-        ggml_set_param(ctx, layer.ffn_down);
-        ggml_set_param(ctx, layer.ffn_up);
-    }
-}
-
-static void init_model(struct my_llama_model * model) {
-    const auto & hparams = model->hparams;
-
-    const uint32_t n_embd  = hparams.n_embd;
-    const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_vocab = hparams.n_vocab;
-    const uint32_t n_ff    = hparams.n_ff;
-
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-    auto tn = [&tn_buf](const char * key) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
-        return tn_buf.data();
-    };
-    auto tni = [&tn_buf](const char * key, int bid) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
-        return tn_buf.data();
-    };
-
-    // context for model tensors without their data
-    struct ggml_init_params ctx_model_params;
-    ctx_model_params.mem_size   = ggml_tensor_overhead()*2*(6 + n_layer*18);
-    ctx_model_params.mem_buffer = NULL;
-    ctx_model_params.no_alloc   = true;
-
-    struct ggml_context * ctx = ggml_init(ctx_model_params);
-    model->ctx = ctx;
-
-    model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
-    model->norm           = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-    model->output         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
-
-    ggml_set_name(model->tok_embeddings, tn(LLM_TENSOR_TOKEN_EMBD));
-    ggml_set_name(model->norm,           tn(LLM_TENSOR_OUTPUT_NORM));
-    ggml_set_name(model->output,         tn(LLM_TENSOR_OUTPUT));
-
-    model->layers.resize(n_layer);
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-        layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
-        layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-
-        layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
-        layer.ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
-        layer.ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_F32,   n_ff, n_embd);
-        layer.ffn_up   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
-
-        ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i));
-
-        ggml_set_name(layer.wq,             tni(LLM_TENSOR_ATTN_Q, i));
-        ggml_set_name(layer.wk,             tni(LLM_TENSOR_ATTN_K, i));
-        ggml_set_name(layer.wv,             tni(LLM_TENSOR_ATTN_V, i));
-        ggml_set_name(layer.wo,             tni(LLM_TENSOR_ATTN_OUT, i));
-
-        ggml_set_name(layer.ffn_norm,       tni(LLM_TENSOR_FFN_NORM, i));
-
-        ggml_set_name(layer.ffn_gate,       tni(LLM_TENSOR_FFN_GATE, i));
-        ggml_set_name(layer.ffn_down,       tni(LLM_TENSOR_FFN_DOWN, i));
-        ggml_set_name(layer.ffn_up,         tni(LLM_TENSOR_FFN_UP, i));
-    }
-
-    set_param_model(model);
-
-    // allocate data
-    model->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type());
-}
-
-static void randomize_model(struct my_llama_model * model, int seed, float mean, float std, float min, float max) {
-    const auto & hparams = model->hparams;
-
-    const uint32_t n_layer = hparams.n_layer;
-
-    struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
-
-    randomize_tensor_normal(model->tok_embeddings, rnd);
-    randomize_tensor_normal(model->norm,           rnd);
-    randomize_tensor_normal(model->output,         rnd);
-
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = model->layers[i];
-        randomize_tensor_normal(layer.attention_norm, rnd);
-
-        randomize_tensor_normal(layer.wq, rnd);
-        randomize_tensor_normal(layer.wk, rnd);
-        randomize_tensor_normal(layer.wv, rnd);
-        randomize_tensor_normal(layer.wo, rnd);
-
-        randomize_tensor_normal(layer.ffn_norm, rnd);
-
-        randomize_tensor_normal(layer.ffn_gate, rnd);
-        randomize_tensor_normal(layer.ffn_down, rnd);
-        randomize_tensor_normal(layer.ffn_up,   rnd);
-    }
-
-    free_random_normal_distribution(rnd);
-}
-
-static struct ggml_tensor * llama_build_train_graphs(
-        struct my_llama_model * model,
-        ggml_gallocr_t          alloc,
-        struct ggml_context   * ctx,
-        struct ggml_cgraph    * gf,
-        struct ggml_cgraph    * gb,
-        struct ggml_cgraph    * gb_tmp,
-        struct ggml_tensor  * * logits,
-        struct ggml_tensor    * tokens_input,
-        struct ggml_tensor    * targets,
-        const  int              n_tokens,
-        const  int              n_batch,
-        const  bool             enable_flash_attn,
-        const  bool             enable_checkpointing,
-        const  bool             measure_only) {
-
-    ggml_set_scratch(ctx, { 0, 0, nullptr, });
-    const int n_past = 0;
-    const int N = n_tokens;
-    const auto & hparams = model->hparams;
-    const int n_ctx      = hparams.n_ctx;
-    const int n_vocab    = hparams.n_vocab;
-    const int n_embd     = hparams.n_embd;
-    const int n_layer    = hparams.n_layer;
-    const int n_head     = hparams.n_head;
-    const int n_rot      = hparams.n_rot;
-    const int n_ff       = hparams.n_ff;
-    const float f_norm_rms_eps  = hparams.f_norm_rms_eps;
-    const float rope_freq_base  = hparams.rope_freq_base;
-    const float rope_freq_scale = hparams.rope_freq_scale;
-
-    auto set_name = [](struct ggml_tensor * t, const char * n) {
-        ggml_set_name(t, n);
-        if (t->grad) {
-            ggml_format_name(t->grad, "%s->grad", n);
-        }
-    };
-
-    // KQ_pos - contains the positions
-    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
-    ggml_set_input(KQ_pos);
-
-    // rope has so much parameters that we make a custom function for it
-    auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
-                (struct ggml_tensor * t) -> struct ggml_tensor * {
-        // not capturing these, to silcence warnings
-        const int rope_mode = 0;
-
-        return ggml_rope_custom(
-            ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
-        );
-    };
-
-    set_name(tokens_input, "tokens_input");
-    set_name(targets,      "targets");
-
-    GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
-    struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch);  set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch);
-    struct ggml_tensor * t01 = ggml_get_rows(ctx, model->tok_embeddings, t00); set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch);
-
-    struct ggml_tensor * cur = t01;
-
-    std::vector<struct ggml_tensor *> checkpoints;
-    checkpoints.push_back(tokens_input);
-    checkpoints.push_back(targets);
-    checkpoints.push_back(t00);
-    checkpoints.push_back(t01);
-
-    const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head);
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct my_llama_layer & layer = model->layers[il];
-        struct ggml_tensor * t02 = ggml_rms_norm     (ctx, cur, f_norm_rms_eps);                    set_name(t02, "t02");     assert_shape_2d(t02, n_embd, N*n_batch);
-        struct ggml_tensor * t03 = ggml_repeat       (ctx, layer.attention_norm, t02);              set_name(t03, "t03");     assert_shape_2d(t03, n_embd, N*n_batch);
-        struct ggml_tensor * t04 = ggml_mul          (ctx, t03, t02);                               set_name(t04, "t04");     assert_shape_2d(t04, n_embd, N*n_batch);
-        struct ggml_tensor * t05 = ggml_mul_mat      (ctx, layer.wq, t04);                          set_name(t05, "t05");     assert_shape_2d(t05, n_embd, N*n_batch);
-        struct ggml_tensor * t06 = ggml_reshape_4d   (ctx, t05, n_embd/n_head, n_head, N, n_batch); set_name(t06, "t06");     assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t07 = rope              (t06);                                         set_name(t07, "t07");     assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t08 = ggml_mul_mat      (ctx, layer.wk, t04);                          set_name(t08, "t08");     assert_shape_2d(t08, n_embd, N*n_batch);
-        struct ggml_tensor * t09 = ggml_reshape_4d   (ctx, t08, n_embd/n_head, n_head, N, n_batch); set_name(t09, "t09");     assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t10 = rope              (t09);                                         set_name(t10, "t10");     assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t11 = ggml_mul_mat      (ctx, t04, layer.wv);                          set_name(t11, "t11");     assert_shape_2d(t11, N*n_batch, n_embd);
-        struct ggml_tensor * t12 = ggml_reshape_4d   (ctx, t11, N, n_batch, n_embd/n_head, n_head); set_name(t12, "t12");     assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
-        struct ggml_tensor * t13 = ggml_permute      (ctx, t07, 0, 2, 1, 3);                        set_name(t13, "t13");     assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch);
-        struct ggml_tensor * t14 = ggml_permute      (ctx, t10, 0, 2, 1, 3);                        set_name(t14, "t14");     assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch);
-        struct ggml_tensor * t15 = ggml_permute      (ctx, t12, 0, 3, 1, 2);                        set_name(t15, "t15");     assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch);
-        struct ggml_tensor * t16;
-        if (enable_flash_attn) {
-            t16 = ggml_flash_attn(ctx, t13, t14, t15, true);                                        set_name(t16, "t16");     assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
-        } else {
-            struct ggml_tensor * t16_0 = ggml_mul_mat              (ctx, t14, t13);                 set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_1 = ggml_scale_inplace        (ctx, t16_0, kv_scale);          set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past);            set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_3 = ggml_soft_max_inplace     (ctx, t16_2);                    set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch);
-            t16 = ggml_mul_mat(ctx, t15, t16_3);                                                    set_name(t16, "t16");     assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
-        }
-        struct ggml_tensor * t17 = ggml_permute      (ctx, t16, 0, 2, 1, 3);                        set_name(t17, "t17");     assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t18 = ggml_cont         (ctx, t17);                                    set_name(t18, "t18");     assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t19 = ggml_reshape_2d   (ctx, t18, n_embd, N*n_batch);                 set_name(t19, "t19");     assert_shape_2d(t19, n_embd, N*n_batch);
-        struct ggml_tensor * t20 = ggml_mul_mat      (ctx, layer.wo, t19);                          set_name(t20, "t20");     assert_shape_2d(t20, n_embd, N*n_batch);
-        struct ggml_tensor * t21 = ggml_add          (ctx, t20, cur);                               set_name(t21, "t21");     assert_shape_2d(t21, n_embd, N*n_batch);
-        struct ggml_tensor * t22 = ggml_rms_norm     (ctx, t21, f_norm_rms_eps);                    set_name(t22, "t22");     assert_shape_2d(t22, n_embd, N*n_batch);
-        struct ggml_tensor * t23 = ggml_repeat       (ctx, layer.ffn_norm, t22);                    set_name(t23, "t23");     assert_shape_2d(t23, n_embd, N*n_batch);
-        struct ggml_tensor * t24 = ggml_mul          (ctx, t23, t22);                               set_name(t24, "t24");     assert_shape_2d(t24, n_embd, N*n_batch);
-        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, layer.ffn_up, t24);                      set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
-        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, layer.ffn_gate, t24);                    set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
-        struct ggml_tensor * t27 = ggml_silu         (ctx, t26);                                    set_name(t27, "t27");     assert_shape_2d(t27, n_ff, N*n_batch);
-        struct ggml_tensor * t28 = ggml_mul          (ctx, t27, t25);                               set_name(t28, "t28");     assert_shape_2d(t28, n_ff, N*n_batch);
-        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, layer.ffn_down, t28);                    set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
-        struct ggml_tensor * t30 = ggml_add          (ctx, t29, t21);                               set_name(t30, "t30");     assert_shape_2d(t30, n_embd, N*n_batch);
-        cur = t30;
-        checkpoints.push_back(cur);
-    }
-    struct ggml_tensor * t31   = ggml_rms_norm          (ctx, cur, f_norm_rms_eps);                 set_name(t31, "t31");     assert_shape_2d(t31, n_embd, N*n_batch);
-    struct ggml_tensor * t32   = ggml_repeat            (ctx, model->norm, t31);                    set_name(t32, "t32");     assert_shape_2d(t32, n_embd, N*n_batch);
-    struct ggml_tensor * t33   = ggml_mul               (ctx, t32, t31);                            set_name(t33, "t33");     assert_shape_2d(t33, n_embd, N*n_batch);
-    struct ggml_tensor * t34   = ggml_mul_mat           (ctx, model->output, t33);                  set_name(t34, "t34");     assert_shape_2d(t34, n_vocab, N*n_batch);
-    struct ggml_tensor * t35   = ggml_reshape_3d        (ctx, t34, n_vocab, N, n_batch);            set_name(t35, "t35");     assert_shape_3d(t35, n_vocab, N, n_batch);
-    struct ggml_tensor * t36   = ggml_cross_entropy_loss(ctx, t35, targets);                        set_name(t36, "t36");     assert_shape_1d(t36, 1);
-
-    checkpoints.push_back(t31);
-    checkpoints.push_back(t32);
-    checkpoints.push_back(t33);
-    checkpoints.push_back(t34);
-    checkpoints.push_back(t35);
-    checkpoints.push_back(t36);
-
-    ggml_build_forward_expand(gf, t36);
-
-    if (enable_checkpointing) {
-        ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
-    } else {
-        ggml_graph_cpy(gf, gb);
-        ggml_build_backward_expand(ctx, gf, gb, true);
-    }
-
-    if (alloc) {
-        // make sure some tensors are not reallocated by inserting new temporary nodes depending on them
-        int n_leafs_before = gb->n_leafs;
-        int n_nodes_before = gb->n_nodes;
-        // output tensors
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f));
-        // input gradient
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
-        // KQ_pos
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
-        GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
-        ggml_set_input(t36->grad);
-
-        // allocating checkpoints in one block to reduce memory fragmentation
-        // note: they will be freed in reverse order
-        for (int i = 0; i < (int) checkpoints.size(); ++i) {
-            if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
-                ggml_set_input(checkpoints[i]);
-            }
-        }
-
-        //int n_leafs_after = gb->n_leafs;
-        //int n_nodes_after = gb->n_nodes;
-        if (measure_only) {
-            // FIXME: will still allocate
-            ggml_gallocr_reserve(alloc, gb);
-        } else {
-            ggml_gallocr_alloc_graph(alloc, gb);
-
-            if (!measure_only) {
-                int * data = (int *) KQ_pos->data;
-                for (int i = 0; i < N; ++i) {
-                    data[i] = n_past + i;
-                }
-            }
-        }
-
-        // remove the additional nodes and leafs
-        for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
-            gb->leafs[i] = NULL;
-        }
-        for (int i = n_nodes_before; i < gb->n_nodes; ++i) {
-            gb->nodes[i] = NULL;
-        }
-        gb->n_leafs = n_leafs_before;
-        gb->n_nodes = n_nodes_before;
-    }
-
-    *logits = t35;
-    return t36;
-}
-
-#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
-do { \
-    const std::string skey(key); \
-    const int kid = gguf_find_key(ctx, skey.c_str()); \
-    if (kid >= 0) { \
-        enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
-        if (ktype != (type)) { \
-            die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
-        } \
-        (dst) = func(ctx, kid); \
-    } else if (req) { \
-        die_fmt("key not found in model: %s", skey.c_str()); \
-    } \
-} while (0)
-
-static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model) {
-    // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
-    std::string arch;
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-    auto kv = [&arch, &keybuf](const char * key) -> const char * {
-        snprintf(keybuf.data(), keybuf.size(), key, arch.c_str());
-        return keybuf.data();
-    };
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-    auto tn = [&tn_buf](const char * key) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
-        return tn_buf.data();
-    };
-    auto tni = [&tn_buf](const char * key, int bid) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
-        return tn_buf.data();
-    };
-
-    GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
-    GGML_ASSERT(arch == "llama");
-
-    uint32_t ftype_u;
-    GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE);
-    GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32);
-
-    // n_ctx was not saved in earlier checkpoint file versions, so we make it optional here
-    GGUF_GET_KEY(fctx, model->hparams.n_ctx,   gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));
-
-    GGUF_GET_KEY(fctx, model->hparams.n_embd,  gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
-    GGUF_GET_KEY(fctx, model->hparams.n_ff,    gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
-    GGUF_GET_KEY(fctx, model->hparams.n_head,  gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
-    GGUF_GET_KEY(fctx, model->hparams.n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));
-
-    model->hparams.n_rot = model->hparams.n_embd / model->hparams.n_head;
-    GGUF_GET_KEY(fctx, model->hparams.n_rot,   gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));
-
-    float rope_freq_scale = 1.0f;
-    GGUF_GET_KEY(fctx, model->hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
-    GGUF_GET_KEY(fctx, model->hparams.rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
-    GGUF_GET_KEY(fctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
-    if (rope_freq_scale != 1.0f) {
-        model->hparams.rope_freq_scale = 1.0f / rope_freq_scale;
-    }
-
-    init_model(model);
-
-    copy_tensor_by_name(model->tok_embeddings, f_ggml_ctx, tn(LLM_TENSOR_TOKEN_EMBD));
-    copy_tensor_by_name(model->norm,           f_ggml_ctx, tn(LLM_TENSOR_OUTPUT_NORM));
-    copy_tensor_by_name(model->output,         f_ggml_ctx, tn(LLM_TENSOR_OUTPUT));
-
-    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-        copy_tensor_by_name(layer.attention_norm, f_ggml_ctx, tni(LLM_TENSOR_ATTN_NORM, i));
-        copy_tensor_by_name(layer.wq,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_Q, i));
-        copy_tensor_by_name(layer.wk,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_K, i));
-        copy_tensor_by_name(layer.wv,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i));
-        copy_tensor_by_name(layer.wo,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i));
-        copy_tensor_by_name(layer.ffn_norm,       f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i));
-        copy_tensor_by_name(layer.ffn_gate,       f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i));
-        copy_tensor_by_name(layer.ffn_down,       f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i));
-        copy_tensor_by_name(layer.ffn_up,         f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i));
-    }
-}
-
-static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) {
-    const char * arch = "llama";
-
-    enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-    auto kv = [arch, &keybuf](const char * key) -> const char * {
-        snprintf(keybuf.data(), keybuf.size(), key, arch);
-        return keybuf.data();
-    };
-
-    // set arch
-    gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
-    gguf_set_val_str(fctx, LLM_KV_GENERAL_NAME, arch);
-    gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
-
-    // set hparams
-    gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH),              model->hparams.n_ctx                  );
-    gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH),            model->hparams.n_embd                 );
-    gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH),         model->hparams.n_ff                   );
-    gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT),        model->hparams.n_head                 );
-    gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT),                 model->hparams.n_layer                );
-    gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT),        model->hparams.n_rot                  );
-
-    gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps         );
-    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE),              model->hparams.rope_freq_base         ); // TODO load in llama.cpp
-    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR),           1.0f / model->hparams.rope_freq_scale );
-
-    // set vocab by copying from vocab_model gguf file
-    {
-        struct gguf_init_params params = {
-            /*.no_alloc = */ false,
-            /*.ctx      = */ NULL,
-        };
-        struct gguf_context * vctx = gguf_init_from_file(fn_vocab_model, params);
-
-        const int token_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_LIST));
-        if (token_idx == -1) {
-            die("cannot find tokenizer vocab in model file");
-        }
-        const uint32_t n_vocab = gguf_get_arr_n(vctx, token_idx);
-
-        const int score_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_SCORES));
-        if (score_idx == -1) {
-            die("cannot find tokenizer scores in model file");
-        }
-
-        const float * scores = (const float * ) gguf_get_arr_data(vctx, score_idx);
-
-        const int toktype_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE));
-        if (toktype_idx == -1) {
-            die("cannot find token type list in GGUF file");
-        }
-
-        const int * toktypes = (const int * ) gguf_get_arr_data(vctx, toktype_idx);
-
-        std::string tokenizer_name;
-        GGUF_GET_KEY(vctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL));
-
-        gguf_set_val_str(fctx, kv(LLM_KV_TOKENIZER_MODEL), tokenizer_name.c_str());
-        gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_SCORES), GGUF_TYPE_FLOAT32, scores, n_vocab);
-        gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE), GGUF_TYPE_INT32, toktypes, n_vocab);
-
-        int32_t special_bos_id = 1;
-        int32_t special_eos_id = 2;
-        int32_t special_unk_id = 0;
-        int32_t special_sep_id = -1;
-        int32_t special_pad_id = -1;
-        if (tokenizer_name == "llama") {
-            // default special tokens
-            special_bos_id = 1;
-            special_eos_id = 2;
-            special_unk_id = 0;
-            special_sep_id = -1;
-            special_pad_id = -1;
-        } else if (tokenizer_name == "gpt2") {
-            // read and copy bpe merges
-            const int merges_keyidx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_MERGES));
-            if (merges_keyidx == -1) {
-                die("cannot find tokenizer merges in model file");
-            }
-
-            const int n_merges = gguf_get_arr_n(vctx, merges_keyidx);
-
-            std::vector<const char*> merges;
-            merges.resize(n_merges);
-            for (int i = 0; i < n_merges; i++) {
-                merges[i] = gguf_get_arr_str(vctx, merges_keyidx, i);
-            }
-            gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_MERGES), merges.data(), n_merges);
-
-            // default special tokens
-            special_bos_id = 11;
-            special_eos_id = 11;
-            special_unk_id = -1;
-            special_sep_id = -1;
-            special_pad_id = -1;
-        } else {
-            fprintf(stderr, "%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
-            fprintf(stderr, "%s: using default tokenizer: 'llama'", __func__);
-        }
-
-        std::vector<const char*> tokens;
-        tokens.resize(n_vocab);
-        for (uint32_t i = 0; i < n_vocab; i++) {
-            tokens[i] = gguf_get_arr_str(vctx, token_idx, i);
-        }
-        gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_LIST), tokens.data(), n_vocab);
-
-        GGUF_GET_KEY(vctx, special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
-        GGUF_GET_KEY(vctx, special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID));
-        GGUF_GET_KEY(vctx, special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID));
-        GGUF_GET_KEY(vctx, special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID));
-        GGUF_GET_KEY(vctx, special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID));
-
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_BOS_ID), special_bos_id);
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_EOS_ID), special_eos_id);
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_UNK_ID), special_unk_id);
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_SEP_ID), special_sep_id);
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_PAD_ID), special_pad_id);
-
-        gguf_free(vctx);
-    }
-
-    // add tensors
-    gguf_add_tensor(fctx, model->tok_embeddings);
-    gguf_add_tensor(fctx, model->norm);
-    gguf_add_tensor(fctx, model->output);
-    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-
-        gguf_add_tensor(fctx, layer.attention_norm);
-        gguf_add_tensor(fctx, layer.wq);
-        gguf_add_tensor(fctx, layer.wk);
-        gguf_add_tensor(fctx, layer.wv);
-        gguf_add_tensor(fctx, layer.wo);
-        gguf_add_tensor(fctx, layer.ffn_norm);
-        gguf_add_tensor(fctx, layer.ffn_gate);
-        gguf_add_tensor(fctx, layer.ffn_down);
-        gguf_add_tensor(fctx, layer.ffn_up);
-    }
-}
-
-static void save_llama_model_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model) {
-    printf("%s: saving to %s\n", __func__, filename);
-    struct gguf_context * fctx = gguf_init_empty();
-
-    save_llama_model_gguf(fctx, fn_vocab_model, model);
-
-    // write file
-    const bool only_meta = false;
-    gguf_write_to_file(fctx, filename, only_meta);
-    gguf_free(fctx);
-}
-
-static void load_checkpoint_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct train_state * train) {
-    load_llama_model_gguf(fctx, f_ggml_ctx, model);
-    if (load_train_state_gguf(fctx, f_ggml_ctx, train)) {
-        std::string train_type = LLM_KV_TRAINING_TYPE_TRAIN_MODEL;
-        GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
-        GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_TRAIN_MODEL);
-    } else {
-        printf("%s: loaded llama model as checkpoint\n", __func__);
-    }
-}
-
-static void save_checkpoint_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) {
-    gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_TRAIN_MODEL);
-    save_llama_model_gguf(fctx, fn_vocab_model, model);
-    save_train_state_gguf(fctx, train);
-}
-
-static bool load_checkpoint_file(const char * filename, struct my_llama_model * model, struct train_state * train) {
-    struct ggml_context * f_ggml_ctx;
-    struct gguf_init_params params;
-    params.no_alloc = false;
-    params.ctx = &f_ggml_ctx;
-    struct gguf_context * fctx = gguf_init_from_file(filename, params);
-    if (fctx == NULL) {
-        return false;
-    }
-
-    load_checkpoint_gguf(fctx, f_ggml_ctx, model, train);
-
-    gguf_free(fctx);
-    return true;
-}
-
-static void save_checkpoint_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) {
-    printf("%s: saving to %s\n", __func__, filename);
-    struct gguf_context * fctx = gguf_init_empty();
-
-    save_checkpoint_gguf(fctx, fn_vocab_model, model, train);
-
-    // write file
-    const bool only_meta = false;
-    gguf_write_to_file(fctx, filename, only_meta);
-    gguf_free(fctx);
-}
-
-struct train_params {
-    struct train_params_common common;
-
-    const char * fn_vocab_model;
-    const char * fn_model_out;
-
-    bool only_write_model;
-
-    int n_ctx;
-    int n_embd;
-    int n_head;
-    int n_layer;
-    int n_ff;
-
-    float f_norm_rms_eps;
-    float rope_freq_base;
-    float rope_freq_scale;
-};
-
-static struct train_params get_default_train_params() {
-    struct train_params params;
-    params.common = get_default_train_params_common();
-    params.fn_vocab_model    = "ggml-vic7b-uncensored-q4_0.bin";
-    params.fn_model_out      = "ggml-checkpoint-f32.bin";
-
-    params.only_write_model = false;
-
-    params.n_ctx      =  128;
-    params.n_embd     =  256;
-    params.n_head     =    8;
-    params.n_layer    =   16;
-    params.n_ff       =  768;
-
-    params.f_norm_rms_eps  = 1e-5f;
-    params.rope_freq_base  = 10000.0f;
-    params.rope_freq_scale = 1.0f;
-
-    return params;
-}
-
-static void train_print_usage(int argc, char ** argv, const struct train_params * params) {
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h, --help                 show this help message and exit\n");
-
-    fprintf(stderr, "  --vocab-model FNAME        model path from which to load vocab (default '%s')\n", params->fn_vocab_model);
-    fprintf(stderr, "  --model-out FNAME          path to save ggml model (default '%s')\n", params->fn_model_out);
-    fprintf(stderr, "  --only-write-model         only save llama model, don't do any training. use this if you only want to convert a checkpoint to a model.\n");
-    fprintf(stderr, "  --embd N                   Embedding size used for new models (default %d)\n", params->n_embd);
-    fprintf(stderr, "  --ff N                     Feedforward size used for new models. (default %d)\n", params->n_ff);
-    fprintf(stderr, "  --head N                   Number of heads for new models (default %d)\n", params->n_head);
-    fprintf(stderr, "  --layer N                  Number of layers for new models (default %d)\n", params->n_layer);
-    fprintf(stderr, "  --norm-rms-eps F           RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
-    fprintf(stderr, "  --rope-freq-base F         Frequency base for ROPE (default %f)\n", params->rope_freq_base);
-    fprintf(stderr, "  --rope-freq-scale F        Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
-
-    print_common_train_usage(argc, argv, &params->common);
-}
-
-static bool train_params_parse(int argc, char ** argv, struct train_params * params) {
-    bool invalid_param = false;
-    std::string arg;
-    struct train_params default_params = get_default_train_params();
-    const std::string arg_prefix = "--";
-
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
-        }
-
-        if (consume_common_train_arg(argc, argv, &i, &params->common, &invalid_param)) {
-            if (invalid_param) {
-                break;
-            } else if (params->common.print_usage) {
-                train_print_usage(argc, argv, &default_params);
-                exit(0);
-            }
-        } else if (arg == "--vocab-model") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_vocab_model = argv[i];
-        } else if (arg == "--model-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_out = argv[i];
-        } else if (arg == "--only-write-model") {
-            params->only_write_model = true;
-        } else if (arg == "--embd") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_embd = std::stoi(argv[i]);
-        } else if (arg == "--ff") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_ff = std::stoi(argv[i]);
-        } else if (arg == "--head") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_head = std::stoi(argv[i]);
-        } else if (arg == "--layer") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_layer = std::stoi(argv[i]);
-        } else if (arg == "--norm-rms-eps") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->f_norm_rms_eps = std::stof(argv[i]);
-        } else if (arg == "--rope-freq-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->rope_freq_base = std::stof(argv[i]);
-        } else if (arg == "--rope-freq-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->rope_freq_scale = std::stof(argv[i]);
-        } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            train_print_usage(argc, argv, &default_params);
-            exit(1);
-        }
-    }
-    if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
-        train_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    finish_processing_train_args(&params->common);
-
-    return true;
-}
-
-struct save_train_files_data {
-    const char            * fn_checkpoint_out;
-    const char            * fn_model_out;
-    const char            * fn_vocab_model;
-    const char            * pattern_fn_it;
-    const char            * fn_latest;
-    struct my_llama_model * model;
-};
-
-static void save_train_files(void * vdata, struct train_state * train) {
-    struct save_train_files_data * data   = (struct save_train_files_data *) vdata;
-    int64_t iter = train->opt->iter;
-
-    if (strlen(data->fn_checkpoint_out) > 0) {
-        save_checkpoint_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->fn_vocab_model, data->model, train);
-        save_checkpoint_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1  ).c_str(), data->fn_vocab_model, data->model, train);
-
-    }
-    if (strlen(data->fn_model_out) > 0) {
-        save_llama_model_file(get_train_filename(data->fn_model_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->fn_vocab_model, data->model);
-        save_llama_model_file(get_train_filename(data->fn_model_out, data->pattern_fn_it, data->fn_latest, -1  ).c_str(), data->fn_vocab_model, data->model);
-    }
-}
-
-static int64_t get_parameter_count(struct my_llama_model* model) {
-    int64_t nx = 0;
-    nx += ggml_nelements(model->tok_embeddings);
-    nx += ggml_nelements(model->norm);
-    nx += ggml_nelements(model->output);
-
-    for (uint32_t i = 0; i < model->layers.size(); ++i) {
-        auto & layer = model->layers[i];
-        nx += ggml_nelements(layer.attention_norm);
-        nx += ggml_nelements(layer.wq);
-        nx += ggml_nelements(layer.wk);
-        nx += ggml_nelements(layer.wv);
-        nx += ggml_nelements(layer.wo);
-        nx += ggml_nelements(layer.ffn_norm);
-        nx += ggml_nelements(layer.ffn_gate);
-        nx += ggml_nelements(layer.ffn_down);
-        nx += ggml_nelements(layer.ffn_up);
-    }
-    return nx;
-}
-
-int main(int argc, char ** argv) {
-    struct train_params params = get_default_train_params();
-
-    if (!train_params_parse(argc, argv, &params)) {
-        return 1;
-    }
-
-    if (params.common.seed == LLAMA_DEFAULT_SEED) {
-        params.common.seed = time(NULL);
-    }
-    printf("%s: seed: %u\n", __func__, params.common.seed);
-    srand(params.common.seed);
-
-    struct llama_model_params mparams = llama_model_default_params();
-    mparams.vocab_only = true;
-
-    struct llama_context_params cparams = llama_context_default_params();
-
-    struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, mparams);
-    struct llama_context * lctx = llama_new_context_with_model(lmodel, cparams);
-
-    struct my_llama_model model;
-    model.hparams.n_vocab = llama_n_vocab(lmodel);
-    model.hparams.n_ctx   = params.common.n_ctx;
-    model.hparams.n_embd  = params.n_embd;
-    model.hparams.n_head  = params.n_head;
-    model.hparams.n_layer = params.n_layer;
-    model.hparams.n_ff    = params.n_ff;
-    // llama.cpp requires n_rot to be exactly n_embd / n_head
-    model.hparams.n_rot   = model.hparams.n_embd / model.hparams.n_head;
-    model.hparams.f_norm_rms_eps  = params.f_norm_rms_eps;
-    model.hparams.rope_freq_base  = params.rope_freq_base;
-    model.hparams.rope_freq_scale = params.rope_freq_scale;
-
-    struct train_state      * train = init_train_state();
-    struct ggml_opt_context * opt   = train->opt;
-
-    // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
-    opt->params.print_forward_graph     = false;
-    opt->params.print_backward_graph    = false;
-    opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;
-    opt->params.n_threads               = params.common.n_threads;
-    opt->params.past                    = params.common.opt_past;
-    opt->params.delta                   = params.common.opt_delta;
-    opt->params.max_no_improvement      = params.common.opt_max_no_improvement;
-    opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation;
-    opt->params.adam.n_iter             = params.common.adam_n_iter;
-    opt->params.adam.sched              = 1.0f;
-    opt->params.adam.alpha              = params.common.adam_alpha;
-    opt->params.adam.decay              = params.common.adam_decay;
-    opt->params.adam.decay_min_ndim     = params.common.adam_decay_min_ndim;
-    opt->params.adam.beta1              = params.common.adam_beta1;
-    opt->params.adam.beta2              = params.common.adam_beta2;
-    opt->params.adam.gclip              = params.common.adam_gclip;
-    opt->params.adam.eps_f              = params.common.adam_eps_f;
-
-    printf("%s: init model\n", __func__);
-    bool existed = load_checkpoint_file(params.common.fn_checkpoint_in, &model, train);
-    if (existed) {
-        // overwrite last n_ctx with user provided n_ctx
-        if (params.common.custom_n_ctx) {
-            model.hparams.n_ctx = params.common.n_ctx;
-        }
-
-        const bool opt_past_changed = opt->params.past != params.common.opt_past;
-
-        if (opt_past_changed) {
-            die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value train from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting");
-            // need to discard previous optimizer past function value statistics and opt_init with new shapes
-            // TODO
-        }
-    } else {
-        init_model(&model);
-        randomize_model(&model, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f);
-        if (!params.only_write_model) {
-            ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&model));
-        }
-    }
-    opt->iter = train->train_its;
-
-    print_params(&model.hparams);
-    printf("%s: total train_iterations %llu\n", __func__, (long long unsigned) train->train_its);
-    printf("%s: seen train_samples     %llu\n", __func__, (long long unsigned) train->train_samples);
-    printf("%s: seen train_tokens      %llu\n", __func__, (long long unsigned) train->train_tokens);
-    printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs);
-    printf("%s: model_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)), (float) (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)) / (1024.0f*1024.0f));
-
-    if (params.only_write_model) {
-        save_train_files_data save_data;
-        save_data.fn_checkpoint_out = "";
-        save_data.fn_model_out      = params.fn_model_out;
-        save_data.fn_vocab_model    = params.fn_vocab_model;
-        save_data.pattern_fn_it     = params.common.pattern_fn_it;
-        save_data.fn_latest         = params.common.fn_latest;
-        save_data.model             = &model;
-
-        save_train_files(&save_data, train);
-
-        free_train_state(train);
-        ggml_free(model.ctx);
-        llama_free(lctx);
-        llama_free_model(lmodel);
-        return 0;
-    }
-
-    printf("%s: opt_size  = %zu bytes (%.1f MB)\n", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f));
-    printf("%s: opt iter %d\n", __func__, opt->iter);
-
-    int n_tokens = model.hparams.n_ctx;
-    int n_vocab  = model.hparams.n_vocab;
-    int n_batch  = params.common.n_batch;
-
-    // context for input tensors without their data
-    struct ggml_init_params ctx_input_params = {
-        ggml_tensor_overhead() * 2, // mem_size
-        NULL,                       // mem_buffer
-        true,                       // no_alloc
-    };
-    struct ggml_context * ctx_input = ggml_init(ctx_input_params);
-
-    // the input tensors
-    struct ggml_tensor * tokens_input  = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch);
-    struct ggml_tensor * target_probs  = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
-
-    // measure required memory for input tensors
-    // allocate input tensors
-    ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type());
-    size_t max_input_size = ggml_backend_buffer_get_size(input_data);
-    printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
-
-    // context for compute tensors without their data
-    const size_t estimated_compute_size_wo_data = (
-            2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() +
-            (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true))
-    );
-    struct ggml_init_params ctx_compute_params = {
-        estimated_compute_size_wo_data, // mem_size
-        NULL,                           // mem_buffer
-        true,                           // no_alloc
-    };
-    struct ggml_context * ctx_compute = NULL;
-
-    struct ggml_tensor * loss   = NULL;
-    struct ggml_tensor * logits = NULL;
-
-    struct ggml_cgraph * gf     = NULL;
-    struct ggml_cgraph * gb     = NULL;
-    struct ggml_cgraph * gb_tmp = NULL;
-
-    // measure required memory for compute tensors
-    size_t best_compute_size = SIZE_MAX;
-    enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT;
-    // find best evaluation order
-    for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
-        ctx_compute = ggml_init(ctx_compute_params);
-        ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-        gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-        gf->order = (enum ggml_cgraph_eval_order) order;
-        gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-        gb_tmp = params.common.use_checkpointing
-            ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
-            : NULL;
-        loss = llama_build_train_graphs(
-            &model, alloc, ctx_compute,
-            gf, gb, gb_tmp,
-            &logits, tokens_input, target_probs,
-            n_tokens, n_batch,
-            params.common.use_flash,
-            params.common.use_checkpointing,
-            true
-        );
-        size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer
-        if (max_compute_size < best_compute_size) {
-            best_compute_size = max_compute_size;
-            best_order = gf->order;
-        }
-        ggml_free(ctx_compute);
-    }
-    size_t max_compute_size = best_compute_size;
-    printf("%s: compute_size = %zu bytes (%.1f MB)\n", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f));
-    printf("%s: evaluation order = %s\n", __func__,
-        (best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? "LEFT_TO_RIGHT" :
-        (best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? "RIGHT_TO_LEFT" :
-        "invalid");
-
-    // allocate compute tensors
-    ctx_compute = ggml_init(ctx_compute_params);
-    ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-    gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-    gf->order = best_order;
-    gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-    gb_tmp = params.common.use_checkpointing
-        ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
-        : NULL;
-    loss = llama_build_train_graphs(
-        &model, alloc, ctx_compute,
-        gf, gb, gb_tmp,
-        &logits, tokens_input, target_probs,
-        n_tokens, n_batch,
-        params.common.use_flash,
-        params.common.use_checkpointing,
-        false
-    );
-
-    std::vector<llama_token> train_tokens;
-    std::vector<size_t> train_samples_begin;
-    std::vector<size_t> train_samples_size;
-    printf("%s: tokenize training data\n", __func__);
-    tokenize_file(lctx,
-            params.common.fn_train_data,
-            params.common.sample_start,
-            params.common.include_sample_start,
-            params.common.overlapping_samples,
-            n_tokens,
-            train_tokens,
-            train_samples_begin,
-            train_samples_size);
-    GGML_ASSERT(train_samples_begin.size() == train_samples_size.size());
-
-    printf("%s: number of training tokens: %zu\n", __func__, train_tokens.size());
-
-    size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size());
-    const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size());
-    if (changed_train_data) {
-        printf("%s: train data seems to have changed. restarting shuffled epoch.\n", __func__);
-    }
-    if (params.common.force_reshuffle) {
-        printf("%s: forced reshuffling of data. restarting with newly shuffled epoch.\n", __func__);
-    }
-    if ((train->shuffle_rng_state_current == "") || changed_train_data || params.common.force_reshuffle) {
-        train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed);
-        train->shuffle_sample_count = train_samples_size.size();
-        train->shuffle_next_sample = 0;
-        train->shuffle_samples_hash = shuffle_samples_hash;
-    }
-    std::vector<size_t> train_shuffled_samples_offs;
-    std::vector<size_t> train_shuffled_samples_begin;
-    std::vector<size_t> train_shuffled_samples_size;
-    train_shuffled_samples_offs.resize(train_samples_begin.size());
-    train_shuffled_samples_begin.resize(train_samples_begin.size());
-    train_shuffled_samples_size.resize(train_samples_size.size());
-    train->shuffle_rng_state_next = shuffle_samples(
-        train->shuffle_rng_state_current,
-        train_shuffled_samples_offs.data(),
-        train_shuffled_samples_begin.data(),
-        train_shuffled_samples_size.data(),
-        train_samples_begin.data(),
-        train_samples_size.data(),
-        train_samples_size.size());
-    printf("%s: begin training\n", __func__);
-
-    save_train_files_data save_data;
-    save_data.fn_checkpoint_out = params.common.fn_checkpoint_out;
-    save_data.fn_model_out      = params.fn_model_out;
-    save_data.fn_vocab_model    = params.fn_vocab_model;
-    save_data.pattern_fn_it     = params.common.pattern_fn_it;
-    save_data.fn_latest         = params.common.fn_latest;
-    save_data.model             = &model;
-
-    struct train_opt_callback_data opt_cb_data;
-    opt_cb_data.params                 = &params.common;
-    opt_cb_data.train                  = train;
-    opt_cb_data.save_cb                = &save_train_files;
-    opt_cb_data.save_data              = &save_data;
-    opt_cb_data.lctx                   = lctx;
-    opt_cb_data.last_save_iter         = opt->iter;
-    opt_cb_data.tokens_data            = train_tokens.data();
-    opt_cb_data.tokens_size            = train_tokens.size();
-    opt_cb_data.samples_begin          = train_samples_begin.data();
-    opt_cb_data.samples_size           = train_samples_size.data();
-    opt_cb_data.shuffled_samples_offs  = train_shuffled_samples_offs.data();
-    opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data();
-    opt_cb_data.shuffled_samples_size  = train_shuffled_samples_size.data();
-    opt_cb_data.samples_count          = train_samples_size.size();
-    opt_cb_data.tokens_input           = tokens_input;
-    opt_cb_data.target_probs           = target_probs;
-    opt_cb_data.first_iter             = opt->iter;
-    opt_cb_data.first_epoch            = train->train_epochs;
-    opt_cb_data.iter_at_last_epoch     = -1;
-    opt_cb_data.last_time              = ggml_time_ms();
-    opt_cb_data.millis_per_iter        = 0.0;
-
-    // measure required memory for work buffer
-    size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE;
-    printf("%s: work_size = %zu bytes (%.1f MB)\n", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f));
-
-    // context for work buffer
-    struct ggml_init_params ctx_work_params = {
-        max_work_size, // mem_size
-        NULL,          // mem_buffer
-        false,         // no_alloc
-    };
-    struct ggml_context * ctx_work = ggml_init(ctx_work_params);
-
-    int64_t t0 = ggml_time_ms();
-
-    ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data);
-
-    ggml_free(ctx_work);
-    ggml_free(ctx_compute);
-    ggml_free(ctx_input);
-
-    int64_t t1 = ggml_time_ms();
-    printf("%s: total training time: ", __func__);
-    print_duration((double) (t1 - t0));
-    printf("\n");
-
-    int new_iters = opt->iter - opt_cb_data.last_save_iter;
-    if (new_iters > 0) {
-        train->train_its     += new_iters;
-        train->train_tokens  += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens;
-
-        save_train_files(&save_data, train);
-        opt_cb_data.last_save_iter = opt->iter;
-    }
-
-    ggml_free(opt->ctx);
-    free_train_state(train);
-    ggml_free(model.ctx);
-    llama_free(lctx);
-    llama_free_model(lmodel);
-    return 0;
-}
diff --git a/flake.lock b/flake.lock
index c9ead0bf70cb4..f9e1548a2aca5 100644
--- a/flake.lock
+++ b/flake.lock
@@ -5,11 +5,11 @@
         "nixpkgs-lib": "nixpkgs-lib"
       },
       "locked": {
-        "lastModified": 1714641030,
-        "narHash": "sha256-yzcRNDoyVP7+SCNX0wmuDju1NUCt8Dz9+lyUXEI0dbI=",
+        "lastModified": 1722555600,
+        "narHash": "sha256-XOQkdLafnb/p9ij77byFQjDf5m5QYl9b2REiVClC+x4=",
         "owner": "hercules-ci",
         "repo": "flake-parts",
-        "rev": "e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e",
+        "rev": "8471fe90ad337a8074e957b69ca4d0089218391d",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1714635257,
-        "narHash": "sha256-4cPymbty65RvF1DWQfc+Bc8B233A1BWxJnNULJKQ1EY=",
+        "lastModified": 1723175592,
+        "narHash": "sha256-M0xJ3FbDUc4fRZ84dPGx5VvgFsOzds77KiBMW/mMTnI=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "63c3a29ca82437c87573e4c6919b09a24ea61b0f",
+        "rev": "5e0ca22929f3342b19569b21b2f3462f053e497b",
         "type": "github"
       },
       "original": {
@@ -36,14 +36,14 @@
     },
     "nixpkgs-lib": {
       "locked": {
-        "lastModified": 1714640452,
-        "narHash": "sha256-QBx10+k6JWz6u7VsohfSw8g8hjdBZEf8CFzXH1/1Z94=",
+        "lastModified": 1722555339,
+        "narHash": "sha256-uFf2QeW7eAHlYXuDktm9c25OxOyCoUOQmh5SZ9amE5Q=",
         "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz"
+        "url": "https://github.com/NixOS/nixpkgs/archive/a5d394176e64ab29c852d03346c1fc9b0b7d33eb.tar.gz"
       },
       "original": {
         "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz"
+        "url": "https://github.com/NixOS/nixpkgs/archive/a5d394176e64ab29c852d03346c1fc9b0b7d33eb.tar.gz"
       }
     },
     "root": {
diff --git a/flake.nix b/flake.nix
index 9cd3756e53e51..c69637d111784 100644
--- a/flake.nix
+++ b/flake.nix
@@ -63,7 +63,7 @@
   # nix-repl> :lf github:ggerganov/llama.cpp
   # Added 13 variables.
   # nix-repl> outputs.apps.x86_64-linux.quantize
-  # { program = "/nix/store/00000000000000000000000000000000-llama.cpp/bin/quantize"; type = "app"; }
+  # { program = "/nix/store/00000000000000000000000000000000-llama.cpp/bin/llama-quantize"; type = "app"; }
   # ```
   outputs =
     { self, flake-parts, ... }@inputs:
@@ -159,7 +159,6 @@
                 windows = config.legacyPackages.llamaPackagesWindows.llama-cpp;
               }
               // lib.optionalAttrs pkgs.stdenv.isLinux {
-                opencl = config.packages.default.override { useOpenCL = true; };
                 cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp;
 
                 mpi-cpu = config.packages.default.override { useMpi = true; };
diff --git a/ggml-cuda/concat.cu b/ggml-cuda/concat.cu
deleted file mode 100644
index 2941d2f1770a8..0000000000000
--- a/ggml-cuda/concat.cu
+++ /dev/null
@@ -1,49 +0,0 @@
-#include "concat.cuh"
-
-static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) {
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
-    // operation
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-    if (blockIdx.z < ne02) { // src0
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            blockIdx.z * ne0 * gridDim.y;
-        dst[offset_dst] = x[offset_src];
-    } else {
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            (blockIdx.z - ne02) * ne0 *  gridDim.y;
-        dst[offset_dst] = y[offset_src];
-    }
-}
-
-static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) {
-    int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, ne1, ne2);
-    concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
-}
-
-void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    const float * src0_d = (const float *)src0->data;
-    const float * src1_d = (const float *)src1->data;
-    float * dst_d = (float *)dst->data;
-    cudaStream_t stream = ctx.stream();
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
-        concat_f32_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream);
-    }
-}
diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh
deleted file mode 100644
index 1dd519bdee7f1..0000000000000
--- a/ggml-cuda/fattn-common.cuh
+++ /dev/null
@@ -1,162 +0,0 @@
-#include "common.cuh"
-
-#include <cstdint>
-
-#define FATTN_KQ_STRIDE       256
-#define HALF_MAX_HALF         __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
-#define SOFTMAX_FTZ_THRESHOLD -20.0f                   // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.
-
-typedef void (* fattn_kernel_t)(
-        const char * __restrict__ Q,
-        const char * __restrict__ K,
-        const char * __restrict__ V,
-        const char * __restrict__ mask,
-        float      * __restrict__ dst,
-        float2     * __restrict__ dst_meta,
-        const float scale,
-        const float max_bias,
-        const float m0,
-        const float m1,
-        const uint32_t n_head_log2,
-        const int ne00,
-        const int ne01,
-        const int ne02,
-        const int ne03,
-        const int ne10,
-        const int ne11,
-        const int ne12,
-        const int ne13,
-        const int ne31,
-        const int nb31,
-        const int nb01,
-        const int nb02,
-        const int nb03,
-        const int nb11,
-        const int nb12,
-        const int nb13,
-        const int ne0,
-        const int ne1,
-        const int ne2,
-        const int ne3);
-
-template<int D, int parallel_blocks> // D == head size
-#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-__launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-static __global__ void flash_attn_combine_results(
-        const float  * __restrict__ VKQ_parts,
-        const float2 * __restrict__ VKQ_meta,
-        float * __restrict__ dst) {
-    VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x;
-    VKQ_meta  += parallel_blocks   * gridDim.y*blockIdx.x;
-    dst       +=                 D * gridDim.y*blockIdx.x;
-
-    const int tid = threadIdx.x;
-    __builtin_assume(tid < D);
-
-    __shared__ float2 meta[parallel_blocks];
-    if (tid < 2*parallel_blocks) {
-        ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid];
-    }
-
-    __syncthreads();
-
-    float kqmax = meta[0].x;
-#pragma unroll
-    for (int l = 1; l < parallel_blocks; ++l) {
-        kqmax = max(kqmax, meta[l].x);
-    }
-
-    float VKQ_numerator   = 0.0f;
-    float VKQ_denominator = 0.0f;
-#pragma unroll
-    for (int l = 0; l < parallel_blocks; ++l) {
-        const float diff = meta[l].x - kqmax;
-        const float KQ_max_scale = expf(diff);
-        const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
-        *((uint32_t *) &KQ_max_scale) &= ftz_mask;
-
-        VKQ_numerator   += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid];
-        VKQ_denominator += KQ_max_scale * meta[l].y;
-    }
-
-    dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
-}
-
-template <int D, int parallel_blocks>
-void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) {
-    const ggml_tensor * Q = dst->src[0];
-    const ggml_tensor * K = dst->src[1];
-    const ggml_tensor * V = dst->src[2];
-
-    const ggml_tensor * mask = dst->src[3];
-
-    ggml_tensor * KQV = dst;
-
-    GGML_ASSERT(Q->type == GGML_TYPE_F32);
-    GGML_ASSERT(K->type == GGML_TYPE_F16);
-    GGML_ASSERT(V->type == GGML_TYPE_F16);
-    GGML_ASSERT(KQV->type == GGML_TYPE_F32);
-
-    GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
-    GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
-                                "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");
-
-    GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
-
-    ggml_cuda_pool & pool = ctx.pool();
-    cudaStream_t main_stream = ctx.stream();
-
-    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
-    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
-
-    if (parallel_blocks > 1) {
-        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
-        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
-    }
-
-    const dim3 block_dim(WARP_SIZE, nwarps, 1);
-    const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
-    const int  shmem = 0;
-
-    float scale    = 1.0f;
-    float max_bias = 0.0f;
-
-    memcpy(&scale,    (float *) KQV->op_params + 0, sizeof(float));
-    memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
-
-    const uint32_t n_head      = Q->ne[2];
-    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
-
-    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
-
-    fattn_kernel<<<blocks_num, block_dim, shmem, main_stream>>>(
-        (const char *) Q->data,
-        (const char *) K->data,
-        (const char *) V->data,
-        mask ? ((const char *) mask->data) : nullptr,
-        (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
-        scale, max_bias, m0, m1, n_head_log2,
-        Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
-        K->ne[0], K->ne[1], K->ne[2], K->ne[3],
-        mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
-        Q->nb[1], Q->nb[2], Q->nb[3],
-        K->nb[1], K->nb[2], K->nb[3],
-        KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
-    );
-    CUDA_CHECK(cudaGetLastError());
-
-    if ((parallel_blocks) == 1) {
-        return;
-    }
-
-    const dim3 block_dim_combine(D, 1, 1);
-    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
-    const int  shmem_combine = 0;
-
-    flash_attn_combine_results<D, parallel_blocks>
-        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
-        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
-    CUDA_CHECK(cudaGetLastError());
-}
diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu
deleted file mode 100644
index 54e1ac5d16050..0000000000000
--- a/ggml-cuda/fattn-vec-f16.cu
+++ /dev/null
@@ -1,326 +0,0 @@
-#include "common.cuh"
-#include "fattn-common.cuh"
-#include "fattn-vec-f16.cuh"
-
-template<int D, int ncols, int parallel_blocks> // D == head size
-#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-__launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-static __global__ void flash_attn_vec_ext_f16(
-        const char * __restrict__ Q,
-        const char * __restrict__ K,
-        const char * __restrict__ V,
-        const char * __restrict__ mask,
-        float      * __restrict__ dst,
-        float2     * __restrict__ dst_meta,
-        const float scale,
-        const float max_bias,
-        const float m0,
-        const float m1,
-        const uint32_t n_head_log2,
-        const int ne00,
-        const int ne01,
-        const int ne02,
-        const int ne03,
-        const int ne10,
-        const int ne11,
-        const int ne12,
-        const int ne13,
-        const int ne31,
-        const int nb31,
-        const int nb01,
-        const int nb02,
-        const int nb03,
-        const int nb11,
-        const int nb12,
-        const int nb13,
-        const int ne0,
-        const int ne1,
-        const int ne2,
-        const int ne3) {
-#if FP16_AVAILABLE
-    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
-
-    const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
-    const int ip  =  blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
-
-    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
-    const float2 * Q_f2  = (const float2 *) (Q    + nb02* blockIdx.y              + nb01*ic0);
-    const half2  * K_h2  = (const half2  *) (K    + nb12*(blockIdx.y / gqa_ratio));
-    const half   * V_h   = (const half   *) (V    + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
-    const half   * maskh = (const half   *)  mask + ne11*ic0;
-
-    const int stride_KV  = nb11 / sizeof(half);
-    const int stride_KV2 = nb11 / sizeof(half2);
-
-    const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
-    const half  slopeh = __float2half(slopef);
-
-    static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
-    constexpr int nwarps = D / WARP_SIZE;
-    const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
-    __builtin_assume(tid < D);
-
-    __shared__ half KQ[ncols*D];
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-        KQ[j*D + tid] = -HALF_MAX_HALF;
-    }
-    half2 * KQ2 = (half2 *) KQ;
-
-    half kqmax[ncols];
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-        kqmax[j] = -HALF_MAX_HALF;
-    }
-    half kqsum[ncols] = {0.0f};
-
-    __shared__ half kqmax_shared[ncols][WARP_SIZE];
-    __shared__ half kqsum_shared[ncols][WARP_SIZE];
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-        if (threadIdx.y == 0) {
-            kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
-            kqsum_shared[j][threadIdx.x] = 0.0f;
-        }
-    }
-    __syncthreads();
-
-    // Convert Q to half2 and store in registers:
-    half2 Q_h2[ncols][D/(2*WARP_SIZE)];
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-#pragma unroll
-        for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
-
-            const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i];
-            Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
-        }
-    }
-
-    half2 VKQ[ncols] = {{0.0f, 0.0f}};
-
-    const int k_start = parallel_blocks == 1 ? 0 : ip*D;
-    for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
-        // Calculate KQ tile and keep track of new maximum KQ values:
-
-        // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
-        // see https://github.com/ggerganov/llama.cpp/pull/7061 .
-        // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
-        half kqmax_new = kqmax[0];
-        half kqmax_new_arr[ncols];
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            kqmax_new_arr[j] = kqmax[j];
-        }
-
-#pragma unroll
-        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
-            const int i_KQ = i_KQ_0 + threadIdx.y;
-
-            if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
-                break;
-            }
-
-            half2 sum2[ncols] = {{0.0f, 0.0f}};
-#pragma unroll
-            for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
-                const int k_KQ = k_KQ_0 + threadIdx.x;
-
-                const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
-#pragma unroll
-                for (int j = 0; j < ncols; ++j) {
-                    sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE];
-                }
-            }
-
-#pragma unroll
-            for (int j = 0; j < ncols; ++j) {
-                sum2[j] = warp_reduce_sum(sum2[j]);
-                half sum = __low2half(sum2[j]) + __high2half(sum2[j]);
-                sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
-
-                if (ncols == 1) {
-                    kqmax_new        = ggml_cuda_hmax(kqmax_new,        sum);
-                } else {
-                    kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
-                }
-
-                if (threadIdx.x == 0) {
-                    KQ[j*D + i_KQ] = sum;
-                }
-            }
-        }
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
-
-            kqmax_new_j = warp_reduce_max(kqmax_new_j);
-            if (threadIdx.x == 0) {
-                kqmax_shared[j][threadIdx.y] = kqmax_new_j;
-            }
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            half kqmax_new_j = kqmax_shared[j][threadIdx.x];
-            kqmax_new_j = warp_reduce_max(kqmax_new_j);
-
-            const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
-            kqmax[j] = kqmax_new_j;
-
-            const half val = hexp(KQ[j*D + tid] - kqmax[j]);
-            kqsum[j] = kqsum[j]*KQ_max_scale + val;
-            KQ[j*D + tid] = val;
-
-            VKQ[j] *= __half2half2(KQ_max_scale);
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int k0 = 0; k0 < D; k0 += 2) {
-            if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
-                break;
-            }
-
-            half2 V_k;
-            reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
-            reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
-#pragma unroll
-            for (int j = 0; j < ncols; ++j) {
-                VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
-            }
-        }
-
-        __syncthreads();
-    }
-
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-        kqsum[j] = warp_reduce_sum(kqsum[j]);
-        if (threadIdx.x == 0) {
-            kqsum_shared[j][threadIdx.y] = kqsum[j];
-        }
-    }
-
-    __syncthreads();
-
-#pragma unroll
-    for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
-        kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
-        kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
-
-        half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
-        if (parallel_blocks == 1) {
-            dst_val /= kqsum[j_VKQ];
-        }
-        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
-        dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
-    }
-
-    if (parallel_blocks != 1 && tid < ncols) {
-        dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
-    }
-#else
-   NO_DEVICE_CODE;
-#endif // FP16_AVAILABLE
-}
-
-void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    ggml_tensor * KQV = dst;
-    ggml_tensor * Q   = dst->src[0];
-
-    const int32_t precision = KQV->op_params[2];
-    GGML_ASSERT(precision == GGML_PREC_DEFAULT);
-
-    constexpr int cols_per_block  = 1;
-    constexpr int parallel_blocks = 4;
-    switch (Q->ne[0]) {
-        case  64: {
-            constexpr int      D = 64;
-            constexpr int nwarps = D/WARP_SIZE;
-            fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
-        } break;
-        case 128: {
-            constexpr int      D = 128;
-            constexpr int nwarps = D/WARP_SIZE;
-            fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
-        } break;
-        case 256: {
-            constexpr int      D = 256;
-            constexpr int nwarps = D/WARP_SIZE;
-            fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
-        } break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-}
-
-template <int cols_per_block, int parallel_blocks>
-void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * Q = dst->src[0];
-    switch (Q->ne[0]) {
-        case  64: {
-            constexpr int      D = 64;
-            constexpr int nwarps = D/WARP_SIZE;
-            fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
-        } break;
-        case 128: {
-            constexpr int      D = 128;
-            constexpr int nwarps = D/WARP_SIZE;
-            fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
-        } break;
-        default: {
-            GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
-        } break;
-    }
-}
-
-void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * KQV = dst;
-    const ggml_tensor * Q   = dst->src[0];
-
-    const int32_t precision = KQV->op_params[2];
-    GGML_ASSERT(precision == GGML_PREC_DEFAULT);
-
-    if (Q->ne[1] == 1) {
-        ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
-        return;
-    }
-
-    if (Q->ne[1] == 2) {
-        constexpr int cols_per_block  = 2;
-        constexpr int parallel_blocks = 4;
-        launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-        return;
-    }
-
-    if (Q->ne[1] <= 4) {
-        constexpr int cols_per_block  = 4;
-        constexpr int parallel_blocks = 4;
-        launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-        return;
-    }
-
-    if (Q->ne[1] <= 8) {
-        constexpr int cols_per_block  = 8;
-        constexpr int parallel_blocks = 4;
-        launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-        return;
-    }
-
-    constexpr int cols_per_block  = 8;
-    constexpr int parallel_blocks = 1;
-    launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-}
diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh
deleted file mode 100644
index c7023610ab2d4..0000000000000
--- a/ggml-cuda/fattn-vec-f16.cuh
+++ /dev/null
@@ -1,5 +0,0 @@
-#include "common.cuh"
-
-void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
-
-void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu
deleted file mode 100644
index 5bcabd0928451..0000000000000
--- a/ggml-cuda/fattn-vec-f32.cu
+++ /dev/null
@@ -1,275 +0,0 @@
-#include "common.cuh"
-#include "fattn-common.cuh"
-#include "fattn-vec-f32.cuh"
-
-template<int D, int ncols, int parallel_blocks> // D == head size
-#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-__launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-static __global__ void flash_attn_vec_ext_f32(
-        const char * __restrict__ Q,
-        const char * __restrict__ K,
-        const char * __restrict__ V,
-        const char * __restrict__ mask,
-        float      * __restrict__ dst,
-        float2     * __restrict__ dst_meta,
-        const float scale,
-        const float max_bias,
-        const float m0,
-        const float m1,
-        const uint32_t n_head_log2,
-        const int ne00,
-        const int ne01,
-        const int ne02,
-        const int ne03,
-        const int ne10,
-        const int ne11,
-        const int ne12,
-        const int ne13,
-        const int ne31,
-        const int nb31,
-        const int nb01,
-        const int nb02,
-        const int nb03,
-        const int nb11,
-        const int nb12,
-        const int nb13,
-        const int ne0,
-        const int ne1,
-        const int ne2,
-        const int ne3) {
-    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
-
-    const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
-    const int ip  =  blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
-
-    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
-    const float2 * Q_f2  = (const float2 *) (Q    + nb02* blockIdx.y              + nb01*ic0);
-    const half2  * K_h2  = (const half2  *) (K    + nb12*(blockIdx.y / gqa_ratio));
-    const half   * V_h   = (const half   *) (V    + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
-    const half   * maskh = (const half   *)  mask + ne11*ic0;
-
-    const int stride_KV  = nb11 / sizeof(half);
-    const int stride_KV2 = nb11 / sizeof(half2);
-
-    const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
-
-    static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
-    constexpr int nwarps = D / WARP_SIZE;
-    const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
-    __builtin_assume(tid < D);
-
-    __shared__ float KQ[ncols*D];
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-        KQ[j*D + tid] = -FLT_MAX/2.0f;
-    }
-
-    float kqmax[ncols];
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-        kqmax[j] = -FLT_MAX/2.0f;
-    }
-    float kqsum[ncols] = {0.0f};
-
-    __shared__ float kqmax_shared[ncols][WARP_SIZE];
-    __shared__ float kqsum_shared[ncols][WARP_SIZE];
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-        if (threadIdx.y == 0) {
-            kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f;
-            kqsum_shared[j][threadIdx.x] = 0.0f;
-        }
-    }
-    __syncthreads();
-
-    // Convert Q to half2 and store in registers:
-    float2 Q_h2[ncols][D/(2*WARP_SIZE)];
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-#pragma unroll
-        for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
-
-            Q_h2[j][i0/WARP_SIZE]    = Q_f2[j*(nb01/sizeof(float2)) + i];
-            Q_h2[j][i0/WARP_SIZE].x *= scale;
-            Q_h2[j][i0/WARP_SIZE].y *= scale;
-        }
-    }
-
-    float VKQ[ncols] = {0.0f};
-
-    const int k_start = parallel_blocks == 1 ? 0 : ip*D;
-    for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
-        // Calculate KQ tile and keep track of new maximum KQ values:
-
-        float kqmax_new_arr[ncols];
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            kqmax_new_arr[j] = kqmax[j];
-        }
-
-#pragma unroll
-        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
-            const int i_KQ = i_KQ_0 + threadIdx.y;
-
-            if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
-                break;
-            }
-
-            float sum[ncols] = {0.0f};
-#pragma unroll
-            for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
-                const int k_KQ = k_KQ_0 + threadIdx.x;
-
-                const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
-#pragma unroll
-                for (int j = 0; j < ncols; ++j) {
-                    sum[j] +=  __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x;
-                    sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y;
-                }
-            }
-
-#pragma unroll
-            for (int j = 0; j < ncols; ++j) {
-                sum[j] = warp_reduce_sum(sum[j]);
-                sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
-
-                kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]);
-
-                if (threadIdx.x == 0) {
-                    KQ[j*D + i_KQ] = sum[j];
-                }
-            }
-        }
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            float kqmax_new_j = kqmax_new_arr[j];
-
-            kqmax_new_j = warp_reduce_max(kqmax_new_j);
-            if (threadIdx.x == 0) {
-                kqmax_shared[j][threadIdx.y] = kqmax_new_j;
-            }
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            float kqmax_new_j = kqmax_shared[j][threadIdx.x];
-            kqmax_new_j = warp_reduce_max(kqmax_new_j);
-
-            const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
-            kqmax[j] = kqmax_new_j;
-
-            const float val = expf(KQ[j*D + tid] - kqmax[j]);
-            kqsum[j] = kqsum[j]*KQ_max_scale + val;
-            KQ[j*D + tid] = val;
-
-            VKQ[j] *= KQ_max_scale;
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int k = 0; k < D; ++k) {
-            if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) {
-                break;
-            }
-
-            const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]);
-#pragma unroll
-            for (int j = 0; j < ncols; ++j) {
-                VKQ[j] += V_ki*KQ[j*D + k];
-            }
-        }
-
-        __syncthreads();
-    }
-
-#pragma unroll
-    for (int j = 0; j < ncols; ++j) {
-        kqsum[j] = warp_reduce_sum(kqsum[j]);
-        if (threadIdx.x == 0) {
-            kqsum_shared[j][threadIdx.y] = kqsum[j];
-        }
-    }
-
-    __syncthreads();
-
-#pragma unroll
-    for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
-        kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
-        kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
-
-        float dst_val = VKQ[j_VKQ];
-        if (parallel_blocks == 1) {
-            dst_val /= kqsum[j_VKQ];
-        }
-        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
-        dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
-    }
-
-    if (parallel_blocks != 1 && tid < ncols) {
-        dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
-    }
-}
-
-template <int cols_per_block, int parallel_blocks>
-void launch_fattn_vec_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * Q = dst->src[0];
-    switch (Q->ne[0]) {
-        case  64: {
-            constexpr int      D = 64;
-            constexpr int nwarps = D/WARP_SIZE;
-            fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
-        } break;
-        case 128: {
-            constexpr int      D = 128;
-            constexpr int nwarps = D/WARP_SIZE;
-            fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
-        } break;
-        default: {
-            GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
-        } break;
-    }
-}
-
-void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * Q = dst->src[0];
-
-    if (Q->ne[1] == 1) {
-        constexpr int cols_per_block  = 1;
-        constexpr int parallel_blocks = 4;
-        launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-        return;
-    }
-
-    if (Q->ne[1] == 2) {
-        constexpr int cols_per_block  = 2;
-        constexpr int parallel_blocks = 4;
-        launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-        return;
-    }
-
-    if (Q->ne[1] <= 4) {
-        constexpr int cols_per_block  = 4;
-        constexpr int parallel_blocks = 4;
-        launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-        return;
-    }
-
-    if (Q->ne[1] <= 8) {
-        constexpr int cols_per_block  = 8;
-        constexpr int parallel_blocks = 4;
-        launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-        return;
-    }
-
-    constexpr int cols_per_block  = 8;
-    constexpr int parallel_blocks = 1;
-    launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
-}
diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh
deleted file mode 100644
index 614d54ae39253..0000000000000
--- a/ggml-cuda/fattn-vec-f32.cuh
+++ /dev/null
@@ -1,3 +0,0 @@
-#include "common.cuh"
-
-void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu
deleted file mode 100644
index 7948f1b1237fa..0000000000000
--- a/ggml-cuda/mmq.cu
+++ /dev/null
@@ -1,2255 +0,0 @@
-#include "mmq.cuh"
-#include "vecdotq.cuh"
-
-typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
-typedef void (*load_tiles_cuda_t)(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
-typedef float (*vec_dot_q_mul_mat_cuda_t)(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
-typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh);
-    GGML_UNUSED(x_sc);
-
-    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
-
-    *x_ql = tile_x_qs;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_0;
-    const int kqsx = k % QI4_0;
-
-    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
-
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
-        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const float * x_dmf = (const float *) x_dm;
-
-    int u[2*VDR_Q4_0_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
-    }
-
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
-         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    __shared__ int   tile_x_qs[mmq_y * (WARP_SIZE) +     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
-
-    *x_ql = tile_x_qs;
-    *x_dm = tile_x_dm;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_1;
-    const int kqsx = k % QI4_1;
-
-    const block_q4_1 * bx0 = (const block_q4_1 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
-        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-
-    int u[2*VDR_Q4_1_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
-    }
-
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
-         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    __shared__ int  tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];
-
-    *x_ql = tile_x_ql;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_0;
-    const int kqsx = k % QI5_0;
-
-    const block_q5_0 * bx0 = (const block_q5_0 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        const int ql = get_int_from_uint8(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
-
-        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
-        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
-        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
-        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
-        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
-        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
-
-        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
-        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
-        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
-        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
-        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
-        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
-        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    int u[2*VDR_Q5_0_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
-    }
-
-    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
-        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset < nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_1;
-    const int kqsx = k % QI5_1;
-
-    const block_q5_1 * bx0 = (const block_q5_1 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
-
-        int qs0 = (ql >>  0) & 0x0F0F0F0F;
-        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
-        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
-        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
-        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
-
-        int qs1 = (ql >>  4) & 0x0F0F0F0F;
-        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
-        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
-        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
-        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
-        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
-
-    int u[2*VDR_Q5_1_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
-    }
-
-    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
-        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];
-
-    *x_ql = tile_x_qs;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI8_0;
-    const int kqsx = k % QI8_0;
-    float * x_dmf = (float *) x_dm;
-
-    const block_q8_0 * bx0 = (const block_q8_0 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
-        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
-
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
-         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh);
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh);
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI2_K;
-    const int kqsx = k % QI2_K;
-
-    const block_q2_K * bx0 = (const block_q2_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
-        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
-
-        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh);
-
-    const int kbx = k / QI2_K;
-    const int ky  = (k % QI2_K) * QR2_K;
-    const float * y_df = (const float *) y_ds;
-
-    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
-
-    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
-    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
-
-#pragma unroll
-    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
-        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
-    }
-
-    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
-
-    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
-    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
-    __shared__ int   tile_x_qh[mmq_y * (WARP_SIZE/2)     + mmq_y/2];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_qh = tile_x_qh;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI3_K;
-    const int kqsx = k % QI3_K;
-
-    const block_q3_K * bx0 = (const block_q3_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
-    const int kbxd = k % blocks_per_tile_x_row;
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
-        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
-        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
-
-        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
-
-        const int ksc = k % (QI3_K/4);
-
-        const int ksc_low = ksc % (QI3_K/8);
-        const int shift_low = 4 * (ksc / (QI3_K/8));
-        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
-
-        const int ksc_high = QI3_K/8;
-        const int shift_high = 2 * ksc;
-        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
-
-        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
-
-        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-
-    const int kbx  = k / QI3_K;
-    const int ky  = (k % QI3_K) * QR3_K;
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
-
-    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
-        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
-        const int shift = 2 * ((ky % 32) / 8);
-        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
-
-        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
-        const int vlh = (vh << 2) & 0x04040404;
-
-        v[l] = __vsubss4(vll, vlh);
-    }
-
-    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
-    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh);
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh);
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI4_K; // == k if QK_K == 256
-
-    const block_q4_K * bx0 = (const block_q4_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
-        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-#if QK_K == 256
-        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
-#else
-        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
-#endif
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
-
-        const int * scales = (const int *) bxi->scales;
-
-        const int ksc = k % (WARP_SIZE/8);
-
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh);
-
-    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
-
-    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
-    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
-                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh);
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh);
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI5_K; // == k if QK_K == 256
-
-    const block_q5_K * bx0 = (const block_q5_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
-        const int ky = QR5_K*kqsx;
-
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
-        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
-
-        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
-        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
-        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
-
-        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
-        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
-
-        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
-        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
-        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-#if QK_K == 256
-        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
-#endif
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
-
-        const int * scales = (const int *) bxi->scales;
-
-        const int ksc = k % (WARP_SIZE/8);
-
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh);
-
-    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
-
-    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
-    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
-    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
-                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    GGML_UNUSED(x_qh);
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    GGML_UNUSED(x_qh);
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI6_K; // == k if QK_K == 256
-
-    const block_q6_K * bx0 = (const block_q6_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
-        const int ky = QR6_K*kqsx;
-
-        const int ql = get_int_from_uint8(bxi->ql, kqsx);
-        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
-        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
-
-        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
-        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
-        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
-
-        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
-        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
-
-        x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
-        x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
-        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    GGML_UNUSED(x_qh);
-
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
-
-    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
-    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
-    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
-}
-
-#define  MMQ_X_Q4_0_RDNA2  64
-#define  MMQ_Y_Q4_0_RDNA2  128
-#define NWARPS_Q4_0_RDNA2  8
-#define  MMQ_X_Q4_0_RDNA1  64
-#define  MMQ_Y_Q4_0_RDNA1  64
-#define NWARPS_Q4_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_0_AMPERE 4
-#define  MMQ_Y_Q4_0_AMPERE 32
-#define NWARPS_Q4_0_AMPERE 4
-#else
-#define  MMQ_X_Q4_0_AMPERE 64
-#define  MMQ_Y_Q4_0_AMPERE 128
-#define NWARPS_Q4_0_AMPERE 4
-#endif
-#define  MMQ_X_Q4_0_PASCAL 64
-#define  MMQ_Y_Q4_0_PASCAL 64
-#define NWARPS_Q4_0_PASCAL 8
-
-template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
-              allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
-static __device__ __forceinline__ void mul_mat_q(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    const int blocks_per_row_x = ncols_x / qk;
-    const int blocks_per_col_y = nrows_y / QK8_1;
-    const int blocks_per_warp = WARP_SIZE / qi;
-
-    const int & ncols_dst = ncols_y;
-
-    const int row_dst_0 = blockIdx.x*mmq_y;
-    const int & row_x_0 = row_dst_0;
-
-    const int col_dst_0 = blockIdx.y*mmq_x;
-    const int & col_y_0 = col_dst_0;
-
-    int   * tile_x_ql = nullptr;
-    half2 * tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-    allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
-
-    __shared__ int    tile_y_qs[mmq_x * WARP_SIZE];
-    __shared__ half2  tile_y_ds[mmq_x * WARP_SIZE/QI8_1];
-
-    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
-
-    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
-
-        load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
-                   threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
-
-#pragma unroll
-        for (int ir = 0; ir < qr; ++ir) {
-            const int kqs = ir*WARP_SIZE + threadIdx.x;
-            const int kbxd = kqs / QI8_1;
-
-#pragma unroll
-            for (int i = 0; i < mmq_x; i += nwarps) {
-                const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
-
-                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
-
-                const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
-                tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
-            }
-
-#pragma unroll
-            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
-                const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
-                const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
-                const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
-
-                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
-                const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
-                half2       * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
-                if (need_sum) {
-                    *dsi_dst = *dsi_src;
-                } else {
-                    float * dfi_dst = (float *) dsi_dst;
-                    *dfi_dst = __low2float(*dsi_src);
-                }
-            }
-
-            __syncthreads();
-
-// #pragma unroll // unrolling this loop causes too much register pressure
-            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
-#pragma unroll
-                for (int j = 0; j < mmq_x; j += nwarps) {
-#pragma unroll
-                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
-                        sum[i/WARP_SIZE][j/nwarps] += vec_dot(
-                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
-                            threadIdx.x + i, threadIdx.y + j, k);
-                    }
-                }
-            }
-
-            __syncthreads();
-        }
-    }
-
-#pragma unroll
-    for (int j = 0; j < mmq_x; j += nwarps) {
-        const int col_dst = col_dst_0 + j + threadIdx.y;
-
-        if (col_dst >= ncols_dst) {
-            return;
-        }
-
-#pragma unroll
-        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
-            const int row_dst = row_dst_0 + threadIdx.x + i;
-
-            if (row_dst >= nrows_dst) {
-                continue;
-            }
-
-            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
-        }
-    }
-}
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q4_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_0_RDNA2;
-    const int nwarps = NWARPS_Q4_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_0_RDNA1;
-    const int nwarps = NWARPS_Q4_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
-    const int nwarps = NWARPS_Q4_0_AMPERE;
-
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_0_PASCAL;
-    const int nwarps = NWARPS_Q4_0_PASCAL;
-
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q4_1_RDNA2  64
-#define  MMQ_Y_Q4_1_RDNA2  128
-#define NWARPS_Q4_1_RDNA2  8
-#define  MMQ_X_Q4_1_RDNA1  64
-#define  MMQ_Y_Q4_1_RDNA1  64
-#define NWARPS_Q4_1_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_1_AMPERE 4
-#define  MMQ_Y_Q4_1_AMPERE 32
-#define NWARPS_Q4_1_AMPERE 4
-#else
-#define  MMQ_X_Q4_1_AMPERE 64
-#define  MMQ_Y_Q4_1_AMPERE 128
-#define NWARPS_Q4_1_AMPERE 4
-#endif
-#define  MMQ_X_Q4_1_PASCAL 64
-#define  MMQ_Y_Q4_1_PASCAL 64
-#define NWARPS_Q4_1_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q4_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_1_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_1_RDNA2;
-    const int nwarps = NWARPS_Q4_1_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_1_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_1_RDNA1;
-    const int nwarps = NWARPS_Q4_1_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
-    const int nwarps = NWARPS_Q4_1_AMPERE;
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_1_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_1_PASCAL;
-    const int nwarps = NWARPS_Q4_1_PASCAL;
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q5_0_RDNA2  64
-#define  MMQ_Y_Q5_0_RDNA2  128
-#define NWARPS_Q5_0_RDNA2  8
-#define  MMQ_X_Q5_0_RDNA1  64
-#define  MMQ_Y_Q5_0_RDNA1  64
-#define NWARPS_Q5_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_0_AMPERE 4
-#define  MMQ_Y_Q5_0_AMPERE 32
-#define NWARPS_Q5_0_AMPERE 4
-#else
-#define  MMQ_X_Q5_0_AMPERE 128
-#define  MMQ_Y_Q5_0_AMPERE 64
-#define NWARPS_Q5_0_AMPERE 4
-#endif
-#define  MMQ_X_Q5_0_PASCAL 64
-#define  MMQ_Y_Q5_0_PASCAL 64
-#define NWARPS_Q5_0_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q5_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_0_RDNA2;
-    const int nwarps = NWARPS_Q5_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_0_RDNA1;
-    const int nwarps = NWARPS_Q5_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
-    const int nwarps = NWARPS_Q5_0_AMPERE;
-
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_0_PASCAL;
-    const int nwarps = NWARPS_Q5_0_PASCAL;
-
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q5_1_RDNA2  64
-#define  MMQ_Y_Q5_1_RDNA2  128
-#define NWARPS_Q5_1_RDNA2  8
-#define  MMQ_X_Q5_1_RDNA1  64
-#define  MMQ_Y_Q5_1_RDNA1  64
-#define NWARPS_Q5_1_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_1_AMPERE 4
-#define  MMQ_Y_Q5_1_AMPERE 32
-#define NWARPS_Q5_1_AMPERE 4
-#else
-#define  MMQ_X_Q5_1_AMPERE 128
-#define  MMQ_Y_Q5_1_AMPERE 64
-#define NWARPS_Q5_1_AMPERE 4
-#endif
-#define  MMQ_X_Q5_1_PASCAL 64
-#define  MMQ_Y_Q5_1_PASCAL 64
-#define NWARPS_Q5_1_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q5_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_1_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_1_RDNA2;
-    const int nwarps = NWARPS_Q5_1_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_1_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_1_RDNA1;
-    const int nwarps = NWARPS_Q5_1_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
-    const int nwarps = NWARPS_Q5_1_AMPERE;
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_1_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_1_PASCAL;
-    const int nwarps = NWARPS_Q5_1_PASCAL;
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q8_0_RDNA2  64
-#define  MMQ_Y_Q8_0_RDNA2  128
-#define NWARPS_Q8_0_RDNA2  8
-#define  MMQ_X_Q8_0_RDNA1  64
-#define  MMQ_Y_Q8_0_RDNA1  64
-#define NWARPS_Q8_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q8_0_AMPERE 4
-#define  MMQ_Y_Q8_0_AMPERE 32
-#define NWARPS_Q8_0_AMPERE 4
-#else
-#define  MMQ_X_Q8_0_AMPERE 128
-#define  MMQ_Y_Q8_0_AMPERE 64
-#define NWARPS_Q8_0_AMPERE 4
-#endif
-#define  MMQ_X_Q8_0_PASCAL 64
-#define  MMQ_Y_Q8_0_PASCAL 64
-#define NWARPS_Q8_0_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q8_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q8_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q8_0_RDNA2;
-    const int nwarps = NWARPS_Q8_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q8_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q8_0_RDNA1;
-    const int nwarps = NWARPS_Q8_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
-    const int nwarps = NWARPS_Q8_0_AMPERE;
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q8_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q8_0_PASCAL;
-    const int nwarps = NWARPS_Q8_0_PASCAL;
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q2_K_RDNA2  64
-#define  MMQ_Y_Q2_K_RDNA2  128
-#define NWARPS_Q2_K_RDNA2  8
-#define  MMQ_X_Q2_K_RDNA1  128
-#define  MMQ_Y_Q2_K_RDNA1  32
-#define NWARPS_Q2_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q2_K_AMPERE 4
-#define  MMQ_Y_Q2_K_AMPERE 32
-#define NWARPS_Q2_K_AMPERE 4
-#else
-#define  MMQ_X_Q2_K_AMPERE 64
-#define  MMQ_Y_Q2_K_AMPERE 128
-#define NWARPS_Q2_K_AMPERE 4
-#endif
-#define  MMQ_X_Q2_K_PASCAL 64
-#define  MMQ_Y_Q2_K_PASCAL 64
-#define NWARPS_Q2_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q2_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q2_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q2_K_RDNA2;
-    const int nwarps = NWARPS_Q2_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q2_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q2_K_RDNA1;
-    const int nwarps = NWARPS_Q2_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
-    const int nwarps = NWARPS_Q2_K_AMPERE;
-
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q2_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q2_K_PASCAL;
-    const int nwarps = NWARPS_Q2_K_PASCAL;
-
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q3_K_RDNA2  128
-#define  MMQ_Y_Q3_K_RDNA2  64
-#define NWARPS_Q3_K_RDNA2  8
-#define  MMQ_X_Q3_K_RDNA1  32
-#define  MMQ_Y_Q3_K_RDNA1  128
-#define NWARPS_Q3_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q3_K_AMPERE 4
-#define  MMQ_Y_Q3_K_AMPERE 32
-#define NWARPS_Q3_K_AMPERE 4
-#else
-#define  MMQ_X_Q3_K_AMPERE 128
-#define  MMQ_Y_Q3_K_AMPERE 128
-#define NWARPS_Q3_K_AMPERE 4
-#endif
-#define  MMQ_X_Q3_K_PASCAL 64
-#define  MMQ_Y_Q3_K_PASCAL 64
-#define NWARPS_Q3_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q3_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q3_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q3_K_RDNA2;
-    const int nwarps = NWARPS_Q3_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q3_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q3_K_RDNA1;
-    const int nwarps = NWARPS_Q3_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
-    const int nwarps = NWARPS_Q3_K_AMPERE;
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q3_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q3_K_PASCAL;
-    const int nwarps = NWARPS_Q3_K_PASCAL;
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q4_K_RDNA2  64
-#define  MMQ_Y_Q4_K_RDNA2  128
-#define NWARPS_Q4_K_RDNA2  8
-#define  MMQ_X_Q4_K_RDNA1  32
-#define  MMQ_Y_Q4_K_RDNA1  64
-#define NWARPS_Q4_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_K_AMPERE 4
-#define  MMQ_Y_Q4_K_AMPERE 32
-#define NWARPS_Q4_K_AMPERE 4
-#else
-#define  MMQ_X_Q4_K_AMPERE 64
-#define  MMQ_Y_Q4_K_AMPERE 128
-#define NWARPS_Q4_K_AMPERE 4
-#endif
-#define  MMQ_X_Q4_K_PASCAL 64
-#define  MMQ_Y_Q4_K_PASCAL 64
-#define NWARPS_Q4_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q4_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_K_RDNA2;
-    const int nwarps = NWARPS_Q4_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_K_RDNA1;
-    const int nwarps = NWARPS_Q4_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
-    const int nwarps = NWARPS_Q4_K_AMPERE;
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_K_PASCAL;
-    const int nwarps = NWARPS_Q4_K_PASCAL;
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q5_K_RDNA2  64
-#define  MMQ_Y_Q5_K_RDNA2  128
-#define NWARPS_Q5_K_RDNA2  8
-#define  MMQ_X_Q5_K_RDNA1  32
-#define  MMQ_Y_Q5_K_RDNA1  64
-#define NWARPS_Q5_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_K_AMPERE 4
-#define  MMQ_Y_Q5_K_AMPERE 32
-#define NWARPS_Q5_K_AMPERE 4
-#else
-#define  MMQ_X_Q5_K_AMPERE 64
-#define  MMQ_Y_Q5_K_AMPERE 128
-#define NWARPS_Q5_K_AMPERE 4
-#endif
-#define  MMQ_X_Q5_K_PASCAL 64
-#define  MMQ_Y_Q5_K_PASCAL 64
-#define NWARPS_Q5_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q5_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_K_RDNA2;
-    const int nwarps = NWARPS_Q5_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_K_RDNA1;
-    const int nwarps = NWARPS_Q5_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
-    const int nwarps = NWARPS_Q5_K_AMPERE;
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_K_PASCAL;
-    const int nwarps = NWARPS_Q5_K_PASCAL;
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q6_K_RDNA2  64
-#define  MMQ_Y_Q6_K_RDNA2  128
-#define NWARPS_Q6_K_RDNA2  8
-#define  MMQ_X_Q6_K_RDNA1  32
-#define  MMQ_Y_Q6_K_RDNA1  64
-#define NWARPS_Q6_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q6_K_AMPERE 4
-#define  MMQ_Y_Q6_K_AMPERE 32
-#define NWARPS_Q6_K_AMPERE 4
-#else
-#define  MMQ_X_Q6_K_AMPERE 64
-#define  MMQ_Y_Q6_K_AMPERE 64
-#define NWARPS_Q6_K_AMPERE 4
-#endif
-#define  MMQ_X_Q6_K_PASCAL 64
-#define  MMQ_Y_Q6_K_PASCAL 64
-#define NWARPS_Q6_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q6_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q6_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q6_K_RDNA2;
-    const int nwarps = NWARPS_Q6_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q6_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q6_K_RDNA1;
-    const int nwarps = NWARPS_Q6_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
-    const int nwarps = NWARPS_Q6_K_AMPERE;
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q6_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q6_K_PASCAL;
-    const int nwarps = NWARPS_Q6_K_PASCAL;
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat);
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-static void ggml_mul_mat_q4_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
-        nwarps = NWARPS_Q4_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
-        nwarps = NWARPS_Q4_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
-        nwarps = NWARPS_Q4_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
-        nwarps = NWARPS_Q4_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q4_1_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_1_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
-        nwarps = NWARPS_Q4_1_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_1_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
-        nwarps = NWARPS_Q4_1_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_1_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
-        nwarps = NWARPS_Q4_1_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_1_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
-        nwarps = NWARPS_Q4_1_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
-        nwarps = NWARPS_Q5_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
-        nwarps = NWARPS_Q5_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
-        nwarps = NWARPS_Q5_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
-        nwarps = NWARPS_Q5_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_1_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_1_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
-        nwarps = NWARPS_Q5_1_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_1_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
-        nwarps = NWARPS_Q5_1_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_1_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
-        nwarps = NWARPS_Q5_1_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_1_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
-        nwarps = NWARPS_Q5_1_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q8_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q8_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
-        nwarps = NWARPS_Q8_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q8_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
-        nwarps = NWARPS_Q8_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q8_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
-        nwarps = NWARPS_Q8_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q8_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
-        nwarps = NWARPS_Q8_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q2_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q2_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
-        nwarps = NWARPS_Q2_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q2_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
-        nwarps = NWARPS_Q2_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q2_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
-        nwarps = NWARPS_Q2_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q2_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
-        nwarps = NWARPS_Q2_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q3_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-#if QK_K == 256
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q3_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
-        nwarps = NWARPS_Q3_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q3_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
-        nwarps = NWARPS_Q3_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q3_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
-        nwarps = NWARPS_Q3_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q3_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
-        nwarps = NWARPS_Q3_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-#endif
-}
-
-static void ggml_mul_mat_q4_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
-        nwarps = NWARPS_Q4_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
-        nwarps = NWARPS_Q4_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
-        nwarps = NWARPS_Q4_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
-        nwarps = NWARPS_Q4_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
-        nwarps = NWARPS_Q5_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
-        nwarps = NWARPS_Q5_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
-        nwarps = NWARPS_Q5_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
-        nwarps = NWARPS_Q5_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q6_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id = ggml_cuda_get_device();
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q6_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
-        nwarps = NWARPS_Q6_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q6_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
-        nwarps = NWARPS_Q6_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q6_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
-        nwarps = NWARPS_Q6_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q6_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
-        nwarps = NWARPS_Q6_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-void ggml_cuda_op_mul_mat_q(
-    ggml_backend_cuda_context & ctx,
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream) {
-
-    const int64_t ne00 = src0->ne[0];
-
-    const int64_t ne10 = src1->ne[0];
-    GGML_ASSERT(ne10 % QK8_1 == 0);
-
-    const int64_t ne0 = dst->ne[0];
-
-    const int64_t row_diff = row_high - row_low;
-
-    int id = ggml_cuda_get_device();
-
-    // the main device has a larger memory buffer to hold the results from all GPUs
-    // nrows_dst == nrows of the matrix that the kernel writes into
-    const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    GGML_UNUSED(src1);
-    GGML_UNUSED(dst);
-    GGML_UNUSED(src1_ddf_i);
-}
-
-bool ggml_cuda_supports_mmq(enum ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            return true;
-        default:
-            return false;
-    }
-}
diff --git a/ggml-cuda/mmq.cuh b/ggml-cuda/mmq.cuh
deleted file mode 100644
index 807817c4a715f..0000000000000
--- a/ggml-cuda/mmq.cuh
+++ /dev/null
@@ -1,9 +0,0 @@
-#include "common.cuh"
-
-void ggml_cuda_op_mul_mat_q(
-    ggml_backend_cuda_context & ctx,
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream);
-
-bool ggml_cuda_supports_mmq(enum ggml_type type);
diff --git a/ggml-cuda/quantize.cu b/ggml-cuda/quantize.cu
deleted file mode 100644
index 7578c4b6c7cab..0000000000000
--- a/ggml-cuda/quantize.cu
+++ /dev/null
@@ -1,45 +0,0 @@
-#include "quantize.cuh"
-
-static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx_padded) {
-    const int64_t ix = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (ix >= kx_padded) {
-        return;
-    }
-
-    const int64_t iy = (int64_t)blockDim.y*blockIdx.y + threadIdx.y;
-
-    const int64_t i_padded = (int64_t)iy*kx_padded + ix;
-
-    block_q8_1 * y = (block_q8_1 *) vy;
-
-    const int64_t ib = i_padded / QK8_1; // block index
-    const int64_t iqs = i_padded % QK8_1; // quant index
-
-    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
-    float amax = fabsf(xi);
-    float sum = xi;
-
-    amax = warp_reduce_max(amax);
-    sum = warp_reduce_sum(sum);
-
-    const float d = amax / 127;
-    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
-
-    y[ib].qs[iqs] = q;
-
-    if (iqs > 0) {
-        return;
-    }
-
-    reinterpret_cast<half&>(y[ib].ds.x) = d;
-    reinterpret_cast<half&>(y[ib].ds.y) = sum;
-}
-
-void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream) {
-    const int64_t block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
-    const dim3 num_blocks(block_num_x, ky, 1);
-    const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
-    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
-}
-
diff --git a/ggml-cuda/quantize.cuh b/ggml-cuda/quantize.cuh
deleted file mode 100644
index b37a4752f2d24..0000000000000
--- a/ggml-cuda/quantize.cuh
+++ /dev/null
@@ -1,5 +0,0 @@
-#include "common.cuh"
-
-#define CUDA_QUANTIZE_BLOCK_SIZE 256
-
-void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream);
diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu
deleted file mode 100644
index 4b0d2e5adbbc5..0000000000000
--- a/ggml-cuda/rope.cu
+++ /dev/null
@@ -1,308 +0,0 @@
-#include "rope.cuh"
-
-struct rope_corr_dims {
-    float v[4];
-};
-
-static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
-    const float y = (i0 / 2 - low) / max(0.001f, high - low);
-    return 1.0f - min(1.0f, max(0.0f, y));
-}
-
-// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
-// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
-static __device__ void rope_yarn(
-    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
-    float * cos_theta, float * sin_theta
-) {
-    // Get n-d rotational scaling corrected for extrapolation
-    float theta_interp = freq_scale * theta_extrap;
-    float theta = theta_interp;
-    if (ext_factor != 0.0f) {
-        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
-        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-
-        // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
-    }
-    *cos_theta = cosf(theta) * mscale;
-    *sin_theta = sinf(theta) * mscale;
-}
-
-// rope == RoPE == rotary positional embedding
-template<typename T, bool has_pos>
-static __global__ void rope(
-    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims
-) {
-    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p*powf(freq_base, -float(col)/ncols);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + 1];
-
-    dst[i + 0] = x0*cos_theta - x1*sin_theta;
-    dst[i + 1] = x0*sin_theta + x1*cos_theta;
-}
-
-template<typename T, bool has_pos>
-static __global__ void rope_neox(
-    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
-) {
-    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-    const int ib = col / n_dims;
-    const int ic = col % n_dims;
-
-    if (ib > 0) {
-        const int i = row*ncols + ib*n_dims + ic;
-
-        dst[i + 0] = x[i + 0];
-        dst[i + 1] = x[i + 1];
-
-        return;
-    }
-
-    const int i  = row*ncols + ib*n_dims + ic/2;
-    const int i2 = row/p_delta_rows;
-
-    float cur_rot = inv_ndims * ic - ib;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims/2];
-
-    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
-}
-
-static __global__ void rope_glm_f32(
-    const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    int n_ctx
-) {
-    const int col = blockDim.x*blockIdx.x + threadIdx.x;
-    const int half_n_dims = ncols/4;
-
-    if (col >= half_n_dims) {
-        return;
-    }
-
-    const int row = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const float col_theta_scale = powf(freq_base, -2.0f*col/ncols);
-     // FIXME: this is likely wrong
-    const int p = pos != nullptr ? pos[i2] : 0;
-
-    const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
-    const float sin_theta = sinf(theta);
-    const float cos_theta = cosf(theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + half_n_dims];
-
-    dst[i + 0]           = x0*cos_theta - x1*sin_theta;
-    dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
-
-    const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
-    const float sin_block_theta = sinf(block_theta);
-    const float cos_block_theta = cosf(block_theta);
-
-    const float x2 = x[i + half_n_dims * 2];
-    const float x3 = x[i + half_n_dims * 3];
-
-    dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
-    dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
-}
-
-
-template<typename T>
-static void rope_cuda(
-    const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
-    const dim3 block_nums(nrows, num_blocks_x, 1);
-    if (pos == nullptr) {
-        rope<T, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
-        );
-    } else {
-        rope<T, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
-        );
-    }
-}
-
-template<typename T>
-static void rope_neox_cuda(
-    const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
-    const dim3 block_nums(nrows, num_blocks_x, 1);
-
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.0f / n_dims;
-
-    if (pos == nullptr) {
-        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-            theta_scale, inv_ndims
-        );
-    } else {
-        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-            theta_scale, inv_ndims
-        );
-    }
-}
-
-static void rope_glm_f32_cuda(
-    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, int n_ctx, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 4 == 0);
-    const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
-    const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
-    const dim3 block_nums(num_blocks_x, nrows, 1);
-    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx);
-}
-
-static void rope_cuda_f16(
-    const half * x, half * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
-
-    rope_cuda<half>(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
-}
-
-static void rope_cuda_f32(
-    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
-
-    rope_cuda<float>(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
-}
-
-static void rope_neox_cuda_f16(
-    const half * x, half * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
-
-    rope_neox_cuda<half>(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
-}
-
-static void rope_neox_cuda_f32(
-    const float * x, float * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
-) {
-
-    rope_neox_cuda<float>(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
-}
-
-void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    const float * src0_d = (const float *)src0->data;
-    const float * src1_d = (const float *)src1->data;
-    float * dst_d = (float *)dst->data;
-    cudaStream_t stream = ctx.stream();
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
-    GGML_ASSERT(src0->type == dst->type);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t nrows = ggml_nrows(src0);
-
-    //const int n_past      = ((int32_t *) dst->op_params)[0];
-    const int n_dims      = ((int32_t *) dst->op_params)[1];
-    const int mode        = ((int32_t *) dst->op_params)[2];
-    const int n_ctx       = ((int32_t *) dst->op_params)[3];
-    const int n_orig_ctx  = ((int32_t *) dst->op_params)[4];
-
-    // RoPE alteration for extended context
-    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
-    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
-    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
-    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
-    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
-    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
-    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
-
-    const int32_t * pos = nullptr;
-    if ((mode & 1) == 0) {
-        GGML_ASSERT(src1->type == GGML_TYPE_I32);
-        GGML_ASSERT(src1->ne[0] == ne2);
-        pos = (const int32_t *) src1_d;
-    }
-
-    const bool is_neox = mode & 2;
-    const bool is_glm  = mode & 4;
-
-    rope_corr_dims corr_dims;
-    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
-
-    // compute
-    if (is_glm) {
-        GGML_ASSERT(false);
-        rope_glm_f32_cuda(src0_d, dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, stream);
-    } else if (is_neox) {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_neox_cuda_f32(
-                (const float *)src0_d, (float *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_neox_cuda_f16(
-                (const half *)src0_d, (half *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, stream
-            );
-        } else {
-            GGML_ASSERT(false);
-        }
-    } else {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_cuda_f32(
-                (const float *)src0_d, (float *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_cuda_f16(
-                (const half *)src0_d, (half *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, stream
-            );
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-}
diff --git a/ggml-cuda/vecdotq.cuh b/ggml-cuda/vecdotq.cuh
deleted file mode 100644
index 86b87fa936d85..0000000000000
--- a/ggml-cuda/vecdotq.cuh
+++ /dev/null
@@ -1,1280 +0,0 @@
-#include "common.cuh"
-
-static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
-    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
-
-    int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
-
-    return x32;
-}
-
-static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
-    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
-
-    int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
-
-    return x32;
-}
-
-static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
-    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
-}
-
-static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
-    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
-}
-
-
-// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
-// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
-
-#define VDR_Q4_0_Q8_1_MMVQ 2
-#define VDR_Q4_0_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
-    const int * v, const int * u, const float & d4, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
-        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
-
-        // SIMD dot product of quantized values
-        sumi = __dp4a(vi0, u[2*i+0], sumi);
-        sumi = __dp4a(vi1, u[2*i+1], sumi);
-    }
-
-    const float2 ds8f = __half22float2(ds8);
-
-    // second part effectively subtracts 8 from each quant value
-    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q4_1_Q8_1_MMVQ 2
-#define VDR_Q4_1_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
-    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
-        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
-
-        // SIMD dot product of quantized values
-        sumi = __dp4a(vi0, u[2*i+0], sumi);
-        sumi = __dp4a(vi1, u[2*i+1], sumi);
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
-    const float d4d8 = tmp.x;
-    const float m4s8 = tmp.y;
-#else
-    const float2 dm4f = __half22float2(dm4);
-    const float2 ds8f = __half22float2(ds8);
-    const float d4d8 = dm4f.x * ds8f.x;
-    const float m4s8 = dm4f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
-    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_0_Q8_1_MMVQ 2
-#define VDR_Q5_0_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
-    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
-        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
-        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
-        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
-        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
-
-        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
-        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
-        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
-        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
-        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
-    }
-
-    const float2 ds8f = __half22float2(ds8);
-
-    // second part effectively subtracts 16 from each quant value
-    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_1_Q8_1_MMVQ 2
-#define VDR_Q5_1_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
-    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
-        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
-        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
-        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
-        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
-
-        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
-        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
-        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
-        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
-        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
-    const float d5d8 = tmp.x;
-    const float m5s8 = tmp.y;
-#else
-    const float2 dm5f = __half22float2(dm5);
-    const float2 ds8f = __half22float2(ds8);
-    const float d5d8 = dm5f.x * ds8f.x;
-    const float m5s8 = dm5f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
-    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q8_0_Q8_1_MMVQ 2
-#define VDR_Q8_0_Q8_1_MMQ 8
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
-    const int * v, const int * u, const float & d8_0, const float & d8_1) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        // SIMD dot product of quantized values
-        sumi = __dp4a(v[i], u[i], sumi);
-    }
-
-    return d8_0*d8_1 * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
-    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        // SIMD dot product of quantized values
-        sumi = __dp4a(v[i], u[i], sumi);
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
-    const float d8d8 = tmp.x;
-    const float m8s8 = tmp.y;
-#else
-    const float2 dm8f = __half22float2(dm8);
-    const float2 ds8f = __half22float2(ds8);
-    const float d8d8 = dm8f.x * ds8f.x;
-    const float m8s8 = dm8f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
-    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q2_K_Q8_1_MMVQ 1
-#define VDR_Q2_K_Q8_1_MMQ  2
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
-    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const half2 & dm2, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR2_K; ++i) {
-        const int sc = scales[2*i];
-
-        const int vi = (v >> (2*i)) & 0x03030303;
-
-        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
-
-        // fill int with 4x m
-        int m = sc >> 4;
-        m |= m <<  8;
-        m |= m << 16;
-        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
-    }
-
-    const float2 dm2f = __half22float2(dm2);
-
-    return dm2f.x*sumf_d - dm2f.y*sumf_m;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const half2 & dm2, const float & d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi_d = 0;
-    int sumi_m = 0;
-
-#pragma unroll
-    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
-        int sumi_d_sc = 0;
-
-        const int sc = scales[i0 / (QI8_1/2)];
-
-        // fill int with 4x m
-        int m = sc >> 4;
-        m |= m <<  8;
-        m |= m << 16;
-
-#pragma unroll
-        for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
-            sumi_m    = __dp4a(m,    u[i], sumi_m); // multiply sum of q8_1 values with m
-        }
-
-        sumi_d += sumi_d_sc * (sc & 0xF);
-    }
-
-    const float2 dm2f = __half22float2(dm2);
-
-    return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q3_K_Q8_1_MMVQ 1
-#define VDR_Q3_K_Q8_1_MMQ  2
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
-    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR3_K; ++i) {
-        const int isc = scale_offset + 2*i;
-
-        const int isc_low = isc % (QK_K/32);
-        const int sc_shift_low = 4 * (isc / (QK_K/32));
-        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
-
-        const int isc_high = isc % (QK_K/64);
-        const int sc_shift_high = 2 * (isc / (QK_K/64));
-        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
-
-        const int sc = (sc_low | sc_high) - 32;
-
-        const int vil = (vl >> (2*i)) & 0x03030303;
-
-        const int vih = ((vh >> i) << 2) & 0x04040404;
-
-        const int vi = __vsubss4(vil, vih);
-
-        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
-    }
-
-    return d3 * sumf;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
-    const float & d3, const float & d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
-        int sumi_sc = 0;
-
-        for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
-        }
-
-        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
-    }
-
-    return d3*d8 * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q4_K_Q8_1_MMVQ 2
-#define VDR_Q4_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR4_K; ++i) {
-        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
-        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
-
-        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
-
-        sumf_d += d8[i] * (dot1 * sc[i]);
-        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
-        int sumi_d = 0;
-
-#pragma unroll
-        for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
-        }
-
-        const float2 ds8f = __half22float2(ds8[i]);
-
-        sumf_d += ds8f.x * (sc[i] * sumi_d);
-        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_K_Q8_1_MMVQ 2
-#define VDR_Q5_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
-    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K; ++i) {
-        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
-        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
-
-        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
-        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
-
-        const int v0i = vl0i | vh0i;
-        const int v1i = vl1i | vh1i;
-
-        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
-
-        sumf_d += d8[i] * (dot1 * sc[i]);
-        sumf_m += d8[i] * (dot2 * m[i]);
-
-    }
-
-    const float2 dm5f = __half22float2(dm5);
-
-    return dm5f.x*sumf_d - dm5f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
-        int sumi_d = 0;
-
-#pragma unroll
-        for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
-        }
-
-        const float2 ds8f = __half22float2(ds8[i]);
-
-        sumf_d += ds8f.x * (sc[i] * sumi_d);
-        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q6_K_Q8_1_MMVQ 1
-#define VDR_Q6_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
-    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
-    const float & d, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR6_K; ++i) {
-        const int sc = scales[4*i];
-
-        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
-
-        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
-
-        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
-
-        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
-    }
-
-    return d*sumf;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
-    const float & d6, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-
-#pragma unroll
-    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
-        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
-
-#pragma unroll
-        for (int i = i0; i < i0 + 2; ++i) {
-            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
-            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
-
-            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
-            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
-        }
-
-        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
-    }
-
-    return d6 * sumf_d;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
-
-    int v[VDR_Q4_0_Q8_1_MMVQ];
-    int u[2*VDR_Q4_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
-        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
-    }
-
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
-}
-
-
-static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
-
-    int v[VDR_Q4_1_Q8_1_MMVQ];
-    int u[2*VDR_Q4_1_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
-        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
-    }
-
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
-}
-
-static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
-
-    int vl[VDR_Q5_0_Q8_1_MMVQ];
-    int vh[VDR_Q5_0_Q8_1_MMVQ];
-    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
-        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
-        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
-    }
-
-    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
-}
-
-static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
-
-    int vl[VDR_Q5_1_Q8_1_MMVQ];
-    int vh[VDR_Q5_1_Q8_1_MMVQ];
-    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
-        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
-        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
-    }
-
-    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
-}
-
-static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
-
-    int v[VDR_Q8_0_Q8_1_MMVQ];
-    int u[VDR_Q8_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
-        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
-        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-    }
-
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
-}
-
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
-
-    const int bq8_offset = QR2_K * (iqs / QI8_1);
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
-
-    const uint8_t * scales = bq2_K->scales + scale_offset;
-
-    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
-    int    u[QR2_K];
-    float d8[QR2_K];
-
-#pragma unroll
-    for (int i = 0; i < QR2_K; ++ i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
-    }
-
-    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
-}
-
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
-
-    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
-
-    const float d = bq3_K->d;
-
-    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
-
-    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
-
-    int    u[QR3_K];
-    float d8[QR3_K];
-
-#pragma unroll
-    for (int i = 0; i < QR3_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
-    }
-
-    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
-}
-
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-#ifndef GGML_QKK_64
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
-
-    int    v[2];
-    int    u[2*QR4_K];
-    float d8[QR4_K];
-
-    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
-    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
-
-    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
-    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
-    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
-    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
-
-    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
-    v[0] = q4[0];
-    v[1] = q4[4];
-
-    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
-    } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
-    }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
-
-    for (int i = 0; i < QR4_K; ++i) {
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = __low2float(bq8i->ds);
-
-        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
-        u[2*i+0] = q8[0];
-        u[2*i+1] = q8[4];
-    }
-
-    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
-
-#else
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
-
-    const uint16_t * a = (const uint16_t *)bq4_K->scales;
-    aux16[0] = a[0] & 0x0f0f;
-    aux16[1] = (a[0] >> 4) & 0x0f0f;
-
-    const float dall = bq4_K->dm[0];
-    const float dmin = bq4_K->dm[1];
-
-    const float d8_1 = __low2float(bq8_1[0].ds);
-    const float d8_2 = __low2float(bq8_1[1].ds);
-
-    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
-    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
-    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
-
-    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
-    const int v1 = q4[0];
-    const int v2 = q4[4];
-
-    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
-    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
-    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
-    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
-
-    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
-    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
-
-    return dall * sumf_d - dmin * sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-#ifndef GGML_QKK_64
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
-
-    int   vl[2];
-    int   vh[2];
-    int    u[2*QR5_K];
-    float d8[QR5_K];
-
-    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
-    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
-    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
-
-    vl[0] = ql[0];
-    vl[1] = ql[4];
-
-    vh[0] = qh[0] >> bq8_offset;
-    vh[1] = qh[4] >> bq8_offset;
-
-    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
-    } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
-    }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K; ++i) {
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = __low2float(bq8i->ds);
-
-        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
-        u[2*i+0] = q8[0];
-        u[2*i+1] = q8[4];
-    }
-
-    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
-
-#else
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
-
-    const int8_t * s = bq5_K->scales;
-
-    const float d = bq5_K->d;
-
-    const float d8_1 = __low2half(bq8_1[0].ds);
-    const float d8_2 = __low2half(bq8_1[1].ds);
-
-    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
-    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
-    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
-
-    const int * ql = (const int *)bq5_K->qs + (iqs/2);
-    const int vl1 = ql[0];
-    const int vl2 = ql[4];
-
-    const int step = 4 * (iqs/2); // 0, 4, 8, 12
-    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
-    const int in = step%8; // 0, 4, 0, 4
-    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
-
-    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
-    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
-    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
-    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
-
-    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
-                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
-
-    return d * sumf_d;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
-
-    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
-    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
-    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
-
-    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
-    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
-
-    const int8_t * scales = bq6_K->scales + scale_offset;
-
-    int    u[QR6_K];
-    float d8[QR6_K];
-
-#pragma unroll
-    for (int i = 0; i < QR6_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
-        d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds);
-    }
-
-    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
-}
-
-static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if QK_K == 256
-    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
-
-#if QR2_XXS == 8
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = q2[2] | (q2[3] << 16);
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
-        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
-        for (int j = 0; j < 8; ++j) {
-            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-        }
-        q8 += 8;
-        aux32 >>= 7;
-    }
-    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f;
-    return d * sumi;
-#else
-    // iqs is 0...15
-    const int ib32 = iqs/2;
-    const int il = iqs%2;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f;
-    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
-    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
-    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
-    int sumi1 = 0, sumi2 = 0;
-    for (int j = 0; j < 8; ++j) {
-        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
-        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
-    }
-    return d * (sumi1 + sumi2);
-#endif
-#else
-    NO_DEVICE_CODE;
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-#if QK_K == 256
-    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
-
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
-    const uint8_t ls2 = bq2->scales[ib32] >>  4;
-    int sumi1 = 0;
-    for (int l = 0; l < 2; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
-        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
-        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
-        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
-        q8 += 8;
-    }
-    int sumi2 = 0;
-    for (int l = 2; l < 4; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
-        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
-        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
-        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
-        q8 += 8;
-    }
-    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
-    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
-#else
-    GGML_UNUSED(ksigns64);
-    NO_DEVICE_CODE;
-#endif
-#else
-    GGML_UNUSED(ksigns64);
-    NO_DEVICE_CODE;
-#endif
-}
-
-// TODO
-static __device__ __forceinline__ float vec_dot_iq2_s_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-#if QK_K == 256
-    const block_iq2_s * bq2 = (const block_iq2_s *) vbq;
-
-    const int ib32 = iqs;
-    const int8_t  * q8 = bq8_1[ib32].qs;
-    const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
-    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
-    const uint8_t ls2 = bq2->scales[ib32] >>  4;
-    int sumi1 = 0;
-    for (int l = 0; l < 2; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
-        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
-        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
-        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
-        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
-        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
-        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
-        q8 += 8;
-    }
-    int sumi2 = 0;
-    for (int l = 2; l < 4; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
-        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
-        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
-        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
-        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
-        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
-        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
-        q8 += 8;
-    }
-    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
-    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
-#else
-    GGML_UNUSED(ksigns64);
-    NO_DEVICE_CODE;
-#endif
-#else
-    GGML_UNUSED(ksigns64);
-    NO_DEVICE_CODE;
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-#if QK_K == 256
-    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
-
-    const int ib32 = iqs;
-    const uint8_t  * q3 = bq2->qs + 8*ib32;
-    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = gas[0] | (gas[1] << 16);
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
-        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
-        const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]);
-        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
-        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
-        q8 += 8;
-        aux32 >>= 7;
-    }
-    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
-    return d * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif
-#else
-    NO_DEVICE_CODE;
-#endif
-}
-
-// TODO: don't use lookup table for signs
-static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-#if QK_K == 256
-    const block_iq3_s * bq2 = (const block_iq3_s *) vbq;
-
-    const int ib32 = iqs;
-    const uint8_t  * qs = bq2->qs + 8*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
-        const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
-        uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
-        uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
-        const int grid_l = __vsub4(grid1[0] ^ signs0, signs0);
-        const int grid_h = __vsub4(grid2[0] ^ signs1, signs1);
-        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
-        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
-        q8 += 8;
-    }
-    const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds);
-    return d * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif
-#else
-    NO_DEVICE_CODE;
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if QK_K == 256
-    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
-
-    const int ib32 = iqs;
-    int sumi = 0;
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const int * q8 = (const int *)bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
-        int grid0 = grid[0] & 0x0f0f0f0f;
-        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
-        sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi));
-    }
-#else
-    const int8_t * q8 = bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
-        for (int j = 0; j < 4; ++j) {
-            sumi += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
-        }
-        q8 += 8;
-    }
-#endif
-    const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA;
-    const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1);
-    const float d = d1q * __low2float (bq8_1[ib32].ds);
-    const float m = d1q * __high2float(bq8_1[ib32].ds);
-    return d * sumi + m * delta;
-#else
-    NO_DEVICE_CODE;
-#endif
-}
-
-static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if QK_K == 256
-    const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
-
-    const int ib32 = iqs;
-    int   sumi[2] = {0, 0};
-    float sumf[2] = {0.f, 0.f};
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const int * q8 = (const int *)bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
-        int grid0 = grid[0] & 0x0f0f0f0f;
-        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
-        sumi[l/2] = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi[l/2]));
-        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
-        const int sumy = __dp4a(q8[2*l+1], 0x01010101, __dp4a(q8[2*l+0], 0x01010101, 0));
-        sumf[l/2] += delta*sumy;
-    }
-#else
-    const int8_t * q8 = bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
-        int sumy = 0;
-        for (int j = 0; j < 4; ++j) {
-            sumi[l/2] += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
-            sumy += q8[j] + q8[j+4];
-        }
-        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
-        sumf[l/2] += delta*sumy;
-        q8 += 8;
-    }
-#endif
-    iq1m_scale_t scale;
-    const uint16_t * sc = (const uint16_t *)bq1->scales;
-    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-    const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds);
-    return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
-#else
-    NO_DEVICE_CODE;
-#endif
-}
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values,
-        int & val1, int & val2) {
-
-    uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32;
-    aux32 = q4 & 0x0f0f0f0f;
-    uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8);
-    uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8);
-    val1 = v1 | (v2 << 16);
-    aux32 = (q4 >> 4) & 0x0f0f0f0f;
-    v1 = values[q8[0]] | (values[q8[1]] << 8);
-    v2 = values[q8[2]] | (values[q8[3]] << 8);
-    val2 = v1 | (v2 << 16);
-}
-#endif
-
-static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_iq4_nl * bq = (const block_iq4_nl *) vbq;
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs;
-    const int32_t  * q8 = (const int32_t  *)bq8_1->qs + iqs;
-
-    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
-
-    int v1, v2;
-    int sumi1 = 0, sumi2 = 0;
-    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
-        const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16);
-        get_int_from_table_16(aux, values, v1, v2);
-        sumi1 = __dp4a(v1, q8[l+0], sumi1);
-        sumi2 = __dp4a(v2, q8[l+4], sumi2);
-    }
-
-#else
-    const uint8_t * q4 = bq->qs + 4*iqs;
-    const int8_t  * q8 = bq8_1->qs + 4*iqs;
-
-    int sumi1 = 0, sumi2 = 0;
-    for (int l = 0; l < 4*VDR_Q4_0_Q8_1_MMVQ; ++l) {
-        sumi1 += q8[l+ 0] * kvalues_iq4nl[q4[l] & 0xf];
-        sumi2 += q8[l+16] * kvalues_iq4nl[q4[l] >>  4];
-    }
-#endif
-    const float d = (float)bq->d * __low2float(bq8_1->ds);
-    return d * (sumi1 + sumi2);
-}
-
-static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-#if QK_K == 256
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-
-    const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
-    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
-
-    // iqs is 0...7
-    const int ib32 = iqs;
-    const int32_t  * q8 = (const int *)bq8_1[ib32].qs;
-    const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
-    const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
-    const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
-    int v1, v2;
-    int sumi1 = 0, sumi2 = 0;
-    for (int j = 0; j < 4; ++j) {
-        get_int_from_table_16(q4[j], values, v1, v2);
-        sumi1 = __dp4a(v1, q8[j+0], sumi1);
-        sumi2 = __dp4a(v2, q8[j+4], sumi2);
-    }
-    return d * (sumi1 + sumi2);
-
-#else
-    NO_DEVICE_CODE;
-#endif
-#else
-    return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs);
-#endif
-}
diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp
deleted file mode 100644
index 880a14958cec5..0000000000000
--- a/ggml-opencl.cpp
+++ /dev/null
@@ -1,2302 +0,0 @@
-#include "ggml.h"
-#include "ggml-opencl.h"
-#include "ggml-backend-impl.h"
-
-#include <array>
-#include <atomic>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <limits>
-#include <sstream>
-#include <vector>
-
-#define CL_TARGET_OPENCL_VERSION 120
-#include <clblast.h>
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-#define CL_DMMV_LOCAL_SIZE 32
-
-#ifndef K_QUANTS_PER_ITERATION
-#define K_QUANTS_PER_ITERATION 1
-#else
-static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
-#endif
-
-#define MULTILINE_QUOTE(...) #__VA_ARGS__
-static std::string program_source = MULTILINE_QUOTE(
-
-typedef char int8_t;
-typedef uchar uint8_t;
-typedef short int16_t;
-typedef ushort uint16_t;
-typedef int int32_t;
-typedef uint uint32_t;
-
-struct __attribute__ ((packed)) block_q4_0
-{
-    half d;
-    uint8_t qs[QK4_0 / 2];
-};
-
-struct __attribute__ ((packed)) block_q4_1
-{
-    half d;
-    half m;
-    uint8_t qs[QK4_1 / 2];
-};
-
-struct __attribute__ ((packed)) block_q5_0
-{
-    half d;
-    uint32_t qh;
-    uint8_t qs[QK5_0 / 2];
-};
-
-struct __attribute__ ((packed)) block_q5_1
-{
-    half d;
-    half m;
-    uint32_t qh;
-    uint8_t qs[QK5_1 / 2];
-};
-
-struct __attribute__ ((packed)) block_q8_0
-{
-    half d;
-    int8_t qs[QK8_0];
-};
-
-struct __attribute__((packed)) block_q2_K
-{
-    uint8_t scales[16];
-    uint8_t qs[64];
-    half d;
-    half dmin;
-};
-
-struct __attribute__((packed)) block_q3_K
-{
-    uint8_t hmask[32];
-    uint8_t qs[64];
-    uint8_t scales[12];
-    half d;
-};
-
-struct __attribute__((packed)) block_q4_K
-{
-    half d;
-    half dmin;
-    uint8_t scales[12];
-    uint8_t qs[128];
-};
-
-struct __attribute__((packed)) block_q5_K
-{
-    half d;
-    half dmin;
-    uint8_t scales[12];
-    uint8_t qh[32];
-    uint8_t qs[128];
-};
-
-struct __attribute__((packed)) block_q6_K
-{
-    uint8_t ql[128];
-    uint8_t qh[64];
-    int8_t scales[16];
-    half d;
-};
-
-__kernel void convert_fp16_to_fp32(__global half* x, __global float* y) {
-    const uint i = get_global_id(0);
-
-    y[i] = vload_half(0, &x[i]);
-}
-
-void dequantize_q4_0(__global const struct block_q4_0* x, const int ib, const int iqs, float* v0, float* v1) {
-    const float d = vload_half(0, &x[ib].d);
-
-    const uint8_t vui = x[ib].qs[iqs];
-
-    const int8_t vi0 = vui & 0xF;
-    const int8_t vi1 = vui >> 4;
-
-    *v0 = (vi0 - 8)*d;
-    *v1 = (vi1 - 8)*d;
-}
-void dequantize_q4_1(__global const struct block_q4_1* x, const int ib, const int iqs, float* v0, float* v1) {
-    const float d = vload_half(0, &x[ib].d);
-    const float m = vload_half(0, &x[ib].m);
-
-    const uint8_t vui = x[ib].qs[iqs];
-
-    const int8_t vi0 = vui & 0xF;
-    const int8_t vi1 = vui >> 4;
-
-    *v0 = vi0*d + m;
-    *v1 = vi1*d + m;
-}
-void dequantize_q5_0(__global const struct block_q5_0* x, const int ib, const int iqs, float* v0, float* v1) {
-    const float d = vload_half(0, &x[ib].d);
-
-    uint32_t qh = x[ib].qh;
-
-    const uint8_t xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
-    const uint8_t xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
-
-    const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0) - 16;
-    const int32_t x1 = ((x[ib].qs[iqs] >>  4) | xh_1) - 16;
-
-    *v0 = x0*d;
-    *v1 = x1*d;
-}
-void dequantize_q5_1(__global const struct block_q5_1* x, const int ib, const int iqs, float* v0, float* v1) {
-    const float d = vload_half(0, &x[ib].d);
-    const float m = vload_half(0, &x[ib].m);
-
-    uint32_t qh = x[ib].qh;
-
-    const uint8_t xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
-    const uint8_t xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
-
-    const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0);
-    const int32_t x1 = ((x[ib].qs[iqs] >>  4) | xh_1);
-
-    *v0 = x0*d + m;
-    *v1 = x1*d + m;
-}
-void dequantize_q8_0(__global const struct block_q8_0* x, const int ib, const int iqs, float* v0, float* v1) {
-    const float d = vload_half(0, &x[ib].d);
-
-    const int8_t vi0 = x[ib].qs[iqs + 0];
-    const int8_t vi1 = x[ib].qs[iqs + 1];
-
-    *v0 = vi0*d;
-    *v1 = vi1*d;
-}
-void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float* v1){
-    *v0 = vload_half(0, &x[ib + 0]);
-    *v1 = vload_half(0, &x[ib + 1]);
-}
-);
-
-static std::string k_quants_source = MULTILINE_QUOTE(
-inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m)
-{
-    if (j < 4)
-    {
-        *d = q[j] & 63;
-        *m = q[j + 4] & 63;
-    }
-    else
-    {
-        *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
-        *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
-    }
-}
-
-__kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __global float *yy)
-{
-    const int i = get_group_id(0) + get_global_offset(0);
-    const int tid = get_local_id(0);
-    const int n = tid / 32;
-    const int l = tid - 32 * n;
-    const int is = 8 * n + l / 16;
-
-    const uint8_t q = x[i].qs[32 * n + l];
-    __global float *y = yy + get_group_id(0) * QK_K + 128 * n;
-
-    const float dall = vload_half(0, &x[i].d);
-    const float dmin = vload_half(0, &x[i].dmin);
-
-    y[l + 0] = dall * (x[i].scales[is + 0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is + 0] >> 4);
-    y[l + 32] = dall * (x[i].scales[is + 2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is + 2] >> 4);
-    y[l + 64] = dall * (x[i].scales[is + 4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is + 4] >> 4);
-    y[l + 96] = dall * (x[i].scales[is + 6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is + 6] >> 4);
-}
-
-__kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __global float *yy)
-{
-    int r = get_local_id(0) / 4;
-    int i = get_group_id(0) + get_global_offset(0);
-    int tid = r / 2;
-    int is0 = r % 2;
-    int l0 = 16 * is0 + 4 * (get_local_id(0) % 4);
-    int n = tid / 4;
-    int j = tid - 4 * n;
-
-    uint8_t m = 1 << (4 * n + j);
-    int is = 8 * n + 2 * j + is0;
-    int shift = 2 * j;
-
-    int8_t us = is < 4 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 8] >> 0) & 3) << 4)
-              : is < 8 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 4] >> 2) & 3) << 4)
-              : is < 12  ? (x[i].scales[is - 8] >> 4) | (((x[i].scales[is + 0] >> 4) & 3) << 4)
-              : (x[i].scales[is - 8] >> 4) | (((x[i].scales[is - 4] >> 6) & 3) << 4);
-    float d_all = vload_half(0, &x[i].d);
-    float dl = d_all * (us - 32);
-
-    __global float *y = yy + get_group_id(0) * QK_K + 128 * n + 32 * j;
-    const __global uint8_t *q = x[i].qs + 32 * n;
-    const __global uint8_t *hm = x[i].hmask;
-
-    for (int l = l0; l < l0 + 4; ++l)
-        y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
-}
-
-__kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __global float *yy)
-{
-    const int i = get_group_id(0) + get_global_offset(0);
-    const int tid = get_local_id(0);
-    const int il = tid / 8;
-    const int ir = tid % 8;
-    const int is = 2 * il;
-    const int n = 4;
-
-    __global float *y = yy + get_group_id(0) * QK_K + 64 * il + n * ir;
-
-    const float dall = vload_half(0, &x[i].d);
-    const float dmin = vload_half(0, &x[i].dmin);
-
-    __global const uint8_t *q = x[i].qs + 32 * il + n * ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
-    float d1 = dall * sc;
-    float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
-    float d2 = dall * sc;
-    float m2 = dmin * m;
-    for (int l = 0; l < n; ++l)
-    {
-        y[l + 0] = d1 * (q[l] & 0xF) - m1;
-        y[l + 32] = d2 * (q[l] >> 4) - m2;
-    }
-}
-
-__kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __global float *yy)
-{
-    const int i = get_group_id(0) + get_global_offset(0);
-    const int tid = get_local_id(0);
-    const int il = tid / 16;
-    const int ir = tid % 16;
-    const int is = 2 * il;
-
-    __global float *y = yy + get_group_id(0) * QK_K + 64 * il + 2 * ir;
-
-    const float dall = vload_half(0, &x[i].d);
-    const float dmin = vload_half(0, &x[i].dmin);
-
-    __global const uint8_t *ql = x[i].qs + 32 * il + 2 * ir;
-    __global const uint8_t *qh = x[i].qh + 2 * ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
-    const float d1 = dall * sc;
-    const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
-    const float d2 = dall * sc;
-    const float m2 = dmin * m;
-
-    uint8_t hm = 1 << (2 * il);
-    y[0] = d1 * ((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0)) - m1;
-    y[1] = d1 * ((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0)) - m1;
-    hm <<= 1;
-    y[32] = d2 * ((ql[0] >> 4) + (qh[0] & hm ? 16 : 0)) - m2;
-    y[33] = d2 * ((ql[1] >> 4) + (qh[1] & hm ? 16 : 0)) - m2;
-}
-
-__kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __global float *yy)
-{
-    const int i = get_group_id(0) + get_global_offset(0);
-    const int tid = get_local_id(0);
-    const int ip = tid / 32;
-    const int il = tid - 32 * ip;
-    const int is = 8 * ip + il / 16;
-
-    __global float *y = yy + get_group_id(0) * QK_K + 128 * ip + il;
-
-    const float d = vload_half(0, &x[i].d);
-
-    __global const uint8_t *ql = x[i].ql + 64 * ip + il;
-    const uint8_t qh = x[i].qh[32 * ip + il];
-    __global const int8_t *sc = x[i].scales + is;
-
-    y[0] = d * sc[0] * ((int8_t)((ql[0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
-    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
-    y[64] = d * sc[4] * ((int8_t)((ql[0] >> 4) | (((qh >> 4) & 3) << 4)) - 32);
-    y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
-}
-
-__kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
-
-    const int row = get_group_id(0);
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
-    __global const struct block_q2_K * x = xx + ib0;
-
-    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
-    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0,1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;
-
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0...15 or 0...7
-
-    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
-    const int q_offset = 32*im + l0;
-    const int s_offset = 8*im;
-    const int y_offset = 128*im + l0;
-
-    tmp[16 * ix + tid] = 0;
-
-    uint32_t aux[4];
-    const uint8_t * d = (const uint8_t *)aux;
-    const uint8_t * m = (const uint8_t *)(aux + 2);
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        __global const float   * y = yy + i * QK_K + y_offset;
-        __global const uint8_t * q = x[i].qs + q_offset;
-
-        const float dall = vload_half(0, &x[i].d);
-        const float dmin = vload_half(0, &x[i].dmin);
-
-        __global const uint32_t * a = (__global const uint32_t *)(x[i].scales + s_offset);
-        aux[0] = a[0] & 0x0f0f0f0f;
-        aux[1] = a[1] & 0x0f0f0f0f;
-        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
-        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
-
-        float sum1 = 0, sum2 = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
-                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
-                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
-                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
-                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
-                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
-                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
-                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
-            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
-                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
-
-        }
-        tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
-
-    }
-
-    // sum up partial sums and write back result
-    barrier(CLK_LOCAL_MEM_FENCE);
-    for (int s=16; s>0; s>>=1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if (tid == 0) {
-        dst[row] = tmp[0];
-    }
-}
-
-__kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
-    const uint16_t kmask1 = 0x0303;
-    const uint16_t kmask2 = 0x0f0f;
-
-    const int row = get_group_id(0);
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
-    __global const struct block_q3_K * x = xx + ib0;
-
-    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0,1
-
-    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
-    const int step = 16/K_QUANTS_PER_ITERATION;
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0....15 or 0...7
-
-    const uint8_t m = 1 << (4*im);
-
-    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
-    const int q_offset =  32*im + l0;
-    const int y_offset = 128*im + l0;
-
-    uint16_t utmp[4];
-    const int8_t * s = (const int8_t *)utmp;
-
-    const uint16_t s_shift = 4*im;
-
-    tmp[16 * ix + tid] = 0;
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        __global const float   * y  = yy + i * QK_K + y_offset;
-        __global const uint8_t * q = x[i].qs + q_offset;
-        __global const uint8_t * h = x[i].hmask + l0;
-
-        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
-        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
-        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
-        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
-        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
-
-        const float d = vload_half(0, &x[i].d);
-
-        float sum = 0;
-        for (int l = 0; l < n; ++l) {
-            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
-                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
-                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
-                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
-            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
-                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
-                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
-                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
-        }
-        tmp[16 * ix + tid] += d * sum;
-
-    }
-
-    // sum up partial sums and write back result
-    barrier(CLK_LOCAL_MEM_FENCE);
-    for (int s=16; s>0; s>>=1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if (tid == 0) {
-        dst[row] = tmp[0];
-    }
-}
-
-__kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
-
-    //to rename it later, just to test now
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
-    const int row = get_group_id(0);
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
-    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...15
-    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;
-
-    const int step = 8/K_QUANTS_PER_ITERATION;
-
-    const int il  = tid/step;     // 0...3
-    const int ir  = tid - step*il;// 0...3
-    const int n   = 2*K_QUANTS_PER_ITERATION;
-
-    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int in = il%2;
-
-    const int l0 = n*(2*ir + in);
-    const int q_offset = 32*im + l0;
-    const int y_offset = 64*im + l0;
-
-    uint16_t aux[4];
-    const uint8_t * sc = (const uint8_t *)aux;
-
-    __global const struct block_q4_K * x = xx + ib0;
-
-    tmp[16 * ix + tid] = 0;
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        __global const uint8_t * q1 = x[i].qs + q_offset;
-        __global const uint8_t * q2 = q1 + 64;
-        __global const float   * y1 = yy + i*QK_K + y_offset;
-        __global const float   * y2 = y1 + 128;
-
-        const float dall = vload_half(0, &x[i].d);
-        const float dmin = vload_half(0, &x[i].dmin);
-
-        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
-        aux[0] = a[im+0] & kmask1;
-        aux[1] = a[im+2] & kmask1;
-        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
-        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
-        float4 s = (float4)(0.f);
-        float smin = 0;
-        for (int l = 0; l < n; ++l) {
-            s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4);
-            s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4);
-            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
-        }
-        tmp[16 * ix + tid] += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin;
-
-    }
-
-    // sum up partial sums and write back result
-    barrier(CLK_LOCAL_MEM_FENCE);
-    for (int s=16; s>0; s>>=1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if (tid == 0) {
-        dst[row] = tmp[0];
-    }
-}
-
-__kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
-
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
-    const int row = get_group_id(0);
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
-    const int tid = get_local_id(0)/2;  // 0...15
-    const int ix  = get_local_id(0)%2;
-
-    const int il  = tid/4;     // 0...3
-    const int ir  = tid - 4*il;// 0...3
-    const int n   = 2;
-
-    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int in = il%2;
-
-    const int l0 = n*(2*ir + in);
-    const int q_offset = 32*im + l0;
-    const int y_offset = 64*im + l0;
-
-    const uint8_t hm1  = 1 << (2*im);
-    const uint8_t hm2  = hm1 << 4;
-
-    uint16_t aux[4];
-    const uint8_t * sc = (const uint8_t *)aux;
-
-    __global const struct block_q5_K * x = xx + ib0;
-
-    tmp[16 * ix + tid] = 0;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2) {
-
-        __global const uint8_t * ql1 = x[i].qs + q_offset;
-        __global const uint8_t * ql2 = ql1 + 64;
-        __global const uint8_t * qh  = x[i].qh + l0;
-        __global const float   * y1  = yy + i*QK_K + y_offset;
-        __global const float   * y2  = y1 + 128;
-
-        const float dall = vload_half(0, &x[i].d);
-        const float dmin = vload_half(0, &x[i].dmin);
-
-        __global const uint16_t * a = (__global const uint16_t *)x[i].scales;
-        aux[0] = a[im+0] & kmask1;
-        aux[1] = a[im+2] & kmask1;
-        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
-        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
-        float4 sum = (float4)(0.f);
-        float smin = 0;
-        for (int l = 0; l < n; ++l) {
-            sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
-                   + y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0));
-            sum.y += y1[l+32] * ((ql1[l+ 0] >>  4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
-                   + y1[l+48] * ((ql1[l+16] >>  4) + (qh[l+16] & (hm1 << 1) ? 16 : 0));
-            sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
-                   + y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0));
-            sum.w += y2[l+32] * ((ql2[l+ 0] >>  4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
-                   + y2[l+48] * ((ql2[l+16] >>  4) + (qh[l+16] & (hm2 << 1) ? 16 : 0));
-            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
-                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
-        }
-        tmp[16 * ix + tid] += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
-
-    }
-
-    // sum up partial sums and write back result
-    barrier(CLK_LOCAL_MEM_FENCE);
-    for (int s=16; s>0; s>>=1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if (tid == 0) {
-        dst[row] = tmp[0];
-    }
-}
-
-__kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, __local float* tmp, __global const float * yy, __global float * dst, const int ncols) {
-
-    const int row = get_group_id(0);
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
-
-    __global const struct block_q6_K * x = xx + ib0;
-
-    const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
-
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0...15 or 0...7
-
-\n#if K_QUANTS_PER_ITERATION == 1\n
-    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
-    const int is = 0;
-
-\n#else\n
-
-    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
-    const int is = in / 4;
-
-\n#endif\n
-
-    const int ql_offset = 64*im + l0;
-    const int qh_offset = 32*im + l0;
-    const int s_offset  =  8*im + is;
-    const int y_offset = 128*im + l0;
-
-    tmp[16 * ix + tid] = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        __global const float   * y  = yy + i * QK_K + y_offset;
-        __global const uint8_t * ql = x[i].ql + ql_offset;
-        __global const uint8_t * qh = x[i].qh + qh_offset;
-        __global const int8_t  * s  = x[i].scales + s_offset;
-
-        const float d = vload_half(0, &x[i].d);
-
-\n#if K_QUANTS_PER_ITERATION == 1\n
-        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
-                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
-                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
-                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
-                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
-                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
-                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
-                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
-        tmp[16 * ix + tid] += sum;
-\n#else\n
-        float sum = 0;
-        for (int l = 0; l < 4; ++l) {
-            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
-                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
-                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
-                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
-        }
-        tmp[16 * ix + tid] += sum;
-\n#endif\n
-
-    }
-
-    // sum up partial sums and write back result
-    barrier(CLK_LOCAL_MEM_FENCE);
-    for (int s=16; s>0; s>>=1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if (tid == 0) {
-        dst[row] = tmp[0];
-    }
-}
-);
-
-
-std::string dequant_template = MULTILINE_QUOTE(
-__kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
-    const int i = get_group_id(0)*get_local_size(0) + get_local_id(0)*2;
-
-    if (i >= get_global_size(0)) {
-        return;
-    }
-
-    const uint qk = QUANT_K;
-    const uint qr = QUANT_R;
-
-    const int ib = i/qk + get_global_offset(0); // block index
-    const int iqs = (i%qk)/qr; // quant index
-    const int iybs = i - i%qk; // y block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    // dequantize
-    float v0, v1;
-    DEQUANT_FUNC(x, ib, iqs, &v0, &v1);
-    y[iybs + iqs + 0] = v0;
-    y[iybs + iqs + y_offset] = v1;
-}
-);
-
-std::string dequant_mul_mat_vec_template = MULTILINE_QUOTE(
-__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) {
-    const int local_size = get_local_size(0);
-    const int row = get_group_id(0);
-    const int tid = get_local_id(0);
-
-    const uint qk = QUANT_K;
-    const uint qr = QUANT_R;
-
-    const int col_step = local_size * 2;
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    x += get_global_offset(0);
-
-    tmp[tid] = 0;
-
-    for (int col = tid*2; col < ncols; col += col_step) {
-        const int ib = (row*ncols + col)/qk; // block index
-        const int iqs = (col%qk)/qr; // quant index
-        const int iybs = col - col%qk; // y block start index
-
-        // dequantize
-        float v0, v1;
-        DEQUANT_FUNC(x, ib, iqs, &v0, &v1);
-
-        // matrix multiplication
-        tmp[tid] += v0 * y[iybs + iqs + 0];
-        tmp[tid] += v1 * y[iybs + iqs + y_offset];
-    }
-
-    // sum up partial sums and write back result
-    barrier(CLK_LOCAL_MEM_FENCE);
-    for (int s=local_size/2; s>0; s>>=1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if (tid == 0) {
-        dst[row] = tmp[0];
-    }
-}
-
-);
-
-
-std::string mul_template = MULTILINE_QUOTE(
-__kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) {
-    const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
-
-    if (i >= get_global_size(0)) {
-        return;
-    }
-
-    dst[dst_offset + i] = x[x_offset + i] * y[y_offset + i%ky];
-}
-);
-
-std::string add_template = MULTILINE_QUOTE(
-__kernel void add_f32(__global float * x, const int x_offset, __global float * y, const int y_offset, __global float * dst, const int dst_offset, const int ky) {
-    const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
-
-    if (i >= get_global_size(0)) {
-        return;
-    }
-
-    dst[dst_offset + i] = x[x_offset + i] + y[y_offset + i%ky];
-}
-);
-
-#define CL_CHECK(err)                                               \
-    do {                                                            \
-        cl_int err_ = (err);                                        \
-        if (err_ != CL_SUCCESS) {                                   \
-            fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n",  \
-                #err, err_, __FILE__, __LINE__);                    \
-            exit(1);                                                \
-        }                                                           \
-    } while (0)
-
-#define CLBLAST_CHECK(err)                                          \
-    do {                                                            \
-        CLBlastStatusCode err_ = (err);                             \
-        if (err_ != CLBlastSuccess) {                               \
-            fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n",  \
-                #err, err_, __FILE__, __LINE__);                    \
-            exit(1);                                                \
-        }                                                           \
-    } while (0)
-
-std::array<std::string, 5> dequant_str_keys = {
-    "KERNEL_NAME", "X_TYPE", "QUANT_K", "QUANT_R", "DEQUANT_FUNC"
-};
-
-std::array<std::string, 30> dequant_str_values = {
-    "dequantize_row_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0",
-    "dequantize_row_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1",
-    "dequantize_row_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0",
-    "dequantize_row_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1",
-    "dequantize_row_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0",
-    "convert_row_f16", "half", "1", "1", "convert_f16"
-};
-
-std::array<std::string, 30> dequant_mul_mat_vec_str_values = {
-    "dequantize_mul_mat_vec_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0",
-    "dequantize_mul_mat_vec_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1",
-    "dequantize_mul_mat_vec_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0",
-    "dequantize_mul_mat_vec_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1",
-    "dequantize_mul_mat_vec_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0",
-    "convert_mul_mat_vec_f16", "half", "1", "1", "convert_f16"
-};
-
-std::array<std::string, 2> mul_str_keys = {
-    "KERNEL_NAME", "TYPE"
-};
-std::array<std::string, 2> mul_str_values = {
-    "mul_f32", "float"
-};
-
-static std::string& replace(std::string& s, const std::string& from, const std::string& to) {
-    size_t pos = 0;
-    while ((pos = s.find(from, pos)) != std::string::npos) {
-         s.replace(pos, from.length(), to);
-         pos += to.length();
-    }
-    return s;
-}
-
-static std::string generate_kernels() {
-    std::stringstream src;
-    src << program_source << '\n';
-    src << k_quants_source << '\n';
-    for (size_t i = 0; i < dequant_str_values.size(); i += dequant_str_keys.size()) {
-        std::string dequant_kernel = dequant_template;
-        std::string dmmv_kernel = dequant_mul_mat_vec_template;
-        for (size_t j = 0; j < dequant_str_keys.size(); j++) {
-            replace(dequant_kernel, dequant_str_keys[j], dequant_str_values[i + j]);
-            replace(dmmv_kernel, dequant_str_keys[j], dequant_mul_mat_vec_str_values[i + j]);
-        }
-        src << dequant_kernel << '\n';
-        src << dmmv_kernel << '\n';
-    }
-    for (size_t i = 0; i < mul_str_values.size(); i += mul_str_keys.size()) {
-        std::string mul_kernel = mul_template;
-        for (size_t j = 0; j < mul_str_keys.size(); j++) {
-            replace(mul_kernel, mul_str_keys[j], mul_str_values[i + j]);
-        }
-        src << mul_kernel << '\n';
-    }
-    src << add_template << '\n';
-
-    return src.str();
-}
-
-static cl_platform_id platform;
-static cl_device_id device;
-static cl_context context;
-static cl_command_queue queue;
-static cl_program program;
-static cl_kernel convert_row_f16_cl;
-static cl_kernel dequantize_row_q4_0_cl, dequantize_row_q4_1_cl, dequantize_row_q5_0_cl, dequantize_row_q5_1_cl, dequantize_row_q8_0_cl;
-static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, dequantize_mul_mat_vec_q5_0_cl, dequantize_mul_mat_vec_q5_1_cl, dequantize_mul_mat_vec_q8_0_cl, convert_mul_mat_vec_f16_cl;
-static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl;
-static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl;
-static cl_kernel mul_f32_cl;
-static cl_kernel add_f32_cl;
-static bool fp16_support;
-
-static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
-    cl_program p;
-    char *program_log;
-    size_t program_size;
-    size_t log_size;
-    int err;
-
-    program_size = strlen(program_buffer);
-
-    p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
-    if(err < 0) {
-        fprintf(stderr, "OpenCL error creating program");
-        exit(1);
-    }
-
-    std::string compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math "
-                               "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1 "
-                               "-DQK_K=256 -DK_QUANTS_PER_ITERATION=" + std::to_string(K_QUANTS_PER_ITERATION);
-
-    err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL);
-    if(err < 0) {
-
-        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
-        program_log = (char*) malloc(log_size + 1);
-        program_log[log_size] = '\0';
-        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
-        fprintf(stderr, "ggml_opencl: kernel compile error:\n\n%s\n", program_log);
-        free(program_log);
-        exit(1);
-    }
-
-    return p;
-}
-
-void ggml_cl_init(void) {
-    static bool initialized = false;
-    if (initialized) {
-        return;
-    }
-    initialized = true;
-
-    cl_int err;
-
-    struct cl_device;
-    struct cl_platform {
-        cl_platform_id id;
-        unsigned number;
-        char name[128];
-        char vendor[128];
-        struct cl_device * devices;
-        unsigned n_devices;
-        struct cl_device * default_device;
-    };
-
-    struct cl_device {
-        struct cl_platform * platform;
-        cl_device_id id;
-        unsigned number;
-        cl_device_type type;
-        char name[128];
-    };
-
-    enum { NPLAT = 16, NDEV = 16 };
-
-    struct cl_platform platforms[NPLAT];
-    unsigned n_platforms = 0;
-    struct cl_device devices[NDEV];
-    unsigned n_devices = 0;
-    struct cl_device * default_device = NULL;
-
-    platform = NULL;
-    device = NULL;
-
-    cl_platform_id platform_ids[NPLAT];
-    CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms));
-
-    for (unsigned i = 0; i < n_platforms; i++) {
-        struct cl_platform * p = &platforms[i];
-        p->number = i;
-        p->id = platform_ids[i];
-        CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
-        CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));
-
-        cl_device_id device_ids[NDEV];
-        cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
-        if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
-            p->n_devices = 0;
-        } else {
-            CL_CHECK(clGetDeviceIDsError);
-        }
-        p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
-        p->default_device = NULL;
-
-        for (unsigned j = 0; j < p->n_devices; j++) {
-            struct cl_device * d = &devices[n_devices];
-            d->number = n_devices++;
-            d->id = device_ids[j];
-            d->platform = p;
-            CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
-            CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));
-
-            if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
-                p->default_device = d;
-            }
-        }
-
-        if (default_device == NULL && p->default_device != NULL) {
-            default_device = p->default_device;
-        }
-    }
-
-    if (n_devices == 0) {
-        fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n");
-        exit(1);
-    }
-
-    char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
-    char * user_device_string = getenv("GGML_OPENCL_DEVICE");
-    int user_platform_number = -1;
-    int user_device_number = -1;
-
-    unsigned n;
-    if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
-        user_platform_number = (int)n;
-    }
-    if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
-        user_device_number = (int)n;
-    }
-    if (user_platform_number != -1 && user_device_number != -1) {
-        cl_platform* platform = &platforms[user_platform_number];
-        if ((unsigned)user_device_number >= platform->n_devices) {
-            fprintf(stderr, "ggml_opencl: invalid device number %d\n", user_device_number);
-            exit(1);
-        }
-        default_device = &platform->devices[user_device_number];
-    } else {
-
-        struct cl_device * selected_devices = devices;
-        unsigned n_selected_devices = n_devices;
-
-        if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
-            for (unsigned i = 0; i < n_platforms; i++) {
-                struct cl_platform * p = &platforms[i];
-                if (strstr(p->name, user_platform_string) != NULL ||
-                    strstr(p->vendor, user_platform_string) != NULL) {
-                    user_platform_number = (int)i;
-                    break;
-                }
-            }
-            if (user_platform_number == -1) {
-                fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
-                exit(1);
-            }
-        }
-        if (user_platform_number != -1) {
-            struct cl_platform * p = &platforms[user_platform_number];
-            selected_devices = p->devices;
-            n_selected_devices = p->n_devices;
-            default_device = p->default_device;
-            if (n_selected_devices == 0) {
-                fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
-                exit(1);
-            }
-        }
-
-        if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
-            for (unsigned i = 0; i < n_selected_devices; i++) {
-                struct cl_device * d = &selected_devices[i];
-                if (strstr(d->name, user_device_string) != NULL) {
-                    user_device_number = d->number;
-                    break;
-                }
-            }
-            if (user_device_number == -1) {
-                fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string);
-                exit(1);
-            }
-        }
-        if (user_device_number != -1) {
-            selected_devices = &devices[user_device_number];
-            n_selected_devices = 1;
-            default_device = &selected_devices[0];
-        }
-
-        GGML_ASSERT(n_selected_devices > 0);
-
-        if (default_device == NULL) {
-            default_device = &selected_devices[0];
-        }
-    }
-
-    fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
-    fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name);
-    if (default_device->type != CL_DEVICE_TYPE_GPU) {
-        fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
-    }
-
-    platform = default_device->platform->id;
-    device = default_device->id;
-
-    size_t ext_str_size;
-    clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, 0, NULL, &ext_str_size);
-    char *ext_buffer = (char *)alloca(ext_str_size + 1);
-    clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
-    ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated
-    // Disabled due to faulty outputs
-    // Check if ext_buffer contains cl_khr_fp16
-    fp16_support = false;  // strstr(ext_buffer, "cl_khr_fp16") != NULL;
-    // fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
-
-    cl_context_properties properties[] = {
-        (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
-    };
-
-    CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));
-
-    CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
-        (err != CL_INVALID_QUEUE_PROPERTIES && err != CL_INVALID_VALUE ? err :
-        (queue = clCreateCommandQueue(context, device, 0, &err), err)
-    )));
-
-    const std::string kernel_src = generate_kernels();
-
-    program = build_program_from_source(context, device, kernel_src.c_str());
-
-    // FP16 to FP32 kernel
-    CL_CHECK((convert_row_f16_cl = clCreateKernel(program, "convert_row_f16", &err), err));
-
-    // Dequantize kernels
-    CL_CHECK((dequantize_row_q4_0_cl = clCreateKernel(program, "dequantize_row_q4_0", &err), err));
-    CL_CHECK((dequantize_row_q4_1_cl = clCreateKernel(program, "dequantize_row_q4_1", &err), err));
-    CL_CHECK((dequantize_row_q5_0_cl = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
-    CL_CHECK((dequantize_row_q5_1_cl = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
-    CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
-    CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
-    CL_CHECK((dequantize_block_q2_k_cl = clCreateKernel(program, "dequantize_block_q2_K", &err), err));
-    CL_CHECK((dequantize_block_q3_k_cl = clCreateKernel(program, "dequantize_block_q3_K", &err), err));
-    CL_CHECK((dequantize_block_q4_k_cl = clCreateKernel(program, "dequantize_block_q4_K", &err), err));
-    CL_CHECK((dequantize_block_q5_k_cl = clCreateKernel(program, "dequantize_block_q5_K", &err), err));
-    CL_CHECK((dequantize_block_q6_k_cl = clCreateKernel(program, "dequantize_block_q6_K", &err), err));
-
-    // dequant mul mat kernel
-    CL_CHECK((dequantize_mul_mat_vec_q4_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_0", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q4_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_1", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q5_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_0", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q5_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_1", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q8_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q8_0", &err), err));
-    CL_CHECK((convert_mul_mat_vec_f16_cl = clCreateKernel(program, "convert_mul_mat_vec_f16", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q2_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q2_K", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q3_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q3_K", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q4_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_K", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q5_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_K", &err), err));
-    CL_CHECK((dequantize_mul_mat_vec_q6_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q6_K", &err), err));
-
-    // mul kernel
-    CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
-
-    CL_CHECK((add_f32_cl = clCreateKernel(program, "add_f32", &err), err));
-}
-
-static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return &dequantize_row_q4_0_cl;
-        case GGML_TYPE_Q4_1:
-            return &dequantize_row_q4_1_cl;
-        case GGML_TYPE_Q5_0:
-            return &dequantize_row_q5_0_cl;
-        case GGML_TYPE_Q5_1:
-            return &dequantize_row_q5_1_cl;
-        case GGML_TYPE_Q8_0:
-            return &dequantize_row_q8_0_cl;
-        case GGML_TYPE_Q2_K:
-            return &dequantize_block_q2_k_cl;
-        case GGML_TYPE_Q3_K:
-            return &dequantize_block_q3_k_cl;
-        case GGML_TYPE_Q4_K:
-            return &dequantize_block_q4_k_cl;
-        case GGML_TYPE_Q5_K:
-            return &dequantize_block_q5_k_cl;
-        case GGML_TYPE_Q6_K:
-            return &dequantize_block_q6_k_cl;
-        case GGML_TYPE_F16:
-            return &convert_row_f16_cl;
-        default:
-            return nullptr;
-    }
-}
-
-static size_t ggml_cl_global_denom(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return 1;
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-            return 4;
-        case GGML_TYPE_Q4_K:
-            return 8;
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            return 4;
-        case GGML_TYPE_F16:
-        default:
-            return 1;
-    }
-}
-
-static size_t ggml_cl_local_size(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return 0;
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-            return 64;
-        case GGML_TYPE_Q4_K:
-            return 32;
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            return 64;
-        case GGML_TYPE_F16:
-        default:
-            return 0;
-    }
-}
-
-static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return &dequantize_mul_mat_vec_q4_0_cl;
-        case GGML_TYPE_Q4_1:
-            return &dequantize_mul_mat_vec_q4_1_cl;
-        case GGML_TYPE_Q5_0:
-            return &dequantize_mul_mat_vec_q5_0_cl;
-        case GGML_TYPE_Q5_1:
-            return &dequantize_mul_mat_vec_q5_1_cl;
-        case GGML_TYPE_Q8_0:
-            return &dequantize_mul_mat_vec_q8_0_cl;
-        case GGML_TYPE_F16:
-            return &convert_mul_mat_vec_f16_cl;
-        case GGML_TYPE_Q2_K:
-            return &dequantize_mul_mat_vec_q2_K_cl;
-        case GGML_TYPE_Q3_K:
-            return &dequantize_mul_mat_vec_q3_K_cl;
-        case GGML_TYPE_Q4_K:
-            return &dequantize_mul_mat_vec_q4_K_cl;
-        case GGML_TYPE_Q5_K:
-            return &dequantize_mul_mat_vec_q5_K_cl;
-        case GGML_TYPE_Q6_K:
-            return &dequantize_mul_mat_vec_q6_K_cl;
-        default:
-            return nullptr;
-    }
-}
-
-// buffer pool for cl
-#define MAX_CL_BUFFERS 256
-
-struct scoped_spin_lock {
-    std::atomic_flag& lock;
-    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
-        while (lock.test_and_set(std::memory_order_acquire)) {
-            ; // spin
-        }
-    }
-    ~scoped_spin_lock() {
-        lock.clear(std::memory_order_release);
-    }
-    scoped_spin_lock(const scoped_spin_lock&) = delete;
-    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
-};
-
-struct cl_buffer {
-    cl_mem mem;
-    size_t size = 0;
-};
-
-static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS];
-static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT;
-
-static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size) {
-    scoped_spin_lock lock(g_cl_pool_lock);
-    cl_int err;
-
-    int best_i = -1;
-    size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
-    int worst_i = -1;
-    size_t worst_size = 0; //largest unused buffer seen so far
-    for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
-        cl_buffer &b = g_cl_buffer_pool[i];
-        if (b.size > 0 && b.size >= size && b.size < best_size)
-        {
-            best_i = i;
-            best_size = b.size;
-        }
-        if (b.size > 0 && b.size > worst_size)
-        {
-            worst_i = i;
-            worst_size = b.size;
-        }
-    }
-    if(best_i!=-1) //found the smallest buffer that fits our needs
-    {
-        cl_buffer& b = g_cl_buffer_pool[best_i];
-        cl_mem mem = b.mem;
-        *actual_size = b.size;
-        b.size = 0;
-        return mem;
-    }
-    if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory
-    {
-         cl_buffer& b = g_cl_buffer_pool[worst_i];
-         cl_mem mem = b.mem;
-         b.size = 0;
-         clReleaseMemObject(mem);
-    }
-    cl_mem mem;
-    CL_CHECK((mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err));
-    *actual_size = size;
-    return mem;
-}
-
-static void ggml_cl_pool_free(cl_mem mem, size_t size) {
-    scoped_spin_lock lock(g_cl_pool_lock);
-
-    for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
-        cl_buffer& b = g_cl_buffer_pool[i];
-        if (b.size == 0) {
-            b.mem = mem;
-            b.size = size;
-            return;
-        }
-    }
-    fprintf(stderr, "WARNING: cl buffer pool full, increase MAX_CL_BUFFERS\n");
-    clReleaseMemObject(mem);
-}
-
-void ggml_cl_free_data(const struct ggml_tensor* tensor) {
-    if (tensor->backend != GGML_BACKEND_TYPE_GPU) {
-        return;
-    }
-
-    cl_mem mem = (cl_mem)tensor->extra;
-    clReleaseMemObject(mem);
-}
-
-static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t offset, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cl_event* ev) {
-    cl_int err;
-    const uint64_t ne0 = src->ne[0];
-    const uint64_t ne1 = src->ne[1];
-    const uint64_t nb0 = src->nb[0];
-    const uint64_t nb1 = src->nb[1];
-    const uint64_t nb2 = src->nb[2];
-    const uint64_t nb3 = src->nb[3];
-    const enum ggml_type type = src->type;
-    const size_t ts = ggml_type_size(type);
-    const size_t bs = ggml_blck_size(type);
-    const uint64_t row_size = ts*ne0/bs;
-
-    const char * x = (const char *) src->data + i2*nb2 + i3*nb3;
-    if (nb0 == ts && nb1 == row_size) {
-        return clEnqueueWriteBuffer(queue, dst, CL_FALSE, offset, ne1*row_size, x, 0, NULL, ev);
-    }
-    if (nb0 == ts) {
-        const size_t buffer_origin[3] = { offset, 0, 0 };
-        const size_t host_origin[3] = { 0, 0, 0 };
-        const size_t region[3] = { row_size, ne1, 1 };
-        return clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, row_size, 0, nb1, 0, x, 0, NULL, ev);
-    }
-    std::vector<cl_event> events;
-    if (ev && ne1>1) events.reserve(ne1-1);
-    for (uint64_t i1 = 0; i1 < ne1; i1++) {
-        // pretend the row is a matrix with cols=1
-        const size_t buffer_origin[3] = { offset + i1*row_size, 0, 0 };
-        const size_t host_origin[3] = { 0, 0, 0 };
-        const size_t region[3] = { ts, ne0/bs, 1 };
-        // if an event is requested, make the last write wait for all previous writes to complete
-        if (ev && i1) {
-            events.push_back(*ev);
-        }
-        cl_uint nevents = i1 == ne1-1 ? events.size() : 0U;
-        err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, ts, 0, nb0, 0, x + i1*nb1, nevents, nevents ? events.data() : nullptr, ev);
-        if (err != CL_SUCCESS) {
-            for (auto event : events) {
-                clReleaseEvent(event);
-            }
-            return err;
-        }
-    }
-    for (auto event : events) {
-        CL_CHECK(clReleaseEvent(event));
-    }
-    return CL_SUCCESS;
-}
-
-static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
-    size_t x_size;
-    size_t d_size;
-
-    cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
-    cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
-    cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
-
-
-    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        for (int64_t i02 = 0; i02 < ne02; i02++) {
-            cl_event ev;
-
-            // copy src0 to device
-            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
-
-            const int64_t i13 = i03%ne13;
-            const int64_t i12 = i02%ne12;
-            const int i1 = i13*ne12*ne11 + i12*ne11;
-
-            cl_int x_offset = 0;
-            cl_int y_offset = i1*ne10;
-            cl_int d_offset = 0;
-
-            size_t global = ne00 * ne01;
-            cl_int ky = ne10 * ne11;
-
-            CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
-            CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
-            CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
-            CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
-            CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
-            CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
-            CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
-            CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
-
-            CL_CHECK(clReleaseEvent(ev));
-            CL_CHECK(clFinish(queue));
-
-            // copy dst to host
-            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
-            CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
-        }
-    }
-    ggml_cl_pool_free(d_X, x_size);
-    ggml_cl_pool_free(d_D, d_size);
-}
-
-void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
-    GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
-    ggml_cl_mul_f32(src0, src1, dst);
-}
-
-static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
-    size_t x_size;
-    size_t d_size;
-
-    cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
-    cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
-    cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
-
-
-    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        for (int64_t i02 = 0; i02 < ne02; i02++) {
-            cl_event ev;
-
-            // copy src0 to device
-            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
-
-            const int64_t i13 = i03%ne13;
-            const int64_t i12 = i02%ne12;
-            const int i1 = i13*ne12*ne11 + i12*ne11;
-
-            cl_int x_offset = 0;
-            cl_int y_offset = i1*ne10;
-            cl_int d_offset = 0;
-
-            size_t global = ne00 * ne01;
-            cl_int ky = ne10 * ne11;
-
-            CL_CHECK(clSetKernelArg(add_f32_cl, 0, sizeof(cl_mem), &d_X));
-            CL_CHECK(clSetKernelArg(add_f32_cl, 1, sizeof(cl_int), &x_offset));
-            CL_CHECK(clSetKernelArg(add_f32_cl, 2, sizeof(cl_mem), &d_Y));
-            CL_CHECK(clSetKernelArg(add_f32_cl, 3, sizeof(cl_int), &y_offset));
-            CL_CHECK(clSetKernelArg(add_f32_cl, 4, sizeof(cl_mem), &d_D));
-            CL_CHECK(clSetKernelArg(add_f32_cl, 5, sizeof(cl_int), &d_offset));
-            CL_CHECK(clSetKernelArg(add_f32_cl, 6, sizeof(cl_int), &ky));
-            CL_CHECK(clEnqueueNDRangeKernel(queue, add_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
-
-            CL_CHECK(clReleaseEvent(ev));
-            CL_CHECK(clFinish(queue));
-
-            // copy dst to host
-            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
-            CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
-        }
-    }
-    ggml_cl_pool_free(d_X, x_size);
-    ggml_cl_pool_free(d_D, d_size);
-}
-
-void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
-    GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
-    ggml_cl_add_f32(src0, src1, dst);
-}
-
-static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
-
-    const int64_t r2 = ne12 / ne02;
-    const int64_t r3 = ne13 / ne03;
-
-    const float alpha = 1.0f;
-    const float beta = 0.0f;
-    const int x_ne = ne01 * ne00;
-    const int y_ne = ne11 * ne10;
-    const int d_ne = ne11 * ne01;
-
-    size_t x_size;
-    size_t y_size;
-    size_t d_size;
-    cl_mem d_X;
-    if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
-        d_X = (cl_mem) src0->extra;
-    } else {
-        d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
-    }
-    cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
-    cl_mem d_D =  dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem)  dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
-
-    size_t x_offset = 0;
-
-    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        // TODO: copy src0 here when r3>1
-        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
-            for (int64_t i02 = 0; i02 < ne02; i02++) {
-                if (src0->backend == GGML_BACKEND_TYPE_GPU) {
-                    x_offset = (i03 * ne02 + i02) * x_ne;
-                } else {
-                    // copy src0 to device
-                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
-                }
-
-                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
-                    // copy src1 to device
-                    if (src1->backend == GGML_BACKEND_TYPE_CPU) {
-                        CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
-                    }
-
-                    CL_CHECK(clFinish(queue));
-
-                    // compute
-                    cl_event ev_sgemm;
-                    clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
-                                                               clblast::Transpose::kYes, clblast::Transpose::kNo,
-                                                               ne01, ne11, ne10,
-                                                               alpha,
-                                                               d_X, x_offset, ne00,
-                                                               d_Y, 0, ne10,
-                                                               beta,
-                                                               d_D, 0, ne01,
-                                                               &queue, &ev_sgemm);
-
-                    if (status != clblast::StatusCode::kSuccess) {
-                        GGML_ASSERT(false);
-                    }
-
-                    // copy dst to host
-                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
-                        float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
-                        CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
-                    }
-                }
-            }
-        }
-    }
-
-    if (src0->backend != GGML_BACKEND_TYPE_GPU) {
-        ggml_cl_pool_free(d_X, x_size);
-    }
-    if (src1->backend != GGML_BACKEND_TYPE_GPU) {
-        ggml_cl_pool_free(d_Y, y_size);
-    }
-    if (dst->backend != GGML_BACKEND_TYPE_GPU) {
-        ggml_cl_pool_free(d_D, d_size);
-    }
-}
-
-static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) {
-    GGML_ASSERT(fp16_support);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const int nb10 = src1->nb[0];
-    const int nb11 = src1->nb[1];
-    const int nb12 = src1->nb[2];
-    const int nb13 = src1->nb[3];
-
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
-
-    const int64_t r2 = ne12 / ne02;
-    const int64_t r3 = ne13 / ne03;
-
-    const ggml_fp16_t alpha = ggml_fp32_to_fp16(1.0f);
-    const ggml_fp16_t beta = ggml_fp32_to_fp16(0.0f);
-    const int x_ne = ne01 * ne00;
-    const int y_ne = ne11 * ne10;
-    const int d_ne = ne11 * ne01;
-
-    GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * y_ne);
-    GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * d_ne);
-    ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata;
-
-    size_t x_size;
-    size_t y_size;
-    size_t d_size;
-    cl_mem d_X;
-    if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
-        d_X = (cl_mem) src0->extra;
-    } else {
-        d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
-    }
-    cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size);
-    cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size);
-
-    bool src1_cont_rows = nb10 == sizeof(float);
-    bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
-
-    size_t x_offset = 0;
-
-    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        // TODO: copy src0 here when r3>1
-        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
-            for (int64_t i02 = 0; i02 < ne02; i02++) {
-                if (src0->backend == GGML_BACKEND_TYPE_GPU) {
-                    x_offset = (i03 * ne02 + i02) * x_ne;
-                } else {
-                    // copy src0 to device
-                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
-                }
-
-                // FIXME: convert on device
-
-                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
-                    // convert src1 to fp16
-                    // TODO: use multiple threads
-                    char * src1i = (char *) src1->data + i13*nb13 + i12*nb12;
-                    if (src1_cont_rows) {
-                        if (src1_cont_cols) {
-                            ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
-                        }
-                        else {
-                            for (int64_t i11 = 0; i11 < ne11; i11++) {
-                                ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10);
-                            }
-                        }
-                    }
-                    else {
-                        for (int64_t i11 = 0; i11 < ne11; i11++) {
-                            for (int64_t i10 = 0; i10 < ne10; i10++) {
-                                // very slow due to no inlining
-                                tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10));
-                            }
-                        }
-                    }
-
-                    // copy src1 to device
-                    CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL));
-
-                    CL_CHECK(clFinish(queue));
-
-                    // compute
-                    cl_event ev_sgemm;
-                    clblast::StatusCode status = clblast::Gemm<cl_half>(clblast::Layout::kColMajor,
-                                                               clblast::Transpose::kYes, clblast::Transpose::kNo,
-                                                               ne01, ne11, ne10,
-                                                               alpha,
-                                                               d_X, x_offset, ne00,
-                                                               d_Y, 0, ne10,
-                                                               beta,
-                                                               d_D, 0, ne01,
-                                                               &queue, &ev_sgemm);
-
-                    if (status != clblast::StatusCode::kSuccess) {
-                        GGML_ASSERT(false);
-                    }
-
-                    // copy dst to host, then convert to float
-                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
-                        CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
-                        float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
-                        ggml_fp16_to_fp32_row(tmp, d, d_ne);
-                    } else {
-                        // FIXME: convert dst to fp32 on device
-                    }
-                }
-            }
-        }
-    }
-
-    if (src0->backend != GGML_BACKEND_TYPE_GPU) {
-        ggml_cl_pool_free(d_X, x_size);
-    }
-    ggml_cl_pool_free(d_Y, y_size);
-    ggml_cl_pool_free(d_D, d_size);
-}
-
-static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
-    const ggml_type type = src0->type;
-    const bool mul_mat_vec = ne11 == 1 && ne00%2 == 0;
-
-    const int64_t r2 = ne12 / ne02;
-    const int64_t r3 = ne13 / ne03;
-
-    const float alpha = 1.0f;
-    const float beta = 0.0f;
-    const int x_ne = ne01 * ne00;
-    const int y_ne = ne11 * ne10;
-    const int d_ne = ne11 * ne01;
-    const int x_bps = x_ne / ggml_blck_size(type); // blocks per 2D slice
-    const size_t q_sz = ggml_type_size(type) * x_bps;
-
-    size_t x_size;
-    size_t y_size;
-    size_t d_size;
-    size_t q_size;
-    cl_mem d_X;
-    if (!mul_mat_vec) {
-        d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
-    }
-    cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
-    cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
-    cl_mem d_Q;
-    if (src0->backend == GGML_BACKEND_TYPE_CPU) {
-        d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
-    }
-
-    cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type);
-    cl_kernel* dmmv = ggml_get_dequantize_mul_mat_vec_cl(type);
-    GGML_ASSERT(to_fp32_cl != nullptr);
-
-    const size_t global_denom = ggml_cl_global_denom(type);
-    const size_t local = mul_mat_vec ? CL_DMMV_LOCAL_SIZE : ggml_cl_local_size(type);
-
-    size_t ev_idx = 0;
-    std::vector<cl_event> events;
-
-    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        // TODO: copy and dequantize src0 here when r3>1
-        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
-            for (int64_t i02 = 0; i02 < ne02; i02++) {
-                // copy src0 to device if necessary
-                if (src0->backend == GGML_BACKEND_TYPE_CPU) {
-                    events.emplace_back();
-                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
-                } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
-                    d_Q = (cl_mem) src0->extra;
-                } else {
-                    GGML_ASSERT(false);
-                }
-
-                if (!mul_mat_vec) {
-                    // convert src0 to fp32 on device
-                    const size_t global = x_ne / global_denom;
-                    const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
-                    CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
-                    CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
-                    CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
-                }
-
-                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
-                    if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
-                        // copy src1 to device
-                        events.emplace_back();
-                        CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++));
-
-                        // compute
-                        const size_t global = ne01 * local;
-                        const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
-                        const cl_int ncols = ne00;
-                        events.emplace_back();
-                        CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
-                        CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL));
-                        CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y));
-                        CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D));
-                        CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols));
-                        CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
-                    } else { // CLBlast matrix matrix multiplication
-                        // copy src1 to device
-                        CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
-
-                        // wait for conversion
-                        CL_CHECK(clFinish(queue));
-
-                        // compute
-                        events.emplace_back();
-                        clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
-                                                                   clblast::Transpose::kYes, clblast::Transpose::kNo,
-                                                                   ne01, ne11, ne10,
-                                                                   alpha,
-                                                                   d_X, 0, ne00,
-                                                                   d_Y, 0, ne10,
-                                                                   beta,
-                                                                   d_D, 0, ne01,
-                                                                   &queue, events.data() + ev_idx++);
-
-                        if (status != clblast::StatusCode::kSuccess) {
-                            GGML_ASSERT(false);
-                        }
-                    }
-
-                    // copy dst to host
-                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
-                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL));
-                    for (auto *event : events) {
-                        clReleaseEvent(event);
-                    }
-
-                    ev_idx = 0;
-                    events.clear();
-                }
-            }
-        }
-    }
-
-    if (!mul_mat_vec) {
-        ggml_cl_pool_free(d_X, x_size);
-    }
-    ggml_cl_pool_free(d_Y, y_size);
-    ggml_cl_pool_free(d_D, d_size);
-    if (src0->backend == GGML_BACKEND_TYPE_CPU) {
-        ggml_cl_pool_free(d_Q, q_size);
-    }
-}
-
-
-bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst) {
-    const int64_t ne10 = src1->ne[0];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-
-    // TODO: find the optimal values for these
-    if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
-        src1->type == GGML_TYPE_F32 &&
-        dst->type == GGML_TYPE_F32 &&
-        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) {
-        return true;
-    }
-
-    return false;
-}
-
-static bool ggml_cl_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) {
-    // If device doesn't support FP16
-    if (!fp16_support) {
-        return false;
-    }
-
-    size_t src0_sz = ggml_nbytes(src0);
-    size_t src1_sz = ggml_nbytes(src1);
-
-    // mul_mat_q: src0 is converted to fp32 on device
-    size_t mul_mat_q_transfer = src0_sz + src1_sz;
-
-    // mul_mat_f16: src1 is converted to fp16 on cpu
-    size_t mul_mat_f16_transfer = src0_sz + sizeof(ggml_fp16_t) * ggml_nelements(src1);
-
-    // choose the smaller one to transfer to the device
-    // TODO: this is not always the best choice due to the overhead of converting to fp16
-    return mul_mat_f16_transfer < mul_mat_q_transfer;
-}
-
-void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize) {
-    GGML_ASSERT(ggml_cl_can_mul_mat(src0, src1, dst));
-
-    if (src0->type == GGML_TYPE_F32) {
-        ggml_cl_mul_mat_f32(src0, src1, dst);
-    }
-    else if (src0->type == GGML_TYPE_F16) {
-        if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
-            ggml_cl_mul_mat_f16(src0, src1, dst, wdata, wsize);
-        }
-        else {
-            ggml_cl_mul_mat_q_f32(src0, src1, dst);
-        }
-    }
-    else if (ggml_is_quantized(src0->type)) {
-        ggml_cl_mul_mat_q_f32(src0, src1, dst);
-    }
-    else {
-        GGML_ASSERT(false);
-    }
-}
-
-size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
-    if (src0->type == GGML_TYPE_F16 && ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
-        return sizeof(ggml_fp16_t) * std::max(src1->ne[0] * src1->ne[1], dst->ne[0] * dst->ne[1]);
-    }
-    return 0;
-}
-
-void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
-    const int64_t ne0 = tensor->ne[0];
-    const int64_t ne1 = tensor->ne[1];
-    const int64_t ne2 = tensor->ne[2];
-    const int64_t ne3 = tensor->ne[3];
-
-    const ggml_type type = tensor->type;
-    const size_t s_sz = ggml_type_size(type) * (size_t) (ne0 * ne1 / ggml_blck_size(type));
-    const size_t q_sz = s_sz * (size_t) (ne2 * ne3);
-
-    size_t q_size;
-    cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
-
-    tensor->data = data;
-    // copy tensor to device
-    size_t offset = 0;
-    for (int64_t i3 = 0; i3 < ne3; i3++) {
-        for (int64_t i2 = 0; i2 < ne2; i2++) {
-            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, dst, offset, tensor, i3, i2, NULL));
-            offset += s_sz;
-        }
-    }
-
-    CL_CHECK(clFinish(queue));
-
-    tensor->extra = dst;
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
-}
-
-// ggml-backend
-
-// buffer
-
-struct ggml_backend_opencl_buffer_context {
-    ~ggml_backend_opencl_buffer_context() {
-        if (buffer) {
-            clReleaseMemObject(buffer);
-        }
-        for (auto * sub_buffer : sub_buffers) {
-            clReleaseMemObject(sub_buffer);
-        }
-    }
-
-    cl_mem buffer;
-    std::vector<cl_mem> sub_buffers;
-};
-
-static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000;
-
-static const char * ggml_backend_opencl_buffer_get_name(ggml_backend_buffer_t buffer) {
-    return "OpenCL";
-
-    GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
-    delete ctx;
-}
-
-static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) {
-    return cl_ptr_base;
-
-    GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
-    if (tensor->view_src != NULL && tensor->view_offs == 0) {
-        tensor->extra = tensor->view_src->extra;
-    } else {
-        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
-        cl_buffer_region region = {(size_t)((char *)tensor->data - (char *)cl_ptr_base), ggml_nbytes(tensor)};
-        cl_int err;
-        cl_mem sub_buffer = clCreateSubBuffer(ctx->buffer, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
-        CL_CHECK(err);
-        ctx->sub_buffers.push_back(sub_buffer);
-        tensor->extra = sub_buffer;
-    }
-    tensor->backend = GGML_BACKEND_TYPE_GPU;
-}
-
-static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    cl_mem tensor_buffer = (cl_mem) tensor->extra;
-    CL_CHECK(clEnqueueWriteBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL));
-    CL_CHECK(clFinish(queue));
-
-    GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    cl_mem tensor_buffer = (cl_mem) tensor->extra;
-    CL_CHECK(clEnqueueReadBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL));
-    CL_CHECK(clFinish(queue));
-
-    GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
-    CL_CHECK(clEnqueueFillBuffer(queue, ctx->buffer, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL));
-    CL_CHECK(clFinish(queue));
-}
-
-static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) {
-    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
-    for (auto * sub_buffer : ctx->sub_buffers) {
-        clReleaseMemObject(sub_buffer);
-    }
-    ctx->sub_buffers.clear();
-}
-
-static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = {
-    /* .get_name        = */ ggml_backend_opencl_buffer_get_name,
-    /* .free_buffer     = */ ggml_backend_opencl_buffer_free_buffer,
-    /* .get_base        = */ ggml_backend_opencl_buffer_get_base,
-    /* .init_tensor     = */ ggml_backend_opencl_buffer_init_tensor,
-    /* .set_tensor      = */ ggml_backend_opencl_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_opencl_buffer_get_tensor,
-    /* .cpy_tensor      = */ NULL,
-    /* .clear           = */ ggml_backend_opencl_buffer_clear,
-    /* .reset           = */ ggml_backend_opencl_buffer_reset,
-};
-
-// buffer type
-
-static const char * ggml_backend_opencl_buffer_type_name(ggml_backend_buffer_type_t buffer_type) {
-    return "OpenCL";
-
-    GGML_UNUSED(buffer_type);
-}
-
-static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) {
-    ggml_cl_init();
-
-    cl_int err;
-    cl_mem mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err);
-    if (err != CL_SUCCESS) {
-        fprintf(stderr, "%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0);
-        return nullptr;
-    }
-
-    ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context{mem, {}};
-
-    return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size);
-}
-
-static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) {
-    // FIXME: not thread safe, device may not be initialized yet
-    static cl_uint alignment = -1;
-    if (alignment == (cl_uint)-1) {
-        ggml_cl_init();
-        clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &alignment, NULL);
-        alignment /= 8; // bits to bytes
-    }
-    return alignment;
-
-    GGML_UNUSED(buffer_type);
-}
-
-static size_t ggml_backend_opencl_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
-    static size_t max_size = -1;
-    if (max_size == (size_t)-1) {
-        ggml_cl_init();
-        clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &max_size, NULL);
-    }
-    return max_size;
-}
-
-static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buffer_type, ggml_backend_t backend) {
-    //return ggml_backend_is_opencl(backend); // opencl must be used through the cpu backend
-    return ggml_backend_is_cpu(backend);
-
-    GGML_UNUSED(buffer_type);
-}
-
-static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = {
-    /* .get_name         = */ ggml_backend_opencl_buffer_type_name,
-    /* .alloc_buffer     = */ ggml_backend_opencl_buffer_type_alloc_buffer,
-    /* .get_alignment    = */ ggml_backend_opencl_buffer_type_get_alignment,
-    /* .get_max_size     = */ ggml_backend_opencl_buffer_type_get_max_size,
-    /* .get_alloc_size   = */ NULL,
-    /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend,
-    /* .is_host          = */ NULL,
-};
-
-
-ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() {
-    static ggml_backend_buffer_type buffer_type = {
-        /* .iface   = */ ggml_backend_opencl_buffer_type_interface,
-        /* .context = */ nullptr,
-    };
-
-    return &buffer_type;
-}
-
-#if 0
-// host buffer type
-
-static const char * ggml_backend_opencl_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    return "CL_Host";
-
-    GGML_UNUSED(buft);
-}
-
-static const char * ggml_backend_opencl_host_buffer_name(ggml_backend_buffer_t buffer) {
-    return "CL_Host";
-
-    GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_opencl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_cl_host_free(buffer->context);
-}
-
-static ggml_backend_buffer_t ggml_backend_opencl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    void * ptr = ggml_cl_host_malloc(size);
-
-    if (ptr == nullptr) {
-        // fallback to cpu buffer
-        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
-    }
-
-    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
-    buffer->buft = buft;
-    buffer->iface.get_name = ggml_backend_opencl_host_buffer_name;
-    buffer->iface.free_buffer = ggml_backend_opencl_host_buffer_free_buffer;
-
-    return buffer;
-}
-
-ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type() {
-    static struct ggml_backend_buffer_type ggml_backend_opencl_buffer_type_host = {
-        /* .iface    = */ {
-            /* .get_name         = */ ggml_backend_opencl_host_buffer_type_name,
-            /* .alloc_buffer     = */ ggml_backend_opencl_host_buffer_type_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
-            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
-            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
-            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
-        },
-        /* .context  = */ nullptr,
-    };
-
-    return &ggml_backend_opencl_buffer_type_host;
-}
-
-// backend
-
-static const char * ggml_backend_opencl_name(ggml_backend_t backend) {
-    return "OpenCL";
-
-    GGML_UNUSED(backend);
-}
-
-static void ggml_backend_opencl_free(ggml_backend_t backend) {
-    GGML_UNUSED(backend);
-}
-
-static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(ggml_backend_t backend) {
-    return ggml_backend_opencl_buffer_type();
-
-    GGML_UNUSED(backend);
-}
-
-static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
-    for (int i = 0; i < graph->n_nodes; ++i) {
-        ggml_tensor * node = graph->nodes[i];
-
-        if (ggml_is_empty(node)) {
-            continue;
-        }
-
-        switch (node->op) {
-            case GGML_OP_MUL_MAT:
-                ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0);
-                break;
-            case GGML_OP_MUL:
-                ggml_cl_mul(node->src[0], node->src[1], node);
-                break;
-            default:
-                GGML_ASSERT(false);
-        }
-    }
-
-    return GGML_STATUS_SUCCESS;
-
-    GGML_UNUSED(backend);
-}
-
-static bool ggml_backend_opencl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
-    switch (op->op) {
-        case GGML_OP_MUL_MAT:
-            return ggml_cl_can_mul_mat(op->src[0], op->src[1], op);
-        case GGML_OP_MUL:
-            // return ggml_can_repeat_rows(op->src[1], op->src[0]);
-            return true;
-        default:
-            return false;
-    }
-
-    GGML_UNUSED(backend);
-}
-
-static ggml_backend_i opencl_backend_i = {
-    /* .get_name                = */ ggml_backend_opencl_name,
-    /* .free                    = */ ggml_backend_opencl_free,
-    /* .get_default_buffer_type = */ ggml_backend_opencl_get_default_buffer_type,
-    /* .set_tensor_async        = */ NULL,
-    /* .get_tensor_async        = */ NULL,
-    /* .cpy_tensor_from_async   = */ NULL,
-    /* .cpy_tensor_to_async     = */ NULL,
-    /* .synchronize             = */ NULL,
-    /* .graph_plan_create       = */ NULL,
-    /* .graph_plan_free         = */ NULL,
-    /* .graph_plan_compute      = */ NULL,
-    /* .graph_compute           = */ ggml_backend_opencl_graph_compute,
-    /* .supports_op             = */ ggml_backend_opencl_supports_op,
-};
-
-ggml_backend_t ggml_backend_opencl_init() {
-    ggml_backend_t backend = new ggml_backend {
-        /* .interface = */ opencl_backend_i,
-        /* .context   = */ nullptr
-    };
-
-    return backend;
-}
-
-bool ggml_backend_is_opencl(ggml_backend_t backend) {
-    return backend && backend->iface.get_name == ggml_backend_opencl_name;
-}
-#endif
diff --git a/ggml-opencl.h b/ggml-opencl.h
deleted file mode 100644
index 257a6be6af5ec..0000000000000
--- a/ggml-opencl.h
+++ /dev/null
@@ -1,36 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-#include "ggml-backend.h"
-
-#ifdef  __cplusplus
-extern "C" {
-#endif
-
-GGML_API void ggml_cl_init(void);
-
-GGML_API void   ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API void   ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst);
-GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API void   ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
-
-// GGML_API void * ggml_cl_host_malloc(size_t size);
-// GGML_API void   ggml_cl_host_free(void * ptr);
-
-GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor);
-
-GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
-
-// backend API
-
-// GGML_API ggml_backend_t ggml_backend_opencl_init(void);
-
-// GGML_API bool ggml_backend_is_opencl(ggml_backend_t backend);
-
-GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
-// GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
-
-#ifdef  __cplusplus
-}
-#endif
diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp
deleted file mode 100644
index 19d22d63753ab..0000000000000
--- a/ggml-sycl.cpp
+++ /dev/null
@@ -1,17775 +0,0 @@
-//
-// MIT license
-// Copyright (C) 2024 Intel Corporation
-// SPDX-License-Identifier: MIT
-//
-
-//
-// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
-// See https://llvm.org/LICENSE.txt for license information.
-// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-//
-
-#include <algorithm>
-#include <assert.h>
-#include <atomic>
-#include <cinttypes>
-#include <cstddef>
-#include <cstdint>
-#include <cstdlib>
-#include <float.h>
-#include <limits>
-#include <stdint.h>
-#include <stdio.h>
-#include <vector>
-#include <cmath>
-#include <iostream>
-#include <fstream>
-#include <stdio.h>
-#include <stdlib.h>
-#include <regex>
-
-#include <sycl/sycl.hpp>
-#include <sycl/half_type.hpp>
-
-#include "ggml-sycl.h"
-#include "ggml.h"
-#include "ggml-backend-impl.h"
-
-/*
-Following definition copied from DPCT head files, which are used by ggml-sycl.cpp
-*/
-// COPY from DPCT head files
-#include <sycl/sycl.hpp>
-#include <oneapi/mkl.hpp>
-#include <map>
-
-#if defined(__linux__)
-#include <sys/mman.h>
-#elif defined(_WIN64)
-#ifndef NOMINMAX
-#define NOMINMAX
-#endif
-#include <windows.h>
-#else
-#error "Only support Windows and Linux."
-#endif
-
-#if defined(__linux__)
-#include <unistd.h>
-#include <sys/syscall.h>
-#endif
-#if defined(_WIN64)
-#ifndef NOMINMAX
-#define NOMINMAX
-#endif
-#include <windows.h>
-#endif
-
-#define DPCT_COMPATIBILITY_TEMP (900)
-
-#if defined(_MSC_VER)
-#define __dpct_align__(n) __declspec(align(n))
-#define __dpct_inline__ __forceinline
-#else
-#define __dpct_align__(n) __attribute__((aligned(n)))
-#define __dpct_inline__ __inline__ __attribute__((always_inline))
-#endif
-
-#if defined(_MSC_VER)
-#define __dpct_noinline__ __declspec(noinline)
-#else
-#define __dpct_noinline__ __attribute__((noinline))
-#endif
-
-
-std::string get_device_type_name(const sycl::device &Device) {
-    auto DeviceType = Device.get_info<sycl::info::device::device_type>();
-    switch (DeviceType) {
-    case sycl::info::device_type::cpu:
-        return "cpu";
-    case sycl::info::device_type::gpu:
-        return "gpu";
-    case sycl::info::device_type::host:
-        return "host";
-    case sycl::info::device_type::accelerator:
-        return "acc";
-    default:
-        return "unknown";
-    }
-}
-
-std::string get_device_backend_and_type(const sycl::device &device) {
-    std::stringstream device_type;
-    sycl::backend backend = device.get_backend();
-    device_type <<  backend << ":" << get_device_type_name(device);
-    return device_type.str();
-}
-
-namespace dpct
-{
-    typedef sycl::queue *queue_ptr;
-    typedef sycl::event *event_ptr;
-    typedef char *device_ptr;
-    typedef uint8_t byte_t;
-    typedef sycl::buffer<byte_t> buffer_t;
-
-    /// SYCL default exception handler
-    inline auto exception_handler = [](sycl::exception_list exceptions)
-    {
-        for (std::exception_ptr const &e : exceptions)
-        {
-            try
-            {
-                std::rethrow_exception(e);
-            }
-            catch (sycl::exception const &e)
-            {
-                std::cerr << "Caught asynchronous SYCL exception:" << std::endl
-                          << e.what() << std::endl
-                          << "Exception caught at file:" << __FILE__
-                          << ", line:" << __LINE__ << std::endl;
-            }
-        }
-    };
-
-    enum error_code
-    {
-        success = 0,
-        default_error = 999
-    };
-
-    enum memcpy_direction
-    {
-        host_to_host,
-        host_to_device,
-        device_to_host,
-        device_to_device,
-        automatic
-    };
-
-    enum memory_region
-    {
-        global = 0, // device global memory
-        constant,   // device constant memory
-        local,      // device local memory
-        shared,     // memory which can be accessed by host and device
-    };
-
-    enum class library_data_t : unsigned char
-    {
-        real_float = 0,
-        complex_float,
-        real_double,
-        complex_double,
-        real_half,
-        complex_half,
-        real_bfloat16,
-        complex_bfloat16,
-        real_int4,
-        complex_int4,
-        real_uint4,
-        complex_uint4,
-        real_int8,
-        complex_int8,
-        real_uint8,
-        complex_uint8,
-        real_int16,
-        complex_int16,
-        real_uint16,
-        complex_uint16,
-        real_int32,
-        complex_int32,
-        real_uint32,
-        complex_uint32,
-        real_int64,
-        complex_int64,
-        real_uint64,
-        complex_uint64,
-        real_int8_4,
-        real_int8_32,
-        real_uint8_4,
-        library_data_t_size
-    };
-
-    template <typename T>
-    struct DataType
-    {
-        using T2 = T;
-    };
-    template <typename T>
-    struct DataType<sycl::vec<T, 2>>
-    {
-        using T2 = std::complex<T>;
-    };
-
-    static void destroy_event(event_ptr event)
-    {
-        delete event;
-    }
-
-    static inline unsigned int get_tid()
-    {
-#if defined(__linux__)
-        return syscall(SYS_gettid);
-#elif defined(_WIN64)
-        return GetCurrentThreadId();
-#else
-#error "Only support Windows and Linux."
-#endif
-    }
-
-    namespace detail
-    {
-        static void get_version(const sycl::device &dev, int &major, int &minor)
-        {
-            // Version string has the following format:
-            // a. OpenCL<space><major.minor><space><vendor-specific-information>
-            // b. <major.minor>
-            // c. <AmdGcnArchName> e.g gfx1030
-            std::string ver;
-            ver = dev.get_info<sycl::info::device::version>();
-            std::string::size_type i = 0;
-            while (i < ver.size()) {
-              if (isdigit(ver[i]))
-                break;
-              i++;
-            }
-            major = std::stoi(&(ver[i]));
-            while (i < ver.size()) {
-              if (ver[i] == '.')
-                break;
-              i++;
-            }
-            if (i < ver.size()) {
-              // a. and b.
-              i++;
-              minor = std::stoi(&(ver[i]));
-            } else {
-              // c.
-              minor = 0;
-            }
-        }
-
-        template <typename tag, typename T>
-        class generic_error_type
-        {
-        public:
-            generic_error_type() = default;
-            generic_error_type(T value) : value{value} {}
-            operator T() const { return value; }
-
-        private:
-            T value;
-        };
-
-    } // namespace detail
-
-    /// Pitched 2D/3D memory data.
-    class pitched_data
-    {
-    public:
-        pitched_data() : pitched_data(nullptr, 0, 0, 0) {}
-        pitched_data(void *data, size_t pitch, size_t x, size_t y)
-            : _data(data), _pitch(pitch), _x(x), _y(y) {}
-
-        void *get_data_ptr() { return _data; }
-        void set_data_ptr(void *data) { _data = data; }
-
-        size_t get_pitch() { return _pitch; }
-        void set_pitch(size_t pitch) { _pitch = pitch; }
-
-        size_t get_x() { return _x; }
-        void set_x(size_t x) { _x = x; };
-
-        size_t get_y() { return _y; }
-        void set_y(size_t y) { _y = y; }
-
-    private:
-        void *_data;
-        size_t _pitch, _x, _y;
-    };
-
-    class device_info
-    {
-    public:
-        // get interface
-        const char *get_name() const { return _name; }
-        char *get_name() { return _name; }
-        template <typename WorkItemSizesTy = sycl::range<3>,
-                  std::enable_if_t<std::is_same_v<WorkItemSizesTy, sycl::range<3>> ||
-                                       std::is_same_v<WorkItemSizesTy, int *>,
-                                   int> = 0>
-        auto get_max_work_item_sizes() const
-        {
-            if constexpr (std::is_same_v<WorkItemSizesTy, sycl::range<3>>)
-                return sycl::range<3>(_max_work_item_sizes_i[0],
-                                      _max_work_item_sizes_i[1],
-                                      _max_work_item_sizes_i[2]);
-            else
-            {
-                return _max_work_item_sizes_i;
-            }
-        }
-        template <typename WorkItemSizesTy = sycl::range<3>,
-                  std::enable_if_t<std::is_same_v<WorkItemSizesTy, sycl::range<3>> ||
-                                       std::is_same_v<WorkItemSizesTy, int *>,
-                                   int> = 0>
-        auto get_max_work_item_sizes()
-        {
-            if constexpr (std::is_same_v<WorkItemSizesTy, sycl::range<3>>)
-                return sycl::range<3>(_max_work_item_sizes_i[0],
-                                      _max_work_item_sizes_i[1],
-                                      _max_work_item_sizes_i[2]);
-            else
-            {
-                return _max_work_item_sizes_i;
-            }
-        }
-        bool get_host_unified_memory() const { return _host_unified_memory; }
-        int get_major_version() const { return _major; }
-        int get_minor_version() const { return _minor; }
-        int get_integrated() const { return _integrated; }
-        int get_max_clock_frequency() const { return _frequency; }
-        int get_max_compute_units() const { return _max_compute_units; }
-        int get_max_work_group_size() const { return _max_work_group_size; }
-        int get_max_sub_group_size() const { return _max_sub_group_size; }
-        int get_max_work_items_per_compute_unit() const
-        {
-            return _max_work_items_per_compute_unit;
-        }
-        int get_max_register_size_per_work_group() const
-        {
-            return _max_register_size_per_work_group;
-        }
-        template <typename NDRangeSizeTy = size_t *,
-                  std::enable_if_t<std::is_same_v<NDRangeSizeTy, size_t *> ||
-                                       std::is_same_v<NDRangeSizeTy, int *>,
-                                   int> = 0>
-        auto get_max_nd_range_size() const
-        {
-            if constexpr (std::is_same_v<NDRangeSizeTy, size_t *>)
-                return _max_nd_range_size;
-            else
-                return _max_nd_range_size_i;
-        }
-        template <typename NDRangeSizeTy = size_t *,
-                  std::enable_if_t<std::is_same_v<NDRangeSizeTy, size_t *> ||
-                                       std::is_same_v<NDRangeSizeTy, int *>,
-                                   int> = 0>
-        auto get_max_nd_range_size()
-        {
-            if constexpr (std::is_same_v<NDRangeSizeTy, size_t *>)
-                return _max_nd_range_size;
-            else
-                return _max_nd_range_size_i;
-        }
-        size_t get_global_mem_size() const { return _global_mem_size; }
-        size_t get_local_mem_size() const { return _local_mem_size; }
-        size_t get_max_mem_alloc_size() const { return _max_mem_alloc_size; }
-        /// Returns the maximum clock rate of device's global memory in kHz. If
-        /// compiler does not support this API then returns default value 3200000 kHz.
-        unsigned int get_memory_clock_rate() const { return _memory_clock_rate; }
-        /// Returns the maximum bus width between device and memory in bits. If
-        /// compiler does not support this API then returns default value 64 bits.
-        unsigned int get_memory_bus_width() const { return _memory_bus_width; }
-        uint32_t get_device_id() const { return _device_id; }
-        std::array<unsigned char, 16> get_uuid() const { return _uuid; }
-        /// Returns global memory cache size in bytes.
-        unsigned int get_global_mem_cache_size() const
-        {
-            return _global_mem_cache_size;
-        }
-
-        // set interface
-        void set_name(const char *name)
-        {
-            size_t length = strlen(name);
-            if (length < 256)
-            {
-                std::memcpy(_name, name, length + 1);
-            }
-            else
-            {
-                std::memcpy(_name, name, 255);
-                _name[255] = '\0';
-            }
-        }
-        void set_max_work_item_sizes(const sycl::range<3> max_work_item_sizes)
-        {
-            for (int i = 0; i < 3; ++i)
-                _max_work_item_sizes_i[i] = max_work_item_sizes[i];
-        }
-        [[deprecated]] void
-        set_max_work_item_sizes(const sycl::id<3> max_work_item_sizes)
-        {
-            for (int i = 0; i < 3; ++i)
-            {
-                _max_work_item_sizes_i[i] = max_work_item_sizes[i];
-            }
-        }
-        void set_host_unified_memory(bool host_unified_memory)
-        {
-            _host_unified_memory = host_unified_memory;
-        }
-        void set_major_version(int major) { _major = major; }
-        void set_minor_version(int minor) { _minor = minor; }
-        void set_integrated(int integrated) { _integrated = integrated; }
-        void set_max_clock_frequency(int frequency) { _frequency = frequency; }
-        void set_max_compute_units(int max_compute_units)
-        {
-            _max_compute_units = max_compute_units;
-        }
-        void set_global_mem_size(size_t global_mem_size)
-        {
-            _global_mem_size = global_mem_size;
-        }
-        void set_local_mem_size(size_t local_mem_size)
-        {
-            _local_mem_size = local_mem_size;
-        }
-        void set_max_mem_alloc_size(size_t max_mem_alloc_size)
-        {
-            _max_mem_alloc_size = max_mem_alloc_size;
-        }
-        void set_max_work_group_size(int max_work_group_size)
-        {
-            _max_work_group_size = max_work_group_size;
-        }
-        void set_max_sub_group_size(int max_sub_group_size)
-        {
-            _max_sub_group_size = max_sub_group_size;
-        }
-        void
-        set_max_work_items_per_compute_unit(int max_work_items_per_compute_unit)
-        {
-            _max_work_items_per_compute_unit = max_work_items_per_compute_unit;
-        }
-        void set_max_nd_range_size(int max_nd_range_size[])
-        {
-            for (int i = 0; i < 3; i++)
-            {
-                _max_nd_range_size[i] = max_nd_range_size[i];
-                _max_nd_range_size_i[i] = max_nd_range_size[i];
-            }
-        }
-        void set_memory_clock_rate(unsigned int memory_clock_rate)
-        {
-            _memory_clock_rate = memory_clock_rate;
-        }
-        void set_memory_bus_width(unsigned int memory_bus_width)
-        {
-            _memory_bus_width = memory_bus_width;
-        }
-        void
-        set_max_register_size_per_work_group(int max_register_size_per_work_group)
-        {
-            _max_register_size_per_work_group = max_register_size_per_work_group;
-        }
-        void set_device_id(uint32_t device_id)
-        {
-            _device_id = device_id;
-        }
-        void set_uuid(std::array<unsigned char, 16> uuid)
-        {
-            _uuid = std::move(uuid);
-        }
-        void set_global_mem_cache_size(unsigned int global_mem_cache_size)
-        {
-            _global_mem_cache_size = global_mem_cache_size;
-        }
-
-    private:
-        char _name[256];
-        int _max_work_item_sizes_i[3];
-        bool _host_unified_memory = false;
-        int _major;
-        int _minor;
-        int _integrated = 0;
-        int _frequency;
-        // Set estimated value 3200000 kHz as default value.
-        unsigned int _memory_clock_rate = 3200000;
-        // Set estimated value 64 bits as default value.
-        unsigned int _memory_bus_width = 64;
-        unsigned int _global_mem_cache_size;
-        int _max_compute_units;
-        int _max_work_group_size;
-        int _max_sub_group_size;
-        int _max_work_items_per_compute_unit;
-        int _max_register_size_per_work_group;
-        size_t _global_mem_size;
-        size_t _local_mem_size;
-        size_t _max_mem_alloc_size;
-        size_t _max_nd_range_size[3];
-        int _max_nd_range_size_i[3];
-        uint32_t _device_id;
-        std::array<unsigned char, 16> _uuid;
-    };
-
-    static int get_major_version(const sycl::device &dev)
-    {
-        int major, minor;
-        detail::get_version(dev, major, minor);
-        return major;
-    }
-
-    static int get_minor_version(const sycl::device &dev)
-    {
-        int major, minor;
-        detail::get_version(dev, major, minor);
-        return minor;
-    }
-
-    static void get_device_info(device_info &out, const sycl::device &dev)
-    {
-        device_info prop;
-        prop.set_name(dev.get_info<sycl::info::device::name>().c_str());
-
-        int major, minor;
-        detail::get_version(dev, major, minor);
-        prop.set_major_version(major);
-        prop.set_minor_version(minor);
-
-        prop.set_max_work_item_sizes(
-#if (__SYCL_COMPILER_VERSION && __SYCL_COMPILER_VERSION < 20220902)
-            // oneAPI DPC++ compiler older than 2022/09/02, where max_work_item_sizes
-            // is an enum class element
-            dev.get_info<sycl::info::device::max_work_item_sizes>());
-#else
-            // SYCL 2020-conformant code, max_work_item_sizes is a struct templated by
-            // an int
-            dev.get_info<sycl::info::device::max_work_item_sizes<3>>());
-#endif
-        prop.set_host_unified_memory(dev.has(sycl::aspect::usm_host_allocations));
-
-        prop.set_max_clock_frequency(
-            dev.get_info<sycl::info::device::max_clock_frequency>() * 1000);
-
-        prop.set_max_compute_units(
-            dev.get_info<sycl::info::device::max_compute_units>());
-        prop.set_max_work_group_size(
-            dev.get_info<sycl::info::device::max_work_group_size>());
-        prop.set_global_mem_size(dev.get_info<sycl::info::device::global_mem_size>());
-        prop.set_local_mem_size(dev.get_info<sycl::info::device::local_mem_size>());
-        prop.set_max_mem_alloc_size(dev.get_info<sycl::info::device::max_mem_alloc_size>());
-
-#if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6)
-        if (dev.has(sycl::aspect::ext_intel_memory_clock_rate))
-        {
-            unsigned int tmp =
-                dev.get_info<sycl::ext::intel::info::device::memory_clock_rate>();
-            if (tmp != 0)
-                prop.set_memory_clock_rate(1000 * tmp);
-        }
-        if (dev.has(sycl::aspect::ext_intel_memory_bus_width))
-        {
-            prop.set_memory_bus_width(
-                dev.get_info<sycl::ext::intel::info::device::memory_bus_width>());
-        }
-        if (dev.has(sycl::aspect::ext_intel_device_id))
-        {
-            prop.set_device_id(
-                dev.get_info<sycl::ext::intel::info::device::device_id>());
-        }
-        if (dev.has(sycl::aspect::ext_intel_device_info_uuid))
-        {
-            prop.set_uuid(dev.get_info<sycl::ext::intel::info::device::uuid>());
-        }
-#elif defined(_MSC_VER) && !defined(__clang__)
-#pragma message("get_device_info: querying memory_clock_rate and \
-        memory_bus_width are not supported by the compiler used. \
-        Use 3200000 kHz as memory_clock_rate default value. \
-        Use 64 bits as memory_bus_width default value.")
-#else
-#warning "get_device_info: querying memory_clock_rate and \
-        memory_bus_width are not supported by the compiler used. \
-        Use 3200000 kHz as memory_clock_rate default value. \
-        Use 64 bits as memory_bus_width default value."
-#endif
-
-        size_t max_sub_group_size = 1;
-        std::vector<size_t> sub_group_sizes =
-            dev.get_info<sycl::info::device::sub_group_sizes>();
-
-        for (const auto &sub_group_size : sub_group_sizes)
-        {
-            if (max_sub_group_size < sub_group_size)
-                max_sub_group_size = sub_group_size;
-        }
-
-        prop.set_max_sub_group_size(max_sub_group_size);
-
-        prop.set_max_work_items_per_compute_unit(
-            dev.get_info<sycl::info::device::max_work_group_size>());
-        int max_nd_range_size[] = {0x7FFFFFFF, 0x7FFFFFFF, 0x7FFFFFFF};
-        prop.set_max_nd_range_size(max_nd_range_size);
-
-        // Estimates max register size per work group, feel free to update the value
-        // according to device properties.
-        prop.set_max_register_size_per_work_group(65536);
-
-        prop.set_global_mem_cache_size(
-            dev.get_info<sycl::info::device::global_mem_cache_size>());
-        out = prop;
-    }
-
-    /// dpct device extension
-    class device_ext : public sycl::device
-    {
-        typedef std::mutex mutex_type;
-
-    public:
-        device_ext() : sycl::device(), _ctx(*this) {}
-        ~device_ext()
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            clear_queues();
-        }
-        device_ext(const sycl::device &base) : sycl::device(base), _ctx(*this)
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            init_queues();
-        }
-
-        int is_native_atomic_supported() { return 0; }
-        int get_major_version() const
-        {
-            return dpct::get_major_version(*this);
-        }
-
-        int get_minor_version() const
-        {
-            return dpct::get_minor_version(*this);
-        }
-
-        int get_max_compute_units() const
-        {
-            return get_device_info().get_max_compute_units();
-        }
-
-        /// Return the maximum clock frequency of this device in KHz.
-        int get_max_clock_frequency() const
-        {
-            return get_device_info().get_max_clock_frequency();
-        }
-
-        int get_integrated() const { return get_device_info().get_integrated(); }
-
-        int get_max_sub_group_size() const
-        {
-            return get_device_info().get_max_sub_group_size();
-        }
-
-        int get_max_register_size_per_work_group() const
-        {
-            return get_device_info().get_max_register_size_per_work_group();
-        }
-
-        int get_max_work_group_size() const
-        {
-            return get_device_info().get_max_work_group_size();
-        }
-
-        int get_mem_base_addr_align() const
-        {
-            return get_info<sycl::info::device::mem_base_addr_align>();
-        }
-
-        size_t get_global_mem_size() const
-        {
-            return get_device_info().get_global_mem_size();
-        }
-
-        size_t get_max_mem_alloc_size() const
-        {
-            return get_device_info().get_max_mem_alloc_size();
-        }
-
-        /// Get the number of bytes of free and total memory on the SYCL device.
-        /// \param [out] free_memory The number of bytes of free memory on the SYCL device.
-        /// \param [out] total_memory The number of bytes of total memory on the SYCL device.
-        void get_memory_info(size_t &free_memory, size_t &total_memory)
-        {
-            total_memory = get_device_info().get_global_mem_size();
-            const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not "
-                                 "supported (export/set ZES_ENABLE_SYSMAN=1 to support), "
-                                 "use total memory as free memory";
-#if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105)
-            if (!has(sycl::aspect::ext_intel_free_memory))
-            {
-                std::cerr << warning_info << std::endl;
-                free_memory = total_memory;
-            }
-            else
-            {
-                free_memory = get_info<sycl::ext::intel::info::device::free_memory>();
-            }
-#else
-            std::cerr << warning_info << std::endl;
-            free_memory = total_memory;
-#if defined(_MSC_VER) && !defined(__clang__)
-#pragma message("Querying the number of bytes of free memory is not supported")
-#else
-#warning "Querying the number of bytes of free memory is not supported"
-#endif
-#endif
-        }
-
-        void get_device_info(device_info &out) const
-        {
-            dpct::get_device_info(out, *this);
-        }
-
-        device_info get_device_info() const
-        {
-            device_info prop;
-            dpct::get_device_info(prop, *this);
-            return prop;
-        }
-
-        void reset()
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            clear_queues();
-            init_queues();
-        }
-
-        sycl::queue &in_order_queue() { return *_q_in_order; }
-
-        sycl::queue &out_of_order_queue() { return *_q_out_of_order; }
-
-        sycl::queue &default_queue()
-        {
-            return in_order_queue();
-        }
-
-        void queues_wait_and_throw()
-        {
-            std::unique_lock<mutex_type> lock(m_mutex);
-            std::vector<std::shared_ptr<sycl::queue>> current_queues(
-                _queues);
-            lock.unlock();
-            for (const auto &q : current_queues)
-            {
-                q->wait_and_throw();
-            }
-            // Guard the destruct of current_queues to make sure the ref count is safe.
-            lock.lock();
-        }
-
-        sycl::queue *create_queue(bool enable_exception_handler = false)
-        {
-            return create_in_order_queue(enable_exception_handler);
-        }
-
-        sycl::queue *create_queue(sycl::context context, sycl::device device,
-                                bool enable_exception_handler = false) {
-            return create_in_order_queue(context, device, enable_exception_handler);
-        }
-
-        sycl::queue *create_in_order_queue(bool enable_exception_handler = false) {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            return create_queue_impl(enable_exception_handler,
-                                    sycl::property::queue::in_order());
-        }
-
-        sycl::queue *create_in_order_queue(sycl::context context, sycl::device device,
-                                        bool enable_exception_handler = false) {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            return create_queue_impl(context, device, enable_exception_handler,
-                                    sycl::property::queue::in_order());
-        }
-
-        sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            return create_queue_impl(enable_exception_handler);
-        }
-
-        void destroy_queue(sycl::queue *&queue)
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            _queues.erase(std::remove_if(_queues.begin(), _queues.end(),
-                                         [=](const std::shared_ptr<sycl::queue> &q) -> bool
-                                         {
-                                             return q.get() == queue;
-                                         }),
-                          _queues.end());
-            queue = nullptr;
-        }
-        void set_saved_queue(sycl::queue *q)
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            _saved_queue = q;
-        }
-        sycl::queue *get_saved_queue() const
-        {
-            std::lock_guard<mutex_type> lock(m_mutex);
-            return _saved_queue;
-        }
-        sycl::context get_context() const { return _ctx; }
-
-    private:
-        void clear_queues()
-        {
-            _queues.clear();
-            _q_in_order = _q_out_of_order = _saved_queue = nullptr;
-        }
-
-        void init_queues()
-        {
-            _q_in_order = create_queue_impl(true, sycl::property::queue::in_order());
-            _q_out_of_order = create_queue_impl(true);
-            _saved_queue = &default_queue();
-        }
-
-        /// Caller should acquire resource \p m_mutex before calling this function.
-        template <class... Properties>
-        sycl::queue *create_queue_impl(bool enable_exception_handler,
-                                       Properties... properties)
-        {
-            sycl::async_handler eh = {};
-            if (enable_exception_handler)
-            {
-                eh = exception_handler;
-            }
-            _queues.push_back(std::make_shared<sycl::queue>(
-                _ctx, *this, eh,
-                sycl::property_list(
-#ifdef DPCT_PROFILING_ENABLED
-                    sycl::property::queue::enable_profiling(),
-#endif
-                    properties...)));
-
-            return _queues.back().get();
-        }
-
-        template <class... Properties>
-        sycl::queue *create_queue_impl(sycl::context context, sycl::device device,
-                                    bool enable_exception_handler,
-                                    Properties... properties) {
-            sycl::async_handler eh = {};
-            if (enable_exception_handler) {
-                eh = exception_handler;
-            }
-            _queues.push_back(std::make_shared<sycl::queue>(
-                context, device, eh,
-                sycl::property_list(
-        #ifdef DPCT_PROFILING_ENABLED
-                    sycl::property::queue::enable_profiling(),
-        #endif
-                    properties...)));
-
-            return _queues.back().get();
-        }
-
-        void get_version(int &major, int &minor) const
-        {
-            detail::get_version(*this, major, minor);
-        }
-        sycl::queue *_q_in_order, *_q_out_of_order;
-        sycl::queue *_saved_queue;
-        sycl::context _ctx;
-        std::vector<std::shared_ptr<sycl::queue>> _queues;
-        mutable mutex_type m_mutex;
-    };
-
-    /// device manager
-    class dev_mgr
-    {
-    public:
-        device_ext &current_device()
-        {
-            unsigned int dev_id = current_device_id();
-            check_id(dev_id);
-            return *_devs[dev_id];
-        }
-        device_ext &cpu_device() const
-        {
-            std::lock_guard<std::recursive_mutex> lock(m_mutex);
-            if (_cpu_device == -1)
-            {
-                throw std::runtime_error("no valid cpu device");
-            }
-            else
-            {
-                return *_devs[_cpu_device];
-            }
-        }
-        device_ext &get_device(unsigned int id) const
-        {
-            std::lock_guard<std::recursive_mutex> lock(m_mutex);
-            check_id(id);
-            return *_devs[id];
-        }
-        unsigned int current_device_id() const
-        {
-            std::lock_guard<std::recursive_mutex> lock(m_mutex);
-            auto it = _thread2dev_map.find(get_tid());
-            if (it != _thread2dev_map.end())
-                return it->second;
-            return DEFAULT_DEVICE_ID;
-        }
-
-        /// Select device with a device ID.
-        /// \param [in] id The id of the device which can
-        /// be obtained through get_device_id(const sycl::device).
-        void select_device(unsigned int id)
-        {
-            std::lock_guard<std::recursive_mutex> lock(m_mutex);
-            check_id(id);
-            _thread2dev_map[get_tid()] = id;
-        }
-        unsigned int device_count() { return _devs.size(); }
-
-        unsigned int get_device_id(const sycl::device &dev)
-        {
-            unsigned int id = 0;
-            for (auto dev_item : _devs)
-            {
-                if (*dev_item == dev)
-                {
-                    break;
-                }
-                id++;
-            }
-            return id;
-        }
-
-        template <class DeviceSelector>
-        std::enable_if_t<
-            std::is_invocable_r_v<int, DeviceSelector, const sycl::device &>>
-        select_device(const DeviceSelector &selector = sycl::gpu_selector_v)
-        {
-            sycl::device selected_device = sycl::device(selector);
-            unsigned int selected_device_id = get_device_id(selected_device);
-            select_device(selected_device_id);
-        }
-
-        /// Returns the instance of device manager singleton.
-        static dev_mgr &instance()
-        {
-            static dev_mgr d_m;
-            return d_m;
-        }
-        dev_mgr(const dev_mgr &) = delete;
-        dev_mgr &operator=(const dev_mgr &) = delete;
-        dev_mgr(dev_mgr &&) = delete;
-        dev_mgr &operator=(dev_mgr &&) = delete;
-
-    private:
-        mutable std::recursive_mutex m_mutex;
-        static bool compare_dev(sycl::device &device1, sycl::device &device2)
-        {
-            dpct::device_info prop1;
-            dpct::get_device_info(prop1, device1);
-            dpct::device_info prop2;
-            dpct::get_device_info(prop2, device2);
-            return prop1.get_max_compute_units() > prop2.get_max_compute_units();
-        }
-        static int convert_backend_index(std::string & backend) {
-            if (backend == "ext_oneapi_level_zero:gpu") return 0;
-            if (backend == "opencl:gpu") return 1;
-            if (backend == "ext_oneapi_cuda:gpu") return 2;
-            if (backend == "ext_oneapi_hip:gpu") return 3;
-            if (backend == "opencl:cpu") return 4;
-            if (backend == "opencl:acc") return 5;
-            printf("convert_backend_index: can't handle backend=%s\n", backend.c_str());
-            GGML_ASSERT(false);
-        }
-        static bool compare_backend(std::string &backend1, std::string &backend2) {
-            return convert_backend_index(backend1) < convert_backend_index(backend2);
-        }
-        dev_mgr()
-        {
-            sycl::device default_device =
-                sycl::device(sycl::default_selector_v);
-            _devs.push_back(std::make_shared<device_ext>(default_device));
-
-            std::vector<sycl::device> sycl_all_devs;
-            // Collect other devices except for the default device.
-            if (default_device.is_cpu())
-                _cpu_device = 0;
-
-            auto Platforms = sycl::platform::get_platforms();
-            // Keep track of the number of devices per backend
-            std::map<sycl::backend, size_t> DeviceNums;
-            std::map<std::string, std::vector<sycl::device>> backend_devices;
-
-            while (!Platforms.empty()) {
-                auto Platform = Platforms.back();
-                Platforms.pop_back();
-                auto devices = Platform.get_devices();
-                std::string backend_type = get_device_backend_and_type(devices[0]);
-                for (const auto &device : devices) {
-                    backend_devices[backend_type].push_back(device);
-                }
-            }
-
-            std::vector<std::string> keys;
-            for(auto it = backend_devices.begin(); it != backend_devices.end(); ++it) {
-                keys.push_back(it->first);
-            }
-            std::sort(keys.begin(), keys.end(), compare_backend);
-
-            for (auto &key : keys) {
-                std::vector<sycl::device> devs = backend_devices[key];
-                std::sort(devs.begin(), devs.end(), compare_dev);
-                for (const auto &dev : devs) {
-                    sycl_all_devs.push_back(dev);
-                }
-            }
-
-            for (auto &dev : sycl_all_devs)
-            {
-                if (dev == default_device)
-                {
-                    continue;
-                }
-                _devs.push_back(std::make_shared<device_ext>(dev));
-                if (_cpu_device == -1 && dev.is_cpu())
-                {
-                    _cpu_device = _devs.size() - 1;
-                }
-            }
-        }
-        void check_id(unsigned int id) const
-        {
-            if (id >= _devs.size())
-            {
-                throw std::runtime_error("invalid device id");
-            }
-        }
-        std::vector<std::shared_ptr<device_ext>> _devs;
-        /// DEFAULT_DEVICE_ID is used, if current_device_id() can not find current
-        /// thread id in _thread2dev_map, which means default device should be used
-        /// for the current thread.
-        const unsigned int DEFAULT_DEVICE_ID = 0;
-        /// thread-id to device-id map.
-        std::map<unsigned int, unsigned int> _thread2dev_map;
-        int _cpu_device = -1;
-    };
-
-    static inline sycl::queue &get_default_queue()
-    {
-        return dev_mgr::instance().current_device().default_queue();
-    }
-
-    namespace detail
-    {
-        enum class pointer_access_attribute
-        {
-            host_only = 0,
-            device_only,
-            host_device,
-            end
-        };
-
-        static pointer_access_attribute get_pointer_attribute(sycl::queue &q,
-                                                              const void *ptr)
-        {
-            switch (sycl::get_pointer_type(ptr, q.get_context()))
-            {
-            case sycl::usm::alloc::unknown:
-                return pointer_access_attribute::host_only;
-            case sycl::usm::alloc::device:
-                return pointer_access_attribute::device_only;
-            case sycl::usm::alloc::shared:
-            case sycl::usm::alloc::host:
-                return pointer_access_attribute::host_device;
-            }
-        }
-
-        template <typename ArgT>
-        inline constexpr std::uint64_t get_type_combination_id(ArgT Val)
-        {
-            static_assert((unsigned char)library_data_t::library_data_t_size <=
-                              std::numeric_limits<unsigned char>::max() &&
-                          "library_data_t size exceeds limit.");
-            static_assert(std::is_same_v<ArgT, library_data_t>, "Unsupported ArgT");
-            return (std::uint64_t)Val;
-        }
-
-        template <typename FirstT, typename... RestT>
-        inline constexpr std::uint64_t get_type_combination_id(FirstT FirstVal,
-                                                               RestT... RestVal)
-        {
-            static_assert((std::uint8_t)library_data_t::library_data_t_size <=
-                              std::numeric_limits<unsigned char>::max() &&
-                          "library_data_t size exceeds limit.");
-            static_assert(sizeof...(RestT) <= 8 && "Too many parameters");
-            static_assert(std::is_same_v<FirstT, library_data_t>, "Unsupported FirstT");
-            return get_type_combination_id(RestVal...) << 8 | ((std::uint64_t)FirstVal);
-        }
-
-        class mem_mgr
-        {
-            mem_mgr()
-            {
-                // Reserved address space, no real memory allocation happens here.
-#if defined(__linux__)
-                mapped_address_space =
-                    (byte_t *)mmap(nullptr, mapped_region_size, PROT_NONE,
-                                   MAP_PRIVATE | MAP_ANONYMOUS, -1, 0);
-#elif defined(_WIN64)
-                mapped_address_space = (byte_t *)VirtualAlloc(
-                    NULL,               // NULL specified as the base address parameter
-                    mapped_region_size, // Size of allocation
-                    MEM_RESERVE,        // Allocate reserved pages
-                    PAGE_NOACCESS);     // Protection = no access
-#else
-#error "Only support Windows and Linux."
-#endif
-                next_free = mapped_address_space;
-            };
-
-        public:
-            using buffer_id_t = int;
-
-            struct allocation
-            {
-                buffer_t buffer;
-                byte_t *alloc_ptr;
-                size_t size;
-            };
-
-            ~mem_mgr()
-            {
-#if defined(__linux__)
-                munmap(mapped_address_space, mapped_region_size);
-#elif defined(_WIN64)
-                VirtualFree(mapped_address_space, 0, MEM_RELEASE);
-#else
-#error "Only support Windows and Linux."
-#endif
-            };
-
-            mem_mgr(const mem_mgr &) = delete;
-            mem_mgr &operator=(const mem_mgr &) = delete;
-            mem_mgr(mem_mgr &&) = delete;
-            mem_mgr &operator=(mem_mgr &&) = delete;
-
-            /// Allocate
-            void *mem_alloc(size_t size)
-            {
-                if (!size)
-                    return nullptr;
-                std::lock_guard<std::mutex> lock(m_mutex);
-                if (next_free + size > mapped_address_space + mapped_region_size)
-                {
-                    throw std::runtime_error("dpct_malloc: out of memory for virtual memory pool");
-                }
-                // Allocation
-                sycl::range<1> r(size);
-                buffer_t buf(r);
-                allocation A{buf, next_free, size};
-                // Map allocation to device pointer
-                void *result = next_free;
-                m_map.emplace(next_free + size, A);
-                // Update pointer to the next free space.
-                next_free += (size + extra_padding + alignment - 1) & ~(alignment - 1);
-
-                return result;
-            }
-
-            /// Deallocate
-            void mem_free(const void *ptr)
-            {
-                if (!ptr)
-                    return;
-                std::lock_guard<std::mutex> lock(m_mutex);
-                auto it = get_map_iterator(ptr);
-                m_map.erase(it);
-            }
-
-            /// map: device pointer -> allocation(buffer, alloc_ptr, size)
-            allocation translate_ptr(const void *ptr)
-            {
-                std::lock_guard<std::mutex> lock(m_mutex);
-                auto it = get_map_iterator(ptr);
-                return it->second;
-            }
-
-            /// Check if the pointer represents device pointer or not.
-            bool is_device_ptr(const void *ptr) const
-            {
-                std::lock_guard<std::mutex> lock(m_mutex);
-                return (mapped_address_space <= ptr) &&
-                       (ptr < mapped_address_space + mapped_region_size);
-            }
-
-            /// Returns the instance of memory manager singleton.
-            static mem_mgr &instance()
-            {
-                static mem_mgr m;
-                return m;
-            }
-
-        private:
-            std::map<byte_t *, allocation> m_map;
-            mutable std::mutex m_mutex;
-            byte_t *mapped_address_space;
-            byte_t *next_free;
-            const size_t mapped_region_size = 128ull * 1024 * 1024 * 1024;
-            const size_t alignment = 256;
-            /// This padding may be defined to some positive value to debug
-            /// out of bound accesses.
-            const size_t extra_padding = 0;
-
-            std::map<byte_t *, allocation>::iterator get_map_iterator(const void *ptr)
-            {
-                auto it = m_map.upper_bound((byte_t *)ptr);
-                if (it == m_map.end())
-                {
-                    // Not a virtual pointer.
-                    throw std::runtime_error("can not get buffer from non-virtual pointer");
-                }
-                const allocation &alloc = it->second;
-                if (ptr < alloc.alloc_ptr)
-                {
-                    // Out of bound.
-                    // This may happen if there's a gap between allocations due to alignment
-                    // or extra padding and pointer points to this gap.
-                    throw std::runtime_error("invalid virtual pointer");
-                }
-                return it;
-            }
-        };
-
-        template <class T, memory_region Memory, size_t Dimension>
-        class accessor;
-        template <memory_region Memory, class T = byte_t>
-        class memory_traits
-        {
-        public:
-            static constexpr sycl::access::target target =
-                sycl::access::target::device;
-            static constexpr sycl::access_mode mode =
-                (Memory == constant) ? sycl::access_mode::read
-                                     : sycl::access_mode::read_write;
-            static constexpr size_t type_size = sizeof(T);
-            using element_t =
-                typename std::conditional<Memory == constant, const T, T>::type;
-            using value_t = typename std::remove_cv<T>::type;
-            template <size_t Dimension = 1>
-            using accessor_t = typename std::conditional<
-                Memory == local, sycl::local_accessor<value_t, Dimension>,
-                sycl::accessor<T, Dimension, mode, target>>::type;
-            using pointer_t = T *;
-        };
-
-        static inline void *dpct_malloc(size_t size, sycl::queue &q)
-        {
-            return sycl::malloc_device(size, q.get_device(), q.get_context());
-        }
-
-#define PITCH_DEFAULT_ALIGN(x) (((x) + 31) & ~(0x1F))
-        static inline void *dpct_malloc(size_t &pitch, size_t x, size_t y, size_t z,
-                                        sycl::queue &q)
-        {
-            pitch = PITCH_DEFAULT_ALIGN(x);
-            return dpct_malloc(pitch * y * z, q);
-        }
-
-        /**
-         * @brief Sets \p value to the first \p size elements starting from \p dev_ptr in \p q.
-         * @tparam valueT The type of the element to be set.
-         * @param [in] q The queue in which the operation is done.
-         * @param [in] dev_ptr Pointer to the virtual device memory address.
-         * @param [in] value The value to be set.
-         * @param [in] size Number of elements to be set to the value.
-         * @return An event representing the memset operation.
-         */
-        template <typename valueT>
-        static inline sycl::event dpct_memset(sycl::queue &q, void *dev_ptr,
-                                              valueT value, size_t size)
-        {
-            return q.fill(dev_ptr, value, size);
-        }
-
-        /**
-         * @brief Sets \p value to the 3D memory region pointed by \p data in \p q.
-         * @tparam valueT The type of the element to be set.
-         * @param [in] q The queue in which the operation is done.
-         * @param [in] data Pointer to the pitched device memory region.
-         * @param [in] value The value to be set.
-         * @param [in] size 3D memory region by number of elements.
-         * @return An event list representing the memset operations.
-         */
-        template <typename valueT>
-        static inline std::vector<sycl::event>
-        dpct_memset(sycl::queue &q, pitched_data data, valueT value,
-                    sycl::range<3> size)
-        {
-            std::vector<sycl::event> event_list;
-            size_t slice = data.get_pitch() * data.get_y();
-            unsigned char *data_surface = (unsigned char *)data.get_data_ptr();
-            for (size_t z = 0; z < size.get(2); ++z)
-            {
-                unsigned char *data_ptr = data_surface;
-                for (size_t y = 0; y < size.get(1); ++y)
-                {
-                    event_list.push_back(dpct_memset(q, data_ptr, value, size.get(0)));
-                    data_ptr += data.get_pitch();
-                }
-                data_surface += slice;
-            }
-            return event_list;
-        }
-
-        /**
-         * @brief Sets \p val to the pitched 2D memory region pointed by \p ptr in \p q.
-         * @tparam valueT The type of the element to be set.
-         * @param [in] q The queue in which the operation is done.
-         * @param [in] ptr Pointer to the virtual device memory.
-         * @param [in] pitch The pitch size by number of elements, including padding.
-         * @param [in] val The value to be set.
-         * @param [in] x The width of memory region by number of elements.
-         * @param [in] y The height of memory region by number of elements.
-         * @return An event list representing the memset operations.
-         */
-        template <typename valueT>
-        static inline std::vector<sycl::event>
-        dpct_memset(sycl::queue &q, void *ptr, size_t pitch, valueT val, size_t x,
-                    size_t y)
-        {
-            return dpct_memset(q, pitched_data(ptr, pitch, x, 1), val,
-                               sycl::range<3>(x, y, 1));
-        }
-
-        static memcpy_direction deduce_memcpy_direction(sycl::queue &q, void *to_ptr,
-                                                        const void *from_ptr,
-                                                        memcpy_direction dir)
-        {
-            switch (dir)
-            {
-            case memcpy_direction::host_to_host:
-            case memcpy_direction::host_to_device:
-            case memcpy_direction::device_to_host:
-            case memcpy_direction::device_to_device:
-                return dir;
-            case memcpy_direction::automatic:
-            {
-                // table[to_attribute][from_attribute]
-                static const memcpy_direction
-                    direction_table[static_cast<unsigned>(pointer_access_attribute::end)]
-                                   [static_cast<unsigned>(pointer_access_attribute::end)] =
-                                       {{memcpy_direction::host_to_host,
-                                         memcpy_direction::device_to_host,
-                                         memcpy_direction::host_to_host},
-                                        {memcpy_direction::host_to_device,
-                                         memcpy_direction::device_to_device,
-                                         memcpy_direction::device_to_device},
-                                        {memcpy_direction::host_to_host,
-                                         memcpy_direction::device_to_device,
-                                         memcpy_direction::device_to_device}};
-                return direction_table[static_cast<unsigned>(get_pointer_attribute(
-                    q, to_ptr))][static_cast<unsigned>(get_pointer_attribute(q, from_ptr))];
-            }
-            default:
-                throw std::runtime_error("dpct_memcpy: invalid direction value");
-            }
-        }
-
-        static sycl::event
-        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size,
-                    memcpy_direction direction,
-                    const std::vector<sycl::event> &dep_events = {})
-        {
-            if (!size)
-                return sycl::event{};
-            return q.memcpy(to_ptr, from_ptr, size, dep_events);
-            GGML_UNUSED(direction);
-        }
-
-        // Get actual copy range and make sure it will not exceed range.
-        static inline size_t get_copy_range(sycl::range<3> size, size_t slice,
-                                            size_t pitch)
-        {
-            return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0);
-        }
-
-        static inline size_t get_offset(sycl::id<3> id, size_t slice,
-                                        size_t pitch)
-        {
-            return slice * id.get(2) + pitch * id.get(1) + id.get(0);
-        }
-
-        /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr
-        /// and \p from_range to another specified by \p to_ptr and \p to_range.
-        static inline std::vector<sycl::event>
-        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
-                    sycl::range<3> to_range, sycl::range<3> from_range,
-                    sycl::id<3> to_id, sycl::id<3> from_id,
-                    sycl::range<3> size, memcpy_direction direction,
-                    const std::vector<sycl::event> &dep_events = {})
-        {
-            // RAII for host pointer
-            class host_buffer
-            {
-                void *_buf;
-                size_t _size;
-                sycl::queue &_q;
-                const std::vector<sycl::event> &_deps; // free operation depends
-
-            public:
-                host_buffer(size_t size, sycl::queue &q,
-                            const std::vector<sycl::event> &deps)
-                    : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {}
-                void *get_ptr() const { return _buf; }
-                size_t get_size() const { return _size; }
-                ~host_buffer()
-                {
-                    if (_buf)
-                    {
-                        _q.submit([&](sycl::handler &cgh)
-                                  {
-        cgh.depends_on(_deps);
-        cgh.host_task([buf = _buf] { std::free(buf); }); });
-                    }
-                }
-            };
-            std::vector<sycl::event> event_list;
-
-            size_t to_slice = to_range.get(1) * to_range.get(0),
-                   from_slice = from_range.get(1) * from_range.get(0);
-            unsigned char *to_surface =
-                (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0));
-            const unsigned char *from_surface =
-                (const unsigned char *)from_ptr +
-                get_offset(from_id, from_slice, from_range.get(0));
-
-            if (to_slice == from_slice && to_slice == size.get(1) * size.get(0))
-            {
-                return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2),
-                                    direction, dep_events)};
-            }
-            direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction);
-            size_t size_slice = size.get(1) * size.get(0);
-            switch (direction)
-            {
-            case host_to_host:
-                for (size_t z = 0; z < size.get(2); ++z)
-                {
-                    unsigned char *to_ptr = to_surface;
-                    const unsigned char *from_ptr = from_surface;
-                    if (to_range.get(0) == from_range.get(0) &&
-                        to_range.get(0) == size.get(0))
-                    {
-                        event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice,
-                                                         direction, dep_events));
-                    }
-                    else
-                    {
-                        for (size_t y = 0; y < size.get(1); ++y)
-                        {
-                            event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0),
-                                                             direction, dep_events));
-                            to_ptr += to_range.get(0);
-                            from_ptr += from_range.get(0);
-                        }
-                    }
-                    to_surface += to_slice;
-                    from_surface += from_slice;
-                }
-                break;
-            case host_to_device:
-            {
-                host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q,
-                                event_list);
-                std::vector<sycl::event> host_events;
-                if (to_slice == size_slice)
-                {
-                    // Copy host data to a temp host buffer with the shape of target.
-                    host_events =
-                        dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range,
-                                    sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size,
-                                    host_to_host, dep_events);
-                }
-                else
-                {
-                    // Copy host data to a temp host buffer with the shape of target.
-                    host_events = dpct_memcpy(
-                        q, buf.get_ptr(), from_surface, to_range, from_range,
-                        sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host,
-                        // If has padding data, not sure whether it is useless. So fill temp
-                        // buffer with it.
-                        std::vector<sycl::event>{
-                            dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(),
-                                        device_to_host, dep_events)});
-                }
-                // Copy from temp host buffer to device with only one submit.
-                event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(),
-                                                 buf.get_size(), host_to_device,
-                                                 host_events));
-                break;
-            }
-            case device_to_host:
-            {
-                host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q,
-                                event_list);
-                // Copy from host temp buffer to host target with reshaping.
-                event_list = dpct_memcpy(
-                    q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0),
-                    sycl::id<3>(0, 0, 0), size, host_to_host,
-                    // Copy from device to temp host buffer with only one submit.
-                    std::vector<sycl::event>{dpct_memcpy(q, buf.get_ptr(), from_surface,
-                                                         buf.get_size(),
-                                                         device_to_host, dep_events)});
-                break;
-            }
-            case device_to_device:
-                event_list.push_back(q.submit([&](sycl::handler &cgh){
-                cgh.depends_on(dep_events);
-                cgh.parallel_for<class dpct_memcpy_3d_detail>(
-                    size,
-                    [=](sycl::id<3> id) {
-                        to_surface[get_offset(id, to_slice, to_range.get(0))] =
-                            from_surface[get_offset(id, from_slice, from_range.get(0))];
-                    }); }));
-                break;
-            default:
-                throw std::runtime_error("dpct_memcpy: invalid direction value");
-            }
-            return event_list;
-        }
-
-        /// memcpy 2D/3D matrix specified by pitched_data.
-        static inline std::vector<sycl::event>
-        dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id,
-                    pitched_data from, sycl::id<3> from_id, sycl::range<3> size,
-                    memcpy_direction direction = automatic)
-        {
-            return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(),
-                               sycl::range<3>(to.get_pitch(), to.get_y(), 1),
-                               sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id,
-                               size, direction);
-        }
-
-        /// memcpy 2D matrix with pitch.
-        static inline std::vector<sycl::event>
-        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
-                    size_t to_pitch, size_t from_pitch, size_t x, size_t y,
-                    memcpy_direction direction = automatic)
-        {
-            return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1),
-                               sycl::range<3>(from_pitch, y, 1),
-                               sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0),
-                               sycl::range<3>(x, y, 1), direction);
-        }
-
-        namespace deprecated
-        {
-
-            template <typename T, sycl::usm::alloc AllocKind>
-            class usm_allocator
-            {
-            private:
-                using Alloc = sycl::usm_allocator<T, AllocKind>;
-                Alloc _impl;
-
-            public:
-                using value_type = typename std::allocator_traits<Alloc>::value_type;
-                using pointer = typename std::allocator_traits<Alloc>::pointer;
-                using const_pointer = typename std::allocator_traits<Alloc>::const_pointer;
-                using void_pointer = typename std::allocator_traits<Alloc>::void_pointer;
-                using const_void_pointer =
-                    typename std::allocator_traits<Alloc>::const_void_pointer;
-                using reference = typename std::allocator_traits<Alloc>::value_type &;
-                using const_reference =
-                    const typename std::allocator_traits<Alloc>::value_type &;
-                using difference_type =
-                    typename std::allocator_traits<Alloc>::difference_type;
-                using size_type = typename std::allocator_traits<Alloc>::size_type;
-                using propagate_on_container_copy_assignment = typename std::allocator_traits<
-                    Alloc>::propagate_on_container_copy_assignment;
-                using propagate_on_container_move_assignment = typename std::allocator_traits<
-                    Alloc>::propagate_on_container_move_assignment;
-                using propagate_on_container_swap =
-                    typename std::allocator_traits<Alloc>::propagate_on_container_swap;
-                using is_always_equal =
-                    typename std::allocator_traits<Alloc>::is_always_equal;
-
-                template <typename U>
-                struct rebind
-                {
-                    typedef usm_allocator<U, AllocKind> other;
-                };
-
-                usm_allocator() : _impl(dpct::get_default_queue()) {}
-                ~usm_allocator() {}
-                usm_allocator(const usm_allocator &other) : _impl(other._impl) {}
-                usm_allocator(usm_allocator &&other) : _impl(std::move(other._impl)) {}
-                pointer address(reference r) { return &r; }
-                const_pointer address(const_reference r) { return &r; }
-                pointer allocate(size_type cnt, const_void_pointer hint = nullptr)
-                {
-                    return std::allocator_traits<Alloc>::allocate(_impl, cnt, hint);
-                }
-                void deallocate(pointer p, size_type cnt)
-                {
-                    std::allocator_traits<Alloc>::deallocate(_impl, p, cnt);
-                }
-                size_type max_size() const
-                {
-                    return std::allocator_traits<Alloc>::max_size(_impl);
-                }
-                bool operator==(const usm_allocator &other) const { return _impl == other._impl; }
-                bool operator!=(const usm_allocator &other) const { return _impl != other._impl; }
-            };
-
-        } // namespace deprecated
-
-        inline void dpct_free(void *ptr,
-                              const sycl::queue &q)
-        {
-            if (ptr)
-            {
-                sycl::free(ptr, q.get_context());
-            }
-        }
-
-        template <typename T>
-        inline auto get_memory(const void *x)
-        {
-            T *new_x = reinterpret_cast<T *>(const_cast<void *>(x));
-            return new_x;
-        }
-
-        template <typename T>
-        inline typename DataType<T>::T2 get_value(const T *s, sycl::queue &q)
-        {
-            using Ty = typename DataType<T>::T2;
-            Ty s_h;
-            if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only)
-                detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host)
-                    .wait();
-            else
-                s_h = *reinterpret_cast<const Ty *>(s);
-            return s_h;
-        }
-
-    } // namespace detail
-
-    template <typename T>
-    inline auto get_value(const T *s, sycl::queue &q)
-    {
-        return detail::get_value(s, q);
-    }
-
-    namespace detail
-    {
-        template <class Ta, class Tb, class Tc, class Ts>
-        inline void gemm_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
-                              oneapi::mkl::transpose b_trans, int m, int n, int k,
-                              const void *alpha, const void *a, int lda, const void *b,
-                              int ldb, const void *beta, void *c, int ldc)
-        {
-            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
-            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
-            auto data_a = get_memory<const Ta>(a);
-            auto data_b = get_memory<const Tb>(b);
-            auto data_c = get_memory<Tc>(c);
-            oneapi::mkl::blas::column_major::gemm(
-                q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda,
-                data_b, ldb, beta_value, data_c, ldc);
-        }
-
-        template <typename VecT, class BinaryOperation, class = void>
-        class vectorized_binary
-        {
-        public:
-            inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op)
-            {
-                VecT v4;
-                for (size_t i = 0; i < v4.size(); ++i)
-                {
-                    v4[i] = binary_op(a[i], b[i]);
-                }
-                return v4;
-            }
-        };
-
-        template <typename VecT, class BinaryOperation>
-        class vectorized_binary<
-            VecT, BinaryOperation,
-            std::void_t<std::invoke_result_t<BinaryOperation, VecT, VecT>>>
-        {
-        public:
-            inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op)
-            {
-                return binary_op(a, b).template as<VecT>();
-            }
-        };
-
-        template <class Ta, class Tb, class Tc, class Ts>
-        inline void gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
-                                    oneapi::mkl::transpose b_trans, int m, int n, int k,
-                                    const void *alpha, const void **a, int lda,
-                                    const void **b, int ldb, const void *beta, void **c,
-                                    int ldc, int batch_size)
-        {
-            struct matrix_info_t
-            {
-                oneapi::mkl::transpose transpose_info[2];
-                Ts value_info[2];
-                std::int64_t size_info[3];
-                std::int64_t ld_info[3];
-                std::int64_t groupsize_info;
-            };
-
-            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
-            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
-
-            matrix_info_t *matrix_info =
-                (matrix_info_t *)std::malloc(sizeof(matrix_info_t));
-            matrix_info->transpose_info[0] = a_trans;
-            matrix_info->transpose_info[1] = b_trans;
-            matrix_info->value_info[0] = alpha_value;
-            matrix_info->value_info[1] = beta_value;
-            matrix_info->size_info[0] = m;
-            matrix_info->size_info[1] = n;
-            matrix_info->size_info[2] = k;
-            matrix_info->ld_info[0] = lda;
-            matrix_info->ld_info[1] = ldb;
-            matrix_info->ld_info[2] = ldc;
-            matrix_info->groupsize_info = batch_size;
-
-            sycl::event e = oneapi::mkl::blas::column_major::gemm_batch(
-                q, matrix_info->transpose_info, matrix_info->transpose_info + 1,
-                matrix_info->size_info, matrix_info->size_info + 1,
-                matrix_info->size_info + 2, matrix_info->value_info,
-                reinterpret_cast<const Ta **>(a), matrix_info->ld_info,
-                reinterpret_cast<const Tb **>(b), matrix_info->ld_info + 1,
-                matrix_info->value_info + 1, reinterpret_cast<Tc **>(c),
-                matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
-
-            q.submit([&](sycl::handler &cgh)
-                     {
-    cgh.depends_on(e);
-    cgh.host_task([=] { std::free(matrix_info); }); });
-        }
-
-        template <class Ta, class Tb, class Tc, class Ts>
-        inline void
-        gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
-                        oneapi::mkl::transpose b_trans, int m, int n,
-                        int k, const void *alpha, const void *a, int lda,
-                        long long int stride_a, const void *b, int ldb,
-                        long long int stride_b, const void *beta, void *c,
-                        int ldc, long long int stride_c, int batch_size)
-        {
-            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
-            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
-            auto data_a = get_memory<const Ta>(a);
-            auto data_b = get_memory<const Tb>(b);
-            auto data_c = get_memory<Tc>(c);
-            oneapi::mkl::blas::column_major::gemm_batch(
-                q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda,
-                stride_a, data_b, ldb, stride_b, beta_value,
-                data_c, ldc, stride_c, batch_size);
-        }
-
-    } // namespace detail
-
-    template <typename VecT, class BinaryOperation>
-    inline unsigned vectorized_binary(unsigned a, unsigned b,
-                                      const BinaryOperation binary_op)
-    {
-        sycl::vec<unsigned, 1> v0{a}, v1{b};
-        auto v2 = v0.as<VecT>();
-        auto v3 = v1.as<VecT>();
-        auto v4 =
-            detail::vectorized_binary<VecT, BinaryOperation>()(v2, v3, binary_op);
-        v0 = v4.template as<sycl::vec<unsigned, 1>>();
-        return v0;
-    }
-
-    static void async_dpct_memcpy(void *to_ptr, const void *from_ptr, size_t size,
-                                  memcpy_direction direction = automatic,
-                                  sycl::queue &q = dpct::get_default_queue())
-    {
-        detail::dpct_memcpy(q, to_ptr, from_ptr, size, direction);
-    }
-
-    static inline unsigned int select_device(unsigned int id)
-    {
-        dev_mgr::instance().select_device(id);
-        return id;
-    }
-
-    template <typename T>
-    T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask,
-                               unsigned int logical_sub_group_size = 32)
-    {
-        unsigned int id = g.get_local_linear_id();
-        unsigned int start_index =
-            id / logical_sub_group_size * logical_sub_group_size;
-        unsigned int target_offset = (id % logical_sub_group_size) ^ mask;
-        return sycl::select_from_group(g, x,
-                                       target_offset < logical_sub_group_size
-                                           ? start_index + target_offset
-                                           : id);
-    }
-
-    template <typename T>
-    sycl::vec<T, 4> extract_and_sign_or_zero_extend4(T val)
-    {
-        return sycl::vec<T, 1>(val)
-            .template as<sycl::vec<
-                std::conditional_t<std::is_signed_v<T>, int8_t, uint8_t>, 4>>()
-            .template convert<T>();
-    }
-
-    template <typename T1, typename T2>
-    using dot_product_acc_t =
-        std::conditional_t<std::is_unsigned_v<T1> && std::is_unsigned_v<T2>,
-                           uint32_t, int32_t>;
-
-    template <typename T1, typename T2, typename T3>
-    inline auto dp4a(T1 a, T2 b, T3 c)
-    {
-        dot_product_acc_t<T1, T2> res = c;
-        auto va = extract_and_sign_or_zero_extend4(a);
-        auto vb = extract_and_sign_or_zero_extend4(b);
-        res += va[0] * vb[0];
-        res += va[1] * vb[1];
-        res += va[2] * vb[2];
-        res += va[3] * vb[3];
-        return res;
-    }
-
-    struct sub_sat
-    {
-        template <typename T>
-        auto operator()(const T x, const T y) const
-        {
-            return sycl::sub_sat(x, y);
-        }
-    };
-
-    template <typename S, typename T>
-    inline T vectorized_min(T a, T b)
-    {
-        sycl::vec<T, 1> v0{a}, v1{b};
-        auto v2 = v0.template as<S>();
-        auto v3 = v1.template as<S>();
-        auto v4 = sycl::min(v2, v3);
-        v0 = v4.template as<sycl::vec<T, 1>>();
-        return v0;
-    }
-
-    inline float pow(const float a, const int b) { return sycl::pown(a, b); }
-    inline double pow(const double a, const int b) { return sycl::pown(a, b); }
-    inline float pow(const float a, const float b) { return sycl::pow(a, b); }
-    inline double pow(const double a, const double b) { return sycl::pow(a, b); }
-    template <typename T, typename U>
-    inline typename std::enable_if_t<std::is_floating_point_v<T>, T>
-    pow(const T a, const U b)
-    {
-        return sycl::pow(a, static_cast<T>(b));
-    }
-    template <typename T, typename U>
-    inline typename std::enable_if_t<!std::is_floating_point_v<T>, double>
-    pow(const T a, const U b)
-    {
-        return sycl::pow(static_cast<double>(a), static_cast<double>(b));
-    }
-
-    inline double min(const double a, const float b)
-    {
-        return sycl::fmin(a, static_cast<double>(b));
-    }
-    inline double min(const float a, const double b)
-    {
-        return sycl::fmin(static_cast<double>(a), b);
-    }
-    inline float min(const float a, const float b) { return sycl::fmin(a, b); }
-    inline double min(const double a, const double b) { return sycl::fmin(a, b); }
-    inline std::uint32_t min(const std::uint32_t a, const std::int32_t b)
-    {
-        return sycl::min(a, static_cast<std::uint32_t>(b));
-    }
-    inline std::uint32_t min(const std::int32_t a, const std::uint32_t b)
-    {
-        return sycl::min(static_cast<std::uint32_t>(a), b);
-    }
-    inline std::int32_t min(const std::int32_t a, const std::int32_t b)
-    {
-        return sycl::min(a, b);
-    }
-    inline std::uint32_t min(const std::uint32_t a, const std::uint32_t b)
-    {
-        return sycl::min(a, b);
-    }
-    inline std::uint64_t min(const std::uint64_t a, const std::int64_t b)
-    {
-        return sycl::min(a, static_cast<std::uint64_t>(b));
-    }
-    inline std::uint64_t min(const std::int64_t a, const std::uint64_t b)
-    {
-        return sycl::min(static_cast<std::uint64_t>(a), b);
-    }
-    inline std::int64_t min(const std::int64_t a, const std::int64_t b)
-    {
-        return sycl::min(a, b);
-    }
-    inline std::uint64_t min(const std::uint64_t a, const std::uint64_t b)
-    {
-        return sycl::min(a, b);
-    }
-    inline std::uint64_t min(const std::uint64_t a, const std::int32_t b)
-    {
-        return sycl::min(a, static_cast<std::uint64_t>(b));
-    }
-    inline std::uint64_t min(const std::int32_t a, const std::uint64_t b)
-    {
-        return sycl::min(static_cast<std::uint64_t>(a), b);
-    }
-    inline std::uint64_t min(const std::uint64_t a, const std::uint32_t b)
-    {
-        return sycl::min(a, static_cast<std::uint64_t>(b));
-    }
-    inline std::uint64_t min(const std::uint32_t a, const std::uint64_t b)
-    {
-        return sycl::min(static_cast<std::uint64_t>(a), b);
-    }
-    // max function overloads.
-    // For floating-point types, `float` or `double` arguments are acceptable.
-    // For integer types, `std::uint32_t`, `std::int32_t`, `std::uint64_t` or
-    // `std::int64_t` type arguments are acceptable.
-    inline double max(const double a, const float b)
-    {
-        return sycl::fmax(a, static_cast<double>(b));
-    }
-    inline double max(const float a, const double b)
-    {
-        return sycl::fmax(static_cast<double>(a), b);
-    }
-    inline float max(const float a, const float b) { return sycl::fmax(a, b); }
-    inline double max(const double a, const double b) { return sycl::fmax(a, b); }
-    inline std::uint32_t max(const std::uint32_t a, const std::int32_t b)
-    {
-        return sycl::max(a, static_cast<std::uint32_t>(b));
-    }
-    inline std::uint32_t max(const std::int32_t a, const std::uint32_t b)
-    {
-        return sycl::max(static_cast<std::uint32_t>(a), b);
-    }
-    inline std::int32_t max(const std::int32_t a, const std::int32_t b)
-    {
-        return sycl::max(a, b);
-    }
-    inline std::uint32_t max(const std::uint32_t a, const std::uint32_t b)
-    {
-        return sycl::max(a, b);
-    }
-    inline std::uint64_t max(const std::uint64_t a, const std::int64_t b)
-    {
-        return sycl::max(a, static_cast<std::uint64_t>(b));
-    }
-    inline std::uint64_t max(const std::int64_t a, const std::uint64_t b)
-    {
-        return sycl::max(static_cast<std::uint64_t>(a), b);
-    }
-    inline std::int64_t max(const std::int64_t a, const std::int64_t b)
-    {
-        return sycl::max(a, b);
-    }
-    inline std::uint64_t max(const std::uint64_t a, const std::uint64_t b)
-    {
-        return sycl::max(a, b);
-    }
-    inline std::uint64_t max(const std::uint64_t a, const std::int32_t b)
-    {
-        return sycl::max(a, static_cast<std::uint64_t>(b));
-    }
-    inline std::uint64_t max(const std::int32_t a, const std::uint64_t b)
-    {
-        return sycl::max(static_cast<std::uint64_t>(a), b);
-    }
-    inline std::uint64_t max(const std::uint64_t a, const std::uint32_t b)
-    {
-        return sycl::max(a, static_cast<std::uint64_t>(b));
-    }
-    inline std::uint64_t max(const std::uint32_t a, const std::uint64_t b)
-    {
-        return sycl::max(static_cast<std::uint64_t>(a), b);
-    }
-
-    inline void
-    has_capability_or_fail(const sycl::device &dev,
-                           const std::initializer_list<sycl::aspect> &props)
-    {
-        for (const auto &it : props)
-        {
-            if (dev.has(it))
-                continue;
-            switch (it)
-            {
-            case sycl::aspect::fp64:
-                throw std::runtime_error("'double' is not supported in '" +
-                                         dev.get_info<sycl::info::device::name>() +
-                                         "' device");
-                break;
-            case sycl::aspect::fp16:
-                throw std::runtime_error("'half' is not supported in '" +
-                                         dev.get_info<sycl::info::device::name>() +
-                                         "' device");
-                break;
-            default:
-#define __SYCL_ASPECT(ASPECT, ID) \
-    case sycl::aspect::ASPECT:    \
-        return #ASPECT;
-#define __SYCL_ASPECT_DEPRECATED(ASPECT, ID, MESSAGE) __SYCL_ASPECT(ASPECT, ID)
-#define __SYCL_ASPECT_DEPRECATED_ALIAS(ASPECT, ID, MESSAGE)
-                auto getAspectNameStr = [](sycl::aspect AspectNum) -> std::string
-                {
-                    switch (AspectNum)
-                    {
-#include <sycl/info/aspects.def>
-#include <sycl/info/aspects_deprecated.def>
-                    default:
-                        return "unknown aspect";
-                    }
-                };
-#undef __SYCL_ASPECT_DEPRECATED_ALIAS
-#undef __SYCL_ASPECT_DEPRECATED
-#undef __SYCL_ASPECT
-                throw std::runtime_error(
-                    "'" + getAspectNameStr(it) + "' is not supported in '" +
-                    dev.get_info<sycl::info::device::name>() + "' device");
-            }
-            break;
-        }
-    }
-
-    static inline unsigned int get_current_device_id()
-    {
-        return dev_mgr::instance().current_device_id();
-    }
-
-    static inline device_ext &get_current_device()
-    {
-        return dev_mgr::instance().current_device();
-    }
-
-    static inline sycl::queue &get_in_order_queue()
-    {
-        return dev_mgr::instance().current_device().in_order_queue();
-    }
-
-    static sycl::event
-    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size,
-                memcpy_direction direction,
-                const std::vector<sycl::event> &dep_events = {})
-    {
-        if (!size)
-            return sycl::event{};
-        return q.memcpy(to_ptr, from_ptr, size, dep_events);
-        GGML_UNUSED(direction);
-    }
-
-    // Get actual copy range and make sure it will not exceed range.
-    static inline size_t get_copy_range(sycl::range<3> size, size_t slice,
-                                        size_t pitch)
-    {
-        return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0);
-    }
-
-    static inline size_t get_offset(sycl::id<3> id, size_t slice,
-                                    size_t pitch)
-    {
-        return slice * id.get(2) + pitch * id.get(1) + id.get(0);
-    }
-
-    /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr
-    /// and \p from_range to another specified by \p to_ptr and \p to_range.
-    static inline std::vector<sycl::event>
-    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
-                sycl::range<3> to_range, sycl::range<3> from_range,
-                sycl::id<3> to_id, sycl::id<3> from_id,
-                sycl::range<3> size, memcpy_direction direction,
-                const std::vector<sycl::event> &dep_events = {})
-    {
-        // RAII for host pointer
-        class host_buffer
-        {
-            void *_buf;
-            size_t _size;
-            sycl::queue &_q;
-            const std::vector<sycl::event> &_deps; // free operation depends
-
-        public:
-            host_buffer(size_t size, sycl::queue &q,
-                        const std::vector<sycl::event> &deps)
-                : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {}
-            void *get_ptr() const { return _buf; }
-            size_t get_size() const { return _size; }
-            ~host_buffer()
-            {
-                if (_buf)
-                {
-                    _q.submit([&](sycl::handler &cgh)
-                              {
-            cgh.depends_on(_deps);
-            cgh.host_task([buf = _buf] { std::free(buf); }); });
-                }
-            }
-        };
-        std::vector<sycl::event> event_list;
-
-        size_t to_slice = to_range.get(1) * to_range.get(0),
-               from_slice = from_range.get(1) * from_range.get(0);
-        unsigned char *to_surface =
-            (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0));
-        const unsigned char *from_surface =
-            (const unsigned char *)from_ptr +
-            get_offset(from_id, from_slice, from_range.get(0));
-
-        if (to_slice == from_slice && to_slice == size.get(1) * size.get(0))
-        {
-            return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2),
-                                direction, dep_events)};
-        }
-        direction = detail::deduce_memcpy_direction(q, to_ptr, from_ptr, direction);
-        size_t size_slice = size.get(1) * size.get(0);
-        switch (direction)
-        {
-        case host_to_host:
-            for (size_t z = 0; z < size.get(2); ++z)
-            {
-                unsigned char *to_ptr = to_surface;
-                const unsigned char *from_ptr = from_surface;
-                if (to_range.get(0) == from_range.get(0) &&
-                    to_range.get(0) == size.get(0))
-                {
-                    event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice,
-                                                     direction, dep_events));
-                }
-                else
-                {
-                    for (size_t y = 0; y < size.get(1); ++y)
-                    {
-                        event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0),
-                                                         direction, dep_events));
-                        to_ptr += to_range.get(0);
-                        from_ptr += from_range.get(0);
-                    }
-                }
-                to_surface += to_slice;
-                from_surface += from_slice;
-            }
-            break;
-        case host_to_device:
-        {
-            host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q,
-                            event_list);
-            std::vector<sycl::event> host_events;
-            if (to_slice == size_slice)
-            {
-                // Copy host data to a temp host buffer with the shape of target.
-                host_events =
-                    dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range,
-                                sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size,
-                                host_to_host, dep_events);
-            }
-            else
-            {
-                // Copy host data to a temp host buffer with the shape of target.
-                host_events = dpct_memcpy(
-                    q, buf.get_ptr(), from_surface, to_range, from_range,
-                    sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host,
-                    // If has padding data, not sure whether it is useless. So fill temp
-                    // buffer with it.
-                    std::vector<sycl::event>{
-                        dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(),
-                                    device_to_host, dep_events)});
-            }
-            // Copy from temp host buffer to device with only one submit.
-            event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(),
-                                             buf.get_size(), host_to_device,
-                                             host_events));
-            break;
-        }
-        case device_to_host:
-        {
-            host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q,
-                            event_list);
-            // Copy from host temp buffer to host target with reshaping.
-            event_list = dpct_memcpy(
-                q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0),
-                sycl::id<3>(0, 0, 0), size, host_to_host,
-                // Copy from device to temp host buffer with only one submit.
-                std::vector<sycl::event>{dpct_memcpy(q, buf.get_ptr(), from_surface,
-                                                     buf.get_size(),
-                                                     device_to_host, dep_events)});
-            break;
-        }
-        case device_to_device:
-            event_list.push_back(q.submit([&](sycl::handler &cgh)
-                                          {
-        cgh.depends_on(dep_events);
-        cgh.parallel_for<class dpct_memcpy_3d_detail>(
-            size,
-            [=](sycl::id<3> id) {
-                to_surface[get_offset(id, to_slice, to_range.get(0))] =
-                    from_surface[get_offset(id, from_slice, from_range.get(0))];
-            }); }));
-        break;
-        default:
-            throw std::runtime_error("dpct_memcpy: invalid direction value");
-        }
-        return event_list;
-    }
-
-    /// memcpy 2D/3D matrix specified by pitched_data.
-    static inline std::vector<sycl::event>
-    dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id,
-                pitched_data from, sycl::id<3> from_id, sycl::range<3> size,
-                memcpy_direction direction = automatic)
-    {
-        return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(),
-                           sycl::range<3>(to.get_pitch(), to.get_y(), 1),
-                           sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id,
-                           size, direction);
-    }
-
-    /// memcpy 2D matrix with pitch.
-    static inline std::vector<sycl::event>
-    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
-                size_t to_pitch, size_t from_pitch, size_t x, size_t y,
-                memcpy_direction direction = automatic)
-    {
-        return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1),
-                           sycl::range<3>(from_pitch, y, 1),
-                           sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0),
-                           sycl::range<3>(x, y, 1), direction);
-    }
-
-    inline void gemm(sycl::queue &q, oneapi::mkl::transpose a_trans,
-                     oneapi::mkl::transpose b_trans, int m, int n, int k,
-                     const void *alpha, const void *a, library_data_t a_type,
-                     int lda, const void *b, library_data_t b_type, int ldb,
-                     const void *beta, void *c, library_data_t c_type, int ldc,
-                     library_data_t scaling_type)
-    {
-        if (scaling_type == library_data_t::real_float &&
-            c_type == library_data_t::complex_float)
-        {
-            scaling_type = library_data_t::complex_float;
-        }
-        else if (scaling_type == library_data_t::real_double &&
-                 c_type == library_data_t::complex_double)
-        {
-            scaling_type = library_data_t::complex_double;
-        }
-
-        std::uint64_t key =
-            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
-        switch (key)
-        {
-        case detail::get_type_combination_id(
-            library_data_t::real_float, library_data_t::real_float,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_impl<float, float, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_double, library_data_t::real_double,
-            library_data_t::real_double, library_data_t::real_double):
-        {
-            detail::gemm_impl<double, double, double, double>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::complex_float, library_data_t::complex_float,
-            library_data_t::complex_float, library_data_t::complex_float):
-        {
-            detail::gemm_impl<std::complex<float>, std::complex<float>,
-                              std::complex<float>, std::complex<float>>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::complex_double, library_data_t::complex_double,
-            library_data_t::complex_double, library_data_t::complex_double):
-        {
-            detail::gemm_impl<std::complex<double>, std::complex<double>,
-                              std::complex<double>, std::complex<double>>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_half, library_data_t::real_half):
-        {
-            detail::gemm_impl<sycl::half, sycl::half, sycl::half,
-                              sycl::half>(q, a_trans, b_trans, m, n, k, alpha, a,
-                                          lda, b, ldb, beta, c, ldc);
-            break;
-        }
-#ifdef __INTEL_MKL__
-        case detail::get_type_combination_id(
-            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
-                              float>(q, a_trans, b_trans, m, n, k, alpha, a, lda, b,
-                                     ldb, beta, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_impl<sycl::half, sycl::half, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_half, library_data_t::real_float):
-        {
-            float alpha_value =
-                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
-            float beta_value =
-                dpct::get_value(reinterpret_cast<const float *>(beta), q);
-            sycl::half alpha_half(alpha_value);
-            sycl::half beta_half(beta_value);
-            detail::gemm_impl<sycl::half, sycl::half, sycl::half,
-                              sycl::half>(q, a_trans, b_trans, m, n, k, &alpha_half,
-                                          a, lda, b, ldb, &beta_half, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_int8, library_data_t::real_int8,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_impl<std::int8_t, std::int8_t, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
-            library_data_t::real_bfloat16, library_data_t::real_float):
-        {
-            detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
-                              oneapi::mkl::bfloat16, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_int8, library_data_t::real_int8,
-            library_data_t::real_int32, library_data_t::real_int32):
-        {
-            float alpha_float =
-                dpct::get_value(reinterpret_cast<const std::int32_t *>(alpha), q);
-            float beta_float =
-                dpct::get_value(reinterpret_cast<const std::int32_t *>(beta), q);
-            detail::gemm_impl<std::int8_t, std::int8_t, std::int32_t, float>(
-                q, a_trans, b_trans, m, n, k, &alpha_float, a, lda, b, ldb, &beta_float, c, ldc);
-            break;
-        }
-#endif // __INTEL_MKL__
-        default:
-            throw std::runtime_error("the combination of data type is unsupported");
-        }
-    } // gemm()
-
-    /// Computes a batch of matrix-matrix product with general matrices.
-    /// \param [in] q The queue where the routine should be executed.
-    /// \param [in] a_trans Specifies the operation applied to A.
-    /// \param [in] b_trans Specifies the operation applied to B.
-    /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C.
-    /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C.
-    /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B).
-    /// \param [in] alpha Scaling factor for the matrix-matrix product.
-    /// \param [in] a Input matrix A.
-    /// \param [in] a_type Data type of the matrix A.
-    /// \param [in] lda Leading dimension of A.
-    /// \param [in] b Input matrix B.
-    /// \param [in] b_type Data type of the matrix B.
-    /// \param [in] ldb Leading dimension of B.
-    /// \param [in] beta Scaling factor for matrix C.
-    /// \param [in, out] c Input/Output matrix C.
-    /// \param [in] c_type Data type of the matrix C.
-    /// \param [in] ldc Leading dimension of C.
-    /// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
-    /// \param [in] scaling_type Data type of the scaling factors.
-    inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans,
-                           oneapi::mkl::transpose b_trans, int m, int n, int k,
-                           const void *alpha, const void *a[],
-                           library_data_t a_type, int lda, const void *b[],
-                           library_data_t b_type, int ldb, const void *beta,
-                           void *c[], library_data_t c_type, int ldc,
-                           int batch_size, library_data_t scaling_type)
-    {
-        if (scaling_type == library_data_t::real_float &&
-            c_type == library_data_t::complex_float)
-        {
-            scaling_type = library_data_t::complex_float;
-        }
-        else if (scaling_type == library_data_t::real_double &&
-                 c_type == library_data_t::complex_double)
-        {
-            scaling_type = library_data_t::complex_double;
-        }
-
-        std::uint64_t key =
-            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
-        switch (key)
-        {
-        case detail::get_type_combination_id(
-            library_data_t::real_float, library_data_t::real_float,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<float, float, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_double, library_data_t::real_double,
-            library_data_t::real_double, library_data_t::real_double):
-        {
-            detail::gemm_batch_impl<double, double, double, double>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::complex_float, library_data_t::complex_float,
-            library_data_t::complex_float, library_data_t::complex_float):
-        {
-            detail::gemm_batch_impl<std::complex<float>, std::complex<float>,
-                                    std::complex<float>, std::complex<float>>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::complex_double, library_data_t::complex_double,
-            library_data_t::complex_double, library_data_t::complex_double):
-        {
-            detail::gemm_batch_impl<std::complex<double>, std::complex<double>,
-                                    std::complex<double>, std::complex<double>>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_half, library_data_t::real_half):
-        {
-            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half,
-                                    sycl::half>(q, a_trans, b_trans, m, n, k, alpha,
-                                                a, lda, b, ldb, beta, c, ldc,
-                                                batch_size);
-            break;
-        }
-#ifdef __INTEL_MKL__
-        case detail::get_type_combination_id(
-            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
-            library_data_t::real_bfloat16, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
-                                    oneapi::mkl::bfloat16, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
-                                    float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
-                                           b, ldb, beta, c, ldc, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_int8, library_data_t::real_int8,
-            library_data_t::real_int32, library_data_t::real_int32):
-        {
-            float alpha_float =
-                dpct::get_value(reinterpret_cast<const std::int32_t *>(alpha), q);
-            float beta_float =
-                dpct::get_value(reinterpret_cast<const std::int32_t *>(beta), q);
-            detail::gemm_batch_impl<std::int8_t, std::int8_t, std::int32_t,
-                                    float>(q, a_trans, b_trans, m, n, k, &alpha_float,
-                                           a, lda, b, ldb, &beta_float, c, ldc,
-                                           batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_int8, library_data_t::real_int8,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<std::int8_t, std::int8_t, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<sycl::half, sycl::half, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
-                batch_size);
-            break;
-        }
-#endif
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_half, library_data_t::real_float):
-        {
-            float alpha_value =
-                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
-            float beta_value =
-                dpct::get_value(reinterpret_cast<const float *>(beta), q);
-            sycl::half alpha_half(alpha_value);
-            sycl::half beta_half(beta_value);
-            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half, sycl::half>(
-                q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, b, ldb, &beta_half, c, ldc,
-                batch_size);
-            break;
-        }
-        default:
-            throw std::runtime_error("the combination of data type is unsupported");
-        }
-    }
-
-    /// Computes a batch of matrix-matrix product with general matrices.
-    /// \param [in] q The queue where the routine should be executed.
-    /// \param [in] a_trans Specifies the operation applied to A.
-    /// \param [in] b_trans Specifies the operation applied to B.
-    /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C.
-    /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C.
-    /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B).
-    /// \param [in] alpha Scaling factor for the matrix-matrix product.
-    /// \param [in] a Input matrix A.
-    /// \param [in] a_type Data type of the matrix A.
-    /// \param [in] lda Leading dimension of A.
-    /// \param [in] stride_a Stride between the different A matrices.
-    /// \param [in] b Input matrix B.
-    /// \param [in] b_type Data type of the matrix B.
-    /// \param [in] ldb Leading dimension of B.
-    /// \param [in] stride_b Stride between the different B matrices.
-    /// \param [in] beta Scaling factor for matrix C.
-    /// \param [in, out] c Input/Output matrix C.
-    /// \param [in] c_type Data type of the matrix C.
-    /// \param [in] ldc Leading dimension of C.
-    /// \param [in] stride_c Stride between the different C matrices.
-    /// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
-    /// \param [in] scaling_type Data type of the scaling factors.
-    inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans,
-                           oneapi::mkl::transpose b_trans, int m, int n, int k,
-                           const void *alpha, const void *a, library_data_t a_type,
-                           int lda, long long int stride_a, const void *b,
-                           library_data_t b_type, int ldb, long long int stride_b,
-                           const void *beta, void *c, library_data_t c_type,
-                           int ldc, long long int stride_c, int batch_size,
-                           library_data_t scaling_type)
-    {
-        if (scaling_type == library_data_t::real_float &&
-            c_type == library_data_t::complex_float)
-        {
-            scaling_type = library_data_t::complex_float;
-        }
-        else if (scaling_type == library_data_t::real_double &&
-                 c_type == library_data_t::complex_double)
-        {
-            scaling_type = library_data_t::complex_double;
-        }
-
-        std::uint64_t key =
-            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
-        switch (key)
-        {
-        case detail::get_type_combination_id(
-            library_data_t::real_float, library_data_t::real_float,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<float, float, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
-                beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_double, library_data_t::real_double,
-            library_data_t::real_double, library_data_t::real_double):
-        {
-            detail::gemm_batch_impl<double, double, double, double>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
-                beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::complex_float, library_data_t::complex_float,
-            library_data_t::complex_float, library_data_t::complex_float):
-        {
-            detail::gemm_batch_impl<std::complex<float>, std::complex<float>,
-                                    std::complex<float>, std::complex<float>>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
-                beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::complex_double, library_data_t::complex_double,
-            library_data_t::complex_double, library_data_t::complex_double):
-        {
-            detail::gemm_batch_impl<std::complex<double>, std::complex<double>,
-                                    std::complex<double>, std::complex<double>>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
-                beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_half, library_data_t::real_half):
-        {
-            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half,
-                                    sycl::half>(q, a_trans, b_trans, m, n, k, alpha,
-                                                a, lda, stride_a, b, ldb, stride_b,
-                                                beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-#ifdef __INTEL_MKL__
-        case detail::get_type_combination_id(
-            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
-            library_data_t::real_bfloat16, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
-                                    oneapi::mkl::bfloat16, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
-                beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
-                                    float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
-                                           stride_a, b, ldb, stride_b, beta, c, ldc,
-                                           stride_c, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_int8, library_data_t::real_int8,
-            library_data_t::real_int32, library_data_t::real_int32):
-        {
-            detail::gemm_batch_impl<std::int8_t, std::int8_t, std::int32_t,
-                                    std::int32_t>(q, a_trans, b_trans, m, n, k, alpha,
-                                                  a, lda, stride_a, b, ldb, stride_b,
-                                                  beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_int8, library_data_t::real_int8,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<std::int8_t, std::int8_t, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
-                beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_float, library_data_t::real_float):
-        {
-            detail::gemm_batch_impl<sycl::half, sycl::half, float, float>(
-                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
-                beta, c, ldc, stride_c, batch_size);
-            break;
-        }
-#endif
-        case detail::get_type_combination_id(
-            library_data_t::real_half, library_data_t::real_half,
-            library_data_t::real_half, library_data_t::real_float):
-        {
-            float alpha_value =
-                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
-            float beta_value =
-                dpct::get_value(reinterpret_cast<const float *>(beta), q);
-            sycl::half alpha_half(alpha_value);
-            sycl::half beta_half(beta_value);
-            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half, sycl::half>(
-                q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, stride_a, b, ldb, stride_b,
-                &beta_half, c, ldc, stride_c, batch_size);
-            break;
-        }
-        default:
-            throw std::runtime_error("the combination of data type is unsupported");
-        }
-    }
-
-    static inline void
-    async_dpct_memcpy(void *to_ptr, size_t to_pitch, const void *from_ptr,
-                      size_t from_pitch, size_t x, size_t y,
-                      memcpy_direction direction = automatic,
-                      sycl::queue &q = get_default_queue())
-    {
-        detail::dpct_memcpy(q, to_ptr, from_ptr, to_pitch, from_pitch, x, y,
-                            direction);
-    }
-
-    using err0 = detail::generic_error_type<struct err0_tag, int>;
-    using err1 = detail::generic_error_type<struct err1_tag, int>;
-
-    static inline void dpct_free(void *ptr, sycl::queue &q = get_default_queue()) {
-        detail::dpct_free(ptr, q);
-    }
-
-    /// dpct accessor used as device function parameter.
-    template <class T, memory_region Memory, size_t Dimension> class accessor;
-    template <class T, memory_region Memory> class accessor<T, Memory, 3> {
-    public:
-        using memory_t = detail::memory_traits<Memory, T>;
-        using element_t = typename memory_t::element_t;
-        using pointer_t = typename memory_t::pointer_t;
-        using accessor_t = typename memory_t::template accessor_t<3>;
-        accessor(pointer_t data, const sycl::range<3> &in_range)
-            : _data(data), _range(in_range) {}
-        template <memory_region M = Memory>
-        accessor(typename std::enable_if<M != local, const accessor_t>::type &acc)
-            : accessor(acc, acc.get_range()) {}
-        accessor(const accessor_t &acc, const sycl::range<3> &in_range)
-            : accessor(acc.get_pointer(), in_range) {}
-        accessor<T, Memory, 2> operator[](size_t index) const {
-            sycl::range<2> sub(_range.get(1), _range.get(2));
-            return accessor<T, Memory, 2>(_data + index * sub.size(), sub);
-        }
-
-        pointer_t get_ptr() const { return _data; }
-
-    private:
-        pointer_t _data;
-        sycl::range<3> _range;
-    };
-    template <class T, memory_region Memory> class accessor<T, Memory, 2> {
-    public:
-        using memory_t = detail::memory_traits<Memory, T>;
-        using element_t = typename memory_t::element_t;
-        using pointer_t = typename memory_t::pointer_t;
-        using accessor_t = typename memory_t::template accessor_t<2>;
-        accessor(pointer_t data, const sycl::range<2> &in_range)
-            : _data(data), _range(in_range) {}
-        template <memory_region M = Memory>
-        accessor(typename std::enable_if<M != local, const accessor_t>::type &acc)
-            : accessor(acc, acc.get_range()) {}
-        accessor(const accessor_t &acc, const sycl::range<2> &in_range)
-            : accessor(acc.get_pointer(), in_range) {}
-
-        pointer_t operator[](size_t index) const {
-            return _data + _range.get(1) * index;
-        }
-
-        pointer_t get_ptr() const { return _data; }
-
-    private:
-        pointer_t _data;
-        sycl::range<2> _range;
-    };
-
-    namespace detail {
-        /// Device variable with address space of shared, global or constant.
-        template <class T, memory_region Memory, size_t Dimension> class device_memory {
-        public:
-            using accessor_t =
-                typename detail::memory_traits<Memory,
-                                            T>::template accessor_t<Dimension>;
-            using value_t = typename detail::memory_traits<Memory, T>::value_t;
-            using dpct_accessor_t = dpct::accessor<T, Memory, Dimension>;
-
-            device_memory() : device_memory(sycl::range<Dimension>(1)) {}
-
-            /// Constructor of 1-D array with initializer list
-            device_memory(const sycl::range<Dimension> &in_range,
-                        std::initializer_list<value_t> &&init_list)
-                : device_memory(in_range) {
-                assert(init_list.size() <= in_range.size());
-                _host_ptr = (value_t *)std::malloc(_size);
-                std::memset(_host_ptr, 0, _size);
-                std::memcpy(_host_ptr, init_list.begin(), init_list.size() * sizeof(T));
-            }
-
-            /// Constructor of 2-D array with initializer list
-            template <size_t D = Dimension>
-            device_memory(
-                const typename std::enable_if<D == 2, sycl::range<2>>::type &in_range,
-                std::initializer_list<std::initializer_list<value_t>> &&init_list)
-                : device_memory(in_range) {
-                assert(init_list.size() <= in_range[0]);
-                _host_ptr = (value_t *)std::malloc(_size);
-                std::memset(_host_ptr, 0, _size);
-                auto tmp_data = _host_ptr;
-                for (auto sub_list : init_list) {
-                    assert(sub_list.size() <= in_range[1]);
-                    std::memcpy(tmp_data, sub_list.begin(),
-                                sub_list.size() * sizeof(T));
-                    tmp_data += in_range[1];
-                }
-            }
-
-            /// Constructor with range
-            device_memory(const sycl::range<Dimension> &range_in)
-                : _size(range_in.size() * sizeof(T)), _range(range_in),
-                _reference(false), _host_ptr(nullptr), _device_ptr(nullptr) {
-                static_assert(
-                    (Memory == global) || (Memory == constant) || (Memory == shared),
-                    "device memory region should be global, constant or shared");
-                // Make sure that singleton class mem_mgr and dev_mgr will destruct
-                // later than this.
-                detail::mem_mgr::instance();
-                dev_mgr::instance();
-            }
-
-            /// Constructor with range
-            template <class... Args>
-            device_memory(Args... Arguments)
-                : device_memory(sycl::range<Dimension>(Arguments...)) {}
-
-            ~device_memory() {
-                if (_device_ptr && !_reference)
-                    dpct::dpct_free(_device_ptr);
-                if (_host_ptr)
-                    std::free(_host_ptr);
-            }
-
-            /// Allocate memory with default queue, and init memory if has initial
-            /// value.
-            void init() { init(dpct::get_default_queue()); }
-            /// Allocate memory with specified queue, and init memory if has initial
-            /// value.
-            void init(sycl::queue &q) {
-                if (_device_ptr)
-                    return;
-                if (!_size)
-                    return;
-                allocate_device(q);
-                if (_host_ptr)
-                    detail::dpct_memcpy(q, _device_ptr, _host_ptr, _size,
-                                        host_to_device);
-            }
-
-            /// The variable is assigned to a device pointer.
-            void assign(value_t *src, size_t size) {
-                this->~device_memory();
-                new (this) device_memory(src, size);
-            }
-
-            /// Get memory pointer of the memory object, which is virtual pointer when
-            /// usm is not used, and device pointer when usm is used.
-            value_t *get_ptr() { return get_ptr(get_default_queue()); }
-            /// Get memory pointer of the memory object, which is virtual pointer when
-            /// usm is not used, and device pointer when usm is used.
-            value_t *get_ptr(sycl::queue &q) {
-                init(q);
-                return _device_ptr;
-            }
-
-            /// Get the device memory object size in bytes.
-            size_t get_size() { return _size; }
-
-            template <size_t D = Dimension>
-            typename std::enable_if<D == 1, T>::type &operator[](size_t index) {
-                init();
-                return _device_ptr[index];
-            }
-
-            /// Get dpct::accessor with dimension info for the device memory object
-            /// when usm is used and dimension is greater than 1.
-            template <size_t D = Dimension>
-            typename std::enable_if<D != 1, dpct_accessor_t>::type
-            get_access(sycl::handler &cgh) {
-                return dpct_accessor_t((T *)_device_ptr, _range);
-            }
-
-        private:
-            device_memory(value_t *memory_ptr, size_t size)
-                : _size(size), _range(size / sizeof(T)), _reference(true),
-                _device_ptr(memory_ptr) {}
-
-            void allocate_device(sycl::queue &q) {
-        #ifndef DPCT_USM_LEVEL_NONE
-                if (Memory == shared) {
-                    _device_ptr = (value_t *)sycl::malloc_shared(_size, q.get_device(),
-                                                                q.get_context());
-                    return;
-                }
-        #ifdef SYCL_EXT_ONEAPI_USM_DEVICE_READ_ONLY
-                if (Memory == constant) {
-                    _device_ptr = (value_t *)sycl::malloc_device(
-                        _size, q.get_device(), q.get_context(),
-                        sycl::ext::oneapi::property::usm::device_read_only());
-                    return;
-                }
-        #endif
-        #endif
-                _device_ptr = (value_t *)detail::dpct_malloc(_size, q);
-            }
-
-            size_t _size;
-            sycl::range<Dimension> _range;
-            bool _reference;
-            value_t *_host_ptr;
-            value_t *_device_ptr;
-        };
-        template <class T, memory_region Memory>
-        class device_memory<T, Memory, 0> : public device_memory<T, Memory, 1> {
-        public:
-            using base = device_memory<T, Memory, 1>;
-            using value_t = typename base::value_t;
-            using accessor_t =
-                typename detail::memory_traits<Memory, T>::template accessor_t<0>;
-
-            /// Constructor with initial value.
-            device_memory(const value_t &val) : base(sycl::range<1>(1), {val}) {}
-
-            /// Default constructor
-            device_memory() : base(1) {}
-        };
-        } // namespace detail
-
-    template <class T, size_t Dimension>
-    using global_memory = detail::device_memory<T, global, Dimension>;
-    template <class T, size_t Dimension>
-    using constant_memory = detail::device_memory<T, constant, Dimension>;
-    template <class T, size_t Dimension>
-    using shared_memory = detail::device_memory<T, shared, Dimension>;
-
-
-} // COPY from DPCT head files
-
-#define GGML_COMMON_DECL_SYCL
-#define GGML_COMMON_IMPL_SYCL
-#include "ggml-common.h"
-
-static int g_ggml_sycl_debug=0;
-#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) fprintf(stderr, __VA_ARGS__);}while(0)
-
-#define CHECK_TRY_ERROR(expr)                                                  \
-  [&]() {                                                                      \
-    try {                                                                      \
-      expr;                                                                    \
-      return dpct::success;                                                    \
-    } catch (std::exception const &e) {                                        \
-      std::cerr << e.what()<< "\nException caught at file:" << __FILE__        \
-        << ", line:" << __LINE__ <<", func:"<<__func__<< std::endl;            \
-      return dpct::default_error;                                              \
-    }                                                                          \
-  }()
-
-// #define DEBUG_SYCL_MALLOC
-
-static int g_work_group_size = 0;
-// typedef sycl::half ggml_fp16_t;
-
-#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP
-#define VER_4VEC   610          //todo for hardward optimize.
-#define VER_GEN9      700       //todo for hardward optimize.
-#define VER_GEN12 1000000       //todo for hardward optimize.
-#define VER_GEN13      (VER_GEN12 + 1030)   //todo for hardward optimize.
-
-#define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares
-
-
-//define for XMX in Intel GPU
-//TODO: currently, it's not used for XMX really.
-#define SYCL_USE_XMX
-
-// max batch size to use MMQ kernels when tensor cores are available
-#define XMX_MAX_BATCH_SIZE 32
-
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-// dmmv = dequantize_mul_mat_vec
-#ifndef GGML_SYCL_DMMV_X
-#define GGML_SYCL_DMMV_X 32
-#endif
-#ifndef GGML_SYCL_MMV_Y
-#define GGML_SYCL_MMV_Y 1
-#endif
-
-enum ggml_sycl_backend_gpu_mode {
-    SYCL_UNSET_GPU_MODE = -1,
-    SYCL_SINGLE_GPU_MODE = 0,
-    SYCL_MUL_GPU_MODE
-};
-
-static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size");
-
-static void crash(){
-    int *ptr = NULL;
-    *ptr = 0;
-}
-
-static void ggml_sycl_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) {
-    fprintf(stderr, "SYCL error: %s: %s\n", stmt, msg);
-    fprintf(stderr, "  in function %s at %s:%d\n", func, file, line);
-    GGML_ASSERT(!"SYCL error");
-}
-
-#define SYCL_CHECK(err) do {                                                   \
-    auto err_ = (err); if (err_ != 0) ggml_sycl_error(                         \
-        #err, __func__, __FILE__, __LINE__,                                    \
-        "Meet error in this line code!");   \
-} while (0)
-
-#if DPCT_COMPAT_RT_VERSION >= 11100
-#define GGML_SYCL_ASSUME(x) __builtin_assume(x)
-#else
-#define GGML_SYCL_ASSUME(x)
-#endif // DPCT_COMPAT_RT_VERSION >= 11100
-
-#ifdef GGML_SYCL_F16
-typedef sycl::half dfloat; // dequantize float
-typedef sycl::half2 dfloat2;
-#else
-typedef float dfloat; // dequantize float
-typedef sycl::float2 dfloat2;
-#endif //GGML_SYCL_F16
-
-#define MMVQ_MAX_BATCH_SIZE  8
-
-static const int8_t kvalues_iq4nl[16]={-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
-
-bool   ggml_sycl_loaded(void);
-void * ggml_sycl_host_malloc(size_t size);
-void   ggml_sycl_host_free(void * ptr);
-bool   ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-void   ggml_sycl_free_data(struct ggml_tensor * tensor);
-void   ggml_sycl_assign_buffers(struct ggml_tensor * tensor);
-void   ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor);
-void   ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor);
-void   ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor);
-void   ggml_sycl_copy_to_device(struct ggml_tensor * tensor);
-void   ggml_sycl_set_main_device(int main_device);
-void   ggml_sycl_set_mul_mat_q(bool mul_mat_q);
-void   ggml_sycl_set_scratch_size(size_t scratch_size);
-void   ggml_sycl_free_scratch(void);
-void   ggml_sycl_get_device_description(int device, char * description, size_t description_size);
-bool   ggml_backend_is_sycl(ggml_backend_t backend);
-int    ggml_backend_sycl_get_device(ggml_backend_t backend);
-int    get_main_device();
-void   print_ggml_tensor(const char*name, struct ggml_tensor *src);
-void   log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt);
-
-void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst,
-                    const void *ptr_src, size_t size) {
-    char *host_buf = (char *)malloc(size);
-    q_src.memcpy(host_buf, (const char *)ptr_src, size).wait();
-    q_dst.memcpy((char *)ptr_dst, host_buf, size).wait();
-    free(host_buf);
-}
-
-static __dpct_inline__ int get_int_from_int8(const int8_t *x8, const int &i32) {
-    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
-
-    int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
-
-    return x32;
-}
-
-static __dpct_inline__ int get_int_from_uint8(const uint8_t *x8,
-                                              const int &i32) {
-    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
-
-    int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
-
-    return x32;
-}
-
-static __dpct_inline__ int get_int_from_int8_aligned(const int8_t *x8,
-                                                     const int &i32) {
-    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
-}
-
-static __dpct_inline__ int get_int_from_uint8_aligned(const uint8_t *x8,
-                                                      const int &i32) {
-    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
-}
-
-template <typename T>
-using to_t_sycl_t = void (*)(const void *__restrict__ x, T *__restrict__ y,
-                             int k, dpct::queue_ptr stream);
-typedef to_t_sycl_t<float> to_fp32_sycl_t;
-typedef to_t_sycl_t<sycl::half> to_fp16_sycl_t;
-
-typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
-typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
-typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
-typedef void (*ggml_sycl_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
-typedef void (*ggml_sycl_op_mul_mat_t)(
-    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
-    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
-    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
-    const int64_t src1_ncols, const int64_t src1_padded_row_size,
-    const dpct::queue_ptr &stream);
-typedef void (*ggml_sycl_op_flatten_t)(const ggml_tensor *src0,
-                                       const ggml_tensor *src1,
-                                       ggml_tensor *dst, const float *src0_dd,
-                                       const float *src1_dd, float *dst_dd,
-                                       const dpct::queue_ptr &main_stream);
-
-typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
-typedef void (*allocate_tiles_sycl_t)(int **x_ql, sycl::half2 **x_dm,
-                                      int **x_qh, int **x_sc);
-typedef void (*load_tiles_sycl_t)(const void *__restrict__ vx,
-                                  int *__restrict__ x_ql,
-                                  sycl::half2 *__restrict__ x_dm,
-                                  int *__restrict__ x_qh,
-                                  int *__restrict__ x_sc, const int &i_offset,
-                                  const int &i_max, const int &k,
-                                  const int &blocks_per_row);
-typedef float (*vec_dot_q_mul_mat_sycl_t)(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ms,
-    const int &i, const int &j, const int &k);
-
-#define WARP_SIZE 32
-#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
-
-#define SYCL_GELU_BLOCK_SIZE 256
-#define SYCL_SILU_BLOCK_SIZE 256
-#define SYCL_TANH_BLOCK_SIZE 256
-#define SYCL_RELU_BLOCK_SIZE 256
-#define SYCL_HARDSIGMOID_BLOCK_SIZE 256
-#define SYCL_HARDSWISH_BLOCK_SIZE 256
-#define SYCL_SQR_BLOCK_SIZE 256
-#define SYCL_CPY_BLOCK_SIZE 32
-#define SYCL_SCALE_BLOCK_SIZE 256
-#define SYCL_CLAMP_BLOCK_SIZE 256
-#define SYCL_ROPE_BLOCK_SIZE 256
-#define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32
-#define SYCL_QUANTIZE_BLOCK_SIZE 256
-#define SYCL_DEQUANTIZE_BLOCK_SIZE 256
-#define SYCL_GET_ROWS_BLOCK_SIZE 256
-#define SYCL_UPSCALE_BLOCK_SIZE 256
-#define SYCL_CONCAT_BLOCK_SIZE 256
-#define SYCL_PAD_BLOCK_SIZE 256
-#define SYCL_ACC_BLOCK_SIZE 256
-#define SYCL_IM2COL_BLOCK_SIZE 256
-#define SYCL_POOL2D_BLOCK_SIZE 256
-
-// dmmv = dequantize_mul_mat_vec
-#ifndef GGML_SYCL_DMMV_X
-#define GGML_SYCL_DMMV_X 32
-#endif
-#ifndef GGML_SYCL_MMV_Y
-#define GGML_SYCL_MMV_Y 1
-#endif
-
-#ifndef K_QUANTS_PER_ITERATION
-#define K_QUANTS_PER_ITERATION 2
-#else
-static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
-#endif
-
-#ifndef GGML_SYCL_PEER_MAX_BATCH_SIZE
-#define GGML_SYCL_PEER_MAX_BATCH_SIZE 128
-#endif // GGML_SYCL_PEER_MAX_BATCH_SIZE
-
-#define MUL_MAT_SRC1_COL_STRIDE 128
-
-#define MAX_STREAMS 8
-static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = {{0}};
-
-struct ggml_tensor_extra_gpu {
-    void * data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split tensors
-    dpct::event_ptr
-        events[GGML_SYCL_MAX_DEVICES]
-              [MAX_STREAMS]; // events for synchronizing multiple GPUs
-};
-
-class sycl_gpu_mgr {
-    public:
-        std::vector<int> gpus;
-        std::vector<sycl::device> devices;
-        sycl::queue *first_queue;
-        sycl::context co_ctx;
-        int max_compute_units = 0;
-        int work_group_size = 0;
-        std::string gpus_list = "";
-
-        /*
-        Use all GPUs with same top max compute units
-        */
-        sycl_gpu_mgr() {
-            detect_sycl_gpu_list_with_max_cu();
-            get_allow_gpus();
-            create_context_with_gpus();
-        }
-
-        /*
-        Only use the assigned GPU
-        */
-        sycl_gpu_mgr(int main_gpu_id) {
-            sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id);
-            dpct::device_info prop;
-            dpct::get_device_info(prop, device);
-            gpus.push_back(main_gpu_id);
-            devices.push_back(device);
-            work_group_size = prop.get_max_work_group_size();
-            max_compute_units = prop.get_max_compute_units();
-
-            get_allow_gpus();
-            create_context_with_gpus();
-        }
-
-        void create_context_with_gpus() {
-            sycl::context ctx = sycl::context(devices);
-            assert(gpus.size() > 0);
-            first_queue = dpct::get_current_device().create_queue(ctx, devices[0]);
-            co_ctx = first_queue->get_context();
-        }
-
-        sycl::context &get_co_ctx() { return co_ctx; }
-
-        void get_allow_gpus() {
-            gpus_list = "";
-            for (size_t i = 0; i < gpus.size(); ++i) {
-                gpus_list += std::to_string(gpus[i]);
-                gpus_list += ",";
-            }
-            if (gpus_list.length() > 1) {
-                gpus_list.pop_back();
-            }
-        }
-
-        bool is_allowed_gpu(int device_id) {
-            return std::find(gpus.begin(), gpus.end(), device_id) != gpus.end();
-        }
-
-        void detect_sycl_gpu_list_with_max_cu() try {
-            int device_count = dpct::dev_mgr::instance().device_count();
-
-            for (int id = 0; id < device_count; id++) {
-                sycl::device device = dpct::dev_mgr::instance().get_device(id);
-                if (!device.is_gpu())
-                    continue;
-                dpct::device_info prop;
-                dpct::get_device_info(prop, device);
-                if (max_compute_units < prop.get_max_compute_units())
-                    max_compute_units = prop.get_max_compute_units();
-            }
-
-            for (int id = 0; id < device_count; id++) {
-                sycl::device device = dpct::dev_mgr::instance().get_device(id);
-                if (!device.is_gpu())
-                    continue;
-                dpct::device_info prop;
-                dpct::get_device_info(prop, device);
-                if (max_compute_units == prop.get_max_compute_units() &&
-                    is_ext_oneapi_device(device)) {
-                    gpus.push_back(id);
-                    devices.push_back(device);
-                    work_group_size = prop.get_max_work_group_size();
-                }
-            }
-            return;
-        } catch (sycl::exception const &exc) {
-            std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-                    << ", line:" << __LINE__ << std::endl;
-            std::exit(1);
-        }
-
-        int get_gpu_count() { return (int)gpus.size(); }
-
-        int get_index(int id) {
-            for (int i = 0; i < (int)gpus.size(); i++) {
-                if (gpus[i] == id)
-                    return i;
-            }
-            printf("miss to get device index by id=%d\n", id);
-            GGML_ASSERT(false);
-        }
-
-        int get_next_index(int id) {
-            int cur_index = get_index(id);
-            for (int i = cur_index + 1; i < (int)gpus.size(); i++) {
-                if (gpus[i] == id)
-                    return i;
-            }
-            GGML_ASSERT(false);
-        }
-
-        bool is_ext_oneapi_device(const sycl::device &dev) {
-            sycl::backend dev_backend = dev.get_backend();
-            if (dev_backend == sycl::backend::ext_oneapi_level_zero ||
-                dev_backend == sycl::backend::ext_oneapi_cuda ||
-                dev_backend == sycl::backend::ext_oneapi_hip)
-                return true;
-            return false;
-        }
-};
-
-static sycl_gpu_mgr *g_sycl_gpu_mgr = NULL;
-static int g_device_count = -1;
-static int g_all_sycl_device_count = -1;
-static int g_main_device = -1;
-static int g_main_device_id = -1;
-static bool g_ggml_backend_sycl_buffer_type_initialized = false;
-
-static std::array<float, GGML_SYCL_MAX_DEVICES> g_default_tensor_split = {};
-
-static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0};
-
-static ggml_sycl_backend_gpu_mode g_ggml_sycl_backend_gpu_mode = SYCL_UNSET_GPU_MODE;
-
-struct sycl_device_capabilities {
-    int     cc;                 // compute capability
-    bool    vmm;                // virtual memory support
-    size_t  vmm_granularity;    // granularity of virtual memory
-    int device_id;
-};
-
-static sycl_device_capabilities g_device_caps[GGML_SYCL_MAX_DEVICES] = { {0, false, 0, -1} };
-
-struct sycl_device_id2index {
-    int index;
-};
-
-static void * g_scratch_buffer = nullptr;
-static size_t g_scratch_size = 0; // disabled by default
-static size_t g_scratch_offset = 0;
-
-static dpct::queue_ptr g_sycl_handles[GGML_SYCL_MAX_DEVICES] = {nullptr};
-
-int get_main_device(){
-    return g_main_device;
-}
-
-[[noreturn]]
-static void bad_arch(const sycl::stream &stream_ct1) {
-    stream_ct1 << "ERROR: ggml-sycl was compiled without support for the "
-                  "current GPU architecture.\n";
-    // __trap();
-    std::exit(1);
-
-    (void) bad_arch; // suppress unused function warning
-}
-
-/*
-device_index: device index from 0 to n (continue numbers).
-    It is used for device select/set in SYCL backend internal data structure.
-*/
-void check_allow_gpu_index(const int device_index) {
-    if (device_index >= g_device_count) {
-        char error_buf[256];
-        snprintf(error_buf, sizeof(error_buf),
-                 "%s error: device_index:%d is out of range: [0-%d]", __func__,
-                 device_index, g_device_count - 1);
-        fprintf(stderr, "%s\n", error_buf);
-        assert(false);
-    }
-}
-
-/*
-device_id: device ID is shown by ggml_backend_sycl_print_sycl_devices().
-    It is only used to set current working device.
-*/
-void check_allow_gpu_id(const int device_id) {
-    if (!g_sycl_gpu_mgr->is_allowed_gpu(device_id)) {
-        char error_buf[256];
-        snprintf(error_buf, sizeof(error_buf),
-                 "error: cannot set device=%d, which is not allowed. Please "
-                 "set GPU ID in: [%s]",
-                 device_id, g_sycl_gpu_mgr->gpus_list.c_str());
-        fprintf(stderr, "%s\n", error_buf);
-        throw std::invalid_argument(error_buf);
-    }
-}
-
-int get_current_device_id() {
-    return dpct::dev_mgr::instance().current_device_id();
-}
-
-inline dpct::err0 ggml_sycl_set_device(const int device) try {
-
-    int device_id = g_sycl_gpu_mgr->gpus[device];
-    check_allow_gpu_id(device_id);
-
-    int current_device_id;
-    SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id()));
-
-    // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d,
-    // current_device_id=%d\n", device, current_device);
-    if (device_id == current_device_id) {
-        return 0;
-    }
-
-    return CHECK_TRY_ERROR(dpct::select_device(device_id));
-} catch (sycl::exception const &exc) {
-    std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-              << ", line:" << __LINE__ << std::endl;
-    crash();
-    std::exit(1);
-}
-
-void log_ggml_var_device(const char*name, float *src, size_t total_elements, bool src_on_device){
-    if(!g_ggml_sycl_debug) return;
-    if(!src){
-        printf("GGML Tensor:%s skip to save for NULL pointer\n", name);
-        return;
-    }
-    char filename[1024];
-    sprintf(filename, "%s.txt", name);
-    printf("GGML Tensor:%s save to %s\n", name, filename);
-
-    size_t total_size = total_elements*sizeof(float);
-    float *local_buf = NULL;
-    if(src_on_device) {
-        local_buf = (float *) ggml_sycl_host_malloc(total_size);
-        ggml_sycl_set_device(g_main_device);
-        dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
-        main_stream->memcpy(local_buf, src, total_size).wait();
-    }
-    else {
-        local_buf = (float *)src;
-    }
-
-    std::ofstream logfile;
-    logfile.open(filename);
-    for(size_t i=0; i<total_elements; i++){
-        logfile << local_buf[i] <<" ";
-        if((i+1)%20 ==0) logfile <<std::endl;
-    }
-    logfile <<std::endl;
-    logfile.close();
-
-    if(src_on_device) ggml_sycl_host_free(local_buf);
-}
-
-void log_ggml_var_device_fp16(const char*name, sycl::half *src, size_t total_elements, bool src_on_device){
-    if(!g_ggml_sycl_debug) return;
-    if(!src){
-        printf("GGML Tensor:%s skip to save for NULL pointer\n", name);
-        return;
-    }
-    char filename[1024];
-    sprintf(filename, "%s.txt", name);
-    printf("GGML Tensor:%s save to %s\n", name, filename);
-
-    size_t total_size = total_elements*sizeof(sycl::half);
-    sycl::half *local_buf = NULL;
-    if(src_on_device) {
-        local_buf = (sycl::half *) ggml_sycl_host_malloc(total_size);
-        ggml_sycl_set_device(g_main_device);
-        dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
-        main_stream->memcpy(local_buf, src, total_size).wait();
-    }
-    else {
-        local_buf = (sycl::half *)src;
-    }
-
-    std::ofstream logfile;
-    logfile.open(filename);
-    for(size_t i=0; i<total_elements; i++){
-        logfile << local_buf[i] <<" ";
-        if((i+1)%20 ==0) logfile <<std::endl;
-    }
-    logfile <<std::endl;
-    logfile.close();
-
-    if(src_on_device) ggml_sycl_host_free(local_buf);
-}
-
-//todo: debug for crash in some case
-void print_ggml_tensor(const char*name, struct ggml_tensor *src){
-    if(!g_ggml_sycl_debug) return;
-    if(!src){
-        printf("GGML Tensor:%s skip to save for NULL pointer\n", name);
-        return;
-    }
-
-    size_t total_elements = ggml_nelements(src);
-
-    const bool src_on_device = src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
-    float *src_data =NULL;
-    if(src_on_device) {
-        ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *)  src->extra;
-        src_data = (float*)src_extra->data_device[g_main_device];
-    }
-    else {
-        src_data = (float *)src->data;
-    }
-
-    log_ggml_var_device(name, src_data, total_elements, src_on_device);
-}
-
-static int log_file_name_idx=0;
-void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt) {
-    stop_cnt = 4;
-    if(log_file_name_idx>=stop_cnt) return;
-    char filename[1280];
-    sprintf(filename, "%s_%07d", name, log_file_name_idx);
-    log_file_name_idx++;
-    print_ggml_tensor(filename, src);
-}
-
-static __dpct_inline__ float warp_reduce_sum(float x,
-                                             const sycl::nd_item<3> &item_ct1) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        /*
-        DPCT1096:98: The right-most dimension of the work-group used in the SYCL
-        kernel that calls this function may be less than "32". The function
-        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
-        CPU device. Modify the size of the work-group to ensure that the value
-        of the right-most dimension is a multiple of "32".
-        */
-        x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask);
-    }
-    return x;
-}
-
-static __dpct_inline__ sycl::float2
-warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3> &item_ct1) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(),
-                                                mask);
-        a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(),
-                                                mask);
-    }
-    return a;
-}
-
-static __dpct_inline__ float warp_reduce_max(float x,
-                                             const sycl::nd_item<3> &item_ct1) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        /*
-        DPCT1096:97: The right-most dimension of the work-group used in the SYCL
-        kernel that calls this function may be less than "32". The function
-        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
-        CPU device. Modify the size of the work-group to ensure that the value
-        of the right-most dimension is a multiple of "32".
-        */
-        x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
-                              item_ct1.get_sub_group(), x, mask));
-    }
-    return x;
-}
-
-static __dpct_inline__ float op_repeat(const float a, const float b) {
-    return b;
-    GGML_UNUSED(a);
-}
-
-static __dpct_inline__ float op_add(const float a, const float b) {
-    return a + b;
-}
-
-static __dpct_inline__ float op_mul(const float a, const float b) {
-    return a * b;
-}
-
-static __dpct_inline__ float op_div(const float a, const float b) {
-    return a / b;
-}
-
-template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
-static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
-        int ne0, int ne1, int ne2, int ne3,
-        int ne10, int ne11, int ne12, int ne13,
-        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s10,*/ int s11, int s12, int s13,
-        const sycl::nd_item<3> &item_ct1) {
-    const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                    item_ct1.get_local_id(2);
-    const int i1 = (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                    item_ct1.get_local_id(1));
-    const int i2 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) +
-                    item_ct1.get_local_id(0)) /
-                   ne3;
-    const int i3 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) +
-                    item_ct1.get_local_id(0)) %
-                   ne3;
-
-    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
-        return;
-    }
-
-    const int i11 = i1 % ne11;
-    const int i12 = i2 % ne12;
-    const int i13 = i3 % ne13;
-
-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
-
-    const src0_t * src0_row = src0 + i_src0;
-    const src1_t * src1_row = src1 + i_src1;
-    dst_t * dst_row = dst + i_dst;
-
-    for (int i0 = i0s; i0 < ne0;
-         i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) {
-        const int i10 = i0 % ne10;
-        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
-    }
-}
-
-template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
-static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
-        int ne0, int ne1, int ne2, int ne3,
-        int ne10, int ne11, int ne12, int ne13,
-        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s10,*/ int s11, int s12, int s13,
-        const sycl::nd_item<3> &item_ct1) {
-
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    const int i3 = i/(ne2*ne1*ne0);
-    const int i2 = (i/(ne1*ne0)) % ne2;
-    const int i1 = (i/ne0) % ne1;
-    const int i0 = i % ne0;
-
-    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
-        return;
-    }
-
-    const int i11 = i1 % ne11;
-    const int i12 = i2 % ne12;
-    const int i13 = i3 % ne13;
-
-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
-
-    const src0_t * src0_row = src0 + i_src0;
-    const src1_t * src1_row = src1 + i_src1;
-    dst_t * dst_row = dst + i_dst;
-
-    const int i10 = i0 % ne10;
-    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
-}
-
-static void acc_f32(const float * x, const float * y, float * dst, const int ne,
-    const int ne10, const int ne11, const int ne12,
-    const int nb1, const int nb2, int offset, const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-    if (i >= ne) {
-        return;
-    }
-    int src1_idx = i - offset;
-    int oz = src1_idx / nb2;
-    int oy = (src1_idx - (oz * nb2)) / nb1;
-    int ox = src1_idx % nb1;
-    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
-        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
-    } else {
-        dst[i] = x[i];
-    }
-}
-
-static void gelu_f32(const float * x, float * dst, const int k,
-                     const sycl::nd_item<3> &item_ct1) {
-    const float GELU_COEF_A    = 0.044715f;
-    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-
-    float xi = x[i];
-    dst[i] = 0.5f * xi *
-             (1.0f +
-              sycl::tanh(SQRT_2_OVER_PI * xi * (1.0f + GELU_COEF_A * xi * xi)));
-}
-
-static void silu_f32(const float * x, float * dst, const int k,
-                     const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] / (1.0f + sycl::native::exp(-x[i]));
-}
-
-static void gelu_quick_f32(const float *x, float *dst, int k,
-                           const sycl::nd_item<3> &item_ct1) {
-    const float GELU_QUICK_COEF = -1.702f;
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] * (1.0f / (1.0f + sycl::native::exp(GELU_QUICK_COEF * x[i])));
-}
-
-static void tanh_f32(const float *x, float *dst, int k,
-                     const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-    if (i >= k) {
-        return;
-    }
-    dst[i] = sycl::tanh((float)(x[i]));
-}
-
-static void relu_f32(const float * x, float * dst, const int k,
-                     const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = sycl::fmax((float)(x[i]), (float)0);
-}
-
-static void hardsigmoid_f32(const float * x, float * dst, const int k,
-                            const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f));
-}
-
-static void hardswish_f32(const float * x, float * dst, const int k,
-                          const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f));
-}
-
-static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope,
-                           const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-    if (i >= k) {
-        return;
-    }
-    dst[i] = sycl::fmax((float)(x[i]), (float)0) +
-             sycl::fmin((float)(x[i]), 0.0f) * negative_slope;
-}
-
-static void sqr_f32(const float * x, float * dst, const int k,
-                    const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] * x[i];
-}
-
-static void norm_f32(const float * x, float * dst, const int ncols, const float eps,
-                     const sycl::nd_item<3> &item_ct1, sycl::float2 *s_sum, int block_size) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-    const int tid = item_ct1.get_local_id(2);
-
-    sycl::float2 mean_var = sycl::float2(0.f, 0.f);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
-        mean_var.x() += xi;
-        mean_var.y() += xi * xi;
-    }
-
-    // sum up partial sums
-    mean_var = warp_reduce_sum(mean_var, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = mean_var;
-        }
-        /*
-        DPCT1118:0: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-        mean_var = s_sum[lane_id];
-        mean_var = warp_reduce_sum(mean_var, item_ct1);
-    }
-
-    const float mean = mean_var.x() / ncols;
-    const float var = mean_var.y() / ncols - mean * mean;
-    const float inv_std = sycl::rsqrt(var + eps);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
-    }
-}
-
-static void concat_f32(const float  *x,const float  *y, float *dst, const int ne0, const int ne02,
-                       const sycl::nd_item<3> &item_ct1) {
-    int nidx = item_ct1.get_local_id(2) +
-               item_ct1.get_group(2) * item_ct1.get_local_range(2);
-    if (nidx >= ne0) {
-        return;
-    }
-    // operation
-    int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
-                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-    if (item_ct1.get_group(0) < ne02) { // src0
-        int offset_src =
-            nidx + item_ct1.get_group(1) * ne0 +
-            item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-            dst[offset_dst] = x[offset_src];
-    } else {
-        int offset_src =
-            nidx + item_ct1.get_group(1) * ne0 +
-            (item_ct1.get_group(0) - ne02) * ne0 * item_ct1.get_group_range(1);
-            dst[offset_dst] = y[offset_src];
-    }
-}
-
-static void upscale_f32(const float  *x, float *dst, const int ne00, const int nb02, const int scale_factor,
-                        const sycl::nd_item<3> &item_ct1) {
-    int ne0 = ne00 * scale_factor;
-    int nidx = item_ct1.get_local_id(2) +
-               item_ct1.get_group(2) * item_ct1.get_local_range(2);
-    if (nidx >= ne0) {
-        return;
-    }
-    // operation
-    int i00 = nidx / scale_factor;
-    int i01 = item_ct1.get_group(1) / scale_factor;
-    int offset_src = i00 + i01 * ne00 + item_ct1.get_group(0) * nb02;
-    int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
-                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-    dst[offset_dst] = x[offset_src];
-}
-
-static void pad_f32(const float  *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02,
-                    const sycl::nd_item<3> &item_ct1) {
-    int nidx = item_ct1.get_local_id(2) +
-               item_ct1.get_group(2) * item_ct1.get_local_range(2);
-    if (nidx >= ne0) {
-        return;
-    }
-
-    // operation
-    int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
-                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-    if (nidx < ne00 && item_ct1.get_group(1) < ne01 &&
-        item_ct1.get_group(0) < ne02) {
-        int offset_src = nidx + item_ct1.get_group(1) * ne00 +
-                         item_ct1.get_group(0) * ne00 * ne01;
-            dst[offset_dst] = x[offset_src];
-    } else {
-        dst[offset_dst] = 0.0f;
-    }
-}
-
-static void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps,
-                           const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) {
-    int start = item_ct1.get_group(2) * group_size;
-    int end = start + group_size;
-
-    start += item_ct1.get_local_id(2);
-
-    if (end >= ne_elements) {
-        end = ne_elements;
-    }
-
-    float tmp = 0.0f; // partial sum for thread in warp
-
-    for (int j = start; j < end; j += block_size) {
-        tmp += x[j];
-    }
-
-    tmp = warp_reduce_sum(tmp, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        /*
-        DPCT1118:1: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        /*
-        DPCT1065:54: Consider replacing sycl::nd_item::barrier() with
-        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-        better performance if there is no access to global memory.
-        */
-        item_ct1.barrier();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp, item_ct1);
-    }
-
-    float mean = tmp / group_size;
-    tmp = 0.0f;
-
-    for (int j = start; j < end; j += block_size) {
-        float xi = x[j] - mean;
-        dst[j] = xi;
-        tmp += xi * xi;
-    }
-
-    tmp = warp_reduce_sum(tmp, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        /*
-        DPCT1118:2: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        /*
-        DPCT1065:55: Consider replacing sycl::nd_item::barrier() with
-        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-        better performance if there is no access to global memory.
-        */
-        item_ct1.barrier();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp, item_ct1);
-    }
-
-    float variance = tmp / group_size;
-    float scale = sycl::rsqrt(variance + eps);
-    for (int j = start; j < end; j += block_size) {
-        dst[j] *= scale;
-    }
-}
-
-static void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps,
-                         const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-    const int tid = item_ct1.get_local_id(2);
-
-    float tmp = 0.0f; // partial sum for thread in warp
-
-    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
-        tmp += xi * xi;
-    }
-
-    // sum up partial sums
-    tmp = warp_reduce_sum(tmp, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        /*
-        DPCT1118:3: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp, item_ct1);
-    }
-
-    const float mean = tmp / ncols;
-    const float scale = sycl::rsqrt(mean + eps);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = scale * x[row*ncols + col];
-    }
-}
-
-static __dpct_inline__ void dequantize_q4_0(const void *vx, const int ib,
-                                            const int iqs, dfloat2 &v) {
-    const block_q4_0 * x = (const block_q4_0 *) vx;
-
-    const dfloat d = x[ib].d;
-
-    const int vui = x[ib].qs[iqs];
-
-    v.x() = vui & 0xF;
-    v.y() = vui >> 4;
-
-#ifdef GGML_SYCL_F16
-    // v = v - {8.0f, 8.0f};
-    // v = v * {d, d};
-    v.s0() = (v.s0() - 8.0f) * d;
-    v.s1() = (v.s1() - 8.0f) * d;
-
-#else
-    v.x() = (v.x() - 8.0f) * d;
-    v.y() = (v.y() - 8.0f) * d;
-#endif // GGML_SYCL_F16
-}
-
-static __dpct_inline__ void dequantize_q4_1(const void *vx, const int ib,
-                                            const int iqs, dfloat2 &v) {
-    const block_q4_1 * x = (const block_q4_1 *) vx;
-
-    const dfloat d = x[ib].dm[0];
-    const dfloat m = x[ib].dm[1];
-
-    const int vui = x[ib].qs[iqs];
-
-    v.x() = vui & 0xF;
-    v.y() = vui >> 4;
-
-#ifdef GGML_SYCL_F16
-    // v = v * {d, d};
-    // v = v + {m, m};
-    v.s0() = (v.s0() * d) + m;
-    v.s1() = (v.s1() * d) + m;
-
-#else
-    v.x() = (v.x() * d) + m;
-    v.y() = (v.y() * d) + m;
-#endif // GGML_SYCL_F16
-}
-
-static __dpct_inline__ void dequantize_q5_0(const void *vx, const int ib,
-                                            const int iqs, dfloat2 &v) {
-    const block_q5_0 * x = (const block_q5_0 *) vx;
-
-    const dfloat d = x[ib].d;
-
-    uint32_t qh;
-    memcpy(&qh, x[ib].qh, sizeof(qh));
-
-    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
-    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
-
-    v.x() = ((x[ib].qs[iqs] & 0xf) | xh_0);
-    v.y() = ((x[ib].qs[iqs] >> 4) | xh_1);
-
-#ifdef GGML_SYCL_F16
-    // v = v - {16.0f, 16.0f};
-    // v = v * {d, d};
-    v.s0() = (v.s0() - 16.0f) * d;
-    v.s1() = (v.s1() - 16.0f) * d;
-
-#else
-    v.x() = (v.x() - 16.0f) * d;
-    v.y() = (v.y() - 16.0f) * d;
-#endif // GGML_SYCL_F16
-}
-
-static __dpct_inline__ void dequantize_q5_1(const void *vx, const int ib,
-                                            const int iqs, dfloat2 &v) {
-    const block_q5_1 * x = (const block_q5_1 *) vx;
-
-    const dfloat d = x[ib].dm[0];
-    const dfloat m = x[ib].dm[1];
-
-    uint32_t qh;
-    memcpy(&qh, x[ib].qh, sizeof(qh));
-
-    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
-    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
-
-    v.x() = ((x[ib].qs[iqs] & 0xf) | xh_0);
-    v.y() = ((x[ib].qs[iqs] >> 4) | xh_1);
-
-#ifdef GGML_SYCL_F16
-    // v = v * {d, d};
-    // v = v + {m, m};
-    v.s0() = (v.s0() * d) + m;
-    v.s1() = (v.s1() * d) + m;
-#else
-    v.x() = (v.x() * d) + m;
-    v.y() = (v.y() * d) + m;
-#endif // GGML_SYCL_F16
-}
-
-static __dpct_inline__ void dequantize_q8_0(const void *vx, const int ib,
-                                            const int iqs, dfloat2 &v) {
-    const block_q8_0 * x = (const block_q8_0 *) vx;
-
-    const dfloat d = x[ib].d;
-
-    v.x() = x[ib].qs[iqs + 0];
-    v.y() = x[ib].qs[iqs + 1];
-
-#ifdef GGML_SYCL_F16
-    // v = v * {d, d};
-    v.s0() *= d;
-    v.s1() *= d;
-#else
-    v.x() *= d;
-    v.y() *= d;
-#endif // GGML_SYCL_F16
-}
-
-template<typename dst_t>
-static void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32,
-                                  const sycl::nd_item<3> &item_ct1) {
-
-    const int i = item_ct1.get_group(2);
-
-    // assume 32 threads
-    const int tid = item_ct1.get_local_id(2);
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int ib = 8*i + ir;
-    if (ib >= nb32) {
-        return;
-    }
-
-    dst_t * y = yy + 256*i + 32*ir + 4*il;
-
-    const block_q4_0 * x = (const block_q4_0 *)vx + ib;
-    const float d = sycl::vec<sycl::half, 1>(x->d)
-                        .convert<float, sycl::rounding_mode::automatic>()[0];
-    const float dm = -8*d;
-
-    const uint8_t * q = x->qs + 4*il;
-
-    for (int l = 0; l < 4; ++l) {
-        y[l+ 0] = d * (q[l] & 0xF) + dm;
-        y[l+16] = d * (q[l] >>  4) + dm;
-    }
-}
-
-template<typename dst_t>
-static void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32,
-                                  const sycl::nd_item<3> &item_ct1) {
-
-    const int i = item_ct1.get_group(2);
-
-    // assume 32 threads
-    const int tid = item_ct1.get_local_id(2);
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int ib = 8*i + ir;
-    if (ib >= nb32) {
-        return;
-    }
-
-    dst_t * y = yy + 256*i + 32*ir + 4*il;
-
-    const block_q4_1 * x = (const block_q4_1 *)vx + ib;
-    const sycl::float2 d =
-        x->dm.convert<float, sycl::rounding_mode::automatic>();
-
-    const uint8_t * q = x->qs + 4*il;
-
-    for (int l = 0; l < 4; ++l) {
-        y[l + 0] = d.x() * (q[l] & 0xF) + d.y();
-        y[l + 16] = d.x() * (q[l] >> 4) + d.y();
-    }
-}
-
-
-//================================== k-quants
-
-template<typename dst_t>
-static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                  const sycl::nd_item<3> &item_ct1) {
-
-    const int i = item_ct1.get_group(2);
-    const block_q2_K * x = (const block_q2_K *) vx;
-
-    const int tid = item_ct1.get_local_id(2);
-#if QK_K == 256
-    const int n   = tid/32;
-    const int l   = tid - 32*n;
-    const int is  = 8*n + l/16;
-
-    const uint8_t q = x[i].qs[32*n + l];
-    dst_t * y = yy + i*QK_K + 128*n;
-
-    float dall = x[i].dm[0];
-    float dmin = x[i].dm[1];
-    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
-    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
-    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
-    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
-#else
-    const int is = tid/16;  // 0 or 1
-    const int il = tid%16;  // 0...15
-    const uint8_t q = x[i].qs[il] >> (2*is);
-    dst_t * y = yy + i*QK_K + 16*is + il;
-
-    float dall = x[i].dm[0];
-    float dmin = x[i].dm[1];
-    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
-    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
-#endif
-
-}
-
-template<typename dst_t>
-static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                  const sycl::nd_item<3> &item_ct1) {
-
-    const int i = item_ct1.get_group(2);
-    const block_q3_K * x = (const block_q3_K *) vx;
-
-#if QK_K == 256
-    const int r = item_ct1.get_local_id(2) / 4;
-    const int tid = r/2;
-    const int is0 = r%2;
-    const int l0 = 16 * is0 + 4 * (item_ct1.get_local_id(2) % 4);
-    const int n = tid / 4;
-    const int j = tid - 4*n;
-
-    uint8_t m = 1 << (4*n + j);
-    int is = 8*n + 2*j + is0;
-    int shift = 2*j;
-
-    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
-                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
-                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
-                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
-    float d_all = x[i].d;
-    float dl = d_all * (us - 32);
-
-    dst_t * y = yy + i*QK_K + 128*n + 32*j;
-    const uint8_t * q = x[i].qs + 32*n;
-    const uint8_t * hm = x[i].hmask;
-
-    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
-#else
-    const int tid = item_ct1.get_local_id(2);
-    const int is  = tid/16;  // 0 or 1
-    const int il  = tid%16;  // 0...15
-    const int im  = il/8;    // 0...1
-    const int in  = il%8;    // 0...7
-
-    dst_t * y = yy + i*QK_K + 16*is + il;
-
-    const uint8_t q = x[i].qs[il] >> (2*is);
-    const uint8_t h = x[i].hmask[in] >> (2*is + im);
-    const float   d = (float)x[i].d;
-
-    if (is == 0) {
-        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
-        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
-    } else {
-        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
-        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
-    }
-#endif
-
-}
-
-#if QK_K == 256
-static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
-    if (j < 4) {
-        d = q[j] & 63; m = q[j + 4] & 63;
-    } else {
-        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
-        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
-    }
-}
-#endif
-
-template<typename dst_t>
-static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                  const sycl::nd_item<3> &item_ct1) {
-    const block_q4_K * x = (const block_q4_K *) vx;
-
-    const int i = item_ct1.get_group(2);
-
-#if QK_K == 256
-    // assume 32 threads
-    const int tid = item_ct1.get_local_id(2);
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int is  = 2*il;
-    const int n   = 4;
-
-    dst_t * y = yy + i*QK_K + 64*il + n*ir;
-
-    const float dall = x[i].dm[0];
-    const float dmin = x[i].dm[1];
-
-    const uint8_t * q = x[i].qs + 32*il + n*ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
-    for (int l = 0; l < n; ++l) {
-        y[l + 0] = d1 * (q[l] & 0xF) - m1;
-        y[l +32] = d2 * (q[l] >>  4) - m2;
-    }
-#else
-    const int tid = item_ct1.get_local_id(2);
-    const uint8_t * q = x[i].qs;
-    dst_t * y = yy + i*QK_K;
-    const float d = (float)x[i].dm[0];
-    const float m = (float)x[i].dm[1];
-    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
-    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                  const sycl::nd_item<3> &item_ct1) {
-    const block_q5_K * x = (const block_q5_K *) vx;
-
-    const int i = item_ct1.get_group(2);
-
-#if QK_K == 256
-    // assume 64 threads - this is very slightly better than the one below
-    const int tid = item_ct1.get_local_id(2);
-    const int il  = tid/16;   // il is in 0...3
-    const int ir  = tid%16;   // ir is in 0...15
-    const int is  = 2*il;     // is is in 0...6
-
-    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
-
-    const float dall = x[i].dm[0];
-    const float dmin = x[i].dm[1];
-
-    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
-    const uint8_t * qh = x[i].qh + 2*ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
-
-    uint8_t   hm  = 1 << (2*il);
-    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
-    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
-    hm <<= 1;
-    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
-    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
-#else
-    const int tid = item_ct1.get_local_id(2);
-    const uint8_t q = x[i].qs[tid];
-    const int im = tid/8;  // 0...3
-    const int in = tid%8;  // 0...7
-    const int is = tid/16; // 0 or 1
-    const uint8_t h = x[i].qh[in] >> im;
-    const float d = x[i].d;
-    dst_t * y = yy + i*QK_K + tid;
-    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
-    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                  const sycl::nd_item<3> &item_ct1) {
-    const block_q6_K * x = (const block_q6_K *) vx;
-
-    const int i = item_ct1.get_group(2);
-#if QK_K == 256
-
-    // assume 64 threads - this is very slightly better than the one below
-    const int tid = item_ct1.get_local_id(2);
-    const int ip  = tid/32;   // ip is 0 or 1
-    const int il  = tid - 32*ip; // 0...32
-    const int is  = 8*ip + il/16;
-
-    dst_t * y = yy + i*QK_K + 128*ip + il;
-
-    const float d = x[i].d;
-
-    const uint8_t * ql = x[i].ql + 64*ip + il;
-    const uint8_t   qh = x[i].qh[32*ip + il];
-    const int8_t  * sc = x[i].scales + is;
-
-    y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
-    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
-    y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
-    y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
-#else
-
-    // assume 32 threads
-    const int tid = item_ct1.get_local_id(2);
-    const int ip  = tid/16;         // 0 or 1
-    const int il  = tid - 16*ip;    // 0...15
-
-    dst_t * y = yy + i*QK_K + 16*ip + il;
-
-    const float d = x[i].d;
-
-    const uint8_t   ql = x[i].ql[16*ip + il];
-    const uint8_t   qh = x[i].qh[il] >> (2*ip);
-    const int8_t  * sc = x[i].scales;
-
-    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
-    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                     const sycl::nd_item<3> &item_ct1,
-                                     const uint64_t *iq2xxs_grid_ptr,
-                                     const uint8_t *ksigns_iq2xs_ptr,
-                                     const uint8_t *kmask_iq2xs_ptr) {
-
-    const int i = item_ct1.get_group(2);
-    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
-
-    const int tid = item_ct1.get_local_id(2);
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * q2 = x[i].qs + 4*ib;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid_ptr + aux8[il]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
-    const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127];
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f);
-#else
-    assert(false);
-#endif
-
-}
-
-template<typename dst_t>
-static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                    const sycl::nd_item<3> &item_ct1,
-                                    const uint64_t *iq2xs_grid,
-                                    const uint8_t *ksigns_iq2xs,
-                                    const uint8_t *kmask_iq2xs) {
-
-    const int i = item_ct1.get_group(2);
-    const block_iq2_xs * x = (const block_iq2_xs *) vx;
-
-    const int tid = item_ct1.get_local_id(2);
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * q2 = x[i].qs + 4*ib;
-    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
-    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
-    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
-#else
-    assert(false);
-#endif
-
-}
-
-template <typename dst_t>
-__dpct_inline__ static void
-dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
-                       const sycl::nd_item<3> &item_ct1) {
-
-    const int i = item_ct1.get_group(2);
-    const block_iq2_s * x = (const block_iq2_s *) vx;
-
-    const int tid = item_ct1.get_local_id(2);
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
-    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
-    const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
-#pragma unroll
-    for (int j = 0; j < 8; ++j)
-        y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
-#else
-    assert(false);
-
-#endif
-
-}
-
-template<typename dst_t>
-static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                     const sycl::nd_item<3> &item_ct1,
-                                     const uint32_t *iq3xxs_grid,
-                                     const uint8_t *ksigns_iq2xs,
-                                     const uint8_t *kmask_iq2xs) {
-
-    const int i = item_ct1.get_group(2);
-    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
-
-    const int tid = item_ct1.get_local_id(2);
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint8_t  * q3 = x[i].qs + 8*ib;
-    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
-    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
-    const uint32_t aux32 = gas[0] | (gas[1] << 16);
-    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
-    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
-    for (int j = 0; j < 4; ++j) {
-        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
-        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
-    }
-#else
-    assert(false);
-#endif
-
-}
-
-template <typename dst_t>
-__dpct_inline__ static void
-dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
-                       const sycl::nd_item<3> &item_ct1,
-                       const uint8_t *kmask_iq2xs, const uint32_t *iq3s_grid) {
-
-    const int i = item_ct1.get_group(2);
-    const block_iq3_s * x = (const block_iq3_s *) vx;
-
-    const int tid = item_ct1.get_local_id(2);
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint8_t * qs = x[i].qs + 8*ib;
-    const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
-    const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
-    const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf));
-    const uint8_t signs = x[i].signs[4*ib + il];
-#pragma unroll
-    for (int j = 0; j < 4; ++j) {
-        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
-        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
-    }
-#else
-    assert(false);
-#endif
-
-}
-
-template <typename dst_t>
-__dpct_inline__ static void
-dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
-                       const sycl::nd_item<3> &item_ct1,
-                       const uint32_t *iq1s_grid_gpu) {
-
-    const int i = item_ct1.get_group(2);
-    const block_iq1_s * x = (const block_iq1_s  *) vx;
-
-    const int tid = item_ct1.get_local_id(2);
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
-    const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1);
-    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
-    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)];
-    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
-    grid32[0] &= 0x0f0f0f0f;
-#pragma unroll
-    for (int j = 0; j < 8; ++j) {
-        y[j] = d * (q[j] + delta);
-    }
-#else
-    assert(false);
-#endif
-
-}
-
-template <typename dst_t>
-__dpct_inline__ static void
-dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy,
-                       const sycl::nd_item<3> &item_ct1,
-                       const uint32_t *iq1s_grid_gpu) {
-
-    const int i = item_ct1.get_group(2);
-    const block_iq1_m * x = (const block_iq1_m  *) vx;
-
-    const int tid = item_ct1.get_local_id(2);
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * sc = (const uint16_t *)x[i].scales;
-    iq1m_scale_t scale;
-    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-    const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
-    const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
-    const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
-    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
-    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
-    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
-    grid32[0] &= 0x0f0f0f0f;
-#pragma unroll
-    for (int j = 0; j < 8; ++j) {
-        y[j] = d * (q[j] + delta);
-    }
-#else
-    assert(false);
-#endif
-
-}
-
-template <typename dst_t>
-__dpct_inline__ static void
-dequantize_block_iq4_nl(const void *__restrict__ vx, dst_t *__restrict__ yy,
-                        const sycl::nd_item<3> &item_ct1) {
-
-    const int i = item_ct1.get_group(2);
-    const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);
-
-    const int tid = item_ct1.get_local_id(2);
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
-    const uint8_t  * q4 = x[ib].qs + 4*il;
-    const float d = (float)x[ib].d;
-#pragma unroll
-    for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
-        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
-    }
-
-}
-
-
-template <typename dst_t>
-__dpct_inline__ static void
-dequantize_block_iq4_xs(const void *__restrict__ vx, dst_t *__restrict__ yy,
-                        const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_group(2);
-    const block_iq4_xs * x = (const block_iq4_xs *)vx;
-
-    const int tid = item_ct1.get_local_id(2);
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
-    const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
-    const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
-#pragma unroll
-    for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
-        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
-    }
-}
-
-
-
-/*
-DPCT1110:4: The total declared local variable size in device function
-dequantize_mul_mat_vec_q2_k exceeds 128 bytes and may cause high register
-pressure. Consult with your hardware vendor to find the total register size
-available and adjust the code, or use smaller sub-group size to avoid high
-register pressure.
-*/
-static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
-                                        const float *__restrict__ yy,
-                                        float *__restrict__ dst,
-                                        const int ncols, int nrows,
-                                        const sycl::nd_item<3> &item_ct1) {
-
-    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
-
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q2_K * x = (const block_q2_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-    const int tid =
-        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...15
-    const int ix =
-        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;
-
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0...15 or 0...7
-
-    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
-    const int q_offset = 32*im + l0;
-    const int s_offset = 8*im;
-    const int y_offset = 128*im + l0;
-
-    uint32_t aux[4];
-    const uint8_t * d = (const uint8_t *)aux;
-    const uint8_t * m = (const uint8_t *)(aux + 2);
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + y_offset;
-        const uint8_t * q = x[i].qs + q_offset;
-
-        const float dall = x[i].dm[0];
-        const float dmin = x[i].dm[1];
-
-        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
-        aux[0] = a[0] & 0x0f0f0f0f;
-        aux[1] = a[1] & 0x0f0f0f0f;
-        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
-        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
-
-        float sum1 = 0, sum2 = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
-                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
-                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
-                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
-                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
-                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
-                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
-                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
-            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
-                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
-
-        }
-        tmp += dall * sum1 - dmin * sum2;
-
-    }
-#else
-    const int tid = item_ct1.get_local_id(2) /
-                    (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7
-    const int ix = item_ct1.get_local_id(2) %
-                   (2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3
-    const int offset = tid * K_QUANTS_PER_ITERATION;
-
-    uint32_t uaux[2];
-    const uint8_t * d = (const uint8_t *)uaux;
-
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + offset;
-        const uint8_t * q = x[i].qs + offset;
-        const uint32_t * s = (const uint32_t *)x[i].scales;
-
-        uaux[0] = s[0] & 0x0f0f0f0f;
-        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
-
-        const sycl::float2 dall =
-            x[i].dm.convert<float, sycl::rounding_mode::automatic>();
-
-        float sum1 = 0, sum2 = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            const uint8_t ql = q[l];
-            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
-                  + y[l+16] * d[1] * ((ql >> 2) & 3)
-                  + y[l+32] * d[2] * ((ql >> 4) & 3)
-                  + y[l+48] * d[3] * ((ql >> 6) & 3);
-            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
-        }
-        tmp += dall.x() * sum1 - dall.y() * sum2;
-    }
-
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-/*
-DPCT1110:5: The total declared local variable size in device function
-dequantize_mul_mat_vec_q3_k exceeds 128 bytes and may cause high register
-pressure. Consult with your hardware vendor to find the total register size
-available and adjust the code, or use smaller sub-group size to avoid high
-register pressure.
-*/
-static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx,
-                                        const float *__restrict__ yy,
-                                        float *__restrict__ dst,
-                                        const int ncols, int nrows,
-                                        const sycl::nd_item<3> &item_ct1) {
-
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q3_K * x = (const block_q3_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-
-    const uint16_t kmask1 = 0x0303;
-    const uint16_t kmask2 = 0x0f0f;
-
-    const int tid =
-        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
-    const int ix =
-        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
-
-    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
-    const int step = 16/K_QUANTS_PER_ITERATION;
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0....15 or 0...7
-
-    const uint8_t m = 1 << (4*im);
-
-    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
-    const int q_offset =  32*im + l0;
-    const int y_offset = 128*im + l0;
-
-    uint16_t utmp[4];
-    const int8_t * s = (const int8_t *)utmp;
-
-    const uint16_t s_shift = 4*im;
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + y_offset;
-        const uint8_t * q = x[i].qs + q_offset;
-        const uint8_t * h = x[i].hmask + l0;
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
-        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
-        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
-        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
-
-        const float d = x[i].d;
-
-        float sum = 0;
-        for (int l = 0; l < n; ++l) {
-            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
-                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
-                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
-                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
-            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
-                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
-                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
-                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
-        }
-        tmp += d * sum;
-
-    }
-#else
-
-    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
-    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
-    const int in = offset/8;                                 // 0 or 1
-    const int im = offset%8;                                 // 0...7
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + offset;
-        const uint8_t * q = x[i].qs + offset;
-        const uint8_t * s = x[i].scales;
-
-        const float dall = (float)x[i].d;
-
-        float sum = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            const uint8_t hl = x[i].hmask[im+l] >> in;
-            const uint8_t ql = q[l];
-            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
-                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
-                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
-                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
-        }
-        tmp += sum;
-    }
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-/*
-DPCT1110:6: The total declared local variable size in device function
-dequantize_mul_mat_vec_q4_k exceeds 128 bytes and may cause high register
-pressure. Consult with your hardware vendor to find the total register size
-available and adjust the code, or use smaller sub-group size to avoid high
-register pressure.
-*/
-static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
-                                        const float *__restrict__ yy,
-                                        float *__restrict__ dst,
-                                        const int ncols, int nrows,
-                                        const sycl::nd_item<3> &item_ct1) {
-
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-    if (row > nrows) return;
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q4_K * x = (const block_q4_K *)vx + ib0;
-
-#if QK_K == 256
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
-    const int tid =
-        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
-    const int ix =
-        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
-
-    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4
-
-    const int il  = tid/step;                            // 0...3
-    const int ir  = tid - step*il;                       // 0...7 or 0...3
-    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4
-
-    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int in = il%2;
-
-    const int l0 = n*(2*ir + in);
-    const int q_offset = 32*im + l0;
-    const int y_offset = 64*im + l0;
-
-    uint16_t aux[4];
-    const uint8_t * sc = (const uint8_t *)aux;
-
-#if K_QUANTS_PER_ITERATION == 2
-    uint32_t q32[4];
-    const uint8_t * q4 = (const uint8_t *)q32;
-#else
-    uint16_t q16[4];
-    const uint8_t * q4 = (const uint8_t *)q16;
-#endif
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y1 = yy + i*QK_K + y_offset;
-        const float   * y2 = y1 + 128;
-
-        const float dall = x[i].dm[0];
-        const float dmin = x[i].dm[1];
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux[0] = a[im+0] & kmask1;
-        aux[1] = a[im+2] & kmask1;
-        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
-        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
-#if K_QUANTS_PER_ITERATION == 2
-        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
-        const uint32_t * q2 = q1 + 16;
-
-        q32[0] = q1[0] & 0x0f0f0f0f;
-        q32[1] = q1[0] & 0xf0f0f0f0;
-        q32[2] = q2[0] & 0x0f0f0f0f;
-        q32[3] = q2[0] & 0xf0f0f0f0;
-
-        sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        for (int l = 0; l < 4; ++l) {
-            s.x() += y1[l] * q4[l + 0]; s.y() += y1[l + 32] * q4[l + 4];
-            s.z() += y2[l] * q4[l + 8]; s.w() += y2[l + 32] * q4[l + 12];
-            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
-        }
-        tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f / 16.f +
-                       s.z() * sc[4] + s.w() * sc[5] * 1.f / 16.f) -
-               dmin * smin;
-#else
-        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
-        const uint16_t * q2 = q1 + 32;
-
-        q16[0] = q1[0] & 0x0f0f;
-        q16[1] = q1[0] & 0xf0f0;
-        q16[2] = q2[0] & 0x0f0f;
-        q16[3] = q2[0] & 0xf0f0;
-
-        float4 s = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        for (int l = 0; l < 2; ++l) {
-            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
-            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
-            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
-        }
-        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
-#endif
-
-    }
-#else
-    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
-
-    const int step = tid * K_QUANTS_PER_ITERATION;
-
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
-
-    float tmp = 0;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-        const uint8_t * q = x[i].qs + step;
-        const float   * y = yy + i*QK_K + step;
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux16[0] = a[0] & 0x0f0f;
-        aux16[1] = (a[0] >> 4) & 0x0f0f;
-        const float d = (float)x[i].dm[0];
-        const float m = (float)x[i].dm[1];
-        float sum = 0.f;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
-                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
-                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
-                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
-        }
-        tmp += sum;
-    }
-
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (tid == 0) {
-        dst[row] = tmp;
-    }
-}
-
-/*
-DPCT1110:7: The total declared local variable size in device function
-dequantize_mul_mat_vec_q5_k exceeds 128 bytes and may cause high register
-pressure. Consult with your hardware vendor to find the total register size
-available and adjust the code, or use smaller sub-group size to avoid high
-register pressure.
-*/
-static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx,
-                                        const float *__restrict__ yy,
-                                        float *__restrict__ dst,
-                                        const int ncols,
-                                        const sycl::nd_item<3> &item_ct1) {
-
-    const int row = item_ct1.get_group(2);
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q5_K * x = (const block_q5_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
-    const int tid = item_ct1.get_local_id(2) / 2; // 0...15
-    const int ix = item_ct1.get_local_id(2) % 2;
-
-    const int il  = tid/4;     // 0...3
-    const int ir  = tid - 4*il;// 0...3
-    const int n   = 2;
-
-    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int in = il%2;
-
-    const int l0 = n*(2*ir + in);
-    const int q_offset = 32*im + l0;
-    const int y_offset = 64*im + l0;
-
-    const uint8_t hm1  = 1 << (2*im);
-    const uint8_t hm2  = hm1 << 4;
-
-    uint16_t aux[4];
-    const uint8_t * sc = (const uint8_t *)aux;
-
-    uint16_t q16[8];
-    const uint8_t * q4 = (const uint8_t *)q16;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2) {
-
-        const uint8_t * ql1 = x[i].qs + q_offset;
-        const uint8_t * qh  = x[i].qh + l0;
-        const float   * y1  = yy + i*QK_K + y_offset;
-        const float   * y2  = y1 + 128;
-
-        const float dall = x[i].dm[0];
-        const float dmin = x[i].dm[1];
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux[0] = a[im+0] & kmask1;
-        aux[1] = a[im+2] & kmask1;
-        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
-        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
-        sycl::float4 sum = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        const uint16_t * q1 = (const uint16_t *)ql1;
-        const uint16_t * q2 = q1 + 32;
-        q16[0] = q1[0] & 0x0f0f;
-        q16[1] = q1[8] & 0x0f0f;
-        q16[2] = (q1[0] >> 4) & 0x0f0f;
-        q16[3] = (q1[8] >> 4) & 0x0f0f;
-        q16[4] = q2[0] & 0x0f0f;
-        q16[5] = q2[8] & 0x0f0f;
-        q16[6] = (q2[0] >> 4) & 0x0f0f;
-        q16[7] = (q2[8] >> 4) & 0x0f0f;
-        for (int l = 0; l < n; ++l) {
-            sum.x() +=
-                y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) +
-                y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0));
-            sum.y() +=
-                y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) +
-                y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0));
-            sum.z() +=
-                y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) +
-                y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0));
-            sum.w() +=
-                y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) +
-                y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0));
-            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
-                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
-        }
-        tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] +
-                       sum.w() * sc[5]) -
-               dmin * smin;
-    }
-
-#else
-    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
-    const int step = tid * K_QUANTS_PER_ITERATION;
-    const int im = step/8;
-    const int in = step%8;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-        const uint8_t * q = x[i].qs + step;
-        const int8_t  * s = x[i].scales;
-        const float   * y = yy + i*QK_K + step;
-        const float     d = x[i].d;
-        float sum = 0.f;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            const uint8_t h = x[i].qh[in+j] >> im;
-            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
-                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
-                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
-                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
-        }
-        tmp += sum;
-    }
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows,
-                                        const sycl::nd_item<3> &item_ct1) {
-
-    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
-
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q6_K * x = (const block_q6_K *)vx + ib0;
-
-#if QK_K == 256
-
-    const int tid =
-        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
-    const int ix =
-        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0, 1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
-
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0...15 or 0...7
-
-#if K_QUANTS_PER_ITERATION == 1
-    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
-    const int is = 0;
-#else
-    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
-    const int is = in / 4;
-#endif
-    const int ql_offset = 64*im + l0;
-    const int qh_offset = 32*im + l0;
-    const int s_offset  =  8*im + is;
-    const int y_offset = 128*im + l0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + y_offset;
-        const uint8_t * ql = x[i].ql + ql_offset;
-        const uint8_t * qh = x[i].qh + qh_offset;
-        const int8_t  * s  = x[i].scales + s_offset;
-
-        const float d = x[i].d;
-
-#if K_QUANTS_PER_ITERATION == 1
-        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
-                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
-                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
-                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
-                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
-                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
-                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
-                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
-        tmp += sum;
-#else
-        float sum = 0;
-        for (int l = 0; l < 4; ++l) {
-            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
-                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
-                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
-                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
-        }
-        tmp += sum;
-#endif
-
-    }
-
-#else
-
-    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...7
-    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);  // 0...3
-
-    const int step = tid * K_QUANTS_PER_ITERATION;
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + step;
-        const uint8_t * ql = x[i].ql + step;
-        const uint8_t * qh = x[i].qh + step;
-        const int8_t  * s  = x[i].scales;
-
-        const float d = x[i+0].d;
-
-        float sum = 0;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
-                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
-                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
-                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
-        }
-        tmp += sum;
-
-    }
-
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (tid == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const sycl::half *x = (const sycl::half *)vx;
-
-    // automatic half -> float type cast if dfloat == float
-    v.x() = x[ib + iqs + 0];
-    v.y() = x[ib + iqs + 1];
-}
-
-static void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const float * x = (const float *) vx;
-
-    // automatic half -> float type cast if dfloat == float
-    v.x() = x[ib + iqs + 0];
-    v.y() = x[ib + iqs + 1];
-}
-
-static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded,
-                          const sycl::nd_item<3> &item_ct1) {
-    const int ix = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                   item_ct1.get_local_id(2);
-
-    if (ix >= kx_padded) {
-        return;
-    }
-
-    const int iy = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                   item_ct1.get_local_id(1);
-
-    const int i_padded = iy*kx_padded + ix;
-
-    block_q8_1 * y = (block_q8_1 *) vy;
-
-    const int ib = i_padded / QK8_1; // block index
-    const int iqs = i_padded % QK8_1; // quant index
-
-    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
-    float amax = sycl::fabs((float)xi);
-    float sum = xi;
-
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        amax = sycl::fmax(amax, dpct::permute_sub_group_by_xor(
-                                    item_ct1.get_sub_group(), amax, mask));
-        sum +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), sum, mask);
-    }
-
-    const float d = amax / 127;
-    const int8_t q = amax == 0.0f ? 0 : sycl::round(xi / d);
-
-    y[ib].qs[iqs] = q;
-
-    if (iqs > 0) {
-        return;
-    }
-
-    reinterpret_cast<sycl::half &>(y[ib].ds.x()) = d;
-    reinterpret_cast<sycl::half &>(y[ib].ds.y()) = sum;
-}
-
-template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static void k_get_rows(
-            const void * src0, const int32_t * src1, dst_t * dst,
-            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
-            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
-            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
-            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
-            size_t s10, size_t s11, size_t s12,
-            const sycl::nd_item<3> &item_ct1/*, size_t s13*/) {
-
-    const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) +
-                     item_ct1.get_local_id(2)) *
-                    2;
-    const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                    item_ct1.get_local_id(1);
-    const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
-                     item_ct1.get_local_id(0)) /
-                    ne12;
-    const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
-                     item_ct1.get_local_id(0)) %
-                    ne12;
-
-    if (i00 >= ne00) {
-        return;
-    }
-
-    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
-
-    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
-
-    const int ib = i00/qk; // block index
-    const int iqs = (i00%qk)/qr; // quant index
-    const int iybs = i00 - i00%qk; // dst block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    // dequantize
-    dfloat2 v;
-    dequantize_kernel(src0_row, ib, iqs, v);
-
-    dst_row[iybs + iqs + 0] = v.x();
-    dst_row[iybs + iqs + y_offset] = v.y();
-}
-
-template<typename src0_t, typename dst_t>
-static void k_get_rows_float(
-            const src0_t * src0, const int32_t * src1, dst_t * dst,
-            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
-            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
-            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
-            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
-            size_t s10, size_t s11, size_t s12,
-            const sycl::nd_item<3> &item_ct1/*, size_t s13*/) {
-
-    const int i00 = item_ct1.get_group(2) * item_ct1.get_local_range(2) +
-                    item_ct1.get_local_id(2);
-    const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                    item_ct1.get_local_id(1);
-    const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
-                     item_ct1.get_local_id(0)) /
-                    ne12;
-    const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
-                     item_ct1.get_local_id(0)) %
-                    ne12;
-
-    if (i00 >= ne00) {
-        return;
-    }
-
-    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
-
-    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
-
-    dst_row[i00] = src0_row[i00];
-}
-
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k,
-                             const sycl::nd_item<3> &item_ct1) {
-    const int i = 2 * (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                       item_ct1.get_local_id(2));
-
-    if (i >= k) {
-        return;
-    }
-
-    const int ib = i/qk; // block index
-    const int iqs = (i%qk)/qr; // quant index
-    const int iybs = i - i%qk; // y block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    // dequantize
-    dfloat2 v;
-    dequantize_kernel(vx, ib, iqs, v);
-
-    y[iybs + iqs + 0] = v.x();
-    y[iybs + iqs + y_offset] = v.y();
-}
-
-template <typename src_t, typename dst_t>
-static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k,
-                          const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-
-    const src_t * x = (src_t *) vx;
-
-    y[i] = x[i];
-}
-
-// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
-// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
-
-#define VDR_Q4_0_Q8_1_MMVQ 2
-#define VDR_Q4_0_Q8_1_MMQ  4
-
-template <int vdr>
-static __dpct_inline__ float vec_dot_q4_0_q8_1_impl(const int *v, const int *u,
-                                                    const float &d4,
-                                                    const sycl::half2 &ds8) {
-    int sumi = 0;
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
-        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
-
-        // SIMD dot product of quantized values
-        sumi = dpct::dp4a(vi0, u[2 * i + 0], sumi);
-        sumi = dpct::dp4a(vi1, u[2 * i + 1], sumi);
-    }
-
-    const sycl::float2 ds8f =
-        ds8.convert<float, sycl::rounding_mode::automatic>();
-
-    // second part effectively subtracts 8 from each quant value
-    return d4 * (sumi * ds8f.x() - (8 * vdr / QI4_0) * ds8f.y());
-}
-
-#define VDR_Q4_1_Q8_1_MMVQ 2
-#define VDR_Q4_1_Q8_1_MMQ  4
-
-template <int vdr>
-static __dpct_inline__ float vec_dot_q4_1_q8_1_impl(const int *v, const int *u,
-                                                    const sycl::half2 &dm4,
-                                                    const sycl::half2 &ds8) {
-
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
-        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
-
-        // SIMD dot product of quantized values
-        sumi = dpct::dp4a(vi0, u[2 * i + 0], sumi);
-        sumi = dpct::dp4a(vi1, u[2 * i + 1], sumi);
-    }
-
-#ifdef GGML_SYCL_F16
-    const sycl::float2 tmp =
-        (dm4 * ds8).convert<float, sycl::rounding_mode::automatic>();
-    const float d4d8 = tmp.x();
-    const float m4s8 = tmp.y();
-#else
-    const sycl::float2 dm4f =
-        dm4.convert<float, sycl::rounding_mode::automatic>();
-    const sycl::float2 ds8f =
-        ds8.convert<float, sycl::rounding_mode::automatic>();
-    const float d4d8 = dm4f.x() * ds8f.x();
-    const float m4s8 = dm4f.y() * ds8f.y();
-#endif // GGML_SYCL_F16
-
-    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
-    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
-}
-
-#define VDR_Q5_0_Q8_1_MMVQ 2
-#define VDR_Q5_0_Q8_1_MMQ  4
-
-template <int vdr>
-static __dpct_inline__ float
-vec_dot_q5_0_q8_1_impl(const int *vl, const int *vh, const int *u,
-                       const float &d5, const sycl::half2 &ds8) {
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
-        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
-        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
-        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
-        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = dpct::dp4a(vi0, u[2 * i + 0],
-                          sumi); // SIMD dot product of quantized values
-
-        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
-        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
-        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
-        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
-        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = dpct::dp4a(vi1, u[2 * i + 1],
-                          sumi); // SIMD dot product of quantized values
-    }
-
-    const sycl::float2 ds8f =
-        ds8.convert<float, sycl::rounding_mode::automatic>();
-
-    // second part effectively subtracts 16 from each quant value
-    return d5 * (sumi * ds8f.x() - (16 * vdr / QI5_0) * ds8f.y());
-}
-
-#define VDR_Q5_1_Q8_1_MMVQ 2
-#define VDR_Q5_1_Q8_1_MMQ  4
-
-template <int vdr>
-static __dpct_inline__ float
-vec_dot_q5_1_q8_1_impl(const int *vl, const int *vh, const int *u,
-                       const sycl::half2 &dm5, const sycl::half2 &ds8) {
-
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
-        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
-        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
-        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
-        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = dpct::dp4a(vi0, u[2 * i + 0],
-                          sumi); // SIMD dot product of quantized values
-
-        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
-        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
-        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
-        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
-        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = dpct::dp4a(vi1, u[2 * i + 1],
-                          sumi); // SIMD dot product of quantized values
-    }
-
-#ifdef GGML_SYCL_F16
-     const sycl::float2 tmp =
-        (dm5 * ds8).convert<float, sycl::rounding_mode::automatic>();
-    const float d5d8 = tmp.x();
-    const float m5s8 = tmp.y();
-
-
-#else
-    const sycl::float2 dm5f =
-        dm5.convert<float, sycl::rounding_mode::automatic>();
-    const sycl::float2 ds8f =
-        ds8.convert<float, sycl::rounding_mode::automatic>();
-    const float d5d8 = dm5f.x() * ds8f.x();
-    const float m5s8 = dm5f.y() * ds8f.y();
-#endif // GGML_SYCL_F16
-
-    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
-    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
-}
-
-#define VDR_Q8_0_Q8_1_MMVQ 2
-#define VDR_Q8_0_Q8_1_MMQ 8
-
-template <int vdr>
-static __dpct_inline__ float vec_dot_q8_0_q8_1_impl(const int *v, const int *u,
-                                                    const float &d8_0,
-                                                    const float &d8_1) {
-
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        // SIMD dot product of quantized values
-        sumi = dpct::dp4a(v[i], u[i], sumi);
-    }
-
-    return d8_0*d8_1 * sumi;
-}
-
-template <int vdr>
-static __dpct_inline__ float vec_dot_q8_1_q8_1_impl(const int *v, const int *u,
-                                                    const sycl::half2 &dm8,
-                                                    const sycl::half2 &ds8) {
-
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        // SIMD dot product of quantized values
-        sumi = dpct::dp4a(v[i], u[i], sumi);
-    }
-
-#ifdef GGML_SYCL_F16
-    const sycl::float2 tmp =
-        (dm8 * ds8).convert<float, sycl::rounding_mode::automatic>();
-    const float d8d8 = tmp.x();
-    const float m8s8 = tmp.y();
-#else
-    const sycl::float2 dm8f =
-        dm8.convert<float, sycl::rounding_mode::automatic>();
-    const sycl::float2 ds8f =
-        ds8.convert<float, sycl::rounding_mode::automatic>();
-    const float d8d8 = dm8f.x() * ds8f.x();
-    const float m8s8 = dm8f.y() * ds8f.y();
-#endif // GGML_SYCL_F16
-
-    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
-    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
-}
-
-#define VDR_Q2_K_Q8_1_MMVQ 1
-#define VDR_Q2_K_Q8_1_MMQ  2
-
-// contiguous v/x values
-static __dpct_inline__ float vec_dot_q2_K_q8_1_impl_mmvq(
-    const int &v, const int *__restrict__ u, const uint8_t *__restrict__ scales,
-    const sycl::half2 &dm2, const float *__restrict__ d8) {
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR2_K; ++i) {
-        const int sc = scales[2*i];
-
-        const int vi = (v >> (2*i)) & 0x03030303;
-
-        sumf_d +=
-            d8[i] * (dpct::dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
-
-        // fill int with 4x m
-        int m = sc >> 4;
-        m |= m <<  8;
-        m |= m << 16;
-        sumf_m += d8[i] *
-                  dpct::dp4a(
-                      m, u[i],
-                      0); // multiply constant q2_K part with sum of q8_1 values
-    }
-
-    const sycl::float2 dm2f =
-        dm2.convert<float, sycl::rounding_mode::automatic>();
-
-    return dm2f.x() * sumf_d - dm2f.y() * sumf_m;
-}
-
-// contiguous u/y values
-static __dpct_inline__ float
-vec_dot_q2_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
-                           const uint8_t *__restrict__ scales,
-                           const sycl::half2 &dm2, const float &d8) {
-
-    int sumi_d = 0;
-    int sumi_m = 0;
-
-#pragma unroll
-    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
-        int sumi_d_sc = 0;
-
-        const int sc = scales[i0 / (QI8_1/2)];
-
-        // fill int with 4x m
-        int m = sc >> 4;
-        m |= m <<  8;
-        m |= m << 16;
-
-#pragma unroll
-        for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            sumi_d_sc = dpct::dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
-            sumi_m = dpct::dp4a(m, u[i],
-                                sumi_m); // multiply sum of q8_1 values with m
-        }
-
-        sumi_d += sumi_d_sc * (sc & 0xF);
-    }
-
-    const sycl::float2 dm2f =
-        dm2.convert<float, sycl::rounding_mode::automatic>();
-
-    return d8 * (dm2f.x() * sumi_d - dm2f.y() * sumi_m);
-}
-
-#define VDR_Q3_K_Q8_1_MMVQ 1
-#define VDR_Q3_K_Q8_1_MMQ  2
-
-// contiguous v/x values
-static __dpct_inline__ float vec_dot_q3_K_q8_1_impl_mmvq(
-    const int &vl, const int &vh, const int *__restrict__ u,
-    const uint8_t *__restrict__ scales, const int &scale_offset,
-    const float &d3, const float *__restrict__ d8) {
-
-    float sumf = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR3_K; ++i) {
-        const int isc = scale_offset + 2*i;
-
-        const int isc_low = isc % (QK_K/32);
-        const int sc_shift_low = 4 * (isc / (QK_K/32));
-        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
-
-        const int isc_high = isc % (QK_K/64);
-        const int sc_shift_high = 2 * (isc / (QK_K/64));
-        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
-
-        const int sc = (sc_low | sc_high) - 32;
-
-        const int vil = (vl >> (2*i)) & 0x03030303;
-
-        const int vih = ((vh >> i) << 2) & 0x04040404;
-
-        const int vi =
-            dpct::vectorized_binary<sycl::char4>(vil, vih, dpct::sub_sat());
-
-        sumf += d8[i] * (dpct::dp4a(vi, u[i], 0) * sc); // SIMD dot product
-    }
-
-    return d3 * sumf;
-}
-
-// contiguous u/y values
-static __dpct_inline__ float
-vec_dot_q3_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
-                           const int8_t *__restrict__ scales, const float &d3,
-                           const float &d8) {
-
-    int sumi = 0;
-
-#pragma unroll
-    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
-        int sumi_sc = 0;
-
-        for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            sumi_sc = dpct::dp4a(v[i], u[i], sumi_sc); // SIMD dot product
-        }
-
-        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
-    }
-
-    return d3*d8 * sumi;
-}
-
-#define VDR_Q4_K_Q8_1_MMVQ 2
-#define VDR_Q4_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __dpct_inline__ float vec_dot_q4_K_q8_1_impl_vmmq(
-    const int *__restrict__ v, const int *__restrict__ u,
-    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
-    const sycl::half2 &dm4, const float *__restrict__ d8) {
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR4_K; ++i) {
-        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
-        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
-
-        const int dot1 =
-            dpct::dp4a(v1i, u[2 * i + 1],
-                       dpct::dp4a(v0i, u[2 * i + 0], 0)); // SIMD dot product
-        const int dot2 =
-            dpct::dp4a(0x01010101, u[2 * i + 1],
-                       dpct::dp4a(0x01010101, u[2 * i + 0], 0)); // sum of u
-
-        sumf_d += d8[i] * (dot1 * sc[i]);
-        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
-    }
-
-    const sycl::float2 dm4f =
-        dm4.convert<float, sycl::rounding_mode::automatic>();
-
-    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
-}
-
-// contiguous u/y values
-static __dpct_inline__ float vec_dot_q4_K_q8_1_impl_mmq(
-    const int *__restrict__ v, const int *__restrict__ u,
-    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
-    const sycl::half2 &dm4, const sycl::half2 *__restrict__ ds8) {
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
-        int sumi_d = 0;
-
-#pragma unroll
-        for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = dpct::dp4a((v[j] >> (4 * i)) & 0x0F0F0F0F,
-                                u[i * QI8_1 + j], sumi_d); // SIMD dot product
-        }
-
-        const sycl::float2 ds8f =
-            ds8[i].convert<float, sycl::rounding_mode::automatic>();
-
-        sumf_d += ds8f.x() * (sc[i] * sumi_d);
-        sumf_m += ds8f.y() * m[i]; // sum of q8_1 block * q4_K min val
-    }
-
-    const sycl::float2 dm4f =
-        dm4.convert<float, sycl::rounding_mode::automatic>();
-
-    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
-}
-
-#define VDR_Q5_K_Q8_1_MMVQ 2
-#define VDR_Q5_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __dpct_inline__ float vec_dot_q5_K_q8_1_impl_vmmq(
-    const int *__restrict__ vl, const int *__restrict__ vh,
-    const int *__restrict__ u, const uint8_t *__restrict__ sc,
-    const uint8_t *__restrict__ m, const sycl::half2 &dm5,
-    const float *__restrict__ d8) {
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K; ++i) {
-        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
-        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
-
-        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
-        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
-
-        const int v0i = vl0i | vh0i;
-        const int v1i = vl1i | vh1i;
-
-        const int dot1 =
-            dpct::dp4a(v0i, u[2 * i + 0],
-                       dpct::dp4a(v1i, u[2 * i + 1], 0)); // SIMD dot product
-        const int dot2 =
-            dpct::dp4a(0x01010101, u[2 * i + 0],
-                       dpct::dp4a(0x01010101, u[2 * i + 1], 0)); // sum of u
-
-        sumf_d += d8[i] * (dot1 * sc[i]);
-        sumf_m += d8[i] * (dot2 * m[i]);
-
-    }
-
-    const sycl::float2 dm5f =
-        dm5.convert<float, sycl::rounding_mode::automatic>();
-
-    return dm5f.x() * sumf_d - dm5f.y() * sumf_m;
-}
-
-// contiguous u/y values
-static __dpct_inline__ float vec_dot_q5_K_q8_1_impl_mmq(
-    const int *__restrict__ v, const int *__restrict__ u,
-    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
-    const sycl::half2 &dm4, const sycl::half2 *__restrict__ ds8) {
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
-        int sumi_d = 0;
-
-#pragma unroll
-        for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = dpct::dp4a(v[i * QI8_1 + j], u[i * QI8_1 + j],
-                                sumi_d); // SIMD dot product
-        }
-
-        const sycl::float2 ds8f =
-            ds8[i].convert<float, sycl::rounding_mode::automatic>();
-
-        sumf_d += ds8f.x() * (sc[i] * sumi_d);
-        sumf_m += ds8f.y() * m[i]; // sum of q8_1 block * q4_K min val
-    }
-
-    const sycl::float2 dm4f =
-        dm4.convert<float, sycl::rounding_mode::automatic>();
-
-    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
-}
-
-#define VDR_Q6_K_Q8_1_MMVQ 1
-#define VDR_Q6_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __dpct_inline__ float
-vec_dot_q6_K_q8_1_impl_mmvq(const int &vl, const int &vh,
-                            const int *__restrict__ u,
-                            const int8_t *__restrict__ scales, const float &d,
-                            const float *__restrict__ d8) {
-
-    float sumf = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR6_K; ++i) {
-        const int sc = scales[4*i];
-
-        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
-
-        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
-
-        const int vi = dpct::vectorized_binary<sycl::char4>(
-            (vil | vih), 0x20202020, dpct::sub_sat()); // vi = (vil | vih) - 32
-
-        sumf += d8[i] * (dpct::dp4a(vi, u[i], 0) * sc); // SIMD dot product
-    }
-
-    return d*sumf;
-}
-
-// contiguous u/y values
-static __dpct_inline__ float
-vec_dot_q6_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
-                           const int8_t *__restrict__ sc, const float &d6,
-                           const float *__restrict__ d8) {
-
-    float sumf_d = 0.0f;
-
-#pragma unroll
-    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
-        sycl::int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
-
-#pragma unroll
-        for (int i = i0; i < i0 + 2; ++i) {
-            sumi_d.x() = dpct::dp4a(v[2 * i + 0], u[2 * i + 0],
-                                    sumi_d.x()); // SIMD dot product
-            sumi_d.x() = dpct::dp4a(v[2 * i + 1], u[2 * i + 1],
-                                    sumi_d.x()); // SIMD dot product
-
-            sumi_d.y() = dpct::dp4a(v[2 * i + 4], u[2 * i + 4],
-                                    sumi_d.y()); // SIMD dot product
-            sumi_d.y() = dpct::dp4a(v[2 * i + 5], u[2 * i + 5],
-                                    sumi_d.y()); // SIMD dot product
-        }
-
-        sumf_d += d8[i0 / 4] *
-                  (sc[i0 / 2 + 0] * sumi_d.x() + sc[i0 / 2 + 1] * sumi_d.y());
-    }
-
-    return d6 * sumf_d;
-}
-
-static __dpct_inline__ float
-vec_dot_q4_0_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
-
-    int v[VDR_Q4_0_Q8_1_MMVQ];
-    int u[2*VDR_Q4_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
-        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
-    }
-
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q4_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_qs_q4_0, float *tile_x_d_q4_0) {
-    (void)x_qh; (void)x_sc;
-
-    *x_ql = tile_x_qs_q4_0;
-    *x_dm = (sycl::half2 *)tile_x_d_q4_0;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q4_0(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_0;
-    const int kqsx = k % QI4_0;
-
-    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
-
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
-        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
-    }
-}
-
-static __dpct_inline__ float vec_dot_q4_0_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const float * x_dmf = (const float *) x_dm;
-
-    int u[2*VDR_Q4_0_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
-    }
-
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
-         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __dpct_inline__ float
-vec_dot_q4_1_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
-
-    int v[VDR_Q4_1_Q8_1_MMVQ];
-    int u[2*VDR_Q4_1_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
-        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
-    }
-
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q4_1(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_qs_q4_1, sycl::half2 *tile_x_dm_q4_1) {
-    (void)x_qh; (void)x_sc;
-
-    *x_ql = tile_x_qs_q4_1;
-    *x_dm = tile_x_dm_q4_1;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q4_1(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_1;
-    const int kqsx = k % QI4_1;
-
-    const block_q4_1 * bx0 = (const block_q4_1 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
-        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
-    }
-}
-
-static __dpct_inline__ float vec_dot_q4_1_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-
-    int u[2*VDR_Q4_1_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
-    }
-
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
-         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __dpct_inline__ float
-vec_dot_q5_0_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
-
-    int vl[VDR_Q5_0_Q8_1_MMVQ];
-    int vh[VDR_Q5_0_Q8_1_MMVQ];
-    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
-        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
-        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
-    }
-
-    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q5_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_ql_q5_0, float *tile_x_d_q5_0) {
-    (void)x_qh; (void)x_sc;
-
-    *x_ql = tile_x_ql_q5_0;
-    *x_dm = (sycl::half2 *)tile_x_d_q5_0;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q5_0(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_0;
-    const int kqsx = k % QI5_0;
-
-    const block_q5_0 * bx0 = (const block_q5_0 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        const int ql = get_int_from_uint8(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
-
-        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
-        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
-        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
-        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
-        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
-        qs0 = dpct::vectorized_binary<sycl::char4>(
-            qs0, 0x10101010, dpct::sub_sat()); // subtract 16
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
-
-        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
-        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
-        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
-        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
-        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
-        qs1 = dpct::vectorized_binary<sycl::char4>(
-            qs1, 0x10101010, dpct::sub_sat()); // subtract 16
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
-        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
-    }
-}
-
-static __dpct_inline__ float vec_dot_q5_0_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    int u[2*VDR_Q5_0_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
-    }
-
-    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
-        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __dpct_inline__ float
-vec_dot_q5_1_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
-
-    int vl[VDR_Q5_1_Q8_1_MMVQ];
-    int vh[VDR_Q5_1_Q8_1_MMVQ];
-    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
-        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
-        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
-    }
-
-    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q5_1(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_ql_q5_1, sycl::half2 *tile_x_dm_q5_1) {
-    (void)x_qh; (void)x_sc;
-
-    *x_ql = tile_x_ql_q5_1;
-    *x_dm = tile_x_dm_q5_1;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q5_1(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset < nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_1;
-    const int kqsx = k % QI5_1;
-
-    const block_q5_1 * bx0 = (const block_q5_1 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
-
-        int qs0 = (ql >>  0) & 0x0F0F0F0F;
-        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
-        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
-        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
-        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
-
-        int qs1 = (ql >>  4) & 0x0F0F0F0F;
-        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
-        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
-        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
-        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
-        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
-    }
-}
-
-static __dpct_inline__ float vec_dot_q5_1_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
-
-    int u[2*VDR_Q5_1_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
-    }
-
-    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
-        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __dpct_inline__ float
-vec_dot_q8_0_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
-
-    int v[VDR_Q8_0_Q8_1_MMVQ];
-    int u[VDR_Q8_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
-        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
-        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-    }
-
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d,
-                                                      bq8_1->ds[0]);
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q8_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_qs_q8_0, float *tile_x_d_q8_0) {
-    (void)x_qh; (void)x_sc;
-
-    *x_ql = tile_x_qs_q8_0;
-    *x_dm = (sycl::half2 *)tile_x_d_q8_0;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q8_0(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI8_0;
-    const int kqsx = k % QI8_0;
-    float * x_dmf = (float *) x_dm;
-
-    const block_q8_0 * bx0 = (const block_q8_0 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
-        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
-    }
-}
-
-static __dpct_inline__ float vec_dot_q8_0_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh; (void)x_sc;
-
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
-         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
-}
-
-static __dpct_inline__ float
-vec_dot_q2_K_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
-
-    const int bq8_offset = QR2_K * (iqs / QI8_1);
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
-
-    const uint8_t * scales = bq2_K->scales + scale_offset;
-
-    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
-    int    u[QR2_K];
-    float d8[QR2_K];
-
-#pragma unroll
-    for (int i = 0; i < QR2_K; ++ i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + i].ds[0];
-    }
-
-    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q2_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_ql_q2_K, sycl::half2 *tile_x_dm_q2_K,
-                    int *tile_x_sc_q2_K) {
-    (void)x_qh;
-
-    *x_ql = tile_x_ql_q2_K;
-    *x_dm = tile_x_dm_q2_K;
-    *x_sc = tile_x_sc_q2_K;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q2_K(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh;
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI2_K;
-    const int kqsx = k % QI2_K;
-
-    const block_q2_K * bx0 = (const block_q2_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
-        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
-
-        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
-    }
-}
-
-static __dpct_inline__ float vec_dot_q2_K_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh;
-
-    const int kbx = k / QI2_K;
-    const int ky  = (k % QI2_K) * QR2_K;
-    const float * y_df = (const float *) y_ds;
-
-    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
-
-    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
-    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
-
-#pragma unroll
-    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
-        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
-    }
-
-    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
-
-    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
-    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
-}
-
-static __dpct_inline__ float
-vec_dot_q3_K_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
-
-    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
-
-    const float d = bq3_K->d;
-
-    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
-
-    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
-
-    int    u[QR3_K];
-    float d8[QR3_K];
-
-#pragma unroll
-    for (int i = 0; i < QR3_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + i].ds[0];
-    }
-
-    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q3_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_ql_q3_K, sycl::half2 *tile_x_dm_q3_K,
-                    int *tile_x_qh_q3_K, int *tile_x_sc_q3_K) {
-
-    *x_ql = tile_x_ql_q3_K;
-    *x_dm = tile_x_dm_q3_K;
-    *x_qh = tile_x_qh_q3_K;
-    *x_sc = tile_x_sc_q3_K;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q3_K(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI3_K;
-    const int kqsx = k % QI3_K;
-
-    const block_q3_K * bx0 = (const block_q3_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
-    const int kbxd = k % blocks_per_tile_x_row;
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
-        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
-        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
-
-        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
-
-        const int ksc = k % (QI3_K/4);
-
-        const int ksc_low = ksc % (QI3_K/8);
-        const int shift_low = 4 * (ksc / (QI3_K/8));
-        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
-
-        const int ksc_high = QI3_K/8;
-        const int shift_high = 2 * ksc;
-        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
-
-        const int sc = dpct::vectorized_binary<sycl::char4>(
-            sc_low | sc_high, 0x20202020, dpct::sub_sat());
-
-        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
-    }
-}
-
-static __dpct_inline__ float vec_dot_q3_K_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-
-    const int kbx  = k / QI3_K;
-    const int ky  = (k % QI3_K) * QR3_K;
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
-
-    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
-        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
-        const int shift = 2 * ((ky % 32) / 8);
-        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
-
-        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
-        const int vlh = (vh << 2) & 0x04040404;
-
-        v[l] = dpct::vectorized_binary<sycl::char4>(vll, vlh, dpct::sub_sat());
-    }
-
-    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
-    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
-}
-
-static __dpct_inline__ float
-vec_dot_q4_K_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-#ifndef GGML_QKK_64
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
-
-    int    v[2];
-    int    u[2*QR4_K];
-    float d8[QR4_K];
-
-    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
-    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
-
-    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
-    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
-    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
-    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
-
-    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
-    v[0] = q4[0];
-    v[1] = q4[4];
-
-    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
-    } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
-    }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
-
-    for (int i = 0; i < QR4_K; ++i) {
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = bq8i->ds[0];
-
-        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
-        u[2*i+0] = q8[0];
-        u[2*i+1] = q8[4];
-    }
-
-    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
-
-#else
-
-#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
-
-    const uint16_t * a = (const uint16_t *)bq4_K->scales;
-    aux16[0] = a[0] & 0x0f0f;
-    aux16[1] = (a[0] >> 4) & 0x0f0f;
-
-    const float dall = bq4_K->dm[0];
-    const float dmin = bq4_K->dm[1];
-
-    const float d8_1 = bq8_1[0].ds[0];
-    const float d8_2 = bq8_1[1].ds[1];
-
-    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
-    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
-    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
-
-    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
-    const int v1 = q4[0];
-    const int v2 = q4[4];
-
-    const int dot1 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0));
-    const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
-    const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0));
-    const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::dp4a(0x01010101, ui3, 0));
-
-    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
-    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
-
-    return dall * sumf_d - dmin * sumf_m;
-
-#else
-    bad_arch();
-#endif // __SYCL_ARCH__ >= VER_4VEC
-
-#endif
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q4_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_ql_q4_K, sycl::half2 *tile_x_dm_q4_K,
-                    int *tile_x_sc_q4_K) {
-    (void)x_qh;
-
-    *x_ql = tile_x_ql_q4_K;
-    *x_dm = tile_x_dm_q4_K;
-    *x_sc = tile_x_sc_q4_K;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh;
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI4_K; // == k if QK_K == 256
-
-    const block_q4_K * bx0 = (const block_q4_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
-        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-#if QK_K == 256
-        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
-#else
-        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
-#endif
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
-
-        const int * scales = (const int *) bxi->scales;
-
-        const int ksc = k % (WARP_SIZE/8);
-
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
-    }
-}
-
-static __dpct_inline__ float vec_dot_q4_K_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh;
-
-    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
-
-    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
-    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
-                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
-}
-
-static __dpct_inline__ float
-vec_dot_q5_K_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-#ifndef GGML_QKK_64
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
-
-    int   vl[2];
-    int   vh[2];
-    int    u[2*QR5_K];
-    float d8[QR5_K];
-
-    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
-    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
-    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
-
-    vl[0] = ql[0];
-    vl[1] = ql[4];
-
-    vh[0] = qh[0] >> bq8_offset;
-    vh[1] = qh[4] >> bq8_offset;
-
-    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
-    } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
-    }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K; ++i) {
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = bq8i->ds[0];
-
-        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
-        u[2*i+0] = q8[0];
-        u[2*i+1] = q8[4];
-    }
-
-    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
-
-#else
-
-#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
-
-    const int8_t * s = bq5_K->scales;
-
-    const float d = bq5_K->d;
-
-    const float d8_1 = bq8_1[0].ds[0];
-    const float d8_2 = bq8_1[1].ds[1];
-
-    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
-    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
-    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
-
-    const int * ql = (const int *)bq5_K->qs + (iqs/2);
-    const int vl1 = ql[0];
-    const int vl2 = ql[4];
-
-    const int step = 4 * (iqs/2); // 0, 4, 8, 12
-    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
-    const int in = step%8; // 0, 4, 0, 4
-    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
-
-    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
-    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
-    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
-    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
-
-    const float sumf_d = d8_1 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1])
-                       + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]);
-
-    return d * sumf_d;
-
-#else
-    bad_arch();
-#endif // __SYCL_ARCH__ >= VER_4VEC
-
-#endif
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q5_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_ql_q5_K, sycl::half2 *tile_x_dm_q5_K,
-                    int *tile_x_sc_q5_K) {
-    (void)x_qh;
-
-    *x_ql = tile_x_ql_q5_K;
-    *x_dm = tile_x_dm_q5_K;
-    *x_sc = tile_x_sc_q5_K;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh;
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI5_K; // == k if QK_K == 256
-
-    const block_q5_K * bx0 = (const block_q5_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
-        const int ky = QR5_K*kqsx;
-
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
-        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
-
-        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
-        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
-        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
-
-        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
-        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
-
-        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
-        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
-        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-#if QK_K == 256
-        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
-#endif
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
-
-        const int * scales = (const int *) bxi->scales;
-
-        const int ksc = k % (WARP_SIZE/8);
-
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
-    }
-}
-
-static __dpct_inline__ float vec_dot_q5_K_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh;
-
-    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
-
-    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
-    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
-    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
-                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
-}
-
-static __dpct_inline__ float
-vec_dot_q6_K_q8_1(const void *__restrict__ vbq,
-                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
-
-    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
-    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
-    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
-
-    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
-    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
-
-    const int8_t * scales = bq6_K->scales + scale_offset;
-
-    int    u[QR6_K];
-    float d8[QR6_K];
-
-#pragma unroll
-    for (int i = 0; i < QR6_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + 2 * i].ds[0];
-    }
-
-    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
-}
-
-template <int mmq_y>
-static __dpct_inline__ void
-allocate_tiles_q6_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
-                    int *tile_x_ql, sycl::half2 *tile_x_dm, int *tile_x_sc) {
-    (void)x_qh;
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check>
-static __dpct_inline__ void
-load_tiles_q6_K(const void *__restrict__ vx, int *__restrict__ x_ql,
-                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
-                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
-                const int &k, const int &blocks_per_row) {
-    (void)x_qh;
-
-    GGML_SYCL_ASSUME(i_offset >= 0);
-    GGML_SYCL_ASSUME(i_offset <  nwarps);
-    GGML_SYCL_ASSUME(k >= 0);
-    GGML_SYCL_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI6_K; // == k if QK_K == 256
-
-    const block_q6_K * bx0 = (const block_q6_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
-        const int ky = QR6_K*kqsx;
-
-        const int ql = get_int_from_uint8(bxi->ql, kqsx);
-        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
-        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
-
-        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
-        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
-        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
-
-        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
-        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
-
-        x_ql[i * (2 * WARP_SIZE + 1) + kq0] =
-            dpct::vectorized_binary<sycl::char4>(ql0 | qh0, 0x20202020,
-                                                 dpct::sub_sat());
-        x_ql[i * (2 * WARP_SIZE + 1) + kq1] =
-            dpct::vectorized_binary<sycl::char4>(ql1 | qh1, 0x20202020,
-                                                 dpct::sub_sat());
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
-        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = sycl::min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
-    }
-}
-
-static __dpct_inline__ float vec_dot_q6_K_q8_1_mul_mat(
-    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
-    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
-    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
-    const int &i, const int &j, const int &k) {
-    (void)x_qh;
-
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
-
-    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
-    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
-    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
-}
-
-
-static __dpct_inline__ float
-vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq,
-                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
-                     const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs,
-                     const uint8_t *kmask_iq2xs) {
-#if QK_K == 256
-    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
-
-#if QR2_XXS == 8
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = q2[2] | (q2[3] << 16);
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
-        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
-        for (int j = 0; j < 8; ++j) {
-            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-        }
-        q8 += 8;
-        aux32 >>= 7;
-    }
-    const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f;
-    return d * sumi;
-#else
-    // iqs is 0...15
-    const int ib32 = iqs/2;
-    const int il = iqs%2;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * bq8_1[ib32].ds[0] * 0.25f;
-    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
-    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
-    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
-    int sumi1 = 0, sumi2 = 0;
-    for (int j = 0; j < 8; ++j) {
-        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
-        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
-    }
-    return d * (sumi1 + sumi2);
-#endif
-#else
-    assert(false);
-    return 0.f;
-#endif
-}
-
-static __dpct_inline__ float
-vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq,
-                    const block_q8_1 *__restrict__ bq8_1, const int &iqs,
-                    const uint64_t *iq2xs_grid, const uint64_t *ksigns64) {
-#if DPCT_COMPATIBILITY_TEMP >=                                                 \
-    MIN_CC_DP4A // lowest compute capability for integer intrinsics
-#if QK_K == 256
-    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
-
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
-    const uint8_t ls2 = bq2->scales[ib32] >>  4;
-    int sumi1 = 0;
-    for (int l = 0; l < 2; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
-        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
-            grid[0] ^ signs[0], signs[0], std::minus<>());
-        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
-            grid[1] ^ signs[1], signs[1], std::minus<>());
-        sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1);
-        sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1);
-        q8 += 8;
-    }
-    int sumi2 = 0;
-    for (int l = 2; l < 4; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
-        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
-            grid[0] ^ signs[0], signs[0], std::minus<>());
-        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
-            grid[1] ^ signs[1], signs[1], std::minus<>());
-        sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2);
-        sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2);
-        q8 += 8;
-    }
-    const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f;
-    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
-#else
-    assert(false);
-    return 0.f;
-#endif
-#else
-    assert(false);
-    return 0.f;
-#endif
-}
-
-static __dpct_inline__ float
-vec_dot_iq2_s_q8_1(const void *__restrict__ vbq,
-                   const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-#if QK_K == 256
-    const block_iq2_s * bq2 = (const block_iq2_s *) vbq;
-
-    const int ib32 = iqs;
-    const int8_t  * q8 = bq8_1[ib32].qs;
-    const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
-    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
-    const uint8_t ls2 = bq2->scales[ib32] >>  4;
-    int sumi1 = 0;
-    for (int l = 0; l < 2; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
-        const uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
-            ((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201,
-            std::equal_to<>());
-        const uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
-            ((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201,
-            std::equal_to<>());
-        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
-            grid[0] ^ signs0, signs0, std::minus<>());
-        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
-            grid[1] ^ signs1, signs1, std::minus<>());
-        sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1);
-        sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1);
-        q8 += 8;
-    }
-    int sumi2 = 0;
-    for (int l = 2; l < 4; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
-        const uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
-            ((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201,
-            std::equal_to<>());
-        const uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
-            ((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201,
-            std::equal_to<>());
-        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
-            grid[0] ^ signs0, signs0, std::minus<>());
-        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
-            grid[1] ^ signs1, signs1, std::minus<>());
-        sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2);
-        sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2);
-        q8 += 8;
-    }
-    const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f;
-    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
-#else
-    assert(false);
-#endif
-}
-
-static __dpct_inline__ float
-vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq,
-                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
-                     const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) {
-#if DPCT_COMPATIBILITY_TEMP >=                                                 \
-    MIN_CC_DP4A // lowest compute capability for integer intrinsics
-#if QK_K == 256
-    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
-
-    const int ib32 = iqs;
-    const uint8_t  * q3 = bq2->qs + 8*ib32;
-    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = gas[0] | (gas[1] << 16);
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
-        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
-        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
-            grid1[0] ^ signs[0], signs[0], std::minus<>());
-        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
-            grid2[0] ^ signs[1], signs[1], std::minus<>());
-        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
-        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
-        q8 += 8;
-        aux32 >>= 7;
-    }
-    const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.5f;
-    return d * sumi;
-#else
-    assert(false);
-    return 0.f;
-#endif
-#else
-    assert(false);
-    return 0.f;
-#endif
-}
-
-static __dpct_inline__ float
-vec_dot_iq3_s_q8_1(const void *__restrict__ vbq,
-                   const block_q8_1 *__restrict__ bq8_1, const int &iqs,
-                   const uint32_t *iq3s_grid) {
-#if QK_K == 256
-    const block_iq3_s * bq2 = (const block_iq3_s *) vbq;
-
-    const int ib32 = iqs;
-    const uint8_t  * qs = bq2->qs + 8*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
-        const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
-        uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
-            ((bq2->signs[4 * ib32 + l] & 0xf) * 0x01010101) & 0x08040201,
-            0x08040201, std::equal_to<>());
-        uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
-            ((bq2->signs[4 * ib32 + l] >> 4) * 0x01010101) & 0x08040201,
-            0x08040201, std::equal_to<>());
-        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
-            grid1[0] ^ signs0, signs0, std::minus<>());
-        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
-            grid2[0] ^ signs1, signs1, std::minus<>());
-        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
-        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
-        q8 += 8;
-    }
-    const float d =
-        (float)bq2->d *
-        (1 + 2 * ((bq2->scales[ib32 / 2] >> 4 * (ib32 % 2)) & 0xf)) *
-        bq8_1[ib32].ds[0];
-    return d * sumi;
-#else
-    assert(false);
-#endif
-}
-
-static __dpct_inline__ float
-vec_dot_iq1_s_q8_1(const void *__restrict__ vbq,
-                   const block_q8_1 *__restrict__ bq8_1, const int &iqs,
-                   const uint32_t *iq1s_grid_gpu) {
-#if QK_K == 256
-    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
-
-    const int ib32 = iqs;
-    int sumi = 0;
-    const int * q8 = (const int *)bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
-        int grid0 = grid[0] & 0x0f0f0f0f;
-        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
-        sumi = dpct::dp4a(q8[2 * l + 1], grid1,
-                          dpct::dp4a(q8[2 * l + 0], grid0, sumi));
-    }
-
-    const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA;
-    const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1);
-    const float d = d1q * bq8_1[ib32].ds[0];
-    const float m = d1q * bq8_1[ib32].ds[1];
-    return d * sumi + m * delta;
-#else
-    assert(false);
-#endif
-}
-
-static __dpct_inline__ float
-vec_dot_iq1_m_q8_1(const void *__restrict__ vbq,
-                   const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-#if QK_K == 256
-    const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
-
-    const int ib32 = iqs;
-    int   sumi[2] = {0, 0};
-    float sumf[2] = {0.f, 0.f};
-
-    const int * q8 = (const int *)bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
-        int grid0 = grid[0] & 0x0f0f0f0f;
-        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
-        sumi[l / 2] = dpct::dp4a(q8[2 * l + 1], grid1,
-                                 dpct::dp4a(q8[2 * l + 0], grid0, sumi[l / 2]));
-        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
-        const int sumy = dpct::dp4a(q8[2 * l + 1], 0x01010101,
-                                    dpct::dp4a(q8[2 * l + 0], 0x01010101, 0));
-        sumf[l/2] += delta*sumy;
-    }
-
-    iq1m_scale_t scale;
-    const uint16_t * sc = (const uint16_t *)bq1->scales;
-    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-    const float d = (float)scale.f16 * bq8_1[ib32].ds[0];
-    return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
-#else
-    assert(false);
-#endif
-}
-
-static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4,
-                                                  const uint8_t *values,
-                                                  int &val1, int &val2) {
-
-    uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32;
-    aux32 = q4 & 0x0f0f0f0f;
-    uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8);
-    uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8);
-    val1 = v1 | (v2 << 16);
-    aux32 = (q4 >> 4) & 0x0f0f0f0f;
-    v1 = values[q8[0]] | (values[q8[1]] << 8);
-    v2 = values[q8[2]] | (values[q8[3]] << 8);
-    val2 = v1 | (v2 << 16);
-}
-
-
-static __dpct_inline__ float
-vec_dot_iq4_nl_q8_1(const void *__restrict__ vbq,
-                    const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-    const block_iq4_nl * bq = (const block_iq4_nl *) vbq;
-
-    const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs;
-    const int32_t  * q8 = (const int32_t  *)bq8_1->qs + iqs;
-
-    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
-
-    int v1, v2;
-    int sumi1 = 0, sumi2 = 0;
-    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
-        const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16);
-        get_int_from_table_16(aux, values, v1, v2);
-        sumi1 = dpct::dp4a(v1, q8[l + 0], sumi1);
-        sumi2 = dpct::dp4a(v2, q8[l + 4], sumi2);
-    }
-
-    const float d = (float)bq->d * bq8_1->ds[0];
-    return d * (sumi1 + sumi2);
-}
-
-
-static __dpct_inline__ float
-vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq,
-                    const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
-
-#if QK_K == 256
-    const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
-    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
-
-    // iqs is 0...7
-    const int ib32 = iqs;
-    const int32_t  * q8 = (const int *)bq8_1[ib32].qs;
-    const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
-    const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
-    const float d = (float)bq4->d * (ls - 32) * bq8_1[ib32].ds[0];
-    int v1, v2;
-    int sumi1 = 0, sumi2 = 0;
-    for (int j = 0; j < 4; ++j) {
-        get_int_from_table_16(q4[j], values, v1, v2);
-        sumi1 = dpct::dp4a(v1, q8[j + 0], sumi1);
-        sumi2 = dpct::dp4a(v2, q8[j + 4], sumi2);
-    }
-    return d * (sumi1 + sumi2);
-#else
-    assert(false);
-#endif
-}
-
-template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x,
-          int mmq_y, int nwarps, load_tiles_sycl_t load_tiles, int vdr,
-          vec_dot_q_mul_mat_sycl_t vec_dot>
-/*
-DPCT1110:8: The total declared local variable size in device function mul_mat_q
-exceeds 128 bytes and may cause high register pressure. Consult with your
-hardware vendor to find the total register size available and adjust the code,
-or use smaller sub-group size to avoid high register pressure.
-*/
-static __dpct_inline__ void
-mul_mat_q(const void *__restrict__ vx, const void *__restrict__ vy,
-          float *__restrict__ dst, const int ncols_x, const int nrows_x,
-          const int ncols_y, const int nrows_y, const int nrows_dst,
-          int *tile_x_ql, sycl::half2 *tile_x_dm, int *tile_x_qh,
-          int *tile_x_sc, const sycl::nd_item<3> &item_ct1, int *tile_y_qs,
-          sycl::half2 *tile_y_ds) {
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    const int blocks_per_row_x = ncols_x / qk;
-    const int blocks_per_col_y = nrows_y / QK8_1;
-    const int blocks_per_warp = WARP_SIZE / qi;
-
-    const int & ncols_dst = ncols_y;
-
-    const int row_dst_0 = item_ct1.get_group(2) * mmq_y;
-    const int & row_x_0 = row_dst_0;
-
-    const int col_dst_0 = item_ct1.get_group(1) * mmq_x;
-    const int & col_y_0 = col_dst_0;
-
-    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
-
-    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
-
-        load_tiles(x + row_x_0 * blocks_per_row_x + ib0, tile_x_ql, tile_x_dm,
-                   tile_x_qh, tile_x_sc, item_ct1.get_local_id(1),
-                   nrows_x - row_x_0 - 1, item_ct1.get_local_id(2),
-                   blocks_per_row_x);
-
-#pragma unroll
-        for (int ir = 0; ir < qr; ++ir) {
-            const int kqs = ir * WARP_SIZE + item_ct1.get_local_id(2);
-            const int kbxd = kqs / QI8_1;
-
-#pragma unroll
-            for (int i = 0; i < mmq_x; i += nwarps) {
-                const int col_y_eff = dpct::min(
-                    (unsigned int)(col_y_0 + item_ct1.get_local_id(1) + i),
-                    ncols_y - 1); // to prevent out-of-bounds memory accesses
-
-                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
-
-                const int index_y = (item_ct1.get_local_id(1) + i) * WARP_SIZE +
-                                    kqs % WARP_SIZE;
-                tile_y_qs[index_y] = get_int_from_int8_aligned(
-                    by0->qs, item_ct1.get_local_id(2) % QI8_1);
-            }
-
-#pragma unroll
-            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
-                const int ids =
-                    (ids0 + item_ct1.get_local_id(1) * QI8_1 +
-                     item_ct1.get_local_id(2) / (WARP_SIZE / QI8_1)) %
-                    mmq_x;
-                const int kby = item_ct1.get_local_id(2) % (WARP_SIZE / QI8_1);
-                const int col_y_eff = sycl::min(col_y_0 + ids, ncols_y - 1);
-
-                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
-                const sycl::half2 *dsi_src =
-                    &y[col_y_eff * blocks_per_col_y + ib0 * (qk / QK8_1) +
-                       ir * (WARP_SIZE / QI8_1) + kby]
-                         .ds;
-                sycl::half2 *dsi_dst =
-                    &tile_y_ds[ids * (WARP_SIZE / QI8_1) + kby];
-                if (need_sum) {
-                    *dsi_dst = *dsi_src;
-                } else {
-                    float * dfi_dst = (float *) dsi_dst;
-                    *dfi_dst = (*dsi_src)[0];
-                }
-            }
-
-            /*
-            DPCT1118:9: SYCL group functions and algorithms must be encountered
-            in converged control flow. You may need to adjust the code.
-            */
-            /*
-            DPCT1065:56: Consider replacing sycl::nd_item::barrier() with
-            sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-            better performance if there is no access to global memory.
-            */
-            item_ct1.barrier();
-
-// #pragma unroll // unrolling this loop causes too much register pressure
-            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
-#pragma unroll
-                for (int j = 0; j < mmq_x; j += nwarps) {
-#pragma unroll
-                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
-                        sum[i / WARP_SIZE][j / nwarps] += vec_dot(
-                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
-                            tile_y_qs, tile_y_ds, item_ct1.get_local_id(2) + i,
-                            item_ct1.get_local_id(1) + j, k);
-                    }
-                }
-            }
-
-            /*
-            DPCT1118:10: SYCL group functions and algorithms must be encountered
-            in converged control flow. You may need to adjust the code.
-            */
-            /*
-            DPCT1065:57: Consider replacing sycl::nd_item::barrier() with
-            sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-            better performance if there is no access to global memory.
-            */
-            item_ct1.barrier();
-        }
-    }
-
-#pragma unroll
-    for (int j = 0; j < mmq_x; j += nwarps) {
-        const int col_dst = col_dst_0 + j + item_ct1.get_local_id(1);
-
-        if (col_dst >= ncols_dst) {
-            return;
-        }
-
-#pragma unroll
-        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
-            const int row_dst = row_dst_0 + item_ct1.get_local_id(2) + i;
-
-            if (row_dst >= nrows_dst) {
-                continue;
-            }
-
-            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
-        }
-    }
-}
-
-#define  MMQ_X_Q4_0_RDNA2  64
-#define  MMQ_Y_Q4_0_RDNA2  128
-#define NWARPS_Q4_0_RDNA2  8
-#define  MMQ_X_Q4_0_RDNA1  64
-#define  MMQ_Y_Q4_0_RDNA1  64
-#define NWARPS_Q4_0_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q4_0_AMPERE 4
-#define  MMQ_Y_Q4_0_AMPERE 32
-#define NWARPS_Q4_0_AMPERE 4
-#else
-#define  MMQ_X_Q4_0_AMPERE 64
-#define  MMQ_Y_Q4_0_AMPERE 128
-#define NWARPS_Q4_0_AMPERE 4
-#endif
-#define  MMQ_X_Q4_0_PASCAL 64
-#define  MMQ_Y_Q4_0_PASCAL 64
-#define NWARPS_Q4_0_PASCAL 8
-
-template <bool need_check> static void
-    mul_mat_q4_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q4_0, float *tile_x_d_q4_0,
-    int *tile_y_qs, sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-
-    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
-    const int nwarps = NWARPS_Q4_0_AMPERE;
-    allocate_tiles_q4_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_qs_q4_0, tile_x_d_q4_0);
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps,
-              load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ,
-              vec_dot_q4_0_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q4_1_RDNA2  64
-#define  MMQ_Y_Q4_1_RDNA2  128
-#define NWARPS_Q4_1_RDNA2  8
-#define  MMQ_X_Q4_1_RDNA1  64
-#define  MMQ_Y_Q4_1_RDNA1  64
-#define NWARPS_Q4_1_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q4_1_AMPERE 4
-#define  MMQ_Y_Q4_1_AMPERE 32
-#define NWARPS_Q4_1_AMPERE 4
-#else
-#define  MMQ_X_Q4_1_AMPERE 64
-#define  MMQ_Y_Q4_1_AMPERE 128
-#define NWARPS_Q4_1_AMPERE 4
-#endif
-#define  MMQ_X_Q4_1_PASCAL 64
-#define  MMQ_Y_Q4_1_PASCAL 64
-#define NWARPS_Q4_1_PASCAL 8
-
-template <bool need_check> static void
-    mul_mat_q4_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q4_1,
-    sycl::half2 *tile_x_dm_q4_1, int *tile_y_qs, sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
-    const int nwarps = NWARPS_Q4_1_AMPERE;
-    allocate_tiles_q4_1<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_qs_q4_1, tile_x_dm_q4_1);
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps,
-              load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ,
-              vec_dot_q4_1_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q5_0_RDNA2  64
-#define  MMQ_Y_Q5_0_RDNA2  128
-#define NWARPS_Q5_0_RDNA2  8
-#define  MMQ_X_Q5_0_RDNA1  64
-#define  MMQ_Y_Q5_0_RDNA1  64
-#define NWARPS_Q5_0_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q5_0_AMPERE 4
-#define  MMQ_Y_Q5_0_AMPERE 32
-#define NWARPS_Q5_0_AMPERE 4
-#else
-#define  MMQ_X_Q5_0_AMPERE 128
-#define  MMQ_Y_Q5_0_AMPERE 64
-#define NWARPS_Q5_0_AMPERE 4
-#endif
-#define  MMQ_X_Q5_0_PASCAL 64
-#define  MMQ_Y_Q5_0_PASCAL 64
-#define NWARPS_Q5_0_PASCAL 8
-
-template <bool need_check> static void
-    mul_mat_q5_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_0, float *tile_x_d_q5_0,
-    int *tile_y_qs, sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
-    const int nwarps = NWARPS_Q5_0_AMPERE;
-    allocate_tiles_q5_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_ql_q5_0, tile_x_d_q5_0);
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps,
-              load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ,
-              vec_dot_q5_0_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q5_1_RDNA2  64
-#define  MMQ_Y_Q5_1_RDNA2  128
-#define NWARPS_Q5_1_RDNA2  8
-#define  MMQ_X_Q5_1_RDNA1  64
-#define  MMQ_Y_Q5_1_RDNA1  64
-#define NWARPS_Q5_1_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q5_1_AMPERE 4
-#define  MMQ_Y_Q5_1_AMPERE 32
-#define NWARPS_Q5_1_AMPERE 4
-#else
-#define  MMQ_X_Q5_1_AMPERE 128
-#define  MMQ_Y_Q5_1_AMPERE 64
-#define NWARPS_Q5_1_AMPERE 4
-#endif
-#define  MMQ_X_Q5_1_PASCAL 64
-#define  MMQ_Y_Q5_1_PASCAL 64
-#define NWARPS_Q5_1_PASCAL 8
-
-template <bool need_check> static void
-mul_mat_q5_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_1,
-    sycl::half2 *tile_x_dm_q5_1, int *tile_y_qs, sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
-    const int nwarps = NWARPS_Q5_1_AMPERE;
-    allocate_tiles_q5_1<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_ql_q5_1, tile_x_dm_q5_1);
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps,
-              load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ,
-              vec_dot_q5_1_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q8_0_RDNA2  64
-#define  MMQ_Y_Q8_0_RDNA2  128
-#define NWARPS_Q8_0_RDNA2  8
-#define  MMQ_X_Q8_0_RDNA1  64
-#define  MMQ_Y_Q8_0_RDNA1  64
-#define NWARPS_Q8_0_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q8_0_AMPERE 4
-#define  MMQ_Y_Q8_0_AMPERE 32
-#define NWARPS_Q8_0_AMPERE 4
-#else
-#define  MMQ_X_Q8_0_AMPERE 128
-#define  MMQ_Y_Q8_0_AMPERE 64
-#define NWARPS_Q8_0_AMPERE 4
-#endif
-#define  MMQ_X_Q8_0_PASCAL 64
-#define  MMQ_Y_Q8_0_PASCAL 64
-#define NWARPS_Q8_0_PASCAL 8
-
-template <bool need_check> static void
-    mul_mat_q8_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q8_0, float *tile_x_d_q8_0,
-    int *tile_y_qs, sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
-    const int nwarps = NWARPS_Q8_0_AMPERE;
-    allocate_tiles_q8_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_qs_q8_0, tile_x_d_q8_0);
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps,
-              load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ,
-              vec_dot_q8_0_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q2_K_RDNA2  64
-#define  MMQ_Y_Q2_K_RDNA2  128
-#define NWARPS_Q2_K_RDNA2  8
-#define  MMQ_X_Q2_K_RDNA1  128
-#define  MMQ_Y_Q2_K_RDNA1  32
-#define NWARPS_Q2_K_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q2_K_AMPERE 4
-#define  MMQ_Y_Q2_K_AMPERE 32
-#define NWARPS_Q2_K_AMPERE 4
-#else
-#define  MMQ_X_Q2_K_AMPERE 64
-#define  MMQ_Y_Q2_K_AMPERE 128
-#define NWARPS_Q2_K_AMPERE 4
-#endif
-#define  MMQ_X_Q2_K_PASCAL 64
-#define  MMQ_Y_Q2_K_PASCAL 64
-#define NWARPS_Q2_K_PASCAL 8
-
-template <bool need_check> static void
-mul_mat_q2_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q2_K,
-    sycl::half2 *tile_x_dm_q2_K, int *tile_x_sc_q2_K, int *tile_y_qs,
-    sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
-    const int nwarps = NWARPS_Q2_K_AMPERE;
-    allocate_tiles_q2_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_ql_q2_K, tile_x_dm_q2_K, tile_x_sc_q2_K);
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps,
-              load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ,
-              vec_dot_q2_K_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q3_K_RDNA2  128
-#define  MMQ_Y_Q3_K_RDNA2  64
-#define NWARPS_Q3_K_RDNA2  8
-#define  MMQ_X_Q3_K_RDNA1  32
-#define  MMQ_Y_Q3_K_RDNA1  128
-#define NWARPS_Q3_K_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q3_K_AMPERE 4
-#define  MMQ_Y_Q3_K_AMPERE 32
-#define NWARPS_Q3_K_AMPERE 4
-#else
-#define  MMQ_X_Q3_K_AMPERE 128
-#define  MMQ_Y_Q3_K_AMPERE 128
-#define NWARPS_Q3_K_AMPERE 4
-#endif
-#define  MMQ_X_Q3_K_PASCAL 64
-#define  MMQ_Y_Q3_K_PASCAL 64
-#define NWARPS_Q3_K_PASCAL 8
-
-template <bool need_check> static void
-mul_mat_q3_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q3_K,
-    sycl::half2 *tile_x_dm_q3_K, int *tile_x_qh_q3_K, int *tile_x_sc_q3_K,
-    int *tile_y_qs, sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
-    const int nwarps = NWARPS_Q3_K_AMPERE;
-    allocate_tiles_q3_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_ql_q3_K, tile_x_dm_q3_K, tile_x_qh_q3_K,
-                               tile_x_sc_q3_K);
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps,
-              load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ,
-              vec_dot_q3_K_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q4_K_RDNA2  64
-#define  MMQ_Y_Q4_K_RDNA2  128
-#define NWARPS_Q4_K_RDNA2  8
-#define  MMQ_X_Q4_K_RDNA1  32
-#define  MMQ_Y_Q4_K_RDNA1  64
-#define NWARPS_Q4_K_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q4_K_AMPERE 4
-#define  MMQ_Y_Q4_K_AMPERE 32
-#define NWARPS_Q4_K_AMPERE 4
-#else
-#define  MMQ_X_Q4_K_AMPERE 64
-#define  MMQ_Y_Q4_K_AMPERE 128
-#define NWARPS_Q4_K_AMPERE 4
-#endif
-#define  MMQ_X_Q4_K_PASCAL 64
-#define  MMQ_Y_Q4_K_PASCAL 64
-#define NWARPS_Q4_K_PASCAL 8
-
-template <bool need_check> static void
-    mul_mat_q4_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q4_K,
-    sycl::half2 *tile_x_dm_q4_K, int *tile_x_sc_q4_K, int *tile_y_qs,
-    sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
-    const int nwarps = NWARPS_Q4_K_AMPERE;
-    allocate_tiles_q4_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_ql_q4_K, tile_x_dm_q4_K, tile_x_sc_q4_K);
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps,
-              load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ,
-              vec_dot_q4_K_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q5_K_RDNA2  64
-#define  MMQ_Y_Q5_K_RDNA2  128
-#define NWARPS_Q5_K_RDNA2  8
-#define  MMQ_X_Q5_K_RDNA1  32
-#define  MMQ_Y_Q5_K_RDNA1  64
-#define NWARPS_Q5_K_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q5_K_AMPERE 4
-#define  MMQ_Y_Q5_K_AMPERE 32
-#define NWARPS_Q5_K_AMPERE 4
-#else
-#define  MMQ_X_Q5_K_AMPERE 64
-#define  MMQ_Y_Q5_K_AMPERE 128
-#define NWARPS_Q5_K_AMPERE 4
-#endif
-#define  MMQ_X_Q5_K_PASCAL 64
-#define  MMQ_Y_Q5_K_PASCAL 64
-#define NWARPS_Q5_K_PASCAL 8
-
-template <bool need_check> static void
-mul_mat_q5_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_K,
-    sycl::half2 *tile_x_dm_q5_K, int *tile_x_sc_q5_K, int *tile_y_qs,
-    sycl::half2 *tile_y_ds) {
-    int   * tile_x_ql = nullptr;
-    sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
-    const int nwarps = NWARPS_Q5_K_AMPERE;
-    allocate_tiles_q5_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_ql_q5_K, tile_x_dm_q5_K, tile_x_sc_q5_K);
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps,
-              load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ,
-              vec_dot_q5_K_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-#define  MMQ_X_Q6_K_RDNA2  64
-#define  MMQ_Y_Q6_K_RDNA2  128
-#define NWARPS_Q6_K_RDNA2  8
-#define  MMQ_X_Q6_K_RDNA1  32
-#define  MMQ_Y_Q6_K_RDNA1  64
-#define NWARPS_Q6_K_RDNA1  8
-#if defined(SYCL_USE_XMX)
-#define  MMQ_X_Q6_K_AMPERE 4
-#define  MMQ_Y_Q6_K_AMPERE 32
-#define NWARPS_Q6_K_AMPERE 4
-#else
-#define  MMQ_X_Q6_K_AMPERE 64
-#define  MMQ_Y_Q6_K_AMPERE 64
-#define NWARPS_Q6_K_AMPERE 4
-#endif
-#define  MMQ_X_Q6_K_PASCAL 64
-#define  MMQ_Y_Q6_K_PASCAL 64
-#define NWARPS_Q6_K_PASCAL 8
-
-template <bool need_check> static void
-    mul_mat_q6_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
-    const sycl::nd_item<3> &item_ct1, int *tile_x_ql, sycl::half2 *tile_x_dm,
-    int *tile_x_sc, int *tile_y_qs, sycl::half2 *tile_y_ds) {
-    // int   * tile_x_ql = nullptr;
-    // sycl::half2 *tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    // int   * tile_x_sc = nullptr;
-
-//sycl_todo: change according to hardware
-    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
-    const int nwarps = NWARPS_Q6_K_AMPERE;
-    allocate_tiles_q6_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
-                               tile_x_ql, tile_x_dm, tile_x_sc);
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps,
-              load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ,
-              vec_dot_q6_K_q8_1_mul_mat>(
-        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
-        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
-}
-
-template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_sycl_t vec_dot_q_sycl>
-static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
-                          const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-    const int qi_vdr = (qi / vdr); // N_threads processing 1 qk block
-
-    // partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / qi_vdr; i < blocks_per_row;
-         i += blocks_per_warp) {
-      const int ibx = row * blocks_per_row + i; // x block index
-
-      const int iby = i * (qk / QK8_1); // y block index that aligns with ibx
-
-      const int iqs =
-          vdr *
-          (item_ct1.get_local_id(2) -
-           i * qi_vdr); // x block quant index when casting the quants to int
-
-      tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx,
-                                       const void *__restrict__ vy,
-                                       float *__restrict__ dst, const int ncols,
-                                       const int nrows,
-                                       const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq2_xxs_q8_1(&x[ibx], &y[iby], iqs, iq2xxs_grid, ksigns_iq2xs, kmask_iq2xs);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx,
-                                      const void *__restrict__ vy,
-                                      float *__restrict__ dst, const int ncols,
-                                      const int nrows,
-                                      const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq2_xs_q8_1(&x[ibx], &y[iby], iqs, iq2xs_grid, ksigns64);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx,
-                                     const void *__restrict__ vy,
-                                     float *__restrict__ dst, const int ncols,
-                                     const int nrows,
-                                     const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq2_s_q8_1(&x[ibx], &y[iby], iqs);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx,
-                                       const void *__restrict__ vy,
-                                       float *__restrict__ dst, const int ncols,
-                                       const int nrows,
-                                       const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq3_xxs_q8_1(&x[ibx], &y[iby], iqs, iq3xxs_grid, ksigns64);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx,
-                                     const void *__restrict__ vy,
-                                     float *__restrict__ dst, const int ncols,
-                                     const int nrows,
-                                     const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq3_s_q8_1(&x[ibx], &y[iby], iqs, iq3s_grid);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx,
-                                     const void *__restrict__ vy,
-                                     float *__restrict__ dst, const int ncols,
-                                     const int nrows,
-                                     const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq1_s_q8_1(&x[ibx], &y[iby], iqs, iq1s_grid_gpu);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx,
-                                     const void *__restrict__ vy,
-                                     float *__restrict__ dst, const int ncols,
-                                     const int nrows,
-                                     const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq1_m_q8_1(&x[ibx], &y[iby], iqs);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx,
-                                      const void *__restrict__ vy,
-                                      float *__restrict__ dst, const int ncols,
-                                      const int nrows,
-                                      const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq4_nl_q8_1(&x[ibx], &y[iby], iqs);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-
-template <int qk, int qi, typename block_q_t, int vdr>
-static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx,
-                                      const void *__restrict__ vy,
-                                      float *__restrict__ dst, const int ncols,
-                                      const int nrows,
-                                      const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
-
-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_iq4_xs_q8_1(&x[ibx], &y[iby], iqs);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[row] = tmp;
-    }
-}
-
-
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
-static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows,
-                                   const sycl::nd_item<3> &item_ct1) {
-    // qk = quantized weights per x block
-    // qr = number of quantized weights per data value in x block
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-
-    if (row >= nrows) {
-        return;
-    }
-
-    const int tid = item_ct1.get_local_id(2);
-
-    const int iter_stride = 2*GGML_SYCL_DMMV_X;
-    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-// partial sum for each thread
-#ifdef GGML_SYCL_F16
-    sycl::half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
-#else
-    float tmp = 0.0f;
-#endif // GGML_SYCL_F16
-
-    for (int i = 0; i < ncols; i += iter_stride) {
-        const int col = i + vals_per_iter*tid;
-        const int ib = (row*ncols + col)/qk; // x block index
-        const int iqs = (col%qk)/qr; // x quant index
-        const int iybs = col - col%qk; // y block start index
-
-// processing >2 values per i iter is faster for fast GPUs
-#pragma unroll
-        for (int j = 0; j < vals_per_iter; j += 2) {
-            // process 2 vals per j iter
-
-            // dequantize
-            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
-            dfloat2 v;
-            dequantize_kernel(vx, ib, iqs + j/qr, v);
-
-            // matrix multiplication
-            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
-#ifdef GGML_SYCL_F16
-            dfloat2 t1{y[iybs + iqs + j / qr + 0],
-                        y[iybs + iqs + j / qr + y_offset]};
-
-            tmp += v * t1;
-#else
-            tmp += v.x() * y[iybs + iqs + j / qr + 0];
-            tmp += v.y() * y[iybs + iqs + j / qr + y_offset];
-#endif // GGML_SYCL_F16
-        }
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (tid == 0) {
-#ifdef GGML_SYCL_F16
-        dst[row] = tmp.x() + tmp.y();
-#else
-        dst[row] = tmp;
-#endif // GGML_SYCL_F16
-    }
-}
-
-static void mul_mat_p021_f16_f32(
-    const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y,
-    const sycl::nd_item<3> &item_ct1) {
-
-    const sycl::half *x = (const sycl::half *)vx;
-
-    const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                      item_ct1.get_local_id(1);
-    const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) +
-                        item_ct1.get_local_id(0);
-    const int channel_x = channel / (nchannels_y / nchannels_x);
-
-    const int nrows_y = ncols_x;
-    const int nrows_dst = nrows_x;
-    const int row_dst = row_x;
-
-    float tmp = 0.0f;
-
-    for (int col_x0 = 0; col_x0 < ncols_x;
-         col_x0 += item_ct1.get_local_range(2)) {
-        const int col_x = col_x0 + item_ct1.get_local_id(2);
-
-        if (col_x >= ncols_x) {
-            break;
-        }
-
-        // x is transposed and permuted
-        const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x;
-        const float xi =
-            sycl::vec<sycl::half, 1>(x[ix])
-                .convert<float, sycl::rounding_mode::automatic>()[0];
-
-        const int row_y = col_x;
-
-
-        // y is not transposed but permuted
-        const int iy = channel*nrows_y + row_y;
-
-        tmp += xi * y[iy];
-    }
-
-    // dst is not transposed and not permuted
-    const int idst = channel*nrows_dst + row_dst;
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[idst] = tmp;
-    }
-}
-
-static void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
-    const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x,
-    const int row_stride_x, const int channel_stride_x, const int channel_x_divisor,
-    const sycl::nd_item<3> &item_ct1) {
-
-    const sycl::half *x = (const sycl::half *)vx;
-
-    const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                      item_ct1.get_local_id(1);
-    const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) +
-                        item_ct1.get_local_id(0);
-    const int channel_x = channel / channel_x_divisor;
-
-    const int nrows_y   = ncols_x;
-    const int nrows_dst = nrows_x;
-    const int row_dst   = row_x;
-
-    const int idst = channel*nrows_dst + row_dst;
-
-    float tmp = 0.0f;
-
-    for (int col_x0 = 0; col_x0 < ncols_x;
-         col_x0 += item_ct1.get_local_range(2)) {
-        const int col_x = col_x0 + item_ct1.get_local_id(2);
-
-        if (col_x >= ncols_x) {
-            break;
-        }
-
-        const int row_y = col_x;
-
-        const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x;
-        const int iy = channel*nrows_y + row_y;
-
-        const float xi =
-            sycl::vec<sycl::half, 1>(x[ix])
-                .convert<float, sycl::rounding_mode::automatic>()[0];
-
-        tmp += xi * y[iy];
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
-    }
-
-    if (item_ct1.get_local_id(2) == 0) {
-        dst[idst] = tmp;
-    }
-}
-
-static void cpy_1_f32_f32(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    float * dsti = (float *) cdsti;
-
-    *dsti = *xi;
-}
-
-static void cpy_1_f32_f16(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    sycl::half *dsti = (sycl::half *)cdsti;
-
-    *dsti = sycl::vec<float, 1>(*xi)
-                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
-}
-
-static void cpy_1_f16_f16(const char * cxi, char * cdsti) {
-    const sycl::half *xi = (const sycl::half *)cxi;
-    sycl::half *dsti = (sycl::half *)cdsti;
-
-    *dsti = *xi;
-}
-
-static void cpy_1_f16_f32(const char * cxi, char * cdsti) {
-    const sycl::half *xi = (const sycl::half *)cxi;
-    float * dsti = (float *) cdsti;
-
-    *dsti = *xi;
-}
-
-static void cpy_1_i16_i16(const char * cxi, char * cdsti) {
-    const int16_t *xi = (const int16_t *)cxi;
-    int16_t *dsti = (int16_t *)cdsti;
-
-    *dsti = *xi;
-}
-
-static void cpy_1_i32_i32(const char * cxi, char * cdsti) {
-    const int32_t *xi = (const int32_t *)cxi;
-    int32_t *dsti = (int32_t *)cdsti;
-
-    *dsti = *xi;
-}
-
-template <cpy_kernel_t cpy_1>
-static void cpy_f32_f16(const char * cx, char * cdst, const int ne,
-                        const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-                        const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
-                        const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= ne) {
-        return;
-    }
-
-    // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
-    // then combine those indices with the corresponding byte offsets to get the total offsets
-    const int i03 = i/(ne00 * ne01 * ne02);
-    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
-    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
-    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
-    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
-
-    const int i13 = i/(ne10 * ne11 * ne12);
-    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
-    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
-    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
-    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
-
-    cpy_1(cx + x_offset, cdst + dst_offset);
-}
-
-static void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q8_0 * dsti = (block_q8_0 *) cdsti;
-
-    float amax = 0.0f; // absolute max
-
-    for (int j = 0; j < QK8_0; j++) {
-        const float v = xi[j];
-        amax = sycl::fmax(amax, sycl::fabs((float)v));
-    }
-
-    const float d = amax / ((1 << 7) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    for (int j = 0; j < QK8_0; ++j) {
-        const float x0 = xi[j]*id;
-
-        dsti->qs[j] = sycl::round((float)x0);
-    }
-}
-
-static void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q4_0 * dsti = (block_q4_0 *) cdsti;
-
-    float amax = 0.0f;
-    float vmax = 0.0f;
-
-    for (int j = 0; j < QK4_0; ++j) {
-        const float v = xi[j];
-        if (amax < sycl::fabs((float)v)) {
-            amax = sycl::fabs((float)v);
-            vmax = v;
-        }
-    }
-
-    const float d  = vmax / -8;
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    for (int j = 0; j < QK4_0/2; ++j) {
-        const float x0 = xi[0       + j]*id;
-        const float x1 = xi[QK4_0/2 + j]*id;
-
-        const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 8.5f));
-        const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 8.5f));
-
-        dsti->qs[j]  = xi0;
-        dsti->qs[j] |= xi1 << 4;
-    }
-}
-
-static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q4_1 * dsti = (block_q4_1 *) cdsti;
-
-    float vmin = FLT_MAX;
-    float vmax = -FLT_MAX;
-
-    for (int j = 0; j < QK4_1; ++j) {
-        const float v = xi[j];
-
-        if (v < vmin) vmin = v;
-        if (v > vmax) vmax = v;
-    }
-
-    const float d  = (vmax - vmin) / ((1 << 4) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->dm.x() = d;
-    dsti->dm.y() = vmin;
-
-    for (int j = 0; j < QK4_1/2; ++j) {
-        const float x0 = (xi[0       + j] - vmin)*id;
-        const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
-
-        const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 0.5f));
-        const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 0.5f));
-
-        dsti->qs[j]  = xi0;
-        dsti->qs[j] |= xi1 << 4;
-    }
-}
-
-template <cpy_kernel_t cpy_blck, int qk>
-static void cpy_f32_q(const char * cx, char * cdst, const int ne,
-                      const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
-                      const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
-                      const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
-    const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                   item_ct1.get_local_id(2)) *
-                  qk;
-
-    if (i >= ne) {
-        return;
-    }
-
-    const int i03 = i/(ne00 * ne01 * ne02);
-    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
-    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
-    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
-    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
-
-    const int i13 = i/(ne10 * ne11 * ne12);
-    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
-    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
-    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
-    const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
-
-    cpy_blck(cx + x_offset, cdst + dst_offset);
-}
-
-static float rope_yarn_ramp(const float low, const float high, const int i0) {
-    const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low);
-    return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y));
-}
-
-struct rope_corr_dims {
-    float v[4];
-};
-
-// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
-// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
-static void rope_yarn(
-    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
-    float * cos_theta, float * sin_theta
-) {
-    // Get n-d rotational scaling corrected for extrapolation
-    float theta_interp = freq_scale * theta_extrap;
-    float theta = theta_interp;
-    if (ext_factor != 0.0f) {
-        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
-        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-
-        // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale);
-    }
-    *cos_theta = sycl::cos(theta) * mscale;
-    *sin_theta = sycl::sin(theta) * mscale;
-}
-
-// rope == RoPE == rotary positional embedding
-template<typename T, bool has_pos>
-static void rope(
-    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims
-,
-    const sycl::nd_item<3> &item_ct1) {
-    const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                         item_ct1.get_local_id(1));
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                    item_ct1.get_local_id(2);
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p * dpct::pow(freq_base, -float(col) / ncols);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + 1];
-
-    dst[i + 0] = x0*cos_theta - x1*sin_theta;
-    dst[i + 1] = x0*sin_theta + x1*cos_theta;
-}
-
-template<typename T, bool has_pos>
-static void rope_neox(
-    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
-,
-    const sycl::nd_item<3> &item_ct1) {
-    const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                         item_ct1.get_local_id(1));
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                    item_ct1.get_local_id(2);
-    const int ib = col / n_dims;
-    const int ic = col % n_dims;
-
-    if (ib > 0) {
-        const int i = row*ncols + ib*n_dims + ic;
-
-        dst[i + 0] = x[i + 0];
-        dst[i + 1] = x[i + 1];
-
-        return;
-    }
-
-    const int i  = row*ncols + ib*n_dims + ic/2;
-    const int i2 = row/p_delta_rows;
-
-    float cur_rot = inv_ndims * ic - ib;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base =
-        p * freq_scale * dpct::pow(theta_scale, col / 2.0f);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims/2];
-
-    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
-}
-
-static void rope_glm_f32(
-    const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    int n_ctx
-, const sycl::nd_item<3> &item_ct1) {
-    const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                    item_ct1.get_local_id(2);
-    const int half_n_dims = ncols/4;
-
-    if (col >= half_n_dims) {
-        return;
-    }
-
-    const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                    item_ct1.get_local_id(1);
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const float col_theta_scale = dpct::pow(freq_base, -2.0f * col / ncols);
-     // FIXME: this is likely wrong
-    const int p = pos != nullptr ? pos[i2] : 0;
-
-    const float theta = sycl::min(p, n_ctx - 2) * freq_scale * col_theta_scale;
-    const float sin_theta = sycl::sin((float)theta);
-    const float cos_theta = sycl::cos((float)theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + half_n_dims];
-
-    dst[i + 0]           = x0*cos_theta - x1*sin_theta;
-    dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
-
-    const float block_theta =
-        ((float)sycl::max(p - n_ctx - 2, 0)) * col_theta_scale;
-    const float sin_block_theta = sycl::sin((float)block_theta);
-    const float cos_block_theta = sycl::cos((float)block_theta);
-
-    const float x2 = x[i + half_n_dims * 2];
-    const float x3 = x[i + half_n_dims * 3];
-
-    dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
-    dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
-}
-
-static void k_sum_rows_f32(const float * x, float * dst, const int ncols,
-                           const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(1);
-    const int col = item_ct1.get_local_id(2);
-
-    float sum = 0.0f;
-    for (int i = col; i < ncols; i += item_ct1.get_local_range(2)) {
-        sum += x[row * ncols + i];
-    }
-
-    sum = warp_reduce_sum(sum, item_ct1);
-
-    if (col == 0) {
-        dst[row] = sum;
-    }
-}
-
-
-template<typename T>
-static inline void ggml_sycl_swap(T & a, T & b) {
-    T tmp = a;
-    a = b;
-    b = tmp;
-}
-
-template <ggml_sort_order order>
-__dpct_inline__ static void
-k_argsort_f32_i32(const float *x, int *dst, const int ncols, int ncols_pad,
-                  const sycl::nd_item<3> &item_ct1, uint8_t *dpct_local) {
-    // bitonic sort
-    int col = item_ct1.get_local_id(2);
-    int row = item_ct1.get_group(1);
-
-    if (col >= ncols_pad) {
-        return;
-    }
-
-    const float * x_row = x + row * ncols;
-    auto dst_row = (int *)dpct_local;
-
-    // initialize indices
-    dst_row[col] = col;
-
-    item_ct1.barrier(sycl::access::fence_space::local_space);
-
-    for (int k = 2; k <= ncols_pad; k *= 2) {
-        for (int j = k / 2; j > 0; j /= 2) {
-            int ixj = col ^ j;
-            if (ixj > col) {
-                if ((col & k) == 0) {
-                    if (dst_row[col] >= ncols ||
-                        (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ?
-                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
-                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
-                    ) {
-                        ggml_sycl_swap(dst_row[col], dst_row[ixj]);
-                    }
-                } else {
-                    if (dst_row[ixj] >= ncols ||
-                        (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ?
-                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
-                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
-                    ) {
-                        ggml_sycl_swap(dst_row[col], dst_row[ixj]);
-                    }
-                }
-            }
-            /*
-            DPCT1118:1: SYCL group functions and algorithms must be encountered
-            in converged control flow. You may need to adjust the code.
-            */
-            item_ct1.barrier(sycl::access::fence_space::local_space);
-        }
-    }
-
-    // copy the result to dst without the padding
-    if (col < ncols) {
-        dst[row * ncols + col] = dst_row[col];
-    }
-}
-
-
-static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past,
-                              const sycl::nd_item<3> &item_ct1) {
-    const int col = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                    item_ct1.get_local_id(1);
-    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                    item_ct1.get_local_id(2);
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int i = row*ncols + col;
-    //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i];
-    //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
-    dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
-}
-
-
-template <bool vals_smem, int ncols_template, int block_size_template>
-static void soft_max_f32(const float * x, const float * mask, float * dst, const int ncols_par,
-                         const int nrows_y, const float scale, const float max_bias, const float m0,
-                         const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) {
-    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
-
-    const int tid = item_ct1.get_local_id(2);
-    const int rowx = item_ct1.get_group(2);
-    const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
-
-    const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template;
-
-    const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-    const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-
-    float slope = 1.0f;
-
-    // ALiBi
-    if (max_bias > 0.0f) {
-        const uint32_t h = rowx/nrows_y; // head index
-
-        const float base = h < n_head_log2 ? m0 : m1;
-        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
-
-        slope = sycl::pow(base, float(exp));
-    }
-
-    float * vals = vals_smem ? buf + WARP_SIZE : dst + rowx*ncols;
-    float max_val = -INFINITY;
-
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-
-        if (ncols_template == 0 && col >= ncols) {
-            break;
-        }
-
-        const int ix = rowx*ncols + col;
-        const int iy = rowy*ncols + col;
-
-        const float val = x[ix]*scale + (mask ? slope*mask[iy] : 0.0f);
-
-        vals[col] = val;
-        max_val = sycl::max(max_val, val);
-    }
-
-    // find the max value in the block
-    max_val = warp_reduce_max(max_val, item_ct1);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf[lane_id] = -INFINITY;
-        }
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-
-        if (lane_id == 0) {
-            buf[warp_id] = max_val;
-        }
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-
-        max_val = buf[lane_id];
-        max_val = warp_reduce_max(max_val, item_ct1);
-    }
-
-    float tmp = 0.f;
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-                if (ncols_template == 0 && col >= ncols) {
-            break;
-        }
-
-        const float val = sycl::native::exp(vals[col] - max_val);
-        tmp += val;
-        vals[col] = val;
-    }
-
-    // find the sum of exps in the block
-    tmp = warp_reduce_sum(tmp, item_ct1);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf[lane_id] = 0.f;
-        }
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-
-        if (lane_id == 0) {
-            buf[warp_id] = tmp;
-        }
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-
-        tmp = buf[lane_id];
-        tmp = warp_reduce_sum(tmp, item_ct1);
-    }
-
-    const float inv_sum = 1.f / tmp;
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-
-        if (ncols_template == 0 && col >= ncols) {
-            return;
-        }
-
-        const int idst = rowx*ncols + col;
-        dst[idst] = vals[col] * inv_sum;
-    }
-}
-
-static void scale_f32(const float * x, float * dst, const float scale, const int k,
-                      const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-
-    dst[i] = scale * x[i];
-}
-
-static void clamp_f32(const float * x, float * dst, const float min, const float max, const int k,
-                      const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-
-    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
-}
-
-template <typename T>
-static void im2col_kernel(const float *x, T *dst, int offset_delta,
-                           int IW, int IH, int OW, int KW, int KH,
-                           int pelements, int CHW, int s0, int s1, int p0,
-                           int p1, int d0, int d1,
-                           const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_id(2) +
-                  item_ct1.get_group(2) * item_ct1.get_local_range(2);
-    if (i >= pelements) {
-        return;
-    }
-
-    const int ksize = OW * (KH > 1 ? KW : 1);
-    const int kx = i / ksize;
-    const int kd = kx * ksize;
-    const int ky = (i - kd) / OW;
-    const int ix = i % OW;
-
-    const int64_t iiw = ix * s0 + kx * d0 - p0;
-    const int64_t iih = item_ct1.get_group(1) * s1 + ky * d1 - p1;
-
-    const int64_t offset_dst =
-        (item_ct1.get_group(1) * OW + ix) * CHW +
-        (item_ct1.get_group(0) * (KW * KH) + ky * KW + kx);
-
-    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-        dst[offset_dst] =
-            sycl::vec<float, 1>(0.0f)
-                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
-    } else {
-        const int64_t offset_src = item_ct1.get_group(0) * offset_delta;
-        dst[offset_dst] =
-            sycl::vec<float, 1>(x[offset_src + iih * IW + iiw])
-                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
-    }
-}
-
-template <typename Ti, typename To>
-static  void pool2d_nchw_kernel(
-        const int ih, const int iw, const int oh, const int ow,
-        const int kh, const int kw, const int sh, const int sw,
-        const int ph, const int pw, const int parallel_elements,
-        const Ti* src, To* dst, const enum ggml_op_pool op,
-        const sycl::nd_item<3> &item_ct1) {
-        int idx = item_ct1.get_local_id(2) +
-                  item_ct1.get_group(2) * item_ct1.get_local_range(2);
-        if (idx >= parallel_elements) {
-            return;
-        }
-
-        const int I_HW = ih * iw;
-        const int O_HW = oh * ow;
-        const int nc = idx / O_HW;
-        const int cur_oh = idx % O_HW / ow;
-        const int cur_ow = idx % O_HW % ow;
-        const Ti* i_ptr = src + nc * I_HW;
-        To* o_ptr = dst + nc * O_HW;
-        const int start_h = cur_oh * sh - ph;
-        const int bh = sycl::max(0, start_h);
-        const int eh = sycl::min(ih, start_h + kh);
-        const int start_w = cur_ow * sw - pw;
-        const int bw = sycl::max(0, start_w);
-        const int ew = sycl::min(iw, start_w + kw);
-
-        To res = 0;
-
-        switch (op) {
-            case GGML_OP_POOL_AVG: res = 0; break;
-            case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
-        }
-
-        for (int i = bh; i < eh; i += 1) {
-            for (int j = bw; j < ew; j += 1) {
-#if DPCT_COMPATIBILITY_TEMP >= 350
-                /*
-                DPCT1098:106: The '*' expression is used instead of the __ldg
-                call. These two expressions do not provide the exact same
-                functionality. Check the generated code for potential precision
-                and/or performance issues.
-                */
-                Ti cur = *(i_ptr + i * iw + j);
-#else
-                Ti cur = i_ptr[i * iw + j];
-#endif
-                switch (op) {
-                    case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break;
-                    case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break;
-                }
-            }
-        }
-        o_ptr[cur_oh * ow + cur_ow] = res;
-}
-
-template <int qk, int qr, dequantize_kernel_t dq>
-static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1,
-                          ggml_tensor *dst, const void *src0_dd,
-                          const int32_t *src1_dd, float *dst_dd,
-                          dpct::queue_ptr stream) {
-
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE);
-    const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE);
-    const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x);
-
-    // strides in elements
-    //const size_t s0 = nb0 / ggml_element_size(dst);
-    const size_t s1 = nb1 / ggml_element_size(dst);
-    const size_t s2 = nb2 / ggml_element_size(dst);
-    const size_t s3 = nb3 / ggml_element_size(dst);
-
-    const size_t s10 = nb10 / ggml_element_size(src1);
-    const size_t s11 = nb11 / ggml_element_size(src1);
-    const size_t s12 = nb12 / ggml_element_size(src1);
-    //const size_t s13 = nb13 / ggml_element_size(src1);
-
-    GGML_ASSERT(ne00 % 2 == 0);
-
-    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                         [=](sycl::nd_item<3> item_ct1) {
-                             k_get_rows<qk, qr, dq>(
-                                 src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
-                                 s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
-                         });
-
-    (void) dst;
-}
-
-template <typename src0_t>
-static void get_rows_sycl_float(const ggml_tensor *src0,
-                                const ggml_tensor *src1, ggml_tensor *dst,
-                                const src0_t *src0_dd, const int32_t *src1_dd,
-                                float *dst_dd, dpct::queue_ptr stream) {
-
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE);
-    const int block_num_x = (ne00 + SYCL_GET_ROWS_BLOCK_SIZE - 1) / SYCL_GET_ROWS_BLOCK_SIZE;
-    const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x);
-
-    // strides in elements
-    //const size_t s0 = nb0 / ggml_element_size(dst);
-    const size_t s1 = nb1 / ggml_element_size(dst);
-    const size_t s2 = nb2 / ggml_element_size(dst);
-    const size_t s3 = nb3 / ggml_element_size(dst);
-
-    const size_t s10 = nb10 / ggml_element_size(src1);
-    const size_t s11 = nb11 / ggml_element_size(src1);
-    const size_t s12 = nb12 / ggml_element_size(src1);
-    //const size_t s13 = nb13 / ggml_element_size(src1);
-
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                k_get_rows_float(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
-                                 s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
-            });
-    }
-
-    (void) dst;
-}
-
-template<float (*bin_op)(const float, const float)>
-struct bin_bcast_sycl {
-    template <typename src0_t, typename src1_t, typename dst_t>
-    void operator()(const struct ggml_tensor *src0,
-                    const struct ggml_tensor *src1, struct ggml_tensor *dst,
-                    const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd,
-                    dpct::queue_ptr stream) {
-
-        GGML_TENSOR_BINARY_OP_LOCALS
-
-        int nr0 = ne10/ne0;
-        int nr1 = ne11/ne1;
-        int nr2 = ne12/ne2;
-        int nr3 = ne13/ne3;
-
-        int nr[4] = { nr0, nr1, nr2, nr3 };
-
-        // collapse dimensions until first broadcast dimension
-        int64_t cne0[] = {ne0, ne1, ne2, ne3};
-        int64_t cne1[] = {ne10, ne11, ne12, ne13};
-        size_t cnb0[] = {nb0, nb1, nb2, nb3};
-        size_t cnb1[] = {nb10, nb11, nb12, nb13};
-        auto collapse = [](int64_t cne[]) {
-            cne[0] *= cne[1];
-            cne[1] = cne[2];
-            cne[2] = cne[3];
-            cne[3] = 1;
-        };
-
-        auto collapse_nb = [](size_t cnb[], int64_t cne[]) {
-            cnb[1] *= cne[1];
-            cnb[2] *= cne[2];
-            cnb[3] *= cne[3];
-        };
-
-        for (int i = 0; i < 4; i++) {
-            if (nr[i] != 1) {
-                break;
-            }
-            if (i > 0) {
-                collapse_nb(cnb0, cne0);
-                collapse_nb(cnb1, cne1);
-                collapse(cne0);
-                collapse(cne1);
-            }
-        }
-        {
-            int64_t ne0 = cne0[0];
-            int64_t ne1 = cne0[1];
-            int64_t ne2 = cne0[2];
-            int64_t ne3 = cne0[3];
-
-            int64_t ne10 = cne1[0];
-            int64_t ne11 = cne1[1];
-            int64_t ne12 = cne1[2];
-            int64_t ne13 = cne1[3];
-
-            size_t nb0 = cnb0[0];
-            size_t nb1 = cnb0[1];
-            size_t nb2 = cnb0[2];
-            size_t nb3 = cnb0[3];
-
-            size_t nb10 = cnb1[0];
-            size_t nb11 = cnb1[1];
-            size_t nb12 = cnb1[2];
-            size_t nb13 = cnb1[3];
-
-            size_t s0 = nb0 / sizeof(dst_t);
-            size_t s1 = nb1 / sizeof(dst_t);
-            size_t s2 = nb2 / sizeof(dst_t);
-            size_t s3 = nb3 / sizeof(dst_t);
-
-            size_t s10 = nb10 / sizeof(src1_t);
-            size_t s11 = nb11 / sizeof(src1_t);
-            size_t s12 = nb12 / sizeof(src1_t);
-            size_t s13 = nb13 / sizeof(src1_t);
-
-            GGML_ASSERT(s0 == 1);
-            GGML_ASSERT(s10 == 1);
-
-            const int block_size = 128;
-
-            int64_t hne0 = std::max(ne0/2LL, 1LL);
-
-            sycl::range<3> block_dims(1, 1, 1);
-            block_dims[2] = std::min<unsigned int>(hne0, block_size);
-            block_dims[1] = std::min<unsigned int>(
-                ne1, block_size / (unsigned int)block_dims[2]);
-            block_dims[0] = std::min(
-                std::min<unsigned int>(
-                    ne2 * ne3, block_size / (unsigned int)block_dims[2] /
-                                   (unsigned int)block_dims[1]),
-                64U);
-
-            sycl::range<3> block_nums(
-                (ne2 * ne3 + block_dims[0] - 1) / block_dims[0],
-                (ne1 + block_dims[1] - 1) / block_dims[1],
-                (hne0 + block_dims[2] - 1) / block_dims[2]);
-
-            if (block_nums[0] > 65535) {
-                // this is the maximum number of blocks in z direction, fallback to 1D grid kernel
-                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
-                {
-                    dpct::has_capability_or_fail(stream->get_device(),
-                                                 {sycl::aspect::fp16});
-
-                    stream->parallel_for(
-                        sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) *
-                                              sycl::range<3>(1, 1, block_size),
-                                          sycl::range<3>(1, 1, block_size)),
-                        [=](sycl::nd_item<3> item_ct1) {
-                            k_bin_bcast_unravel<bin_op>(
-                                src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3,
-                                ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12,
-                                s13, item_ct1);
-                        });
-                }
-            } else {
-                /*
-                DPCT1049:16: The work-group size passed to the SYCL kernel may
-                exceed the limit. To get the device limit, query
-                info::device::max_work_group_size. Adjust the work-group size if
-                needed.
-                */
-                dpct::has_capability_or_fail(stream->get_device(),
-                                             {sycl::aspect::fp16});
-
-                stream->parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        k_bin_bcast<bin_op>(src0_dd, src1_dd, dst_dd, ne0, ne1,
-                                            ne2, ne3, ne10, ne11, ne12, ne13,
-                                            s1, s2, s3, s11, s12, s13,
-                                            item_ct1);
-                    });
-            }
-        }
-    }
-};
-
-static void acc_f32_sycl(const float *x, const float *y, float *dst,
-                         const int n_elements, const int ne10, const int ne11,
-                         const int ne12, const int nb1, const int nb2,
-                         const int offset, dpct::queue_ptr stream) {
-    int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset,
-                    item_ct1);
-        });
-}
-
-static void gelu_f32_sycl(const float *x, float *dst, const int k,
-                          dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            gelu_f32(x, dst, k, item_ct1);
-        });
-}
-
-static void silu_f32_sycl(const float *x, float *dst, const int k,
-                          dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            silu_f32(x, dst, k, item_ct1);
-        });
-}
-
-static void gelu_quick_f32_sycl(const float *x, float *dst, const int k,
-                                dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            gelu_quick_f32(x, dst, k, item_ct1);
-        });
-}
-
-static void tanh_f32_sycl(const float *x, float *dst, const int k,
-                          dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            tanh_f32(x, dst, k, item_ct1);
-        });
-}
-
-static void relu_f32_sycl(const float *x, float *dst, const int k,
-                          dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            relu_f32(x, dst, k, item_ct1);
-        });
-}
-
-static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k,
-                                 dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            hardsigmoid_f32(x, dst, k, item_ct1);
-        });
-}
-
-static void hardswish_f32_sycl(const float *x, float *dst, const int k,
-                               dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            hardswish_f32(x, dst, k, item_ct1);
-        });
-}
-
-static void leaky_relu_f32_sycl(const float *x, float *dst, const int k,
-                                const float negative_slope,
-                                dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            leaky_relu_f32(x, dst, k, negative_slope, item_ct1);
-        });
-}
-
-static void sqr_f32_sycl(const float *x, float *dst, const int k,
-                         dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            sqr_f32(x, dst, k, item_ct1);
-        });
-}
-
-static void norm_f32_sycl(const float *x, float *dst, const int ncols,
-                          const int nrows, const float eps,
-                          dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
-    if (ncols < 1024) {
-        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
-                sycl::range<1>(32), cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        norm_f32(x, dst, ncols, eps, item_ct1,
-                                            s_sum_acc_ct1.get_pointer(), WARP_SIZE);
-                    });
-        });
-    } else {
-        const int work_group_size = g_work_group_size;
-        const sycl::range<3> block_dims(1, 1, work_group_size);
-        /*
-        DPCT1049:17: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
-                sycl::range<1>(32), cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        norm_f32(x, dst, ncols, eps, item_ct1,
-                                       s_sum_acc_ct1.get_pointer(), work_group_size);
-                    });
-        });
-    }
-}
-
-static void group_norm_f32_sycl(const float *x, float *dst,
-                                const int num_groups, const int group_size,
-                                const int ne_elements, dpct::queue_ptr stream) {
-    static const float eps = 1e-6f;
-    if (group_size < 1024) {
-        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
-                                                         cgh);
-
-            const float eps_ct4 = eps;
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        group_norm_f32(
-                            x, dst, group_size, ne_elements, eps_ct4, item_ct1,
-                            s_sum_acc_ct1.get_pointer(), WARP_SIZE);
-                    });
-        });
-    } else {
-        const int work_group_size = g_work_group_size;
-        const sycl::range<3> block_dims(1, 1, work_group_size);
-        /*
-        DPCT1049:18: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
-                                                         cgh);
-
-            const float eps_ct4 = eps;
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        group_norm_f32(x, dst, group_size, ne_elements,
-                                             eps_ct4, item_ct1,
-                                             s_sum_acc_ct1.get_pointer(), work_group_size);
-                    });
-        });
-    }
-}
-
-static void concat_f32_sycl(const float *x, const float *y, float *dst,
-                            const int ne0, int ne1, int ne2, int ne02,
-                            dpct::queue_ptr stream) {
-    int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE;
-    sycl::range<3> gridDim(ne2, ne1, num_blocks);
-    stream->parallel_for(
-        sycl::nd_range<3>(gridDim *
-                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            concat_f32(x, y, dst, ne0, ne02, item_ct1);
-        });
-}
-
-static void upscale_f32_sycl(const float *x, float *dst, const int ne00,
-                             const int ne01, const int ne02,
-                             const int scale_factor, dpct::queue_ptr stream) {
-    int ne0 = (ne00 * scale_factor);
-    int num_blocks = (ne0 + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE;
-    sycl::range<3> gridDim(ne02, (ne01 * scale_factor), num_blocks);
-    stream->parallel_for(
-        sycl::nd_range<3>(gridDim *
-                              sycl::range<3>(1, 1, SYCL_UPSCALE_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_UPSCALE_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            upscale_f32(x, dst, ne00, ne00 * ne01, scale_factor, item_ct1);
-        });
-}
-
-static void pad_f32_sycl(const float *x, float *dst, const int ne00,
-                         const int ne01, const int ne02, const int ne0,
-                         const int ne1, const int ne2, dpct::queue_ptr stream) {
-    int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE;
-    sycl::range<3> gridDim(ne2, ne1, num_blocks);
-    stream->parallel_for(
-        sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            pad_f32(x, dst, ne0, ne00, ne01, ne02, item_ct1);
-        });
-}
-
-static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols,
-                              const int nrows, const float eps,
-                              dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
-    // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
-    if (ncols < 1024) {
-        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
-                                                         cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        rms_norm_f32(x, dst, ncols, eps, item_ct1,
-                                                s_sum_acc_ct1.get_pointer(), WARP_SIZE);
-                    });
-        });
-    } else {
-        const int work_group_size = g_work_group_size;
-        const sycl::range<3> block_dims(1, 1, work_group_size);
-        /*
-        DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
-                                                         cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        rms_norm_f32(x, dst, ncols, eps, item_ct1,
-                                           s_sum_acc_ct1.get_pointer(), work_group_size);
-                    });
-        });
-    }
-}
-
-static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx,
-                                   const int ky, const int kx_padded,
-                                   dpct::queue_ptr stream) {
-    const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE;
-    const sycl::range<3> num_blocks(1, ky, block_num_x);
-    const sycl::range<3> block_size(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(num_blocks * block_size, block_size),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                quantize_q8_1(x, vy, kx, kx_padded, item_ct1);
-            });
-    }
-}
-
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static void dequantize_block_sycl(const void *__restrict__ vx,
-                                  dst_t *__restrict__ y, const int k,
-                                  dpct::queue_ptr stream) {
-    const int num_blocks = (k + 2*SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2*SYCL_DEQUANTIZE_BLOCK_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-        stream->parallel_for(
-            sycl::nd_range<3>(
-                sycl::range<3>(1, 1, num_blocks) *
-                    sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
-                sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                dequantize_block<qk, qr, dequantize_kernel>(vx, y, k, item_ct1);
-            });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k,
-                                     dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 64),
-                                               sycl::range<3>(1, 1, 64)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q2_K(vx, y, item_ct1);
-                             });
-    }
-#else
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q2_K(vx, y, item_ct1);
-                             });
-    }
-
-#endif
-}
-
-template <typename dst_t>
-static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k,
-                                     dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 64),
-                                               sycl::range<3>(1, 1, 64)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q3_K(vx, y, item_ct1);
-                             });
-    }
-#else
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q3_K(vx, y, item_ct1);
-                             });
-    }
-#endif
-}
-
-template <typename dst_t>
-static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int k,
-                                     dpct::queue_ptr stream) {
-    const int nb32 = k / 32;
-    const int nb = (k + 255) / 256;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q4_0(vx, y, nb32, item_ct1);
-                             });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int k,
-                                     dpct::queue_ptr stream) {
-    const int nb32 = k / 32;
-    const int nb = (k + 255) / 256;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q4_1(vx, y, nb32, item_ct1);
-                             });
-    }
-}
-
-
-template <typename dst_t>
-static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k,
-                                     dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q4_K(vx, y, item_ct1);
-                             });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k,
-                                     dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 64),
-                                               sycl::range<3>(1, 1, 64)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q5_K(vx, y, item_ct1);
-                             });
-    }
-#else
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q5_K(vx, y, item_ct1);
-                             });
-    }
-
-#endif
-}
-
-template <typename dst_t>
-static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k,
-                                     dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 64),
-                                               sycl::range<3>(1, 1, 64)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q6_K(vx, y, item_ct1);
-                             });
-    }
-#else
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q6_K(vx, y, item_ct1);
-                             });
-    }
-
-#endif
-}
-
-template <typename dst_t>
-static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k,
-                                        dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_iq1_s(
-                                     vx, y, item_ct1, iq1s_grid_gpu
-                                     );
-                             });
-        });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_iq1_m_sycl(const void *vx, dst_t *y, const int k,
-                                        dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_iq1_m(
-                                     vx, y, item_ct1, iq1s_grid_gpu
-                                     );
-                             });
-        });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int k,
-                                        dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_iq2_xxs(
-                                     vx, y, item_ct1, iq2xxs_grid,
-                                     ksigns_iq2xs, kmask_iq2xs);
-                             });
-        });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int k,
-                                       dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_iq2_xs(
-                                     vx, y, item_ct1, iq2xs_grid,
-                                     ksigns_iq2xs, kmask_iq2xs);
-                             });
-        });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_iq2_s_sycl(const void *vx, dst_t *y, const int k,
-                                      dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_iq2_s(vx, y, item_ct1);
-                             });
-        });
-    }
-}
-
-
-template <typename dst_t>
-static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k,
-                                        dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_iq3_xxs(
-                                     vx, y, item_ct1, iq3xxs_grid,
-                                     ksigns_iq2xs, kmask_iq2xs);
-                             });
-        });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int k,
-                                        dpct::queue_ptr stream) {
-    const int nb = k / QK_K;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                                   sycl::range<3>(1, 1, 32),
-                                               sycl::range<3>(1, 1, 32)),
-                             [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_iq3_s(
-                                     vx, y, item_ct1, kmask_iq2xs, iq3s_grid);
-                             });
-        });
-    }
-}
-
-template <typename dst_t>
-static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k,
-                                       dpct::queue_ptr stream) {
-    const int nb = (k + QK_K - 1) / QK_K;
-#if QK_K == 64
-    dequantize_row_iq4_nl_sycl(vx, y, k, stream);
-#else
-      {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                  cgh.parallel_for(
-                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                            sycl::range<3>(1, 1, 32),
-                                        sycl::range<3>(1, 1, 32)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                            dequantize_block_iq4_xs(vx, y, item_ct1);
-                      });
-            });
-      }
-#endif
-}
-
-
-template <typename dst_t>
-static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int k,
-                                       dpct::queue_ptr stream) {
-    const int nb = (k + QK_K - 1) / QK_K;
-      {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                  cgh.parallel_for(
-                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
-                                            sycl::range<3>(1, 1, 32),
-                                        sycl::range<3>(1, 1, 32)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                            dequantize_block_iq4_nl(vx, y, item_ct1);
-                      });
-            });
-      }
-}
-
-
-
-template <typename src_t, typename dst_t>
-static void convert_unary_sycl(const void *__restrict__ vx,
-                               dst_t *__restrict__ y, const int k,
-                               dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(
-                sycl::range<3>(1, 1, num_blocks) *
-                    sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
-                sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                convert_unary<src_t>(vx, y, k, item_ct1);
-            });
-    }
-}
-
-
-static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) try {
-    int id;
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return dequantize_block_sycl<QK4_0, QR4_0, dequantize_q4_0>;
-        case GGML_TYPE_Q4_1:
-            return dequantize_block_sycl<QK4_1, QR4_1, dequantize_q4_1>;
-        case GGML_TYPE_Q5_0:
-            return dequantize_block_sycl<QK5_0, QR5_0, dequantize_q5_0>;
-        case GGML_TYPE_Q5_1:
-            return dequantize_block_sycl<QK5_1, QR5_1, dequantize_q5_1>;
-        case GGML_TYPE_Q8_0:
-            return dequantize_block_sycl<QK8_0, QR8_0, dequantize_q8_0>;
-        case GGML_TYPE_Q2_K:
-            return dequantize_row_q2_K_sycl;
-        case GGML_TYPE_Q3_K:
-            return dequantize_row_q3_K_sycl;
-        case GGML_TYPE_Q4_K:
-            return dequantize_row_q4_K_sycl;
-        case GGML_TYPE_Q5_K:
-            return dequantize_row_q5_K_sycl;
-        case GGML_TYPE_Q6_K:
-            return dequantize_row_q6_K_sycl;
-        case GGML_TYPE_IQ1_S:
-            return dequantize_row_iq1_s_sycl;
-        case GGML_TYPE_IQ1_M:
-            return dequantize_row_iq1_m_sycl;
-        case GGML_TYPE_IQ2_XXS:
-            return dequantize_row_iq2_xxs_sycl;
-        case GGML_TYPE_IQ2_XS:
-            return dequantize_row_iq2_xs_sycl;
-        case GGML_TYPE_IQ2_S:
-            return dequantize_row_iq2_s_sycl;
-        case GGML_TYPE_IQ3_XXS:
-            return dequantize_row_iq3_xxs_sycl;
-        case GGML_TYPE_IQ3_S:
-            return dequantize_row_iq3_s_sycl;
-        case GGML_TYPE_IQ4_XS:
-            return dequantize_row_iq4_xs_sycl;
-        case GGML_TYPE_IQ4_NL:
-            return dequantize_row_iq4_nl_sycl;
-        case GGML_TYPE_F32:
-            return convert_unary_sycl<float>;
-        default:
-            return nullptr;
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return dequantize_row_q4_0_sycl;
-        case GGML_TYPE_Q4_1:
-            return dequantize_row_q4_1_sycl;
-        case GGML_TYPE_Q5_0:
-            return dequantize_block_sycl<QK5_0, QR5_0, dequantize_q5_0>;
-        case GGML_TYPE_Q5_1:
-            return dequantize_block_sycl<QK5_1, QR5_1, dequantize_q5_1>;
-        case GGML_TYPE_Q8_0:
-            return dequantize_block_sycl<QK8_0, QR8_0, dequantize_q8_0>;
-        case GGML_TYPE_Q2_K:
-            return dequantize_row_q2_K_sycl;
-        case GGML_TYPE_Q3_K:
-            return dequantize_row_q3_K_sycl;
-        case GGML_TYPE_Q4_K:
-            return dequantize_row_q4_K_sycl;
-        case GGML_TYPE_Q5_K:
-            return dequantize_row_q5_K_sycl;
-        case GGML_TYPE_Q6_K:
-            return dequantize_row_q6_K_sycl;
-        case GGML_TYPE_IQ1_S:
-            return dequantize_row_iq1_s_sycl;
-        case GGML_TYPE_IQ1_M:
-            return dequantize_row_iq1_m_sycl;
-        case GGML_TYPE_IQ2_XXS:
-            return dequantize_row_iq2_xxs_sycl;
-        case GGML_TYPE_IQ2_XS:
-            return dequantize_row_iq2_xs_sycl;
-        case GGML_TYPE_IQ2_S:
-            return dequantize_row_iq2_s_sycl;
-        case GGML_TYPE_IQ3_XXS:
-            return dequantize_row_iq3_xxs_sycl;
-        case GGML_TYPE_IQ3_S:
-            return dequantize_row_iq3_s_sycl;
-        case GGML_TYPE_IQ4_XS:
-            return dequantize_row_iq4_xs_sycl;
-        case GGML_TYPE_IQ4_NL:
-            return dequantize_row_iq4_nl_sycl;
-        case GGML_TYPE_F16:
-            return convert_unary_sycl<sycl::half>;
-        default:
-            return nullptr;
-    }
-}
-
-static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(
-                    vx, y, dst, ncols, nrows, item_ct1);
-            });
-    }
-}
-
-static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(
-                    vx, y, dst, ncols, nrows, item_ct1);
-            });
-    }
-}
-
-static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(
-                    vx, y, dst, ncols, nrows, item_ct1);
-            });
-    }
-}
-
-static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(
-                    vx, y, dst, ncols, nrows, item_ct1);
-            });
-    }
-}
-
-static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(
-                    vx, y, dst, ncols, nrows, item_ct1);
-            });
-    }
-}
-
-static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, ny, 32);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
-        });
-}
-
-static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, ny, 32);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
-        });
-}
-
-static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, ny, 32);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
-        });
-}
-
-static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const sycl::range<3> block_dims(1, 1, 32);
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1);
-        });
-}
-
-static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
-                                             float *dst, const int ncols,
-                                             const int nrows,
-                                             dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, ny, 32);
-    stream->parallel_for(
-        sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-            dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
-        });
-}
-
-static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
-                                         float *dst, const int ncols,
-                                         const int nrows,
-                                         dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols,
-                                                          nrows, item_ct1);
-            });
-    }
-}
-
-
-static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK4_0 == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK4_0, QI4_0, block_q4_0,
-                                      VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK4_1 == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
-                                      VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK5_0 == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
-                                      VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK5_1 == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
-                                      VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK8_0 == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
-                                      VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
-                                      VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
-                                      VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
-                                      VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
-                                      VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
-                                       float *dst, const int ncols,
-                                       const int nrows,
-                                       dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
-                                      VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-
-static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
-                                          float *dst, const int ncols,
-                                          const int nrows,
-                                          dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS, block_iq2_xxs, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
-                                         float *dst, const int ncols,
-                                         const int nrows,
-                                         dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
-            auto ksigns64_ptr_ct1 = &ksigns64[0];
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS, block_iq2_xs, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
-                                         float *dst, const int ncols,
-                                         const int nrows,
-                                         dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
-            auto ksigns64_ptr_ct1 = &ksigns64[0];
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S, block_iq2_s, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
-                                          float *dst, const int ncols,
-                                          const int nrows,
-                                          dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-            auto iq3xxs_grid_ptr_ct1 = &iq3xxs_grid[0];
-            auto ksigns64_ptr_ct1 = &ksigns64[0];
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS, block_iq3_xxs, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
-                                          float *dst, const int ncols,
-                                          const int nrows,
-                                          dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-            auto iq3s_grid_ptr_ct1 = &iq3s_grid[0];
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_XS, block_iq3_s, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
-                                          float *dst, const int ncols,
-                                          const int nrows,
-                                          dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-            auto iq1s_grid_ptr_ct1 = &iq1s_grid_gpu[0];
-            auto ksigns64_ptr_ct1 = &ksigns64[0];
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
-                                          float *dst, const int ncols,
-                                          const int nrows,
-                                          dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
-                                          float *dst, const int ncols,
-                                          const int nrows,
-                                          dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK4_NL == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
-                                          float *dst, const int ncols,
-                                          const int nrows,
-                                          dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
-    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
-    {
-
-        stream->submit([&](sycl::handler &cgh) {
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS, block_iq4_xs, 1>(
-                            vx, vy, dst, ncols, nrows, item_ct1);
-                    });
-        });
-    }
-}
-
-static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q4_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
-        nwarps = NWARPS_Q4_0_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q4_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
-        nwarps = NWARPS_Q4_0_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q4_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
-        nwarps = NWARPS_Q4_0_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q4_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
-        nwarps = NWARPS_Q4_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:20: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q4_0<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_qs_q4_0_acc_ct1.get_pointer(),
-                            tile_x_d_q4_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:21: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q4_0<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_qs_q4_0_acc_ct1.get_pointer(),
-                            tile_x_d_q4_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q4_1_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
-        nwarps = NWARPS_Q4_1_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q4_1_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
-        nwarps = NWARPS_Q4_1_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q4_1_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
-        nwarps = NWARPS_Q4_1_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q4_1_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
-        nwarps = NWARPS_Q4_1_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:22: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q4_1<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_qs_q4_1_acc_ct1.get_pointer(),
-                            tile_x_dm_q4_1_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:23: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q4_1<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_qs_q4_1_acc_ct1.get_pointer(),
-                            tile_x_dm_q4_1_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q5_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
-        nwarps = NWARPS_Q5_0_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q5_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
-        nwarps = NWARPS_Q5_0_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q5_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
-        nwarps = NWARPS_Q5_0_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q5_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
-        nwarps = NWARPS_Q5_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:24: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q5_0<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q5_0_acc_ct1.get_pointer(),
-                            tile_x_d_q5_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:25: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q5_0<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q5_0_acc_ct1.get_pointer(),
-                            tile_x_d_q5_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q5_1_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
-        nwarps = NWARPS_Q5_1_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q5_1_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
-        nwarps = NWARPS_Q5_1_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q5_1_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
-        nwarps = NWARPS_Q5_1_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q5_1_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
-        nwarps = NWARPS_Q5_1_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:26: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
-                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q5_1<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q5_1_acc_ct1.get_pointer(),
-                            tile_x_dm_q5_1_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:27: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
-                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q5_1<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q5_1_acc_ct1.get_pointer(),
-                            tile_x_dm_q5_1_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q8_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
-        nwarps = NWARPS_Q8_0_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q8_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
-        nwarps = NWARPS_Q8_0_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q8_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
-        nwarps = NWARPS_Q8_0_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q8_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
-        nwarps = NWARPS_Q8_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:28: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q8_0<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_qs_q8_0_acc_ct1.get_pointer(),
-                            tile_x_d_q8_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:29: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q8_0<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_qs_q8_0_acc_ct1.get_pointer(),
-                            tile_x_d_q8_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q2_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
-        nwarps = NWARPS_Q2_K_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q2_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
-        nwarps = NWARPS_Q2_K_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q2_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
-        nwarps = NWARPS_Q2_K_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q2_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
-        nwarps = NWARPS_Q2_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:30: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_q2_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q2_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q2_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q2_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q2_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:31: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_q2_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q2_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q2_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q2_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q2_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-#if QK_K == 256
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q3_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
-        nwarps = NWARPS_Q3_K_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q3_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
-        nwarps = NWARPS_Q3_K_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q3_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
-        nwarps = NWARPS_Q3_K_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q3_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
-        nwarps = NWARPS_Q3_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:32: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_qh_q3_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_q3_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q3_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q3_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q3_K_acc_ct1.get_pointer(),
-                            tile_x_qh_q3_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q3_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:33: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_qh_q3_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_q3_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q3_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q3_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q3_K_acc_ct1.get_pointer(),
-                            tile_x_qh_q3_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q3_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-#endif
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q4_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
-        nwarps = NWARPS_Q4_K_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q4_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
-        nwarps = NWARPS_Q4_K_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q4_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
-        nwarps = NWARPS_Q4_K_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q4_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
-        nwarps = NWARPS_Q4_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:34: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_q4_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q4_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q4_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q4_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q4_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:35: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_q4_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q4_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q4_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q4_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q4_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q5_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
-        nwarps = NWARPS_Q5_K_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q5_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
-        nwarps = NWARPS_Q5_K_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q5_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
-        nwarps = NWARPS_Q5_K_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q5_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
-        nwarps = NWARPS_Q5_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:36: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_q5_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q5_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q5_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q5_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q5_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:37: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_q5_K_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q5_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_q5_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q5_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q5_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
-                                        float *dst, const int ncols_x,
-                                        const int nrows_x, const int ncols_y,
-                                        const int nrows_y, const int nrows_dst,
-                                        dpct::queue_ptr stream) try {
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= VER_GEN13) {
-        mmq_x  =  MMQ_X_Q6_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
-        nwarps = NWARPS_Q6_K_RDNA2;
-    } else if (compute_capability >= VER_GEN12) {
-        mmq_x  =  MMQ_X_Q6_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
-        nwarps = NWARPS_Q6_K_RDNA1;
-    } else if (compute_capability >= VER_GEN9) {
-        mmq_x  =  MMQ_X_Q6_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
-        nwarps = NWARPS_Q6_K_AMPERE;
-    } else if (compute_capability >= VER_4VEC) {
-        mmq_x  =  MMQ_X_Q6_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
-        nwarps = NWARPS_Q6_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
-    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        /*
-        DPCT1049:38: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
-                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q6_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_acc_ct1.get_pointer(),
-                            tile_x_dm_acc_ct1.get_pointer(),
-                            tile_x_sc_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    } else {
-        const bool need_check = true;
-        /*
-        DPCT1049:39: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            stream->submit([&](sycl::handler &cgh) {
-                sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
-                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K),
-                    cgh);
-                sycl::local_accessor<int, 1> tile_x_sc_acc_ct1(
-                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
-                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
-                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
-                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
-
-                cgh.parallel_for(
-                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                    [=](sycl::nd_item<3> item_ct1) {
-                        mul_mat_q6_K<need_check>(
-                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
-                            nrows_dst, item_ct1,
-                            tile_x_ql_acc_ct1.get_pointer(),
-                            tile_x_dm_acc_ct1.get_pointer(),
-                            tile_x_sc_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
-                    });
-            });
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y,
-                                           float *dst, const int ncols_x,
-                                           const int nrows_x,
-                                           const int nchannels_x,
-                                           const int nchannels_y,
-                                           dpct::queue_ptr stream) {
-
-    const sycl::range<3> block_nums(nchannels_y, nrows_x, 1);
-    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                mul_mat_p021_f16_f32(vx, y, dst, ncols_x, nrows_x, nchannels_x,
-                                     nchannels_y, item_ct1);
-            });
-    }
-}
-
-static void ggml_mul_mat_vec_nc_f16_f32_sycl(
-    const void *vx, const float *y, float *dst, const int ncols_x,
-    const int nrows_x, const int row_stride_x, const int nchannels_x,
-    const int nchannels_y, const int channel_stride_x, dpct::queue_ptr stream) {
-
-    const sycl::range<3> block_nums(nchannels_y, nrows_x, 1);
-    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                mul_mat_vec_nc_f16_f32(vx, y, dst, ncols_x, nrows_x,
-                                       row_stride_x, channel_stride_x,
-                                       nchannels_y / nchannels_x, item_ct1);
-            });
-    }
-}
-
-static void
-ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00,
-                      const int ne01, const int ne02, const int nb00,
-                      const int nb01, const int nb02, const int nb03,
-                      const int ne10, const int ne11, const int ne12,
-                      const int nb10, const int nb11, const int nb12,
-                      const int nb13, dpct::queue_ptr stream) {
-
-    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_f16_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00,
-                                           nb01, nb02, nb03, ne10, ne11, ne12,
-                                           nb10, nb11, nb12, nb13, item_ct1);
-            });
-    }
-}
-
-static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
-                                  const int ne00, const int ne01,
-                                  const int ne02, const int nb00,
-                                  const int nb01, const int nb02,
-                                  const int nb03, const int ne10,
-                                  const int ne11, const int ne12,
-                                  const int nb10, const int nb11,
-                                  const int nb12, const int nb13,
-                                  dpct::queue_ptr stream) {
-
-    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_f32_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
-                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
-                                           item_ct1);
-            });
-    }
-}
-
-static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
-                                  const int ne00, const int ne01,
-                                  const int ne02, const int nb00,
-                                  const int nb01, const int nb02,
-                                  const int nb03, const int ne10,
-                                  const int ne11, const int ne12,
-                                  const int nb10, const int nb11,
-                                  const int nb12, const int nb13,
-                                  dpct::queue_ptr stream) {
-
-    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_f32_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
-                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
-                                           item_ct1);
-            });
-    }
-}
-
-static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne,
-                                   const int ne00, const int ne01,
-                                   const int ne02, const int nb00,
-                                   const int nb01, const int nb02,
-                                   const int nb03, const int ne10,
-                                   const int ne11, const int ne12,
-                                   const int nb10, const int nb11,
-                                   const int nb12, const int nb13,
-                                   dpct::queue_ptr stream) {
-
-    GGML_ASSERT(ne % QK8_0 == 0);
-    const int num_blocks = ne / QK8_0;
-    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
-                                           sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
-                             cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(
-                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
-                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
-                                 item_ct1);
-                         });
-}
-
-static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne,
-                                   const int ne00, const int ne01,
-                                   const int ne02, const int nb00,
-                                   const int nb01, const int nb02,
-                                   const int nb03, const int ne10,
-                                   const int ne11, const int ne12,
-                                   const int nb10, const int nb11,
-                                   const int nb12, const int nb13,
-                                   dpct::queue_ptr stream) {
-
-    GGML_ASSERT(ne % QK4_0 == 0);
-    const int num_blocks = ne / QK4_0;
-    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
-                                           sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
-                             cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(
-                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
-                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
-                                 item_ct1);
-                         });
-}
-
-static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne,
-                                   const int ne00, const int ne01,
-                                   const int ne02, const int nb00,
-                                   const int nb01, const int nb02,
-                                   const int nb03, const int ne10,
-                                   const int ne11, const int ne12,
-                                   const int nb10, const int nb11,
-                                   const int nb12, const int nb13,
-                                   dpct::queue_ptr stream) {
-
-    GGML_ASSERT(ne % QK4_1 == 0);
-    const int num_blocks = ne / QK4_1;
-    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
-                                           sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
-                             cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(
-                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
-                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
-                                 item_ct1);
-                         });
-}
-
-static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
-                                  const int ne00, const int ne01,
-                                  const int ne02, const int nb00,
-                                  const int nb01, const int nb02,
-                                  const int nb03, const int ne10,
-                                  const int ne11, const int ne12,
-                                  const int nb10, const int nb11,
-                                  const int nb12, const int nb13,
-                                  dpct::queue_ptr stream) {
-
-    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_f16_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
-                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
-                                           item_ct1);
-            });
-    }
-}
-
-static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
-                                  const int ne00, const int ne01,
-                                  const int ne02, const int nb00,
-                                  const int nb01, const int nb02,
-                                  const int nb03, const int ne10,
-                                  const int ne11, const int ne12,
-                                  const int nb10, const int nb11,
-                                  const int nb12, const int nb13,
-                                  dpct::queue_ptr stream) {
-
-    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
-    {
-        // dpct::has_capability_or_fail(stream->get_device(),
-        //                              {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_i16_i16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
-                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
-                                           item_ct1);
-            });
-    }
-}
-
-static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne,
-                                  const int ne00, const int ne01,
-                                  const int ne02, const int nb00,
-                                  const int nb01, const int nb02,
-                                  const int nb03, const int ne10,
-                                  const int ne11, const int ne12,
-                                  const int nb10, const int nb11,
-                                  const int nb12, const int nb13,
-                                  dpct::queue_ptr stream) {
-
-    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
-    {
-        // dpct::has_capability_or_fail(stream->get_device(),
-        //                              {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                cpy_f32_f16<cpy_1_i32_i32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
-                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
-                                           item_ct1);
-            });
-    }
-}
-
-static void scale_f32_sycl(const float *x, float *dst, const float scale,
-                           const int k, dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_SCALE_BLOCK_SIZE - 1) / SYCL_SCALE_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            scale_f32(x, dst, scale, k, item_ct1);
-        });
-}
-
-static void clamp_f32_sycl(const float *x, float *dst, const float min,
-                           const float max, const int k,
-                           dpct::queue_ptr stream) {
-    const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            clamp_f32(x, dst, min, max, k, item_ct1);
-        });
-}
-
-template <typename T>
-static void rope_sycl(const T *x, T *dst, int ncols, int nrows,
-                      const int32_t *pos, float freq_scale, int p_delta_rows,
-                      float freq_base, float ext_factor, float attn_factor,
-                      rope_corr_dims corr_dims, dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
-    const sycl::range<3> block_nums(1, num_blocks_x, nrows);
-    if (pos == nullptr) {
-        /*
-        DPCT1049:40: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                rope<T, false>(x, dst, ncols, pos, freq_scale, p_delta_rows,
-                               freq_base, ext_factor, attn_factor, corr_dims,
-                               item_ct1);
-            });
-    } else {
-        /*
-        DPCT1049:41: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                rope<T, true>(x, dst, ncols, pos, freq_scale, p_delta_rows,
-                              freq_base, ext_factor, attn_factor, corr_dims,
-                              item_ct1);
-            });
-    }
-}
-
-template <typename T>
-static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
-                           const int32_t *pos, float freq_scale,
-                           int p_delta_rows, float freq_base, float ext_factor,
-                           float attn_factor, rope_corr_dims corr_dims,
-                           dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
-    const sycl::range<3> block_nums(1, num_blocks_x, nrows);
-
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.0f / n_dims;
-
-    if (pos == nullptr) {
-        /*
-        DPCT1049:42: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                rope_neox<T, false>(x, dst, ncols, n_dims, pos, freq_scale,
-                                    p_delta_rows, ext_factor, attn_factor,
-                                    corr_dims, theta_scale, inv_ndims,
-                                    item_ct1);
-            });
-    } else {
-        /*
-        DPCT1049:43: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                rope_neox<T, true>(x, dst, ncols, n_dims, pos, freq_scale,
-                                   p_delta_rows, ext_factor, attn_factor,
-                                   corr_dims, theta_scale, inv_ndims, item_ct1);
-            });
-    }
-}
-
-static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows,
-                              const int32_t *pos, float freq_scale,
-                              int p_delta_rows, float freq_base, int n_ctx,
-                              dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % 4 == 0);
-    const sycl::range<3> block_dims(1, 1, SYCL_ROPE_BLOCK_SIZE / 4);
-    const int num_blocks_x = (ncols + SYCL_ROPE_BLOCK_SIZE - 1) / SYCL_ROPE_BLOCK_SIZE;
-    const sycl::range<3> block_nums(1, nrows, num_blocks_x);
-    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                         [=](sycl::nd_item<3> item_ct1) {
-                             rope_glm_f32(x, dst, ncols, pos, freq_scale,
-                                          p_delta_rows, freq_base, n_ctx,
-                                          item_ct1);
-                         });
-}
-
-static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols,
-                              const int nrows, dpct::queue_ptr stream) {
-    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-    const sycl::range<3> block_nums(1, nrows, 1);
-    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                         [=](sycl::nd_item<3> item_ct1)
-                             [[intel::reqd_sub_group_size(32)]] {
-                                 k_sum_rows_f32(x, dst, ncols, item_ct1);
-                             });
-}
-
-static int next_power_of_2(int x) {
-    int n = 1;
-    while (n < x) {
-        n *= 2;
-    }
-    return n;
-}
-
-static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
-                                 const int nrows, ggml_sort_order order,
-                                 dpct::queue_ptr stream) {
-    // bitonic sort requires ncols to be power of 2
-    const int ncols_pad = next_power_of_2(ncols);
-
-    const sycl::range<3> block_dims(1, 1, ncols_pad);
-    const sycl::range<3> block_nums(1, nrows, 1);
-    const size_t shared_mem = ncols_pad * sizeof(int);
-
-    // GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb);
-
-    if (order == GGML_SORT_ORDER_ASC) {
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
-                sycl::range<1>(shared_mem), cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    k_argsort_f32_i32<GGML_SORT_ORDER_ASC>(
-                        x, dst, ncols, ncols_pad, item_ct1,
-                        dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
-                            .get());
-                });
-        });
-    } else if (order == GGML_SORT_ORDER_DESC) {
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
-                sycl::range<1>(shared_mem), cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    k_argsort_f32_i32<GGML_SORT_ORDER_DESC>(
-                        x, dst, ncols, ncols_pad, item_ct1,
-                        dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
-                            .get());
-                });
-        });
-    } else {
-        GGML_ASSERT(false);
-    }
-}
-
-static void diag_mask_inf_f32_sycl(const float *x, float *dst,
-                                   const int ncols_x, const int nrows_x,
-                                   const int rows_per_channel, const int n_past,
-                                   dpct::queue_ptr stream) {
-    const sycl::range<3> block_dims(1, SYCL_DIAG_MASK_INF_BLOCK_SIZE, 1);
-    const int block_num_x = (ncols_x + SYCL_DIAG_MASK_INF_BLOCK_SIZE - 1) / SYCL_DIAG_MASK_INF_BLOCK_SIZE;
-    const sycl::range<3> block_nums(1, block_num_x, nrows_x);
-    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                         [=](sycl::nd_item<3> item_ct1) {
-                             diag_mask_inf_f32(x, dst, ncols_x,
-                                               rows_per_channel, n_past,
-                                               item_ct1);
-                         });
-}
-
-template <bool vals_smem, int ncols_template, int block_size_template>
-static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par,
-                                   const int nrows_y, const float scale, const float max_bias, const float m0,
-                                   const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
-                                   const size_t n_local_scratch, dpct::queue_ptr stream) {
-    stream->submit([&](sycl::handler &cgh) {
-        sycl::local_accessor<float, 1> local_buf_acc(n_local_scratch, cgh);
-
-        cgh.parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
-                                                                             nrows_y, scale, max_bias, m0,
-                                                                             m1, n_head_log2, item_ct1,
-                                                                             local_buf_acc.get_pointer());
-            });
-    });
-}
-
-static void soft_max_f32_sycl(const float * x, const float * mask,
-                              float * dst, const int ncols_x, const int nrows_x,
-                              const int nrows_y, const float scale, const float max_bias,
-                              dpct::queue_ptr stream) {
-    int nth = WARP_SIZE;
-    int max_block_size = g_work_group_size;
-    while (nth < ncols_x && nth < max_block_size) nth *= 2;
-    if (nth>max_block_size) nth = max_block_size;
-
-    const sycl::range<3> block_dims(1, 1, nth);
-    const sycl::range<3> block_nums(1, 1, nrows_x);
-    const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE);
-
-    const uint32_t n_head_kv   = nrows_x/nrows_y;
-    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
-
-    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
-
-    const size_t local_mem_size = stream->get_device().get_info<sycl::info::device::local_mem_size>();
-    if (n_local_scratch*sizeof(float) < local_mem_size) {
-        if (ncols_x > max_block_size) {
-            soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
-                                               max_bias, m0, m1, n_head_log2, block_nums,
-                                               block_dims, n_local_scratch, stream);
-            return;
-        }
-        switch (ncols_x) {
-            case 32:
-                soft_max_f32_submitter<true, 32, 32>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                     max_bias, m0, m1, n_head_log2, block_nums,
-                                                     block_dims, n_local_scratch, stream);
-                break;
-            case 64:
-                soft_max_f32_submitter<true, 64, 64>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                     max_bias, m0, m1, n_head_log2, block_nums,
-                                                     block_dims, n_local_scratch, stream);
-                break;
-            case 128:
-                soft_max_f32_submitter<true, 128, 128>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                       max_bias, m0, m1, n_head_log2, block_nums,
-                                                       block_dims, n_local_scratch, stream);
-                break;
-            case 256:
-                soft_max_f32_submitter<true, 256, 256>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                       max_bias, m0, m1, n_head_log2, block_nums,
-                                                       block_dims, n_local_scratch, stream);
-                break;
-            case 512:
-                soft_max_f32_submitter<true, 512, 512>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                       max_bias, m0, m1, n_head_log2, block_nums,
-                                                       block_dims, n_local_scratch, stream);
-                break;
-            case 1024:
-                soft_max_f32_submitter<true, 1024, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                         max_bias, m0, m1, n_head_log2, block_nums,
-                                                         block_dims, n_local_scratch, stream);
-                break;
-            case 2048:
-                soft_max_f32_submitter<true, 2048, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                         max_bias, m0, m1, n_head_log2, block_nums,
-                                                         block_dims, n_local_scratch, stream);
-                break;
-            case 4096:
-                soft_max_f32_submitter<true, 4096, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                         max_bias, m0, m1, n_head_log2, block_nums,
-                                                         block_dims, n_local_scratch, stream);
-                break;
-            default:
-                soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
-                                                   max_bias, m0, m1, n_head_log2, block_nums,
-                                                   block_dims, n_local_scratch, stream);
-                break;
-        }
-    } else {
-        soft_max_f32_submitter<false, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
-                                            max_bias, m0, m1, n_head_log2, block_nums,
-                                            block_dims, WARP_SIZE, stream);
-    }
-}
-
-template <typename T>
-static void im2col_sycl(const float *x, T *dst, int IW, int IH,
-                                int OW, int OH, int KW, int KH, int IC,
-                                int offset_delta, int s0, int s1, int p0,
-                                int p1, int d0, int d1,
-                                dpct::queue_ptr stream) {
-    const int parallel_elements = OW * KW * KH;
-    const int num_blocks = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE;
-    sycl::range<3> block_nums(IC, OH, num_blocks);
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums *
-                                  sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
-                im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH,
-                               parallel_elements, (IC * KH * KW), s0, s1, p0,
-                               p1, d0, d1, item_ct1);
-            });
-    }
-}
-
-// buffer pool for sycl
-#define MAX_SYCL_BUFFERS 256
-
-struct scoped_spin_lock {
-    std::atomic_flag& lock;
-    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
-        while (lock.test_and_set(std::memory_order_acquire)) {
-            ; // spin
-        }
-    }
-    ~scoped_spin_lock() {
-        lock.clear(std::memory_order_release);
-    }
-    scoped_spin_lock(const scoped_spin_lock&) = delete;
-    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
-};
-
-static std::atomic_flag g_sycl_pool_lock = ATOMIC_FLAG_INIT;
-
-// #define DEBUG_SYCL_MALLOC
-struct sycl_buffer {
-    void * ptr = nullptr;
-    size_t size = 0;
-};
-
-static sycl_buffer g_sycl_buffer_pool[GGML_SYCL_MAX_DEVICES][MAX_SYCL_BUFFERS];
-static size_t g_sycl_pool_size[GGML_SYCL_MAX_DEVICES] = {0};
-
-static void *ggml_sycl_pool_malloc_leg(int device_index, size_t size, size_t *actual_size) try {
-    scoped_spin_lock lock(g_sycl_pool_lock);
-    // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg device_index %d size=%lu\n", device_index, size);
-#ifdef DEBUG_SYCL_MALLOC
-    int nnz = 0;
-    size_t max_size = 0;
-#endif
-    size_t best_diff = 1ull << 36;
-    int ibest = -1;
-    for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
-        sycl_buffer& b = g_sycl_buffer_pool[device_index][i];
-        if (b.ptr != nullptr) {
-#ifdef DEBUG_SYCL_MALLOC
-            ++nnz;
-            if (b.size > max_size) max_size = b.size;
-#endif
-            if (b.size >= size) {
-                size_t diff = b.size - size;
-                if (diff < best_diff) {
-                    best_diff = diff;
-                    ibest = i;
-                    if (!best_diff) {
-                        void * ptr = b.ptr;
-                        *actual_size = b.size;
-                        b.ptr = nullptr;
-                        b.size = 0;
-                        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p and rm in pool\n", ptr);
-                        return ptr;
-                    }
-                }
-            }
-        }
-    }
-    if (ibest >= 0) {
-        sycl_buffer& b = g_sycl_buffer_pool[device_index][ibest];
-        void * ptr = b.ptr;
-        *actual_size = b.size;
-        b.ptr = nullptr;
-        b.size = 0;
-        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p and rm in pool\n", ptr);
-        return ptr;
-    }
-    void * ptr;
-    size_t look_ahead_size = (size_t) (1.05 * size);
-    look_ahead_size = 256 * ((look_ahead_size + 255)/256);
-
-    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device(
-                             look_ahead_size, *stream)));
-    *actual_size = look_ahead_size;
-    g_sycl_pool_size[device_index] += look_ahead_size;
-
-#ifdef DEBUG_SYCL_MALLOC
-    fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz,
-            (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024));
-#endif
-    // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr);
-    return ptr;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_sycl_pool_free_leg(int device_index, void *ptr, size_t size) try {
-    scoped_spin_lock lock(g_sycl_pool_lock);
-    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
-    for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
-        sycl_buffer& b = g_sycl_buffer_pool[device_index][i];
-        if (b.ptr == nullptr) {
-            b.ptr = ptr;
-            b.size = size;
-            return;
-        }
-    }
-    fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_SYCL_BUFFERS\n");
-    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *stream)));
-    g_sycl_pool_size[device_index] -= size;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-// pool with virtual memory
-/*
-DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported.
-*/
-// static std::vector<CUmemGenericAllocationHandle>
-//     g_sycl_pool_handles[GGML_SYCL_MAX_DEVICES];
-static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0};
-static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0};
-
-static void *ggml_sycl_pool_malloc_vmm(int device_index, size_t size, size_t *actual_size) try {
-    GGML_UNUSED(device_index);
-    GGML_UNUSED(size);
-    GGML_UNUSED(actual_size);
-    return NULL;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_sycl_pool_free_vmm(int device_index, void *ptr, size_t size) try {
-    scoped_spin_lock lock(g_sycl_pool_lock);
-#ifdef DEBUG_SYCL_MALLOC
-    printf("sycl pool[%d]: freed %llu bytes at %llx\n", device_index, (unsigned long long) size, ptr);
-#endif
-
-    g_sycl_pool_used[device_index] -= size;
-
-    // all deallocations must be in reverse order of the allocations
-    GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[device_index] + g_sycl_pool_used[device_index]));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void *ggml_sycl_pool_malloc(int device_index, size_t size, size_t *actual_size) try {
-    if (g_device_caps[device_index].vmm) {
-        return ggml_sycl_pool_malloc_vmm(device_index, size, actual_size);
-    } else {
-        return ggml_sycl_pool_malloc_leg(device_index, size, actual_size);
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_sycl_pool_free(int device_index, void *ptr, size_t size) try {
-    if (g_device_caps[device_index].vmm) {
-        ggml_sycl_pool_free_vmm(device_index, ptr, size);
-    } else {
-        ggml_sycl_pool_free_leg(device_index, ptr, size);
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-
-template<typename T>
-struct sycl_pool_alloc {
-    int device_index = -1;
-    int device_id = -1;
-    T * ptr = nullptr;
-    size_t actual_size = 0;
-
-    // size is in number of elements
-    T * alloc(size_t size) {
-        GGML_ASSERT(ptr == nullptr);
-        device_id = get_current_device_id();
-        device_index = g_sycl_gpu_mgr->get_index(device_id);
-        ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size);
-        // GGML_SYCL_DEBUG("sycl_pool_alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size);
-        return ptr;
-    }
-
-    sycl_pool_alloc(size_t size) {
-        alloc(size);
-    }
-
-    ~sycl_pool_alloc() {
-        if (ptr != nullptr) {
-            ggml_sycl_pool_free(device_index, ptr, actual_size);
-        }
-    }
-
-    T * get() {
-        return ptr;
-    }
-
-    sycl_pool_alloc() = default;
-    sycl_pool_alloc(const sycl_pool_alloc &) = delete;
-    sycl_pool_alloc(sycl_pool_alloc &&) = delete;
-    sycl_pool_alloc& operator=(const sycl_pool_alloc &) = delete;
-    sycl_pool_alloc& operator=(sycl_pool_alloc &&) = delete;
-};
-
-static bool g_sycl_loaded = false;
-
-bool ggml_sycl_loaded(void) {
-    return g_sycl_loaded;
-}
-
-void print_device_detail(int id, sycl::device &device, std::string device_type) {
-
-    dpct::device_info prop;
-    SYCL_CHECK(CHECK_TRY_ERROR(
-        dpct::get_device_info(prop, device)));
-
-    std::string version;
-    version += std::to_string(prop.get_major_version());
-    version += ".";
-    version += std::to_string(prop.get_minor_version());
-
-    device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), "");
-    std::string name = std::string(prop.get_name());
-    name = std::regex_replace(name, std::regex("\\(R\\)"), "");
-    name = std::regex_replace(name, std::regex("\\(TM\\)"), "");
-
-    auto global_mem_size = prop.get_global_mem_size()/1000000;
-
-    fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(),
-            name.c_str(), version.c_str(), prop.get_max_compute_units(),
-            prop.get_max_work_group_size(), prop.get_max_sub_group_size(),
-            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str());
-}
-
-void ggml_backend_sycl_print_sycl_devices() {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n");
-    int device_count = dpct::dev_mgr::instance().device_count();
-    std::map<std::string, size_t> DeviceNums;
-    fprintf(stderr, "found %d SYCL devices:\n", device_count);
-    fprintf(stderr, "|  |                   |                                       |       |Max    |        |Max  |Global |                     |\n");
-    fprintf(stderr, "|  |                   |                                       |       |compute|Max work|sub  |mem    |                     |\n");
-    fprintf(stderr, "|ID|        Device Type|                                   Name|Version|units  |group   |group|size   |       Driver version|\n");
-    fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n");
-    for (int id = 0; id < device_count; ++id) {
-        sycl::device device = dpct::dev_mgr::instance().get_device(id);
-        sycl::backend backend = device.get_backend();
-        std::string backend_type = get_device_backend_and_type(device);
-        int type_id=DeviceNums[backend_type]++;
-        std::stringstream device_type;
-        device_type << "[" <<  backend_type << ":" << std::to_string(type_id) << "]";
-        print_device_detail(id, device, device_type.str());
-    }
-}
-
-void print_gpu_device_list() {
-    GGML_ASSERT(g_sycl_gpu_mgr);
-
-    char* hint=NULL;
-    if (g_ggml_sycl_backend_gpu_mode == SYCL_SINGLE_GPU_MODE) {
-        hint = "use %d SYCL GPUs: [%s] with Max compute units:%d\n";
-    } else {
-        hint = "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n";
-    }
-    fprintf(stderr, hint,
-        g_sycl_gpu_mgr->get_gpu_count(),
-        g_sycl_gpu_mgr->gpus_list.c_str(),
-        g_sycl_gpu_mgr->max_compute_units);
-}
-
-int get_sycl_env(const char *env_name, int default_val) {
-    char *user_device_string = getenv(env_name);
-    int user_number = default_val;
-
-    unsigned n;
-    if (user_device_string != NULL &&
-        sscanf(user_device_string, " %u", &n) == 1) {
-        user_number = (int)n;
-    } else {
-        user_number = default_val;
-    }
-    return user_number;
-}
-
-int get_work_group_size(int user_device_id) {
-    dpct::device_info prop;
-    dpct::get_device_info(prop,
-                          dpct::dev_mgr::instance().get_device(user_device_id));
-    return prop.get_max_work_group_size();
-}
-
-static void ggml_init_sycl() try {
-    static bool initialized = false;
-
-    if (!initialized) {
-        fprintf(stderr, "[SYCL] call ggml_init_sycl\n");
-        g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0);
-
-        fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug);
-
-#if defined(GGML_SYCL_F16)
-        fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__);
-#else
-        fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__);
-#endif
-
-/* NOT REMOVE, keep it for next optimize for XMX.
-#if defined(SYCL_USE_XMX)
-        fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__);
-#else
-        fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__);
-#endif
-*/
-
-        if (CHECK_TRY_ERROR(g_all_sycl_device_count =
-                            dpct::dev_mgr::instance().device_count()) != 0) {
-            initialized = true;
-            g_sycl_loaded = false;
-            return;
-        }
-        GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES);
-        ggml_backend_sycl_print_sycl_devices();
-        initialized = true;
-        g_sycl_loaded = true;
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-void ggml_init_by_gpus(int device_count) try {
-    g_device_count = device_count;
-    g_work_group_size = g_sycl_gpu_mgr->work_group_size;
-
-    int64_t total_vram = 0;
-
-    print_gpu_device_list();
-
-    for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) {
-        g_device_caps[id].vmm = 0;
-        g_device_caps[id].device_id = -1;
-        g_device_caps[id].cc = 0;
-        g_tensor_split[id] = 0;
-        g_default_tensor_split[id] = 0;
-    }
-
-    for (int i = 0; i < g_device_count; ++i) {
-        int device_id = g_sycl_gpu_mgr->gpus[i];
-        g_device_caps[i].vmm = 0;
-
-        dpct::device_info prop;
-        SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
-            prop, dpct::dev_mgr::instance().get_device(device_id))));
-
-        g_default_tensor_split[i] = total_vram;
-        total_vram += prop.get_global_mem_size();
-
-        g_device_caps[i].cc =
-            100 * prop.get_major_version() + 10 * prop.get_minor_version();
-    }
-
-    for (int i = 0; i < g_device_count; ++i) {
-        g_default_tensor_split[i] /= total_vram;
-    }
-
-    for (int i = 0; i < g_device_count; ++i) {
-        SYCL_CHECK(ggml_sycl_set_device(i));
-
-        // create sycl streams
-        for (int is = 0; is < MAX_STREAMS; ++is) {
-            SYCL_CHECK(CHECK_TRY_ERROR(
-                g_syclStreams[i][is] =
-                    dpct::get_current_device().create_queue(
-                        g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device())));
-        }
-
-        const dpct::queue_ptr stream = g_syclStreams[i][0];
-        // create sycl handle
-        SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream));
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-void *ggml_sycl_host_malloc(size_t size) try {
-    if (getenv("GGML_SYCL_NO_PINNED") != nullptr) {
-        return nullptr;
-    }
-
-    void * ptr = nullptr;
-    //allow to use dpct::get_in_order_queue() for host malloc
-    dpct::err0 err = CHECK_TRY_ERROR(
-        ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue()));
-
-    if (err != 0) {
-        // clear the error
-        fprintf(
-            stderr,
-            "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
-            size / 1024.0 / 1024.0,
-            "syclGetErrorString is not supported");
-        return nullptr;
-    }
-
-    return ptr;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-void ggml_sycl_host_free(void *ptr) try {
-    //allow to use dpct::get_in_order_queue() for host malloc
-    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, dpct::get_in_order_queue())));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst,
-                                          const struct ggml_tensor *src,
-                                          int64_t i3, int64_t i2,
-                                          int64_t i1_low, int64_t i1_high,
-                                          dpct::queue_ptr stream) try {
-
-    dpct::memcpy_direction kind;
-    char * src_ptr;
-    if (src->backend == GGML_BACKEND_TYPE_CPU) {
-        kind = dpct::host_to_device;
-        src_ptr = (char *) src->data;
-        // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d  GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr);
-    } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
-        GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
-        kind = dpct::device_to_device;
-        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
-        int id;
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            id = get_current_device_id()));
-        // GGML_SYCL_DEBUG("current device index %d\n", id);
-        src_ptr = (char *) extra->data_device[id];
-    } else {
-        // GGML_SYCL_DEBUG("GGML_ASSERT(false)\n");
-        GGML_ASSERT(false);
-    }
-    char * dst_ptr = (char *) dst;
-
-    GGML_TENSOR_LOCALS_1(int64_t, ne, src, ne);
-    GGML_TENSOR_LOCALS(int64_t, nb, src, nb);
-    const enum ggml_type type = src->type;
-    const int64_t ts = ggml_type_size(type);
-    const int64_t bs = ggml_blck_size(type);
-    int64_t i1_diff = i1_high - i1_low;
-
-    const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
-    if (nb0 == ts && nb1 == ts*ne0/bs) {
-        // GGML_SYCL_DEBUG("stream->memcpy: dst_ptr=%p, x=%p, size=%lu\n", dst_ptr, x, i1_diff * nb1);
-        // return CHECK_TRY_ERROR(stream->memcpy(dst_ptr, x, i1_diff * nb1));
-        return CHECK_TRY_ERROR(dpct::async_dpct_memcpy(dst_ptr, x, i1_diff * nb1,
-                                    kind, *stream));
-
-    } else if (nb0 == ts) {
-        return CHECK_TRY_ERROR(
-            dpct::async_dpct_memcpy(dst_ptr, ts * ne0 / bs, x, nb1,
-                                    ts * ne0 / bs, i1_diff, kind, *stream));
-    } else {
-        for (int64_t i1 = 0; i1 < i1_diff; i1++) {
-            const void * rx = (const void *) ((const char *) x + i1*nb1);
-            void * rd = (void *) (dst_ptr + i1*ts*ne0/bs);
-            // pretend the row is a matrix with cols=1
-            dpct::err0 r = CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
-                rd, ts / bs, rx, nb0, ts / bs, ne0, kind, *stream));
-            /*
-            DPCT1001:85: The statement could not be removed.
-            */
-            /*
-            DPCT1000:86: Error handling if-stmt was detected but could not be
-            rewritten.
-            */
-            if (r != 0) return r;
-        }
-        return 0;
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_sycl_op_get_rows(const ggml_tensor *src0,
-                                  const ggml_tensor *src1, ggml_tensor *dst,
-                                  const float *src0_d, const float *src1_d,
-                                  float *dst_d, const dpct::queue_ptr &stream) {
-
-    GGML_ASSERT(src1->type == GGML_TYPE_I32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
-    GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
-    GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
-
-    const int32_t * src1_i32 = (const int32_t *) src1_d;
-
-    switch (src0->type) {
-        case GGML_TYPE_F16:
-            get_rows_sycl_float(src0, src1, dst, (const sycl::half *)src0_d,
-                                src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_F32:
-            get_rows_sycl_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q4_0:
-            get_rows_sycl<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            get_rows_sycl<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            get_rows_sycl<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            get_rows_sycl<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            get_rows_sycl<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        default:
-            // TODO: k-quants
-            fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
-            GGML_ASSERT(false);
-            break;
-    }
-}
-
-template <class op>
-inline void ggml_sycl_op_bin_bcast(const ggml_tensor *src0,
-                                   const ggml_tensor *src1, ggml_tensor *dst,
-                                   const float *src0_dd, const float *src1_dd,
-                                   float *dst_dd,
-                                   const dpct::queue_ptr &main_stream) {
-
-    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-        op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-        op()(src0, src1, dst, (const sycl::half *)src0_dd, src1_dd,
-             (sycl::half *)dst_dd, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
-        op()(src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd,
-             main_stream);
-    } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) {
-        op()(src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd,
-             main_stream);
-    } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) {
-        op()(src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd,
-             main_stream);
-    } else {
-        fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
-            ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
-    }
-}
-
-static void ggml_sycl_op_repeat(const ggml_tensor *src0,
-                                const ggml_tensor *src1, ggml_tensor *dst,
-                                const float *src0_d, const float *src1_d,
-                                float *dst_d,
-                                const dpct::queue_ptr &main_stream) {
-
-    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_repeat>>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream);
-
-    (void) src1;
-    (void) src1_d;
-}
-
-inline void ggml_sycl_op_add(const ggml_tensor *src0, const ggml_tensor *src1,
-                             ggml_tensor *dst, const float *src0_dd,
-                             const float *src1_dd, float *dst_dd,
-                             const dpct::queue_ptr &main_stream) {
-
-    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_add>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-inline void ggml_sycl_op_acc(const ggml_tensor *src0, const ggml_tensor *src1,
-                             ggml_tensor *dst, const float *src0_dd,
-                             const float *src1_dd, float *dst_dd,
-                             const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
-
-    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
-    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
-    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
-    int offset = dst->op_params[3] / 4; // offset in bytes
-
-    acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream);
-
-    (void) dst;
-}
-
-inline void ggml_sycl_op_mul(const ggml_tensor *src0, const ggml_tensor *src1,
-                             ggml_tensor *dst, const float *src0_dd,
-                             const float *src1_dd, float *dst_dd,
-                             const dpct::queue_ptr &main_stream) {
-
-    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_mul>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-inline void ggml_sycl_op_div(const ggml_tensor *src0, const ggml_tensor *src1,
-                             ggml_tensor *dst, const float *src0_dd,
-                             const float *src1_dd, float *dst_dd,
-                             const dpct::queue_ptr &main_stream) {
-
-    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_div>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-inline void ggml_sycl_op_gelu(const ggml_tensor *src0, const ggml_tensor *src1,
-                              ggml_tensor *dst, const float *src0_dd,
-                              const float *src1_dd, float *dst_dd,
-                              const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_silu(const ggml_tensor *src0, const ggml_tensor *src1,
-                              ggml_tensor *dst, const float *src0_dd,
-                              const float *src1_dd, float *dst_dd,
-                              const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_gelu_quick(const ggml_tensor *src0,
-                                    const ggml_tensor *src1, ggml_tensor *dst,
-                                    const float *src0_dd, const float *src1_dd,
-                                    float *dst_dd,
-                                    const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1,
-                              ggml_tensor *dst, const float *src0_dd,
-                              const float *src1_dd, float *dst_dd,
-                              const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1,
-                              ggml_tensor *dst, const float *src0_dd,
-                              const float *src1_dd, float *dst_dd,
-                              const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_sycl_op_hardsigmoid(const ggml_tensor *src0,
-                                     const ggml_tensor *src1, ggml_tensor *dst,
-                                     const float *src0_dd, const float *src1_dd,
-                                     float *dst_dd,
-                                     const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_sycl_op_hardswish(const ggml_tensor *src0,
-                                   const ggml_tensor *src1, ggml_tensor *dst,
-                                   const float *src0_dd, const float *src1_dd,
-                                   float *dst_dd, const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0,
-                                    const ggml_tensor *src1, ggml_tensor *dst,
-                                    const float *src0_dd, const float *src1_dd,
-                                    float *dst_dd,
-                                    const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float negative_slope;
-    memcpy(&negative_slope, dst->op_params, sizeof(float));
-
-    leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_sqr(const ggml_tensor *src0, const ggml_tensor *src1,
-                             ggml_tensor *dst, const float *src0_dd,
-                             const float *src1_dd, float *dst_dd,
-                             const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_norm(const ggml_tensor *src0, const ggml_tensor *src1,
-                              ggml_tensor *dst, const float *src0_dd,
-                              const float *src1_dd, float *dst_dd,
-                              const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
-    norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_group_norm(const ggml_tensor *src0,
-                                    const ggml_tensor *src1, ggml_tensor *dst,
-                                    const float *src0_dd, const float *src1_dd,
-                                    float *dst_dd,
-                                    const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    int num_groups = dst->op_params[0];
-    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
-    group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_concat(const ggml_tensor *src0,
-                                const ggml_tensor *src1, ggml_tensor *dst,
-                                const float *src0_dd, const float *src1_dd,
-                                float *dst_dd,
-                                const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
-        concat_f32_sycl(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream);
-    }
-
-    (void) src1;
-    (void) dst;
-}
-
-inline void ggml_sycl_op_upscale(const ggml_tensor *src0,
-                                 const ggml_tensor *src1, ggml_tensor *dst,
-                                 const float *src0_dd, const float *src1_dd,
-                                 float *dst_dd,
-                                 const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
-
-#pragma message("TODO: generalize upscale operator")
-#pragma message("      https://github.com/ggerganov/ggml/pull/814")
-    GGML_ASSERT(false && "TODO: generalize upscale operator");
-
-    const int scale_factor = dst->op_params[0];
-
-    upscale_f32_sycl(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_pad(const ggml_tensor *src0, const ggml_tensor *src1,
-                             ggml_tensor *dst, const float *src0_dd,
-                             const float *src1_dd, float *dst_dd,
-                             const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
-
-    pad_f32_sycl(src0_dd, dst_dd,
-        src0->ne[0], src0->ne[1], src0->ne[2],
-        dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_rms_norm(const ggml_tensor *src0,
-                                  const ggml_tensor *src1, ggml_tensor *dst,
-                                  const float *src0_dd, const float *src1_dd,
-                                  float *dst_dd,
-                                  const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
-    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_mul_mat_q(
-    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
-    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
-    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
-    const int64_t src1_ncols, const int64_t src1_padded_row_size,
-    const dpct::queue_ptr &stream) try {
-
-    const int64_t ne00 = src0->ne[0];
-
-    const int64_t ne10 = src1->ne[0];
-    GGML_ASSERT(ne10 % QK8_1 == 0);
-
-    const int64_t ne0 = dst->ne[0];
-
-    const int64_t row_diff = row_high - row_low;
-
-    int device_id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(device_id = get_current_device_id()));
-
-    // the main device has a larger memory buffer to hold the results from all GPUs
-    // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff;
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            ggml_mul_mat_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            ggml_mul_mat_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            ggml_mul_mat_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            ggml_mul_mat_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            ggml_mul_mat_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            ggml_mul_mat_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            ggml_mul_mat_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            ggml_mul_mat_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            ggml_mul_mat_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            ggml_mul_mat_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddf_i;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split) {
-    int64_t min_compute_capability = INT_MAX;
-    int64_t max_compute_capability = INT_MIN;
-    for (int i = 0; i < g_device_count; ++i) {
-        if (tensor_split[i] < (i + 1 < g_device_count ? tensor_split[i + 1] : 1.0f)) {
-            if (min_compute_capability > g_device_caps[i].cc) {
-                min_compute_capability = g_device_caps[i].cc;
-            }
-            if (max_compute_capability < g_device_caps[i].cc) {
-                max_compute_capability = g_device_caps[i].cc;
-            }
-        }
-    }
-
-    switch(type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-            return max_compute_capability >= VER_GEN9 ? 128 : 64;
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return 64;
-        case GGML_TYPE_F16:
-        case GGML_TYPE_F32:
-            return 1;
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_IQ2_XXS:
-        case GGML_TYPE_IQ2_XS:
-        case GGML_TYPE_IQ2_S:
-        case GGML_TYPE_IQ1_S:
-        case GGML_TYPE_IQ1_M:
-        case GGML_TYPE_IQ3_XXS:
-        case GGML_TYPE_IQ4_XS:
-        case GGML_TYPE_IQ4_NL:
-            return max_compute_capability >= VER_GEN9 ? 128 : 64;
-        case GGML_TYPE_IQ3_S:
-            return max_compute_capability >= VER_GEN9 ? 128 : 64;
-        case GGML_TYPE_Q6_K:
-            return 64;
-        default:
-            GGML_ASSERT(false);
-    }
-
-}
-
-inline void ggml_sycl_op_mul_mat_vec_q(
-    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
-    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
-    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
-    const int64_t src1_ncols, const int64_t src1_padded_row_size,
-    const dpct::queue_ptr &stream) {
-
-    const int64_t ne10 = src1->ne[0];
-    GGML_ASSERT(ne10 % QK8_1 == 0);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t row_diff = row_high - row_low;
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-
-    // the main device has a larger memory buffer to hold the results from all GPUs
-    // nrows_dst == nrows of the matrix that the kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne00 : row_diff;
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ1_S:
-            mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ1_M:
-            mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ2_XXS:
-            mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ2_XS:
-            mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ2_S:
-            mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ3_XXS:
-            mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ3_S:
-            mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ4_NL:
-            mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ4_XS:
-            mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddf_i;
-    (void) src1_ncols;
-    (void) src1_padded_row_size;
-}
-
-
-inline void ggml_sycl_op_dequantize_mul_mat_vec(
-    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
-    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
-    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
-    const int64_t src1_ncols, const int64_t src1_padded_row_size,
-    const dpct::queue_ptr &stream) {
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t row_diff = row_high - row_low;
-
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
-#ifdef GGML_SYCL_F16
-    sycl_pool_alloc<sycl::half> src1_dfloat_a;
-    sycl::half *src1_dfloat = nullptr; // dfloat == half
-
-    bool src1_convert_f16 =
-        src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
-        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
-        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
-
-    if (src1_convert_f16) {
-        src1_dfloat = src1_dfloat_a.alloc(ne00);
-        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
-        GGML_ASSERT(to_fp16_sycl != nullptr);
-        to_fp16_sycl(src1_ddf_i, src1_dfloat, ne00, stream);
-    }
-#else
-    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
-#endif // GGML_SYCL_F16
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            dequantize_mul_mat_vec_q4_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            dequantize_mul_mat_vec_q4_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            dequantize_mul_mat_vec_q5_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            dequantize_mul_mat_vec_q5_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            dequantize_mul_mat_vec_q8_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            dequantize_mul_mat_vec_q2_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            dequantize_mul_mat_vec_q3_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            dequantize_mul_mat_vec_q4_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            dequantize_mul_mat_vec_q5_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            dequantize_mul_mat_vec_q6_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_F16:
-            convert_mul_mat_vec_f16_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        default:
-            printf("ggml_sycl_op_dequantize_mul_mat_vec unsupported GGML_TYPE %d\n", src0->type);
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddq_i;
-    (void) src1_ncols;
-    (void) src1_padded_row_size;
-}
-
-inline void ggml_sycl_op_mul_mat_sycl(
-    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
-    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
-    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
-    const int64_t src1_ncols, const int64_t src1_padded_row_size,
-    const dpct::queue_ptr &stream) try {
-
-    GGML_ASSERT(src0_dd_i  != nullptr);
-    GGML_ASSERT(src1_ddf_i != nullptr);
-    GGML_ASSERT(dst_dd_i   != nullptr);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne10 = src1->ne[0];
-
-    const int64_t ne0 = dst->ne[0];
-
-    const int64_t row_diff = row_high - row_low;
-
-    int id;
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(id = get_current_device_id()));
-
-    // the main device has a larger memory buffer to hold the results from all GPUs
-    // ldc == nrows of the matrix that cuBLAS writes into
-    int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
-
-#ifdef GGML_SYCL_F16
-    bool use_fp16 = true;  // TODO(Yu) SYCL capability check
-#else
-    bool use_fp16 = false;
-#endif
-    if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
-        use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] &&
-        dst->op_params[0] == GGML_PREC_DEFAULT) {
-
-        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n");
-        sycl_pool_alloc<sycl::half> src0_as_f16;
-        if (src0->type != GGML_TYPE_F16) {
-            const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src0->type);
-            GGML_ASSERT(to_fp16_sycl != nullptr);
-            size_t ne = row_diff*ne00;
-            src0_as_f16.alloc(ne);
-            to_fp16_sycl(src0_dd_i, src0_as_f16.get(), ne, stream);
-        }
-        const sycl::half *src0_ptr = src0->type == GGML_TYPE_F16
-                                         ? (const sycl::half *)src0_dd_i
-                                         : src0_as_f16.get();
-
-        sycl_pool_alloc<sycl::half> src1_as_f16;
-        if (src1->type != GGML_TYPE_F16) {
-            const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
-            GGML_ASSERT(to_fp16_sycl != nullptr);
-            size_t ne = src1_ncols*ne10;
-            src1_as_f16.alloc(ne);
-            to_fp16_sycl(src1_ddf_i, src1_as_f16.get(), ne, stream);
-        }
-        const sycl::half *src1_ptr = src1->type == GGML_TYPE_F16
-                ? (const sycl::half *)src1->data + src1_padded_row_size
-                                         : src1_as_f16.get();
-        sycl_pool_alloc<sycl::half> dst_f16(row_diff * src1_ncols);
-
-        const sycl::half alpha_f16 = 1.0f;
-        const sycl::half beta_f16 = 0.0f;
-        SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream));
-        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
-            *g_sycl_handles[id], oneapi::mkl::transpose::trans,
-            oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
-            &alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00,
-            src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16,
-            dst_f16.get(), dpct::library_data_t::real_half, ldc,
-            dpct::library_data_t::real_half)));
-        g_sycl_handles[id]->wait();
-        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
-        to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
-    }
-    else {
-        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n");
-        sycl_pool_alloc<float> src0_ddq_as_f32;
-        sycl_pool_alloc<float> src1_ddq_as_f32;
-        if (src0->type != GGML_TYPE_F32) {
-            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type);
-            GGML_ASSERT(to_fp32_sycl != nullptr);
-            src0_ddq_as_f32.alloc(row_diff*ne00);
-            to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream);
-        }
-        if (src1->type != GGML_TYPE_F32) {
-            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type);
-            GGML_ASSERT(to_fp32_sycl != nullptr);
-            src1_ddq_as_f32.alloc(src1_ncols*ne10);
-            to_fp32_sycl(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream);
-        }
-        const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get();
-        const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get();
-
-        const float alpha = 1.0f;
-        const float beta = 0.0f;
-
-        SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream));
-        SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
-            *g_sycl_handles[id], oneapi::mkl::transpose::trans,
-            oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
-            dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00,
-            src1_ddf1_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]),
-            dst_dd_i, ldc)));
-        g_sycl_handles[id]->wait();
-    }
-    (void) dst;
-    (void) src1_ddq_i;
-    (void) src1_padded_row_size;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
-                              ggml_tensor *dst, const float *src0_dd,
-                              const float *src1_dd, float *dst_dd,
-                              const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
-    GGML_ASSERT(src0->type == dst->type);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t nrows = ggml_nrows(src0);
-
-    //const int n_past      = ((int32_t *) dst->op_params)[0];
-    const int n_dims      = ((int32_t *) dst->op_params)[1];
-    const int mode        = ((int32_t *) dst->op_params)[2];
-    const int n_ctx       = ((int32_t *) dst->op_params)[3];
-    const int n_orig_ctx  = ((int32_t *) dst->op_params)[4];
-
-    // RoPE alteration for extended context
-    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
-    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
-    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
-    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
-    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
-    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
-    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
-
-    const int32_t * pos = nullptr;
-    if ((mode & 1) == 0) {
-        GGML_ASSERT(src1->type == GGML_TYPE_I32);
-        GGML_ASSERT(src1->ne[0] == ne2);
-        pos = (const int32_t *) src1_dd;
-    }
-
-    const bool is_neox = mode & 2;
-    const bool is_glm  = mode & 4;
-
-    rope_corr_dims corr_dims;
-    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
-
-    // compute
-    if (is_glm) {
-        GGML_ASSERT(false);
-        rope_glm_f32_sycl(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream);
-    } else if (is_neox) {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_neox_sycl(
-                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd,
-                           ne00, n_dims, nrows, pos, freq_scale, ne01,
-                           freq_base, ext_factor, attn_factor, corr_dims,
-                           main_stream);
-        } else {
-            GGML_ASSERT(false);
-        }
-    } else {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_sycl(
-                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00,
-                      nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                      attn_factor, corr_dims, main_stream);
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_sycl_op_pool2d(const ggml_tensor *src0,
-                                const ggml_tensor *src1, ggml_tensor *dst,
-                                const float *src0_dd, const float *src1_dd,
-                                float *dst_dd, const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int32_t * opts = (const int32_t *)dst->op_params;
-    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
-    const int k0 = opts[1];
-    const int k1 = opts[2];
-    const int s0 = opts[3];
-    const int s1 = opts[4];
-    const int p0 = opts[5];
-    const int p1 = opts[6];
-
-    const int64_t IH = src0->ne[1];
-    const int64_t IW = src0->ne[0];
-
-    const int64_t N = dst->ne[3];
-    const int64_t OC = dst->ne[2];
-    const int64_t OH = dst->ne[1];
-    const int64_t OW = dst->ne[0];
-
-    const int parallel_elements = N * OC * OH * OW;
-    const int num_blocks = (parallel_elements + SYCL_POOL2D_BLOCK_SIZE - 1) / SYCL_POOL2D_BLOCK_SIZE;
-    sycl::range<3> block_nums(1, 1, num_blocks);
-    main_stream->parallel_for(
-        sycl::nd_range<3>(block_nums *
-                              sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            pool2d_nchw_kernel(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0,
-                               parallel_elements, src0_dd, dst_dd, op,
-                               item_ct1);
-        });
-
-    (void) src1;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_im2col(const ggml_tensor *src0,
-                                const ggml_tensor *src1, ggml_tensor *dst,
-                                const float *src0_dd, const float *src1_dd,
-                                float *dst_dd,
-                                const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
-
-    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
-    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
-    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
-    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
-    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
-    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
-
-    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
-
-    const int64_t IC = src1->ne[is_2D ? 2 : 1];
-    const int64_t IH = is_2D ? src1->ne[1] : 1;
-    const int64_t IW =         src1->ne[0];
-
-    const int64_t KH = is_2D ? src0->ne[1] : 1;
-    const int64_t KW =         src0->ne[0];
-
-    const int64_t OH = is_2D ? dst->ne[2] : 1;
-    const int64_t OW =         dst->ne[1];
-
-    const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
-
-    if (dst->type == GGML_TYPE_F16) {
-        im2col_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
-    } else {
-        im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
-    }
-
-    (void) src0;
-    (void) src0_dd;
-}
-
-inline void ggml_sycl_op_sum_rows(const ggml_tensor *src0,
-                                  const ggml_tensor *src1, ggml_tensor *dst,
-                                  const float *src0_dd, const float *src1_dd,
-                                  float *dst_dd,
-                                  const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ncols = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_argsort(const ggml_tensor *src0,
-                                 const ggml_tensor *src1, ggml_tensor *dst,
-                                 const float *src0_dd, const float *src1_dd,
-                                 float *dst_dd,
-                                 const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_I32);
-
-    const int64_t ncols = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
-
-    argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_diag_mask_inf(const ggml_tensor *src0,
-                                       const ggml_tensor *src1,
-                                       ggml_tensor *dst, const float *src0_dd,
-                                       const float *src1_dd, float *dst_dd,
-                                       const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int nrows0 = ggml_nrows(src0);
-
-    const int n_past = ((int32_t *) dst->op_params)[0];
-
-    diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_soft_max(const ggml_tensor *src0,
-                                  const ggml_tensor *src1, ggml_tensor *dst,
-                                  const float *src0_dd, const float *src1_dd,
-                                  float *dst_dd,
-                                  const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 support")
-#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
-    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows_x = ggml_nrows(src0);
-    const int64_t nrows_y = src0->ne[1];
-
-    float scale = 1.0f;
-    float max_bias = 0.0f;
-
-    memcpy(&scale, dst->op_params + 0, sizeof(float));
-    memcpy(&max_bias, dst->op_params + 1, sizeof(float));
-
-    soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00,
-                      nrows_x, nrows_y, scale, max_bias, main_stream);
-}
-
-inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1,
-                               ggml_tensor *dst, const float *src0_dd,
-                               const float *src1_dd, float *dst_dd,
-                               const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float scale;
-    memcpy(&scale, dst->op_params, sizeof(float));
-
-    scale_f32_sycl(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream);
-    /*
-    DPCT1010:87: SYCL uses exceptions to report errors and does not use the
-    error codes. The call was replaced with 0. You need to rewrite this code.
-    */
-    SYCL_CHECK(0);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_clamp(const ggml_tensor *src0, const ggml_tensor *src1,
-                               ggml_tensor *dst, const float *src0_dd,
-                               const float *src1_dd, float *dst_dd,
-                               const dpct::queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float min;
-    float max;
-    memcpy(&min, dst->op_params, sizeof(float));
-    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
-
-    clamp_f32_sycl(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
-    /*
-    DPCT1010:88: SYCL uses exceptions to report errors and does not use the
-    error codes. The call was replaced with 0. You need to rewrite this code.
-    */
-    SYCL_CHECK(0);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_sycl_op_flatten(const ggml_tensor *src0,
-                                 const ggml_tensor *src1, ggml_tensor *dst,
-                                 const ggml_sycl_op_flatten_t op) try {
-    const int64_t nrows0 = ggml_nrows(src0);
-
-    const bool use_src1 = src1 != nullptr;
-    const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
-
-    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-
-    ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
-    ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
-
-    const bool src0_on_device =             src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
-    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU;
-    const bool  dst_on_device =              dst->backend == GGML_BACKEND_TYPE_GPU;
-
-    // dd = data device
-    float * src0_ddf = nullptr;
-    float * src1_ddf = nullptr;
-    float *  dst_ddf = nullptr;
-
-    sycl_pool_alloc<float> src0_f;
-    sycl_pool_alloc<float> src1_f;
-    sycl_pool_alloc<float>  dst_f;
-
-    ggml_sycl_set_device(g_main_device);
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
-    // GGML_SYCL_DEBUG("g_main_device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n",
-        // g_main_device, main_stream, src0_on_device, src1_on_device, dst_on_device);
-
-    if (src0_on_device) {
-        src0_ddf = (float *) src0_extra->data_device[g_main_device];
-    } else {
-        src0_ddf = src0_f.alloc(ggml_nelements(src0));
-        // GGML_SYCL_DEBUG("before ggml_sycl_cpy_tensor_2d src0_ddf=%p, src0=%p\n", src0_ddf, src0);
-        SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream));
-    }
-
-    if (use_src1) {
-        if (src1_on_device) {
-            src1_ddf = (float *) src1_extra->data_device[g_main_device];
-        } else {
-            src1_ddf = src1_f.alloc(ggml_nelements(src1));
-            SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream));
-        }
-    }
-    if (dst_on_device) {
-        dst_ddf = (float *) dst_extra->data_device[g_main_device];
-    } else {
-        dst_ddf = dst_f.alloc(ggml_nelements(dst));
-    }
-
-    // GGML_SYCL_DEBUG("op src0=%p, src1=%p, dst=%p, src0_ddf=%p, src1_ddf=%p, dst_ddf=%p, main_stream=%p\n",
-        // src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
-    // do the computation
-    op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
-    /*
-    DPCT1010:89: SYCL uses exceptions to report errors and does not use the
-    error codes. The call was replaced with 0. You need to rewrite this code.
-    */
-    SYCL_CHECK(0);
-
-    // copy dst to host if necessary
-    if (!dst_on_device) {
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)).wait()));
-    }
-
-    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            dpct::get_current_device().queues_wait_and_throw()));
-    }
-    // print_ggml_tensor("tensor", dst);
-}
-catch (sycl::exception const &exc) {
-
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_sycl_set_peer_access(const int n_tokens) {
-    static bool peer_access_enabled = false;
-
-    const bool enable_peer_access = n_tokens <= GGML_SYCL_PEER_MAX_BATCH_SIZE;
-
-    if (peer_access_enabled == enable_peer_access) {
-        return;
-    }
-
-#ifdef NDEBUG
-    for (int i = 0; i < g_device_count; ++i) {
-        SYCL_CHECK(ggml_sycl_set_device(i));
-        // SYCL_CHECK(syclDeviceSynchronize());
-    }
-
-    for (int i = 0; i < g_device_count; ++i) {
-        SYCL_CHECK(ggml_sycl_set_device(i));
-
-        for (int id_other = 0; id_other < g_device_count; ++id_other) {
-            if (i == id_other) {
-                continue;
-            }
-            if (i != g_main_device && id_other != g_main_device) {
-                continue;
-            }
-
-            // int can_access_peer;
-            // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other));
-            // if (can_access_peer) {
-            //     if (enable_peer_access) {
-            //         SYCL_CHECK(syclDeviceEnablePeerAccess(id_other, 0));
-            //     } else {
-            //         SYCL_CHECK(syclDeviceDisablePeerAccess(id_other));
-            //     }
-            // }
-        }
-    }
-#endif // NDEBUG
-
-    peer_access_enabled = enable_peer_access;
-}
-
-struct ggml_backend_sycl_split_buffer_type_context {
-    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split;
-};
-
-static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
-                                 const ggml_tensor *src1, ggml_tensor *dst,
-                                 ggml_sycl_op_mul_mat_t op,
-                                 const bool convert_src1_to_q8_1) try {
-
-    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
-
-    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
-    const int64_t nrows1 = ggml_nrows(src1);
-
-    GGML_ASSERT(ne03 == ne13);
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-
-    const int nb2 = dst->nb[2];
-    const int nb3 = dst->nb[3];
-
-    GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
-
-    GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
-
-    const int64_t i02_divisor = ne12 / ne02;
-
-    const size_t src0_ts = ggml_type_size(src0->type);
-    const size_t src0_bs = ggml_blck_size(src0->type);
-    const size_t q8_1_ts = sizeof(block_q8_1);
-    const size_t q8_1_bs = QK8_1;
-
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
-
-    const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
-    const bool src0_is_contiguous = ggml_is_contiguous(src0);
-    const bool src1_is_contiguous = ggml_is_contiguous(src1);
-
-    int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
-
-    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
-    GGML_ASSERT(!(split && ne02 > 1));
-    GGML_ASSERT(!(split && ne03 > 1));
-    GGML_ASSERT(!(split && ne02 < ne12));
-
-    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split;
-    if (split) {
-        // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check
-        // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...);
-        ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context;
-        tensor_split = buft_ctx->tensor_split;
-    }
-
-    struct dev_data {
-        sycl_pool_alloc<char> src0_dd_alloc;
-        sycl_pool_alloc<float> src1_ddf_alloc;
-        sycl_pool_alloc<char> src1_ddq_alloc;
-        sycl_pool_alloc<float> dst_dd_alloc;
-
-        char *src0_dd = nullptr;
-        float *src1_ddf = nullptr; // float
-        char *src1_ddq = nullptr;  // q8_1
-        float *dst_dd = nullptr;
-
-        int64_t row_low;
-        int64_t row_high;
-    };
-
-    dev_data dev[GGML_SYCL_MAX_DEVICES];
-
-    int used_devices = 0;
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
-
-    for (int i = 0; i < g_device_count; ++i) {
-        // by default, use all rows
-        dev[i].row_low  = 0;
-        dev[i].row_high = ne01;
-
-        // for multi GPU, get the row boundaries from tensor split
-        // and round to mul_mat_q tile sizes
-        if (split) {
-            const int64_t rounding = get_row_rounding(src0->type, tensor_split);
-
-            if (i != 0) {
-                dev[i].row_low  = ne01*tensor_split[i];
-                if (dev[i].row_low < ne01) {
-                    dev[i].row_low -= dev[i].row_low % rounding;
-                }
-            }
-
-            if (i != g_device_count - 1) {
-                dev[i].row_high  = ne01*tensor_split[i + 1];
-                if (dev[i].row_high < ne01) {
-                    dev[i].row_high -= dev[i].row_high % rounding;
-                }
-            }
-        }
-    }
-
-    for (int i = 0; i < g_device_count; ++i) {
-        if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) {
-            continue;
-        }
-
-        used_devices++;
-
-        const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
-        const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
-
-        ggml_sycl_set_device(i);
-        dpct::queue_ptr stream = g_syclStreams[i][0];
-
-        if (src0_on_device && src0_is_contiguous) {
-            dev[i].src0_dd = (char *) src0_extra->data_device[i];
-        } else {
-            dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ggml_nbytes(src0));
-        }
-
-        if (src1_on_device && src1_is_contiguous) {
-            dev[i].src1_ddf = (float *) src1_extra->data_device[i];
-        } else {
-            dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ggml_nelements(src1));
-        }
-
-        if (convert_src1_to_q8_1) {
-            dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
-
-            if (src1_on_device && src1_is_contiguous) {
-                quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
-                /*
-                DPCT1010:90: SYCL uses exceptions to report errors and does not
-                use the error codes. The call was replaced with 0. You need to
-                rewrite this code.
-                */
-                SYCL_CHECK(0);
-            }
-        }
-
-        if (dst_on_device) {
-            dev[i].dst_dd = (float *) dst_extra->data_device[i];
-        } else {
-            const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst);
-            dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(size_dst_ddf);
-        }
-    }
-
-    // if multiple devices are used they need to wait for the main device
-    // here an event is recorded that signals that the main device has finished calculating the input data
-    if (split && used_devices > 1) {
-        ggml_sycl_set_device(g_main_device);
-        /*
-        DPCT1024:91: The original code returned the error code that was further
-        consumed by the program logic. This original code was replaced with 0.
-        You may need to rewrite the program logic consuming the error code.
-        */
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            *src0_extra->events[g_main_device][0] =
-                g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier()));
-    }
-
-    const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
-    for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
-        const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
-        const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
-
-        for (int i = 0; i < g_device_count; ++i) {
-            if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) {
-                continue;
-            }
-
-            const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
-            const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
-            const int64_t row_diff = dev[i].row_high - dev[i].row_low;
-
-            ggml_sycl_set_device(i);
-            dpct::queue_ptr stream = g_syclStreams[i][is];
-
-            // wait for main GPU data if necessary
-            if (split && (i != g_main_device || is != 0)) {
-                /*
-                DPCT1009:163: SYCL uses exceptions to report errors and does not
-                use the error codes. The original code was commented out and a
-                warning string was inserted. You need to rewrite this code.
-                */
-                SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier(
-                    {*src0_extra->events[g_main_device][0]})));
-            }
-
-            for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
-                const int64_t i03 = i0 / ne12;
-                const int64_t i02 = i0 % ne12;
-
-                const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs;
-
-                // for split tensors the data begins at i0 == i0_offset_low
-                char  *  src0_dd_i =  dev[i].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
-                float * src1_ddf_i = dev[i].src1_ddf + (i0*ne11 + src1_col_0) * ne10;
-                char  * src1_ddq_i = dev[i].src1_ddq +  src1_ddq_i_offset;
-                float *   dst_dd_i =   dev[i].dst_dd + (i0*ne1  + src1_col_0) * (dst_on_device ? ne0 : row_diff);
-
-                // the main device memory buffer can be on VRAM scratch, with space for all partial results
-                // in that case an offset on dst_ddf_i is needed
-                if (dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device) {
-                    dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split
-                }
-
-                // copy src0, src1 to device if necessary
-                if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) {
-                    if (i != g_main_device) {
-                        if (convert_src1_to_q8_1) {
-                            char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset;
-                          SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
-                                src1_ddq_i, src1_ddq_i_source,
-                                src1_ncols * src1_padded_col_size * q8_1_ts /
-                                    q8_1_bs).wait()));
-                        } else {
-
-                            float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
-                            src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
-
-                            SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream,
-                                src1_ddf_i, src1_ddf_i_source,
-                                src1_ncols * ne10 * sizeof(float))));
-                        }
-                    }
-                } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) {
-                    SYCL_CHECK(ggml_sycl_cpy_tensor_2d(
-                                   src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
-                } else {
-                    GGML_ASSERT(false);
-                }
-
-                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) {
-                    quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
-                    /*
-                    DPCT1010:92: SYCL uses exceptions to report errors and does
-                    not use the error codes. The call was replaced with 0. You
-                    need to rewrite this code.
-                    */
-                    SYCL_CHECK(0);
-                }
-
-                if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) {
-                    SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream));
-                }
-                if (src1->type == GGML_TYPE_F16) {
-                    src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10;
-                }
-                // do the computation
-                SYCL_CHECK(CHECK_TRY_ERROR(op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
-                    dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream)));
-                /*
-                DPCT1010:93: SYCL uses exceptions to report errors and does not
-                use the error codes. The call was replaced with 0. You need to
-                rewrite this code.
-                */
-                SYCL_CHECK(0);
-
-                // copy dst to host or other device if necessary
-                if (!dst_on_device) {
-                    void * dst_off_device;
-                    dpct::memcpy_direction kind;
-                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
-                        dst_off_device = dst->data;
-                        kind = dpct::device_to_host;
-                    } else if (dst->backend == GGML_BACKEND_TYPE_GPU) {
-                        dst_off_device = dst_extra->data_device[g_main_device];
-                        kind = dpct::device_to_device;
-                    } else {
-                        GGML_ASSERT(false);
-                    }
-                    if (split) {
-                        // src0 = weight matrix is saved as a transposed matrix for better memory layout.
-                        // dst is NOT transposed.
-                        // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU.
-                        // Instead they need to be copied to the correct slice in ne0 = dst row index.
-                        // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results.
-                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
-                        GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
-                        dhf_dst_i += src1_col_0*ne0 + dev[i].row_low;
-
-                        //todo, dirty solution. Need be updated when device2device memcpy() is supported.
-                        if (kind == dpct::device_to_device) {
-                            size_t dst_size = ggml_nbytes_pad(dst);
-                            float *host_buf = (float *)malloc(dst_size);
-                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
-                                host_buf, ne0 * sizeof(float), dst_dd_i,
-                                row_diff * sizeof(float), row_diff * sizeof(float),
-                                src1_ncols, dpct::device_to_host, *stream)));
-                            dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[i]).queues_wait_and_throw();
-                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
-                                dhf_dst_i, ne0 * sizeof(float), host_buf,
-                                row_diff * sizeof(float), row_diff * sizeof(float),
-                                src1_ncols, dpct::host_to_device, *main_stream)));
-                            dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[g_main_device]).queues_wait_and_throw();
-                            free(host_buf);
-                        } else {
-                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
-                                dhf_dst_i, ne0 * sizeof(float), dst_dd_i,
-                                row_diff * sizeof(float), row_diff * sizeof(float),
-                                src1_ncols, kind, *stream)));
-                        }
-                    } else {
-                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
-                        GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
-                        dhf_dst_i += src1_col_0*ne0;
-                        SYCL_CHECK(CHECK_TRY_ERROR(
-                            stream->memcpy(dhf_dst_i, dst_dd_i,
-                                           src1_ncols * ne0 * sizeof(float)).wait()));
-                    }
-                }
-
-                // add event for the main device to wait on until other device is done
-                if (split && (i != g_main_device || is != 0)) {
-                    /*
-                    DPCT1024:94: The original code returned the error code that
-                    was further consumed by the program logic. This original
-                    code was replaced with 0. You may need to rewrite the
-                    program logic consuming the error code.
-                    */
-                    SYCL_CHECK(CHECK_TRY_ERROR(
-                        *src0_extra->events[i][is] =
-                            stream->ext_oneapi_submit_barrier()));
-                }
-            }
-        }
-    }
-
-    // main device waits for all other devices to be finished
-    if (split && g_device_count > 1) {
-        int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
-        is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS;
-
-        ggml_sycl_set_device(g_main_device);
-        for (int i = 0; i < g_device_count; ++i) {
-            if (dev[i].row_low == dev[i].row_high) {
-                continue;
-            }
-            for (int64_t is = 0; is < is_max; ++is) {
-                SYCL_CHECK(CHECK_TRY_ERROR(
-                    g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier(
-                        {*src0_extra->events[i][is]})));
-            }
-        }
-    }
-
-    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
-        SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            dpct::get_current_device().queues_wait_and_throw()));
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-
-static void ggml_sycl_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_repeat);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_get_rows);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_add);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_acc);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_mul);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_div);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_silu);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu_quick);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_tanh);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_relu);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardsigmoid);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardswish);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_leaky_relu);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sqr);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_norm);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_group_norm);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_concat);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_upscale);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-static void ggml_sycl_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pad);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-
-static void ggml_sycl_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rms_norm);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
-bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
-    if (!g_sycl_loaded) return false;
-
-    const int64_t ne10 = src1->ne[0];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-
-    // TODO: find the optimal values for these
-    return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
-            src1->type == GGML_TYPE_F32 &&
-             dst->type == GGML_TYPE_F32 &&
-            (ne0 >= 32 && ne1 >= 32 && ne10 >= 32);
-}
-
-static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0,
-                                       const ggml_tensor *src1,
-                                       ggml_tensor *dst) try {
-    GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
-    GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-
-    const int64_t ne12 = src1->ne[2];
-
-    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
-
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
-
-    ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0,
-                                     const ggml_tensor *src1,
-                                     ggml_tensor *dst) try {
-    GGML_ASSERT(!ggml_is_transposed(src0));
-    GGML_ASSERT(!ggml_is_transposed(src1));
-    GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-
-    const int64_t nb01 = src0->nb[1];
-    const int64_t nb02 = src0->nb[2];
-
-    const int64_t ne12 = src1->ne[2];
-
-    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
-
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
-
-    const int64_t row_stride_x = nb01 / sizeof(sycl::half);
-    const int64_t channel_stride_x = nb02 / sizeof(sycl::half);
-
-    ggml_mul_mat_vec_nc_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void k_compute_batched_ptrs(const sycl::half *src0_as_f16,
-                                   const sycl::half *src1_as_f16, char *dst,
-                                   const void **ptrs_src, void **ptrs_dst,
-                                   int64_t ne12, int64_t ne13, int64_t ne23,
-                                   size_t nb02, size_t nb03, size_t nb12,
-                                   size_t nb13, size_t nbd2, size_t nbd3,
-                                   int64_t r2, int64_t r3,
-                                   const sycl::nd_item<3> &item_ct1) {
-    int64_t i13 = item_ct1.get_group(2) * item_ct1.get_local_range(2) +
-                  item_ct1.get_local_id(2);
-    int64_t i12 = item_ct1.get_group(1) * item_ct1.get_local_range(1) +
-                  item_ct1.get_local_id(1);
-
-    if (i13 >= ne13 || i12 >= ne12) {
-        return;
-    }
-
-    int64_t i03 = i13 / r3;
-    int64_t i02 = i12 / r2;
-
-    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
-    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13;
-    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)         dst + i12*nbd2 + i13*nbd3;
-}
-
-static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
-                                             const ggml_tensor *src1,
-                                             ggml_tensor *dst) try {
-    GGML_ASSERT(!ggml_is_transposed(src0));
-    GGML_ASSERT(!ggml_is_transposed(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
-
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    const int64_t ne_dst = ggml_nelements(dst);
-
-    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
-
-    bool no_mixed_dtypes = main_stream->get_backend() == sycl::backend::ext_oneapi_cuda ||
-                           main_stream->get_backend() == sycl::backend::ext_oneapi_hip;
-
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream));
-
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
-    sycl::half *src0_as_f16 = (sycl::half *)src0_ddq;
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
-
-    // convert src1 to fp16
-    sycl_pool_alloc<sycl::half> src1_f16_alloc;
-    if (src1->type != GGML_TYPE_F16) {
-        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
-        const int64_t ne_src1 = ggml_nelements(src1);
-        src1_f16_alloc.alloc(ne_src1);
-        GGML_ASSERT(to_fp16_sycl != nullptr);
-        to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream);
-    }
-    sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf
-                                                       : src1_f16_alloc.get();
-
-    sycl_pool_alloc<sycl::half> dst_f16;
-    char * dst_t;
-
-    dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float;
-    dpct::library_data_t cu_data_type = dpct::library_data_t::real_float;
-    if (no_mixed_dtypes) {
-        cu_compute_type = dpct::library_data_t::real_half;
-        cu_data_type = dpct::library_data_t::real_half;
-    }
-
-    // dst strides
-    size_t nbd2 = dst->nb[2];
-    size_t nbd3 = dst->nb[3];
-
-    const float alpha_f32 = 1.0f;
-    const float beta_f32 = 0.0f;
-
-    const sycl::half alpha_f16 = 1.0f;
-    const sycl::half beta_f16 = 0.0f;
-
-    const void * alpha = &alpha_f32;
-    const void * beta  = &beta_f32;
-    if (no_mixed_dtypes) {
-        alpha = &alpha_f16;
-        beta  = &beta_f16;
-    }
-
-    // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway
-    // when oneMKL open source supports half, half, float, float: datatypes
-
-    dst_t = (char *) dst_ddf;
-    if (no_mixed_dtypes) {
-        dst_t = (char *) dst_f16.alloc(ne_dst);
-
-        nbd2 /= sizeof(float) / sizeof(sycl::half);
-        nbd3 /= sizeof(float) / sizeof(sycl::half);
-    }
-
-    GGML_ASSERT(ne12 % ne02 == 0);
-    GGML_ASSERT(ne13 % ne03 == 0);
-
-    // broadcast factors
-    const int64_t r2 = ne12/ne02;
-    const int64_t r3 = ne13/ne03;
-
-    if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
-        // there is no broadcast and src0, src1 are contiguous across dims 2, 3
-        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
-            *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans,
-            oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
-            (const char *)src0_as_f16, dpct::library_data_t::real_half,
-            nb01 / nb00, nb02 / nb00,
-            (const char *)src1_f16, dpct::library_data_t::real_half,
-            nb11 / nb10, nb12 / nb10, beta,
-            (char *)dst_t, cu_data_type, ne01, nb2 / nb0,
-            ne12 * ne13, cu_compute_type)));
-    } else {
-        const int ne23 = ne12*ne13;
-
-        sycl_pool_alloc<const void *> ptrs_src(2*ne23);
-        sycl_pool_alloc<      void *> ptrs_dst(1*ne23);
-
-        sycl::range<3> block_dims(1, ne12, ne13);
-        /*
-        DPCT1049:47: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        {
-            dpct::has_capability_or_fail(main_stream->get_device(),
-                                         {sycl::aspect::fp16});
-
-            main_stream->submit([&](sycl::handler &cgh) {
-                const void **ptrs_src_get = ptrs_src.get();
-                void **ptrs_dst_get = ptrs_dst.get();
-                size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2;
-                size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2;
-                cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims),
-                                 [=](sycl::nd_item<3> item_ct1) {
-                                     k_compute_batched_ptrs(
-                                         src0_as_f16, src1_f16,
-                                         dst_t, ptrs_src_get,
-                                         ptrs_dst_get, ne12, ne13, ne23,
-                                         nb02, nb03, nb12_scaled, nb13_scaled,
-                                         nbd2, nbd3, r2, r3, item_ct1);
-                                 });
-            });
-        }
-        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
-            *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans,
-            oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
-            (const void **)(ptrs_src.get() + 0 * ne23),
-            dpct::library_data_t::real_half, nb01 / nb00,
-            (const void **)(ptrs_src.get() + 1 * ne23),
-            dpct::library_data_t::real_half, nb11 / nb10, beta,
-            (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23,
-            cu_compute_type)));
-    }
-
-    if (no_mixed_dtypes) {
-        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
-        to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream);
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-
-static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const bool all_on_device =
-        (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) &&
-        (src1->backend == GGML_BACKEND_TYPE_GPU) &&
-        ( dst->backend == GGML_BACKEND_TYPE_GPU);
-
-    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
-
-    int64_t min_compute_capability = INT_MAX;
-    for (int i = 0; i < g_device_count; ++i) {
-        if (min_compute_capability > g_device_caps[i].cc && g_tensor_split[i] < (i + 1 < g_device_count ? g_tensor_split[i + 1] : 1.0f)) {
-            min_compute_capability = g_device_caps[i].cc;
-        }
-    }
-
-#ifdef SYCL_USE_XMX
-    const bool use_xmx = true;
-#else
-    const bool use_xmx = false;
-#endif
-
-    // debug helpers
-    //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
-    //printf("      %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
-    //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
-    //printf("      %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
-    //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
-    //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
-
-    if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
-        // KQ single-batch
-        // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_p021\n");
-        ggml_sycl_mul_mat_vec_p021(src0, src1, dst);
-    } else if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
-        // KQV single-batch
-        // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n");
-        ggml_sycl_mul_mat_vec_nc(src0, src1, dst);
-    } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
-        // KQ + KQV multi-batch
-        // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n");
-        ggml_sycl_mul_mat_batched_sycl(src0, src1, dst);
-    } else if (src0->type == GGML_TYPE_F32) {
-        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n");
-        ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
-    } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
-        // GGML_SYCL_DEBUG("ggml_is_quantized or GGML_TYPE_F16\n");
-        if (src1->ne[1] == 1 && src0->ne[0] % GGML_SYCL_DMMV_X == 0) {
-#ifdef GGML_SYCL_FORCE_DMMV
-            const bool use_mul_mat_vec_q = false;
-#else
-            bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
-            use_mul_mat_vec_q = use_mul_mat_vec_q ||
-                (src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) ||
-                (src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) ||
-                (src0->type == GGML_TYPE_IQ4_NL) || (src0->type == GGML_TYPE_IQ4_XS) ||
-                (src0->type == GGML_TYPE_IQ1_S) || (src0->type == GGML_TYPE_IQ1_M);
-
-
-#endif // GGML_SYCL_FORCE_DMMV
-
-            if (use_mul_mat_vec_q) {
-                // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n");
-                ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
-            } else {
-                // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_dequantize_mul_mat_vec path\n");
-                ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false);
-            }
-        } else {
-            bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
-
-            if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) {
-                use_mul_mat_q = false;
-            }
-
-            if (use_mul_mat_q) {
-                // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_q path\n");
-                ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true);
-            } else {
-                // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_sycl path\n");
-                ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
-            }
-        }
-    } else {
-        GGML_ASSERT(false);
-    }
-}
-
-#if 0
-template<typename ... Srcs>
-static __global__ void k_compute_batched_ptrs_id(
-        const void ** ptrs_src, void ** ptrs_dst,
-        int ne12, int ne13,
-        int ne23,
-        int nb02, int nb03,
-        int nb12, int nb13,
-        int nb2, int nb3,
-        int r2, int r3,
-        ggml_type src0_type, half * src0_as_f16, int64_t src0_ne,
-        const half * src1_f16, half * dst_f16,
-        const int32_t * ids, const int id,
-        Srcs... src0s) {
-
-    int i = ids[id];
-
-    half * src0_f16;
-    const void * srcs_ar[] = { (const half *) src0s... };
-    if (src0_type == GGML_TYPE_F16) {
-        src0_f16 = (half *) srcs_ar[i];
-    } else {
-        src0_f16 = src0_as_f16;
-        if (item_ct1.get_local_id(2) == 0 && threadIdx.y == 0) {
-            const to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(src0_type);
-            to_fp16(srcs_ar[i], src0_f16, src0_ne, syclStreamFireAndForget);
-        }
-    }
-
-    int i13 = blockIdx.x * blockDim.x + item_ct1.get_local_id(2);
-    int i12 = blockIdx.y * blockDim.y + threadIdx.y;
-
-    if (i13 >= ne13 || i12 >= ne12) {
-        return;
-    }
-
-    int i03 = i13 / r3;
-    int i02 = i12 / r2;
-
-    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02   + i03*nb03;
-    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2;
-    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)  dst_f16 + i12* nb2/2 + i13* nb3/2;
-}
-
-static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
-    const struct ggml_tensor * ids = dst->src[0];
-    const struct ggml_tensor * src1 = dst->src[1];
-    const struct ggml_tensor * src00 = dst->src[2];
-
-    const int id = dst->op_params[0];
-
-    GGML_ASSERT(!ggml_is_transposed(src00));
-    GGML_ASSERT(!ggml_is_transposed(src1));
-
-    GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne);
-
-    //const int64_t nb01 = src00->nb[1];
-    GGML_TENSOR_LOCALS(int64_t, nb0, src00, nb);
-
-    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
-
-    GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb);
-    //const int64_t nb11 = src1->nb[1];
-
-    const int64_t ne1 = ggml_nelements(src1);
-    const int64_t ne  = ggml_nelements(dst);
-
-    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    syclStream_t main_stream = g_syclStreams[g_main_device][0];
-
-    SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device], main_stream));
-
-    //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    //void * src0_ddq = src0_extra->data_device[g_main_device];
-    //half * src0_as_f16 = (half *) src0_ddq;
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
-
-    // convert src1 to fp16
-    const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
-    GGML_ASSERT(to_fp16_sycl != nullptr);
-
-    size_t src1_as = 0;
-    half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne1 * sizeof(half), &src1_as);
-    to_fp16_sycl(src1_ddf, src1_as_f16, ne1, main_stream);
-
-    size_t dst_as = 0;
-    half * dst_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne * sizeof(half), &dst_as);
-
-    GGML_ASSERT(ne12 % ne02 == 0);
-    GGML_ASSERT(ne13 % ne03 == 0);
-
-    // broadcast factors
-    const int64_t r2 = ne12/ne02;
-    const int64_t r3 = ne13/ne03;
-
-    const half alpha_f16 = 1.0f;
-    const half beta_f16  = 0.0f;
-
-    // use syclGemmBatchedEx
-    const int ne23 = ne12*ne13;
-
-    const void ** ptrs_src = nullptr;
-          void ** ptrs_dst = nullptr;
-
-    size_t ptrs_src_s = 0;
-    size_t ptrs_dst_s = 0;
-
-    ptrs_src = (const void **) ggml_sycl_pool_malloc(g_main_device, 2*ne23*sizeof(void *), &ptrs_src_s);
-    ptrs_dst = (      void **) ggml_sycl_pool_malloc(g_main_device, 1*ne23*sizeof(void *), &ptrs_dst_s);
-
-    int64_t src0_ne = ggml_nelements(src00);
-    half * src0_as_f16 = nullptr;
-    size_t src0_as = 0;
-    if (src00->type != GGML_TYPE_F16) {
-        src0_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, src0_ne * sizeof(half), &src0_as);
-    }
-
-    static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6");
-    dim3 block_dims(ne13, ne12);
-    k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>(
-            ptrs_src, ptrs_dst,
-            ne12, ne13,
-            ne23,
-            ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half),
-            nb12, nb13,
-            dst->nb[2], dst->nb[3],
-            r2, r3,
-            src00->type, src0_as_f16, src0_ne,
-            src1_as_f16, dst_f16,
-            (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id,
-            dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr
-    );
-    SYCL_CHECK(syclGetLastError());
-
-    SYCL_CHECK(
-    syclGemmBatchedEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
-            ne01, ne11, ne10,
-            &alpha_f16, (const void **) (ptrs_src + 0*ne23), SYCL_R_16F, ne00,
-                        (const void **) (ptrs_src + 1*ne23), SYCL_R_16F, ne10,
-            &beta_f16,  (      void **) (ptrs_dst + 0*ne23), SYCL_R_16F, ne01,
-            ne23,
-            CUBLAS_COMPUTE_16F,
-            CUBLAS_GEMM_DEFAULT_TENSOR_OP));
-
-    if (src0_as != 0) {
-        ggml_sycl_pool_free(g_main_device, src0_as_f16, src0_as);
-    }
-    if (ptrs_src_s != 0) {
-        ggml_sycl_pool_free(g_main_device, ptrs_src, ptrs_src_s);
-    }
-    if (ptrs_dst_s != 0) {
-        ggml_sycl_pool_free(g_main_device, ptrs_dst, ptrs_dst_s);
-    }
-
-    const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
-    to_fp32_sycl(dst_f16, dst_ddf, ne, main_stream);
-
-    ggml_sycl_pool_free(g_main_device, src1_as_f16, src1_as);
-    ggml_sycl_pool_free(g_main_device, dst_f16, dst_as);
-}
-#endif
-
-static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
-                                 const ggml_tensor *src1,
-                                 ggml_tensor *dst) try {
-    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT &&
-                "mul_mat_id does not support split buffers");
-    const ggml_tensor *ids = dst->src[2];
-    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
-
-    const size_t nb11 = src1->nb[1];
-    const size_t nb1 = dst->nb[1];
-
-    const int32_t id = ((int32_t *)dst->op_params)[0];
-    const int32_t n_as = src0->ne[2];
-
-    std::vector<char> ids_host(ggml_nbytes(ids));
-    const char *ids_dev = (const char *)ids->data;
-
-    SYCL_CHECK(CHECK_TRY_ERROR(
-        stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
-    SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
-
-    const ggml_tensor_extra_gpu *src0_extra =
-        (const ggml_tensor_extra_gpu *)src0->extra;
-    const ggml_tensor_extra_gpu *src1_extra =
-        (const ggml_tensor_extra_gpu *)src1->extra;
-    const ggml_tensor_extra_gpu *dst_extra =
-        (const ggml_tensor_extra_gpu *)dst->extra;
-
-    ggml_tensor_extra_gpu src0_row_extra;
-    ggml_tensor_extra_gpu src1_row_extra;
-    ggml_tensor_extra_gpu dst_row_extra;
-
-    ggml_tensor src0_row = *src0;
-    ggml_tensor src1_row = *src1;
-    ggml_tensor dst_row = *dst;
-
-    src1_row.backend = GGML_BACKEND_TYPE_GPU;
-    dst_row.backend  = GGML_BACKEND_TYPE_GPU;
-
-    src0_row.extra = &src0_row_extra;
-    src1_row.extra = &src1_row_extra;
-    dst_row.extra = &dst_row_extra;
-
-    char *src0_original = src1->backend == GGML_BACKEND_TYPE_CPU
-                              ? (char *)src0->data
-                              : (char *)src0_extra->data_device[g_main_device];
-    char *src1_original = src1->backend == GGML_BACKEND_TYPE_CPU
-                              ? (char *)src1->data
-                              : (char *)src1_extra->data_device[g_main_device];
-    char *dst_original = dst->backend == GGML_BACKEND_TYPE_CPU
-                             ? (char *)dst->data
-                             : (char *)dst_extra->data_device[g_main_device];
-
-    src0_row.ne[2] = 1;
-    src0_row.ne[3] = 1;
-    src0_row.nb[3] = src0->nb[2];
-
-    if (src1->ne[1] == 1) {
-        for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-            const int32_t row_id =
-                *(const int32_t *)(ids_host.data() + i01 * ids->nb[1] +
-                                   id * ids->nb[0]);
-
-            GGML_ASSERT(row_id >= 0 && row_id < n_as);
-
-            src0_row_extra.data_device[g_main_device] =
-                src0_original + row_id * src0->nb[2];
-            src1_row_extra.data_device[g_main_device] =
-                src1_original + i01 * src1->nb[1];
-            dst_row_extra.data_device[g_main_device] =
-                dst_original + i01 * dst->nb[1];
-
-            ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row);
-        }
-    } else {
-        sycl_pool_alloc<char> src1_contiguous(sizeof(float)*ggml_nelements(src1));
-        sycl_pool_alloc<char>  dst_contiguous(sizeof(float)*ggml_nelements(dst));
-
-        src1_row_extra.data_device[g_main_device] = src1_contiguous.get();
-        dst_row_extra.data_device[g_main_device]  =  dst_contiguous.get();
-
-        for (int32_t row_id = 0; row_id < n_as; ++row_id) {
-            int64_t num_src1_rows = 0;
-            for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-                const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
-
-                if (row_id_i != row_id) {
-                    continue;
-                }
-
-                GGML_ASSERT(row_id >= 0 && row_id < n_as);
-
-                SYCL_CHECK(CHECK_TRY_ERROR(
-                    stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11,
-                                   src1_original + i01 * nb11, nb11)));
-                num_src1_rows++;
-            }
-
-            if (num_src1_rows == 0) {
-                continue;
-            }
-
-            src0_row_extra.data_device[g_main_device] =
-                src0_original + row_id * src0->nb[2];
-
-            src1_row.ne[1] = num_src1_rows;
-            dst_row.ne[1] = num_src1_rows;
-
-            src1_row.nb[1] = nb11;
-            src1_row.nb[2] = num_src1_rows*nb11;
-            src1_row.nb[3] = num_src1_rows*nb11;
-
-            dst_row.nb[1] = nb1;
-            dst_row.nb[2] = num_src1_rows*nb1;
-            dst_row.nb[3] = num_src1_rows*nb1;
-
-            ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row);
-
-            num_src1_rows = 0;
-            for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-                const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
-
-                if (row_id_i != row_id) {
-                    continue;
-                }
-
-                GGML_ASSERT(row_id >= 0 && row_id < n_as);
-
-                SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
-                    dst_original + i01 * nb1,
-                    dst_contiguous.get() + num_src1_rows * nb1, nb1)));
-                num_src1_rows++;
-            }
-        }
-    }
-
-    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
-        SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_sycl_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_scale);
-}
-
-static void ggml_sycl_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_clamp);
-}
-
-static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
-                          ggml_tensor *dst) try {
-    const int64_t ne = ggml_nelements(src0);
-    GGML_ASSERT(ne == ggml_nelements(src1));
-
-    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
-    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
-
-    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
-    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
-
-    GGML_TENSOR_BINARY_OP_LOCALS;
-
-    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
-
-    const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-
-    char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
-    char * src1_ddc = (char *) src1_extra->data_device[g_main_device];
-
-    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
-        ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
-        ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
-        ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_f16_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) {
-        ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) {
-        ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-    } else {
-        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
-                ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
-    }
-
-    (void) dst;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_sycl_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    // TODO: why do we pass dst as src1 here?
-    ggml_sycl_cpy(src0, dst, nullptr);
-    (void) src1;
-}
-
-static void ggml_sycl_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_diag_mask_inf);
-}
-
-static void ggml_sycl_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_soft_max);
-}
-
-static void ggml_sycl_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rope);
-}
-
-static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d);
-}
-
-static void ggml_sycl_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_im2col);
-}
-
-static void ggml_sycl_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sum_rows);
-}
-
-static void ggml_sycl_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_argsort);
-}
-
-static void ggml_sycl_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    (void) src0;
-    (void) src1;
-    (void) dst;
-}
-
-static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
-    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
-
-    return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
-}
-
-void ggml_sycl_free_data(struct ggml_tensor *tensor) try {
-    if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) {
-        return;
-    }
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-    for (int i = 0; i < g_device_count; ++i) {
-        const dpct::queue_ptr stream = g_syclStreams[i][0];
-        if (extra->data_device[i] != nullptr) {
-            SYCL_CHECK(ggml_sycl_set_device(i));
-            SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[i], *stream)));
-        }
-
-        for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-            if (extra->events[i][is] != nullptr) {
-                SYCL_CHECK(ggml_sycl_set_device(i));
-                SYCL_CHECK(CHECK_TRY_ERROR(
-                    dpct::destroy_event(extra->events[i][is])));
-            }
-        }
-    }
-
-    delete extra;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr;
-static size_t g_temp_tensor_extra_index = 0;
-
-static ggml_tensor_extra_gpu * ggml_sycl_alloc_temp_tensor_extra() {
-    if (g_temp_tensor_extras == nullptr) {
-        g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_SYCL_MAX_NODES];
-    }
-
-    size_t alloc_index = g_temp_tensor_extra_index;
-    g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_SYCL_MAX_NODES;
-    ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
-    memset(extra, 0, sizeof(*extra));
-
-    return extra;
-}
-
-static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
-                                          bool scratch, bool force_inplace,
-                                          bool no_alloc) try {
-    if (scratch && g_scratch_size == 0) {
-        return;
-    }
-
-    tensor->backend = GGML_BACKEND_TYPE_GPU;
-
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU) {
-        const ggml_op src0_op = tensor->src[0]->op;
-        if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
-            ggml_sycl_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
-        }
-    }
-    if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU) {
-        ggml_sycl_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
-    }
-
-    if (scratch && no_alloc) {
-        return;
-    }
-
-    ggml_tensor_extra_gpu * extra;
-
-    const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
-        tensor->op == GGML_OP_VIEW ||
-        force_inplace;
-    const size_t size = ggml_nbytes(tensor);
-
-    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
-
-    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) {
-        ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
-        char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
-        size_t offset = 0;
-        if (tensor->op == GGML_OP_VIEW) {
-            memcpy(&offset, tensor->op_params, sizeof(size_t));
-        }
-        extra = ggml_sycl_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device] = src0_ddc + offset;
-    } else if (tensor->op == GGML_OP_CPY) {
-        ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra;
-        void * src1_ddv = src1_extra->data_device[g_main_device];
-        extra = ggml_sycl_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device] = src1_ddv;
-    } else if (scratch) {
-        GGML_ASSERT(size <= g_scratch_size);
-        if (g_scratch_offset + size > g_scratch_size) {
-            g_scratch_offset = 0;
-        }
-
-        char * data = (char *) g_scratch_buffer;
-        if (data == nullptr) {
-            SYCL_CHECK(CHECK_TRY_ERROR(
-                data = (char *)sycl::malloc_device(
-                    g_scratch_size, *stream)));
-            g_scratch_buffer = data;
-        }
-        extra = ggml_sycl_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device] = data + g_scratch_offset;
-
-        g_scratch_offset += size;
-
-        GGML_ASSERT(g_scratch_offset <= g_scratch_size);
-    } else { // allocate new buffers outside of scratch
-        void * data;
-        SYCL_CHECK(CHECK_TRY_ERROR(data = (void *)sycl::malloc_device(
-                                        size, *stream)));
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            (*stream).memset(data, 0, size).wait()));
-        extra = new ggml_tensor_extra_gpu;
-        memset(extra, 0, sizeof(*extra));
-        extra->data_device[g_main_device] = data;
-    }
-
-    tensor->extra = extra;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
-    GGML_ASSERT(ggml_is_contiguous(tensor));
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
-    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
-    SYCL_CHECK(CHECK_TRY_ERROR((*stream)
-                                    .memcpy(extra->data_device[g_main_device],
-                                            tensor->data, ggml_nbytes(tensor))
-                                    .wait()));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-void ggml_sycl_assign_buffers(struct ggml_tensor * tensor) {
-    ggml_sycl_assign_buffers_impl(tensor, true, false, false);
-}
-
-void ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor) {
-    ggml_sycl_assign_buffers_impl(tensor, true, false, true);
-}
-
-void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
-    ggml_sycl_assign_buffers_impl(tensor, false, false, false);
-}
-
-void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
-    ggml_sycl_assign_buffers_impl(tensor, false, true, false);
-}
-
-void ggml_sycl_set_main_device(const int main_device) try {
-    if (g_main_device == main_device) return;
-    check_allow_gpu_index(main_device);
-    g_main_device = main_device;
-    g_main_device_id = g_sycl_gpu_mgr->gpus[main_device];
-
-    if (g_ggml_sycl_debug) {
-        dpct::device_info prop;
-        SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
-            prop, dpct::dev_mgr::instance().get_device(g_main_device_id))));
-        fprintf(stderr, "Using device %d (%s) as main device\n",
-                g_main_device_id, prop.get_name());
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-void ggml_sycl_set_scratch_size(const size_t scratch_size) {
-    // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously
-    // it still won't always work as expected, but it's better than nothing
-    if (scratch_size > g_scratch_size) {
-        ggml_sycl_free_scratch();
-    }
-    g_scratch_size = std::max(g_scratch_size, scratch_size);
-}
-
-void ggml_sycl_free_scratch() try {
-    if (g_scratch_buffer == nullptr) {
-        return;
-    }
-    ggml_sycl_set_device(g_main_device);
-    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
-
-    SYCL_CHECK(CHECK_TRY_ERROR(
-        sycl::free(g_scratch_buffer, *stream)));
-    g_scratch_buffer = nullptr;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
-    if (!g_sycl_loaded) return false;
-
-    ggml_sycl_func_t func;
-    const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
-        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
-        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
-
-    if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
-        return false;
-    }
-
-    if (tensor->op == GGML_OP_MUL_MAT) {
-        if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
-#ifndef NDEBUG
-            fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
-#endif
-            return false;
-        }
-    }
-
-    switch (tensor->op) {
-        case GGML_OP_REPEAT:
-            func = ggml_sycl_repeat;
-            break;
-        case GGML_OP_GET_ROWS:
-            func = ggml_sycl_get_rows;
-            break;
-        case GGML_OP_DUP:
-            func = ggml_sycl_dup;
-            break;
-        case GGML_OP_ADD:
-            func = ggml_sycl_add;
-            break;
-        case GGML_OP_ACC:
-            func = ggml_sycl_acc;
-            break;
-        case GGML_OP_MUL:
-            func = ggml_sycl_mul;
-            break;
-        case GGML_OP_DIV:
-            func = ggml_sycl_div;
-            break;
-        case GGML_OP_UNARY:
-            switch (ggml_get_unary_op(tensor)) {
-                case GGML_UNARY_OP_GELU:
-                    func = ggml_sycl_gelu;
-                    break;
-                case GGML_UNARY_OP_SILU:
-                    func = ggml_sycl_silu;
-                    break;
-                case GGML_UNARY_OP_GELU_QUICK:
-                    func = ggml_sycl_gelu_quick;
-                    break;
-                case GGML_UNARY_OP_TANH:
-                    func = ggml_sycl_tanh;
-                    break;
-                case GGML_UNARY_OP_RELU:
-                    func = ggml_sycl_relu;
-                    break;
-                case GGML_UNARY_OP_HARDSIGMOID:
-                    func = ggml_sycl_hardsigmoid;
-                    break;
-                case GGML_UNARY_OP_HARDSWISH:
-                    func = ggml_sycl_hardswish;
-                    break;
-                default:
-                    return false;
-            }
-            break;
-        case GGML_OP_NORM:
-            func = ggml_sycl_norm;
-            break;
-        case GGML_OP_GROUP_NORM:
-            func = ggml_sycl_group_norm;
-            break;
-        case GGML_OP_CONCAT:
-            func = ggml_sycl_concat;
-            break;
-        case GGML_OP_UPSCALE:
-            func = ggml_sycl_upscale;
-            break;
-        case GGML_OP_PAD:
-            func = ggml_sycl_pad;
-            break;
-        case GGML_OP_LEAKY_RELU:
-            func = ggml_sycl_leaky_relu;
-            break;
-        case GGML_OP_RMS_NORM:
-            func = ggml_sycl_rms_norm;
-            break;
-        case GGML_OP_MUL_MAT:
-            if (!any_on_device && !ggml_sycl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
-                return false;
-            }
-            func = ggml_sycl_mul_mat;
-            break;
-        case GGML_OP_MUL_MAT_ID:
-            if (!any_on_device && !ggml_sycl_can_mul_mat(tensor->src[2], tensor->src[1], tensor)) {
-                return false;
-            }
-            func = ggml_sycl_mul_mat_id;
-            break;
-        case GGML_OP_SCALE:
-            func = ggml_sycl_scale;
-            break;
-        case GGML_OP_SQR:
-            func = ggml_sycl_sqr;
-            break;
-        case GGML_OP_CLAMP:
-            func = ggml_sycl_clamp;
-            break;
-        case GGML_OP_CPY:
-            func = ggml_sycl_cpy;
-            break;
-        case GGML_OP_CONT:
-            func = ggml_sycl_dup;
-            break;
-        case GGML_OP_NONE:
-        case GGML_OP_RESHAPE:
-        case GGML_OP_VIEW:
-        case GGML_OP_PERMUTE:
-        case GGML_OP_TRANSPOSE:
-            func = ggml_sycl_nop;
-            break;
-        case GGML_OP_DIAG_MASK_INF:
-            func = ggml_sycl_diag_mask_inf;
-            break;
-        case GGML_OP_SOFT_MAX:
-            func = ggml_sycl_soft_max;
-            break;
-        case GGML_OP_ROPE:
-            func = ggml_sycl_rope;
-            break;
-        case GGML_OP_IM2COL:
-            func = ggml_sycl_im2col;
-            break;
-        case GGML_OP_POOL_2D:
-            func = ggml_sycl_pool2d;
-            break;
-        case GGML_OP_SUM_ROWS:
-            func = ggml_sycl_sum_rows;
-            break;
-        case GGML_OP_ARGSORT:
-            func = ggml_sycl_argsort;
-            break;
-        default:
-            return false;
-    }
-
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
-        ggml_sycl_set_peer_access(tensor->src[1]->ne[1]);
-    }
-
-    if (params->ith != 0) {
-        return true;
-    }
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return true;
-    }
-    func(tensor->src[0], tensor->src[1], tensor);
-    return true;
-}
-
-GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len) try {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n");
-    for(int i=0;i<max_len;i++) id_list[i] = -1;
-
-    if (!g_sycl_gpu_mgr) {
-        g_sycl_gpu_mgr = new sycl_gpu_mgr();
-    }
-
-    for (int i=0;i< g_sycl_gpu_mgr->gpus.size();i++){
-        if (i>=max_len) break;
-        id_list[i] = g_sycl_gpu_mgr->gpus[i];
-    }
-    return;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-int ggml_sycl_get_device_count() try {
-    int device_count;
-    if (CHECK_TRY_ERROR(device_count =
-                             dpct::dev_mgr::instance().device_count()) != 0) {
-        return 0;
-    }
-    return device_count;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description,
-                                      size_t description_size) try {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n");
-    dpct::device_info prop;
-    int device_id = g_sycl_gpu_mgr->gpus[device];
-    SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
-        prop, dpct::dev_mgr::instance().get_device(device_id))));
-    snprintf(description, description_size, "%s", prop.get_name());
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free,
-                                                   size_t *total) try {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_memory\n");
-    ggml_sycl_set_device(device);
-
-    /*
-    DPCT1009:218: SYCL uses exceptions to report errors and does not use the
-    error codes. The original code was commented out and a warning string was
-    inserted. You need to rewrite this code.
-    */
-    /*
-    DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for
-    device information which may not be supported by all compilers or runtimes.
-    You may need to adjust the code.
-    */
-   int device_id = g_sycl_gpu_mgr->gpus[device];
-    SYCL_CHECK(CHECK_TRY_ERROR(
-        dpct::dev_mgr::instance().get_device(device_id).get_memory_info(*free, *total)));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-////////////////////////////////////////////////////////////////////////////////
-
-// backend interface
-
-#define UNUSED GGML_UNUSED
-
-// sycl buffer
-
-struct ggml_backend_sycl_buffer_context {
-    int device;
-    void * dev_ptr = nullptr;
-    ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
-    size_t temp_tensor_extra_index = 0;
-    std::string name;
-
-     ggml_backend_sycl_buffer_context(int device, void * dev_ptr) :
-        device(device), dev_ptr(dev_ptr) {
-            check_allow_gpu_index(device);
-            int id = g_sycl_gpu_mgr->gpus[device];
-            name = (GGML_SYCL_NAME + std::to_string(id));
-        }
-
-    ~ ggml_backend_sycl_buffer_context() {
-        delete[] temp_tensor_extras;
-    }
-
-    ggml_tensor_extra_gpu * ggml_sycl_alloc_temp_tensor_extra() {
-        if (temp_tensor_extras == nullptr) {
-            temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_SYCL_MAX_NODES];
-        }
-
-        size_t alloc_index = temp_tensor_extra_index;
-        temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_SYCL_MAX_NODES;
-        ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
-        memset(extra, 0, sizeof(*extra));
-
-        return extra;
-    }
-};
-
-GGML_CALL static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) {
-    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
-    return ctx->name.c_str();
-}
-
-GGML_CALL static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) {
-    return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name;
-}
-
-static void
-ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try {
-    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
-    ggml_sycl_set_device(ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
-
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(sycl::free(ctx->dev_ptr, *stream)));
-    delete ctx;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) {
-    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
-    return ctx->dev_ptr;
-}
-
-GGML_CALL static void
-ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
-                                     ggml_tensor *tensor) try {
-    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
-
-    if (tensor->view_src != NULL && tensor->view_offs == 0) {
-        assert(tensor->view_src->buffer->buft == buffer->buft);
-        tensor->backend = tensor->view_src->backend;
-        tensor->extra = tensor->view_src->extra;
-        return;
-    }
-
-    ggml_tensor_extra_gpu * extra = ctx->ggml_sycl_alloc_temp_tensor_extra();
-
-    extra->data_device[ctx->device] = tensor->data;
-    tensor->backend = GGML_BACKEND_TYPE_GPU;
-    tensor->extra = extra;
-
-    if (ggml_is_quantized(tensor->type)) {
-        // initialize padding to 0 to avoid possible NaN values
-        size_t original_size = ggml_nbytes(tensor);
-        size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
-
-        if (padded_size > original_size && tensor->view_src == nullptr) {
-            SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[ctx->device][0]->memset(
-                (char *)tensor->data + original_size, 0,
-                padded_size - original_size).wait()));
-        }
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
-                                                ggml_tensor *tensor,
-                                                const void *data, size_t offset,
-                                                size_t size) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
-
-    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
-
-    ggml_sycl_set_device(ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
-    char* host_buf = (char*)malloc(size);
-    memcpy(host_buf, data, size);
-    SYCL_CHECK(
-        CHECK_TRY_ERROR((*stream)
-                             .memcpy((char *)tensor->data + offset, host_buf, size)
-                             .wait()));
-    free(host_buf);
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer,
-                                                const ggml_tensor *tensor,
-                                                void *data, size_t offset,
-                                                size_t size) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
-
-    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
-
-    ggml_sycl_set_device(ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
-
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
-
-    SYCL_CHECK(CHECK_TRY_ERROR(
-        (*stream)
-            .memcpy(data, (const char *)tensor->data + offset, size)
-            .wait()));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL static bool
-ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
-                                    const ggml_tensor *src,
-                                    ggml_tensor *dst) try {
-    if (ggml_backend_buffer_is_sycl(src->buffer)) {
-        ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context;
-        ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
-
-        ggml_sycl_set_device(src_ctx->device);
-        /*
-        DPCT1009:198: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw()));
-        ggml_sycl_set_device(dst_ctx->device);
-        /*
-        DPCT1009:199: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw()));
-        /*
-        DPCT1009:200: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-
-        dpct::queue_ptr stream_dst = g_syclStreams[dst_ctx->device][0];
-        dpct::queue_ptr stream_src = g_syclStreams[src_ctx->device][0];
-        size_t size = ggml_nbytes(src);
-
-        //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs.
-        dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size);
-
-//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove
-#if 0
-        SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy(
-            (char *)dst->data, (const char *)src->data, size).wait()));
-
-        /*
-        DPCT1009:201: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw()));
-#endif
-        return true;
-    }
-    return false;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-
-static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer,
-                                           uint8_t value) try {
-     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
-
-    ggml_sycl_set_device(ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
-    SYCL_CHECK(
-        CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw()));
-
-    SYCL_CHECK(CHECK_TRY_ERROR((*stream)
-                                    .memset(ctx->dev_ptr, value, buffer->size)
-                                    .wait()));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = {
-    /* .get_name        = */ ggml_backend_sycl_buffer_get_name,
-    /* .free_buffer     = */ ggml_backend_sycl_buffer_free_buffer,
-    /* .get_base        = */ ggml_backend_sycl_buffer_get_base,
-    /* .init_tensor     = */ ggml_backend_sycl_buffer_init_tensor,
-    /* .set_tensor      = */ ggml_backend_sycl_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_sycl_buffer_get_tensor,
-    /* .cpy_tensor      = */ ggml_backend_sycl_buffer_cpy_tensor,
-    /* .clear           = */ ggml_backend_sycl_buffer_clear,
-    /* .reset           = */ NULL,
-};
-
-// sycl buffer type
-struct ggml_backend_sycl_buffer_type_context {
-    int device;
-    std::string name;
-};
-
-struct ggml_backend_sycl_context {
-    int device;
-    std::string name;
-};
-
-GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
-
-    return ctx->name.c_str();
-}
-GGML_CALL static ggml_backend_buffer_t
-ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
-                                           size_t size) try {
-    ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
-    ggml_sycl_set_device(buft_ctx->device);
-    const dpct::queue_ptr stream = g_syclStreams[buft_ctx->device][0];
-    size = std::max(size, (size_t)1); // syclMalloc returns null for size 0
-
-    void * dev_ptr;
-    SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device(
-                                    size, *stream)));
-    ggml_backend_sycl_buffer_context * ctx = new  ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr);
-    return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size);
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return 128;
-    UNUSED(buft);
-}
-
-static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
-    return dpct::get_current_device().get_max_mem_alloc_size();
-
-    UNUSED(buft);
-}
-
-GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
-    size_t size = ggml_nbytes(tensor);
-    int64_t ne0 = tensor->ne[0];
-
-    if (ggml_is_quantized(tensor->type)) {
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-    }
-
-    return size;
-
-    UNUSED(buft);
-}
-
-GGML_CALL static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    if (!ggml_backend_is_sycl(backend)) {
-        return false;
-    }
-    ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-    return buft_ctx->device == sycl_ctx->device;
-}
-
-static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = {
-    /* .get_name         = */ ggml_backend_sycl_buffer_type_name,
-    /* .alloc_buffer     = */ ggml_backend_sycl_buffer_type_alloc_buffer,
-    /* .get_alignment    = */ ggml_backend_sycl_buffer_type_get_alignment,
-    /* .get_max_size     = */ ggml_backend_sycl_buffer_type_get_max_size,
-    /* .get_alloc_size   = */ ggml_backend_sycl_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_sycl_buffer_type_supports_backend,
-    /* .is_host          = */ nullptr,
-};
-
-ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n");
-
-    if (device_index>=g_device_count or device_index<0) {
-        printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
-            device_index, g_device_count-1);
-        GGML_ASSERT(device_index<g_device_count);
-    }
-    static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_types[GGML_SYCL_MAX_DEVICES];
-
-    if (!g_ggml_backend_sycl_buffer_type_initialized) {
-        for (int i = 0; i < g_device_count; i++) {
-            ggml_backend_sycl_buffer_types[i] = {
-                /* .iface    = */ ggml_backend_sycl_buffer_type_interface,
-                /* .context  = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(g_sycl_gpu_mgr->gpus[i])},
-            };
-        }
-        g_ggml_backend_sycl_buffer_type_initialized = true;
-    }
-    return &ggml_backend_sycl_buffer_types[device_index];
-}
-
-// sycl split buffer type
-static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split, int id) {
-    const int64_t nrows = ggml_nrows(tensor);
-    const int64_t rounding = get_row_rounding(tensor->type, tensor_split);
-
-    *row_low = id == 0 ? 0 : nrows*tensor_split[id];
-    *row_low -= *row_low % rounding;
-    if (id == g_device_count - 1) {
-        *row_high = nrows;
-    } else {
-        *row_high = nrows*tensor_split[id + 1];
-        *row_high -= *row_high % rounding;
-    }
-}
-
-struct ggml_backend_sycl_split_buffer_context {
-    ~ggml_backend_sycl_split_buffer_context() try {
-        for (ggml_tensor_extra_gpu * extra : tensor_extras) {
-            for (int i = 0; i < g_device_count; ++i) {
-                // int id = g_sycl_gpu_mgr->gpus[i];
-                for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-                    if (extra->events[i][is] != nullptr) {
-                        /*
-                        DPCT1009:206: SYCL uses exceptions to report errors and
-                        does not use the error codes. The original code was
-                        commented out and a warning string was inserted. You
-                        need to rewrite this code.
-                        */
-                        SYCL_CHECK(CHECK_TRY_ERROR(
-                            dpct::destroy_event(extra->events[i][is])));
-                    }
-                }
-                if (extra->data_device[i] != nullptr) {
-                    /*
-                    DPCT1009:207: SYCL uses exceptions to report errors and does
-                    not use the error codes. The original code was commented out
-                    and a warning string was inserted. You need to rewrite this
-                    code.
-                    */
-                    ggml_sycl_set_device(i);
-                    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(
-                        extra->data_device[i], *g_syclStreams[i][0])));
-                }
-            }
-            delete extra;
-        }
-    }
-    catch (sycl::exception const &exc) {
-      std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-                << ", line:" << __LINE__ << std::endl;
-      std::exit(1);
-    }
-
-    std::vector<ggml_tensor_extra_gpu *> tensor_extras;
-};
-
-GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) {
-    return GGML_SYCL_NAME "_Split";
-
-    UNUSED(buffer);
-}
-
-// unused at the moment
-//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) {
-//    return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name;
-//}
-
-GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
-    delete ctx;
-}
-
-GGML_CALL static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) {
-    // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
-    return (void *)0x1000;
-
-    UNUSED(buffer);
-}
-
-GGML_CALL static void
-ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
-                                           ggml_tensor *tensor) try {
-    GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
-
-    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
-    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
-
-    const int64_t ne0 = tensor->ne[0];
-
-    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
-
-    ctx->tensor_extras.push_back(extra);
-
-    for (int i = 0; i < g_device_count; ++i) {
-        // int id = g_sycl_gpu_mgr->gpus[i];
-        int64_t row_low, row_high;
-        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
-
-        int64_t nrows_split = row_high - row_low;
-        if (nrows_split == 0) {
-            continue;
-        }
-
-        size_t size = ggml_nbytes_split(tensor, nrows_split);
-        const size_t original_size = size;
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-
-        // FIXME: do not crash if cudaMalloc fails
-        // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
-        ggml_sycl_set_device(i);
-        char * buf;
-        /*
-        DPCT1009:208: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-        SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device(
-                                        size, *g_syclStreams[i][0])));
-
-        // set padding to 0 to avoid possible NaN values
-        if (size > original_size) {
-            /*
-            DPCT1009:209: SYCL uses exceptions to report errors and does not use
-            the error codes. The original code was commented out and a warning
-            string was inserted. You need to rewrite this code.
-            */
-            SYCL_CHECK(CHECK_TRY_ERROR(
-                (*g_syclStreams[i][0])
-                    .memset(buf + original_size, 0, size - original_size)
-                    .wait()));
-        }
-
-        extra->data_device[i] = buf;
-
-        for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-            /*
-            DPCT1009:210: SYCL uses exceptions to report errors and does not use
-            the error codes. The original code was commented out and a warning
-            string was inserted. You need to rewrite this code.
-            */
-            SYCL_CHECK(
-                CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event()));
-        }
-    }
-    tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT;
-    tensor->extra = extra;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL static void
-ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer,
-                                          ggml_tensor *tensor, const void *data,
-                                          size_t offset, size_t size) try {
-    // split tensors must always be set in their entirety at once
-    GGML_ASSERT(offset == 0);
-    GGML_ASSERT(size == ggml_nbytes(tensor));
-
-    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
-
-    const int64_t ne0 = tensor->ne[0];
-    const size_t nb1 = tensor->nb[1];
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
-
-    for (int i = 0; i < g_device_count; ++i) {
-        // int id = g_sycl_gpu_mgr->gpus[i];
-        int64_t row_low, row_high;
-        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
-
-        int64_t nrows_split = row_high - row_low;
-        if (nrows_split == 0) {
-            continue;
-        }
-
-        const size_t offset_split = row_low*nb1;
-        size_t size = ggml_nbytes_split(tensor, nrows_split);
-        const size_t original_size = size;
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-
-        const char * buf_host = (const char *)data + offset_split;
-        /*
-        DPCT1009:211: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-        ggml_sycl_set_device(i);
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            (*g_syclStreams[i][0])
-                .memcpy(extra->data_device[i], buf_host, original_size)
-                .wait()));
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL static void
-ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer,
-                                          const ggml_tensor *tensor, void *data,
-                                          size_t offset, size_t size) try {
-    // split tensors must always be set in their entirety at once
-    GGML_ASSERT(offset == 0);
-    GGML_ASSERT(size == ggml_nbytes(tensor));
-
-    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
-
-    const int64_t ne0 = tensor->ne[0];
-    const size_t nb1 = tensor->nb[1];
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
-
-    for (int i = 0; i < g_device_count; ++i) {
-        // int id = g_sycl_gpu_mgr->gpus[i];
-        int64_t row_low, row_high;
-        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
-
-        int64_t nrows_split = row_high - row_low;
-        if (nrows_split == 0) {
-            continue;
-        }
-
-        const size_t offset_split = row_low*nb1;
-        size_t size = ggml_nbytes_split(tensor, nrows_split);
-        const size_t original_size = size;
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-
-        char * buf_host = (char *)data + offset_split;
-        /*
-        DPCT1009:212: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-        ggml_sycl_set_device(i);
-        SYCL_CHECK(CHECK_TRY_ERROR(
-            (*g_syclStreams[i][0])
-                .memcpy(buf_host, extra->data_device[i], original_size)
-                .wait()));
-    }
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    UNUSED(buffer);
-    UNUSED(value);
-}
-
-static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = {
-    /* .get_name        = */ ggml_backend_sycl_split_buffer_get_name,
-    /* .free_buffer     = */ ggml_backend_sycl_split_buffer_free_buffer,
-    /* .get_base        = */ ggml_backend_sycl_split_buffer_get_base,
-    /* .init_tensor     = */ ggml_backend_sycl_split_buffer_init_tensor,
-    /* .set_tensor      = */ ggml_backend_sycl_split_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_sycl_split_buffer_get_tensor,
-    /* .cpy_tensor      = */ NULL,
-    /* .clear           = */ ggml_backend_sycl_split_buffer_clear,
-    /* .reset           = */ NULL,
-};
-
-GGML_CALL static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    return GGML_SYCL_NAME "_Split";
-
-    UNUSED(buft);
-}
-
-GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
-    // instead, we allocate them for each tensor separately in init_tensor
-    // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
-    // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct.
-    ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context();
-
-    return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size);
-}
-
-GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return 128;
-    UNUSED(buft);
-}
-
-GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
-    ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context;
-
-    size_t total_size = 0;
-
-    const int64_t ne0 = tensor->ne[0];
-
-    for (int i = 0; i < g_device_count; ++i) {
-        // int id = g_sycl_gpu_mgr->gpus[i];
-        int64_t row_low, row_high;
-        get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i);
-
-        int64_t nrows_split = row_high - row_low;
-        if (nrows_split == 0) {
-            continue;
-        }
-
-        total_size += ggml_nbytes_split(tensor, nrows_split);
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-    }
-
-    return total_size;
-}
-
-GGML_CALL static bool ggml_backend_sycl_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    return ggml_backend_is_sycl(backend);
-
-    UNUSED(buft);
-}
-
-GGML_CALL static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
-    return false;
-
-    UNUSED(buft);
-}
-
-static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = {
-    /* .get_name         = */ ggml_backend_sycl_split_buffer_type_name,
-    /* .alloc_buffer     = */ ggml_backend_sycl_split_buffer_type_alloc_buffer,
-    /* .get_alignment    = */ ggml_backend_sycl_split_buffer_type_get_alignment,
-    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-    /* .get_alloc_size   = */ ggml_backend_sycl_split_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_sycl_split_buffer_type_supports_backend,
-    /* .is_host          = */ ggml_backend_sycl_split_buffer_type_is_host,
-};
-
-GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n");
-    ggml_init_sycl();
-    // FIXME: this is not thread safe
-    static std::map<std::array<float, GGML_SYCL_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
-
-    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split_arr = {};
-
-    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; });
-    if (all_zero) {
-        tensor_split_arr = g_default_tensor_split;
-    } else {
-        float split_sum = 0.0f;
-        for (int i = 0; i < g_device_count; ++i) {
-            // int id = g_sycl_gpu_mgr->gpus[i];
-            tensor_split_arr[i] = split_sum;
-            split_sum += tensor_split[i];
-        }
-        for (int i = 0; i < g_device_count; ++i) {
-            // int id = g_sycl_gpu_mgr->gpus[i];
-            tensor_split_arr[i] /= split_sum;
-        }
-    }
-
-    auto it = buft_map.find(tensor_split_arr);
-    if (it != buft_map.end()) {
-        return &it->second;
-    }
-
-    struct ggml_backend_buffer_type buft {
-        /* .iface   = */ ggml_backend_sycl_split_buffer_type_interface,
-        /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr},
-    };
-
-    auto result = buft_map.emplace(tensor_split_arr, buft);
-    return &result.first->second;
-}
-
-// host buffer type
-
-GGML_CALL static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    return GGML_SYCL_NAME "_Host";
-
-    UNUSED(buft);
-}
-
-GGML_CALL static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) {
-    return GGML_SYCL_NAME "_Host";
-
-    UNUSED(buffer);
-}
-
-static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_sycl_host_free(buffer->context);
-}
-
-static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    void * ptr = ggml_sycl_host_malloc(size);
-
-    if (ptr == nullptr) {
-        // fallback to cpu buffer
-        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
-    }
-
-    // FIXME: this is a hack to avoid having to implement a new buffer type
-    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
-    buffer->buft = buft;
-    buffer->iface.get_name = ggml_backend_sycl_host_buffer_name;
-    buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer;
-
-    return buffer;
-}
-
-ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n");
-    static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = {
-        /* .iface    = */ {
-            /* .get_name         = */ ggml_backend_sycl_host_buffer_type_name,
-            /* .alloc_buffer     = */ ggml_backend_sycl_host_buffer_type_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
-            /* .get_max_size     = */ NULL, // TODO: return device.maxBufferLength
-            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
-            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
-            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
-        },
-        /* .context  = */ nullptr,
-    };
-
-    return &ggml_backend_sycl_buffer_type_host;
-}
-
-// backend
-
-GGML_CALL static const char * ggml_backend_sycl_name(ggml_backend_t backend) {
-
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-
-    return sycl_ctx->name.c_str();
-}
-
-GGML_CALL static void ggml_backend_sycl_free(ggml_backend_t backend) {
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-
-    delete sycl_ctx;
-    delete backend;
-}
-
-
-GGML_CALL static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) {
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-    return ggml_backend_sycl_buffer_type(sycl_ctx->device);
-}
-
-GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
-                                               ggml_tensor *tensor,
-                                               const void *data, size_t offset,
-                                               size_t size) try {
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-    GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
-    SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
-        (char *)tensor->data + offset, data, size).wait()));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
-                                               const ggml_tensor *tensor,
-                                               void *data, size_t offset,
-                                               size_t size) try {
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-    GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
-    SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
-        data, (const char *)tensor->data + offset, size).wait()));
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend,
-                                                         const ggml_tensor *src,
-                                                         ggml_tensor *dst) try {
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-    if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) {
-        /*
-        DPCT1009:215: SYCL uses exceptions to report errors and does not use the
-        error codes. The original code was commented out and a warning string
-        was inserted. You need to rewrite this code.
-        */
-        SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
-            dst->data, src->data, ggml_nbytes(dst)).wait()));
-        return true;
-    }
-
-    return false;
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try {
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-    SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait()));
-
-    UNUSED(backend);
-}
-catch (sycl::exception const &exc) {
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
-            << ", line:" << __LINE__ << std::endl;
-  std::exit(1);
-}
-
-GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
-    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-    ggml_sycl_set_main_device(sycl_ctx->device);
-
-    ggml_compute_params params = {};
-    params.type = GGML_TASK_TYPE_COMPUTE;
-    params.ith = 0;
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_tensor * node = cgraph->nodes[i];
-        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
-            continue;
-        }
-#ifndef NDEBUG
-        assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
-        assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
-        assert(node->extra != nullptr);
-
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            if (node->src[j] != nullptr) {
-                assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
-                assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
-                assert(node->src[j]->extra != nullptr);
-            }
-        }
-#endif
-        bool ok = ggml_sycl_compute_forward(&params, node);
-        if (!ok) {
-            fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
-        }
-        GGML_ASSERT(ok);
-    }
-
-    return GGML_STATUS_SUCCESS;
-}
-
-GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
-    switch (op->op) {
-        case GGML_OP_UNARY:
-            switch (ggml_get_unary_op(op)) {
-                case GGML_UNARY_OP_GELU:
-                case GGML_UNARY_OP_SILU:
-                case GGML_UNARY_OP_RELU:
-                case GGML_UNARY_OP_HARDSIGMOID:
-                case GGML_UNARY_OP_HARDSWISH:
-                case GGML_UNARY_OP_GELU_QUICK:
-                case GGML_UNARY_OP_TANH:
-                    return true;
-                default:
-                    return false;
-            }
-            break;
-        case GGML_OP_MUL_MAT:
-        case GGML_OP_MUL_MAT_ID:
-            {
-                struct ggml_tensor * a;
-                struct ggml_tensor * b;
-                if (op->op == GGML_OP_MUL_MAT) {
-                    a = op->src[0];
-                    b = op->src[1];
-                } else {
-                    a = op->src[2];
-                    b = op->src[1];
-                }
-                if (a->ne[3] != b->ne[3]) {
-                    return false;
-                }
-                ggml_type a_type = a->type;
-                if (a_type == GGML_TYPE_IQ4_NL  || a_type == GGML_TYPE_IQ4_XS ||
-                    a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ3_S  ||
-                    a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ2_S ||
-                    a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ1_M
-                    ) {
-                    if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
-                        return false;
-                    }
-                }
-                return true;
-            } break;
-        case GGML_OP_GET_ROWS:
-            {
-                switch (op->src[0]->type) {
-                    case GGML_TYPE_F16:
-                    case GGML_TYPE_F32:
-                    case GGML_TYPE_Q4_0:
-                    case GGML_TYPE_Q4_1:
-                    case GGML_TYPE_Q5_0:
-                    case GGML_TYPE_Q5_1:
-                    case GGML_TYPE_Q8_0:
-                        return true;
-                    default:
-                        return false;
-                }
-            } break;
-        case GGML_OP_CPY:
-            {
-                ggml_type src0_type = op->src[0]->type;
-                ggml_type src1_type = op->src[1]->type;
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                return false;
-            } break;
-        case GGML_OP_CONCAT:
-            {
-                ggml_type src0_type = op->src[0]->type;
-                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
-            } break;
-        case GGML_OP_DUP:
-        case GGML_OP_NONE:
-        case GGML_OP_RESHAPE:
-        case GGML_OP_REPEAT:
-        case GGML_OP_VIEW:
-        case GGML_OP_PERMUTE:
-        case GGML_OP_TRANSPOSE:
-        case GGML_OP_NORM:
-        case GGML_OP_ADD:
-        case GGML_OP_MUL:
-        case GGML_OP_DIV:
-        case GGML_OP_RMS_NORM:
-        case GGML_OP_SCALE:
-        case GGML_OP_SQR:
-        case GGML_OP_CLAMP:
-        case GGML_OP_CONT:
-        case GGML_OP_DIAG_MASK_INF:
-        case GGML_OP_SOFT_MAX:
-        case GGML_OP_ROPE:
-        case GGML_OP_IM2COL:
-        case GGML_OP_POOL_2D:
-        case GGML_OP_SUM_ROWS:
-        case GGML_OP_ARGSORT:
-        case GGML_OP_ACC:
-        case GGML_OP_GROUP_NORM:
-        case GGML_OP_UPSCALE:
-        case GGML_OP_PAD:
-        case GGML_OP_LEAKY_RELU:
-            return true;
-        default:
-            return false;
-    }
-
-    UNUSED(backend);
-}
-
-GGML_CALL static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
-    const int min_batch_size = 32;
-    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID;
-    GGML_UNUSED(backend);
-}
-
-
-static ggml_backend_i ggml_backend_sycl_interface = {
-    /* .get_name                = */ ggml_backend_sycl_name,
-    /* .free                    = */ ggml_backend_sycl_free,
-    /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type,
-    /* .set_tensor_async        = */ ggml_backend_sycl_set_tensor_async,
-    /* .get_tensor_async        = */ ggml_backend_sycl_get_tensor_async,
-    /* .cpy_tensor_async        = */ NULL, //ggml_backend_sycl_cpy_tensor_async, // TODO: update for the new interface
-    /* .synchronize             = */ ggml_backend_sycl_synchronize,
-    /* .graph_plan_create       = */ NULL,
-    /* .graph_plan_free         = */ NULL,
-    /* .graph_plan_compute      = */ NULL,
-    /* .graph_compute           = */ ggml_backend_sycl_graph_compute,
-    /* .supports_op             = */ ggml_backend_sycl_supports_op,
-    /* .offload_op              = */ ggml_backend_sycl_offload_op,
-    /* .event_new               = */ NULL,
-    /* .event_free              = */ NULL,
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
-    /* .event_synchronize       = */ NULL,
-};
-
-static ggml_guid_t ggml_backend_sycl_guid() {
-    static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 };
-    return &guid;
-}
-
-GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_init\n");
-    ggml_init_sycl();
-
-    check_allow_gpu_index(device);
-
-    // not strictly necessary, but it may reduce the overhead of the first graph_compute
-    ggml_sycl_set_main_device(device);
-    int id = g_sycl_gpu_mgr->gpus[device];
-    ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context {
-        /* .device = */ device,
-        /* .name   = */ GGML_SYCL_NAME + std::to_string(id),
-    };
-
-    ggml_backend_t sycl_backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_sycl_guid(),
-        /* .interface = */ ggml_backend_sycl_interface,
-        /* .context   = */ ctx
-    };
-
-    return sycl_backend;
-}
-
-bool ggml_backend_is_sycl(ggml_backend_t backend) {
-    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid());
-}
-
-GGML_CALL int ggml_backend_sycl_get_device_count() {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n");
-    if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr();
-    return g_sycl_gpu_mgr->get_gpu_count();
-}
-
-GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) {
-    ggml_backend_t sycl_backend = ggml_backend_sycl_init((int) (intptr_t) user_data);
-    return sycl_backend;
-
-    UNUSED(params);
-}
-
-GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_index\n");
-    return g_sycl_gpu_mgr->get_index(device_id);
-}
-
-GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index) {
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_id\n");
-    return g_sycl_gpu_mgr->gpus[device_index];
-}
-
-GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id) {
-    ggml_init_sycl();
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_single_device_mode\n");
-    fprintf(stderr, "ggml_backend_sycl_set_single_device: use single device: [%d]\n", main_gpu_id);
-    GGML_ASSERT(main_gpu_id<g_all_sycl_device_count);
-
-    if (g_sycl_gpu_mgr) {
-        delete g_sycl_gpu_mgr;
-    }
-    g_sycl_gpu_mgr = new sycl_gpu_mgr(main_gpu_id);
-    g_ggml_sycl_backend_gpu_mode = SYCL_SINGLE_GPU_MODE;
-    ggml_init_by_gpus(g_sycl_gpu_mgr->get_gpu_count());
-    g_ggml_backend_sycl_buffer_type_initialized = false;
-}
-
-GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode() {
-    ggml_init_sycl();
-    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_mul_device_mode\n");
-
-    if (g_ggml_sycl_backend_gpu_mode == SYCL_MUL_GPU_MODE) {
-        return;
-    }
-
-    fprintf(stderr, "ggml_backend_sycl_set_mul_device_mode: true\n");
-
-    if (g_sycl_gpu_mgr) {
-        delete g_sycl_gpu_mgr;
-    }
-    g_sycl_gpu_mgr = new sycl_gpu_mgr();
-    g_ggml_sycl_backend_gpu_mode = SYCL_MUL_GPU_MODE;
-    ggml_init_by_gpus(g_sycl_gpu_mgr->get_gpu_count());
-    g_ggml_backend_sycl_buffer_type_initialized = false;
-}
-
-extern "C" int ggml_backend_sycl_reg_devices();
-
-int ggml_backend_sycl_reg_devices() {
-    ggml_backend_sycl_set_mul_device_mode();
-    assert(g_device_count>0);
-    for (int i = 0; i < g_device_count; i++) {
-        int id = g_sycl_gpu_mgr->gpus[i];
-        char name[128];
-        snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, id);
-        ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i);
-    }
-    return g_device_count;
-}
diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp
deleted file mode 100644
index 70c4043d3f3cf..0000000000000
--- a/ggml-vulkan-shaders.hpp
+++ /dev/null
@@ -1,81142 +0,0 @@
-#include <cstdint>
-
-unsigned char add_f32_data[] = {
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x01,0x00,0x00,
-0xca,0x01,0x00,0x00,0xc3,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x01,0x00,0x00,0xce,0x01,0x00,0x00,
-0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0xd4,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xdc,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,
-0xdd,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe4,0x01,0x00,0x00,0xe1,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xe4,0x01,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x01,0x00,0x00,
-0xe9,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf5,0x01,0x00,0x00,0xec,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x01,0x00,0x00,0xe9,0x01,0x00,0x00,0xf5,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xf8,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xf9,0x01,0x00,0x00,0xf8,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x01,0x00,0x00,
-0xca,0x01,0x00,0x00,0xf9,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xfc,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xfd,0x01,0x00,0x00,0xfc,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,
-0xfd,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xff,0x01,0x00,0x00,0xfa,0x01,0x00,0x00,0xfe,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x02,0x02,0x00,0x00,0x01,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x02,0x00,0x00,
-0xec,0x01,0x00,0x00,0x02,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x04,0x02,0x00,0x00,0xff,0x01,0x00,0x00,
-0x03,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x06,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,
-0x06,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x08,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x07,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x02,0x00,0x00,
-0x04,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x34,0x01,0x00,0x00,0x09,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
-0xca,0x01,0x00,0x00,0x4a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x17,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x50,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
-0x50,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x52,0x02,0x00,0x00,0xdd,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
-0x55,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x58,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
-0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
-0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
-0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t add_f32_len = 4276;
-
-unsigned char argsort_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x41,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x30,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x30,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x89,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0f,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x12,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x12,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1c,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x00,0x04,0x00,0x00,0x1c,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x22,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x22,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x22,0x00,0x00,0x00,
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-0x9f,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7f,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7f,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x37,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x87,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x76,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x76,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x37,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xb5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xb3,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb4,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x2a,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x25,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x29,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbc,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc0,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x37,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0xa8,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xc6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0xc9,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x37,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xcf,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xce,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x34,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x37,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0xd1,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd2,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x93,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0xdc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0xe1,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x93,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x88,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xb8,0x00,0x05,0x00,0x37,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0xdd,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x93,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x88,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf5,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x93,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0x37,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x37,0x00,0x00,0x00,
-0x3f,0x01,0x00,0x00,0xe6,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x37,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
-0xd5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc6,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x37,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfb,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
-0x13,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x32,0x01,0x00,0x00,
-0x37,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xbe,0x00,0x00,0x00,
-0x33,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x6c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x65,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x65,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x57,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x57,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x3d,0x01,0x00,0x00,0x45,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x54,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x56,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4b,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x07,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,0x45,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x48,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4a,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x37,0x00,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
-0x0e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x41,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x44,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x1a,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
-0x12,0x01,0x00,0x00,0x3e,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1b,0x01,0x00,0x00,0x19,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x1d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t argsort_f32_len = 4096;
-
-unsigned char clamp_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x6d,0x01,0x00,0x00,
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-0x86,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x71,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x01,0x00,0x00,0x80,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
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-0x9f,0x01,0x00,0x00,0x92,0x01,0x00,0x00,0x9e,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
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-0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
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-0xa6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa8,0x01,0x00,0x00,0xa3,0x01,0x00,0x00,0xa7,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xaa,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
-0x95,0x01,0x00,0x00,0xab,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00,0xa8,0x01,0x00,0x00,
-0xac,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
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-0xb1,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0xb0,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,
-0xad,0x01,0x00,0x00,0xb1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0xb2,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0xef,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xb8,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf2,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf8,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xef,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xa9,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf5,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,
-0xb4,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xb5,0x01,0x00,0x00,0xf8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xde,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x07,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t clamp_f32_len = 3512;
-
-unsigned char cpy_f16_f16_data[] = {
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-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
-0x7a,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x92,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
-0x91,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x94,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
-0x94,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
-0x92,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x97,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xd4,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xe9,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0xd8,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf0,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t cpy_f16_f16_len = 3252;
-
-unsigned char cpy_f32_f16_data[] = {
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00,
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-0x8a,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x89,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,
-0x6d,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x8e,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x91,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x92,0x01,0x00,0x00,
-0x91,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x93,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x92,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x97,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
-0x97,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x99,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x98,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xed,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t cpy_f32_f16_len = 3284;
-
-unsigned char cpy_f32_f32_data[] = {
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-0x6a,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
-0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
-0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
-0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
-0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x96,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xe8,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xeb,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t cpy_f32_f32_len = 3232;
-
-unsigned char dequant_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x2d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
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-0x86,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8b,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x16,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
-0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xa5,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x17,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x39,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0xb0,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xb2,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3d,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xbf,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x19,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x39,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xd9,0x00,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe1,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3d,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x1c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x39,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x00,0x01,0x00,0x00,
-0xff,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x0a,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3d,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x45,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x45,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_f32_len = 3200;
-
-unsigned char dequant_q2_K_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x10,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
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-0x5e,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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-0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
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-0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x5a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x5c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x5c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x5e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x5e,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x77,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x00,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
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-0xfa,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x67,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6c,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x01,0x01,0x00,0x00,
-0x07,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x01,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q2_K_len = 4032;
-
-unsigned char dequant_q3_K_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x77,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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-0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x44,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0x44,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
-0x01,0x01,0x00,0x00,0xf6,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x02,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x39,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0e,0x01,0x00,0x00,0xf7,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x7c,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
-0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x10,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
-0x10,0x01,0x00,0x00,0x5d,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
-0x13,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0x15,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x8b,0x00,0x00,0x00,
-0x72,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x15,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x17,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x7c,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0x77,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x39,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x1b,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
-0x57,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
-0x21,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
-0x21,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0xdb,0x00,0x00,0x00,
-0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0xdb,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0xea,0x00,0x00,0x00,
-0x23,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
-0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xdf,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,
-0x35,0x01,0x00,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x2f,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
-0x2c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x33,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x3e,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q3_K_len = 4804;
-
-unsigned char dequant_q4_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x83,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x49,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x64,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x66,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
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-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,
-0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x16,0x00,0x03,0x00,0x45,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x48,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x1e,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x4a,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
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-0x42,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x00,0x00,0x00,0xc1,
-0x1d,0x00,0x03,0x00,0x63,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
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-0x2c,0x00,0x00,0x00,0x62,0x01,0x00,0x00,0x61,0x01,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x2c,0x00,0x00,0x00,0x63,0x01,0x00,0x00,
-0x62,0x01,0x00,0x00,0x76,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x64,0x01,0x00,0x00,0x63,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,0x66,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x64,0x01,0x00,0x00,0x57,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x45,0x00,0x00,0x00,0x67,0x01,0x00,0x00,0x66,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,0x68,0x01,0x00,0x00,
-0x66,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x68,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,
-0x41,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x46,0x00,0x00,0x00,0x6d,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,
-0x6d,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x6f,0x01,0x00,0x00,0x57,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x45,0x00,0x00,0x00,0x72,0x01,0x00,0x00,0x71,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
-0x66,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x73,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x95,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x95,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q4_0_len = 5184;
-
-unsigned char dequant_q4_1_data[] = {
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-0x85,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-};
-const uint64_t dequant_q4_1_len = 5248;
-
-unsigned char dequant_q4_K_data[] = {
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-0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
-0x8d,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x43,0x00,0x00,0x00,
-0x9c,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,0x98,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x9d,0x01,0x00,0x00,
-0x9c,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x3e,0x00,0x00,0x00,
-0x9f,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x9d,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,
-0x9f,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4e,0x00,0x00,0x00,
-0xa1,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x98,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa1,0x01,0x00,0x00,
-0xa0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x45,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x3a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x3a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x34,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q4_K_len = 5916;
-
-unsigned char dequant_q5_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb7,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x4e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x7d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xab,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1e,0x00,0x07,0x00,0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x32,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x00,0x04,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x42,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x45,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x48,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
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-0xa0,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
-0x9d,0x01,0x00,0x00,0xa0,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,
-0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,
-0xa2,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x42,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
-0xa3,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0xa5,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x53,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q5_0_len = 6644;
-
-unsigned char dequant_q5_1_data[] = {
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-0x3e,0x00,0x03,0x00,0x96,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x01,0x00,0x00,
-0x41,0x00,0x00,0x00,0xb3,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x9a,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,
-0x47,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x01,0x00,0x00,0x99,0x01,0x00,0x00,0x9c,0x01,0x00,0x00,
-0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,
-0x9d,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
-0xa0,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
-0xa0,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
-0xa2,0x01,0x00,0x00,0x7a,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x98,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa2,0x01,0x00,0x00,
-0xa1,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q5_1_len = 6412;
-
-unsigned char dequant_q5_K_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x9e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x45,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4a,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0e,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x0f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x0f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x0f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x11,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x11,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x3e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x41,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x45,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x47,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
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-0x48,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x4a,0x00,0x00,0x00,
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-0x49,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
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-0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
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-0x2c,0x00,0x06,0x00,0x14,0x00,0x00,0x00,0x87,0x01,0x00,0x00,
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-0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,
-0x5d,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
-0x21,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,
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-0x63,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x43,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,0x35,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x43,0x00,0x00,0x00,0x70,0x01,0x00,0x00,
-0x6f,0x01,0x00,0x00,0x9c,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x72,0x01,0x00,0x00,
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-0x7b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
-0x21,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,
-0x26,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
-0x7d,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x7f,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x3e,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,
-0x9d,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,
-0x83,0x01,0x00,0x00,0x82,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x50,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x69,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x84,0x01,0x00,0x00,0x83,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,
-0x90,0x01,0x00,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
-0x8a,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
-0x2c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8e,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x97,0x01,0x00,0x00,
-0x88,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x88,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q5_K_len = 5980;
-
-unsigned char dequant_q6_K_data[] = {
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-0x0d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x43,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x7c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0xc1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
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-0xd0,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
-0x8e,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0xd3,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0x72,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0xd3,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0xd5,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xdb,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x53,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
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-0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x53,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
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-0xf3,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
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-0x72,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfe,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x05,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
-0x09,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x03,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x03,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q6_K_len = 4272;
-
-unsigned char dequant_q8_0_data[] = {
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-0x4c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
-0x41,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0x40,0x00,0x00,0x00,
-0x4a,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,
-0x2f,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0x42,0x00,0x00,0x00,0x30,0x01,0x00,0x00,0x52,0x00,0x00,0x00,
-0x2f,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0x31,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x4f,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x36,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x46,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x38,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x52,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x34,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x3c,0x01,0x00,0x00,
-0x3b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x88,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q8_0_len = 4780;
-
-unsigned char diag_mask_inf_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x38,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x43,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x43,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x1e,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x36,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x37,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x38,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x39,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x39,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x42,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x43,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x44,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x44,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x09,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x00,0x00,0x80,0xff,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0xae,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x35,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x34,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3e,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x3f,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3e,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3e,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4c,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4c,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t diag_mask_inf_f32_len = 1504;
-
-unsigned char gelu_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x36,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x21,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x2a,0x42,0x4c,0x3f,
-0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x13,0x27,0x37,0x3d,0x1d,0x00,0x03,0x00,
-0x35,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x36,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x00,0x00,0x00,0x3f,0x2b,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x00,0x00,0x00,0x40,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x09,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x49,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x11,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x88,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x26,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x49,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x49,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t gelu_f32_len = 1484;
-
-unsigned char get_rows_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
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-0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x46,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7f,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7f,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x82,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x85,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_f16_len = 2996;
-
-unsigned char get_rows_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x35,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x71,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x71,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x73,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
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-0x36,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x77,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x87,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_f16_f32_len = 3028;
-
-unsigned char get_rows_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x35,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x72,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1e,0x00,0x1e,0x00,0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x2c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x32,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x48,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x70,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x71,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x72,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x73,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x78,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x79,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x7a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x0c,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x2f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7f,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x87,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_f32_len = 3028;
-
-unsigned char get_rows_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
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-0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7e,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x7e,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_f32_f32_len = 2976;
-
-unsigned char get_rows_q4_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x66,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x67,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd2,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0xab,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0xab,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,
-0xe0,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xa8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
-0xe1,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x2a,0x00,0x00,0x00,
-0xe7,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xa8,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0xe7,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc6,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x20,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd0,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd3,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_0_len = 3704;
-
-unsigned char get_rows_q4_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x66,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x67,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd1,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
-0x20,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xe1,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x2a,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0xe7,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xe8,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0xe8,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc6,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xcf,0x00,0x00,0x00,
-0xce,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_0_f32_len = 3688;
-
-unsigned char get_rows_q4_1_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x57,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x6f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc2,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc5,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xda,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
-0x20,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x32,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0xff,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x03,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x01,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x20,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd8,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_1_len = 3788;
-
-unsigned char get_rows_q4_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x57,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x6f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc2,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc5,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd9,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
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-0x4f,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x20,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x32,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0xfe,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x03,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xb4,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xcd,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xce,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xcd,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xd7,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xda,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xda,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_1_f32_len = 3772;
-
-unsigned char get_rows_q5_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x72,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x7d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x94,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x95,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xe9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xe9,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xe9,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xeb,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xeb,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x00,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd5,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
-0x23,0x00,0x00,0x00,0x10,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x11,0x01,0x00,0x00,
-0x10,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x12,0x01,0x00,0x00,0x11,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x2c,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x17,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
-0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
-0x18,0x01,0x00,0x00,0x30,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x2c,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x19,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
-0xda,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,
-0xda,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
-0x29,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x51,0x00,0x00,0x00,
-0x32,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x33,0x01,0x00,0x00,
-0x32,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x34,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x56,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x39,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
-0x39,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x3b,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x40,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x40,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x42,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,
-0x42,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x43,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
-0x45,0x01,0x00,0x00,0x12,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x47,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x23,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x23,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x01,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q5_0_len = 4232;
-
-unsigned char get_rows_q5_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x72,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0x79,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x81,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x9e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xa9,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0xa8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0xab,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x81,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb8,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x23,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0x10,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x2c,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
-0x16,0x01,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x18,0x01,0x00,0x00,0x17,0x01,0x00,0x00,0x30,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x2c,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x1d,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x18,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
-0x1f,0x01,0x00,0x00,0xda,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x28,0x01,0x00,0x00,0xda,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,0x29,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x51,0x00,0x00,0x00,0x31,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0x56,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x35,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00,
-0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x42,0x01,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x83,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0x42,0x01,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x46,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0x46,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xf3,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf4,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x46,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xf3,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q5_0_f32_len = 4216;
-
-unsigned char get_rows_q5_1_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x43,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0xef,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x42,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xe7,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xfa,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfd,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_q5_1_len = 4128;
-
-unsigned char get_rows_q5_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x91,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x91,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x91,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x93,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe5,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xe5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe5,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe7,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe7,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfb,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
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-0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x6b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x02,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x79,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x4c,0x00,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xd2,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x51,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x32,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x2f,0x01,0x00,0x00,
-0x32,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x34,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x39,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
-0x39,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x3b,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x3b,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x3e,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
-0x14,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x40,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xef,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x41,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xef,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q5_1_f32_len = 4112;
-
-unsigned char get_rows_q8_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x40,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x64,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x65,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x65,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xb8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xcf,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0xe3,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xa5,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xa9,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x29,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
-0xae,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbf,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xbf,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x20,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xcd,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd0,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q8_0_len = 3644;
-
-unsigned char get_rows_q8_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x40,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x64,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x65,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x65,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xb8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xce,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x29,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
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-0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x29,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x72,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0xa9,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xc1,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xc2,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xbf,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc2,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xcc,0x00,0x00,0x00,
-0xcb,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q8_0_f32_len = 3628;
-
-unsigned char matmul_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
-0x38,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x7f,0x02,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x81,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
-0x55,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x61,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
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-0xf6,0x00,0x04,0x00,0x40,0x02,0x00,0x00,0x41,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x44,0x02,0x00,0x00,
-0x3f,0x02,0x00,0x00,0x40,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x46,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x3f,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,
-0xa9,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x48,0x02,0x00,0x00,0x49,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x4c,0x02,0x00,0x00,0x47,0x02,0x00,0x00,
-0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x50,0x02,0x00,0x00,
-0xa9,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,0x27,0x02,0x00,0x00,
-0x50,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x53,0x02,0x00,0x00,0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,
-0x51,0x02,0x00,0x00,0x53,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x58,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xc5,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x59,0x02,0x00,0x00,0x2d,0x02,0x00,0x00,0x58,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
-0x5b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x47,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x64,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x60,0x02,0x00,0x00,0x61,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x64,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x69,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x68,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x6c,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x54,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x72,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x74,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x72,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x74,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0x5c,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x79,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc1,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
-0x67,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x7d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7b,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x5c,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0x86,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x3c,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x94,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x9c,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x9d,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x69,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x49,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x41,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_len = 10316;
-
-unsigned char matmul_f16_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x08,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x69,0x01,0x00,0x00,
-0x68,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x76,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xd9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
-0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x78,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x7c,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x81,0x02,0x00,0x00,0x57,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x83,0x02,0x00,0x00,
-0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x81,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xf5,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0xd1,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x90,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x94,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x98,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x97,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xa4,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xa8,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xad,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xab,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
-0x6c,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
-0xd9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0xca,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0xcc,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xb1,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x91,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_aligned_len = 11200;
-
-unsigned char matmul_f16_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,
-0x50,0x01,0x00,0x00,0x38,0x02,0x00,0x00,0x81,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xca,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x9c,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x9d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x49,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x46,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_aligned_fp32_len = 10288;
-
-unsigned char matmul_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
-0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3a,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x80,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x81,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x81,0x02,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x83,0x02,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x83,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
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-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x42,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_len = 10332;
-
-unsigned char matmul_f16_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0x7c,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
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-0xe3,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
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-0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x9b,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x9f,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xd9,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa3,0x02,0x00,0x00,
-0xa4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xa7,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xaf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc2,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xb6,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xce,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd1,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xd5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x01,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd7,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_aligned_len = 11360;
-
-unsigned char matmul_f16_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
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-0xa8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x97,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0xca,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x53,0x01,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x46,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_aligned_fp32_len = 10240;
-
-unsigned char matmul_f16_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
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-0x4a,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
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-0x59,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
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-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x45,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x5f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
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-0x66,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x72,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x70,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x75,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x76,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
-0x75,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x7b,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x79,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x83,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x3a,0x02,0x00,0x00,
-0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x95,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5c,0x01,0x00,0x00,0x9a,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_fp32_len = 10260;
-
-unsigned char matmul_f16_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x9a,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x9b,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x46,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_fp32_len = 10276;
-
-unsigned char matmul_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
-0x52,0x01,0x00,0x00,0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x22,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x50,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x50,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x50,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x52,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3a,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x80,0x02,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x81,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x81,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x81,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x83,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x83,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0x54,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
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-0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xaa,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x41,0x02,0x00,0x00,
-0xa4,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x4a,0x02,0x00,0x00,
-0x4b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x4e,0x02,0x00,0x00,0x49,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x52,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x53,0x02,0x00,0x00,0x29,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
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-0x55,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xc7,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x2f,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5e,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xac,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x63,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x62,0x02,0x00,0x00,
-0x63,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x66,0x02,0x00,0x00,0x61,0x02,0x00,0x00,0x62,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x61,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x6b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x6e,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x6a,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6e,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x69,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x71,0x02,0x00,0x00,0x56,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x74,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x76,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x74,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x79,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x76,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
-0x7d,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x7d,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x89,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x89,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x3e,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x56,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0x98,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x11,0x01,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x63,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x43,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_len = 10324;
-
-unsigned char matmul_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x17,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
-0x71,0x01,0x00,0x00,0x78,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
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-0x0f,0x00,0x00,0x00,0x72,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x48,0x00,0x00,0x00,
-0x72,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x79,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x73,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x84,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x80,0x02,0x00,0x00,0x81,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x84,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x7f,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0xe8,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x8c,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0xe8,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,0x67,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x93,0x02,0x00,0x00,0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x05,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x87,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xea,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xa0,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xa4,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa6,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xec,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xde,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa8,0x02,0x00,0x00,
-0xa9,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xac,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xb4,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xb2,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc2,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xbd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xc8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,
-0xca,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xda,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x07,0x01,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0xce,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xdc,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_aligned_len = 11432;
-
-unsigned char matmul_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xce,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
-0x45,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,0x2f,0x02,0x00,0x00,
-0x78,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
-0xa3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x05,0x01,0x00,0x00,
-0x93,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0x85,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x93,0x02,0x00,0x00,
-0x92,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x38,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x35,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_aligned_fp32_len = 10124;
-
-unsigned char matmul_f32_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x47,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
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-0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x83,0x02,0x00,0x00,
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-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x42,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_f16_len = 10332;
-
-unsigned char matmul_f32_f16_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
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-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
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-0x05,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
-0x73,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
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-0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
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-0xc2,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xb6,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,
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-0x97,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
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-0xc3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xd5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x07,0x01,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd7,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_f16_aligned_len = 11360;
-
-unsigned char matmul_f32_f16_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x45,0x01,0x00,0x00,
-0x4d,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x8c,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
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-0xa5,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
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-0x41,0x00,0x06,0x00,0x05,0x01,0x00,0x00,0x9a,0x02,0x00,0x00,
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-0x3e,0x00,0x03,0x00,0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x46,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_f16_aligned_fp32_len = 10240;
-
-unsigned char matmul_f32_f16_fp32_data[] = {
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-0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xa2,0x02,0x00,0x00,0x3f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x42,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
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-0x3f,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
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-0x4a,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
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-0x59,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
-0x57,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x45,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x5f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x67,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x66,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x6a,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x72,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x70,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x75,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x76,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
-0x75,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x7b,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x79,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x83,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x3a,0x02,0x00,0x00,
-0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x95,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x10,0x01,0x00,0x00,0x9a,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_f16_fp32_len = 10260;
-
-unsigned char matmul_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x44,0x01,0x00,0x00,0x4f,0x01,0x00,0x00,0x33,0x02,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
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-0x95,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x10,0x01,0x00,0x00,
-0x97,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0x89,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x97,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x39,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_fp32_len = 10208;
-
-unsigned char matmul_q2_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
-0x8b,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x80,0x02,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
-0xf8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
-0xf3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q2_k_f32_len = 11240;
-
-unsigned char matmul_q2_k_f32_aligned_data[] = {
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-0xd2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xd5,0x02,0x00,0x00,0x19,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xd1,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd5,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x19,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xd8,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
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-0xf8,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x17,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xe1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdd,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
-0xe4,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xe6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe4,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
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-0xf0,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,
-0xf0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x19,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,
-0x14,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
-0x17,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfd,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xff,0x02,0x00,0x00,
-0x15,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,
-0xff,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x02,0x03,0x00,0x00,0x00,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
-0xca,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x03,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0x97,0x01,0x00,0x00,0x05,0x03,0x00,0x00,
-0xea,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x05,0x03,0x00,0x00,0x04,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
-0x17,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x15,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q2_k_f32_aligned_len = 11800;
-
-unsigned char matmul_q2_k_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x59,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
-0x78,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x19,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1c,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1c,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
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-0x64,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x65,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x90,0x01,0x00,0x00,
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-0x91,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x91,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x91,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x93,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x93,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x02,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbe,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xba,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xbd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xbb,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xee,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xc7,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
-0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0x7c,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x94,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xce,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
-0xee,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd9,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
-0xca,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x95,0x01,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xdc,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
-0xee,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q2_k_f32_aligned_fp32_len = 11068;
-
-unsigned char matmul_q2_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x47,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,
-0x58,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x94,0x01,0x00,0x00,
-0x79,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x17,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x1d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x91,0x01,0x00,0x00,
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-0x92,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x92,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x92,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x94,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x94,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x79,0x02,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbf,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xc0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc0,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xc0,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc2,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
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-0x9d,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xc7,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xf1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd1,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
-0xd1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
-0xf1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x9f,0x01,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xdd,0x02,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xf1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q2_k_f32_fp32_len = 11120;
-
-unsigned char matmul_q3_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,0x0b,0x02,0x00,0x00,
-0x16,0x02,0x00,0x00,0x00,0x03,0x00,0x00,0x49,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
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-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x32,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x34,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x36,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x36,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x14,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,0x6f,0x03,0x00,0x00,
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-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q3_k_f32_len = 13144;
-
-unsigned char matmul_q3_k_f32_aligned_data[] = {
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-
-};
-const uint64_t matmul_q3_k_f32_aligned_len = 13704;
-
-unsigned char matmul_q3_k_f32_aligned_fp32_data[] = {
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-0x3f,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x60,0x03,0x00,0x00,
-0x21,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x24,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
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-0x5c,0x03,0x00,0x00,0x5b,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x29,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5e,0x03,0x00,0x00,0x6c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x26,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x28,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x21,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x21,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x60,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1e,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x20,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x62,0x03,0x00,0x00,
-0x68,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x64,0x03,0x00,0x00,0x67,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x00,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q3_k_f32_aligned_fp32_len = 12972;
-
-unsigned char matmul_q3_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xa0,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,
-0x09,0x02,0x00,0x00,0x14,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
-0x42,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x31,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x32,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x34,0x01,0x00,0x00,
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-0x36,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x36,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x36,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x21,0x03,0x00,0x00,
-0x22,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x25,0x03,0x00,0x00,0x20,0x03,0x00,0x00,0x21,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x20,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x27,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x6d,0x03,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x20,0x03,0x00,0x00,0x5f,0x03,0x00,0x00,
-0x2a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x2d,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
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-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x33,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
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-0xf8,0x00,0x02,0x00,0x34,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x38,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,
-0x6b,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x39,0x03,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,
-0x39,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x3b,0x03,0x00,0x00,0x38,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x35,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
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-0x3b,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x3e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x3c,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,0x3e,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3d,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
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-0xfd,0x02,0x00,0x00,0x4a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4d,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,
-0x15,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4f,0x03,0x00,0x00,0x4d,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x51,0x03,0x00,0x00,
-0x68,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x53,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
-0x6b,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x58,0x03,0x00,0x00,0x55,0x03,0x00,0x00,
-0x57,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x03,0x00,0x00,0x58,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x5b,0x03,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x5a,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0x41,0x00,0x06,0x00,0x1f,0x02,0x00,0x00,0x5d,0x03,0x00,0x00,
-0x42,0x03,0x00,0x00,0x35,0x00,0x00,0x00,0x4f,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x5d,0x03,0x00,0x00,0x5c,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5f,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x27,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x29,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x22,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x03,0x00,0x00,
-0x6b,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x1f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x21,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x63,0x03,0x00,0x00,0x69,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x09,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x02,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x65,0x03,0x00,0x00,0x68,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q3_k_f32_fp32_len = 13024;
-
-unsigned char matmul_q4_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x1b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-0x5a,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x4f,0x02,0x00,0x00,
-0x98,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_0_f32_len = 10564;
-
-unsigned char matmul_q4_0_f32_aligned_data[] = {
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-0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_0_f32_aligned_len = 11156;
-
-unsigned char matmul_q4_0_f32_aligned_fp32_data[] = {
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-0xc2,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x58,0x02,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x57,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
-0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x70,0x02,0x00,0x00,
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-0x74,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
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-0x7c,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,
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-0x7f,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x84,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x82,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x8d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x8b,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x99,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x4c,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xca,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x64,0x01,0x00,0x00,0xac,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_0_f32_aligned_fp32_len = 10408;
-
-unsigned char matmul_q4_0_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x14,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
-0x27,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x63,0x01,0x00,0x00,
-0x48,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x6e,0x01,0x00,0x00,0xac,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_0_f32_fp32_len = 10444;
-
-unsigned char matmul_q4_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x5f,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x54,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xba,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x65,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_1_f32_len = 10632;
-
-unsigned char matmul_q4_1_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x18,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x62,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x77,0x02,0x00,0x00,
-0xc0,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
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-0xdf,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
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-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
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-0xb1,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
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-0xeb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
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-0xb9,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
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-0xd5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
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-0xca,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0x41,0x00,0x06,0x00,0x6c,0x01,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xc0,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xdb,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x88,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x87,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_1_f32_aligned_len = 11224;
-
-unsigned char matmul_q4_1_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xee,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x74,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x81,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x7d,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x81,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
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-0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0x9a,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
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-0x14,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa1,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
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-0x69,0x01,0x00,0x00,0xb1,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb1,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x92,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb3,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x56,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_1_f32_aligned_fp32_len = 10476;
-
-unsigned char matmul_q4_1_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xee,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
-0x2c,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0x68,0x01,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x03,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x65,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x66,0x01,0x00,0x00,
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-0x66,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x66,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x68,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x68,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x56,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_1_f32_fp32_len = 10512;
-
-unsigned char matmul_q4_k_f32_data[] = {
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-0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xec,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x21,0x03,0x00,0x00,0x31,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xeb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x25,0x03,0x00,0x00,
-0x2d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x27,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_k_f32_len = 12072;
-
-unsigned char matmul_q4_k_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x0d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x06,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x45,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x03,0x03,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x49,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_k_f32_aligned_len = 12648;
-
-unsigned char matmul_q4_k_f32_aligned_fp32_data[] = {
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-0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xc7,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xc2,0x02,0x00,0x00,0x1b,0x03,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0x27,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xdb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xdf,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xda,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x29,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
-0x19,0x03,0x00,0x00,0xe4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,0x29,0x03,0x00,0x00,
-0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe3,0x02,0x00,0x00,
-0xe4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe7,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x29,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xed,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xef,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xed,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
-0xef,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,
-0xd7,0x02,0x00,0x00,0x27,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc2,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,
-0xf8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x00,0x03,0x00,0x00,
-0xd7,0x02,0x00,0x00,0x27,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x14,0x00,0x00,0x00,
-0x01,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x03,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x00,0x03,0x00,0x00,
-0x03,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0xb7,0x02,0x00,0x00,0x04,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
-0x05,0x03,0x00,0x00,0xcf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
-0x29,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x24,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x0e,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x11,0x03,0x00,0x00,0x25,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x11,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x12,0x03,0x00,0x00,
-0x29,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0x15,0x03,0x00,0x00,0xca,0x00,0x00,0x00,0x14,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x16,0x03,0x00,0x00,
-0x15,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xcf,0x01,0x00,0x00,
-0x17,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0x09,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x17,0x03,0x00,0x00,
-0x16,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
-0x29,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1b,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,
-0x24,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_k_f32_aligned_fp32_len = 11900;
-
-unsigned char matmul_q4_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x86,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x78,0x01,0x00,0x00,
-0xc4,0x01,0x00,0x00,0xcf,0x01,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0xba,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbc,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_k_f32_fp32_len = 11952;
-
-unsigned char matmul_q5_0_f32_data[] = {
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-0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,0xa7,0x00,0x00,0x00,
-0x6c,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x71,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x70,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x73,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
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-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xeb,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xe4,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x80,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x84,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x86,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x89,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
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-0x94,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0x05,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x98,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xee,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xa0,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa4,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x9f,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0xf0,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xac,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
-0x94,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
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-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb2,0x02,0x00,0x00,
-0xb3,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xb3,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xbd,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcc,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
-0xcc,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xd9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x99,0x01,0x00,0x00,0xdc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xdc,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xde,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe4,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_0_f32_len = 11076;
-
-unsigned char matmul_q5_0_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x61,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
-0x11,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xab,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_0_f32_aligned_len = 11668;
-
-unsigned char matmul_q5_0_f32_aligned_fp32_data[] = {
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-0xe4,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
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-0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x97,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
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-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xe9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xa1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
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-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xad,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xab,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb0,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb6,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,
-0xb5,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x75,0x02,0x00,0x00,
-0xc2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcd,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x8d,0x01,0x00,0x00,0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_0_f32_aligned_fp32_len = 10920;
-
-unsigned char matmul_q5_0_f32_fp32_data[] = {
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-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
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-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
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-0xd2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x97,0x01,0x00,0x00,0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_0_f32_fp32_len = 10956;
-
-unsigned char matmul_q5_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x16,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x7e,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x73,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_1_f32_len = 10956;
-
-unsigned char matmul_q5_1_f32_aligned_data[] = {
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-0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
-0x0c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x00,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x09,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_1_f32_aligned_len = 11548;
-
-unsigned char matmul_q5_1_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x0f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x7f,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
-0x6c,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x3f,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x98,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
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-0x93,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0x88,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa8,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa6,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0xa6,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb1,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xaf,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcd,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
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-0xcf,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x88,0x01,0x00,0x00,0xd0,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd0,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x95,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x74,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_1_f32_aligned_fp32_len = 10800;
-
-unsigned char matmul_q5_1_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x0f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
-0x6c,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x03,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb1,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xaf,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x92,0x01,0x00,0x00,0xd0,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd0,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x95,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x74,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_1_f32_fp32_len = 10836;
-
-unsigned char matmul_q5_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x2e,0x01,0x00,0x00,0x83,0x01,0x00,0x00,0xeb,0x01,0x00,0x00,
-0xf6,0x01,0x00,0x00,0xe0,0x02,0x00,0x00,0x29,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x27,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x28,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x29,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2a,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x01,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0xb0,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2c,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2e,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2e,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc5,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc6,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xf3,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xf4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf4,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xf4,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf6,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf6,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe0,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x26,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x27,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x27,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x27,0x03,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x03,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x29,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
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-0xf9,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x09,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x48,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x08,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x03,0x00,0x00,0x52,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4c,0x03,0x00,0x00,
-0x51,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_k_f32_len = 12596;
-
-unsigned char matmul_q5_k_f32_aligned_data[] = {
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-0xf8,0x00,0x02,0x00,0x47,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x33,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x33,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x68,0x03,0x00,0x00,
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-0x30,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x32,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x2b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6a,0x03,0x00,0x00,0x76,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x28,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x13,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x13,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,0x74,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x12,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x03,0x00,0x00,
-0x73,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_k_f32_aligned_len = 13172;
-
-unsigned char matmul_q5_k_f32_aligned_fp32_data[] = {
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xde,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xde,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x49,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0x44,0x03,0x00,0x00,0xe1,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0x49,0x03,0x00,0x00,0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xe0,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe4,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdf,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x4a,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0x42,0x03,0x00,0x00,0xe9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0x4a,0x03,0x00,0x00,
-0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe8,0x02,0x00,0x00,
-0xe9,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xec,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0x4a,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf1,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
-0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
-0xf3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf8,0x02,0x00,0x00,0x49,0x03,0x00,0x00,0x67,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfe,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x4c,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x40,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x4c,0x03,0x00,0x00,
-0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x00,0x03,0x00,0x00,
-0x01,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x04,0x03,0x00,0x00,0xff,0x02,0x00,0x00,0x00,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xff,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x06,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x4e,0x03,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0x3e,0x03,0x00,0x00,
-0x09,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x0c,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x08,0x03,0x00,0x00,0x09,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0c,0x03,0x00,0x00,
-0x07,0x03,0x00,0x00,0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x07,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x03,0x00,0x00,0xf4,0x02,0x00,0x00,0x4e,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x14,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x12,0x03,0x00,0x00,0x13,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x13,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
-0x4c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x18,0x03,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
-0x18,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x17,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x14,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x14,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
-0x1b,0x03,0x00,0x00,0x12,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x13,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x1d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x1b,0x03,0x00,0x00,0x1c,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x25,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
-0x4c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x27,0x03,0x00,0x00,0x14,0x00,0x00,0x00,0x26,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
-0x27,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x29,0x03,0x00,0x00,0x25,0x03,0x00,0x00,0x28,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x03,0x00,0x00,
-0xdc,0x02,0x00,0x00,0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
-0xf4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
-0x49,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x32,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
-0x4c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x34,0x03,0x00,0x00,0x32,0x03,0x00,0x00,0x33,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,
-0x4a,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x37,0x03,0x00,0x00,0x34,0x03,0x00,0x00,
-0x36,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x39,0x03,0x00,0x00,0x37,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xca,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0xf4,0x01,0x00,0x00,0x3c,0x03,0x00,0x00,
-0x21,0x03,0x00,0x00,0x35,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x3c,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3e,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x03,0x00,0x00,
-0x4c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x42,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x44,0x03,0x00,0x00,0x49,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xde,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_k_f32_aligned_fp32_len = 12424;
-
-unsigned char matmul_q5_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xab,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
-0xe9,0x01,0x00,0x00,0xf4,0x01,0x00,0x00,0xd9,0x02,0x00,0x00,
-0x22,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x4f,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x15,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x13,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
-0x15,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x14,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x18,0x03,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x4d,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x19,0x03,0x00,0x00,0x14,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x19,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,0x18,0x03,0x00,0x00,
-0x1a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x15,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x15,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc1,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
-0x08,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
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-0xfa,0x00,0x04,0x00,0x1c,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,
-0x1e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x1d,0x03,0x00,0x00,
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-0x27,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x2b,0x03,0x00,0x00,0xdd,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,
-0x2b,0x03,0x00,0x00,0xf5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,
-0x4f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x33,0x03,0x00,0x00,
-0x31,0x03,0x00,0x00,0x4d,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,0x33,0x03,0x00,0x00,
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-0x37,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x38,0x03,0x00,0x00,
-0x35,0x03,0x00,0x00,0x37,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
-0x4f,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
-0x3b,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,
-0x3b,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xff,0x01,0x00,0x00,
-0x3d,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0x35,0x00,0x00,0x00,
-0x2f,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x3d,0x03,0x00,0x00,
-0x3c,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x1e,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,
-0x4f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x02,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x41,0x03,0x00,0x00,0x4d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xea,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x43,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x03,0x00,0x00,
-0x4a,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_k_f32_fp32_len = 12476;
-
-unsigned char matmul_q6_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x43,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x33,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0xaf,0x01,0x00,0x00,
-0xba,0x01,0x00,0x00,0xa4,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
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-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2a,0x01,0x00,0x00,
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-0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x01,0x00,0x00,
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-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xea,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
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-0xe9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x18,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x16,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0e,0x03,0x00,0x00,0x14,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q6_k_f32_len = 11764;
-
-unsigned char matmul_q6_k_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,0x37,0x03,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0xce,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0x31,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcd,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q6_k_f32_aligned_len = 12324;
-
-unsigned char matmul_q6_k_f32_aligned_fp32_data[] = {
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-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
-0x10,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xcc,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xd0,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x10,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xd8,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd6,0x02,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd8,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xe1,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xdf,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
-0xe9,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,
-0xf0,0x02,0x00,0x00,0x10,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0x0b,0x03,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfb,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
-0xfb,0x02,0x00,0x00,0x10,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcd,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
-0xff,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb8,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0xe5,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x00,0x03,0x00,0x00,0xff,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x02,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
-0x0c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x08,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa4,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q6_k_f32_aligned_fp32_len = 11592;
-
-unsigned char matmul_q6_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x44,0x01,0x00,0x00,
-0xad,0x01,0x00,0x00,0xb8,0x01,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xe6,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2a,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2f,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x31,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x31,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x31,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x33,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x33,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x88,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb5,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xb6,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xb6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xb6,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0xab,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x0d,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0x07,0x03,0x00,0x00,0xae,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xad,0x02,0x00,0x00,
-0xae,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xb1,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb6,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,0x2c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x92,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc3,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0x05,0x03,0x00,0x00,0xc6,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,
-0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xc5,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xc9,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0x03,0x03,0x00,0x00,
-0xce,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0xd1,0x02,0x00,0x00,0x11,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd1,0x02,0x00,0x00,
-0xcc,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x11,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd7,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xdd,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xe2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe0,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xec,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xee,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf3,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x11,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
-0x0c,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf9,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,
-0x0d,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
-0xfb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfe,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,0x11,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xff,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xff,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0xc3,0x01,0x00,0x00,0x01,0x03,0x00,0x00,
-0xe6,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x01,0x03,0x00,0x00,0x00,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xce,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xce,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x11,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x03,0x00,0x00,
-0x0f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xae,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x07,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xab,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q6_k_f32_fp32_len = 11644;
-
-unsigned char matmul_q8_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf1,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
-0x5b,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x50,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
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-0xb6,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
-0xc2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q8_0_f32_len = 10608;
-
-unsigned char matmul_q8_0_f32_aligned_data[] = {
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-0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,
-0xe4,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
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-0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
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-0x3e,0x00,0x03,0x00,0xd7,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q8_0_f32_aligned_len = 11200;
-
-unsigned char matmul_q8_0_f32_aligned_fp32_data[] = {
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-0xea,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0xb5,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x55,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x51,0x02,0x00,0x00,
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-0x55,0x02,0x00,0x00,0x50,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x50,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x5d,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x59,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x5d,0x02,0x00,0x00,
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-0x6c,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x6a,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x58,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x72,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x75,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x75,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x70,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x70,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x83,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x85,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x83,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,
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-0x84,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8c,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcd,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
-0xaa,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
-0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x65,0x01,0x00,0x00,0xad,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q8_0_f32_aligned_fp32_len = 10452;
-
-unsigned char matmul_q8_0_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xea,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x28,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
-0x49,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x70,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x83,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x85,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x83,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8c,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xaa,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x6f,0x01,0x00,0x00,0xad,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q8_0_f32_fp32_len = 10488;
-
-unsigned char mul_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x15,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2f,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x3d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3d,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x3d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3f,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x48,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x49,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x49,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x53,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x54,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x54,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x01,0x00,0x00,
-0xa5,0x01,0x00,0x00,0xb6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x01,0x00,0x00,0xb3,0x01,0x00,0x00,
-0xb7,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0xb8,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc3,0x01,0x00,0x00,0xc2,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xc4,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x01,0x00,0x00,0xc4,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x01,0x00,0x00,
-0xc3,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xc7,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x01,0x00,0x00,0xc7,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc9,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x01,0x00,0x00,
-0xca,0x01,0x00,0x00,0xc3,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x01,0x00,0x00,0xce,0x01,0x00,0x00,
-0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0xd4,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xdc,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,
-0xdd,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe4,0x01,0x00,0x00,0xe1,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xe4,0x01,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x01,0x00,0x00,
-0xe9,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf5,0x01,0x00,0x00,0xec,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x01,0x00,0x00,0xe9,0x01,0x00,0x00,0xf5,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xf8,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xf9,0x01,0x00,0x00,0xf8,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x01,0x00,0x00,
-0xca,0x01,0x00,0x00,0xf9,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xfc,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xfd,0x01,0x00,0x00,0xfc,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,
-0xfd,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xff,0x01,0x00,0x00,0xfa,0x01,0x00,0x00,0xfe,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x02,0x02,0x00,0x00,0x01,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x02,0x00,0x00,
-0xec,0x01,0x00,0x00,0x02,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x04,0x02,0x00,0x00,0xff,0x01,0x00,0x00,
-0x03,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x06,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,
-0x06,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x08,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x07,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x02,0x00,0x00,
-0x04,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x34,0x01,0x00,0x00,0x09,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
-0xca,0x01,0x00,0x00,0x4a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x17,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x50,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
-0x50,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x52,0x02,0x00,0x00,0xdd,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
-0x55,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x58,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
-0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
-0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
-0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_f32_len = 4276;
-
-unsigned char mul_mat_vec_f16_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
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-0x7e,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x80,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x79,0x00,0x00,0x00,
-0x78,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x81,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x78,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x7e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x94,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x88,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x1a,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x1a,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x02,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0x94,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x1a,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xb0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
-0xb0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x1a,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xc0,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x75,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
-0x85,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x85,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcb,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0xca,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
-0x80,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,
-0xce,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd1,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x80,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,
-0xd5,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x74,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd6,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xce,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xce,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0xe1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xe7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe5,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x74,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xf1,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf2,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_f16_f16_f32_len = 3568;
-
-unsigned char mul_mat_vec_f16_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3b,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x8f,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x94,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x88,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x1a,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x1a,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x00,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0x94,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xb1,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xb0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xb1,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,
-0xc0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x85,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x77,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xca,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x79,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0xac,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xcc,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd3,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x74,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xdd,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x85,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcd,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0xe0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xca,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x80,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xe6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe5,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x74,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe6,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_f16_f32_f32_len = 3536;
-
-unsigned char mul_mat_vec_nc_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x6d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x6d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x78,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xa9,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xab,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xab,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8c,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x46,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa7,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,
-0xa6,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa6,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,
-0xaf,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xaf,0x00,0x00,0x00,
-0xae,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa7,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_nc_f16_f32_len = 2824;
-
-unsigned char mul_mat_vec_p021_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xbc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
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-0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x64,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x76,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x77,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x79,0x00,0x00,0x00,
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-0xad,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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-0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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-0x1e,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x3c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x44,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x44,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3e,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x68,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7b,0x00,0x00,0x00,
-0x7c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x32,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x3a,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x3f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x3e,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x8e,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0x92,0x00,0x00,0x00,0xad,0x00,0x05,0x00,0x44,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x91,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x95,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x90,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x98,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x44,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x98,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x9b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x99,0x00,0x00,0x00,
-0x9a,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x39,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x32,0x00,0x00,0x00,
-0xa5,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x3a,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x92,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x00,0x00,0x00,
-0xc3,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0xb7,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x44,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xac,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xaa,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xab,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x39,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7b,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_p021_f16_f32_len = 2768;
-
-unsigned char mul_mat_vec_q2_K_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x11,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xa1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa2,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xa3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa3,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xa3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xc1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xc1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfc,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0xf7,0x00,0x03,0x00,0xeb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe9,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
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-0x87,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0x80,0x00,0x00,0x00,
-0xef,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
-0xf1,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x87,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0x80,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
-0xf3,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0x81,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
-0xf1,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xf2,0x02,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe3,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x02,0x00,0x00,0x09,0x03,0x00,0x00,0xdf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe2,0x02,0x00,0x00,0xaa,0x00,0x05,0x00,0x92,0x00,0x00,0x00,
-0xf9,0x02,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xfb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf9,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x87,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x80,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
-0x04,0x03,0x00,0x00,0x03,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0x05,0x03,0x00,0x00,0x06,0x03,0x00,0x00,0xff,0x02,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x06,0x03,0x00,0x00,0x04,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfb,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q2_K_f16_f32_len = 8612;
-
-unsigned char mul_mat_vec_q2_K_f32_f32_data[] = {
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-0x01,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x70,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
-0x72,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x7d,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
-0x7d,0x02,0x00,0x00,0x91,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x9b,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x99,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x33,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
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-0x86,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0xd8,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
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-0x8b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
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-0xb9,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x9b,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xc1,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x9b,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
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-0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xd8,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x7d,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x7d,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x7d,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xe1,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x9b,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xe9,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,
-0xb3,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0x33,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xb3,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x7d,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x81,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xfb,0x02,0x00,0x00,0xdf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xb4,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,0x7f,0x00,0x04,0x00,
-0x7d,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xc7,0x02,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x7d,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0xfc,0x02,0x00,0x00,0x00,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x7d,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x88,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,
-0xca,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x88,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
-0xf8,0x02,0x00,0x00,0x5c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8d,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0x81,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0xac,0x00,0x05,0x00,0x92,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
-0xf9,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xd3,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xd7,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x92,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
-0x5d,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xdc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xda,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0x5d,0x00,0x00,0x00,
-0xf9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xe1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
-0xe3,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0x81,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xe3,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdc,0x02,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xf9,0x02,0x00,0x00,0xdf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x92,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xec,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xea,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
-0xf4,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xf4,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0xcb,0x00,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xf3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xf6,0x02,0x00,0x00,
-0xf5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q2_K_f32_f32_len = 8356;
-
-unsigned char mul_mat_vec_q3_K_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xaa,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x37,0x03,0x00,0x00,0x49,0x03,0x00,0x00,0x6e,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x20,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x22,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
-0x39,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x3b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x39,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
-0x3b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x42,0x03,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x8f,0x00,0x00,0x00,0x43,0x03,0x00,0x00,0x88,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
-0x44,0x03,0x00,0x00,0x43,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0x45,0x03,0x00,0x00,0x46,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x42,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x46,0x03,0x00,0x00,0x44,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q3_K_f16_f32_len = 10028;
-
-unsigned char mul_mat_vec_q3_K_f32_f32_data[] = {
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-0xf8,0x00,0x02,0x00,0x1c,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,0x41,0x03,0x00,0x00,
-0x6e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x19,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1b,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x9d,0x00,0x00,0x00,0x32,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x34,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x32,0x03,0x00,0x00,
-0x33,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3b,0x03,0x00,0x00,0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x8f,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,
-0x88,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x85,0x00,0x00,0x00,0x3d,0x03,0x00,0x00,0x3c,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0xcf,0x00,0x00,0x00,0x3e,0x03,0x00,0x00,
-0x38,0x03,0x00,0x00,0x4c,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x3e,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x34,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q3_K_f32_f32_len = 9900;
-
-unsigned char mul_mat_vec_q4_0_f16_f32_data[] = {
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-0x18,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1b,0x01,0x00,0x00,0x19,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,
-0x1b,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x19,0x01,0x00,0x00,0x33,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x20,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,
-0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
-0x20,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
-0x12,0x01,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0x7c,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
-0x24,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,
-0xd2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xd1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xd3,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xcb,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xd5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdc,0x00,0x00,0x00,0x27,0x01,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0xac,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x42,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe6,0x00,0x00,0x00,
-0xe1,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe1,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xeb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x89,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x8a,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xeb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xdd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe3,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe3,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
-0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe2,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x95,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x42,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xfb,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfa,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x02,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x89,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
-0x86,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x05,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
-0xff,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x06,0x01,0x00,0x00,0x04,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfb,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q4_0_f16_f32_len = 3960;
-
-unsigned char mul_mat_vec_q4_0_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x29,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x51,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x51,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x51,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x51,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x51,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x51,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xbe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xbe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xbe,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfb,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xfc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xfc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xfc,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfe,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfe,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
-0xa5,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
-0x16,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x19,0x01,0x00,0x00,0x17,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
-0x19,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1c,0x01,0x00,0x00,0x17,0x01,0x00,0x00,0x33,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
-0x1c,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,
-0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
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-0x7c,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
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-0xc7,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd1,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xc6,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0xc0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
-0xd2,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x8a,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
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-0x9a,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdf,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x26,0x01,0x00,0x00,0xde,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
-0x95,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
-0x42,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe5,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x95,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x26,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xea,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe8,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x89,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xea,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0x26,0x01,0x00,0x00,0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x95,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xfa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xf8,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x82,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
-0x02,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
-0x02,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xc6,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0xfe,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
-0x03,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_0_f32_f32_len = 3928;
-
-unsigned char mul_mat_vec_q4_1_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x39,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x8e,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x59,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x59,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x59,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x59,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x9a,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x18,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0x37,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
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-0x08,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
-0x34,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0xd3,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x08,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xdd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xe0,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x92,0x00,0x00,0x00,
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-0xe4,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0xa2,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x94,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x96,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x38,0x01,0x00,0x00,
-0xe7,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xac,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xea,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xee,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe9,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x38,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf1,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x91,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x92,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf3,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0xe5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x38,0x01,0x00,0x00,
-0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xea,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x9d,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x51,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x03,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x01,0x01,0x00,0x00,
-0x02,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x02,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0a,0x01,0x00,0x00,0x8a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x91,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x8e,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x0d,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x07,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x03,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x03,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q4_1_f16_f32_len = 4044;
-
-unsigned char mul_mat_vec_q4_1_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x37,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x8e,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0xea,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
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-0xf8,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x9d,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x36,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xf2,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf0,0x00,0x00,0x00,
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-0xf6,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x36,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x91,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x8e,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0x92,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x92,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf2,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xea,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x36,0x01,0x00,0x00,0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x51,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x00,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x91,0x00,0x00,0x00,
-0x0a,0x01,0x00,0x00,0x8e,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x0a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xce,0x00,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x09,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x0c,0x01,0x00,0x00,
-0x0b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x02,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x02,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_1_f32_f32_len = 4012;
-
-unsigned char mul_mat_vec_q4_K_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd4,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xa1,0x02,0x00,0x00,0x73,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
-0x72,0x03,0x00,0x00,0x71,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x83,0x00,0x00,0x00,0x76,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x72,0x03,0x00,0x00,0xf0,0x02,0x00,0x00,
-0x6b,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,
-0x7c,0x03,0x00,0x00,0xcd,0x02,0x00,0x00,0x73,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x7d,0x03,0x00,0x00,0x7c,0x03,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,0x81,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x7d,0x03,0x00,0x00,
-0xfb,0x02,0x00,0x00,0x76,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x89,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x83,0x00,0x00,0x00,0x8d,0x03,0x00,0x00,
-0xdd,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x83,0x00,0x00,0x00,
-0x8e,0x03,0x00,0x00,0x7c,0x02,0x00,0x00,0x8d,0x03,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,0x8f,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,
-0x89,0x03,0x00,0x00,0x8e,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x92,0x03,0x00,0x00,0x18,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,0x94,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
-0x92,0x03,0x00,0x00,0x8f,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x97,0x03,0x00,0x00,0x34,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x83,0x00,0x00,0x00,0x99,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x97,0x03,0x00,0x00,0x94,0x03,0x00,0x00,0x85,0x00,0x05,0x00,
-0x83,0x00,0x00,0x00,0x9d,0x03,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x81,0x03,0x00,0x00,0x7f,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
-0xd3,0x03,0x00,0x00,0x9d,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x83,0x00,0x00,0x00,0x9e,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x99,0x03,0x00,0x00,
-0xd3,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
-0xa0,0x03,0x00,0x00,0x8e,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x83,0x00,0x00,0x00,0xa1,0x03,0x00,0x00,0xa0,0x03,0x00,0x00,
-0x9e,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,
-0xa1,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x03,0x00,0x00,0xcf,0x03,0x00,0x00,0x5c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x91,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x93,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
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-0xa7,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xd0,0x03,0x00,0x00,
-0x87,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xbe,0x03,0x00,0x00,
-0xaa,0x03,0x00,0x00,0xac,0x00,0x05,0x00,0x98,0x00,0x00,0x00,
-0xad,0x03,0x00,0x00,0xd0,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xa9,0x03,0x00,0x00,0xaa,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xad,0x03,0x00,0x00,
-0xa8,0x03,0x00,0x00,0xa9,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa8,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0x98,0x00,0x00,0x00,
-0xb0,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,0xd0,0x03,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xb2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xb0,0x03,0x00,0x00,0xb1,0x03,0x00,0x00,
-0xb2,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x03,0x00,0x00,
-0x5d,0x00,0x00,0x00,0xd0,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x8d,0x00,0x00,0x00,0xb7,0x03,0x00,0x00,0x86,0x00,0x00,0x00,
-0xb6,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
-0xb8,0x03,0x00,0x00,0xb7,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x8d,0x00,0x00,0x00,0xb9,0x03,0x00,0x00,0x86,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
-0xba,0x03,0x00,0x00,0xb9,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
-0x83,0x00,0x00,0x00,0xbb,0x03,0x00,0x00,0xba,0x03,0x00,0x00,
-0xb8,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0xb9,0x03,0x00,0x00,
-0xbb,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xb2,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb2,0x03,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xaa,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xaa,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbe,0x03,0x00,0x00,0xd0,0x03,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa7,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa9,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,0x98,0x00,0x00,0x00,
-0xc0,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xc2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xc0,0x03,0x00,0x00,0xc1,0x03,0x00,0x00,
-0xc2,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xc1,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x03,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x8d,0x00,0x00,0x00,0xca,0x03,0x00,0x00,0x86,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
-0xcb,0x03,0x00,0x00,0xca,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0xcc,0x03,0x00,0x00,0xcd,0x03,0x00,0x00,0xc6,0x03,0x00,0x00,
-0x4c,0x00,0x00,0x00,0xc9,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xcd,0x03,0x00,0x00,0xcb,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc2,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xc2,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_K_f16_f32_len = 10400;
-
-unsigned char mul_mat_vec_q4_K_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb4,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
-0x23,0x02,0x00,0x00,0xa7,0x03,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xab,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xab,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xab,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x20,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x21,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x21,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x21,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x23,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x23,0x02,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x48,0x00,0x04,0x00,0xa5,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa5,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xa5,0x03,0x00,0x00,0x02,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x89,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
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-0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x86,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8b,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x03,0x00,0x00,0xb0,0x03,0x00,0x00,
-0xc3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8a,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x98,0x00,0x00,0x00,0xa1,0x03,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa3,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa1,0x03,0x00,0x00,
-0xa2,0x03,0x00,0x00,0xa3,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaa,0x03,0x00,0x00,0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,0xab,0x03,0x00,0x00,
-0x86,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0xac,0x03,0x00,0x00,0xab,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0x27,0x02,0x00,0x00,0xad,0x03,0x00,0x00,
-0xa7,0x03,0x00,0x00,0x4c,0x00,0x00,0x00,0xaa,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xad,0x03,0x00,0x00,0xac,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa3,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa3,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q4_K_f32_f32_len = 9888;
-
-unsigned char mul_mat_vec_q5_0_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x80,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
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-0xf6,0x00,0x04,0x00,0x10,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x14,0x01,0x00,0x00,
-0x0f,0x01,0x00,0x00,0x10,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0f,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xc4,0x00,0x00,0x00,
-0x17,0x01,0x00,0x00,0x79,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x19,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x17,0x01,0x00,0x00,0x18,0x01,0x00,0x00,
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-0xb8,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0xb5,0x00,0x00,0x00,
-0x1d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0xb9,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
-0x21,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb9,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x19,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x19,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x0b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x11,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x11,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x7d,0x01,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0e,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x10,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xc4,0x00,0x00,0x00,0x27,0x01,0x00,0x00,0x79,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x29,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x27,0x01,0x00,0x00,
-0x28,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x28,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x30,0x01,0x00,0x00,0xb1,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
-0xb5,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x33,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0x30,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x34,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x29,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x29,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q5_0_f16_f32_len = 4488;
-
-unsigned char mul_mat_vec_q5_0_f32_f32_data[] = {
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-0x58,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x0f,0x01,0x00,0x00,
-0x10,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x13,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
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-0x3e,0x00,0x03,0x00,0xb9,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
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-0x14,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x10,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x10,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,
-0x7b,0x01,0x00,0x00,0x2b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0f,0x01,0x00,0x00,
-0xaa,0x00,0x05,0x00,0xc4,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
-0x79,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x28,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x26,0x01,0x00,0x00,0x27,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x27,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x30,0x01,0x00,0x00,0xb5,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
-0x30,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xf4,0x00,0x00,0x00,
-0x32,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x32,0x01,0x00,0x00,
-0x31,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x28,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x28,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_0_f32_f32_len = 4456;
-
-unsigned char mul_mat_vec_q5_1_f16_f32_data[] = {
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-0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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-0x7b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x7b,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x0d,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x10,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
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-0x41,0x00,0x05,0x00,0xb3,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
-0xb0,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
-0xb4,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xb4,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x15,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x15,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
-0x78,0x01,0x00,0x00,0x28,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x01,0x00,0x00,
-0xaa,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
-0x74,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x25,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x23,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x24,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0xac,0x00,0x00,0x00,
-0x70,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xb3,0x00,0x00,0x00,
-0x2d,0x01,0x00,0x00,0xb0,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x2f,0x01,0x00,0x00,
-0x30,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x2c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x30,0x01,0x00,0x00,
-0x2e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x25,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x25,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_1_f16_f32_len = 4384;
-
-unsigned char mul_mat_vec_q5_1_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x02,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0xff,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xb4,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x75,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb8,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x09,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x09,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x08,0x01,0x00,0x00,
-0xb8,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
-0xac,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
-0x76,0x01,0x00,0x00,0x53,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x0b,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0f,0x01,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x0b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x01,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
-0x74,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x14,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x12,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x14,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x13,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x74,0x00,0x00,0x00,
-0x76,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0xb3,0x00,0x00,0x00,
-0x19,0x01,0x00,0x00,0xb0,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
-0x19,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x1c,0x01,0x00,0x00,0xb4,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,
-0x1a,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xb4,0x00,0x00,0x00,
-0x1d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x14,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x14,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0c,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x20,0x01,0x00,0x00,0x76,0x01,0x00,0x00,0x28,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x09,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0b,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,
-0x22,0x01,0x00,0x00,0x74,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x24,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x22,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x23,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
-0xac,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0xb3,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xf0,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x2e,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x24,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x24,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_1_f32_f32_len = 4352;
-
-unsigned char mul_mat_vec_q5_K_f16_f32_data[] = {
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-0xc0,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x2e,0x02,0x00,0x00,0xb2,0x04,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb6,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xba,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2b,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x2c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2c,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2e,0x02,0x00,0x00,
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-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1e,0x00,0x0d,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x85,0x00,0x05,0x00,0x90,0x00,0x00,0x00,0x7a,0x04,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x79,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x90,0x00,0x00,0x00,0x7b,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x75,0x04,0x00,0x00,
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-0x90,0x00,0x00,0x00,0x80,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x74,0x03,0x00,0x00,0x7e,0x04,0x00,0x00,
-0x7b,0x04,0x00,0x00,0x70,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
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-0x90,0x00,0x00,0x00,0x85,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xe3,0x03,0x00,0x00,0x83,0x04,0x00,0x00,
-0x80,0x04,0x00,0x00,0x85,0x00,0x05,0x00,0x90,0x00,0x00,0x00,
-0x89,0x04,0x00,0x00,0xca,0x00,0x00,0x00,0x6d,0x04,0x00,0x00,
-0x7f,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0xbf,0x04,0x00,0x00,
-0x89,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,0x90,0x00,0x00,0x00,
-0x8a,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0x85,0x04,0x00,0x00,0xbf,0x04,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x8c,0x04,0x00,0x00,
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-0x95,0x04,0x00,0x00,0x96,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x99,0x04,0x00,0x00,0x94,0x04,0x00,0x00,
-0x95,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x94,0x04,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,0x9c,0x04,0x00,0x00,
-0x5d,0x00,0x00,0x00,0xbc,0x04,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x9e,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x9c,0x04,0x00,0x00,0x9d,0x04,0x00,0x00,0x9e,0x04,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9d,0x04,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x9e,0x04,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x91,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x96,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x04,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x04,0x00,0x00,
-0xbc,0x04,0x00,0x00,0x86,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x93,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x95,0x04,0x00,0x00,
-0xaa,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,0xac,0x04,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xae,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xac,0x04,0x00,0x00,0xad,0x04,0x00,0x00,0xae,0x04,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xad,0x04,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x04,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x9a,0x00,0x00,0x00,
-0xb6,0x04,0x00,0x00,0x93,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0xb7,0x04,0x00,0x00,
-0xb6,0x04,0x00,0x00,0x41,0x00,0x06,0x00,0xb8,0x04,0x00,0x00,
-0xb9,0x04,0x00,0x00,0xb2,0x04,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xb5,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,0xb9,0x04,0x00,0x00,
-0xb7,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,0xae,0x04,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xae,0x04,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_K_f16_f32_len = 13288;
-
-unsigned char mul_mat_vec_q5_K_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xa0,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-0x2e,0x02,0x00,0x00,0x93,0x04,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
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-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0xb5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x48,0x00,0x04,0x00,0x2c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xfa,0x00,0x04,0x00,0x7d,0x04,0x00,0x00,0x7e,0x04,0x00,0x00,
-0x7f,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x04,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0x77,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x77,0x04,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8b,0x04,0x00,0x00,0x9c,0x04,0x00,0x00,0x86,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x74,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x76,0x04,0x00,0x00,0xaa,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,
-0x8d,0x04,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x8f,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x8d,0x04,0x00,0x00,0x8e,0x04,0x00,0x00,
-0x8f,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x04,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x04,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x9a,0x00,0x00,0x00,0x97,0x04,0x00,0x00,0x93,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
-0x98,0x04,0x00,0x00,0x97,0x04,0x00,0x00,0x41,0x00,0x06,0x00,
-0x32,0x02,0x00,0x00,0x99,0x04,0x00,0x00,0x93,0x04,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x96,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x99,0x04,0x00,0x00,0x98,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x04,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_K_f32_f32_len = 12776;
-
-unsigned char mul_mat_vec_q6_K_f16_f32_data[] = {
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-0xa4,0x03,0x00,0x00,0xa3,0x03,0x00,0x00,0x33,0x00,0x00,0x00,
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-0xaf,0x03,0x00,0x00,0x33,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
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-0xb0,0x03,0x00,0x00,0x72,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,
-0xb2,0x03,0x00,0x00,0xb1,0x03,0x00,0x00,0x72,0x00,0x04,0x00,
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-0x3d,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0xc2,0x03,0x00,0x00,
-0x83,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
-0xc3,0x03,0x00,0x00,0xc2,0x03,0x00,0x00,0x85,0x00,0x05,0x00,
-0x89,0x00,0x00,0x00,0xc4,0x03,0x00,0x00,0xbe,0x03,0x00,0x00,
-0xc3,0x03,0x00,0x00,0x85,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
-0xc5,0x03,0x00,0x00,0xc4,0x03,0x00,0x00,0xb9,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0xcb,0x03,0x00,0x00,
-0x7a,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0xa7,0x00,0x00,0x00,
-0xcc,0x03,0x00,0x00,0xcb,0x03,0x00,0x00,0x33,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xcd,0x03,0x00,0x00,
-0xcc,0x03,0x00,0x00,0x7c,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0xce,0x03,0x00,0x00,0xcd,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xa7,0x00,0x00,0x00,0xd3,0x03,0x00,0x00,0x5b,0x03,0x00,0x00,
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-0xd3,0x03,0x00,0x00,0x2d,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xd5,0x03,0x00,0x00,0xd4,0x03,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0xd6,0x03,0x00,0x00,
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-0xd7,0x03,0x00,0x00,0xd6,0x03,0x00,0x00,0xb5,0x00,0x00,0x00,
-0xc4,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xd8,0x03,0x00,0x00,
-0xd7,0x03,0x00,0x00,0x33,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xd9,0x03,0x00,0x00,0xce,0x03,0x00,0x00,
-0xd8,0x03,0x00,0x00,0x72,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,
-0xda,0x03,0x00,0x00,0xd9,0x03,0x00,0x00,0x72,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0xdb,0x03,0x00,0x00,0xda,0x03,0x00,0x00,
-0x82,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xdc,0x03,0x00,0x00,
-0xdb,0x03,0x00,0x00,0x00,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x89,0x00,0x00,0x00,0xdd,0x03,0x00,0x00,0xdc,0x03,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x89,0x00,0x00,0x00,0xdf,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xc5,0x03,0x00,0x00,
-0xdd,0x03,0x00,0x00,0xb7,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
-0x89,0x00,0x00,0x00,0xe0,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
-0xdf,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
-0xb5,0x01,0x00,0x00,0x94,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x89,0x00,0x00,0x00,0xb6,0x01,0x00,0x00,0xb5,0x01,0x00,0x00,
-0xe0,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x94,0x00,0x00,0x00,
-0xb6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x01,0x00,0x00,0xe4,0x01,0x00,0x00,0x5c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x97,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x99,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0xba,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbc,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x01,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe5,0x01,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0xd3,0x01,0x00,0x00,
-0xbf,0x01,0x00,0x00,0xac,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,
-0xc2,0x01,0x00,0x00,0xe5,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xbe,0x01,0x00,0x00,0xbf,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc2,0x01,0x00,0x00,
-0xbd,0x01,0x00,0x00,0xbe,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbd,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,
-0xc5,0x01,0x00,0x00,0x5d,0x00,0x00,0x00,0xe5,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xc7,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xc5,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,
-0xc7,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xc6,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x01,0x00,0x00,
-0x5d,0x00,0x00,0x00,0xe5,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x93,0x00,0x00,0x00,0xcc,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,
-0xcb,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
-0xcd,0x01,0x00,0x00,0xcc,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x93,0x00,0x00,0x00,0xce,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
-0xcf,0x01,0x00,0x00,0xce,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
-0x89,0x00,0x00,0x00,0xd0,0x01,0x00,0x00,0xcf,0x01,0x00,0x00,
-0xcd,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xce,0x01,0x00,0x00,
-0xd0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xc7,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc7,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xba,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbf,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbf,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd3,0x01,0x00,0x00,0xe5,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbc,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbe,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,
-0xd5,0x01,0x00,0x00,0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xd7,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xd5,0x01,0x00,0x00,0xd6,0x01,0x00,0x00,
-0xd7,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x01,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x93,0x00,0x00,0x00,0xdf,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
-0xe0,0x01,0x00,0x00,0xdf,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe1,0x01,0x00,0x00,0xe2,0x01,0x00,0x00,0xdb,0x01,0x00,0x00,
-0x4c,0x00,0x00,0x00,0xde,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xe2,0x01,0x00,0x00,0xe0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd7,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q6_K_f16_f32_len = 11960;
-
-unsigned char mul_mat_vec_q6_K_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xde,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0xd8,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
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-0xdd,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd4,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd4,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q6_K_f32_f32_len = 11704;
-
-unsigned char mul_mat_vec_q8_0_f16_f32_data[] = {
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-0xe1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0x27,0x01,0x00,0x00,0x27,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xde,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x94,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xf9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xf7,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x81,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x85,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x01,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x03,0x01,0x00,0x00,
-0x04,0x01,0x00,0x00,0xfd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
-0x02,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xf9,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf9,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q8_0_f16_f32_len = 3916;
-
-unsigned char mul_mat_vec_q8_0_f32_f32_data[] = {
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-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
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-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x47,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
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-0xe6,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x25,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x89,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x99,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe0,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x27,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdf,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x94,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf6,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf7,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x88,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x85,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xc4,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0xfc,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x02,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf8,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q8_0_f32_f32_len = 3884;
-
-unsigned char norm_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x27,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x27,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x27,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x34,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x98,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x9a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x1c,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2c,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x27,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x2f,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x33,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x34,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x35,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x3d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x2a,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x87,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x97,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x98,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x99,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x99,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0xb0,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x21,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x21,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x23,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x30,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x42,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x43,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x42,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x14,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x4c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x21,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x23,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x57,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x2a,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0xad,0x00,0x05,0x00,
-0x2f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x59,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x5d,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x63,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x63,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x52,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x00,0x00,0x00,0xc3,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x42,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x88,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x42,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x88,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x7f,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x14,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x78,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x87,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,0x14,0x00,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x90,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x96,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0xa8,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0x78,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x9a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xad,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t norm_f32_len = 2624;
-
-unsigned char relu_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x20,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x22,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x27,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x1f,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x20,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x24,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x25,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x0c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x32,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x2e,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x32,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x32,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t relu_f32_len = 1212;
-
-unsigned char rms_norm_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x9e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x25,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x25,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x32,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x34,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x83,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x83,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x85,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x25,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x26,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x31,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x32,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x3b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x72,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x82,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x83,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x83,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x84,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1f,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3b,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x14,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x21,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x28,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0xad,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x59,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x50,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x00,0x00,0x00,
-0xc3,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x9c,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4f,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x88,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x72,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
-0x14,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x79,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x7a,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x81,0x00,0x00,0x00,
-0x7a,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3b,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t rms_norm_f32_len = 2344;
-
-unsigned char rope_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x1c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2a,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x88,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x89,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xaa,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xab,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xab,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xab,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd5,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x07,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x6f,0x12,0x83,0x3a,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x00,0x00,0x80,0x3f,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x1e,0x00,0x09,0x00,0x2a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
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-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
-0x04,0x01,0x00,0x00,0x19,0x01,0x00,0x00,0x0c,0x00,0x06,0x00,
-0x06,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x08,0x01,0x00,0x00,
-0x19,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xb0,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb0,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x42,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xa9,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
-0xb8,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x7f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xb0,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xc8,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
-0x0a,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0xa9,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xb0,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xd3,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd6,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t rope_f16_len = 3156;
-
-unsigned char rope_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x17,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x67,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x89,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xaa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xaa,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xac,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xac,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xb8,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x6f,0x12,0x83,0x3a,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x07,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x1e,0x00,0x09,0x00,0x2a,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x39,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x3c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0xcd,0xcc,0xcc,0x3d,0x17,0x00,0x04,0x00,0x65,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x66,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x69,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x88,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x8d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xa9,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xaa,0x00,0x00,0x00,
-0xa9,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xab,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0xab,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xb7,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xb8,0x00,0x00,0x00,
-0xb7,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x2c,0x00,0x06,0x00,0x65,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xd1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x6e,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd2,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x69,0x00,0x00,0x00,
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-0x75,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x76,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0x84,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
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-0x8e,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x92,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x2f,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x93,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x7f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x97,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x88,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
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-0xdc,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
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-0xde,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
-0xb7,0x00,0x05,0x00,0x3c,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe4,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe5,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x2f,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x2f,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0xe8,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00,
-0x09,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x09,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x01,0x00,0x00,0x0a,0x01,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,
-0xe9,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,
-0x88,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x0b,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,0x0c,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x10,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x0c,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x11,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x10,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x12,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0x11,0x01,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x12,0x01,0x00,0x00,0xe3,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x15,0x01,0x00,0x00,0xe3,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x9e,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0x88,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x14,0x01,0x00,0x00,0xdd,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
-0xe0,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xe5,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x13,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x03,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xaf,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0x42,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xaf,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0x42,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x7f,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xaf,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0x42,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xaf,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd1,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t rope_f32_len = 3072;
-
-unsigned char rope_neox_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x5f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
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-0xa9,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0xa6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x94,0x00,0x00,0x00,
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-0x42,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
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-0x07,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
-0x83,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x07,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
-0xb8,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x73,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00,
-0xbf,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x83,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x7f,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0xc8,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xd1,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x42,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0xd3,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe0,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,
-0x0c,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
-0xe0,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0x6d,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,
-0x22,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
-0x22,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x26,0x01,0x00,0x00,0xd8,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x2f,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
-0xb7,0x00,0x05,0x00,0x3c,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,
-0x29,0x01,0x00,0x00,0x20,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x43,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x2a,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,0x43,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x2f,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x2f,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,
-0x2e,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00,
-0x4f,0x01,0x00,0x00,0xe5,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x01,0x00,0x00,
-0x4f,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x51,0x01,0x00,0x00,0x50,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
-0x2f,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,0x0c,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x53,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
-0x88,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x0c,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
-0x0c,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x56,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x58,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0x57,0x01,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,
-0x58,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x83,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x57,0x01,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
-0x36,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x5d,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0xe2,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
-0x39,0x01,0x00,0x00,0x88,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3d,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x3e,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
-0x40,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x43,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x43,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
-0x87,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00,
-0x26,0x01,0x00,0x00,0x87,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x59,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x59,0x01,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x49,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xa1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
-0x42,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x94,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xa1,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
-0x42,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x94,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x03,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x7f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,
-0x03,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x94,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x04,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xa1,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x98,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x06,0x01,0x00,0x00,
-0x05,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x11,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x11,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
-0x94,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xa1,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
-0x98,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x14,0x01,0x00,0x00,0x13,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x17,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t rope_neox_f16_len = 3876;
-
-unsigned char rope_neox_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x5a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x12,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t rope_neox_f32_len = 3792;
-
-unsigned char scale_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd5,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd7,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xe0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xe1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe1,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe3,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf3,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0xc5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0xcf,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xd4,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0xd6,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xe0,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xe1,0x00,0x00,0x00,
-0xe0,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0xe2,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xe8,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0xec,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,
-0xc5,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfb,0x00,0x03,0x00,0xc8,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc9,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0xcf,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
-0xce,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xd2,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0xd1,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x05,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x04,0x01,0x00,0x00,
-0x06,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
-0x08,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0a,0x01,0x00,0x00,0x07,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0xcb,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x04,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x12,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
-0x12,0x01,0x00,0x00,0x09,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,
-0x15,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1d,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x18,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x06,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,
-0x18,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,
-0x09,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x36,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
-0x2a,0x01,0x00,0x00,0x36,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x39,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x3d,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x3d,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
-0x3b,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x43,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x43,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x40,0x01,0x00,0x00,0x44,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
-0x37,0x01,0x00,0x00,0x48,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x45,0x01,0x00,0x00,
-0x49,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x56,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x56,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00,
-0x55,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5c,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00,0x60,0x01,0x00,0x00,
-0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x66,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
-0xcb,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,0x57,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6f,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
-0x6f,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7b,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x76,0x01,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
-0x7b,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
-0x7e,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
-0x95,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x98,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
-0x98,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x99,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
-0x96,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xec,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe8,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t scale_f32_len = 3320;
-
-unsigned char silu_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x21,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x29,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x39,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x0c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x39,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x7f,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x88,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x35,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x39,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x39,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t silu_f32_len = 1264;
-
-unsigned char soft_max_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x6f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x74,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x75,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x8d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8f,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x09,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x0a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x0a,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x09,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x28,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x4e,0x01,0x00,0x00,0x4d,0x01,0x00,0x00,0x2f,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x4c,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x34,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x51,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x9d,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x31,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t soft_max_f32_len = 4836;
-
-unsigned char soft_max_f32_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x73,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x10,0x01,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x75,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x8e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x8e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x8e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x90,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0d,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x10,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x10,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x56,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x16,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x09,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x28,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x74,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x76,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x7e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x8c,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x8d,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x8f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x8f,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x1c,0x00,0x04,0x00,
-0xa3,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0xa3,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,
-0xa5,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x0d,0x01,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x0e,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0f,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0f,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x56,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x72,0x01,0x00,0x00,0x00,0x00,0x80,0xff,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0xba,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2a,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0xe6,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7e,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0xac,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xf5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xf2,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x97,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x5c,0x01,0x00,0x00,
-0xff,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x16,0x00,0x00,0x00,0x65,0x01,0x00,0x00,
-0x00,0x01,0x00,0x00,0xf4,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x16,0x00,0x00,0x00,
-0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x65,0x01,0x00,0x00,
-0x83,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x03,0x01,0x00,0x00,0xca,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
-0x16,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x0a,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa9,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7e,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
-0x64,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x13,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
-0x61,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xce,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd0,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x17,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x1a,0x00,0x00,0x00,0x69,0x01,0x00,0x00,0xad,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x30,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,
-0xad,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
-0x69,0x01,0x00,0x00,0x64,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x19,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x1d,0x01,0x00,0x00,0x18,0x01,0x00,0x00,
-0x19,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x18,0x01,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x69,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
-0x21,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x23,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x21,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
-0x23,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0xa8,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x28,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x2a,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x2c,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa9,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x23,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x23,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1a,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1a,0x01,0x00,0x00,0xc3,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x30,0x01,0x00,0x00,0x69,0x01,0x00,0x00,
-0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x19,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x35,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x6a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
-0x55,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,
-0x66,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x37,0x01,0x00,0x00,
-0x38,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x3d,0x01,0x00,0x00,0x36,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x36,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,
-0x0f,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x66,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x47,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x37,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x47,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x13,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7e,0x00,0x00,0x00,0x50,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
-0x64,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x51,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
-0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x50,0x01,0x00,0x00,0x52,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x38,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x38,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,
-0x6a,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x35,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t soft_max_f32_f16_len = 4904;
-
-unsigned char split_k_reduce_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x30,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x3f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x3f,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x41,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x19,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x2d,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x2e,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x2f,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x3e,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x3f,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x40,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x40,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x46,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x09,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x46,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x48,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x49,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x48,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x23,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x23,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x25,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x24,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x23,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x25,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x37,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x44,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x48,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x48,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t split_k_reduce_len = 1416;
-
-unsigned char sqr_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xd7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd7,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd9,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe4,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf3,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
-0x7e,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
-0x95,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x98,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
-0x98,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x99,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
-0x96,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x9b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xde,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf4,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t sqr_f32_len = 3252;
-
diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp
deleted file mode 100644
index aff451b6354e5..0000000000000
--- a/ggml-vulkan.cpp
+++ /dev/null
@@ -1,7028 +0,0 @@
-#include "ggml-vulkan.h"
-
-#ifdef GGML_VULKAN_RUN_TESTS
-#include <chrono>
-#endif
-
-#include <vulkan/vulkan.hpp>
-
-#include <algorithm>
-#include <cmath>
-#include <iostream>
-#include <limits>
-#include <tuple>
-#include <vector>
-#include <sstream>
-#include <utility>
-#include <memory>
-
-#include "ggml.h"
-#include "ggml-backend-impl.h"
-
-#include "ggml-vulkan-shaders.hpp"
-
-#define VK_API_VERSION VK_API_VERSION_1_2
-
-#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
-
-#define VK_VENDOR_ID_AMD 0x1002
-#define VK_VENDOR_ID_APPLE 0x106b
-#define VK_VENDOR_ID_INTEL 0x8086
-#define VK_VENDOR_ID_NVIDIA 0x10de
-
-#define VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN 0
-#define VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI 1
-#define VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE 2
-
-#define VK_NUM_TYPES 16
-
-#define GGML_VK_MAX_NODES 8192
-
-#define MAX_VK_BUFFERS 256
-
-#ifndef K_QUANTS_PER_ITERATION
-#define K_QUANTS_PER_ITERATION 1
-#else
-static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
-#endif
-
-#define VK_CHECK(err, msg)                                          \
-    do {                                                            \
-        vk::Result err_ = (err);                                    \
-        if (err_ != vk::Result::eSuccess) {                         \
-            fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n",  \
-                #err, to_string(err_).c_str(), __FILE__, __LINE__); \
-            exit(1);                                                \
-        }                                                           \
-    } while (0)
-
-struct ggml_backend_vk_context;
-
-struct vk_queue {
-    uint32_t queue_family_index;
-    vk::Queue queue;
-    vk::CommandPool pool;
-    uint32_t cmd_buffer_idx;
-    std::vector<vk::CommandBuffer> cmd_buffers;
-
-    vk::PipelineStageFlags stage_flags;
-};
-
-struct vk_pipeline_struct {
-    std::string name;
-    vk::ShaderModule shader_module;
-    vk::DescriptorSetLayout dsl;
-    std::vector<vk::DescriptorPool> descriptor_pools;
-    std::vector<vk::DescriptorSet> descriptor_sets;
-    uint32_t descriptor_set_idx;
-    vk::PipelineLayout layout;
-    vk::Pipeline pipeline;
-    uint32_t push_constant_size;
-    uint32_t parameter_count;
-    std::array<uint32_t, 3> wg_denoms;
-    uint32_t align;
-};
-
-typedef std::shared_ptr<vk_pipeline_struct> vk_pipeline;
-typedef std::weak_ptr<vk_pipeline_struct> vk_pipeline_ref;
-
-static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
-
-struct vk_matmul_pipeline_struct {
-    vk_pipeline l, m, s;
-    vk_pipeline a_l, a_m, a_s;
-};
-
-typedef std::shared_ptr<vk_matmul_pipeline_struct> vk_matmul_pipeline;
-
-struct vk_device {
-    vk::PhysicalDevice physical_device;
-    vk::PhysicalDeviceProperties properties;
-    std::string name;
-    uint64_t max_memory_allocation_size;
-    bool fp16;
-    vk::Device device;
-    uint32_t vendor_id;
-    vk_queue compute_queue;
-    vk_queue transfer_queue;
-    bool single_queue;
-    uint32_t descriptor_set_mode;
-    uint32_t subgroup_size;
-    bool uma;
-
-    bool initialized;
-    size_t idx;
-
-    vk_matmul_pipeline pipeline_matmul_f32;
-    vk_matmul_pipeline pipeline_matmul_f32_f16;
-    vk_matmul_pipeline pipeline_matmul_f16;
-    vk_matmul_pipeline pipeline_matmul_f16_f32;
-    vk_pipeline pipeline_matmul_split_k_reduce;
-
-    vk_matmul_pipeline pipeline_dequant_mul_mat_mat[VK_NUM_TYPES];
-
-    vk_matmul_pipeline pipeline_matmul_id_f32;
-    vk_matmul_pipeline pipeline_matmul_id_f16;
-    vk_matmul_pipeline pipeline_matmul_id_f16_f32;
-
-    vk_matmul_pipeline pipeline_dequant_mul_mat_mat_id[VK_NUM_TYPES];
-
-    vk_pipeline pipeline_dequant[VK_NUM_TYPES];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[VK_NUM_TYPES];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[VK_NUM_TYPES];
-    vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[VK_NUM_TYPES];
-
-    vk_pipeline pipeline_mul_mat_vec_p021_f16_f32;
-    vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
-    vk_pipeline pipeline_get_rows[VK_NUM_TYPES];
-    vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES];
-    vk_pipeline pipeline_mul_f32;
-    vk_pipeline pipeline_add_f32;
-    vk_pipeline pipeline_scale_f32;
-    vk_pipeline pipeline_sqr_f32;
-    vk_pipeline pipeline_clamp_f32;
-    vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16;
-    vk_pipeline pipeline_norm_f32;
-    vk_pipeline pipeline_rms_norm_f32;
-    vk_pipeline pipeline_gelu_f32;
-    vk_pipeline pipeline_silu_f32;
-    vk_pipeline pipeline_relu_f32;
-    vk_pipeline pipeline_diag_mask_inf_f32;
-    vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
-    vk_pipeline pipeline_rope_f32, pipeline_rope_f16;
-    vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
-    vk_pipeline pipeline_argsort_f32;
-
-    std::vector<vk_pipeline_ref> pipelines;
-
-    ~vk_device() {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "destroy device " << name << std::endl;
-#endif
-        device.destroyCommandPool(compute_queue.pool);
-        if (!single_queue) {
-            device.destroyCommandPool(transfer_queue.pool);
-        }
-
-        for (auto& pipeline : pipelines) {
-            if (pipeline.expired()) {
-                continue;
-            }
-
-            vk_pipeline pl = pipeline.lock();
-            ggml_vk_destroy_pipeline(device, pl);
-        }
-        pipelines.clear();
-
-        device.destroy();
-    }
-};
-
-struct vk_buffer_struct {
-    vk::Buffer buffer;
-    vk::DeviceMemory device_memory;
-    vk::MemoryPropertyFlags memory_property_flags;
-    void * ptr;
-    size_t size = 0;
-
-    ggml_backend_vk_context * ctx;
-
-    std::shared_ptr<vk_device> device;
-
-    ~vk_buffer_struct() {
-        if (size == 0) {
-            return;
-        }
-#ifdef GGML_VULKAN_DEBUG
-        std::cerr << "~vk_buffer_struct(" << buffer << ", " << size << ")" << std::endl;
-#endif
-
-        device->device.freeMemory(device_memory);
-        device->device.destroyBuffer(buffer);
-    }
-};
-
-typedef std::shared_ptr<vk_buffer_struct> vk_buffer;
-typedef std::weak_ptr<vk_buffer_struct> vk_buffer_ref;
-
-struct vk_subbuffer {
-    vk_buffer buffer;
-    uint64_t offset;
-    uint64_t size;
-};
-
-struct vk_semaphore {
-    vk::Semaphore s;
-    uint64_t value;
-};
-
-struct vk_submission {
-    vk::CommandBuffer buffer;
-    std::vector<vk_semaphore> wait_semaphores;
-    std::vector<vk_semaphore> signal_semaphores;
-};
-
-typedef std::vector<vk_submission> vk_sequence;
-
-struct vk_mat_mat_push_constants {
-    uint32_t M; uint32_t N; uint32_t K;
-    uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; uint32_t k_split;
-    uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
-    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
-    uint32_t expert_stride_b; uint32_t expert_stride_d;
-    uint32_t idx; uint32_t nbi1; uint32_t n_as;
-};
-
-struct vk_mat_vec_push_constants {
-    uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
-    uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
-    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
-};
-
-struct vk_op_push_constants {
-    uint32_t KX;
-    uint32_t KY;
-    float param1;
-    float param2;
-};
-
-struct vk_op_unary_push_constants {
-    uint32_t ne;
-    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
-    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
-    uint32_t d_offset;
-    float param1; float param2;
-};
-
-struct vk_op_binary_push_constants {
-    uint32_t ne;
-    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
-    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
-    uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23;
-    uint32_t d_offset;
-    float param1; float param2;
-};
-
-struct vk_op_diag_mask_push_constants {
-    uint32_t ncols;
-    uint32_t rows_per_channel;
-    int32_t n_past;
-};
-
-struct vk_op_rope_push_constants {
-    uint32_t ncols;
-    float freq_scale;
-    uint32_t p_delta_rows;
-    float freq_base;
-    float ext_factor;
-    float attn_factor;
-    float corr_dims[4];
-};
-
-struct vk_op_rope_neox_push_constants {
-    uint32_t ncols;
-    uint32_t ndims;
-    float freq_scale;
-    uint32_t p_delta_rows;
-    float freq_base;
-    float ext_factor;
-    float attn_factor;
-    float corr_dims[4];
-    float theta_scale;
-    float inv_ndims;
-};
-
-struct vk_op_soft_max_push_constants {
-    uint32_t KX;
-    uint32_t KY;
-    float scale;
-    float max_bias;
-    float m0;
-    float m1;
-    uint32_t n_head_log2;
-};
-
-struct vk_op_argsort_push_constants {
-    uint32_t ncols;
-    uint32_t ncols_pad;
-    int32_t order;
-};
-
-// Allow pre-recording command buffers
-struct vk_staging_memcpy {
-    vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
-
-    void * dst;
-    const void * src;
-    size_t n;
-};
-
-struct vk_context {
-    size_t idx;
-
-    vk_submission * s;
-    std::vector<vk_sequence> seqs;
-
-    ggml_tensor * exit_tensor;
-
-    std::vector<vk_staging_memcpy> in_memcpys;
-    std::vector<vk_staging_memcpy> out_memcpys;
-
-    vk_queue * q;
-};
-
-struct ggml_tensor_extra_gpu {
-    bool ready;
-
-    size_t ctx_idx;
-
-    vk_buffer_ref buffer_gpu;
-    uint64_t offset;
-
-    void reset() {
-        ready = false;
-        ctx_idx = 0;
-        buffer_gpu.reset();
-        offset = 0;
-    }
-};
-
-struct ggml_vk_garbage_collector {
-    std::vector<vk_semaphore> tl_semaphores;
-    std::vector<vk_semaphore> semaphores;
-    std::vector<vk::Event> events;
-    std::vector<vk_buffer> temp_buffers;
-    std::vector<vk_context> contexts;
-};
-
-struct ggml_backend_vk_context {
-    std::string name;
-
-    std::shared_ptr<vk_device> device;
-
-    size_t semaphore_idx, event_idx;
-    ggml_vk_garbage_collector gc;
-    std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;
-    size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k;
-    vk_buffer prealloc_x, prealloc_y, prealloc_split_k;
-    vk::Fence fence;
-    vk_buffer staging;
-    size_t staging_size;
-    size_t staging_offset;
-    vk_buffer sync_staging;
-
-    vk_buffer buffer_pool[MAX_VK_BUFFERS];
-
-    vk_context * compute_ctx;
-    vk_context * transfer_ctx;
-
-    bool initialized;
-
-    size_t idx;
-};
-
-struct vk_instance_t {
-    vk::Instance instance;
-
-    std::vector<size_t> device_indices;
-
-    ggml_backend_t backends[GGML_VK_MAX_DEVICES];
-    ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES];
-    ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES];
-    bool initialized[GGML_VK_MAX_DEVICES];
-};
-
-static std::shared_ptr<vk_device> ggml_vk_get_device(size_t idx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_get_device(" << idx << ")" << std::endl;
-#endif
-    static std::weak_ptr<vk_device> devices[GGML_VK_MAX_DEVICES];
-
-    if (devices[idx].expired()) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "Initializing new vk_device" << std::endl;
-#endif
-        std::shared_ptr<vk_device> device = std::make_shared<vk_device>();
-        device->initialized = false;
-        devices[idx] = device;
-        return device;
-    }
-
-    return devices[idx].lock();
-}
-
-#ifdef GGML_VULKAN_CHECK_RESULTS
-static size_t vk_skip_checks;
-static size_t vk_output_tensor;
-
-static void ggml_vk_print_tensor(ggml_backend * ctx, const ggml_tensor * tensor, const char * name);
-static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor);
-static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor);
-#endif
-
-typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
-
-static bool vk_instance_initialized = false;
-static vk_instance_t vk_instance;
-
-GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend);
-
-static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t>&& specialization_constants, uint32_t align) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl;
-#endif
-    GGML_ASSERT(parameter_count > 0);
-    GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
-
-    pipeline = std::make_shared<vk_pipeline_struct>();
-    pipeline->name = name;
-    pipeline->parameter_count = parameter_count;
-    pipeline->push_constant_size = push_constant_size;
-    pipeline->wg_denoms = wg_denoms;
-    pipeline->align = align;
-
-    vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast<const uint32_t *>(spv_data));
-    pipeline->shader_module = ctx->device->device.createShaderModule(shader_module_create_info);
-
-    std::vector<vk::DescriptorSetLayoutBinding> dsl_binding;
-    std::vector<vk::DescriptorBindingFlags> dsl_binding_flags;
-    for (uint32_t i = 0; i < parameter_count; i++) {
-        dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute});
-        dsl_binding_flags.push_back({});
-    }
-
-    vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags };
-
-    vk::PushConstantRange pcr(
-        vk::ShaderStageFlagBits::eCompute,
-        0,
-        pipeline->push_constant_size
-    );
-
-    vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info(
-        {},
-        dsl_binding);
-    descriptor_set_layout_create_info.setPNext(&dslbfci);
-    pipeline->dsl = ctx->device->device.createDescriptorSetLayout(descriptor_set_layout_create_info);
-
-    // Check if device supports multiple descriptors per pool
-    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) {
-        const uint32_t alloc_count = 2;
-
-        // Try allocating multiple sets from one pool
-        // This fails on AMD for some reason, so add a fall back to allocating one pool per set
-        vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
-        vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size);
-        vk::DescriptorPool pool = ctx->device->device.createDescriptorPool(descriptor_pool_create_info);
-
-        std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
-        for (uint32_t i = 0; i < alloc_count; i++) {
-            layouts[i] = pipeline->dsl;
-        }
-        try {
-            vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data());
-            std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
-        } catch(vk::OutOfPoolMemoryError const&) {
-            ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE;
-        }
-
-        ctx->device->device.destroyDescriptorPool(pool);
-    }
-
-    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
-        vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
-        vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size);
-        pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info));
-    }
-
-    pipeline->descriptor_set_idx = 0;
-
-    vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr);
-    pipeline->layout = ctx->device->device.createPipelineLayout(pipeline_layout_create_info);
-
-    std::vector<vk::SpecializationMapEntry> specialization_entries(specialization_constants.size());
-
-    for (size_t i = 0; i < specialization_constants.size(); i++) {
-        specialization_entries[i].constantID = i;
-        specialization_entries[i].offset = i * sizeof(uint32_t);
-        specialization_entries[i].size = sizeof(uint32_t);
-    }
-
-    vk::SpecializationInfo specialization_info(
-        specialization_entries.size(),
-        specialization_entries.data(),
-        specialization_constants.size() * sizeof(uint32_t),
-        specialization_constants.data()
-    );
-
-    vk::PipelineShaderStageCreateInfo pipeline_shader_create_info(
-            vk::PipelineShaderStageCreateFlags(),
-            vk::ShaderStageFlagBits::eCompute,
-            pipeline->shader_module,
-            entrypoint.c_str(),
-            &specialization_info);
-    vk::ComputePipelineCreateInfo compute_pipeline_create_info(
-        vk::PipelineCreateFlags(),
-        pipeline_shader_create_info,
-        pipeline->layout);
-    pipeline->pipeline = ctx->device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
-
-    ctx->device->pipelines.push_back(pipeline);
-}
-
-static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_pipeline_destroy_pipeline(" << pipeline->name << ")" << std::endl;
-#endif
-    for (auto& pool : pipeline->descriptor_pools) {
-        device.destroyDescriptorPool(pool);
-    }
-    pipeline->descriptor_pools.clear();
-    pipeline->descriptor_sets.clear();
-    pipeline->descriptor_set_idx = 0;
-
-    device.destroyDescriptorSetLayout(pipeline->dsl);
-
-    device.destroyPipelineLayout(pipeline->layout);
-
-    device.destroyShaderModule(pipeline->shader_module);
-
-    device.destroyPipeline(pipeline->pipeline);
-}
-
-static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")" << std::endl;
-#endif
-    if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) {
-        // Enough descriptors are available
-        return;
-    }
-
-    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
-        const uint32_t alloc_count = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size();
-
-        std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
-        for (uint32_t i = 0; i < alloc_count; i++) {
-            layouts[i] = pipeline->dsl;
-        }
-        vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[0], alloc_count, layouts.data());
-        std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
-        pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end());
-    } else {
-        for (uint32_t i = pipeline->descriptor_sets.size(); i < pipeline->descriptor_set_idx + n; i++) {
-            vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
-            vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size);
-            pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info));
-
-            vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[i], 1, &pipeline->dsl);
-            std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
-            pipeline->descriptor_sets.push_back(sets[0]);
-        }
-    }
-}
-
-static void ggml_pipeline_cleanup(vk_pipeline& pipeline) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_pipeline_cleanup(" << pipeline->name << ")" << std::endl;
-#endif
-    pipeline->descriptor_set_idx = 0;
-}
-
-static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl;
-#endif
-    if (q.cmd_buffers.size() > q.cmd_buffer_idx) {
-        // Reuse command buffer
-        return q.cmd_buffers[q.cmd_buffer_idx++];
-    }
-
-    vk::CommandBufferAllocateInfo command_buffer_alloc_info(
-        q.pool,
-        vk::CommandBufferLevel::ePrimary,
-        1);
-    const std::vector<vk::CommandBuffer> cmd_buffers = ctx->device->device.allocateCommandBuffers(command_buffer_alloc_info);
-    auto buf = cmd_buffers.front();
-
-    q.cmd_buffers.push_back(buf);
-    q.cmd_buffer_idx++;
-
-    return buf;
-}
-
-static vk_submission ggml_vk_create_submission(ggml_backend_vk_context * ctx, vk_queue& q, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_submission()" << std::endl;
-#endif
-    vk_submission s;
-    s.buffer = ggml_vk_create_cmd_buffer(ctx, q);
-    s.wait_semaphores = std::move(wait_semaphores);
-    s.signal_semaphores = std::move(signal_semaphores);
-    return s;
-}
-
-static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl;
-#endif
-    if (ctx->seqs.empty()) {
-        return;
-    }
-
-    std::vector<std::vector<uint64_t>> tl_wait_vals;
-    std::vector<std::vector<uint64_t>> tl_signal_vals;
-    std::vector<std::vector<vk::Semaphore>> tl_wait_semaphores;
-    std::vector<std::vector<vk::Semaphore>> tl_signal_semaphores;
-    std::vector<vk::TimelineSemaphoreSubmitInfo> tl_submit_infos;
-    std::vector<vk::SubmitInfo> submit_infos;
-    int idx = -1;
-    std::vector<std::vector<vk::PipelineStageFlags>> stage_flags;
-
-    size_t reserve = 0;
-
-    for (const auto& sequence : ctx->seqs) {
-        reserve += sequence.size();
-    }
-
-    // Pre-reserve vectors to prevent reallocation, which invalidates pointers
-    tl_wait_semaphores.reserve(reserve);
-    tl_wait_vals.reserve(reserve);
-    tl_signal_semaphores.reserve(reserve);
-    tl_signal_vals.reserve(reserve);
-    tl_submit_infos.reserve(reserve);
-    submit_infos.reserve(reserve);
-    stage_flags.reserve(reserve);
-
-    for (const auto& sequence : ctx->seqs) {
-        for (const auto& submission : sequence) {
-            stage_flags.push_back({});
-            idx++;
-            tl_wait_vals.push_back({});
-            tl_wait_semaphores.push_back({});
-            tl_signal_vals.push_back({});
-            tl_signal_semaphores.push_back({});
-            for (size_t i = 0; i < submission.wait_semaphores.size(); i++) {
-                stage_flags[idx].push_back(ctx->q->stage_flags);
-                tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value);
-                tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s);
-            }
-            for (size_t i = 0; i < submission.signal_semaphores.size(); i++) {
-                tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value);
-                tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s);
-            }
-            tl_submit_infos.push_back({
-                (uint32_t) submission.wait_semaphores.size(),
-                tl_wait_vals[idx].data(),
-                (uint32_t) submission.signal_semaphores.size(),
-                tl_signal_vals[idx].data(),
-            });
-            tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo;
-            tl_submit_infos[idx].pNext = nullptr;
-            vk::SubmitInfo si{
-                (uint32_t) submission.wait_semaphores.size(),
-                tl_wait_semaphores[idx].data(),
-                stage_flags[idx].data(),
-                1,
-                &submission.buffer,
-                (uint32_t) submission.signal_semaphores.size(),
-                tl_signal_semaphores[idx].data(),
-            };
-            si.setPNext(&tl_submit_infos[idx]);
-            submit_infos.push_back(si);
-        }
-    }
-
-    ctx->q->queue.submit(submit_infos, fence);
-
-    ctx->seqs.clear();
-}
-
-static uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyProperties>& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_find_queue_family_index()" << std::endl;
-#endif
-    const uint32_t qfsize = queue_family_props.size();
-
-    // Try with avoid preferences first
-    for (uint32_t i = 0; i < qfsize; i++) {
-        if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) {
-            return i;
-        }
-    }
-
-    // Fall back to only required
-    for (size_t i = 0; i < qfsize; i++) {
-        if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) {
-            return i;
-        }
-    }
-
-    // Fall back to reusing compute queue
-    for (size_t i = 0; i < qfsize; i++) {
-        if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) {
-            return i;
-        }
-    }
-
-    // Fall back to ignoring min_num_queries
-    for (size_t i = 0; i < qfsize; i++) {
-        if (queue_family_props[i].queueFlags & required) {
-            return i;
-        }
-    }
-
-    // All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
-    // Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
-    if (compute_index >= 0) {
-        return compute_index;
-    }
-
-    std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;
-
-    for(auto &q_family : queue_family_props) {
-        std::cerr << "Queue number: "  + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl;
-    }
-    abort();
-}
-
-static void ggml_vk_create_queue(ggml_backend_vk_context * ctx, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_queue()" << std::endl;
-#endif
-    q.queue_family_index = queue_family_index;
-
-    vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index);
-    q.pool = ctx->device->device.createCommandPool(command_pool_create_info_compute);
-
-    q.cmd_buffer_idx = 0;
-
-    q.queue = ctx->device->device.getQueue(queue_family_index, queue_index);
-
-    q.stage_flags = stage_flags;
-}
-
-static vk_context * ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_context()" << std::endl;
-#endif
-    ctx->gc.contexts.emplace_back();
-    vk_context * result = &ctx->gc.contexts[ctx->gc.contexts.size() - 1];
-    memset((void *) result, 0, sizeof(vk_context));
-    result->idx = ctx->gc.contexts.size() - 1;
-    result->q = &q;
-    return result;
-}
-
-static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl;
-#endif
-    vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 };
-    vk::SemaphoreCreateInfo ci{};
-    ci.setPNext(&tci);
-    vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
-    ctx->gc.semaphores.push_back({ semaphore, 0 });
-    return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1];
-}
-
-static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl;
-#endif
-    if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) {
-        vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
-        vk::SemaphoreCreateInfo ci{};
-        ci.setPNext(&tci);
-        vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
-        ctx->gc.tl_semaphores.push_back({ semaphore, 0 });
-    }
-    return &ctx->gc.tl_semaphores[ctx->semaphore_idx++];
-}
-
-static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) {
-    if (ctx->event_idx >= ctx->gc.events.size()) {
-        ctx->gc.events.push_back(ctx->device->device.createEvent({}));
-    }
-    return ctx->gc.events[ctx->event_idx++];
-}
-
-static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_queue_cleanup()" << std::endl;
-#endif
-    // Requires command buffers to be done
-
-    ctx->device->device.resetCommandPool(q.pool);
-    q.cmd_buffer_idx = 0;
-}
-
-static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) {
-    for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) {
-        vk::MemoryType memory_type = mem_props->memoryTypes[i];
-        if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) &&
-            (flags & memory_type.propertyFlags) == flags &&
-            mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) {
-            return static_cast<int32_t>(i);
-        }
-    }
-    return UINT32_MAX;
-}
-
-static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_buffer(device " << ctx->idx << ", " << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")" << std::endl;
-#endif
-    vk_buffer buf = std::make_shared<vk_buffer_struct>();
-
-    if (size == 0) {
-        buf->size = 0;
-        return buf;
-    }
-
-    buf->size = size;
-    vk::BufferCreateInfo buffer_create_info{
-        vk::BufferCreateFlags(),
-        size,
-        vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst,
-        vk::SharingMode::eExclusive,
-        0,
-        nullptr,
-    };
-
-    buf->buffer = ctx->device->device.createBuffer(buffer_create_info);
-
-    vk::MemoryRequirements mem_req = ctx->device->device.getBufferMemoryRequirements(buf->buffer);
-
-    vk::PhysicalDeviceMemoryProperties mem_props = ctx->device->physical_device.getMemoryProperties();
-
-    uint32_t memory_type_index = UINT32_MAX;
-
-    memory_type_index = find_properties(&mem_props, &mem_req, req_flags);
-    buf->memory_property_flags = req_flags;
-
-    if (memory_type_index == UINT32_MAX && fallback_flags) {
-        memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags);
-        buf->memory_property_flags = fallback_flags;
-    }
-
-    if (memory_type_index == UINT32_MAX) {
-        ctx->device->device.destroyBuffer(buf->buffer);
-        buf->size = 0;
-        throw vk::OutOfDeviceMemoryError("No suitable memory type found");
-    }
-
-    try {
-        buf->device_memory = ctx->device->device.allocateMemory({ mem_req.size, memory_type_index });
-    } catch (const vk::SystemError& e) {
-        // Out of Host/Device memory, clean up buffer
-        ctx->device->device.destroyBuffer(buf->buffer);
-        buf->size = 0;
-        throw e;
-    }
-    buf->ptr = nullptr;
-
-    if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
-        buf->ptr = ctx->device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
-    }
-
-    ctx->device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0);
-
-    buf->ctx = ctx;
-
-    buf->device = ctx->device;
-
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "Created buffer " << buf->buffer << std::endl;
-#endif
-
-    return buf;
-}
-
-static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
-    try {
-        return ggml_vk_create_buffer(ctx, size, req_flags, fallback_flags);
-    } catch (const vk::SystemError& e) {
-        std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl;
-        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
-        throw e;
-    }
-}
-
-static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) {
-    vk_buffer buf;
-    try {
-        if (ctx->device->uma) {
-            // Fall back to host memory type
-            buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
-        } else {
-            buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
-        }
-    } catch (const vk::SystemError& e) {
-        std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
-        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
-        throw e;
-    }
-
-    return buf;
-}
-
-static void ggml_vk_destroy_buffer(vk_buffer& buf) {
-    buf.reset();
-}
-
-static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) {
-    return { buf, 0, VK_WHOLE_SIZE };
-}
-
-static void ggml_vk_sync_buffers(vk_context * ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_sync_buffers()" << std::endl;
-#endif
-    const std::vector<vk::MemoryBarrier> mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } };
-
-    ctx->s->buffer.pipelineBarrier(
-        ctx->q->stage_flags,
-        ctx->q->stage_flags,
-        {},
-        mem_barriers,
-        {},
-        {}
-    );
-}
-
-static void ggml_vk_wait_events(vk_context * ctx, std::vector<vk::Event>&& events) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_wait_events()" << std::endl;
-#endif
-    if (events.empty()) {
-        return;
-    }
-
-    ctx->s->buffer.waitEvents(
-        events,
-        ctx->q->stage_flags,
-        ctx->q->stage_flags,
-        {},
-        {},
-        {}
-    );
-}
-
-static bool ggml_vk_build_shader(ggml_type type) {
-    switch(type) {
-    case GGML_TYPE_F16:
-    case GGML_TYPE_Q4_0:
-    case GGML_TYPE_Q4_1:
-    case GGML_TYPE_Q5_0:
-    case GGML_TYPE_Q5_1:
-    case GGML_TYPE_Q8_0:
-    case GGML_TYPE_Q2_K:
-    case GGML_TYPE_Q3_K:
-    case GGML_TYPE_Q4_K:
-    case GGML_TYPE_Q5_K:
-    case GGML_TYPE_Q6_K:
-        return true;
-    default:
-        return false;
-    }
-}
-
-static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl;
-#endif
-
-    const std::shared_ptr<vk_device> device = ctx->device;
-
-    // mulmat
-    std::initializer_list<uint32_t> warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
-    std::initializer_list<uint32_t> warptile_m = { 128,  64,  64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
-    std::initializer_list<uint32_t> warptile_s = { device->subgroup_size,  32,  32, 16, 32, 32, 2, 2, 2, device->subgroup_size };
-
-    std::initializer_list<uint32_t> warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
-    std::initializer_list<uint32_t> warptile_mmq_m = { 128,  64,  64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
-    std::initializer_list<uint32_t> warptile_mmq_s = { device->subgroup_size,  32,  32, 32, 32, 32, 2, 2, 2, device->subgroup_size };
-
-    std::array<uint32_t, 3> l_wg_denoms = {128, 128, 1 };
-    std::array<uint32_t, 3> m_wg_denoms = { 64,  64, 1 };
-    std::array<uint32_t, 3> s_wg_denoms = { 32,  32, 1 };
-
-    uint32_t l_align = 128;
-    uint32_t m_align =  64;
-    uint32_t s_align =  32;
-
-    ctx->device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_matmul_f32_f16 = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_matmul_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_matmul_f16 = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();
-
-    /*ctx->device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_matmul_id_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_matmul_id_f16 = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
-    ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();*/
-
-    if (device->fp16) {
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);*/
-    } else {
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
-        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);*/
-    }
-
-    // mul mat vec
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32",  mul_mat_vec_f16_f32_f32_len,  mul_mat_vec_f16_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32_f32", mul_mat_vec_q2_K_f32_f32_len, mul_mat_vec_q2_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32_f32", mul_mat_vec_q3_K_f32_f32_len, mul_mat_vec_q3_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32_f32", mul_mat_vec_q4_K_f32_f32_len, mul_mat_vec_q4_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32_f32", mul_mat_vec_q5_K_f32_f32_len, mul_mat_vec_q5_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32_f32", mul_mat_vec_q6_K_f32_f32_len, mul_mat_vec_q6_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f16_f32", mul_mat_vec_q2_K_f16_f32_len, mul_mat_vec_q2_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f16_f32", mul_mat_vec_q3_K_f16_f32_len, mul_mat_vec_q3_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f16_f32", mul_mat_vec_q4_K_f16_f32_len, mul_mat_vec_q4_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f16_f32", mul_mat_vec_q5_K_f16_f32_len, mul_mat_vec_q5_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f16_f32", mul_mat_vec_q6_K_f16_f32_len, mul_mat_vec_q6_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-
-    /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_K_f32", mul_mat_vec_id_q2_K_f32_len, mul_mat_vec_id_q2_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_K_f32", mul_mat_vec_id_q3_K_f32_len, mul_mat_vec_id_q3_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_K_f32", mul_mat_vec_id_q4_K_f32_len, mul_mat_vec_id_q4_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_K_f32", mul_mat_vec_id_q5_K_f32_len, mul_mat_vec_id_q5_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_K_f32", mul_mat_vec_id_q6_K_f32_len, mul_mat_vec_id_q6_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);*/
-
-    // dequant shaders
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
-
-    // get_rows
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16",  get_rows_f16_len,  get_rows_f16_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
-
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
-}
-
-static void ggml_vk_print_gpu_info(size_t idx) {
-    GGML_ASSERT(idx < vk_instance.device_indices.size());
-    size_t dev_num = vk_instance.device_indices[idx];
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_print_gpu_info(" << dev_num << ")" << std::endl;
-#endif
-    GGML_ASSERT(vk_instance.initialized);
-
-    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
-
-    if (dev_num >= devices.size()) {
-        std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
-        throw std::runtime_error("Device not found");
-    }
-
-    vk::PhysicalDevice physical_device = devices[dev_num];
-    std::vector<vk::ExtensionProperties> ext_props = physical_device.enumerateDeviceExtensionProperties();
-
-    vk::PhysicalDeviceProperties2 props2;
-    vk::PhysicalDeviceMaintenance3Properties props3;
-    vk::PhysicalDeviceSubgroupProperties subgroup_props;
-    props2.pNext = &props3;
-    props3.pNext = &subgroup_props;
-    physical_device.getProperties2(&props2);
-
-    const size_t subgroup_size = subgroup_props.subgroupSize;
-    const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
-
-    bool fp16_storage = false;
-    bool fp16_compute = false;
-
-    for (auto properties : ext_props) {
-        if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
-            fp16_storage = true;
-        } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
-            fp16_compute = true;
-        }
-    }
-
-    const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
-    bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
-
-    bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
-
-    vk::PhysicalDeviceFeatures device_features = physical_device.getFeatures();
-
-    VkPhysicalDeviceFeatures2 device_features2;
-    device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
-    device_features2.pNext = nullptr;
-    device_features2.features = (VkPhysicalDeviceFeatures)device_features;
-
-    VkPhysicalDeviceVulkan11Features vk11_features;
-    vk11_features.pNext = nullptr;
-    vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
-    device_features2.pNext = &vk11_features;
-
-    VkPhysicalDeviceVulkan12Features vk12_features;
-    vk12_features.pNext = nullptr;
-    vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
-    vk11_features.pNext = &vk12_features;
-
-    vkGetPhysicalDeviceFeatures2(physical_device, &device_features2);
-
-    fp16 = fp16 && vk12_features.shaderFloat16;
-
-    std::string device_name = props2.properties.deviceName.data();
-    std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl;
-
-    if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) {
-        std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl;
-    }
-}
-
-static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
-static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
-
-void ggml_vk_instance_init() {
-    if (vk_instance_initialized) {
-        return;
-    }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_instance_init()" << std::endl;
-#endif
-
-    vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION };
-
-    const std::vector<vk::ExtensionProperties> instance_extensions = vk::enumerateInstanceExtensionProperties();
-    const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions);
-#ifdef __APPLE__
-    const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions);
-#endif
-
-    std::vector<const char*> layers;
-
-    if (validation_ext) {
-        layers.push_back("VK_LAYER_KHRONOS_validation");
-    }
-    std::vector<const char*> extensions;
-    if (validation_ext) {
-        extensions.push_back("VK_EXT_validation_features");
-    }
-#ifdef __APPLE__
-    if (portability_enumeration_ext) {
-        extensions.push_back("VK_KHR_portability_enumeration");
-    }
-#endif
-    vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions);
-#ifdef __APPLE__
-    if (portability_enumeration_ext) {
-        instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR;
-    }
-#endif
-
-    std::vector<vk::ValidationFeatureEnableEXT> features_enable;
-    vk::ValidationFeaturesEXT validation_features;
-
-    if (validation_ext) {
-        features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices };
-        validation_features = {
-            features_enable,
-            {},
-        };
-        validation_features.setPNext(nullptr);
-        instance_create_info.setPNext(&validation_features);
-
-        std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl;
-    }
-    vk_instance.instance = vk::createInstance(instance_create_info);
-
-    memset(vk_instance.initialized, 0, sizeof(bool) * GGML_VK_MAX_DEVICES);
-
-    size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();
-
-    // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
-    char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES");
-    if (devices_env != nullptr) {
-        std::string devices(devices_env);
-        std::replace(devices.begin(), devices.end(), ',', ' ');
-
-        std::stringstream ss(devices);
-        size_t tmp;
-        while (ss >> tmp) {
-            if(tmp >= num_available_devices) {
-                std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl;
-                throw std::runtime_error("Invalid Vulkan device index");
-            }
-            vk_instance.device_indices.push_back(tmp);
-        }
-    } else {
-        std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
-
-        // Make sure at least one device exists
-        if (devices.empty()) {
-            std::cerr << "ggml_vulkan: Error: No devices found." << std::endl;
-            GGML_ASSERT(false);
-        }
-
-        // Default to using all dedicated GPUs
-        for (size_t i = 0; i < devices.size(); i++) {
-            vk::PhysicalDeviceProperties props = devices[i].getProperties();
-
-            if (props.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) {
-                vk_instance.device_indices.push_back(i);
-            }
-        }
-
-        // If no dedicated GPUs found, fall back to GPU 0
-        if (vk_instance.device_indices.empty()) {
-            vk_instance.device_indices.push_back(0);
-        }
-    }
-
-    std::cerr << "ggml_vulkan: Found " << vk_instance.device_indices.size() << " Vulkan devices:" << std::endl;
-
-    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
-        ggml_vk_print_gpu_info(i);
-    }
-
-    vk_instance_initialized = true;
-}
-
-static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
-    GGML_ASSERT(idx < vk_instance.device_indices.size());
-    size_t dev_num = vk_instance.device_indices[idx];
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_init(" << ctx->name << ", " << dev_num << ")" << std::endl;
-#endif
-    ggml_vk_instance_init();
-
-    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
-
-    if (dev_num >= devices.size()) {
-        std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
-        throw std::runtime_error("Device not found");
-    }
-
-    ctx->device = ggml_vk_get_device(idx);
-    if (!ctx->device->initialized) {
-        ctx->device->physical_device = devices[dev_num];
-        const std::vector<vk::ExtensionProperties> ext_props = ctx->device->physical_device.enumerateDeviceExtensionProperties();
-
-        bool maintenance4_support = false;
-
-        // Check if maintenance4 is supported
-        for (const auto& properties : ext_props) {
-            if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
-                maintenance4_support = true;
-            }
-        }
-
-        vk::PhysicalDeviceProperties2 props2;
-        vk::PhysicalDeviceMaintenance3Properties props3;
-        vk::PhysicalDeviceMaintenance4Properties props4;
-        vk::PhysicalDeviceSubgroupProperties subgroup_props;
-        props2.pNext = &props3;
-        props3.pNext = &subgroup_props;
-        if (maintenance4_support) {
-            subgroup_props.pNext = &props4;
-        }
-        ctx->device->physical_device.getProperties2(&props2);
-        ctx->device->properties = props2.properties;
-
-        const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
-
-        if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) {
-            ctx->device->max_memory_allocation_size = std::stoi(GGML_VK_FORCE_MAX_ALLOCATION_SIZE);
-        } else if (maintenance4_support) {
-            ctx->device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize);
-        } else {
-            ctx->device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
-        }
-
-        ctx->device->vendor_id = ctx->device->properties.vendorID;
-        ctx->device->subgroup_size = subgroup_props.subgroupSize;
-        ctx->device->uma = ctx->device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
-
-        bool fp16_storage = false;
-        bool fp16_compute = false;
-
-        for (const auto& properties : ext_props) {
-            if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
-                fp16_storage = true;
-            } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
-                fp16_compute = true;
-            }
-        }
-
-        const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
-        const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
-
-        ctx->device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
-
-        std::vector<vk::QueueFamilyProperties> queue_family_props = ctx->device->physical_device.getQueueFamilyProperties();
-
-        // Try to find a non-graphics compute queue and transfer-focused queues
-        const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1);
-        const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1);
-
-        const float priorities[] = { 1.0f, 1.0f };
-        ctx->device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1;
-
-        std::vector<vk::DeviceQueueCreateInfo> device_queue_create_infos;
-        if (compute_queue_family_index != transfer_queue_family_index) {
-            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
-            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1});
-        } else if(!ctx->device->single_queue) {
-            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities});
-        } else {
-            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
-        }
-        vk::DeviceCreateInfo device_create_info;
-        std::vector<const char *> device_extensions;
-        vk::PhysicalDeviceFeatures device_features = ctx->device->physical_device.getFeatures();
-
-        VkPhysicalDeviceFeatures2 device_features2;
-        device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
-        device_features2.pNext = nullptr;
-        device_features2.features = (VkPhysicalDeviceFeatures)device_features;
-
-        VkPhysicalDeviceVulkan11Features vk11_features;
-        vk11_features.pNext = nullptr;
-        vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
-        device_features2.pNext = &vk11_features;
-
-        VkPhysicalDeviceVulkan12Features vk12_features;
-        vk12_features.pNext = nullptr;
-        vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
-        vk11_features.pNext = &vk12_features;
-
-        vkGetPhysicalDeviceFeatures2(ctx->device->physical_device, &device_features2);
-
-        ctx->device->fp16 = ctx->device->fp16 && vk12_features.shaderFloat16;
-
-        if (!vk11_features.storageBuffer16BitAccess) {
-            std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
-            throw std::runtime_error("Unsupported device");
-        }
-
-        device_extensions.push_back("VK_KHR_16bit_storage");
-
-#ifdef GGML_VULKAN_VALIDATE
-        device_extensions.push_back("VK_KHR_shader_non_semantic_info");
-#endif
-
-        if (ctx->device->fp16) {
-            device_extensions.push_back("VK_KHR_shader_float16_int8");
-        }
-        ctx->device->name = ctx->device->properties.deviceName.data();
-
-        device_create_info = {
-            vk::DeviceCreateFlags(),
-            device_queue_create_infos,
-            {},
-            device_extensions
-        };
-        device_create_info.setPNext(&device_features2);
-        ctx->device->device = ctx->device->physical_device.createDevice(device_create_info);
-
-        ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN;
-
-        // Queues
-        ggml_vk_create_queue(ctx, ctx->device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer });
-
-        // Shaders
-        ggml_vk_load_shaders(ctx);
-
-        if (!ctx->device->single_queue) {
-            const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0;
-            ggml_vk_create_queue(ctx, ctx->device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer });
-        } else {
-            // TODO: Use pointer or reference to avoid copy
-            ctx->device->transfer_queue = ctx->device->compute_queue;
-        }
-
-        ctx->device->idx = dev_num;
-        ctx->device->initialized = true;
-    } else if (ctx->device->idx != dev_num) {
-        std::cerr << "ggml_vulkan: Device " << ctx->device->name << " already initialized with index " << ctx->device->idx << ", but trying to reinitialize with index " << dev_num << std::endl;
-        throw std::runtime_error("Device already initialized");
-    }
-
-    ctx->fence = ctx->device->device.createFence({});
-
-    ctx->compute_ctx = nullptr;
-    ctx->transfer_ctx = nullptr;
-
-    ctx->initialized = true;
-
-    ctx->idx = idx;
-
-#ifdef GGML_VULKAN_CHECK_RESULTS
-    const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS");
-    vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks));
-    const char* output_tensor = getenv("GGML_VULKAN_OUTPUT_TENSOR");
-    vk_output_tensor = (output_tensor == NULL ? 0 : atoi(output_tensor));
-#endif
-}
-
-static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_get_to_fp16()" << std::endl;
-#endif
-    switch (type) {
-        case GGML_TYPE_F32:
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            break;
-        default:
-            return nullptr;
-    }
-
-    return ctx->device->pipeline_dequant[type];
-}
-
-static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_get_mul_mat_mat_pipeline()" << std::endl;
-#endif
-    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
-        return ctx->device->pipeline_matmul_f32;
-    }
-    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
-        return ctx->device->pipeline_matmul_f32_f16;
-    }
-    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
-        return ctx->device->pipeline_matmul_f16_f32;
-    }
-    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
-        return ctx->device->pipeline_matmul_f16;
-    }
-
-    GGML_ASSERT(src1_type == GGML_TYPE_F32);
-
-    switch (src0_type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            break;
-        default:
-            return nullptr;
-    }
-
-    return ctx->device->pipeline_dequant_mul_mat_mat[src0_type];
-}
-
-static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_get_mul_mat_mat_id_pipeline()" << std::endl;
-#endif
-    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
-        return ctx->device->pipeline_matmul_id_f32;
-    }
-    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
-        return ctx->device->pipeline_matmul_id_f16_f32;
-    }
-    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
-        return ctx->device->pipeline_matmul_id_f16;
-    }
-
-    GGML_ASSERT(src1_type == GGML_TYPE_F32);
-
-    switch (src0_type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            break;
-        default:
-            return nullptr;
-    }
-
-    return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type];
-}
-
-static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl;
-#endif
-    GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16);
-
-    switch (a_type) {
-        case GGML_TYPE_F16:
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            break;
-        default:
-            return nullptr;
-    }
-
-    return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type];
-}
-
-static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl;
-#endif
-    int best_i = -1;
-    size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
-    int worst_i = -1;
-    size_t worst_size = 0; //largest unused buffer seen so far
-    for (int i = 0; i < MAX_VK_BUFFERS; ++i) {
-        vk_buffer &b = ctx->buffer_pool[i];
-        if (b != nullptr && b->size >= size && b->size < best_size) {
-            best_i = i;
-            best_size = b->size;
-        }
-        if (b != nullptr && b->size > worst_size) {
-            worst_i = i;
-            worst_size = b->size;
-        }
-    }
-    if(best_i != -1) {
-        //found the smallest buffer that fits our needs
-        vk_buffer b = ctx->buffer_pool[best_i];
-        ctx->buffer_pool[best_i].reset();
-        return b;
-    }
-    if(worst_i != -1) {
-        //no buffer that fits our needs, resize largest one to save memory
-        vk_buffer& b = ctx->buffer_pool[worst_i];
-        ggml_vk_destroy_buffer(b);
-    }
-
-    return ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
-}
-
-static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_pool_free(" << buffer->size << ")" << std::endl;
-#endif
-    for (int i = 0; i < MAX_VK_BUFFERS; ++i) {
-        vk_buffer& b = ctx->buffer_pool[i];
-        if (b == nullptr) {
-            b = buffer;
-            return;
-        }
-    }
-    std::cerr << "ggml_vulkan: WARNING: vk buffer pool full, increase MAX_VK_BUFFERS" << std::endl;
-    ggml_vk_destroy_buffer(buffer);
-}
-
-// Returns an available temporary buffer that may only be used temporarily, it will be reused
-static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_t size) {
-    // Try to find existing temp buffer with enough capacity
-    for (auto& buffer : ctx->gc.temp_buffers) {
-        if (buffer->size >= size) {
-            return buffer;
-        }
-    }
-
-    // Otherwise create new buffer
-    vk_buffer buf = ggml_vk_pool_malloc(ctx, size);
-    ctx->gc.temp_buffers.push_back(buf);
-
-    return buf;
-}
-
-static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl;
-#endif
-    vk_buffer buf = ggml_vk_create_buffer(ctx, size,
-        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
-        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
-
-    if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
-        fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
-            size/1024.0/1024.0);
-        ctx->device->device.freeMemory(buf->device_memory);
-        ctx->device->device.destroyBuffer(buf->buffer);
-        return nullptr;
-    }
-
-    ctx->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf));
-
-    return buf->ptr;
-}
-
-static void ggml_vk_host_free(ggml_backend_vk_context * ctx, void* ptr) {
-    if (ptr == nullptr) {
-        return;
-    }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl;
-#endif
-    vk_buffer buf;
-    size_t index;
-    for (size_t i = 0; i < ctx->pinned_memory.size(); i++) {
-        const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]);
-        const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]);
-        if (ptr >= addr && ptr < endr) {
-            buf = std::get<2>(ctx->pinned_memory[i]);
-            index = i;
-            break;
-        }
-    }
-    if (buf == nullptr) {
-        fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n");
-        return;
-    }
-
-    ggml_vk_destroy_buffer(buf);
-
-    ctx->pinned_memory.erase(ctx->pinned_memory.begin() + index);
-}
-
-static void ggml_vk_host_get(ggml_backend_vk_context * ctx, const void * ptr, vk_buffer& buf, size_t& buf_offset) {
-    buf = nullptr;
-    buf_offset = 0;
-    for (size_t i = 0; i < ctx->pinned_memory.size(); i++) {
-        const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]);
-        const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]);
-        if (ptr >= addr && ptr < endr) {
-            buf = std::get<2>(ctx->pinned_memory[i]);
-            buf_offset = ((const uint8_t *)ptr) - addr;
-            break;
-        }
-    }
-}
-
-static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_queue& q, bool one_time = true) {
-    vk_submission s;
-    s.buffer = ggml_vk_create_cmd_buffer(ctx, q);
-    if (one_time) {
-        s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit });
-    } else {
-        s.buffer.begin({ vk::CommandBufferUsageFlags{} });
-    }
-
-    return s;
-}
-
-static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector<vk_subbuffer>&& buffers, size_t push_constant_size, const void* push_constants, std::array<uint32_t, 3> elements) {
-    const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]);
-    const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]);
-    const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]);
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl;
-#endif
-    std::vector<vk::DescriptorBufferInfo> descriptor_buffer_infos;
-    std::vector<vk::WriteDescriptorSet> write_descriptor_sets;
-    GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size());
-    GGML_ASSERT(buffers.size() == pipeline->parameter_count);
-    vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++];
-    for (uint32_t i = 0; i < pipeline->parameter_count; i++) {
-        descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size});
-    }
-    for (uint32_t i = 0; i < pipeline->parameter_count; i++) {
-        write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]});
-    }
-
-    ctx->device->device.updateDescriptorSets(write_descriptor_sets, {});
-
-    subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants);
-    subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline);
-    subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute,
-                                pipeline->layout,
-                                0,
-                                { descriptor_set },
-                                {});
-    subctx->s->buffer.dispatch(wg0, wg1, wg2);
-}
-
-static void ggml_vk_end_submission(vk_submission& s, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
-    s.buffer.end();
-
-    s.wait_semaphores = std::move(wait_semaphores);
-    s.signal_semaphores = std::move(signal_semaphores);
-}
-
-static void ggml_vk_ctx_end(vk_context * ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")" << std::endl;
-#endif
-    if (ctx->s == nullptr) {
-        return;
-    }
-
-    ctx->s->buffer.end();
-    ctx->s = nullptr;
-}
-
-static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl;
-#endif
-    if (subctx->s != nullptr) {
-        ggml_vk_ctx_end(subctx);
-    }
-
-    subctx->seqs.push_back({ ggml_vk_begin_submission(ctx, *subctx->q) });
-    subctx->s = subctx->seqs[subctx->seqs.size() - 1].data();
-}
-
-static size_t ggml_vk_align_size(size_t width, size_t align) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_align_size(" << width << ", " << align << ")" << std::endl;
-#endif
-    return CEIL_DIV(width, align) * align;
-}
-
-static void deferred_memcpy(void * dst, const void * src, size_t size, std::vector<vk_staging_memcpy>* memcpys = nullptr) {
-    if (memcpys == nullptr) {
-        memcpy(dst, src, size);
-    } else {
-        memcpys->emplace_back(dst, src, size);
-    }
-}
-
-static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) {
-    if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) {
-        ggml_vk_destroy_buffer(ctx->sync_staging);
-        ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size,
-            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
-            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
-    }
-}
-
-static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl;
-#endif
-    GGML_ASSERT(!ggml_is_contiguous(tensor));
-    // Buffer is already mapped
-    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
-        std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl;
-        GGML_ASSERT(false);
-    }
-    // Check if src is pinned memory
-    vk_buffer buf;
-    size_t buf_offset;
-    ggml_vk_host_get(ctx, tensor->data, buf, buf_offset);
-
-    const uint64_t ne0 = tensor->ne[0];
-    const uint64_t ne1 = tensor->ne[1];
-    const uint64_t ne2 = tensor->ne[2];
-    const uint64_t ne3 = tensor->ne[3];
-    const uint64_t nb0 = tensor->nb[0];
-    const uint64_t nb1 = tensor->nb[1];
-    const uint64_t nb2 = tensor->nb[2];
-    const uint64_t nb3 = tensor->nb[3];
-    const ggml_type type = tensor->type;
-    const uint64_t ts = ggml_type_size(type);
-    const uint64_t bs = ggml_blck_size(type);
-
-    const uint64_t dstnb0 = ts;
-    const uint64_t dstnb1 = dstnb0*(ne0/bs);
-    const uint64_t dstnb2 = dstnb1*ne1;
-    const uint64_t dstnb3 = dstnb2*ne2;
-
-    const uint64_t ne = ggml_nelements(tensor);
-
-    if (buf != nullptr) {
-        // Memory is pinned, use as staging buffer
-        std::vector<vk::BufferCopy> slices;
-
-        for (uint64_t i3 = 0; i3 < ne3; i3++) {
-            for (uint64_t i2 = 0; i2 < ne2; i2++) {
-                // Find longest contiguous slice
-                if (ne1*nb1 == dstnb2) {
-                    slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 });
-                } else {
-                    for (uint64_t i1 = 0; i1 < ne1; i1++) {
-                        if (ne0*nb0/bs == dstnb1) {
-                            slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 });
-                        } else {
-                            const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
-                            const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
-                            for (uint64_t i0 = 0; i0 < ne0; i0++) {
-                                slices.push_back({ s_off + i1*nb0, d_off + i0*dstnb0, dstnb0 });
-                            }
-                        }
-                    }
-                }
-            }
-        }
-
-        ggml_vk_sync_buffers(subctx);
-        subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
-        return;
-    }
-
-    // Staging buffer required
-    vk_buffer staging = ctx->staging;
-    size_t staging_offset = ctx->staging_offset;
-    const size_t copy_size = ts*ne/bs;
-    if (ctx->staging->size < ctx->staging_offset + copy_size) {
-        if (sync_staging) {
-            // Create temporary larger buffer
-            ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
-
-            staging = ctx->sync_staging;
-            staging_offset = 0;
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-
-    VkBufferCopy buf_copy{ staging_offset, offset, copy_size };
-
-    ggml_vk_sync_buffers(subctx);
-    vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy);
-
-    for (uint64_t i3 = 0; i3 < ne3; i3++) {
-        for (uint64_t i2 = 0; i2 < ne2; i2++) {
-            // Find longest contiguous slice
-            if (ne1*nb1 == dstnb2) {
-                deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys);
-            } else {
-                for (uint64_t i1 = 0; i1 < ne1; i1++) {
-                    if (ne0*nb0/bs == dstnb1) {
-                        deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys);
-                    } else {
-                        const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
-                        const uint64_t d_off = staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
-                        for (uint64_t i0 = 0; i0 < ne0; i0++) {
-                            deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys);
-                        }
-                    }
-                }
-            }
-        }
-    }
-}
-
-static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl;
-#endif
-    // Make sure ctx owns the buffer
-    GGML_ASSERT(dst->ctx == ctx);
-
-    // Buffer is already mapped
-    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
-        std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl;
-        GGML_ASSERT(false);
-    }
-    // Check if src is pinned memory
-    vk_buffer buf = nullptr;
-    size_t buf_offset;
-    ggml_vk_host_get(ctx, src, buf, buf_offset);
-
-    if (buf != nullptr) {
-        // Memory is pinned, use as staging buffer
-        std::vector<vk::BufferCopy> slices(1);
-        if (width == spitch) {
-            // Only do single write if stride is equal
-            slices[0].srcOffset = buf_offset;
-            slices[0].dstOffset = offset;
-            slices[0].size = width * height;
-        } else {
-            slices.resize(height);
-            for (size_t i = 0; i < height; i++) {
-                slices[i].srcOffset = buf_offset + i * spitch;
-                slices[i].dstOffset = offset + i * width;
-                slices[i].size = width;
-            }
-        }
-
-        ggml_vk_sync_buffers(subctx);
-        subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
-        return;
-    }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "STAGING" << std::endl;
-#endif
-
-    // Staging buffer required
-    vk_buffer staging = ctx->staging;
-    size_t staging_offset = ctx->staging_offset;
-    const size_t copy_size = width*height;
-    if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) {
-        if (sync_staging) {
-            ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
-
-            staging = ctx->sync_staging;
-            staging_offset = 0;
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-
-    VkBufferCopy buf_copy = {
-        staging_offset,
-        offset,
-        copy_size};
-
-    ggml_vk_sync_buffers(subctx);
-    vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy);
-
-    if (width == spitch) {
-        deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &subctx->in_memcpys);
-    } else {
-        for (size_t i = 0; i < height; i++) {
-            deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys);
-        }
-    }
-}
-
-static void ggml_vk_buffer_write_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl;
-#endif
-    return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, size, size, 1, sync_staging);
-}
-
-static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl;
-#endif
-    // Buffer is already mapped
-    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
-        GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
-
-        for (size_t i = 0; i < height; i++) {
-            memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width);
-        }
-    } else {
-        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
-        ggml_vk_ctx_begin(ctx, subctx);
-        ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true);
-        ggml_vk_ctx_end(subctx);
-
-        for (auto& cpy : subctx->in_memcpys) {
-            memcpy(cpy.dst, cpy.src, cpy.n);
-        }
-
-        ggml_vk_submit(subctx, ctx->fence);
-        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
-        ctx->device->device.resetFences({ ctx->fence });
-    }
-}
-
-static void ggml_vk_buffer_write(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl;
-#endif
-    ggml_vk_buffer_write_2d(ctx, dst, offset, src, 0, size, 1);
-}
-
-static void ggml_vk_buffer_read_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl;
-#endif
-    GGML_ASSERT(width > 0);
-    GGML_ASSERT(height > 0);
-    GGML_ASSERT(src != nullptr);
-    // Make sure ctx owns the buffer
-    GGML_ASSERT(src->ctx == ctx);
-
-    // Check if dst is pinned memory
-    vk_buffer buf = nullptr;
-    size_t buf_offset;
-    ggml_vk_host_get(ctx, dst, buf, buf_offset);
-
-    std::vector<vk::BufferCopy> slices(1);
-    if (width == spitch && width == dpitch) {
-        // Only do single write if stride is equal
-        slices[0].srcOffset = offset;
-        slices[0].dstOffset = buf_offset;
-        slices[0].size = width * height;
-    } else {
-        slices.resize(height);
-        for (size_t i = 0; i < height; i++) {
-            slices[i].srcOffset = offset + i * spitch;
-            slices[i].dstOffset = buf_offset + i * dpitch;
-            slices[i].size = width;
-        }
-    }
-
-    if (buf != nullptr) {
-        // Memory is pinned, use as staging buffer
-        ggml_vk_sync_buffers(subctx);
-        subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices);
-
-        return;
-    }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "STAGING" << std::endl;
-#endif
-
-    // Fall back to staging buffer
-    vk_buffer staging = ctx->staging;
-    const size_t copy_size = dpitch * height;
-    if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) {
-        if (sync_staging) {
-            // Create temporary larger buffer
-            ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
-
-            staging = ctx->sync_staging;
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-
-    ggml_vk_sync_buffers(subctx);
-    subctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices);
-
-    deferred_memcpy(dst, staging->ptr, copy_size, &subctx->out_memcpys);
-}
-
-static void ggml_vk_buffer_read_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) {
-    return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst, size, size, size, 1, sync_staging);
-}
-
-static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, size_t offset, void * dst, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl;
-#endif
-    if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
-        GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
-
-        memcpy(dst, (uint8_t *) src->ptr + offset, size);
-    } else {
-        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
-        ggml_vk_ctx_begin(ctx, subctx);
-        ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true);
-        ggml_vk_ctx_end(subctx);
-
-        ggml_vk_submit(subctx, ctx->fence);
-        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences");
-        ctx->device->device.resetFences({ ctx->fence });
-
-        for (auto& cpy : subctx->out_memcpys) {
-            memcpy(cpy.dst, cpy.src, cpy.n);
-        }
-    }
-}
-
-static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl;
-#endif
-    // Make sure both buffers are on same ctx
-    GGML_ASSERT(src->ctx == dst->ctx);
-
-    VkBufferCopy bc{ src_offset, dst_offset, size };
-
-    vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc);
-}
-
-static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
-    if (src->ctx == dst->ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")" << std::endl;
-#endif
-        // Copy within the device
-        ggml_backend_vk_context * ctx = src->ctx;
-
-        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
-        ggml_vk_ctx_begin(ctx, subctx);
-        ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
-        ggml_vk_ctx_end(subctx);
-        ggml_vk_submit(subctx, ctx->fence);
-        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
-        ctx->device->device.resetFences({ ctx->fence });
-    } else {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl;
-#endif
-        // Copy device to device
-        ggml_backend_vk_context * src_ctx = src->ctx;
-        ggml_backend_vk_context * dst_ctx = dst->ctx;
-
-        ggml_vk_ensure_sync_staging_buffer(src_ctx, size);
-        ggml_vk_ensure_sync_staging_buffer(dst_ctx, size);
-
-        // Copy to src staging buffer
-        ggml_vk_buffer_copy(src_ctx->sync_staging, 0, src, src_offset, size);
-        // memcpy to dst staging buffer
-        memcpy(dst_ctx->sync_staging->ptr, src_ctx->sync_staging->ptr, size);
-        // Copy to dst buffer
-        ggml_vk_buffer_copy(dst, dst_offset, dst_ctx->sync_staging, 0, size);
-    }
-}
-
-static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl;
-#endif
-    // Make sure ctx owns the buffer
-    GGML_ASSERT(dst->ctx == ctx);
-
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
-    ggml_vk_ctx_begin(ctx, subctx);
-    subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c);
-    ggml_vk_ctx_end(subctx);
-
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-}
-
-static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl;
-#endif
-    const uint64_t ne0 = src->ne[0];
-    const uint64_t ne1 = src->ne[1];
-    const uint64_t nb0 = src->nb[0];
-    const uint64_t nb1 = src->nb[1];
-    const uint64_t nb2 = src->nb[2];
-    const uint64_t nb3 = src->nb[3];
-    const enum ggml_type type = src->type;
-    const size_t ts = ggml_type_size(type);
-    const size_t bs = ggml_blck_size(type);
-    const size_t row_length = ts*ne0/bs;
-
-    const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
-    if (nb0 == ts && nb1 == row_length) {
-        return ggml_vk_buffer_write_async(ctx, subctx, dst, offset, x, i1*nb1);
-    }
-    if (nb0 == ts && (i1 == ne1 || !ggml_is_permuted(src))) {
-        return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, x, nb1, row_length, i1);
-    }
-
-    GGML_ASSERT(i3 == 0);
-    GGML_ASSERT(i2 == 0);
-    GGML_ASSERT(i1 == (uint64_t) ggml_nrows(src));
-
-    return ggml_vk_buffer_write_nc_async(ctx, subctx, dst, offset, src);
-}
-
-static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, const ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl;
-#endif
-    const uint64_t ne0 = dst->ne[0];
-    const uint64_t ne1 = dst->ne[1];
-    const uint64_t ne2 = dst->ne[2];
-    const uint64_t ne3 = dst->ne[3];
-    const uint64_t nb0 = dst->nb[0];
-    const uint64_t nb1 = dst->nb[1];
-    // const uint64_t nb2 = dst->nb[2];
-    // const uint64_t nb3 = dst->nb[3];
-    const enum ggml_type type = dst->type;
-    const size_t ts = ggml_type_size(type);
-    const size_t bs = ggml_blck_size(type);
-    const size_t row_length = ts*ne0/bs;
-
-    if (ggml_is_contiguous(dst)) {
-        return ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst->data, ne1*nb1*ne2*ne3);
-    }
-    if (nb0 == ts) {
-        return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3);
-    }
-    GGML_ASSERT(false);
-}
-
-static uint32_t ggml_vk_guess_split_k(int m, int n, int k) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")" << std::endl;
-#endif
-    // if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) {
-    //     return 4;
-    // }
-
-    return 1;
-
-    GGML_UNUSED(m); GGML_UNUSED(n); GGML_UNUSED(k);
-}
-
-static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
-    if (m <= 32 || n <= 32) {
-        return aligned ? mmp->a_s : mmp->s;
-    }
-    return aligned ? mmp->a_m : mmp->m;
-
-    GGML_UNUSED(ctx);
-}
-
-static vk_pipeline ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
-    return aligned ? mmp->a_m : mmp->m;
-
-    GGML_UNUSED(ctx);
-}
-
-static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
-    return aligned ? mmp->a_s : mmp->s;
-
-    GGML_UNUSED(ctx);
-}
-
-static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")" << std::endl;
-#endif
-    switch (ctx->device->vendor_id) {
-    case VK_VENDOR_ID_AMD:
-        return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned);
-    case VK_VENDOR_ID_APPLE:
-        return ggml_vk_guess_matmul_pipeline_apple(ctx, mmp, aligned);
-    case VK_VENDOR_ID_INTEL:
-        return ggml_vk_guess_matmul_pipeline_intel(ctx, mmp, aligned);
-    default:
-        break;
-    }
-
-    if (m <= 32 || n <= 32) {
-        return aligned ? mmp->a_s : mmp->s;
-    }
-    if (m <= 64 || n <= 64) {
-        return aligned ? mmp->a_m : mmp->m;
-    }
-    return aligned ? mmp->a_l : mmp->l;
-}
-
-static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl;
-#endif
-    return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true)->align;
-}
-
-static void ggml_vk_matmul(
-        ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline,
-        vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer,
-        uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d,
-        uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3,
-        uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d,
-        uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl;
-#endif
-    ggml_vk_sync_buffers(subctx);
-    if (split_k == 1) {
-        const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as };
-        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch });
-        return;
-    }
-
-    GGML_ASSERT(batch_stride_d == m * n);
-
-    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as };
-    // Make sure enough workgroups get assigned for split k to work
-    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
-    ggml_vk_sync_buffers(subctx);
-    const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
-}
-
-static void ggml_vk_matmul_id(
-        ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline,
-        vk_subbuffer&& ids, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& a, vk_subbuffer&& split_k_buffer,
-        uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d,
-        uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3,
-        uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d,
-        uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl;
-#endif
-    ggml_vk_sync_buffers(subctx);
-    if (split_k == 1) {
-        const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as };
-        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { ids, b, d, a }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch });
-        return;
-    }
-
-    GGML_ASSERT(batch_stride_d == m * n);
-
-    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as };
-    // Make sure enough workgroups get assigned for split k to work
-    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { ids, b, split_k_buffer, a }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
-    ggml_vk_sync_buffers(subctx);
-    const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
-}
-
-static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) {
-    return
-        tensor->nb[0] == ggml_type_size(tensor->type) &&
-        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
-        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
-}
-
-static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) {
-    if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) {
-        return ctx->device->pipeline_cpy_f32_f32;
-    }
-    if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) {
-        return ctx->device->pipeline_cpy_f32_f16;
-    }
-    if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) {
-        return ctx->device->pipeline_cpy_f16_f16;
-    }
-
-    std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl;
-    GGML_ASSERT(false);
-}
-
-static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
-    std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl;
-#endif
-    const int tensor_type_size = ggml_type_size(tensor->type);
-
-    const uint32_t ne = ggml_nelements(tensor);
-
-    const vk_op_unary_push_constants pc = {
-        (uint32_t)ne,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
-        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3],                       1                   , (uint32_t)tensor->ne[0]                   , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]),
-        0,
-        0.0f, 0.0f,
-    };
-    ggml_vk_sync_buffers(subctx);
-    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 });
-}
-
-static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
-    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
-    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
-#endif
-    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
-    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
-
-    const uint64_t ne00 = src0->ne[0];
-    const uint64_t ne01 = src0->ne[1];
-    const uint64_t ne02 = src0->ne[2];
-    const uint64_t ne03 = src0->ne[3];
-
-    const uint64_t ne10 = src1->ne[0];
-    const uint64_t ne11 = src1->ne[1];
-    const uint64_t ne12 = src1->ne[2];
-    const uint64_t ne13 = src1->ne[3];
-
-    const uint64_t ne20 = dst->ne[0];
-    const uint64_t ne21 = dst->ne[1];
-
-    const uint64_t r2 = ne12 / ne02;
-    const uint64_t r3 = ne13 / ne03;
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
-
-    vk_buffer d_Qx;
-    size_t qx_buf_offset = 0;
-    vk_buffer d_Qy;
-    size_t qy_buf_offset = 0;
-
-    bool src0_uma = false;
-    bool src1_uma = false;
-
-    if (ctx->device->uma) {
-        ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
-        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
-        src0_uma = d_Qx != nullptr;
-        src1_uma = d_Qy != nullptr;
-    }
-
-    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
-    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
-
-    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
-
-    vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
-
-    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
-    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
-
-    if (mmp == nullptr) {
-        // Fall back to dequant + f16 mulmat
-        mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16);
-    }
-
-    // Not implemented
-    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
-
-    const int x_ne = ne01 * ne00;
-    const int y_ne = ne11 * ne10;
-    const int d_ne = ne11 * ne01;
-
-    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11));
-    const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8;
-
-    const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
-
-    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned);
-
-    const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
-    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
-    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
-    const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
-    const uint64_t d_sz = sizeof(float) * d_ne;
-
-    vk_buffer d_D = extra->buffer_gpu.lock();
-    const uint64_t d_buf_offset = extra->offset;
-    GGML_ASSERT(d_D != nullptr);
-    GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03);
-    vk_buffer d_X;
-    uint64_t x_buf_offset = 0;
-    vk_buffer d_Y;
-    uint64_t y_buf_offset = 0;
-    if (!src0_uma) {
-        d_Qx = extra_src0->buffer_gpu.lock();
-        qx_buf_offset = extra_src0->offset;
-        GGML_ASSERT(d_Qx != nullptr);
-    }
-    if (!src1_uma) {
-        d_Qy = extra_src1->buffer_gpu.lock();
-        qy_buf_offset = extra_src1->offset;
-        GGML_ASSERT(d_Qy != nullptr);
-    }
-    if (qx_needs_dequant) {
-        d_X = ctx->prealloc_x;
-        GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03);
-    } else {
-        d_X = d_Qx;
-        x_buf_offset = qx_buf_offset;
-        GGML_ASSERT(qx_sz == x_sz);
-    }
-    if (qy_needs_dequant) {
-        d_Y = ctx->prealloc_y;
-        GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03);
-    } else {
-        d_Y = d_Qy;
-        y_buf_offset = qy_buf_offset;
-        GGML_ASSERT(qy_sz == y_sz);
-    }
-
-    vk_pipeline to_fp16_vk_0 = nullptr;
-    vk_pipeline to_fp16_vk_1 = nullptr;
-
-    if (x_non_contig) {
-        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16);
-    } else {
-        to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
-    }
-    if (y_non_contig) {
-        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16);
-    } else {
-        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
-    }
-    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
-    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
-
-    // Allocate descriptor sets
-    ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
-    if (qx_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
-    }
-    if (qy_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1);
-    }
-    if (split_k > 1) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1);
-    }
-
-    if (x_non_contig) {
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
-    } else if (qx_needs_dequant) {
-        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
-        ggml_vk_sync_buffers(subctx);
-        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
-    }
-    if (y_non_contig) {
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
-    }
-
-    uint32_t stride_batch_x = ne00*ne01;
-    uint32_t stride_batch_y = ne10*ne11;
-
-    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
-        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
-    }
-
-    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
-        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
-    }
-
-    // compute
-    ggml_vk_matmul(
-        ctx, subctx, pipeline,
-        { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 },
-        { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k },
-        ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21,
-        0, 0, 0, 0, 1
-    );  // NOLINT
-}
-
-static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
-    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
-    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
-#endif
-    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
-    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
-
-    const uint64_t ne00 = src0->ne[0];
-    const uint64_t ne01 = src0->ne[1];
-    const uint64_t ne02 = src0->ne[2];
-    const uint64_t ne03 = src0->ne[3];
-
-    const uint64_t ne10 = src1->ne[0];
-    const uint64_t ne11 = src1->ne[1];
-    const uint64_t ne12 = src1->ne[2];
-    const uint64_t ne13 = src1->ne[3];
-
-    GGML_ASSERT(ne11 == 1);
-
-    const uint64_t ne20 = dst->ne[0];
-    const uint64_t ne21 = dst->ne[1];
-    const uint64_t ne22 = dst->ne[2];
-    const uint64_t ne23 = dst->ne[3];
-
-    const uint64_t r2 = ne12 / ne02;
-    const uint64_t r3 = ne13 / ne03;
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
-
-    vk_buffer d_Qx;
-    size_t qx_buf_offset = 0;
-    vk_buffer d_Qy;
-    size_t qy_buf_offset = 0;
-
-    bool src0_uma = false;
-    bool src1_uma = false;
-
-    if (ctx->device->uma) {
-        ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
-        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
-        src0_uma = d_Qx != nullptr;
-        src1_uma = d_Qy != nullptr;
-    }
-
-    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
-    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
-
-    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
-
-    const bool qx_needs_dequant = x_non_contig;
-    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
-
-    // Not implemented
-    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
-
-    const uint64_t x_ne = ne01 * ne00;
-    const uint64_t y_ne = ne11 * ne10;
-    const uint64_t d_ne = ne11 * ne01;
-
-    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
-    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
-    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
-    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
-    const uint64_t d_sz = sizeof(float) * d_ne;
-
-    vk_buffer d_D = extra->buffer_gpu.lock();
-    const uint64_t d_buf_offset = extra->offset;
-    GGML_ASSERT(d_D != nullptr);
-    vk_buffer d_X;
-    uint64_t x_buf_offset = 0;
-    vk_buffer d_Y;
-    uint64_t y_buf_offset = 0;
-    if(!src0_uma) {
-        d_Qx = extra_src0->buffer_gpu.lock();
-        qx_buf_offset = extra_src0->offset;
-        GGML_ASSERT(d_Qx != nullptr);
-    }
-    if(!src1_uma) {
-        d_Qy = extra_src1->buffer_gpu.lock();
-        qy_buf_offset = extra_src1->offset;
-        GGML_ASSERT(d_Qy != nullptr);
-    }
-    if (qx_needs_dequant) {
-        d_X = ctx->prealloc_x;
-    } else {
-        d_X = d_Qx;
-        x_buf_offset = qx_buf_offset;
-        GGML_ASSERT(qx_sz == x_sz);
-    }
-    if (qy_needs_dequant) {
-        d_Y = ctx->prealloc_y;
-    } else {
-        d_Y = d_Qy;
-        y_buf_offset = qy_buf_offset;
-        GGML_ASSERT(qy_sz == y_sz);
-    }
-
-    vk_pipeline to_fp16_vk_0 = nullptr;
-    vk_pipeline to_fp16_vk_1 = nullptr;
-    if (x_non_contig) {
-        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type);
-    }
-    if (y_non_contig) {
-        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type);
-    } else {
-        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
-    }
-    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type);
-    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
-    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
-    GGML_ASSERT(dmmv != nullptr);
-
-    // Allocate descriptor sets
-    if (qx_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
-    }
-    if (qy_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13);
-    }
-    ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13);
-
-    if (x_non_contig) {
-        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
-    }
-    if (y_non_contig) {
-        GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
-    }
-
-    uint32_t stride_batch_x = ne00*ne01;
-    uint32_t stride_batch_y = ne10*ne11;
-
-    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
-        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
-    }
-
-    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
-        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
-    }
-
-    // compute
-    const vk_mat_vec_push_constants pc = {
-        (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
-        (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
-        stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21),
-    };
-    ggml_vk_sync_buffers(subctx);
-    ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { (uint32_t)ne01, (uint32_t)(ne12 * ne13), 1});
-}
-
-static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
-    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
-    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
-#endif
-    GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]);  // NOLINT
-    GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]);  // NOLINT
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    const uint64_t ne00 = src0->ne[0];
-    const uint64_t ne01 = src0->ne[1];
-    const uint64_t ne02 = src0->ne[2];
-    // const uint64_t ne03 = src0->ne[3];
-
-    const uint64_t ne10 = src1->ne[0];
-    const uint64_t ne11 = src1->ne[1];
-    const uint64_t ne12 = src1->ne[2];
-    // const uint64_t ne13 = src1->ne[3];
-
-    GGML_ASSERT(ne11 == 1);
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
-
-    vk_buffer d_Qy;
-    size_t qy_buf_offset = 0;
-
-    bool src1_uma = false;
-
-    if (ctx->device->uma) {
-        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
-        src1_uma = d_Qy != nullptr;
-    }
-
-    const uint64_t x_ne = ne00 * ne01 * ne02;
-    const uint64_t y_ne = ne10 * ne11 * ne12;
-    const uint64_t d_ne = ne01 * ne11 * ne12;
-
-    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
-    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
-    const uint64_t d_sz = sizeof(float) * d_ne;
-
-    vk_buffer d_D = extra->buffer_gpu.lock();
-    const uint64_t d_buf_offset = extra->offset;
-    GGML_ASSERT(d_D != nullptr);
-    vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
-    const uint64_t qx_buf_offset = extra_src0->offset;
-    GGML_ASSERT(d_Qx != nullptr);
-    if (!src1_uma) {
-        d_Qy = extra_src1->buffer_gpu.lock();
-        qy_buf_offset = extra_src1->offset;
-        GGML_ASSERT(d_Qx != nullptr);
-    }
-
-    // Allocate descriptor sets
-    ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1);
-
-    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
-    const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
-
-    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
-    const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
-
-    // compute
-    const std::array<uint32_t, 6> pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
-    ggml_vk_sync_buffers(subctx);
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
-}
-
-static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
-    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
-    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
-#endif
-    GGML_ASSERT(!ggml_is_transposed(src0));
-    GGML_ASSERT(!ggml_is_transposed(src1));
-    GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    const uint64_t ne00 = src0->ne[0];
-    const uint64_t ne01 = src0->ne[1];
-    const uint64_t ne02 = src0->ne[2];
-    // const uint64_t ne03 = src0->ne[3];
-
-    const uint64_t nb01 = src0->nb[1];
-    const uint64_t nb02 = src0->nb[2];
-
-    // const uint64_t ne10 = src1->ne[0];
-    const uint64_t ne11 = src1->ne[1];
-    const uint64_t ne12 = src1->ne[2];
-    // const uint64_t ne13 = src1->ne[3];
-
-    GGML_ASSERT(ne11 == 1);
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
-
-    vk_buffer d_Qy = nullptr;
-    size_t qy_buf_offset = 0;
-
-    bool src1_uma = false;
-
-    if (ctx->device->uma) {
-        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
-        src1_uma = d_Qy != nullptr;
-    }
-
-    const uint64_t d_ne = ne01 * ne11 * ne12;
-
-    const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t);
-    const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t);
-
-    const uint64_t qx_sz = ggml_nbytes(src0);
-    const uint64_t qy_sz = ggml_nbytes(src1);
-    const uint64_t d_sz = sizeof(float) * d_ne;
-
-    vk_buffer d_D = extra->buffer_gpu.lock();
-    const uint64_t d_buf_offset = extra->offset;
-    GGML_ASSERT(d_D != nullptr);
-    vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
-    const uint64_t qx_buf_offset = extra_src0->offset;
-    GGML_ASSERT(d_Qx != nullptr);
-    if (!src1_uma) {
-        d_Qy = extra_src1->buffer_gpu.lock();
-        qy_buf_offset = extra_src1->offset;
-        GGML_ASSERT(d_Qx != nullptr);
-    }
-
-    // Allocate descriptor sets
-    ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1);
-
-    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
-    const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
-
-    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
-    const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
-
-    // compute
-    const std::array<uint32_t, 7> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
-    ggml_vk_sync_buffers(subctx);
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
-}
-
-static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl;
-#endif
-    if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
-        ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst);
-    } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
-        ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst);
-    } else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
-        ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst);
-    } else {
-        ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst);
-    }
-}
-
-/*static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
-    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
-    std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", backend=" << ids->backend << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3];
-    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
-#endif
-    GGML_ASSERT(src0->type == GGML_TYPE_I32);
-    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
-
-    const uint64_t ne00 = src0->ne[0];
-    const uint64_t ne01 = src0->ne[1];
-    const uint64_t ne02 = src0->ne[2];
-    const uint64_t ne03 = src0->ne[3];
-
-    const uint64_t ne10 = src1->ne[0];
-    const uint64_t ne11 = src1->ne[1];
-    const uint64_t ne12 = src1->ne[2];
-    const uint64_t ne13 = src1->ne[3];
-
-    const uint32_t nb11 = src1->nb[1];
-
-    const uint64_t ne20 = dst->ne[0];
-    const uint64_t ne21 = dst->ne[1];
-
-    const uint64_t r2 = ne12 / ne02;
-    const uint64_t r3 = ne13 / ne03;
-
-    const uint32_t nbi1 = src0->nb[1];
-    const uint32_t idx = ((uint32_t *) dst->op_params)[0];
-    const uint64_t n_as = ne02;
-
-    GGML_ASSERT(n_as <= 8);
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
-    ggml_tensor_extra_gpu * extra_ids = (ggml_tensor_extra_gpu *) ids->extra;
-
-    vk_buffer d_Qx;
-    size_t qx_buf_offset = 0;
-    vk_buffer d_Qy;
-    size_t qy_buf_offset = 0;
-
-    bool src0_uma = false;
-    bool src1_uma = false;
-
-    if (ctx->device->uma) {
-        ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
-        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
-        src0_uma = d_Qx != nullptr;
-        src1_uma = d_Qy != nullptr;
-    }
-
-    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
-    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
-
-    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
-
-    vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
-
-    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
-    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
-
-    if (mmp == nullptr) {
-        GGML_ASSERT(false);
-    }
-
-    // Not implemented
-    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
-
-    const int x_ne = ne01 * ne00;
-    const int y_ne = ne11 * ne10;
-    const int d_ne = ne11 * ne01;
-
-    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11));
-    const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8;
-
-    const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
-
-    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned);
-
-    const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
-    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
-    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
-    const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
-    const uint64_t d_sz = sizeof(float) * d_ne;
-
-    vk_buffer d_D = extra->buffer_gpu.lock();
-    const uint64_t d_buf_offset = extra->offset;
-    GGML_ASSERT(d_D != nullptr);
-    GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03);
-    vk_buffer d_X;
-    uint64_t x_buf_offset = 0;
-    vk_buffer d_Y;
-    uint64_t y_buf_offset = 0;
-    if (!src0_uma) {
-        d_Qx = extra_src0->buffer_gpu.lock();
-        qx_buf_offset = extra_src0->offset;
-        GGML_ASSERT(d_Qx != nullptr);
-    }
-    if (!src1_uma) {
-        d_Qy = extra_src1->buffer_gpu.lock();
-        qy_buf_offset = extra_src1->offset;
-        GGML_ASSERT(d_Qy != nullptr);
-    }
-    if (qx_needs_dequant) {
-        d_X = ctx->prealloc_x;
-        GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03);
-    } else {
-        d_X = d_Qx;
-        x_buf_offset = qx_buf_offset;
-        GGML_ASSERT(qx_sz == x_sz);
-    }
-    if (qy_needs_dequant) {
-        d_Y = ctx->prealloc_y;
-        GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03);
-    } else {
-        d_Y = d_Qy;
-        y_buf_offset = qy_buf_offset;
-        GGML_ASSERT(qy_sz == y_sz);
-    }
-
-    vk_pipeline to_fp16_vk_0 = nullptr;
-    vk_pipeline to_fp16_vk_1 = nullptr;
-
-    if (x_non_contig) {
-        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16);
-    } else {
-        to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
-    }
-    if (y_non_contig) {
-        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16);
-    } else {
-        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
-    }
-    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
-    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
-
-    // Allocate descriptor sets
-    ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
-    if (qx_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
-    }
-    if (qy_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1);
-    }
-    if (split_k > 1) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1);
-    }
-
-    if (x_non_contig) {
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
-    } else if (qx_needs_dequant) {
-        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
-        ggml_vk_sync_buffers(subctx);
-        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
-    }
-    if (y_non_contig) {
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
-    }
-
-    uint32_t stride_batch_x = ne00*ne01;
-    uint32_t stride_batch_y = ne10*ne11;
-
-    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
-        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
-    }
-
-    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
-        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
-    }
-
-    // compute
-    ggml_vk_matmul(
-        ctx, subctx, pipeline,
-        { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 },
-        { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k },
-        ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21,
-        nb11 / ggml_type_size(src1->type), ne20, idx, nbi1, n_as
-    );  // NOLINT
-}
-
-static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ",  backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
-    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ",  backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
-    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ",  backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
-#endif
-    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
-    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
-
-    const uint64_t ne00 = src0->ne[0];
-    const uint64_t ne01 = src0->ne[1];
-    const uint64_t ne02 = src0->ne[2];
-    const uint64_t ne03 = src0->ne[3];
-
-    const uint64_t ne10 = src1->ne[0];
-    const uint64_t ne11 = src1->ne[1];
-    const uint64_t ne12 = src1->ne[2];
-    const uint64_t ne13 = src1->ne[3];
-
-    GGML_ASSERT(ne11 == 1);
-
-    const uint64_t ne20 = dst->ne[0];
-    const uint64_t ne21 = dst->ne[1];
-    const uint64_t ne22 = dst->ne[2];
-    const uint64_t ne23 = dst->ne[3];
-
-    const uint64_t nb22  = dst->nb[2];
-    const uint64_t nb23  = dst->nb[3];
-
-    const uint64_t r2 = ne12 / ne02;
-    const uint64_t r3 = ne13 / ne03;
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
-
-    vk_buffer d_Qx;
-    size_t qx_buf_offset = 0;
-    vk_buffer d_Qy;
-    size_t qy_buf_offset = 0;
-
-    bool src0_uma = false;
-    bool src1_uma = false;
-
-    if (ctx->device->uma) {
-        ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
-        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
-        src0_uma = d_Qx != nullptr;
-        src1_uma = d_Qy != nullptr;
-    }
-
-    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
-    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
-
-    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
-
-    const bool qx_needs_dequant = x_non_contig;
-    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
-
-    // Not implemented
-    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
-
-    const uint64_t x_ne = ne01 * ne00;
-    const uint64_t y_ne = ne11 * ne10;
-    const uint64_t d_ne = ne11 * ne01;
-
-    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
-    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
-    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
-    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
-    const uint64_t d_sz = sizeof(float) * d_ne;
-
-    vk_buffer d_D = extra->buffer_gpu.lock();
-    const uint64_t d_buf_offset = extra->offset;
-    GGML_ASSERT(d_D != nullptr);
-    vk_buffer d_X;
-    uint64_t x_buf_offset = 0;
-    vk_buffer d_Y;
-    uint64_t y_buf_offset = 0;
-    if(!src0_uma) {
-        d_Qx = extra_src0->buffer_gpu.lock();
-        qx_buf_offset = extra_src0->offset;
-        GGML_ASSERT(d_Qx != nullptr);
-    }
-    if(!src1_uma) {
-        d_Qy = extra_src1->buffer_gpu.lock();
-        qy_buf_offset = extra_src1->offset;
-        GGML_ASSERT(d_Qy != nullptr);
-    }
-    if (qx_needs_dequant) {
-        d_X = ctx->prealloc_x;
-    } else {
-        d_X = d_Qx;
-        x_buf_offset = qx_buf_offset;
-        GGML_ASSERT(qx_sz == x_sz);
-    }
-    if (qy_needs_dequant) {
-        d_Y = ctx->prealloc_y;
-    } else {
-        d_Y = d_Qy;
-        y_buf_offset = qy_buf_offset;
-        GGML_ASSERT(qy_sz == y_sz);
-    }
-
-    vk_pipeline to_fp16_vk_0 = nullptr;
-    vk_pipeline to_fp16_vk_1 = nullptr;
-    if (x_non_contig) {
-        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type);
-    }
-    if (y_non_contig) {
-        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type);
-    } else {
-        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
-    }
-    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type);
-    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
-    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
-    GGML_ASSERT(dmmv != nullptr);
-
-    // Allocate descriptor sets
-    if (qx_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
-    }
-    if (qy_needs_dequant) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13);
-    }
-    ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13);
-
-    if (x_non_contig) {
-        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
-    }
-    if (y_non_contig) {
-        GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
-        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
-    }
-
-    uint32_t stride_batch_x = ne00*ne01;
-    uint32_t stride_batch_y = ne10*ne11;
-
-    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
-        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
-    }
-
-    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
-        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
-    }
-
-    // compute
-    const vk_mat_vec_push_constants pc = {
-        (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
-        (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
-        stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21),
-        // 0, 0, 0, 0, 1
-    };
-    ggml_vk_sync_buffers(subctx);
-    ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { (uint32_t)ne01, (uint32_t)(ne12 * ne13), 1});
-}*/
-
-static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    // guaranteed to be an integer due to the check in ggml_can_repeat
-    const uint64_t ne0 = dst->ne[0];
-    const uint64_t ne1 = dst->ne[1];
-    const uint64_t ne2 = dst->ne[2];
-    const uint64_t ne3 = dst->ne[3];
-
-    const uint64_t ne00 = src0->ne[0];
-    const uint64_t ne01 = src0->ne[1];
-    const uint64_t ne02 = src0->ne[2];
-    const uint64_t ne03 = src0->ne[3];
-
-    const uint64_t nb0 = dst->nb[0];
-    const uint64_t nb1 = dst->nb[1];
-    const uint64_t nb2 = dst->nb[2];
-    const uint64_t nb3 = dst->nb[3];
-
-    const uint64_t nb00 = src0->nb[0];
-    const uint64_t nb01 = src0->nb[1];
-    const uint64_t nb02 = src0->nb[2];
-    const uint64_t nb03 = src0->nb[3];
-
-    const uint64_t nr0 = ne0/ne00;
-    const uint64_t nr1 = ne1/ne01;
-    const uint64_t nr2 = ne2/ne02;
-    const uint64_t nr3 = ne3/ne03;
-
-    // TODO: support for transposed / permuted tensors
-    GGML_ASSERT(nb0  == sizeof(float));
-    GGML_ASSERT(nb00 == sizeof(float));
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-
-    const vk_buffer src_buf = extra_src0->buffer_gpu.lock();
-    const uint64_t src_offset = extra_src0->offset;
-    vk_buffer dst_buf = extra->buffer_gpu.lock();
-    const uint64_t dst_offset = extra->offset;
-
-    std::vector<vk::BufferCopy> copies;
-
-    for                         (uint64_t i3 = 0; i3 < nr3;  i3++) {
-        for                     (uint64_t k3 = 0; k3 < ne03; k3++) {
-            for                 (uint64_t i2 = 0; i2 < nr2;  i2++) {
-                for             (uint64_t k2 = 0; k2 < ne02; k2++) {
-                    for         (uint64_t i1 = 0; i1 < nr1;  i1++) {
-                        for     (uint64_t k1 = 0; k1 < ne01; k1++) {
-                            for (uint64_t i0 = 0; i0 < nr0;  i0++) {
-                                copies.push_back({
-                                    src_offset + (i3*ne03 + k3)*nb3  + (i2*ne02 + k2)*nb2  + (i1*ne01 + k1)*nb1  + (i0*ne00)*nb0,
-                                    dst_offset + (          k3)*nb03 + (          k2)*nb02 + (          k1)*nb01,
-                                    ne00*nb0,
-                                });
-                            }
-                        }
-                    }
-                }
-            }
-        }
-    }
-
-    ggml_vk_sync_buffers(subctx);
-    subctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies);
-
-    GGML_UNUSED(ctx);
-    GGML_UNUSED(src1);
-}
-
-
-static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) {
-    switch (op) {
-    case GGML_OP_ADD:
-        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_add_f32;
-        }
-        return nullptr;
-    case GGML_OP_GET_ROWS:
-        GGML_ASSERT(src1->type == GGML_TYPE_I32);
-        if (dst->type == GGML_TYPE_F16) {
-            return ctx->device->pipeline_get_rows[src0->type];
-        }
-        if (dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_get_rows_f32[src0->type];
-        }
-        return nullptr;
-    case GGML_OP_MUL:
-        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_mul_f32;
-        }
-        return nullptr;
-    case GGML_OP_SCALE:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_scale_f32;
-        }
-        return nullptr;
-    case GGML_OP_SQR:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_sqr_f32;
-        }
-        return nullptr;
-    case GGML_OP_CLAMP:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_clamp_f32;
-        }
-        return nullptr;
-    case GGML_OP_CPY:
-    case GGML_OP_CONT:
-    case GGML_OP_DUP:
-        return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type);
-    case GGML_OP_NORM:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_norm_f32;
-        }
-        return nullptr;
-    case GGML_OP_RMS_NORM:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_rms_norm_f32;
-        }
-        return nullptr;
-    case GGML_OP_UNARY:
-        switch (ggml_get_unary_op(dst)) {
-            case GGML_UNARY_OP_SILU:
-                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return ctx->device->pipeline_silu_f32;
-                }
-                break;
-            case GGML_UNARY_OP_GELU:
-                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return ctx->device->pipeline_gelu_f32;
-                }
-                break;
-            case GGML_UNARY_OP_RELU:
-                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return ctx->device->pipeline_relu_f32;
-                }
-                break;
-            default:
-                break;
-        }
-        return nullptr;
-    case GGML_OP_DIAG_MASK_INF:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_diag_mask_inf_f32;
-        }
-        return nullptr;
-    case GGML_OP_SOFT_MAX:
-        GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
-
-        if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_soft_max_f32;
-        }
-        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_soft_max_f32_f16;
-        }
-        return nullptr;
-    case GGML_OP_ROPE:
-        {
-            const int mode = ((const int32_t *) dst->op_params)[2];
-            const bool is_neox = mode & 2;
-            const bool is_glm  = mode & 4;
-
-            if (is_glm) {
-                return nullptr;
-            }
-
-            if (is_neox) {
-                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return ctx->device->pipeline_rope_neox_f32;
-                }
-                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-                    return ctx->device->pipeline_rope_neox_f16;
-                }
-            } else {
-                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return ctx->device->pipeline_rope_f32;
-                }
-                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-                    return ctx->device->pipeline_rope_f16;
-                }
-            }
-            return nullptr;
-        }
-    case GGML_OP_ARGSORT:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
-            return ctx->device->pipeline_argsort_f32;
-        }
-        return nullptr;
-    default:
-        return nullptr;
-    }
-}
-
-static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) {
-    switch(op) {
-    case GGML_OP_REPEAT:
-        return ggml_vk_op_repeat;
-    default:
-        return nullptr;
-    }
-}
-
-static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
-    switch (op) {
-    case GGML_OP_CPY:
-    case GGML_OP_GET_ROWS:
-    case GGML_OP_ADD:
-    case GGML_OP_MUL:
-    case GGML_OP_SCALE:
-    case GGML_OP_SQR:
-    case GGML_OP_CLAMP:
-        return true;
-    default:
-        return false;
-    }
-}
-
-template<typename PC>
-static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
-    if (src1 != nullptr) {
-        std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
-    }
-    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl;
-#endif
-    GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
-    GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0));  // NOLINT
-    GGML_ASSERT(dst->extra != nullptr);
-    const uint64_t ne00 = src0->ne[0];
-    const uint64_t ne01 = src0->ne[1];
-    const uint64_t ne02 = src0->ne[2];
-    const uint64_t ne03 = src0->ne[3];
-    const uint64_t ne0 = ne00 * ne01;
-    const bool use_src1 = src1 != nullptr;
-    const uint64_t ne10 = use_src1 ? src1->ne[0] : 0;
-    const uint64_t ne11 = use_src1 ? src1->ne[1] : 0;
-    const uint64_t ne12 = use_src1 ? src1->ne[2] : 0;
-    const uint64_t ne13 = use_src1 ? src1->ne[3] : 0;
-    const uint64_t ne1 = ne10 * ne11;
-    // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0;
-
-    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op);
-    ggml_vk_func_t op_func;
-
-    if (pipeline == nullptr) {
-        op_func = ggml_vk_op_get_func(op);
-        if (op_func == nullptr) {
-            std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type);
-            if (src1 != nullptr) {
-                std::cerr << " and " << ggml_type_name(src1->type);
-            }
-            std::cerr << " to " << ggml_type_name(dst->type) << std::endl;
-            GGML_ASSERT(false);
-        }
-
-        op_func(ctx, subctx, src0, src1, dst);
-        return;
-    }
-
-    const bool op_supports_incontiguous = ggml_vk_op_supports_incontiguous(op);
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
-
-    vk_buffer d_X = nullptr;
-    size_t x_buf_offset = 0;
-    vk_buffer d_Y = nullptr;
-    size_t y_buf_offset = 0;
-    vk_buffer d_Z = nullptr;
-
-    bool src0_uma = false;
-    bool src1_uma = false;
-
-    if (ctx->device->uma) {
-        ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset);
-        src0_uma = d_X != nullptr;
-        if (use_src1) {
-            ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset);
-            src1_uma = d_Y != nullptr;
-        }
-    }
-
-    uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment);
-    uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0;
-    uint64_t d_sz = ggml_type_size(dst->type) * ne0;
-
-    vk_buffer d_D = extra->buffer_gpu.lock();
-
-    // Workaround for tiny tensor inputs on ROPE
-    if (use_src1 && y_sz > d_D->size) {
-        y_sz = VK_WHOLE_SIZE;
-    }
-
-    GGML_ASSERT(d_D != nullptr);
-    uint64_t d_buf_offset = (extra->offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
-    GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY);  // NOLINT
-    if(!src0_uma) {
-        d_X = extra_src0->buffer_gpu.lock();
-        x_buf_offset = extra_src0->offset;
-        GGML_ASSERT(d_X != nullptr);
-    }
-    if (use_src1 && !src1_uma) {
-        d_Y = extra_src1->buffer_gpu.lock();
-        y_buf_offset = extra_src1->offset;
-        GGML_ASSERT(d_Y != nullptr);
-    }
-
-    if (op_supports_incontiguous) {
-        x_sz = ggml_nbytes(src0);
-        y_sz = use_src1 ? ggml_nbytes(src1) : 0;
-        d_sz = ggml_nbytes(dst);
-
-        if (x_buf_offset + x_sz >= d_X->size) {
-            x_sz = VK_WHOLE_SIZE;
-        }
-        if (use_src1 && y_buf_offset + y_sz >= d_Y->size) {
-            y_sz = VK_WHOLE_SIZE;
-        }
-        if (d_buf_offset + d_sz >= d_D->size) {
-            d_sz = VK_WHOLE_SIZE;
-        }
-    }
-
-    std::array<uint32_t, 3> elements;
-
-    // Single call if dimension 2 is contiguous
-    if (op_supports_incontiguous || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
-
-        switch (dst->op) {
-        case GGML_OP_NORM:
-        case GGML_OP_RMS_NORM:
-        case GGML_OP_SOFT_MAX:
-            elements = { (uint32_t)ggml_nrows(src0), 1, 1 };
-            break;
-        case GGML_OP_DIAG_MASK_INF:
-        case GGML_OP_ROPE:
-            elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 };
-            break;
-        case GGML_OP_GET_ROWS:
-            elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
-            break;
-        case GGML_OP_ARGSORT:
-            elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 };
-            break;
-        default:
-            elements = { (uint32_t)ggml_nelements(src0), 1, 1 };
-            break;
-        }
-
-        if (!op_supports_incontiguous) {
-            if (x_sz != VK_WHOLE_SIZE) {
-                x_sz *= ne02 * ne03;
-            }
-            if (use_src1 && y_sz != VK_WHOLE_SIZE) {
-                y_sz *= ne12 * ne13;
-            }
-            if (d_sz != VK_WHOLE_SIZE) {
-                d_sz *= ne02 * ne03;
-            }
-        }
-
-        if (op == GGML_OP_SOFT_MAX) {
-            // Empty src1 is possible on soft_max, but the shader needs a buffer
-            vk_subbuffer subbuf_y;
-            if (use_src1) {
-                subbuf_y = { d_Y, y_buf_offset, y_sz };
-            } else {
-                subbuf_y = { d_X, 0, d_X->size };
-            }
-
-            ggml_vk_sync_buffers(subctx);
-            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
-        } else if (use_src1) {
-            ggml_vk_sync_buffers(subctx);
-            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
-        } else {
-            ggml_vk_sync_buffers(subctx);
-            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
-        }
-    } else {
-        GGML_ASSERT(op != GGML_OP_SOFT_MAX);
-        GGML_ASSERT(op != GGML_OP_ARGSORT);
-
-        ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03);
-
-        switch (dst->op) {
-        case GGML_OP_NORM:
-        case GGML_OP_RMS_NORM:
-            elements = { (uint32_t)ne01, 1, 1 };
-            break;
-        case GGML_OP_DIAG_MASK_INF:
-        case GGML_OP_ROPE:
-            elements = { (uint32_t)ne01, (uint32_t)ne00, 1 };
-            break;
-        case GGML_OP_GET_ROWS:
-            elements = {  (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
-            break;
-        default:
-            elements = { (uint32_t)ne0, 1, 1 };
-            break;
-        }
-
-        for (uint64_t i03 = 0; i03 < ne03; i03++) {
-            for (uint64_t i02 = 0; i02 < ne02; i02++) {
-                const uint32_t it_idx0 = (i03 * ne02 + i02);
-                const uint32_t it_idx1 = use_src1 ? ((i03 % ne13) * ne12 + (i02 % ne12)) : 0;
-                const uint32_t x_offset = x_sz * it_idx0;
-                const uint32_t y_offset = y_sz * it_idx1;
-                const uint32_t d_offset = d_sz * it_idx0;
-
-                if (use_src1) {
-                    ggml_vk_sync_buffers(subctx);
-                    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
-                } else {
-                    ggml_vk_sync_buffers(subctx);
-                    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
-                }
-            }
-        }
-    }
-}
-
-static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
-}
-
-static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t src1_type_size = ggml_type_size(src1->type);
-    const uint32_t dst_type_size = ggml_type_size(dst->type);
-
-    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, {
-        (uint32_t)ggml_nelements(src0),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
-        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
-        0,
-        0.0f, 0.0f,
-    });
-}
-
-static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t src1_type_size = ggml_type_size(src1->type);
-    const uint32_t dst_type_size = ggml_type_size(dst->type);
-
-    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_ADD, {
-        (uint32_t)ggml_nelements(src0),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
-        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
-        0,
-        0.0f, 0.0f,
-    });
-}
-
-static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t src1_type_size = ggml_type_size(src1->type);
-    const uint32_t dst_type_size = ggml_type_size(dst->type);
-
-    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_MUL, {
-        (uint32_t)ggml_nelements(src0),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
-        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
-        0,
-        0.0f, 0.0f,
-    });
-}
-
-static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    float * op_params = (float *)dst->op_params;
-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t dst_type_size = ggml_type_size(dst->type);
-
-    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, {
-        (uint32_t)ggml_nelements(src0),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
-        0,
-        op_params[0], 0.0f
-    });
-}
-
-static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t dst_type_size = ggml_type_size(dst->type);
-
-    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, {
-        (uint32_t)ggml_nelements(src0),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
-        0,
-        0.0f, 0.0f,
-    });
-}
-
-static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    float * op_params = (float *)dst->op_params;
-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t dst_type_size = ggml_type_size(dst->type);
-
-    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, {
-        (uint32_t)ggml_nelements(src0),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
-        0,
-        op_params[0], op_params[1],
-    });
-}
-
-static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t dst_type_size = ggml_type_size(dst->type);
-    const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
-
-    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, {
-        (uint32_t)ggml_nelements(src0),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
-        d_offset,
-        0.0f, 0.0f,
-    });
-}
-
-static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    float * op_params = (float *)dst->op_params;
-
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f });
-}
-
-static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    float * op_params = (float *)dst->op_params;
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f });
-}
-
-static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f });
-}
-
-static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    int32_t * op_params = (int32_t *)dst->op_params;
-    ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] });
-}
-
-static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    float * op_params = (float *)dst->op_params;
-
-    float scale = op_params[0];
-    float max_bias = op_params[1];
-
-    const uint32_t ncols =   (uint32_t)src0->ne[0];
-    const uint32_t nrows_x = (uint32_t)ggml_nrows(src0);
-    const uint32_t nrows_y = (uint32_t)src0->ne[1];
-
-    const uint32_t n_head_kv   = nrows_x/nrows_y;
-    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
-
-    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
-
-    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, {
-        ncols,
-        src1 != nullptr ? nrows_y : (uint32_t)0,
-        scale, max_bias,
-        m0, m1,
-        n_head_log2,
-    });
-}
-
-static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const int n_dims        = ((int32_t *) dst->op_params)[1];
-    const int mode          = ((int32_t *) dst->op_params)[2];
-    // const int n_ctx         = ((int32_t *) dst->op_params)[3];
-    const int n_orig_ctx    = ((int32_t *) dst->op_params)[4];
-    const float freq_base   = ((float *)   dst->op_params)[5];
-    const float freq_scale  = ((float *)   dst->op_params)[6];
-    const float ext_factor  = ((float *)   dst->op_params)[7];
-    const float attn_factor = ((float *)   dst->op_params)[8];
-    const float beta_fast   = ((float *)   dst->op_params)[9];
-    const float beta_slow   = ((float *)   dst->op_params)[10];
-
-    const bool is_neox = mode & 2;
-    const bool is_glm  = mode & 4;
-
-    GGML_ASSERT(!is_glm);
-
-    float corr_dims[2];
-    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
-
-    if (is_neox) {
-        const float theta_scale = powf(freq_base, -2.0f/n_dims);
-        const float inv_ndims = -1.0f / n_dims;
-        ggml_vk_op_f32<vk_op_rope_neox_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, {
-            (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1],
-            freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims
-        });
-    } else {
-        ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, {
-            (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1],
-            freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}
-        });
-    }
-}
-
-static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
-    int32_t * op_params = (int32_t *)dst->op_params;
-
-    uint32_t ncols = src0->ne[0];
-
-    uint32_t ncols_pad = 1;
-    while (ncols_pad < ncols) {
-        ncols_pad *= 2;
-    }
-
-    GGML_ASSERT(ncols_pad <= 1024);
-
-    std::cerr << "ncols=" << ncols << " ncols_pad=" << ncols_pad << " ascending=" << op_params[0] << std::endl;
-
-    std::cerr << ((ggml_sort_order) op_params[0]) << " " << GGML_SORT_ORDER_ASC << std::endl;
-
-    ggml_vk_op_f32<vk_op_argsort_push_constants>(ctx, subctx, src0, nullptr, dst, GGML_OP_ARGSORT, {
-        ncols,
-        ncols_pad,
-        op_params[0],
-    });
-}
-
-#ifdef GGML_VULKAN_RUN_TESTS
-static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) {
-    if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) {
-        return;
-    }
-    i0 = std::max(i0, 5);
-    i1 = std::max(i1, 5);
-    i2 = std::max(i2, 0);
-    fprintf(stderr, "         ");
-    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
-        fprintf(stderr, "%7d ", idx1);
-    }
-    fprintf(stderr, "\n");
-    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
-        fprintf(stderr, "%7d: ", idx0);
-        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
-            if (idx0 >= 0 && idx0 < ne0 && idx1 >= 0 && idx1 < ne1) {
-                float val;
-                if (type == GGML_TYPE_F32) {
-                    val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0);
-                } else if (type == GGML_TYPE_F16) {
-                    val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0));
-                } else {
-                    GGML_ASSERT(false);
-                }
-                fprintf(stderr, "% 7.2f ", val);
-            } else {
-                fprintf(stderr, "        ");
-            }
-        }
-        fprintf(stderr, "\n");
-    }
-}
-
-template <typename X_TYPE, typename Y_TYPE>
-static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl;
-#endif
-    const size_t x_ne = m * k * batch;
-    const size_t y_ne = k * n * batch;
-    const size_t d_ne = m * n * batch;
-
-    vk_pipeline p;
-    std::string shname;
-    if (shader_size == 0) {
-        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f32->a_s;
-            shname = "F32_ALIGNED_S";
-        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f32_f16->a_s;
-            shname = "F32_F16_ALIGNED_S";
-        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f16_f32->a_s;
-            shname = "F16_F32_ALIGNED_S";
-        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f16->a_s;
-            shname = "F16_ALIGNED_S";
-        } else {
-            GGML_ASSERT(false);
-        }
-    } else if (shader_size == 1) {
-        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f32->a_m;
-            shname = "F32_ALIGNED_M";
-        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f32_f16->a_m;
-            shname = "F32_F16_ALIGNED_M";
-        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f16_f32->a_m;
-            shname = "F16_F32_ALIGNED_M";
-        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f16->a_m;
-            shname = "F16_ALIGNED_M";
-        } else {
-            GGML_ASSERT(false);
-        }
-    } else if (shader_size == 2) {
-        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f32->a_l;
-            shname = "F32_ALIGNED_L";
-        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f32_f16->a_l;
-            shname = "F32_F16_ALIGNED_L";
-        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f16_f32->a_l;
-            shname = "F16_F32_ALIGNED_L";
-        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            p = ctx->device->pipeline_matmul_f16->a_l;
-            shname = "F16_ALIGNED_L";
-        } else {
-            GGML_ASSERT(false);
-        }
-    } else {
-        GGML_ASSERT(0);
-    }
-
-    const size_t kpad = ggml_vk_align_size(k, p->align);
-
-    if (k != kpad) {
-        if (shader_size == 0) {
-            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f32->s;
-                shname = "F32_S";
-            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f32_f16->s;
-                shname = "F32_F16_S";
-            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f16_f32->s;
-                shname = "F16_F32_S";
-            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f16->s;
-                shname = "F16_S";
-            }
-        } else if (shader_size == 1) {
-            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f32->m;
-                shname = "F32_M";
-            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f32_f16->m;
-                shname = "F32_F16_M";
-            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f16_f32->m;
-                shname = "F16_F32_M";
-            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f16->m;
-                shname = "F16_M";
-            }
-        } else if (shader_size == 2) {
-            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f32->l;
-                shname = "F32_L";
-            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f32_f16->l;
-                shname = "F32_F16_L";
-            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f16_f32->l;
-                shname = "F16_F32_L";
-            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
-                p = ctx->device->pipeline_matmul_f16->l;
-                shname = "F16_L";
-            }
-        }
-    }
-
-    ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
-    if (split_k > 1) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
-
-        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
-            // Resize buffer
-            if (ctx->prealloc_split_k != nullptr) {
-                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
-            }
-            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
-        }
-    }
-
-    vk_buffer d_X = ggml_vk_create_buffer_check(ctx, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_Y = ggml_vk_create_buffer_check(ctx, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_D = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-
-    X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne);
-    Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne);
-    float* d = (float *) malloc(sizeof(float) * d_ne);
-
-    for (size_t i = 0; i < x_ne; i++) {
-        if (std::is_same<float, X_TYPE>()) {
-            x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
-        } else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
-            x[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-    for (size_t i = 0; i < y_ne; i++) {
-        if (std::is_same<float, Y_TYPE>()) {
-            // y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
-            y[i] = (i % k == i / k) ? 1.0f : 0.0f;
-        } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
-            // y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
-            y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f);
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-
-    ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
-    ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
-
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
-    for (size_t i = 0; i < num_it; i++) {
-        ggml_vk_ctx_begin(ctx, subctx);
-        ggml_vk_matmul(
-            ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k),
-            m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n, 0, 0, 0, 0, 1
-        );
-        ggml_vk_ctx_end(subctx);
-    }
-
-    auto begin = std::chrono::high_resolution_clock::now();
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    auto end = std::chrono::high_resolution_clock::now();
-    double time = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
-
-    // copy dst to host
-    ggml_vk_buffer_read(ctx, d_D, 0, d, sizeof(float) * d_ne);
-
-    float * d_chk = (float *) malloc(sizeof(float) * d_ne);
-
-    ggml_init_params iparams = {
-        /*.mem_size   =*/ 1024*1024*1024,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
-
-    ggml_context * ggml_ctx = ggml_init(iparams);
-
-    ggml_type src0_type;
-    ggml_type src1_type;
-
-    if (std::is_same<float, X_TYPE>()) {
-        src0_type = GGML_TYPE_F32;
-    } else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
-        src0_type = GGML_TYPE_F16;
-    } else {
-        GGML_ASSERT(false);
-    }
-    if (std::is_same<float, Y_TYPE>()) {
-        src1_type = GGML_TYPE_F32;
-    } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
-        src1_type = GGML_TYPE_F16;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, src0_type, k, m, batch);
-    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, src1_type, k, n, batch);
-    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
-
-    src0_ggml->data = x;
-    src1_ggml->data = y;
-    tensor_ggml->data = d_chk;
-
-    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
-    ggml_build_forward_expand(cgraph, tensor_ggml);
-
-    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
-
-    ggml_free(ggml_ctx);
-
-    double avg_err = 0.0;
-    int first_err_n = -1;
-    int first_err_m = -1;
-    int first_err_b = -1;
-
-    for (size_t i = 0; i < m*n*batch; i++) {
-        double err = std::fabs(d[i] - d_chk[i]);
-        avg_err += err;
-
-        if (err > 0.05f && first_err_n == -1) {
-            first_err_b = i / (m * n);
-            first_err_n = (i % (m * n)) / m;
-            first_err_m = (i % (m * n)) % m;
-        }
-    }
-
-    avg_err /= m * n;
-
-    std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms avg_err=" << avg_err << std::endl;
-
-    if (avg_err > 0.1) {
-        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
-        std::cerr << "Actual result: " << std::endl << std::endl;
-        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-        std::cerr << std::endl;
-        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n + 15, first_err_b);
-        std::cerr << "Expected result: " << std::endl << std::endl;
-        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-        if (split_k > 1) {
-            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
-            ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
-
-            std::cerr << "d_buf0: " << std::endl << std::endl;
-            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-            std::cerr << "d_buf1: " << std::endl << std::endl;
-            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-            std::cerr << "d_buf2: " << std::endl << std::endl;
-            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-            std::cerr << "d_buf3: " << std::endl << std::endl;
-            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-            free(split_k_buf);
-        }
-    }
-
-    free(d_chk);
-
-    ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
-    ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
-
-    ggml_vk_destroy_buffer(d_X);
-    ggml_vk_destroy_buffer(d_Y);
-    ggml_vk_destroy_buffer(d_D);
-
-    ggml_pipeline_cleanup(p);
-    ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce);
-
-    free(x);
-    free(y);
-    free(d);
-}
-
-static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
-    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
-        return;
-    }
-    i0 = std::max(i0, 5);
-    i1 = std::max(i1, 5);
-    i2 = std::max(i2, 0);
-    i3 = std::max(i3, 0);
-    fprintf(stderr, "         ");
-    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
-        fprintf(stderr, "%7d ", idx1);
-    }
-    fprintf(stderr, "\n");
-    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
-        fprintf(stderr, "%7d: ", idx0);
-        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
-            if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
-                float val;
-                if (tensor->type == GGML_TYPE_F32) {
-                    val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
-                } else if (tensor->type == GGML_TYPE_F16) {
-                    val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
-                } else {
-                    GGML_ASSERT(false);
-                }
-                fprintf(stderr, "% 7.2f ", val);
-            } else {
-                fprintf(stderr, "        ");
-            }
-        }
-        fprintf(stderr, "\n");
-    }
-}
-
-static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_t ne1, size_t ne2, size_t ne3) {
-    const size_t ne = ne0 * ne1 * ne2 * ne3;
-
-    ggml_init_params iparams = {
-        /*.mem_size   =*/ 1024*1024*1024,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
-
-    ggml_context * ggml_ctx = ggml_init(iparams);
-
-    ggml_tensor * tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne2, ne1, ne3);  // NOLINT
-    ggml_tensor * result_tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne1, ne2, ne3);
-
-    float * data = (float *) ggml_vk_host_malloc(ctx, ggml_nbytes(tensor));
-    tensor->data = data;
-
-    float * result_data = (float *) malloc(ggml_nbytes(tensor));
-    result_tensor->data = result_data;
-
-    // Permute
-    {
-        size_t tmp = tensor->nb[2];
-        tensor->nb[2] = tensor->nb[1];
-        tensor->nb[1] = tmp;
-
-        tensor->ne[2] = ne2;
-        tensor->ne[1] = ne1;
-    }
-
-    for (size_t i = 0; i < ne; i++) {
-        data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
-    }
-
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
-    ggml_vk_ctx_begin(ctx, subctx);
-
-    vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal);
-
-    ggml_vk_h2d_tensor_2d(ctx, subctx, buffer, 0, tensor, 0, 0, ggml_nrows(tensor));
-
-    ggml_vk_ctx_end(subctx);
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor));
-
-    double avg_err = 0.0;
-    int first_err_i0 = -1;
-    int first_err_i1 = -1;
-    int first_err_i2 = -1;
-    int first_err_i3 = -1;
-
-    for (size_t i3 = 0; i3 < ne3; i3++) {
-        for (size_t i2 = 0; i2 < ne2; i2++) {
-            for (size_t i1 = 0; i1 < ne1; i1++) {
-                for (size_t i0 = 0; i0 < ne0; i0++) {
-                    float correct = *(float *) ((char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
-                    float result = *(float *) ((char *) result_data + i3*ne2*ne1*ne0*sizeof(float) + i2*ne1*ne0*sizeof(float) + i1*ne0*sizeof(float) + i0*sizeof(float));
-                    double err = std::fabs(result - correct);
-
-                    avg_err += err;
-
-                    if (err > 0.05f && first_err_i0 == -1) {
-                        first_err_i0 = i0;
-                        first_err_i1 = i1;
-                        first_err_i2 = i2;
-                        first_err_i3 = i3;
-                    }
-                }
-            }
-        }
-    }
-
-    avg_err /= ne;
-
-    std::cerr << "TEST nc copy ne0=" << ne0 << " ne1=" << ne1 << " ne2=" << ne2 << " ne3=" << ne3 << " avg_err=" << avg_err << std::endl;
-
-    if (avg_err > 0.1) {
-        std::cerr << "i0 = " << first_err_i0 << " i1 = " << first_err_i1 << " i2 = " << first_err_i2 << " i3 = " << first_err_i3 << std::endl;
-        std::cerr << "Actual result: " << std::endl << std::endl;
-        ggml_vk_print_tensor_area(result_tensor, first_err_i0, first_err_i1, first_err_i2, first_err_i3);
-        std::cerr << "Expected result: " << std::endl << std::endl;
-        ggml_vk_print_tensor_area(tensor, first_err_i0, first_err_i1, first_err_i2, first_err_i3);
-    }
-
-    ggml_free(ggml_ctx);
-
-    ggml_vk_destroy_buffer(buffer);
-
-    ggml_vk_host_free(ctx, data);
-    free(result_data);
-}
-
-static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool pinned) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl;
-#endif
-    // Check transfers are correct
-    vk_buffer buffer = ggml_vk_create_buffer_check(ctx, sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-
-    float * x;
-    float * y;
-    if (pinned) {
-        x = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne);
-        y = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne);
-    } else {
-        x = (float *) malloc(sizeof(float) * ne);
-        y = (float *) malloc(sizeof(float) * ne);
-    }
-
-    for (size_t i = 0; i < ne; i++) {
-        x[i] = rand() / (float)RAND_MAX;
-    }
-
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
-    ggml_vk_ctx_begin(ctx, subctx);
-
-    auto begin = std::chrono::high_resolution_clock::now();
-
-    ggml_vk_buffer_write_async(ctx, subctx, buffer, 0, x, sizeof(float) * ne);
-
-    for (auto& cpy : subctx->in_memcpys) {
-        memcpy(cpy.dst, cpy.src, cpy.n);
-    }
-    subctx->in_memcpys.clear();
-
-    ggml_vk_ctx_end(subctx);
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    auto end = std::chrono::high_resolution_clock::now();
-
-    double ms_to_gpu = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
-
-    ggml_vk_ctx_begin(ctx, subctx);
-
-    begin = std::chrono::high_resolution_clock::now();
-
-    ggml_vk_buffer_read_async(ctx, subctx, buffer, 0, y, sizeof(float) * ne);
-
-    ggml_vk_ctx_end(subctx);
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    for (auto& cpy : subctx->out_memcpys) {
-        memcpy(cpy.dst, cpy.src, cpy.n);
-    }
-    subctx->out_memcpys.clear();
-
-    end = std::chrono::high_resolution_clock::now();
-
-    double ms_from_gpu = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
-
-    double avg_err = 0.0;
-    for (size_t i = 0; i < ne; i++) {
-        avg_err += std::fabs(x[i] - y[i]);
-    }
-
-    double kb = ne * sizeof(float) / 1024.0;
-
-    std::cerr << "TEST TRANSFER " << kb << " KB to_gpu " << ms_to_gpu << "ms (" << kb / ms_to_gpu * 1000.0 / 1024.0 << " MB/s) from_gpu " << ms_from_gpu << "ms (" << kb / ms_from_gpu * 1000.0 / 1024.0 << " MB/s) avg_err=" << avg_err / ne << std::endl;
-
-    ggml_vk_destroy_buffer(buffer);
-
-    if (pinned) {
-        ggml_vk_host_free(ctx, x);
-        ggml_vk_host_free(ctx, y);
-    } else {
-        free(x);
-        free(y);
-    }
-}
-
-static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
-    ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
-}
-
-static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl;
-#endif
-    const size_t x_sz = sizeof(float) * ne;
-    const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne;
-    const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant);
-    float * x = (float *) malloc(x_sz);
-    void * qx = malloc(qx_sz);
-    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16);
-
-    for (size_t i = 0; i < ne; i++) {
-        x[i] = rand() / (float)RAND_MAX;
-    }
-
-    vk_pipeline p = ctx->device->pipeline_dequant[quant];
-
-    ggml_vk_quantize_data(x, qx, ne, quant);
-
-    ggml_pipeline_allocate_descriptor_sets(ctx, p, 1);
-
-    ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
-
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
-    ggml_vk_ctx_begin(ctx, subctx);
-    const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
-    ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1});
-    ggml_vk_ctx_end(subctx);
-
-    auto begin = std::chrono::high_resolution_clock::now();
-
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    auto end = std::chrono::high_resolution_clock::now();
-
-    double ms_dequant = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
-    ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16);
-
-    int first_err = -1;
-
-    double avg_err = 0.0;
-    for (size_t i = 0; i < ne; i++) {
-        double error = std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i]));
-        avg_err += error;
-
-        if (first_err < 0 && error > 0.05) {
-            first_err = i;
-        }
-    }
-
-    avg_err /= ne;
-
-    std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl;
-
-    if (avg_err > 0.1) {
-        std::cerr << "first_error = " << first_err << std::endl;
-        std::cerr << "Actual result: " << std::endl << std::endl;
-        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
-            std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", ";
-        }
-        std::cerr << std::endl << "Expected result: " << std::endl << std::endl;
-        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
-            std::cerr << x[i] << ", ";
-        }
-        std::cerr << std::endl;
-    }
-
-    ggml_vk_destroy_buffer(x_buf);
-    ggml_vk_destroy_buffer(qx_buf);
-
-    free(x);
-    free(qx);
-    free(x_chk);
-}
-
-static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")" << std::endl;
-#endif
-    const size_t x_ne = m * k * batch;
-    const size_t y_ne = k * n * batch;
-    const size_t d_ne = m * n * batch;
-
-    vk_pipeline p;
-    std::string shname;
-    if (shader_size == 0) {
-        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s;
-        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S";
-    } else if (shader_size == 1) {
-        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m;
-        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M";
-    } else if (shader_size == 2) {
-        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l;
-        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L";
-    } else {
-        GGML_ASSERT(0);
-    }
-
-    const size_t kpad = ggml_vk_align_size(k, p->align);
-
-    if (k != kpad) {
-        if (shader_size == 0) {
-            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s;
-            shname = std::string(ggml_type_name(quant)) + "_S";
-        } else if (shader_size == 1) {
-            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m;
-            shname = std::string(ggml_type_name(quant)) + "_M";
-        } else if (shader_size == 2) {
-            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l;
-            shname = std::string(ggml_type_name(quant)) + "_L";
-        } else {
-            GGML_ASSERT(0);
-        }
-    }
-
-    const size_t x_sz = sizeof(float) * x_ne;
-    const size_t y_sz = sizeof(float) * y_ne;
-    const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant);
-    const size_t d_sz = sizeof(float) * d_ne;
-    float * x = (float *) malloc(x_sz);
-    float * y = (float *) malloc(y_sz);
-    void * qx = malloc(qx_sz);
-    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    float * d = (float *) malloc(d_sz);
-    float * d_chk = (float *) malloc(d_sz);
-
-    for (size_t i = 0; i < x_ne; i++) {
-        x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
-    }
-
-    ggml_vk_quantize_data(x, qx, x_ne, quant);
-
-    for (size_t i = 0; i < y_ne; i++) {
-        // y[i] = rand() / (float)RAND_MAX;
-        y[i] = (i % k == i / k) ? 1.0f : 0.0f;
-    }
-
-    ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
-    if (split_k > 1) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
-
-        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
-            // Resize buffer
-            if (ctx->prealloc_split_k != nullptr) {
-                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
-            }
-            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
-        }
-    }
-
-    ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
-    ggml_vk_buffer_write(ctx, y_buf, 0, y, y_sz);
-
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
-    for (size_t i = 0; i < num_it; i++) {
-        ggml_vk_ctx_begin(ctx, subctx);
-        ggml_vk_matmul(
-            ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k),
-            m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n, 0, 0, 0, 0, 1
-        );
-        ggml_vk_ctx_end(subctx);
-    }
-
-    auto begin = std::chrono::high_resolution_clock::now();
-
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    auto end = std::chrono::high_resolution_clock::now();
-
-    double time_ms = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
-    ggml_vk_buffer_read(ctx, d_buf, 0, d, d_sz);
-
-    ggml_init_params iparams = {
-        /*.mem_size   =*/ 1024*1024*1024,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
-
-    ggml_context * ggml_ctx = ggml_init(iparams);
-
-    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch);
-    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch);
-    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
-
-    src0_ggml->data = qx;
-    src1_ggml->data = y;
-    tensor_ggml->data = d_chk;
-
-    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
-    ggml_build_forward_expand(cgraph, tensor_ggml);
-
-    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
-
-    ggml_free(ggml_ctx);
-
-    double avg_err = 0.0;
-    int first_err_n = -1;
-    int first_err_m = -1;
-    int first_err_b = -1;
-
-    for (size_t i = 0; i < m*n*batch; i++) {
-        double err = std::fabs(d[i] - d_chk[i]);
-        avg_err += err;
-
-        if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) {
-            first_err_b = i / (m * n);
-            first_err_n = (i % (m * n)) / m;
-            first_err_m = (i % (m * n)) % m;
-        }
-    }
-
-    avg_err /= m * n;
-
-    std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl;
-
-    if (avg_err > 0.01 || std::isnan(avg_err)) {
-        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
-        std::cerr << "Actual result: " << std::endl << std::endl;
-        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-        std::cerr << std::endl;
-        std::cerr << "Expected result: " << std::endl << std::endl;
-        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-        if (split_k > 1) {
-            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
-            ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
-
-            std::cerr << "d_buf0: " << std::endl << std::endl;
-            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-            std::cerr << "d_buf1: " << std::endl << std::endl;
-            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-            std::cerr << "d_buf2: " << std::endl << std::endl;
-            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-            std::cerr << "d_buf3: " << std::endl << std::endl;
-            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
-
-            free(split_k_buf);
-        }
-    }
-
-    ggml_vk_destroy_buffer(qx_buf);
-    ggml_vk_destroy_buffer(y_buf);
-    ggml_vk_destroy_buffer(d_buf);
-
-    free(x);
-    free(qx);
-    free(y);
-    free(d);
-    free(d_chk);
-}
-#endif
-
-static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))" << std::endl;
-#endif
-    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu;
-    extra->reset();
-    tensor->extra = extra;
-    return extra;
-}
-
-static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl;
-#endif
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
-
-    if (extra == nullptr) {
-        return;
-    }
-
-    ggml_tensor * src0 = node->src[0];
-    ggml_tensor * src1 = node->src[1];
-
-    const bool use_src0 = src0 != nullptr;
-    const int64_t ne00 = use_src0 ? src0->ne[0] : 0;
-    const int64_t ne01 = use_src0 ? src0->ne[1] : 0;
-    const int64_t ne02 = use_src0 ? src0->ne[2] : 0;
-    const int64_t ne03 = use_src0 ? src0->ne[3] : 0;
-    const bool use_src1 = src1 != nullptr && node->op != GGML_OP_CPY && node->op != GGML_OP_CONT && node->op != GGML_OP_DUP;
-    const int64_t ne10 = use_src1 ? src1->ne[0] : 0;
-    const int64_t ne11 = use_src1 ? src1->ne[1] : 0;
-    const int64_t ne12 = use_src1 ? src1->ne[2] : 0;
-    const int64_t ne13 = use_src1 ? src1->ne[3] : 0;
-    const int64_t ne20 = node->ne[0];
-    const int64_t ne21 = node->ne[1];
-    const int64_t ne22 = node->ne[2];
-    const int64_t ne23 = node->ne[3];
-
-    const ggml_type src0_type = (use_src0 && src0->type == GGML_TYPE_F32) ? src0->type : GGML_TYPE_F16;
-    const ggml_type src1_type = (use_src1 && src1->type == GGML_TYPE_F32) ? src1->type : GGML_TYPE_F16;
-
-    const bool x_non_contig = use_src0 && !ggml_vk_dim01_contiguous(src0);
-    const bool y_non_contig = use_src1 && !ggml_vk_dim01_contiguous(src1);
-
-    const bool y_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32 && !y_non_contig;
-
-    bool mmp = (use_src0 && use_src1 && src1_type == GGML_TYPE_F32) ? ggml_vk_get_mul_mat_mat_pipeline(ctx, src0_type, y_non_contig ? GGML_TYPE_F16 : src1->type) != nullptr : false;
-
-    const bool qx_needs_dequant = use_src0 && (mmp || x_non_contig);
-    const bool qy_needs_dequant = use_src1 && ((src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig);
-
-    int split_k;
-    if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
-        split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
-    } else {
-        split_k = 1;
-    }
-    const uint32_t x_ne = ne00 * ne01;
-    const uint32_t y_ne = ne10 * ne11;
-    const uint32_t d_ne = ne20 * ne21;
-
-    const uint64_t x_sz = (use_src0 && qx_needs_dequant) ? ggml_vk_align_size(sizeof(src0_type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0;
-    const uint64_t y_sz = (use_src1 && qy_needs_dequant) ? ggml_vk_align_size(sizeof(src1_type) * y_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0;
-    uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23;
-    const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0;
-
-    if (extra->buffer_gpu.expired()) {
-        // Workaround for CPU backend BLAS matmul calls
-        extra->buffer_gpu = ggml_vk_create_buffer_temp(ctx, d_sz);
-    }
-
-    switch (node->op) {
-    case GGML_OP_REPEAT:
-    case GGML_OP_GET_ROWS:
-    case GGML_OP_RESHAPE:
-    case GGML_OP_VIEW:
-    case GGML_OP_PERMUTE:
-    case GGML_OP_TRANSPOSE:
-    case GGML_OP_ADD:
-    case GGML_OP_SCALE:
-    case GGML_OP_SQR:
-    case GGML_OP_CLAMP:
-    case GGML_OP_CPY:
-    case GGML_OP_CONT:
-    case GGML_OP_DUP:
-    case GGML_OP_MUL:
-    case GGML_OP_NORM:
-    case GGML_OP_RMS_NORM:
-    case GGML_OP_DIAG_MASK_INF:
-    case GGML_OP_SOFT_MAX:
-    case GGML_OP_ROPE:
-    case GGML_OP_ARGSORT:
-        break;
-    case GGML_OP_UNARY:
-        switch (ggml_get_unary_op(node)) {
-        case GGML_UNARY_OP_SILU:
-        case GGML_UNARY_OP_GELU:
-        case GGML_UNARY_OP_RELU:
-            break;
-        default:
-            return;
-        }
-        break;
-    case GGML_OP_MUL_MAT:
-    case GGML_OP_MUL_MAT_ID:
-        if (ctx->prealloc_size_x < x_sz) {
-            ctx->prealloc_size_x = x_sz;
-        }
-        if (ctx->prealloc_size_y < y_sz) {
-            ctx->prealloc_size_y = y_sz;
-        }
-        if (ctx->prealloc_size_split_k < split_k_size) {
-            ctx->prealloc_size_split_k = split_k_size;
-        }
-        if (ctx->staging_size < x_sz + y_sz) {
-            ctx->staging_size = x_sz + y_sz;
-        }
-        break;
-    default:
-        return;
-    }
-}
-
-static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl;
-#endif
-#if defined(GGML_VULKAN_RUN_TESTS)
-    ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul,
-        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
-        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
-    ggml_vk_test_transfer(ctx, 8192 * 1000, false);
-    ggml_vk_test_transfer(ctx, 8192 * 1000, true);
-
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K);
-
-    ggml_vk_test_matmul<ggml_fp16_t, ggml_fp16_t>(ctx, 512, 512, 100, 32, 100, 1, 2);
-
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 0);
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 1);
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 2);
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 0);
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 1);
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 2);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q2_K);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q3_K);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_K);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_K);
-
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q6_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q6_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q6_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q6_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q6_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q6_K);
-
-    std::cerr << std::endl;
-
-    const std::vector<size_t> vals {
-        8, 8, 8,
-        100, 46, 576,
-        623, 111, 128,
-        100, 46, 558,
-        512, 1, 256,
-        128, 110, 622,
-        511, 511, 127,
-        511, 511, 7,
-        511, 511, 17,
-        49, 49, 128,
-        128, 49, 49,
-        4096, 49, 4096,
-        11008, 49, 4096,
-        4096, 49, 11008,
-        32000, 49, 4096,
-        512, 512, 128,
-        128, 512, 512,
-        4096, 512, 4096,
-        11008, 512, 4096,
-        4096, 512, 11008,
-        32000, 512, 4096,
-    };
-    const size_t num_it = 1;
-    for (size_t i = 0; i < vals.size(); i += 3) {
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0);
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1);
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2);
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0);
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1);
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2);
-        std::cerr << std::endl;
-    }
-
-    GGML_ASSERT(false);
-#endif
-
-    if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) {
-        // Resize buffer
-        if (ctx->prealloc_x != nullptr) {
-            ggml_vk_destroy_buffer(ctx->prealloc_x);
-        }
-        ctx->prealloc_x = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_x);
-    }
-    if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) {
-        // Resize buffer
-        if (ctx->prealloc_y != nullptr) {
-            ggml_vk_destroy_buffer(ctx->prealloc_y);
-        }
-        ctx->prealloc_y = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_y);
-    }
-    if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) {
-        // Resize buffer
-        if (ctx->prealloc_split_k != nullptr) {
-            ggml_vk_destroy_buffer(ctx->prealloc_split_k);
-        }
-        ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_split_k);
-    }
-    if (ctx->staging == nullptr || (ctx->staging_size > 0 && ctx->staging->size < ctx->staging_size)) {
-        // Resize buffer
-        if (ctx->staging != nullptr) {
-            ggml_vk_destroy_buffer(ctx->staging);
-        }
-        ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size,
-            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
-            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
-    }
-}
-
-static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
-
-    if (ggml_is_empty(node) || extra == nullptr) {
-        return;
-    }
-
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl;
-#endif
-    ctx->semaphore_idx = 0;
-    ctx->staging_offset = 0;
-
-    const ggml_tensor * src0 = node->src[0];
-    const ggml_tensor * src1 = node->src[1];
-
-    switch (node->op) {
-    case GGML_OP_UNARY:
-        switch (ggml_get_unary_op(node)) {
-        case GGML_UNARY_OP_SILU:
-        case GGML_UNARY_OP_GELU:
-        case GGML_UNARY_OP_RELU:
-            break;
-        default:
-            return;
-        }
-        break;
-    case GGML_OP_REPEAT:
-    case GGML_OP_GET_ROWS:
-    case GGML_OP_ADD:
-    case GGML_OP_MUL:
-    case GGML_OP_SCALE:
-    case GGML_OP_SQR:
-    case GGML_OP_CLAMP:
-    case GGML_OP_CPY:
-    case GGML_OP_CONT:
-    case GGML_OP_DUP:
-    case GGML_OP_RESHAPE:
-    case GGML_OP_VIEW:
-    case GGML_OP_PERMUTE:
-    case GGML_OP_TRANSPOSE:
-    case GGML_OP_NORM:
-    case GGML_OP_RMS_NORM:
-    case GGML_OP_DIAG_MASK_INF:
-    case GGML_OP_SOFT_MAX:
-    case GGML_OP_ROPE:
-    case GGML_OP_MUL_MAT:
-    case GGML_OP_MUL_MAT_ID:
-    case GGML_OP_NONE:
-    case GGML_OP_ARGSORT:
-        break;
-    default:
-        std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
-        GGML_ASSERT(false);
-        return;
-    }
-
-    if (ctx->compute_ctx == nullptr) {
-        ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
-        ggml_vk_ctx_begin(ctx, ctx->compute_ctx);
-    }
-
-    switch (node->op) {
-    case GGML_OP_REPEAT:
-        ggml_vk_repeat(ctx, ctx->compute_ctx, src0, src1, node);
-
-        break;
-    case GGML_OP_GET_ROWS:
-        ggml_vk_get_rows(ctx, ctx->compute_ctx, src0, src1, node);
-
-        break;
-    case GGML_OP_ADD:
-        ggml_vk_add(ctx, ctx->compute_ctx, src0, src1, node);
-
-        break;
-    case GGML_OP_MUL:
-        ggml_vk_mul(ctx, ctx->compute_ctx, src0, src1, node);
-
-        break;
-    case GGML_OP_SCALE:
-        ggml_vk_scale(ctx, ctx->compute_ctx, src0, node);
-
-        break;
-    case GGML_OP_SQR:
-        ggml_vk_sqr(ctx, ctx->compute_ctx, src0, node);
-
-        break;
-    case GGML_OP_CLAMP:
-        ggml_vk_clamp(ctx, ctx->compute_ctx, src0, node);
-
-        break;
-    case GGML_OP_CPY:
-    case GGML_OP_CONT:
-    case GGML_OP_DUP:
-        ggml_vk_cpy(ctx, ctx->compute_ctx, src0, node);
-
-        break;
-    case GGML_OP_RESHAPE:
-    case GGML_OP_VIEW:
-    case GGML_OP_PERMUTE:
-    case GGML_OP_TRANSPOSE:
-    case GGML_OP_NONE:
-        break;
-    case GGML_OP_NORM:
-        ggml_vk_norm(ctx, ctx->compute_ctx, src0, node);
-
-        break;
-    case GGML_OP_RMS_NORM:
-        ggml_vk_rms_norm(ctx, ctx->compute_ctx, src0, node);
-
-        break;
-    case GGML_OP_UNARY:
-        switch (ggml_get_unary_op(node)) {
-        case GGML_UNARY_OP_SILU:
-        case GGML_UNARY_OP_GELU:
-        case GGML_UNARY_OP_RELU:
-            ggml_vk_unary(ctx, ctx->compute_ctx, src0, node);
-            break;
-        default:
-            return;
-        }
-        break;
-    case GGML_OP_DIAG_MASK_INF:
-        ggml_vk_diag_mask_inf(ctx, ctx->compute_ctx, src0, node);
-
-        break;
-    case GGML_OP_SOFT_MAX:
-        ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node);
-
-        break;
-    case GGML_OP_ROPE:
-        ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node);
-
-        break;
-    case GGML_OP_ARGSORT:
-        ggml_vk_argsort(ctx, ctx->compute_ctx, src0, node);
-        break;
-    case GGML_OP_MUL_MAT:
-        ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node);
-
-        break;
-    case GGML_OP_MUL_MAT_ID:
-        //ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, node);
-        std::cerr << "ggml_vulkan: GGML_OP_MUL_MAT_ID not implemented yet." << std::endl;
-        GGML_ASSERT(false);
-
-        break;
-    default:
-        return;
-    }
-
-    extra->ready = true;
-    extra->ctx_idx = ctx->compute_ctx->idx;
-
-#ifdef GGML_VULKAN_CHECK_RESULTS
-    // Force context reset on each node so that each tensor ends up in its own context
-    // and can be run and compared to its CPU equivalent separately
-    last_node = true;
-#endif
-
-    if (last_node) {
-        ggml_vk_ctx_end(ctx->compute_ctx);
-        ctx->compute_ctx->exit_tensor = node;
-        ctx->compute_ctx = nullptr;
-    }
-}
-
-static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){
-    ggml_tensor_extra_gpu * extra = nullptr;
-
-    switch (tensor->op) {
-    case GGML_OP_ADD:
-    case GGML_OP_GET_ROWS:
-    case GGML_OP_MUL:
-    case GGML_OP_SCALE:
-    case GGML_OP_SQR:
-    case GGML_OP_CLAMP:
-    case GGML_OP_CPY:
-    case GGML_OP_CONT:
-    case GGML_OP_DUP:
-    case GGML_OP_NORM:
-    case GGML_OP_RMS_NORM:
-    case GGML_OP_DIAG_MASK_INF:
-    case GGML_OP_SOFT_MAX:
-    case GGML_OP_ROPE:
-    case GGML_OP_RESHAPE:
-    case GGML_OP_VIEW:
-    case GGML_OP_PERMUTE:
-    case GGML_OP_TRANSPOSE:
-    case GGML_OP_NONE:
-    case GGML_OP_ARGSORT:
-        extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-        break;
-    case GGML_OP_UNARY:
-        switch (ggml_get_unary_op(tensor)) {
-        case GGML_UNARY_OP_SILU:
-        case GGML_UNARY_OP_GELU:
-        case GGML_UNARY_OP_RELU:
-            extra = (ggml_tensor_extra_gpu *) tensor->extra;
-            break;
-        default:
-            return false;
-        }
-        break;
-    case GGML_OP_MUL_MAT:
-    case GGML_OP_MUL_MAT_ID:
-        extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-        break;
-    default:
-        return false;
-    }
-
-    if (extra == nullptr) {
-        return false;
-    }
-
-    if (params->ith != 0) {
-        return true;
-    }
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return true;
-    }
-
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl;
-#endif
-
-#ifdef GGML_VULKAN_CHECK_RESULTS
-    ggml_vk_check_results_0(ctx, params, tensor);
-#endif
-
-    GGML_ASSERT(extra->ready);
-
-    vk_context& subctx = ctx->gc.contexts[extra->ctx_idx];
-
-    // Only run if ctx hasn't been submitted yet
-    if (!subctx.seqs.empty()) {
-        // Do staging buffer copies
-        for (auto& cpy : subctx.in_memcpys) {
-            memcpy(cpy.dst, cpy.src, cpy.n);
-        }
-
-        ggml_vk_submit(&subctx, ctx->fence);
-    }
-
-    if (tensor == subctx.exit_tensor) {
-        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
-        ctx->device->device.resetFences({ ctx->fence });
-
-        // Do staging buffer copies
-        for (auto& cpy : subctx.out_memcpys) {
-            memcpy(cpy.dst, cpy.src, cpy.n);
-        }
-        subctx.in_memcpys.clear();
-        subctx.out_memcpys.clear();
-    }
-
-    extra->ready = false;
-
-    return true;
-}
-
-// Clean up after graph processing is done
-static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_graph_cleanup()" << std::endl;
-#endif
-    for (auto& buffer : ctx->gc.temp_buffers) {
-        ggml_vk_pool_free(ctx, buffer);
-    }
-    ctx->gc.temp_buffers.clear();
-
-    for (auto& pipeline : ctx->device->pipelines) {
-        if (pipeline.expired()) {
-            continue;
-        }
-
-        vk_pipeline pl = pipeline.lock();
-        ggml_pipeline_cleanup(pl);
-    }
-
-    ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
-    ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
-
-    for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) {
-        ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s });
-    }
-    ctx->gc.semaphores.clear();
-
-    for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) {
-        ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s });
-    }
-    ctx->gc.tl_semaphores.clear();
-    ctx->semaphore_idx = 0;
-
-    ctx->event_idx = 0;
-
-    for (auto& event : ctx->gc.events) {
-        ctx->device->device.resetEvent(event);
-    }
-
-    ctx->staging_offset = 0;
-
-    ctx->compute_ctx = nullptr;
-    ctx->transfer_ctx = nullptr;
-    ctx->gc.contexts.clear();
-}
-
-// Clean up on backend free
-static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_cleanup(" << ctx->idx << ")" << std::endl;
-#endif
-    ggml_vk_graph_cleanup(ctx);
-
-    ggml_vk_destroy_buffer(ctx->prealloc_x);
-    ggml_vk_destroy_buffer(ctx->prealloc_y);
-    ggml_vk_destroy_buffer(ctx->prealloc_split_k);
-    ggml_vk_destroy_buffer(ctx->staging);
-    ggml_vk_destroy_buffer(ctx->sync_staging);
-
-    for (auto& buffer : ctx->buffer_pool) {
-        ggml_vk_destroy_buffer(buffer);
-    }
-
-    ctx->prealloc_size_x = 0;
-    ctx->prealloc_size_y = 0;
-    ctx->prealloc_size_split_k = 0;
-    ctx->staging_size = 0;
-
-    for (auto& event : ctx->gc.events) {
-        ctx->device->device.destroyEvent(event);
-    }
-    ctx->gc.events.clear();
-
-    ctx->device->device.destroyFence(ctx->fence);
-}
-
-GGML_CALL static int ggml_vk_get_device_count() {
-    ggml_vk_instance_init();
-
-    return vk_instance.device_indices.size();
-}
-
-GGML_CALL static void ggml_vk_get_device_description(int device, char * description, size_t description_size) {
-    ggml_vk_instance_init();
-
-    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
-
-    vk::PhysicalDeviceProperties props;
-    devices[device].getProperties(&props);
-
-    snprintf(description, description_size, "%s", props.deviceName.data());
-}
-
-// backend interface
-
-#define UNUSED GGML_UNUSED
-
-// device backend
-
-static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000;  // NOLINT
-
-struct ggml_backend_vk_buffer_context {
-    ggml_backend_vk_context * ctx;
-    vk_buffer dev_buffer;
-    ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
-    size_t temp_tensor_extra_index = 0;
-    std::string name;
-
-    ggml_backend_vk_buffer_context(ggml_backend_vk_context * ctx, vk_buffer&& dev_buffer, std::string& name) :
-        ctx(ctx),
-        dev_buffer(dev_buffer),
-        name(name) {
-    }
-
-    ~ggml_backend_vk_buffer_context() {
-        ggml_vk_destroy_buffer(dev_buffer);
-        delete[] temp_tensor_extras;
-    }
-
-    ggml_tensor_extra_gpu * ggml_vk_alloc_temp_tensor_extra() {
-        if (temp_tensor_extras == nullptr) {
-            temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_VK_MAX_NODES];
-        }
-
-        size_t alloc_index = temp_tensor_extra_index;
-        temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_VK_MAX_NODES;
-        ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
-        extra->reset();
-
-        return extra;
-    }
-};
-
-GGML_CALL static const char * ggml_backend_vk_buffer_get_name(ggml_backend_buffer_t buffer) {
-    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
-    return ctx->name.c_str();
-}
-
-GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) {
-    return buffer->iface.get_name == ggml_backend_vk_buffer_get_name;
-}
-
-GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_buffer_free_buffer()" << std::endl;
-#endif
-    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
-    ggml_vk_destroy_buffer(ctx->dev_buffer);
-    delete ctx;
-}
-
-GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) {
-    return vk_ptr_base;
-
-    UNUSED(buffer);
-}
-
-GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")" << std::endl;
-#endif
-    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
-
-    ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra();
-    if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) {
-        GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
-        ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra;
-        extra->buffer_gpu = extra_view->buffer_gpu;
-        extra->offset = extra_view->offset + tensor->view_offs;
-    } else {
-        extra->buffer_gpu = ctx->dev_buffer;
-        extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
-    }
-
-    tensor->extra = extra;
-}
-
-GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
-#endif
-    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-    vk_buffer buf = extra->buffer_gpu.lock();
-
-    ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + offset, data, size);
-}
-
-GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
-#endif
-    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-    vk_buffer buf = extra->buffer_gpu.lock();
-
-    ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + offset, data, size);
-}
-
-GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
-    if (ggml_backend_buffer_is_vk(src->buffer)) {
-        ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
-        ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-
-        vk_buffer src_buf = src_extra->buffer_gpu.lock();
-        vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
-
-        ggml_vk_buffer_copy(dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src));
-
-        return true;
-    }
-    return false;
-
-    UNUSED(buffer);
-}
-
-GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
-
-    ggml_vk_buffer_memset(ctx->ctx, ctx->dev_buffer, 0, value, buffer->size);
-}
-
-static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
-    /* .get_name        = */ ggml_backend_vk_buffer_get_name,
-    /* .free_buffer     = */ ggml_backend_vk_buffer_free_buffer,
-    /* .get_base        = */ ggml_backend_vk_buffer_get_base,
-    /* .init_tensor     = */ ggml_backend_vk_buffer_init_tensor,
-    /* .set_tensor      = */ ggml_backend_vk_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_vk_buffer_get_tensor,
-    /* .cpy_tensor      = */ ggml_backend_vk_buffer_cpy_tensor,
-    /* .clear           = */ ggml_backend_vk_buffer_clear,
-    /* .reset           = */ NULL,
-};
-
-// vk buffer type
-struct ggml_backend_vk_buffer_type_context {
-    std::string name;
-    ggml_backend_vk_context * ctx;
-};
-
-GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
-
-    return ctx->name.c_str();
-}
-
-GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl;
-#endif
-    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
-    vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size);
-
-    ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name);
-
-    return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size);
-}
-
-GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
-    return ctx->ctx->device->properties.limits.minStorageBufferOffsetAlignment;
-}
-
-GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
-    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
-    return ctx->ctx->device->max_memory_allocation_size;
-}
-
-GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
-    return ggml_nbytes(tensor);
-
-    UNUSED(buft);
-}
-
-GGML_CALL static bool ggml_backend_vk_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    if (!ggml_backend_is_vk(backend)) {
-        return false;
-    }
-
-    ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-
-    return buft_ctx->ctx->idx == ctx->idx;
-}
-
-static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
-    /* .get_name         = */ ggml_backend_vk_buffer_type_name,
-    /* .alloc_buffer     = */ ggml_backend_vk_buffer_type_alloc_buffer,
-    /* .get_alignment    = */ ggml_backend_vk_buffer_type_get_alignment,
-    /* .get_max_size     = */ ggml_backend_vk_buffer_type_get_max_size,
-    /* .get_alloc_size   = */ ggml_backend_vk_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_vk_buffer_type_supports_backend,
-    /* .is_host          = */ NULL,
-};
-
-GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_buffer_type(" << dev_num << ")" << std::endl;
-#endif
-
-    GGML_ASSERT(dev_num < vk_instance.device_indices.size());
-
-    ggml_backend_vk_init(dev_num);
-
-    return &vk_instance.buffer_types[dev_num];
-}
-
-// host buffer type
-
-GGML_CALL static const char * ggml_backend_vk_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    return GGML_VK_NAME "_Host";
-
-    UNUSED(buft);
-}
-
-GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffer) {
-    return GGML_VK_NAME "_Host";
-
-    UNUSED(buffer);
-}
-
-GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_host_buffer_free_buffer()" << std::endl;
-#endif
-    ggml_vk_host_free(&vk_instance.contexts[0], buffer->context);
-}
-
-GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl;
-#endif
-    size += 32;  // Behave like the CPU buffer type
-    void * ptr = nullptr;
-    try {
-        ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size);
-    } catch (vk::SystemError& e) {
-        std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl;
-        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
-        // fallback to cpu buffer
-        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
-    }
-
-    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
-    buffer->buft = buft;
-    buffer->iface.get_name = ggml_backend_vk_host_buffer_name;
-    buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer;
-
-    return buffer;
-}
-
-GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return vk_instance.contexts[0].device->properties.limits.minMemoryMapAlignment;
-
-    UNUSED(buft);
-}
-
-GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() {
-    static struct ggml_backend_buffer_type ggml_backend_vk_buffer_type_host = {
-        /* .iface    = */ {
-            /* .get_name         = */ ggml_backend_vk_host_buffer_type_name,
-            /* .alloc_buffer     = */ ggml_backend_vk_host_buffer_type_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_vk_host_buffer_type_get_alignment,
-            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
-            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
-            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
-            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
-        },
-        /* .context  = */ nullptr,
-    };
-
-    if (!vk_instance.contexts[0].initialized) {
-        // Fall back to CPU
-        return ggml_backend_cpu_buffer_type();
-    }
-
-    return &ggml_backend_vk_buffer_type_host;
-}
-
-// backend
-
-GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) {
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-
-    return ctx->name.c_str();
-}
-
-GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) {
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_free(" << ctx->name << ")" << std::endl;
-#endif
-
-    size_t idx = ctx->idx;
-
-    ggml_vk_cleanup(ctx);
-
-    ctx->device.reset();
-    ctx->initialized = false;
-
-    vk_instance.initialized[idx] = false;
-    vk_instance.backends[idx] = nullptr;
-    memset(&vk_instance.buffer_types[idx], 0, sizeof(ggml_backend_buffer_type));
-    delete backend;
-}
-
-GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) {
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-
-    GGML_ASSERT(ctx->initialized);
-
-    return ggml_backend_vk_buffer_type(ctx->idx);
-}
-
-GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl;
-#endif
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-    GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-    if (ctx->transfer_ctx == nullptr) {
-        // Initialize new transfer context
-        ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
-        ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
-    }
-
-    vk_buffer buf = extra->buffer_gpu.lock();
-
-    ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size);
-}
-
-GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl;
-#endif
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-    GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-    if (ctx->transfer_ctx == nullptr) {
-        // Initialize new transfer context
-        ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
-        ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
-    }
-
-    vk_buffer buf = extra->buffer_gpu.lock();
-
-    ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size);
-}
-
-GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl;
-#endif
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-    if ((dst->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) {
-        ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
-        ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-
-        if (ctx->transfer_ctx == nullptr) {
-            // Initialize new transfer context
-            ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
-            ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
-        }
-
-        vk_buffer src_buf = src_extra->buffer_gpu.lock();
-        vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
-
-        ggml_vk_buffer_copy_async(ctx->transfer_ctx, dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src));
-        return true;
-    }
-
-    return false;
-}
-
-GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_synchronize()" << std::endl;
-#endif
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-    if(ctx->transfer_ctx == nullptr) {
-        return;
-    }
-
-    ggml_vk_ctx_end(ctx->transfer_ctx);
-
-    for (auto& cpy : ctx->transfer_ctx->in_memcpys) {
-        memcpy(cpy.dst, cpy.src, cpy.n);
-    }
-
-    ggml_vk_submit(ctx->transfer_ctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    for (auto& cpy : ctx->transfer_ctx->out_memcpys) {
-        memcpy(cpy.dst, cpy.src, cpy.n);
-    }
-
-    ctx->transfer_ctx = nullptr;
-}
-
-static bool ggml_vk_is_empty(ggml_tensor * node) {
-    return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE;
-}
-
-GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)" << std::endl;
-#endif
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_vk_preallocate_buffers_graph(ctx, cgraph->nodes[i]);
-    }
-    ggml_vk_preallocate_buffers(ctx);
-
-    int last_node = cgraph->n_nodes - 1;
-
-    // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly
-    while (last_node > 0 && ggml_vk_is_empty(cgraph->nodes[last_node])) {
-        last_node -= 1;
-    }
-
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_vk_build_graph(ctx,cgraph->nodes[i], i == last_node);
-    }
-
-    ggml_compute_params params = {};
-    params.type = GGML_TASK_TYPE_COMPUTE;
-    params.ith = 0;
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_tensor * node = cgraph->nodes[i];
-
-        if (ggml_vk_is_empty(node)) {
-            continue;
-        }
-
-        bool ok = ggml_vk_compute_forward(ctx, &params, node);
-        if (!ok) {
-            fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
-        }
-#ifdef GGML_VULKAN_CHECK_RESULTS
-        else {
-            ggml_vk_check_results_1(ctx, &params, node);
-        }
-#endif
-        GGML_ASSERT(ok);
-    }
-
-    ggml_vk_graph_cleanup(ctx);
-
-    return GGML_STATUS_SUCCESS;
-
-    UNUSED(backend);
-}
-
-GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
-    // ggml_backend_vk_context * ctx = (ggml_backend_vk_context *) backend->context;
-
-    switch (op->op) {
-        case GGML_OP_UNARY:
-            switch (ggml_get_unary_op(op)) {
-                case GGML_UNARY_OP_GELU:
-                case GGML_UNARY_OP_SILU:
-                case GGML_UNARY_OP_RELU:
-                    return true;
-                default:
-                    return false;
-            }
-            break;
-        case GGML_OP_MUL_MAT:
-        // case GGML_OP_MUL_MAT_ID:
-            {
-                switch (op->src[0]->type) {
-                    case GGML_TYPE_F32:
-                    case GGML_TYPE_F16:
-                    case GGML_TYPE_Q4_0:
-                    case GGML_TYPE_Q4_1:
-                    case GGML_TYPE_Q5_0:
-                    case GGML_TYPE_Q5_1:
-                    case GGML_TYPE_Q8_0:
-                    case GGML_TYPE_Q2_K:
-                    case GGML_TYPE_Q3_K:
-                    case GGML_TYPE_Q4_K:
-                    case GGML_TYPE_Q5_K:
-                    case GGML_TYPE_Q6_K:
-                        break;
-                    default:
-                        return false;
-                }
-                struct ggml_tensor * a;
-                struct ggml_tensor * b;
-                if (op->op == GGML_OP_MUL_MAT) {
-                    a = op->src[0];
-                    b = op->src[1];
-                } else {
-                    a = op->src[2];
-                    b = op->src[1];
-                }
-                if (a->ne[3] != b->ne[3]) {
-                    return false;
-                }
-                return true;
-            } break;
-        case GGML_OP_GET_ROWS:
-            {
-                switch (op->src[0]->type) {
-                    case GGML_TYPE_F32:
-                    case GGML_TYPE_F16:
-                    case GGML_TYPE_Q4_0:
-                    case GGML_TYPE_Q4_1:
-                    case GGML_TYPE_Q5_0:
-                    case GGML_TYPE_Q5_1:
-                    case GGML_TYPE_Q8_0:
-                        return true;
-                    default:
-                        return false;
-                }
-            } break;
-        case GGML_OP_CPY:
-        case GGML_OP_DUP:
-            {
-                ggml_type src0_type = op->src[0]->type;
-                ggml_type src1_type = op->src[1] != nullptr ? op->src[1]->type : src0_type;
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
-                    return true;
-                }
-                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
-                    return true;
-                }
-                return false;
-            } break;
-        // case GGML_OP_REPEAT:
-        //     {
-        //         ggml_type src0_type = op->src[0]->type;
-        //         return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
-        //     } break;
-        case GGML_OP_ROPE:
-            {
-                const int mode = ((const int32_t *) op->op_params)[2];
-                const bool is_glm  = mode & 4;
-
-                return !is_glm;
-            } break;
-        case GGML_OP_NONE:
-        case GGML_OP_RESHAPE:
-        case GGML_OP_VIEW:
-        case GGML_OP_PERMUTE:
-        case GGML_OP_TRANSPOSE:
-        case GGML_OP_NORM:
-        case GGML_OP_ADD:
-        case GGML_OP_MUL:
-        case GGML_OP_RMS_NORM:
-        case GGML_OP_SCALE:
-        case GGML_OP_SQR:
-        case GGML_OP_CLAMP:
-        case GGML_OP_CONT:
-        case GGML_OP_DIAG_MASK_INF:
-        case GGML_OP_SOFT_MAX:
-        case GGML_OP_ARGSORT:
-            return true;
-        default:
-            return false;
-    }
-
-    UNUSED(backend);
-}
-
-GGML_CALL static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
-    const ggml_tensor * dst = op;
-
-    const int min_batch_size = 32;
-
-    if (dst->ne[1] > min_batch_size && dst->op != GGML_OP_GET_ROWS) {
-        return true;
-    }
-
-    return false;
-
-    UNUSED(backend);
-}
-
-// TODO: enable async and synchronize
-static ggml_backend_i ggml_backend_vk_interface = {
-    /* .get_name                = */ ggml_backend_vk_name,
-    /* .free                    = */ ggml_backend_vk_free,
-    /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type,
-    /* .set_tensor_async        = */ NULL,  // ggml_backend_vk_set_tensor_async,
-    /* .get_tensor_async        = */ NULL,  // ggml_backend_vk_get_tensor_async,
-    /* .cpy_tensor_async        = */ NULL,  // ggml_backend_vk_cpy_tensor_async,
-    /* .synchronize             = */ NULL,  // ggml_backend_vk_synchronize,
-    /* .graph_plan_create       = */ NULL,
-    /* .graph_plan_free         = */ NULL,
-    /* .graph_plan_compute      = */ NULL,
-    /* .graph_compute           = */ ggml_backend_vk_graph_compute,
-    /* .supports_op             = */ ggml_backend_vk_supports_op,
-    /* .offload_op              = */ ggml_backend_vk_offload_op,
-    /* .event_new               = */ NULL,
-    /* .event_free              = */ NULL,
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
-    /* .event_synchronize       = */ NULL,
-};
-
-static ggml_guid_t ggml_backend_vk_guid() {
-    static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
-    return &guid;
-}
-
-GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
-    if (vk_instance.initialized[dev_num]) {
-        return vk_instance.backends[dev_num];
-    }
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_backend_vk_init(" << dev_num << ")" << std::endl;
-#endif
-
-    ggml_backend_vk_context * ctx = &vk_instance.contexts[dev_num];
-    ggml_vk_init(ctx, dev_num);
-    ctx->name = GGML_VK_NAME + std::to_string(dev_num);
-    vk_instance.buffer_types[dev_num] = {
-        /* .iface    = */ ggml_backend_vk_buffer_type_interface,
-        /* .context  = */ new ggml_backend_vk_buffer_type_context{ ctx->name, ctx },
-    };
-    vk_instance.initialized[dev_num] = true;
-
-    ggml_backend_t vk_backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_vk_guid(),
-        /* .interface = */ ggml_backend_vk_interface,
-        /* .context   = */ &vk_instance.contexts[ctx->idx],
-    };
-
-    vk_instance.backends[dev_num] = vk_backend;
-
-    return vk_backend;
-}
-
-GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) {
-    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid());
-}
-
-GGML_CALL int ggml_backend_vk_get_device_count() {
-    return ggml_vk_get_device_count();
-}
-
-GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) {
-    ggml_vk_get_device_description(device, description, description_size);
-}
-
-GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
-    GGML_ASSERT(device < (int) vk_instance.device_indices.size());
-
-    vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];
-
-    vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties();
-
-    for (const vk::MemoryHeap& heap : memprops.memoryHeaps) {
-        if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) {
-            *total = heap.size;
-            *free = heap.size;
-            break;
-        }
-    }
-}
-
-// backend registry
-GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, void * user_data) {
-    ggml_backend_t vk_backend = ggml_backend_vk_init((int) (intptr_t) user_data);
-    return vk_backend;
-
-    UNUSED(params);
-}
-
-extern "C" GGML_CALL int ggml_backend_vk_reg_devices();
-
-GGML_CALL int ggml_backend_vk_reg_devices() {
-    ggml_vk_instance_init();
-
-    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
-        char name[128];
-        snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, i);
-        ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(i), (void *) (intptr_t) i);  // NOLINT
-    }
-    return vk_instance.device_indices.size();
-}
-
-// Extension availability
-static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
-#ifdef GGML_VULKAN_VALIDATE
-    bool portability_enumeration_ext = false;
-    // Check for portability enumeration extension for MoltenVK support
-    for (const auto& properties : instance_extensions) {
-        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
-            return true;
-        }
-    }
-    if (!portability_enumeration_ext) {
-        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
-    }
-#endif
-    return false;
-
-    UNUSED(instance_extensions);
-}
-static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
-#ifdef __APPLE__
-    bool portability_enumeration_ext = false;
-    // Check for portability enumeration extension for MoltenVK support
-    for (const auto& properties : instance_extensions) {
-        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
-            return true;
-        }
-    }
-    if (!portability_enumeration_ext) {
-        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
-    }
-#endif
-    return false;
-
-    UNUSED(instance_extensions);
-}
-
-// checks
-
-#ifdef GGML_VULKAN_CHECK_RESULTS
-static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector<const ggml_tensor *>& done, int level = 0) {
-    if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) {
-        return;
-    }
-    for (int j = 0; j < level; j++) {
-        std::cerr << " ";
-    }
-    std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << " backend=" << tensor->backend << std::endl;
-
-    done.push_back(tensor);
-
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (tensor->src[i] != nullptr) {
-            ggml_vk_print_graph_origin(tensor->src[i], done, level + 1);
-        }
-    }
-}
-
-static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, int i0, int i1, int i2, int i3) {
-    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16 && tensor->type != GGML_TYPE_I32) {
-        return;
-    }
-    i0 = std::max(i0, 5);
-    i1 = std::max(i1, 5);
-    i2 = std::max(i2, 0);
-    i3 = std::max(i3, 0);
-    fprintf(stderr, "         ");
-    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
-        fprintf(stderr, "%7d ", idx1);
-    }
-    fprintf(stderr, "\n");
-    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
-        fprintf(stderr, "%7d: ", idx0);
-        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
-            if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
-                float val;
-                if (tensor->type == GGML_TYPE_F32) {
-                    val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
-                } else if (tensor->type == GGML_TYPE_F16) {
-                    val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
-                } else if (tensor->type == GGML_TYPE_I32) {
-                    val = *(const int32_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
-                } else {
-                    GGML_ASSERT(false);
-                }
-                fprintf(stderr, "% 7.2f ", val);
-            } else {
-                fprintf(stderr, "        ");
-            }
-        }
-        fprintf(stderr, "\n");
-    }
-}
-
-static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) {
-    void * tensor_data = tensor->data;
-
-    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
-        const size_t tensor_size = ggml_nbytes(tensor);
-        tensor_data = malloc(tensor_size);
-
-        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-        vk_buffer buffer_gpu = extra->buffer_gpu.lock();
-        ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
-    }
-
-    std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
-    std::cerr << "tensor=" << tensor << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl;
-    if (tensor->src[0] != nullptr) {
-        std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " backend=" << tensor->src[0]->backend << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl;
-    }
-    if (tensor->src[1] != nullptr) {
-        std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " backend=" << tensor->src[1]->backend << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl;
-    }
-    std::cerr << std::endl << "Result:" << std::endl;
-    ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
-    std::cerr << std::endl;
-    std::cerr << std::endl << "Result:" << std::endl;
-    ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 1, 0);
-    std::cerr << std::endl;
-    std::vector<const ggml_tensor *> done;
-    ggml_vk_print_graph_origin(tensor, done);
-
-    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
-        free(tensor_data);
-    }
-}
-
-static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) {
-    return;
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU);
-    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
-        return;
-    }
-    for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
-        for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
-            for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
-                for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
-                    float val = 0.0f;
-                    if (tensor->type == GGML_TYPE_F32) {
-                        val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
-                    } else if (tensor->type == GGML_TYPE_F16) {
-                        val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
-                    }
-                    if (std::isnan(val)) {
-                        std::cerr << "ERROR: TENSOR CHECK " << name << ": Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " val=" << val << std::endl;
-                        std::cerr << "tensor=" << tensor << " tensor->type=" << ggml_type_name(tensor->type) << " tensor->backend: " << tensor->backend << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl;
-                        std::cerr << std::endl;
-                        ggml_vk_print_tensor_area(tensor, tensor->data, i0, i1, i2, i3);
-                        std::cerr << std::endl;
-                        std::vector<const ggml_tensor *> done;
-                        ggml_vk_print_graph_origin(tensor, done);
-                        GGML_ASSERT(false);
-                    }
-                }
-            }
-        }
-    }
-}
-
-void * comp_result;
-size_t comp_size;
-size_t comp_nb[GGML_MAX_DIMS];
-size_t check_counter = 0;
-static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) {
-    if (params->ith != 0) {
-        return;
-    }
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
-        return;
-    }
-
-    check_counter++;
-    if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
-        return;
-    }
-
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_check_results_0(" << tensor->name << ")" << std::endl;
-#endif
-
-    ggml_tensor * src0 = tensor->src[0];
-    ggml_tensor * src1 = tensor->src[1];
-
-    struct ggml_init_params iparams = {
-        /*.mem_size   =*/ 1024*1024*1024,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ false,
-    };
-
-    struct ggml_context * ggml_ctx = ggml_init(iparams);
-
-    struct ggml_tensor * src0_clone = nullptr;
-    struct ggml_tensor * src1_clone = nullptr;
-    struct ggml_tensor * src2_clone = nullptr;
-    struct ggml_tensor * tensor_clone = nullptr;
-
-    size_t src0_size;
-    size_t src1_size;
-    size_t src2_size;
-
-    void * src0_buffer;
-    void * src1_buffer;
-    void * src2_buffer;
-
-    if (src0 != nullptr) {
-        src0_clone = ggml_dup_tensor(ggml_ctx, src0);
-
-        src0_size = ggml_nbytes(src0);
-
-        src0_buffer = malloc(src0_size);
-        src0_clone->data = src0_buffer;
-        if (src0->backend == GGML_BACKEND_TYPE_CPU) {
-            memcpy(src0_clone->data, src0->data, src0_size);
-            memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
-        } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
-            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra;
-            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
-            uint64_t offset = extra->offset;
-            if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) {
-                for (int i3 = 0; i3 < src0->ne[3]; i3++) {
-                    for (int i2 = 0; i2 < src0->ne[2]; i2++) {
-                        const int idx = i3*src0->ne[2] + i2;
-                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
-                    }
-                }
-
-                src0_clone->nb[0] = src0->nb[0];
-                src0_clone->nb[1] = src0->nb[1];
-                for (int i = 2; i < GGML_MAX_DIMS; i++) {
-                    src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1];
-                }
-            } else {
-                if (offset + src0_size >= buffer_gpu->size) {
-                    src0_size = buffer_gpu->size - offset;
-                }
-                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size);
-                memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
-            }
-        } else {
-            GGML_ASSERT(false);
-        }
-
-        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
-            ggml_vk_print_tensor(ctx, src0, "src0");
-        }
-
-        ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone);
-    }
-    if (src1 != nullptr) {
-        src1_clone = ggml_dup_tensor(ggml_ctx, src1);
-
-        src1_size = ggml_nbytes(src1);
-
-        src1_buffer = malloc(src1_size);
-        src1_clone->data = src1_buffer;
-        if (src1->backend == GGML_BACKEND_TYPE_CPU) {
-            memcpy(src1_clone->data, src1->data, src1_size);
-            memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
-        } else if (src1->backend == GGML_BACKEND_TYPE_GPU) {
-            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra;
-            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
-            uint64_t offset = extra->offset;
-            if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) {
-                for (int i3 = 0; i3 < src1->ne[3]; i3++) {
-                    for (int i2 = 0; i2 < src1->ne[2]; i2++) {
-                        const int idx = i3*src1->ne[2] + i2;
-                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
-                    }
-                }
-
-                src1_clone->nb[0] = src1->nb[0];
-                src1_clone->nb[1] = src1->nb[1];
-                for (int i = 2; i < GGML_MAX_DIMS; i++) {
-                    src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1];
-                }
-            } else {
-                if (offset + src1_size >= buffer_gpu->size) {
-                    src1_size = buffer_gpu->size - offset;
-                }
-                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size);
-                memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
-            }
-        } else {
-            GGML_ASSERT(false);
-        }
-
-        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
-            ggml_vk_print_tensor(ctx, src1, "src1");
-            std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl;
-            std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl;
-            if (src1->src[0] != nullptr) {
-                std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " backend=" << src1->src[0]->backend << " ne0=" << src1->src[0]->ne[0] << " nb0=" << src1->src[0]->nb[0] << " ne1=" << src1->src[0]->ne[1] << " nb1=" << src1->src[0]->nb[1] << " ne2=" << src1->src[0]->ne[2] << " nb2=" << src1->src[0]->nb[2] << " ne3=" << src1->src[0]->ne[3] << " nb3=" << src1->src[0]->nb[3] << std::endl;
-            }
-            if (src1->src[1] != nullptr) {
-                std::cerr << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " backend=" << src1->src[1]->backend << " ne0=" << src1->src[1]->ne[0] << " nb0=" << src1->src[1]->nb[0] << " ne1=" << src1->src[1]->ne[1] << " nb1=" << src1->src[1]->nb[1] << " ne2=" << src1->src[1]->ne[2] << " nb2=" << src1->src[1]->nb[2] << " ne3=" << src1->src[1]->ne[3] << " nb3=" << src1->src[1]->nb[3] << std::endl;
-            }
-            std::cerr << std::endl << "Result:" << std::endl;
-            ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 0, 0);
-            std::cerr << std::endl;
-            std::cerr << std::endl << "Result:" << std::endl;
-            ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 1, 0);
-            std::cerr << std::endl;
-            std::vector<const ggml_tensor *> done;
-            ggml_vk_print_graph_origin(src1_clone, done);
-        }
-
-        ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone);
-    }
-
-    if (tensor->op == GGML_OP_MUL_MAT) {
-        tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone);
-    } else if (tensor->op == GGML_OP_MUL) {
-        tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone);
-    } else if (tensor->op == GGML_OP_SCALE) {
-        tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]);
-    } else if (tensor->op == GGML_OP_SQR) {
-        tensor_clone = ggml_sqr(ggml_ctx, src0_clone);
-    } else if (tensor->op == GGML_OP_CLAMP) {
-        tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
-    } else if (tensor->op == GGML_OP_ADD) {
-        tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone);
-    } else if (tensor->op == GGML_OP_NORM) {
-        tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
-    } else if (tensor->op == GGML_OP_RMS_NORM) {
-        tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
-    } else if (tensor->op == GGML_OP_SOFT_MAX) {
-        if (src1 != nullptr) {
-            tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
-        } else {
-            tensor_clone = ggml_soft_max(ggml_ctx, src0_clone);
-        }
-    } else if (tensor->op == GGML_OP_DIAG_MASK_INF) {
-        tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(int *)tensor->op_params);
-    } else if (tensor->op == GGML_OP_ROPE) {
-        const int n_dims      = ((int32_t *) tensor->op_params)[1];
-        const int mode        = ((int32_t *) tensor->op_params)[2];
-        const int n_ggml_ctx       = ((int32_t *) tensor->op_params)[3];
-        const int n_orig_ggml_ctx  = ((int32_t *) tensor->op_params)[4];
-        float freq_base       = ((float *)   tensor->op_params)[5];
-        float freq_scale      = ((float *)   tensor->op_params)[6];
-        float ext_factor      = ((float *)   tensor->op_params)[7];
-        float attn_factor     = ((float *)   tensor->op_params)[8];
-        float beta_fast       = ((float *)   tensor->op_params)[9];
-        float beta_slow       = ((float *)   tensor->op_params)[10];
-        tensor_clone = ggml_rope_custom(ggml_ctx, src0_clone, src1_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
-    } else if (tensor->op == GGML_OP_UNARY) {
-        switch (ggml_get_unary_op(tensor)) {
-        case GGML_UNARY_OP_SILU:
-            tensor_clone = ggml_silu(ggml_ctx, src0_clone);
-            break;
-        case GGML_UNARY_OP_GELU:
-            tensor_clone = ggml_gelu(ggml_ctx, src0_clone);
-            break;
-        case GGML_UNARY_OP_RELU:
-            tensor_clone = ggml_relu(ggml_ctx, src0_clone);
-            break;
-        default:
-            std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
-            GGML_ASSERT(false);
-        }
-    } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) {
-        if (src1 == nullptr) {
-            tensor_clone = ggml_dup(ggml_ctx, src0_clone);
-            tensor_clone->type = tensor->type;
-        } else {
-            tensor_clone = ggml_cpy(ggml_ctx, src0_clone, src1_clone);
-        }
-    } else if (tensor->op == GGML_OP_CONT) {
-        tensor_clone = ggml_cont_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
-    } else if (tensor->op == GGML_OP_RESHAPE) {
-        tensor_clone = ggml_reshape_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
-    } else if (tensor->op == GGML_OP_VIEW) {
-        tensor_clone = ggml_view_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]);
-    } else if (tensor->op == GGML_OP_PERMUTE) {
-        int32_t * params = (int32_t *)tensor->op_params;
-        tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]);
-    } else if (tensor->op == GGML_OP_TRANSPOSE) {
-        tensor_clone = ggml_transpose(ggml_ctx, src0_clone);
-    } else if (tensor->op == GGML_OP_GET_ROWS) {
-        tensor_clone = ggml_get_rows(ggml_ctx, src0_clone, src1_clone);
-    } else if (tensor->op == GGML_OP_ARGSORT) {
-        tensor_clone = ggml_argsort(ggml_ctx, src0_clone, (ggml_sort_order) *(int *)tensor->op_params);
-    } else {
-        std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
-        GGML_ASSERT(false);
-    }
-
-    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
-    ggml_build_forward_expand(cgraph, tensor_clone);
-
-    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8);
-
-    ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone);
-    if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
-        ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone");
-    }
-
-    comp_size = ggml_nbytes(tensor_clone);
-
-    comp_result = malloc(comp_size);
-    memcpy(comp_result, tensor_clone->data, comp_size);
-    memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS);
-
-    if (src0 != nullptr) {
-        free(src0_buffer);
-    }
-    if (src1 != nullptr) {
-        free(src1_buffer);
-    }
-
-    ggml_free(ggml_ctx);
-}
-
-static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) {
-    if (params->ith != 0) {
-        return;
-    }
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
-        return;
-    }
-    if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
-        return;
-    }
-
-#ifdef GGML_VULKAN_DEBUG
-    std::cerr << "ggml_vk_check_results_1(" << tensor->name << ")" << std::endl;
-#endif
-
-    ggml_tensor * src0 = tensor->src[0];
-    ggml_tensor * src1 = tensor->src[1];
-
-    void * tensor_data = tensor->data;
-
-    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
-        size_t tensor_size = ggml_nbytes(tensor);
-        tensor_data = malloc(tensor_size);
-
-        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-        vk_buffer buffer_gpu = extra->buffer_gpu.lock();
-        if (extra->offset + tensor_size >= buffer_gpu->size) {
-            tensor_size = buffer_gpu->size - (extra->offset);
-        }
-
-        ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
-    }
-
-    float first_error_result = -1.0f;
-    float first_error_correct = -1.0f;
-    std::array<int, 4> first_error = { -1, -1, -1, -1 };
-    double avg_err = 0.0;
-    size_t counter = 0;
-
-    for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
-        for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
-            for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
-                for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
-                    const bool buffer_size_fit = i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0] < comp_size;
-                    float correct = 0.0f;
-                    float result = 0.0f;
-
-                    if (buffer_size_fit) {
-                        if (tensor->type == GGML_TYPE_F32) {
-                            correct = *(float *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
-                            result  = *(float *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
-                        } else if (tensor->type == GGML_TYPE_F16) {
-                            correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]));
-                            result  = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
-                        } else if (tensor->type == GGML_TYPE_I32) {
-                            correct = *(int32_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
-                            result  = *(int32_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
-                        } else {
-                            std::cerr << "Results check not implemented for type " << ggml_type_name(tensor->type) << std::endl;
-                        }
-                    } else {
-                        std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl;
-                        GGML_ASSERT(false);
-                    }
-
-                    if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) {
-                        std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl;
-                        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
-                        if (src0 != nullptr) {
-                            std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
-                        }
-                        if (src1 != nullptr) {
-                            std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
-                        }
-                        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
-                        std::cerr << std::endl << "Result:" << std::endl;
-                        ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3);
-                        std::cerr << std::endl << "Correct:" << std::endl;
-                        ggml_vk_print_tensor_area(tensor, comp_result, i0, i1, i2, i3);
-                        std::cerr << std::endl;
-                        std::vector<const ggml_tensor *> done;
-                        ggml_vk_print_graph_origin(tensor, done);
-                        GGML_ASSERT(false);
-                    }
-                    if (first_error[0] == -1 && std::fabs(correct - result) > 0.1f) {
-                        first_error[0] = i0;
-                        first_error[1] = i1;
-                        first_error[2] = i2;
-                        first_error[3] = i3;
-                        first_error_result = result;
-                        first_error_correct = correct;
-                    }
-
-                    // Special case, value is infinite, avoid NaN result in avg_err
-                    // NaN also appears in results, if both are nan error is 0
-                    if (!std::isinf(correct) && !std::isinf(result) && !std::isnan(correct) && !std::isnan(result)) {
-                        avg_err += std::fabs(correct - result);
-                    }
-                    counter++;
-                }
-            }
-        }
-    }
-
-    avg_err /= counter;
-
-    if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
-        std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
-        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
-        if (src0 != nullptr) {
-            std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
-        }
-        if (src1 != nullptr) {
-            std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
-        }
-        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
-        std::cerr << std::endl << "Result:" << std::endl;
-        ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
-        std::cerr << std::endl << "Correct:" << std::endl;
-        ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 0, 0);
-        std::cerr << std::endl;
-        std::cerr << std::endl << "Result:" << std::endl;
-        ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 1, 0);
-        std::cerr << std::endl << "Correct:" << std::endl;
-        ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 1, 0);
-        std::cerr << std::endl;
-        std::vector<const ggml_tensor *> done;
-        ggml_vk_print_graph_origin(tensor, done);
-    }
-
-    if (avg_err > 0.05 || std::isnan(avg_err)) {
-        std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
-        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
-        if (src0 != nullptr) {
-            std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
-        }
-        if (src1 != nullptr) {
-            std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
-        }
-        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
-        std::cerr << std::endl << "Result:" << std::endl;
-        ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]);
-        std::cerr << std::endl << "Correct:" << std::endl;
-        ggml_vk_print_tensor_area(tensor, comp_result, first_error[0], first_error[1], first_error[2], first_error[3]);
-        std::cerr << std::endl;
-        std::vector<const ggml_tensor *> done;
-        ggml_vk_print_graph_origin(tensor, done);
-        GGML_ASSERT(false);
-    } else {
-        std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " backend=" << tensor->backend << " avg_err=" << avg_err << std::endl;
-    }
-
-    free(comp_result);
-    comp_result = nullptr;
-    comp_size = 0;
-
-    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
-        free(tensor_data);
-    }
-}
-#endif
diff --git a/ggml/.gitignore b/ggml/.gitignore
new file mode 100644
index 0000000000000..c82d8e69295ac
--- /dev/null
+++ b/ggml/.gitignore
@@ -0,0 +1,2 @@
+src/ggml-vulkan-shaders.hpp
+src/ggml-vulkan-shaders.cpp
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
new file mode 100644
index 0000000000000..cc16858849783
--- /dev/null
+++ b/ggml/CMakeLists.txt
@@ -0,0 +1,254 @@
+cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories.
+project("ggml" C CXX)
+include(CheckIncludeFileCXX)
+
+set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
+
+if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
+    set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
+    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
+endif()
+
+if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
+    set(GGML_STANDALONE ON)
+
+    set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+
+    # configure project version
+    # TODO
+else()
+    set(GGML_STANDALONE OFF)
+endif()
+
+if (EMSCRIPTEN)
+    set(BUILD_SHARED_LIBS_DEFAULT OFF)
+
+    option(GGML_WASM_SINGLE_FILE "ggml: embed WASM inside the generated ggml.js" ON)
+else()
+    if (MINGW)
+        set(BUILD_SHARED_LIBS_DEFAULT OFF)
+    else()
+        set(BUILD_SHARED_LIBS_DEFAULT ON)
+    endif()
+endif()
+
+option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
+
+#
+# option list
+#
+
+# TODO: mark all options as advanced when not GGML_STANDALONE
+
+if (APPLE)
+    set(GGML_METAL_DEFAULT ON)
+    set(GGML_BLAS_DEFAULT ON)
+    set(GGML_BLAS_VENDOR_DEFAULT "Apple")
+else()
+    set(GGML_METAL_DEFAULT OFF)
+    set(GGML_BLAS_DEFAULT OFF)
+    set(GGML_BLAS_VENDOR_DEFAULT "Generic")
+endif()
+
+if (CMAKE_CROSSCOMPILING)
+    set(GGML_NATIVE_DEFAULT OFF)
+else()
+    set(GGML_NATIVE_DEFAULT ON)
+endif()
+
+# general
+option(GGML_STATIC "ggml: static link libraries"         OFF)
+option(GGML_NATIVE "ggml: enable -march=native flag"     ${GGML_NATIVE_DEFAULT})
+option(GGML_LTO    "ggml: enable link time optimization" OFF)
+option(GGML_CCACHE "ggml: use ccache if available"       ON)
+
+# debug
+option(GGML_ALL_WARNINGS           "ggml: enable all compiler warnings"                   ON)
+option(GGML_ALL_WARNINGS_3RD_PARTY "ggml: enable all compiler warnings in 3rd party libs" OFF)
+option(GGML_GPROF                  "ggml: enable gprof"                                   OFF)
+
+# build
+option(GGML_FATAL_WARNINGS    "ggml: enable -Werror flag"    OFF)
+
+# sanitizers
+option(GGML_SANITIZE_THREAD    "ggml: enable thread sanitizer"    OFF)
+option(GGML_SANITIZE_ADDRESS   "ggml: enable address sanitizer"   OFF)
+option(GGML_SANITIZE_UNDEFINED "ggml: enable undefined sanitizer" OFF)
+
+# instruction set specific
+if (GGML_NATIVE OR NOT GGML_NATIVE_DEFAULT)
+    set(INS_ENB OFF)
+else()
+    set(INS_ENB ON)
+endif()
+
+option(GGML_CPU_HBM     "ggml: use memkind for CPU HBM" OFF)
+
+option(GGML_AVX         "ggml: enable AVX"              ${INS_ENB})
+option(GGML_AVX2        "ggml: enable AVX2"             ${INS_ENB})
+option(GGML_AVX512      "ggml: enable AVX512"           OFF)
+option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI"      OFF)
+option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI"      OFF)
+option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16"      OFF)
+option(GGML_FMA         "ggml: enable FMA"              ${INS_ENB})
+if (NOT MSVC)
+    option(GGML_F16C    "ggml: enable F16C"             ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512
+endif()
+option(GGML_LASX        "ggml: enable lasx"             ON)
+option(GGML_LSX         "ggml: enable lsx"              ON)
+option(GGML_SVE         "ggml: enable SVE"              OFF)
+
+if (WIN32)
+    set(GGML_WIN_VER "0x602" CACHE STRING "ggml: Windows Version")
+endif()
+
+# ggml core
+set(GGML_SCHED_MAX_COPIES  "4" CACHE STRING "ggml: max input copies for pipeline parallelism")
+
+# 3rd party libs / backends
+option(GGML_ACCELERATE                      "ggml: enable Accelerate framework"               ON)
+option(GGML_BLAS                            "ggml: use BLAS"                                  ${GGML_BLAS_DEFAULT})
+set(GGML_BLAS_VENDOR ${GGML_BLAS_VENDOR_DEFAULT} CACHE STRING
+                                            "ggml: BLAS library vendor")
+option(GGML_LLAMAFILE                       "ggml: use LLAMAFILE"                             OFF)
+
+option(GGML_CUDA                            "ggml: use CUDA"                                  OFF)
+option(GGML_MUSA                            "ggml: use MUSA"                                  OFF)
+option(GGML_CUDA_FORCE_DMMV                 "ggml: use dmmv instead of mmvq CUDA kernels"     OFF)
+option(GGML_CUDA_FORCE_MMQ                  "ggml: use mmq kernels instead of cuBLAS"         OFF)
+option(GGML_CUDA_FORCE_CUBLAS               "ggml: always use cuBLAS instead of mmq kernels"  OFF)
+set   (GGML_CUDA_DMMV_X   "32" CACHE STRING "ggml: x stride for dmmv CUDA kernels")
+set   (GGML_CUDA_MMV_Y     "1" CACHE STRING "ggml: y block size for mmv CUDA kernels")
+option(GGML_CUDA_F16                        "ggml: use 16 bit floats for some calculations"   OFF)
+set   (GGML_CUDA_KQUANTS_ITER "2" CACHE STRING
+                                            "ggml: iters./thread per block for Q2_K/Q6_K")
+set   (GGML_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
+                                            "ggml: max. batch size for using peer access")
+option(GGML_CUDA_NO_PEER_COPY               "ggml: do not use peer to peer copies"            OFF)
+option(GGML_CUDA_NO_VMM                     "ggml: do not try to use CUDA VMM"                OFF)
+option(GGML_CUDA_FA_ALL_QUANTS              "ggml: compile all quants for FlashAttention"     OFF)
+option(GGML_CUDA_USE_GRAPHS                 "ggml: use CUDA graphs (llama.cpp only)"          OFF)
+
+option(GGML_HIPBLAS                         "ggml: use hipBLAS"                               OFF)
+option(GGML_HIP_UMA                         "ggml: use HIP unified memory architecture"       OFF)
+option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)
+option(GGML_VULKAN_CHECK_RESULTS            "ggml: run Vulkan op checks"                      OFF)
+option(GGML_VULKAN_DEBUG                    "ggml: enable Vulkan debug output"                OFF)
+option(GGML_VULKAN_MEMORY_DEBUG             "ggml: enable Vulkan memory debug output"         OFF)
+option(GGML_VULKAN_PERF                     "ggml: enable Vulkan perf output"                 OFF)
+option(GGML_VULKAN_VALIDATE                 "ggml: enable Vulkan validation"                  OFF)
+option(GGML_VULKAN_RUN_TESTS                "ggml: run Vulkan tests"                          OFF)
+option(GGML_KOMPUTE                         "ggml: use Kompute"                               OFF)
+option(GGML_METAL                           "ggml: use Metal"                                 ${GGML_METAL_DEFAULT})
+option(GGML_METAL_NDEBUG                    "ggml: disable Metal debugging"                   OFF)
+option(GGML_METAL_SHADER_DEBUG              "ggml: compile Metal with -fno-fast-math"         OFF)
+option(GGML_METAL_EMBED_LIBRARY             "ggml: embed Metal library"                       ${GGML_METAL})
+set   (GGML_METAL_MACOSX_VERSION_MIN "" CACHE STRING
+                                            "ggml: metal minimum macOS version")
+set   (GGML_METAL_STD "" CACHE STRING       "ggml: metal standard version (-std flag)")
+option(GGML_OPENMP                          "ggml: use OpenMP"                                ON)
+option(GGML_RPC                             "ggml: use RPC"                                   OFF)
+option(GGML_SYCL                            "ggml: use SYCL"                                  OFF)
+option(GGML_SYCL_F16                        "ggml: use 16 bit floats for sycl calculations"   OFF)
+set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
+                                            "ggml: sycl target device")
+
+# extra artifacts
+option(GGML_BUILD_TESTS    "ggml: build tests"    ${GGML_STANDALONE})
+option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
+
+#
+# dependencies
+#
+
+set(CMAKE_C_STANDARD 11)
+set(CMAKE_C_STANDARD_REQUIRED true)
+
+if (GGML_SYCL)
+    set(CMAKE_CXX_STANDARD 17)
+else()
+    set(CMAKE_CXX_STANDARD 11)
+endif()
+set(CMAKE_CXX_STANDARD_REQUIRED true)
+
+set(THREADS_PREFER_PTHREAD_FLAG ON)
+
+find_package(Threads REQUIRED)
+
+#
+# build the library
+#
+
+add_subdirectory(src)
+
+#
+# tests and examples
+#
+
+if (GGML_BUILD_TESTS)
+    enable_testing()
+    add_subdirectory(tests)
+endif ()
+
+if (GGML_BUILD_EXAMPLES)
+    add_subdirectory(examples)
+endif ()
+
+#
+# install
+#
+
+include(GNUInstallDirs)
+include(CMakePackageConfigHelpers)
+
+# all public headers
+set(GGML_PUBLIC_HEADERS
+    include/ggml.h
+    include/ggml-alloc.h
+    include/ggml-backend.h
+    include/ggml-blas.h
+    include/ggml-cann.h
+    include/ggml-cuda.h
+    include/ggml.h
+    include/ggml-kompute.h
+    include/ggml-metal.h
+    include/ggml-rpc.h
+    include/ggml-sycl.h
+    include/ggml-vulkan.h)
+
+set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
+#if (GGML_METAL)
+#    set_target_properties(ggml PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/src/ggml-metal.metal")
+#endif()
+install(TARGETS ggml PUBLIC_HEADER)
+
+if (BUILD_SHARED_LIBS)
+    install(TARGETS ggml LIBRARY)
+endif()
+
+if (GGML_METAL)
+    install(
+        FILES src/ggml-metal.metal
+        PERMISSIONS
+            OWNER_READ
+            OWNER_WRITE
+            GROUP_READ
+            WORLD_READ
+        DESTINATION ${CMAKE_INSTALL_BINDIR})
+
+    if (NOT GGML_METAL_EMBED_LIBRARY)
+        install(
+            FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
+            DESTINATION ${CMAKE_INSTALL_BINDIR}
+        )
+    endif()
+endif()
+
+if (GGML_STANDALONE)
+    configure_file(${CMAKE_CURRENT_SOURCE_DIR}/ggml.pc.in
+        ${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
+        @ONLY)
+
+    install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
+        DESTINATION share/pkgconfig)
+endif()
diff --git a/cmake/FindSIMD.cmake b/ggml/cmake/FindSIMD.cmake
similarity index 94%
rename from cmake/FindSIMD.cmake
rename to ggml/cmake/FindSIMD.cmake
index 33377ec44de12..5533668ec4ab1 100644
--- a/cmake/FindSIMD.cmake
+++ b/ggml/cmake/FindSIMD.cmake
@@ -79,22 +79,22 @@ endmacro()
 # flags are for MSVC only!
 check_sse("AVX" " ;/arch:AVX")
 if (NOT ${AVX_FOUND})
-    set(LLAMA_AVX OFF)
+    set(GGML_AVX OFF)
 else()
-    set(LLAMA_AVX ON)
+    set(GGML_AVX ON)
 endif()
 
 check_sse("AVX2" " ;/arch:AVX2")
 check_sse("FMA" " ;/arch:AVX2")
 if ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND}))
-    set(LLAMA_AVX2 OFF)
+    set(GGML_AVX2 OFF)
 else()
-    set(LLAMA_AVX2 ON)
+    set(GGML_AVX2 ON)
 endif()
 
 check_sse("AVX512" " ;/arch:AVX512")
 if (NOT ${AVX512_FOUND})
-    set(LLAMA_AVX512 OFF)
+    set(GGML_AVX512 OFF)
 else()
-    set(LLAMA_AVX512 ON)
+    set(GGML_AVX512 ON)
 endif()
diff --git a/ggml-alloc.h b/ggml/include/ggml-alloc.h
similarity index 100%
rename from ggml-alloc.h
rename to ggml/include/ggml-alloc.h
diff --git a/ggml-backend.h b/ggml/include/ggml-backend.h
similarity index 82%
rename from ggml-backend.h
rename to ggml/include/ggml-backend.h
index 744b6a77457d7..5f3f1e286990e 100644
--- a/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -23,28 +23,29 @@ extern "C" {
     GGML_API           size_t                ggml_backend_buft_get_alignment   (ggml_backend_buffer_type_t buft);
     GGML_API           size_t                ggml_backend_buft_get_max_size    (ggml_backend_buffer_type_t buft);
     GGML_API GGML_CALL size_t                ggml_backend_buft_get_alloc_size  (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
-    GGML_API           bool                  ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
     GGML_API           bool                  ggml_backend_buft_is_host         (ggml_backend_buffer_type_t buft);
 
     // buffer
     enum ggml_backend_buffer_usage {
         GGML_BACKEND_BUFFER_USAGE_ANY = 0,
         GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
+        GGML_BACKEND_BUFFER_USAGE_COMPUTE = 2,
     };
 
-    GGML_API           const char *               ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
-    GGML_API           void                       ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
-    GGML_API           void *                     ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
-    GGML_API           size_t                     ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
-    GGML_API GGML_CALL void                       ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API           size_t                     ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
-    GGML_API           size_t                     ggml_backend_buffer_get_max_size  (ggml_backend_buffer_t buffer);
-    GGML_API           size_t                     ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API           void                       ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
-    GGML_API           bool                       ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
-    GGML_API           void                       ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
-    GGML_API           ggml_backend_buffer_type_t ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
-    GGML_API           void                       ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
+    GGML_API           const char *                   ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
+    GGML_API           void                           ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
+    GGML_API           void *                         ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                         ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
+    GGML_API GGML_CALL void                           ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           size_t                         ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                         ggml_backend_buffer_get_max_size  (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                         ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           void                           ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
+    GGML_API           bool                           ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
+    GGML_API           void                           ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
+    GGML_API           enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage     (ggml_backend_buffer_t buffer);
+    GGML_API           ggml_backend_buffer_type_t     ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
+    GGML_API           void                           ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
 
     //
     // Backend
@@ -74,6 +75,7 @@ extern "C" {
     GGML_API enum ggml_status ggml_backend_graph_compute      (ggml_backend_t backend, struct ggml_cgraph * cgraph);
     GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
     GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
     GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
 
     // tensor copy between different backends
@@ -90,7 +92,7 @@ extern "C" {
     GGML_API void                   ggml_backend_event_free       (ggml_backend_event_t event);
     GGML_API void                   ggml_backend_event_record     (ggml_backend_event_t event);
     GGML_API void                   ggml_backend_event_synchronize(ggml_backend_event_t event);
-    GGML_API void                   ggml_backend_event_wait       (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
+    GGML_API void                   ggml_backend_event_wait       (ggml_backend_t backend, ggml_backend_event_t event);
 
     //
     // CPU backend
@@ -119,7 +121,7 @@ extern "C" {
 
     GGML_API size_t                     ggml_backend_reg_get_count(void);
     GGML_API size_t                     ggml_backend_reg_find_by_name(const char * name);
-    GGML_API ggml_backend_t             ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is name[:params]
+    GGML_API ggml_backend_t             ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is backend_name:params (params is optional)
     GGML_API const char *               ggml_backend_reg_get_name(size_t i);
     GGML_API ggml_backend_t             ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific
     GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i);
@@ -182,6 +184,9 @@ extern "C" {
     // Initialize backend buffers from a measure graph
     GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
 
+    GGML_API int                  ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
+    GGML_API ggml_backend_t       ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i);
+
     // Get the number of splits of the last graph
     GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
     GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
@@ -225,7 +230,7 @@ extern "C" {
 
     // Tensor initialization
     GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
-    GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
 
 
 #ifdef  __cplusplus
diff --git a/ggml/include/ggml-blas.h b/ggml/include/ggml-blas.h
new file mode 100644
index 0000000000000..f2e37de06f609
--- /dev/null
+++ b/ggml/include/ggml-blas.h
@@ -0,0 +1,23 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+// backend API
+GGML_API GGML_CALL ggml_backend_t ggml_backend_blas_init(void);
+
+GGML_API GGML_CALL bool ggml_backend_is_blas(ggml_backend_t backend);
+
+// number of threads used for conversion to float
+// for openblas and blis, this will also set the number of threads used for blas operations
+GGML_API GGML_CALL void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads);
+
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/include/ggml-cann.h b/ggml/include/ggml-cann.h
new file mode 100644
index 0000000000000..ca73211fea736
--- /dev/null
+++ b/ggml/include/ggml-cann.h
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Maximum number of CANN devices supported.
+ */
+#define GGML_CANN_MAX_DEVICES 16
+
+/**
+ * @brief Initializes the CANN backend for a specified device.
+ *
+ * This function initializes the CANN backend for the given device.
+ * It verifies the device index, allocates a context, and creates a backend
+ * instance.
+ *
+ * @param device The index of the device to initialize.
+ * @return A pointer to the initialized backend instance, or nullptr on failure.
+ */
+GGML_API GGML_CALL ggml_backend_t ggml_backend_cann_init(int32_t device);
+
+/**
+ * @brief Checks if a given backend is a CANN backend.
+ *
+ * This function verifies if the provided backend is a CANN backend by comparing
+ * its GUID with the CANN backend's GUID.
+ *
+ * @param backend The backend instance to check.
+ * @return True if the backend is a CANN backend, false otherwise.
+ */
+GGML_API GGML_CALL bool ggml_backend_is_cann(ggml_backend_t backend);
+
+/**
+ * @brief Retrieves the CANN buffer type for a specified device.
+ *
+ * This function initializes and returns the buffer type interface associated
+ * with the given device. It ensures thread-safe access using a mutex.
+ *
+ * @param device The device index for which to retrieve the buffer type.
+ * @return A pointer to the buffer type interface for the specified device, or
+ * nullptr if the device index is out of range.
+ */
+GGML_API GGML_CALL ggml_backend_buffer_type_t
+ggml_backend_cann_buffer_type(int32_t device);
+
+/**
+ * @brief Retrieves the number of CANN devices available.
+ *
+ * This function returns the number of CANN devices available based on
+ * information obtained from `ggml_cann_info()`.
+ *
+ * @return The number of CANN devices available.
+ */
+GGML_API GGML_CALL int32_t ggml_backend_cann_get_device_count(void);
+
+/**
+ * @brief Retrieves the description of a specific CANN device.
+ *
+ * This function sets the specified device, retrieves the SoC name,
+ * and writes it into the provided description buffer.
+ *
+ * @param device The device index to retrieve the description for.
+ * @param description Pointer to a buffer where the description will be written.
+ * @param description_size Size of the description buffer.
+ */
+GGML_API GGML_CALL void ggml_backend_cann_get_device_description(
+    int32_t device, char* description, size_t description_size);
+
+/**
+ * @brief Retrieves the memory information of a specific CANN device.
+ *
+ * This function sets the specified device, retrieves the free and total
+ * memory information of the specified type (ACL_HBM_MEM), and stores them
+ * in the provided pointers.
+ *
+ * @param device The device index to retrieve memory information for.
+ * @param free Pointer to a variable where the free memory size will be stored.
+ * @param total Pointer to a variable where the total memory size will be
+ * stored.
+ */
+GGML_API GGML_CALL void ggml_backend_cann_get_device_memory(int32_t device,
+                                                            size_t* free,
+                                                            size_t* total);
+
+/**
+ * @brief Set the logging callback for GGML.
+ *
+ * This function sets the logging callback and user data for logging.
+ *
+ * @param log_callback The logging callback to set.
+ * @param user_data User data to pass to the logging callback.
+ */
+GGML_API void ggml_backend_cann_log_set_callback(ggml_log_callback log_callback,
+                                                 void* user_data);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml-cuda.h b/ggml/include/ggml-cuda.h
similarity index 94%
rename from ggml-cuda.h
rename to ggml/include/ggml-cuda.h
index d7903c666cebf..71bb6dcf07975 100644
--- a/ggml-cuda.h
+++ b/ggml/include/ggml-cuda.h
@@ -6,6 +6,9 @@
 #ifdef GGML_USE_HIPBLAS
 #define GGML_CUDA_NAME "ROCm"
 #define GGML_CUBLAS_NAME "hipBLAS"
+#elif defined(GGML_USE_MUSA)
+#define GGML_CUDA_NAME "MUSA"
+#define GGML_CUBLAS_NAME "muBLAS"
 #else
 #define GGML_CUDA_NAME "CUDA"
 #define GGML_CUBLAS_NAME "cuBLAS"
diff --git a/ggml-kompute.h b/ggml/include/ggml-kompute.h
similarity index 100%
rename from ggml-kompute.h
rename to ggml/include/ggml-kompute.h
diff --git a/ggml-metal.h b/ggml/include/ggml-metal.h
similarity index 93%
rename from ggml-metal.h
rename to ggml/include/ggml-metal.h
index a5c542189c295..d483cf1ac40c6 100644
--- a/ggml-metal.h
+++ b/ggml/include/ggml-metal.h
@@ -1,7 +1,7 @@
 // An interface allowing to compute ggml_cgraph with Metal
 //
 // This is a fully functional interface that extends ggml with GPU support for Apple devices.
-// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
+// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, etc.)
 //
 // How it works?
 //
@@ -50,6 +50,8 @@ GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void
 
 GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
 
+GGML_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);
+
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
 
 // helper to check if the device supports a specific family
@@ -63,4 +65,3 @@ GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend);
 #ifdef __cplusplus
 }
 #endif
-
diff --git a/ggml-rpc.h b/ggml/include/ggml-rpc.h
similarity index 100%
rename from ggml-rpc.h
rename to ggml/include/ggml-rpc.h
diff --git a/ggml-sycl.h b/ggml/include/ggml-sycl.h
similarity index 76%
rename from ggml-sycl.h
rename to ggml/include/ggml-sycl.h
index a9f776fc1dd59..43ab1519cd05d 100644
--- a/ggml-sycl.h
+++ b/ggml/include/ggml-sycl.h
@@ -9,13 +9,13 @@
 #include "ggml.h"
 #include "ggml-backend.h"
 
+#define GGML_SYCL_NAME "SYCL"
+#define GGML_SYCL_MAX_DEVICES 48
+
 #ifdef  __cplusplus
 extern "C" {
 #endif
 
-#define GGML_SYCL_MAX_DEVICES       48
-#define GGML_SYCL_NAME "SYCL"
-
 // backend API
 GGML_API ggml_backend_t ggml_backend_sycl_init(int device);
 
@@ -33,13 +33,6 @@ GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len);
 GGML_API GGML_CALL void   ggml_sycl_get_device_description(int device, char *description, size_t description_size);
 GGML_API GGML_CALL int   ggml_backend_sycl_get_device_count();
 GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
-GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id);
-
-// TODO: these are temporary
-//       ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670
-GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index);
-GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id);
-GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode();
 
 // SYCL doesn't support registering host memory, keep here for reference
 // GGML_API GGML_CALL bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size);
diff --git a/ggml-vulkan.h b/ggml/include/ggml-vulkan.h
similarity index 100%
rename from ggml-vulkan.h
rename to ggml/include/ggml-vulkan.h
diff --git a/ggml.h b/ggml/include/ggml.h
similarity index 93%
rename from ggml.h
rename to ggml/include/ggml.h
index 77475710129d7..1d2a354024675 100644
--- a/ggml.h
+++ b/ggml/include/ggml.h
@@ -244,6 +244,8 @@
 #define GGML_EXIT_SUCCESS 0
 #define GGML_EXIT_ABORTED 1
 
+#define GGML_ROPE_TYPE_NEOX 2
+
 #define GGUF_MAGIC "GGUF"
 
 #define GGUF_VERSION 3
@@ -254,18 +256,8 @@
 
 #define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
 
-#define GGML_ASSERT(x) \
-    do { \
-        if (!(x)) { \
-            fflush(stdout); \
-            fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
-            ggml_print_backtrace(); \
-            abort(); \
-        } \
-    } while (0)
-
 #ifndef NDEBUG
-#define GGML_UNREACHABLE() GGML_ASSERT(!"statement should not be reached")
+#define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0)
 #elif defined(__GNUC__)
 #define GGML_UNREACHABLE() __builtin_unreachable()
 #elif defined(_MSC_VER)
@@ -274,6 +266,17 @@
 #define GGML_UNREACHABLE() ((void) 0)
 #endif
 
+#ifdef __cplusplus
+#define GGML_NORETURN [[noreturn]]
+#elif defined(_MSC_VER)
+#define GGML_NORETURN __declspec(noreturn)
+#else
+#define GGML_NORETURN _Noreturn
+#endif
+
+#define GGML_ABORT(...) ggml_abort(__FILE__, __LINE__, __VA_ARGS__)
+#define GGML_ASSERT(x) if (!(x)) GGML_ABORT("GGML_ASSERT(%s) failed", #x)
+
 // used to copy the number of elements and stride in bytes of tensors into local variables.
 // main purpose is to reduce code duplication and improve readability.
 //
@@ -312,10 +315,19 @@
     GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
     GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
 
+#define GGML_TENSOR_BINARY_OP_LOCALS01 \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb)
+
 #ifdef  __cplusplus
 extern "C" {
 #endif
 
+    GGML_NORETURN GGML_ATTRIBUTE_FORMAT(3, 4)
+    GGML_API void ggml_abort(const char * file, int line, const char * fmt, ...);
+
     enum ggml_status {
         GGML_STATUS_ALLOC_FAILED = -2,
         GGML_STATUS_FAILED = -1,
@@ -339,6 +351,7 @@ extern "C" {
     GGML_API ggml_bf16_t ggml_fp32_to_bf16(float);
     GGML_API float       ggml_bf16_to_fp32(ggml_bf16_t);  // consider just doing << 16
     GGML_API void        ggml_bf16_to_fp32_row(const ggml_bf16_t *, float *, int64_t);
+    GGML_API void        ggml_fp32_to_bf16_row_ref(const float *, ggml_bf16_t *, int64_t);
     GGML_API void        ggml_fp32_to_bf16_row(const float *, ggml_bf16_t *, int64_t);
 
     struct ggml_object;
@@ -377,6 +390,9 @@ extern "C" {
         GGML_TYPE_F64     = 28,
         GGML_TYPE_IQ1_M   = 29,
         GGML_TYPE_BF16    = 30,
+        GGML_TYPE_Q4_0_4_4 = 31,
+        GGML_TYPE_Q4_0_4_8 = 32,
+        GGML_TYPE_Q4_0_8_8 = 33,
         GGML_TYPE_COUNT,
     };
 
@@ -418,6 +434,9 @@ extern "C" {
         GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
         GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0_4_4 = 25, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0_4_8 = 26, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0_8_8 = 27, // except 1d tensors
     };
 
     // available tensor operations:
@@ -481,9 +500,7 @@ extern "C" {
         GGML_OP_ARGSORT,
         GGML_OP_LEAKY_RELU,
 
-        GGML_OP_FLASH_ATTN,
         GGML_OP_FLASH_ATTN_EXT,
-        GGML_OP_FLASH_FF,
         GGML_OP_FLASH_ATTN_BACK,
         GGML_OP_SSM_CONV,
         GGML_OP_SSM_SCAN,
@@ -587,11 +604,7 @@ extern "C" {
         struct ggml_tensor * grad;
         struct ggml_tensor * src[GGML_MAX_SRC];
 
-        // performance
-        int     perf_runs;
-        int64_t perf_cycles;
-        int64_t perf_time_us;
-
+        // source tensor and offset for views
         struct ggml_tensor * view_src;
         size_t               view_offs;
 
@@ -601,7 +614,7 @@ extern "C" {
 
         void * extra; // extra things e.g. for ggml-cuda.cu
 
-        char padding[8];
+        // char padding[4];
     };
 
     static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
@@ -630,8 +643,11 @@ extern "C" {
         GGML_CGRAPH_EVAL_ORDER_COUNT
     };
 
+    typedef uint32_t ggml_bitset_t;
+
     struct ggml_hash_set {
         size_t size;
+        ggml_bitset_t * used;
         struct ggml_tensor ** keys;
     };
 
@@ -645,14 +661,9 @@ extern "C" {
         struct ggml_tensor ** grads;
         struct ggml_tensor ** leafs;
 
-        struct ggml_hash_set visited_hash_table;
+        struct ggml_hash_set visited_hash_set;
 
         enum ggml_cgraph_eval_order order;
-
-        // performance
-        int     perf_runs;
-        int64_t perf_cycles;
-        int64_t perf_time_us;
     };
 
     // scratch buffer
@@ -669,28 +680,6 @@ extern "C" {
         bool   no_alloc;   // don't allocate memory for the tensor data
     };
 
-
-    // compute types
-
-    // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
-    // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
-    enum ggml_task_type {
-        GGML_TASK_TYPE_INIT = 0,
-        GGML_TASK_TYPE_COMPUTE,
-        GGML_TASK_TYPE_FINALIZE,
-    };
-
-    struct ggml_compute_params {
-        enum ggml_task_type type;
-
-        // ith = thread index, nth = number of threads
-        int ith, nth;
-
-        // work buffer for all threads
-        size_t wsize;
-        void * wdata;
-    };
-
     // numa strategies
     enum ggml_numa_strategy {
         GGML_NUMA_STRATEGY_DISABLED   = 0,
@@ -719,8 +708,6 @@ extern "C" {
     GGML_API int64_t ggml_cycles(void);
     GGML_API int64_t ggml_cycles_per_ms(void);
 
-    GGML_API void    ggml_print_backtrace(void);
-
     // accepts a UTF-8 path, even on Windows
     GGML_API FILE *  ggml_fopen(const char * fname, const char * mode);
 
@@ -735,9 +722,9 @@ extern "C" {
     GGML_API GGML_CALL size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
     GGML_API           size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
 
-    GGML_API GGML_CALL int    ggml_blck_size(enum ggml_type type);
-    GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
-    GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
+    GGML_API GGML_CALL int64_t ggml_blck_size(enum ggml_type type);
+    GGML_API GGML_CALL size_t  ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
+    GGML_API GGML_CALL size_t  ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
 
     GGML_DEPRECATED(
     GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
@@ -758,7 +745,6 @@ extern "C" {
     GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
 
     GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
-    GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor);
     GGML_API GGML_CALL bool ggml_is_permuted  (const struct ggml_tensor * tensor);
     GGML_API GGML_CALL bool ggml_is_empty     (const struct ggml_tensor * tensor);
     GGML_API           bool ggml_is_scalar    (const struct ggml_tensor * tensor);
@@ -767,9 +753,16 @@ extern "C" {
     GGML_API           bool ggml_is_3d        (const struct ggml_tensor * tensor);
     GGML_API           int  ggml_n_dims       (const struct ggml_tensor * tensor); // returns 1 for scalars
 
+    GGML_API GGML_CALL bool ggml_is_contiguous  (const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous()
+    GGML_API GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1
+    GGML_API GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2
+
     GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1);
     GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
 
+    GGML_API bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
+
     // use this to compute the memory overhead of a tensor
     GGML_API size_t ggml_tensor_overhead(void);
 
@@ -1009,12 +1002,13 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
-    // concat a and b on dim 2
+    // concat a and b along dim
     // used in stable-diffusion
     GGML_API struct ggml_tensor * ggml_concat(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            int                   dim);
 
     GGML_API struct ggml_tensor * ggml_abs(
             struct ggml_context * ctx,
@@ -1148,16 +1142,17 @@ extern "C" {
 
     // group normalize along ne0*ne1*n_groups
     // used in stable-diffusion
-    // TODO: eps is hardcoded to 1e-6 for now
     GGML_API struct ggml_tensor * ggml_group_norm(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            int                   n_groups);
+            int                   n_groups,
+            float                 eps);
 
     GGML_API struct ggml_tensor * ggml_group_norm_inplace(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            int                   n_groups);
+            int                   n_groups,
+            float                 eps);
 
     // a - x
     // b - dy
@@ -1460,9 +1455,8 @@ extern "C" {
             struct ggml_tensor  * b);
 
     // rotary position embedding
-    // if mode & 1 == 1, skip n_past elements (DEPRECATED)
-    // if mode & 2 == 1, GPT-NeoX style
-    // if mode & 4 == 1, ChatGLM style
+    // if (mode & 1) - skip n_past elements (NOT SUPPORTED)
+    // if (mode & GGML_ROPE_TYPE_NEOX) - GPT-NeoX style
     //
     // b is an int32 vector with size a->ne[2], it contains the positions
     GGML_API struct ggml_tensor * ggml_rope(
@@ -1470,8 +1464,7 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
             int                   n_dims,
-            int                   mode,
-            int                   n_ctx);
+            int                   mode);
 
     // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_rope_inplace(
@@ -1479,18 +1472,18 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
             int                   n_dims,
-            int                   mode,
-            int                   n_ctx);
+            int                   mode);
 
     // custom RoPE
-    GGML_API struct ggml_tensor * ggml_rope_custom(
+    // c is freq factors (e.g. phi3-128k), (optional)
+    GGML_API struct ggml_tensor * ggml_rope_ext(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
             int                   n_dims,
             int                   mode,
-            int                   n_ctx,
-            int                   n_orig_ctx,
+            int                   n_ctx_orig,
             float                 freq_base,
             float                 freq_scale,
             float                 ext_factor,
@@ -1499,14 +1492,14 @@ extern "C" {
             float                 beta_slow);
 
     // in-place, returns view(a)
-    GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
+    GGML_API struct ggml_tensor * ggml_rope_ext_inplace(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
             int                   n_dims,
             int                   mode,
-            int                   n_ctx,
-            int                   n_orig_ctx,
+            int                   n_ctx_orig,
             float                 freq_base,
             float                 freq_scale,
             float                 ext_factor,
@@ -1514,18 +1507,39 @@ extern "C" {
             float                 beta_fast,
             float                 beta_slow);
 
-    // compute correction dims for YaRN RoPE scaling
-    GGML_CALL void ggml_rope_yarn_corr_dims(
-        int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]);
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow),
+        "use ggml_rope_ext instead");
 
-    // xPos RoPE, in-place, returns view(a)
-    GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
             int                   n_dims,
-            float                 base,
-            bool                  down);
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow),
+        "use ggml_rope_ext_inplace instead");
+
+    // compute correction dims for YaRN RoPE scaling
+    GGML_CALL void ggml_rope_yarn_corr_dims(
+        int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]);
 
     // rotary position embedding backward, i.e compute dx from dy
     // a - dy
@@ -1533,18 +1547,16 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
             int                   n_dims,
             int                   mode,
-            int                   n_ctx,
-            int                   n_orig_ctx,
+            int                   n_ctx_orig,
             float                 freq_base,
             float                 freq_scale,
             float                 ext_factor,
             float                 attn_factor,
             float                 beta_fast,
-            float                 beta_slow,
-            float                 xpos_base,
-            bool                  xpos_down);
+            float                 beta_slow);
 
     // clamp
     // in-place, returns view(a)
@@ -1734,13 +1746,6 @@ extern "C" {
             struct ggml_tensor  * a,
             int                   k);
 
-    GGML_API struct ggml_tensor * ggml_flash_attn(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * q,
-            struct ggml_tensor  * k,
-            struct ggml_tensor  * v,
-            bool                  masked);
-
 #define GGML_KQ_MASK_PAD 32
 
     // q:    [n_embd, n_batch,     n_head,    1]
@@ -1761,6 +1766,7 @@ extern "C" {
             struct ggml_tensor * a,
             enum ggml_prec       prec);
 
+    // TODO: needs to be adapted to ggml_flash_attn_ext
     GGML_API struct ggml_tensor * ggml_flash_attn_back(
            struct ggml_context * ctx,
            struct ggml_tensor  * q,
@@ -1769,14 +1775,6 @@ extern "C" {
            struct ggml_tensor  * d,
            bool                  masked);
 
-    GGML_API struct ggml_tensor * ggml_flash_ff(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b0,
-            struct ggml_tensor  * b1,
-            struct ggml_tensor  * c0,
-            struct ggml_tensor  * c1);
-
     GGML_API struct ggml_tensor * ggml_ssm_conv(
             struct ggml_context * ctx,
             struct ggml_tensor  * s,
@@ -2016,8 +2014,8 @@ extern "C" {
 
     // ggml_graph_plan() has to be called before ggml_graph_compute()
     // when plan.work_size > 0, caller must allocate memory for plan.work_data
-    GGML_API struct ggml_cplan ggml_graph_plan            (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
-    GGML_API enum ggml_status  ggml_graph_compute         (      struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
+    GGML_API struct ggml_cplan ggml_graph_plan   (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
+    GGML_API enum ggml_status  ggml_graph_compute(      struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
     // same as ggml_graph_compute() but the work data is allocated as a part of the context
     // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
     GGML_API enum ggml_status  ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
@@ -2393,6 +2391,7 @@ extern "C" {
     GGML_API int ggml_cpu_has_avx512_bf16(void);
     GGML_API int ggml_cpu_has_fma        (void);
     GGML_API int ggml_cpu_has_neon       (void);
+    GGML_API int ggml_cpu_has_sve        (void);
     GGML_API int ggml_cpu_has_arm_fma    (void);
     GGML_API int ggml_cpu_has_metal      (void);
     GGML_API int ggml_cpu_has_f16c       (void);
@@ -2400,15 +2399,17 @@ extern "C" {
     GGML_API int ggml_cpu_has_wasm_simd  (void);
     GGML_API int ggml_cpu_has_blas       (void);
     GGML_API int ggml_cpu_has_cuda       (void);
-    GGML_API int ggml_cpu_has_clblast    (void);
     GGML_API int ggml_cpu_has_vulkan     (void);
     GGML_API int ggml_cpu_has_kompute    (void);
     GGML_API int ggml_cpu_has_gpublas    (void);
     GGML_API int ggml_cpu_has_sse3       (void);
     GGML_API int ggml_cpu_has_ssse3      (void);
     GGML_API int ggml_cpu_has_sycl       (void);
+    GGML_API int ggml_cpu_has_rpc        (void);
     GGML_API int ggml_cpu_has_vsx        (void);
     GGML_API int ggml_cpu_has_matmul_int8(void);
+    GGML_API int ggml_cpu_has_cann       (void);
+    GGML_API int ggml_cpu_has_llamafile  (void);
 
     //
     // Internal types and functions exposed for tests and benchmarks
@@ -2422,20 +2423,31 @@ extern "C" {
 #endif
     typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
     typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t k);
-    typedef void (*ggml_vec_dot_t)   (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
-                                      const void * GGML_RESTRICT y, size_t by, int nrc);
+    typedef void (*ggml_from_float_to_mat_t)
+                                     (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs);
+    typedef void (*ggml_vec_dot_t)  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
+                                       const void * GGML_RESTRICT y, size_t by, int nrc);
+    typedef void (*ggml_gemv_t)     (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
+                                       const void * GGML_RESTRICT y, int nr, int nc);
+    typedef void (*ggml_gemm_t)     (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
+                                       const void * GGML_RESTRICT y, int nr, int nc);
 
     typedef struct {
-        const char      * type_name;
-        int               blck_size;
-        size_t            type_size;
-        bool              is_quantized;
-        ggml_to_float_t   to_float;
-        ggml_from_float_t from_float;
-        ggml_from_float_t from_float_reference;
-        ggml_vec_dot_t    vec_dot;
-        enum ggml_type    vec_dot_type;
-        int64_t           nrows; // number of rows to process simultaneously;
+        const char             * type_name;
+        int64_t                  blck_size;
+        int64_t                  blck_size_interleave; // interleave elements in blocks
+        size_t                   type_size;
+        bool                     is_quantized;
+        ggml_to_float_t          to_float;
+        ggml_from_float_t        from_float;
+        ggml_from_float_t        from_float_ref;
+        ggml_from_float_to_mat_t from_float_to_mat;
+        ggml_vec_dot_t           vec_dot;
+        enum ggml_type           vec_dot_type;
+        int64_t                  nrows; // number of rows to process simultaneously
+        int64_t                  ncols; // number of columns to process simultaneously
+        ggml_gemv_t              gemv;
+        ggml_gemm_t              gemm;
     } ggml_type_traits_t;
 
     GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
new file mode 100644
index 0000000000000..1775ef3cc9146
--- /dev/null
+++ b/ggml/src/CMakeLists.txt
@@ -0,0 +1,1329 @@
+include(CheckCXXCompilerFlag)
+
+unset(GGML_CDEF_PUBLIC)
+
+add_compile_definitions(GGML_SCHED_MAX_COPIES=${GGML_SCHED_MAX_COPIES})
+
+# enable libstdc++ assertions for debug builds
+if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+    add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
+endif()
+
+if (NOT MSVC)
+    if (GGML_SANITIZE_THREAD)
+        add_compile_options(-fsanitize=thread)
+        link_libraries     (-fsanitize=thread)
+    endif()
+
+    if (GGML_SANITIZE_ADDRESS)
+        add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
+        link_libraries     (-fsanitize=address)
+    endif()
+
+    if (GGML_SANITIZE_UNDEFINED)
+        add_compile_options(-fsanitize=undefined)
+        link_libraries     (-fsanitize=undefined)
+    endif()
+endif()
+
+if (APPLE AND GGML_ACCELERATE)
+    find_library(ACCELERATE_FRAMEWORK Accelerate)
+    if (ACCELERATE_FRAMEWORK)
+        message(STATUS "Accelerate framework found")
+
+        add_compile_definitions(GGML_USE_ACCELERATE)
+        add_compile_definitions(ACCELERATE_NEW_LAPACK)
+        add_compile_definitions(ACCELERATE_LAPACK_ILP64)
+
+        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
+    else()
+        message(WARNING "Accelerate framework not found")
+    endif()
+endif()
+
+if (GGML_METAL)
+    find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
+    find_library(METAL_FRAMEWORK    Metal      REQUIRED)
+    find_library(METALKIT_FRAMEWORK MetalKit   REQUIRED)
+
+    message(STATUS "Metal framework found")
+    set(GGML_HEADERS_METAL ../include/ggml-metal.h)
+    set(GGML_SOURCES_METAL ggml-metal.m)
+
+    list(APPEND GGML_CDEF_PUBLIC GGML_USE_METAL)
+    if (GGML_METAL_NDEBUG)
+        add_compile_definitions(GGML_METAL_NDEBUG)
+    endif()
+
+    # copy ggml-common.h and ggml-metal.metal to bin directory
+    configure_file(ggml-common.h    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h    COPYONLY)
+    configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
+
+    if (GGML_METAL_EMBED_LIBRARY)
+        enable_language(ASM)
+
+        add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
+
+        set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h")
+        set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
+
+        file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated")
+
+        # merge ggml-common.h and ggml-metal.metal into a single file
+        set(METALLIB_EMBED_ASM    "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
+        set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
+
+        add_custom_command(
+            OUTPUT ${METALLIB_EMBED_ASM}
+            COMMAND echo "Embedding Metal library"
+            COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED}
+            COMMAND echo ".section __DATA,__ggml_metallib"          >  ${METALLIB_EMBED_ASM}
+            COMMAND echo ".globl _ggml_metallib_start"              >> ${METALLIB_EMBED_ASM}
+            COMMAND echo "_ggml_metallib_start:"                    >> ${METALLIB_EMBED_ASM}
+            COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM}
+            COMMAND echo ".globl _ggml_metallib_end"                >> ${METALLIB_EMBED_ASM}
+            COMMAND echo "_ggml_metallib_end:"                      >> ${METALLIB_EMBED_ASM}
+            DEPENDS ggml-metal.metal ggml-common.h
+            COMMENT "Generate assembly for embedded Metal library"
+        )
+
+        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM})
+    else()
+        if (GGML_METAL_SHADER_DEBUG)
+            # custom command to do the following:
+            #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
+            #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
+            #
+            # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
+            #       disabling fast math is needed in order to pass tests/test-backend-ops
+            # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
+            # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
+            #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
+            set(XC_FLAGS -fno-fast-math -fno-inline -g)
+        else()
+            set(XC_FLAGS -O3)
+        endif()
+
+        # Append macOS metal versioning flags
+        if (GGML_METAL_MACOSX_VERSION_MIN)
+            message(STATUS "Adding  -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN} flag to metal compilation")
+            list   (APPEND XC_FLAGS -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN})
+        endif()
+
+        if (GGML_METAL_STD)
+            message(STATUS "Adding  -std=${GGML_METAL_STD} flag to metal compilation")
+            list   (APPEND XC_FLAGS -std=${GGML_METAL_STD})
+        endif()
+
+        add_custom_command(
+            OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
+            COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
+            COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
+            DEPENDS ggml-metal.metal ggml-common.h
+            COMMENT "Compiling Metal kernels"
+            )
+
+        add_custom_target(
+            ggml-metal ALL
+            DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
+            )
+    endif() # GGML_METAL_EMBED_LIBRARY
+
+    set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS}
+        ${FOUNDATION_LIBRARY}
+        ${METAL_FRAMEWORK}
+        ${METALKIT_FRAMEWORK}
+        )
+endif()
+
+if (GGML_MUSA)
+    set(CMAKE_C_COMPILER clang)
+    set(CMAKE_C_EXTENSIONS OFF)
+    set(CMAKE_CXX_COMPILER clang++)
+    set(CMAKE_CXX_EXTENSIONS OFF)
+
+    set(GGML_CUDA ON)
+
+    list(APPEND GGML_CDEF_PUBLIC GGML_USE_MUSA)
+endif()
+
+if (GGML_OPENMP)
+    find_package(OpenMP)
+    if (OpenMP_FOUND)
+        message(STATUS "OpenMP found")
+
+        add_compile_definitions(GGML_USE_OPENMP)
+
+        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
+
+        if (GGML_MUSA)
+            set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} "/usr/lib/llvm-10/include/openmp")
+            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} "/usr/lib/llvm-10/lib/libomp.so")
+        endif()
+    else()
+        message(WARNING "OpenMP not found")
+    endif()
+endif()
+
+if (GGML_BLAS)
+    if (GGML_STATIC)
+        set(BLA_STATIC ON)
+    endif()
+    #if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22)
+    #    set(BLA_SIZEOF_INTEGER 8)
+    #endif()
+
+    set(BLA_VENDOR ${GGML_BLAS_VENDOR})
+    find_package(BLAS)
+
+    if (BLAS_FOUND)
+        message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}")
+
+        if (("${BLAS_INCLUDE_DIRS}" STREQUAL "") AND NOT (${GGML_BLAS_VENDOR} MATCHES "Apple"))
+            # BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake.
+            # see https://gitlab.kitware.com/cmake/cmake/-/issues/20268
+            find_package(PkgConfig REQUIRED)
+            if (${GGML_BLAS_VENDOR} MATCHES "Generic")
+                pkg_check_modules(DepBLAS REQUIRED blas)
+            elseif (${GGML_BLAS_VENDOR} MATCHES "OpenBLAS")
+                # As of openblas v0.3.22, the 64-bit is named openblas64.pc
+                pkg_check_modules(DepBLAS openblas64)
+                if (NOT DepBLAS_FOUND)
+                    pkg_check_modules(DepBLAS REQUIRED openblas)
+                endif()
+            elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME")
+                pkg_check_modules(DepBLAS REQUIRED blis)
+            elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS")
+                pkg_check_modules(DepBLAS REQUIRED blas-atlas)
+            elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS")
+                pkg_check_modules(DepBLAS REQUIRED flexiblas_api)
+            elseif (${GGML_BLAS_VENDOR} MATCHES "Intel")
+                # all Intel* libraries share the same include path
+                pkg_check_modules(DepBLAS REQUIRED mkl-sdl)
+            elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC")
+                # this doesn't provide pkg-config
+                # suggest to assign BLAS_INCLUDE_DIRS on your own
+                if ("${NVHPC_VERSION}" STREQUAL "")
+                    message(WARNING "Better to set NVHPC_VERSION")
+                else()
+                    set(DepBLAS_FOUND ON)
+                    set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include")
+                endif()
+            endif()
+            if (DepBLAS_FOUND)
+                set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS})
+            else()
+                message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically"
+                " detected by pkgconfig, trying to find cblas.h from possible paths...")
+                find_path(BLAS_INCLUDE_DIRS
+                    NAMES cblas.h
+                    HINTS
+                        /usr/include
+                        /usr/local/include
+                        /usr/include/openblas
+                        /opt/homebrew/opt/openblas/include
+                        /usr/local/opt/openblas/include
+                        /usr/include/x86_64-linux-gnu/openblas/include
+                )
+            endif()
+        endif()
+
+        message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}")
+
+        add_compile_options(${BLAS_LINKER_FLAGS})
+
+        list(APPEND GGML_CDEF_PUBLIC GGML_USE_BLAS)
+
+        if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel"))
+            add_compile_definitions(GGML_BLAS_USE_MKL)
+        endif()
+
+        set(GGML_HEADERS_BLAS ../include/ggml-blas.h)
+        set(GGML_SOURCES_BLAS ggml-blas.cpp)
+
+        set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS}     ${BLAS_LIBRARIES})
+        set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS})
+    else()
+        message(WARNING "BLAS not found, please refer to "
+        "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
+        " to set correct GGML_BLAS_VENDOR")
+    endif()
+endif()
+
+if (GGML_LLAMAFILE)
+    message(STATUS "Using llamafile")
+
+    add_compile_definitions(GGML_USE_LLAMAFILE)
+
+    set(GGML_HEADERS_LLAMAFILE llamafile/sgemm.h)
+    set(GGML_SOURCES_LLAMAFILE llamafile/sgemm.cpp)
+endif()
+
+if (GGML_CUDA)
+    cmake_minimum_required(VERSION 3.18)  # for CMAKE_CUDA_ARCHITECTURES
+
+    if (GGML_MUSA)
+        list(APPEND CMAKE_MODULE_PATH "/usr/local/musa/cmake/")
+        find_package(MUSAToolkit)
+        set(CUDAToolkit_FOUND ${MUSAToolkit_FOUND})
+    else()
+        find_package(CUDAToolkit)
+    endif()
+
+    if (CUDAToolkit_FOUND)
+        message(STATUS "CUDA found")
+
+        if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
+            # 52 == lowest CUDA 12 standard
+            # 60 == FP16 CUDA intrinsics
+            # 61 == integer CUDA intrinsics
+            # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster
+            if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16)
+                set(CMAKE_CUDA_ARCHITECTURES "60;61;70;75")
+            else()
+                set(CMAKE_CUDA_ARCHITECTURES "52;61;70;75")
+                #set(CMAKE_CUDA_ARCHITECTURES "OFF") # use this to compile much faster, but only F16 models work
+            endif()
+        endif()
+        message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
+
+        if (GGML_MUSA)
+            set(CMAKE_CUDA_COMPILER ${MUSAToolkit_MCC_EXECUTABLE})
+        else()
+            enable_language(CUDA)
+        endif()
+
+        file(GLOB   GGML_HEADERS_CUDA "ggml-cuda/*.cuh")
+        list(APPEND GGML_HEADERS_CUDA "../include/ggml-cuda.h")
+
+        file(GLOB   GGML_SOURCES_CUDA "ggml-cuda/*.cu")
+        list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
+        file(GLOB   SRCS "ggml-cuda/template-instances/fattn-wmma*.cu")
+        list(APPEND GGML_SOURCES_CUDA ${SRCS})
+        file(GLOB   SRCS "ggml-cuda/template-instances/mmq*.cu")
+        list(APPEND GGML_SOURCES_CUDA ${SRCS})
+
+        if (GGML_CUDA_FA_ALL_QUANTS)
+            file(GLOB   SRCS "ggml-cuda/template-instances/fattn-vec*.cu")
+            list(APPEND GGML_SOURCES_CUDA ${SRCS})
+            add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS)
+        else()
+            file(GLOB   SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu")
+            list(APPEND GGML_SOURCES_CUDA ${SRCS})
+            file(GLOB   SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu")
+            list(APPEND GGML_SOURCES_CUDA ${SRCS})
+            file(GLOB   SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu")
+            list(APPEND GGML_SOURCES_CUDA ${SRCS})
+        endif()
+
+        list(APPEND GGML_CDEF_PUBLIC GGML_USE_CUDA)
+
+        add_compile_definitions(GGML_CUDA_DMMV_X=${GGML_CUDA_DMMV_X})
+        add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_MMV_Y})
+        add_compile_definitions(K_QUANTS_PER_ITERATION=${GGML_CUDA_KQUANTS_ITER})
+        add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE})
+
+        if (GGML_CUDA_USE_GRAPHS)
+            add_compile_definitions(GGML_CUDA_USE_GRAPHS)
+        endif()
+
+        if (GGML_CUDA_FORCE_DMMV)
+            add_compile_definitions(GGML_CUDA_FORCE_DMMV)
+        endif()
+
+        if (GGML_CUDA_FORCE_MMQ)
+            add_compile_definitions(GGML_CUDA_FORCE_MMQ)
+        endif()
+
+        if (GGML_CUDA_FORCE_CUBLAS)
+            add_compile_definitions(GGML_CUDA_FORCE_CUBLAS)
+        endif()
+
+        if (GGML_CUDA_NO_VMM)
+            add_compile_definitions(GGML_CUDA_NO_VMM)
+        endif()
+
+        if (DEFINED GGML_CUDA_DMMV_Y)
+            add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_DMMV_Y}) # for backwards compatibility
+        endif()
+
+        if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16)
+            add_compile_definitions(GGML_CUDA_F16)
+        endif()
+
+        if (GGML_CUDA_NO_PEER_COPY)
+            add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
+        endif()
+
+        if (GGML_MUSA)
+            set_source_files_properties(${GGML_SOURCES_CUDA} PROPERTIES LANGUAGE CXX)
+            foreach(SOURCE ${GGML_SOURCES_CUDA})
+                set_property(SOURCE ${SOURCE} PROPERTY COMPILE_FLAGS "-x musa -mtgpu --cuda-gpu-arch=mp_22")
+            endforeach()
+        endif()
+
+        if (GGML_STATIC)
+            if (WIN32)
+                # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library
+                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
+            else ()
+                if (GGML_MUSA)
+                    set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} MUSA::musart_static MUSA::mublas_static)
+                else()
+                    set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+                endif()
+            endif()
+        else()
+            if (GGML_MUSA)
+                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} MUSA::musart MUSA::mublas)
+            else()
+                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
+            endif()
+        endif()
+
+        if (GGML_CUDA_NO_VMM)
+            # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so)
+        else()
+            if (GGML_MUSA)
+                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} MUSA::musa_driver) # required by muDeviceGetAttribute(), muMemGetAllocationGranularity(...), ...
+            else()
+                set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ...
+            endif()
+        endif()
+    else()
+        message(WARNING "CUDA not found")
+    endif()
+endif()
+
+if (GGML_HIPBLAS)
+    if (NOT EXISTS $ENV{ROCM_PATH})
+        if (NOT EXISTS /opt/rocm)
+            set(ROCM_PATH /usr)
+        else()
+            set(ROCM_PATH /opt/rocm)
+        endif()
+    else()
+        set(ROCM_PATH $ENV{ROCM_PATH})
+    endif()
+
+    list(APPEND CMAKE_PREFIX_PATH  ${ROCM_PATH})
+    list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake")
+
+    # CMake on Windows doesn't support the HIP language yet
+    if (WIN32)
+        set(CXX_IS_HIPCC TRUE)
+    else()
+        string(REGEX MATCH "hipcc(\.bat)?$" CXX_IS_HIPCC "${CMAKE_CXX_COMPILER}")
+    endif()
+
+    if (CXX_IS_HIPCC)
+        if (LINUX)
+            if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang")
+                message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++")
+            endif()
+
+            message(WARNING "Setting hipcc as the C++ compiler is legacy behavior."
+                    " Prefer setting the HIP compiler directly. See README for details.")
+        endif()
+    else()
+        # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES.
+        if (AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES)
+            set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS})
+        endif()
+        cmake_minimum_required(VERSION 3.21)
+        enable_language(HIP)
+    endif()
+
+    find_package(hip     REQUIRED)
+    find_package(hipblas REQUIRED)
+    find_package(rocblas REQUIRED)
+
+    message(STATUS "HIP and hipBLAS found")
+
+    file(GLOB   GGML_HEADERS_ROCM "ggml-cuda/*.cuh")
+    list(APPEND GGML_HEADERS_ROCM "../include/ggml-cuda.h")
+
+    file(GLOB   GGML_SOURCES_ROCM "ggml-cuda/*.cu")
+    list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu")
+    file(GLOB   SRCS "ggml-cuda/template-instances/fattn-wmma*.cu")
+    list(APPEND GGML_SOURCES_ROCM ${SRCS})
+    file(GLOB   SRCS "ggml-cuda/template-instances/mmq*.cu")
+    list(APPEND GGML_SOURCES_ROCM ${SRCS})
+
+    if (GGML_CUDA_FA_ALL_QUANTS)
+        file(GLOB   SRCS "ggml-cuda/template-instances/fattn-vec*.cu")
+        list(APPEND GGML_SOURCES_ROCM ${SRCS})
+        add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS)
+    else()
+        file(GLOB   SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu")
+        list(APPEND GGML_SOURCES_ROCM ${SRCS})
+        file(GLOB   SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu")
+        list(APPEND GGML_SOURCES_ROCM ${SRCS})
+        file(GLOB   SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu")
+        list(APPEND GGML_SOURCES_ROCM ${SRCS})
+    endif()
+
+    list(APPEND GGML_CDEF_PUBLIC GGML_USE_CUDA)
+
+    add_compile_definitions(GGML_USE_HIPBLAS)
+    add_compile_definitions(GGML_CUDA_DMMV_X=${GGML_CUDA_DMMV_X})
+    add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_MMV_Y})
+    add_compile_definitions(K_QUANTS_PER_ITERATION=${GGML_CUDA_KQUANTS_ITER})
+
+    if (GGML_HIP_UMA)
+        add_compile_definitions(GGML_HIP_UMA)
+    endif()
+
+    if (GGML_CUDA_FORCE_DMMV)
+        add_compile_definitions(GGML_CUDA_FORCE_DMMV)
+    endif()
+
+    if (GGML_CUDA_FORCE_MMQ)
+        add_compile_definitions(GGML_CUDA_FORCE_MMQ)
+    endif()
+
+    if (GGML_CUDA_FORCE_CUBLAS)
+        add_compile_definitions(GGML_CUDA_FORCE_CUBLAS)
+    endif()
+
+    if (GGML_CUDA_NO_PEER_COPY)
+        add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
+    endif()
+
+    if (CXX_IS_HIPCC)
+        set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX)
+        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} hip::device)
+    else()
+        set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP)
+    endif()
+
+    if (GGML_STATIC)
+        message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
+    endif()
+
+    set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} PUBLIC hip::host roc::rocblas roc::hipblas)
+endif()
+
+if (GGML_SYCL)
+    if (NOT GGML_SYCL_TARGET MATCHES "^(INTEL|NVIDIA)$")
+        message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA")
+    endif()
+
+    check_cxx_compiler_flag("-fsycl" SUPPORTS_SYCL)
+    if ( DEFINED ENV{ONEAPI_ROOT})
+        message(STATUS "Using oneAPI Release SYCL compiler (icpx).")
+    elseif(SUPPORTS_SYCL)
+        message(WARNING "Using open-source SYCL compiler (clang++). Didn't detect ENV {ONEAPI_ROOT}.
+         If you expected the oneAPI Release compiler, please install oneAPI & source it, like:
+         source /opt/intel/oneapi/setvars.sh")
+    else()
+        message(FATAL_ERROR, "C++ compiler lacks SYCL support.")
+    endif()
+    message(STATUS "SYCL found")
+    #todo: AOT
+
+    list(APPEND GGML_CDEF_PUBLIC GGML_USE_SYCL)
+
+    if (GGML_SYCL_F16)
+        add_compile_definitions(GGML_SYCL_F16)
+    endif()
+
+    if (GGML_CUDA_FORCE_MMQ)
+        add_compile_definitions(GGML_SYCL_FORCE_MMQ)
+    endif()
+
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl")
+
+    if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
+        add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
+    else()
+        add_compile_definitions(GGML_SYCL_WARP_SIZE=16)
+    endif()
+
+    file(GLOB   GGML_HEADERS_SYCL "ggml-sycl/*.hpp")
+    list(APPEND GGML_HEADERS_SYCL "../include/ggml-sycl.h")
+
+    file(GLOB   GGML_SOURCES_SYCL "ggml-sycl/*.cpp")
+    list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp")
+
+    if (WIN32)
+        find_package(IntelSYCL REQUIRED)
+        find_package(MKL REQUIRED)
+        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
+    else()
+        if (GGML_SYCL_TARGET STREQUAL "INTEL")
+            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+        elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA")
+            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
+            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl pthread m dl onemkl)
+        endif()
+    endif()
+endif()
+
+if (GGML_RPC)
+    message(STATUS "RPC found")
+
+    list(APPEND GGML_CDEF_PUBLIC GGML_USE_RPC)
+
+    if (WIN32)
+        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} ws2_32)
+    endif()
+
+    set(GGML_HEADERS_RPC ../include/ggml-rpc.h)
+    set(GGML_SOURCES_RPC ggml-rpc.cpp)
+endif()
+
+if (GGML_VULKAN)
+    find_package(Vulkan COMPONENTS glslc REQUIRED)
+
+    if (Vulkan_FOUND)
+        message(STATUS "Vulkan found")
+
+        list(APPEND GGML_CDEF_PUBLIC GGML_USE_VULKAN)
+
+        # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build
+        # Posssibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector
+        if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
+            add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0)
+        endif()
+
+        if (GGML_VULKAN_CHECK_RESULTS)
+            add_compile_definitions(GGML_VULKAN_CHECK_RESULTS)
+        endif()
+
+        if (GGML_VULKAN_DEBUG)
+            add_compile_definitions(GGML_VULKAN_DEBUG)
+        endif()
+
+        if (GGML_VULKAN_MEMORY_DEBUG)
+            add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG)
+        endif()
+
+        if (GGML_VULKAN_PERF)
+            add_compile_definitions(GGML_VULKAN_PERF)
+        endif()
+
+        if (GGML_VULKAN_VALIDATE)
+            add_compile_definitions(GGML_VULKAN_VALIDATE)
+        endif()
+
+        if (GGML_VULKAN_RUN_TESTS)
+            add_compile_definitions(GGML_VULKAN_RUN_TESTS)
+        endif()
+
+        add_subdirectory(vulkan-shaders)
+
+        set (_ggml_vk_genshaders_cmd vulkan-shaders-gen)
+        set (_ggml_vk_header     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp)
+        set (_ggml_vk_source     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp)
+        set (_ggml_vk_input_dir  ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders)
+        set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv)
+
+        file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp")
+
+        add_custom_command(
+            OUTPUT ${_ggml_vk_header}
+                   ${_ggml_vk_source}
+
+            COMMAND ${_ggml_vk_genshaders_cmd}
+                --glslc      ${Vulkan_GLSLC_EXECUTABLE}
+                --input-dir  ${_ggml_vk_input_dir}
+                --output-dir ${_ggml_vk_output_dir}
+                --target-hpp ${_ggml_vk_header}
+                --target-cpp ${_ggml_vk_source}
+                --no-clean
+
+            DEPENDS ${_ggml_vk_shader_deps}
+            COMMENT "Generate vulkan shaders"
+        )
+
+        set(GGML_HEADERS_VULKAN ${CMAKE_CURRENT_SOURCE_DIR}/../include/ggml-vulkan.h ${_ggml_vk_header})
+        set(GGML_SOURCES_VULKAN ggml-vulkan.cpp ${_ggml_vk_source})
+
+        set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS} Vulkan::Vulkan)
+        set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CMAKE_CURRENT_BINARY_DIR})
+    else()
+        message(WARNING "Vulkan not found")
+    endif()
+endif()
+
+if (GGML_KOMPUTE)
+    add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1)
+
+    find_package(Vulkan COMPONENTS glslc REQUIRED)
+    find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc)
+
+    if (NOT glslc_executable)
+        message(FATAL_ERROR "glslc not found")
+    endif()
+
+    function(compile_shader)
+        set(options)
+        set(oneValueArgs)
+        set(multiValueArgs SOURCES)
+        cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+        foreach(source ${compile_shader_SOURCES})
+            get_filename_component(filename ${source} NAME)
+            set(spv_file ${filename}.spv)
+            add_custom_command(
+                OUTPUT ${spv_file}
+                DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source}
+                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp
+                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp
+                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp
+                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp
+                COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source}
+                COMMENT "Compiling ${source} to ${spv_file}"
+                )
+
+            get_filename_component(RAW_FILE_NAME ${spv_file} NAME)
+            set(FILE_NAME "shader${RAW_FILE_NAME}")
+            string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME})
+            string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE)
+            string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}")
+            set(OUTPUT_HEADER_FILE "${HEADER_FILE}")
+            message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}")
+            if(CMAKE_GENERATOR MATCHES "Visual Studio")
+                add_custom_command(
+                    OUTPUT ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_BINARY_DIR}/bin/$<CONFIG>/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    DEPENDS ${spv_file} xxd
+                    COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$<CONFIG>/xxd"
+                    )
+            else()
+                add_custom_command(
+                    OUTPUT ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    DEPENDS ${spv_file} xxd
+                    COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd"
+                    )
+            endif()
+        endforeach()
+    endfunction()
+
+    if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt")
+        message(STATUS "Kompute found")
+        set(KOMPUTE_OPT_LOG_LEVEL Error CACHE STRING "Kompute log level")
+        add_subdirectory(kompute)
+
+        # Compile our shaders
+        compile_shader(SOURCES
+            kompute-shaders/op_scale.comp
+            kompute-shaders/op_scale_8.comp
+            kompute-shaders/op_add.comp
+            kompute-shaders/op_addrow.comp
+            kompute-shaders/op_mul.comp
+            kompute-shaders/op_silu.comp
+            kompute-shaders/op_relu.comp
+            kompute-shaders/op_gelu.comp
+            kompute-shaders/op_softmax.comp
+            kompute-shaders/op_norm.comp
+            kompute-shaders/op_rmsnorm.comp
+            kompute-shaders/op_diagmask.comp
+            kompute-shaders/op_mul_mat_mat_f32.comp
+            kompute-shaders/op_mul_mat_f16.comp
+            kompute-shaders/op_mul_mat_q8_0.comp
+            kompute-shaders/op_mul_mat_q4_0.comp
+            kompute-shaders/op_mul_mat_q4_1.comp
+            kompute-shaders/op_mul_mat_q6_k.comp
+            kompute-shaders/op_getrows_f32.comp
+            kompute-shaders/op_getrows_f16.comp
+            kompute-shaders/op_getrows_q4_0.comp
+            kompute-shaders/op_getrows_q4_1.comp
+            kompute-shaders/op_getrows_q6_k.comp
+            kompute-shaders/op_rope_f16.comp
+            kompute-shaders/op_rope_f32.comp
+            kompute-shaders/op_cpy_f16_f16.comp
+            kompute-shaders/op_cpy_f16_f32.comp
+            kompute-shaders/op_cpy_f32_f16.comp
+            kompute-shaders/op_cpy_f32_f32.comp
+        )
+
+        # Create a custom target for our generated shaders
+        add_custom_target(generated_shaders DEPENDS
+            shaderop_scale.h
+            shaderop_scale_8.h
+            shaderop_add.h
+            shaderop_addrow.h
+            shaderop_mul.h
+            shaderop_silu.h
+            shaderop_relu.h
+            shaderop_gelu.h
+            shaderop_softmax.h
+            shaderop_norm.h
+            shaderop_rmsnorm.h
+            shaderop_diagmask.h
+            shaderop_mul_mat_mat_f32.h
+            shaderop_mul_mat_f16.h
+            shaderop_mul_mat_q8_0.h
+            shaderop_mul_mat_q4_0.h
+            shaderop_mul_mat_q4_1.h
+            shaderop_mul_mat_q6_k.h
+            shaderop_getrows_f32.h
+            shaderop_getrows_f16.h
+            shaderop_getrows_q4_0.h
+            shaderop_getrows_q4_1.h
+            shaderop_getrows_q6_k.h
+            shaderop_rope_f16.h
+            shaderop_rope_f32.h
+            shaderop_cpy_f16_f16.h
+            shaderop_cpy_f16_f32.h
+            shaderop_cpy_f32_f16.h
+            shaderop_cpy_f32_f32.h
+        )
+
+        # Create a custom command that depends on the generated_shaders
+        add_custom_command(
+            OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp
+            COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp
+            DEPENDS generated_shaders
+            COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp"
+        )
+
+        # Add the stamp to the main sources to ensure dependency tracking
+        set(GGML_SOURCES_KOMPUTE ggml-kompute.cpp           ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp)
+        set(GGML_HEADERS_KOMPUTE ../include/ggml-kompute.h  ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp)
+
+        list(APPEND GGML_CDEF_PUBLIC GGML_USE_KOMPUTE)
+
+        set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS}     kompute)
+        set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CMAKE_CURRENT_BINARY_DIR})
+    else()
+        message(WARNING "Kompute not found")
+    endif()
+endif()
+
+if (GGML_CPU_HBM)
+    find_library(memkind memkind REQUIRED)
+
+    message(STATUS "Using memkind for CPU HBM")
+
+    add_compile_definitions(GGML_USE_CPU_HBM)
+
+    target_link_libraries(ggml PUBLIC memkind)
+endif()
+
+if (GGML_CANN)
+    if ("cann${CANN_INSTALL_DIR}" STREQUAL "cann" AND DEFINED ENV{ASCEND_TOOLKIT_HOME})
+        set(CANN_INSTALL_DIR $ENV{ASCEND_TOOLKIT_HOME})
+        message(STATUS "CANN: updated CANN_INSTALL_DIR from ASCEND_TOOLKIT_HOME=$ENV{ASCEND_TOOLKIT_HOME}")
+    endif()
+
+    if (CANN_INSTALL_DIR)
+        # Only Support Linux.
+        if (GGML_CANN)
+            if (NOT UNIX)
+                set(GGML_CANN OFF)
+                message(WARNING "CANN: CANN toolkit supports unix but not ${CMAKE_SYSTEM_NAME}. Turning off GGML_CANN")
+            endif()
+        endif()
+
+        # Supported platforms: x86-64, arm64
+        if (GGML_CANN)
+            if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64")
+            elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "amd64")
+            else()
+                set(GGML_CANN OFF)
+                message(WARNING "CANN: CANN toolkit supports x86-64 and arm64 but not ${CMAKE_SYSTEM_PROCESSOR}. Turning off GGML_CANN")
+            endif()
+        endif()
+
+        # Set header and libs
+        if(GGML_CANN)
+            set(CANN_INCLUDE_DIRS
+                ${CANN_INSTALL_DIR}/include
+                ${CANN_INSTALL_DIR}/include/aclnn
+                ${CANN_INSTALL_DIR}/acllib/include
+            )
+
+            add_subdirectory(ggml-cann/kernels)
+            list(APPEND CANN_LIBRARIES
+                ascendcl
+                nnopbase
+                opapi
+                acl_op_compiler
+                ascendc_kernels
+            )
+
+            set(GGML_HEADERS_CANN "../include/ggml-cann.h")
+            file(GLOB GGML_SOURCES_CANN "ggml-cann/*.cpp")
+            list(APPEND GGML_SOURCES_CANN "ggml-cann.cpp")
+
+            message(STATUS "CANN: CANN_INCLUDE_DIRS =  ${CANN_INCLUDE_DIRS}")
+            message(STATUS "CANN: CANN_LIBRARIES =  ${CANN_LIBRARIES}")
+
+            set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS}     ${CANN_LIBRARIES} )
+            set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CANN_INCLUDE_DIRS})
+            set(GGML_EXTRA_LIBDIRS  ${GGML_EXTRA_LIBDIRS}  ${CANN_INSTALL_DIR}/lib64)
+            list(APPEND GGML_CDEF_PUBLIC GGML_USE_CANN)
+        endif()
+    else()
+        set(GGML_CANN OFF)
+        message(WARNING "CANN: Can't find CANN_INSTALL_DIR, do you forget to source set_var.sh. Turning off GGML_CANN")
+    endif()
+
+    if(NOT GGML_CANN)
+        message(WARNING "CANN: GGML_CANN is turned OFF, see above for details.")
+    endif()
+endif()
+
+function(get_flags CCID CCVER)
+    set(C_FLAGS "")
+    set(CXX_FLAGS "")
+
+    if (CCID MATCHES "Clang")
+        set(C_FLAGS   -Wunreachable-code-break -Wunreachable-code-return)
+        set(CXX_FLAGS -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi)
+
+        if (
+            (CCID STREQUAL "Clang"      AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR
+            (CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0)
+        )
+            list(APPEND C_FLAGS -Wdouble-promotion)
+        endif()
+    elseif (CCID STREQUAL "GNU")
+        set(C_FLAGS   -Wdouble-promotion)
+        set(CXX_FLAGS -Wno-array-bounds)
+
+        if (NOT GGML_MUSA)
+            if (CCVER VERSION_GREATER_EQUAL 7.1.0)
+                list(APPEND CXX_FLAGS -Wno-format-truncation)
+            endif()
+        endif()
+        if (CCVER VERSION_GREATER_EQUAL 8.1.0)
+            list(APPEND CXX_FLAGS -Wextra-semi)
+        endif()
+    endif()
+
+    set(GF_C_FLAGS   ${C_FLAGS}   PARENT_SCOPE)
+    set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE)
+endfunction()
+
+if (GGML_FATAL_WARNINGS)
+    if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+        list(APPEND C_FLAGS   -Werror)
+        list(APPEND CXX_FLAGS -Werror)
+    elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+        add_compile_options(/WX)
+    endif()
+endif()
+
+if (GGML_ALL_WARNINGS)
+    if (NOT MSVC)
+        list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
+        list(APPEND C_FLAGS       -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
+                                  -Werror=implicit-int -Werror=implicit-function-declaration)
+        list(APPEND CXX_FLAGS     -Wmissing-declarations -Wmissing-noreturn)
+
+        list(APPEND C_FLAGS   ${WARNING_FLAGS})
+        list(APPEND CXX_FLAGS ${WARNING_FLAGS})
+
+        get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
+
+        add_compile_options("$<$<COMPILE_LANGUAGE:C>:${C_FLAGS};${GF_C_FLAGS}>"
+                            "$<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS};${GF_CXX_FLAGS}>")
+    else()
+        # todo : msvc
+        set(C_FLAGS   "")
+        set(CXX_FLAGS "")
+    endif()
+endif()
+
+set(CUDA_CXX_FLAGS "")
+
+if (GGML_CUDA)
+    set(CUDA_FLAGS -use_fast_math)
+
+    if (GGML_FATAL_WARNINGS)
+        list(APPEND CUDA_FLAGS -Werror all-warnings)
+    endif()
+
+    if (GGML_ALL_WARNINGS AND NOT MSVC)
+        set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c)
+        if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "")
+            list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER})
+        endif()
+
+        execute_process(
+            COMMAND ${NVCC_CMD} -Xcompiler --version
+            OUTPUT_VARIABLE CUDA_CCFULLVER
+            ERROR_QUIET
+        )
+
+        if (NOT CUDA_CCFULLVER MATCHES clang)
+            set(CUDA_CCID "GNU")
+            execute_process(
+                COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion"
+                OUTPUT_VARIABLE CUDA_CCVER
+                ERROR_QUIET
+            )
+        else()
+            if (CUDA_CCFULLVER MATCHES Apple)
+                set(CUDA_CCID "AppleClang")
+            else()
+                set(CUDA_CCID "Clang")
+            endif()
+            string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER})
+        endif()
+
+        message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}")
+
+        get_flags(${CUDA_CCID} ${CUDA_CCVER})
+        list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS})  # This is passed to -Xcompiler later
+    endif()
+
+    if (NOT MSVC)
+        list(APPEND CUDA_CXX_FLAGS -Wno-pedantic)
+    endif()
+endif()
+
+if (GGML_LTO)
+    include(CheckIPOSupported)
+    check_ipo_supported(RESULT result OUTPUT output)
+    if (result)
+        set(CMAKE_INTERPROCEDURAL_OPTIMIZATION TRUE)
+    else()
+        message(WARNING "IPO is not supported: ${output}")
+    endif()
+endif()
+
+if (GGML_CCACHE)
+    find_program(GGML_CCACHE_FOUND ccache)
+
+    if (GGML_CCACHE_FOUND)
+        # TODO: should not be set globally
+        set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
+        set(ENV{CCACHE_SLOPPINESS} time_macros)
+        message(STATUS "ccache found, compilation results will be cached. Disable with GGML_CCACHE=OFF.")
+    else()
+        message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF")
+    endif ()
+endif()
+
+# this version of Apple ld64 is buggy
+execute_process(
+    COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v
+    ERROR_VARIABLE output
+    OUTPUT_QUIET
+)
+
+if (output MATCHES "dyld-1015\.7")
+    add_compile_definitions(HAVE_BUGGY_APPLE_LINKER)
+endif()
+
+# architecture specific
+# TODO: probably these flags need to be tweaked on some architectures
+#       feel free to update the Makefile for your architecture and send a pull request or issue
+message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
+if (MSVC)
+    string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR)
+    message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}")
+else ()
+    set(CMAKE_GENERATOR_PLATFORM_LWR "")
+endif ()
+
+if (NOT MSVC)
+    if (GGML_STATIC)
+        add_link_options(-static)
+        if (MINGW)
+            add_link_options(-static-libgcc -static-libstdc++)
+        endif()
+    endif()
+    if (GGML_GPROF)
+        add_compile_options(-pg)
+    endif()
+endif()
+
+set(ARCH_FLAGS "")
+
+if (CMAKE_OSX_ARCHITECTURES      STREQUAL "arm64" OR
+    CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
+    (NOT CMAKE_OSX_ARCHITECTURES      AND
+     NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+         CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
+
+    message(STATUS "ARM detected")
+
+    if (MSVC)
+        add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead
+        add_compile_definitions(__ARM_NEON)
+        add_compile_definitions(__ARM_FEATURE_FMA)
+
+        set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS})
+        string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2")
+
+        check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
+        if (GGML_COMPILER_SUPPORT_DOTPROD)
+            add_compile_definitions(__ARM_FEATURE_DOTPROD)
+        endif ()
+
+        check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8)
+
+        if (GGML_COMPILER_SUPPORT_MATMUL_INT8)
+            add_compile_definitions(__ARM_FEATURE_MATMUL_INT8)
+        endif ()
+
+        check_cxx_source_compiles("#include <arm_neon.h>\nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
+        if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
+            add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+        endif ()
+
+        set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV})
+    else()
+        check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)
+        if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
+            list(APPEND ARCH_FLAGS -mfp16-format=ieee)
+        endif()
+        if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
+            # Raspberry Pi 1, Zero
+            list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access)
+        endif()
+        if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
+            if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android")
+                # Android armeabi-v7a
+                list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations)
+            else()
+                # Raspberry Pi 2
+                list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
+            endif()
+        endif()
+        if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
+            # Android arm64-v8a
+            # Raspberry Pi 3, 4, Zero 2 (32-bit)
+            list(APPEND ARCH_FLAGS -mno-unaligned-access)
+        endif()
+        if (GGML_SVE)
+            list(APPEND ARCH_FLAGS -march=armv8.6-a+sve)
+        endif()
+    endif()
+elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
+        (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+         CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$"))
+    message(STATUS "x86 detected")
+    if (MSVC)
+        # instruction set detection for MSVC only
+        if (GGML_NATIVE)
+            # TODO: improve, should not reference files from the parent folder
+            include(../cmake/FindSIMD.cmake)
+        endif ()
+        if (GGML_AVX512)
+            list(APPEND ARCH_FLAGS /arch:AVX512)
+            # MSVC has no compile-time flags enabling specific
+            # AVX512 extensions, neither it defines the
+            # macros corresponding to the extensions.
+            # Do it manually.
+            if (GGML_AVX512_VBMI)
+                add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VBMI__>)
+                add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VBMI__>)
+            endif()
+            if (GGML_AVX512_VNNI)
+                add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VNNI__>)
+                add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VNNI__>)
+            endif()
+            if (GGML_AVX512_BF16)
+                add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512BF16__>)
+                add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512BF16__>)
+            endif()
+        elseif (GGML_AVX2)
+            list(APPEND ARCH_FLAGS /arch:AVX2)
+        elseif (GGML_AVX)
+            list(APPEND ARCH_FLAGS /arch:AVX)
+        endif()
+    else()
+        if (GGML_NATIVE)
+            list(APPEND ARCH_FLAGS -march=native)
+        endif()
+        if (GGML_F16C)
+            list(APPEND ARCH_FLAGS -mf16c)
+        endif()
+        if (GGML_FMA)
+            list(APPEND ARCH_FLAGS -mfma)
+        endif()
+        if (GGML_AVX)
+            list(APPEND ARCH_FLAGS -mavx)
+        endif()
+        if (GGML_AVX2)
+            list(APPEND ARCH_FLAGS -mavx2)
+        endif()
+        if (GGML_AVX512)
+            list(APPEND ARCH_FLAGS -mavx512f)
+            list(APPEND ARCH_FLAGS -mavx512bw)
+        endif()
+        if (GGML_AVX512_VBMI)
+            list(APPEND ARCH_FLAGS -mavx512vbmi)
+        endif()
+        if (GGML_AVX512_VNNI)
+            list(APPEND ARCH_FLAGS -mavx512vnni)
+        endif()
+        if (GGML_AVX512_BF16)
+            list(APPEND ARCH_FLAGS -mavx512bf16)
+        endif()
+    endif()
+elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
+    message(STATUS "PowerPC detected")
+    if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le")
+        list(APPEND ARCH_FLAGS -mcpu=powerpc64le)
+    else()
+        list(APPEND ARCH_FLAGS -mcpu=native -mtune=native)
+        #TODO: Add  targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be)
+    endif()
+elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
+    message(STATUS "loongarch64 detected")
+
+    list(APPEND ARCH_FLAGS -march=loongarch64)
+    if (GGML_LASX)
+        list(APPEND ARCH_FLAGS -mlasx)
+    endif()
+    if (GGML_LSX)
+        list(APPEND ARCH_FLAGS -mlsx)
+    endif()
+else()
+    message(STATUS "Unknown architecture")
+endif()
+
+add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:${ARCH_FLAGS}>")
+add_compile_options("$<$<COMPILE_LANGUAGE:C>:${ARCH_FLAGS}>")
+
+if (GGML_CUDA)
+    list(APPEND CUDA_CXX_FLAGS ${ARCH_FLAGS})
+    list(JOIN   CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED)  # pass host compiler flags as a single argument
+
+    if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "")
+        list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED})
+    endif()
+
+    add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>")
+endif()
+
+if (MINGW)
+    # Target Windows 8 for PrefetchVirtualMemory
+    add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
+endif()
+
+#
+# POSIX conformance
+#
+
+# clock_gettime came in POSIX.1b (1993)
+# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
+# posix_memalign came in POSIX.1-2001 / SUSv3
+# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985)
+add_compile_definitions(_XOPEN_SOURCE=600)
+
+# Somehow in OpenBSD whenever POSIX conformance is specified
+# some string functions rely on locale_t availability,
+# which was introduced in POSIX.1-2008, forcing us to go higher
+if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
+    remove_definitions(-D_XOPEN_SOURCE=600)
+    add_compile_definitions(_XOPEN_SOURCE=700)
+endif()
+
+# Data types, macros and functions related to controlling CPU affinity and
+# some memory allocation are available on Linux through GNU extensions in libc
+if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+    add_compile_definitions(_GNU_SOURCE)
+endif()
+
+# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1,
+# and on macOS its availability depends on enabling Darwin extensions
+# similarly on DragonFly, enabling BSD extensions is necessary
+if (
+    CMAKE_SYSTEM_NAME MATCHES "Darwin" OR
+    CMAKE_SYSTEM_NAME MATCHES "iOS"    OR
+    CMAKE_SYSTEM_NAME MATCHES "tvOS"   OR
+    CMAKE_SYSTEM_NAME MATCHES "DragonFly"
+)
+    add_compile_definitions(_DARWIN_C_SOURCE)
+endif()
+
+# alloca is a non-standard interface that is not visible on BSDs when
+# POSIX conformance is specified, but not all of them provide a clean way
+# to enable it in such cases
+if (CMAKE_SYSTEM_NAME MATCHES "FreeBSD")
+    add_compile_definitions(__BSD_VISIBLE)
+endif()
+if (CMAKE_SYSTEM_NAME MATCHES "NetBSD")
+    add_compile_definitions(_NETBSD_SOURCE)
+endif()
+if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
+    add_compile_definitions(_BSD_SOURCE)
+endif()
+
+if (WIN32)
+    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
+
+    if (BUILD_SHARED_LIBS)
+        # TODO: should not use this
+        set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
+    endif()
+endif()
+
+#
+# libraries
+#
+
+# ggml
+
+add_library(ggml
+            ../include/ggml.h
+            ../include/ggml-alloc.h
+            ../include/ggml-backend.h
+            ggml.c
+            ggml-alloc.c
+            ggml-backend.c
+            ggml-quants.c
+            ggml-quants.h
+            ${GGML_SOURCES_CUDA}      ${GGML_HEADERS_CUDA}
+            ${GGML_SOURCES_METAL}     ${GGML_HEADERS_METAL}
+            ${GGML_SOURCES_RPC}       ${GGML_HEADERS_RPC}
+            ${GGML_SOURCES_EXTRA}     ${GGML_HEADERS_EXTRA}
+            ${GGML_SOURCES_SYCL}      ${GGML_HEADERS_SYCL}
+            ${GGML_SOURCES_KOMPUTE}   ${GGML_HEADERS_KOMPUTE}
+            ${GGML_SOURCES_VULKAN}    ${GGML_HEADERS_VULKAN}
+            ${GGML_SOURCES_ROCM}      ${GGML_HEADERS_ROCM}
+            ${GGML_SOURCES_BLAS}      ${GGML_HEADERS_BLAS}
+            ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE}
+            ${GGML_SOURCES_CANN}      ${GGML_HEADERS_CANN}
+            ggml-aarch64.c            ggml-aarch64.h
+            )
+
+if (EMSCRIPTEN)
+    set_target_properties(ggml PROPERTIES COMPILE_FLAGS "-msimd128")
+endif()
+
+target_compile_definitions(ggml PUBLIC  ${GGML_CDEF_PUBLIC})
+target_include_directories(ggml PUBLIC ../include)
+target_include_directories(ggml PRIVATE . ${GGML_EXTRA_INCLUDES})
+target_link_directories(ggml PRIVATE ${GGML_EXTRA_LIBDIRS})
+target_compile_features   (ggml PRIVATE c_std_11) # don't bump
+
+target_link_libraries(ggml PRIVATE Threads::Threads ${GGML_EXTRA_LIBS})
+
+find_library(MATH_LIBRARY m)
+if (MATH_LIBRARY)
+    if (NOT WIN32 OR NOT GGML_SYCL)
+        target_link_libraries(ggml PRIVATE ${MATH_LIBRARY})
+    endif()
+endif()
+
+if (BUILD_SHARED_LIBS)
+    set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    target_compile_definitions(ggml PRIVATE GGML_SHARED GGML_BUILD)
+endif()
diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c
new file mode 100644
index 0000000000000..7adaadc92d70a
--- /dev/null
+++ b/ggml/src/ggml-aarch64.c
@@ -0,0 +1,2195 @@
+// SPDX-FileCopyrightText: Copyright 2024 Arm Ltd.
+#define GGML_COMMON_IMPL_C
+#include "ggml-common.h"
+
+#include "ggml-quants.h"
+#include "ggml-impl.h"
+
+#include <math.h>
+#include <string.h>
+#include <assert.h>
+#include <float.h>
+#include <stdlib.h> // for qsort
+#include <stdio.h>  // for GGML_ASSERT
+
+#include "ggml-aarch64.h"
+
+#if defined(__GNUC__)
+#pragma GCC diagnostic ignored "-Woverlength-strings"
+#elif defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+#define UNUSED GGML_UNUSED
+
+// Functions to create the interleaved data layout formats
+
+// interleave 4 block_q4_0s in blocks of blck_size_interleave
+// returns an interleaved block_q4_0x4
+// in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks
+// first, then interleave quants from 4 block_q4_0s in blocks of blck_size_interleave
+//
+// - in                  : an array of block_q4_0 pointers
+// - blck_size_interleave : the block_q4_0 quants bytes are interleaved in blocks of
+//                         blck_size_interleave bytes
+// - xor_mask            : the mask to convert the nibbles in block_q4_0 quants bytes
+//                         from bias offset form to pure sign form (this saves subtract
+//                         operations durin unpacking)
+//
+static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) {
+    block_q4_0x4 out;
+
+    for (int i = 0; i < 4; i++) {
+        out.d[i] = in[i].d;
+    }
+
+    for (int i = 0; i < QK4_0 * 2; i++) {
+        int src_offset = (i / (4 * blck_size_interleave)) * blck_size_interleave;
+        int src_id = (i % (4 * blck_size_interleave)) / blck_size_interleave;
+        src_offset += (i % blck_size_interleave);
+
+        out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask;
+    }
+
+    return out;
+}
+
+// interleave 8 block_q4_0s in blocks of blck_size_interleave
+// returns an interleaved block_q4_0x8
+// in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks
+// first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave
+static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) {
+    block_q4_0x8 out;
+
+    for (int i = 0; i < 8; i++) {
+        out.d[i] = in[i].d;
+    }
+
+    for (int i = 0; i < QK4_0 * 4; i++) {
+        int src_offset = (i / (8 * blck_size_interleave)) * blck_size_interleave;
+        int src_id = (i % (8 * blck_size_interleave)) / blck_size_interleave;
+        src_offset += (i % blck_size_interleave);
+
+        out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask;
+    }
+
+    return out;
+}
+
+void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k) {
+    assert(QK8_0 == 32);
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    block_q8_0x4 * restrict y = (block_q8_0x4 *) vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t srcv[4][8];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        float32x4_t asrcv[8];
+        float32x4_t amaxv[8];
+
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j);
+            for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]);
+
+            for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]);
+            for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]);
+            for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]);
+
+            const float amax = vmaxvq_f32(amaxv[0]);
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < 8; j++) {
+            float32x4_t v = vmulq_n_f32(srcv[0][j], id[0]);
+            int32x4_t vi = vcvtnq_s32_f32(v);
+            y[i].qs[16 * j + 0] = vgetq_lane_s32(vi, 0);
+            y[i].qs[16 * j + 1] = vgetq_lane_s32(vi, 1);
+            y[i].qs[16 * j + 2] = vgetq_lane_s32(vi, 2);
+            y[i].qs[16 * j + 3] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[1][j], id[1]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[16 * j + 4] = vgetq_lane_s32(vi, 0);
+            y[i].qs[16 * j + 5] = vgetq_lane_s32(vi, 1);
+            y[i].qs[16 * j + 6] = vgetq_lane_s32(vi, 2);
+            y[i].qs[16 * j + 7] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[2][j], id[2]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[16 * j + 8] = vgetq_lane_s32(vi, 0);
+            y[i].qs[16 * j + 9] = vgetq_lane_s32(vi, 1);
+            y[i].qs[16 * j + 10] = vgetq_lane_s32(vi, 2);
+            y[i].qs[16 * j + 11] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[3][j], id[3]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[16 * j + 12] = vgetq_lane_s32(vi, 0);
+            y[i].qs[16 * j + 13] = vgetq_lane_s32(vi, 1);
+            y[i].qs[16 * j + 14] = vgetq_lane_s32(vi, 2);
+            y[i].qs[16 * j + 15] = vgetq_lane_s32(vi, 3);
+        }
+    }
+#else
+    // scalar
+    const int blck_size_interleave = 4;
+    float srcv[4][QK8_0];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            float amax = 0.0f; // absolute max
+
+            for (int j = 0; j < QK8_0; j++) {
+                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
+                amax = MAX(amax, fabsf(srcv[row_iter][j]));
+            }
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < QK8_0 * 4; j++) {
+            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
+            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
+            src_offset += (j % blck_size_interleave);
+
+            float x0 = srcv[src_id][src_offset] * id[src_id];
+            y[i].qs[j] = roundf(x0);
+        }
+    }
+#endif
+}
+
+void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k) {
+    assert(QK8_0 == 32);
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    block_q8_0x4 * restrict y = (block_q8_0x4 *) vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t srcv[4][8];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        float32x4_t asrcv[8];
+        float32x4_t amaxv[8];
+
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j);
+            for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]);
+
+            for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]);
+            for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]);
+            for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]);
+
+            const float amax = vmaxvq_f32(amaxv[0]);
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < 4; j++) {
+            float32x4_t v = vmulq_n_f32(srcv[0][2 * j], id[0]);
+            int32x4_t vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 0] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 1] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 2] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 3] = vgetq_lane_s32(vi, 3);
+            v = vmulq_n_f32(srcv[0][2 * j + 1], id[0]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 4] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 5] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 6] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 7] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[1][2 * j], id[1]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 8] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 9] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 10] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 11] = vgetq_lane_s32(vi, 3);
+            v = vmulq_n_f32(srcv[1][2 * j + 1], id[1]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 12] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 13] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 14] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 15] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[2][2 * j], id[2]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 16] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 17] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 18] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 19] = vgetq_lane_s32(vi, 3);
+            v = vmulq_n_f32(srcv[2][2 * j + 1], id[2]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 20] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 21] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 22] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 23] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[3][2 * j], id[3]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 24] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 25] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 26] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 27] = vgetq_lane_s32(vi, 3);
+            v = vmulq_n_f32(srcv[3][2 * j + 1], id[3]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 28] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 29] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 30] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 31] = vgetq_lane_s32(vi, 3);
+        }
+    }
+#else
+    // scalar
+    const int blck_size_interleave = 8;
+    float srcv[4][QK8_0];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            float amax = 0.0f; // absolute max
+
+            for (int j = 0; j < QK8_0; j++) {
+                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
+                amax = MAX(amax, fabsf(srcv[row_iter][j]));
+            }
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < QK8_0 * 4; j++) {
+            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
+            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
+            src_offset += (j % blck_size_interleave);
+
+            float x0 = srcv[src_id][src_offset] * id[src_id];
+            y[i].qs[j] = roundf(x0);
+        }
+    }
+#endif
+}
+
+void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) {
+    assert(nrow == 4);
+    UNUSED(nrow);
+    if (blck_size_interleave == 4) {
+        quantize_q8_0_4x4(x, vy, n_per_row);
+    } else if (blck_size_interleave == 8) {
+        quantize_q8_0_4x8(x, vy, n_per_row);
+    } else {
+        assert(false);
+    }
+}
+
+static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int blck_size_interleave) {
+    assert(n_per_row % QK4_0 == 0);
+    const int nb = n_per_row / QK4_0;
+
+    void * out_ptr = NULL;
+    if (nrows_interleaved == 8) {
+        out_ptr = (block_q4_0x8 *) dst;
+    }
+    else if (nrows_interleaved == 4) {
+        out_ptr = (block_q4_0x4 *) dst;
+    }
+    assert(nrows_interleaved <= 8);
+    block_q4_0 dst_tmp[8];
+
+    for (int b = 0; b < (nrow * n_per_row); b += nrows_interleaved * n_per_row) {
+
+        for (int64_t x = 0; x < nb; x++) {
+
+            for (int i  = 0; i < nrows_interleaved; i++ ) {
+                quantize_row_q4_0_ref(src + b + i * n_per_row + x * QK4_0, (block_q4_0 *) dst_tmp + i, QK4_0);
+            }
+
+            if (nrows_interleaved == 8) {
+                *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave, 0x88);
+                out_ptr = (block_q4_0x8 *) out_ptr + 1;
+            }
+            else if (nrows_interleaved == 4) {
+                *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave, 0x88);
+                out_ptr = (block_q4_0x4 *) out_ptr + 1;
+            }
+        }
+    }
+
+    return ((nrow * n_per_row) / QK4_0 * sizeof(block_q4_0));
+}
+
+size_t quantize_q4_0_4x4(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 4);
+    }
+    else {
+        assert(false);
+        return 0;
+    }
+}
+
+size_t quantize_q4_0_4x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 8);
+    }
+    else {
+        assert(false);
+        return 0;
+    }
+}
+
+size_t quantize_q4_0_8x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8);
+    }
+    else {
+        assert(false);
+        return 0;
+    }
+}
+
+void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 4;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE)
+    if (ggml_sve_cnt_b == QK8_0) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
+                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    GGML_ASSERT(!(ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) &&
+                "__ARM_NEON and __ARM_FEATURE_MATMUL_INT8 defined, use the Q4_0_4_8 quantization format for optimal performance");
+#elif defined(__ARM_NEON) && defined(__aarch64__) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
+    const void * b_ptr = vx;
+    const void * a_ptr = vy;
+    float * res_ptr = s;
+
+    __asm__ __volatile__(
+        "movi v31.16b, #0x4\n"
+        "movi v30.16b, #0xf0\n"
+        "add %x[b_ptr], %x[b_ptr], #0x8\n"
+        "1:"  // Column loop
+        "add x22, %x[a_ptr], #0x2\n"
+        "movi v29.16b, #0x0\n"
+        "mov x21, %x[nb]\n"
+        "2:"  // Block loop
+        "ldr q28, [%x[b_ptr], #0x0]\n"
+        "ldr q27, [x22, #0x0]\n"
+        "movi v26.4s, #0x0\n"
+        "sub x20, x22, #0x2\n"
+        "ldr q25, [x22, #0x10]\n"
+        "ldr q24, [%x[b_ptr], #0x10]\n"
+        "sub x21, x21, #0x1\n"
+        "add x22, x22, #0x22\n"
+        "ldr q23, [%x[b_ptr], #0x20]\n"
+        "ldr q22, [%x[b_ptr], #0x30]\n"
+        "ld1r { v21.8h }, [x20]\n"
+        "ldr q20, [%x[b_ptr], #-0x8]\n"
+        "sshl v16.16b, v28.16b, v31.16b\n"
+        "and v28.16b, v28.16b, v30.16b\n"
+        "sshl v19.16b, v24.16b, v31.16b\n"
+        "and v24.16b, v24.16b, v30.16b\n"
+        "add %x[b_ptr], %x[b_ptr], #0x48\n"
+        "sshl v18.16b, v23.16b, v31.16b\n"
+        "and v23.16b, v23.16b, v30.16b\n"
+        ".inst 0x4f9be21a  // sdot v26.4s, v16.16b, v27.4b[0]\n"
+        "sshl v17.16b, v22.16b, v31.16b\n"
+        "and v22.16b, v22.16b, v30.16b\n"
+        "fcvtl v21.4s, v21.4h\n"
+        "fcvtl v16.4s, v20.4h\n"
+        ".inst 0x4f99e39a  // sdot v26.4s, v28.16b, v25.4b[0]\n"
+        "fmul v16.4s, v16.4s, v21.4s\n"
+        ".inst 0x4fbbe27a  // sdot v26.4s, v19.16b, v27.4b[1]\n"
+        ".inst 0x4fb9e31a  // sdot v26.4s, v24.16b, v25.4b[1]\n"
+        ".inst 0x4f9bea5a  // sdot v26.4s, v18.16b, v27.4b[2]\n"
+        ".inst 0x4f99eafa  // sdot v26.4s, v23.16b, v25.4b[2]\n"
+        ".inst 0x4fbbea3a  // sdot v26.4s, v17.16b, v27.4b[3]\n"
+        ".inst 0x4fb9eada  // sdot v26.4s, v22.16b, v25.4b[3]\n"
+        "scvtf v26.4s, v26.4s, #0x4\n"
+        "fmla v29.4s, v26.4s, v16.4s\n"
+        "cbnz x21, 2b\n"
+        "sub %x[nc], %x[nc], #0x4\n"
+        "str q29, [%x[res_ptr], #0x0]\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "cbnz %x[nc], 1b\n"
+        : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc)
+        : [a_ptr] "r" (a_ptr), [nb] "r" (nb)
+        : "memory", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22"
+    );
+#else
+    float sumf[4];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                    }
+                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+#endif
+}
+
+void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE)
+    if (ggml_sve_cnt_b == QK8_0) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
+                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
+    const void * b_ptr = vx;
+    const void * a_ptr = vy;
+    float * res_ptr = s;
+
+    __asm__ __volatile__(
+        "movi v2.16b, #0x4\n"
+        "movi v1.16b, #0xf0\n"
+        "add %x[b_ptr], %x[b_ptr], #0x8\n"
+        "1:"  // Column loop
+        "add x23, %x[a_ptr], #0x2\n"
+        "movi v0.16b, #0x0\n"
+        "mov x22, %x[nb]\n"
+        "2:"  // Block loop
+        "ldr q31, [%x[b_ptr], #0x0]\n"
+        "ldr q30, [%x[b_ptr], #0x10]\n"
+        "mov x21, x23\n"
+        "movi v29.4s, #0x0\n"
+        "ldr q28, [%x[b_ptr], #0x20]\n"
+        "ldr q27, [%x[b_ptr], #0x30]\n"
+        "movi v26.4s, #0x0\n"
+        "sub x20, x23, #0x2\n"
+        "ld1r { v25.8h }, [x20]\n"
+        "ldr q24, [%x[b_ptr], #-0x8]\n"
+        "sub x22, x22, #0x1\n"
+        "add x23, x23, #0x22\n"
+        "ld1r { v23.2d }, [x21], #0x8\n"
+        "sshl v22.16b, v31.16b, v2.16b\n"
+        "sshl v16.16b, v30.16b, v2.16b\n"
+        "add %x[b_ptr], %x[b_ptr], #0x48\n"
+        "ld1r { v21.2d }, [x21], #0x8\n"
+        "sshl v20.16b, v28.16b, v2.16b\n"
+        "sshl v19.16b, v27.16b, v2.16b\n"
+        "ld1r { v18.2d }, [x21], #0x8\n"
+        "ld1r { v17.2d }, [x21], #0x8\n"
+        "and v31.16b, v31.16b, v1.16b\n"
+        "and v30.16b, v30.16b, v1.16b\n"
+        ".inst 0x4e9796dd  // sdot v29.4s, v22.16b, v23.16b\n"
+        ".inst 0x4e97961a  // sdot v26.4s, v16.16b, v23.16b\n"
+        "and v28.16b, v28.16b, v1.16b\n"
+        "and v27.16b, v27.16b, v1.16b\n"
+        "fcvtl v25.4s, v25.4h\n"
+        "fcvtl v16.4s, v24.4h\n"
+        ".inst 0x4e95969d  // sdot v29.4s, v20.16b, v21.16b\n"
+        ".inst 0x4e95967a  // sdot v26.4s, v19.16b, v21.16b\n"
+        "fmul v16.4s, v16.4s, v25.4s\n"
+        ".inst 0x4e9297fd  // sdot v29.4s, v31.16b, v18.16b\n"
+        ".inst 0x4e9297da  // sdot v26.4s, v30.16b, v18.16b\n"
+        ".inst 0x4e91979d  // sdot v29.4s, v28.16b, v17.16b\n"
+        ".inst 0x4e91977a  // sdot v26.4s, v27.16b, v17.16b\n"
+        "addp v29.4s, v29.4s, v26.4s\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "fmla v0.4s, v29.4s, v16.4s\n"
+        "cbnz x22, 2b\n"
+        "sub %x[nc], %x[nc], #0x4\n"
+        "str q0, [%x[res_ptr], #0x0]\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "cbnz %x[nc], 1b\n"
+        : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc)
+        : [a_ptr] "r" (a_ptr), [nb] "r" (nb)
+        : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23"
+    );
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
+                "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
+                "performance");
+#else
+    float sumf[4];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                    }
+                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+#endif
+}
+
+void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
+    if (ggml_sve_cnt_b == QK8_0) {
+        const void * b_ptr = vx;
+        const void * a_ptr = vy;
+        float * res_ptr = s;
+
+        __asm__ __volatile__(
+            "ptrue p0.b\n"
+            "add %x[b_ptr], %x[b_ptr], #0x10\n"
+            "1:"  // Column loop
+            "add x22, %x[a_ptr], #0x2\n"
+            "mov z31.b, #0x0\n"
+            "mov x21, %x[nb]\n"
+            "2:"  // Block loop
+            "ld1b { z30.b }, p0/Z, [%x[b_ptr]]\n"
+            "ld1b { z29.b }, p0/Z, [%x[b_ptr], #1, MUL VL]\n"
+            "mov z28.s, #0x0\n"
+            "mov z27.s, #0x0\n"
+            "ld1rd { z26.d }, p0/Z, [x22]\n"
+            "ld1b { z25.b }, p0/Z, [%x[b_ptr], #2, MUL VL]\n"
+            "sub x20, x22, #0x2\n"
+            "sub x21, x21, #0x1\n"
+            "ld1b { z24.b }, p0/Z, [%x[b_ptr], #3, MUL VL]\n"
+            "ld1rd { z23.d }, p0/Z, [x22, #8]\n"
+            "lsl z22.b, z30.b, #0x4\n"
+            "lsl z16.b, z29.b, #0x4\n"
+            "and z30.b, z30.b, #0xf0\n"
+            "and z29.b, z29.b, #0xf0\n"
+            "ld1rd { z21.d }, p0/Z, [x22, #16]\n"
+            "ld1rd { z20.d }, p0/Z, [x22, #24]\n"
+            "lsl z19.b, z25.b, #0x4\n"
+            "and z25.b, z25.b, #0xf0\n"
+            "ld1rh { z17.h }, p0/Z, [x20]\n"
+            "ld1h { z18.s }, p0/Z, [%x[b_ptr], #-1, MUL VL]\n"
+            "sdot z28.s, z22.b, z26.b\n"
+            "sdot z27.s, z16.b, z26.b\n"
+            "lsl z16.b, z24.b, #0x4\n"
+            "add x22, x22, #0x22\n"
+            "and z24.b, z24.b, #0xf0\n"
+            "add %x[b_ptr], %x[b_ptr], #0x90\n"
+            "fcvt z17.s, p0/m, z17.h\n"
+            "fcvt z18.s, p0/m, z18.h\n"
+            "sdot z28.s, z19.b, z23.b\n"
+            "sdot z27.s, z16.b, z23.b\n"
+            "fmul z18.s, z18.s, z17.s\n"
+            "sdot z28.s, z30.b, z21.b\n"
+            "sdot z27.s, z29.b, z21.b\n"
+            "sdot z28.s, z25.b, z20.b\n"
+            "sdot z27.s, z24.b, z20.b\n"
+            "uzp1 z17.s, z28.s, z27.s\n"
+            "uzp2 z16.s, z28.s, z27.s\n"
+            "add z17.s, z17.s, z16.s\n"
+            "asr z17.s, z17.s, #0x4\n"
+            "scvtf z17.s, p0/m, z17.s\n"
+            "fmla z31.s, p0/M, z17.s, z18.s\n"
+            "cbnz x21, 2b\n"
+            "sub %x[nc], %x[nc], #0x8\n"
+            "st1w { z31.s }, p0, [%x[res_ptr]]\n"
+            "add %x[res_ptr], %x[res_ptr], #0x20\n"
+            "cbnz %x[nc], 1b\n"
+            : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc)
+            : [a_ptr] "r" (a_ptr), [nb] "r" (nb)
+            : "memory", "p0", "x20", "x21", "x22", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31"
+        );
+        return;
+    }
+    else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
+        GGML_ASSERT((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
+                    "__ARM_FEATURE_SVE for vector size of 256-bits not defined, use the Q4_0_4_8 quantization format for optimal "
+                    "performance");
+    }
+    else if (ggml_cpu_has_neon()) {
+        GGML_ASSERT(((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) || ggml_cpu_has_matmul_int8()) &&
+                    "__ARM_FEATURE_SVE for vector size of 256-bits and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 "
+                    "quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    GGML_ASSERT(ggml_cpu_has_sve() &&
+                "__ARM_FEATURE_SVE not defined, use the Q4_0_4_8 quantization format for optimal performance");
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
+                "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
+                "performance");
+#else
+    float sumf[8];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                    }
+                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+#endif
+}
+
+void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 4;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
+    if (ggml_sve_cnt_b == QK8_0) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
+                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    GGML_ASSERT(!(ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) &&
+                "__ARM_NEON and __ARM_FEATURE_MATMUL_INT8 defined, use the Q4_0_4_8 quantization format for optimal performance");
+#elif defined(__ARM_NEON) && defined(__aarch64__) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
+    const void * b_ptr = vx;
+    const void * a_ptr = vy;
+    float * res_ptr = s;
+    size_t res_stride = bs * sizeof(float);
+
+    __asm__ __volatile__(
+        "mov x10, %x[nr]\n"
+        "mov x9, #0x88\n"
+        "cmp x10, #0x10\n"
+        "mul x9, %x[nb], x9\n"
+        "blt 4f\n"
+        "1:"  // Row loop
+        "add x28, %x[b_ptr], #0x8\n"
+        "mov x27, %x[nc]\n"
+        "add x26, %x[res_ptr], %x[res_stride], LSL #4\n"
+        "2:"  // Column loop
+        "add x25, %x[a_ptr], #0x8\n"
+        "movi v15.16b, #0x0\n"
+        "movi v19.16b, #0x0\n"
+        "mov x24, %x[nb]\n"
+        "add x23, x25, x9\n"
+        "movi v18.16b, #0x0\n"
+        "movi v14.16b, #0x0\n"
+        "add x22, x23, x9\n"
+        "movi v11.16b, #0x0\n"
+        "movi v13.16b, #0x0\n"
+        "add x21, x22, x9\n"
+        "movi v23.16b, #0x0\n"
+        "movi v16.16b, #0x0\n"
+        "movi v25.16b, #0x0\n"
+        "movi v7.16b, #0x0\n"
+        "movi v0.16b, #0x0\n"
+        "movi v4.16b, #0x0\n"
+        "movi v5.16b, #0x0\n"
+        "movi v21.16b, #0x0\n"
+        "movi v8.16b, #0x0\n"
+        "movi v1.16b, #0x0\n"
+        "3:"  // Block loop
+        "ldr q3, [x28, #0x0]\n"
+        "ldr q31, [x25, #0x0]\n"
+        "movi v28.16b, #0x4\n"
+        "movi v10.4s, #0x0\n"
+        "ldr q22, [x28, #0x10]\n"
+        "ldr q6, [x25, #0x10]\n"
+        "movi v29.4s, #0x0\n"
+        "movi v9.4s, #0x0\n"
+        "ldr q27, [x28, #0x20]\n"
+        "ldr q30, [x28, #0x30]\n"
+        "movi v20.4s, #0x0\n"
+        "movi v24.16b, #0xf0\n"
+        "ldr d2, [x25, #-0x8]\n"
+        "ldr d26, [x23, #-0x8]\n"
+        "sshl v12.16b, v3.16b, v28.16b\n"
+        "sub x20, x28, #0x8\n"
+        "ldr d17, [x20, #0x0]\n"
+        "and v3.16b, v3.16b, v24.16b\n"
+        "subs x24, x24, #0x1\n"
+        "add x28, x28, #0x48\n"
+        ".inst 0x4f9fe18a  // sdot v10.4s, v12.16b, v31.4b[0]\n"
+        ".inst 0x4fbfe19d  // sdot v29.4s, v12.16b, v31.4b[1]\n"
+        ".inst 0x4f9fe989  // sdot v9.4s, v12.16b, v31.4b[2]\n"
+        ".inst 0x4fbfe994  // sdot v20.4s, v12.16b, v31.4b[3]\n"
+        "sshl v31.16b, v22.16b, v28.16b\n"
+        "and v22.16b, v22.16b, v24.16b\n"
+        "fcvtl v17.4s, v17.4h\n"
+        "fcvtl v2.4s, v2.4h\n"
+        "fcvtl v26.4s, v26.4h\n"
+        ".inst 0x4f86e3ea  // sdot v10.4s, v31.16b, v6.4b[0]\n"
+        ".inst 0x4fa6e3fd  // sdot v29.4s, v31.16b, v6.4b[1]\n"
+        ".inst 0x4f86ebe9  // sdot v9.4s, v31.16b, v6.4b[2]\n"
+        ".inst 0x4fa6ebf4  // sdot v20.4s, v31.16b, v6.4b[3]\n"
+        "sshl v6.16b, v27.16b, v28.16b\n"
+        "sshl v28.16b, v30.16b, v28.16b\n"
+        "and v27.16b, v27.16b, v24.16b\n"
+        "and v30.16b, v30.16b, v24.16b\n"
+        "ldr q24, [x25, #0x20]\n"
+        ".inst 0x4f98e0ca  // sdot v10.4s, v6.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e0dd  // sdot v29.4s, v6.16b, v24.4b[1]\n"
+        ".inst 0x4f98e8c9  // sdot v9.4s, v6.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e8d4  // sdot v20.4s, v6.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x30]\n"
+        ".inst 0x4f98e38a  // sdot v10.4s, v28.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e39d  // sdot v29.4s, v28.16b, v24.4b[1]\n"
+        ".inst 0x4f98eb89  // sdot v9.4s, v28.16b, v24.4b[2]\n"
+        ".inst 0x4fb8eb94  // sdot v20.4s, v28.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x40]\n"
+        ".inst 0x4f98e06a  // sdot v10.4s, v3.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e07d  // sdot v29.4s, v3.16b, v24.4b[1]\n"
+        ".inst 0x4f98e869  // sdot v9.4s, v3.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e874  // sdot v20.4s, v3.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x50]\n"
+        ".inst 0x4f98e2ca  // sdot v10.4s, v22.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e2dd  // sdot v29.4s, v22.16b, v24.4b[1]\n"
+        ".inst 0x4f98eac9  // sdot v9.4s, v22.16b, v24.4b[2]\n"
+        ".inst 0x4fb8ead4  // sdot v20.4s, v22.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x60]\n"
+        ".inst 0x4f98e36a  // sdot v10.4s, v27.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e37d  // sdot v29.4s, v27.16b, v24.4b[1]\n"
+        ".inst 0x4f98eb69  // sdot v9.4s, v27.16b, v24.4b[2]\n"
+        ".inst 0x4fb8eb74  // sdot v20.4s, v27.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x70]\n"
+        "add x25, x25, #0x88\n"
+        ".inst 0x4f98e3ca  // sdot v10.4s, v30.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e3dd  // sdot v29.4s, v30.16b, v24.4b[1]\n"
+        ".inst 0x4f98ebc9  // sdot v9.4s, v30.16b, v24.4b[2]\n"
+        ".inst 0x4fb8ebd4  // sdot v20.4s, v30.16b, v24.4b[3]\n"
+        "fmul v24.4s, v17.4s, v2.s[0]\n"
+        "scvtf v10.4s, v10.4s, #0x4\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "scvtf v9.4s, v9.4s, #0x4\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "fmla v15.4s, v10.4s, v24.4s\n"
+        "ldr q24, [x23, #0x0]\n"
+        "fmul v10.4s, v17.4s, v2.s[1]\n"
+        "fmla v19.4s, v29.4s, v10.4s\n"
+        "ldr q10, [x23, #0x10]\n"
+        "fmul v29.4s, v17.4s, v2.s[2]\n"
+        "fmul v2.4s, v17.4s, v2.s[3]\n"
+        "fmla v18.4s, v9.4s, v29.4s\n"
+        "movi v9.4s, #0x0\n"
+        "movi v29.4s, #0x0\n"
+        ".inst 0x4f98e189  // sdot v9.4s, v12.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e19d  // sdot v29.4s, v12.16b, v24.4b[1]\n"
+        "fmla v14.4s, v20.4s, v2.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v2.4s, #0x0\n"
+        ".inst 0x4f98e994  // sdot v20.4s, v12.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e982  // sdot v2.4s, v12.16b, v24.4b[3]\n"
+        "ldr q24, [x23, #0x20]\n"
+        ".inst 0x4f8ae3e9  // sdot v9.4s, v31.16b, v10.4b[0]\n"
+        ".inst 0x4faae3fd  // sdot v29.4s, v31.16b, v10.4b[1]\n"
+        ".inst 0x4f8aebf4  // sdot v20.4s, v31.16b, v10.4b[2]\n"
+        ".inst 0x4faaebe2  // sdot v2.4s, v31.16b, v10.4b[3]\n"
+        "ldr q10, [x23, #0x30]\n"
+        ".inst 0x4f98e0c9  // sdot v9.4s, v6.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e0dd  // sdot v29.4s, v6.16b, v24.4b[1]\n"
+        ".inst 0x4f98e8d4  // sdot v20.4s, v6.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e8c2  // sdot v2.4s, v6.16b, v24.4b[3]\n"
+        "ldr q24, [x23, #0x40]\n"
+        ".inst 0x4f8ae389  // sdot v9.4s, v28.16b, v10.4b[0]\n"
+        ".inst 0x4faae39d  // sdot v29.4s, v28.16b, v10.4b[1]\n"
+        ".inst 0x4f8aeb94  // sdot v20.4s, v28.16b, v10.4b[2]\n"
+        ".inst 0x4faaeb82  // sdot v2.4s, v28.16b, v10.4b[3]\n"
+        "ldr q10, [x23, #0x50]\n"
+        ".inst 0x4f98e069  // sdot v9.4s, v3.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e07d  // sdot v29.4s, v3.16b, v24.4b[1]\n"
+        ".inst 0x4f98e874  // sdot v20.4s, v3.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e862  // sdot v2.4s, v3.16b, v24.4b[3]\n"
+        "ldr q24, [x23, #0x60]\n"
+        ".inst 0x4f8ae2c9  // sdot v9.4s, v22.16b, v10.4b[0]\n"
+        ".inst 0x4faae2dd  // sdot v29.4s, v22.16b, v10.4b[1]\n"
+        ".inst 0x4f8aead4  // sdot v20.4s, v22.16b, v10.4b[2]\n"
+        ".inst 0x4faaeac2  // sdot v2.4s, v22.16b, v10.4b[3]\n"
+        "ldr q10, [x23, #0x70]\n"
+        "add x23, x23, #0x88\n"
+        ".inst 0x4f98e369  // sdot v9.4s, v27.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e37d  // sdot v29.4s, v27.16b, v24.4b[1]\n"
+        ".inst 0x4f98eb74  // sdot v20.4s, v27.16b, v24.4b[2]\n"
+        ".inst 0x4fb8eb62  // sdot v2.4s, v27.16b, v24.4b[3]\n"
+        "ldr q24, [x22, #0x0]\n"
+        ".inst 0x4f8ae3c9  // sdot v9.4s, v30.16b, v10.4b[0]\n"
+        ".inst 0x4faae3dd  // sdot v29.4s, v30.16b, v10.4b[1]\n"
+        ".inst 0x4f8aebd4  // sdot v20.4s, v30.16b, v10.4b[2]\n"
+        ".inst 0x4faaebc2  // sdot v2.4s, v30.16b, v10.4b[3]\n"
+        "fmul v10.4s, v17.4s, v26.s[0]\n"
+        "scvtf v9.4s, v9.4s, #0x4\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "scvtf v2.4s, v2.4s, #0x4\n"
+        "fmla v11.4s, v9.4s, v10.4s\n"
+        "ldr q9, [x22, #0x10]\n"
+        "fmul v10.4s, v17.4s, v26.s[1]\n"
+        "fmla v13.4s, v29.4s, v10.4s\n"
+        "ldr d29, [x22, #-0x8]\n"
+        "fmul v10.4s, v17.4s, v26.s[2]\n"
+        "fmul v26.4s, v17.4s, v26.s[3]\n"
+        "fcvtl v29.4s, v29.4h\n"
+        "fmla v23.4s, v20.4s, v10.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v10.4s, #0x0\n"
+        "fmla v16.4s, v2.4s, v26.4s\n"
+        "movi v26.4s, #0x0\n"
+        "movi v2.4s, #0x0\n"
+        ".inst 0x4f98e194  // sdot v20.4s, v12.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e18a  // sdot v10.4s, v12.16b, v24.4b[1]\n"
+        ".inst 0x4f98e99a  // sdot v26.4s, v12.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e982  // sdot v2.4s, v12.16b, v24.4b[3]\n"
+        "ldr q24, [x22, #0x20]\n"
+        ".inst 0x4f89e3f4  // sdot v20.4s, v31.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e3ea  // sdot v10.4s, v31.16b, v9.4b[1]\n"
+        ".inst 0x4f89ebfa  // sdot v26.4s, v31.16b, v9.4b[2]\n"
+        ".inst 0x4fa9ebe2  // sdot v2.4s, v31.16b, v9.4b[3]\n"
+        "ldr q9, [x22, #0x30]\n"
+        ".inst 0x4f98e0d4  // sdot v20.4s, v6.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e0ca  // sdot v10.4s, v6.16b, v24.4b[1]\n"
+        ".inst 0x4f98e8da  // sdot v26.4s, v6.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e8c2  // sdot v2.4s, v6.16b, v24.4b[3]\n"
+        "ldr q24, [x22, #0x40]\n"
+        ".inst 0x4f89e394  // sdot v20.4s, v28.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e38a  // sdot v10.4s, v28.16b, v9.4b[1]\n"
+        ".inst 0x4f89eb9a  // sdot v26.4s, v28.16b, v9.4b[2]\n"
+        ".inst 0x4fa9eb82  // sdot v2.4s, v28.16b, v9.4b[3]\n"
+        "ldr q9, [x22, #0x50]\n"
+        ".inst 0x4f98e074  // sdot v20.4s, v3.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e06a  // sdot v10.4s, v3.16b, v24.4b[1]\n"
+        ".inst 0x4f98e87a  // sdot v26.4s, v3.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e862  // sdot v2.4s, v3.16b, v24.4b[3]\n"
+        "ldr q24, [x22, #0x60]\n"
+        ".inst 0x4f89e2d4  // sdot v20.4s, v22.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e2ca  // sdot v10.4s, v22.16b, v9.4b[1]\n"
+        ".inst 0x4f89eada  // sdot v26.4s, v22.16b, v9.4b[2]\n"
+        ".inst 0x4fa9eac2  // sdot v2.4s, v22.16b, v9.4b[3]\n"
+        "ldr q9, [x22, #0x70]\n"
+        "add x22, x22, #0x88\n"
+        ".inst 0x4f98e374  // sdot v20.4s, v27.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e36a  // sdot v10.4s, v27.16b, v24.4b[1]\n"
+        ".inst 0x4f98eb7a  // sdot v26.4s, v27.16b, v24.4b[2]\n"
+        ".inst 0x4fb8eb62  // sdot v2.4s, v27.16b, v24.4b[3]\n"
+        "ldr q24, [x21, #0x0]\n"
+        ".inst 0x4f89e3d4  // sdot v20.4s, v30.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e3ca  // sdot v10.4s, v30.16b, v9.4b[1]\n"
+        ".inst 0x4f89ebda  // sdot v26.4s, v30.16b, v9.4b[2]\n"
+        ".inst 0x4fa9ebc2  // sdot v2.4s, v30.16b, v9.4b[3]\n"
+        "fmul v9.4s, v17.4s, v29.s[0]\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "scvtf v10.4s, v10.4s, #0x4\n"
+        "scvtf v26.4s, v26.4s, #0x4\n"
+        "scvtf v2.4s, v2.4s, #0x4\n"
+        "fmla v25.4s, v20.4s, v9.4s\n"
+        "ldr q9, [x21, #0x10]\n"
+        "fmul v20.4s, v17.4s, v29.s[1]\n"
+        "fmla v7.4s, v10.4s, v20.4s\n"
+        "ldr d20, [x21, #-0x8]\n"
+        "fmul v10.4s, v17.4s, v29.s[2]\n"
+        "fmul v29.4s, v17.4s, v29.s[3]\n"
+        "fcvtl v20.4s, v20.4h\n"
+        "fmla v0.4s, v26.4s, v10.4s\n"
+        "movi v26.4s, #0x0\n"
+        "movi v10.4s, #0x0\n"
+        "fmla v4.4s, v2.4s, v29.4s\n"
+        "movi v2.4s, #0x0\n"
+        "movi v29.4s, #0x0\n"
+        ".inst 0x4f98e19a  // sdot v26.4s, v12.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e18a  // sdot v10.4s, v12.16b, v24.4b[1]\n"
+        ".inst 0x4f98e982  // sdot v2.4s, v12.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e99d  // sdot v29.4s, v12.16b, v24.4b[3]\n"
+        "ldr q12, [x21, #0x20]\n"
+        "fmul v24.4s, v17.4s, v20.s[0]\n"
+        ".inst 0x4f89e3fa  // sdot v26.4s, v31.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e3ea  // sdot v10.4s, v31.16b, v9.4b[1]\n"
+        ".inst 0x4f89ebe2  // sdot v2.4s, v31.16b, v9.4b[2]\n"
+        ".inst 0x4fa9ebfd  // sdot v29.4s, v31.16b, v9.4b[3]\n"
+        "ldr q9, [x21, #0x30]\n"
+        "fmul v31.4s, v17.4s, v20.s[1]\n"
+        ".inst 0x4f8ce0da  // sdot v26.4s, v6.16b, v12.4b[0]\n"
+        ".inst 0x4face0ca  // sdot v10.4s, v6.16b, v12.4b[1]\n"
+        ".inst 0x4f8ce8c2  // sdot v2.4s, v6.16b, v12.4b[2]\n"
+        ".inst 0x4face8dd  // sdot v29.4s, v6.16b, v12.4b[3]\n"
+        "ldr q12, [x21, #0x40]\n"
+        "fmul v6.4s, v17.4s, v20.s[2]\n"
+        "fmul v20.4s, v17.4s, v20.s[3]\n"
+        ".inst 0x4f89e39a  // sdot v26.4s, v28.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e38a  // sdot v10.4s, v28.16b, v9.4b[1]\n"
+        ".inst 0x4f89eb82  // sdot v2.4s, v28.16b, v9.4b[2]\n"
+        ".inst 0x4fa9eb9d  // sdot v29.4s, v28.16b, v9.4b[3]\n"
+        "ldr q9, [x21, #0x50]\n"
+        ".inst 0x4f8ce07a  // sdot v26.4s, v3.16b, v12.4b[0]\n"
+        ".inst 0x4face06a  // sdot v10.4s, v3.16b, v12.4b[1]\n"
+        ".inst 0x4f8ce862  // sdot v2.4s, v3.16b, v12.4b[2]\n"
+        ".inst 0x4face87d  // sdot v29.4s, v3.16b, v12.4b[3]\n"
+        "ldr q12, [x21, #0x60]\n"
+        ".inst 0x4f89e2da  // sdot v26.4s, v22.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e2ca  // sdot v10.4s, v22.16b, v9.4b[1]\n"
+        ".inst 0x4f89eac2  // sdot v2.4s, v22.16b, v9.4b[2]\n"
+        ".inst 0x4fa9eadd  // sdot v29.4s, v22.16b, v9.4b[3]\n"
+        "ldr q17, [x21, #0x70]\n"
+        "add x21, x21, #0x88\n"
+        ".inst 0x4f8ce37a  // sdot v26.4s, v27.16b, v12.4b[0]\n"
+        ".inst 0x4face36a  // sdot v10.4s, v27.16b, v12.4b[1]\n"
+        ".inst 0x4f8ceb62  // sdot v2.4s, v27.16b, v12.4b[2]\n"
+        ".inst 0x4faceb7d  // sdot v29.4s, v27.16b, v12.4b[3]\n"
+        ".inst 0x4f91e3da  // sdot v26.4s, v30.16b, v17.4b[0]\n"
+        ".inst 0x4fb1e3ca  // sdot v10.4s, v30.16b, v17.4b[1]\n"
+        ".inst 0x4f91ebc2  // sdot v2.4s, v30.16b, v17.4b[2]\n"
+        ".inst 0x4fb1ebdd  // sdot v29.4s, v30.16b, v17.4b[3]\n"
+        "scvtf v26.4s, v26.4s, #0x4\n"
+        "scvtf v10.4s, v10.4s, #0x4\n"
+        "fmla v5.4s, v26.4s, v24.4s\n"
+        "scvtf v2.4s, v2.4s, #0x4\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "fmla v21.4s, v10.4s, v31.4s\n"
+        "fmla v8.4s, v2.4s, v6.4s\n"
+        "fmla v1.4s, v29.4s, v20.4s\n"
+        "bgt 3b\n"
+        "mov x20, %x[res_ptr]\n"
+        "subs x27, x27, #0x4\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "str q15, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q19, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q18, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q14, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q11, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q13, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q23, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q16, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q25, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q7, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q0, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q4, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q5, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q21, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q8, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q1, [x20, #0x0]\n"
+        "bne 2b\n"
+        "mov x20, #0x4\n"
+        "sub x10, x10, #0x10\n"
+        "cmp x10, #0x10\n"
+        "mov %x[res_ptr], x26\n"
+        "madd %x[a_ptr], x20, x9, %x[a_ptr]\n"
+        "bge 1b\n"
+        "4:"  // Row loop skip
+        "cbz x10, 9f\n"
+        "5:"  // Row tail: Row loop
+        "add x24, %x[b_ptr], #0x8\n"
+        "mov x23, %x[nc]\n"
+        "add x22, %x[res_ptr], %x[res_stride], LSL #2\n"
+        "6:"  // Row tail: Column loop
+        "movi v15.16b, #0x0\n"
+        "movi v19.16b, #0x0\n"
+        "add x25, %x[a_ptr], #0x8\n"
+        "mov x21, %x[nb]\n"
+        "movi v18.16b, #0x0\n"
+        "movi v14.16b, #0x0\n"
+        "7:"  // Row tail: Block loop
+        "ldr q7, [x24, #0x0]\n"
+        "ldr q5, [x25, #0x0]\n"
+        "movi v9.16b, #0x4\n"
+        "movi v4.4s, #0x0\n"
+        "ldr q3, [x24, #0x10]\n"
+        "ldr q2, [x25, #0x10]\n"
+        "movi v1.4s, #0x0\n"
+        "movi v0.4s, #0x0\n"
+        "ldr q13, [x24, #0x20]\n"
+        "ldr q31, [x25, #0x20]\n"
+        "movi v30.4s, #0x0\n"
+        "movi v29.16b, #0xf0\n"
+        "ldr q28, [x24, #0x30]\n"
+        "ldr q27, [x25, #0x30]\n"
+        "sshl v20.16b, v7.16b, v9.16b\n"
+        "sub x20, x24, #0x8\n"
+        "ldr q26, [x25, #0x40]\n"
+        "ldr q25, [x25, #0x50]\n"
+        "sshl v17.16b, v3.16b, v9.16b\n"
+        "and v7.16b, v7.16b, v29.16b\n"
+        "ldr q24, [x25, #0x60]\n"
+        "ldr q16, [x25, #0x70]\n"
+        "sshl v22.16b, v13.16b, v9.16b\n"
+        "and v3.16b, v3.16b, v29.16b\n"
+        "ldr d21, [x20, #0x0]\n"
+        "ldr d12, [x25, #-0x8]\n"
+        ".inst 0x4f85e284  // sdot v4.4s, v20.16b, v5.4b[0]\n"
+        ".inst 0x4fa5e281  // sdot v1.4s, v20.16b, v5.4b[1]\n"
+        ".inst 0x4f85ea80  // sdot v0.4s, v20.16b, v5.4b[2]\n"
+        ".inst 0x4fa5ea9e  // sdot v30.4s, v20.16b, v5.4b[3]\n"
+        "sshl v9.16b, v28.16b, v9.16b\n"
+        "subs x21, x21, #0x1\n"
+        "and v13.16b, v13.16b, v29.16b\n"
+        "and v28.16b, v28.16b, v29.16b\n"
+        "add x25, x25, #0x88\n"
+        "add x24, x24, #0x48\n"
+        "fcvtl v21.4s, v21.4h\n"
+        "fcvtl v12.4s, v12.4h\n"
+        ".inst 0x4f82e224  // sdot v4.4s, v17.16b, v2.4b[0]\n"
+        ".inst 0x4fa2e221  // sdot v1.4s, v17.16b, v2.4b[1]\n"
+        ".inst 0x4f82ea20  // sdot v0.4s, v17.16b, v2.4b[2]\n"
+        ".inst 0x4fa2ea3e  // sdot v30.4s, v17.16b, v2.4b[3]\n"
+        "fmul v11.4s, v21.4s, v12.s[0]\n"
+        "fmul v23.4s, v21.4s, v12.s[1]\n"
+        "fmul v17.4s, v21.4s, v12.s[2]\n"
+        ".inst 0x4f9fe2c4  // sdot v4.4s, v22.16b, v31.4b[0]\n"
+        "fmul v6.4s, v21.4s, v12.s[3]\n"
+        ".inst 0x4fbfe2c1  // sdot v1.4s, v22.16b, v31.4b[1]\n"
+        ".inst 0x4f9feac0  // sdot v0.4s, v22.16b, v31.4b[2]\n"
+        ".inst 0x4fbfeade  // sdot v30.4s, v22.16b, v31.4b[3]\n"
+        ".inst 0x4f9be124  // sdot v4.4s, v9.16b, v27.4b[0]\n"
+        ".inst 0x4fbbe121  // sdot v1.4s, v9.16b, v27.4b[1]\n"
+        ".inst 0x4f9be920  // sdot v0.4s, v9.16b, v27.4b[2]\n"
+        ".inst 0x4fbbe93e  // sdot v30.4s, v9.16b, v27.4b[3]\n"
+        ".inst 0x4f9ae0e4  // sdot v4.4s, v7.16b, v26.4b[0]\n"
+        ".inst 0x4fbae0e1  // sdot v1.4s, v7.16b, v26.4b[1]\n"
+        ".inst 0x4f9ae8e0  // sdot v0.4s, v7.16b, v26.4b[2]\n"
+        ".inst 0x4fbae8fe  // sdot v30.4s, v7.16b, v26.4b[3]\n"
+        ".inst 0x4f99e064  // sdot v4.4s, v3.16b, v25.4b[0]\n"
+        ".inst 0x4fb9e061  // sdot v1.4s, v3.16b, v25.4b[1]\n"
+        ".inst 0x4f99e860  // sdot v0.4s, v3.16b, v25.4b[2]\n"
+        ".inst 0x4fb9e87e  // sdot v30.4s, v3.16b, v25.4b[3]\n"
+        ".inst 0x4f98e1a4  // sdot v4.4s, v13.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e1a1  // sdot v1.4s, v13.16b, v24.4b[1]\n"
+        ".inst 0x4f98e9a0  // sdot v0.4s, v13.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e9be  // sdot v30.4s, v13.16b, v24.4b[3]\n"
+        ".inst 0x4f90e384  // sdot v4.4s, v28.16b, v16.4b[0]\n"
+        ".inst 0x4fb0e381  // sdot v1.4s, v28.16b, v16.4b[1]\n"
+        ".inst 0x4f90eb80  // sdot v0.4s, v28.16b, v16.4b[2]\n"
+        ".inst 0x4fb0eb9e  // sdot v30.4s, v28.16b, v16.4b[3]\n"
+        "scvtf v4.4s, v4.4s, #0x4\n"
+        "scvtf v1.4s, v1.4s, #0x4\n"
+        "scvtf v0.4s, v0.4s, #0x4\n"
+        "fmla v15.4s, v4.4s, v11.4s\n"
+        "scvtf v30.4s, v30.4s, #0x4\n"
+        "fmla v19.4s, v1.4s, v23.4s\n"
+        "fmla v18.4s, v0.4s, v17.4s\n"
+        "fmla v14.4s, v30.4s, v6.4s\n"
+        "bgt 7b\n"
+        "mov x20, %x[res_ptr]\n"
+        "cmp x10, #0x1\n"
+        "str q15, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "cmp x10, #0x2\n"
+        "str q19, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "cmp x10, #0x3\n"
+        "str q18, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "str q14, [x20, #0x0]\n"
+        "8:"  // Row tail: Accumulator store skip
+        "subs x23, x23, #0x4\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "bne 6b\n"
+        "subs x10, x10, #0x4\n"
+        "add %x[a_ptr], %x[a_ptr], x9\n"
+        "mov %x[res_ptr], x22\n"
+        "bgt 5b\n"
+        "9:"  // Row tail: Row loop skip
+        : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr)
+        : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc)
+        : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28"
+    );
+#else
+    float sumf[4][4];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+#endif
+}
+
+void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
+    if (ggml_sve_cnt_b == QK8_0) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
+                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
+    const void * b_ptr = vx;
+    const void * a_ptr = vy;
+    float * res_ptr = s;
+    size_t res_stride = bs * sizeof(float);
+
+    __asm__ __volatile__(
+        "mov x10, %x[nr]\n"
+        "mov x9, #0x88\n"
+        "cmp x10, #0x10\n"
+        "mul x9, %x[nb], x9\n"
+        "blt 4f\n"
+        "1:"  // Row loop
+        "add x28, %x[b_ptr], #0x8\n"
+        "mov x27, %x[nc]\n"
+        "add x26, %x[res_ptr], %x[res_stride], LSL #4\n"
+        "2:"  // Column loop
+        "add x25, %x[a_ptr], #0x8\n"
+        "movi v2.16b, #0x0\n"
+        "movi v10.16b, #0x0\n"
+        "mov x24, %x[nb]\n"
+        "add x23, x25, x9\n"
+        "movi v12.16b, #0x0\n"
+        "movi v28.16b, #0x0\n"
+        "add x22, x23, x9\n"
+        "movi v11.16b, #0x0\n"
+        "movi v13.16b, #0x0\n"
+        "add x21, x22, x9\n"
+        "movi v22.16b, #0x0\n"
+        "movi v23.16b, #0x0\n"
+        "movi v25.16b, #0x0\n"
+        "movi v5.16b, #0x0\n"
+        "movi v7.16b, #0x0\n"
+        "movi v4.16b, #0x0\n"
+        "movi v6.16b, #0x0\n"
+        "movi v30.16b, #0x0\n"
+        "movi v24.16b, #0x0\n"
+        "movi v14.16b, #0x0\n"
+        "3:"  // Block loop
+        "ldr q21, [x28, #0x0]\n"
+        "ldr q16, [x28, #0x10]\n"
+        "movi v1.16b, #0x4\n"
+        "movi v19.4s, #0x0\n"
+        "ldr q27, [x25, #0x0]\n"
+        "ldr q15, [x25, #0x10]\n"
+        "movi v26.4s, #0x0\n"
+        "movi v18.4s, #0x0\n"
+        "ldr q29, [x28, #0x20]\n"
+        "ldr q3, [x28, #0x30]\n"
+        "movi v17.4s, #0x0\n"
+        "movi v0.16b, #0xf0\n"
+        "ldr d20, [x25, #-0x8]\n"
+        "ldr d9, [x23, #-0x8]\n"
+        "sshl v8.16b, v21.16b, v1.16b\n"
+        "sshl v31.16b, v16.16b, v1.16b\n"
+        "and v21.16b, v21.16b, v0.16b\n"
+        "and v16.16b, v16.16b, v0.16b\n"
+        "sub x20, x28, #0x8\n"
+        "subs x24, x24, #0x1\n"
+        "add x28, x28, #0x48\n"
+        ".inst 0x4e88a773  // smmla v19.4s, v27.16b, v8.16b\n"
+        ".inst 0x4e9fa77a  // smmla v26.4s, v27.16b, v31.16b\n"
+        "ldr q27, [x25, #0x20]\n"
+        ".inst 0x4e88a5f2  // smmla v18.4s, v15.16b, v8.16b\n"
+        ".inst 0x4e9fa5f1  // smmla v17.4s, v15.16b, v31.16b\n"
+        "sshl v15.16b, v29.16b, v1.16b\n"
+        "sshl v1.16b, v3.16b, v1.16b\n"
+        "and v29.16b, v29.16b, v0.16b\n"
+        "and v3.16b, v3.16b, v0.16b\n"
+        "ldr q0, [x25, #0x30]\n"
+        "fcvtl v20.4s, v20.4h\n"
+        ".inst 0x4e8fa773  // smmla v19.4s, v27.16b, v15.16b\n"
+        "fcvtl v9.4s, v9.4h\n"
+        ".inst 0x4e81a77a  // smmla v26.4s, v27.16b, v1.16b\n"
+        "ldr q27, [x25, #0x40]\n"
+        ".inst 0x4e8fa412  // smmla v18.4s, v0.16b, v15.16b\n"
+        ".inst 0x4e81a411  // smmla v17.4s, v0.16b, v1.16b\n"
+        "ldr q0, [x25, #0x50]\n"
+        ".inst 0x4e95a773  // smmla v19.4s, v27.16b, v21.16b\n"
+        ".inst 0x4e90a77a  // smmla v26.4s, v27.16b, v16.16b\n"
+        "ldr q27, [x25, #0x60]\n"
+        ".inst 0x4e95a412  // smmla v18.4s, v0.16b, v21.16b\n"
+        ".inst 0x4e90a411  // smmla v17.4s, v0.16b, v16.16b\n"
+        "ldr q0, [x25, #0x70]\n"
+        "add x25, x25, #0x88\n"
+        ".inst 0x4e9da773  // smmla v19.4s, v27.16b, v29.16b\n"
+        ".inst 0x4e83a77a  // smmla v26.4s, v27.16b, v3.16b\n"
+        "ldr d27, [x20, #0x0]\n"
+        ".inst 0x4e9da412  // smmla v18.4s, v0.16b, v29.16b\n"
+        ".inst 0x4e83a411  // smmla v17.4s, v0.16b, v3.16b\n"
+        "fcvtl v27.4s, v27.4h\n"
+        "uzp1 v0.2d, v19.2d, v26.2d\n"
+        "uzp2 v26.2d, v19.2d, v26.2d\n"
+        "fmul v19.4s, v27.4s, v20.s[0]\n"
+        "scvtf v0.4s, v0.4s, #0x4\n"
+        "scvtf v26.4s, v26.4s, #0x4\n"
+        "fmla v2.4s, v0.4s, v19.4s\n"
+        "ldr q19, [x23, #0x0]\n"
+        "uzp1 v0.2d, v18.2d, v17.2d\n"
+        "uzp2 v18.2d, v18.2d, v17.2d\n"
+        "fmul v17.4s, v27.4s, v20.s[1]\n"
+        "scvtf v0.4s, v0.4s, #0x4\n"
+        "scvtf v18.4s, v18.4s, #0x4\n"
+        "fmla v10.4s, v26.4s, v17.4s\n"
+        "ldr q17, [x23, #0x10]\n"
+        "fmul v26.4s, v27.4s, v20.s[2]\n"
+        "fmul v20.4s, v27.4s, v20.s[3]\n"
+        "fmla v12.4s, v0.4s, v26.4s\n"
+        "ldr d0, [x22, #-0x8]\n"
+        "ldr d26, [x21, #-0x8]\n"
+        "fcvtl v0.4s, v0.4h\n"
+        "fmla v28.4s, v18.4s, v20.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v18.4s, #0x0\n"
+        ".inst 0x4e88a674  // smmla v20.4s, v19.16b, v8.16b\n"
+        ".inst 0x4e9fa672  // smmla v18.4s, v19.16b, v31.16b\n"
+        "ldr q19, [x23, #0x20]\n"
+        "fcvtl v26.4s, v26.4h\n"
+        ".inst 0x4e8fa674  // smmla v20.4s, v19.16b, v15.16b\n"
+        ".inst 0x4e81a672  // smmla v18.4s, v19.16b, v1.16b\n"
+        "ldr q19, [x23, #0x40]\n"
+        ".inst 0x4e95a674  // smmla v20.4s, v19.16b, v21.16b\n"
+        ".inst 0x4e90a672  // smmla v18.4s, v19.16b, v16.16b\n"
+        "ldr q19, [x23, #0x60]\n"
+        ".inst 0x4e9da674  // smmla v20.4s, v19.16b, v29.16b\n"
+        ".inst 0x4e83a672  // smmla v18.4s, v19.16b, v3.16b\n"
+        "uzp1 v19.2d, v20.2d, v18.2d\n"
+        "scvtf v19.4s, v19.4s, #0x4\n"
+        "uzp2 v20.2d, v20.2d, v18.2d\n"
+        "fmul v18.4s, v27.4s, v9.s[0]\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "fmla v11.4s, v19.4s, v18.4s\n"
+        "ldr q18, [x22, #0x0]\n"
+        "fmul v19.4s, v27.4s, v9.s[1]\n"
+        "fmla v13.4s, v20.4s, v19.4s\n"
+        "movi v19.4s, #0x0\n"
+        "movi v20.4s, #0x0\n"
+        ".inst 0x4e88a633  // smmla v19.4s, v17.16b, v8.16b\n"
+        ".inst 0x4e9fa634  // smmla v20.4s, v17.16b, v31.16b\n"
+        "ldr q17, [x23, #0x30]\n"
+        ".inst 0x4e8fa633  // smmla v19.4s, v17.16b, v15.16b\n"
+        ".inst 0x4e81a634  // smmla v20.4s, v17.16b, v1.16b\n"
+        "ldr q17, [x23, #0x50]\n"
+        ".inst 0x4e95a633  // smmla v19.4s, v17.16b, v21.16b\n"
+        ".inst 0x4e90a634  // smmla v20.4s, v17.16b, v16.16b\n"
+        "ldr q17, [x23, #0x70]\n"
+        "add x23, x23, #0x88\n"
+        ".inst 0x4e9da633  // smmla v19.4s, v17.16b, v29.16b\n"
+        ".inst 0x4e83a634  // smmla v20.4s, v17.16b, v3.16b\n"
+        "uzp1 v17.2d, v19.2d, v20.2d\n"
+        "scvtf v17.4s, v17.4s, #0x4\n"
+        "uzp2 v20.2d, v19.2d, v20.2d\n"
+        "fmul v19.4s, v27.4s, v9.s[2]\n"
+        "fmul v9.4s, v27.4s, v9.s[3]\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "fmla v22.4s, v17.4s, v19.4s\n"
+        "ldr q17, [x22, #0x10]\n"
+        "movi v19.4s, #0x0\n"
+        ".inst 0x4e88a653  // smmla v19.4s, v18.16b, v8.16b\n"
+        "fmla v23.4s, v20.4s, v9.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v9.4s, #0x0\n"
+        ".inst 0x4e9fa654  // smmla v20.4s, v18.16b, v31.16b\n"
+        "ldr q18, [x22, #0x20]\n"
+        ".inst 0x4e88a629  // smmla v9.4s, v17.16b, v8.16b\n"
+        ".inst 0x4e8fa653  // smmla v19.4s, v18.16b, v15.16b\n"
+        ".inst 0x4e81a654  // smmla v20.4s, v18.16b, v1.16b\n"
+        "ldr q18, [x22, #0x40]\n"
+        ".inst 0x4e95a653  // smmla v19.4s, v18.16b, v21.16b\n"
+        ".inst 0x4e90a654  // smmla v20.4s, v18.16b, v16.16b\n"
+        "ldr q18, [x22, #0x60]\n"
+        ".inst 0x4e9da653  // smmla v19.4s, v18.16b, v29.16b\n"
+        ".inst 0x4e83a654  // smmla v20.4s, v18.16b, v3.16b\n"
+        "movi v18.4s, #0x0\n"
+        ".inst 0x4e9fa632  // smmla v18.4s, v17.16b, v31.16b\n"
+        "ldr q17, [x22, #0x30]\n"
+        ".inst 0x4e8fa629  // smmla v9.4s, v17.16b, v15.16b\n"
+        ".inst 0x4e81a632  // smmla v18.4s, v17.16b, v1.16b\n"
+        "ldr q17, [x22, #0x50]\n"
+        ".inst 0x4e95a629  // smmla v9.4s, v17.16b, v21.16b\n"
+        ".inst 0x4e90a632  // smmla v18.4s, v17.16b, v16.16b\n"
+        "ldr q17, [x22, #0x70]\n"
+        "add x22, x22, #0x88\n"
+        ".inst 0x4e9da629  // smmla v9.4s, v17.16b, v29.16b\n"
+        ".inst 0x4e83a632  // smmla v18.4s, v17.16b, v3.16b\n"
+        "uzp1 v17.2d, v19.2d, v20.2d\n"
+        "uzp2 v20.2d, v19.2d, v20.2d\n"
+        "fmul v19.4s, v27.4s, v0.s[0]\n"
+        "scvtf v17.4s, v17.4s, #0x4\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "fmla v25.4s, v17.4s, v19.4s\n"
+        "ldr q19, [x21, #0x0]\n"
+        "fmul v17.4s, v27.4s, v0.s[1]\n"
+        "fmla v5.4s, v20.4s, v17.4s\n"
+        "ldr q17, [x21, #0x10]\n"
+        "uzp1 v20.2d, v9.2d, v18.2d\n"
+        "uzp2 v9.2d, v9.2d, v18.2d\n"
+        "fmul v18.4s, v27.4s, v0.s[2]\n"
+        "fmul v0.4s, v27.4s, v0.s[3]\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "scvtf v9.4s, v9.4s, #0x4\n"
+        "fmla v7.4s, v20.4s, v18.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v18.4s, #0x0\n"
+        ".inst 0x4e88a674  // smmla v20.4s, v19.16b, v8.16b\n"
+        ".inst 0x4e9fa672  // smmla v18.4s, v19.16b, v31.16b\n"
+        "ldr q19, [x21, #0x20]\n"
+        "fmla v4.4s, v9.4s, v0.4s\n"
+        "movi v9.4s, #0x0\n"
+        "movi v0.4s, #0x0\n"
+        ".inst 0x4e88a629  // smmla v9.4s, v17.16b, v8.16b\n"
+        "fmul v8.4s, v27.4s, v26.s[0]\n"
+        ".inst 0x4e9fa620  // smmla v0.4s, v17.16b, v31.16b\n"
+        "ldr q17, [x21, #0x30]\n"
+        ".inst 0x4e8fa674  // smmla v20.4s, v19.16b, v15.16b\n"
+        "fmul v31.4s, v27.4s, v26.s[1]\n"
+        ".inst 0x4e81a672  // smmla v18.4s, v19.16b, v1.16b\n"
+        "ldr q19, [x21, #0x40]\n"
+        ".inst 0x4e8fa629  // smmla v9.4s, v17.16b, v15.16b\n"
+        "fmul v15.4s, v27.4s, v26.s[2]\n"
+        "fmul v27.4s, v27.4s, v26.s[3]\n"
+        ".inst 0x4e81a620  // smmla v0.4s, v17.16b, v1.16b\n"
+        "ldr q1, [x21, #0x50]\n"
+        ".inst 0x4e95a674  // smmla v20.4s, v19.16b, v21.16b\n"
+        ".inst 0x4e90a672  // smmla v18.4s, v19.16b, v16.16b\n"
+        "ldr q26, [x21, #0x60]\n"
+        ".inst 0x4e95a429  // smmla v9.4s, v1.16b, v21.16b\n"
+        ".inst 0x4e90a420  // smmla v0.4s, v1.16b, v16.16b\n"
+        "ldr q21, [x21, #0x70]\n"
+        "add x21, x21, #0x88\n"
+        ".inst 0x4e9da754  // smmla v20.4s, v26.16b, v29.16b\n"
+        ".inst 0x4e83a752  // smmla v18.4s, v26.16b, v3.16b\n"
+        ".inst 0x4e9da6a9  // smmla v9.4s, v21.16b, v29.16b\n"
+        ".inst 0x4e83a6a0  // smmla v0.4s, v21.16b, v3.16b\n"
+        "uzp1 v29.2d, v20.2d, v18.2d\n"
+        "uzp2 v21.2d, v20.2d, v18.2d\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "uzp1 v18.2d, v9.2d, v0.2d\n"
+        "uzp2 v16.2d, v9.2d, v0.2d\n"
+        "scvtf v21.4s, v21.4s, #0x4\n"
+        "fmla v6.4s, v29.4s, v8.4s\n"
+        "scvtf v18.4s, v18.4s, #0x4\n"
+        "scvtf v16.4s, v16.4s, #0x4\n"
+        "fmla v30.4s, v21.4s, v31.4s\n"
+        "fmla v24.4s, v18.4s, v15.4s\n"
+        "fmla v14.4s, v16.4s, v27.4s\n"
+        "bgt 3b\n"
+        "mov x20, %x[res_ptr]\n"
+        "subs x27, x27, #0x4\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "str q2, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q10, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q12, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q28, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q11, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q13, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q22, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q23, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q25, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q5, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q7, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q4, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q6, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q30, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q24, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q14, [x20, #0x0]\n"
+        "bne 2b\n"
+        "mov x20, #0x4\n"
+        "sub x10, x10, #0x10\n"
+        "cmp x10, #0x10\n"
+        "mov %x[res_ptr], x26\n"
+        "madd %x[a_ptr], x20, x9, %x[a_ptr]\n"
+        "bge 1b\n"
+        "4:"  // Row loop skip
+        "cbz x10, 9f\n"
+        "5:"  // Row tail: Row loop
+        "add x24, %x[b_ptr], #0x8\n"
+        "mov x23, %x[nc]\n"
+        "add x22, %x[res_ptr], %x[res_stride], LSL #2\n"
+        "6:"  // Row tail: Column loop
+        "movi v2.16b, #0x0\n"
+        "movi v10.16b, #0x0\n"
+        "add x25, %x[a_ptr], #0x8\n"
+        "mov x21, %x[nb]\n"
+        "movi v12.16b, #0x0\n"
+        "movi v28.16b, #0x0\n"
+        "7:"  // Row tail: Block loop
+        "ldr q6, [x24, #0x0]\n"
+        "ldr q5, [x24, #0x10]\n"
+        "movi v17.16b, #0x4\n"
+        "movi v8.4s, #0x0\n"
+        "ldr q4, [x25, #0x0]\n"
+        "ldr q13, [x25, #0x10]\n"
+        "movi v27.4s, #0x0\n"
+        "movi v0.4s, #0x0\n"
+        "ldr q31, [x24, #0x20]\n"
+        "ldr q14, [x24, #0x30]\n"
+        "movi v29.4s, #0x0\n"
+        "movi v22.16b, #0xf0\n"
+        "ldr q11, [x25, #0x20]\n"
+        "ldr q23, [x25, #0x30]\n"
+        "sshl v21.16b, v6.16b, v17.16b\n"
+        "sshl v16.16b, v5.16b, v17.16b\n"
+        "ldr q20, [x25, #0x40]\n"
+        "ldr q26, [x25, #0x50]\n"
+        "and v6.16b, v6.16b, v22.16b\n"
+        "and v5.16b, v5.16b, v22.16b\n"
+        "ldr q25, [x25, #0x60]\n"
+        "ldr q3, [x25, #0x70]\n"
+        "sshl v19.16b, v31.16b, v17.16b\n"
+        "sshl v18.16b, v14.16b, v17.16b\n"
+        "ldr d17, [x25, #-0x8]\n"
+        ".inst 0x4e95a488  // smmla v8.4s, v4.16b, v21.16b\n"
+        ".inst 0x4e90a49b  // smmla v27.4s, v4.16b, v16.16b\n"
+        "and v31.16b, v31.16b, v22.16b\n"
+        ".inst 0x4e95a5a0  // smmla v0.4s, v13.16b, v21.16b\n"
+        ".inst 0x4e90a5bd  // smmla v29.4s, v13.16b, v16.16b\n"
+        "and v14.16b, v14.16b, v22.16b\n"
+        "sub x20, x24, #0x8\n"
+        "ldr d16, [x20, #0x0]\n"
+        "subs x21, x21, #0x1\n"
+        "add x25, x25, #0x88\n"
+        "fcvtl v17.4s, v17.4h\n"
+        "add x24, x24, #0x48\n"
+        ".inst 0x4e93a568  // smmla v8.4s, v11.16b, v19.16b\n"
+        ".inst 0x4e92a57b  // smmla v27.4s, v11.16b, v18.16b\n"
+        ".inst 0x4e93a6e0  // smmla v0.4s, v23.16b, v19.16b\n"
+        ".inst 0x4e92a6fd  // smmla v29.4s, v23.16b, v18.16b\n"
+        "fcvtl v16.4s, v16.4h\n"
+        ".inst 0x4e86a688  // smmla v8.4s, v20.16b, v6.16b\n"
+        ".inst 0x4e85a69b  // smmla v27.4s, v20.16b, v5.16b\n"
+        "fmul v23.4s, v16.4s, v17.s[0]\n"
+        "fmul v21.4s, v16.4s, v17.s[1]\n"
+        "fmul v1.4s, v16.4s, v17.s[2]\n"
+        "fmul v20.4s, v16.4s, v17.s[3]\n"
+        ".inst 0x4e86a740  // smmla v0.4s, v26.16b, v6.16b\n"
+        ".inst 0x4e85a75d  // smmla v29.4s, v26.16b, v5.16b\n"
+        ".inst 0x4e9fa728  // smmla v8.4s, v25.16b, v31.16b\n"
+        ".inst 0x4e8ea73b  // smmla v27.4s, v25.16b, v14.16b\n"
+        ".inst 0x4e9fa460  // smmla v0.4s, v3.16b, v31.16b\n"
+        ".inst 0x4e8ea47d  // smmla v29.4s, v3.16b, v14.16b\n"
+        "uzp1 v19.2d, v8.2d, v27.2d\n"
+        "uzp2 v18.2d, v8.2d, v27.2d\n"
+        "scvtf v19.4s, v19.4s, #0x4\n"
+        "uzp1 v17.2d, v0.2d, v29.2d\n"
+        "uzp2 v16.2d, v0.2d, v29.2d\n"
+        "scvtf v18.4s, v18.4s, #0x4\n"
+        "fmla v2.4s, v19.4s, v23.4s\n"
+        "scvtf v17.4s, v17.4s, #0x4\n"
+        "scvtf v16.4s, v16.4s, #0x4\n"
+        "fmla v10.4s, v18.4s, v21.4s\n"
+        "fmla v12.4s, v17.4s, v1.4s\n"
+        "fmla v28.4s, v16.4s, v20.4s\n"
+        "bgt 7b\n"
+        "mov x20, %x[res_ptr]\n"
+        "cmp x10, #0x1\n"
+        "str q2, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "cmp x10, #0x2\n"
+        "str q10, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "cmp x10, #0x3\n"
+        "str q12, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "str q28, [x20, #0x0]\n"
+        "8:"  // Row tail: Accumulator store skip
+        "subs x23, x23, #0x4\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "bne 6b\n"
+        "subs x10, x10, #0x4\n"
+        "add %x[a_ptr], %x[a_ptr], x9\n"
+        "mov %x[res_ptr], x22\n"
+        "bgt 5b\n"
+        "9:"  // Row tail: Row loop skip
+        : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr)
+        : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc)
+        : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28"
+    );
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
+                "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
+                "performance");
+#else
+    float sumf[4][4];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+#endif
+}
+
+void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
+    if (ggml_sve_cnt_b == QK8_0) {
+        const void * b_ptr = vx;
+        const void * a_ptr = vy;
+        float * res_ptr = s;
+        size_t res_stride = bs * sizeof(float);
+
+        __asm__ __volatile__(
+            "mov x20, #0x4\n"
+            "mov x13, %x[nr]\n"
+            "mov z28.s, #-0x4\n"
+            "mov x12, #0x88\n"
+            "ptrue p1.b\n"
+            "whilelt p0.s, XZR, x20\n"
+            "cmp x13, #0x10\n"
+            "mul x12, %x[nb], x12\n"
+            "blt 4f\n"
+            "1:"  // Row loop
+            "add x11, %x[b_ptr], #0x10\n"
+            "mov x10, %x[nc]\n"
+            "add x9, %x[res_ptr], %x[res_stride], LSL #4\n"
+            "2:"  // Column loop
+            "add x28, %x[a_ptr], #0x8\n"
+            "mov z24.b, #0x0\n"
+            "mov z15.b, #0x0\n"
+            "mov x27, %x[nb]\n"
+            "add x26, x28, x12\n"
+            "mov z12.b, #0x0\n"
+            "mov z0.b, #0x0\n"
+            "add x25, x26, x12\n"
+            "mov z13.b, #0x0\n"
+            "mov z1.b, #0x0\n"
+            "add x24, x25, x12\n"
+            "mov z20.b, #0x0\n"
+            "mov z25.b, #0x0\n"
+            "mov z11.b, #0x0\n"
+            "mov z16.b, #0x0\n"
+            "mov z19.b, #0x0\n"
+            "mov z26.b, #0x0\n"
+            "mov z8.b, #0x0\n"
+            "mov z29.b, #0x0\n"
+            "mov z27.b, #0x0\n"
+            "mov z10.b, #0x0\n"
+            "3:"  // Block loop
+            "ld1b { z30.b }, p1/Z, [x11]\n"
+            "ld1b { z21.b }, p1/Z, [x11, #1, MUL VL]\n"
+            "mov z18.s, #0x0\n"
+            "mov z7.s, #0x0\n"
+            "ld1rqb { z3.b }, p1/Z, [x28]\n"
+            "ld1rqb { z5.b }, p1/Z, [x28, #16]\n"
+            "mov z9.s, #0x0\n"
+            "mov z22.s, #0x0\n"
+            "ld1b { z4.b }, p1/Z, [x11, #2, MUL VL]\n"
+            "ld1b { z17.b }, p1/Z, [x11, #3, MUL VL]\n"
+            "sub x20, x11, #0x10\n"
+            "sub x23, x28, #0x8\n"
+            "lsl z31.b, z30.b, #0x4\n"
+            "lsl z6.b, z21.b, #0x4\n"
+            "ld1h { z23.s }, p1/Z, [x20]\n"
+            "sub x22, x26, #0x8\n"
+            "and z30.b, z30.b, #0xf0\n"
+            "and z21.b, z21.b, #0xf0\n"
+            "sub x21, x25, #0x8\n"
+            "sub x20, x24, #0x8\n"
+            "lsl z14.b, z4.b, #0x4\n"
+            "lsl z2.b, z17.b, #0x4\n"
+            "subs x27, x27, #0x1\n"
+            "add x11, x11, #0x90\n"
+            ".inst 0x451f9872  // smmla z18.s, z3.b, z31.b\n"
+            ".inst 0x45069867  // smmla z7.s, z3.b, z6.b\n"
+            "ld1rqb { z3.b }, p1/Z, [x28, #32]\n"
+            "and z4.b, z4.b, #0xf0\n"
+            ".inst 0x451f98a9  // smmla z9.s, z5.b, z31.b\n"
+            ".inst 0x450698b6  // smmla z22.s, z5.b, z6.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x28, #48]\n"
+            "and z17.b, z17.b, #0xf0\n"
+            "fcvt z23.s, p1/m, z23.h\n"
+            ".inst 0x450e9872  // smmla z18.s, z3.b, z14.b\n"
+            ".inst 0x45029867  // smmla z7.s, z3.b, z2.b\n"
+            "ld1rqb { z3.b }, p1/Z, [x28, #64]\n"
+            ".inst 0x450e98a9  // smmla z9.s, z5.b, z14.b\n"
+            ".inst 0x450298b6  // smmla z22.s, z5.b, z2.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x28, #80]\n"
+            "fscale z23.s, p1/m, z23.s, z28.s\n"
+            ".inst 0x451e9872  // smmla z18.s, z3.b, z30.b\n"
+            ".inst 0x45159867  // smmla z7.s, z3.b, z21.b\n"
+            "ld1rqb { z3.b }, p1/Z, [x28, #96]\n"
+            ".inst 0x451e98a9  // smmla z9.s, z5.b, z30.b\n"
+            ".inst 0x451598b6  // smmla z22.s, z5.b, z21.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x28, #112]\n"
+            "add x28, x28, #0x88\n"
+            ".inst 0x45049872  // smmla z18.s, z3.b, z4.b\n"
+            ".inst 0x45119867  // smmla z7.s, z3.b, z17.b\n"
+            "ld1h { z3.s }, p0/Z, [x23]\n"
+            ".inst 0x450498a9  // smmla z9.s, z5.b, z4.b\n"
+            ".inst 0x451198b6  // smmla z22.s, z5.b, z17.b\n"
+            "fcvt z3.s, p1/m, z3.h\n"
+            "uzp1 z5.d, z18.d, z7.d\n"
+            "uzp2 z18.d, z18.d, z7.d\n"
+            "mov z3.q, z3.q[0]\n"
+            "uzp1 z7.d, z9.d, z22.d\n"
+            "uzp2 z22.d, z9.d, z22.d\n"
+            "fmul z9.s, z23.s, z3.s[0]\n"
+            "scvtf z5.s, p1/m, z5.s\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "scvtf z7.s, p1/m, z7.s\n"
+            "scvtf z22.s, p1/m, z22.s\n"
+            "fmla z24.s, p1/M, z5.s, z9.s\n"
+            "ld1rqb { z5.b }, p1/Z, [x26]\n"
+            "fmul z9.s, z23.s, z3.s[1]\n"
+            "fmla z15.s, p1/M, z18.s, z9.s\n"
+            "ld1rqb { z18.b }, p1/Z, [x26, #16]\n"
+            "fmul z9.s, z23.s, z3.s[2]\n"
+            "fmul z3.s, z23.s, z3.s[3]\n"
+            "fmla z12.s, p1/M, z7.s, z9.s\n"
+            "mov z9.s, #0x0\n"
+            "ld1h { z7.s }, p0/Z, [x22]\n"
+            ".inst 0x451f98a9  // smmla z9.s, z5.b, z31.b\n"
+            "fmla z0.s, p1/M, z22.s, z3.s\n"
+            "mov z22.s, #0x0\n"
+            "ld1h { z3.s }, p0/Z, [x21]\n"
+            ".inst 0x450698b6  // smmla z22.s, z5.b, z6.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x26, #32]\n"
+            "fcvt z7.s, p1/m, z7.h\n"
+            "fcvt z3.s, p1/m, z3.h\n"
+            ".inst 0x450e98a9  // smmla z9.s, z5.b, z14.b\n"
+            ".inst 0x450298b6  // smmla z22.s, z5.b, z2.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x26, #64]\n"
+            "mov z7.q, z7.q[0]\n"
+            "mov z3.q, z3.q[0]\n"
+            ".inst 0x451e98a9  // smmla z9.s, z5.b, z30.b\n"
+            ".inst 0x451598b6  // smmla z22.s, z5.b, z21.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x26, #96]\n"
+            ".inst 0x450498a9  // smmla z9.s, z5.b, z4.b\n"
+            ".inst 0x451198b6  // smmla z22.s, z5.b, z17.b\n"
+            "uzp1 z5.d, z9.d, z22.d\n"
+            "scvtf z5.s, p1/m, z5.s\n"
+            "uzp2 z22.d, z9.d, z22.d\n"
+            "fmul z9.s, z23.s, z7.s[0]\n"
+            "scvtf z22.s, p1/m, z22.s\n"
+            "fmla z13.s, p1/M, z5.s, z9.s\n"
+            "ld1rqb { z9.b }, p1/Z, [x25]\n"
+            "fmul z5.s, z23.s, z7.s[1]\n"
+            "fmla z1.s, p1/M, z22.s, z5.s\n"
+            "mov z5.s, #0x0\n"
+            "mov z22.s, #0x0\n"
+            ".inst 0x451f9a45  // smmla z5.s, z18.b, z31.b\n"
+            ".inst 0x45069a56  // smmla z22.s, z18.b, z6.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x26, #48]\n"
+            ".inst 0x450e9a45  // smmla z5.s, z18.b, z14.b\n"
+            ".inst 0x45029a56  // smmla z22.s, z18.b, z2.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x26, #80]\n"
+            ".inst 0x451e9a45  // smmla z5.s, z18.b, z30.b\n"
+            ".inst 0x45159a56  // smmla z22.s, z18.b, z21.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x26, #112]\n"
+            "add x26, x26, #0x88\n"
+            ".inst 0x45049a45  // smmla z5.s, z18.b, z4.b\n"
+            ".inst 0x45119a56  // smmla z22.s, z18.b, z17.b\n"
+            "uzp1 z18.d, z5.d, z22.d\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "uzp2 z22.d, z5.d, z22.d\n"
+            "fmul z5.s, z23.s, z7.s[2]\n"
+            "fmul z7.s, z23.s, z7.s[3]\n"
+            "scvtf z22.s, p1/m, z22.s\n"
+            "fmla z20.s, p1/M, z18.s, z5.s\n"
+            "ld1rqb { z18.b }, p1/Z, [x25, #16]\n"
+            "ld1h { z5.s }, p0/Z, [x20]\n"
+            "fcvt z5.s, p1/m, z5.h\n"
+            "fmla z25.s, p1/M, z22.s, z7.s\n"
+            "mov z22.s, #0x0\n"
+            "mov z7.s, #0x0\n"
+            ".inst 0x451f9936  // smmla z22.s, z9.b, z31.b\n"
+            ".inst 0x45069927  // smmla z7.s, z9.b, z6.b\n"
+            "ld1rqb { z9.b }, p1/Z, [x25, #32]\n"
+            "mov z5.q, z5.q[0]\n"
+            ".inst 0x450e9936  // smmla z22.s, z9.b, z14.b\n"
+            ".inst 0x45029927  // smmla z7.s, z9.b, z2.b\n"
+            "ld1rqb { z9.b }, p1/Z, [x25, #64]\n"
+            ".inst 0x451e9936  // smmla z22.s, z9.b, z30.b\n"
+            ".inst 0x45159927  // smmla z7.s, z9.b, z21.b\n"
+            "ld1rqb { z9.b }, p1/Z, [x25, #96]\n"
+            ".inst 0x45049936  // smmla z22.s, z9.b, z4.b\n"
+            ".inst 0x45119927  // smmla z7.s, z9.b, z17.b\n"
+            "uzp1 z9.d, z22.d, z7.d\n"
+            "scvtf z9.s, p1/m, z9.s\n"
+            "uzp2 z22.d, z22.d, z7.d\n"
+            "fmul z7.s, z23.s, z3.s[0]\n"
+            "scvtf z22.s, p1/m, z22.s\n"
+            "fmla z11.s, p1/M, z9.s, z7.s\n"
+            "ld1rqb { z9.b }, p1/Z, [x24]\n"
+            "fmul z7.s, z23.s, z3.s[1]\n"
+            "fmla z16.s, p1/M, z22.s, z7.s\n"
+            "mov z22.s, #0x0\n"
+            "mov z7.s, #0x0\n"
+            ".inst 0x451f9a56  // smmla z22.s, z18.b, z31.b\n"
+            ".inst 0x45069a47  // smmla z7.s, z18.b, z6.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x25, #48]\n"
+            ".inst 0x450e9a56  // smmla z22.s, z18.b, z14.b\n"
+            ".inst 0x45029a47  // smmla z7.s, z18.b, z2.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x25, #80]\n"
+            ".inst 0x451e9a56  // smmla z22.s, z18.b, z30.b\n"
+            ".inst 0x45159a47  // smmla z7.s, z18.b, z21.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x25, #112]\n"
+            "add x25, x25, #0x88\n"
+            ".inst 0x45049a56  // smmla z22.s, z18.b, z4.b\n"
+            ".inst 0x45119a47  // smmla z7.s, z18.b, z17.b\n"
+            "uzp1 z18.d, z22.d, z7.d\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "uzp2 z7.d, z22.d, z7.d\n"
+            "fmul z22.s, z23.s, z3.s[2]\n"
+            "fmul z3.s, z23.s, z3.s[3]\n"
+            "scvtf z7.s, p1/m, z7.s\n"
+            "fmla z19.s, p1/M, z18.s, z22.s\n"
+            "ld1rqb { z18.b }, p1/Z, [x24, #16]\n"
+            "fmul z22.s, z23.s, z5.s[0]\n"
+            "fmla z26.s, p1/M, z7.s, z3.s\n"
+            "mov z3.s, #0x0\n"
+            "mov z7.s, #0x0\n"
+            ".inst 0x451f9923  // smmla z3.s, z9.b, z31.b\n"
+            ".inst 0x45069927  // smmla z7.s, z9.b, z6.b\n"
+            "ld1rqb { z9.b }, p1/Z, [x24, #32]\n"
+            ".inst 0x450e9923  // smmla z3.s, z9.b, z14.b\n"
+            ".inst 0x45029927  // smmla z7.s, z9.b, z2.b\n"
+            "mov z9.s, #0x0\n"
+            ".inst 0x451f9a49  // smmla z9.s, z18.b, z31.b\n"
+            "mov z31.s, #0x0\n"
+            ".inst 0x45069a5f  // smmla z31.s, z18.b, z6.b\n"
+            "ld1rqb { z6.b }, p1/Z, [x24, #48]\n"
+            "ld1rqb { z18.b }, p1/Z, [x24, #64]\n"
+            ".inst 0x450e98c9  // smmla z9.s, z6.b, z14.b\n"
+            "fmul z14.s, z23.s, z5.s[1]\n"
+            ".inst 0x450298df  // smmla z31.s, z6.b, z2.b\n"
+            "ld1rqb { z6.b }, p1/Z, [x24, #80]\n"
+            "fmul z2.s, z23.s, z5.s[2]\n"
+            "fmul z23.s, z23.s, z5.s[3]\n"
+            ".inst 0x451e9a43  // smmla z3.s, z18.b, z30.b\n"
+            ".inst 0x45159a47  // smmla z7.s, z18.b, z21.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x24, #96]\n"
+            ".inst 0x451e98c9  // smmla z9.s, z6.b, z30.b\n"
+            ".inst 0x451598df  // smmla z31.s, z6.b, z21.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x24, #112]\n"
+            "add x24, x24, #0x88\n"
+            ".inst 0x450498a3  // smmla z3.s, z5.b, z4.b\n"
+            ".inst 0x451198a7  // smmla z7.s, z5.b, z17.b\n"
+            ".inst 0x45049a49  // smmla z9.s, z18.b, z4.b\n"
+            ".inst 0x45119a5f  // smmla z31.s, z18.b, z17.b\n"
+            "uzp1 z18.d, z3.d, z7.d\n"
+            "uzp2 z5.d, z3.d, z7.d\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "uzp1 z6.d, z9.d, z31.d\n"
+            "uzp2 z9.d, z9.d, z31.d\n"
+            "scvtf z5.s, p1/m, z5.s\n"
+            "fmla z8.s, p1/M, z18.s, z22.s\n"
+            "scvtf z6.s, p1/m, z6.s\n"
+            "scvtf z9.s, p1/m, z9.s\n"
+            "fmla z29.s, p1/M, z5.s, z14.s\n"
+            "fmla z27.s, p1/M, z6.s, z2.s\n"
+            "fmla z10.s, p1/M, z9.s, z23.s\n"
+            "bgt 3b\n"
+            "mov x20, %x[res_ptr]\n"
+            "subs x10, x10, #0x8\n"
+            "add %x[res_ptr], %x[res_ptr], #0x20\n"
+            "st1w { z24.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z15.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z12.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z0.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z13.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z1.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z20.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z25.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z11.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z16.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z19.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z26.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z8.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z29.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z27.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z10.s }, p1, [x20]\n"
+            "bne 2b\n"
+            "mov x20, #0x4\n"
+            "sub x13, x13, #0x10\n"
+            "cmp x13, #0x10\n"
+            "mov %x[res_ptr], x9\n"
+            "madd %x[a_ptr], x20, x12, %x[a_ptr]\n"
+            "bge 1b\n"
+            "4:"  // Row loop skip
+            "cbz x13, 9f\n"
+            "5:"  // Row tail: Row loop
+            "add x25, %x[b_ptr], #0x10\n"
+            "mov x24, %x[nc]\n"
+            "add x23, %x[res_ptr], %x[res_stride], LSL #2\n"
+            "6:"  // Row tail: Column loop
+            "mov z24.b, #0x0\n"
+            "mov z15.b, #0x0\n"
+            "add x28, %x[a_ptr], #0x8\n"
+            "mov x22, %x[nb]\n"
+            "mov z12.b, #0x0\n"
+            "mov z0.b, #0x0\n"
+            "7:"  // Row tail: Block loop
+            "ld1b { z3.b }, p1/Z, [x25]\n"
+            "ld1b { z6.b }, p1/Z, [x25, #1, MUL VL]\n"
+            "mov z2.s, #0x0\n"
+            "mov z25.s, #0x0\n"
+            "ld1rqb { z26.b }, p1/Z, [x28]\n"
+            "ld1rqb { z21.b }, p1/Z, [x28, #16]\n"
+            "mov z27.s, #0x0\n"
+            "mov z19.s, #0x0\n"
+            "ld1b { z29.b }, p1/Z, [x25, #2, MUL VL]\n"
+            "ld1b { z16.b }, p1/Z, [x25, #3, MUL VL]\n"
+            "sub x21, x25, #0x10\n"
+            "sub x20, x28, #0x8\n"
+            "lsl z20.b, z3.b, #0x4\n"
+            "lsl z4.b, z6.b, #0x4\n"
+            "ld1rqb { z10.b }, p1/Z, [x28, #32]\n"
+            "ld1rqb { z23.b }, p1/Z, [x28, #48]\n"
+            "and z3.b, z3.b, #0xf0\n"
+            "and z6.b, z6.b, #0xf0\n"
+            "ld1rqb { z11.b }, p1/Z, [x28, #64]\n"
+            "ld1rqb { z7.b }, p1/Z, [x28, #80]\n"
+            "lsl z8.b, z29.b, #0x4\n"
+            "lsl z14.b, z16.b, #0x4\n"
+            "ld1rqb { z18.b }, p1/Z, [x28, #96]\n"
+            "ld1rqb { z30.b }, p1/Z, [x28, #112]\n"
+            ".inst 0x45149b42  // smmla z2.s, z26.b, z20.b\n"
+            ".inst 0x45049b59  // smmla z25.s, z26.b, z4.b\n"
+            "and z29.b, z29.b, #0xf0\n"
+            "ld1h { z17.s }, p1/Z, [x21]\n"
+            ".inst 0x45149abb  // smmla z27.s, z21.b, z20.b\n"
+            ".inst 0x45049ab3  // smmla z19.s, z21.b, z4.b\n"
+            "and z16.b, z16.b, #0xf0\n"
+            "ld1h { z4.s }, p0/Z, [x20]\n"
+            "subs x22, x22, #0x1\n"
+            "add x28, x28, #0x88\n"
+            "fcvt z17.s, p1/m, z17.h\n"
+            "add x25, x25, #0x90\n"
+            ".inst 0x45089942  // smmla z2.s, z10.b, z8.b\n"
+            ".inst 0x450e9959  // smmla z25.s, z10.b, z14.b\n"
+            "fcvt z4.s, p1/m, z4.h\n"
+            ".inst 0x45089afb  // smmla z27.s, z23.b, z8.b\n"
+            ".inst 0x450e9af3  // smmla z19.s, z23.b, z14.b\n"
+            "fscale z17.s, p1/m, z17.s, z28.s\n"
+            "mov z4.q, z4.q[0]\n"
+            ".inst 0x45039962  // smmla z2.s, z11.b, z3.b\n"
+            ".inst 0x45069979  // smmla z25.s, z11.b, z6.b\n"
+            "fmul z23.s, z17.s, z4.s[0]\n"
+            "fmul z9.s, z17.s, z4.s[1]\n"
+            "fmul z21.s, z17.s, z4.s[2]\n"
+            "fmul z4.s, z17.s, z4.s[3]\n"
+            ".inst 0x450398fb  // smmla z27.s, z7.b, z3.b\n"
+            ".inst 0x450698f3  // smmla z19.s, z7.b, z6.b\n"
+            ".inst 0x451d9a42  // smmla z2.s, z18.b, z29.b\n"
+            ".inst 0x45109a59  // smmla z25.s, z18.b, z16.b\n"
+            ".inst 0x451d9bdb  // smmla z27.s, z30.b, z29.b\n"
+            ".inst 0x45109bd3  // smmla z19.s, z30.b, z16.b\n"
+            "uzp1 z31.d, z2.d, z25.d\n"
+            "uzp2 z13.d, z2.d, z25.d\n"
+            "scvtf z31.s, p1/m, z31.s\n"
+            "uzp1 z17.d, z27.d, z19.d\n"
+            "uzp2 z18.d, z27.d, z19.d\n"
+            "scvtf z13.s, p1/m, z13.s\n"
+            "fmla z24.s, p1/M, z31.s, z23.s\n"
+            "scvtf z17.s, p1/m, z17.s\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "fmla z15.s, p1/M, z13.s, z9.s\n"
+            "fmla z12.s, p1/M, z17.s, z21.s\n"
+            "fmla z0.s, p1/M, z18.s, z4.s\n"
+            "bgt 7b\n"
+            "mov x20, %x[res_ptr]\n"
+            "cmp x13, #0x1\n"
+            "st1w { z24.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "ble 8f\n"
+            "cmp x13, #0x2\n"
+            "st1w { z15.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "ble 8f\n"
+            "cmp x13, #0x3\n"
+            "st1w { z12.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "ble 8f\n"
+            "st1w { z0.s }, p1, [x20]\n"
+            "8:"  // Row tail: Accumulator store skip
+            "subs x24, x24, #0x8\n"
+            "add %x[res_ptr], %x[res_ptr], #0x20\n"
+            "bne 6b\n"
+            "subs x13, x13, #0x4\n"
+            "add %x[a_ptr], %x[a_ptr], x12\n"
+            "mov %x[res_ptr], x23\n"
+            "bgt 5b\n"
+            "9:"  // Row tail: Row loop skip
+            : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr)
+            : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc)
+            : "cc", "memory", "p0", "p1", "x9", "x10", "x11", "x12", "x13", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "z0", "z1", "z2", "z3", "z4", "z5", "z6", "z7", "z8", "z9", "z10", "z11", "z12", "z13", "z14", "z15", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31"
+        );
+        return;
+    }
+    else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
+        GGML_ASSERT((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
+                    "__ARM_FEATURE_SVE for vector size of 256-bits not defined, use the Q4_0_4_8 quantization format for optimal "
+                    "performance");
+    }
+    else if (ggml_cpu_has_neon()) {
+        GGML_ASSERT(((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) || ggml_cpu_has_matmul_int8()) &&
+                    "__ARM_FEATURE_SVE for vector size of 256-bits and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 "
+                    "quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    GGML_ASSERT(ggml_cpu_has_sve() &&
+                "__ARM_FEATURE_SVE not defined, use the Q4_0_4_8 quantization format for optimal performance");
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
+                "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
+                "performance");
+#else
+    float sumf[4][8];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+#endif
+}
diff --git a/ggml/src/ggml-aarch64.h b/ggml/src/ggml-aarch64.h
new file mode 100644
index 0000000000000..517babaf1691b
--- /dev/null
+++ b/ggml/src/ggml-aarch64.h
@@ -0,0 +1,39 @@
+// SPDX-FileCopyrightText: Copyright 2024 Arm Ltd.
+#pragma once
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+
+#include "ggml.h"
+
+// GGML internal header
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// Quantization
+void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+
+void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t blck_size_interleave);
+
+// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
+size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+
+// GEMV
+void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+
+// GEMM
+void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+
+#ifdef __cplusplus
+}
+#endif
+
diff --git a/ggml-alloc.c b/ggml/src/ggml-alloc.c
similarity index 88%
rename from ggml-alloc.c
rename to ggml/src/ggml-alloc.c
index 1fbd376edf410..e485326abc45d 100644
--- a/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -91,8 +91,7 @@ void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tenso
     if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) {
         fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
                 __func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset);
-        GGML_ASSERT(!"not enough space in the buffer");
-        return;
+        GGML_ABORT("not enough space in the buffer");
     }
 
     void * addr = (char *)ggml_backend_buffer_get_base(talloc->buffer) + talloc->offset;
@@ -133,7 +132,7 @@ static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset,
             return;
         }
     }
-    GGML_ASSERT(!"out of allocated_tensors");
+    GGML_ABORT("out of allocated_tensors");
 }
 static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
     for (int i = 0; i < 1024; i++) {
@@ -142,8 +141,7 @@ static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offs
             return;
         }
     }
-    fprintf(stderr, "tried to free tensor %s not found\n", tensor->name);
-    GGML_ASSERT(!"tensor not found");
+    GGML_ABORT("tried to free tensor %s not found\n", tensor->name);
 }
 #endif
 
@@ -176,8 +174,7 @@ static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t siz
             // this should never happen
             fprintf(stderr, "%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
                     __func__, size, max_avail);
-            GGML_ASSERT(!"not enough space in the buffer");
-            GGML_UNREACHABLE();
+            GGML_ABORT("not enough space in the buffer");
         }
     }
 
@@ -339,6 +336,7 @@ struct hash_node {
 };
 
 struct tensor_alloc {
+    int buffer_id;
     size_t offset;
     size_t size_max; // 0 = pre-allocated, unused, or view
 };
@@ -349,7 +347,6 @@ struct leaf_alloc {
 };
 
 struct node_alloc {
-    int buffer_id;
     struct tensor_alloc dst;
     struct tensor_alloc src[GGML_MAX_SRC];
 };
@@ -377,7 +374,7 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
     galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t));
     GGML_ASSERT(galloc->bufts != NULL);
 
-    galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs);
+    galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t));
     GGML_ASSERT(galloc->buffers != NULL);
 
     galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
@@ -386,8 +383,19 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
     for (int i = 0; i < n_bufs; i++) {
         galloc->bufts[i] = bufts[i];
         galloc->buffers[i] = NULL;
-        size_t alignment = ggml_backend_buft_get_alignment(bufts[i]);
-        galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment);
+
+        // check if the same buffer type is used multiple times and reuse the same allocator
+        for (int j = 0; j < i; j++) {
+            if (bufts[i] == bufts[j]) {
+                galloc->buf_tallocs[i] = galloc->buf_tallocs[j];
+                break;
+            }
+        }
+
+        if (galloc->buf_tallocs[i] == NULL) {
+            size_t alignment = ggml_backend_buft_get_alignment(bufts[i]);
+            galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment);
+        }
     }
     galloc->n_buffers = n_bufs;
 
@@ -405,14 +413,34 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) {
 
     for (int i = 0; i < galloc->n_buffers; i++) {
         if (galloc->buffers != NULL) {
-            ggml_backend_buffer_free(galloc->buffers[i]);
+            // skip if already freed
+            bool freed = false;
+            for (int j = 0; j < i; j++) {
+                if (galloc->buffers[j] == galloc->buffers[i]) {
+                    freed = true;
+                    break;
+                }
+            }
+            if (!freed) {
+                ggml_backend_buffer_free(galloc->buffers[i]);
+            }
         }
         if (galloc->buf_tallocs != NULL) {
-            ggml_dyn_tallocr_free(galloc->buf_tallocs[i]);
+            // skip if already freed
+            bool freed = false;
+            for (int j = 0; j < i; j++) {
+                if (galloc->buf_tallocs[j] == galloc->buf_tallocs[i]) {
+                    freed = true;
+                    break;
+                }
+            }
+            if (!freed) {
+                ggml_dyn_tallocr_free(galloc->buf_tallocs[i]);
+            }
         }
     }
 
-    free(galloc->hash_set.keys);
+    ggml_hash_set_free(&galloc->hash_set);
     free(galloc->hash_values);
     free(galloc->bufts);
     free(galloc->buffers);
@@ -425,7 +453,7 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) {
 typedef struct ggml_gallocr * ggml_gallocr_t;
 
 static struct hash_node * ggml_gallocr_hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) {
-    size_t i = ggml_hash_find_or_insert(galloc->hash_set, t);
+    size_t i = ggml_hash_find_or_insert(&galloc->hash_set, t);
     return &galloc->hash_values[i];
 }
 
@@ -511,17 +539,18 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
     }
 }
 
-static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) {
+static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) {
     // graph outputs are never freed
     if (node->flags & GGML_TENSOR_FLAG_OUTPUT) {
         AT_PRINTF("not freeing output %s\n", node->name);
         return;
     }
 
-    struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
-    ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
     struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
     size_t offset = hn->offset;
+    int buffer_id = hn->buffer_id;
+    struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
+    ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
     size_t size = ggml_backend_buft_get_alloc_size(buft, node);
     ggml_dyn_tallocr_free_tensor(alloc, offset, size, node);
     hn->allocated = false;
@@ -533,8 +562,8 @@ static int get_node_buffer_id(const int * node_buffer_ids, int i) {
 
 static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
     // clear hash tables
-    memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *));
-    memset(galloc->hash_values,   0, galloc->hash_set.size * sizeof(struct hash_node));
+    ggml_hash_set_reset(&galloc->hash_set);
+    memset(galloc->hash_values, 0, sizeof(struct hash_node) * galloc->hash_set.size);
 
     // allocate leafs
     // these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes
@@ -626,11 +655,11 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
                     AT_PRINTF("view_src %s: %d children, %d views\n",
                         view_src->name, view_src_hn->n_children, view_src_hn->n_views);
                     if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src_hn->allocated) {
-                        ggml_gallocr_free_node(galloc, view_src, buffer_id);
+                        ggml_gallocr_free_node(galloc, view_src);
                     }
                 }
                 else if (p_hn->allocated) {
-                    ggml_gallocr_free_node(galloc, parent, buffer_id);
+                    ggml_gallocr_free_node(galloc, parent);
                 }
             }
             AT_PRINTF("\n");
@@ -639,21 +668,19 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
 }
 
 bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
-    size_t hash_size = graph->visited_hash_table.size;
+    size_t min_hash_size = graph->n_nodes + graph->n_leafs;
+    // add 25% margin to avoid hash collisions
+    min_hash_size += min_hash_size / 4;
 
     // initialize hash table
-    if (galloc->hash_set.size < hash_size) {
-        free(galloc->hash_set.keys);
-        free(galloc->hash_values);
-        galloc->hash_set.size = hash_size;
-        galloc->hash_set.keys = calloc(hash_size, sizeof(struct ggml_tensor *));
-        galloc->hash_values   = calloc(hash_size, sizeof(struct hash_node));
+    if (galloc->hash_set.size < min_hash_size) {
+        ggml_hash_set_free(&galloc->hash_set);
+        galloc->hash_set = ggml_hash_set_new(min_hash_size);
         GGML_ASSERT(galloc->hash_set.keys != NULL);
+
+        free(galloc->hash_values);
+        galloc->hash_values = malloc(sizeof(struct hash_node) * galloc->hash_set.size);
         GGML_ASSERT(galloc->hash_values != NULL);
-    } else {
-        // reset hash table
-        memset(galloc->hash_set.keys, 0, sizeof(struct ggml_tensor *) * galloc->hash_set.size);
-        memset(galloc->hash_values,   0, sizeof(struct hash_node) * galloc->hash_set.size);
     }
 
     // reset allocators
@@ -674,22 +701,25 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
         struct node_alloc * node_alloc = &galloc->node_allocs[i];
-        node_alloc->buffer_id = get_node_buffer_id(node_buffer_ids, i);
         if (node->view_src || node->data) {
+            node_alloc->dst.buffer_id = -1;
             node_alloc->dst.offset = SIZE_MAX;
             node_alloc->dst.size_max = 0;
         } else {
             struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
-            node_alloc->dst.offset   = hn->offset;
-            node_alloc->dst.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node);
+            node_alloc->dst.buffer_id = hn->buffer_id;
+            node_alloc->dst.offset    = hn->offset;
+            node_alloc->dst.size_max  = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node);
         }
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             struct ggml_tensor * src = node->src[j];
             if (!src || src->view_src || src->data) {
+                node_alloc->src[j].buffer_id = -1;
                 node_alloc->src[j].offset = SIZE_MAX;
                 node_alloc->src[j].size_max = 0;
             } else {
                 struct hash_node * hn = ggml_gallocr_hash_get(galloc, src);
+                node_alloc->src[j].buffer_id = hn->buffer_id;
                 node_alloc->src[j].offset   = hn->offset;
                 node_alloc->src[j].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], src);
             }
@@ -706,9 +736,11 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
         galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
         if (leaf->view_src || leaf->data) {
+            galloc->leaf_allocs[i].leaf.buffer_id = -1;
             galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
             galloc->leaf_allocs[i].leaf.size_max = 0;
         } else {
+            galloc->leaf_allocs[i].leaf.buffer_id = hn->buffer_id;
             galloc->leaf_allocs[i].leaf.offset = hn->offset;
             galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
         }
@@ -716,6 +748,14 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
 
     // reallocate buffers if needed
     for (int i = 0; i < galloc->n_buffers; i++) {
+        // if the buffer type is used multiple times, we reuse the same buffer
+        for (int j = 0; j < i; j++) {
+            if (galloc->buf_tallocs[j] == galloc->buf_tallocs[i]) {
+                galloc->buffers[i] = galloc->buffers[j];
+                break;
+            }
+        }
+
         size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
         size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
 
@@ -724,12 +764,14 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
 #ifndef NDEBUG
             fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
+
             ggml_backend_buffer_free(galloc->buffers[i]);
             galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size);
             if (galloc->buffers[i] == NULL) {
                 fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
                 return false;
             }
+            ggml_backend_buffer_set_usage(galloc->buffers[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
         }
     }
 
@@ -740,7 +782,8 @@ bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
     return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
 }
 
-static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) {
+static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, struct tensor_alloc * tensor_alloc) {
+    int buffer_id = tensor_alloc->buffer_id;
     assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
 
     if (tensor->view_src != NULL) {
@@ -750,7 +793,7 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor *
                 // this tensor was allocated without ggml-backend
                 return;
             }
-            ggml_backend_view_init(galloc->buffers[buffer_id], tensor);
+            ggml_backend_view_init(tensor);
         }
     } else {
         if (tensor->data == NULL) {
@@ -768,9 +811,8 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor *
     }
 }
 
-static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct node_alloc * nalloc, struct tensor_alloc * talloc) {
-    ggml_backend_buffer_type_t buft = galloc->bufts[nalloc->buffer_id];
-    size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(buft, node);
+static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) {
+    size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node);
     return talloc->size_max >= node_size;
 }
 
@@ -793,7 +835,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph
         struct ggml_tensor * node = graph->nodes[i];
         struct node_alloc * node_alloc = &galloc->node_allocs[i];
 
-        if (!ggml_gallocr_node_needs_realloc(galloc, node, node_alloc, &node_alloc->dst)) {
+        if (!ggml_gallocr_node_needs_realloc(galloc, node, &node_alloc->dst)) {
 #ifndef NDEBUG
             fprintf(stderr, "%s: node %s is not valid\n", __func__, node->name);
 #endif
@@ -805,7 +847,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph
             if (src == NULL) {
                 continue;
             }
-            if (!ggml_gallocr_node_needs_realloc(galloc, src, node_alloc, &node_alloc->src[j])) {
+            if (!ggml_gallocr_node_needs_realloc(galloc, src, &node_alloc->src[j])) {
 #ifndef NDEBUG
                 fprintf(stderr, "%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name);
 #endif
@@ -846,7 +888,7 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
     for (int i = 0; i < graph->n_leafs; i++) {
         struct ggml_tensor * leaf = graph->leafs[i];
         struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i];
-        ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf);
+        ggml_gallocr_init_tensor(galloc, leaf, &leaf_alloc->leaf);
     }
     // nodes
     for (int i = 0; i < graph->n_nodes; i++) {
@@ -857,9 +899,9 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
             if (src == NULL) {
                 continue;
             }
-            ggml_gallocr_init_tensor(galloc, src, node_alloc->buffer_id, &node_alloc->src[j]);
+            ggml_gallocr_init_tensor(galloc, src, &node_alloc->src[j]);
         }
-        ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst);
+        ggml_gallocr_init_tensor(galloc, node, &node_alloc->dst);
     }
 
     return true;
@@ -871,6 +913,15 @@ size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
     if (galloc->buffers[buffer_id] == NULL) {
         return 0;
     }
+
+    for (int i = 0; i < buffer_id; i++) {
+        if (galloc->buffers[i] == galloc->buffers[buffer_id]) {
+            // this buffer is the same as a previous one due to the same buffer type being used multiple times
+            // only return the buffer size the first time it appears to avoid double counting
+            return 0;
+        }
+    }
+
     return ggml_backend_buffer_get_size(galloc->buffers[buffer_id]);
 }
 
@@ -886,7 +937,7 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
 #endif
         for (size_t i = 0; i < *n_buffers; i++) {
-            ggml_backend_buffer_free(*buffers[i]);
+            ggml_backend_buffer_free((*buffers)[i]);
         }
         free(*buffers);
         return false;
@@ -899,12 +950,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
             if (t->view_src == NULL) {
                 ggml_tallocr_alloc(&tallocr, t);
             } else if (t->buffer == NULL) {
-                ggml_backend_view_init(buffer, t);
+                ggml_backend_view_init(t);
             }
         } else {
             if (t->view_src != NULL && t->buffer == NULL) {
                 // view of a pre-allocated tensor
-                ggml_backend_view_init(buffer, t);
+                ggml_backend_view_init(t);
             }
         }
     }
diff --git a/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h
similarity index 83%
rename from ggml-backend-impl.h
rename to ggml/src/ggml-backend-impl.h
index f121e1de420fa..36ca370867c9e 100644
--- a/ggml-backend-impl.h
+++ b/ggml/src/ggml-backend-impl.h
@@ -17,13 +17,15 @@ extern "C" {
 
     struct ggml_backend_buffer_type_i {
         const char *          (*GGML_CALL get_name)        (ggml_backend_buffer_type_t buft);
+        // allocate a buffer of this type
         ggml_backend_buffer_t (*GGML_CALL alloc_buffer)    (ggml_backend_buffer_type_t buft, size_t size);
-        size_t                (*GGML_CALL get_alignment)   (ggml_backend_buffer_type_t buft); // tensor alignment
-        size_t                (*GGML_CALL get_max_size)    (ggml_backend_buffer_type_t buft); // allocation max size
-        size_t                (*GGML_CALL get_alloc_size)  (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
-        bool                  (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
+        // tensor alignment
+        size_t                (*GGML_CALL get_alignment)   (ggml_backend_buffer_type_t buft);
+        // max buffer size that can be allocated
+        size_t                (*GGML_CALL get_max_size)    (ggml_backend_buffer_type_t buft);
+        // data size needed to allocate the tensor, including padding
+        size_t                (*GGML_CALL get_alloc_size)  (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor);
         // check if tensor data is in host memory
-        // should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
         bool                  (*GGML_CALL is_host)         (ggml_backend_buffer_type_t buft);
     };
 
@@ -92,27 +94,37 @@ extern "C" {
         void (*GGML_CALL synchronize)(ggml_backend_t backend);
 
         // compute graph with a plan (not used currently)
+        // create a new plan for a graph
         ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
         void                      (*GGML_CALL graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+        // update the plan with a new graph - this should be faster than creating a new plan when the graph has the same topology
+        void                      (*GGML_CALL graph_plan_update) (ggml_backend_t backend, ggml_backend_graph_plan_t plan, const struct ggml_cgraph * cgraph);
+        // compute the graph with the plan
+        enum ggml_status          (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
-        // compute graph with a plan
-        enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
         // compute graph without a plan (async)
         enum ggml_status (*GGML_CALL graph_compute)     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
 
-        // check if the backend supports an operation
+        // check if the backend can compute an operation
         bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
 
+        // check if the backend can use tensors allocated in a buffer type
+        bool (*GGML_CALL supports_buft)(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
+
         // check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
         // these should be expensive operations with large batch sizes that may benefit from running on this backend
         // even if the weight has to be copied from the CPU temporarily
         bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op);
 
         // (optional) event synchronization
+        // create a new event that can record events on this backend instance
         ggml_backend_event_t (*GGML_CALL event_new)         (ggml_backend_t backend);
         void                 (*GGML_CALL event_free)        (ggml_backend_event_t event);
+        // record an event on the backend instance that created it
         void                 (*GGML_CALL event_record)      (ggml_backend_event_t event);
+        // wait for an event on on a different backend instance
         void                 (*GGML_CALL event_wait)        (ggml_backend_t backend, ggml_backend_event_t event);
+        // block until an event is recorded
         void                 (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
     };
 
diff --git a/ggml-backend.c b/ggml/src/ggml-backend.c
similarity index 80%
rename from ggml-backend.c
rename to ggml/src/ggml-backend.c
index 9e35ce98d7ace..e1651cc645c42 100644
--- a/ggml-backend.c
+++ b/ggml/src/ggml-backend.c
@@ -44,10 +44,6 @@ GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buf
     return ggml_nbytes(tensor);
 }
 
-bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    return buft->iface.supports_backend(buft, backend);
-}
-
 bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
     if (buft->iface.is_host) {
         return buft->iface.is_host(buft);
@@ -138,6 +134,10 @@ void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backe
     }
 }
 
+enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage(ggml_backend_buffer_t buffer) {
+    return buffer->usage;
+}
+
 ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
     return buffer->buft;
 }
@@ -151,7 +151,7 @@ void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
 bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) {
     ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer;
     if (dst_buf->iface.cpy_tensor) {
-        return src->buffer->iface.cpy_tensor(dst_buf, src, dst);
+        return dst_buf->iface.cpy_tensor(dst_buf, src, dst);
     }
     return false;
 }
@@ -286,6 +286,10 @@ bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor *
     return backend->iface.supports_op(backend, op);
 }
 
+bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    return backend->iface.supports_buft(backend, buft);
+}
+
 bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
     if (backend->iface.offload_op != NULL) {
         return backend->iface.offload_op(backend, op);
@@ -347,15 +351,10 @@ void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t b
     }
 
     // an async copy would normally happen after all the queued operations on both backends are completed
-    // sync src, set_async dst
-    if (ggml_backend_buffer_is_host(src->buffer)) {
-        ggml_backend_synchronize(backend_src);
-        ggml_backend_tensor_set_async(backend_dst, dst, src->data, 0, ggml_nbytes(src));
-    } else {
-        ggml_backend_synchronize(backend_src);
-        ggml_backend_tensor_copy(src, dst);
-        ggml_backend_synchronize(backend_dst);
-    }
+    // to simulate the same behavior, we need to synchronize both backends first, and do a blocking copy
+    ggml_backend_synchronize(backend_src);
+    ggml_backend_synchronize(backend_dst);
+    ggml_backend_tensor_copy(src, dst);
 }
 
 // events
@@ -394,7 +393,7 @@ void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event)
 
 // backend registry
 
-#define GGML_REG_MAX_BACKENDS 16
+#define GGML_REG_MAX_BACKENDS 64
 
 struct ggml_backend_reg {
     char name[128];
@@ -445,6 +444,11 @@ GGML_CALL static void ggml_backend_registry_init(void) {
     extern GGML_CALL void ggml_backend_kompute_reg_devices(void);
     ggml_backend_kompute_reg_devices();
 #endif
+
+#ifdef GGML_USE_CANN
+    extern GGML_CALL int ggml_backend_cann_reg_devices(void);
+    ggml_backend_cann_reg_devices();
+#endif
 }
 
 GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
@@ -639,12 +643,6 @@ GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_
     GGML_UNUSED(buft);
 }
 
-GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    return ggml_backend_is_cpu(backend);
-
-    GGML_UNUSED(buft);
-}
-
 GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
     return true;
 
@@ -659,7 +657,6 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
             /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
             /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
             /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
-            /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend,
             /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
         },
         /* .context = */ NULL,
@@ -715,7 +712,6 @@ ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
             /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
             /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
             /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
-            /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend,
             /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
         },
         /* .context  = */ NULL,
@@ -836,6 +832,12 @@ GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const
     GGML_UNUSED(backend);
 }
 
+GGML_CALL static bool ggml_backend_cpu_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    return ggml_backend_buft_is_host(buft);
+
+    GGML_UNUSED(backend);
+}
+
 static struct ggml_backend_i cpu_backend_i = {
     /* .get_name                = */ ggml_backend_cpu_name,
     /* .free                    = */ ggml_backend_cpu_free,
@@ -846,9 +848,11 @@ static struct ggml_backend_i cpu_backend_i = {
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ ggml_backend_cpu_graph_plan_create,
     /* .graph_plan_free         = */ ggml_backend_cpu_graph_plan_free,
+    /* .graph_plan_update       = */ NULL,
     /* .graph_plan_compute      = */ ggml_backend_cpu_graph_plan_compute,
     /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
     /* .supports_op             = */ ggml_backend_cpu_supports_op,
+    /* .supports_buft           = */ ggml_backend_cpu_supports_buft,
     /* .offload_op              = */ NULL,
     /* .event_new               = */ NULL,
     /* .event_free              = */ NULL,
@@ -1046,17 +1050,19 @@ struct ggml_backend_sched {
     ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS];
     ggml_gallocr_t galloc;
 
-    // hash keys of the nodes in the graph
-    struct ggml_hash_set    hash_set;
-    // hash values
-    int * tensor_backend_id;
-    struct ggml_tensor * (* tensor_copies)[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
+    // hash map of the nodes in the graph
+    struct ggml_hash_set  hash_set;
+    int                 * hv_tensor_backend_ids; // [hash_set.size]
+    struct ggml_tensor ** hv_tensor_copies;      // [hash_set.size][n_backends][n_copies]
 
     int * node_backend_ids; // [graph_size]
     int * leaf_backend_ids; // [graph_size]
 
+    int * prev_node_backend_ids; // [graph_size]
+    int * prev_leaf_backend_ids; // [graph_size]
+
     // copy of the graph with modified inputs
-    struct ggml_cgraph * graph;
+    struct ggml_cgraph graph;
 
     // graph splits
     struct ggml_backend_sched_split * splits;
@@ -1075,17 +1081,16 @@ struct ggml_backend_sched {
     ggml_backend_sched_eval_callback callback_eval;
     void * callback_eval_user_data;
 
-    // align context_buffer to GGML_MEM_ALIGN
-#ifdef _MSC_VER
-    __declspec(align(GGML_MEM_ALIGN))
-#else
-    __attribute__((aligned(GGML_MEM_ALIGN)))
-#endif
-    char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
+    char * context_buffer;
+    size_t context_buffer_size;
+
+    bool debug;
 };
 
-#define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor)
-#define tensor_backend_id(tensor) sched->tensor_backend_id[hash_id(tensor)]
+#define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor)
+#define tensor_backend_id(tensor) sched->hv_tensor_backend_ids[hash_id(tensor)]
+#define tensor_id_copy(id, backend_id, copy_id) sched->hv_tensor_copies[(id) * sched->n_backends * sched->n_copies + (backend_id) * sched->n_copies + (copy_id)]
+#define tensor_copy(tensor, backend_id, copy_id) tensor_id_copy(hash_id(tensor), backend_id, copy_id)
 
 // returns the priority of the backend, lower id is higher priority
 static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) {
@@ -1097,22 +1102,24 @@ static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backen
     return -1;
 }
 
-static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor) {
+static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor, const struct ggml_tensor * op) {
     ggml_backend_buffer_t buffer = tensor->buffer;
     if (buffer == NULL) {
         return -1;
     }
 
-    // find highest prio backend that supports the buffer type
+    // find highest prio backend that supports the buffer type and the op
     for (int i = 0; i < sched->n_backends; i++) {
-        if (ggml_backend_buft_supports_backend(buffer->buft, sched->backends[i])) {
+        if (ggml_backend_supports_buft(sched->backends[i], buffer->buft) &&
+            ggml_backend_supports_op(sched->backends[i], op)) {
             return i;
         }
     }
 
-    fprintf(stderr, "%s: error: no backend supports buffer type %s used in tensor %s\n",
-        __func__, ggml_backend_buffer_name(buffer), tensor->name);
-    GGML_ASSERT(false);
+#ifndef NDEBUG
+    fprintf(stderr, "%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n",
+        __func__, ggml_op_desc(tensor), ggml_backend_buffer_name(buffer), tensor->name);
+#endif
 
     return -1;
 }
@@ -1131,7 +1138,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
     // TODO: use supports_op to check if the backend supports the op
 
     // assign pre-allocated nodes to their backend
-    int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor);
+    int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor, tensor);
     if (cur_backend_id != -1) {
         SET_CAUSE(tensor, "1.dst");
         return cur_backend_id;
@@ -1139,7 +1146,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
 
     // view_src
     if (tensor->view_src != NULL) {
-        cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src);
+        cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src, tensor);
         if (cur_backend_id != -1) {
             SET_CAUSE(tensor, "1.vsrc");
             return cur_backend_id;
@@ -1153,7 +1160,6 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
         return cur_backend_id;
     }
 
-    // assign nodes that use weights to the backend of the weights
     // operations with weights are preferably run on the same backend as the weights
     for (int i = 0; i < GGML_MAX_SRC; i++) {
         const struct ggml_tensor * src = tensor->src[i];
@@ -1161,11 +1167,11 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
             continue;
         }
         if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
-            int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src);
+            int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
             // check if a backend with higher prio wants to offload the op
             if (src_backend_id == sched->n_backends - 1) {
                 for (int b = 0; b < src_backend_id; b++) {
-                    if (ggml_backend_offload_op(sched->backends[b], tensor)) {
+                    if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
                         SET_CAUSE(tensor, "1.off");
                         return b;
                     }
@@ -1223,10 +1229,33 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
     }
 }
 
-//#define DEBUG_PASS1
-//#define DEBUG_PASS2
-//#define DEBUG_PASS3
-//#define DEBUG_PASS4
+static bool ggml_backend_sched_buffer_supported(ggml_backend_sched_t sched, struct ggml_tensor * t, int backend_id) {
+    ggml_backend_buffer_t buf = t->view_src ? t->view_src->buffer : t->buffer;
+    ggml_backend_buffer_type_t buft = NULL;
+
+    if (buf) {
+        // the tensor is already allocated
+        buft = buf->buft;
+    } else {
+        // see if the tensor already has a backend assigned, and use the buffer type of that backend
+        int tensor_backend_id = tensor_backend_id(t);
+        if (tensor_backend_id == -1 && t->view_src) {
+            tensor_backend_id = tensor_backend_id(t->view_src);
+        }
+        if (tensor_backend_id != -1) {
+            buft = sched->bufts[tensor_backend_id];
+        }
+    }
+
+    return buft != NULL && ggml_backend_supports_buft(sched->backends[backend_id], buft);
+}
+
+static void ggml_backend_sched_set_if_supported(ggml_backend_sched_t sched, struct ggml_tensor * node, int cur_backend_id, int * node_backend_id) {
+    if (ggml_backend_supports_op(sched->backends[cur_backend_id], node)) {
+        *node_backend_id = cur_backend_id;
+        SET_CAUSE(node, "2.sup");
+    }
+}
 
 // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
 static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
@@ -1236,7 +1265,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
     sched->is_reset = false;
 
     struct ggml_init_params params = {
-        /* .mem_size =   */ sizeof(sched->context_buffer),
+        /* .mem_size =   */ sched->context_buffer_size,
         /* .mem_buffer = */ sched->context_buffer,
         /* .no_alloc =   */ true
     };
@@ -1245,52 +1274,52 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
     sched->ctx = ggml_init(params);
     if (sched->ctx == NULL) {
-        fprintf(stderr, "%s: failed to initialize context\n", __func__);
-        GGML_ASSERT(false);
+        GGML_ABORT("%s: failed to initialize context\n", __func__);
     }
 
     // pass 1: assign backends to ops with pre-allocated inputs
     for (int i = 0; i < graph->n_leafs; i++) {
         struct ggml_tensor * leaf = graph->leafs[i];
         int * leaf_backend_id = &tensor_backend_id(leaf);
-        if (*leaf_backend_id != -1) {
-            // do not overwrite user assignments
-            continue;
+        // do not overwrite user assignments
+        if (*leaf_backend_id == -1) {
+            *leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf);
         }
-        *leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf);
     }
 
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
         int * node_backend_id = &tensor_backend_id(node);
-        if (*node_backend_id != -1) {
-            // do not overwrite user assignments
-            continue;
-        }
-        *node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node);
-        // src
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            struct ggml_tensor * src = node->src[j];
-            if (src == NULL) {
+        // do not overwrite user assignments
+        if (*node_backend_id == -1) {
+            *node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node);
+
+#if 0
+            // src
+            if (node->op == GGML_OP_NONE) {
                 continue;
             }
-            int * src_backend_id = &tensor_backend_id(src);
-            if (*src_backend_id == -1) {
-                *src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src);
+
+            for (int j = 0; j < GGML_MAX_SRC; j++) {
+                struct ggml_tensor * src = node->src[j];
+                if (src == NULL) {
+                    continue;
+                }
+                int * src_backend_id = &tensor_backend_id(src);
+                if (*src_backend_id == -1) {
+                    *src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src);
+                }
             }
+#endif
         }
     }
-#ifdef DEBUG_PASS1
-    fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
-#endif
 
     // pass 2: expand current backend assignments
     // assign the same backend to adjacent nodes
     // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend)
     // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
-
-
-    // pass 2.2 expand gpu down
+    // ops unsupported by the backend being expanded will be left unassigned so that they can be assigned later when the locations of its inputs are known
+    // expand gpu down
     {
         int cur_backend_id = -1;
         for (int i = 0; i < graph->n_nodes; i++) {
@@ -1306,13 +1335,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 } else {
                     cur_backend_id = *node_backend_id;
                 }
-            } else {
-                *node_backend_id = cur_backend_id;
-                SET_CAUSE(node, "2.2");
+            } else if (cur_backend_id != -1) {
+                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
             }
         }
     }
-    // pass 2.1 expand gpu up
+    // expand gpu up
     {
         int cur_backend_id = -1;
         for (int i = graph->n_nodes - 1; i >= 0; i--) {
@@ -1328,13 +1356,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 } else {
                     cur_backend_id = *node_backend_id;
                 }
-            } else {
-                *node_backend_id = cur_backend_id;
-                SET_CAUSE(node, "2.1");
+            } else if (cur_backend_id != -1) {
+                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
             }
         }
     }
-    // pass 2.4 expand rest down
+    // expand rest down
     {
         int cur_backend_id = -1;
         for (int i = 0; i < graph->n_nodes; i++) {
@@ -1345,13 +1372,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             int * node_backend_id = &tensor_backend_id(node);
             if (*node_backend_id != -1) {
                 cur_backend_id = *node_backend_id;
-            } else {
-                *node_backend_id = cur_backend_id;
-                SET_CAUSE(node, "2.4");
+            } else if (cur_backend_id != -1) {
+                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
             }
         }
     }
-    // pass 2.3 expand rest up
+    // expand rest up
     {
         int cur_backend_id = -1;
         for (int i = graph->n_nodes - 1; i >= 0; i--) {
@@ -1362,24 +1388,80 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             int * node_backend_id = &tensor_backend_id(node);
             if (*node_backend_id != -1) {
                 cur_backend_id = *node_backend_id;
-            } else {
-                *node_backend_id = cur_backend_id;
-                SET_CAUSE(node, "2.3");
+            } else if (cur_backend_id != -1) {
+                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
             }
         }
     }
 
-#ifdef DEBUG_PASS2
-    fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
-#endif
+    // pass 3: upgrade nodes to higher prio backends with compatible buffer types
+    // if the tensor is already in the same buffer type (*) as another higher priority backend, we should move it there
+    // however, we also need to verify that the sources are in compatible buffer types
+    // (*) the actual requirement is more relaxed, the buffer type of the backend should be supported by all the users of this tensor further down the graph
+    // however, this is slow to verify, so we have a more strict requirement that the buffer type is the same
+    // this is not uncommon since multiple backends can use host memory, with the same buffer type (eg. BLAS and CPU)
+    // additionally, set remaining unassigned nodes to the backend with the most supported inputs
+    // only nodes that could not be assigned during expansion due to the backend not supporting the op should be unassigned at this point
+    for (int i = 0; i < graph->n_nodes; i++) {
+        struct ggml_tensor * node = graph->nodes[i];
+        if (ggml_is_view_op(node->op)) {
+            continue;
+        }
+        int * node_backend_id = &tensor_backend_id(node);
+        if (*node_backend_id == -1) {
+            // unassigned node: find the backend with the most supported inputs
+            int n_supported_best = -1;
+            for (int b = 0; b < sched->n_backends; b++) {
+                if (ggml_backend_supports_op(sched->backends[b], node)) {
+                    int n_supported = 0;
+                    for (int j = 0; j < GGML_MAX_SRC; j++) {
+                        struct ggml_tensor * src = node->src[j];
+                        if (src == NULL) {
+                            continue;
+                        }
+                        if ((tensor_backend_id(src) != -1 || tensor_backend_id(src->view_src) != -1) && ggml_backend_sched_buffer_supported(sched, src, b)) {
+                            n_supported++;
+                        }
+                    }
+                    if (n_supported > n_supported_best) {
+                        n_supported_best = n_supported;
+                        *node_backend_id = b;
+                        SET_CAUSE(node, "3.best");
+                    }
+                }
+            }
+        } else {
+            // assigned node: upgrade to higher prio backend if possible
+            for (int b = 0; b < *node_backend_id; b++) {
+                if (sched->bufts[b] == sched->bufts[*node_backend_id] && ggml_backend_supports_op(sched->backends[b], node)) {
+                    bool supported = true;
+                    for (int j = 0; j < GGML_MAX_SRC; j++) {
+                        struct ggml_tensor * src = node->src[j];
+                        if (src == NULL) {
+                            continue;
+                        }
+                        if (!ggml_backend_sched_buffer_supported(sched, src, b)) {
+                            supported = false;
+                            break;
+                        }
+                    }
+                    if (supported) {
+                        *node_backend_id = b;
+                        SET_CAUSE(node, "3.upg");
+                        break;
+                    }
+                }
+            }
+        }
+    }
 
-    // pass 3: assign backends to remaining src from dst and view_src
+    // pass 4: assign backends to remaining src from dst and view_src
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
         int * cur_backend_id = &tensor_backend_id(node);
         if (node->view_src != NULL && *cur_backend_id == -1) {
             *cur_backend_id = tensor_backend_id(node->view_src);
-            SET_CAUSE(node, "3.vsrc");
+            SET_CAUSE(node, "4.vsrc");
         }
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             struct ggml_tensor * src = node->src[j];
@@ -1391,24 +1473,22 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 if (src->view_src != NULL) {
                     // views are always on the same backend as the source
                     *src_backend_id = tensor_backend_id(src->view_src);
-                    SET_CAUSE(src, "3.vsrc");
+                    SET_CAUSE(src, "4.vsrc");
                 } else {
                     *src_backend_id = *cur_backend_id;
-                    SET_CAUSE(src, "3.cur");
+                    SET_CAUSE(src, "4.cur");
                 }
             }
         }
     }
-#ifdef DEBUG_PASS3
-    fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
-#endif
 
-    // pass 4: split graph, find tensors that need to be copied
+    // pass 5: split graph, find tensors that need to be copied
     {
         int i_split = 0;
         struct ggml_backend_sched_split * split = &sched->splits[0];
         // find the backend of the first split, skipping view ops
-        for (int i = 0; i < graph->n_nodes; i++) {
+        int i = 0;
+        for (; i < graph->n_nodes; i++) {
             struct ggml_tensor * node = graph->nodes[i];
             if (!ggml_is_view_op(node->op)) {
                 split->backend_id = tensor_backend_id(node);
@@ -1417,9 +1497,8 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
         }
         split->i_start = 0;
         split->n_inputs = 0;
-        memset(split->inputs, 0, sizeof(split->inputs)); //HACK
         int cur_backend_id = split->backend_id;
-        for (int i = 0; i < graph->n_nodes; i++) {
+        for (; i < graph->n_nodes; i++) {
             struct ggml_tensor * node = graph->nodes[i];
 
             if (ggml_is_view_op(node->op)) {
@@ -1428,7 +1507,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
             const int node_backend_id = tensor_backend_id(node);
 
-            GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now
+            assert(node_backend_id != -1); // all nodes should be assigned by now
 
             // check if we should start a new split based on the sources of the current node
             bool need_new_split = false;
@@ -1442,16 +1521,18 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                     // by starting a new split, the memory of the previously offloaded weights can be reused
                     if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
                         int src_backend_id = tensor_backend_id(src);
-                        if (src_backend_id != -1 && src_backend_id != cur_backend_id) {
+                        if (src_backend_id != cur_backend_id) {
                             need_new_split = true;
                             break;
                         }
                     }
                     // check if the split has too many inputs
+                    // FIXME: count the number of inputs instead of only checking when full
                     if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) {
                         const size_t id = hash_id(src);
-                        int src_backend_id = sched->tensor_backend_id[id];
-                        if (src_backend_id != cur_backend_id && sched->tensor_copies[hash_id(src)][cur_backend_id][0] == NULL) {
+                        int src_backend_id = sched->hv_tensor_backend_ids[id];
+                        bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id);
+                        if (src_backend_id != cur_backend_id && tensor_id_copy(id, cur_backend_id, 0) == NULL && !supported) {
                             //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name);
                             need_new_split = true;
                             break;
@@ -1483,12 +1564,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                     continue;
                 }
 
-                const int src_backend_id = tensor_backend_id(src);
+                size_t src_id = hash_id(src);
+                const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
                 assert(src_backend_id != -1); // all inputs should be assigned by now
 
-                if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1)  {
-                    size_t id = hash_id(src);
-                    if (sched->tensor_copies[id][src_backend_id][0] == NULL) {
+                if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
+                    if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {
                         ggml_backend_t backend = sched->backends[src_backend_id];
                         for (int c = 0; c < sched->n_copies; c++) {
                             struct ggml_tensor * tensor_copy;
@@ -1502,7 +1583,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                                 ggml_set_input(tensor_copy);
                                 ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
                             }
-                            sched->tensor_copies[id][src_backend_id][c] = tensor_copy;
+                            tensor_id_copy(src_id, src_backend_id, c) = tensor_copy;
                             SET_CAUSE(tensor_copy, "4.cpy");
                         }
                         int n_graph_inputs = sched->n_graph_inputs++;
@@ -1511,10 +1592,9 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                     }
                 }
 
-                if (src_backend_id != node_backend_id) {
+                if (src_backend_id != cur_backend_id && !ggml_backend_sched_buffer_supported(sched, src, cur_backend_id)) {
                     // create a copy of the input in the split's backend
-                    const size_t id = hash_id(src);
-                    if (sched->tensor_copies[id][cur_backend_id][0] == NULL) {
+                    if (tensor_id_copy(src_id, cur_backend_id, 0) == NULL) {
                         ggml_backend_t backend = sched->backends[cur_backend_id];
                         for (int c = 0; c < sched->n_copies; c++) {
                             struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
@@ -1523,27 +1603,49 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                                 ggml_set_input(tensor_copy);
                                 ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
                             }
-                            sched->tensor_copies[id][cur_backend_id][c] = tensor_copy;
+                            tensor_id_copy(src_id, cur_backend_id, c) = tensor_copy;
                             SET_CAUSE(tensor_copy, "4.cpy");
                         }
                         int n_inputs = split->n_inputs++;
                         GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
                         split->inputs[n_inputs] = src;
                     }
-                    node->src[j] = sched->tensor_copies[id][cur_backend_id][sched->cur_copy];
+                    node->src[j] = tensor_id_copy(src_id, cur_backend_id, sched->cur_copy);
                 }
             }
         }
         split->i_end = graph->n_nodes;
         sched->n_splits = i_split + 1;
     }
-#ifdef DEBUG_PASS4
-    fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
-#endif
 
-    // create copies of the graph for each split
-    // TODO: avoid this copy
-    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2, false);
+    if (sched->debug) {
+        ggml_backend_sched_print_assignments(sched, graph);
+    }
+
+    // swap node_backend_ids and leaf _backend_ids with prevs
+    {
+        int * tmp = sched->node_backend_ids;
+        sched->node_backend_ids = sched->prev_node_backend_ids;
+        sched->prev_node_backend_ids = tmp;
+
+        tmp = sched->leaf_backend_ids;
+        sched->leaf_backend_ids = sched->prev_leaf_backend_ids;
+        sched->prev_leaf_backend_ids = tmp;
+    }
+
+    int graph_size = graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2;
+    if (sched->graph.size < graph_size) {
+        sched->graph.size = graph_size;
+        sched->graph.nodes = realloc(sched->graph.nodes, graph_size * sizeof(struct ggml_tensor *));
+        sched->graph.leafs = realloc(sched->graph.leafs, graph_size * sizeof(struct ggml_tensor *));
+        GGML_ASSERT(sched->graph.nodes != NULL);
+        GGML_ASSERT(sched->graph.leafs != NULL);
+    }
+    sched->graph.n_nodes = 0;
+    sched->graph.n_leafs = 0;
+
+    struct ggml_cgraph * graph_copy = &sched->graph;
+
     for (int i = 0; i < sched->n_splits; i++) {
         struct ggml_backend_sched_split * split = &sched->splits[i];
         split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
@@ -1554,12 +1656,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
             struct ggml_tensor * input = split->inputs[j];
             const size_t input_id = hash_id(input);
-            struct ggml_tensor * input_cpy = sched->tensor_copies[input_id][split->backend_id][sched->cur_copy];
+            struct ggml_tensor * input_cpy = tensor_id_copy(input_id, split->backend_id, sched->cur_copy);
 
             // add a dependency to the input source so that it is not freed before the copy is done
             struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input);
             input_dep->src[0] = input;
-            sched->node_backend_ids[graph_copy->n_nodes] = sched->tensor_backend_id[input_id];
+            sched->node_backend_ids[graph_copy->n_nodes] = sched->hv_tensor_backend_ids[input_id];
             graph_copy->nodes[graph_copy->n_nodes++] = input_dep;
 
             // add a dependency to the input copy so that it is allocated at the start of the split
@@ -1581,7 +1683,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             size_t id = hash_id(input);
             int backend_id = tensor_backend_id(input);
             for (int c = 0; c < sched->n_copies; c++) {
-                struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
+                struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
                 sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
                 graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
             }
@@ -1594,7 +1696,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 struct ggml_tensor * input = split->inputs[j];
                 size_t id = hash_id(input);
                 for (int c = 0; c < sched->n_copies; c++) {
-                    struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
+                    struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
                     sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
                     graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
                 }
@@ -1608,20 +1710,36 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
         sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
         graph_copy->leafs[graph_copy->n_leafs++] = leaf;
     }
-
-    sched->graph = graph_copy;
 }
 
 static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
+    bool backend_ids_changed = false;
+    for (int i = 0; i < sched->graph.n_nodes; i++) {
+        if (sched->node_backend_ids[i] != sched->prev_node_backend_ids[i] &&
+            sched->bufts[sched->node_backend_ids[i]] != sched->bufts[sched->prev_node_backend_ids[i]]) {
+            backend_ids_changed = true;
+            break;
+        }
+    }
+    if (!backend_ids_changed) {
+        for (int i = 0; i < sched->graph.n_leafs; i++) {
+            if (sched->leaf_backend_ids[i] != sched->prev_leaf_backend_ids[i] &&
+                sched->bufts[sched->leaf_backend_ids[i]] != sched->bufts[sched->prev_leaf_backend_ids[i]]) {
+                backend_ids_changed = true;
+                break;
+            }
+        }
+    }
+
     // allocate graph
-    if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
+    if (backend_ids_changed || !ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
         // the re-allocation may cause the split inputs to be moved to a different address
         ggml_backend_sched_synchronize(sched);
 #ifndef NDEBUG
-        fprintf(stderr, "%s: failed to allocate graph, reserving\n", __func__);
+        fprintf(stderr, "%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
 #endif
-        ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
-        if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
+        ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
+        if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
             fprintf(stderr, "%s: failed to allocate graph\n", __func__);
             return false;
         }
@@ -1642,7 +1760,7 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
         for (int j = 0; j < split->n_inputs; j++) {
             ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
             struct ggml_tensor * input = split->inputs[j];
-            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id][sched->cur_copy];
+            struct ggml_tensor * input_cpy = tensor_copy(input, split_backend_id, sched->cur_copy);
 
             if (input->flags & GGML_TENSOR_FLAG_INPUT) {
                 // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
@@ -1659,7 +1777,17 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 } else {
                     ggml_backend_synchronize(split_backend);
                 }
-                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
+                // try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events
+                // TODO: add public function to facilitate this, since applications do not have direct access to the backend interface
+                if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) {
+                    ggml_backend_synchronize(input_backend);
+                    if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                        ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
+                    } else {
+                        ggml_backend_synchronize(split_backend);
+                    }
+                    ggml_backend_tensor_copy(input, input_cpy);
+                }
             }
         }
 
@@ -1727,18 +1855,24 @@ ggml_backend_sched_t ggml_backend_sched_new(
 
     struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched));
 
+    sched->debug = getenv("GGML_SCHED_DEBUG") != NULL;
+    sched->n_backends = n_backends;
+    sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
+
     // initialize hash table
-    sched->hash_set          = ggml_hash_set_new(graph_size);
-    sched->tensor_backend_id = calloc(sched->hash_set.size, sizeof(sched->tensor_backend_id[0]));
-    sched->tensor_copies     = calloc(sched->hash_set.size, sizeof(sched->tensor_copies[0]));
+    // FIXME: needs to be size*2 to account for leafs (do it in graph_split instead)
+    sched->hash_set    = ggml_hash_set_new(graph_size);
+    sched->hv_tensor_backend_ids = malloc(sched->hash_set.size * sizeof(sched->hv_tensor_backend_ids[0]));
+    sched->hv_tensor_copies      = malloc(sched->hash_set.size * sched->n_backends * sched->n_copies * sizeof(struct ggml_tensor *));
 
     const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2;
-    sched->node_backend_ids  = calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
-    sched->leaf_backend_ids  = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));
-
-    sched->n_backends = n_backends;
+    sched->node_backend_ids = calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
+    sched->leaf_backend_ids = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));
+    sched->prev_node_backend_ids = calloc(nodes_size, sizeof(sched->prev_node_backend_ids[0]));
+    sched->prev_leaf_backend_ids = calloc(nodes_size, sizeof(sched->prev_leaf_backend_ids[0]));
 
-    sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
+    sched->context_buffer_size = GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + ggml_graph_overhead_custom(graph_size, false);
+    sched->context_buffer = malloc(sched->context_buffer_size);
 
     const int initial_splits_capacity = 16;
     sched->splits = calloc(initial_splits_capacity, sizeof(sched->splits[0]));
@@ -1747,7 +1881,7 @@ ggml_backend_sched_t ggml_backend_sched_new(
     for (int b = 0; b < n_backends; b++) {
         sched->backends[b] = backends[b];
         sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
-        GGML_ASSERT(ggml_backend_buft_supports_backend(sched->bufts[b], backends[b]));
+        GGML_ASSERT(ggml_backend_supports_buft(backends[b], sched->bufts[b]));
         if (sched->n_copies > 1) {
             for (int c = 0; c < sched->n_copies; c++) {
                 sched->events[b][c] = ggml_backend_event_new(backends[b]);
@@ -1773,35 +1907,37 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
     }
     ggml_gallocr_free(sched->galloc);
     ggml_free(sched->ctx);
+    ggml_hash_set_free(&sched->hash_set);
     free(sched->splits);
-    free(sched->hash_set.keys);
-    free(sched->tensor_backend_id);
-    free(sched->tensor_copies);
+    free(sched->hv_tensor_backend_ids);
+    free(sched->hv_tensor_copies);
     free(sched->node_backend_ids);
     free(sched->leaf_backend_ids);
+    free(sched->prev_node_backend_ids);
+    free(sched->prev_leaf_backend_ids);
+    free(sched->context_buffer);
+    free(sched->graph.nodes);
+    free(sched->graph.leafs);
     free(sched);
 }
 
 void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
     // reset state for the next run
     if (!sched->is_reset) {
-        size_t hash_size = sched->hash_set.size;
-        memset(sched->hash_set.keys,      0, sizeof(sched->hash_set.keys[0])     * hash_size); // NOLINT
-        memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
-        memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);
-
+        ggml_hash_set_reset(&sched->hash_set);
+        memset(sched->hv_tensor_backend_ids, -1, sched->hash_set.size * sizeof(sched->hv_tensor_backend_ids[0]));
+        memset(sched->hv_tensor_copies,       0, sched->hash_set.size * sched->n_backends * sched->n_copies * sizeof(struct ggml_tensor *));
         sched->is_reset = true;
     }
     sched->is_alloc = false;
 }
 
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
-    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes);
+    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
 
     ggml_backend_sched_split_graph(sched, measure_graph);
 
-    // TODO: extract this to a separate function
-    if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
+    if (!ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
         return false;
     }
 
@@ -1812,10 +1948,11 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
 }
 
 bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes);
+    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + graph->n_leafs);
 
     ggml_backend_sched_split_graph(sched, graph);
 
+
     if (!ggml_backend_sched_alloc_splits(sched)) {
         return false;
     }
@@ -1864,6 +2001,15 @@ int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
     return sched->n_copies;
 }
 
+int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) {
+    return sched->n_backends;
+}
+
+ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) {
+    GGML_ASSERT(i >= 0 && i < sched->n_backends);
+    return sched->backends[i];
+}
+
 size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
@@ -1875,6 +2021,8 @@ void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct gg
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
     tensor_backend_id(node) = backend_index;
+    SET_CAUSE(node, "usr");
+    sched->is_reset = false;
 }
 
 ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
@@ -1887,15 +2035,15 @@ ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched,
 
 // utils
 
-void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+void ggml_backend_view_init(struct ggml_tensor * tensor) {
     GGML_ASSERT(tensor->buffer == NULL);
     GGML_ASSERT(tensor->view_src != NULL);
     GGML_ASSERT(tensor->view_src->buffer != NULL);
     GGML_ASSERT(tensor->view_src->data != NULL);
 
-    tensor->buffer = buffer;
+    tensor->buffer = tensor->view_src->buffer;
     tensor->data = (char *)tensor->view_src->data + tensor->view_offs;
-    ggml_backend_buffer_init_tensor(buffer, tensor);
+    ggml_backend_buffer_init_tensor(tensor->buffer, tensor);
 }
 
 void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) {
@@ -1917,9 +2065,9 @@ static struct ggml_tensor * graph_copy_dup_tensor(struct ggml_hash_set hash_set,
     GGML_ASSERT(src != NULL);
     GGML_ASSERT(src->data && "graph must be allocated");
 
-    size_t id = ggml_hash_insert(hash_set, src);
-    if (id == GGML_HASHTABLE_ALREADY_EXISTS) {
-        return node_copies[ggml_hash_find(hash_set, src)];
+    size_t id = ggml_hash_insert(&hash_set, src);
+    if (id == GGML_HASHSET_ALREADY_EXISTS) {
+        return node_copies[ggml_hash_find(&hash_set, src)];
     }
 
     struct ggml_tensor * dst = ggml_dup_tensor_layout(src->data && !src->view_src ? ctx_allocated : ctx_unallocated, src);
@@ -1944,7 +2092,7 @@ static struct ggml_tensor * graph_copy_dup_tensor(struct ggml_hash_set hash_set,
     return dst;
 }
 
-static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) {
+static void graph_copy_init_tensor(struct ggml_hash_set * hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) {
     size_t id = ggml_hash_find(hash_set, src);
     if (node_init[id]) {
         return;
@@ -1954,7 +2102,7 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te
     struct ggml_tensor * dst = node_copies[id];
     if (dst->view_src != NULL) {
         graph_copy_init_tensor(hash_set, node_copies, node_init, src->view_src);
-        ggml_backend_view_init(dst->view_src->buffer, dst);
+        ggml_backend_view_init(dst);
     }
     else {
         ggml_backend_tensor_copy(src, dst);
@@ -1971,10 +2119,7 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te
 }
 
 struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
-    struct ggml_hash_set hash_set = {
-        /* .size = */ graph->visited_hash_table.size,
-        /* .keys = */ calloc(graph->visited_hash_table.size, sizeof(hash_set.keys[0])) // NOLINT
-    };
+    struct ggml_hash_set hash_set = ggml_hash_set_new(graph->visited_hash_set.size);
     struct ggml_tensor ** node_copies = calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
     bool * node_init = calloc(hash_set.size, sizeof(node_init[0]));
 
@@ -1989,7 +2134,7 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s
 
     if (ctx_allocated == NULL || ctx_unallocated == NULL) {
         fprintf(stderr, "failed to allocate context for graph copy\n");
-        free(hash_set.keys);
+        ggml_hash_set_free(&hash_set);
         free(node_copies);
         free(node_init);
         ggml_free(ctx_allocated);
@@ -2012,7 +2157,7 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s
     ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend);
     if (buffer == NULL) {
         fprintf(stderr, "failed to allocate buffer for graph copy\n");
-        free(hash_set.keys);
+        ggml_hash_set_free(&hash_set);
         free(node_copies);
         free(node_init);
         ggml_free(ctx_allocated);
@@ -2030,19 +2175,19 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s
     // copy data and init views
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
-        graph_copy_init_tensor(hash_set, node_copies, node_init, node);
+        graph_copy_init_tensor(&hash_set, node_copies, node_init, node);
     }
 
     // build graph copy
     struct ggml_cgraph * graph_copy = ggml_new_graph_custom(ctx_allocated, graph->size, false);
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
-        struct ggml_tensor * node_copy = node_copies[ggml_hash_find(hash_set, node)];
+        struct ggml_tensor * node_copy = node_copies[ggml_hash_find(&hash_set, node)];
         graph_copy->nodes[i] = node_copy;
     }
     graph_copy->n_nodes = graph->n_nodes;
 
-    free(hash_set.keys);
+    ggml_hash_set_free(&hash_set);
     free(node_copies);
     free(node_init);
 
diff --git a/ggml/src/ggml-blas.cpp b/ggml/src/ggml-blas.cpp
new file mode 100644
index 0000000000000..71373173598c7
--- /dev/null
+++ b/ggml/src/ggml-blas.cpp
@@ -0,0 +1,367 @@
+#include "ggml-blas.h"
+#include "ggml-backend-impl.h"
+
+#include <future>
+#include <vector>
+
+#if defined(GGML_USE_ACCELERATE)
+#   include <Accelerate/Accelerate.h>
+#elif defined(GGML_BLAS_USE_MKL)
+#   include <mkl.h>
+#elif defined(GGML_BLAS_USE_BLIS)
+#   include <blis.h>
+#elif defined(GGML_BLAS_USE_NVPL)
+#   include <nvpl_blas.h>
+#else
+#   include <cblas.h>
+#endif
+
+struct ggml_backend_blas_context {
+    int n_threads = GGML_DEFAULT_N_THREADS;
+    std::unique_ptr<char[]> work_data;
+    size_t work_size = 0;
+#ifndef GGML_USE_OPENMP
+    std::vector<std::future<void>> tasks;
+#endif
+};
+
+// helper function to determine if it is better to use BLAS or not
+// for large matrices, BLAS is faster
+static bool ggml_backend_blas_use_blas(const struct ggml_tensor * dst) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    // TODO: find the optimal values for these
+    if (ggml_is_contiguous(src0) &&
+        ggml_is_contiguous(src1) &&
+        src1->type == GGML_TYPE_F32 &&
+        (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
+
+        /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
+        return true;
+    }
+
+    return false;
+}
+
+static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const enum ggml_type type = src0->type;
+
+    GGML_ASSERT(ne0 == ne01);
+    GGML_ASSERT(ne1 == ne11);
+    GGML_ASSERT(ne2 == ne12);
+    GGML_ASSERT(ne3 == ne13);
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == ggml_type_size(type));
+    GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
+
+    const int64_t ne_plane      = ne01*ne00;
+    const size_t  desired_wsize = type == GGML_TYPE_F32 ? 0 : ne03*ne02*ne_plane*sizeof(float);
+
+    if (ctx->work_size < desired_wsize) {
+        ctx->work_data.reset(new char[desired_wsize]);
+        ctx->work_size = desired_wsize;
+    }
+    void * wdata = ctx->work_data.get();
+
+    // convert src0 to float
+    if (type != GGML_TYPE_F32) {
+        ggml_type_traits_t type_traits = ggml_internal_get_type_traits(type);
+        ggml_to_float_t const to_float = type_traits.to_float;
+
+        for (int64_t i03 = 0; i03 < ne03; i03++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                const void  *       x      = (char *)  src0->data + i02*nb02          + i03*nb03;
+                      float * const wplane = (float *) wdata      + i02*ne_plane      + i03*ne02*ne_plane;
+
+                const int min_cols_per_thread = 4096;
+                const int min_rows_per_thread = std::max((int)(min_cols_per_thread/ne00), 1);
+                const int n_threads = std::max(std::min(ctx->n_threads, (int)(ne01/min_rows_per_thread)), 1);
+
+#ifdef GGML_USE_OPENMP
+                #pragma omp parallel for num_threads(n_threads)
+                for (int64_t i01 = 0; i01 < ne01; i01++) {
+                    to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
+                }
+#else
+                for (int i = 1; i < n_threads; i++) {
+                    const int64_t start =       i*ne01/n_threads;
+                    const int64_t end   = (i + 1)*ne01/n_threads;
+                    if (start < end) {
+                        ctx->tasks.push_back(std::async(std::launch::async, [=]() {
+                            for (int64_t i01 = start; i01 < end; i01++) {
+                                to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
+                            }
+                        }));
+                    }
+                }
+                {
+                    // reuse the current thread for the first task
+                    const int64_t start = 0;
+                    const int64_t end   = ne01/n_threads;
+                    for (int64_t i01 = start; i01 < end; i01++) {
+                        to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
+                    }
+                }
+#endif
+            }
+        }
+
+#ifndef GGML_USE_OPENMP
+        // wait for all tasks to finish
+        for (auto & task : ctx->tasks) {
+            task.get();
+        }
+        ctx->tasks.clear();
+#endif
+    }
+
+#if defined(OPENBLAS_VERSION)
+    openblas_set_num_threads(ctx->n_threads);
+#endif
+
+#if defined(GGML_BLAS_USE_BLIS)
+    bli_thread_set_num_threads(ctx->n_threads);
+#endif
+
+#if defined(GGML_BLAS_USE_NVPL)
+    nvpl_blas_set_num_threads(ctx->n_threads);
+#endif
+
+    for (int64_t i13 = 0; i13 < ne13; i13++) {
+        for (int64_t i12 = 0; i12 < ne12; i12++) {
+            const int64_t i03 = i13/r3;
+            const int64_t i02 = i12/r2;
+
+            const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
+            const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
+                  float * d = (float *) ((char *)  dst->data + i12*nb2  + i13*nb3);
+
+            if (type != GGML_TYPE_F32) {
+                x = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane;
+            }
+
+            cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
+                        ne1, ne01, ne10,
+                        1.0f,   y, ne10,
+                                x, ne00,
+                        0.0f,   d, ne01);
+        }
+    }
+}
+
+static void ggml_backend_blas_out_prod(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(ne0  == ne00);
+    GGML_ASSERT(ne1  == ne10);
+    GGML_ASSERT(ne2  == ne02);
+    GGML_ASSERT(ne02 == ne12);
+    GGML_ASSERT(ne3  == ne13);
+    GGML_ASSERT(ne03 == ne13);
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == sizeof(float));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    // GGML_ASSERT(nb0 <= nb1);
+    // GGML_ASSERT(nb1 <= nb2);
+    // GGML_ASSERT(nb2 <= nb3);
+
+    // Arguments to ggml_compute_forward_out_prod (expressed as major,minor)
+    // src0: (k,n)
+    // src1: (k,m)
+    // dst:  (m,n)
+    //
+    // Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f)
+    // Also expressed as (major,minor)
+    // a: (m,k): so src1 transposed
+    // b: (k,n): so src0
+    // c: (m,n)
+    //
+    // However, if ggml_is_transposed(src1) is true, then
+    // src1->data already contains a transposed version, so sgemm mustn't
+    // transpose it further.
+
+    int n = src0->ne[0];
+    int k = src0->ne[1];
+    int m = src1->ne[0];
+
+    CBLAS_TRANSPOSE transposeA;
+    int lda;
+
+    if (!ggml_is_transposed(src1)) {
+        transposeA = CblasTrans;
+        lda = m;
+    } else {
+        transposeA = CblasNoTrans;
+        lda = k;
+    }
+
+    float * a = (float *) ((char *) src1->data);
+    float * b = (float *) ((char *) src0->data);
+    float * c = (float *) ((char *) dst->data);
+
+    cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n);
+
+    GGML_UNUSED(ctx);
+}
+
+// backend interface
+
+GGML_CALL static const char * ggml_backend_blas_name(ggml_backend_t backend) {
+    return "BLAS";
+
+    GGML_UNUSED(backend);
+}
+
+GGML_CALL static void ggml_backend_blas_free(ggml_backend_t backend) {
+    ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
+    delete ctx;
+    delete backend;
+}
+
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_blas_get_default_buffer_type(ggml_backend_t backend) {
+    return ggml_backend_cpu_buffer_type();
+
+    GGML_UNUSED(backend);
+}
+
+GGML_CALL static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        struct ggml_tensor * node = cgraph->nodes[i];
+
+        switch (node->op) {
+            case GGML_OP_MUL_MAT:
+                ggml_backend_blas_mul_mat(ctx, node);
+                break;
+
+            case GGML_OP_OUT_PROD:
+                ggml_backend_blas_out_prod(ctx, node);
+                break;
+
+            case GGML_OP_NONE:
+            case GGML_OP_RESHAPE:
+            case GGML_OP_VIEW:
+            case GGML_OP_PERMUTE:
+            case GGML_OP_TRANSPOSE:
+                break;
+
+            default:
+                GGML_ABORT("%s: unsupported op %s\n", __func__, ggml_op_desc(node));
+        }
+    }
+
+    return GGML_STATUS_SUCCESS;
+
+    GGML_UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_blas_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    const struct ggml_tensor * src0 = op->src[0];
+    const struct ggml_tensor * src1 = op->src[1];
+
+    return (op->op == GGML_OP_MUL_MAT  && ggml_backend_blas_use_blas(op)) ||
+           (op->op == GGML_OP_OUT_PROD && op->src[0]->type == GGML_TYPE_F32 &&
+                                          op->src[1]->type == GGML_TYPE_F32 &&
+                                          ggml_is_matrix(src0) &&
+                                          ggml_is_matrix(src1) &&
+                                          ggml_is_contiguous(src0) &&
+                                          (ggml_is_contiguous(src1) || ggml_is_transposed(src1)));
+
+    GGML_UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_blas_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    return ggml_backend_buft_is_host(buft);
+
+    GGML_UNUSED(backend);
+}
+
+static struct ggml_backend_i blas_backend_i = {
+    /* .get_name                = */ ggml_backend_blas_name,
+    /* .free                    = */ ggml_backend_blas_free,
+    /* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type,
+    /* .set_tensor_async        = */ NULL,
+    /* .get_tensor_async        = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,
+    /* .synchronize             = */ NULL,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_blas_graph_compute,
+    /* .supports_op             = */ ggml_backend_blas_supports_op,
+    /* .supports_buft           = */ ggml_backend_blas_supports_buft,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_blas_guid(void) {
+    static ggml_guid guid = { 0x12, 0xa8, 0xae, 0xf4, 0xc0, 0x1e, 0x61, 0x97, 0x8f, 0xeb, 0x33, 0x04, 0xa1, 0x33, 0x51, 0x2d };
+    return &guid;
+}
+
+ggml_backend_t ggml_backend_blas_init(void) {
+    ggml_backend_blas_context * ctx = new ggml_backend_blas_context;
+
+    ggml_backend_t backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_blas_guid(),
+        /* .interface = */ blas_backend_i,
+        /* .context   = */ ctx,
+    };
+
+#if !defined(NDEBUG) && defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
+    if (openblas_get_parallel() != OPENBLAS_OPENMP) {
+        fprintf(stderr, "%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
+    }
+#endif
+
+#if !defined(NDEBUG) && defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP)
+    fprintf(stderr, "%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__);
+#endif
+
+    return backend;
+}
+
+GGML_CALL bool ggml_backend_is_blas(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_blas_guid());
+}
+
+void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads) {
+    GGML_ASSERT(ggml_backend_is_blas(backend_blas));
+
+    ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context;
+    ctx->n_threads = n_threads;
+}
diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann.cpp
new file mode 100644
index 0000000000000..06930ba2e5bee
--- /dev/null
+++ b/ggml/src/ggml-cann.cpp
@@ -0,0 +1,2020 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include "ggml-cann.h"
+
+#include <acl/acl.h>
+#include <stdarg.h>
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <mutex>
+
+#include "ggml-backend-impl.h"
+#include "ggml-cann/aclnn_ops.h"
+#include "ggml-cann/common.h"
+
+#define GGML_COMMON_DECL_C
+
+#include "ggml-common.h"
+
+/**
+ * @brief Default logging callback for GGML.
+ *
+ * This function is the default logging callback that logs messages to stderr.
+ *
+ * @param level The log level.
+ * @param msg The log message.
+ * @param user_data User data passed to the callback.
+ */
+static void ggml_cann_default_log_callback(enum ggml_log_level level,
+                                           const char* msg, void* user_data) {
+    GGML_UNUSED(level);
+    GGML_UNUSED(user_data);
+    fprintf(stderr, "%s", msg);
+}
+
+ggml_log_callback ggml_cann_log_callback = ggml_cann_default_log_callback;
+void* ggml_cann_log_user_data = NULL;
+
+GGML_API void ggml_backend_cann_log_set_callback(ggml_log_callback log_callback,
+                                                 void* user_data) {
+    ggml_cann_log_callback = log_callback;
+    ggml_cann_log_user_data = user_data;
+}
+
+#define GGML_CANN_LOG_INFO(...) ggml_cann_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
+#define GGML_CANN_LOG_WARN(...) ggml_cann_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
+#define GGML_CANN_LOG_ERROR(...) \
+    ggml_cann_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+
+GGML_ATTRIBUTE_FORMAT(2, 3)
+
+/**
+ * @brief Log a message using the current logging callback.
+ *
+ * This function formats a log message and passes it to the current logging
+ * callback.
+ *
+ * @param level The log level.
+ * @param format The format string for the log message.
+ * @param ... The arguments for the format string.
+ */
+static void ggml_cann_log(enum ggml_log_level level, const char* format, ...) {
+    if (ggml_cann_log_callback != NULL) {
+        va_list args;
+        va_start(args, format);
+        char buffer[128];
+        int len = vsnprintf(buffer, 128, format, args);
+        if (len < 128) {
+            ggml_cann_log_callback(level, buffer, ggml_cann_log_user_data);
+        } else {
+             // vsnprintf adds a null terminator
+            std::vector<char> buffer2(len + 1);
+            va_end(args);
+            va_start(args, format);
+            vsnprintf(&buffer2[0], buffer2.size(), format, args);
+            ggml_cann_log_callback(level, buffer2.data(),
+                                   ggml_cann_log_user_data);
+        }
+        va_end(args);
+    }
+}
+
+/**
+ * @brief Handles CANN errors by printing an error message and aborting.
+ *
+ * @param stmt The statement that caused the error.
+ * @param func The function in which the error occurred.
+ * @param file The file in which the error occurred.
+ * @param line The line number where the error occurred.
+ * @param msg The error message.
+ */
+[[noreturn]] void ggml_cann_error(const char* stmt, const char* func,
+                                  const char* file, int line, const char* msg) {
+    int32_t id = -1;
+    aclrtGetDevice(&id);
+
+    GGML_CANN_LOG_ERROR("CANN error: %s\n", msg);
+    GGML_CANN_LOG_ERROR("  current device: %d, in function %s at %s:%d\n", id, func,
+            file, line);
+    GGML_CANN_LOG_ERROR("  %s\n", stmt);
+    // abort with GGML_ASSERT to get a stack trace
+    GGML_ABORT("CANN error");
+}
+
+/**
+ * @brief Sets the device to be used by CANN.
+ *
+ * @param device The device ID to set.
+ */
+void ggml_cann_set_device(const int32_t device) {
+    // TODO: uncomment these lines after empty context has fixed.
+    // int current_device;
+    // ACL_CHECK(aclrtGetDevice(&current_device));
+
+    // if (device == current_device) {
+    //   return;
+    // }
+    ACL_CHECK(aclrtSetDevice(device));
+}
+
+/**
+ * @brief Retrieves the current device ID.
+ *
+ * @return The current device ID.
+ */
+int32_t ggml_cann_get_device() {
+    int32_t id;
+    ACL_CHECK(aclrtGetDevice(&id));
+    return id;
+}
+
+/**
+ * @brief Initialize the CANN device information.
+ *
+ * This function initializes the CANN device information by obtaining the
+ * device count and setting the memory allocation granularity for each device.
+ *
+ * @return A structure containing the device information.
+ */
+static ggml_cann_device_info ggml_cann_init() {
+    ggml_cann_device_info info = {};
+
+    aclError err = aclrtGetDeviceCount((uint32_t*)&info.device_count);
+
+    if (err != ACL_SUCCESS) {
+        GGML_CANN_LOG_ERROR("%s: failed to initialize CANN: %s\n",
+                __func__, aclGetRecentErrMsg());
+        return info;
+    }
+
+    GGML_ASSERT(info.device_count <= GGML_CANN_MAX_DEVICES);
+
+    for (int id = 0; id < info.device_count; ++id) {
+        aclrtPhysicalMemProp prop = {};
+        prop.handleType = ACL_MEM_HANDLE_TYPE_NONE;
+        prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED;
+        prop.memAttr = ACL_HBM_MEM_HUGE;
+        prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE;
+        prop.location.id = id;
+        prop.reserve = 0;
+        ACL_CHECK(aclrtMemGetAllocationGranularity(
+            &prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED,
+            &info.devices[id].vmm_granularity));
+    }
+
+    // TODO: add more device info later.
+    return info;
+}
+
+/**
+ * @brief Retrieve the CANN device information.
+ *
+ * This function returns a reference to a structure containing the CANN device
+ * information. The device information is initialized once and reused on
+ * subsequent calls.
+ *
+ * @return A reference to the structure containing the device information.
+ */
+const ggml_cann_device_info& ggml_cann_info() {
+    static ggml_cann_device_info info = ggml_cann_init();
+    return info;
+}
+
+//#define DEBUG_CANN_MALLOC
+/**
+ * @brief A pool of CANN buffers(legacy).
+ *
+ * This class manages a pool of CANN buffers for a specific device.
+ */
+struct ggml_cann_pool_leg : public ggml_cann_pool {
+    /**
+     * @brief The maximum number of buffers in the pool.
+     */
+    static const int MAX_BUFFERS = 256;
+
+    /**
+     * @brief The device ID associated with this buffer pool.
+     */
+    int device;
+
+    /**
+     * @brief Structure representing a CANN buffer.
+     */
+    struct ggml_cann_buffer {
+        void* ptr = nullptr;  ///< Pointer to the buffer memory.
+        size_t size = 0;      ///< Size of the buffer.
+    };
+
+    /**
+     * @brief Array of CANN buffers in the pool.
+     */
+    ggml_cann_buffer buffer_pool[MAX_BUFFERS] = {};
+
+    /**
+     * @brief Total size of all buffers in the pool.
+     */
+    size_t pool_size = 0;
+
+    /**
+     * @brief Constructor to initialize the buffer pool for a specific device.
+     *
+     * @param device The device ID to associate with this buffer pool.
+     */
+    explicit ggml_cann_pool_leg(int device) : device(device) {}
+
+    /**
+     * @brief Destructor to free all buffers in the pool.
+     */
+    ~ggml_cann_pool_leg() {
+        ggml_cann_set_device(device);
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cann_buffer& b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+                ACL_CHECK(aclrtFree(b.ptr));
+                pool_size -= b.size;
+            }
+        }
+        GGML_ASSERT(pool_size == 0);
+    }
+
+    /**
+     * @brief Allocate a buffer of the given size.
+     *
+     * @param size The size of the buffer to allocate.
+     * @param actual_size A pointer to a variable to receive the actual size of
+     * the allocated buffer.
+     * @return A pointer to the allocated buffer.
+     */
+    void* alloc(size_t size, size_t* actual_size) override {
+#ifdef DEBUG_CANN_MALLOC
+        int nnz = 0;
+        size_t max_size = 0;
+#endif
+        size_t best_diff = 1ull << 36;
+        int ibest = -1;
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cann_buffer& b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+#ifdef DEBUG_CANN_MALLOC
+                ++nnz;
+                if (b.size > max_size) max_size = b.size;
+#endif
+                if (b.size >= size) {
+                    size_t diff = b.size - size;
+                    if (diff < best_diff) {
+                        best_diff = diff;
+                        ibest = i;
+                        if (!best_diff) {
+                            void* ptr = b.ptr;
+                            *actual_size = b.size;
+                            b.ptr = nullptr;
+                            b.size = 0;
+                            return ptr;
+                        }
+                    }
+                }
+            }
+        }
+        if (ibest >= 0) {
+            ggml_cann_buffer& b = buffer_pool[ibest];
+            void* ptr = b.ptr;
+            *actual_size = b.size;
+            b.ptr = nullptr;
+            b.size = 0;
+            return ptr;
+        }
+        void* ptr;
+        size_t look_ahead_size = (size_t)(1.05 * size);
+        look_ahead_size = 256 * ((look_ahead_size + 255) / 256);
+        ggml_cann_set_device(device);
+        ACL_CHECK(
+            aclrtMalloc(&ptr, look_ahead_size, ACL_MEM_MALLOC_HUGE_FIRST));
+        *actual_size = look_ahead_size;
+        pool_size += look_ahead_size;
+#ifdef DEBUG_CANN_MALLOC
+        GGML_CANN_LOG_INFO(
+            "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, "
+            "requested %u MB\n",
+            __func__, device, nnz, (uint32_t)(max_size / 1024 / 1024),
+            (uint32_t)(pool_size / 1024 / 1024),
+            (uint32_t)(size / 1024 / 1024));
+#endif
+        return ptr;
+    }
+
+    /**
+     * @brief Free a buffer and return it to the pool.
+     *
+     * @param ptr Pointer to the buffer to free.
+     * @param size Size of the buffer to free.
+     */
+    void free(void* ptr, size_t size) override {
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cann_buffer& b = buffer_pool[i];
+            if (b.ptr == nullptr) {
+                b.ptr = ptr;
+                b.size = size;
+                return;
+            }
+        }
+        // memory should always buffered. these memory may still needed by
+        // tasks in stream.
+        // TODO, fix me.
+        GGML_ABORT("Cann buffer pool full, increase MAX_CANN_BUFFERS\n");
+    }
+};
+
+/**
+ * @brief A pool of CANN buffers with virtual memory.
+ *
+ * This class manages a pool of CANN buffers with virtual memory for a specific
+ * device.
+ */
+struct ggml_cann_pool_vmm : public ggml_cann_pool {
+    /**
+     * @brief The maximum size of the virtual memory pool (32 GB).
+     */
+    static const size_t CANN_POOL_VMM_MAX_SIZE = 1ull << 35;  // 32 GB
+
+    /**
+     * @brief The device ID associated with this buffer pool.
+     */
+    int device;
+
+    /**
+     * @brief Pointer to the start of the virtual memory pool.
+     */
+    void* pool_addr = 0;
+
+    /**
+     * @brief Amount of virtual memory used in the pool.
+     */
+    size_t pool_used = 0;
+
+    /**
+     * @brief Total size of the virtual memory pool.
+     */
+    size_t pool_size = 0;
+
+    /**
+     * @brief Allocation granularity for the virtual memory pool.
+     */
+    size_t granularity;
+
+    /**
+     * @brief Handles for the physical memory allocated.
+     */
+    std::vector<aclrtDrvMemHandle> handles;
+
+    /**
+     * @brief Offsets for the mapped memory regions.
+     */
+    std::vector<void*> map_offsets;
+
+    /**
+     * @brief Constructor to initialize the buffer pool with virtual memory for
+     * a specific device.
+     *
+     * @param device The device ID to associate with this buffer pool.
+     */
+    explicit ggml_cann_pool_vmm(int device)
+        : device(device),
+          granularity(ggml_cann_info().devices[device].vmm_granularity) {}
+
+    /**
+     * @brief Destructor to free all buffers in the virtual memory pool.
+     */
+    ~ggml_cann_pool_vmm() {
+        if (pool_addr != 0) {
+            for (auto& offset : map_offsets) {
+                ACL_CHECK(aclrtUnmapMem(offset));
+            }
+            for (auto& handle : handles) {
+                ACL_CHECK(aclrtFreePhysical(handle));
+            }
+            ACL_CHECK(aclrtReleaseMemAddress(pool_addr));
+        }
+    }
+
+    /**
+     * @brief Allocate a buffer of the given size in the virtual memory pool.
+     *
+     * @param size The size of the buffer to allocate.
+     * @param actual_size A pointer to a variable to receive the actual size of
+     * the allocated buffer.
+     * @return A pointer to the allocated buffer.
+     */
+    void* alloc(size_t size, size_t* actual_size) override {
+        // round up the allocation size to the alignment to ensure that all
+        // allocations are aligned for all data types
+        const size_t alignment = 128;
+        size = alignment * ((size + alignment - 1) / alignment);
+
+        size_t avail = pool_size - pool_used;
+
+        if (size > avail) {
+            // round up to the next multiple of the granularity
+            size_t reserve_size = size - avail;
+            reserve_size =
+                granularity * ((reserve_size + granularity - 1) / granularity);
+
+            GGML_ASSERT(pool_size + reserve_size <= CANN_POOL_VMM_MAX_SIZE);
+
+            // allocate more physical memory
+            aclrtPhysicalMemProp prop = {};
+            prop.handleType = ACL_MEM_HANDLE_TYPE_NONE;
+            prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED;
+            prop.memAttr = ACL_HBM_MEM_HUGE;
+            prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE;
+            prop.location.id = device;
+            prop.reserve = 0;
+            aclrtDrvMemHandle handle;
+            ACL_CHECK(aclrtMallocPhysical(&handle, reserve_size, &prop, 0));
+
+            // reserve virtual address space (if not already reserved)
+            if (pool_addr == 0) {
+                ACL_CHECK(aclrtReserveMemAddress(
+                    &pool_addr, CANN_POOL_VMM_MAX_SIZE, 0, NULL, 1));
+            }
+
+            // map at the end of the pool
+            ACL_CHECK(aclrtMapMem((char*)pool_addr + pool_size, reserve_size, 0,
+                                  handle, 0));
+
+            handles.push_back(handle);
+            map_offsets.push_back((char*)pool_addr + pool_size);
+
+            // add to the pool
+            pool_size += reserve_size;
+
+            // GGML_CANN_LOG_INFO("cann pool[%d]: size increased to %llu MB (
+            // reserved %llu MB)\n",
+            //       device, (unsigned long long) (pool_size/1024/1024),
+            //       (unsigned long long) (reserve_size/1024/1024));
+        }
+
+        GGML_ASSERT(pool_addr != 0);
+
+        void* ptr = (void*)((char*)pool_addr + pool_used);
+        *actual_size = size;
+        pool_used += size;
+
+#ifdef DEBUG_CANN_MALLOC
+        GGML_CANN_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device,
+               (unsigned long long)size, (unsigned long long)ptr);
+#endif
+        return ptr;
+    }
+
+    /**
+     * @brief Free a buffer and return it to the virtual memory pool.
+     *
+     * @param ptr Pointer to the buffer to free.
+     * @param size Size of the buffer to free.
+     */
+    void free(void* ptr, size_t size) override {
+#ifdef DEBUG_CANN_MALLOC
+        GGML_CANN_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device,
+               (unsigned long long)size, (unsigned long long)ptr);
+#endif
+
+        pool_used -= size;
+
+        // all deallocations must be in reverse order of the allocations
+        GGML_ASSERT(ptr == (void*)((char*)pool_addr + pool_used));
+    }
+};
+
+/**
+ * @brief Create a new CANN pool for a specific device.
+ *
+ * Factory method to create a new CANN pool object based on the device type.
+ *
+ * @param device The device ID for which to create the pool.
+ * @return A unique pointer to the created CANN pool.
+ */
+std::unique_ptr<ggml_cann_pool> ggml_backend_cann_context::new_pool_for_device(
+    int device) {
+    // return std::unique_ptr<ggml_cann_pool>(new ggml_cann_pool_leg(device));
+    return std::unique_ptr<ggml_cann_pool>(new ggml_cann_pool_vmm(device));
+}
+
+// cann buffer
+/**
+ * @brief Context for managing a CANN buffer associated with a specific device.
+ *
+ * This structure holds information about a CANN buffer, including the device
+ * ID, device pointer, and a name derived from GGML_CANN_NAME and the device ID.
+ */
+struct ggml_backend_cann_buffer_context {
+    int32_t device;  ///< The device ID associated with this buffer context.
+    void* dev_ptr =
+        nullptr;  ///< Pointer to the device memory allocated for the buffer.
+
+    /**
+     * @brief Constructor to initialize the CANN buffer context.
+     *
+     * @param device The device ID associated with this buffer context.
+     * @param dev_ptr Pointer to the device memory allocated for the buffer.
+     */
+    ggml_backend_cann_buffer_context(int32_t device, void* dev_ptr)
+        : device(device),
+          dev_ptr(dev_ptr) {}
+
+    /**
+     * @brief Destructor to free the device memory allocated for the buffer.
+     */
+    ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); }
+};
+
+/**
+ * @brief Retrieve the name associated with a CANN buffer.
+ *
+ * This function returns the name of a CANN buffer, which is stored in the
+ * context of the buffer.
+ *
+ * @param buffer The CANN buffer whose name is to be retrieved.
+ * @return A pointer to a C-string containing the name of the buffer.
+ */
+
+GGML_CALL static const char* ggml_backend_cann_buffer_get_name(
+    ggml_backend_buffer_t buffer) {
+    return "CANN";
+
+    GGML_UNUSED(buffer);
+}
+
+/**
+ * @brief Check if a buffer is a CANN buffer.
+ *
+ * This function checks if a given buffer is a CANN buffer by comparing its
+ * `get_name` function pointer to `ggml_backend_cann_buffer_get_name`.
+ *
+ * @param buffer The buffer to check.
+ * @return true if the buffer is a CANN buffer, false otherwise.
+ */
+GGML_CALL static bool ggml_backend_buffer_is_cann(
+    ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_cann_buffer_get_name;
+}
+
+/**
+ * @brief Free resources associated with a CANN buffer.
+ *
+ * This function frees the resources associated with a CANN buffer, including
+ * its context.
+ *
+ * @param buffer The CANN buffer to free.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_free_buffer(
+    ggml_backend_buffer_t buffer) {
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+    delete ctx;
+}
+
+/**
+ * @brief Retrieve the base pointer of a CANN buffer.
+ *
+ * This function returns the base pointer of a CANN buffer, which points to the
+ * device memory allocated for the buffer.
+ *
+ * @param buffer The CANN buffer whose base pointer is to be retrieved.
+ * @return A pointer to the base of the device memory allocated for the buffer.
+ */
+GGML_CALL static void* ggml_backend_cann_buffer_get_base(
+    ggml_backend_buffer_t buffer) {
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+    return ctx->dev_ptr;
+}
+
+/**
+ * @brief Transform quantized Q4.0 tensor data into a format suitable for CANN
+ * processing.
+ *
+ * This function transforms quantized Q4.0 tensor data into a format suitable
+ * for CANN processing. It extracts quantization values and scales from the
+ * source data and prepares them in a format expected by CANN operations.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source data in Q4.0 format.
+ * @param dst Pointer to the destination buffer where transformed data will be
+ * stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor,
+                                                       const void* src,
+                                                       void* dst) {
+
+    int64_t n_elems = ggml_nelements(tensor);
+    int64_t groups = n_elems / QK4_0;
+    size_t quant_bytes = n_elems * sizeof(uint8_t) / 2;
+
+    uint8_t* quant_offset = (uint8_t*)dst;
+    uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes);
+
+    for (int i = 0; i < groups; i++) {
+        const block_q4_0* group =
+            (const block_q4_0*)((const char*)src + i * sizeof(block_q4_0));
+        *scale_offset = group->d;
+        scale_offset++;
+
+        // 0-15
+        for (int j = 0; j < QK4_0 / 2; j += 2) {
+            (*quant_offset) = (group->qs[j] & 0x0F);
+            (*quant_offset) |= ((group->qs[j + 1] << 4));
+            quant_offset++;
+        }
+
+        // 16-31
+        for (int j = 0; j < QK4_0 / 2; j += 2) {
+            (*quant_offset) = (group->qs[j] >> 4);
+            (*quant_offset) |= (group->qs[j + 1] & 0xF0);
+            quant_offset++;
+        }
+    }
+
+    // put (uint4b_t -8) into int4b_t
+    for (quant_offset = (uint8_t*)dst;
+         quant_offset < (uint8_t*)dst + quant_bytes; quant_offset++) {
+        (*quant_offset) ^= 0x88;
+    }
+}
+
+/**
+ * @brief Transform CANN processed data back into quantized Q4.0 format.
+ *
+ * This function transforms CANN processed data back into quantized Q4.0 format.
+ * It reverses the transformation performed by
+ * ggml_backend_cann_transform_q4_0(), converting the data back into its
+ * original quantized form.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source buffer containing transformed data.
+ * @param dst Pointer to the destination buffer where the Q4.0 formatted data
+ * will be stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_back_q4_0(
+    const ggml_tensor* tensor, void* src, void* dst) {
+
+    int64_t n_elems = ggml_nelements(tensor);
+    int64_t groups = n_elems / QK4_0;
+    size_t quant_bytes = n_elems * sizeof(uint8_t) / 2;
+
+    uint8_t* quant_offset = (uint8_t*)src;
+    uint16_t* scale_offset = (uint16_t*)((char*)src + quant_bytes);
+
+    for (; quant_offset < (uint8_t*)src + quant_bytes; quant_offset++) {
+        (*quant_offset) ^= 0x88;
+    }
+    quant_offset = (uint8_t*)src;
+
+    for (int i = 0; i < groups; i++) {
+        block_q4_0* group = (block_q4_0*)((char*)dst + i * sizeof(block_q4_0));
+        group->d = *scale_offset;
+        scale_offset++;
+
+        // 0-15
+        for (int j = 0; j < QK4_0 / 2; j += 2) {
+            group->qs[j] = ((*quant_offset) & 0x0F);
+            group->qs[j + 1] = ((*quant_offset) >> 4);
+            quant_offset++;
+        }
+
+        // 16-31
+        for (int j = 0; j < QK4_0 / 2; j += 2) {
+            group->qs[j] |= ((*quant_offset) << 4);
+            group->qs[j + 1] |= ((*quant_offset) & 0xF0);
+            quant_offset++;
+        }
+    }
+}
+
+/**
+ * @brief Transform quantized Q8.0 tensor data into a format suitable for CANN
+ * processing.
+ *
+ * This function transforms quantized Q8.0 tensor data into a format suitable
+ * for CANN processing. It extracts quantization values and scales from the
+ * source data and prepares them in a format expected by CANN operations.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source data in Q8.0 format.
+ * @param dst Pointer to the destination buffer where transformed data will be
+ * stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_q8_0(ggml_tensor* tensor,
+                                                       const void* src,
+                                                       void* dst) {
+    int64_t n_elems = ggml_nelements(tensor);
+    int64_t groups = n_elems / QK8_0;
+    size_t quant_bytes = n_elems * sizeof(uint8_t);
+
+    uint8_t* quant_offset = (uint8_t*)dst;
+    uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes);
+
+    for (int i = 0; i < groups; i++) {
+        const block_q8_0* group =
+            (const block_q8_0*)((const char*)src + i * sizeof(block_q8_0));
+        *scale_offset = group->d;
+        scale_offset++;
+        size_t group_quant_size = QK8_0 * sizeof(uint8_t);
+        memcpy(quant_offset, group->qs, group_quant_size);
+        quant_offset += group_quant_size;
+    }
+}
+
+/**
+ * @brief Transform CANN processed data back into quantized Q8.0 format.
+ *
+ * This function transforms CANN processed data back into quantized Q8.0 format.
+ * It reverses the transformation performed by
+ * ggml_backend_cann_transform_q8_0(), converting the data back into its
+ * original quantized form.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source buffer containing transformed data.
+ * @param dst Pointer to the destination buffer where the Q8.0 formatted data
+ * will be stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_back_q8_0(
+    const ggml_tensor* tensor, const void* src, void* dst) {
+    int64_t n_elems = ggml_nelements(tensor);
+    int64_t groups = n_elems / QK8_0;
+    size_t quant_bytes = n_elems * sizeof(uint8_t);
+
+    const uint8_t* quant_offset = (const uint8_t*)src;
+    const uint16_t* scale_offset =
+        (const uint16_t*)((const char*)src + quant_bytes);
+
+    for (int i = 0; i < groups; i++) {
+        block_q8_0* group = (block_q8_0*)((char*)dst + i * sizeof(block_q8_0));
+        group->d = *scale_offset;
+        scale_offset++;
+        size_t group_quant_size = QK8_0 * sizeof(uint8_t);
+        memcpy(group->qs, quant_offset, group_quant_size);
+        quant_offset += group_quant_size;
+    }
+}
+
+/**
+ * @brief Transform tensor data based on its type for CANN processing.
+ *
+ * This function transforms tensor data based on its quantization type for CANN
+ * processing. It dispatches the transformation based on the tensor's type to
+ * specialized functions handling Q4.0 and Q8.0 formats.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source data to be transformed.
+ * @param dst Pointer to the destination buffer where transformed data will be
+ * stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform(ggml_tensor* tensor,
+                                                  const void* src, void* dst) {
+    switch (tensor->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_backend_cann_transform_q4_0(tensor, src, dst);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_backend_cann_transform_q8_0(tensor, src, dst);
+            break;
+        default:
+            break;
+    }
+}
+
+/**
+ * @brief Transform CANN processed data back into tensor data based on its type.
+ *
+ * This function transforms CANN processed data back into tensor data based on
+ * its quantization type for Q4.0 and Q8.0 formats. It dispatches the
+ * transformation based on the tensor's type to specialized functions.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source data containing CANN processed data.
+ * @param dst Pointer to the destination buffer where transformed tensor data
+ * will be stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_back(
+    const ggml_tensor* tensor, void* src, void* dst) {
+    switch (tensor->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_backend_cann_transform_back_q4_0(tensor, src, dst);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_backend_cann_transform_back_q8_0(tensor, src, dst);
+            break;
+        default:
+            break;
+    }
+}
+
+/**
+ * @brief Check if transformation is needed for a given tensor type.
+ *
+ * This function checks if transformation is needed for a given tensor type
+ * to prepare data for CANN processing.
+ *
+ * @param type The tensor type to check.
+ * @return true if transformation is needed, false otherwise.
+ */
+GGML_CALL static bool need_transform(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+            return true;
+        default:
+            return false;
+    }
+}
+
+/**
+ * @brief Initialize a tensor using data from a CANN buffer.
+ *
+ * This function initializes a tensor using data from a CANN buffer.
+ * It handles special cases such as views and quantization.
+ *
+ * @param buffer The CANN buffer from which to initialize the tensor.
+ * @param tensor Pointer to the tensor to be initialized.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_init_tensor(
+    ggml_backend_buffer_t buffer, ggml_tensor* tensor) {
+    if (tensor->view_src != NULL && tensor->view_offs == 0) {
+        GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
+        return;
+    }
+
+    // TODO: can backend doesn't support quantized yet. Just leave the code
+    // here.
+    if (ggml_is_quantized(tensor->type)) {
+        // Initialize padding to 0 to avoid possible NaN values
+        size_t original_size = ggml_nbytes(tensor);
+        size_t padded_size =
+            ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
+
+        if (padded_size > original_size && tensor->view_src == nullptr) {
+            size_t memset_size = padded_size - original_size;
+            ACL_CHECK(aclrtMemset((char*)tensor->data + original_size,
+                                  memset_size, 0, memset_size));
+        }
+    }
+}
+
+// TODO: need handle tensor which has paddings.
+/**
+ * @brief Set tensor data in a CANN buffer.
+ *
+ * This function sets tensor data in a CANN buffer, handling transformations
+ * if needed based on the tensor's type.
+ *
+ * @param buffer The CANN buffer where the tensor data will be set.
+ * @param tensor Pointer to the tensor whose data will be set.
+ * @param data Pointer to the source data to be copied into the tensor.
+ * @param offset Offset in the source data from where to start copying.
+ * @param size Size of the data to be copied, in bytes.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_set_tensor(
+    ggml_backend_buffer_t buffer, ggml_tensor *tensor, const void *data,
+    size_t offset, size_t size) {
+    ggml_backend_cann_buffer_context *ctx =
+        (ggml_backend_cann_buffer_context *)buffer->context;
+
+    ggml_cann_set_device(ctx->device);
+    // TODO: refer to cann(#6017), it use thread's default stream.
+    // For acl, synchronous functions use this default stream.
+    // Why aclrtSynchronizeDevice?
+
+    if (!need_transform(tensor->type)) {
+        ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, data, size,
+                              ACL_MEMCPY_HOST_TO_DEVICE));
+    } else {
+        void *transform_buffer = malloc(size);
+        ggml_backend_cann_transform(tensor, data, transform_buffer);
+
+#ifndef NDEBUG
+        void *check_buffer = malloc(size);
+        ggml_backend_cann_transform_back(tensor, transform_buffer,
+                                         check_buffer);
+        GGML_ASSERT(memcmp(data, check_buffer, size) == 0);
+        free(check_buffer);
+#endif
+        ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size,
+                              transform_buffer, size,
+                              ACL_MEMCPY_HOST_TO_DEVICE));
+        free(transform_buffer);
+    }
+}
+
+/**
+ * @brief Get tensor data from a CANN buffer.
+ *
+ * This function retrieves tensor data from a CANN buffer, handling
+ * transformations if needed based on the tensor's type.
+ *
+ * @param buffer The CANN buffer from which to retrieve tensor data.
+ * @param tensor Pointer to the tensor whose data will be retrieved.
+ * @param data Pointer to the destination buffer where the tensor data will be
+ * copied.
+ * @param offset Offset in the destination buffer where to start copying.
+ * @param size Size of the data to be copied, in bytes.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_get_tensor(
+    ggml_backend_buffer_t buffer, const ggml_tensor* tensor, void* data,
+    size_t offset, size_t size) {
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+
+    ggml_cann_set_device(ctx->device);
+
+    if (!need_transform(tensor->type)) {
+        ACL_CHECK(aclrtMemcpy(data, size, (char*)tensor->data + offset, size,
+                              ACL_MEMCPY_DEVICE_TO_HOST));
+    } else {
+        void* transform_buffer = malloc(size);
+        ACL_CHECK(aclrtMemcpy(transform_buffer, size,
+                              (char*)tensor->data + offset, size,
+                              ACL_MEMCPY_DEVICE_TO_HOST));
+        ggml_backend_cann_transform_back(tensor, transform_buffer, data);
+        free(transform_buffer);
+    }
+}
+
+/**
+ * @brief Copy tensor data between CANN buffers if possible.
+ *
+ * This function copies tensor data between CANN buffers if the source and
+ * destination buffers are CANN buffers and they meet the necessary conditions
+ * (same device or devices can access each other).
+ *
+ * @param buffer The destination CANN buffer where the tensor data will be
+ * copied.
+ * @param src Pointer to the source tensor whose data will be copied.
+ * @param dst Pointer to the destination tensor where the data will be copied.
+ * @return true if the copy operation succeeded, false otherwise.
+ */
+GGML_CALL static bool ggml_backend_cann_buffer_cpy_tensor(
+    ggml_backend_buffer_t buffer, const ggml_tensor* src, ggml_tensor* dst) {
+    if (ggml_backend_buffer_is_cann(src->buffer)) {
+        ggml_backend_cann_buffer_context* src_ctx =
+            (ggml_backend_cann_buffer_context*)src->buffer->context;
+        ggml_backend_cann_buffer_context* dst_ctx =
+            (ggml_backend_cann_buffer_context*)buffer->context;
+
+        size_t memcpy_size = ggml_nbytes(src);
+        // Same device.
+        if (src_ctx->device == dst_ctx->device) {
+            ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size,
+                                  (const char*)src->data, memcpy_size,
+                                  ACL_MEMCPY_DEVICE_TO_DEVICE));
+            return true;
+        } else {
+            // Different device but can access by peer.
+            int32_t canAccessPeer = 0;
+            ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device,
+                                               dst_ctx->device));
+            if (canAccessPeer) {
+                ggml_cann_set_device(src_ctx->device);
+                ACL_CHECK(aclrtDeviceEnablePeerAccess(dst_ctx->device, 0));
+                ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size,
+                                      (const char*)src->data, memcpy_size,
+                                      ACL_MEMCPY_DEVICE_TO_DEVICE));
+                return true;
+            }
+        }
+    }
+    return false;
+}
+
+/**
+ * @brief Clear a CANN buffer by setting all its memory to a specified value.
+ *
+ * This function clears a CANN buffer by setting all its memory to a specified
+ * value.
+ *
+ * @param buffer The CANN buffer to be cleared.
+ * @param value The value to which each byte in the buffer will be set.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_clear(
+    ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+
+    ggml_cann_set_device(ctx->device);
+    ACL_CHECK(aclrtMemset(ctx->dev_ptr, buffer->size, value, buffer->size));
+}
+
+/**
+ * @brief Interface for a CANN buffer in the backend.
+ *
+ * This structure defines function pointers to operations that can be performed
+ * on a CANN buffer within the backend.
+ */
+static ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
+    /* .get_name        = */ ggml_backend_cann_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_cann_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_cann_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_cann_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_cann_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_cann_buffer_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_cann_buffer_cpy_tensor,
+    /* .clear           = */ ggml_backend_cann_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// cann buffer type
+/**
+ * @brief Structure representing context information for a specific backend
+ * buffer type.
+ */
+struct ggml_backend_cann_buffer_type_context {
+    int32_t
+        device; /**< Device identifier associated with the buffer context. */
+    std::string name; /**< Name associated with the buffer context. */
+};
+
+/**
+ * @brief Retrieves the name associated with a CANN buffer type.
+ *
+ * This function returns the descriptive name associated with the specified
+ * CANN buffer type context.
+ *
+ * @param buft Pointer to the buffer type context.
+ * @return Const pointer to the C-style string containing the name.
+ */
+GGML_CALL static const char* ggml_backend_cann_buffer_type_name(
+    ggml_backend_buffer_type_t buft) {
+    return "CANN";
+
+    GGML_UNUSED(buft);
+}
+
+/**
+ * @brief Allocates a new CANN buffer of the specified type and size.
+ *
+ * This function allocates a new CANN buffer on the specified device with the
+ * given size.
+ *
+ * @param buft Pointer to the buffer type context.
+ * @param size Size in bytes of the buffer to allocate.
+ * @return Pointer to the allocated buffer, or nullptr if allocation fails.
+ */
+GGML_CALL static ggml_backend_buffer_t
+ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
+                                           size_t size) {
+    ggml_backend_cann_buffer_type_context* buft_ctx =
+        (ggml_backend_cann_buffer_type_context*)buft->context;
+
+    ggml_cann_set_device(buft_ctx->device);
+
+    size = std::max(size, (size_t)1);
+
+    void* dev_ptr;
+    aclError err = aclrtMalloc(&dev_ptr, size, ACL_MEM_MALLOC_HUGE_FIRST);
+    if (err != ACL_SUCCESS) {
+        GGML_CANN_LOG_ERROR(
+            "%s: allocating %.2f MiB on device %d: aclrtMalloc failed: %s\n",
+            __func__, size / 1024.0 / 1024.0, buft_ctx->device,
+            aclGetRecentErrMsg());
+        return nullptr;
+    }
+
+    ggml_backend_cann_buffer_context* ctx =
+        new ggml_backend_cann_buffer_context(buft_ctx->device, dev_ptr);
+
+    return ggml_backend_buffer_init(buft, ggml_backend_cann_buffer_interface,
+                                    ctx, size);
+}
+
+/**
+ * @brief Retrieves the memory alignment requirement for CANN buffers of this
+ * type.
+ *
+ * This function returns the alignment requirement in bytes for memory allocated
+ * by the CANN buffer type.
+ *
+ * @param buft Pointer to the buffer type context (unused in this
+ * implementation).
+ * @return The alignment requirement in bytes (fixed at 128 bytes for CANN
+ * buffers).
+ */
+GGML_CALL static size_t ggml_backend_cann_buffer_type_get_alignment(
+    ggml_backend_buffer_type_t buft) {
+    return 128;
+
+    GGML_UNUSED(buft);
+}
+
+/**
+ * @brief Calculates the allocation size required for a tensor in a CANN buffer.
+ *
+ * Computes the total allocation size needed for storing the tensor's data in a
+ * CANN buffer, considering any necessary padding or adjustments for quantized
+ * types.
+ *
+ * @param buft Pointer to the buffer type context (unused in this
+ * implementation).
+ * @param tensor Pointer to the tensor for which the allocation size is
+ * calculated.
+ * @return The total allocation size in bytes required for the tensor in the
+ * CANN buffer.
+ */
+GGML_CALL static size_t ggml_backend_cann_buffer_type_get_alloc_size(
+    ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) {
+    size_t size = ggml_nbytes(tensor);
+    int64_t ne0 = tensor->ne[0];
+
+    // last line must bigger than 32, because every single op deal at
+    // least 32 bytes.
+    // TODO: quantized type?
+    // int64_t line_size = ne0 * ggml_element_size(tensor);
+    // int64_t line_size_align_32 = (line_size + 31) & ~31;
+    // size += (line_size_align_32 - line_size);
+
+    // TODO: not support quantized yet.
+    // TODO: consider un-continue tensor.
+    if (ggml_is_quantized(tensor->type)) {
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(
+                tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+    }
+
+    return size;
+
+    GGML_UNUSED(buft);
+}
+
+/**
+ * @brief Interface for managing CANN buffer types in the GGML backend.
+ *
+ * Provides function pointers for allocating, querying properties, and managing
+ * memory for CANN buffer types in the GGML backend.
+ */
+static ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_cann_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_cann_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_cann_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL,  // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_cann_buffer_type_get_alloc_size,
+    /* .is_host          = */ NULL,
+};
+
+/**
+ * @brief Retrieves the CANN buffer type for a specified device.
+ *
+ * This function initializes and returns the buffer type interface associated
+ * with the given device. It ensures thread-safe access using a mutex.
+ *
+ * @param device The device index for which to retrieve the buffer type.
+ * @return A pointer to the buffer type interface for the specified device, or
+ * nullptr if the device index is out of range.
+ */
+GGML_CALL ggml_backend_buffer_type_t
+ggml_backend_cann_buffer_type(int32_t device) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+
+    if (device >= ggml_backend_cann_get_device_count()) {
+        return nullptr;
+    }
+
+    static ggml_backend_buffer_type
+        ggml_backend_cann_buffer_types[GGML_CANN_MAX_DEVICES];
+
+    static bool ggml_backend_cann_buffer_type_initialized = false;
+
+    if (!ggml_backend_cann_buffer_type_initialized) {
+        for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) {
+            ggml_backend_cann_buffer_types[i] = {
+                /* .iface    = */ ggml_backend_cann_buffer_type_interface,
+                /* .context  = */
+                 new ggml_backend_cann_buffer_type_context{
+                    i, "CANN" + std::to_string(i)},
+            };
+        }
+        ggml_backend_cann_buffer_type_initialized = true;
+    }
+
+    return &ggml_backend_cann_buffer_types[device];
+}
+
+/**
+ * @brief Computes the forward operation for a given tensor using CANN
+ * operations.
+ *
+ * This function selects the appropriate CANN operation based on the type of
+ * operation specified in the tensor and performs the computation.
+ *
+ * @param ctx The CANN context containing necessary resources and
+ * configurations.
+ * @param dst The destination tensor where the result of the computation will be
+ * stored.
+ * @return true if the computation was successful; false otherwise.
+ */
+static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
+                                      struct ggml_tensor* dst) {
+    switch (dst->op) {
+        case GGML_OP_REPEAT:
+            ggml_cann_repeat(ctx, dst);
+            break;
+        case GGML_OP_GET_ROWS:
+            ggml_cann_get_rows(ctx, dst);
+            break;
+        case GGML_OP_DUP:
+            ggml_cann_dup(ctx, dst);
+            break;
+        case GGML_OP_ADD:
+            ggml_cann_add(ctx, dst);
+            break;
+        case GGML_OP_ACC:
+            ggml_cann_acc(ctx, dst);
+            break;
+        case GGML_OP_MUL:
+            ggml_cann_mul_div<aclnnMulGetWorkspaceSize, aclnnMul>(ctx, dst);
+            break;
+        case GGML_OP_DIV:
+            ggml_cann_mul_div<aclnnDivGetWorkspaceSize, aclnnDiv>(ctx, dst);
+            break;
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(dst)) {
+                case GGML_UNARY_OP_GELU:
+                    ggml_cann_activation<aclnnGeluGetWorkspaceSize, aclnnGelu>(
+                        ctx, dst);
+                    break;
+                case GGML_UNARY_OP_SILU:
+                    ggml_cann_activation<aclnnSiluGetWorkspaceSize, aclnnSilu>(
+                        ctx, dst);
+                    break;
+                // TODO: Use faster gelu??
+                case GGML_UNARY_OP_GELU_QUICK:
+                    ggml_cann_activation<aclnnGeluGetWorkspaceSize, aclnnGelu>(
+                        ctx, dst);
+                    break;
+                case GGML_UNARY_OP_TANH:
+                    ggml_cann_activation<aclnnTanhGetWorkspaceSize, aclnnTanh>(
+                        ctx, dst);
+                    break;
+                case GGML_UNARY_OP_RELU:
+                    ggml_cann_activation<aclnnReluGetWorkspaceSize, aclnnRelu>(
+                        ctx, dst);
+                    break;
+                case GGML_UNARY_OP_HARDSIGMOID:
+                    ggml_cann_activation<aclnnHardsigmoidGetWorkspaceSize,
+                                         aclnnHardsigmoid>(ctx, dst);
+                    break;
+                case GGML_UNARY_OP_HARDSWISH:
+                    ggml_cann_activation<aclnnHardswishGetWorkspaceSize,
+                                         aclnnHardswish>(ctx, dst);
+                    break;
+                default:
+                    return false;
+            }
+            break;
+        case GGML_OP_NORM:
+            ggml_cann_norm(ctx, dst);
+            break;
+        case GGML_OP_GROUP_NORM:
+            ggml_cann_group_norm(ctx, dst);
+            break;
+        case GGML_OP_CONCAT:
+            ggml_cann_concat(ctx, dst);
+            break;
+        case GGML_OP_UPSCALE:
+            ggml_cann_upsample_nearest2d(ctx, dst);
+            break;
+        case GGML_OP_PAD:
+            ggml_cann_pad(ctx, dst);
+            break;
+        case GGML_OP_ARANGE:
+            ggml_cann_arange(ctx, dst);
+            break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            ggml_cann_timestep_embedding(ctx, dst);
+            break;
+        case GGML_OP_LEAKY_RELU:
+            ggml_cann_leaky_relu(ctx, dst);
+            break;
+        case GGML_OP_RMS_NORM:
+            ggml_cann_rms_norm(ctx, dst);
+            break;
+        case GGML_OP_MUL_MAT:
+            ggml_cann_mul_mat(ctx, dst);
+            break;
+        case GGML_OP_MUL_MAT_ID:
+            return false;
+        case GGML_OP_SCALE:
+            ggml_cann_scale(ctx, dst);
+            break;
+        case GGML_OP_SQR:
+            ggml_cann_sqr(ctx, dst);
+            break;
+        case GGML_OP_CLAMP:
+            ggml_cann_clamp(ctx, dst);
+            break;
+        case GGML_OP_CPY:
+            ggml_cann_cpy(ctx, dst);
+            break;
+        case GGML_OP_CONT:
+            ggml_cann_dup(ctx, dst);
+            break;
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+            break;
+        case GGML_OP_DIAG_MASK_INF:
+            ggml_cann_diag_mask(ctx, dst, -INFINITY);
+            break;
+        case GGML_OP_SOFT_MAX:
+            ggml_cann_softmax(ctx, dst);
+            break;
+        case GGML_OP_ROPE:
+            ggml_cann_rope(ctx, dst);
+            break;
+        case GGML_OP_IM2COL:
+            ggml_cann_im2col(ctx, dst);
+            break;
+        case GGML_OP_POOL_2D:
+            ggml_cann_pool2d(ctx, dst);
+            break;
+        case GGML_OP_SUM_ROWS:
+            ggml_cann_sum_rows(ctx, dst);
+            break;
+        case GGML_OP_ARGSORT:
+            ggml_cann_argsort(ctx, dst);
+            break;
+        default:
+            return false;
+    }
+
+    return true;
+}
+
+// backend
+/**
+ * @brief Retrieves the name associated with the CANN backend.
+ *
+ * This function returns the name assigned to the CANN backend, which is stored
+ * in the context of the provided backend structure.
+ *
+ * @param backend Pointer to the CANN backend structure.
+ * @return A pointer to a constant string representing the backend name.
+ */
+GGML_CALL static const char* ggml_backend_cann_name(ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    return cann_ctx->name.c_str();
+}
+
+/**
+ * @brief Frees resources associated with the CANN backend.
+ *
+ * This function releases resources associated with the CANN backend context
+ * and resets the device associated with the backend to its initial state.
+ *
+ * @param backend Pointer to the CANN backend structure to be freed.
+ */
+GGML_CALL static void ggml_backend_cann_free(ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+    ACL_CHECK(aclrtSynchronizeDevice());
+    ACL_CHECK(aclrtResetDevice(cann_ctx->device));
+
+    // finalize when last backend freed.
+    if (cann_ctx->device == ggml_backend_cann_get_device_count() - 1) {
+        ACL_CHECK(aclFinalize());
+    }
+
+    delete cann_ctx;
+    delete backend;
+}
+
+/**
+ * @brief Retrieves the default buffer type associated with the CANN backend.
+ *
+ * This function returns the buffer type specific to the device associated
+ * with the CANN backend. It is used to allocate buffers for computations
+ * performed by the backend.
+ *
+ * @param backend Pointer to the CANN backend structure.
+ * @return Pointer to the buffer type structure for the CANN backend.
+ */
+GGML_CALL static ggml_backend_buffer_type_t
+ggml_backend_cann_get_default_buffer_type(ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    return ggml_backend_cann_buffer_type(cann_ctx->device);
+}
+
+/**
+ * @brief Sets tensor data asynchronously in the CANN backend.
+ *
+ * This function asynchronously sets tensor data in the CANN backend. Depending
+ * on the tensor type, it may perform data transformations before copying data
+ * to the device.
+ *
+ * @param backend Pointer to the CANN backend structure.
+ * @param tensor Pointer to the tensor structure to set data for.
+ * @param data Pointer to the host data to copy to the tensor.
+ * @param offset Offset in bytes within the host data.
+ * @param size Size of the data to copy in bytes.
+ */
+GGML_CALL static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend,
+                                                         ggml_tensor *tensor,
+                                                         const void *data,
+                                                         size_t offset,
+                                                         size_t size) {
+    ggml_backend_cann_context *cann_ctx =
+        (ggml_backend_cann_context *)backend->context;
+
+    if (!need_transform(tensor->type)) {
+        ACL_CHECK(aclrtMemcpyAsync((char *)tensor->data + offset, size, data,
+                                   size, ACL_MEMCPY_HOST_TO_DEVICE,
+                                   cann_ctx->stream()));
+    } else {
+        void *transform_buffer = malloc(size);
+        ggml_backend_cann_transform(tensor, data, transform_buffer);
+
+#ifndef NDEBUG
+        void *check_buffer = malloc(size);
+        ggml_backend_cann_transform_back(tensor, transform_buffer,
+                                         check_buffer);
+        GGML_ASSERT(memcmp(data, check_buffer, size));
+        free(check_buffer);
+#endif
+        ACL_CHECK(aclrtMemcpyAsync(
+            (char *)tensor->data + offset, size, transform_buffer, size,
+            ACL_MEMCPY_HOST_TO_DEVICE, cann_ctx->stream()));
+        ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
+        free(transform_buffer);
+    }
+}
+
+GGML_CALL static void ggml_backend_cann_get_tensor_async(
+    ggml_backend_t backend, const ggml_tensor *tensor, void *data,
+    size_t offset, size_t size) {
+    ggml_backend_cann_context *cann_ctx =
+        (ggml_backend_cann_context *)backend->context;
+    ggml_backend_buffer_t buf =
+        tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+    GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) &&
+                "unsupported buffer type");
+
+    if (!need_transform(tensor->type)) {
+        ACL_CHECK(aclrtMemcpyAsync(data, size, (char *)tensor->data + offset,
+                                   size, ACL_MEMCPY_DEVICE_TO_HOST,
+                                   cann_ctx->stream()));
+    } else {
+        void *transform_buffer = malloc(size);
+        ACL_CHECK(aclrtMemcpyAsync(
+            transform_buffer, size, (char *)tensor->data + offset, size,
+            ACL_MEMCPY_DEVICE_TO_HOST, cann_ctx->stream()));
+        ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
+        ggml_backend_cann_transform_back(tensor, transform_buffer, data);
+        free(transform_buffer);
+    }
+}
+
+/**
+ * @brief Asynchronously copies tensor data between CANN backends.
+ *
+ * This function copies tensor data asynchronously between two CANN backends. It
+ * checks if both tensors reside in CANN buffers and whether the devices support
+ * peer-to-peer access for direct copying. If not, it returns false.
+ *
+ * @param backend_src Pointer to the source CANN backend structure.
+ * @param backend_dst Pointer to the destination CANN backend structure.
+ * @param src Pointer to the source tensor to copy data from.
+ * @param dst Pointer to the destination tensor to copy data to.
+ * @return true if the copy operation succeeds, false otherwise.
+ */
+GGML_CALL static bool ggml_backend_cann_cpy_tensor_async(
+    ggml_backend_t backend_src, ggml_backend_t backend_dst,
+    const ggml_tensor* src, ggml_tensor* dst) {
+    GGML_ASSERT(ggml_backend_is_cann(backend_src) ||
+                ggml_backend_is_cann(backend_dst));
+
+    if (!ggml_backend_buffer_is_cann(src->buffer) ||
+        !ggml_backend_buffer_is_cann(dst->buffer)) {
+        return false;
+    }
+
+    ggml_backend_buffer_t buf_src =
+        src->view_src ? src->view_src->buffer : src->buffer;
+    ggml_backend_buffer_t buf_dst =
+        dst->view_src ? dst->view_src->buffer : dst->buffer;
+
+    ggml_backend_cann_context* cann_ctx_src =
+        (ggml_backend_cann_context*)backend_src->context;
+    ggml_backend_cann_context* cann_ctx_dst =
+        (ggml_backend_cann_context*)backend_dst->context;
+
+    size_t copy_size = ggml_nbytes(dst);
+    if (backend_src != backend_dst) {
+        ggml_backend_cann_buffer_context* buf_ctx_src =
+            (ggml_backend_cann_buffer_context*)buf_src->context;
+        ggml_backend_cann_buffer_context* buf_ctx_dst =
+            (ggml_backend_cann_buffer_context*)buf_dst->context;
+
+        GGML_ASSERT(cann_ctx_src->device == buf_ctx_src->device);
+        GGML_ASSERT(cann_ctx_dst->device == buf_ctx_dst->device);
+
+        int32_t canAccessPeer = 0;
+        ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, cann_ctx_src->device,
+                                           cann_ctx_dst->device));
+        if (!canAccessPeer) {
+            return false;
+        }
+
+        // need open both directions for memcpyasync between devices.
+        ggml_cann_set_device(cann_ctx_dst->device);
+        ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_src->device, 0));
+        ggml_cann_set_device(cann_ctx_src->device);
+        ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_dst->device, 0));
+
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE,
+                                   cann_ctx_src->stream()));
+
+        //TODO: workaround for Event didn`t work here.
+        aclrtSynchronizeStream(cann_ctx_src->stream());
+    } else {
+        // src and dst are on the same backend
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE,
+                                   cann_ctx_dst->stream()));
+    }
+
+    return true;
+}
+
+/**
+ * @brief Synchronizes a CANN backend.
+ *
+ * This function synchronizes the specified CANN backend by waiting for all
+ * operations in its associated stream to complete.
+ *
+ * @param backend Pointer to the CANN backend structure to synchronize.
+ */
+GGML_CALL static void ggml_backend_cann_synchronize(ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    ggml_cann_set_device(cann_ctx->device);
+
+    ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
+}
+
+/**
+ * @brief Computes a computational graph using a CANN backend.
+ *
+ * This function computes the operations defined in the computational graph
+ * using the specified CANN backend.
+ *
+ * @param backend Pointer to the CANN backend structure to use for computation.
+ * @param cgraph Pointer to the computational graph structure containing nodes
+ *               representing operations to be computed.
+ * @return enum ggml_status Returns GGML_STATUS_SUCCESS if computation
+ *         completes successfully, otherwise an appropriate error status.
+ */
+GGML_CALL static enum ggml_status ggml_backend_cann_graph_compute(
+    ggml_backend_t backend, ggml_cgraph* cgraph) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    ggml_cann_set_device(cann_ctx->device);
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor* node = cgraph->nodes[i];
+
+        if (ggml_is_empty(node) || node->op == GGML_OP_NONE) {
+            continue;
+        }
+
+        bool ok = ggml_cann_compute_forward(*cann_ctx, node);
+
+        if (!ok) {
+            GGML_CANN_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
+                    node->name, ggml_op_name(node->op));
+        }
+        GGML_ASSERT(ok);
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
+/**
+ * @brief Checks if the CANN backend supports a specific operation.
+ *
+ * This function checks whether the specified operation is supported by the
+ * CANN backend.
+ *
+ * @param backend Pointer to the CANN backend structure to check support for
+ *                the operation.
+ * @param op Pointer to the tensor representing the operation to check.
+ * @return bool Returns true if the operation is supported by the backend,
+ *              otherwise false.
+ */
+GGML_CALL static bool ggml_backend_cann_supports_op(ggml_backend_t backend,
+                                                    const ggml_tensor* op) {
+    switch (op->op) {
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_SILU:
+                case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_HARDSIGMOID:
+                case GGML_UNARY_OP_HARDSWISH:
+                case GGML_UNARY_OP_GELU_QUICK:
+                case GGML_UNARY_OP_TANH:
+                    return true;
+                default:
+                    return false;
+            }
+        case GGML_OP_MUL_MAT: {
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F16:
+                case GGML_TYPE_F32:
+                case GGML_TYPE_Q8_0:
+                    // TODO: fix me
+                    // Current groupsize should not be greater than k-1 in
+                    // aclnnWeightQuantBatchMatmulV2GetWorkspaceSize().
+                case GGML_TYPE_Q4_0:
+                    return true;
+                default:
+                    return false;
+            }
+        }
+        case GGML_OP_MUL_MAT_ID:
+            return false;
+        // embedding
+        case GGML_OP_GET_ROWS: {
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q8_0:
+                    return true;
+                default:
+                    return false;
+            }
+        } break;
+        case GGML_OP_CPY: {
+            switch (op->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q8_0:
+                case GGML_TYPE_Q4_0:
+                    return true;
+                default:
+                    return false;
+            }
+        }
+        case GGML_OP_DUP:
+        case GGML_OP_REPEAT:
+        case GGML_OP_CONCAT:
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_NORM:
+        case GGML_OP_ADD:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SCALE:
+        case GGML_OP_SQR:
+        case GGML_OP_CLAMP:
+        case GGML_OP_CONT:
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_ROPE:
+        case GGML_OP_IM2COL:
+        case GGML_OP_POOL_2D:
+        case GGML_OP_SUM_ROWS:
+        case GGML_OP_ARGSORT:
+        case GGML_OP_ACC:
+        case GGML_OP_GROUP_NORM:
+        case GGML_OP_UPSCALE:
+        case GGML_OP_PAD:
+        case GGML_OP_ARANGE:
+        case GGML_OP_TIMESTEP_EMBEDDING:
+        case GGML_OP_LEAKY_RELU:
+            return true;
+        default:
+            return false;
+    }
+
+    GGML_UNUSED(backend);
+}
+
+/**
+ * @brief Checks if the backend buffer type is associated with the CANN backend.
+ *
+ * This function checks whether the provided backend buffer type is associated
+ * with the CANN backend based on the comparison of its name retrieval function
+ * pointer.
+ *
+ * @param buft Pointer to the backend buffer type to check.
+ * @return bool Returns true if the buffer type is associated with the CANN
+ * backend, otherwise false.
+ */
+static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
+}
+
+/**
+ * @brief Checks if the CANN backend supports a specific backend buffer type.
+ *
+ * This function determines whether the CANN backend supports the given backend
+ * buffer type by comparing the device context of the backend and buffer type.
+ * It returns true if the devices are same between the backend context and
+ * buffer type context.
+ *
+ * @param backend Pointer to the CANN backend.
+ * @param buft Pointer to the backend buffer type to check.
+ * @return bool Returns true if the CANN backend supports the buffer type,
+ *              otherwise false.
+ */
+GGML_CALL static bool ggml_backend_cann_supports_buft(
+    ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    if (ggml_backend_buft_is_cann(buft)) {
+        ggml_backend_cann_context * cann_ctx =
+                        (ggml_backend_cann_context *)backend->context;
+        ggml_backend_cann_buffer_type_context * buft_ctx =
+                        (ggml_backend_cann_buffer_type_context *)buft->context;
+        return buft_ctx->device == cann_ctx->device;
+    }
+    return false;
+}
+
+/**
+ * @brief Determines if a tensor operation should be offloaded to the CANN
+ * backend.
+ *
+ * This function checks if a given tensor operation should be offloaded to the
+ * CANN backend based on the operation type and the size of the tensor. It
+ * returns true if the second dimension (ne[1]) of the tensor is greater than or
+ * equal to the minimum batch size and the operation is not GGML_OP_GET_ROWS.
+ *
+ * @param backend Pointer to the CANN backend.
+ * @param op Pointer to the tensor operation to check.
+ * @return bool Returns true if the operation should be offloaded, otherwise
+ * false.
+ */
+GGML_CALL static bool ggml_backend_cann_offload_op(ggml_backend_t backend,
+                                                   const ggml_tensor* op) {
+    const int min_batch_size = 32;
+    GGML_UNUSED(backend);
+
+    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS;
+}
+
+/**
+ * @brief Creates a new event for the CANN backend.
+ *
+ * This function initializes a new event for the CANN backend by setting the
+ * device and creating an ACL runtime event. The created event is then wrapped
+ * in a ggml_backend_event structure and returned.
+ *
+ * @param backend Pointer to the CANN backend.
+ * @return ggml_backend_event_t Returns a pointer to the new event structure.
+ */
+static ggml_backend_event_t ggml_backend_cann_event_new(
+    ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    ggml_cann_set_device(cann_ctx->device);
+
+    aclrtEvent event;
+    ACL_CHECK(aclrtCreateEvent(&event));
+
+    return new ggml_backend_event{
+        /* .backend = */ backend,
+        /* .context = */ event,
+    };
+}
+
+/**
+ * @brief Frees a CANN backend event.
+ *
+ * This function destroys the ACL runtime event associated with the given CANN
+ * backend event and then deletes the event structure itself.
+ *
+ * @param event Pointer to the event structure to be freed.
+ */
+static void ggml_backend_cann_event_free(ggml_backend_event_t event) {
+    ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context));
+
+    delete event;
+}
+
+/**
+ * @brief Records an event on the CANN backend stream.
+ *
+ * This function records the given event on the ACL runtime stream associated
+ * with the backend context.
+ *
+ * @param event Pointer to the event structure to be recorded.
+ */
+static void ggml_backend_cann_event_record(ggml_backend_event_t event) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)event->backend->context;
+
+    ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream()));
+}
+
+/**
+ * @brief Waits for a recorded event to complete on the CANN backend stream.
+ *
+ * This function makes the given backend wait for the event to complete on its
+ * ACL runtime stream.
+ *
+ * @param backend Pointer to the backend structure.
+ * @param event Pointer to the event structure that the backend needs to wait
+ * for.
+ */
+static void ggml_backend_cann_event_wait(ggml_backend_t backend,
+                                         ggml_backend_event_t event) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    if (ggml_backend_is_cann(event->backend)) {
+        ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(),
+                                       (aclrtEvent)event->context));
+    } else {
+        GGML_ABORT("fatal error");
+    }
+}
+
+/**
+ * @brief Synchronizes the given event on the CANN backend.
+ *
+ * This function waits for the specified event to complete on the ACL runtime.
+ *
+ * @param event Pointer to the event structure to be synchronized.
+ */
+static void ggml_backend_cann_event_synchronize(ggml_backend_event_t event) {
+    ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context));
+}
+
+/**
+ * @brief Structure defining the interface for the CANN backend.
+ *
+ * This structure contains function pointers for various operations
+ * supported by the CANN backend, including name retrieval, memory
+ * management, tensor operations, synchronization, and event handling.
+ */
+static ggml_backend_i ggml_backend_cann_interface = {
+    /* .get_name                = */ ggml_backend_cann_name,
+    /* .free                    = */ ggml_backend_cann_free,
+    /* .get_default_buffer_type = */ ggml_backend_cann_get_default_buffer_type,
+    /* .set_tensor_async        = */ ggml_backend_cann_set_tensor_async,
+    /* .get_tensor_async        = */ ggml_backend_cann_get_tensor_async,
+    /* .cpy_tensor_async        = */ ggml_backend_cann_cpy_tensor_async,
+    /* .synchronize             = */ ggml_backend_cann_synchronize,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_cann_graph_compute,
+    /* .supports_op             = */ ggml_backend_cann_supports_op,
+    /* .supports_buft           = */ ggml_backend_cann_supports_buft,
+    /* .offload_op              = */ ggml_backend_cann_offload_op,
+    /* .event_new               = */ ggml_backend_cann_event_new,
+    /* .event_free              = */ ggml_backend_cann_event_free,
+    /* .event_record            = */ ggml_backend_cann_event_record,
+    /* .event_wait              = */ ggml_backend_cann_event_wait,
+    /* .event_synchronize       = */ ggml_backend_cann_event_synchronize,
+};
+
+/**
+ * @brief Return the hardcoded GUID for the CANN backend.
+ *
+ * This function returns a static GUID which uniquely identifies the CANN
+ * backend.
+ *
+ * @return A pointer to the static GUID.
+ */
+static ggml_guid_t ggml_backend_cann_guid() {
+    static ggml_guid guid = {0xa1, 0x94, 0xaf, 0xac, 0xbd, 0x4f, 0x47, 0x34,
+                             0xbe, 0x1a, 0x9e, 0x71, 0x1f, 0x9e, 0xed, 0x64};
+    return &guid;
+}
+
+GGML_CALL ggml_backend_t ggml_backend_cann_init(int32_t device) {
+    aclInit(nullptr);
+    if (device < 0 || device >= ggml_backend_cann_get_device_count()) {
+        GGML_CANN_LOG_ERROR("%s: error: invalid device %d\n", __func__, device);
+        return nullptr;
+    }
+
+    ggml_backend_cann_context* ctx = new ggml_backend_cann_context(device);
+    if (ctx == nullptr) {
+        GGML_CANN_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
+        return nullptr;
+    }
+
+    ggml_backend_t cann_backend =
+        new ggml_backend{/* .guid      = */ ggml_backend_cann_guid(),
+                         /* .interface = */ ggml_backend_cann_interface,
+                         /* .context   = */ ctx};
+
+    return cann_backend;
+}
+
+GGML_CALL bool ggml_backend_is_cann(ggml_backend_t backend) {
+    return backend != NULL &&
+           ggml_guid_matches(backend->guid, ggml_backend_cann_guid());
+}
+
+GGML_CALL int32_t ggml_backend_cann_get_device_count() {
+    return ggml_cann_info().device_count;
+}
+
+GGML_CALL void ggml_backend_cann_get_device_description(
+    int32_t device, char* description, size_t description_size) {
+    ggml_cann_set_device(device);
+    const char* soc_name = aclrtGetSocName();
+    snprintf(description, description_size, "%s", soc_name);
+}
+
+GGML_CALL void ggml_backend_cann_get_device_memory(int32_t device, size_t* free,
+                                                   size_t* total) {
+    ggml_cann_set_device(device);
+    ACL_CHECK(aclrtGetMemInfo(ACL_HBM_MEM, free, total));
+}
+
+// backend registry
+/**
+ * @brief Initializes a CANN backend based on the provided parameters.
+ *
+ * This function initializes a CANN backend using the device index and then
+ * initializes the backend using `ggml_backend_cann_init`.
+ *
+ * @param params Parameters for initialization (unused in this implementation).
+ * @param user_data User data containing the device index to initialize the
+ * backend.
+ * @return ggml_backend_t The initialized CANN backend.
+ */
+GGML_CALL static ggml_backend_t ggml_backend_reg_cann_init(const char* params,
+                                                           void* user_data) {
+    ggml_backend_t cann_backend =
+        ggml_backend_cann_init((int)(intptr_t)user_data);
+    return cann_backend;
+
+    GGML_UNUSED(params);
+}
+
+extern "C" GGML_CALL int ggml_backend_cann_reg_devices();
+
+/**
+ * @brief Registers CANN (Ascend) devices as backend options.
+ *
+ * This function initializes ACL, retrieves the number of available CANN
+ * devices, and registers each device as a backend option using
+ * `ggml_backend_register`. Each device is given a unique name based on
+ * `GGML_CANN_NAME` followed by its index.
+ *
+ * @return int The number of CANN devices registered.
+ */
+GGML_CALL int ggml_backend_cann_reg_devices() {
+    uint32_t device_count = ggml_backend_cann_get_device_count();
+    // initialization
+    for (uint32_t i = 0; i < device_count; i++) {
+        char name[128];
+        snprintf(name, sizeof(name), "CANN%d", i);
+        ggml_backend_register(name, ggml_backend_reg_cann_init,
+                              ggml_backend_cann_buffer_type(i),
+                              (void*)(intptr_t)i);
+    }
+    return device_count;
+}
diff --git a/ggml/src/ggml-cann/.clang-format b/ggml/src/ggml-cann/.clang-format
new file mode 100644
index 0000000000000..2ad03d7391936
--- /dev/null
+++ b/ggml/src/ggml-cann/.clang-format
@@ -0,0 +1,168 @@
+---
+Language:        Cpp
+# BasedOnStyle:  Google
+AccessModifierOffset: -1
+AlignAfterOpenBracket: Align
+AlignConsecutiveMacros: false
+AlignConsecutiveAssignments: false
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlines: Left
+AlignOperands:   true
+AlignTrailingComments: true
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+AllowShortLambdasOnASingleLine: All
+AllowShortIfStatementsOnASingleLine: WithoutElse
+AllowShortLoopsOnASingleLine: true
+AlwaysBreakAfterDefinitionReturnType: None
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: Yes
+BinPackArguments: true
+BinPackParameters: true
+BraceWrapping:
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+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   false
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
+  AfterExternBlock: false
+  BeforeCatch:     false
+  BeforeElse:      false
+  IndentBraces:    false
+  SplitEmptyFunction: true
+  SplitEmptyRecord: true
+  SplitEmptyNamespace: true
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Attach
+BreakBeforeInheritanceComma: false
+BreakInheritanceList: BeforeColon
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializersBeforeComma: false
+BreakConstructorInitializers: BeforeColon
+BreakAfterJavaFieldAnnotations: false
+BreakStringLiterals: true
+ColumnLimit:     80
+CommentPragmas:  '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DeriveLineEnding: true
+DerivePointerAlignment: true
+DisableFormat:   false
+ExperimentalAutoDetectBinPacking: false
+FixNamespaceComments: true
+ForEachMacros:
+  - foreach
+  - Q_FOREACH
+  - BOOST_FOREACH
+IncludeBlocks:   Regroup
+IncludeCategories:
+  - Regex:           '^<ext/.*\.h>'
+    Priority:        2
+    SortPriority:    0
+  - Regex:           '^<.*\.h>'
+    Priority:        1
+    SortPriority:    0
+  - Regex:           '^<.*'
+    Priority:        2
+    SortPriority:    0
+  - Regex:           '.*'
+    Priority:        3
+    SortPriority:    0
+IncludeIsMainRegex: '([-_](test|unittest))?$'
+IncludeIsMainSourceRegex: ''
+IndentCaseLabels: true
+IndentGotoLabels: true
+IndentPPDirectives: None
+IndentWidth:     4
+IndentWrappedFunctionNames: false
+JavaScriptQuotes: Leave
+JavaScriptWrapImports: true
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBinPackProtocolList: Never
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: true
+PenaltyBreakAssignment: 2
+PenaltyBreakBeforeFirstCallParameter: 1
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyBreakTemplateDeclaration: 10
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 200
+PointerAlignment: Left
+RawStringFormats:
+  - Language:        Cpp
+    Delimiters:
+      - cc
+      - CC
+      - cpp
+      - Cpp
+      - CPP
+      - 'c++'
+      - 'C++'
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+  - Language:        TextProto
+    Delimiters:
+      - pb
+      - PB
+      - proto
+      - PROTO
+    EnclosingFunctions:
+      - EqualsProto
+      - EquivToProto
+      - PARSE_PARTIAL_TEXT_PROTO
+      - PARSE_TEST_PROTO
+      - PARSE_TEXT_PROTO
+      - ParseTextOrDie
+      - ParseTextProtoOrDie
+    CanonicalDelimiter: ''
+    BasedOnStyle:    google
+ReflowComments:  true
+SortIncludes:    true
+SortUsingDeclarations: true
+SpaceAfterCStyleCast: false
+SpaceAfterLogicalNot: false
+SpaceAfterTemplateKeyword: true
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeCpp11BracedList: false
+SpaceBeforeCtorInitializerColon: true
+SpaceBeforeInheritanceColon: true
+SpaceBeforeParens: ControlStatements
+SpaceBeforeRangeBasedForLoopColon: true
+SpaceInEmptyBlock: false
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 2
+SpacesInAngles:  false
+SpacesInConditionalStatement: false
+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+SpaceBeforeSquareBrackets: false
+Standard:        Auto
+StatementMacros:
+  - Q_UNUSED
+  - QT_REQUIRE_VERSION
+TabWidth:        8
+UseCRLF:         false
+UseTab:          Never
+...
+
diff --git a/ggml/src/ggml-cann/Doxyfile b/ggml/src/ggml-cann/Doxyfile
new file mode 100644
index 0000000000000..2b009e8f9bd26
--- /dev/null
+++ b/ggml/src/ggml-cann/Doxyfile
@@ -0,0 +1,2579 @@
+# Doxyfile 1.8.17
+
+# This file describes the settings to be used by the documentation system
+# doxygen (www.doxygen.org) for a project.
+#
+# All text after a double hash (##) is considered a comment and is placed in
+# front of the TAG it is preceding.
+#
+# All text after a single hash (#) is considered a comment and will be ignored.
+# The format is:
+# TAG = value [value, ...]
+# For lists, items can also be appended using:
+# TAG += value [value, ...]
+# Values that contain spaces should be placed between quotes (\" \").
+
+#---------------------------------------------------------------------------
+# Project related configuration options
+#---------------------------------------------------------------------------
+
+# This tag specifies the encoding used for all characters in the configuration
+# file that follow. The default is UTF-8 which is also the encoding used for all
+# text before the first occurrence of this tag. Doxygen uses libiconv (or the
+# iconv built into libc) for the transcoding. See
+# https://www.gnu.org/software/libiconv/ for the list of possible encodings.
+# The default value is: UTF-8.
+
+DOXYFILE_ENCODING      = UTF-8
+
+# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
+# double-quotes, unless you are using Doxywizard) that should identify the
+# project for which the documentation is generated. This name is used in the
+# title of most generated pages and in a few other places.
+# The default value is: My Project.
+
+PROJECT_NAME           = "llama.cpp"
+
+# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
+# could be handy for archiving the generated documentation or if some version
+# control system is used.
+
+PROJECT_NUMBER         =
+
+# Using the PROJECT_BRIEF tag one can provide an optional one line description
+# for a project that appears at the top of each page and should give viewer a
+# quick idea about the purpose of the project. Keep the description short.
+
+PROJECT_BRIEF          = "llama inference engine"
+
+# With the PROJECT_LOGO tag one can specify a logo or an icon that is included
+# in the documentation. The maximum height of the logo should not exceed 55
+# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
+# the logo to the output directory.
+
+PROJECT_LOGO           =
+
+# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
+# into which the generated documentation will be written. If a relative path is
+# entered, it will be relative to the location where doxygen was started. If
+# left blank the current directory will be used.
+
+OUTPUT_DIRECTORY       = docs
+
+# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
+# directories (in 2 levels) under the output directory of each output format and
+# will distribute the generated files over these directories. Enabling this
+# option can be useful when feeding doxygen a huge amount of source files, where
+# putting all generated files in the same directory would otherwise causes
+# performance problems for the file system.
+# The default value is: NO.
+
+CREATE_SUBDIRS         = NO
+
+# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
+# characters to appear in the names of generated files. If set to NO, non-ASCII
+# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
+# U+3044.
+# The default value is: NO.
+
+ALLOW_UNICODE_NAMES    = NO
+
+# The OUTPUT_LANGUAGE tag is used to specify the language in which all
+# documentation generated by doxygen is written. Doxygen will use this
+# information to generate all constant output in the proper language.
+# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
+# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
+# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
+# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
+# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
+# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
+# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
+# Ukrainian and Vietnamese.
+# The default value is: English.
+
+OUTPUT_LANGUAGE        = English
+
+# The OUTPUT_TEXT_DIRECTION tag is used to specify the direction in which all
+# documentation generated by doxygen is written. Doxygen will use this
+# information to generate all generated output in the proper direction.
+# Possible values are: None, LTR, RTL and Context.
+# The default value is: None.
+
+OUTPUT_TEXT_DIRECTION  = None
+
+# If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member
+# descriptions after the members that are listed in the file and class
+# documentation (similar to Javadoc). Set to NO to disable this.
+# The default value is: YES.
+
+BRIEF_MEMBER_DESC      = YES
+
+# If the REPEAT_BRIEF tag is set to YES, doxygen will prepend the brief
+# description of a member or function before the detailed description
+#
+# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
+# brief descriptions will be completely suppressed.
+# The default value is: YES.
+
+REPEAT_BRIEF           = YES
+
+# This tag implements a quasi-intelligent brief description abbreviator that is
+# used to form the text in various listings. Each string in this list, if found
+# as the leading text of the brief description, will be stripped from the text
+# and the result, after processing the whole list, is used as the annotated
+# text. Otherwise, the brief description is used as-is. If left blank, the
+# following values are used ($name is automatically replaced with the name of
+# the entity):The $name class, The $name widget, The $name file, is, provides,
+# specifies, contains, represents, a, an and the.
+
+ABBREVIATE_BRIEF       = "The $name class" \
+                         "The $name widget" \
+                         "The $name file" \
+                         is \
+                         provides \
+                         specifies \
+                         contains \
+                         represents \
+                         a \
+                         an \
+                         the
+
+# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
+# doxygen will generate a detailed section even if there is only a brief
+# description.
+# The default value is: NO.
+
+ALWAYS_DETAILED_SEC    = NO
+
+# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all
+# inherited members of a class in the documentation of that class as if those
+# members were ordinary class members. Constructors, destructors and assignment
+# operators of the base classes will not be shown.
+# The default value is: NO.
+
+INLINE_INHERITED_MEMB  = NO
+
+# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path
+# before files name in the file list and in the header files. If set to NO the
+# shortest path that makes the file name unique will be used
+# The default value is: YES.
+
+FULL_PATH_NAMES        = YES
+
+# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
+# Stripping is only done if one of the specified strings matches the left-hand
+# part of the path. The tag can be used to show relative paths in the file list.
+# If left blank the directory from which doxygen is run is used as the path to
+# strip.
+#
+# Note that you can specify absolute paths here, but also relative paths, which
+# will be relative from the directory where doxygen is started.
+# This tag requires that the tag FULL_PATH_NAMES is set to YES.
+
+STRIP_FROM_PATH        =
+
+# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
+# path mentioned in the documentation of a class, which tells the reader which
+# header file to include in order to use a class. If left blank only the name of
+# the header file containing the class definition is used. Otherwise one should
+# specify the list of include paths that are normally passed to the compiler
+# using the -I flag.
+
+STRIP_FROM_INC_PATH    =
+
+# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
+# less readable) file names. This can be useful is your file systems doesn't
+# support long names like on DOS, Mac, or CD-ROM.
+# The default value is: NO.
+
+SHORT_NAMES            = NO
+
+# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
+# first line (until the first dot) of a Javadoc-style comment as the brief
+# description. If set to NO, the Javadoc-style will behave just like regular Qt-
+# style comments (thus requiring an explicit @brief command for a brief
+# description.)
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+
+JAVADOC_AUTOBRIEF      = NO
+
+# If the JAVADOC_BANNER tag is set to YES then doxygen will interpret a line
+# such as
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+# as being the beginning of a Javadoc-style comment "banner". If set to NO, the
+# Javadoc-style will behave just like regular comments and it will not be
+# interpreted by doxygen.
+# The default value is: NO.
+
+JAVADOC_BANNER         = NO
+
+# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
+# line (until the first dot) of a Qt-style comment as the brief description. If
+# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
+# requiring an explicit \brief command for a brief description.)
+# The default value is: NO.
+
+QT_AUTOBRIEF           = NO
+
+# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
+# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
+# a brief description. This used to be the default behavior. The new default is
+# to treat a multi-line C++ comment block as a detailed description. Set this
+# tag to YES if you prefer the old behavior instead.
+#
+# Note that setting this tag to YES also means that rational rose comments are
+# not recognized any more.
+# The default value is: NO.
+
+MULTILINE_CPP_IS_BRIEF = NO
+
+# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
+# documentation from any documented member that it re-implements.
+# The default value is: YES.
+
+INHERIT_DOCS           = YES
+
+# If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new
+# page for each member. If set to NO, the documentation of a member will be part
+# of the file/class/namespace that contains it.
+# The default value is: NO.
+
+SEPARATE_MEMBER_PAGES  = NO
+
+# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
+# uses this value to replace tabs by spaces in code fragments.
+# Minimum value: 1, maximum value: 16, default value: 4.
+
+TAB_SIZE               = 4
+
+# This tag can be used to specify a number of aliases that act as commands in
+# the documentation. An alias has the form:
+# name=value
+# For example adding
+# "sideeffect=@par Side Effects:\n"
+# will allow you to put the command \sideeffect (or @sideeffect) in the
+# documentation, which will result in a user-defined paragraph with heading
+# "Side Effects:". You can put \n's in the value part of an alias to insert
+# newlines (in the resulting output). You can put ^^ in the value part of an
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+# When you need a literal { or } or , in the value part of an alias you have to
+# escape them by means of a backslash (\), this can lead to conflicts with the
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+# a double escape (\\{ and \\})
+
+ALIASES                =
+
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+
+TCL_SUBST              =
+
+# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
+# only. Doxygen will then generate output that is more tailored for C. For
+# instance, some of the names that are used will be different. The list of all
+# members will be omitted, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_FOR_C  = NO
+
+# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
+# Python sources only. Doxygen will then generate output that is more tailored
+# for that language. For instance, namespaces will be presented as packages,
+# qualified scopes will look different, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_JAVA   = NO
+
+# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
+# sources. Doxygen will then generate output that is tailored for Fortran.
+# The default value is: NO.
+
+OPTIMIZE_FOR_FORTRAN   = NO
+
+# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
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+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_VHDL   = NO
+
+# Set the OPTIMIZE_OUTPUT_SLICE tag to YES if your project consists of Slice
+# sources only. Doxygen will then generate output that is more tailored for that
+# language. For instance, namespaces will be presented as modules, types will be
+# separated into more groups, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_SLICE  = NO
+
+# Doxygen selects the parser to use depending on the extension of the files it
+# parses. With this tag you can assign which parser to use for a given
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+# using this tag. The format is ext=language, where ext is a file extension, and
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+# Csharp (C#), C, C++, D, PHP, md (Markdown), Objective-C, Python, Slice,
+# Fortran (fixed format Fortran: FortranFixed, free formatted Fortran:
+# FortranFree, unknown formatted Fortran: Fortran. In the later case the parser
+# tries to guess whether the code is fixed or free formatted code, this is the
+# default for Fortran type files), VHDL, tcl. For instance to make doxygen treat
+# .inc files as Fortran files (default is PHP), and .f files as C (default is
+# Fortran), use: inc=Fortran f=C.
+#
+# Note: For files without extension you can use no_extension as a placeholder.
+#
+# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
+# the files are not read by doxygen.
+
+EXTENSION_MAPPING      =
+
+# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
+# according to the Markdown format, which allows for more readable
+# documentation. See https://daringfireball.net/projects/markdown/ for details.
+# The output of markdown processing is further processed by doxygen, so you can
+# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
+# case of backward compatibilities issues.
+# The default value is: YES.
+
+MARKDOWN_SUPPORT       = YES
+
+# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up
+# to that level are automatically included in the table of contents, even if
+# they do not have an id attribute.
+# Note: This feature currently applies only to Markdown headings.
+# Minimum value: 0, maximum value: 99, default value: 5.
+# This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
+
+TOC_INCLUDE_HEADINGS   = 5
+
+# When enabled doxygen tries to link words that correspond to documented
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+# be prevented in individual cases by putting a % sign in front of the word or
+# globally by setting AUTOLINK_SUPPORT to NO.
+# The default value is: YES.
+
+AUTOLINK_SUPPORT       = YES
+
+# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
+# to include (a tag file for) the STL sources as input, then you should set this
+# tag to YES in order to let doxygen match functions declarations and
+# definitions whose arguments contain STL classes (e.g. func(std::string);
+# versus func(std::string) {}). This also make the inheritance and collaboration
+# diagrams that involve STL classes more complete and accurate.
+# The default value is: NO.
+
+BUILTIN_STL_SUPPORT    = NO
+
+# If you use Microsoft's C++/CLI language, you should set this option to YES to
+# enable parsing support.
+# The default value is: NO.
+
+CPP_CLI_SUPPORT        = NO
+
+# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
+# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen
+# will parse them like normal C++ but will assume all classes use public instead
+# of private inheritance when no explicit protection keyword is present.
+# The default value is: NO.
+
+SIP_SUPPORT            = NO
+
+# For Microsoft's IDL there are propget and propput attributes to indicate
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+# should set this option to NO.
+# The default value is: YES.
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+IDL_PROPERTY_SUPPORT   = YES
+
+# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
+# tag is set to YES then doxygen will reuse the documentation of the first
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+# The default value is: NO.
+
+DISTRIBUTE_GROUP_DOC   = NO
+
+# If one adds a struct or class to a group and this option is enabled, then also
+# any nested class or struct is added to the same group. By default this option
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+# The default value is: NO.
+
+GROUP_NESTED_COMPOUNDS = NO
+
+# Set the SUBGROUPING tag to YES to allow class member groups of the same type
+# (for instance a group of public functions) to be put as a subgroup of that
+# type (e.g. under the Public Functions section). Set it to NO to prevent
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+# \nosubgrouping command.
+# The default value is: YES.
+
+SUBGROUPING            = YES
+
+# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
+# are shown inside the group in which they are included (e.g. using \ingroup)
+# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
+# and RTF).
+#
+# Note that this feature does not work in combination with
+# SEPARATE_MEMBER_PAGES.
+# The default value is: NO.
+
+INLINE_GROUPED_CLASSES = NO
+
+# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
+# with only public data fields or simple typedef fields will be shown inline in
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+# to NO, structs, classes, and unions are shown on a separate page (for HTML and
+# Man pages) or section (for LaTeX and RTF).
+# The default value is: NO.
+
+INLINE_SIMPLE_STRUCTS  = NO
+
+# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
+# enum is documented as struct, union, or enum with the name of the typedef. So
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+# namespace, or class. And the struct will be named TypeS. This can typically be
+# useful for C code in case the coding convention dictates that all compound
+# types are typedef'ed and only the typedef is referenced, never the tag name.
+# The default value is: NO.
+
+TYPEDEF_HIDES_STRUCT   = NO
+
+# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
+# cache is used to resolve symbols given their name and scope. Since this can be
+# an expensive process and often the same symbol appears multiple times in the
+# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
+# doxygen will become slower. If the cache is too large, memory is wasted. The
+# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
+# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
+# symbols. At the end of a run doxygen will report the cache usage and suggest
+# the optimal cache size from a speed point of view.
+# Minimum value: 0, maximum value: 9, default value: 0.
+
+LOOKUP_CACHE_SIZE      = 0
+
+#---------------------------------------------------------------------------
+# Build related configuration options
+#---------------------------------------------------------------------------
+
+# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
+# documentation are documented, even if no documentation was available. Private
+# class members and static file members will be hidden unless the
+# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
+# Note: This will also disable the warnings about undocumented members that are
+# normally produced when WARNINGS is set to YES.
+# The default value is: NO.
+
+EXTRACT_ALL            = YES
+
+# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
+# be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIVATE        = YES
+
+# If the EXTRACT_PRIV_VIRTUAL tag is set to YES, documented private virtual
+# methods of a class will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIV_VIRTUAL   = YES
+
+# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
+# scope will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PACKAGE        = YES
+
+# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be
+# included in the documentation.
+# The default value is: NO.
+
+EXTRACT_STATIC         = YES
+
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
+# locally in source files will be included in the documentation. If set to NO,
+# only classes defined in header files are included. Does not have any effect
+# for Java sources.
+# The default value is: YES.
+
+EXTRACT_LOCAL_CLASSES  = YES
+
+# This flag is only useful for Objective-C code. If set to YES, local methods,
+# which are defined in the implementation section but not in the interface are
+# included in the documentation. If set to NO, only methods in the interface are
+# included.
+# The default value is: NO.
+
+EXTRACT_LOCAL_METHODS  = YES
+
+# If this flag is set to YES, the members of anonymous namespaces will be
+# extracted and appear in the documentation as a namespace called
+# 'anonymous_namespace{file}', where file will be replaced with the base name of
+# the file that contains the anonymous namespace. By default anonymous namespace
+# are hidden.
+# The default value is: NO.
+
+EXTRACT_ANON_NSPACES   = NO
+
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
+# undocumented members inside documented classes or files. If set to NO these
+# members will be included in the various overviews, but no documentation
+# section is generated. This option has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_MEMBERS     = NO
+
+# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
+# undocumented classes that are normally visible in the class hierarchy. If set
+# to NO, these classes will be included in the various overviews. This option
+# has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_CLASSES     = NO
+
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
+# declarations. If set to NO, these declarations will be included in the
+# documentation.
+# The default value is: NO.
+
+HIDE_FRIEND_COMPOUNDS  = NO
+
+# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
+# documentation blocks found inside the body of a function. If set to NO, these
+# blocks will be appended to the function's detailed documentation block.
+# The default value is: NO.
+
+HIDE_IN_BODY_DOCS      = NO
+
+# The INTERNAL_DOCS tag determines if documentation that is typed after a
+# \internal command is included. If the tag is set to NO then the documentation
+# will be excluded. Set it to YES to include the internal documentation.
+# The default value is: NO.
+
+INTERNAL_DOCS          = NO
+
+# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
+# names in lower-case letters. If set to YES, upper-case letters are also
+# allowed. This is useful if you have classes or files whose names only differ
+# in case and if your file system supports case sensitive file names. Windows
+# (including Cygwin) ands Mac users are advised to set this option to NO.
+# The default value is: system dependent.
+
+CASE_SENSE_NAMES       = YES
+
+# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# their full class and namespace scopes in the documentation. If set to YES, the
+# scope will be hidden.
+# The default value is: NO.
+
+HIDE_SCOPE_NAMES       = NO
+
+# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
+# append additional text to a page's title, such as Class Reference. If set to
+# YES the compound reference will be hidden.
+# The default value is: NO.
+
+HIDE_COMPOUND_REFERENCE= NO
+
+# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# the files that are included by a file in the documentation of that file.
+# The default value is: YES.
+
+SHOW_INCLUDE_FILES     = YES
+
+# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
+# grouped member an include statement to the documentation, telling the reader
+# which file to include in order to use the member.
+# The default value is: NO.
+
+SHOW_GROUPED_MEMB_INC  = NO
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# files with double quotes in the documentation rather than with sharp brackets.
+# The default value is: NO.
+
+FORCE_LOCAL_INCLUDES   = NO
+
+# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
+# documentation for inline members.
+# The default value is: YES.
+
+INLINE_INFO            = YES
+
+# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# (detailed) documentation of file and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order.
+# The default value is: YES.
+
+SORT_MEMBER_DOCS       = YES
+
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# descriptions of file, namespace and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order. Note that
+# this will also influence the order of the classes in the class list.
+# The default value is: NO.
+
+SORT_BRIEF_DOCS        = NO
+
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# (brief and detailed) documentation of class members so that constructors and
+# destructors are listed first. If set to NO the constructors will appear in the
+# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
+# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
+# member documentation.
+# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
+# detailed member documentation.
+# The default value is: NO.
+
+SORT_MEMBERS_CTORS_1ST = NO
+
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# of group names into alphabetical order. If set to NO the group names will
+# appear in their defined order.
+# The default value is: NO.
+
+SORT_GROUP_NAMES       = NO
+
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
+# fully-qualified names, including namespaces. If set to NO, the class list will
+# be sorted only by class name, not including the namespace part.
+# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
+# Note: This option applies only to the class list, not to the alphabetical
+# list.
+# The default value is: NO.
+
+SORT_BY_SCOPE_NAME     = NO
+
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# type resolution of all parameters of a function it will reject a match between
+# the prototype and the implementation of a member function even if there is
+# only one candidate or it is obvious which candidate to choose by doing a
+# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# accept a match between prototype and implementation in such cases.
+# The default value is: NO.
+
+STRICT_PROTO_MATCHING  = NO
+
+# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
+# list. This list is created by putting \todo commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TODOLIST      = YES
+
+# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
+# list. This list is created by putting \test commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TESTLIST      = YES
+
+# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
+# list. This list is created by putting \bug commands in the documentation.
+# The default value is: YES.
+
+GENERATE_BUGLIST       = YES
+
+# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
+# the deprecated list. This list is created by putting \deprecated commands in
+# the documentation.
+# The default value is: YES.
+
+GENERATE_DEPRECATEDLIST= YES
+
+# The ENABLED_SECTIONS tag can be used to enable conditional documentation
+# sections, marked by \if <section_label> ... \endif and \cond <section_label>
+# ... \endcond blocks.
+
+ENABLED_SECTIONS       =
+
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
+# initial value of a variable or macro / define can have for it to appear in the
+# documentation. If the initializer consists of more lines than specified here
+# it will be hidden. Use a value of 0 to hide initializers completely. The
+# appearance of the value of individual variables and macros / defines can be
+# controlled using \showinitializer or \hideinitializer command in the
+# documentation regardless of this setting.
+# Minimum value: 0, maximum value: 10000, default value: 30.
+
+MAX_INITIALIZER_LINES  = 30
+
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
+# the bottom of the documentation of classes and structs. If set to YES, the
+# list will mention the files that were used to generate the documentation.
+# The default value is: YES.
+
+SHOW_USED_FILES        = YES
+
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
+# will remove the Files entry from the Quick Index and from the Folder Tree View
+# (if specified).
+# The default value is: YES.
+
+SHOW_FILES             = YES
+
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
+# page. This will remove the Namespaces entry from the Quick Index and from the
+# Folder Tree View (if specified).
+# The default value is: YES.
+
+SHOW_NAMESPACES        = YES
+
+# The FILE_VERSION_FILTER tag can be used to specify a program or script that
+# doxygen should invoke to get the current version for each file (typically from
+# the version control system). Doxygen will invoke the program by executing (via
+# popen()) the command command input-file, where command is the value of the
+# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
+# by doxygen. Whatever the program writes to standard output is used as the file
+# version. For an example see the documentation.
+
+FILE_VERSION_FILTER    =
+
+# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
+# by doxygen. The layout file controls the global structure of the generated
+# output files in an output format independent way. To create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option. You can
+# optionally specify a file name after the option, if omitted DoxygenLayout.xml
+# will be used as the name of the layout file.
+#
+# Note that if you run doxygen from a directory containing a file called
+# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# tag is left empty.
+
+LAYOUT_FILE            =
+
+# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
+# the reference definitions. This must be a list of .bib files. The .bib
+# extension is automatically appended if omitted. This requires the bibtex tool
+# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info.
+# For LaTeX the style of the bibliography can be controlled using
+# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
+# search path. See also \cite for info how to create references.
+
+CITE_BIB_FILES         =
+
+#---------------------------------------------------------------------------
+# Configuration options related to warning and progress messages
+#---------------------------------------------------------------------------
+
+# The QUIET tag can be used to turn on/off the messages that are generated to
+# standard output by doxygen. If QUIET is set to YES this implies that the
+# messages are off.
+# The default value is: NO.
+
+QUIET                  = NO
+
+# The WARNINGS tag can be used to turn on/off the warning messages that are
+# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
+# this implies that the warnings are on.
+#
+# Tip: Turn warnings on while writing the documentation.
+# The default value is: YES.
+
+WARNINGS               = YES
+
+# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
+# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: YES.
+
+WARN_IF_UNDOCUMENTED   = YES
+
+# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some parameters
+# in a documented function, or documenting parameters that don't exist or using
+# markup commands wrongly.
+# The default value is: YES.
+
+WARN_IF_DOC_ERROR      = YES
+
+# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
+# are documented, but have no documentation for their parameters or return
+# value. If set to NO, doxygen will only warn about wrong or incomplete
+# parameter documentation, but not about the absence of documentation. If
+# EXTRACT_ALL is set to YES then this flag will automatically be disabled.
+# The default value is: NO.
+
+WARN_NO_PARAMDOC       = NO
+
+# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
+# a warning is encountered.
+# The default value is: NO.
+
+WARN_AS_ERROR          = NO
+
+# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# can produce. The string should contain the $file, $line, and $text tags, which
+# will be replaced by the file and line number from which the warning originated
+# and the warning text. Optionally the format may contain $version, which will
+# be replaced by the version of the file (if it could be obtained via
+# FILE_VERSION_FILTER)
+# The default value is: $file:$line: $text.
+
+WARN_FORMAT            = "$file:$line: $text"
+
+# The WARN_LOGFILE tag can be used to specify a file to which warning and error
+# messages should be written. If left blank the output is written to standard
+# error (stderr).
+
+WARN_LOGFILE           =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the input files
+#---------------------------------------------------------------------------
+
+# The INPUT tag is used to specify the files and/or directories that contain
+# documented source files. You may enter file names like myfile.cpp or
+# directories like /usr/src/myproject. Separate the files or directories with
+# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
+# Note: If this tag is empty the current directory is searched.
+
+INPUT                  =
+
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
+# documentation (see: https://www.gnu.org/software/libiconv/) for the list of
+# possible encodings.
+# The default value is: UTF-8.
+
+INPUT_ENCODING         = UTF-8
+
+# If the value of the INPUT tag contains directories, you can use the
+# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
+# *.h) to filter out the source-files in the directories.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# read by doxygen.
+#
+# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
+# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
+# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
+# *.m, *.markdown, *.md, *.mm, *.dox (to be provided as doxygen C comment),
+# *.doc (to be provided as doxygen C comment), *.txt (to be provided as doxygen
+# C comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, *.f, *.for, *.tcl, *.vhd,
+# *.vhdl, *.ucf, *.qsf and *.ice.
+
+FILE_PATTERNS          = *.c \
+                         *.cc \
+                         *.cxx \
+                         *.cpp \
+                         *.c++ \
+                         *.java \
+                         *.ii \
+                         *.ixx \
+                         *.ipp \
+                         *.i++ \
+                         *.inl \
+                         *.idl \
+                         *.ddl \
+                         *.odl \
+                         *.h \
+                         *.hh \
+                         *.hxx \
+                         *.hpp \
+                         *.h++ \
+                         *.cs \
+                         *.d \
+                         *.php \
+                         *.php4 \
+                         *.php5 \
+                         *.phtml \
+                         *.inc \
+                         *.m \
+                         *.markdown \
+                         *.md \
+                         *.mm \
+                         *.dox \
+                         *.doc \
+                         *.txt \
+                         *.py \
+                         *.pyw \
+                         *.f90 \
+                         *.f95 \
+                         *.f03 \
+                         *.f08 \
+                         *.f \
+                         *.for \
+                         *.tcl \
+                         *.vhd \
+                         *.vhdl \
+                         *.ucf \
+                         *.qsf \
+                         *.ice
+
+# The RECURSIVE tag can be used to specify whether or not subdirectories should
+# be searched for input files as well.
+# The default value is: NO.
+
+RECURSIVE              = YES
+
+# The EXCLUDE tag can be used to specify files and/or directories that should be
+# excluded from the INPUT source files. This way you can easily exclude a
+# subdirectory from a directory tree whose root is specified with the INPUT tag.
+#
+# Note that relative paths are relative to the directory from which doxygen is
+# run.
+
+EXCLUDE                =
+
+# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
+# directories that are symbolic links (a Unix file system feature) are excluded
+# from the input.
+# The default value is: NO.
+
+EXCLUDE_SYMLINKS       = NO
+
+# If the value of the INPUT tag contains directories, you can use the
+# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
+# certain files from those directories.
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories for example use the pattern */test/*
+
+EXCLUDE_PATTERNS       =
+
+# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
+# (namespaces, classes, functions, etc.) that should be excluded from the
+# output. The symbol name can be a fully qualified name, a word, or if the
+# wildcard * is used, a substring. Examples: ANamespace, AClass,
+# AClass::ANamespace, ANamespace::*Test
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories use the pattern */test/*
+
+EXCLUDE_SYMBOLS        =
+
+# The EXAMPLE_PATH tag can be used to specify one or more files or directories
+# that contain example code fragments that are included (see the \include
+# command).
+
+EXAMPLE_PATH           =
+
+# If the value of the EXAMPLE_PATH tag contains directories, you can use the
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank all
+# files are included.
+
+EXAMPLE_PATTERNS       = *
+
+# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
+# searched for input files to be used with the \include or \dontinclude commands
+# irrespective of the value of the RECURSIVE tag.
+# The default value is: NO.
+
+EXAMPLE_RECURSIVE      = NO
+
+# The IMAGE_PATH tag can be used to specify one or more files or directories
+# that contain images that are to be included in the documentation (see the
+# \image command).
+
+IMAGE_PATH             =
+
+# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# invoke to filter for each input file. Doxygen will invoke the filter program
+# by executing (via popen()) the command:
+#
+# <filter> <input-file>
+#
+# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
+# name of an input file. Doxygen will then use the output that the filter
+# program writes to standard output. If FILTER_PATTERNS is specified, this tag
+# will be ignored.
+#
+# Note that the filter must not add or remove lines; it is applied before the
+# code is scanned, but not when the output code is generated. If lines are added
+# or removed, the anchors will not be placed correctly.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+INPUT_FILTER           =
+
+# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
+# basis. Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match. The filters are a list of the form: pattern=filter
+# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
+# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
+# patterns match the file name, INPUT_FILTER is applied.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+FILTER_PATTERNS        =
+
+# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
+# INPUT_FILTER) will also be used to filter the input files that are used for
+# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# The default value is: NO.
+
+FILTER_SOURCE_FILES    = NO
+
+# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
+# it is also possible to disable source filtering for a specific pattern using
+# *.ext= (so without naming a filter).
+# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
+
+FILTER_SOURCE_PATTERNS =
+
+# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
+# is part of the input, its contents will be placed on the main page
+# (index.html). This can be useful if you have a project on for instance GitHub
+# and want to reuse the introduction page also for the doxygen output.
+
+USE_MDFILE_AS_MAINPAGE =
+
+#---------------------------------------------------------------------------
+# Configuration options related to source browsing
+#---------------------------------------------------------------------------
+
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
+# generated. Documented entities will be cross-referenced with these sources.
+#
+# Note: To get rid of all source code in the generated output, make sure that
+# also VERBATIM_HEADERS is set to NO.
+# The default value is: NO.
+
+SOURCE_BROWSER         = NO
+
+# Setting the INLINE_SOURCES tag to YES will include the body of functions,
+# classes and enums directly into the documentation.
+# The default value is: NO.
+
+INLINE_SOURCES         = NO
+
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# special comment blocks from generated source code fragments. Normal C, C++ and
+# Fortran comments will always remain visible.
+# The default value is: YES.
+
+STRIP_CODE_COMMENTS    = YES
+
+# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
+# entity all documented functions referencing it will be listed.
+# The default value is: NO.
+
+REFERENCED_BY_RELATION = NO
+
+# If the REFERENCES_RELATION tag is set to YES then for each documented function
+# all documented entities called/used by that function will be listed.
+# The default value is: NO.
+
+REFERENCES_RELATION    = NO
+
+# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
+# to YES then the hyperlinks from functions in REFERENCES_RELATION and
+# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
+# link to the documentation.
+# The default value is: YES.
+
+REFERENCES_LINK_SOURCE = YES
+
+# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
+# source code will show a tooltip with additional information such as prototype,
+# brief description and links to the definition and documentation. Since this
+# will make the HTML file larger and loading of large files a bit slower, you
+# can opt to disable this feature.
+# The default value is: YES.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+SOURCE_TOOLTIPS        = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code will
+# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# source browser. The htags tool is part of GNU's global source tagging system
+# (see https://www.gnu.org/software/global/global.html). You will need version
+# 4.8.6 or higher.
+#
+# To use it do the following:
+# - Install the latest version of global
+# - Enable SOURCE_BROWSER and USE_HTAGS in the configuration file
+# - Make sure the INPUT points to the root of the source tree
+# - Run doxygen as normal
+#
+# Doxygen will invoke htags (and that will in turn invoke gtags), so these
+# tools must be available from the command line (i.e. in the search path).
+#
+# The result: instead of the source browser generated by doxygen, the links to
+# source code will now point to the output of htags.
+# The default value is: NO.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+USE_HTAGS              = NO
+
+# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# verbatim copy of the header file for each class for which an include is
+# specified. Set to NO to disable this.
+# See also: Section \class.
+# The default value is: YES.
+
+VERBATIM_HEADERS       = YES
+
+# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the
+# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
+# cost of reduced performance. This can be particularly helpful with template
+# rich C++ code for which doxygen's built-in parser lacks the necessary type
+# information.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse_libclang=ON option for CMake.
+# The default value is: NO.
+
+CLANG_ASSISTED_PARSING = NO
+
+# If clang assisted parsing is enabled you can provide the compiler with command
+# line options that you would normally use when invoking the compiler. Note that
+# the include paths will already be set by doxygen for the files and directories
+# specified with INPUT and INCLUDE_PATH.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+CLANG_OPTIONS          =
+
+# If clang assisted parsing is enabled you can provide the clang parser with the
+# path to the compilation database (see:
+# http://clang.llvm.org/docs/HowToSetupToolingForLLVM.html) used when the files
+# were built. This is equivalent to specifying the "-p" option to a clang tool,
+# such as clang-check. These options will then be passed to the parser.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse_libclang=ON option for CMake.
+
+CLANG_DATABASE_PATH    =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the alphabetical class index
+#---------------------------------------------------------------------------
+
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
+# compounds will be generated. Enable this if the project contains a lot of
+# classes, structs, unions or interfaces.
+# The default value is: YES.
+
+ALPHABETICAL_INDEX     = YES
+
+# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
+# which the alphabetical index list will be split.
+# Minimum value: 1, maximum value: 20, default value: 5.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+COLS_IN_ALPHA_INDEX    = 5
+
+# In case all classes in a project start with a common prefix, all classes will
+# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
+# can be used to specify a prefix (or a list of prefixes) that should be ignored
+# while generating the index headers.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+IGNORE_PREFIX          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the HTML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
+# The default value is: YES.
+
+GENERATE_HTML          = YES
+
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_OUTPUT            = html
+
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
+# generated HTML page (for example: .htm, .php, .asp).
+# The default value is: .html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FILE_EXTENSION    = .html
+
+# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
+# each generated HTML page. If the tag is left blank doxygen will generate a
+# standard header.
+#
+# To get valid HTML the header file that includes any scripts and style sheets
+# that doxygen needs, which is dependent on the configuration options used (e.g.
+# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
+# default header using
+# doxygen -w html new_header.html new_footer.html new_stylesheet.css
+# YourConfigFile
+# and then modify the file new_header.html. See also section "Doxygen usage"
+# for information on how to generate the default header that doxygen normally
+# uses.
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. For a description
+# of the possible markers and block names see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_HEADER            =
+
+# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
+# generated HTML page. If the tag is left blank doxygen will generate a standard
+# footer. See HTML_HEADER for more information on how to generate a default
+# footer and what special commands can be used inside the footer. See also
+# section "Doxygen usage" for information on how to generate the default footer
+# that doxygen normally uses.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FOOTER            =
+
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
+# sheet that is used by each HTML page. It can be used to fine-tune the look of
+# the HTML output. If left blank doxygen will generate a default style sheet.
+# See also section "Doxygen usage" for information on how to generate the style
+# sheet that doxygen normally uses.
+# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
+# it is more robust and this tag (HTML_STYLESHEET) will in the future become
+# obsolete.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_STYLESHEET        =
+
+# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# cascading style sheets that are included after the standard style sheets
+# created by doxygen. Using this option one can overrule certain style aspects.
+# This is preferred over using HTML_STYLESHEET since it does not replace the
+# standard style sheet and is therefore more robust against future updates.
+# Doxygen will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list). For an example see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_STYLESHEET  =
+
+# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the HTML output directory. Note
+# that these files will be copied to the base HTML output directory. Use the
+# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
+# files will be copied as-is; there are no commands or markers available.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_FILES       =
+
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
+# will adjust the colors in the style sheet and background images according to
+# this color. Hue is specified as an angle on a colorwheel, see
+# https://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
+# purple, and 360 is red again.
+# Minimum value: 0, maximum value: 359, default value: 220.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_HUE    = 220
+
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
+# in the HTML output. For a value of 0 the output will use grayscales only. A
+# value of 255 will produce the most vivid colors.
+# Minimum value: 0, maximum value: 255, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_SAT    = 100
+
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
+# luminance component of the colors in the HTML output. Values below 100
+# gradually make the output lighter, whereas values above 100 make the output
+# darker. The value divided by 100 is the actual gamma applied, so 80 represents
+# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
+# change the gamma.
+# Minimum value: 40, maximum value: 240, default value: 80.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_GAMMA  = 80
+
+# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
+# page will contain the date and time when the page was generated. Setting this
+# to YES can help to show when doxygen was last run and thus if the
+# documentation is up to date.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_TIMESTAMP         = NO
+
+# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
+# documentation will contain a main index with vertical navigation menus that
+# are dynamically created via JavaScript. If disabled, the navigation index will
+# consists of multiple levels of tabs that are statically embedded in every HTML
+# page. Disable this option to support browsers that do not have JavaScript,
+# like the Qt help browser.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_MENUS     = YES
+
+# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
+# documentation will contain sections that can be hidden and shown after the
+# page has loaded.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_SECTIONS  = NO
+
+# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
+# shown in the various tree structured indices initially; the user can expand
+# and collapse entries dynamically later on. Doxygen will expand the tree to
+# such a level that at most the specified number of entries are visible (unless
+# a fully collapsed tree already exceeds this amount). So setting the number of
+# entries 1 will produce a full collapsed tree by default. 0 is a special value
+# representing an infinite number of entries and will result in a full expanded
+# tree by default.
+# Minimum value: 0, maximum value: 9999, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_INDEX_NUM_ENTRIES = 100
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files will be
+# generated that can be used as input for Apple's Xcode 3 integrated development
+# environment (see: https://developer.apple.com/xcode/), introduced with OSX
+# 10.5 (Leopard). To create a documentation set, doxygen will generate a
+# Makefile in the HTML output directory. Running make will produce the docset in
+# that directory and running make install will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
+# startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy
+# genXcode/_index.html for more information.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_DOCSET        = NO
+
+# This tag determines the name of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# The default value is: Doxygen generated docs.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDNAME        = "Doxygen generated docs"
+
+# This tag specifies a string that should uniquely identify the documentation
+# set bundle. This should be a reverse domain-name style string, e.g.
+# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_BUNDLE_ID       = org.doxygen.Project
+
+# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
+# the documentation publisher. This should be a reverse domain-name style
+# string, e.g. com.mycompany.MyDocSet.documentation.
+# The default value is: org.doxygen.Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
+
+# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
+# The default value is: Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_NAME  = Publisher
+
+# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
+# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
+# (see: https://www.microsoft.com/en-us/download/details.aspx?id=21138) on
+# Windows.
+#
+# The HTML Help Workshop contains a compiler that can convert all HTML output
+# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# files are now used as the Windows 98 help format, and will replace the old
+# Windows help format (.hlp) on all Windows platforms in the future. Compressed
+# HTML files also contain an index, a table of contents, and you can search for
+# words in the documentation. The HTML workshop also contains a viewer for
+# compressed HTML files.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_HTMLHELP      = NO
+
+# The CHM_FILE tag can be used to specify the file name of the resulting .chm
+# file. You can add a path in front of the file if the result should not be
+# written to the html output directory.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_FILE               =
+
+# The HHC_LOCATION tag can be used to specify the location (absolute path
+# including file name) of the HTML help compiler (hhc.exe). If non-empty,
+# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# The file has to be specified with full path.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+HHC_LOCATION           =
+
+# The GENERATE_CHI flag controls if a separate .chi index file is generated
+# (YES) or that it should be included in the master .chm file (NO).
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+GENERATE_CHI           = NO
+
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
+# and project file content.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_INDEX_ENCODING     =
+
+# The BINARY_TOC flag controls whether a binary table of contents is generated
+# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
+# enables the Previous and Next buttons.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+BINARY_TOC             = NO
+
+# The TOC_EXPAND flag can be set to YES to add extra items for group members to
+# the table of contents of the HTML help documentation and to the tree view.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+TOC_EXPAND             = NO
+
+# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
+# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
+# (.qch) of the generated HTML documentation.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_QHP           = NO
+
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
+# the file name of the resulting .qch file. The path specified is relative to
+# the HTML output folder.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QCH_FILE               =
+
+# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
+# Project output. For more information please see Qt Help Project / Namespace
+# (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_NAMESPACE          = org.doxygen.Project
+
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
+# Help Project output. For more information please see Qt Help Project / Virtual
+# Folders (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-
+# folders).
+# The default value is: doc.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_VIRTUAL_FOLDER     = doc
+
+# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
+# filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_NAME   =
+
+# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_ATTRS  =
+
+# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
+# project's filter section matches. Qt Help Project / Filter Attributes (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#filter-attributes).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_SECT_FILTER_ATTRS  =
+
+# The QHG_LOCATION tag can be used to specify the location of Qt's
+# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
+# generated .qhp file.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHG_LOCATION           =
+
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
+# generated, together with the HTML files, they form an Eclipse help plugin. To
+# install this plugin and make it available under the help contents menu in
+# Eclipse, the contents of the directory containing the HTML and XML files needs
+# to be copied into the plugins directory of eclipse. The name of the directory
+# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
+# After copying Eclipse needs to be restarted before the help appears.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_ECLIPSEHELP   = NO
+
+# A unique identifier for the Eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have this
+# name. Each documentation set should have its own identifier.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
+
+ECLIPSE_DOC_ID         = org.doxygen.Project
+
+# If you want full control over the layout of the generated HTML pages it might
+# be necessary to disable the index and replace it with your own. The
+# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
+# of each HTML page. A value of NO enables the index and the value YES disables
+# it. Since the tabs in the index contain the same information as the navigation
+# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+DISABLE_INDEX          = NO
+
+# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
+# structure should be generated to display hierarchical information. If the tag
+# value is set to YES, a side panel will be generated containing a tree-like
+# index structure (just like the one that is generated for HTML Help). For this
+# to work a browser that supports JavaScript, DHTML, CSS and frames is required
+# (i.e. any modern browser). Windows users are probably better off using the
+# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
+# further fine-tune the look of the index. As an example, the default style
+# sheet generated by doxygen has an example that shows how to put an image at
+# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
+# the same information as the tab index, you could consider setting
+# DISABLE_INDEX to YES when enabling this option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_TREEVIEW      = NO
+
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
+# doxygen will group on one line in the generated HTML documentation.
+#
+# Note that a value of 0 will completely suppress the enum values from appearing
+# in the overview section.
+# Minimum value: 0, maximum value: 20, default value: 4.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+ENUM_VALUES_PER_LINE   = 4
+
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
+# to set the initial width (in pixels) of the frame in which the tree is shown.
+# Minimum value: 0, maximum value: 1500, default value: 250.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+TREEVIEW_WIDTH         = 250
+
+# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
+# external symbols imported via tag files in a separate window.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+EXT_LINKS_IN_WINDOW    = NO
+
+# Use this tag to change the font size of LaTeX formulas included as images in
+# the HTML documentation. When you change the font size after a successful
+# doxygen run you need to manually remove any form_*.png images from the HTML
+# output directory to force them to be regenerated.
+# Minimum value: 8, maximum value: 50, default value: 10.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_FONTSIZE       = 10
+
+# Use the FORMULA_TRANSPARENT tag to determine whether or not the images
+# generated for formulas are transparent PNGs. Transparent PNGs are not
+# supported properly for IE 6.0, but are supported on all modern browsers.
+#
+# Note that when changing this option you need to delete any form_*.png files in
+# the HTML output directory before the changes have effect.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_TRANSPARENT    = YES
+
+# The FORMULA_MACROFILE can contain LaTeX \newcommand and \renewcommand commands
+# to create new LaTeX commands to be used in formulas as building blocks. See
+# the section "Including formulas" for details.
+
+FORMULA_MACROFILE      =
+
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
+# https://www.mathjax.org) which uses client side JavaScript for the rendering
+# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
+# installed or if you want to formulas look prettier in the HTML output. When
+# enabled you may also need to install MathJax separately and configure the path
+# to it using the MATHJAX_RELPATH option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+USE_MATHJAX            = YES
+
+# When MathJax is enabled you can set the default output format to be used for
+# the MathJax output. See the MathJax site (see:
+# http://docs.mathjax.org/en/latest/output.html) for more details.
+# Possible values are: HTML-CSS (which is slower, but has the best
+# compatibility), NativeMML (i.e. MathML) and SVG.
+# The default value is: HTML-CSS.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_FORMAT         = HTML-CSS
+
+# When MathJax is enabled you need to specify the location relative to the HTML
+# output directory using the MATHJAX_RELPATH option. The destination directory
+# should contain the MathJax.js script. For instance, if the mathjax directory
+# is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
+# Content Delivery Network so you can quickly see the result without installing
+# MathJax. However, it is strongly recommended to install a local copy of
+# MathJax from https://www.mathjax.org before deployment.
+# The default value is: https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_RELPATH        = https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/
+
+# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
+# extension names that should be enabled during MathJax rendering. For example
+# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_EXTENSIONS     =
+
+# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# of code that will be used on startup of the MathJax code. See the MathJax site
+# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# example see the documentation.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_CODEFILE       =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
+# the HTML output. The underlying search engine uses javascript and DHTML and
+# should work on any modern browser. Note that when using HTML help
+# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
+# there is already a search function so this one should typically be disabled.
+# For large projects the javascript based search engine can be slow, then
+# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
+# search using the keyboard; to jump to the search box use <access key> + S
+# (what the <access key> is depends on the OS and browser, but it is typically
+# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
+# key> to jump into the search results window, the results can be navigated
+# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
+# the search. The filter options can be selected when the cursor is inside the
+# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
+# to select a filter and <Enter> or <escape> to activate or cancel the filter
+# option.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SEARCHENGINE           = YES
+
+# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
+# implemented using a web server instead of a web client using JavaScript. There
+# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
+# setting. When disabled, doxygen will generate a PHP script for searching and
+# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
+# and searching needs to be provided by external tools. See the section
+# "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SERVER_BASED_SEARCH    = NO
+
+# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# script for searching. Instead the search results are written to an XML file
+# which needs to be processed by an external indexer. Doxygen will invoke an
+# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
+# search results.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: https://xapian.org/).
+#
+# See the section "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH        = NO
+
+# The SEARCHENGINE_URL should point to a search engine hosted by a web server
+# which will return the search results when EXTERNAL_SEARCH is enabled.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: https://xapian.org/). See the section "External Indexing and
+# Searching" for details.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHENGINE_URL       =
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
+# search data is written to a file for indexing by an external tool. With the
+# SEARCHDATA_FILE tag the name of this file can be specified.
+# The default file is: searchdata.xml.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHDATA_FILE        = searchdata.xml
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
+# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
+# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
+# projects and redirect the results back to the right project.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH_ID     =
+
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# projects other than the one defined by this configuration file, but that are
+# all added to the same external search index. Each project needs to have a
+# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
+# to a relative location where the documentation can be found. The format is:
+# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTRA_SEARCH_MAPPINGS  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the LaTeX output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
+# The default value is: YES.
+
+GENERATE_LATEX         = YES
+
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_OUTPUT           = latex
+
+# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
+# invoked.
+#
+# Note that when not enabling USE_PDFLATEX the default is latex when enabling
+# USE_PDFLATEX the default is pdflatex and when in the later case latex is
+# chosen this is overwritten by pdflatex. For specific output languages the
+# default can have been set differently, this depends on the implementation of
+# the output language.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_CMD_NAME         =
+
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
+# index for LaTeX.
+# Note: This tag is used in the Makefile / make.bat.
+# See also: LATEX_MAKEINDEX_CMD for the part in the generated output file
+# (.tex).
+# The default file is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+MAKEINDEX_CMD_NAME     = makeindex
+
+# The LATEX_MAKEINDEX_CMD tag can be used to specify the command name to
+# generate index for LaTeX. In case there is no backslash (\) as first character
+# it will be automatically added in the LaTeX code.
+# Note: This tag is used in the generated output file (.tex).
+# See also: MAKEINDEX_CMD_NAME for the part in the Makefile / make.bat.
+# The default value is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_MAKEINDEX_CMD    = makeindex
+
+# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+COMPACT_LATEX          = NO
+
+# The PAPER_TYPE tag can be used to set the paper type that is used by the
+# printer.
+# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
+# 14 inches) and executive (7.25 x 10.5 inches).
+# The default value is: a4.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PAPER_TYPE             = a4
+
+# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
+# that should be included in the LaTeX output. The package can be specified just
+# by its name or with the correct syntax as to be used with the LaTeX
+# \usepackage command. To get the times font for instance you can specify :
+# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
+# To use the option intlimits with the amsmath package you can specify:
+# EXTRA_PACKAGES=[intlimits]{amsmath}
+# If left blank no extra packages will be included.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+EXTRA_PACKAGES         =
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
+# generated LaTeX document. The header should contain everything until the first
+# chapter. If it is left blank doxygen will generate a standard header. See
+# section "Doxygen usage" for information on how to let doxygen write the
+# default header to a separate file.
+#
+# Note: Only use a user-defined header if you know what you are doing! The
+# following commands have a special meaning inside the header: $title,
+# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
+# string, for the replacement values of the other commands the user is referred
+# to HTML_HEADER.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HEADER           =
+
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
+# generated LaTeX document. The footer should contain everything after the last
+# chapter. If it is left blank doxygen will generate a standard footer. See
+# LATEX_HEADER for more information on how to generate a default footer and what
+# special commands can be used inside the footer.
+#
+# Note: Only use a user-defined footer if you know what you are doing!
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_FOOTER           =
+
+# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# LaTeX style sheets that are included after the standard style sheets created
+# by doxygen. Using this option one can overrule certain style aspects. Doxygen
+# will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list).
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_STYLESHEET =
+
+# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the LATEX_OUTPUT output
+# directory. Note that the files will be copied as-is; there are no commands or
+# markers available.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_FILES      =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
+# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
+# contain links (just like the HTML output) instead of page references. This
+# makes the output suitable for online browsing using a PDF viewer.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PDF_HYPERLINKS         = YES
+
+# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
+# the PDF file directly from the LaTeX files. Set this option to YES, to get a
+# higher quality PDF documentation.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+USE_PDFLATEX           = YES
+
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
+# command to the generated LaTeX files. This will instruct LaTeX to keep running
+# if errors occur, instead of asking the user for help. This option is also used
+# when generating formulas in HTML.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BATCHMODE        = NO
+
+# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# index chapters (such as File Index, Compound Index, etc.) in the output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HIDE_INDICES     = NO
+
+# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
+# code with syntax highlighting in the LaTeX output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_SOURCE_CODE      = NO
+
+# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
+# bibliography, e.g. plainnat, or ieeetr. See
+# https://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# The default value is: plain.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BIB_STYLE        = plain
+
+# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_TIMESTAMP        = NO
+
+# The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)
+# path from which the emoji images will be read. If a relative path is entered,
+# it will be relative to the LATEX_OUTPUT directory. If left blank the
+# LATEX_OUTPUT directory will be used.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EMOJI_DIRECTORY  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the RTF output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
+# RTF output is optimized for Word 97 and may not look too pretty with other RTF
+# readers/editors.
+# The default value is: NO.
+
+GENERATE_RTF           = NO
+
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: rtf.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_OUTPUT             = rtf
+
+# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+COMPACT_RTF            = NO
+
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
+# contain hyperlink fields. The RTF file will contain links (just like the HTML
+# output) instead of page references. This makes the output suitable for online
+# browsing using Word or some other Word compatible readers that support those
+# fields.
+#
+# Note: WordPad (write) and others do not support links.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_HYPERLINKS         = NO
+
+# Load stylesheet definitions from file. Syntax is similar to doxygen's
+# configuration file, i.e. a series of assignments. You only have to provide
+# replacements, missing definitions are set to their default value.
+#
+# See also section "Doxygen usage" for information on how to generate the
+# default style sheet that doxygen normally uses.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_STYLESHEET_FILE    =
+
+# Set optional variables used in the generation of an RTF document. Syntax is
+# similar to doxygen's configuration file. A template extensions file can be
+# generated using doxygen -e rtf extensionFile.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_EXTENSIONS_FILE    =
+
+# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
+# with syntax highlighting in the RTF output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_SOURCE_CODE        = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the man page output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
+# classes and files.
+# The default value is: NO.
+
+GENERATE_MAN           = NO
+
+# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it. A directory man3 will be created inside the directory specified by
+# MAN_OUTPUT.
+# The default directory is: man.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_OUTPUT             = man
+
+# The MAN_EXTENSION tag determines the extension that is added to the generated
+# man pages. In case the manual section does not start with a number, the number
+# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
+# optional.
+# The default value is: .3.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_EXTENSION          = .3
+
+# The MAN_SUBDIR tag determines the name of the directory created within
+# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
+# MAN_EXTENSION with the initial . removed.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_SUBDIR             =
+
+# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# will generate one additional man file for each entity documented in the real
+# man page(s). These additional files only source the real man page, but without
+# them the man command would be unable to find the correct page.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_LINKS              = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the XML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
+# captures the structure of the code including all documentation.
+# The default value is: NO.
+
+GENERATE_XML           = NO
+
+# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: xml.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_OUTPUT             = xml
+
+# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
+# listings (including syntax highlighting and cross-referencing information) to
+# the XML output. Note that enabling this will significantly increase the size
+# of the XML output.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_PROGRAMLISTING     = YES
+
+# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include
+# namespace members in file scope as well, matching the HTML output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_NS_MEMB_FILE_SCOPE = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the DOCBOOK output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
+# that can be used to generate PDF.
+# The default value is: NO.
+
+GENERATE_DOCBOOK       = NO
+
+# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
+# front of it.
+# The default directory is: docbook.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_OUTPUT         = docbook
+
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
+# program listings (including syntax highlighting and cross-referencing
+# information) to the DOCBOOK output. Note that enabling this will significantly
+# increase the size of the DOCBOOK output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_PROGRAMLISTING = NO
+
+#---------------------------------------------------------------------------
+# Configuration options for the AutoGen Definitions output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
+# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures
+# the structure of the code including all documentation. Note that this feature
+# is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_AUTOGEN_DEF   = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the Perl module output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
+# file that captures the structure of the code including all documentation.
+#
+# Note that this feature is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_PERLMOD       = NO
+
+# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
+# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
+# output from the Perl module output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_LATEX          = NO
+
+# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
+# formatted so it can be parsed by a human reader. This is useful if you want to
+# understand what is going on. On the other hand, if this tag is set to NO, the
+# size of the Perl module output will be much smaller and Perl will parse it
+# just the same.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_PRETTY         = YES
+
+# The names of the make variables in the generated doxyrules.make file are
+# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
+# so different doxyrules.make files included by the same Makefile don't
+# overwrite each other's variables.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_MAKEVAR_PREFIX =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the preprocessor
+#---------------------------------------------------------------------------
+
+# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
+# C-preprocessor directives found in the sources and include files.
+# The default value is: YES.
+
+ENABLE_PREPROCESSING   = YES
+
+# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
+# in the source code. If set to NO, only conditional compilation will be
+# performed. Macro expansion can be done in a controlled way by setting
+# EXPAND_ONLY_PREDEF to YES.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+MACRO_EXPANSION        = NO
+
+# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
+# the macro expansion is limited to the macros specified with the PREDEFINED and
+# EXPAND_AS_DEFINED tags.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_ONLY_PREDEF     = NO
+
+# If the SEARCH_INCLUDES tag is set to YES, the include files in the
+# INCLUDE_PATH will be searched if a #include is found.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SEARCH_INCLUDES        = YES
+
+# The INCLUDE_PATH tag can be used to specify one or more directories that
+# contain include files that are not input files but should be processed by the
+# preprocessor.
+# This tag requires that the tag SEARCH_INCLUDES is set to YES.
+
+INCLUDE_PATH           =
+
+# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
+# patterns (like *.h and *.hpp) to filter out the header-files in the
+# directories. If left blank, the patterns specified with FILE_PATTERNS will be
+# used.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+INCLUDE_FILE_PATTERNS  =
+
+# The PREDEFINED tag can be used to specify one or more macro names that are
+# defined before the preprocessor is started (similar to the -D option of e.g.
+# gcc). The argument of the tag is a list of macros of the form: name or
+# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
+# is assumed. To prevent a macro definition from being undefined via #undef or
+# recursively expanded use the := operator instead of the = operator.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+PREDEFINED             =
+
+# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
+# tag can be used to specify a list of macro names that should be expanded. The
+# macro definition that is found in the sources will be used. Use the PREDEFINED
+# tag if you want to use a different macro definition that overrules the
+# definition found in the source code.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_AS_DEFINED      =
+
+# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
+# remove all references to function-like macros that are alone on a line, have
+# an all uppercase name, and do not end with a semicolon. Such function macros
+# are typically used for boiler-plate code, and will confuse the parser if not
+# removed.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SKIP_FUNCTION_MACROS   = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to external references
+#---------------------------------------------------------------------------
+
+# The TAGFILES tag can be used to specify one or more tag files. For each tag
+# file the location of the external documentation should be added. The format of
+# a tag file without this location is as follows:
+# TAGFILES = file1 file2 ...
+# Adding location for the tag files is done as follows:
+# TAGFILES = file1=loc1 "file2 = loc2" ...
+# where loc1 and loc2 can be relative or absolute paths or URLs. See the
+# section "Linking to external documentation" for more information about the use
+# of tag files.
+# Note: Each tag file must have a unique name (where the name does NOT include
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+# dependency graph for every global function or class method.
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+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable call graphs for selected
+# functions only using the \callgraph command. Disabling a call graph can be
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+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
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+# functions only using the \callergraph command. Disabling a caller graph can be
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diff --git a/ggml/src/ggml-cann/acl_tensor.cpp b/ggml/src/ggml-cann/acl_tensor.cpp
new file mode 100644
index 0000000000000..d120ce6acf8a7
--- /dev/null
+++ b/ggml/src/ggml-cann/acl_tensor.cpp
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include "acl_tensor.h"
+
+#include <algorithm>
+#include <cstring>
+
+aclDataType ggml_cann_type_mapping(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_F32:
+            return ACL_FLOAT;
+        case GGML_TYPE_F16:
+            return ACL_FLOAT16;
+        case GGML_TYPE_I8:
+            return ACL_INT8;
+        case GGML_TYPE_I16:
+            return ACL_INT16;
+        case GGML_TYPE_I32:
+            return ACL_INT32;
+        case GGML_TYPE_Q4_0:
+            return ACL_INT4;
+        case GGML_TYPE_Q8_0:
+            return ACL_INT8;
+        default:
+            return ACL_DT_UNDEFINED;
+    }
+    return ACL_DT_UNDEFINED;
+}
+
+aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne,
+                                   size_t* nb, int64_t dims, aclFormat format,
+                                   size_t offset) {
+    // If tensor is bcasted, Up to GGML_MAX_DIMS additional dimensions will be
+    // added.
+    int64_t acl_ne[GGML_MAX_DIMS * 2], acl_stride[GGML_MAX_DIMS * 2];
+
+    int64_t acl_storage_len = 0;
+    if (ne == nullptr) {
+        acl_storage_len = ggml_nbytes(tensor);
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            acl_ne[i] = tensor->ne[i];
+            // The step size of acl is in elements.
+            acl_stride[i] = tensor->nb[i] / ggml_element_size(tensor);
+        }
+    } else {
+        // With bcast
+        for (int i = 0; i < dims; i++) {
+            acl_storage_len += (ne[i] - 1) * nb[i];
+            acl_ne[i] = ne[i];
+            acl_stride[i] = nb[i] / ggml_element_size(tensor);
+        }
+    }
+
+    // Reverse ne and stride.
+    int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims);
+    std::reverse(acl_ne, acl_ne + final_dims);
+    std::reverse(acl_stride, acl_stride + final_dims);
+
+    aclTensor* acl_tensor = aclCreateTensor(
+        acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride,
+        offset / ggml_element_size(tensor), format, &acl_storage_len, 1,
+        tensor->data);
+
+    return acl_tensor;
+}
+
+bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) {
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (t1->ne[i] != t0->ne[i] && t1->ne[i] != 1) {
+            return true;
+        }
+    }
+    return false;
+}
+
+int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0,
+                                  const ggml_tensor* src1,
+                                  int64_t* bcast_src0_ne,
+                                  int64_t* bcast_src1_ne, size_t* bcast_src0_nb,
+                                  size_t* bcast_src1_nb) {
+    GGML_ASSERT(ggml_can_repeat(src1, src0));
+    int bcast_dim_cnt = 0;
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        int64_t nr = src0->ne[i] / src1->ne[i];
+        bcast_src0_ne[bcast_dim_cnt] = src0->ne[i] / nr;
+        bcast_src1_ne[bcast_dim_cnt] = src1->ne[i];
+        bcast_src0_nb[bcast_dim_cnt] = src0->nb[i];
+        bcast_src1_nb[bcast_dim_cnt] = src1->nb[i];
+        bcast_dim_cnt++;
+        if (nr != 1) {
+            // Need to add an extra dim.
+            bcast_src0_ne[bcast_dim_cnt] = nr;
+            bcast_src1_ne[bcast_dim_cnt] = 1;
+            bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] *
+                                           bcast_src0_ne[bcast_dim_cnt - 1];
+            bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] *
+                                           bcast_src1_ne[bcast_dim_cnt - 1];
+            bcast_dim_cnt++;
+        }
+    }
+    return bcast_dim_cnt;
+}
+
+int64_t ggml_cann_get_mulmat_bcast_shape(
+    const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne,
+    const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb,
+    int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne,
+    size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb) {
+    // input and dst shoule in same shape, except first two dims.
+    GGML_ASSERT(input_ne[2] == dst_ne[2]);
+    GGML_ASSERT(input_ne[3] == dst_ne[3]);
+
+    int bcast_dim_cnt = 0;
+
+    // For mul_mat, a dimension needs to be added before the dimension that
+    // weight needs to be expanded to satisfy the bcast rule of matrix
+    // multiplication.
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        int64_t nr = input_ne[i] / weight_ne[i];
+        // Do not use bcast in the first two dimensions because we only support
+        // the bcast batch dimension. Just copy them.
+        if (i < 2 || nr == 1) {
+            bcast_input_ne[bcast_dim_cnt] = input_ne[i];
+            bcast_weight_ne[bcast_dim_cnt] = weight_ne[i];
+            bcast_dst_ne[bcast_dim_cnt] = dst_ne[i];
+
+            bcast_input_nb[bcast_dim_cnt] = input_nb[i];
+            bcast_weight_nb[bcast_dim_cnt] = weight_nb[i];
+            bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
+            bcast_dim_cnt++;
+        } else {
+            // Need to add an extra dim.
+            bcast_input_ne[bcast_dim_cnt] = nr;
+            bcast_dst_ne[bcast_dim_cnt] = nr;
+            bcast_weight_ne[bcast_dim_cnt] = 1;
+            bcast_input_nb[bcast_dim_cnt] = input_nb[i];
+            bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
+            bcast_weight_nb[bcast_dim_cnt] = weight_nb[i];
+            bcast_dim_cnt++;
+
+            bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr;
+            bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr;
+            bcast_weight_ne[bcast_dim_cnt] = weight_ne[i];
+            bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] *
+                                            bcast_input_ne[bcast_dim_cnt - 1];
+            bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] *
+                                          bcast_dst_ne[bcast_dim_cnt - 1];
+            bcast_weight_nb[bcast_dim_cnt] =
+                bcast_weight_nb[bcast_dim_cnt - 1] *
+                bcast_weight_ne[bcast_dim_cnt - 1];
+            bcast_dim_cnt++;
+        }
+    }
+    return bcast_dim_cnt;
+}
diff --git a/ggml/src/ggml-cann/acl_tensor.h b/ggml/src/ggml-cann/acl_tensor.h
new file mode 100644
index 0000000000000..4734a9cb8c301
--- /dev/null
+++ b/ggml/src/ggml-cann/acl_tensor.h
@@ -0,0 +1,258 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#ifndef CANN_ACL_TENSOR_H
+#define CANN_ACL_TENSOR_H
+
+#include <algorithm>
+#include <cstring>
+
+#include <aclnn/aclnn_base.h>
+#include "common.h"
+
+/**
+ * @brief	Maps a ggml_type to its corresponding aclDataType.
+ *
+ * @details	This function takes a ggml_type as input and returns the corresponding
+ *			aclDataType. It supports mapping for various ggml_types. If the input type
+ *			does not match any of the predefined ggml_types, the function returns
+ *          ACL_DT_UNDEFINED.
+ *
+ * @param	type    The ggml_type to be mapped.
+ * @return	The corresponding aclDataType. If the input type is not recognized,
+ *			ACL_DT_UNDEFINED is returned.
+ */
+aclDataType ggml_cann_type_mapping(ggml_type type);
+
+/**
+ * @brief   Creates an ACL tensor from a ggml_tensor with optional shape.
+ *
+ * @details This function creates an ACL tensor based on the properties of the
+ *          provided ggml_tensor. It supports customer shape by adjusting dimensions
+ *          and strides accordingly. If customer shape is applied, additional
+ *          dimensions and strides are calculated based on the provided parameters.
+ *
+ * @param   tensor      Pointer to the ggml_tensor to be converted to ACL tensor.
+ * @param   ne          Pointer to an array containing dimensions. Defaults to nullptr
+ *                      if no customer shape is applied.
+ * @param   nb          Pointer to an array containing strides. Defaults to nullptr
+ *                      if no customer shape is applied.
+ * @param   dims        Number of dimensions in the tensor. Defaults to 0 if no customer
+ *                      shape is applied.
+ * @param   format      ACL tensor format. Defaults to ACL_FORMAT_ND.
+ * @param   offset      Offset in bytes for the ACL tensor data. Defaults to 0.
+ * @return  Pointer to the created ACL tensor.
+ */
+aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = nullptr,
+                             size_t* nb = nullptr, int64_t dims = 0,
+                             aclFormat format = ACL_FORMAT_ND,
+                             size_t offset = 0);
+
+/**
+ * @brief   Template for creating an ACL tensor from provided parameters. typename TYPE
+ *          should be size_t or float.
+ *
+ * @details This function creates an ACL tensor using the provided data pointer,
+ *          data type, dimensions, strides, format, offset, and additional parameters.
+ *          It calculates necessary dimensions and strides based on the provided ne and nb
+ *          arrays, adjusting them for the ACL tensor creation. The ACL storage length
+ *          is also calculated based on the provided dimensions and strides.
+ *
+ * @param   data_ptr    Pointer to the data buffer for the ACL tensor.
+ * @param   dtype       ACL data type of the tensor.
+ * @param   type_size   Size of each element in the tensor data buffer.
+ * @param   ne          Pointer to an array containing tensor dimensions.
+ * @param   nb          Pointer to an array containing tensor strides.
+ * @param   dims        Number of dimensions of the tensor.
+ * @param   format      ACL tensor format. Defaults to ACL_FORMAT_ND.
+ * @param   offset      Offset in bytes for the ACL tensor data. Defaults to 0.
+ * @return  Pointer to the created ACL tensor.
+ */
+template<typename TYPE>
+aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype,
+                                   TYPE type_size, int64_t* ne, TYPE* nb,
+                                   int64_t dims,
+                                   aclFormat format = ACL_FORMAT_ND,
+                                   size_t offset = 0) {
+    int64_t tmp_ne[GGML_MAX_DIMS * 2];
+    int64_t tmp_stride[GGML_MAX_DIMS * 2];
+
+    memcpy(tmp_ne, ne, dims * sizeof(int64_t));
+    for (int i = 0; i < dims; i++) {
+        tmp_stride[i] = nb[i] / type_size;
+    }
+
+    std::reverse(tmp_ne, tmp_ne + dims);
+    std::reverse(tmp_stride, tmp_stride + dims);
+
+    int64_t acl_storage_len = 0;
+    for (int i = 0; i < dims; i++) {
+        acl_storage_len += (ne[i] - 1) * nb[i];
+    }
+
+    aclTensor* acl_tensor =
+        aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size,
+                        format, &acl_storage_len, 1, data_ptr);
+
+    return acl_tensor;
+}
+
+/**
+ * @brief   Checks if tensors require broadcasting based on their shapes.
+ *
+ * @details This function determines if two ggml_tensors need to be broadcasted for
+ *          element-wise operations. Broadcasting is necessary if the shapes of the
+ *          tensors are not identical and no dimension in either tensor equals 1.
+ *
+ * @param   t0      Pointer to the first ggml_tensor.
+ * @param   t1      Pointer to the second ggml_tensor.
+ * @return  True if broadcasting is needed, False otherwise.
+ *
+ * @remarks This function iterates over the dimensions of t0 and t1. It checks if each
+ *          dimension in t1 differs from t0's corresponding dimension and is not equal
+ *          to 1. If such a dimension is found, broadcasting is required to align t1
+ *          with t0 for element-wise operations.
+ */
+bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1);
+
+/**
+ * @brief   Computes broadcast shapes and strides for two ggml_tensors.
+ *
+ * @details This function calculates the broadcast shapes and strides for two ggml_tensors,
+ *          following the broadcasting rules similar to numpy. It adjusts dimensions and
+ *          strides to ensure compatibility for element-wise operations where one tensor
+ *          can be broadcasted to match the shape of another tensor.
+ *
+ * @param   src0                Pointer to the first ggml_tensor.
+ * @param   src1                Pointer to the second ggml_tensor.
+ * @param   bcast_ne_src0       Output array to store broadcasted dimensions for src0.
+ * @param   bcast_ne_src1       Output array to store broadcasted dimensions for src1.
+ * @param   bcast_nb_src0       Output array to store broadcasted strides for src0.
+ * @param   bcast_nb_src1       Output array to store broadcasted strides for src1.
+ * @return  Number of dimensions in the broadcasted shape.
+ *
+ * @pre     ggml_can_repeat(src1, src0) must return true, indicating src1 can be broadcasted
+ *          to match src0.
+ *
+ * @remarks This function iterates over the dimensions of src0 and src1, calculating the
+ *          necessary broadcast dimensions and strides. If a dimension requires broadcasting
+ *          (i.e., its size in src1 is smaller than in src0), an additional dimension is
+ *          added with size calculated to match src0's dimension. This adjustment ensures
+ *          that src1 can be element-wise broadcasted to src0's shape.
+ *
+ *  How it works:
+ *
+ *  if dim0 has padding.
+ *  a -> (2, 2) padding = 2
+ *   a: [[1, 2, *, *]
+ *       [2, 3, *, *]]
+ *  nb = (8, 4, 2)
+ *
+ *  if a should bcast with b -> (2, 4)
+ *  b' -> (2, 2, 2)
+ *  b : [[1, 2, 3, 4, *, *]
+ *       [5, 6, 7, 8, *, *]]
+ *  nb = (12, 6, 1)
+ *
+ *  after bcast:
+ *  a' -> (2, 1, 2)
+ *  a': [[[1, 2], *, *]
+ *       [[2, 3], *, *]]
+ *  nb = (8, 4, 2, 1)
+ *
+ *  b' : [[[1, 2], [3, 4], *, *]
+ *        [[5, 6], [7, 8], *, *]]
+ *  nb = (12, 6, 2, 1)
+ *  \endcode
+ *
+ *  dim1 in a inserted dim, should add nb for dim1,
+ *  and all other nb moves to next in order.
+ */
+int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1,
+                        int64_t* bcast_ne_src0, int64_t* bcast_ne_src1,
+                        size_t* bcast_nb_src0, size_t* bcast_nb_src1);
+
+// Bcast macro to avoid duplicate code.
+#define BCAST_SHAPE(src0, src1)                                              \
+    int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2];                            \
+    int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2];                            \
+    size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2];                             \
+    size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2];                             \
+    int64_t bcast_dims = ggml_cann_get_bcast_shape(                          \
+        src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, bcast_##src0##_nb, \
+        bcast_##src1##_nb);
+
+#define BCAST_PARAM(tensor) bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims
+
+/**
+ * @brief Calculates broadcast shapes for matrix multiplication.
+ *
+ * @details This function computes the broadcast shapes required for matrix multiplication
+ *          based on the input, weight, and destination tensor shapes. It ensures that the
+ *          dimensions of weight tensors are expanded appropriately to satisfy matrix
+ *          multiplication broadcast rules.
+ *
+ * @param input_ne      Array containing the dimensions of the input tensor.
+ * @param weight_ne     Array containing the dimensions of the weight tensor.
+ * @param dst_ne        Array containing the dimensions of the destination tensor.
+ * @param input_nb      Array containing the strides of the input tensor.
+ * @param weight_nb     Array containing the strides of the weight tensor.
+ * @param dst_nb        Array containing the strides of the destination tensor.
+ * @param bcast_input_ne    Output array for broadcasted input tensor dimensions.
+ * @param bcast_weight_ne   Output array for broadcasted weight tensor dimensions.
+ * @param bcast_dst_ne      Output array for broadcasted destination tensor dimensions.
+ * @param bcast_input_nb    Output array for broadcasted input tensor strides.
+ * @param bcast_weight_nb   Output array for broadcasted weight tensor strides.
+ * @param bcast_dst_nb      Output array for broadcasted destination tensor strides.
+ * @return The number of dimensions in the broadcasted tensors.
+ *
+ * @remarks This function iterates over the tensor dimensions and calculates the broadcast
+ *          shapes needed for matrix multiplication. It ensures that dimensions where
+ *          weight tensor requires expansion are appropriately handled to conform with
+ *          broadcasting rules.
+ * @note compare with ggml_cann_get_bcast_shape, mul_mat broadcast need add this new dim
+ *       before cast dim.
+ * @sa ggml_cann_get_bcast_shape
+ */
+int64_t ggml_cann_get_mulmat_bcast_shape(
+    const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne,
+    const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb,
+    int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne,
+    size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb);
+
+// Bcast macro to avoid duplicate code.
+#define BCAST_MUL_MAT_SHAPE(input, weight, dst)                         \
+    int64_t bcast_##input##_ne[GGML_MAX_DIMS * 2];                      \
+    int64_t bcast_##weight##_ne[GGML_MAX_DIMS * 2];                     \
+    int64_t bcast_##dst##_ne[GGML_MAX_DIMS * 2];                        \
+    size_t bcast_##input##_nb[GGML_MAX_DIMS * 2];                       \
+    size_t bcast_##weight##_nb[GGML_MAX_DIMS * 2];                      \
+    size_t bcast_##dst##_nb[GGML_MAX_DIMS * 2];                         \
+    int64_t bcast_dims = ggml_cann_get_mulmat_bcast_shape(              \
+        input->ne, weight->ne, dst->ne, input->nb, weight->nb, dst->nb, \
+        bcast_##input##_ne, bcast_##weight##_ne, bcast_##dst##_ne,      \
+        bcast_##input##_nb, bcast_##weight##_nb, bcast_##dst##_nb);
+
+#define BCAST_MUL_MAT_PARAM(tensor) \
+    bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims
+
+#endif  // CANN_ACL_TENSOR_H
diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
new file mode 100644
index 0000000000000..a4ec8418e2ab3
--- /dev/null
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -0,0 +1,3082 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include "aclnn_ops.h"
+
+#include <aclnnop/aclnn_avgpool2d.h>
+#include <aclnnop/aclnn_cast.h>
+#include <aclnnop/aclnn_constant_pad_nd.h>
+#include <aclnnop/aclnn_copy.h>
+#include <aclnnop/aclnn_cos.h>
+#include <aclnnop/aclnn_exp.h>
+#include <aclnnop/aclnn_fill_scalar.h>
+#include <aclnnop/aclnn_group_norm.h>
+#include <aclnnop/aclnn_index_fill_tensor.h>
+#include <aclnnop/aclnn_layer_norm.h>
+#include <aclnnop/aclnn_matmul.h>
+#include <aclnnop/aclnn_max_pool.h>
+#include <aclnnop/aclnn_permute.h>
+#include <aclnnop/aclnn_pow_tensor_tensor.h>
+#include <aclnnop/aclnn_reduce_sum.h>
+#include <aclnnop/aclnn_repeat.h>
+#include <aclnnop/aclnn_repeat_interleave.h>
+#include <aclnnop/aclnn_roll.h>
+#include <aclnnop/aclnn_sin.h>
+#include <aclnnop/aclnn_softmax.h>
+#include <aclnnop/aclnn_tril.h>
+#include <aclnnop/aclnn_triu.h>
+#include <aclnnop/aclnn_upsample_nearest_2d.h>
+#include <aclnnop/aclnn_weight_quant_batch_matmul_v2.h>
+#include <float.h>
+
+#include <cmath>
+#include <cstring>
+#include <exception>
+#include <vector>
+
+#include "kernels/ascendc_kernels.h"
+
+#define GGML_COMMON_DECL_C
+
+#include "../ggml-common.h"
+
+/**
+ * @brief Repeats elements of a tensor along each dimension according to the
+ * specified repeat array.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor to be repeated.
+ * @param acl_dst The destination tensor after repeating.
+ * @param repeat_array The array specifying the number of repetitions along each
+ * dimension.
+ */
+static void aclnn_repeat(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                         aclTensor* acl_dst, int64_t* repeat_array) {
+    // repeat tensor along each dim with repeat_array
+    aclIntArray* repeats = aclCreateIntArray(repeat_array, GGML_MAX_DIMS);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnRepeatGetWorkspaceSize(acl_src, repeats, acl_dst,
+                                          &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        // Memory from allocator will "free" immediately, and this memory
+        // will be alloced to other pointers, but it won't access before
+        // this async task end because all tasks in same stream will execute
+        // in queue.
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+    ACL_CHECK(
+        aclnnRepeat(workspaceAddr, workspaceSize, executor, ctx.stream()));
+    ACL_CHECK(aclDestroyIntArray(repeats));
+}
+
+void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    GGML_ASSERT(ggml_can_repeat(src, dst));
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t repeatsArray[] = {dst->ne[3] / src->ne[3], dst->ne[2] / src->ne[2],
+                              dst->ne[1] / src->ne[1], dst->ne[0] / src->ne[0]};
+
+    aclnn_repeat(ctx, acl_src, acl_dst, repeatsArray);
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Adds two tensors element-wise and stores the result in a destination
+ * tensor.
+ *
+ * This function performs the operation:
+ * \f[
+ *    dst = acl\_src0 + alpha \times acl\_src1
+ * \f]
+ * where alpha is a scalar value and defaults to 1.0f.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src0 The first source tensor.
+ * @param acl_src1 The second source tensor.
+ * @param acl_dst The destination tensor where the result will be stored.
+ */
+static void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0,
+                      aclTensor* acl_src1, aclTensor* acl_dst) {
+    aclScalar* alpha = nullptr;
+    float alphaValue = 1.0f;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnAddGetWorkspaceSize(acl_src0, acl_src1, alpha, acl_dst,
+                                       &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnAdd(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(alpha));
+}
+
+void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+    GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    aclTensor* acl_src0;
+    aclTensor* acl_src1;
+    aclTensor* acl_dst;
+
+    // Need bcast
+    if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) {
+        BCAST_SHAPE(src0, src1)
+        acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0));
+        acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1));
+        acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0));
+    } else {
+        acl_src0 = ggml_cann_create_tensor(src0);
+        acl_src1 = ggml_cann_create_tensor(src1);
+        acl_dst = ggml_cann_create_tensor(dst);
+    }
+
+    aclnn_add(ctx, acl_src0, acl_src1, acl_dst);
+
+    ACL_CHECK(aclDestroyTensor(acl_src0));
+    ACL_CHECK(aclDestroyTensor(acl_src1));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float negative_slope;
+    memcpy(&negative_slope, dst->op_params, sizeof(float));
+    aclScalar* acl_negative_slope =
+        aclCreateScalar(&negative_slope, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnLeakyReluGetWorkspaceSize(
+        acl_src, acl_negative_slope, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnLeakyRelu(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(acl_negative_slope));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Concatenates a list of tensors along a specified dimension and stores
+ * the result in a destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param tensorList The list of tensors to be concatenated.
+ * @param acl_dst The destination tensor where the concatenated result will be
+ * stored.
+ * @param concat_dim The dimension along which the tensors will be concatenated.
+ */
+static void aclnn_concat(ggml_backend_cann_context& ctx,
+                         aclTensorList* tensorList, aclTensor* acl_dst,
+                         int64_t concat_dim) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnCatGetWorkspaceSize(tensorList, concat_dim, acl_dst,
+                                       &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnCat(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+    aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
+    aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t concat_dim = 1;
+    aclTensor* tensors[] = {acl_src0, acl_src1};
+    aclTensorList* tensorList = aclCreateTensorList(tensors, 2);
+    aclnn_concat(ctx, tensorList, acl_dst, concat_dim);
+
+    ACL_CHECK(aclDestroyTensorList(tensorList));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Creates a tensor with values starting from `start`, incremented by
+ * `step`, and ending before `stop`.
+ *
+ * This function performs the operation:
+ * \f[
+ *    \text {out }_{i+1}=\text {out }_i+\text {step}
+ * \f]
+ * the range is [start, stop).
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_dst The destination tensor where the values will be stored.
+ * @param start The starting value of the range.
+ * @param stop The ending value of the range (exclusive).
+ * @param step The step size between consecutive values.
+ * @param n_elements The number of elements in the destination tensor.
+ */
+static void aclnn_arange(ggml_backend_cann_context& ctx, aclTensor* acl_dst,
+                         float start, float stop, float step,
+                         int64_t n_elements) {
+    int64_t steps = (int64_t)std::ceil((stop - start) / step);
+    GGML_ASSERT(n_elements == steps);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    aclScalar* acl_start = aclCreateScalar(&start, aclDataType::ACL_FLOAT);
+    aclScalar* acl_end = aclCreateScalar(&stop, aclDataType::ACL_FLOAT);
+    aclScalar* acl_step = aclCreateScalar(&step, aclDataType::ACL_FLOAT);
+
+    ACL_CHECK(aclnnArangeGetWorkspaceSize(acl_start, acl_end, acl_step, acl_dst,
+                                          &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnArange(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(acl_start));
+    ACL_CHECK(aclDestroyScalar(acl_end));
+    ACL_CHECK(aclDestroyScalar(acl_step));
+}
+
+void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t n_elements = ggml_nelements(dst);
+    float start;
+    float stop;
+    float step;
+    memcpy(&start, (float*)dst->op_params + 0, sizeof(float));
+    memcpy(&stop, (float*)dst->op_params + 1, sizeof(float));
+    memcpy(&step, (float*)dst->op_params + 2, sizeof(float));
+
+    aclnn_arange(ctx, acl_dst, start, stop, step, n_elements);
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_sqr(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    dst->src[1] = dst->src[0];
+    ggml_cann_mul_div<aclnnMulGetWorkspaceSize, aclnnMul>(ctx, dst);
+}
+
+void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    float min;
+    float max;
+    memcpy(&min, dst->op_params, sizeof(float));
+    memcpy(&max, (float*)dst->op_params + 1, sizeof(float));
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    aclScalar* acl_min = aclCreateScalar(&min, aclDataType::ACL_FLOAT);
+    aclScalar* acl_max = aclCreateScalar(&max, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnClampGetWorkspaceSize(acl_src, acl_min, acl_max, acl_dst,
+                                         &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnClamp(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(acl_min));
+    ACL_CHECK(aclDestroyScalar(acl_max));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    // scale factor
+    float v;
+    memcpy(&v, dst->op_params, sizeof(float));
+
+    aclScalar* scale = aclCreateScalar(&v, aclDataType::ACL_FLOAT);
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnMulsGetWorkspaceSize(acl_src, scale, acl_dst, &workspaceSize,
+                                        &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnMuls(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(scale));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    enum ggml_sort_order order = (enum ggml_sort_order)dst->op_params[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+    ggml_cann_pool_alloc temp_buffer_allocator(
+        ctx.pool(), ggml_nelements(dst) * sizeof(int64_t));
+    void* buffer = temp_buffer_allocator.get();
+    aclTensor* tmp_tensor =
+        ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type),
+                                dst->ne, dst->nb, GGML_MAX_DIMS);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnArgsortGetWorkspaceSize(
+        acl_src, -1, (order == GGML_SORT_ORDER_DESC ? true : false), tmp_tensor,
+        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnArgsort(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    workspaceSize = 0;
+    ACL_CHECK(aclnnCastGetWorkspaceSize(tmp_tensor,
+                                        ggml_cann_type_mapping(dst->type),
+                                        acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnCast(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(tmp_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    std::vector<int64_t> normData = {dst->ne[0]};
+    aclIntArray* norm = aclCreateIntArray(normData.data(), normData.size());
+    ACL_CHECK(aclnnLayerNormGetWorkspaceSize(acl_src, norm, nullptr, nullptr,
+                                             eps, acl_dst, nullptr, nullptr,
+                                             &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnLayerNorm(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(norm));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int n_groups = dst->op_params[0];
+
+    float eps;
+    memcpy(&eps, dst->op_params + 1, sizeof(float));
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    int64_t N = src->ne[3];
+    int64_t C = src->ne[2];
+    int64_t HxW = src->ne[1] * src->ne[0];
+
+    size_t type_size = ggml_type_size(src->type);
+    int64_t ne[] = {n_groups, N};
+    size_t nb[] = {type_size, type_size * n_groups};
+    size_t n_bytes = N * n_groups;
+
+    ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes * 2);
+    void* buffer = temp_buffer_allocator.get();
+    aclTensor* acl_mean_out = ggml_cann_create_tensor(
+        buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND);
+    aclTensor* acl_rstd_out = ggml_cann_create_tensor(
+        (char*)buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND);
+
+    ACL_CHECK(aclnnGroupNormGetWorkspaceSize(
+        acl_src, nullptr, nullptr, N, C, HxW, n_groups, eps, acl_dst,
+        acl_mean_out, acl_rstd_out, &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnGroupNorm(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyTensor(acl_mean_out));
+    ACL_CHECK(aclDestroyTensor(acl_rstd_out));
+}
+
+void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+
+    size_t nb1 = ((int32_t*)dst->op_params)[0];
+    size_t nb2 = ((int32_t*)dst->op_params)[1];
+    size_t nb3 = ((int32_t*)dst->op_params)[2];
+    size_t offset = ((int32_t*)dst->op_params)[3];
+    bool inplace = (bool)((int32_t*)dst->op_params)[4];
+
+    size_t param_nb[] = {ggml_element_size(src0), nb1, nb2, nb3};
+
+    aclTensor* acl_dst = ggml_cann_create_tensor(
+        dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
+    aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
+
+    aclScalar* alpha = nullptr;
+    float alphaValue = 1.0f;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    if (!inplace) {
+        size_t cpy_size = ggml_nbytes(dst);
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, cpy_size, src0->data, cpy_size,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+        aclTensor* acl_src0 = ggml_cann_create_tensor(
+            src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
+        ACL_CHECK(aclnnAddGetWorkspaceSize(acl_src0, acl_src1, alpha, acl_dst,
+                                           &workspaceSize, &executor));
+        if (workspaceSize > 0) {
+            ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+            workspaceAddr = workspace_allocator.get();
+        }
+        ACL_CHECK(
+            aclnnAdd(workspaceAddr, workspaceSize, executor, ctx.stream()));
+        ACL_CHECK(aclDestroyTensor(acl_src0));
+    } else {
+        ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, acl_src1, alpha,
+                                                  &workspaceSize, &executor));
+        if (workspaceSize > 0) {
+            ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+            workspaceAddr = workspace_allocator.get();
+        }
+        ACL_CHECK(aclnnInplaceAdd(workspaceAddr, workspaceSize, executor,
+                                  ctx.stream()));
+    }
+
+    ACL_CHECK(aclDestroyTensor(acl_src1));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+
+    GGML_ASSERT(dst->ne[0] == 1);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t reduce_dims_host[] = {3};
+    aclIntArray* reduce_dims = aclCreateIntArray(reduce_dims_host, 1);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnReduceSumGetWorkspaceSize(
+        acl_src, reduce_dims, true, ggml_cann_type_mapping(src->type), acl_dst,
+        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnReduceSum(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx,
+                                  ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    aclTensor* acl_src =
+        ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    std::vector<int64_t> output_size{dst->ne[1], dst->ne[0]};
+    auto output_size_array = aclCreateIntArray(output_size.data(), 2);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnUpsampleNearest2dGetWorkspaceSize(
+        acl_src, output_size_array, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnUpsampleNearest2d(workspaceAddr, workspaceSize, executor,
+                                     ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(output_size_array));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Pads a tensor with a specified value along each dimension.
+ *
+ * This function performs padding of the source tensor `acl_src` and stores the
+ * result in the destination tensor `acl_dst`. The padding values for each
+ * dimension are specified in the `paddings` array.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor to be padded.
+ * @param acl_dst The destination tensor where the padded result will be stored.
+ * @param paddings An array specifying the padding values for each dimension.
+ * The size of the array should be twice the number of dimensions of the tensor.
+ * @param value The value to be used for padding. The default value is 0.0.
+ */
+static void aclnn_pad(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_dst, int64_t* paddings,
+                      float value = 0.0f) {
+    aclIntArray* acl_pad = aclCreateIntArray(paddings, GGML_MAX_DIMS * 2);
+    aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnConstantPadNdGetWorkspaceSize(
+        acl_src, acl_pad, acl_value, acl_dst, &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnConstantPadNd(workspaceAddr, workspaceSize, executor,
+                                 ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(acl_pad));
+    ACL_CHECK(aclDestroyScalar(acl_value));
+}
+
+void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    // padding: value in the array means how much distance will be padding.
+    // the position of elements in the array means which dirction to padding,
+    // each position means: [dim0.front, dim0.behind, dim1.front, dim1.behind,
+    //                       dim2.front, dim2.behind, dim3.front, dim3.behind]
+    int64_t paddings[] = {
+        0, dst->ne[0] - src->ne[0], 0, dst->ne[1] - src->ne[1],
+        0, dst->ne[2] - src->ne[2], 0, dst->ne[3] - src->ne[3]};
+    aclnn_pad(ctx, acl_src, acl_dst, paddings);
+
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+}
+
+/**
+ * @brief Performs 2D average pooling on the input tensor and stores the result
+ * in the destination tensor.
+ *
+ * This function performs average pooling on the source tensor and stores the
+ * result in the destination tensor. The pooling parameters (kernel size,
+ * strides, padding) are specified in the `op_params` of the destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result will be stored. The source
+ * tensor is referenced by `dst->src[0]`.
+ */
+static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx,
+                                 ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src =
+        ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    const int32_t* opts = (const int32_t*)dst->op_params;
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+
+    std::vector<int64_t> kernel_dims = {k1, k0};
+    std::vector<int64_t> stride_dims = {s1, s0};
+    std::vector<int64_t> padding_avg_dims = {p1, p0};  // (padH, padW)
+
+    auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2);
+    auto* strides = aclCreateIntArray(stride_dims.data(), 2);
+    auto* paddings_avg = aclCreateIntArray(padding_avg_dims.data(), 2);
+
+    bool ceil_mode = false;
+    bool count_include_pad = true;
+    int64_t divisor_override = 0;
+    int8_t cube_math_type = 0;
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnAvgPool2dGetWorkspaceSize(
+        acl_src, kernel_size, strides, paddings_avg, ceil_mode,
+        count_include_pad, divisor_override, cube_math_type, acl_dst,
+        &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+    ACL_CHECK(
+        aclnnAvgPool2d(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyIntArray(kernel_size));
+    ACL_CHECK(aclDestroyIntArray(strides));
+    ACL_CHECK(aclDestroyIntArray(paddings_avg));
+}
+
+/**
+ * @brief Performs 2D max pooling on the input tensor and stores the result in
+ * the destination tensor.
+ *
+ * This function performs max pooling on the source tensor and stores the result
+ * in the destination tensor. The pooling parameters (kernel size, strides,
+ * padding) are specified in the `op_params` of the destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result will be stored. The source
+ * tensor is referenced by `dst->src[0]`.
+ */
+static void ggml_cann_max_pool2d(ggml_backend_cann_context& ctx,
+                                 ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src =
+        ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    const int32_t* opts = (const int32_t*)dst->op_params;
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+
+    int64_t temp_ne[] = {src->ne[0] + p0 * 2, src->ne[1] + p1 * 2, src->ne[2],
+                         src->ne[3]};
+    size_t temp_nb[GGML_MAX_DIMS];
+
+    temp_nb[0] = ggml_element_size(src);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        temp_nb[i] = temp_nb[i - 1] * temp_ne[i - 1];
+    }
+
+    ggml_cann_pool_alloc temp_buffer_allocator(
+        ctx.pool(), ggml_nbytes(src) + p0 * 2 + p1 * 2 * src->nb[1]);
+    void* buffer = temp_buffer_allocator.get();
+    aclTensor* tmp_tensor = ggml_cann_create_tensor(
+        buffer, ACL_FLOAT, ggml_element_size(src), temp_ne, temp_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_NCHW);
+
+    // pad: see padding in ggml_cann_pad()
+    int64_t paddings[] = {p0, p0, p1, p1, 0, 0, 0, 0};
+    float value = -FLT_MAX;
+    aclnn_pad(ctx, acl_src, tmp_tensor, paddings, value);
+
+    // max_pool
+    std::vector<int64_t> kernel_dims = {k1, k0};
+    std::vector<int64_t> stride_dims = {s1, s0};
+    // padding_max_dims: [dim0_start, dim0_end, dim1_start, dim1_end]
+    std::vector<int64_t> padding_max_dims = {0, 0, 0, 0};
+    std::vector<int64_t> dilation_size = {1, 1};
+    auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2);
+    auto* strides = aclCreateIntArray(stride_dims.data(), 2);
+    auto* paddings_max = aclCreateIntArray(padding_max_dims.data(), 4);
+    auto* dilations = aclCreateIntArray(dilation_size.data(), 2);
+
+    bool ceil_mode = false;
+    int64_t auto_pads = 0;
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnMaxPoolGetWorkspaceSize(
+        tmp_tensor, kernel_size, strides, auto_pads, paddings_max, dilations,
+        ceil_mode, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnMaxPool(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyTensor(tmp_tensor));
+    ACL_CHECK(aclDestroyIntArray(kernel_size));
+    ACL_CHECK(aclDestroyIntArray(strides));
+    ACL_CHECK(aclDestroyIntArray(paddings_max));
+    ACL_CHECK(aclDestroyIntArray(dilations));
+}
+
+void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    const int32_t* opts = (const int32_t*)dst->op_params;
+    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
+    switch (op) {
+        case GGML_OP_POOL_AVG:
+            ggml_cann_avg_pool2d(ctx, dst);
+            break;
+        case GGML_OP_POOL_MAX:
+            ggml_cann_max_pool2d(ctx, dst);
+            break;
+        case GGML_OP_POOL_COUNT:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+
+/**
+ * @brief Copies data from the source tensor to the destination tensor.
+ *
+ * This function copies data from the source tensor `acl_src` to the destination
+ * tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor from which data will be copied.
+ * @param acl_dst The destination tensor where the data will be copied to.
+ */
+static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceCopyGetWorkspaceSize(acl_dst, acl_src, &workspaceSize,
+                                               &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceCopy(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    ggml_cann_pool_alloc src_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    ggml_cann_pool_alloc dst_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    src->extra = src_extra_allocator.get();
+    dst->extra = dst_extra_allocator.get();
+    ACL_CHECK(aclrtMemcpyAsync(src->extra, sizeof(ggml_tensor), src,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+    ACL_CHECK(aclrtMemcpyAsync(dst->extra, sizeof(ggml_tensor), dst,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+
+    if ((dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32) &&
+        ggml_are_same_shape(src, dst)) {
+        cann_copy(ctx, acl_src, acl_dst);
+        ACL_CHECK(aclDestroyTensor(acl_src));
+        ACL_CHECK(aclDestroyTensor(acl_dst));
+        return;
+    }
+    // TODO: simplify
+    if (src->type == GGML_TYPE_F16) {
+        if (dst->type == GGML_TYPE_Q8_0) {
+            aclrtlaunch_ascendc_quantize_f16_q8_0(
+                24, ctx.stream(), src->data, dst->data,
+                ((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
+                ((ggml_tensor*)dst->extra)->ne);
+            return;
+        }
+        if (dst->type == GGML_TYPE_Q4_0) {
+            aclrtlaunch_ascendc_quantize_f16_to_q4_0(
+                24, ctx.stream(), src->data, dst->data,
+                ((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
+                ((ggml_tensor*)dst->extra)->ne);
+            return;
+        }
+        if (dst->type == GGML_TYPE_F16) {
+            if (ggml_are_same_shape(src, dst)) {
+                cann_copy(ctx, acl_src, acl_dst);
+                ACL_CHECK(aclDestroyTensor(acl_src));
+                ACL_CHECK(aclDestroyTensor(acl_dst));
+                return;
+            }
+            if (ggml_is_contiguous(dst)) {
+                const size_t src_type_size = ggml_type_size(src->type);
+                if (src->nb[0] == src_type_size) {
+                    // src0 is contigous on first dimension, copy by rows
+                    int64_t rows_num = ggml_nrows(src);
+
+                    aclrtlaunch_ascendc_dup_by_rows_fp16(
+                        rows_num, ctx.stream(), src->data, dst->data,
+                        ((ggml_tensor*)src->extra)->ne,
+                        ((ggml_tensor*)src->extra)->nb,
+                        ((ggml_tensor*)dst->extra)->ne,
+                        ((ggml_tensor*)dst->extra)->nb);
+                    return;
+                }
+                GGML_ABORT("fatal error");
+            }
+            GGML_ABORT("fatal error");
+        }
+        if (dst->type == GGML_TYPE_F32) {
+            if (ggml_are_same_shape(src, dst)) {
+                cann_copy(ctx, acl_src, acl_dst);
+                ACL_CHECK(aclDestroyTensor(acl_src));
+                ACL_CHECK(aclDestroyTensor(acl_dst));
+                return;
+            }
+            if (ggml_is_contiguous(dst)) {
+                const size_t src_type_size = ggml_type_size(src->type);
+                if (src->nb[0] == src_type_size) {
+                    // src0 is contigous on first dimension, copy by rows
+                    int64_t rows_num = ggml_nrows(src);
+                    aclrtlaunch_ascendc_dup_by_rows_fp16_to_fp32(
+                        rows_num, ctx.stream(), src->data, dst->data,
+                        ((ggml_tensor*)src->extra)->ne,
+                        ((ggml_tensor*)src->extra)->nb,
+                        ((ggml_tensor*)dst->extra)->ne,
+                        ((ggml_tensor*)dst->extra)->nb);
+                    return;
+                }
+                GGML_ABORT("fatal error");
+            }
+            GGML_ABORT("fatal error");
+        }
+        // TODO
+        GGML_ABORT("fatal error");
+    } else if (src->type == GGML_TYPE_F32) {
+        // TODO: if (src0->type == dst->type && ne00 == ne0 && nb00 == type_size
+        //          && nb0 == type_size)
+        if (dst->type == GGML_TYPE_Q8_0) {
+            aclrtlaunch_ascendc_quantize_f32_q8_0(
+                24, ctx.stream(), src->data, dst->data,
+                ((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
+                ((ggml_tensor*)dst->extra)->ne);
+            return;
+        }
+        if (dst->type == GGML_TYPE_Q4_0) {
+            aclrtlaunch_ascendc_quantize_f32_to_q4_0(
+                24, ctx.stream(), src->data, dst->data,
+                ((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
+                ((ggml_tensor*)dst->extra)->ne);
+            return;
+        }
+        if (dst->type == GGML_TYPE_F32) {
+            if (ggml_are_same_shape(src, dst)) {
+                cann_copy(ctx, acl_src, acl_dst);
+                ACL_CHECK(aclDestroyTensor(acl_src));
+                ACL_CHECK(aclDestroyTensor(acl_dst));
+                return;
+            }
+            if (ggml_is_contiguous(dst)) {
+                const size_t src_type_size = ggml_type_size(src->type);
+                if (src->nb[0] == src_type_size) {
+                    // src0 is contigous on first dimension, copy by rows
+                    int64_t rows_num = ggml_nrows(src);
+                    aclrtlaunch_ascendc_dup_by_rows_fp32(
+                        rows_num, ctx.stream(), src->data, dst->data,
+                        ((ggml_tensor*)src->extra)->ne,
+                        ((ggml_tensor*)src->extra)->nb,
+                        ((ggml_tensor*)dst->extra)->ne,
+                        ((ggml_tensor*)dst->extra)->nb);
+                    return;
+                }
+                GGML_ABORT("fatal error");
+            } else {
+                // TODO: dst not contiguous
+                GGML_ABORT("fatal error");
+            }
+        }
+        if (dst->type == GGML_TYPE_F16) {
+            if (ggml_are_same_shape(src, dst)) {
+                cann_copy(ctx, acl_src, acl_dst);
+                ACL_CHECK(aclDestroyTensor(acl_src));
+                ACL_CHECK(aclDestroyTensor(acl_dst));
+                return;
+            }
+            if (ggml_is_contiguous(dst)) {
+                const size_t src_type_size = ggml_type_size(src->type);
+                if (src->nb[0] == src_type_size) {
+                    // src0 is contigous on first dimension, copy by rows
+                    int64_t rows_num = ggml_nrows(src);
+                    aclrtlaunch_ascendc_dup_by_rows_fp32_to_fp16(
+                        rows_num, ctx.stream(), src->data, dst->data,
+                        ((ggml_tensor*)src->extra)->ne,
+                        ((ggml_tensor*)src->extra)->nb,
+                        ((ggml_tensor*)dst->extra)->ne,
+                        ((ggml_tensor*)dst->extra)->nb);
+                    return;
+                }
+                GGML_ABORT("fatal error");
+            }
+        }
+        // TODO
+        GGML_ABORT("fatal error");
+    } else {
+        if (ggml_are_same_shape(src, dst)) {
+            cann_copy(ctx, acl_src, acl_dst);
+            ACL_CHECK(aclDestroyTensor(acl_src));
+            ACL_CHECK(aclDestroyTensor(acl_dst));
+            return;
+        }
+        GGML_ABORT("fatal error");
+    }
+}
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+aclnnStatus aclnnRmsNormGetWorkspaceSize(const aclTensor* x,
+                                         const aclTensor* gamma, double epsilon,
+                                         const aclTensor* yOut,
+                                         const aclTensor* rstdOout,
+                                         uint64_t* workspaceSize,
+                                         aclOpExecutor** executor);
+aclnnStatus aclnnRmsNorm(void* workspace, uint64_t workspaceSize,
+                         aclOpExecutor* executor, aclrtStream stream);
+#ifdef __cplusplus
+}
+#endif
+
+/**
+ * @brief Creates an ACL tensor initialized with zeros using a provided buffer.
+ *
+ * This function initializes a tensor with zeros using the specified buffer and
+ * tensor parameters.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param buffer The buffer to be used for the tensor data.
+ * @param n_bytes The size of the buffer in bytes.
+ * @param ne An array specifying the extents (sizes) of each dimension of the
+ * tensor.
+ * @param dims The number of dimensions of the tensor.
+ * @param type The data type of the tensor.
+ * @param type_size The size of each element in the tensor data type.
+ * @return An ACL tensor initialized with zeros.
+ */
+static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer,
+                             size_t n_bytes, int64_t* ne, int64_t dims,
+                             aclDataType type, size_t type_size) {
+    size_t nb[GGML_MAX_DIMS];
+    nb[0] = type_size;
+    for (int i = 1; i < dims; i++) {
+        nb[i] = nb[i - 1] * ne[i - 1];
+    }
+
+    ACL_CHECK(aclrtMemsetAsync(buffer, n_bytes, 0, n_bytes, ctx.stream()));
+    aclTensor* zero =
+        ggml_cann_create_tensor(buffer, type, type_size, ne, nb, dims);
+    return zero;
+}
+
+/**
+ * @brief Creates an ACL tensor initialized with ones using a provided buffer.
+ *
+ * This function initializes a tensor with ones using the specified buffer and
+ * tensor parameters.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param buffer The buffer to be used for the tensor data.
+ * @param n_bytes The size of the buffer in bytes.
+ * @param ne An array specifying the extents (sizes) of each dimension of the
+ * tensor.
+ * @param dims The number of dimensions of the tensor.
+ * @param type The data type of the tensor.
+ * @param type_size The size of each element in the tensor data type.
+ * @param value The value to be used for initializing the tensor (default
+ * is 1.0).
+ * @return An ACL tensor initialized with ones.
+ */
+static aclTensor* aclnn_ones(ggml_backend_cann_context& ctx, void* buffer,
+                             size_t n_bytes, int64_t* ne, int64_t dims,
+                             aclDataType type, size_t type_size,
+                             float value = 1.0f) {
+    aclTensor* acl_tensor =
+        aclnn_zero(ctx, buffer, n_bytes, ne, dims, type, type_size);
+    float alpha_host = 1.0f;
+    aclScalar* alpha = aclCreateScalar(&alpha_host, aclDataType::ACL_FLOAT);
+    aclScalar* other = aclCreateScalar(&value, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceAddsGetWorkspaceSize(acl_tensor, other, alpha,
+                                               &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+    ACL_CHECK(
+        aclnnInplaceAdds(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    return acl_tensor;
+}
+
+void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    GGML_ASSERT(eps > 0.0f);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    size_t one_tensor_n_bytes = src->ne[0] * ggml_element_size(src);
+    ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes);
+
+    aclTensor* acl_gamma = aclnn_ones(
+        ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, 1,
+        ggml_cann_type_mapping(src->type), ggml_element_size(src));
+
+    size_t zero_tensor_n_bytes =
+        src->ne[1] * src->ne[2] * src->ne[3] * ggml_element_size(src);
+    ggml_cann_pool_alloc zero_tensor_allocator(ctx.pool(), zero_tensor_n_bytes);
+    aclTensor* acl_rstd =
+        aclnn_zero(ctx, zero_tensor_allocator.get(), zero_tensor_n_bytes,
+                   src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type),
+                   ggml_element_size(src));
+
+    ACL_CHECK(aclnnRmsNormGetWorkspaceSize(
+        acl_src, acl_gamma, eps, acl_dst, acl_rstd, &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnRmsNorm(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyTensor(acl_gamma));
+    ACL_CHECK(aclDestroyTensor(acl_rstd));
+}
+
+// TODO: performace is low.
+void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst,
+                         float value) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    const int n_past = ((int32_t*)dst->op_params)[0];
+
+    size_t one_tensor_n_bytes = src->ne[0] * src->ne[1] * src->ne[2] *
+                                src->ne[3] * ggml_element_size(src);
+    ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes);
+
+    aclTensor* mask_tensor =
+        aclnn_ones(ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne,
+                   GGML_MAX_DIMS, ggml_cann_type_mapping(src->type),
+                   ggml_element_size(src), value);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceTriuGetWorkspaceSize(mask_tensor, n_past + 1,
+                                               &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceTriu(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclnnTrilGetWorkspaceSize(acl_src, n_past + 1, acl_dst,
+                                        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnTril(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    aclScalar* alpha = nullptr;
+    float alphaValue = 1.0f;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, mask_tensor, alpha,
+                                              &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+    ACL_CHECK(
+        aclnnInplaceAdd(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(alpha));
+    ACL_CHECK(aclDestroyTensor(mask_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Casts the data type of a source tensor to a destination tensor.
+ *
+ * This function casts the data type of the source tensor `acl_src` to the
+ * specified data type `cast_data_type` and stores the result in the destination
+ * tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose data type will be casted.
+ * @param acl_dst The destination tensor where the casted result will be stored.
+ * @param cast_data_type The target data type to which the source tensor will be
+ * casted.
+ */
+static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                       aclTensor* acl_dst, aclDataType cast_data_type) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnCastGetWorkspaceSize(acl_src, cast_data_type, acl_dst,
+                                        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnCast(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Permutes the dimensions of a tensor according to a specified order.
+ *
+ * This function permutes the dimensions of the source tensor `acl_src`
+ * according to the order specified in the `new_dim` array and stores the result
+ * in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose dimensions will be permuted.
+ * @param acl_dst The destination tensor where the permuted result will be
+ * stored.
+ * @param new_dim An array specifying the new order of dimensions for the
+ * tensor.
+ * @param dims The number of dimensions in the tensor.
+ */
+static void aclnn_permute(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                          aclTensor* acl_dst, int64_t* new_dim, uint64_t dims) {
+    aclIntArray* acl_dims = aclCreateIntArray(new_dim, dims);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnPermuteGetWorkspaceSize(acl_src, acl_dims, acl_dst,
+                                           &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnPermute(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(acl_dims));
+}
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+aclnnStatus aclnnIm2colGetWorkspaceSize(const aclTensor* self,
+                                        const aclIntArray* kernelSize,
+                                        const aclIntArray* dilation,
+                                        const aclIntArray* padding,
+                                        const aclIntArray* stride,
+                                        aclTensor* out, uint64_t* workspaceSize,
+                                        aclOpExecutor** executor);
+aclnnStatus aclnnIm2col(void* workspace, uint64_t workspaceSize,
+                        aclOpExecutor* executor, aclrtStream stream);
+#ifdef __cplusplus
+}
+#endif
+
+static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context& ctx,
+                                             ggml_tensor* dst,
+                                             ggml_tensor* src1,
+                                             aclTensor* tmp_cast_tensor,
+                                             aclTensor* tmp_im2col_tensor) {
+    // Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW]
+    int64_t dst_ne[] = {dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3]};
+    size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[3]};
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1);
+
+    int64_t permute_dim[] = {0, 2, 1};
+    if (src1->type != dst->type) {
+        aclnn_permute(ctx, tmp_cast_tensor, acl_dst, permute_dim, 3);
+    } else {
+        aclnn_permute(ctx, tmp_im2col_tensor, acl_dst, permute_dim, 3);
+    }
+
+    // release
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+static void ggml_cann_im2col_1d_post_process(
+    ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_tensor* src1,
+    aclTensor* tmp_cast_tensor, aclTensor* tmp_im2col_tensor,
+    const std::vector<int64_t>& im2col_op_params) {
+    // get params
+    const int64_t KH = im2col_op_params[0];
+    const int64_t KW = im2col_op_params[1];
+    const int64_t IW = im2col_op_params[2];
+    const int64_t IC = im2col_op_params[3];
+    const int64_t N = im2col_op_params[4];
+    const int64_t OH = im2col_op_params[5];
+    const int64_t OW = im2col_op_params[6];
+    const int64_t s0 = im2col_op_params[7];
+    const int64_t p0 = im2col_op_params[8];
+    const int64_t d0 = im2col_op_params[9];
+    const int64_t n_bytes_factor = im2col_op_params[10];
+
+    // Permute: [N, IC * KH * KW, OW * OH] ->
+    // [N, OW * OH * n_bytes_factor, IC * KH * KW]
+    aclTensor* tmp_permute_tensor = nullptr;
+    ggml_cann_pool_alloc tmp_permute_allocator(ctx.pool());
+    tmp_permute_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor);
+    void* tmp_permute_buffer = tmp_permute_allocator.get();
+
+    int64_t tmp_permute_ne[] = {IC * KH * KW, OW * OH * n_bytes_factor, N};
+    size_t tmp_permute_nb[GGML_MAX_DIMS - 1];
+    tmp_permute_nb[0] = ggml_type_size(dst->type);
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1];
+    }
+
+    tmp_permute_tensor = ggml_cann_create_tensor(
+        tmp_permute_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_permute_ne, tmp_permute_nb,
+        GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+
+    int64_t permute_dim[] = {0, 2, 1};
+    if (src1->type != dst->type) {
+        aclnn_permute(ctx, tmp_cast_tensor, tmp_permute_tensor, permute_dim, 3);
+    } else {
+        aclnn_permute(ctx, tmp_im2col_tensor, tmp_permute_tensor, permute_dim,
+                      3);
+    }
+
+    // number of times the kernel moves in W dimension
+    const int n_step_w = (IW + 2 * p0 - d0 * (KW - 1) - 1) / s0 + 1;
+    size_t offset;
+    void *cur_dst_buffer = dst->data, *cur_permute_buffer = tmp_permute_buffer;
+
+    // memory copy with offset to restore 1D im2col from 2d
+    if (IC > 1) {
+        offset = IC * KH * KW * n_step_w * ggml_type_size(dst->type);
+        size_t size_cpy = KH * KW * ggml_type_size(dst->type);
+
+        for (int c = 0; c < IC; c++) {
+            cur_permute_buffer = (char*)tmp_permute_buffer + offset +
+                                 KH * KW * c * ggml_type_size(dst->type);
+            cur_dst_buffer = (char*)dst->data +
+                             c * KH * KW * n_step_w * ggml_type_size(dst->type);
+
+            for (int i = 0; i < n_step_w; i++) {
+                ACL_CHECK(aclrtMemcpyAsync(
+                    cur_dst_buffer, size_cpy, cur_permute_buffer, size_cpy,
+                    ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+                cur_dst_buffer =
+                    (char*)cur_dst_buffer + KH * KW * ggml_type_size(dst->type);
+                cur_permute_buffer = (char*)cur_permute_buffer +
+                                     KH * KW * IC * ggml_type_size(dst->type);
+            }
+        }
+    } else {
+        offset = KH * KW * n_step_w *
+                 ggml_type_size(dst->type);  // equal to ggml_nbytes(dst)
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, offset,
+                                   (char*)tmp_permute_buffer + offset, offset,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+    }
+
+    // release
+    ACL_CHECK(aclDestroyTensor(tmp_permute_tensor));
+}
+
+void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];  // kernel
+    ggml_tensor* src1 = dst->src[1];  // input
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    // aclnnIm2col only works on 2D. set s1, p1, d1 to 1 to perform 2D
+    // im2col and do post-processing to restore it to 1D.
+    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = is_2D ? ((const int32_t*)(dst->op_params))[1] : 1;
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = is_2D ? ((const int32_t*)(dst->op_params))[3] : 1;
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = is_2D ? ((const int32_t*)(dst->op_params))[5] : 1;
+
+    const int64_t N = ne13;
+    const int64_t IC = ne12;
+    const int64_t KH = ne01;
+    const int64_t KW = ne00;
+    const int64_t IW = ne10;
+
+    const int64_t OH = is_2D ? ne2 : 1;
+    const int64_t OW = ne1;
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // memory allocated increased to 3x when is_2D == false
+    const int64_t n_bytes_factor = is_2D ? 1 : 3;
+
+    // im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH * n_bytes_factor]
+    aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
+    int64_t tmp_im2col_ne[] = {OW * OH * n_bytes_factor, IC * KH * KW, N};
+    size_t tmp_im2col_nb[GGML_MAX_DIMS - 1];
+
+    tmp_im2col_nb[0] = ggml_type_size(src1->type);
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        tmp_im2col_nb[i] = tmp_im2col_nb[i - 1] * tmp_im2col_ne[i - 1];
+    }
+
+    // Calculate im2col.
+    // If dst is f16, tmp_buffer is f32, we need alloc src.typesize *
+    // dst.elemcount.
+    ggml_cann_pool_alloc im2col_allocator(
+        ctx.pool(),
+        ggml_nelements(dst) * ggml_element_size(src1) * n_bytes_factor);
+    void* tmp_im2col_buffer = im2col_allocator.get();
+
+    aclTensor* tmp_im2col_tensor = ggml_cann_create_tensor(
+        tmp_im2col_buffer, ggml_cann_type_mapping(src1->type),
+        ggml_type_size(src1->type), tmp_im2col_ne, tmp_im2col_nb,
+        GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+
+    std::vector<int64_t> kernel_dims = {KH, KW};
+    std::vector<int64_t> dilation_size = {d1, d0};
+    std::vector<int64_t> padding_dims = {p1, p0};
+    std::vector<int64_t> stride_dims = {s1, s0};
+    auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2);
+    auto* dilations = aclCreateIntArray(dilation_size.data(), 2);
+    auto* paddings = aclCreateIntArray(padding_dims.data(), 2);
+    auto* strides = aclCreateIntArray(stride_dims.data(), 2);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnIm2colGetWorkspaceSize(acl_src1, kernel_size, dilations,
+                                          paddings, strides, tmp_im2col_tensor,
+                                          &workspaceSize, &executor));
+
+    ggml_cann_pool_alloc workspace_allocator(ctx.pool());
+    if (workspaceSize > 0) {
+        workspace_allocator.alloc(workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnIm2col(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    // Cast if dst is f16.
+    aclTensor* tmp_cast_tensor = nullptr;
+    ggml_cann_pool_alloc tmp_cast_allocator(ctx.pool());
+    void* tmp_cast_buffer = nullptr;
+    if (src1->type != dst->type) {
+        tmp_cast_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor);
+        tmp_cast_buffer = tmp_cast_allocator.get();
+        size_t temp_cast_nb[GGML_MAX_DIMS - 1];
+        temp_cast_nb[0] = ggml_type_size(dst->type);
+        for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+            temp_cast_nb[i] = temp_cast_nb[i - 1] * tmp_im2col_ne[i - 1];
+        }
+
+        tmp_cast_tensor = ggml_cann_create_tensor(
+            tmp_cast_buffer, ggml_cann_type_mapping(dst->type),
+            ggml_type_size(dst->type), tmp_im2col_ne, temp_cast_nb,
+            GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+        aclnn_cast(ctx, tmp_im2col_tensor, tmp_cast_tensor,
+                   ggml_cann_type_mapping(dst->type));
+    }
+
+    // post-processing
+    if (is_2D) {
+        ggml_cann_im2col_2d_post_process(ctx, dst, src1, tmp_cast_tensor,
+                                         tmp_im2col_tensor);
+    } else {
+        std::vector<int64_t> im2col_op_params = {
+            KH, KW, IW, IC, N, OH, OW, s0, p0, d0, n_bytes_factor};
+        ggml_cann_im2col_1d_post_process(ctx, dst, src1, tmp_cast_tensor,
+                                         tmp_im2col_tensor, im2col_op_params);
+    }
+
+    // release
+    ACL_CHECK(aclDestroyTensor(acl_src1));
+    ACL_CHECK(aclDestroyTensor(tmp_im2col_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_cast_tensor));
+    ACL_CHECK(aclDestroyIntArray(kernel_size));
+    ACL_CHECK(aclDestroyIntArray(dilations));
+    ACL_CHECK(aclDestroyIntArray(paddings));
+    ACL_CHECK(aclDestroyIntArray(strides));
+}
+
+/**
+ * @brief Applies element-wise exponential function to the elements of a tensor.
+ *
+ * This function computes the exponential of each element in the source tensor
+ * `acl_src` and stores the result back into the same tensor.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_src }_i=e^{acl\_src_i}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The tensor on which the exponential function will be applied.
+ */
+static void aclnn_exp(ggml_backend_cann_context& ctx, aclTensor* acl_src) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(
+        aclnnInplaceExpGetWorkspaceSize(acl_src, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceExp(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Multiplies elements of a tensor by a scalar value, optionally
+ * in-place.
+ *
+ * This function multiplies each element of the source tensor `acl_src` by the
+ * scalar `scale` and stores the result in the destination tensor `acl_dst`. If
+ * `inplace` is true, `acl_dst` will not be used and the operation is performed
+ *  in-place on `acl_src`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=\text {acl_src }_i \times \text {scale}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be multiplied.
+ * @param scale The scalar value by which each element of `acl_src` will be
+ * multiplied.
+ * @param acl_dst The destination tensor where the result will be stored if
+ * `inplace` is false.
+ * @param inplace Flag indicating whether to perform the operation in-place on
+ * `acl_src`.
+ */
+static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                       float scale, aclTensor* acl_dst, bool inplace) {
+    aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    if (inplace) {
+        ACL_CHECK(aclnnInplaceMulsGetWorkspaceSize(acl_src, acl_scale,
+                                                   &workspaceSize, &executor));
+        if (workspaceSize > 0) {
+            ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+            workspaceAddr = workspace_allocator.get();
+        }
+
+        ACL_CHECK(aclnnInplaceMuls(workspaceAddr, workspaceSize, executor,
+                                   ctx.stream()));
+    } else {
+        ACL_CHECK(aclnnMulsGetWorkspaceSize(acl_src, acl_scale, acl_dst,
+                                            &workspaceSize, &executor));
+        if (workspaceSize > 0) {
+            ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+            workspaceAddr = workspace_allocator.get();
+        }
+
+        ACL_CHECK(
+            aclnnMuls(workspaceAddr, workspaceSize, executor, ctx.stream()));
+    }
+
+    ACL_CHECK(aclDestroyScalar(acl_scale));
+}
+
+/**
+ * @brief Performs an in-place element-wise multiplication of two tensors.
+ *
+ * This function performs an element-wise multiplication of the tensors
+ * `acl_src` and `acl_other` and stores the result in `acl_src`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_src }_i=\text {acl_src }_i \times \text {acl_other }_i
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor where the multiplication result will be
+ * stored.
+ * @param acl_other The tensor whose elements will be multiplied with `acl_src`.
+ */
+static void aclnn_inplace_mul(ggml_backend_cann_context& ctx,
+                              aclTensor* acl_src, aclTensor* acl_other) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceMulGetWorkspaceSize(acl_src, acl_other,
+                                              &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceMul(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Performs element-wise multiplication of two tensors and stores the
+ * result in a destination tensor.
+ *
+ * This function performs element-wise multiplication of the tensors `acl_src`
+ * and `acl_other` and stores the result in the destination tensor `acl_dst`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=\text {acl_src }_i \times \text {acl_other }_i
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The first tensor for element-wise multiplication.
+ * @param acl_other The second tensor for element-wise multiplication.
+ * @param acl_dst The destination tensor where the result will be stored.
+ */
+static void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_other, aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnMulGetWorkspaceSize(acl_src, acl_other, acl_dst,
+                                       &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnMul(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Applies element-wise cosine function to the elements of a tensor.
+ *
+ * This function computes the cosine of each element in the source tensor
+ * `acl_src` and stores the result in the destination tensor `acl_dst`. The
+ * operation is defined as: \f[ \text {acl_dst }_i=\cos \left(\text {acl_src
+ * }_i\right) \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor on which the cosine function will be
+ * applied.
+ * @param acl_dst The destination tensor where the cosine results will be
+ * stored.
+ */
+static void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(
+        aclnnCosGetWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnCos(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Applies element-wise sine function to the elements of a tensor.
+ *
+ * This function computes the sine of each element in the source tensor
+ `acl_src`
+ * and stores the result in the destination tensor `acl_dst`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=\sin \left(\text {acl_src }_i\right)
+ * \f]
+
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor on which the sine function will be applied.
+ * @param acl_dst The destination tensor where the sine results will be stored.
+ */
+static void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(
+        aclnnSinGetWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnSin(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx,
+                                  ggml_tensor* dst) {
+    const ggml_tensor* src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int dim = dst->op_params[0];
+    const int max_period = dst->op_params[1];
+    int half = dim / 2;
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+
+    // arange: [0, ..., half)
+    float start = 0;
+    float stop = half;
+    float step = 1;
+    int64_t n_elements_arange = half;
+    int64_t tmp_arange_ne[] = {half};
+    size_t tmp_arange_nb[] = {sizeof(dst->type)};
+
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(), half * sizeof(dst->type));
+    void* tmp_arange_buffer = arange_allocator.get();
+    aclTensor* tmp_arange_tensor = ggml_cann_create_tensor(
+        tmp_arange_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_arange_ne, tmp_arange_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+    aclnn_arange(ctx, tmp_arange_tensor, start, stop, step, n_elements_arange);
+
+    // freq
+    float freq_param = -logf(max_period) / half;
+    bool inplace = true;
+    aclnn_muls(ctx, tmp_arange_tensor, freq_param, nullptr, inplace);
+    aclnn_exp(ctx, tmp_arange_tensor);
+
+    // permute: src [0,1,2,3]->[0,1,3,2]
+    int64_t tmp_permute_ne[] = {src->ne[1], src->ne[0], src->ne[2], src->ne[3]};
+    size_t tmp_permute_nb[GGML_MAX_DIMS];
+    tmp_permute_nb[0] = ggml_type_size(src->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1];
+    }
+
+    ggml_cann_pool_alloc permute_allocator(ctx.pool(), ggml_nbytes(src));
+    void* tmp_permute_buffer = permute_allocator.get();
+    aclTensor* tmp_permute_tenosr = ggml_cann_create_tensor(
+        tmp_permute_buffer, ggml_cann_type_mapping(src->type),
+        ggml_type_size(src->type), tmp_permute_ne, tmp_permute_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    int64_t permute_dim[] = {0, 1, 3, 2};
+    int64_t num_dims = 4;
+    aclnn_permute(ctx, acl_src, tmp_permute_tenosr, permute_dim, num_dims);
+
+    // timestep * freq
+    int64_t tmp_mul_ne[] = {src->ne[1] * half, src->ne[0], src->ne[2],
+                            src->ne[3]};
+    size_t tmp_mul_nb[GGML_MAX_DIMS];
+    tmp_mul_nb[0] = ggml_type_size(src->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_mul_nb[i] = tmp_mul_nb[i - 1] * tmp_mul_ne[i - 1];
+    }
+
+    int mul_nelements =
+        src->ne[1] * half * src->ne[0] * src->ne[2] * src->ne[3];
+
+    ggml_cann_pool_alloc mul_allocator(
+        ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void* tmp_mul_buffer = mul_allocator.get();
+    aclTensor* tmp_mul_tensor = ggml_cann_create_tensor(
+        tmp_mul_buffer, ggml_cann_type_mapping(src->type),
+        ggml_type_size(src->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+    aclnn_mul(ctx, tmp_permute_tenosr, tmp_arange_tensor, tmp_mul_tensor);
+
+    // cos
+    ggml_cann_pool_alloc cos_allocator(
+        ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void* tmp_cos_buffer = cos_allocator.get();
+    aclTensor* tmp_cos_tensor = ggml_cann_create_tensor(
+        tmp_cos_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+
+    aclnn_cos(ctx, tmp_mul_tensor, tmp_cos_tensor);
+
+    // sin
+    ggml_cann_pool_alloc sin_allocator(
+        ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void* tmp_sin_buffer = sin_allocator.get();
+    aclTensor* tmp_sin_tensor = ggml_cann_create_tensor(
+        tmp_sin_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+
+    aclnn_sin(ctx, tmp_mul_tensor, tmp_sin_tensor);
+
+    // concat
+    int64_t concat_dim = 3;
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+    aclTensor* tensors[] = {tmp_cos_tensor, tmp_sin_tensor};
+    aclTensorList* tensorList = aclCreateTensorList(tensors, 2);
+    aclnn_concat(ctx, tensorList, acl_dst, concat_dim);
+
+    // release
+    // segmentation fault when delete both tensorList and his elements.
+    ACL_CHECK(aclDestroyTensorList(tensorList));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(tmp_arange_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_permute_tenosr));
+    ACL_CHECK(aclDestroyTensor(tmp_mul_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Fills a tensor with a scalar value.
+ *
+ * This function fills the destination tensor `acl_dst` with the scalar value
+ * `scalar`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param scalar The scalar value used to fill the tensor.
+ * @param acl_dst The destination tensor to be filled with the scalar value.
+ */
+static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar,
+                              aclTensor* acl_dst) {
+    auto acl_scalar = aclCreateScalar(&scalar, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceFillScalarGetWorkspaceSize(
+        acl_dst, acl_scalar, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnInplaceFillScalar(workspaceAddr, workspaceSize, executor,
+                                     ctx.stream()));
+    ACL_CHECK(aclDestroyScalar(acl_scalar));
+}
+
+/**
+ * @brief Raises each element of a tensor to the power of the corresponding
+ * element in another tensor.
+ *
+ * This function computes the element-wise power of the destination tensor
+ * `acl_dst` raised to the power of the exponent tensor `acl_exp`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=acl\_dst_i^{\text {acl_exp }_i}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_dst The destination tensor, which also serves as the base tensor.
+ * @param acl_exp The exponent tensor, each element of which is used to raise
+ * the corresponding element in the destination tensor.
+ */
+static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx,
+                                    aclTensor* acl_dst, aclTensor* acl_exp) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplacePowTensorTensorGetWorkspaceSize(
+        acl_dst, acl_exp, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnInplacePowTensorTensor(workspaceAddr, workspaceSize,
+                                          executor, ctx.stream()));
+}
+
+/**
+ * @brief   Applies the Alibi (Attention with Linear Biases) mechanism to the
+ * @details This function implements the Alibi mechanism, which introduces
+ *          learnable biases into the attention scores to simulate relative
+ *          position encoding without the need for explicit positional
+ *          embeddings.
+ *
+ * @param ctx          The backend CANN context for executing operations.
+ * @param acl_src      The source tensor representing the query or key.
+ * @param acl_position The position tensor containing relative positions.
+ * @param acl_dst      The destination tensor where the result will be stored.
+ * @param n_head       The number of attention heads.
+ * @param src_ne       The dimensions of the source tensor.
+ * @param src_nb0      The byte size of the first dimension of the source
+ tensor.
+ * @param max_bias     The maximum bias value used in the Alibi mechanism.
+ * @param dst          The destination tensor object for additional metadata.
+ *
+ * The function performs the following steps:
+ * 1. Calculates the logarithm floor of the number of heads to determine the
+      base for bias calculation.
+ * 2. Initializes arrays with arithmetic sequences and fills them with bias
+      values.
+ * 3. Computes the bias tensor based on the calculated biases and arithmetic
+      sequences.
+ * 4. Reshapes the bias tensor to match the dimensions of the input tensors.
+ * 5. Multiplies the position tensor by the bias tensor.
+ * 6. Adds the result of the multiplication to the source tensor to produce the
+      final output.
+ */
+static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                        aclTensor* acl_position, aclTensor* acl_dst,
+                        const int n_head, int64_t* src_ne, const size_t src_nb0,
+                        float max_bias, ggml_tensor* dst) {
+    const int64_t ne2_ne3 = src_ne[2] * src_ne[3];
+    GGML_ASSERT(src_nb0 == sizeof(float));
+    GGML_ASSERT(n_head == src_ne[2]);
+
+    const int n_heads_log2_floor = 1u << (uint32_t)floor(log2(n_head));
+
+    float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+    float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+    // init arange
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(),
+                                          ne2_ne3 * ggml_type_size(dst->type));
+    void* tmp_arange_buffer = arange_allocator.get();
+
+    // arange1: [1, ..., n_heads_log2_floor+1)
+    float start = 1;
+    float stop = n_heads_log2_floor + 1;
+    float step = 1;
+    int64_t n_elements_arange = n_heads_log2_floor;
+
+    int64_t tmp_arange1_ne[] = {n_heads_log2_floor};
+    size_t tmp_arange1_nb[] = {sizeof(dst->type)};
+    aclTensor* tmp_arange1_tensor = ggml_cann_create_tensor(
+        tmp_arange_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_arange1_ne, tmp_arange1_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+    aclnn_arange(ctx, tmp_arange1_tensor, start, stop, step, n_elements_arange);
+
+    aclTensor* tmp_arange2_tensor = nullptr;
+    if (n_heads_log2_floor < ne2_ne3) {
+        // arange2: [1, ..., 2 * (k - n_heads_log2_floor) + 1)
+        start = 1;
+        stop = 2 * (ne2_ne3 - n_heads_log2_floor) + 1;
+        step = 2;
+        n_elements_arange = ne2_ne3 - n_heads_log2_floor;
+        int64_t tmp_arange2_ne[] = {ne2_ne3 - n_heads_log2_floor};
+        size_t tmp_arange2_nb[] = {sizeof(dst->type)};
+
+        aclTensor* tmp_arange2_tensor = ggml_cann_create_tensor(
+            (char*)tmp_arange_buffer +
+                n_heads_log2_floor * ggml_type_size(dst->type),
+            ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+            tmp_arange2_ne, tmp_arange2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+        aclnn_arange(ctx, tmp_arange2_tensor, start, stop, step,
+                     n_elements_arange);
+    }
+
+    // init mk_base
+    ggml_cann_pool_alloc mk_base_allocator(ctx.pool(),
+                                           ne2_ne3 * ggml_type_size(dst->type));
+    void* tmp_mk_base_buffer = mk_base_allocator.get();
+    int64_t tmp_mk_base1_ne[] = {n_heads_log2_floor};
+    size_t tmp_mk_base1_nb[] = {sizeof(dst->type)};
+    aclTensor* tmp_mk_base1_tensor = ggml_cann_create_tensor(
+        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mk_base1_ne, tmp_mk_base1_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+    aclnn_fill_scalar(ctx, m0, tmp_mk_base1_tensor);
+
+    aclTensor* tmp_mk_base2_tensor = nullptr;
+    if (n_heads_log2_floor < ne2_ne3) {
+        int64_t tmp_mk_base2_ne[] = {ne2_ne3 - n_heads_log2_floor};
+        size_t tmp_mk_base2_nb[] = {sizeof(dst->type)};
+        aclTensor* tmp_mk_base2_tensor = ggml_cann_create_tensor(
+            (char*)tmp_mk_base_buffer +
+                n_heads_log2_floor * ggml_type_size(dst->type),
+            ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+            tmp_mk_base2_ne, tmp_mk_base2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+        aclnn_fill_scalar(ctx, m1, tmp_mk_base2_tensor);
+    }
+
+    // init mk
+    int64_t tmp_mk_base_ne[] = {ne2_ne3};
+    size_t tmp_mk_base_nb[] = {sizeof(dst->type)};
+    aclTensor* tmp_mk_base_tensor = ggml_cann_create_tensor(
+        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mk_base_ne, tmp_mk_base_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+    aclTensor* tmp_arange_tensor = ggml_cann_create_tensor(
+        tmp_arange_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mk_base_ne, tmp_mk_base_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+    aclnn_pow_tensor_tensor(ctx, tmp_mk_base_tensor, tmp_arange_tensor);
+
+    // reshape mk
+    int64_t tmp_mk_ne[] = {1, 1, src_ne[2], src_ne[3]};
+    size_t tmp_mk_nb[GGML_MAX_DIMS];
+    tmp_mk_nb[0] = ggml_type_size(dst->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_mk_nb[i] = tmp_mk_nb[i - 1] * tmp_mk_ne[i - 1];
+    }
+    aclTensor* tmp_mk_tensor = ggml_cann_create_tensor(
+        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mk_ne, tmp_mk_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+
+    // acl_position * mk
+    int64_t tmp_output_ne[] = {src_ne[0], src_ne[1], src_ne[2], src_ne[3]};
+    size_t tmp_output_nb[GGML_MAX_DIMS];
+    tmp_output_nb[0] = ggml_type_size(dst->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_output_nb[i] = tmp_output_nb[i - 1] * tmp_output_ne[i - 1];
+    }
+    ggml_cann_pool_alloc output_allocator(ctx.pool(), ggml_nbytes(dst));
+    void* tmp_output_buffer = output_allocator.get();
+    aclTensor* tmp_output_tensor = ggml_cann_create_tensor(
+        tmp_output_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_output_ne, tmp_output_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+    aclnn_mul(ctx, acl_position, tmp_mk_tensor, tmp_output_tensor);
+
+    // add
+    aclnn_add(ctx, tmp_output_tensor, acl_src, acl_dst);
+
+    ACL_CHECK(aclDestroyTensor(tmp_arange1_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_arange2_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mk_base1_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mk_base2_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mk_base_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_arange_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mk_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_output_tensor));
+}
+
+void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_cann_dup(ctx, dst);
+}
+
+/**
+ * @brief Performs element-wise addition of two tensors in place.
+ *
+ * This function adds the source tensor `acl_src` to the destination tensor
+ * `acl_dst` element-wise and stores the result in the destination tensor
+ * `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor to be added.
+ * @param acl_dst The destination tensor which will hold the result of the
+ * addition.
+ */
+static void aclnn_inplace_add(ggml_backend_cann_context& ctx,
+                              aclTensor* acl_src, aclTensor* acl_dst) {
+    aclScalar* alpha = nullptr;
+    float alphaValue = 1.0f;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, acl_src, alpha,
+                                              &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceAdd(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(alpha));
+}
+
+/**
+ * @brief Applies the softmax function to a tensor along a specified dimension.
+ *
+ * This function computes the softmax of the source tensor `acl_src` along the
+ * specified dimension `dim` and stores the result in the destination tensor
+ * `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor on which the softmax function will be
+ * applied.
+ * @param dim The dimension along which the softmax function will be computed.
+ * @param acl_dst The destination tensor where the softmax results will be
+ * stored.
+ */
+static void aclnn_softmax(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                          int64_t dim, aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnSoftmaxGetWorkspaceSize(acl_src, dim, acl_dst,
+                                           &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    aclrtStream stream = ctx.stream();
+    ACL_CHECK(aclnnSoftmax(workspaceAddr, workspaceSize, executor, stream));
+}
+
+void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];  // mask
+
+    aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float scale = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale, (float*)dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float*)dst->op_params + 1, sizeof(float));
+
+    // input mul scale
+    aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT);
+
+    size_t n_bytes = ggml_nbytes(src0);
+    ggml_cann_pool_alloc mul_scale_allocator(ctx.pool(), n_bytes);
+    void* input_mul_scale_buffer = mul_scale_allocator.get();
+    aclTensor* acl_input_mul_scale_tensor = ggml_cann_create_tensor(
+        input_mul_scale_buffer, ACL_FLOAT, ggml_type_size(src0->type), src0->ne,
+        src0->nb, GGML_MAX_DIMS);
+
+    bool inplace = false;
+    aclnn_muls(ctx, acl_src0, scale, acl_input_mul_scale_tensor, inplace);
+
+    // mask
+    aclTensor* acl_src1_fp32_tensor = nullptr;
+    aclTensor* tmp_mask_tensor = nullptr;
+    ggml_cann_pool_alloc src1_fp32_allocator(ctx.pool());
+    if (src1) {
+        const bool use_f16 = src1->type == GGML_TYPE_F16;
+        if (use_f16) {
+            // cast to fp32
+            size_t n_bytes = ggml_nelements(src1) * sizeof(float_t);
+            size_t src1_fp32_nb[GGML_MAX_DIMS];
+            src1_fp32_nb[0] = sizeof(float_t);
+            for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                src1_fp32_nb[i] = src1_fp32_nb[i - 1] * src1->ne[i - 1];
+            }
+            src1_fp32_allocator.alloc(n_bytes);
+            void* src1_fp32_buffer = src1_fp32_allocator.get();
+            acl_src1_fp32_tensor = ggml_cann_create_tensor(
+                src1_fp32_buffer, ACL_FLOAT, sizeof(float), src1->ne,
+                src1_fp32_nb, GGML_MAX_DIMS);
+            aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
+            aclnn_cast(ctx, acl_src1, acl_src1_fp32_tensor, ACL_FLOAT);
+
+            ACL_CHECK(aclDestroyTensor(acl_src1));
+        } else {
+            acl_src1_fp32_tensor = ggml_cann_create_tensor(src1);
+        }
+
+        // broadcast the mask across rows, only use ne11 of ne01 in mask
+        if (src1->ne[1] != src0->ne[1]) {
+            // mask shape: [1,1,ne11,ne10]
+            int64_t tmp_mask_ne[] = {src0->ne[0], src0->ne[1], 1, 1};
+            size_t tmp_mask_nb[GGML_MAX_DIMS];
+            tmp_mask_nb[0] = sizeof(float_t);
+            for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                tmp_mask_nb[i] = tmp_mask_nb[i - 1] * tmp_mask_ne[i - 1];
+            }
+            tmp_mask_tensor = ggml_cann_create_tensor(
+                src1->data, ACL_FLOAT, sizeof(float), tmp_mask_ne, tmp_mask_nb,
+                GGML_MAX_DIMS, ACL_FORMAT_ND);
+        }
+
+        // alibi
+        const int n_head = src0->ne[2];
+        const size_t src_nb0 = src0->nb[0];
+
+        n_bytes = ggml_nbytes(dst);
+        ggml_cann_pool_alloc output_allocator(ctx.pool(), n_bytes);
+        void* output_buffer = output_allocator.get();
+        aclTensor* alibi_output_tensor = ggml_cann_create_tensor(
+            output_buffer, ACL_FLOAT, ggml_type_size(dst->type), dst->ne,
+            dst->nb, GGML_MAX_DIMS);
+        if (max_bias <= 0.0f) {
+            // slope = 1.0
+            if (tmp_mask_tensor) {
+                aclnn_add(ctx, tmp_mask_tensor, acl_input_mul_scale_tensor,
+                          alibi_output_tensor);
+            } else {
+                aclnn_add(ctx, acl_src1_fp32_tensor, acl_input_mul_scale_tensor,
+                          alibi_output_tensor);
+            }
+        } else {
+            // slope != 1.0
+            if (tmp_mask_tensor) {
+                aclnn_alibi(ctx, acl_input_mul_scale_tensor, tmp_mask_tensor,
+                            alibi_output_tensor, n_head, src0->ne, src_nb0,
+                            max_bias, dst);
+            } else {
+                aclnn_alibi(ctx, acl_input_mul_scale_tensor,
+                            acl_src1_fp32_tensor, alibi_output_tensor, n_head,
+                            src0->ne, src_nb0, max_bias, dst);
+            }
+        }
+
+        // softmax
+        aclnn_softmax(ctx, alibi_output_tensor, 3, acl_dst);
+        ACL_CHECK(aclDestroyTensor(alibi_output_tensor));
+    } else {
+        aclnn_softmax(ctx, acl_input_mul_scale_tensor, 3, acl_dst);
+    }
+
+    ACL_CHECK(aclDestroyTensor(acl_src0));
+    ACL_CHECK(aclDestroyTensor(acl_src1_fp32_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyScalar(acl_scale));
+    ACL_CHECK(aclDestroyTensor(acl_input_mul_scale_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mask_tensor));
+}
+
+void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+
+    ggml_cann_pool_alloc src0_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    ggml_cann_pool_alloc src1_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    ggml_cann_pool_alloc dst_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    src0->extra = src0_extra_allocator.get();
+    src1->extra = src1_extra_allocator.get();
+    dst->extra = dst_extra_allocator.get();
+    ACL_CHECK(aclrtMemcpyAsync(src0->extra, sizeof(ggml_tensor), src0,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+    ACL_CHECK(aclrtMemcpyAsync(src1->extra, sizeof(ggml_tensor), src1,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+    ACL_CHECK(aclrtMemcpyAsync(dst->extra, sizeof(ggml_tensor), dst,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            aclrtlaunch_ascendc_get_row_f32(
+                24, ctx.stream(), src0->data, src1->data, dst->data,
+                ((ggml_tensor*)src0->extra)->ne,
+                ((ggml_tensor*)src0->extra)->nb,
+                ((ggml_tensor*)src1->extra)->ne,
+                ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
+                ((ggml_tensor*)dst->extra)->nb);
+            break;
+        case GGML_TYPE_F16:
+            aclrtlaunch_ascendc_get_row_f16(
+                24, ctx.stream(), src0->data, src1->data, dst->data,
+                ((ggml_tensor*)src0->extra)->ne,
+                ((ggml_tensor*)src0->extra)->nb,
+                ((ggml_tensor*)src1->extra)->ne,
+                ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
+                ((ggml_tensor*)dst->extra)->nb);
+            break;
+        case GGML_TYPE_Q4_0:
+            aclrtlaunch_ascendc_get_row_q4_0(
+                24, ctx.stream(), src0->data, src1->data, dst->data,
+                ((ggml_tensor*)src0->extra)->ne,
+                ((ggml_tensor*)src1->extra)->ne,
+                ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
+                ((ggml_tensor*)dst->extra)->nb);
+            break;
+        case GGML_TYPE_Q8_0:
+            aclrtlaunch_ascendc_get_row_q8_0(
+                24, ctx.stream(), src0->data, src1->data, dst->data,
+                ((ggml_tensor*)src0->extra)->ne,
+                ((ggml_tensor*)src1->extra)->ne,
+                ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
+                ((ggml_tensor*)dst->extra)->nb);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+
+/**
+ * @brief Repeats elements of a tensor along a specified dimension.
+ *
+ * This function repeats each element of the source tensor `acl_src` a specified
+ * number of times (`repeats`) along the specified dimension `dim` and stores
+ * the result in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be repeated.
+ * @param acl_dst The destination tensor where the repeated elements will be
+ * stored.
+ * @param dim The dimension along which the elements will be repeated.
+ * @param repeats The number of times each element will be repeated.
+ * @param output_size The size of the output tensor.
+ */
+static void aclnn_repeat_interleave(ggml_backend_cann_context& ctx,
+                                    aclTensor* acl_src, aclTensor* acl_dst,
+                                    int64_t dim, int64_t repeats,
+                                    int64_t output_size) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnRepeatInterleaveIntWithDimGetWorkspaceSize(
+        acl_src, repeats, dim, output_size, acl_dst, &workspaceSize,
+        &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnRepeatInterleaveIntWithDim(workspaceAddr, workspaceSize,
+                                              executor, ctx.stream()));
+}
+
+/**
+ * @brief Performs matrix multiplication of two tensors.
+ *
+ * This function computes the matrix multiplication of the input tensor
+ * `acl_input` and the weight tensor `acl_weight`, and stores the result in the
+ * destination tensor `acl_dst`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst}=\text {acl_input@acl_weight}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_input The input tensor for the matrix multiplication.
+ * @param acl_weight The weight tensor for the matrix multiplication.
+ * @param acl_dst The destination tensor where the result of the matrix
+ * multiplication will be stored.
+ */
+static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input,
+                          aclTensor* acl_weight, aclTensor* acl_dst) {
+    int8_t cube_math_type = 1;  // ALLOW_FP32_DOWN_PRECISION, when input is
+                                // fp32, atlas a2 will transpose it to HFLOAT32.
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnMatmulGetWorkspaceSize(acl_input, acl_weight, acl_dst,
+                                          cube_math_type, &workspaceSize,
+                                          &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnMatmul(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Performs matrix multiplication with floating-point precision on
+ * tensors using the CANN backend.
+ *
+ * This function performs matrix multiplication of the input tensor and the
+ * weight tensor, handling broadcasting and transposing as needed, and stores
+ * the result in the destination tensor `dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result of the matrix
+ * multiplication will be stored.
+ */
+static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
+                                 ggml_tensor* dst) {
+    ggml_tensor* weight = dst->src[0];  // weight
+    ggml_tensor* input = dst->src[1];   // input
+
+    // when weight ne2 or ne3 is 1, aclnnMatmulGetWorkspaceSize will auto
+    // broadcast, when weight ne2 or ne3 is not 1, weight need repeat.
+    BCAST_MUL_MAT_SHAPE(input, weight, dst);
+
+    // transpose weight: [1,2,3,4] -> [1,2,4,3]
+    int64_t transpose_ne[] = {bcast_weight_ne[1], bcast_weight_ne[0],
+                              bcast_weight_ne[2], bcast_weight_ne[3],
+                              bcast_weight_ne[4], bcast_weight_ne[5]};
+    size_t transpose_nb[] = {bcast_weight_nb[1], bcast_weight_nb[0],
+                             bcast_weight_nb[2], bcast_weight_nb[3],
+                             bcast_weight_nb[4], bcast_weight_nb[5]};
+
+    aclTensor* acl_weight_tensor =
+        ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, bcast_dims);
+    aclTensor* acl_input_tensor =
+        ggml_cann_create_tensor(input, BCAST_MUL_MAT_PARAM(input));
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst, BCAST_MUL_MAT_PARAM(dst));
+    aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst);
+
+    ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Performs matrix multiplication with quantized weights and
+ * floating-point inputs using the CANN backend.
+ *
+ * This function performs matrix multiplication of the input tensor `src1` and
+ * the weight tensor `src0`, handling broadcasting, transposing, and
+ * quantization as needed, and stores the result in the destination tensor
+ * `dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result of the matrix
+ * multiplication will be stored.
+ */
+static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,
+                                   ggml_tensor* dst,
+                                   const enum ggml_type type) {
+    ggml_tensor* src0 = dst->src[0];  // weight
+    ggml_tensor* src1 = dst->src[1];  // input
+
+    // The shape of the weight is NCHW. Matrix multiplication uses HW dims. HC
+    // is regarded as batch. weight need transpose.
+    int64_t weight_ne[] = {src0->ne[1], src0->ne[0]};
+    float weight_elem_size;
+    if (type == GGML_TYPE_Q4_0) {
+        weight_elem_size = float(sizeof(uint8_t)) / 2;
+    }
+    else if (type == GGML_TYPE_Q8_0) {
+        weight_elem_size = float(sizeof(uint8_t));
+    }
+    else {
+        GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT");
+    }
+    float weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size};
+
+    // size of one matrix is element_size * height * width.
+    size_t weight_stride = weight_elem_size * src0->ne[0] * src0->ne[1];
+    size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3];
+
+    // scale stored at the end of weight. Also need transpose.
+    GGML_ASSERT(QK4_0 == QK8_0);
+    int64_t scale_ne[] = {src0->ne[1], src0->ne[0] / QK8_0};
+    size_t scale_elem_size = sizeof(uint16_t);
+    size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size,
+                         scale_elem_size};
+    size_t scale_stride = scale_elem_size * src0->ne[0] * src0->ne[1] / QK8_0;
+    char* scale_offset = (char*)src0->data + weight_size;
+
+    // input
+    void* input_buffer;
+    size_t input_elem_size = sizeof(uint16_t);
+    int64_t input_ne[] = {src1->ne[0], src1->ne[1]};
+    size_t input_nb[] = {input_elem_size, input_elem_size * src1->ne[0]};
+    size_t input_stride = input_elem_size * src1->ne[0] * src1->ne[1];
+
+    ggml_cann_pool_alloc input_alloctor(ctx.pool());
+    if (src1->type != GGML_TYPE_F16) {
+        aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1);
+        input_alloctor.alloc(ggml_nelements(src1) * input_elem_size);
+        input_buffer = input_alloctor.get();
+
+        int64_t* input_cast_ne = src1->ne;
+        size_t input_cast_nb[GGML_MAX_DIMS];
+        input_cast_nb[0] = sizeof(uint16_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_cast_nb[i] = input_cast_nb[i - 1] * input_cast_ne[i - 1];
+        }
+
+        aclTensor* acl_input_tensor = ggml_cann_create_tensor(
+            input_buffer, ACL_FLOAT16, input_elem_size, input_cast_ne,
+            input_cast_nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, acl_src1_tensor, acl_input_tensor, ACL_FLOAT16);
+        ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+        ACL_CHECK(aclDestroyTensor(acl_src1_tensor));
+    } else {
+        input_buffer = src1->data;
+    }
+
+    // output
+    size_t output_elem_size = sizeof(uint16_t);
+    int64_t output_ne[] = {dst->ne[0], dst->ne[1]};
+    size_t output_nb[] = {output_elem_size, output_elem_size * dst->ne[0]};
+    ggml_cann_pool_alloc output_alloctor(
+        ctx.pool(), ggml_nelements(dst) * output_elem_size);
+    void* output_buffer = output_alloctor.get();
+    size_t output_stride = output_elem_size * dst->ne[0] * dst->ne[1];
+
+    // aclnn
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) {
+        for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) {
+            int64_t n0 = n1 / (src1->ne[3] / src0->ne[3]);
+            int64_t c0 = c1 / (src1->ne[2] / src0->ne[2]);
+
+            int64_t batch1 = n1 * src1->ne[2] + c1;
+            int64_t batch0 = n0 * src0->ne[2] + c0;
+
+            aclTensor* acl_input_tensor = ggml_cann_create_tensor(
+                (char*)input_buffer + batch1 * input_stride, ACL_FLOAT16,
+                input_elem_size, input_ne, input_nb, 2);
+            aclTensor* acl_weight_tensor = ggml_cann_create_tensor(
+                (char*)src0->data + batch0 * weight_stride,
+                ggml_cann_type_mapping(type), weight_elem_size, weight_ne,
+                weight_nb, 2);
+            aclTensor* acl_scale_tensor = ggml_cann_create_tensor(
+                scale_offset + batch0 * scale_stride, ACL_FLOAT16,
+                scale_elem_size, scale_ne, scale_nb, 2);
+            aclTensor* acl_output_tensor = ggml_cann_create_tensor(
+                (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16,
+                output_elem_size, output_ne, output_nb, 2);
+
+            ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(
+                acl_input_tensor, acl_weight_tensor, acl_scale_tensor, nullptr,
+                nullptr, nullptr, nullptr, QK8_0, acl_output_tensor,
+                &workspaceSize, &executor));
+
+            if (workspaceSize > 0 && workspaceAddr == nullptr) {
+                ggml_cann_pool_alloc workspace_allocator(ctx.pool(),
+                                                         workspaceSize);
+                workspaceAddr = workspace_allocator.get();
+            }
+
+            ACL_CHECK(aclnnWeightQuantBatchMatmulV2(
+                workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+            ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+            ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
+            ACL_CHECK(aclDestroyTensor(acl_scale_tensor));
+            ACL_CHECK(aclDestroyTensor(acl_output_tensor));
+        }
+    }
+
+    // cast out
+    int64_t* output_cast_ne = dst->ne;
+    size_t output_cast_nb[GGML_MAX_DIMS];
+    output_cast_nb[0] = sizeof(uint16_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1];
+    }
+
+    aclTensor* acl_output_tensor =
+        ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size,
+                                output_cast_ne, output_cast_nb, GGML_MAX_DIMS);
+    aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
+    aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ACL_FLOAT);
+
+    ACL_CHECK(aclDestroyTensor(acl_output_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst_tensor));
+}
+
+void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    const enum ggml_type type = dst->src[0]->type;
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+            ggml_cann_mat_mul_fp(ctx, dst);
+            break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+            ggml_cann_mul_mat_quant(ctx, dst, type);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+
+/**
+ * @brief Rolls the elements of a tensor along a specified dimension.
+ *
+ * This function rolls the elements of the source tensor `acl_src` by the
+ * specified shifts `shifts` along the specified dimensions `dims`, and stores
+ * the result in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be rolled.
+ * @param acl_dst The destination tensor where the rolled elements will be
+ * stored.
+ * @param shifts An array specifying the number of positions by which elements
+ * are shifted.
+ * @param dims An array specifying the dimensions along which elements are
+ * shifted.
+ */
+static void aclnn_roll(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                       aclTensor* acl_dst, int64_t* shifts, int64_t* dims) {
+    aclIntArray* acl_shifts = aclCreateIntArray(shifts, 1);
+    aclIntArray* acl_dims = aclCreateIntArray(dims, 1);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnRollGetWorkspaceSize(acl_src, acl_shifts, acl_dims, acl_dst,
+                                        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnRoll(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(acl_shifts));
+    ACL_CHECK(aclDestroyIntArray(acl_dims));
+}
+
+/**
+ * @brief Fills specified positions of a tensor with a scalar value.
+ *
+ * This function fills the positions in the source tensor `acl_src` specified by
+ * `index` along the dimension `dim` with the scalar value `value`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor where the positions will be filled.
+ * @param dim The dimension along which the positions are specified.
+ * @param index An array specifying the positions to be filled.
+ * @param index_num The number of positions specified in the index array.
+ * @param value The scalar value used to fill the specified positions.
+ */
+static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx,
+                                    aclTensor* acl_src, int64_t dim,
+                                    int64_t* index, int64_t index_num,
+                                    float value) {
+    aclIntArray* acl_index = aclCreateIntArray(index, index_num);
+    aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceIndexFillTensorGetWorkspaceSize(
+        acl_src, dim, acl_index, acl_value, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnInplaceIndexFillTensor(workspaceAddr, workspaceSize,
+                                          executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(acl_index));
+    ACL_CHECK(aclDestroyScalar(acl_value));
+}
+
+static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
+                             aclTensor* acl_cos_repeat_tensor,
+                             aclTensor* acl_sin_repeat_tensor,
+                             float theta_scale, bool is_neox) {
+    // int sin/cos cache, cache has different repeat method depond on
+    // @param.is_neox
+
+    ggml_tensor* src0 = dst->src[0];  // input
+    ggml_tensor* src1 = dst->src[1];  // position
+
+    // arange, [0,1,...,ne0/2]
+    int64_t arange_length = src0->ne[0] / 2;
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(),
+                                          arange_length * sizeof(float_t));
+    void* arange_buffer = arange_allocator.get();
+    int64_t arange_ne[] = {arange_length, 1, 1, 1};
+    size_t arange_nb[] = {sizeof(float_t), sizeof(float_t), sizeof(float_t),
+                          arange_length * sizeof(float_t)};
+
+    aclTensor* acl_arange_tensor =
+        ggml_cann_create_tensor(arange_buffer, ACL_FLOAT, sizeof(float_t),
+                                arange_ne, arange_nb, GGML_MAX_DIMS);
+    float start = 0;
+    float step = 1;
+    float stop = src0->ne[0] / 2;
+    float n_elements = src0->ne[0] / 2;
+    aclnn_arange(ctx, acl_arange_tensor, start, stop, step, n_elements);
+
+    // power
+    // aclnnPowScalarTensor(): @param self is tensor which should be scalar, so
+    // use aclnn_pow_tensor_tensor() until fixed. aclScalar* acl_theta_scale =
+    // aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT);
+    // aclnn_power_scalar_tensor(ctx, acl_theta_scale, acl_arange_tensor,
+    // acl_power_tensor);
+    ggml_cann_pool_alloc theta_scale_allocator(ctx.pool(),
+                                               arange_length * sizeof(float_t));
+    void* theta_scale_buffer = theta_scale_allocator.get();
+    aclTensor* acl_theta_scale_tensor = aclnn_ones(
+        ctx, theta_scale_buffer, arange_length * sizeof(float_t), arange_ne,
+        GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), theta_scale);
+    aclnn_pow_tensor_tensor(ctx, acl_theta_scale_tensor, acl_arange_tensor);
+
+    // position
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    int64_t position_length = src1->ne[0];
+    int64_t position_ne[] = {1, position_length, 1, 1};
+    size_t position_nb[] = {sizeof(int32_t), sizeof(int32_t),
+                            sizeof(int32_t) * position_length,
+                            sizeof(int32_t) * position_length};
+    aclTensor* acl_position_tensor = ggml_cann_create_tensor(
+        src1->data, ggml_cann_type_mapping(src1->type),
+        ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);
+
+    // power * position
+    int64_t theta_length = arange_length * position_length;
+    ggml_cann_pool_alloc theta_allocator(ctx.pool(),
+                                         theta_length * sizeof(float_t));
+    void* theta_buffer = theta_allocator.get();
+    int64_t theta_ne[] = {arange_length, position_length, 1, 1};
+    size_t theta_nb[GGML_MAX_DIMS];
+    theta_nb[0] = sizeof(float_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1];
+    }
+    aclTensor* acl_theta_tensor =
+        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
+                                theta_ne, theta_nb, GGML_MAX_DIMS);
+    aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
+              acl_theta_tensor);
+
+    // permute: [0,1,2,3]->[0,2,1,3]
+    int64_t permute_ne[] = {arange_length, 1, position_length, 1};
+    size_t permute_nb[GGML_MAX_DIMS];
+    permute_nb[0] = sizeof(float_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        permute_nb[i] = permute_nb[i - 1] * permute_ne[i - 1];
+    }
+    ggml_cann_pool_alloc permute_allocator(ctx.pool(),
+                                           theta_length * sizeof(float_t));
+    void* permute_buffer = permute_allocator.get();
+    aclTensor* acl_permute_tensor = ggml_cann_create_tensor(
+        permute_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    int64_t permute_dim[] = {0, 2, 1, 3};
+    int64_t num_dims = 4;
+    aclnn_permute(ctx, acl_theta_tensor, acl_permute_tensor, permute_dim,
+                  num_dims);
+
+    // sin/cos
+    ggml_cann_pool_alloc sin_allocator(ctx.pool(),
+                                       theta_length * sizeof(float_t));
+    void* sin_buffer = sin_allocator.get();
+    aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
+        sin_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_sin(ctx, acl_permute_tensor, acl_sin_tensor);
+
+    ggml_cann_pool_alloc cos_allocator(ctx.pool(),
+                                       theta_length * sizeof(float_t));
+    void* cos_buffer = cos_allocator.get();
+    aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
+        cos_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_cos(ctx, acl_permute_tensor, acl_cos_tensor);
+
+    // repeat
+    if (is_neox) {
+        int64_t repeatsArray[] = {1, 1, 1, 2};
+        aclnn_repeat(ctx, acl_sin_tensor, acl_sin_repeat_tensor, repeatsArray);
+        aclnn_repeat(ctx, acl_cos_tensor, acl_cos_repeat_tensor, repeatsArray);
+    } else {
+        int64_t num_repeats = 2;
+        int64_t dim = 3;
+        int64_t output_size = arange_length * num_repeats;
+        aclnn_repeat_interleave(ctx, acl_sin_tensor, acl_sin_repeat_tensor, dim,
+                                num_repeats, output_size);
+        aclnn_repeat_interleave(ctx, acl_cos_tensor, acl_cos_repeat_tensor, dim,
+                                num_repeats, output_size);
+    }
+
+    // release
+    ACL_CHECK(aclDestroyTensor(acl_arange_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_theta_scale_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_position_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_theta_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_permute_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_sin_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_cos_tensor));
+}
+
+void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    // TODO: use ascendc
+    // Only test with LLAMA model.
+    ggml_tensor* src0 = dst->src[0];  // input
+    ggml_tensor* src2 = dst->src[2];  // freq_factors
+
+    // TODO: with freq_factors
+    GGML_ASSERT(src2 == NULL);
+
+    // param
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    // const int n_past     = ((int32_t *) dst->op_params)[0];
+    const int n_dims = ((int32_t*)dst->op_params)[1];
+    const int mode = ((int32_t*)dst->op_params)[2];
+    // const int n_ctx      = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig = ((int32_t*)dst->op_params)[4];
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    memcpy(&freq_base, (int32_t*)dst->op_params + 5, sizeof(float));
+    memcpy(&freq_scale, (int32_t*)dst->op_params + 6, sizeof(float));
+    memcpy(&ext_factor, (int32_t*)dst->op_params + 7, sizeof(float));
+    memcpy(&attn_factor, (int32_t*)dst->op_params + 8, sizeof(float));
+    memcpy(&beta_fast, (int32_t*)dst->op_params + 9, sizeof(float));
+    memcpy(&beta_slow, (int32_t*)dst->op_params + 10, sizeof(float));
+
+    GGML_ASSERT(n_dims <= ne0);
+    GGML_ASSERT(n_dims % 2 == 0);
+
+    // TODO: ext_factor != 0
+    GGML_ASSERT(ext_factor == 0);
+    // TODO: freq_scale != 1
+    GGML_ASSERT(freq_scale == 1);
+
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
+
+    float corr_dims[2];
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast,
+                             beta_slow, corr_dims);
+
+    const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+
+    // init cos/sin cache
+    ggml_cann_pool_alloc sin_allocator(
+        ctx.pool(), src0->ne[0] * src0->ne[2] * sizeof(float_t));
+    ggml_cann_pool_alloc cos_allocator(
+        ctx.pool(), src0->ne[0] * src0->ne[2] * sizeof(float_t));
+    void* sin_buffer = sin_allocator.get();
+    void* cos_buffer = cos_allocator.get();
+
+    int64_t sin_reshape_ne[4] = {src0->ne[0], 1, src0->ne[2], 1};
+    size_t sin_reshape_nb[GGML_MAX_DIMS];
+    sin_reshape_nb[0] = sizeof(float_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
+    }
+    aclTensor* acl_sin_reshape_tensor =
+        ggml_cann_create_tensor(sin_buffer, ACL_FLOAT, sizeof(float_t),
+                                sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+    aclTensor* acl_cos_reshape_tensor =
+        ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t),
+                                sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+    aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor,
+                     theta_scale, is_neox);
+
+    // roll input
+    void* input_roll_buffer;
+    aclTensor* acl_minus_one_tensor;
+    void* minus_one_scale_buffer = nullptr;
+    ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0));
+    ggml_cann_pool_alloc minus_one_scale_allocator(
+        ctx.pool(), sizeof(float_t) * src0->ne[0]);
+    if (!is_neox) {
+        // roll input: [q0,q1,q2,q3,...] -> [q1,q0,q3,q2,...]
+        input_roll_buffer = roll_allocator.get();
+        int64_t input_roll_ne[4] = {2, src0->ne[1] * (src0->ne[0] / 2),
+                                    src0->ne[2], src0->ne[3]};
+        size_t input_roll_nb[GGML_MAX_DIMS];
+        input_roll_nb[0] = ggml_type_size(src0->type);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_roll_nb[i] = input_roll_nb[i - 1] * input_roll_ne[i - 1];
+        }
+        aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor(
+            input_roll_buffer, ggml_cann_type_mapping(src0->type),
+            ggml_type_size(src0->type), input_roll_ne, input_roll_nb,
+            GGML_MAX_DIMS);
+        aclTensor* acl_input_tensor = ggml_cann_create_tensor(
+            src0->data, ggml_cann_type_mapping(src0->type),
+            ggml_type_size(src0->type), input_roll_ne, input_roll_nb,
+            GGML_MAX_DIMS);
+
+        int64_t shifts[] = {1};
+        int64_t dims[] = {3};
+        aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims);
+        ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor));
+        ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+
+        // init [-1, 1, -1, 1, ...]
+        minus_one_scale_buffer = minus_one_scale_allocator.get();
+
+        int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1};
+        size_t minus_one_nb[GGML_MAX_DIMS];
+        minus_one_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
+        }
+        acl_minus_one_tensor = aclnn_ones(
+            ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0],
+            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1);
+        int64_t dim = 3;
+        int64_t* index = new int64_t[src0->ne[0]];
+        for (int i = 0; i < src0->ne[0]; i++) {
+            index[i] = i / 2 * 2;
+        }
+        int64_t index_num = src0->ne[0];
+        float value = -1;
+        aclnn_index_fill_tensor(ctx, acl_minus_one_tensor, dim, index,
+                                index_num, value);
+    } else {
+        // roll input: [q0,q1,q2,...] ->
+        // [q_half,q_half+1,...,q_end,q0,q1,...q_half-1]
+        input_roll_buffer = roll_allocator.get();
+        aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor(
+            input_roll_buffer, ggml_cann_type_mapping(src0->type),
+            ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS);
+        aclTensor* acl_input_tensor = ggml_cann_create_tensor(src0);
+
+        int64_t shifts[] = {src0->ne[0] / 2};
+        int64_t dims[] = {3};
+        aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims);
+
+        ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor));
+        ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+
+        // init [-1, -1, -1, 1, 1，1，...]
+        minus_one_scale_buffer = minus_one_scale_allocator.get();
+
+        int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1};
+        size_t minus_one_nb[GGML_MAX_DIMS];
+        minus_one_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
+        }
+        acl_minus_one_tensor = aclnn_ones(
+            ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0],
+            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1);
+        // -1 * first half
+        int64_t first_half_ne[4] = {src0->ne[0] / 2, 1, 1, 1};
+        size_t first_half_nb[GGML_MAX_DIMS];
+        first_half_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1];
+        }
+        aclTensor* acl_first_half_tensor = ggml_cann_create_tensor(
+            minus_one_scale_buffer, ACL_FLOAT, sizeof(float_t), first_half_ne,
+            first_half_nb, GGML_MAX_DIMS);
+        bool inplace = true;
+        float scale = -1;
+        aclnn_muls(ctx, acl_first_half_tensor, scale, nullptr, inplace);
+        ACL_CHECK(aclDestroyTensor(acl_first_half_tensor));
+    }
+
+    // TODO: n_dims < ne0
+    GGML_ASSERT(n_dims == src0->ne[0]);
+
+    // input * scale
+    ggml_cann_pool_alloc roll_mul_scale_allocator(ctx.pool(),
+                                                  ggml_nbytes(src0));
+    void* input_roll_mul_scale_buffer = roll_mul_scale_allocator.get();
+    size_t input_nb[GGML_MAX_DIMS];
+    input_nb[0] = ggml_type_size(src0->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        input_nb[i] = input_nb[i - 1] * src0->ne[i - 1];
+    }
+    aclTensor* acl_input_roll_mul_scale_tensor = ggml_cann_create_tensor(
+        input_roll_mul_scale_buffer, ggml_cann_type_mapping(src0->type),
+        ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS);
+    aclTensor* acl_input_roll_reshape_tensor = ggml_cann_create_tensor(
+        input_roll_buffer, ggml_cann_type_mapping(src0->type),
+        ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS);
+
+    aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor,
+              acl_input_roll_mul_scale_tensor);
+
+    // output
+    aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+    void* output_fp32_buffer;
+    if (src0->type == GGML_TYPE_F32) {
+        aclnn_inplace_mul(ctx, acl_src0, acl_cos_reshape_tensor);
+        aclnn_inplace_mul(ctx, acl_input_roll_mul_scale_tensor,
+                          acl_sin_reshape_tensor);
+        aclnn_add(ctx, acl_src0, acl_input_roll_mul_scale_tensor, acl_dst);
+        // TODO: ne0 != n_dims in mode2
+    } else if (src0->type == GGML_TYPE_F16) {
+        size_t input_fp32_nb[GGML_MAX_DIMS];
+        input_fp32_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_fp32_nb[i] = input_fp32_nb[i - 1] * dst->ne[i - 1];
+        }
+        ggml_cann_pool_alloc fp32_allocator1(
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+        void* input_fp32_buffer1 = fp32_allocator1.get();
+        aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor(
+            input_fp32_buffer1, ACL_FLOAT, sizeof(float_t), dst->ne,
+            input_fp32_nb, GGML_MAX_DIMS);
+        ggml_cann_pool_alloc fp32_allocator2(
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+        void* input_fp32_buffer2 = fp32_allocator2.get();
+        aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor(
+            input_fp32_buffer2, ACL_FLOAT, sizeof(float_t), dst->ne,
+            input_fp32_nb, GGML_MAX_DIMS);
+
+        ggml_cann_pool_alloc fp32_allocator(
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+        output_fp32_buffer = fp32_allocator.get();
+        aclTensor* output_fp32_tensor = ggml_cann_create_tensor(
+            output_fp32_buffer, ACL_FLOAT, sizeof(float_t), dst->ne,
+            input_fp32_nb, GGML_MAX_DIMS);
+        aclnn_mul(ctx, acl_src0, acl_cos_reshape_tensor, input_fp32_tensor1);
+        aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor,
+                  input_fp32_tensor2);
+        aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2,
+                  output_fp32_tensor);
+        aclnn_cast(ctx, output_fp32_tensor, acl_dst, ACL_FLOAT16);
+
+        ACL_CHECK(aclDestroyTensor(input_fp32_tensor1));
+        ACL_CHECK(aclDestroyTensor(input_fp32_tensor2));
+        ACL_CHECK(aclDestroyTensor(output_fp32_tensor));
+    }
+
+    ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_minus_one_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_input_roll_mul_scale_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_input_roll_reshape_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_src0));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
diff --git a/ggml/src/ggml-cann/aclnn_ops.h b/ggml/src/ggml-cann/aclnn_ops.h
new file mode 100644
index 0000000000000..680129c76de68
--- /dev/null
+++ b/ggml/src/ggml-cann/aclnn_ops.h
@@ -0,0 +1,592 @@
+#ifndef CANN_ACLNN_OPS
+#define CANN_ACLNN_OPS
+
+/**
+ * @file    acl_tensor
+ * @brief   This file contains related functions of ggml_tensor and acl_tensor.
+ *          Contains conversion from ggml_tensor to acl_tensor, broadcast and other
+ *          functions.
+ * @author  hipudding <huafengchun@gmail.com>
+ * @author  wangshuai09 <391746016@qq.com>
+ * @date    July 15, 2024
+ *
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include <aclnnop/aclnn_add.h>
+#include <aclnnop/aclnn_arange.h>
+#include <aclnnop/aclnn_argsort.h>
+#include <aclnnop/aclnn_cat.h>
+#include <aclnnop/aclnn_clamp.h>
+#include <aclnnop/aclnn_div.h>
+#include <aclnnop/aclnn_gelu.h>
+#include <aclnnop/aclnn_hardsigmoid.h>
+#include <aclnnop/aclnn_hardswish.h>
+#include <aclnnop/aclnn_leaky_relu.h>
+#include <aclnnop/aclnn_mul.h>
+#include <aclnnop/aclnn_relu.h>
+#include <aclnnop/aclnn_silu.h>
+#include <aclnnop/aclnn_tanh.h>
+#include "acl_tensor.h"
+#include "common.h"
+
+/**
+ * @brief   Repeats a ggml tensor along each dimension to match the dimensions
+ *          of another tensor.
+ *
+ * @details This function repeats the elements of a source ggml tensor along
+ *          each dimension to create a destination tensor with the specified
+ *          dimensions. The operation is performed using the ACL backend and
+ *          executed asynchronously on the device.
+ *
+ * @param   ctx The CANN context used for operations.
+ * @param   dst The ggml tensor representing the destination, which op is
+ *              GGML_OP_REPEAT and specifies the desired dimensions.
+ */
+void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Adds two ggml tensors using the CANN backend.
+ *
+ * @details This function performs an element-wise addition of two tensors. In
+ *          case the tensors do not have the same shape, one or both tensors
+ *          will be broadcasted to match the shape of the other before the
+ *          addition is performed.The formula for the operation is given by:
+ *          \f[
+ *              \text{dst} = \text{acl_src0} + \alpha \cdot \text{acl_src1}
+ *          \f]
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The ggml tensor representing the destination, result of the
+ *            addition is stored at dst->data, and dst->op is `GGML_OP_ADD`
+ */
+void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Applies the Leaky ReLU activation function to a tensor using the CANN
+ *          backend.
+ *
+ * @details This function computes the Leaky ReLU activation for each element of
+ *          the input tensor. The Leaky ReLU function allows a small gradient
+ *          when the unit is not active (i.e., when the input is negative). The
+ *          Leaky ReLU function is defined as:
+ *          \f[
+ *              \text{dst} = \max(0, src) + \text{negativeSlope} \cdot \min(0,
+ *               src)
+ *          \f]
+ *          `negativeSlope` is in dst->params.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the result of the Leaky ReLU
+ *            activation is stored, which op is `GGML_OP_LEAKY_RELU`
+ */
+void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief    Concatenates multiple tensors along a specified dimension using the
+ *           CANN backend.
+ *
+ * @param ctx        The CANN context used for operations.
+ * @param tensorList A pointer to the list of tensors to be concatenated.
+ * @param dst        The destination tensor where the result of the
+ *                   concatenation is stored. dst->op is `GGML_OP_CONCAT`.
+ * @param concat_dim The dimension along which the tensors are concatenated.
+ *
+ * @attention tensorList length should be 2 and the dimension using for concat
+ *            default to 1.
+ */
+void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Generates a sequence of evenly spaced values within a specified
+ *          interval for a ggml tensor using the CANN backend.
+ *
+ * @details This function creates a sequence of numbers over a specified i
+ *          nterval, starting from `start`, ending before `stop`, and
+ *          incrementing by `step`. The sequence is stored in the destination
+ *          tensor `dst`.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the generated sequence will be stored.
+ *            `start`, 'stop' and 'step' are in dst->op_params and dst->op is
+ *            `GGML_OP_ARANGE`.
+ */
+void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the square of the elements of a ggml tensor using the CANN
+ *          backend.
+ * @details The function sets the second source tensor of the destination
+ *          tensor `dst` to be equal to the first source tensor. This is
+ *          effectively squaring the elements since the multiplication becomes
+ *          `element * element`.
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the squared values will be stored，
+ *            which dst->op is `GGML_OP_SQR`.
+ */
+void ggml_cann_sqr(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Applies a clamp operation to the elements of a ggml tensor using the
+ *          CANN backend.
+ *
+ * @details This function clamps the elements of the input tensor `src` to a
+ *          specified range defined by `min` and `max` values. The result is
+ *          stored in the destination tensor `dst`. The operation is defined as:
+ *          \f[
+ *              y = \max(\min(x, max\_value), min\_value)
+ *           \f]
+ *          where `x` is an element of the input tensor, and `y` is the
+ *          corresponding element in the output tensor.
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the clamped values will be stored.
+ *            dst->op is `GGML_OP_CLAMP`, `min` and `max` value is in dst->params.
+ */
+void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Scales the elements of a ggml tensor by a constant factor using the
+ *          CANN backend.
+ *
+ * @details This function multiplies each element of the input tensor `src` by
+ *          a scaling factor `scale`, storing the result in the destination
+ *          tensor `dst`. The operation is defined as:
+ *          \f[
+ *             dst = src \times scale
+ *          \f]
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the scaled values will be stored.
+ *            dst->op is `GGML_OP_SCALE` and `scale` value is in dst->params.
+ */
+void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Sorts the elements of a ggml tensor and returns the indices that
+ *          would sort the tensor using the CANN backend.
+ *
+ * @details This function performs an argsort operation on the input tensor
+ *          `src`. It sorts the elements of `src` in either ascending or
+ *          descending order, depending on the `GGML_SORT_ORDER_DESC`,
+ *          and returns the indices that would sort the original tensor.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the sorted indices will be stored.
+ *            dst->op is `GGML_OP_ARGSORT`.
+ */
+void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the Layer Normalization for a ggml tensor using the CANN
+ *          backend.
+ *
+ * @details This function applies the Layer Normalization operation on the
+ *          input tensor `src` and stores the result in the destination tensor
+ *          `dst`. Layer Normalization normalizes the features at each sample in
+ *          a mini-batch independently. It is commonly used in neural networks
+ *          to normalize the activations of a layer by adjusting and scaling
+ *          the outputs.
+ *          The operation is defined as:
+ *          \f[
+ *              \text { out }=\frac{x-\mathrm{E}[x]}{\sqrt{\text{Var}[x]+eps}}
+ *          \f]
+ *          `Var` defaults dst->ne[0]. `eps` is in dst->params.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the normalized values will be stored.
+ * @attention `Var` defaults to dst->ne[0].
+ */
+void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief  Computes the Group Normalization for a ggml tensor using the CANN
+ *         backend.
+ *
+ * @brief  This function applies the Group Normalization operation on the input
+ *         tensor `src` and stores the result in the destination tensor `dst`.
+ *         Group Normalization divides the channels into groups and normalizes
+ *         the features within each group across spatial locations.
+ *         It is commonly used in convolutional neural networks to improve
+ *         training stability and performance.
+ *         The operation is defined as:
+ *         \f[
+ *             \text { out }=\frac{x-\mathrm{E}[x]}{\sqrt{\text{Var}[x]+eps}}
+ *         \f]
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the normalized values will be stored.
+ *            `n_groups` is in dst->params, which split C channel to `n_groups`.
+ *            dst->op is `GGML_OP_GROUP_NORM`.
+ *
+ * @attention eps defaults to 1e-6f.
+ */
+void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the accumulation of tensors using the CANN backend.
+ *
+ * @details This function performs an accumulation operation on two tensors.
+ *          Depending on the `inplace` flag, it either updates the destination
+ *          tensor `dst` in place by adding `alpha * src1` to it, or it creates
+ *          a new tensor as the result of `src0 + alpha * src1` and stores it in
+ *          `dst`.
+ *          The operation is defined as:
+ *          \f[
+ *               dst = src0 + alpha \times src1
+ *          \f]
+ *          if `inplace` is `true`, `src0` is equal to 'dst'.
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the accumulated values will be stored.
+ *            `inplace` is in dst->params, and dst->op is `GGML_OP_ACC`.
+ */
+void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the sum of elements along the last dimension of a ggml tensor
+ *          using the CANN backend.
+ *
+ * @details This function performs a reduction sum operation along the last
+ *          dimension of the input tensor `src`. The result of the sum is stored
+ *          in the destination tensor `dst`.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the reduced values will be stored。
+ *            dst->op is `GGML_OP_SUM_ROWS`.
+ *
+ * @attention `reduce_dims` defaults to 3, which means the last dimension.
+ */
+void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Upsamples a ggml tensor using nearest neighbor interpolation using
+ *          the CANN backend.
+ *
+ * @details This function performs upsampling of the input tensor `src` using
+ *          nearest neighbor interpolation. The upsampling is applied to the
+ *          height and width dimensions (last two dimensions) of the tensor. The
+ *          result is stored in the destination tensor `dst`, which must have
+ *          the appropriate dimensions for the upsampled output.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the upsampled values will be stored.
+ *            dst->op is `GGML_OP_UPSCALE`.
+ */
+void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx,
+                                  ggml_tensor* dst);
+
+/**
+ * @brief   Pads a ggml tensor to match the dimensions of the destination tensor
+ *          using the CANN backend.
+ *
+ * @details This function pads the input tensor `src` so that it matches the
+ *          dimensions of the destination tensor `dst`. The amount of padding
+ *          is calculated based on the difference in sizes between `src` and
+ *          `dst` along each dimension. The padded tensor is stored in `dst`.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor, which specifies the target dimensions for
+ *            padding. dst->op is `GGML_OP_PAD`.
+ */
+void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Executes a 2D pooling operation on a ggml tensor using the CANN
+ *          backend.
+ *
+ * @details This function dispatches the execution of a 2D pooling operation on
+ *          the input tensor `dst`. The type of pooling (average or max) is
+ *          determined by the `op` parameter, which is read from the operation
+ *          parameters of `dst`. The function supports average pooling
+ *          (`GGML_OP_POOL_AVG`) and max pooling (`GGML_OP_POOL_MAX`). If an
+ *          invalid operation is encountered, the function asserts a failure.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor on which the pooling operation is to be
+ *            performed. dst->op is `GGML_OP_POOL_2D`.
+ */
+void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Duplicates a ggml tensor using the CANN backend.
+ *
+ * @details This function duplicates the contents of the source tensor `src` to
+ *          the destination tensor `dst`. The function supports various tensor
+ *          types and configurations, including handling of extra data, type
+ *          conversions, and special cases for contiguous and non-contiguous
+ *          tensors.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the duplicated data will be stored.
+ *            dst->op is `GGML_OP_DUP`
+ *
+ * @attention Only support Fp16/FP32. Not support when src and dst have
+ *            different shape and dst is no-contiguous.
+ * @note:     This func need to simplify.
+ */
+void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the Root Mean Square (RMS) normalization of a ggml tensor
+ *          using the CANN backend.
+ *
+ * @details This function applies RMS normalization to the input tensor `src`
+ *          and stores the result in the destination tensor `dst`. RMS
+ *          normalization involves computing the root mean square of the input
+ *          tensor along a specified dimension and then dividing each element of
+ *          the tensor by this value, adjusted by a small epsilon value to
+ *          prevent division by zero.
+ *          The operation is defined as:
+ *          \f[
+ *               \text{RmsNorm}\left(x_i\right)=\frac{x_i}{\text{Rms}(\mathbf{x})} g_i,
+ *               \quad \text { where } \text{Rms}(\mathbf{x})=\sqrt{\frac{1}{n} \sum_{i=1}^n x_i^2+e p s}
+ *          \f]
+ *          `eps` is in dst->op_params.
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the normalized values will be stored.
+ *            dst->op is `GGML_OP_RMS_NORM`.
+ */
+void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Applies a diagonal mask to the tensor with a specified value.
+ *
+ * @details This function creates a mask tensor filled with ones, then applies
+ *          an upper triangular and lower triangular operation to it based on
+ *          the number of past elements specified. Afterward, it adds the masked
+ *          tensor to the destination tensor in-place.
+ *
+ * @param ctx The backend CANN context used for operations.
+ * @param dst The destination tensor where the result will be stored. dst->op is
+ *            `GGML_OP_DIAG_MASK`
+ * @param value The value to use for masking.
+ */
+void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, float value);
+
+/**
+ * @brief   Performs an image-to-column transformation on the input tensor.
+ *
+ * @details This function takes an input tensor and applies an image-to-column
+ *          operation, converting spatial dimensions into column-like
+ *          structures suitable for convolutional operations. It supports both
+ *          half-precision (F16) and single-precision (F32) floating-point data
+ *          types.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor that stores the result of the operation.
+ *            dst->op is `GGML_OP_IM2COL`.
+ */
+void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes time step embeddings using sine and cosine functions.
+ *
+ * @details This function calculates time step embeddings by applying sine and
+ *          cosine transformations to a given input tensor, which is typically
+ *          used in temporal models like diffusion models or transformers to
+ *          encode time information effectively.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the result of the embedding operation
+ *            will be stored. dst->op is `GGML_OP_TIMESTEP_EMBEDDING`.
+ */
+void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+// @see ggml_cann_dup.
+void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the softmax activation with optional masking.
+ *
+ * @details This function computes the softmax activation over the input tensor,
+ *          optionally applying a mask and scaling factor. It supports both FP16
+ *          and FP32 data types and can handle masking by broadcasting the mask
+ *          across rows if necessary.
+ *          The function performs the following steps:
+ *          1. Multiplies the input tensor by a scale factor.
+ *          2. Optionally casts the mask tensor to FP32 if it is in FP16 format.
+ *          3. Broadcasts the mask tensor if its dimensions do not match the
+ *             input tensor's dimensions.
+ *          4. Adds the mask to the scaled input tensor.
+ *          5. Applies the softmax activation function along the specified
+ *             dimension.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the result will be stored. dst->op is
+ *            `GGML_OP_SOFTMAX`.
+ */
+void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Extracts specific rows from a tensor based on indices.
+ *
+ * @details This function retrieves rows from a source tensor src0 according to
+ *          the indices provided in another tensor src1 and stores the result in
+ *          a destination tensor (\p dst). It supports different data types
+ *          including F32, F16, Q4_0, and Q8_0.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the extracted rows will be stored.
+ *            dst->op is `GGML_OP_GET_ROWS`.
+ */
+void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Executes matrix multiplication for the given tensor.
+ *
+ * @details This function performs matrix multiplication on the source tensors
+ *          associated with the destination tensor. It supports matrix
+ *          multiplication F32, F16, and Q8_0.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor for storing the result of the matrix
+ *            multiplication. dst->op is `GGML_OP_MUL_MAT`.
+ */
+void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief Applies Rotary Positional Embedding (RoPE) to the input tensor.
+ *
+ * @details This function implements the RoPE mechanism, which is a method to
+ *          encode positional information into sequence data, particularly
+ *          useful in transformer models. It supports both F32 and F16 data
+ *          types.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the RoPE-transformed data will be
+ *            stored. dst->op is `GGML_OP_ROPE`.
+ *
+ * @note The function currently does not support cases where the n_dims is less
+ *       than the input tensor's first dimension.
+ * @note The function currently does not support cases where the freq_factors is
+ *       not NULL.
+ * @note The function currently does not support cases where the ext_factor is
+ *       not equal 0.
+ * @note The function currently does not support cases where the freq_scale is
+ *       not equal 1.
+ */
+void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+template <aclnnStatus getWorkspaceSize(const aclTensor*, const aclTensor*,
+                                       aclTensor*, uint64_t*, aclOpExecutor**),
+          aclnnStatus execute(void*, uint64_t, aclOpExecutor*, aclrtStream)>
+void ggml_cann_mul_div(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+    GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    aclTensor* acl_src0;
+    aclTensor* acl_src1;
+    aclTensor* acl_dst;
+
+    // Need bcast
+    if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) {
+        BCAST_SHAPE(src0, src1)
+        acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0));
+        acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1));
+        acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0));
+    } else {
+        acl_src0 = ggml_cann_create_tensor(src0);
+        acl_src1 = ggml_cann_create_tensor(src1);
+        acl_dst = ggml_cann_create_tensor(dst);
+    }
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(getWorkspaceSize(acl_src0, acl_src1, acl_dst, &workspaceSize,
+                               &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    aclrtStream main_stream = ctx.stream();
+    ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
+
+    ACL_CHECK(aclDestroyTensor(acl_src0));
+    ACL_CHECK(aclDestroyTensor(acl_src1));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+// Activation functions template.
+template <aclnnStatus getWorkspaceSize(const aclTensor*, aclTensor*, uint64_t*,
+                                       aclOpExecutor**),
+          aclnnStatus execute(void*, uint64_t, aclOpExecutor*,
+                              const aclrtStream)>
+void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    aclrtStream main_stream = ctx.stream();
+    ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+// Activation functions template for const aclTensors.
+template <aclnnStatus getWorkspaceSize(const aclTensor*, const aclTensor*,
+                                       uint64_t*, aclOpExecutor**),
+          aclnnStatus execute(void*, uint64_t, aclOpExecutor*,
+                              const aclrtStream)>
+void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    aclrtStream main_stream = ctx.stream();
+    ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+#endif  // CANN_ACLNN_OPS
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
new file mode 100644
index 0000000000000..e6a5701075f02
--- /dev/null
+++ b/ggml/src/ggml-cann/common.h
@@ -0,0 +1,282 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#ifndef CANN_COMMON_H
+#define CANN_COMMON_H
+
+#include <acl/acl.h>
+
+#include <cstdio>
+#include <iostream>
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "../include/ggml-cann.h"
+#include "../include/ggml.h"
+
+#define MATRIX_ROW_PADDING 512
+#define GGML_CANN_MAX_STREAMS 8
+
+/**
+ * @brief Handles CANN-related errors by printing an error message and
+ *        terminating the program.
+ * @param stmt The statement that caused the error.
+ * @param func The function in which the error occurred.
+ * @param file The file in which the error occurred.
+ * @param line The line number at which the error occurred.
+ * @param msg The error message.
+ */
+[[noreturn]] void ggml_cann_error(const char* stmt, const char* func,
+                                  const char* file, int line, const char* msg);
+
+/**
+ * @brief Checks the result of a CANN function call and invokes the error
+ *        handler if the call fails.
+ * @param stmt The CANN function call to check.
+ * @param success The success code that indicates the call was successful.
+ * @param error_fn The function to call to retrieve the error message.
+ */
+#define ACL_CHECK_GEN(stmt, success, error_fn)                                \
+    do {                                                                      \
+        int err_code = (stmt);                                                \
+        if (err_code != (success)) {                                          \
+            ggml_cann_error(#stmt, __func__, __FILE__, __LINE__, error_fn()); \
+        }                                                                     \
+    } while (0);
+
+#define ACL_CHECK(stmt) ACL_CHECK_GEN(stmt, 0, aclGetRecentErrMsg)
+
+/**
+ * @brief Contains information about CANN devices.
+ */
+struct ggml_cann_device_info {
+    /**
+     * @brief Number of CANN devices available.
+     */
+    int32_t device_count;
+
+    /**
+     * @brief Information about a single CANN device.
+     */
+    struct cann_device_info {
+        int cc;                 /**< Compute capability.                   */
+        size_t smpb;            /**< Maximum shared memory per block.      */
+        bool vmm;               /**< Virtual memory support.               */
+        size_t vmm_granularity; /**< Granularity of virtual memory.        */
+        size_t total_vram;      /**< Total video RAM available on the device. */
+    };
+
+    cann_device_info devices[GGML_CANN_MAX_DEVICES] =
+        {}; /**< Array of CANN device information. */
+};
+
+const ggml_cann_device_info& ggml_cann_info();
+
+void ggml_cann_set_device(int32_t device);
+int32_t ggml_cann_get_device();
+
+/**
+ * @brief Abstract base class for memory pools used by CANN.
+ */
+struct ggml_cann_pool {
+    /**
+     * @brief Virtual destructor for the memory pool.
+     */
+    virtual ~ggml_cann_pool() = default;
+
+    /**
+     * @brief Allocates memory from the pool.
+     *
+     * @param size         The size of the memory block to allocate.
+     * @param actual_size  Pointer to a variable where the actual allocated size
+     *                     will be stored.
+     * @return             Pointer to the allocated memory block.
+     */
+    virtual void* alloc(size_t size, size_t* actual_size) = 0;
+
+    /**
+     * @brief Frees a previously allocated memory block.
+     *
+     * @param ptr   Pointer to the memory block to free.
+     * @param size  Size of the memory block to free.
+     * @note Note that all CANN opertors are running async. Make sure memory is
+     *       still avaiable before this operator finished.
+     */
+    virtual void free(void* ptr, size_t size) = 0;
+};
+
+/**
+ * @brief RAII wrapper for managing memory allocations from a CANN memory pool.
+ */
+struct ggml_cann_pool_alloc {
+    ggml_cann_pool* pool = nullptr; /**< Pointer to the memory pool. */
+    void* ptr = nullptr;    /**< Pointer to the allocated memory block. */
+    size_t actual_size = 0; /**< Actual size of the allocated memory block. */
+
+    /**
+     * @brief Default constructor.
+     */
+    ggml_cann_pool_alloc() = default;
+
+    /**
+     * @brief Constructor that initializes the memory pool.
+     * @param pool Reference to the memory pool.
+     */
+    explicit ggml_cann_pool_alloc(ggml_cann_pool& pool) : pool(&pool) {}
+
+    /**
+     * @brief Constructor that initializes the memory pool and allocates memory.
+     * @param pool Reference to the memory pool.
+     * @param size Size of the memory block to allocate.
+     */
+    ggml_cann_pool_alloc(ggml_cann_pool& pool, size_t size) : pool(&pool) {
+        alloc(size);
+    }
+
+    /**
+     * @brief Destructor that frees the allocated memory block.
+     */
+    ~ggml_cann_pool_alloc() {
+        if (ptr != nullptr) {
+            pool->free(ptr, actual_size);
+        }
+    }
+
+    /**
+     * @brief Allocates memory from the pool.
+     * @param size Size of the memory block to allocate.
+     * @return Pointer to the allocated memory block.
+     */
+    void* alloc(size_t size) {
+        GGML_ASSERT(pool != nullptr);
+        GGML_ASSERT(ptr == nullptr);
+        ptr = pool->alloc(size, &this->actual_size);
+        return ptr;
+    }
+
+    /**
+     * @brief Allocates memory from a specific memory pool.
+     * @param pool Reference to the memory pool.
+     * @param size Size of the memory block to allocate.
+     * @return Pointer to the allocated memory block.
+     */
+    void* alloc(ggml_cann_pool& pool, size_t size) {
+        this->pool = &pool;
+        return alloc(size);
+    }
+
+    /**
+     * @brief Gets the pointer to the allocated memory block.
+     * @return Pointer to the allocated memory block.
+     */
+    void* get() { return ptr; }
+
+    // Deleted copy constructor
+    ggml_cann_pool_alloc(const ggml_cann_pool_alloc&) = delete;
+
+    // Deleted move constructor
+    ggml_cann_pool_alloc(ggml_cann_pool_alloc&&) = delete;
+
+    // Deleted copy assignment operator
+    ggml_cann_pool_alloc& operator=(const ggml_cann_pool_alloc&) = delete;
+
+    // Deleted move assignment operator
+    ggml_cann_pool_alloc& operator=(ggml_cann_pool_alloc&&) = delete;
+};
+
+/**
+ * @brief Context for managing CANN backend operations.
+ */
+struct ggml_backend_cann_context {
+    int32_t device;                  /**< Device ID. */
+    std::string name;                /**< Name of the device. */
+    aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
+
+    aclrtStream streams[GGML_CANN_MAX_STREAMS] = {
+        {nullptr}}; /**< Array of streams for the device. */
+
+    /**
+     * @brief Constructor for initializing the context with a given device.
+     * @param device Device ID.
+     */
+    explicit ggml_backend_cann_context(int device)
+        : device(device), name("CANN" + std::to_string(device)) {}
+
+    /**
+     * @brief Destructor for cleaning up resources.
+     */
+    ~ggml_backend_cann_context() {
+        if (copy_event != nullptr) {
+            ACL_CHECK(aclrtDestroyEvent(copy_event));
+        }
+        for (int i = 0; i < GGML_CANN_MAX_STREAMS; ++i) {
+            if (streams[i] != nullptr) {
+                ACL_CHECK(aclrtDestroyStream(streams[i]));
+            }
+        }
+    }
+
+    /**
+     * @brief Get or create a stream for a given index.
+     * @param stream Index of the stream.
+     * @return The stream corresponding to the given index.
+     */
+    aclrtStream stream(int stream) {
+        if (streams[stream] == nullptr) {
+            ggml_cann_set_device(device);
+            ACL_CHECK(aclrtCreateStream(&streams[stream]));
+        }
+        return streams[stream];
+    }
+
+    /**
+     * @brief Get or create the default stream (index 0).
+     * @return The default stream.
+     */
+    aclrtStream stream() { return stream(0); }
+
+    // TODO: each stream should have a memory pool.
+    std::unique_ptr<ggml_cann_pool>
+        mem_pool; /**< Memory pool for the device. */
+
+    /**
+     * @brief Create a new memory pool for a given device.
+     * @param device Device ID.
+     * @return A unique pointer to the new memory pool.
+     */
+    static std::unique_ptr<ggml_cann_pool> new_pool_for_device(int device);
+
+    /**
+     * @brief Get or create the memory pool for the context.
+     * @return Reference to the memory pool.
+     */
+    ggml_cann_pool& pool() {
+        if (mem_pool == nullptr) {
+            mem_pool = new_pool_for_device(device);
+        }
+        return *mem_pool;
+    }
+};
+
+#endif  // CANN_COMMON_H
diff --git a/ggml/src/ggml-cann/kernels/CMakeLists.txt b/ggml/src/ggml-cann/kernels/CMakeLists.txt
new file mode 100644
index 0000000000000..5b4fef91b5877
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/CMakeLists.txt
@@ -0,0 +1,33 @@
+if (NOT SOC_TYPE)
+    set (SOC_TYPE "Ascend910B3")
+endif()
+
+file(GLOB SRC_FILES
+    get_row_f32.cpp
+    get_row_f16.cpp
+    get_row_q4_0.cpp
+    get_row_q8_0.cpp
+    quantize_f32_q8_0.cpp
+    quantize_f16_q8_0.cpp
+    quantize_float_to_q4_0.cpp
+    dup.cpp
+)
+
+string(TOLOWER ${SOC_TYPE} SOC_VERSION)
+set(ASCEND_CANN_PACKAGE_PATH ${CANN_INSTALL_DIR})
+set(RUN_MODE "npu" CACHE STRING "run mode: npu/sim")
+
+if(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake)
+    set(ASCENDC_CMAKE_DIR ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake)
+elseif(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/ascendc_devkit/tikcpp/samples/cmake)
+    set(ASCENDC_CMAKE_DIR ${ASCEND_CANN_PACKAGE_PATH}/ascendc_devkit/tikcpp/samples/cmake)
+else()
+    message(FATAL_ERROR "ascendc_kernel_cmake does not exist, please check whether the compiler package is installed.")
+endif()
+include(${ASCENDC_CMAKE_DIR}/ascendc.cmake)
+
+ascendc_library(ascendc_kernels STATIC
+    ${SRC_FILES}
+)
+
+# ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP)
diff --git a/ggml/src/ggml-cann/kernels/ascendc_kernels.h b/ggml/src/ggml-cann/kernels/ascendc_kernels.h
new file mode 100644
index 0000000000000..7e153208cfdbc
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/ascendc_kernels.h
@@ -0,0 +1,19 @@
+#ifndef ASCENDC_KERNELS_H
+#define ASCENDC_KERNELS_H
+
+#include "aclrtlaunch_ascendc_get_row_f32.h"
+#include "aclrtlaunch_ascendc_get_row_f16.h"
+#include "aclrtlaunch_ascendc_get_row_q8_0.h"
+#include "aclrtlaunch_ascendc_get_row_q4_0.h"
+
+#include "aclrtlaunch_ascendc_quantize_f32_q8_0.h"
+#include "aclrtlaunch_ascendc_quantize_f16_q8_0.h"
+#include "aclrtlaunch_ascendc_quantize_f16_to_q4_0.h"
+#include "aclrtlaunch_ascendc_quantize_f32_to_q4_0.h"
+
+#include "aclrtlaunch_ascendc_dup_by_rows_fp16.h"
+#include "aclrtlaunch_ascendc_dup_by_rows_fp32.h"
+#include "aclrtlaunch_ascendc_dup_by_rows_fp32_to_fp16.h"
+#include "aclrtlaunch_ascendc_dup_by_rows_fp16_to_fp32.h"
+
+#endif  // ASCENDC_KERNELS_H
diff --git a/ggml/src/ggml-cann/kernels/dup.cpp b/ggml/src/ggml-cann/kernels/dup.cpp
new file mode 100644
index 0000000000000..e2c651152f486
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/dup.cpp
@@ -0,0 +1,223 @@
+#include "kernel_operator.h"
+
+#include <cmath>
+
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+template <typename SRC_T, typename DST_T>
+class DupByRows {
+   public:
+    __aicore__ inline DupByRows() {}
+    __aicore__ inline void init(GM_ADDR src, GM_ADDR dst, int64_t *input_ne_ub,
+                                size_t *input_nb_ub) {
+        /* Dup by rows when src is contigous on first dimension and dst is
+        contiguous, each kernel process one row.
+        */
+
+        // Input has four dims.
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        // param
+        num_rows = input_ne_ub[1] * input_ne_ub[2] * input_ne_ub[3];
+        num_elem = input_ne_ub[0];
+
+        // index for (ne[1], ne[2], ne[3]): (idx_ne1, idx_ne2, idx_ne3)
+        idx_ne3 = op_block_idx / (input_ne_ub[1] * input_ne_ub[2]);
+        idx_ne2 = (op_block_idx - idx_ne3 * (input_ne_ub[1] * input_ne_ub[2]))
+                  / (input_ne_ub[1]);
+        idx_ne1 = op_block_idx - idx_ne3 * (input_ne_ub[1] * input_ne_ub[2])
+                - idx_ne2 * input_ne_ub[1];
+
+        // src may not contiguous in dim [1,2,3], so stride decited by ne&nb
+        src_stride = input_nb_ub[3] * idx_ne3 + input_nb_ub[2] * idx_ne2
+                     + input_nb_ub[1] * idx_ne1;
+
+        // dst is contiguous
+        dst_stride = op_block_idx * (input_ne_ub[0] * sizeof(DST_T));
+
+        src_gm.SetGlobalBuffer(reinterpret_cast<__gm__ SRC_T *>(src +
+                                                                src_stride));
+        dst_gm.SetGlobalBuffer(reinterpret_cast<__gm__ DST_T *>(dst +
+                                                                dst_stride));
+
+        pipe.InitBuffer(src_queue, BUFFER_NUM, (sizeof(SRC_T) * num_elem +
+                                                32 - 1) / 32 * 32);
+        pipe.InitBuffer(dst_queue, BUFFER_NUM, (sizeof(DST_T) * num_elem +
+                                                32 - 1) / 32 * 32);
+    }
+
+    __aicore__ inline void copy_in() {
+        LocalTensor<SRC_T> src_local = src_queue.AllocTensor<SRC_T>();
+
+        DataCopyExtParams dataCopyParams;
+        dataCopyParams.blockCount = 1;
+        dataCopyParams.blockLen = num_elem * sizeof(SRC_T);
+        DataCopyPadExtParams<SRC_T> padParams;
+        DataCopyPad(src_local, src_gm, dataCopyParams, padParams);
+
+        src_queue.EnQue(src_local);
+    }
+
+    __aicore__ inline void copy_out() {
+        LocalTensor<DST_T> dst_local = dst_queue.DeQue<DST_T>();
+
+        DataCopyExtParams dataCopyParams;
+        dataCopyParams.blockCount = 1;
+        dataCopyParams.blockLen = num_elem * sizeof(DST_T);
+        DataCopyPad(dst_gm, dst_local, dataCopyParams);
+
+        dst_queue.FreeTensor(dst_local);
+    }
+
+    __aicore__ inline void dup() {
+        // main process, copy one row data from src to dst.
+        copy_in();
+
+        LocalTensor<SRC_T> src_local = src_queue.DeQue<SRC_T>();
+        LocalTensor<DST_T> dst_local = dst_queue.AllocTensor<DST_T>();
+
+        int32_t BLOCK_NUM = 32 / sizeof(DST_T);
+        DataCopy(dst_local, src_local, (num_elem + BLOCK_NUM - 1)
+                                        / BLOCK_NUM * BLOCK_NUM);
+        dst_queue.EnQue<DST_T>(dst_local);
+
+        src_queue.FreeTensor(src_local);
+        copy_out();
+    }
+
+    __aicore__ inline void dup_with_cast() {
+        // main process, copy one row data from src to dst.
+        // cast dtype from src to dst.
+        copy_in();
+
+        LocalTensor<SRC_T> src_local = src_queue.DeQue<SRC_T>();
+        LocalTensor<DST_T> dst_local = dst_queue.AllocTensor<DST_T>();
+
+        Cast(dst_local, src_local, RoundMode::CAST_NONE, num_elem);
+        dst_queue.EnQue<DST_T>(dst_local);
+
+        src_queue.FreeTensor(src_local);
+        copy_out();
+    }
+
+   private:
+
+    TPipe pipe;
+    GlobalTensor<SRC_T> src_gm;
+    GlobalTensor<DST_T> dst_gm;
+
+    int64_t num_rows;
+    int64_t num_elem;
+    int64_t idx_ne3;
+    int64_t idx_ne2;
+    int64_t idx_ne1;
+    int64_t src_stride;
+    int64_t dst_stride;
+
+    TQue<QuePosition::VECIN, BUFFER_NUM> src_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> dst_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp16(
+                                                        GM_ADDR src_gm,
+                                                        GM_ADDR dst_gm,
+                                                        GM_ADDR input_ne_gm,
+                                                        GM_ADDR input_nb_gm,
+                                                        GM_ADDR output_ne_gm,
+                                                        GM_ADDR output_nb_gm) {
+
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    DupByRows<half, half> op;
+    op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
+    op.dup();
+}
+
+extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp32(
+                                                        GM_ADDR src_gm,
+                                                        GM_ADDR dst_gm,
+                                                        GM_ADDR input_ne_gm,
+                                                        GM_ADDR input_nb_gm,
+                                                        GM_ADDR output_ne_gm,
+                                                        GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    DupByRows<float_t, float_t> op;
+    op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
+    op.dup();
+}
+
+extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp32_to_fp16(
+                                                        GM_ADDR src_gm,
+                                                        GM_ADDR dst_gm,
+                                                        GM_ADDR input_ne_gm,
+                                                        GM_ADDR input_nb_gm,
+                                                        GM_ADDR output_ne_gm,
+                                                        GM_ADDR output_nb_gm) {
+
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    DupByRows<float_t, half> op;
+    op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
+    op.dup_with_cast();
+}
+
+extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp16_to_fp32(
+                                                        GM_ADDR src_gm,
+                                                        GM_ADDR dst_gm,
+                                                        GM_ADDR input_ne_gm,
+                                                        GM_ADDR input_nb_gm,
+                                                        GM_ADDR output_ne_gm,
+                                                        GM_ADDR output_nb_gm) {
+
+    // copy params from gm to ub.
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    DupByRows<half, float_t> op;
+    op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
+    op.dup_with_cast();
+}
diff --git a/ggml/src/ggml-cann/kernels/get_row_f16.cpp b/ggml/src/ggml-cann/kernels/get_row_f16.cpp
new file mode 100644
index 0000000000000..c704b5b2ec0f3
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/get_row_f16.cpp
@@ -0,0 +1,186 @@
+#include "kernel_operator.h"
+
+// optimize me. Use template to avoid copy code.
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+class GET_ROW_F16 {
+   public:
+    __aicore__ inline GET_ROW_F16() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *indices_ne_ub, size_t *indices_nb_ub,
+                                int64_t *output_ne_ub, size_t *output_nb_ub) {
+        // TODO, use template for F16/f32
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+
+            indices_ne[i] = indices_ne_ub[i];
+            indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
+
+            output_ne[i] = output_ne_ub[i];
+            output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
+        }
+
+        // Indices has two dims. n_elements = all rows should get.
+        // dr, all rows should this thread get.
+        uint64_t n_elements =
+            indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
+        dr = n_elements / op_block_num;
+
+        uint64_t tails = n_elements % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        input_gm.SetGlobalBuffer((__gm__ half *)input);
+        indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
+        output_gm.SetGlobalBuffer((__gm__ float *)output);
+
+        uint64_t input_local_buffer_size = ((input_ne[0] * sizeof(half) + 31)
+                                             & ~31);
+        uint64_t output_local_buffer_size = ((input_ne[0] * sizeof(float) + 31)
+                                              & ~31);
+
+        local_buffer_elems = input_local_buffer_size / sizeof(half);
+
+        // TODO, consider long row that can't put in UB.
+        // All data should asign to 32. It's ok because all data is align to 32.
+        pipe.InitBuffer(input_queue, BUFFER_NUM, input_local_buffer_size);
+        pipe.InitBuffer(output_queue, BUFFER_NUM, output_local_buffer_size);
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset, size_t len) {
+        LocalTensor<half> input_local = input_queue.AllocTensor<half>();
+        size_t tail = len % 32;
+        len = len & ~31;
+        DataCopy(input_local, input_gm[offset], len);
+        if(tail != 0) {
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = tail * sizeof(half);
+            DataCopyPadExtParams<half> padParams;
+            DataCopyPad(input_local[len], input_gm[offset + len],
+                        dataCopyParams, padParams);
+        }
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset, size_t len) {
+        LocalTensor<float> output_local = output_queue.DeQue<float>();
+        size_t tail = len % 32;
+        len = len & ~31;
+        DataCopy(output_gm[offset], output_local, len);
+        if(tail != 0) {
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = tail * sizeof(float);
+            DataCopyPad(output_gm[offset + len], output_local[len],
+                        dataCopyParams);
+        }
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void calculate_row(int64_t idx) {
+        const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
+        const int64_t indices_ne1_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
+            indices_ne[0];
+        const int64_t indices_ne0_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
+             indices_ne1_idx * indices_ne[0]);
+
+        const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
+                                       indices_ne1_idx * indices_stride[1] +
+                                       indices_ne2_idx * indices_stride[2];
+        const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
+
+        const int64_t input_offset = selected_row_idx * input_stride[1] +
+                                     indices_ne1_idx * input_stride[2] +
+                                     indices_ne2_idx * input_stride[3];
+
+        const int64_t output_offset = indices_ne0_idx * output_stride[1] +
+                                      indices_ne1_idx * output_stride[2] +
+                                      indices_ne2_idx * output_stride[3];
+
+        copy_in(input_offset, input_ne[0]);
+        LocalTensor<half> input_local = input_queue.DeQue<half>();
+        LocalTensor<float> output_local = output_queue.AllocTensor<float>();
+
+        Cast(output_local, input_local, RoundMode::CAST_NONE,
+             local_buffer_elems);
+        output_queue.EnQue(output_local);
+        copy_out(output_offset, input_ne[0]);
+
+        input_queue.FreeTensor(input_local);
+    }
+
+    __aicore__ inline void calculate() {
+        for (int64_t i = ir; i < ir + dr; i++) {
+            calculate_row(i);
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t indices_ne[4];
+    size_t indices_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    size_t local_buffer_elems;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<half> input_gm;
+    GlobalTensor<int32_t> indices_gm;
+    GlobalTensor<float> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_get_row_f16(
+    GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
+    GM_ADDR input_ne_gm, GM_ADDR input_nb_gm, GM_ADDR indices_ne_gm,
+    GM_ADDR indices_nb_gm, GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t indices_ne_ub[4];
+    size_t indices_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
+    copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    GET_ROW_F16 op;
+    op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
+            indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/get_row_f32.cpp b/ggml/src/ggml-cann/kernels/get_row_f32.cpp
new file mode 100644
index 0000000000000..9db080af36998
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/get_row_f32.cpp
@@ -0,0 +1,180 @@
+#include "kernel_operator.h"
+
+// optimize me. Use template to avoid copy code.
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+class GET_ROW_F32 {
+   public:
+    __aicore__ inline GET_ROW_F32() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *indices_ne_ub, size_t *indices_nb_ub,
+                                int64_t *output_ne_ub, size_t *output_nb_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+
+            indices_ne[i] = indices_ne_ub[i];
+            indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
+
+            output_ne[i] = output_ne_ub[i];
+            output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
+        }
+
+        // Indices has two dims. n_elements = all rows should get.
+        // dr, all rows should this thread get.
+        uint64_t n_elements =
+            indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
+        dr = n_elements / op_block_num;
+
+        uint64_t tails = n_elements % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        input_gm.SetGlobalBuffer((__gm__ float *)input);
+        indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
+        output_gm.SetGlobalBuffer((__gm__ float *)output);
+
+        uint64_t local_buffer_size = ((input_ne[0] * sizeof(float) + 31) & ~31);
+        local_buffer_elems = local_buffer_size / sizeof(float);
+
+        // TODO, consider long row that can't put in UB.
+        // All data should asign to 32. It's ok because all data is align to 32.
+        pipe.InitBuffer(input_queue, BUFFER_NUM, local_buffer_size);
+        pipe.InitBuffer(output_queue, BUFFER_NUM, local_buffer_size);
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset, size_t len) {
+        LocalTensor<float> input_local = input_queue.AllocTensor<float>();
+        size_t tail = len % 32;
+        len = len & ~31;
+        DataCopy(input_local, input_gm[offset], len);
+        if(tail != 0) {
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = tail * sizeof(float);
+            DataCopyPadExtParams<float> padParams;
+            DataCopyPad(input_local[len], input_gm[offset + len],
+                        dataCopyParams, padParams);
+        }
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset, size_t len) {
+        LocalTensor<float> output_local = output_queue.DeQue<float>();
+        size_t tail = len % 32;
+        len = len & ~31;
+        DataCopy(output_gm[offset], output_local, len);
+        if(tail != 0) {
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = tail * sizeof(float);
+            DataCopyPad(output_gm[offset + len], output_local[len],
+                        dataCopyParams);
+        }
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void calculate_row(int64_t idx) {
+        const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
+        const int64_t indices_ne1_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
+            indices_ne[0];
+        const int64_t indices_ne0_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
+             indices_ne1_idx * indices_ne[0]);
+
+        const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
+                                       indices_ne1_idx * indices_stride[1] +
+                                       indices_ne2_idx * indices_stride[2];
+        const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
+
+        const int64_t input_offset = selected_row_idx * input_stride[1] +
+                                     indices_ne1_idx * input_stride[2] +
+                                     indices_ne2_idx * input_stride[3];
+
+        const int64_t output_offset = indices_ne0_idx * output_stride[1] +
+                                      indices_ne1_idx * output_stride[2] +
+                                      indices_ne2_idx * output_stride[3];
+
+        copy_in(input_offset, input_ne[0]);
+        LocalTensor<float> input_local = input_queue.DeQue<float>();
+        LocalTensor<float> output_local = output_queue.AllocTensor<float>();
+
+        DataCopy(output_local, input_local, local_buffer_elems);
+        output_queue.EnQue(output_local);
+        copy_out(output_offset, input_ne[0]);
+
+        input_queue.FreeTensor(input_local);
+    }
+
+    __aicore__ inline void calculate() {
+        for (int64_t i = ir; i < ir + dr; i++) {
+            calculate_row(i);
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t indices_ne[4];
+    size_t indices_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    size_t local_buffer_elems;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<float> input_gm;
+    GlobalTensor<int32_t> indices_gm;
+    GlobalTensor<float> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_get_row_f32(
+    GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
+    GM_ADDR input_ne_gm, GM_ADDR input_nb_gm, GM_ADDR indices_ne_gm,
+    GM_ADDR indices_nb_gm, GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t indices_ne_ub[4];
+    size_t indices_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
+    copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    GET_ROW_F32 op;
+    op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
+            indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp
new file mode 100644
index 0000000000000..a80bfeec2417d
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp
@@ -0,0 +1,193 @@
+#include "kernel_operator.h"
+
+// optimize me. Use template to avoid copy code.
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+#define QK4_0 32
+
+class GET_ROW_Q4_0 {
+   public:
+    __aicore__ inline GET_ROW_Q4_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
+                                int64_t *input_ne_ub, int64_t *indices_ne_ub,
+                                size_t *indices_nb_ub, int64_t *output_ne_ub,
+                                size_t *output_nb_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            indices_ne[i] = indices_ne_ub[i];
+            indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
+            scale_ne[i] = input_ne_ub[i];
+            output_ne[i] = output_ne_ub[i];
+            output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
+        }
+
+        // one scale for a group.
+        scale_ne[0] /= QK4_0;
+
+        input_stride[0] = 1;
+        scale_stride[0] = 1;
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            input_stride[i] = input_stride[i - 1] * input_ne[i - 1];
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        group_size_in_row = input_ne[0] / QK4_0;
+        int64_t scale_offset = input_ne[0] * input_ne[1] * input_ne[2] *
+                               input_ne[3] / 2;
+
+        // Indices has two dims. n_elements = all rows should get.
+        // dr, all rows should this thread get.
+        uint64_t n_elements =
+            indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
+        dr = n_elements / op_block_num;
+
+        uint64_t tails = n_elements % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        input_gm.SetGlobalBuffer((__gm__ int4b_t *)input);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(input + scale_offset));
+        indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
+        output_gm.SetGlobalBuffer((__gm__ float *)output);
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, QK4_0 * sizeof(int4b_t));
+        pipe.InitBuffer(cast_queue, BUFFER_NUM, QK4_0 * sizeof(half));
+        pipe.InitBuffer(output_queue, BUFFER_NUM, QK4_0 * sizeof(float));
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<int4b_t> input_local = input_queue.AllocTensor<int4b_t>();
+        // 32 * sizeof(int4b_t) = 16, which is not aligned to 32, why no error?
+        DataCopy(input_local, input_gm[offset], QK4_0);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        LocalTensor<float> output_local = output_queue.DeQue<float>();
+        DataCopy(output_gm[offset], output_local, QK4_0);
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void calculate_group(int64_t idx, int64_t group) {
+        const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
+        const int64_t indices_ne1_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
+            indices_ne[0];
+        const int64_t indices_ne0_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
+             indices_ne1_idx * indices_ne[0]);
+
+        const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
+                                       indices_ne1_idx * indices_stride[1] +
+                                       indices_ne2_idx * indices_stride[2];
+        const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
+
+        const int64_t input_offset = selected_row_idx * input_stride[1] +
+                                     indices_ne1_idx * input_stride[2] +
+                                     indices_ne2_idx * input_stride[3] +
+                                     group * QK4_0;
+        const int64_t scale_offset = selected_row_idx * scale_stride[1] +
+                                     indices_ne1_idx * scale_stride[2] +
+                                     indices_ne2_idx * scale_stride[3] + group;
+        const int64_t output_offset = indices_ne0_idx * output_stride[1] +
+                                      indices_ne1_idx * output_stride[2] +
+                                      indices_ne2_idx * output_stride[3] +
+                                      group * QK4_0;
+
+        copy_in(input_offset);
+        LocalTensor<int4b_t> input_local = input_queue.DeQue<int4b_t>();
+        LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
+        LocalTensor<float> output_local = output_queue.AllocTensor<float>();
+
+        // TODO: cast more data to speed up.
+        Cast(cast_local, input_local, RoundMode::CAST_NONE, QK4_0);
+        Cast(output_local, cast_local, RoundMode::CAST_NONE, QK4_0);
+
+        // Only mul need compile by group.
+        half scale = scale_gm.GetValue(scale_offset);
+
+        Muls(output_local, output_local, (float)scale, QK4_0);
+
+        input_queue.FreeTensor(input_local);
+        cast_queue.FreeTensor(cast_local);
+        output_queue.EnQue(output_local);
+
+        copy_out(output_offset);
+    }
+
+    __aicore__ inline void calculate() {
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                calculate_group(i, j);
+            }
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t scale_ne[4];
+    size_t scale_stride[4];
+
+    int64_t indices_ne[4];
+    size_t indices_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t ir;
+    int64_t dr;
+
+    int64_t group_size_in_row;
+
+    TPipe pipe;
+    GlobalTensor<int4b_t> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int32_t> indices_gm;
+    GlobalTensor<float> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, BUFFER_NUM> cast_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_get_row_q4_0(
+    GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
+    GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm,
+    GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    int64_t indices_ne_ub[4];
+    size_t indices_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
+    copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    GET_ROW_Q4_0 op;
+    op.init(input_gm, indices_gm, output_gm, input_ne_ub, indices_ne_ub,
+            indices_nb_ub, output_ne_ub, output_nb_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/get_row_q8_0.cpp b/ggml/src/ggml-cann/kernels/get_row_q8_0.cpp
new file mode 100644
index 0000000000000..ba9ab3c04832f
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/get_row_q8_0.cpp
@@ -0,0 +1,191 @@
+#include "kernel_operator.h"
+
+// optimize me. Use template to avoid copy code.
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+#define QK8_0 32
+
+class GET_ROW_Q8_0 {
+   public:
+    __aicore__ inline GET_ROW_Q8_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
+                                int64_t *input_ne_ub, int64_t *indices_ne_ub,
+                                size_t *indices_nb_ub, int64_t *output_ne_ub,
+                                size_t *output_nb_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            indices_ne[i] = indices_ne_ub[i];
+            indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
+            scale_ne[i] = input_ne_ub[i];
+            output_ne[i] = output_ne_ub[i];
+            output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
+        }
+
+        // one scale for a group.
+        scale_ne[0] /= QK8_0;
+
+        input_stride[0] = 1;
+        scale_stride[0] = 1;
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            input_stride[i] = input_stride[i - 1] * input_ne[i - 1];
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        group_size_in_row = input_ne[0] / QK8_0;
+        int64_t scale_offset = input_ne[0] * input_ne[1] * input_ne[2] *
+                               input_ne[3] * sizeof(int8_t);
+
+        // Indices has two dims. n_elements = all rows should get.
+        // dr, all rows should this thread get.
+        uint64_t n_elements =
+            indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
+        dr = n_elements / op_block_num;
+
+        uint64_t tails = n_elements % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        input_gm.SetGlobalBuffer((__gm__ int8_t *)input);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(input + scale_offset));
+        indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
+        output_gm.SetGlobalBuffer((__gm__ float *)output);
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
+        pipe.InitBuffer(cast_queue, BUFFER_NUM, QK8_0 * sizeof(half));
+        pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(float));
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<int8_t> input_local = input_queue.AllocTensor<int8_t>();
+        DataCopy(input_local, input_gm[offset], QK8_0);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        LocalTensor<float> output_local = output_queue.DeQue<float>();
+        DataCopy(output_gm[offset], output_local, QK8_0);
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void calculate_group(int64_t idx, int64_t group) {
+        const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
+        const int64_t indices_ne1_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
+            indices_ne[0];
+        const int64_t indices_ne0_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
+             indices_ne1_idx * indices_ne[0]);
+
+        const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
+                                       indices_ne1_idx * indices_stride[1] +
+                                       indices_ne2_idx * indices_stride[2];
+        const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
+
+        const int64_t input_offset = selected_row_idx * input_stride[1] +
+                                     indices_ne1_idx * input_stride[2] +
+                                     indices_ne2_idx * input_stride[3] +
+                                     group * QK8_0;
+        const int64_t scale_offset = selected_row_idx * scale_stride[1] +
+                                     indices_ne1_idx * scale_stride[2] +
+                                     indices_ne2_idx * scale_stride[3] + group;
+        const int64_t output_offset = indices_ne0_idx * output_stride[1] +
+                                      indices_ne1_idx * output_stride[2] +
+                                      indices_ne2_idx * output_stride[3] +
+                                      group * QK8_0;
+
+        copy_in(input_offset);
+        LocalTensor<int8_t> input_local = input_queue.DeQue<int8_t>();
+        LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
+        LocalTensor<float> output_local = output_queue.AllocTensor<float>();
+
+        // TODO: cast more data to speed up.
+        Cast(cast_local, input_local, RoundMode::CAST_NONE, QK8_0);
+        Cast(output_local, cast_local, RoundMode::CAST_NONE, QK8_0);
+
+        // Only mul need compile by group.
+        half scale = scale_gm.GetValue(scale_offset);
+        Muls(output_local, output_local, (float)scale, QK8_0);
+
+        input_queue.FreeTensor(input_local);
+        cast_queue.FreeTensor(cast_local);
+        output_queue.EnQue(output_local);
+
+        copy_out(output_offset);
+    }
+
+    __aicore__ inline void calculate() {
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                calculate_group(i, j);
+            }
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t scale_ne[4];
+    size_t scale_stride[4];
+
+    int64_t indices_ne[4];
+    size_t indices_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t ir;
+    int64_t dr;
+
+    int64_t group_size_in_row;
+
+    TPipe pipe;
+    GlobalTensor<int8_t> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int32_t> indices_gm;
+    GlobalTensor<float> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, BUFFER_NUM> cast_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_get_row_q8_0(
+    GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
+    GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm,
+    GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    int64_t indices_ne_ub[4];
+    size_t indices_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
+    copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    GET_ROW_Q8_0 op;
+    op.init(input_gm, indices_gm, output_gm, input_ne_ub, indices_ne_ub,
+            indices_nb_ub, output_ne_ub, output_nb_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp b/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp
new file mode 100644
index 0000000000000..8423b3f02a8f8
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp
@@ -0,0 +1,208 @@
+#include "kernel_operator.h"
+
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+#define QK8_0 32
+
+class QUANTIZE_F16_Q8_0 {
+   public:
+    __aicore__ inline QUANTIZE_F16_Q8_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *output_ne_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+
+            output_ne[i] = output_ne_ub[i];
+        }
+
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
+        }
+
+        scale_ne = input_ne;
+        scale_stride[0] = 1;
+        scale_stride[1] = input_ne[0] / QK8_0;
+        for (int i = 2; i < 4; i++) {
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        // split input tensor by rows.
+        uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
+        dr = nr / op_block_num;
+
+        uint64_t tails = nr % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        group_size_in_row = scale_stride[1];
+        int64_t output_size = output_ne[0] * output_ne[1] * output_ne[2] *
+                              output_ne[3] * sizeof(uint8_t);
+
+        input_gm.SetGlobalBuffer((__gm__ half *)input);
+        output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(output + output_size + ir *
+                                                 group_size_in_row *
+                                                 sizeof(half)));
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(half));
+        pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
+        pipe.InitBuffer(work_queue, 1, 32);
+        pipe.InitBuffer(max_queue, 1, 32);
+        pipe.InitBuffer(abs_queue, 1, QK8_0 * sizeof(float));
+        pipe.InitBuffer(scale_queue, 1, 32);
+        pipe.InitBuffer(cast_queue ,1 ,QK8_0 * sizeof(float));
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<half> input_local = input_queue.AllocTensor<half>();
+        DataCopy(input_local, input_gm[offset], QK8_0);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        LocalTensor<int8_t> output_local = output_queue.DeQue<int8_t>();
+        DataCopy(output_gm[offset], output_local, QK8_0);
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline half calculate_group(int64_t row, int64_t group) {
+        const int64_t i3 = row / (input_ne[1] * input_ne[2]);
+        const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
+        const int64_t i1 =
+            row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
+
+        const int64_t input_offset = i1 * input_stride[1] +
+                                     i2 * input_stride[2] +
+                                     i3 * input_stride[3] + QK8_0 * group;
+
+        const int64_t output_offset = i1 * output_stride[1] +
+                                      i2 * output_stride[2] +
+                                      i3 * output_stride[3] + QK8_0 * group;
+
+        copy_in(input_offset);
+        LocalTensor<half> input_local = input_queue.DeQue<half>();
+        LocalTensor<int8_t> output_local = output_queue.AllocTensor<int8_t>();
+        LocalTensor<float> work_local = work_queue.AllocTensor<float>();
+        LocalTensor<float> abs_local = abs_queue.AllocTensor<float>();
+        LocalTensor<float> max_local = max_queue.AllocTensor<float>();
+        LocalTensor<float> cast_local = cast_queue.AllocTensor<float>();
+
+        Cast(cast_local, input_local, RoundMode::CAST_NONE, QK8_0);
+        Abs(abs_local, cast_local, QK8_0);
+        ReduceMax(max_local, abs_local, work_local, QK8_0);
+
+        pipe_barrier(PIPE_ALL);
+        float d = max_local.GetValue(0);
+        d = d / ((1 << 7) - 1);
+        if (d != 0) {
+            Muls(cast_local, cast_local, 1.0f / d, QK8_0);
+        }
+
+        Cast(cast_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
+        Cast(input_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
+        Cast(output_local, input_local, RoundMode::CAST_ROUND, QK8_0);
+        output_queue.EnQue(output_local);
+        copy_out(output_offset);
+
+        input_queue.FreeTensor(input_local);
+        work_queue.FreeTensor(work_local);
+        abs_queue.FreeTensor(abs_local);
+        max_queue.FreeTensor(max_local);
+        cast_queue.FreeTensor(cast_local);
+        return (half)d;
+    }
+
+    __aicore__ inline void calculate() {
+        LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
+        uint32_t scale_local_offset = 0;
+        uint32_t scale_global_offset = 0;
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                half scale = calculate_group(i, j);
+                scale_local.SetValue(scale_local_offset++, scale);
+                if (scale_local_offset == 16) {
+                    scale_local_offset = 0;
+                    // TODO: OPTIMIZE ME
+                    pipe_barrier(PIPE_ALL);
+                    DataCopy(scale_gm[scale_global_offset], scale_local, 16);
+                    pipe_barrier(PIPE_ALL);
+                    scale_global_offset += 16;
+                }
+            }
+        }
+
+        if (scale_local_offset != 0) {
+            pipe_barrier(PIPE_ALL);
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = scale_local_offset * sizeof(half);
+            DataCopyPad(scale_gm[scale_global_offset], scale_local,
+                        dataCopyParams);
+            pipe_barrier(PIPE_ALL);
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t *scale_ne;
+    size_t scale_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t group_size_in_row;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<half> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int8_t> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, 1> work_queue;
+    TQue<QuePosition::VECOUT, 1> max_queue;
+    TQue<QuePosition::VECIN, 1> abs_queue;
+    TQue<QuePosition::VECOUT, 1> scale_queue;
+    TQue<QuePosition::VECOUT, 1> cast_queue;
+
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_quantize_f16_q8_0(
+    GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
+    GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+
+    QUANTIZE_F16_Q8_0 op;
+    op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp b/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp
new file mode 100644
index 0000000000000..b7c575093e9c1
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp
@@ -0,0 +1,206 @@
+#include "kernel_operator.h"
+
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+#define QK8_0 32
+
+class QUANTIZE_F32_Q8_0 {
+   public:
+    __aicore__ inline QUANTIZE_F32_Q8_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *output_ne_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+
+            output_ne[i] = output_ne_ub[i];
+        }
+
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
+        }
+
+        scale_ne = input_ne;
+        scale_stride[0] = 1;
+        scale_stride[1] = input_ne[0] / QK8_0;
+        for (int i = 2; i < 4; i++) {
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        // split input tensor by rows.
+        uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
+        dr = nr / op_block_num;
+
+        uint64_t tails = nr % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        group_size_in_row = scale_stride[1];
+        int64_t output_size = output_ne[0] * output_ne[1] * output_ne[2] *
+                              output_ne[3] * sizeof(uint8_t);
+
+        input_gm.SetGlobalBuffer((__gm__ float *)input);
+        output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(output + output_size +
+                                                 ir * group_size_in_row *
+                                                 sizeof(half)));
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(float));
+        pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
+        pipe.InitBuffer(work_queue, 1, 32);
+        pipe.InitBuffer(max_queue, 1, 32);
+        pipe.InitBuffer(abs_queue, 1, QK8_0 * sizeof(float));
+        pipe.InitBuffer(cast_queue, 1, QK8_0 * sizeof(half));
+        pipe.InitBuffer(scale_queue, 1, 32);
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<float> input_local = input_queue.AllocTensor<float>();
+        DataCopy(input_local, input_gm[offset], QK8_0);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        LocalTensor<int8_t> output_local = output_queue.DeQue<int8_t>();
+        DataCopy(output_gm[offset], output_local, QK8_0);
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline half calculate_group(int64_t row, int64_t group) {
+        const int64_t i3 = row / (input_ne[1] * input_ne[2]);
+        const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
+        const int64_t i1 =
+            row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
+
+        const int64_t input_offset = i1 * input_stride[1] +
+                                     i2 * input_stride[2] +
+                                     i3 * input_stride[3] + QK8_0 * group;
+
+        const int64_t output_offset = i1 * output_stride[1] +
+                                      i2 * output_stride[2] +
+                                      i3 * output_stride[3] + QK8_0 * group;
+
+        copy_in(input_offset);
+        LocalTensor<float> input_local = input_queue.DeQue<float>();
+        LocalTensor<int8_t> output_local = output_queue.AllocTensor<int8_t>();
+        LocalTensor<float> work_local = work_queue.AllocTensor<float>();
+        LocalTensor<float> abs_local = abs_queue.AllocTensor<float>();
+        LocalTensor<float> max_local = max_queue.AllocTensor<float>();
+        LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
+
+        Abs(abs_local, input_local, QK8_0);
+        ReduceMax(max_local, abs_local, work_local, QK8_0);
+        pipe_barrier(PIPE_ALL);
+        float d = max_local.GetValue(0);
+        d = d / ((1 << 7) - 1);
+        if (d != 0) {
+            Muls(input_local, input_local, 1.0f / d, QK8_0);
+        }
+
+        Cast(input_local, input_local, RoundMode::CAST_ROUND, QK8_0);
+        Cast(cast_local, input_local, RoundMode::CAST_ROUND, QK8_0);
+        Cast(output_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
+        output_queue.EnQue(output_local);
+        copy_out(output_offset);
+
+        input_queue.FreeTensor(input_local);
+        work_queue.FreeTensor(work_local);
+        abs_queue.FreeTensor(abs_local);
+        max_queue.FreeTensor(max_local);
+        cast_queue.FreeTensor(cast_local);
+
+        return (half)d;
+    }
+
+    __aicore__ inline void calculate() {
+        LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
+        uint32_t scale_local_offset = 0;
+        uint32_t scale_global_offset = 0;
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                half scale = calculate_group(i, j);
+                scale_local.SetValue(scale_local_offset++, scale);
+                if (scale_local_offset == 16) {
+                    scale_local_offset = 0;
+                    // TODO: OPTIMIZE ME
+                    pipe_barrier(PIPE_ALL);
+                    DataCopy(scale_gm[scale_global_offset], scale_local, 16);
+                    pipe_barrier(PIPE_ALL);
+                    scale_global_offset += 16;
+                }
+            }
+        }
+
+        if (scale_local_offset != 0) {
+            pipe_barrier(PIPE_ALL);
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = scale_local_offset * sizeof(half);
+            DataCopyPad(scale_gm[scale_global_offset], scale_local,
+                        dataCopyParams);
+            pipe_barrier(PIPE_ALL);
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t *scale_ne;
+    size_t scale_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t group_size_in_row;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<float> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int8_t> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, 1> work_queue;
+    TQue<QuePosition::VECOUT, 1> max_queue;
+    TQue<QuePosition::VECIN, 1> abs_queue;
+    TQue<QuePosition::VECIN, 1> cast_queue;
+    TQue<QuePosition::VECOUT, 1> scale_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_quantize_f32_q8_0(
+    GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
+    GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+
+    QUANTIZE_F32_Q8_0 op;
+    op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp b/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp
new file mode 100644
index 0000000000000..9c8c86b66ad66
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp
@@ -0,0 +1,278 @@
+#include "kernel_operator.h"
+
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+#define Group_Size 32
+
+template <typename SRC_T>
+class QUANTIZE_FLOAT_TO_Q4_0 {
+   public:
+    __aicore__ inline QUANTIZE_FLOAT_TO_Q4_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *output_ne_ub) {
+        // TODO: fix test_case CPY(type_src=f16,type_dst=q4_0,ne=[256,4,4,4],
+        //                         permute=[0,0,0,0]):
+        // [CPY] NMSE = 0.000008343 > 0.000001000 FAIL
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        // input stride of data elements
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+            output_ne[i] = output_ne_ub[i];
+        }
+
+        // output stride of data elements
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
+        }
+
+        // scale saved one by one after data:. [group1_scale, group2_scale, ...]
+        scale_ne = input_ne;
+        scale_stride[0] = 1;
+        scale_stride[1] = input_ne[0] / Group_Size;
+        for (int i = 2; i < 4; i++) {
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        // split input tensor by rows.
+        uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
+        dr = nr / op_block_num;
+
+        uint64_t tails = nr % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        group_size_in_row = scale_stride[1];
+        int64_t scale_offset = output_ne[0] * output_ne[1] * output_ne[2] *
+                              output_ne[3] * sizeof(uint8_t) / 2;
+
+        input_gm.SetGlobalBuffer((__gm__ SRC_T *)input);
+        output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(output + scale_offset + ir *
+                                                 group_size_in_row *
+                                                 sizeof(half)));
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, Group_Size * sizeof(SRC_T));
+        pipe.InitBuffer(output_queue, BUFFER_NUM,
+                            Group_Size * sizeof(int8_t) / 2);
+        pipe.InitBuffer(cast_queue , 1, Group_Size * sizeof(float));
+        pipe.InitBuffer(work_queue, 1, Group_Size * sizeof(float));
+        pipe.InitBuffer(max_queue, 1, Group_Size * sizeof(float));
+        pipe.InitBuffer(min_queue, 1, Group_Size * sizeof(float));
+        pipe.InitBuffer(scale_queue, 1, Group_Size / 2 * sizeof(half));
+        pipe.InitBuffer(int8_queue, 1, Group_Size * sizeof(int8_t));
+        pipe.InitBuffer(half_queue, 1, Group_Size * sizeof(half));
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<SRC_T> input_local = input_queue.AllocTensor<SRC_T>();
+        DataCopy(input_local, input_gm[offset], Group_Size);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        // reinterpretcast Group_Size(32) * int4b_t to Group_Size / 2 * int8_t,
+        // and using DataCopyPad to avoid 32 bits align.
+        LocalTensor<int4b_t> output_local = output_queue.DeQue<int4b_t>();
+        LocalTensor<int8_t> output_int8_local =
+                                    output_local.ReinterpretCast<int8_t>();
+
+        DataCopyExtParams dataCopyParams;
+        dataCopyParams.blockCount = 1;
+        dataCopyParams.blockLen = Group_Size / 2  * sizeof(int8_t);
+        DataCopyPad(output_gm[offset], output_int8_local, dataCopyParams);
+
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void input_to_cast(LocalTensor<float> cast_local,
+                                         LocalTensor<float> input_local) {
+        DataCopy(cast_local, input_local, Group_Size);
+    }
+
+    __aicore__ inline void input_to_cast(LocalTensor<float> cast_local,
+                                         LocalTensor<half> input_local) {
+        Cast(cast_local, input_local, RoundMode::CAST_NONE, Group_Size);
+    }
+
+    __aicore__ inline half calculate_group(int64_t row, int64_t group) {
+        const int64_t i3 = row / (input_ne[1] * input_ne[2]);
+        const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
+        const int64_t i1 =
+            row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
+
+        const int64_t input_offset = i1 * input_stride[1] +
+                                     i2 * input_stride[2] +
+                                     i3 * input_stride[3] + Group_Size * group;
+
+        // output_offset is stride for output_gm which datatype is int8_t and
+        // divided by 2 is needed for int4b_t.
+        const int64_t output_offset = (i1 * output_stride[1] +
+                                       i2 * output_stride[2] +
+                                       i3 * output_stride[3] +
+                                       Group_Size * group) / 2;
+        copy_in(input_offset);
+
+        LocalTensor<SRC_T> input_local = input_queue.DeQue<SRC_T>();
+        LocalTensor<int4b_t> output_local = output_queue.AllocTensor<int4b_t>();
+        LocalTensor<float> cast_local = cast_queue.AllocTensor<float>();
+        LocalTensor<float> work_local = work_queue.AllocTensor<float>();
+        LocalTensor<float> max_local = max_queue.AllocTensor<float>();
+        LocalTensor<float> min_local = min_queue.AllocTensor<float>();
+        LocalTensor<int8_t> int8_local = int8_queue.AllocTensor<int8_t>();
+        LocalTensor<half> half_local = half_queue.AllocTensor<half>();
+
+        input_to_cast(cast_local, input_local);
+
+        ReduceMax(max_local, cast_local, work_local, Group_Size);
+        ReduceMin(min_local, cast_local, work_local, Group_Size);
+        const float max_value = max_local.GetValue(0);
+        const float min_value = min_local.GetValue(0);
+        float d = max_value;
+        if (min_value < 0 && (-1 * min_value) > max_value) {
+            d = min_value;
+        }
+
+        d = d / (-8);
+        if (d != 0) {
+            Muls(cast_local, cast_local, 1.0f / d, Group_Size);
+        }
+
+        // range: [-8,8] -> [0.5,16.5] -> [0,16] -> [0,15] -> [-8,7]
+        float scalar = 8.5f;
+        Adds(cast_local, cast_local, scalar, Group_Size);
+        Cast(cast_local, cast_local, RoundMode::CAST_FLOOR, Group_Size);
+        scalar = 15.0f;
+        Mins(cast_local, cast_local, scalar, Group_Size);
+        scalar = -8.0f;
+        Adds(cast_local, cast_local, scalar, Group_Size);
+
+        // float->half->int4b
+        Cast(half_local, cast_local, RoundMode::CAST_NONE, Group_Size);
+        Cast(output_local, half_local, RoundMode::CAST_NONE, Group_Size);
+
+        output_queue.EnQue(output_local);
+        copy_out(output_offset);
+
+        input_queue.FreeTensor(input_local);
+        work_queue.FreeTensor(work_local);
+        max_queue.FreeTensor(max_local);
+        min_queue.FreeTensor(min_local);
+        int8_queue.FreeTensor(int8_local);
+        half_queue.FreeTensor(half_local);
+        cast_queue.FreeTensor(cast_local);
+        return (half)d;
+    }
+
+    __aicore__ inline void calculate() {
+        LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
+        uint32_t scale_local_offset = 0;
+        uint32_t scale_global_offset = 0;
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                half scale = calculate_group(i, j);
+                scale_local.SetValue(scale_local_offset++, scale);
+                // Copy Group_Size/2 length data each time.
+                if (scale_local_offset == Group_Size / 2) {
+                    scale_local_offset = 0;
+                    // TODO: OPTIMIZE ME
+                    pipe_barrier(PIPE_ALL);
+                    DataCopy(scale_gm[scale_global_offset], scale_local,
+                                      Group_Size / 2);
+                    pipe_barrier(PIPE_ALL);
+                    scale_global_offset += Group_Size / 2;
+                }
+            }
+        }
+
+        if (scale_local_offset != 0) {
+            pipe_barrier(PIPE_ALL);
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = scale_local_offset * sizeof(half);
+            DataCopyPad(scale_gm[scale_global_offset], scale_local,
+                        dataCopyParams);
+            pipe_barrier(PIPE_ALL);
+        }
+        scale_queue.FreeTensor(scale_local);
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t *scale_ne;
+    size_t scale_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t group_size_in_row;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<SRC_T> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int8_t> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, BUFFER_NUM> work_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> max_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> min_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> scale_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> cast_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> int8_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> half_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_quantize_f16_to_q4_0(
+    GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
+    GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+
+    QUANTIZE_FLOAT_TO_Q4_0<half> op;
+    op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
+    op.calculate();
+}
+
+extern "C" __global__ __aicore__ void ascendc_quantize_f32_to_q4_0(
+    GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
+    GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+
+    QUANTIZE_FLOAT_TO_Q4_0<float> op;
+    op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
+    op.calculate();
+}
diff --git a/ggml-common.h b/ggml/src/ggml-common.h
similarity index 98%
rename from ggml-common.h
rename to ggml/src/ggml-common.h
index 43c7978a0982d..e40057632fc5a 100644
--- a/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -19,7 +19,11 @@ typedef half2 ggml_half2;
 
 #define GGML_COMMON_DECL
 #elif defined(GGML_COMMON_DECL_CUDA)
+#if defined(GGML_COMMON_DECL_MUSA)
+#include <musa_fp16.h>
+#else
 #include <cuda_fp16.h>
+#endif
 #include <cstdint>
 
 typedef half  ggml_half;
@@ -65,13 +69,8 @@ typedef sycl::half2 ggml_half2;
 // QK = number of values after dequantization
 // QK_K = super-block size
 
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
 #define QK_K 256
 #define K_SCALE_SIZE 12
-#endif // GGML_QKK_64
 
 #if defined(GGML_COMMON_DECL_CUDA) || defined(GGML_COMMON_DECL_HIP) || defined(GGML_COMMON_DECL_SYCL)
 // QR = QK / number of values before dequantization
@@ -111,33 +110,34 @@ typedef sycl::half2 ggml_half2;
 #define QR6_K 2
 
 #define QI2_XXS (QK_K / (4*QR2_XXS))
-#define QR2_XXS 8
+#define QR2_XXS 4
 
 #define QI2_XS (QK_K / (4*QR2_XS))
-#define QR2_XS 8
+#define QR2_XS 4
 
 #define QI2_S (QK_K / (4*QR2_S))
-#define QR2_S 8
+#define QR2_S 4
 
 #define QI3_XXS (QK_K / (4*QR3_XXS))
-#define QR3_XXS 8
+#define QR3_XXS 4
 
 #define QI3_XS (QK_K / (4*QR3_XS))
-#define QR3_XS 8
+#define QR3_XS 4
 
 #define QI1_S (QK_K / (4*QR1_S))
 #define QR1_S 8
 
+#define QI1_M (QK_K / (4*QR1_M))
+#define QR1_M 8
+
 #define QI4_NL (QK4_NL / (4*QR4_NL))
 #define QR4_NL 2
 
-#if QK_K == 64
-#define QI4_XS QI4_NL
-#define QR4_XS QR4_NL
-#else
 #define QI4_XS (QK_K / (4*QR4_XS))
-#define QR4_XS 8
-#endif
+#define QR4_XS 2
+
+#define QI3_S (QK_K / (4*QR3_S))
+#define QR3_S 4
 
 #endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP
 
@@ -203,6 +203,30 @@ typedef struct {
 } block_q8_1;
 static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_half) + QK8_1, "wrong q8_1 block size/padding");
 
+typedef struct {
+    ggml_half d[4];        // deltas for 4 q4_0 blocks
+    uint8_t qs[QK4_0 * 2]; // nibbles / quants for 4 q4_0 blocks
+} block_q4_0x4;
+static_assert(sizeof(block_q4_0x4) == 4 * sizeof(ggml_half) + QK4_0 * 2, "wrong q4_0x4 block size/padding");
+
+typedef struct {
+    ggml_half d[8];        // deltas for 8 q4_0 blocks
+    uint8_t qs[QK4_0 * 4]; // nibbles / quants for 8 q4_0 blocks
+} block_q4_0x8;
+static_assert(sizeof(block_q4_0x8) == 8 * sizeof(ggml_half) + QK4_0 * 4, "wrong q4_0x8 block size/padding");
+
+typedef struct {
+    ggml_half d[4];        // deltas for 4 q8_0 blocks
+    int8_t qs[QK8_0 * 4];  // quants for 4 q8_0 blocks
+} block_q8_0x4;
+static_assert(sizeof(block_q8_0x4) == 4 * sizeof(ggml_half) + QK8_0 * 4, "wrong q8_0x4 block size/padding");
+
+typedef struct {
+    ggml_half d[8];        // deltas for 8 q8_0 blocks
+    int8_t qs[QK8_0 * 8];  // quants for 8 q8_0 blocks
+} block_q8_0x8;
+static_assert(sizeof(block_q8_0x8) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong q8_0x8 block size/padding");
+
 //
 // Super-block quantization structures
 //
@@ -228,15 +252,6 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wro
 // weight is represented as x = a * q
 // 16 blocks of 16 elements each
 // Effectively 3.4375 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    uint8_t hmask[QK_K/8]; // quants - high bit
-    uint8_t qs[QK_K/4];    // quants - low 2 bits
-    uint8_t scales[2];
-    ggml_half d;           // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
-#else
 typedef struct {
     uint8_t hmask[QK_K/8]; // quants - high bit
     uint8_t qs[QK_K/4];    // quants - low 2 bits
@@ -244,20 +259,11 @@ typedef struct {
     ggml_half d;           // super-block scale
 } block_q3_K;
 static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
-#endif
 
 // 4-bit quantization
 // 8 blocks of 32 elements each
 // weight is represented as x = a * q + b
 // Effectively 4.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_half d[2];     // super-block scales/mins
-    uint8_t scales[2];  // 4-bit block scales/mins
-    uint8_t qs[QK_K/2]; // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
 typedef struct {
     union {
         struct {
@@ -270,21 +276,11 @@ typedef struct {
     uint8_t qs[QK_K/2];           // 4--bit quants
 } block_q4_K;
 static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
-#endif
 
 // 5-bit quantization
 // 8 blocks of 32 elements each
 // weight is represented as x = a * q + b
 // Effectively 5.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_half d;             // super-block scale
-    int8_t  scales[QK_K/16]; // 8-bit block scales
-    uint8_t qh[QK_K/8];      // quants, high bit
-    uint8_t qs[QK_K/2];      // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_half) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
 typedef struct {
     union {
         struct {
@@ -298,7 +294,6 @@ typedef struct {
     uint8_t qs[QK_K/2];           // quants, low 4 bits
 } block_q5_K;
 static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
 
 // 6-bit quantization
 // weight is represented as x = a * q
@@ -356,11 +351,7 @@ typedef struct {
 static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
 
 // 3.4375 bpw
-#if QK_K == 64
-#define IQ3S_N_SCALE 2
-#else
 #define IQ3S_N_SCALE QK_K/64
-#endif
 typedef struct {
     ggml_half d;
     uint8_t qs[QK_K/4];
@@ -381,16 +372,9 @@ static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wron
 typedef struct {
     uint8_t  qs[QK_K/8];      // grid index, low 8 bits
     uint8_t  qh[QK_K/16];     // grid index, high 3 bits + grid shift bit (for two groups of 8)
-#if QK_K == 64
-    ggml_half d;
-#endif
     uint8_t  scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64)
 } block_iq1_m;
-#if QK_K == 64
-static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding");
-#else
 static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
-#endif
 
 // Used by IQ1_M quants
 typedef union {
@@ -406,9 +390,6 @@ typedef struct {
 } block_iq4_nl;
 static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding");
 
-#if QK_K == 64
-#define block_iq4_xs block_iq4_nl
-#else
 typedef struct {
     ggml_half d;
     uint16_t scales_h;
@@ -416,7 +397,6 @@ typedef struct {
     uint8_t  qs[QK_K/2];
 } block_iq4_xs;
 static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
-#endif
 
 #endif // GGML_COMMON_DECL
 #endif // GGML_COMMON_DECL
@@ -439,7 +419,7 @@ static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_
 #define GGML_TABLE_END() };
 
 #define GGML_COMMON_IMPL
-#elif defined(GGML_COMMON_IMPL_CUDA) || defined(GGML_COMMON_IMPL_HIP)
+#elif defined(GGML_COMMON_IMPL_CUDA) || defined(GGML_COMMON_IMPL_HIP) || defined(GGML_COMMON_IMPL_MUSA)
 #include <cstdint>
 
 #define GGML_TABLE_BEGIN(type, name, size) static const __device__ type name[size] = {
diff --git a/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
similarity index 89%
rename from ggml-cuda.cu
rename to ggml/src/ggml-cuda.cu
index b82167cbf7227..682c30d45bcf4 100644
--- a/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -29,6 +29,7 @@
 #include "ggml-cuda/tsembd.cuh"
 #include "ggml-cuda/unary.cuh"
 #include "ggml-cuda/upscale.cuh"
+#include "ggml-cuda/conv-transpose-1d.cuh"
 
 #include <algorithm>
 #include <array>
@@ -97,7 +98,7 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in
     GGML_CUDA_LOG_ERROR("  current device: %d, in function %s at %s:%d\n", id, func, file, line);
     GGML_CUDA_LOG_ERROR("  %s\n", stmt);
     // abort with GGML_ASSERT to get a stack trace
-    GGML_ASSERT(!"CUDA error");
+    GGML_ABORT("CUDA error");
 }
 
 // this is faster on Windows
@@ -119,6 +120,35 @@ int ggml_cuda_get_device() {
     return id;
 }
 
+static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) {
+    ggml_cuda_set_device(device);
+#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA)
+    auto res = hipMallocManaged(ptr, size);
+    if (res == hipSuccess) {
+        // if error we "need" to know why...
+        CUDA_CHECK(hipMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device));
+    }
+    return res;
+#else
+
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA)
+    cudaError_t err;
+    if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr)
+    {
+        err = cudaMallocManaged(ptr, size);
+    }
+    else
+    {
+        err = cudaMalloc(ptr, size);
+    }
+    return err;
+#else
+    return cudaMalloc(ptr, size);
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA)
+
+#endif
+}
+
 static ggml_cuda_device_info ggml_cuda_init() {
 #ifdef __HIP_PLATFORM_AMD__
     // Workaround for a rocBLAS bug when using multiple graphics cards:
@@ -138,21 +168,21 @@ static ggml_cuda_device_info ggml_cuda_init() {
     GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES);
 
     int64_t total_vram = 0;
-#if defined(GGML_CUDA_FORCE_MMQ)
-    GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ:   yes\n", __func__);
+#ifdef GGML_CUDA_FORCE_MMQ
+    GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ:    yes\n", __func__);
 #else
-    GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ:   no\n", __func__);
-#endif
-#if defined(CUDA_USE_TENSOR_CORES)
-    GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
+    GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ:    no\n", __func__);
+#endif // GGML_CUDA_FORCE_MMQ
+#ifdef GGML_CUDA_FORCE_CUBLAS
+    GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: yes\n", __func__);
 #else
-    GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
-#endif
+    GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: no\n", __func__);
+#endif // GGML_CUDA_FORCE_CUBLAS
     GGML_CUDA_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
     for (int id = 0; id < info.device_count; ++id) {
         int device_vmm = 0;
 
-#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
         CUdevice device;
         CU_CHECK(cuDeviceGet(&device, id));
         CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
@@ -164,7 +194,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
             alloc_prop.location.id = id;
             CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
         }
-#endif // !defined(GGML_USE_HIPBLAS)
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
         info.devices[id].vmm = !!device_vmm;
 
         cudaDeviceProp prop;
@@ -174,13 +204,15 @@ static ggml_cuda_device_info ggml_cuda_init() {
         info.default_tensor_split[id] = total_vram;
         total_vram += prop.totalGlobalMem;
 
+        info.devices[id].nsm   = prop.multiProcessorCount;
+        info.devices[id].smpb  = prop.sharedMemPerBlock;
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+        info.devices[id].smpbo = prop.sharedMemPerBlock;
         info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD;
 #else
+        info.devices[id].smpbo = prop.sharedMemPerBlockOptin;
         info.devices[id].cc = 100*prop.major + 10*prop.minor;
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-        info.devices[id].smpb = prop.sharedMemPerBlock;
-        info.devices[id].nsm  = prop.multiProcessorCount;
     }
 
     for (int id = 0; id < info.device_count; ++id) {
@@ -271,7 +303,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
         size_t look_ahead_size = (size_t) (1.05 * size);
         look_ahead_size = 256 * ((look_ahead_size + 255)/256);
         ggml_cuda_set_device(device);
-        CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
+        CUDA_CHECK(ggml_cuda_device_malloc(&ptr, look_ahead_size, device));
         *actual_size = look_ahead_size;
         pool_size += look_ahead_size;
 #ifdef DEBUG_CUDA_MALLOC
@@ -298,7 +330,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
 };
 
 // pool with virtual memory
-#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
 struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
     static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
 
@@ -392,14 +424,14 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
         GGML_ASSERT(ptr == (void *) (pool_addr + pool_used));
     }
 };
-#endif // !defined(GGML_USE_HIPBLAS)
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
 
 std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) {
-#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
     if (ggml_cuda_info().devices[device].vmm) {
         return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
     }
-#endif
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA)
     return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
 }
 
@@ -447,12 +479,12 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t
         return;
     }
 
-    if (ggml_is_quantized(tensor->type)) {
+    if (ggml_is_quantized(tensor->type) && tensor->view_src == nullptr && ggml_backend_buffer_get_usage(buffer) != GGML_BACKEND_BUFFER_USAGE_COMPUTE) {
         // initialize padding to 0 to avoid possible NaN values
         size_t original_size = ggml_nbytes(tensor);
         size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
 
-        if (padded_size > original_size && tensor->view_src == nullptr) {
+        if (padded_size > original_size) {
             ggml_cuda_set_device(ctx->device);
             CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size));
         }
@@ -529,6 +561,10 @@ GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_bu
     return ctx->name.c_str();
 }
 
+static bool ggml_backend_buft_is_cuda(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cuda_buffer_type_name;
+}
+
 GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
 
@@ -537,7 +573,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe
     size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
 
     void * dev_ptr;
-    cudaError_t err = cudaMalloc(&dev_ptr, size);
+    cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device);
     if (err != cudaSuccess) {
         // clear the error
         cudaGetLastError();
@@ -571,24 +607,12 @@ GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backen
     GGML_UNUSED(buft);
 }
 
-GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    if (!ggml_backend_is_cuda(backend)) {
-        return false;
-    }
-
-    ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
-    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
-
-    return buft_ctx->device == cuda_ctx->device;
-}
-
 static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = {
     /* .get_name         = */ ggml_backend_cuda_buffer_type_name,
     /* .alloc_buffer     = */ ggml_backend_cuda_buffer_type_alloc_buffer,
     /* .get_alignment    = */ ggml_backend_cuda_buffer_type_get_alignment,
     /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
     /* .get_alloc_size   = */ ggml_backend_cuda_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend,
     /* .is_host          = */ NULL,
 };
 
@@ -619,88 +643,22 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
 
 // cuda split buffer
 
-static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split) {
-    int64_t min_compute_capability = INT_MAX;
-    int64_t max_compute_capability = INT_MIN;
+static int64_t get_row_rounding(const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split) {
+    int64_t row_rounding = 0;
     for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
-        if (tensor_split[id] < (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) {
-            if (min_compute_capability > ggml_cuda_info().devices[id].cc) {
-                min_compute_capability = ggml_cuda_info().devices[id].cc;
-            }
-            if (max_compute_capability < ggml_cuda_info().devices[id].cc) {
-                max_compute_capability = ggml_cuda_info().devices[id].cc;
-            }
+        if (tensor_split[id] >= (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) {
+            continue;
         }
-    }
 
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    switch(type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
-        case GGML_TYPE_F16:
-        case GGML_TYPE_F32:
-            return 1;
-        case GGML_TYPE_Q2_K:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 32;
-        case GGML_TYPE_Q3_K:
-            return min_compute_capability < CC_RDNA2 ? 128 : 64;
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-        case GGML_TYPE_IQ2_XXS:
-        case GGML_TYPE_IQ2_XS:
-        case GGML_TYPE_IQ2_S:
-        case GGML_TYPE_IQ3_XXS:
-        case GGML_TYPE_IQ1_S:
-        case GGML_TYPE_IQ1_M:
-        case GGML_TYPE_IQ4_NL:
-        case GGML_TYPE_IQ4_XS:
-        case GGML_TYPE_IQ3_S:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
-        default:
-            GGML_ASSERT(false);
-    }
-#else
-    switch(type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-            return max_compute_capability >= CC_VOLTA ? 128 : 64;
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return 64;
-        case GGML_TYPE_F16:
-        case GGML_TYPE_F32:
-            return 1;
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_IQ2_XXS:
-        case GGML_TYPE_IQ2_XS:
-        case GGML_TYPE_IQ2_S:
-        case GGML_TYPE_IQ3_XXS:
-        case GGML_TYPE_IQ1_S:
-        case GGML_TYPE_IQ1_M:
-        case GGML_TYPE_IQ4_NL:
-        case GGML_TYPE_IQ4_XS:
-        case GGML_TYPE_IQ3_S:
-            return max_compute_capability >= CC_VOLTA ? 128 : 64;
-        case GGML_TYPE_Q6_K:
-            return 64;
-        default:
-            GGML_ASSERT(false);
+        const int cc = ggml_cuda_info().devices[id].cc;
+        row_rounding = std::max(row_rounding, (int64_t)get_mmq_y_host(cc));
     }
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    return row_rounding;
 }
 
 static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split, int id) {
     const int64_t nrows = ggml_nrows(tensor);
-    const int64_t rounding = get_row_rounding(tensor->type, tensor_split);
+    const int64_t rounding = get_row_rounding(tensor_split);
 
     *row_low = id == 0 ? 0 : nrows*tensor_split[id];
     *row_low -= *row_low % rounding;
@@ -798,7 +756,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu
         // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
         ggml_cuda_set_device(id);
         char * buf;
-        CUDA_CHECK(cudaMalloc(&buf, size));
+        CUDA_CHECK(ggml_cuda_device_malloc((void**)&buf, size, id));
 
         // set padding to 0 to avoid possible NaN values
         if (size > original_size) {
@@ -915,6 +873,10 @@ GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_back
     GGML_UNUSED(buft);
 }
 
+static bool ggml_backend_buft_is_cuda_split(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cuda_split_buffer_type_name;
+}
+
 GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
     // instead, we allocate them for each tensor separately in init_tensor
@@ -958,12 +920,6 @@ GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_
     return total_size;
 }
 
-GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    return ggml_backend_is_cuda(backend);
-
-    GGML_UNUSED(buft);
-}
-
 GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
     return false;
 
@@ -976,7 +932,6 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface
     /* .get_alignment    = */ ggml_backend_cuda_split_buffer_type_get_alignment,
     /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
     /* .get_alloc_size   = */ ggml_backend_cuda_split_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend,
     /* .is_host          = */ ggml_backend_cuda_split_buffer_type_is_host,
 };
 
@@ -1076,7 +1031,6 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
             /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
             /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
             /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
-            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
             /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
         },
         /* .context  = */ nullptr,
@@ -1399,10 +1353,30 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
     GGML_UNUSED(main_device);
 }
 
+static cudaError_t ggml_cuda_Memcpy2DPeerAsync(
+    void * dst, int dstDevice, size_t dpitch, void * src, int srcDevice, size_t spitch, size_t width, size_t height, cudaStream_t stream) {
+
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA)
+    // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
+    cudaMemcpy3DPeerParms p = {};
+    p.dstDevice = dstDevice;
+    p.dstPtr = make_cudaPitchedPtr(dst, dpitch, dpitch, height);
+    p.srcDevice = srcDevice;
+    p.srcPtr = make_cudaPitchedPtr(src, spitch, spitch, height);
+    p.extent = make_cudaExtent(width, height, 1);
+    return cudaMemcpy3DPeerAsync(&p, stream);
+#else
+    // HIP does not support cudaMemcpy3DPeerAsync or vmm pools
+    GGML_UNUSED(dstDevice);
+    GGML_UNUSED(srcDevice);
+    return cudaMemcpy2DAsync(dst, dpitch, src, spitch, width, height, cudaMemcpyDeviceToDevice, stream);
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA)
+}
+
 static void ggml_cuda_op_mul_mat(
     ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op,
-    const bool convert_src1_to_q8_1) {
+    quantize_cuda_t quantize_src1) {
 
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
@@ -1459,7 +1433,9 @@ static void ggml_cuda_op_mul_mat(
     }
 
     struct dev_data {
-        ggml_cuda_pool_alloc<char>  src0_dd_alloc;
+        int cc;
+
+        ggml_cuda_pool_alloc<char>   src0_dd_alloc;
         ggml_cuda_pool_alloc<float> src1_ddf_alloc;
         ggml_cuda_pool_alloc<char>  src1_ddq_alloc;
         ggml_cuda_pool_alloc<float>   dst_dd_alloc;
@@ -1478,6 +1454,8 @@ static void ggml_cuda_op_mul_mat(
     int used_devices = 0;
 
     for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        dev[id].cc = ggml_cuda_info().devices[id].cc;
+
         // by default, use all rows
         dev[id].row_low  = 0;
         dev[id].row_high = ne01;
@@ -1485,7 +1463,7 @@ static void ggml_cuda_op_mul_mat(
         // for multi GPU, get the row boundaries from tensor split
         // and round to mul_mat_q tile sizes
         if (split) {
-            const int64_t rounding = get_row_rounding(src0->type, tensor_split);
+            const int64_t rounding = get_row_rounding(tensor_split);
 
             if (id != 0) {
                 dev[id].row_low  = ne01*tensor_split[id];
@@ -1522,17 +1500,28 @@ static void ggml_cuda_op_mul_mat(
             dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0));
         }
 
+        // If src0 is on a temporary compute buffers (partial offloading) there may be some padding that needs to be cleared:
+        if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) {
+            const int64_t nbytes_data    = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00);
+            const int64_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
+            CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream));
+        }
+
         if (src1_on_device && src1_is_contiguous) {
             dev[id].src1_ddf = (float *) src1->data;
         } else {
             dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ctx.pool(id), ggml_nelements(src1));
         }
 
-        if (convert_src1_to_q8_1) {
-            dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
+        if (quantize_src1) {
+            size_t src_1_ddq_size = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs;
+            if (quantize_src1 == quantize_mmq_q8_1_cuda) {
+                src_1_ddq_size += get_mmq_x_max_host(dev[id].cc)*sizeof(block_q8_1_mmq);
+            }
+            dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), src_1_ddq_size);
 
             if (src1_on_device && src1_is_contiguous) {
-                quantize_row_q8_1_cuda(dev[id].src1_ddf, dev[id].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
+                quantize_src1(dev[id].src1_ddf, dev[id].src1_ddq, ne10, ne11, ne12*ne13, src1_padded_col_size, src0->type, stream);
                 CUDA_CHECK(cudaGetLastError());
             }
         }
@@ -1578,7 +1567,12 @@ static void ggml_cuda_op_mul_mat(
                 const int64_t i03 = i0 / ne12;
                 const int64_t i02 = i0 % ne12;
 
-                const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs;
+                size_t src1_ddq_i_offset = i0*ne11 * src1_padded_col_size*q8_1_ts/q8_1_bs;
+                if (quantize_src1 == quantize_mmq_q8_1_cuda) {
+                    src1_ddq_i_offset += src1_col_0 * sizeof(block_q8_1_mmq);
+                } else {
+                    src1_ddq_i_offset += src1_col_0 * src1_padded_col_size*q8_1_ts/q8_1_bs;
+                }
 
                 // for split tensors the data begins at i0 == i0_offset_low
                 char  *  src0_dd_i =  dev[id].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
@@ -1595,10 +1589,17 @@ static void ggml_cuda_op_mul_mat(
                 // copy src0, src1 to device if necessary
                 if (src1_is_contiguous) {
                     if (id != ctx.device) {
-                        if (convert_src1_to_q8_1) {
+                        if (quantize_src1) {
                             char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset;
-                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, ctx.device,
-                                                            src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream));
+                            if (quantize_src1 == quantize_mmq_q8_1_cuda) {
+                                const size_t pitch = ne11*sizeof(block_q8_1_mmq);
+                                const size_t width = src1_ncols*sizeof(block_q8_1_mmq);
+                                const size_t height = src1_padded_col_size/(4*QK8_1);
+                                CUDA_CHECK(ggml_cuda_Memcpy2DPeerAsync(src1_ddq_i, id, pitch, src1_ddq_i_source, ctx.device, pitch, width, height, stream));
+                            } else {
+                                CUDA_CHECK(cudaMemcpyPeerAsync(
+                                    src1_ddq_i, id, src1_ddq_i_source, ctx.device, src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream));
+                            }
                         } else {
                             float * src1_ddf_i_source = (float *) src1->data;
                             src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
@@ -1610,11 +1611,11 @@ static void ggml_cuda_op_mul_mat(
                     CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
                                 src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
                 } else {
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
 
-                if (convert_src1_to_q8_1 && !src1_is_contiguous) {
-                    quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
+                if (quantize_src1 && !src1_is_contiguous) {
+                    quantize_src1(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, 1, src1_padded_col_size, src0->type, stream);
                     CUDA_CHECK(cudaGetLastError());
                 }
 
@@ -1639,22 +1640,8 @@ static void ggml_cuda_op_mul_mat(
                         float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
                         GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
                         dhf_dst_i += src1_col_0*ne0 + dev[id].row_low;
-#if !defined(GGML_USE_HIPBLAS)
-                        // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
-                        cudaMemcpy3DPeerParms p = {};
-                        p.dstDevice = ctx.device;
-                        p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols);
-                        p.srcDevice = id;
-                        p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols);
-                        p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1);
-                        CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream));
-#else
-                        // HIP does not support cudaMemcpy3DPeerAsync or vmm pools
-                        CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float),
-                                                        dst_dd_i, row_diff*sizeof(float),
-                                                        row_diff*sizeof(float), src1_ncols,
-                                                        cudaMemcpyDeviceToDevice, stream));
-#endif
+                        CUDA_CHECK(ggml_cuda_Memcpy2DPeerAsync(
+                            dhf_dst_i, ctx.device, ne0*sizeof(float), dst_dd_i, id, row_diff*sizeof(float), row_diff*sizeof(float), src1_ncols, stream));
                     } else {
                         float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
                         GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
@@ -1856,7 +1843,10 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
         }
     }
 #else
-    if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
+#ifdef GGML_USE_MUSA
+    GGML_ASSERT(false);
+#else // !GGML_USE_MUSA
+    if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
         // there is no broadcast and src0, src1 are contiguous across dims 2, 3
         // use cublasGemmStridedBatchedEx
         CUBLAS_CHECK(
@@ -1898,6 +1888,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
                 cu_compute_type,
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
     }
+#endif // GGML_USE_MUSA
 #endif
 
     if (dst->op_params[0] == GGML_PREC_DEFAULT) {
@@ -1909,9 +1900,22 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
 static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
 
-    int64_t min_compute_capability = INT_MAX;
+    bool use_dequantize_mul_mat_vec = ggml_cuda_dmmv_type_supported(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+        && src0->ne[0] % (GGML_CUDA_DMMV_X*2) == 0 && src1->ne[1] == 1;
+    bool          use_mul_mat_vec_q =  ggml_is_quantized(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+        && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
+    bool              use_mul_mat_q =  ggml_is_quantized(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+    // if mmvq is available it's a better choice than dmmv:
+#ifndef GGML_CUDA_FORCE_DMMV
+    use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
+#endif // GGML_CUDA_FORCE_DMMV
+
+    bool any_gpus_with_slow_fp16 = false;
 
-    bool any_pascal_with_slow_fp16 = false;
     if (split) {
         ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context;
         auto & tensor_split = buft_ctx->tensor_split;
@@ -1921,60 +1925,16 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
                 continue;
             }
 
-            if (min_compute_capability > ggml_cuda_info().devices[id].cc) {
-                min_compute_capability = ggml_cuda_info().devices[id].cc;
-            }
-            if (ggml_cuda_info().devices[id].cc == 610) {
-                any_pascal_with_slow_fp16 = true;
-            }
+            const int cc            = ggml_cuda_info().devices[id].cc;
+            use_mul_mat_q           = use_mul_mat_q           && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
+            any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc);
         }
     } else {
-        min_compute_capability    = ggml_cuda_info().devices[ctx.device].cc;
-        any_pascal_with_slow_fp16 = ggml_cuda_info().devices[ctx.device].cc == 610;
+        const int cc            = ggml_cuda_info().devices[ctx.device].cc;
+        use_mul_mat_q           = use_mul_mat_q           && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
+        any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc);
     }
 
-    // check data types and tensor shapes for custom matrix multiplication kernels:
-    bool use_dequantize_mul_mat_vec = (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16)
-        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
-        && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->ne[1] == 1;
-
-    bool          use_mul_mat_vec_q =  ggml_is_quantized(src0->type)
-        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
-        && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
-
-    bool              use_mul_mat_q =  ggml_cuda_supports_mmq(src0->type)
-        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-
-    const bool fp16_performance_good = min_compute_capability >= CC_RDNA1;
-
-#ifdef CUDA_USE_TENSOR_CORES
-    use_mul_mat_q = use_mul_mat_q && min_compute_capability < CC_RDNA3;
-#endif // CUDA_USE_TENSOR_CORES
-
-#else
-
-    // fp16 performance is good on Volta or newer and on P100 (compute capability 6.0)
-    const bool fp16_performance_good = min_compute_capability >= CC_PASCAL && !any_pascal_with_slow_fp16;
-
-    // mmvq and mmq need the __dp4a instruction which on NVIDIA is only available for CC >= 6.1
-    use_mul_mat_vec_q = use_mul_mat_vec_q && min_compute_capability >= MIN_CC_DP4A;
-    use_mul_mat_q     = use_mul_mat_q     && min_compute_capability >= MIN_CC_DP4A;
-
-#ifdef CUDA_USE_TENSOR_CORES
-    // when tensor cores are available, use them for large batch size
-    // ref: https://github.com/ggerganov/llama.cpp/pull/3776
-    use_mul_mat_q     = use_mul_mat_q     && (!fp16_performance_good || src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
-#endif // CUDA_USE_TENSOR_CORES
-
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-
-    // if mmvq is available it's a better choice than dmmv:
-#ifndef GGML_CUDA_FORCE_DMMV
-    use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
-#endif // GGML_CUDA_FORCE_DMMV
-
     // debug helpers
     //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
     //printf("      %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
@@ -1983,23 +1943,24 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
-    if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
-        // KQ single-batch
+    if (!split && any_gpus_with_slow_fp16 && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+        // FP32 precision KQ single-batch for batch size 1 without FlashAttention
         ggml_cuda_mul_mat_vec_p021(ctx, src0, src1, dst);
-    } else if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
-        // KQV single-batch
+    } else if (!split && any_gpus_with_slow_fp16 && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+        // FP32 precision KQV single-batch for batch size 1 without FlashAttention
         ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst);
-    } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || fp16_performance_good) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
-        // KQ + KQV multi-batch
+    } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16)
+               && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
+        // KQ + KQV multi-batch without FlashAttention
         ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
     } else if (use_dequantize_mul_mat_vec) {
-        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, nullptr);
     } else if (use_mul_mat_vec_q) {
-        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda);
     } else if (use_mul_mat_q) {
-        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, quantize_mmq_q8_1_cuda);
     } else {
-        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, nullptr);
     }
 }
 
@@ -2303,6 +2264,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_SQR:
             ggml_cuda_op_sqr(ctx, dst);
             break;
+        case GGML_OP_SQRT:
+            ggml_cuda_op_sqrt(ctx, dst);
+            break;
         case GGML_OP_CLAMP:
             ggml_cuda_op_clamp(ctx, dst);
             break;
@@ -2324,6 +2288,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_IM2COL:
             ggml_cuda_op_im2col(ctx, dst);
             break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            ggml_cuda_op_conv_transpose_1d(ctx,dst);
+            break;
         case GGML_OP_POOL_2D:
             ggml_cuda_op_pool2d(ctx, dst);
             break;
@@ -2391,33 +2358,35 @@ GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend,
 }
 
 GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_backend_is_cuda(backend_src) || ggml_backend_is_cuda(backend_dst));
-
     ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
     ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
 
-    if (!ggml_backend_buffer_is_cuda(src->buffer)) {
+    if (!ggml_backend_is_cuda(backend_src) || !ggml_backend_is_cuda(backend_dst)) {
         return false;
     }
 
-    if (!ggml_backend_buffer_is_cuda(dst->buffer)) {
+    if (!ggml_backend_buffer_is_cuda(src->buffer) || !ggml_backend_buffer_is_cuda(dst->buffer)) {
         return false;
     }
 
-    // device -> device
+    // device -> device copy
     ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *)backend_src->context;
     ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *)backend_dst->context;
 
-    if (backend_src != backend_dst) {
-        ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context;
-        ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context;
+    ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context;
+    ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context;
 
-        GGML_ASSERT(cuda_ctx_src->device == buf_ctx_src->device);
-        GGML_ASSERT(cuda_ctx_dst->device == buf_ctx_dst->device);
+    if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
+#ifndef NDEBUG
+        GGML_CUDA_LOG_WARN("%s: backend and buffer devices do not match\n", __func__);
+#endif
+        return false;
+    }
 
+    if (backend_src != backend_dst) {
         // copy on src stream
         if (cuda_ctx_src->device == cuda_ctx_dst->device) {
-            CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream()));
+            CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream()));
         } else {
 #ifdef GGML_CUDA_NO_PEER_COPY
             return false;
@@ -2426,7 +2395,7 @@ GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_
 #endif
         }
 
-        // record event on src stream
+        // record event on src stream after the copy
         if (!cuda_ctx_src->copy_event) {
             ggml_cuda_set_device(cuda_ctx_src->device);
             CUDA_CHECK(cudaEventCreateWithFlags(&cuda_ctx_src->copy_event, cudaEventDisableTiming));
@@ -2438,7 +2407,7 @@ GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_
         CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx_dst->stream(), cuda_ctx_src->copy_event, 0));
     } else {
         // src and dst are on the same backend
-        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream()));
+        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream()));
     }
     return true;
 }
@@ -2510,9 +2479,9 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
 
     bool use_cuda_graph = true;
     bool cuda_graph_update_required = false;
-    // pointer to CUDA cpy kernel, which is required to identify
+    // vector of pointers to CUDA cpy kernels, which are required to identify
     // kernel parameters which need updated in the graph for each token
-    void * ggml_cuda_cpy_fn_ptr = nullptr;
+    std::vector<void *> ggml_cuda_cpy_fn_ptrs;
 
     if (cuda_ctx->cuda_graph->graph == nullptr) {
         if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
@@ -2588,9 +2557,10 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
             if (node->op == GGML_OP_CPY) {
                 // store the copy op parameter which changes with each token.
                 cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
-                if (ggml_cuda_cpy_fn_ptr == nullptr) {
-                    // store a pointer to the copy op CUDA kernel to identify it later
-                    ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
+                // store a pointer to each copy op CUDA kernel to identify it later
+                void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
+                if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
+                    ggml_cuda_cpy_fn_ptrs.push_back(ptr);
                 }
             }
 
@@ -2687,10 +2657,8 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
 
         if (cuda_graph_update_required) {
             // Extract nodes from graph
-            if (cuda_ctx->cuda_graph->num_nodes == 0) {
-                // First call with null argument gets number of nodes in graph
-                CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
-            }
+            // First call with null argument gets number of nodes in graph
+            CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
             // Subsequent call with non-null argument gets nodes
             cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
             cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
@@ -2720,7 +2688,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
         if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
             int k = 0;
             for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
-                if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) {
+                if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
                     char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
                     cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
                     CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
@@ -2767,7 +2735,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
                 case GGML_UNARY_OP_HARDSWISH:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
-                    return true;
+                    return ggml_is_contiguous(op->src[0]);
                 default:
                     return false;
             }
@@ -2775,27 +2743,41 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
             {
-                struct ggml_tensor * a;
-                struct ggml_tensor * b;
-                if (op->op == GGML_OP_MUL_MAT) {
-                    a = op->src[0];
-                    b = op->src[1];
-                } else {
-                    a = op->src[2];
-                    b = op->src[1];
+                struct ggml_tensor * a = op->src[0];
+                struct ggml_tensor * b = op->src[1];
+                if (b->type == GGML_TYPE_F16 && a->type != GGML_TYPE_F16) {
+                    return false;
                 }
-                if (a->ne[3] != b->ne[3]) {
+                if (op->op == GGML_OP_MUL_MAT && a->ne[3] != b->ne[3]) {
                     return false;
                 }
-                ggml_type a_type = a->type;
-                if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS ||
-                    a_type == GGML_TYPE_IQ1_S   || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S   ||
-                    a_type == GGML_TYPE_IQ1_M   || a_type == GGML_TYPE_IQ2_S  || a_type == GGML_TYPE_IQ4_XS) {
-                    if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
+                switch (a->type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q2_K:
+                    case GGML_TYPE_Q3_K:
+                    case GGML_TYPE_Q4_K:
+                    case GGML_TYPE_Q5_K:
+                    case GGML_TYPE_Q6_K:
+                    case GGML_TYPE_Q8_K:
+                    case GGML_TYPE_IQ1_M:
+                    case GGML_TYPE_IQ1_S:
+                    case GGML_TYPE_IQ2_S:
+                    case GGML_TYPE_IQ2_XS:
+                    case GGML_TYPE_IQ2_XXS:
+                    case GGML_TYPE_IQ3_S:
+                    case GGML_TYPE_IQ3_XXS:
+                    case GGML_TYPE_IQ4_NL:
+                    case GGML_TYPE_IQ4_XS:
+                        return true;
+                    default:
                         return false;
-                    }
                 }
-                return true;
             } break;
         case GGML_OP_GET_ROWS:
             {
@@ -2855,6 +2837,15 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
                 ggml_type src0_type = op->src[0]->type;
                 return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
             } break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                ggml_type src1_type = op->src[1]->type;
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                return false;
+            } break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
@@ -2867,11 +2858,14 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
         case GGML_OP_RMS_NORM:
         case GGML_OP_SCALE:
         case GGML_OP_SQR:
+        case GGML_OP_SQRT:
         case GGML_OP_CLAMP:
         case GGML_OP_CONT:
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
+            return true;
         case GGML_OP_ROPE:
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_IM2COL:
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM_ROWS:
@@ -2886,12 +2880,16 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
             return true;
         case GGML_OP_FLASH_ATTN_EXT:
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-            return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128;
+            return (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) || op->src[0]->ne[0] == 128;
 #else
-            if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) {
+            if (op->src[0]->ne[0] == 128) {
+                return true;
+            }
+            if (op->src[0]->ne[0] ==  64 && op->src[1]->type == GGML_TYPE_F16) {
                 return true;
             }
-            return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA;
+            return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA &&
+                op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
         default:
             return false;
@@ -2900,6 +2898,20 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
     GGML_UNUSED(backend);
 }
 
+GGML_CALL static bool ggml_backend_cuda_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    if (ggml_backend_buft_is_cuda_split(buft)) {
+        return true;
+    }
+
+    if (ggml_backend_buft_is_cuda(buft)) {
+        ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+        ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
+        return buft_ctx->device == cuda_ctx->device;
+    }
+
+    return false;
+}
+
 GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
     const int min_batch_size = 32;
 
@@ -2954,7 +2966,7 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev
 
         CUDA_CHECK(cudaLaunchHostFunc(cuda_ctx->stream(), wait_fn, event));
 #endif
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
@@ -2972,9 +2984,11 @@ static ggml_backend_i ggml_backend_cuda_interface = {
     /* .synchronize             = */ ggml_backend_cuda_synchronize,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_cuda_graph_compute,
     /* .supports_op             = */ ggml_backend_cuda_supports_op,
+    /* .supports_buft           = */ ggml_backend_cuda_supports_buft,
     /* .offload_op              = */ ggml_backend_cuda_offload_op,
     /* .event_new               = */ ggml_backend_cuda_event_new,
     /* .event_free              = */ ggml_backend_cuda_event_free,
@@ -3034,7 +3048,7 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size
         return false;
     }
 
-#if CUDART_VERSION >= 11100
+#if CUDART_VERSION >= 11100 || defined(GGML_USE_MUSA)
     cudaError_t err = cudaHostRegister(buffer, size, cudaHostRegisterPortable | cudaHostRegisterReadOnly);
     if (err != cudaSuccess) {
         // clear the error
diff --git a/ggml-cuda/acc.cu b/ggml/src/ggml-cuda/acc.cu
similarity index 100%
rename from ggml-cuda/acc.cu
rename to ggml/src/ggml-cuda/acc.cu
diff --git a/ggml-cuda/acc.cuh b/ggml/src/ggml-cuda/acc.cuh
similarity index 100%
rename from ggml-cuda/acc.cuh
rename to ggml/src/ggml-cuda/acc.cuh
diff --git a/ggml-cuda/arange.cu b/ggml/src/ggml-cuda/arange.cu
similarity index 100%
rename from ggml-cuda/arange.cu
rename to ggml/src/ggml-cuda/arange.cu
diff --git a/ggml-cuda/arange.cuh b/ggml/src/ggml-cuda/arange.cuh
similarity index 100%
rename from ggml-cuda/arange.cuh
rename to ggml/src/ggml-cuda/arange.cuh
diff --git a/ggml-cuda/argsort.cu b/ggml/src/ggml-cuda/argsort.cu
similarity index 96%
rename from ggml-cuda/argsort.cu
rename to ggml/src/ggml-cuda/argsort.cu
index 1641440617779..607ded8558b45 100644
--- a/ggml-cuda/argsort.cu
+++ b/ggml/src/ggml-cuda/argsort.cu
@@ -73,6 +73,7 @@ static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, co
     const dim3 block_nums(1, nrows, 1);
     const size_t shared_mem = ncols_pad * sizeof(int);
 
+    // FIXME: this limit could be raised by ~2-4x on Ampere or newer
     GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb);
 
     if (order == GGML_SORT_ORDER_ASC) {
@@ -80,7 +81,7 @@ static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, co
     } else if (order == GGML_SORT_ORDER_DESC) {
         k_argsort_f32_i32<GGML_SORT_ORDER_DESC><<<block_nums, block_dims, shared_mem, stream>>>(x, dst, ncols, ncols_pad);
     } else {
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
diff --git a/ggml-cuda/argsort.cuh b/ggml/src/ggml-cuda/argsort.cuh
similarity index 100%
rename from ggml-cuda/argsort.cuh
rename to ggml/src/ggml-cuda/argsort.cuh
diff --git a/ggml-cuda/binbcast.cu b/ggml/src/ggml-cuda/binbcast.cu
similarity index 99%
rename from ggml-cuda/binbcast.cu
rename to ggml/src/ggml-cuda/binbcast.cu
index 19b08b74fb0af..34bc67acdd890 100644
--- a/ggml-cuda/binbcast.cu
+++ b/ggml/src/ggml-cuda/binbcast.cu
@@ -259,7 +259,7 @@ static void ggml_cuda_op_bin_bcast(
     } else {
         fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
             ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
diff --git a/ggml-cuda/binbcast.cuh b/ggml/src/ggml-cuda/binbcast.cuh
similarity index 100%
rename from ggml-cuda/binbcast.cuh
rename to ggml/src/ggml-cuda/binbcast.cuh
diff --git a/ggml-cuda/clamp.cu b/ggml/src/ggml-cuda/clamp.cu
similarity index 100%
rename from ggml-cuda/clamp.cu
rename to ggml/src/ggml-cuda/clamp.cu
diff --git a/ggml-cuda/clamp.cuh b/ggml/src/ggml-cuda/clamp.cuh
similarity index 100%
rename from ggml-cuda/clamp.cuh
rename to ggml/src/ggml-cuda/clamp.cuh
diff --git a/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
similarity index 63%
rename from ggml-cuda/common.cuh
rename to ggml/src/ggml-cuda/common.cuh
index 8f6fd71cfea35..eb39b6d23a6b3 100644
--- a/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -3,6 +3,7 @@
 #include "ggml.h"
 #include "ggml-cuda.h"
 
+#include <cstdint>
 #include <memory>
 
 #if defined(GGML_USE_HIPBLAS)
@@ -11,6 +12,10 @@
 #else
 #define GGML_COMMON_DECL_CUDA
 #define GGML_COMMON_IMPL_CUDA
+#if defined(GGML_USE_MUSA)
+#define GGML_COMMON_DECL_MUSA
+#define GGML_COMMON_IMPL_MUSA
+#endif
 #endif
 #include "ggml-common.h"
 
@@ -22,116 +27,11 @@
 #include <vector>
 
 #if defined(GGML_USE_HIPBLAS)
-#include <hip/hip_runtime.h>
-#include <hipblas/hipblas.h>
-#include <hip/hip_fp16.h>
-#ifdef __HIP_PLATFORM_AMD__
-// for rocblas_initialize()
-#include "rocblas/rocblas.h"
-#endif // __HIP_PLATFORM_AMD__
-#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
-#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
-#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
-#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
-#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
-#define CUBLAS_OP_N HIPBLAS_OP_N
-#define CUBLAS_OP_T HIPBLAS_OP_T
-#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
-#define CUBLAS_TF32_TENSOR_OP_MATH 0
-#define CUDA_R_16F  HIPBLAS_R_16F
-#define CUDA_R_32F  HIPBLAS_R_32F
-#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
-#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
-#define cublasCreate hipblasCreate
-#define cublasDestroy hipblasDestroy
-#define cublasGemmEx hipblasGemmEx
-#define cublasGemmBatchedEx hipblasGemmBatchedEx
-#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
-#define cublasHandle_t hipblasHandle_t
-#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
-#define cublasSetStream hipblasSetStream
-#define cublasSgemm hipblasSgemm
-#define cublasStatus_t hipblasStatus_t
-#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6
-#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
-#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
-#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
-#define cudaDeviceProp hipDeviceProp_t
-#define cudaDeviceSynchronize hipDeviceSynchronize
-#define cudaError_t hipError_t
-#define cudaErrorPeerAccessAlreadyEnabled hipErrorPeerAccessAlreadyEnabled
-#define cudaErrorPeerAccessNotEnabled hipErrorPeerAccessNotEnabled
-#define cudaEventCreateWithFlags hipEventCreateWithFlags
-#define cudaEventDisableTiming hipEventDisableTiming
-#define cudaEventRecord hipEventRecord
-#define cudaEventSynchronize hipEventSynchronize
-#define cudaEvent_t hipEvent_t
-#define cudaEventDestroy hipEventDestroy
-#define cudaFree hipFree
-#define cudaFreeHost hipHostFree
-#define cudaGetDevice hipGetDevice
-#define cudaGetDeviceCount hipGetDeviceCount
-#define cudaGetDeviceProperties hipGetDeviceProperties
-#define cudaGetErrorString hipGetErrorString
-#define cudaGetLastError hipGetLastError
-#define cudaHostRegister hipHostRegister
-#define cudaHostRegisterPortable hipHostRegisterPortable
-#define cudaHostRegisterReadOnly hipHostRegisterReadOnly
-#define cudaHostUnregister hipHostUnregister
-#define cudaLaunchHostFunc hipLaunchHostFunc
-#ifdef GGML_HIP_UMA
-#define cudaMalloc hipMallocManaged
-#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size)
-#else
-#define cudaMalloc hipMalloc
-#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
-#endif
-#define cudaMemcpy hipMemcpy
-#define cudaMemcpyAsync hipMemcpyAsync
-#define cudaMemcpyPeerAsync hipMemcpyPeerAsync
-#define cudaMemcpy2DAsync hipMemcpy2DAsync
-#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
-#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
-#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
-#define cudaMemcpyKind hipMemcpyKind
-#define cudaMemset hipMemset
-#define cudaMemsetAsync hipMemsetAsync
-#define cudaMemGetInfo hipMemGetInfo
-#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
-#define cudaSetDevice hipSetDevice
-#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
-#define cudaStreamDestroy hipStreamDestroy
-#define cudaStreamFireAndForget hipStreamFireAndForget
-#define cudaStreamNonBlocking hipStreamNonBlocking
-#define cudaStreamPerThread hipStreamPerThread
-#define cudaStreamSynchronize hipStreamSynchronize
-#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
-#define cudaStream_t hipStream_t
-#define cudaSuccess hipSuccess
-#define __trap abort
-#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
-#define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED
-#define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED
-#define CUBLAS_STATUS_INVALID_VALUE HIPBLAS_STATUS_INVALID_VALUE
-#define CUBLAS_STATUS_ARCH_MISMATCH HIPBLAS_STATUS_ARCH_MISMATCH
-#define CUBLAS_STATUS_MAPPING_ERROR HIPBLAS_STATUS_MAPPING_ERROR
-#define CUBLAS_STATUS_EXECUTION_FAILED HIPBLAS_STATUS_EXECUTION_FAILED
-#define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
-#define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
+#include "vendors/hip.h"
+#elif defined(GGML_USE_MUSA)
+#include "vendors/musa.h"
 #else
-#include <cuda_runtime.h>
-#include <cuda.h>
-#include <cublas_v2.h>
-#include <cuda_fp16.h>
-
-#if CUDART_VERSION < 11020
-#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
-#define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH
-#define CUBLAS_COMPUTE_16F CUDA_R_16F
-#define CUBLAS_COMPUTE_32F CUDA_R_32F
-#define cublasComputeType_t cudaDataType_t
-#endif // CUDART_VERSION < 11020
-
+#include "vendors/cuda.h"
 #endif // defined(GGML_USE_HIPBLAS)
 
 #define STRINGIZE_IMPL(...) #__VA_ARGS__
@@ -144,29 +44,13 @@
 #define CC_PASCAL     600
 #define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
 #define CC_VOLTA      700
+#define CC_TURING     750
 #define CC_AMPERE     800
 #define CC_OFFSET_AMD 1000000
 #define CC_RDNA1      (CC_OFFSET_AMD + 1010)
 #define CC_RDNA2      (CC_OFFSET_AMD + 1030)
 #define CC_RDNA3      (CC_OFFSET_AMD + 1100)
 
-// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
-// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
-// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
-// -  7B quantum model: +100-200 MB
-// - 13B quantum model: +200-400 MB
-//
-//#define GGML_CUDA_FORCE_MMQ
-
-// TODO: improve this to be correct for more hardware
-//       for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
-#if !defined(GGML_CUDA_FORCE_MMQ)
-#define CUDA_USE_TENSOR_CORES
-#endif
-
-#define MMVQ_MAX_BATCH_SIZE  8 // max batch size to use MMVQ kernels
-#define  MMQ_MAX_BATCH_SIZE 32 // max batch size to use MMQ kernels when tensor cores are available
-
 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
 
 #if defined(_MSC_VER)
@@ -188,7 +72,7 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in
 
 #define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString)
 
-#if CUDART_VERSION >= 12000
+#if CUDART_VERSION >= 12000 || defined(GGML_USE_MUSA)
     static const char * cublas_get_error_str(const cublasStatus_t err) {
         return cublasGetStatusString(err);
     }
@@ -220,7 +104,7 @@ static const char * cu_get_error_str(CUresult err) {
 #define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str)
 #endif
 
-#if CUDART_VERSION >= 11100
+#if CUDART_VERSION >= 11100 || defined(GGML_USE_MUSA)
 #define GGML_CUDA_ASSUME(x) __builtin_assume(x)
 #else
 #define GGML_CUDA_ASSUME(x)
@@ -232,117 +116,36 @@ typedef half2 dfloat2;
 #else
 typedef float dfloat; // dequantize float
 typedef float2 dfloat2;
-#endif //GGML_CUDA_F16
-
-#if defined(GGML_USE_HIPBLAS)
-#define __CUDA_ARCH__ 1300
-
-#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
-    defined(__gfx1150__) || defined(__gfx1151__)
-#define RDNA3
-#endif
-
-#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
-    defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
-#define RDNA2
-#endif
-
-#ifndef __has_builtin
-    #define __has_builtin(x) 0
-#endif
-
-typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
-typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
-static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
-    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
-    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
-#if __has_builtin(__builtin_elementwise_sub_sat)
-    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
-    return reinterpret_cast<const int &>(c);
-#else
-    int8x4_t c;
-    int16_t tmp;
-#pragma unroll
-    for (int i = 0; i < 4; i++) {
-        tmp = va[i] - vb[i];
-        if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
-        if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
-        c[i] = tmp;
-    }
-    return reinterpret_cast<int &>(c);
-#endif // __has_builtin(__builtin_elementwise_sub_sat)
-}
-
-static __device__ __forceinline__ int __vsub4(const int a, const int b) {
-    return __vsubss4(a, b);
-}
+#endif // GGML_CUDA_F16
 
-static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigned int b) {
-    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
-    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
-    unsigned int c;
-    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
-#pragma unroll
-    for (int i = 0; i < 4; ++i) {
-        vc[i] = va[i] == vb[i] ? 0xff : 0x00;
-    }
-    return c;
-}
-
-static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
-#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
-    c = __builtin_amdgcn_sdot4(a, b, c, false);
-#elif defined(RDNA3)
-    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
-#elif defined(__gfx1010__) || defined(__gfx900__)
-    int tmp1;
-    int tmp2;
-    asm("\n \
-        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
-        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
-        v_add3_u32 %0, %1, %2, %0 \n \
-        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
-        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
-        v_add3_u32 %0, %1, %2, %0 \n \
-        "
-        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
-        : "v"(a), "v"(b)
-    );
-#else
-    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
-    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
-    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
-#endif
-    return c;
-}
+#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL
+#define FP16_AVAILABLE
+#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL
 
-#if defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000
-// __shfl_xor() for half2 was added in ROCm 5.6
-static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int width) {
-    typedef union half2_b32 {
-        half2 val;
-        int   b32;
-    } half2_b32_t;
-    half2_b32_t tmp;
-    tmp.val = var;
-    tmp.b32 = __shfl_xor(tmp.b32, laneMask, width);
-    return tmp.val;
-}
-#endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000
-#endif // defined(GGML_USE_HIPBLAS)
+#if defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610
+#define FAST_FP16_AVAILABLE
+#endif // defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610
 
-#define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
+#define FP16_MMA_AVAILABLE
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
 
-#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING
+#define INT8_MMA_AVAILABLE
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING
 
-static bool fast_fp16_available(const int cc) {
+static constexpr bool fast_fp16_available(const int cc) {
     return cc >= CC_PASCAL && cc != 610;
 }
 
-static bool fp16_mma_available(const int cc) {
+static constexpr bool fp16_mma_available(const int cc) {
     return cc < CC_OFFSET_AMD && cc >= CC_VOLTA;
 }
 
+static constexpr bool int8_mma_available(const int cc) {
+    return cc < CC_OFFSET_AMD && cc >= CC_TURING;
+}
+
 [[noreturn]]
 static __device__ void no_device_code(
     const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
@@ -363,7 +166,7 @@ static __device__ void no_device_code(
 #ifdef __CUDA_ARCH__
 #define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__))
 #else
-#define NO_DEVICE_CODE //GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.")
+#define NO_DEVICE_CODE //GGML_ABORT("NO_DEVICE_CODE not valid in host code.")
 #endif // __CUDA_ARCH__
 
 static __device__ __forceinline__ float warp_reduce_sum(float x) {
@@ -384,7 +187,7 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
 }
 
 static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
 
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
 #pragma unroll
@@ -417,7 +220,7 @@ static __device__ __forceinline__ float warp_reduce_max(float x) {
 }
 
 static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
 
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
     return __float2half(fmaxf(__half2float(a), __half2float(b)));
@@ -470,10 +273,50 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half
     const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b)));
     return mask_low | mask_high;
 }
-#endif // CUDART_VERSION < 12000
+#endif // CUDART_VERSION < CUDART_HMASK
+
+static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(RDNA2)
+    c = __builtin_amdgcn_sdot4(a, b, c, false);
+#elif defined(RDNA3)
+    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
+#elif defined(__gfx1010__) || defined(__gfx900__)
+    int tmp1;
+    int tmp2;
+    asm("\n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        "
+        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
+        : "v"(a), "v"(b)
+    );
+#else
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
+#endif
+    return c;
+
+#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A
+    return __dp4a(a, b, c);
+#else // __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int8_t * a8 = (const int8_t *) &a;
+    const int8_t * b8 = (const int8_t *) &b;
+    return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3];
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+}
 
 // TODO: move to ggml-common.h
-static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+static constexpr __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
 
 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);
 
@@ -489,6 +332,148 @@ static __device__ __forceinline__ float get_alibi_slope(
     return powf(base, exph);
 }
 
+template <ggml_type type>
+struct ggml_cuda_type_traits;
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_F16> {
+    static constexpr int qk = 1;
+    static constexpr int qr = 1;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q4_0> {
+    static constexpr int qk = QK4_0;
+    static constexpr int qr = QR4_0;
+    static constexpr int qi = QI4_0;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q4_1> {
+    static constexpr int qk = QK4_1;
+    static constexpr int qr = QR4_1;
+    static constexpr int qi = QI4_1;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q5_0> {
+    static constexpr int qk = QK5_0;
+    static constexpr int qr = QR5_0;
+    static constexpr int qi = QI5_0;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q5_1> {
+    static constexpr int qk = QK5_1;
+    static constexpr int qr = QR5_1;
+    static constexpr int qi = QI5_1;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q8_0> {
+    static constexpr int qk = QK8_0;
+    static constexpr int qr = QR8_0;
+    static constexpr int qi = QI8_0;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q2_K> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR2_K;
+    static constexpr int qi = QI2_K;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q3_K> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR3_K;
+    static constexpr int qi = QI3_K;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q4_K> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR4_K;
+    static constexpr int qi = QI4_K;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q5_K> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR5_K;
+    static constexpr int qi = QI5_K;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q6_K> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR6_K;
+    static constexpr int qi = QI6_K;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ2_XXS> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR2_XXS;
+    static constexpr int qi = QI2_XXS;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ2_XS> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR2_XS;
+    static constexpr int qi = QI2_XS;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ2_S> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR2_S;
+    static constexpr int qi = QI2_S;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ3_XXS> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR3_XXS;
+    static constexpr int qi = QI3_XXS;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ1_S> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR1_S;
+    static constexpr int qi = QI1_S;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ1_M> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR1_M;
+    static constexpr int qi = QI1_M;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ4_NL> {
+    static constexpr int qk = QK4_NL;
+    static constexpr int qr = QR4_NL;
+    static constexpr int qi = QI4_NL;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ4_XS> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR4_XS;
+    static constexpr int qi = QI4_XS;
+};
+
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_IQ3_S> {
+    static constexpr int qk = QK_K;
+    static constexpr int qr = QR3_S;
+    static constexpr int qi = QI3_S;
+};
+
 //////////////////////
 
 struct ggml_cuda_device_info {
@@ -498,6 +483,7 @@ struct ggml_cuda_device_info {
         int     cc;                 // compute capability
         int     nsm;                // number of streaming multiprocessors
         size_t  smpb;               // max. shared memory per block
+        size_t  smpbo;              // max. shared memory per block (with opt-in)
         bool    vmm;                // virtual memory support
         size_t  vmm_granularity;    // granularity of virtual memory
         size_t  total_vram;
diff --git a/ggml/src/ggml-cuda/concat.cu b/ggml/src/ggml-cuda/concat.cu
new file mode 100644
index 0000000000000..dac10ec36b0bd
--- /dev/null
+++ b/ggml/src/ggml-cuda/concat.cu
@@ -0,0 +1,196 @@
+#include "concat.cuh"
+
+// contiguous kernels
+static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) {
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+
+    if (nidx < ne00) { // src0
+        int offset_src =
+            nidx +
+            blockIdx.y * ne00 +
+            blockIdx.z * ne00 * gridDim.y;
+        dst[offset_dst] = x[offset_src];
+    } else {
+        int offset_src =
+            (nidx - ne00) +
+            blockIdx.y * (ne0 - ne00) +
+            blockIdx.z * (ne0 - ne00) * gridDim.y;
+        dst[offset_dst] = y[offset_src];
+    }
+}
+
+static __global__ void concat_f32_dim1(const float * x, const float * y, float * dst, const int ne0, const int ne01) {
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+
+    if (blockIdx.y < ne01) { // src0
+        int offset_src =
+            nidx +
+            blockIdx.y * ne0 +
+            blockIdx.z * ne0 * ne01;
+        dst[offset_dst] = x[offset_src];
+    } else {
+        int offset_src =
+            nidx +
+            (blockIdx.y - ne01) * ne0 +
+            blockIdx.z * ne0 * (gridDim.y - ne01);
+        dst[offset_dst] = y[offset_src];
+    }
+}
+
+static __global__ void concat_f32_dim2(const float * x, const float * y, float * dst, const int ne0, const int ne02) {
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+
+    if (blockIdx.z < ne02) { // src0
+        int offset_src =
+            nidx +
+            blockIdx.y * ne0 +
+            blockIdx.z * ne0 * gridDim.y;
+        dst[offset_dst] = x[offset_src];
+    } else {
+        int offset_src =
+            nidx +
+            blockIdx.y * ne0 +
+            (blockIdx.z - ne02) * ne0 *  gridDim.y;
+        dst[offset_dst] = y[offset_src];
+    }
+}
+
+static void concat_f32_cuda(const float * x, const float * y, float * dst, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, int dim, cudaStream_t stream) {
+    int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, ne1, ne2);
+    if (dim == 0) {
+        concat_f32_dim0<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne00);
+        return;
+    }
+    if (dim == 1) {
+        concat_f32_dim1<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne01);
+        return;
+    }
+    concat_f32_dim2<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
+}
+
+// non-contiguous kernel (slow)
+static __global__ void concat_f32_non_cont(
+        const char * src0,
+        const char * src1,
+              char * dst,
+           int64_t   ne00,
+           int64_t   ne01,
+           int64_t   ne02,
+           int64_t   ne03,
+          uint64_t   nb00,
+          uint64_t   nb01,
+          uint64_t   nb02,
+          uint64_t   nb03,
+           int64_t /*ne10*/,
+           int64_t /*ne11*/,
+           int64_t /*ne12*/,
+           int64_t /*ne13*/,
+          uint64_t   nb10,
+          uint64_t   nb11,
+          uint64_t   nb12,
+          uint64_t   nb13,
+           int64_t   ne0,
+           int64_t /*ne1*/,
+           int64_t /*ne2*/,
+           int64_t /*ne3*/,
+          uint64_t   nb0,
+          uint64_t   nb1,
+          uint64_t   nb2,
+          uint64_t   nb3,
+          int32_t   dim) {
+    const int64_t i3 = blockIdx.z;
+    const int64_t i2 = blockIdx.y;
+    const int64_t i1 = blockIdx.x;
+
+    int64_t o[4] = {0, 0, 0, 0};
+    o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
+
+    const float * x;
+
+    for (int i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
+        if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+            x = (const float *)(src0 + (i3       )*nb03 + (i2       )*nb02 + (i1       )*nb01 + (i0       )*nb00);
+        } else {
+            x = (const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10);
+        }
+
+        float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+        *y = *x;
+    }
+}
+
+
+void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    cudaStream_t stream = ctx.stream();
+
+    const int32_t dim = ((int32_t *) dst->op_params)[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+        const float * src0_d = (const float *)src0->data;
+        const float * src1_d = (const float *)src1->data;
+
+        float * dst_d = (float *)dst->data;
+
+        if (dim != 3) {
+            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_f32_cuda(
+                        src0_d + i3 * (src0->nb[3] / 4),
+                        src1_d + i3 * (src1->nb[3] / 4),
+                        dst_d + i3 * ( dst->nb[3] / 4),
+                        src0->ne[0], src0->ne[1], src0->ne[2],
+                        dst->ne[0],  dst->ne[1],  dst->ne[2], dim, stream);
+            }
+        } else {
+            const size_t size0 = ggml_nbytes(src0);
+            const size_t size1 = ggml_nbytes(src1);
+
+            CUDA_CHECK(cudaMemcpyAsync(dst_d,           src0_d, size0, cudaMemcpyDeviceToDevice, stream));
+            CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream));
+        }
+    } else {
+        dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]);
+        concat_f32_non_cont<<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
+                (const char *)src0->data,
+                (const char *)src1->data,
+                (      char *)dst->data,
+                src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+                src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+                src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
+                src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
+                dst->ne[0],  dst->ne[1],  dst->ne[2],  dst->ne[3],
+                dst->nb[0],  dst->nb[1],  dst->nb[2],  dst->nb[3], dim);
+    }
+}
diff --git a/ggml-cuda/concat.cuh b/ggml/src/ggml-cuda/concat.cuh
similarity index 100%
rename from ggml-cuda/concat.cuh
rename to ggml/src/ggml-cuda/concat.cuh
diff --git a/ggml/src/ggml-cuda/conv-transpose-1d.cu b/ggml/src/ggml-cuda/conv-transpose-1d.cu
new file mode 100644
index 0000000000000..b1e94d6f770aa
--- /dev/null
+++ b/ggml/src/ggml-cuda/conv-transpose-1d.cu
@@ -0,0 +1,87 @@
+#include "conv-transpose-1d.cuh"
+
+static  __global__ void conv_transpose_1d_kernel(
+        const int s0, const int p0, const int d0, const int output_size,
+        const int src0_ne0, const int src0_ne1, const int src0_ne2, const int src0_ne3,
+        const int src1_ne0, const int src1_ne1, const int src1_ne2, const int src1_ne3,
+        const int dst_ne0, const int dst_ne1, const int dst_ne2, const int dst_ne3,
+        const float * src0, const float * src1,  float * dst) {
+    int global_index = threadIdx.x + blockIdx.x * blockDim.x;
+    if (global_index >= output_size) {
+        return;
+    }
+
+    int out_index = global_index / dst_ne0;
+
+    float accumulator = 0;
+
+    for (int c = 0; c < src0_ne2; c++) {
+        int idx = global_index % dst_ne0;
+
+        int kernel_offset = (src0_ne0 * src0_ne1 * c) + (out_index * src0_ne0);
+        int input_offset = src1_ne0 * c;
+
+        for (int i = 0; i < src1_ne0; i++) {
+            if (!(idx >= i*s0 && idx < i*s0 + src0_ne0)) {
+                continue;
+            }
+            int weight_idx = idx - i*s0;
+
+            float kernel_weight = src0[kernel_offset + weight_idx];
+            float input_value =  src1[input_offset+i];
+
+            accumulator += kernel_weight * input_value;
+        }
+    }
+    dst[global_index] = accumulator;
+}
+
+static void conv_transpose_1d_f32_f32_cuda(
+        const int s0, const int p0, const int d0, const int output_size,
+        const int src0_ne0, const int src0_ne1, const int src0_ne2, const int src0_ne3,
+        const int src1_ne0, const int src1_ne1, const int src1_ne2, const int src1_ne3,
+        const int dst_ne0, const int dst_ne1, const int dst_ne2, const int dst_ne3,
+        const float * src0, const float * src1,  float * dst,
+        cudaStream_t stream) {
+
+    const int num_blocks = (output_size + CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE - 1) / CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE;
+    conv_transpose_1d_kernel<<<num_blocks,CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE, 0, stream>>>(
+        s0,p0,d0,output_size,
+        src0_ne0, src0_ne1,  src0_ne2, src0_ne3,
+        src1_ne0, src1_ne1,  src1_ne2, src1_ne3,
+        dst_ne0,  dst_ne1,   dst_ne2,  dst_ne3,
+        src0,src1, dst);
+}
+
+void ggml_cuda_op_conv_transpose_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src1_d = (const float *)src1->data;
+
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+
+    const int s0 = opts[0];
+    const int p0 = 0;//opts[3];
+    const int d0 = 1;//opts[4];
+
+    const int64_t kernel_size = ggml_nelements(src0);
+    const int64_t input_size = ggml_nelements(src1);
+    const int64_t output_size = ggml_nelements(dst);
+
+    conv_transpose_1d_f32_f32_cuda(s0, p0, d0, output_size,
+        src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+        src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
+        dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+        src0_d, src1_d, dst_d, stream);
+}
diff --git a/ggml/src/ggml-cuda/conv-transpose-1d.cuh b/ggml/src/ggml-cuda/conv-transpose-1d.cuh
new file mode 100644
index 0000000000000..6c2cf666b68da
--- /dev/null
+++ b/ggml/src/ggml-cuda/conv-transpose-1d.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE 256
+
+void ggml_cuda_op_conv_transpose_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu
similarity index 87%
rename from ggml-cuda/convert.cu
rename to ggml/src/ggml-cuda/convert.cu
index 830e2d7566162..c0a4447075c6e 100644
--- a/ggml-cuda/convert.cu
+++ b/ggml/src/ggml-cuda/convert.cu
@@ -131,7 +131,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
     const block_q2_K * x = (const block_q2_K *) vx;
 
     const int64_t tid = threadIdx.x;
-#if QK_K == 256
     const int64_t n   = tid/32;
     const int64_t l   = tid - 32*n;
     const int64_t is  = 8*n + l/16;
@@ -145,17 +144,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
     y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
     y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
     y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
-#else
-    const int64_t is = tid/16;  // 0 or 1
-    const int64_t il = tid%16;  // 0...15
-    const uint8_t q = x[i].qs[il] >> (2*is);
-    dst_t * y = yy + i*QK_K + 16*is + il;
-    float dall = __low2half(x[i].dm);
-    float dmin = __high2half(x[i].dm);
-    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
-    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
-#endif
-
 }
 
 template<typename dst_t>
@@ -164,7 +152,6 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t
     const int64_t i = blockIdx.x;
     const block_q3_K * x = (const block_q3_K *) vx;
 
-#if QK_K == 256
     const int64_t r = threadIdx.x/4;
     const int64_t tid = r/2;
     const int64_t is0 = r%2;
@@ -188,31 +175,8 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t
     const uint8_t * hm = x[i].hmask;
 
     for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
-#else
-    const int64_t tid = threadIdx.x;
-    const int64_t is  = tid/16;  // 0 or 1
-    const int64_t il  = tid%16;  // 0...15
-    const int64_t im  = il/8;    // 0...1
-    const int64_t in  = il%8;    // 0...7
-
-    dst_t * y = yy + i*QK_K + 16*is + il;
-
-    const uint8_t q = x[i].qs[il] >> (2*is);
-    const uint8_t h = x[i].hmask[in] >> (2*is + im);
-    const float   d = (float)x[i].d;
-
-    if (is == 0) {
-        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
-        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
-    } else {
-        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
-        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
-    }
-#endif
-
 }
 
-#if QK_K == 256
 static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
     if (j < 4) {
         d = q[j] & 63; m = q[j + 4] & 63;
@@ -221,7 +185,6 @@ static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t
         m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
     }
 }
-#endif
 
 template<typename dst_t>
 static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
@@ -229,7 +192,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t
 
     const int64_t i = blockIdx.x;
 
-#if QK_K == 256
     // assume 32 threads
     const int64_t tid = threadIdx.x;
     const int64_t il  = tid/8;
@@ -253,15 +215,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t
         y[l + 0] = d1 * (q[l] & 0xF) - m1;
         y[l +32] = d2 * (q[l] >>  4) - m2;
     }
-#else
-    const int64_t tid = threadIdx.x;
-    const uint8_t * q = x[i].qs;
-    dst_t * y = yy + i*QK_K;
-    const float d = (float)x[i].dm[0];
-    const float m = (float)x[i].dm[1];
-    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
-    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
-#endif
 }
 
 template<typename dst_t>
@@ -270,7 +223,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t
 
     const int64_t i = blockIdx.x;
 
-#if QK_K == 256
     // assume 64 threads - this is very slightly better than the one below
     const int64_t tid = threadIdx.x;
     const int64_t il  = tid/16;   // il is in 0...3
@@ -297,18 +249,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t
     hm <<= 1;
     y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
     y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
-#else
-    const int64_t tid = threadIdx.x;
-    const uint8_t q = x[i].qs[tid];
-    const int64_t im = tid/8;  // 0...3
-    const int64_t in = tid%8;  // 0...7
-    const int64_t is = tid/16; // 0 or 1
-    const uint8_t h = x[i].qh[in] >> im;
-    const float d = x[i].d;
-    dst_t * y = yy + i*QK_K + tid;
-    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
-    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
-#endif
 }
 
 template<typename dst_t>
@@ -316,7 +256,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t
     const block_q6_K * x = (const block_q6_K *) vx;
 
     const int64_t i = blockIdx.x;
-#if QK_K == 256
 
     // assume 64 threads - this is very slightly better than the one below
     const int64_t tid = threadIdx.x;
@@ -336,24 +275,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t
     y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
     y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
     y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
-#else
-
-    // assume 32 threads
-    const int64_t tid = threadIdx.x;
-    const int64_t ip  = tid/16;         // 0 or 1
-    const int64_t il  = tid - 16*ip;    // 0...15
-
-    dst_t * y = yy + i*QK_K + 16*ip + il;
-
-    const float d = x[i].d;
-
-    const uint8_t   ql = x[i].ql[16*ip + il];
-    const uint8_t   qh = x[i].qh[il] >> (2*ip);
-    const int8_t  * sc = x[i].scales;
-
-    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
-    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
-#endif
 }
 
 template<typename dst_t>
@@ -363,7 +284,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
     const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
 
     const int64_t tid = threadIdx.x;
-#if QK_K == 256
     const int64_t il = tid/8; // 0...3
     const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@@ -374,10 +294,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
     const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
     const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
     for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
-#else
-    NO_DEVICE_CODE;
-#endif
-
 }
 
 template<typename dst_t>
@@ -387,7 +303,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
     const block_iq2_xs * x = (const block_iq2_xs *) vx;
 
     const int64_t tid = threadIdx.x;
-#if QK_K == 256
     const int64_t il = tid/8; // 0...3
     const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@@ -396,10 +311,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
     const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
     const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
     for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
-#else
-    NO_DEVICE_CODE;
-#endif
-
 }
 
 template<typename dst_t>
@@ -409,7 +320,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
     const block_iq2_s * x = (const block_iq2_s *) vx;
 
     const int64_t tid = threadIdx.x;
-#if QK_K == 256
     const int64_t il = tid/8; // 0...3
     const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@@ -417,10 +327,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
     const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
     const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
     for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
-#else
-    NO_DEVICE_CODE;
-#endif
-
 }
 
 template<typename dst_t>
@@ -430,7 +336,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
     const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
 
     const int64_t tid = threadIdx.x;
-#if QK_K == 256
     const int64_t il = tid/8; // 0...3
     const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@@ -445,10 +350,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
         y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
         y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
     }
-#else
-    NO_DEVICE_CODE;
-#endif
-
 }
 
 template<typename dst_t>
@@ -458,7 +359,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
     const block_iq3_s * x = (const block_iq3_s *) vx;
 
     const int64_t tid = threadIdx.x;
-#if QK_K == 256
     const int64_t il = tid/8; // 0...3
     const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@@ -471,10 +371,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
         y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
         y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
     }
-#else
-    NO_DEVICE_CODE;
-#endif
-
 }
 
 template<typename dst_t>
@@ -484,7 +380,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
     const block_iq1_s * x = (const block_iq1_s  *) vx;
 
     const int64_t tid = threadIdx.x;
-#if QK_K == 256
     const int64_t il = tid/8; // 0...3
     const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@@ -497,10 +392,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
     for (int j = 0; j < 8; ++j) {
         y[j] = d * (q[j] + delta);
     }
-#else
-    NO_DEVICE_CODE;
-#endif
-
 }
 
 template<typename dst_t>
@@ -510,7 +401,6 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_
     const block_iq1_m * x = (const block_iq1_m  *) vx;
 
     const int64_t tid = threadIdx.x;
-#if QK_K == 256
     const int64_t il = tid/8; // 0...3
     const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@@ -527,13 +417,8 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_
     for (int j = 0; j < 8; ++j) {
         y[j] = d * (q[j] + delta);
     }
-#else
-    NO_DEVICE_CODE;
-#endif
-
 }
 
-
 template<typename dst_t>
 static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
@@ -550,10 +435,8 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst
         y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
         y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
     }
-
 }
 
-#if QK_K != 64
 template<typename dst_t>
 static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
     const int64_t i   = blockIdx.x;
@@ -570,7 +453,6 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst
         y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
     }
 }
-#endif
 
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) {
@@ -592,21 +474,13 @@ static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half *
 template<typename dst_t>
 static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
     const int nb = k / QK_K;
-#if QK_K == 256
     dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
 }
 
 template<typename dst_t>
 static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
     const int nb = k / QK_K;
-#if QK_K == 256
     dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
 }
 
 template<typename dst_t>
@@ -632,21 +506,13 @@ static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int64_t k
 template<typename dst_t>
 static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
     const int nb = k / QK_K;
-#if QK_K == 256
     dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
 }
 
 template<typename dst_t>
 static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
     const int nb = k / QK_K;
-#if QK_K == 256
     dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
 }
 
 template<typename dst_t>
@@ -700,11 +566,7 @@ static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int64_t
 template<typename dst_t>
 static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
     const int nb = (k + QK_K - 1) / QK_K;
-#if QK_K == 64
-    dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
-#else
     dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
-#endif
 }
 
 template <typename src_t, typename dst_t>
diff --git a/ggml-cuda/convert.cuh b/ggml/src/ggml-cuda/convert.cuh
similarity index 100%
rename from ggml-cuda/convert.cuh
rename to ggml/src/ggml-cuda/convert.cuh
diff --git a/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu
similarity index 99%
rename from ggml-cuda/cpy.cu
rename to ggml/src/ggml-cuda/cpy.cu
index 12d741f017d3b..aad34bfe5b32b 100644
--- a/ggml-cuda/cpy.cu
+++ b/ggml/src/ggml-cuda/cpy.cu
@@ -451,7 +451,7 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
     } else {
         fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
                 ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
@@ -484,7 +484,6 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
     } else {
         fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
                 ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
-
diff --git a/ggml-cuda/cpy.cuh b/ggml/src/ggml-cuda/cpy.cuh
similarity index 100%
rename from ggml-cuda/cpy.cuh
rename to ggml/src/ggml-cuda/cpy.cuh
diff --git a/ggml-cuda/dequantize.cuh b/ggml/src/ggml-cuda/dequantize.cuh
similarity index 100%
rename from ggml-cuda/dequantize.cuh
rename to ggml/src/ggml-cuda/dequantize.cuh
diff --git a/ggml-cuda/diagmask.cu b/ggml/src/ggml-cuda/diagmask.cu
similarity index 100%
rename from ggml-cuda/diagmask.cu
rename to ggml/src/ggml-cuda/diagmask.cu
diff --git a/ggml-cuda/diagmask.cuh b/ggml/src/ggml-cuda/diagmask.cuh
similarity index 100%
rename from ggml-cuda/diagmask.cuh
rename to ggml/src/ggml-cuda/diagmask.cuh
diff --git a/ggml-cuda/dmmv.cu b/ggml/src/ggml-cuda/dmmv.cu
similarity index 80%
rename from ggml-cuda/dmmv.cu
rename to ggml/src/ggml-cuda/dmmv.cu
index 7313e3e175367..96a5adef5b2b5 100644
--- a/ggml-cuda/dmmv.cu
+++ b/ggml/src/ggml-cuda/dmmv.cu
@@ -22,7 +22,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
 
     float tmp = 0; // partial sum for thread in warp
 
-#if QK_K == 256
     const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
     const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
 
@@ -71,37 +70,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
         tmp += dall * sum1 - dmin * sum2;
 
     }
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
-    const int offset = tid * K_QUANTS_PER_ITERATION;
-
-    uint32_t uaux[2];
-    const uint8_t * d = (const uint8_t *)uaux;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + offset;
-        const uint8_t * q = x[i].qs + offset;
-        const uint32_t * s = (const uint32_t *)x[i].scales;
-
-        uaux[0] = s[0] & 0x0f0f0f0f;
-        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
-
-        const float2 dall = __half22float2(x[i].dm);
-
-        float sum1 = 0, sum2 = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            const uint8_t ql = q[l];
-            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
-                  + y[l+16] * d[1] * ((ql >> 2) & 3)
-                  + y[l+32] * d[2] * ((ql >> 4) & 3)
-                  + y[l+48] * d[3] * ((ql >> 6) & 3);
-            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
-        }
-        tmp += dall.x * sum1 - dall.y * sum2;
-    }
-#endif
 
     // sum up partial sums and write back result
     tmp = warp_reduce_sum(tmp);
@@ -123,8 +91,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx,
 
     float tmp = 0; // partial sum for thread in warp
 
-#if QK_K == 256
-
     const uint16_t kmask1 = 0x0303;
     const uint16_t kmask2 = 0x0f0f;
 
@@ -175,34 +141,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx,
         tmp += d * sum;
 
     }
-#else
-
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
-    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
-    const int in = offset/8;                                 // 0 or 1
-    const int im = offset%8;                                 // 0...7
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + offset;
-        const uint8_t * q = x[i].qs + offset;
-        const uint8_t * s = x[i].scales;
-
-        const float dall = (float)x[i].d;
-
-        float sum = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            const uint8_t hl = x[i].hmask[im+l] >> in;
-            const uint8_t ql = q[l];
-            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
-                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
-                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
-                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
-        }
-        tmp += sum;
-    }
-#endif
 
     // sum up partial sums and write back result
     tmp = warp_reduce_sum(tmp);
@@ -221,7 +159,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
 
     const block_q4_K * x = (const block_q4_K *)vx + ib0;
 
-#if QK_K == 256
     const uint16_t kmask1 = 0x3f3f;
     const uint16_t kmask2 = 0x0f0f;
     const uint16_t kmask3 = 0xc0c0;
@@ -306,36 +243,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
 #endif
 
     }
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
-
-    const int step = tid * K_QUANTS_PER_ITERATION;
-
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
-
-    float tmp = 0;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-        const uint8_t * q = x[i].qs + step;
-        const float   * y = yy + i*QK_K + step;
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux16[0] = a[0] & 0x0f0f;
-        aux16[1] = (a[0] >> 4) & 0x0f0f;
-        const float d = (float)x[i].dm[0];
-        const float m = (float)x[i].dm[1];
-        float sum = 0.f;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
-                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
-                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
-                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
-        }
-        tmp += sum;
-    }
-
-#endif
 
     // sum up partial sums and write back result
     tmp = warp_reduce_sum(tmp);
@@ -355,7 +262,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
 
     float tmp = 0; // partial sum for thread in warp
 
-#if QK_K == 256
     const uint16_t kmask1 = 0x3f3f;
     const uint16_t kmask2 = 0x0f0f;
     const uint16_t kmask3 = 0xc0c0;
@@ -426,30 +332,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
         tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
     }
 
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
-    const int step = tid * K_QUANTS_PER_ITERATION;
-    const int im = step/8;
-    const int in = step%8;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-        const uint8_t * q = x[i].qs + step;
-        const int8_t  * s = x[i].scales;
-        const float   * y = yy + i*QK_K + step;
-        const float     d = x[i].d;
-        float sum = 0.f;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            const uint8_t h = x[i].qh[in+j] >> im;
-            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
-                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
-                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
-                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
-        }
-        tmp += sum;
-    }
-#endif
-
     // sum up partial sums and write back result
     tmp = warp_reduce_sum(tmp);
 
@@ -470,8 +352,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx,
 
     const block_q6_K * x = (const block_q6_K *)vx + ib0;
 
-#if QK_K == 256
-
     const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
     const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
 
@@ -526,37 +406,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx,
 
     }
 
-#else
-
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0...3
-
-    const int step = tid * K_QUANTS_PER_ITERATION;
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + step;
-        const uint8_t * ql = x[i].ql + step;
-        const uint8_t * qh = x[i].qh + step;
-        const int8_t  * s  = x[i].scales;
-
-        const float d = x[i+0].d;
-
-        float sum = 0;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
-                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
-                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
-                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
-        }
-        tmp += sum;
-
-    }
-
-#endif
-
     // sum up partial sums and write back result
     tmp = warp_reduce_sum(tmp);
 
@@ -573,10 +422,22 @@ static __device__ void convert_f16(const void * vx, const int64_t ib, const int
     v.y = x[ib + iqs + 1];
 }
 
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
+static constexpr __device__ dequantize_kernel_t get_dequantize_kernel(ggml_type type) {
+    return type == GGML_TYPE_Q4_0 ? dequantize_q4_0 :
+        type == GGML_TYPE_Q4_1 ? dequantize_q4_1 :
+        type == GGML_TYPE_Q5_0 ? dequantize_q5_0 :
+        type == GGML_TYPE_Q5_1 ? dequantize_q5_1 :
+        type == GGML_TYPE_Q8_0 ? dequantize_q8_0 :
+        type == GGML_TYPE_F16 ? convert_f16 :
+        nullptr;
+}
+
+template <ggml_type type>
 static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
-    // qk = quantized weights per x block
-    // qr = number of quantized weights per data value in x block
+    constexpr int qk = ggml_cuda_type_traits<type>::qk; // quantized weights per x block
+    constexpr int qr = ggml_cuda_type_traits<type>::qr; // number of quantized weights per data value in x block
+    constexpr dequantize_kernel_t dequantize_kernel = get_dequantize_kernel(type);
+
     const int64_t row = (int64_t)blockIdx.x*blockDim.y + threadIdx.y;
 
     if (row >= nrows) {
@@ -639,48 +500,48 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
 }
 
 static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
+    dequantize_mul_mat_vec<GGML_TYPE_Q4_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
+    dequantize_mul_mat_vec<GGML_TYPE_Q4_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
+    dequantize_mul_mat_vec<GGML_TYPE_Q5_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
+    dequantize_mul_mat_vec<GGML_TYPE_Q5_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
+    dequantize_mul_mat_vec<GGML_TYPE_Q8_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
@@ -727,11 +588,11 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f
 }
 
 static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<1, 1, convert_f16>
+    dequantize_mul_mat_vec<GGML_TYPE_F16>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
@@ -801,7 +662,7 @@ void ggml_cuda_op_dequantize_mul_mat_vec(
             convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
             break;
         default:
-            GGML_ASSERT(false);
+            GGML_ABORT("fatal error");
             break;
     }
 
@@ -811,3 +672,12 @@ void ggml_cuda_op_dequantize_mul_mat_vec(
     GGML_UNUSED(src1_ncols);
     GGML_UNUSED(src1_padded_row_size);
 }
+
+bool ggml_cuda_dmmv_type_supported(ggml_type src0_type) {
+    return src0_type == GGML_TYPE_Q4_0 || src0_type == GGML_TYPE_Q4_1 ||
+        src0_type == GGML_TYPE_Q5_0 || src0_type == GGML_TYPE_Q5_1 ||
+        src0_type == GGML_TYPE_Q8_0 || src0_type == GGML_TYPE_Q2_K ||
+        src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q4_K ||
+        src0_type == GGML_TYPE_Q5_K || src0_type == GGML_TYPE_Q6_K ||
+        src0_type == GGML_TYPE_F16;
+}
diff --git a/ggml-cuda/dmmv.cuh b/ggml/src/ggml-cuda/dmmv.cuh
similarity index 90%
rename from ggml-cuda/dmmv.cuh
rename to ggml/src/ggml-cuda/dmmv.cuh
index 4c5ebd475fdb5..e727eb97f6aad 100644
--- a/ggml-cuda/dmmv.cuh
+++ b/ggml/src/ggml-cuda/dmmv.cuh
@@ -16,3 +16,5 @@ void ggml_cuda_op_dequantize_mul_mat_vec(
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, cudaStream_t stream);
+
+bool ggml_cuda_dmmv_type_supported(ggml_type src0_type);
diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
new file mode 100644
index 0000000000000..950fd93dfe1ee
--- /dev/null
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -0,0 +1,701 @@
+#pragma once
+
+#include "common.cuh"
+#include "convert.cuh"
+#include "vecdotq.cuh"
+
+#include <cstdint>
+
+#define FATTN_KQ_STRIDE       256
+#define HALF_MAX_HALF         __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
+#define SOFTMAX_FTZ_THRESHOLD -20.0f                   // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.
+
+typedef void (* fattn_kernel_t)(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const float max_bias,
+        const float m0,
+        const float m1,
+        const uint32_t n_head_log2,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3);
+
+typedef half (*vec_dot_KQ_f16_t)(
+    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
+typedef float (*vec_dot_KQ_f32_t)(
+    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
+
+template<typename T, int D>
+static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
+    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
+
+    const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c;
+    GGML_UNUSED(Q_v);
+
+    T sum = 0.0f;
+
+#pragma unroll
+    for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
+        const int k_KQ = k_KQ_0 + threadIdx.x;
+
+        const int ib    = k_KQ /  QI8_1;
+        const int iqs4  = k_KQ %  QI4_0;
+        const int shift = k_KQ & (QI8_1/2);
+
+        const int v = (get_int_b2(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
+        const int u = Q_q8[k_KQ_0/WARP_SIZE];
+
+        const int sumi = ggml_cuda_dp4a(v, u, 0);
+
+#ifdef FP16_AVAILABLE
+        if (std::is_same<T, half>::value) {
+            const half2  * Q_ds = (const half2  *) Q_ds_v;
+
+            const half2 sum2 = __half2half2(K_q4_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE];
+            sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2) /* *8/QI8_1 == 1 */);
+        } else
+#endif // FP16_AVAILABLE
+        {
+            const float2 * Q_ds = (const float2 *) Q_ds_v;
+
+            sum += (T) (__half2float(K_q4_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (8/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y));
+        }
+    }
+
+    return sum;
+}
+
+template<typename T, int D>
+static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
+    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
+
+    const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c;
+    GGML_UNUSED(Q_v);
+
+    T sum = 0.0f;
+
+#pragma unroll
+    for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
+        const int k_KQ = k_KQ_0 + threadIdx.x;
+
+        const int ib    = k_KQ /  QI8_1;
+        const int iqs4  = k_KQ %  QI4_1;
+        const int shift = k_KQ & (QI8_1/2);
+
+        const int v = (get_int_b4(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
+        const int u = Q_q8[k_KQ_0/WARP_SIZE];
+
+        const int sumi = ggml_cuda_dp4a(v, u, 0);
+
+#ifdef FP16_AVAILABLE
+        if (std::is_same<T, half>::value) {
+            const half2  * Q_ds = (const half2  *) Q_ds_v;
+
+            const half2 d4d8_m4s8 = K_q4_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE];
+            const half2 sumid4d8_m4s8scaled = d4d8_m4s8 * make_half2(sumi, 1.0f/QI8_1);
+            sum += (T) (__low2half(sumid4d8_m4s8scaled) + __high2half(sumid4d8_m4s8scaled));
+        } else
+#endif // FP16_AVAILABLE
+        {
+            const float2 * Q_ds = (const float2 *) Q_ds_v;
+
+            const float sumid4d8   =  __low2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi;
+            const float m4s8scaled = __high2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1;
+
+            sum += (T) (sumid4d8 + m4s8scaled);
+        }
+    }
+
+    return sum;
+}
+
+template<typename T, int D>
+static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
+    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
+
+    const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c;
+    GGML_UNUSED(Q_v);
+
+    T sum = 0.0f;
+
+#pragma unroll
+    for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
+        const int k_KQ = k_KQ_0 + threadIdx.x;
+
+        const int ib    = k_KQ /  QI8_1;
+        const int iqs4  = k_KQ %  QI5_0;
+        const int iqs8  = k_KQ %  QI8_1;
+        const int shift = k_KQ & (QI8_1/2);
+
+        int v = (get_int_b2(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
+        const int vh = get_int_b2(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0);
+        v |= (vh <<  4) & 0x00000010; // 0 ->  4
+        v |= (vh << 11) & 0x00001000; // 1 -> 12
+        v |= (vh << 18) & 0x00100000; // 2 -> 20
+        v |= (vh << 25) & 0x10000000; // 3 -> 28
+
+        const int u = Q_q8[k_KQ_0/WARP_SIZE];
+
+        const int sumi = ggml_cuda_dp4a(v, u, 0);
+
+#ifdef FP16_AVAILABLE
+        if (std::is_same<T, half>::value) {
+            const half2  * Q_ds = (const half2  *) Q_ds_v;
+
+            const half2 sum2 = __half2half2(K_q5_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE];
+            sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2)*__float2half(2.0f)) /* *16/QI8_1 == 2 */;
+        } else
+#endif // FP16_AVAILABLE
+        {
+            const float2 * Q_ds = (const float2 *) Q_ds_v;
+
+            sum += (T) (__half2float(K_q5_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (16/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y));
+        }
+    }
+
+    return sum;
+}
+
+template<typename T, int D>
+static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
+    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
+
+    const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c;
+    GGML_UNUSED(Q_v);
+
+    T sum = 0.0f;
+
+#pragma unroll
+    for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
+        const int k_KQ = k_KQ_0 + threadIdx.x;
+
+        const int ib    = k_KQ /  QI8_1;
+        const int iqs4  = k_KQ %  QI5_1;
+        const int iqs8  = k_KQ %  QI8_1;
+        const int shift = k_KQ & (QI8_1/2);
+
+        int v = (get_int_b2(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
+        const int vh = get_int_b2(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1);
+        v |= (vh <<  4) & 0x00000010; // 0 ->  4
+        v |= (vh << 11) & 0x00001000; // 1 -> 12
+        v |= (vh << 18) & 0x00100000; // 2 -> 20
+        v |= (vh << 25) & 0x10000000; // 3 -> 28
+
+        const int u = Q_q8[k_KQ_0/WARP_SIZE];
+
+        const int sumi = ggml_cuda_dp4a(v, u, 0);
+
+#ifdef FP16_AVAILABLE
+        if (std::is_same<T, half>::value) {
+            const half2  * Q_ds = (const half2  *) Q_ds_v;
+
+            const half2 d5d8_m5s8 = K_q5_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE];
+            const half2 sumid5d8_m5s8scaled = d5d8_m5s8 * make_half2(sumi, 1.0f/QI8_1);
+            sum += (T) (__low2half(sumid5d8_m5s8scaled) + __high2half(sumid5d8_m5s8scaled));
+        } else
+#endif // FP16_AVAILABLE
+        {
+            const float2 * Q_ds = (const float2 *) Q_ds_v;
+
+            const float sumid5d8   =  __low2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi;
+            const float m5s8scaled = __high2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1;
+
+            sum += (T) (sumid5d8 + m5s8scaled);
+        }
+    }
+
+    return sum;
+}
+
+template <typename T, int D>
+static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
+    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
+
+    const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c;
+    GGML_UNUSED(Q_v);
+
+    T sum = 0.0f;
+
+#pragma unroll
+    for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
+        const int k_KQ = k_KQ_0 + threadIdx.x;
+
+        const int ib  = k_KQ / QI8_0;
+        const int iqs = k_KQ % QI8_0;
+
+        const int v = get_int_b2(K_q8_0[ib].qs, iqs);
+
+        T Q_d;
+        if (std::is_same<T, half>::value) {
+            const half2  * Q_ds = (const half2  *) Q_ds_v;
+            Q_d = __low2half(Q_ds[k_KQ_0/WARP_SIZE]);
+        } else {
+            const float2 * Q_ds = (const float2 *) Q_ds_v;
+            Q_d = Q_ds[k_KQ_0/WARP_SIZE].x;
+        }
+
+        sum += vec_dot_q8_0_q8_1_impl<T, 1>(&v, &Q_q8[k_KQ_0/WARP_SIZE], K_q8_0[ib].d, Q_d);
+    }
+
+    return sum;
+}
+
+template <typename T, int D>
+static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16(
+    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds_v) {
+
+    const half2 * K_h2 = (const half2 *) K_c;
+    GGML_UNUSED(Q_q8);
+    GGML_UNUSED(Q_ds_v);
+
+#ifdef FP16_AVAILABLE
+    if (std::is_same<T, half>::value) {
+        const half2 * Q_h2 = (const half2 *) Q_v;
+
+        half2 sum2 = make_half2(0.0f, 0.0f);
+
+#pragma unroll
+        for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
+            const int k_KQ = k_KQ_0 + threadIdx.x;
+
+            const half2 K_ik = K_h2[k_KQ];
+            sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE];
+        }
+
+        return __low2half(sum2) + __high2half(sum2);
+    }
+#endif // FP16_AVAILABLE
+
+    const float2 * Q_f2 = (const float2 *) Q_v;
+
+    float sum = 0.0f;
+
+#pragma unroll
+    for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
+        const int k_KQ = k_KQ_0 + threadIdx.x;
+
+        const half2 K_ik = K_h2[k_KQ];
+        sum +=  __low2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].x;
+        sum += __high2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].y;
+    }
+
+    return sum;
+}
+
+template <typename Tds>
+static __device__ __forceinline__ void quantize_q8_1_to_shared(
+    const float * __restrict__ x, const float scale, int * __restrict__ yq32, void * __restrict__ yds) {
+
+    float vals[sizeof(int)] = {0.0f};
+#pragma unroll
+    for (int l = 0; l < sizeof(int); ++l) {
+        vals[l] = scale * x[4*threadIdx.x + l];
+    }
+
+    float amax = fabsf(vals[0]);
+    float sum  = vals[0];
+#pragma unroll
+    for (int l = 1; l < sizeof(int); ++l) {
+        amax = fmaxf(amax, fabsf(vals[l]));
+        sum += vals[l];
+    }
+#pragma unroll
+    for (int mask = QI8_1/2; mask > 0; mask >>= 1) {
+        amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, 32));
+        sum +=             __shfl_xor_sync(0xFFFFFFFF, sum,  mask, 32);
+    }
+
+    const float d = amax / 127;
+    int q32 = 0;
+    int8_t * q8 = (int8_t *) &q32;
+
+    if (d != 0.0f) {
+#pragma unroll
+        for (int l = 0; l < sizeof(int); ++l) {
+            q8[l] = roundf(vals[l] / d);
+        }
+    }
+
+    yq32[threadIdx.x] = q32;
+    if (threadIdx.x % QI8_1 == 0) {
+        if (std::is_same<Tds, half2>::value) {
+            ((half2  *) yds)[threadIdx.x/QI8_1] =  make_half2(d, sum);
+        } else {
+            ((float2 *) yds)[threadIdx.x/QI8_1] = make_float2(d, sum);
+        }
+    }
+}
+
+typedef half  (*dequantize_1_f16_t)(const void *, const int64_t);
+typedef float (*dequantize_1_f32_t)(const void *, const int64_t);
+
+template <typename T>
+static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__ vx, const int64_t i) {
+    const block_q4_0 * x = (const block_q4_0 *) vx;
+
+    const int64_t ib    =  i          /  QK4_0;
+    const int     iqs   =  i          % (QK4_0/2);
+    const int     shift = (i % QK4_0) / (QK4_0/2);
+
+    const T   d  = x[ib].d;
+    const int q0 = x[ib].qs[iqs];
+    const int q  = ((q0 >> (4*shift)) & 0x0F) - 8;
+
+#ifdef FP16_AVAILABLE
+    if (std::is_same<T, half>::value) {
+        return ((half) d)*((half) q);
+    }
+#endif // FP16_AVAILABLE
+
+    return ((float) d)*((float) q);
+}
+
+template <typename T>
+static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__ vx, const int64_t i) {
+    const block_q4_1 * x = (const block_q4_1 *) vx;
+
+    const int64_t ib    =  i          /  QK4_1;
+    const int     iqs   =  i          % (QK4_1/2);
+    const int     shift = (i % QK4_1) / (QK4_1/2);
+
+    const half2 dm = x[ib].dm;
+    const int   q0 = x[ib].qs[iqs];
+    const int   q  = ((q0 >> (4*shift)) & 0x0F);
+
+#ifdef FP16_AVAILABLE
+    if (std::is_same<T, half>::value) {
+        return __low2half(dm)*((half) q) + __high2half(dm);
+    }
+#endif // FP16_AVAILABLE
+
+    return __low2float(dm)*((float) q) + __high2float(dm);
+}
+
+template <typename T>
+static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ vx, const int64_t i) {
+    const block_q5_0 * x = (const block_q5_0 *) vx;
+
+    const int64_t ib    =  i          /  QK5_0;
+    const int     idq   =  i          %  QK5_0;
+    const int     iqs   =  i          % (QK5_0/2);
+    const int     shift = (i % QK5_0) / (QK5_0/2);
+
+    const T   d   = x[ib].d;
+    const int ql0 = x[ib].qs[iqs];
+    const int qh0 = get_int_b2(x[ib].qh, 0);
+    const int ql  = ((ql0 >> (4*shift)) & 0x0F);
+    const int qh  = ((qh0 >> idq) << 4) & 0x10;
+    const int q   = (ql | qh) - 16;
+
+#ifdef FP16_AVAILABLE
+    if (std::is_same<T, half>::value) {
+        return ((half) d)*((half) q);
+    }
+#endif // FP16_AVAILABLE
+
+    return ((float) d)*((float) q);
+}
+
+template <typename T>
+static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ vx, const int64_t i) {
+    const block_q5_1 * x = (const block_q5_1 *) vx;
+
+    const int64_t ib    =  i          /  QK5_1;
+    const int     idq   =  i          %  QK5_1;
+    const int     iqs   =  i          % (QK5_1/2);
+    const int     shift = (i % QK5_1) / (QK5_1/2);
+
+    const half2 dm  = x[ib].dm;
+    const int   ql0 = x[ib].qs[iqs];
+    const int   qh0 = get_int_b4(x[ib].qh, 0);
+    const int   ql  = ((ql0 >> (4*shift)) & 0x0F);
+    const int   qh  = ((qh0 >> idq) << 4) & 0x10;
+    const int   q   = (ql | qh);
+
+#ifdef FP16_AVAILABLE
+    if (std::is_same<T, half>::value) {
+        return __low2half(dm)*((half) q) + __high2half(dm);
+    }
+#endif // FP16_AVAILABLE
+
+    return __low2float(dm)*((float) q) + __high2float(dm);
+}
+
+template <typename T>
+static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__ vx, const int64_t i) {
+    const block_q8_0 * x = (const block_q8_0 *) vx;
+
+    const int64_t ib  = i / QK8_0;
+    const int     iqs = i % QK8_0;
+
+    const T   d = x[ib].d;
+    const int q = x[ib].qs[iqs];
+
+#ifdef FP16_AVAILABLE
+    if (std::is_same<T, half>::value) {
+        return ((half) d)*((half) q);
+    }
+#endif // FP16_AVAILABLE
+
+    return ((float) d)*((float) q);
+}
+
+template <typename T>
+static __device__ __forceinline__ T dequantize_1_f16(const void * __restrict__ vx, const int64_t i) {
+    const half * x = (const half *) vx;
+
+    return x[i];
+}
+
+template <int D>
+constexpr __device__ vec_dot_KQ_f16_t get_vec_dot_KQ_f16(ggml_type type_K) {
+    return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<half, D> :
+        type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<half, D> :
+        type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<half, D> :
+        type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<half, D> :
+        type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<half, D> :
+        type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<half, D> :
+        nullptr;
+}
+
+template <int D>
+constexpr __device__ vec_dot_KQ_f32_t get_vec_dot_KQ_f32(ggml_type type_K) {
+    return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<float, D> :
+        type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<float, D> :
+        type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<float, D> :
+        type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<float, D> :
+        type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<float, D> :
+        type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<float, D> :
+        nullptr;
+}
+
+constexpr __device__ dequantize_1_f16_t get_dequantize_1_f16(ggml_type type_V) {
+    return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0<half> :
+        type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1<half> :
+        type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0<half> :
+        type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1<half> :
+        type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0<half> :
+        type_V == GGML_TYPE_F16 ? dequantize_1_f16<half> :
+        nullptr;
+}
+
+constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
+    return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0<float> :
+        type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1<float> :
+        type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0<float> :
+        type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1<float> :
+        type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0<float> :
+        type_V == GGML_TYPE_F16 ? dequantize_1_f16<float> :
+        nullptr;
+}
+
+template<int D, int parallel_blocks> // D == head size
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(D, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_combine_results(
+        const float  * __restrict__ VKQ_parts,
+        const float2 * __restrict__ VKQ_meta,
+        float * __restrict__ dst) {
+    VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x;
+    VKQ_meta  += parallel_blocks   * gridDim.y*blockIdx.x;
+    dst       +=                 D * gridDim.y*blockIdx.x;
+
+    const int tid = threadIdx.x;
+    __builtin_assume(tid < D);
+
+    __shared__ float2 meta[parallel_blocks];
+    if (tid < 2*parallel_blocks) {
+        ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid];
+    }
+
+    __syncthreads();
+
+    float kqmax = meta[0].x;
+#pragma unroll
+    for (int l = 1; l < parallel_blocks; ++l) {
+        kqmax = max(kqmax, meta[l].x);
+    }
+
+    float VKQ_numerator   = 0.0f;
+    float VKQ_denominator = 0.0f;
+#pragma unroll
+    for (int l = 0; l < parallel_blocks; ++l) {
+        const float diff = meta[l].x - kqmax;
+        const float KQ_max_scale = expf(diff);
+        const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
+        *((uint32_t *) &KQ_max_scale) &= ftz_mask;
+
+        VKQ_numerator   += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid];
+        VKQ_denominator += KQ_max_scale * meta[l].y;
+    }
+
+    dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
+}
+
+static void on_no_fattn_vec_case(const int D) {
+    if (D == 64) {
+        fprintf(stderr, "Unsupported KV type combination for head_size 64.\n");
+        fprintf(stderr, "By default only f16 KV cache is supported.\n");
+        fprintf(stderr, "Compile with GGML_CUDA_FA_ALL_QUANTS for V cache quantization support.\n");
+        GGML_ABORT("fatal error");
+    } else if (D == 128) {
+        fprintf(stderr, "Unsupported KV type combination for head_size 128.\n");
+        fprintf(stderr, "Supported combinations:\n");
+        fprintf(stderr, "  - K == q4_0, V == q4_0,  4.50 BPV\n");
+        fprintf(stderr, "  - K == q8_0, V == q8_0,  8.50 BPV\n");
+        fprintf(stderr, "  - K == f16,  V == f16,  16.00 BPV\n");
+        fprintf(stderr, "Compile with GGML_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n");
+        GGML_ABORT("fatal error");
+    } else {
+        fprintf(stderr, "Unsupported KV type combination for head_size 256.\n");
+        fprintf(stderr, "Only f16 is supported.\n");
+        GGML_ABORT("fatal error");
+    }
+}
+
+template <int D, int parallel_blocks>
+void launch_fattn(
+    ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel,
+    const int nwarps, const int cols_per_block, const bool need_f16_K, const bool need_f16_V
+) {
+    const ggml_tensor * Q = dst->src[0];
+    const ggml_tensor * K = dst->src[1];
+    const ggml_tensor * V = dst->src[2];
+
+    const ggml_tensor * mask = dst->src[3];
+
+    ggml_tensor * KQV = dst;
+
+    GGML_ASSERT(Q->type == GGML_TYPE_F32);
+    GGML_ASSERT(KQV->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
+    GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
+                                "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");
+
+    GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
+
+    ggml_cuda_pool & pool = ctx.pool();
+    cudaStream_t main_stream = ctx.stream();
+
+    ggml_cuda_pool_alloc<half>   K_f16(pool);
+    ggml_cuda_pool_alloc<half>   V_f16(pool);
+    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
+    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
+
+    char * K_data = (char *) K->data;
+    size_t nb11 = K->nb[1];
+    size_t nb12 = K->nb[2];
+    size_t nb13 = K->nb[3];
+
+    char * V_data = (char *) V->data;
+    size_t nb21 = V->nb[1];
+    size_t nb22 = V->nb[2];
+    size_t nb23 = V->nb[3];
+
+    if (need_f16_K && K->type != GGML_TYPE_F16) {
+        K_f16.alloc(ggml_nelements(K));
+        to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type);
+        to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream);
+        K_data = (char *) K_f16.ptr;
+
+        const size_t bs = ggml_blck_size(K->type);
+        const size_t ts = ggml_type_size(K->type);
+
+        nb11 = nb11*bs*sizeof(half)/ts;
+        nb12 = nb12*bs*sizeof(half)/ts;
+        nb13 = nb13*bs*sizeof(half)/ts;
+    }
+
+    if (need_f16_V && V->type != GGML_TYPE_F16) {
+        V_f16.alloc(ggml_nelements(V));
+        to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type);
+        to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream);
+        V_data = (char *) V_f16.ptr;
+
+        const size_t bs = ggml_blck_size(V->type);
+        const size_t ts = ggml_type_size(V->type);
+
+        nb21 = nb21*bs*sizeof(half)/ts;
+        nb22 = nb22*bs*sizeof(half)/ts;
+        nb23 = nb23*bs*sizeof(half)/ts;
+    }
+
+    if (parallel_blocks > 1) {
+        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
+        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
+    }
+
+    const dim3 block_dim(WARP_SIZE, nwarps, 1);
+    const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
+    const int  shmem = 0;
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (float *) KQV->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
+
+    const uint32_t n_head      = Q->ne[2];
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    fattn_kernel<<<blocks_num, block_dim, shmem, main_stream>>>(
+        (const char *) Q->data,
+        K_data,
+        V_data,
+        mask ? ((const char *) mask->data) : nullptr,
+        (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
+        scale, max_bias, m0, m1, n_head_log2,
+        Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
+        K->ne[0], K->ne[1], K->ne[2], K->ne[3],
+        mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
+        Q->nb[1], Q->nb[2], Q->nb[3],
+        nb11, nb12, nb13,
+        nb21, nb22, nb23,
+        KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
+    );
+    CUDA_CHECK(cudaGetLastError());
+
+    if ((parallel_blocks) == 1) {
+        return;
+    }
+
+    const dim3 block_dim_combine(D, 1, 1);
+    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
+    const int  shmem_combine = 0;
+
+    flash_attn_combine_results<D, parallel_blocks>
+        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
+        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
+    CUDA_CHECK(cudaGetLastError());
+}
diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml/src/ggml-cuda/fattn-tile-f16.cu
similarity index 96%
rename from ggml-cuda/fattn-tile-f16.cu
rename to ggml/src/ggml-cuda/fattn-tile-f16.cu
index 4a07ac6adad71..1b2fd500b746c 100644
--- a/ggml-cuda/fattn-tile-f16.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu
@@ -36,11 +36,14 @@ static __global__ void flash_attn_tile_ext_f16(
         const int nb11,
         const int nb12,
         const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
         const int ne0,
         const int ne1,
         const int ne2,
         const int ne3) {
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
@@ -83,7 +86,7 @@ static __global__ void flash_attn_tile_ext_f16(
         for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
             const int i = i0 + threadIdx.x;
 
-            const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i];
+            const float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f);
             Q_h2[j][i] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
         }
     }
@@ -238,6 +241,10 @@ static __global__ void flash_attn_tile_ext_f16(
     for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) {
         const int j_VKQ = j_VKQ_0 + threadIdx.y;
 
+        if (ic0 + j_VKQ >= ne01) {
+            return;
+        }
+
         half kqsum_j = __low2half(kqsum[j_VKQ_0/nwarps]) + __high2half(kqsum[j_VKQ_0/nwarps]);
         kqsum_j = warp_reduce_sum(kqsum_j);
 
@@ -271,16 +278,16 @@ void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
             constexpr int      D = 64;
             constexpr int nwarps = 8;
             fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
+            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
         } break;
         case 128: {
             constexpr int      D = 128;
             constexpr int nwarps = 8;
             fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
+            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
         } break;
         default: {
-            GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
+            GGML_ABORT("FlashAttention without tensor cores only supports head sizes 64 and 128.");
         } break;
     }
 }
diff --git a/ggml-cuda/fattn-tile-f16.cuh b/ggml/src/ggml-cuda/fattn-tile-f16.cuh
similarity index 100%
rename from ggml-cuda/fattn-tile-f16.cuh
rename to ggml/src/ggml-cuda/fattn-tile-f16.cuh
diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml/src/ggml-cuda/fattn-tile-f32.cu
similarity index 95%
rename from ggml-cuda/fattn-tile-f32.cu
rename to ggml/src/ggml-cuda/fattn-tile-f32.cu
index 130e7cbdbe10d..f3e68dbfa6a6a 100644
--- a/ggml-cuda/fattn-tile-f32.cu
+++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu
@@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f32(
         const int nb11,
         const int nb12,
         const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
         const int ne0,
         const int ne1,
         const int ne2,
@@ -79,7 +82,7 @@ static __global__ void flash_attn_tile_ext_f32(
 
 #pragma unroll
         for (int i0 = 0; i0 < D; i0 += 2*WARP_SIZE) {
-            float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x];
+            float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x] : make_float2(0.0f, 0.0f);
             Q_f[j][i0 + 0*WARP_SIZE + threadIdx.x] = tmp.x * scale;
             Q_f[j][i0 + 1*WARP_SIZE + threadIdx.x] = tmp.y * scale;
         }
@@ -237,6 +240,10 @@ static __global__ void flash_attn_tile_ext_f32(
     for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) {
         const int j_VKQ = j_VKQ_0 + threadIdx.y;
 
+        if (ic0 + j_VKQ >= ne01) {
+            return;
+        }
+
         float kqsum_j = kqsum[j_VKQ_0/nwarps];
         kqsum_j = warp_reduce_sum(kqsum_j);
 
@@ -268,26 +275,22 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
             constexpr int      D = 64;
             constexpr int nwarps = 8;
             fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
+            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
         } break;
         case 128: {
             constexpr int      D = 128;
             constexpr int nwarps = 8;
             fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks>;
-            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
+            launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
         } break;
         default: {
-            GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
+            GGML_ABORT("FlashAttention without tensor cores only supports head sizes 64 and 128.");
         } break;
     }
 }
 
 void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * KQV = dst;
-    const ggml_tensor * Q   = dst->src[0];
-
-    const int32_t precision = KQV->op_params[2];
-    GGML_ASSERT(precision == GGML_PREC_DEFAULT);
+    const ggml_tensor * Q = dst->src[0];
 
     if (Q->ne[1] <= 16) {
         constexpr int cols_per_block = 16;
diff --git a/ggml-cuda/fattn-tile-f32.cuh b/ggml/src/ggml-cuda/fattn-tile-f32.cuh
similarity index 100%
rename from ggml-cuda/fattn-tile-f32.cuh
rename to ggml/src/ggml-cuda/fattn-tile-f32.cuh
diff --git a/ggml/src/ggml-cuda/fattn-vec-f16.cuh b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
new file mode 100644
index 0000000000000..02a4ad072bbd6
--- /dev/null
+++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
@@ -0,0 +1,397 @@
+#include "common.cuh"
+#include "fattn-common.cuh"
+
+template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(D, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_vec_ext_f16(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const float max_bias,
+        const float m0,
+        const float m1,
+        const uint32_t n_head_log2,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+#ifdef FP16_AVAILABLE
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16<D>(type_K);
+    constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16;
+    constexpr dequantize_1_f16_t dequantize_1_v = get_dequantize_1_f16(type_V);
+
+    const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
+    const int ip  =  blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    Q += nb02* blockIdx.y              + nb01*ic0;
+    K += nb12*(blockIdx.y / gqa_ratio);
+    V += nb22*(blockIdx.y / gqa_ratio);
+
+    const half * maskh = (const half   *)  mask + ne11*ic0;
+
+    const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
+    const half  slopeh = __float2half(slopef);
+
+    static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
+    constexpr int nwarps = D / WARP_SIZE;
+    const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
+    __builtin_assume(tid < D);
+
+    __shared__ half KQ[ncols*D];
+    half2 * KQ2 = (half2 *) KQ;
+
+    half kqmax[ncols];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        kqmax[j] = -HALF_MAX_HALF;
+    }
+    half kqsum[ncols] = {0.0f};
+
+    __shared__ half kqmax_shared[ncols][WARP_SIZE];
+    __shared__ half kqsum_shared[ncols][WARP_SIZE];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        if (threadIdx.y == 0) {
+            kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
+            kqsum_shared[j][threadIdx.x] = 0.0f;
+        }
+    }
+    __syncthreads();
+
+    // Convert Q to half2 (f16 K) or q8_1 (quantized K) and store in registers:
+    half2  Q_h2[ncols][D/(2*WARP_SIZE)];
+    int   Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D/(sizeof(int)*QK8_1)];
+    half2  Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1];
+    if (Q_q8_1) {
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            if (j0 + nwarps > ncols && j >= ncols) {
+                break;
+            }
+
+            // Reuse KQ as temporary storage for converting Q to q8_1:
+            int   * tmp_q_i32 = (int   *) &KQ[j*D];
+            half2 * tmp_q_ds  = (half2 *) (tmp_q_i32 + D/sizeof(int));
+
+            // Set memory to zero if out of bounds:
+            if (ncols > 2 && ic0 + j >= ne01) {
+#pragma unroll
+                for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
+                    const int i = i0 + threadIdx.x;
+
+                    tmp_q_i32[i] = 0;
+                }
+                if (threadIdx.x < D/QK8_1) {
+                    tmp_q_ds[threadIdx.x] = make_half2(0.0f, 0.0f);
+                }
+                continue;
+            }
+
+            const float * Q_f = (const float *) (Q + j*nb01);
+#pragma unroll
+            for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
+                quantize_q8_1_to_shared<half2>(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds);
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            int   * tmp_q_i32 = (int   *) &KQ[j*D];
+            half2 * tmp_q_ds  = (half2 *) (tmp_q_i32 + D/sizeof(int));
+
+#pragma unroll
+            for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i];
+                Q_ds[j][i0/WARP_SIZE]  = tmp_q_ds[i/QI8_1];
+            }
+        }
+
+        __syncthreads();
+    } else {
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            const float2 * Q_f2_j = (const float2 *) (Q + j*nb01);
+
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2_j[i] : make_float2(0.0f, 0.0f);
+                Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
+            }
+        }
+    }
+
+
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        KQ[j*D + tid] = -HALF_MAX_HALF;
+    }
+
+    half2 VKQ[ncols] = {{0.0f, 0.0f}};
+
+    const int k_start = parallel_blocks == 1 ? 0 : ip*D;
+    for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
+        // Calculate KQ tile and keep track of new maximum KQ values:
+
+        // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
+        // see https://github.com/ggerganov/llama.cpp/pull/7061 .
+        // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
+        half kqmax_new = kqmax[0];
+        half kqmax_new_arr[ncols];
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            kqmax_new_arr[j] = kqmax[j];
+        }
+
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
+            const int i_KQ = i_KQ_0 + threadIdx.y;
+
+            if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
+                break;
+            }
+
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]);
+                sum = warp_reduce_sum(sum);
+                sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
+
+                if (ncols == 1) {
+                    kqmax_new        = ggml_cuda_hmax(kqmax_new,        sum);
+                } else {
+                    kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
+                }
+
+                if (threadIdx.x == 0) {
+                    KQ[j*D + i_KQ] = sum;
+                }
+            }
+        }
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
+
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+            if (threadIdx.x == 0) {
+                kqmax_shared[j][threadIdx.y] = kqmax_new_j;
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            half kqmax_new_j = kqmax_shared[j][threadIdx.x];
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+
+            const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
+            kqmax[j] = kqmax_new_j;
+
+            const half val = hexp(KQ[j*D + tid] - kqmax[j]);
+            kqsum[j] = kqsum[j]*KQ_max_scale + val;
+            KQ[j*D + tid] = val;
+
+            VKQ[j] *= __half2half2(KQ_max_scale);
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int k0 = 0; k0 < D; k0 += 2) {
+            if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
+                break;
+            }
+
+            half2 V_k;
+            reinterpret_cast<half&>(V_k.x) = dequantize_1_v(V + (k_VKQ_0 + k0 + 0)*nb21, tid);
+            reinterpret_cast<half&>(V_k.y) = dequantize_1_v(V + (k_VKQ_0 + k0 + 1)*nb21, tid);
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
+            }
+        }
+
+        __syncthreads();
+    }
+
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        kqsum[j] = warp_reduce_sum(kqsum[j]);
+        if (threadIdx.x == 0) {
+            kqsum_shared[j][threadIdx.y] = kqsum[j];
+        }
+    }
+
+    __syncthreads();
+
+#pragma unroll
+    for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
+        if (ncols > 2 && ic0 + j_VKQ >= ne01) {
+            break;
+        }
+
+        kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
+        kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
+
+        half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
+        if (parallel_blocks == 1) {
+            dst_val /= kqsum[j_VKQ];
+        }
+        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
+        dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
+    }
+
+    if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
+        dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
+    }
+#else
+   NO_DEVICE_CODE;
+#endif // FP16_AVAILABLE
+}
+
+template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
+void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    constexpr int nwarps = D/WARP_SIZE;
+    fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V>;
+    constexpr bool need_f16_K = D != 128;
+    constexpr bool need_f16_V = D != 128 && D != 64;
+    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
+}
+
+template <int D, ggml_type type_K, ggml_type type_V>
+void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * KQV = dst;
+    ggml_tensor * Q   = dst->src[0];
+    ggml_tensor * K   = dst->src[1];
+    ggml_tensor * V   = dst->src[2];
+
+    const int32_t precision = KQV->op_params[2];
+    GGML_ASSERT(precision == GGML_PREC_DEFAULT);
+
+    GGML_ASSERT(K->type == type_K);
+    GGML_ASSERT(V->type == type_V);
+
+    if (Q->ne[1] == 1) {
+        constexpr int cols_per_block  = 1;
+        constexpr int parallel_blocks = 4;
+        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] == 2) {
+        constexpr int cols_per_block  = 2;
+        constexpr int parallel_blocks = 4;
+        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] <= 4) {
+        constexpr int cols_per_block  = 4;
+        constexpr int parallel_blocks = 4;
+        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] <= 8) {
+        constexpr int cols_per_block  = 8;
+        constexpr int parallel_blocks = 4;
+        ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        return;
+    }
+
+    constexpr int cols_per_block  = 8;
+    constexpr int parallel_blocks = 1;
+    ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+}
+
+#define DECL_FATTN_VEC_F16_CASE(D, type_K, type_V)                          \
+    template void ggml_cuda_flash_attn_ext_vec_f16_case                     \
+    <D, type_K, type_V>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
+
+extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16);
+
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q4_0);
+
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q4_1);
+
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q5_0);
+
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q5_1);
+
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q8_0);
+
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_F16);
+
+extern DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/fattn-vec-f32.cuh b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
new file mode 100644
index 0000000000000..11a5e355fd40b
--- /dev/null
+++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
@@ -0,0 +1,374 @@
+#include "common.cuh"
+#include "fattn-common.cuh"
+
+template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(D, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_vec_ext_f32(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const float max_bias,
+        const float m0,
+        const float m1,
+        const uint32_t n_head_log2,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    constexpr vec_dot_KQ_f32_t vec_dot_KQ = get_vec_dot_KQ_f32<D>(type_K);
+    constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16;
+    constexpr dequantize_1_f32_t dequantize_1_v = get_dequantize_1_f32(type_V);
+
+    const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
+    const int ip  =  blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    Q += nb02* blockIdx.y              + nb01*ic0;
+    K += nb12*(blockIdx.y / gqa_ratio);
+    V += nb22*(blockIdx.y / gqa_ratio); // K and V have same shape
+    const half * maskh = (const half   *)  mask + ne11*ic0;
+
+    const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
+
+    static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
+    constexpr int nwarps = D / WARP_SIZE;
+    const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
+    __builtin_assume(tid < D);
+
+    __shared__ float KQ[ncols*D];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        KQ[j*D + tid] = -FLT_MAX/2.0f;
+    }
+
+    float kqmax[ncols];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        kqmax[j] = -FLT_MAX/2.0f;
+    }
+    float kqsum[ncols] = {0.0f};
+
+    __shared__ float kqmax_shared[ncols][WARP_SIZE];
+    __shared__ float kqsum_shared[ncols][WARP_SIZE];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        if (threadIdx.y == 0) {
+            kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f;
+            kqsum_shared[j][threadIdx.x] = 0.0f;
+        }
+    }
+    __syncthreads();
+
+    // Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers:
+    float2  Q_f2[ncols][D/(2*WARP_SIZE)];
+    int    Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D >= D/(sizeof(int)*QK8_1)];
+    float2  Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1];
+    if (Q_q8_1) {
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            if (j0 + nwarps > ncols && j >= ncols) {
+                break;
+            }
+
+            // Reuse KQ as temporary storage for converting Q to q8_1:
+            int    * tmp_q_i32 = (int    *) &KQ[j*D];
+            float2 * tmp_q_ds  = (float2 *) (tmp_q_i32 + D/sizeof(int));
+
+            // Set memory to zero if out of bounds:
+            if (ncols > 2 && ic0 + j >= ne01) {
+#pragma unroll
+                for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
+                    const int i = i0 + threadIdx.x;
+
+                    tmp_q_i32[i] = 0;
+                }
+                if (threadIdx.x < D/QK8_1) {
+                    tmp_q_ds[threadIdx.x] = make_float2(0.0f, 0.0f);
+                }
+                continue;
+            }
+
+            const float * Q_f = (const float *) (Q + j*nb01);
+#pragma unroll
+            for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
+                quantize_q8_1_to_shared<float2>(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds);
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            int    * tmp_q_i32 = (int    *) &KQ[j*D];
+            float2 * tmp_q_ds  = (float2 *) (tmp_q_i32 + D/sizeof(int));
+
+#pragma unroll
+            for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i];
+                Q_ds[j][i0/WARP_SIZE]  = tmp_q_ds[i/QI8_1];
+            }
+        }
+
+        __syncthreads();
+    } else {
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            const float2 * Q_f2_j = (const float2 *) (Q + j*nb01);
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                Q_f2[j][i0/WARP_SIZE]    = ncols <= 2 || ic0 + j < ne01 ? Q_f2_j[i] : make_float2(0.0f, 0.0f);
+                Q_f2[j][i0/WARP_SIZE].x *= scale;
+                Q_f2[j][i0/WARP_SIZE].y *= scale;
+            }
+        }
+    }
+
+    float VKQ[ncols] = {0.0f};
+
+    const int k_start = parallel_blocks == 1 ? 0 : ip*D;
+    for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
+        // Calculate KQ tile and keep track of new maximum KQ values:
+
+        float kqmax_new_arr[ncols];
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            kqmax_new_arr[j] = kqmax[j];
+        }
+
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
+            const int i_KQ = i_KQ_0 + threadIdx.y;
+
+            if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
+                break;
+            }
+
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                float sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_f2[j], Q_i32[j], Q_ds[j]);
+                sum = warp_reduce_sum(sum);
+                sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
+
+                kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum);
+
+                if (threadIdx.x == 0) {
+                    KQ[j*D + i_KQ] = sum;
+                }
+            }
+        }
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            float kqmax_new_j = kqmax_new_arr[j];
+
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+            if (threadIdx.x == 0) {
+                kqmax_shared[j][threadIdx.y] = kqmax_new_j;
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            float kqmax_new_j = kqmax_shared[j][threadIdx.x];
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+
+            const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
+            kqmax[j] = kqmax_new_j;
+
+            const float val = expf(KQ[j*D + tid] - kqmax[j]);
+            kqsum[j] = kqsum[j]*KQ_max_scale + val;
+            KQ[j*D + tid] = val;
+
+            VKQ[j] *= KQ_max_scale;
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int k = 0; k < D; ++k) {
+            if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) {
+                break;
+            }
+
+            const float V_ki = dequantize_1_v(V + (k_VKQ_0 + k)*nb21, tid);
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                VKQ[j] += V_ki*KQ[j*D + k];
+            }
+        }
+
+        __syncthreads();
+    }
+
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        kqsum[j] = warp_reduce_sum(kqsum[j]);
+        if (threadIdx.x == 0) {
+            kqsum_shared[j][threadIdx.y] = kqsum[j];
+        }
+    }
+
+    __syncthreads();
+
+#pragma unroll
+    for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
+        if (ncols > 2 && ic0 + j_VKQ >= ne01) {
+            break;
+        }
+
+        kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
+        kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
+
+        float dst_val = VKQ[j_VKQ];
+        if (parallel_blocks == 1) {
+            dst_val /= kqsum[j_VKQ];
+        }
+        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
+        dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
+    }
+
+    if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
+        dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
+    }
+}
+
+template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
+void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    constexpr int nwarps = D/WARP_SIZE;
+    fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V>;
+    constexpr bool need_f16_K = D != 128;
+    constexpr bool need_f16_V = D != 128 && D != 64;
+    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
+}
+
+template <int D, ggml_type type_K, ggml_type type_V>
+void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * Q   = dst->src[0];
+    ggml_tensor * K   = dst->src[1];
+    ggml_tensor * V   = dst->src[2];
+
+    GGML_ASSERT(K->type == type_K);
+    GGML_ASSERT(V->type == type_V);
+
+    if (Q->ne[1] == 1) {
+        constexpr int cols_per_block  = 1;
+        constexpr int parallel_blocks = 4;
+        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] == 2) {
+        constexpr int cols_per_block  = 2;
+        constexpr int parallel_blocks = 4;
+        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] <= 4) {
+        constexpr int cols_per_block  = 4;
+        constexpr int parallel_blocks = 4;
+        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        return;
+    }
+
+    if (Q->ne[1] <= 8) {
+        constexpr int cols_per_block  = 8;
+        constexpr int parallel_blocks = 4;
+        ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+        return;
+    }
+
+    constexpr int cols_per_block  = 8;
+    constexpr int parallel_blocks = 1;
+    ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
+}
+
+#define DECL_FATTN_VEC_F32_CASE(D, type_K, type_V)                          \
+    template void ggml_cuda_flash_attn_ext_vec_f32_case                     \
+    <D, type_K, type_V>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
+
+extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16);
+
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q4_0);
+
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q4_1);
+
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q5_0);
+
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q5_1);
+
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q8_0);
+
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
+extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_F16);
+
+extern DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
diff --git a/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh
similarity index 73%
rename from ggml-cuda/fattn.cu
rename to ggml/src/ggml-cuda/fattn-wmma-f16.cuh
index af7c95232ddf3..ae23222426097 100644
--- a/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh
@@ -1,16 +1,9 @@
 #include "common.cuh"
 #include "fattn-common.cuh"
-#include "fattn-tile-f16.cuh"
-#include "fattn-tile-f32.cuh"
-#include "fattn-vec-f16.cuh"
-#include "fattn-vec-f32.cuh"
-#include "fattn.cuh"
 
-#include <cstdint>
-
-#if FP16_MMA_AVAILABLE
+#ifdef FP16_MMA_AVAILABLE
 #include <mma.h>
-#endif
+#endif // FP16_MMA_AVAILABLE
 
 // D == head size, VKQ_stride == num VKQ rows calculated in parallel:
 template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
@@ -45,11 +38,14 @@ static __global__ void flash_attn_ext_f16(
         const int nb11,
         const int nb12,
         const int nb13,
+        const int nb21,
+        const int nb22,
+        const int nb23,
         const int ne0,
         const int ne1,
         const int ne2,
         const int ne3) {
-#if FP16_MMA_AVAILABLE
+#ifdef FP16_MMA_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
@@ -429,10 +425,12 @@ static_assert(get_VKQ_stride( 80, 1, 16) ==  16, "Test failed.");
 static_assert(get_VKQ_stride( 80, 2, 16) ==  16, "Test failed.");
 static_assert(get_VKQ_stride( 80, 4, 16) ==  16, "Test failed.");
 
-template <int D, int cols_per_block, int nwarps, typename KQ_acc_t>
-void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+template <int D, int cols_per_block, typename KQ_acc_t>
+void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * Q = dst->src[0];
 
+    constexpr int nwarps = 4;
+
     constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16;
     const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
     const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
@@ -440,199 +438,53 @@ void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     if (4*blocks_num_pb1 < 2*nsm) {
         constexpr int parallel_blocks = 4;
         fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
-        launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
+        launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
         return;
     }
     if (2*blocks_num_pb1 < 2*nsm) {
         constexpr int parallel_blocks = 2;
         fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
-        launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
+        launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
         return;
     }
     constexpr int parallel_blocks = 1;
     fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
-    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
+    launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
 }
 
-void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * KQV = dst;
-    const ggml_tensor * Q   = dst->src[0];
-
-    ggml_cuda_set_device(ctx.device);
-    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-    const int32_t precision = KQV->op_params[2];
-
-    // On AMD the tile kernels perform poorly, use the vec kernel instead:
-    if (cc >= CC_OFFSET_AMD) {
-        if (precision == GGML_PREC_DEFAULT) {
-            ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst);
-        } else {
-            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
-        }
-        return;
-    }
-
-    if (!fast_fp16_available(cc)) {
-        if (Q->ne[1] <= 8) {
-            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
-        } else {
-            ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
-        }
-        return;
-    }
-
-    if (!fp16_mma_available(cc)) {
-        if (Q->ne[1] <= 8) {
-            ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst);
-        } else {
-            ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
-        }
-        return;
-    }
-
-    if (precision != GGML_PREC_DEFAULT) {
-        if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) {
-            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
-            return;
-        }
-
-        if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
-            constexpr int cols_per_block = 16;
-            constexpr int nwarps         =  4;
-            switch (Q->ne[0]) {
-                case 64:
-                    launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 80:
-                    launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 96:
-                    launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 112:
-                    launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 128:
-                    launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 256:
-                    launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                default:
-                    GGML_ASSERT(false);
-                    break;
-            }
-        } else {
-            constexpr int cols_per_block = 32;
-            constexpr int nwarps         =  4;
-            switch (Q->ne[0]) {
-                case 64:
-                    launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 80:
-                    launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 96:
-                    launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 112:
-                    launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                case 128:
-                    launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst);
-                    break;
-                // case 256:
-                //     launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst);
-                //     break;
-                default:
-                    GGML_ASSERT(false);
-                    break;
-            }
-        }
-        return;
-    }
-
-    if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
-        ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
-        return;
-    }
-
-    if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
-        constexpr int cols_per_block = 8;
-        constexpr int nwarps         = 4;
-        switch (Q->ne[0]) {
-            case 64:
-                launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            case 96:
-                launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            case 128:
-                launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            case 256:
-                launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            default:
-                GGML_ASSERT(false);
-                break;
-        }
-        return;
-    }
-
-    if (Q->ne[1] <= 32) {
-        constexpr int cols_per_block = 16;
-        constexpr int nwarps         =  4;
-        switch (Q->ne[0]) {
-            case 64:
-                launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            case 80:
-                launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            case 96:
-                launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            case 112:
-                launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            case 128:
-                launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            case 256:
-                launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst);
-                break;
-            default:
-                GGML_ASSERT(false);
-                break;
-        }
-        return;
-    }
-
-    constexpr int cols_per_block = 32;
-    constexpr int nwarps         =  4;
-    switch (Q->ne[0]) {
-        case 64:
-            launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst);
-            break;
-        case 80:
-            launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst);
-            break;
-        case 96:
-            launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst);
-            break;
-        case 112:
-            launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst);
-            break;
-        case 128:
-            launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst);
-            break;
-        case 256:
-            launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-    return;
-}
+#define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t)                         \
+    template void ggml_cuda_flash_attn_ext_wmma_f16_case                              \
+    <D, cols_per_block, KQ_acc_t>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
+
+extern DECL_FATTN_WMMA_F16_CASE( 64, 16, float);
+extern DECL_FATTN_WMMA_F16_CASE( 80, 16, float);
+extern DECL_FATTN_WMMA_F16_CASE( 96, 16, float);
+extern DECL_FATTN_WMMA_F16_CASE(112, 16, float);
+extern DECL_FATTN_WMMA_F16_CASE(128, 16, float);
+extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
+
+extern DECL_FATTN_WMMA_F16_CASE( 64, 32, float);
+extern DECL_FATTN_WMMA_F16_CASE( 80, 32, float);
+extern DECL_FATTN_WMMA_F16_CASE( 96, 32, float);
+extern DECL_FATTN_WMMA_F16_CASE(112, 32, float);
+extern DECL_FATTN_WMMA_F16_CASE(128, 32, float);
+// extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
+
+extern DECL_FATTN_WMMA_F16_CASE( 64,  8, half);
+extern DECL_FATTN_WMMA_F16_CASE( 96,  8, half);
+extern DECL_FATTN_WMMA_F16_CASE(128,  8, half);
+extern DECL_FATTN_WMMA_F16_CASE(256,  8, half);
+
+extern DECL_FATTN_WMMA_F16_CASE( 64, 16, half);
+extern DECL_FATTN_WMMA_F16_CASE( 80, 16, half);
+extern DECL_FATTN_WMMA_F16_CASE( 96, 16, half);
+extern DECL_FATTN_WMMA_F16_CASE(112, 16, half);
+extern DECL_FATTN_WMMA_F16_CASE(128, 16, half);
+extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
+
+extern DECL_FATTN_WMMA_F16_CASE( 64, 32, half);
+extern DECL_FATTN_WMMA_F16_CASE( 80, 32, half);
+extern DECL_FATTN_WMMA_F16_CASE( 96, 32, half);
+extern DECL_FATTN_WMMA_F16_CASE(112, 32, half);
+extern DECL_FATTN_WMMA_F16_CASE(128, 32, half);
+extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
new file mode 100644
index 0000000000000..29f608b0ff98d
--- /dev/null
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -0,0 +1,345 @@
+#include "common.cuh"
+#include "fattn-common.cuh"
+#include "fattn-tile-f16.cuh"
+#include "fattn-tile-f32.cuh"
+#include "fattn-vec-f16.cuh"
+#include "fattn-vec-f32.cuh"
+#include "fattn-wmma-f16.cuh"
+#include "fattn.cuh"
+
+#include <cstdint>
+
+static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * KQV = dst;
+    const ggml_tensor * Q   = dst->src[0];
+
+    const int32_t precision = KQV->op_params[2];
+
+    if (precision != GGML_PREC_DEFAULT) {
+        if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
+            constexpr int cols_per_block = 16;
+            switch (Q->ne[0]) {
+                case 64:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
+                    break;
+                case 80:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
+                    break;
+                case 96:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
+                    break;
+                case 112:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
+                    break;
+                case 128:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+                    break;
+                case 256:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
+                    break;
+                default:
+                    GGML_ABORT("fatal error");
+                    break;
+            }
+        } else {
+            constexpr int cols_per_block = 32;
+            switch (Q->ne[0]) {
+                case 64:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
+                    break;
+                case 80:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
+                    break;
+                case 96:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
+                    break;
+                case 112:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
+                    break;
+                case 128:
+                    ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+                    break;
+                // case 256:
+                //     ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+                //     break;
+                default:
+                    GGML_ABORT("fatal error");
+                    break;
+            }
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
+        constexpr int cols_per_block = 8;
+        switch (Q->ne[0]) {
+            case 64:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+                break;
+            case 96:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+                break;
+            case 128:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+                break;
+            case 256:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+                break;
+            default:
+                GGML_ABORT("fatal error");
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 32) {
+        constexpr int cols_per_block = 16;
+        switch (Q->ne[0]) {
+            case 64:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+                break;
+            case 80:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
+                break;
+            case 96:
+                ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+                break;
+            case 112:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
+                break;
+            case 128:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+                break;
+            case 256:
+                ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+                break;
+            default:
+                GGML_ABORT("fatal error");
+                break;
+        }
+        return;
+    }
+
+    constexpr int cols_per_block = 32;
+    switch (Q->ne[0]) {
+        case 64:
+            ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+            break;
+        case 80:
+            ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
+            break;
+        case 96:
+            ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+            break;
+        case 112:
+            ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
+            break;
+        case 128:
+            ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+            break;
+        case 256:
+            ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+#define FATTN_VEC_F16_CASE(D, type_K, type_V)                               \
+    if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) {    \
+        ggml_cuda_flash_attn_ext_vec_f16_case<D, type_K, type_V>(ctx, dst); \
+        return;                                                             \
+    }                                                                       \
+
+static void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * Q = dst->src[1];
+    ggml_tensor * K = dst->src[1];
+    ggml_tensor * V = dst->src[2];
+
+#ifdef GGML_CUDA_FA_ALL_QUANTS
+    FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0)
+    FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1)
+    FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0)
+    FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1)
+    FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0)
+    FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16 )
+
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q4_0)
+
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q4_1)
+
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q5_0)
+
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q5_1)
+
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q8_0)
+
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_F16,  GGML_TYPE_F16)
+
+    FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
+#else
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
+
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
+
+    FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
+    FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
+    FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
+#endif // GGML_CUDA_FA_ALL_QUANTS
+
+    on_no_fattn_vec_case(Q->ne[0]);
+}
+
+#define FATTN_VEC_F32_CASE(D, type_K, type_V)                               \
+    if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) {    \
+        ggml_cuda_flash_attn_ext_vec_f32_case<D, type_K, type_V>(ctx, dst); \
+        return;                                                             \
+    }                                                                       \
+
+static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * Q = dst->src[1];
+    ggml_tensor * K = dst->src[1];
+    ggml_tensor * V = dst->src[2];
+
+#ifdef GGML_CUDA_FA_ALL_QUANTS
+    FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0)
+    FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1)
+    FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0)
+    FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1)
+    FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0)
+    FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
+
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q4_0)
+
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q4_1)
+
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q5_0)
+
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q5_1)
+
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_Q8_0)
+
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_F16,  GGML_TYPE_F16)
+
+    FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
+#else
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
+
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
+
+    FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
+    FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
+    FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
+#endif // GGML_CUDA_FA_ALL_QUANTS
+
+    on_no_fattn_vec_case(Q->ne[0]);
+}
+
+void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * KQV = dst;
+    const ggml_tensor * Q   = dst->src[0];
+
+    ggml_cuda_set_device(ctx.device);
+    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+    const int32_t precision = KQV->op_params[2];
+
+    // On AMD the tile kernels perform poorly, use the vec kernel instead:
+    if (cc >= CC_OFFSET_AMD) {
+        if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
+            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+        } else {
+            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+        }
+        return;
+    }
+
+    if (!fast_fp16_available(cc)) {
+        if (Q->ne[1] <= 8) {
+            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+        } else {
+            ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
+        }
+        return;
+    }
+
+    if (!fp16_mma_available(cc)) {
+        if (Q->ne[1] <= 8) {
+            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+        } else {
+            ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
+        }
+        return;
+    }
+
+    if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
+        if (precision == GGML_PREC_DEFAULT) {
+            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+            return;
+        } else if(Q->ne[0] <= 128) {
+            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+            return;
+        }
+    }
+
+    ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
+}
diff --git a/ggml-cuda/fattn.cuh b/ggml/src/ggml-cuda/fattn.cuh
similarity index 100%
rename from ggml-cuda/fattn.cuh
rename to ggml/src/ggml-cuda/fattn.cuh
diff --git a/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu
similarity index 98%
rename from ggml-cuda/getrows.cu
rename to ggml/src/ggml-cuda/getrows.cu
index 55af195fd4542..4c3703238cb6e 100644
--- a/ggml-cuda/getrows.cu
+++ b/ggml/src/ggml-cuda/getrows.cu
@@ -171,8 +171,7 @@ void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
             break;
         default:
             // TODO: k-quants
-            fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
-            GGML_ASSERT(false);
+            GGML_ABORT("%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
             break;
     }
 }
diff --git a/ggml-cuda/getrows.cuh b/ggml/src/ggml-cuda/getrows.cuh
similarity index 100%
rename from ggml-cuda/getrows.cuh
rename to ggml/src/ggml-cuda/getrows.cuh
diff --git a/ggml-cuda/im2col.cu b/ggml/src/ggml-cuda/im2col.cu
similarity index 100%
rename from ggml-cuda/im2col.cu
rename to ggml/src/ggml-cuda/im2col.cu
diff --git a/ggml-cuda/im2col.cuh b/ggml/src/ggml-cuda/im2col.cuh
similarity index 100%
rename from ggml-cuda/im2col.cuh
rename to ggml/src/ggml-cuda/im2col.cuh
diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
new file mode 100644
index 0000000000000..a452a3cc3d152
--- /dev/null
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -0,0 +1,221 @@
+#include "common.cuh"
+
+struct mma_int_A_I16K4 {
+    static constexpr int I  = 16;
+    static constexpr int K  = 4;
+    static constexpr int ne = 2;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = (l%2) * (I/2) + threadIdx.x / K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int /* l */) {
+        const int ret = threadIdx.x % K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+
+    __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
+#if defined(INT8_MMA_AVAILABLE)
+        const int * xs = xs0 + (threadIdx.x%I)*stride;
+        asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
+            : "+r"(x[0]), "+r"(x[1])
+            : "l"(xs));
+#else
+#pragma unroll
+        for (int l = 0; l < ne; ++l) {
+            x[l] = xs0[get_i(l)*stride + get_k(l)];
+        }
+#endif // defined(INT8_MMA_AVAILABLE)
+    }
+};
+
+struct mma_int_A_I16K8 {
+    static constexpr int I  = 16;
+    static constexpr int K  = 8;
+    static constexpr int ne = 4;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = (l%2) * (I/2) + threadIdx.x / (K/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int l) {
+        const int ret = (l/2) * (K/2) + threadIdx.x % (K/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+
+    __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
+#if defined(INT8_MMA_AVAILABLE)
+        const int * xs = xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
+        asm("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
+            : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
+            : "l"(xs));
+#else
+#pragma unroll
+        for (int l = 0; l < ne; ++l) {
+            x[l] = xs0[get_i(l)*stride + get_k(l)];
+        }
+#endif // defined(INT8_MMA_AVAILABLE)
+    }
+
+    __device__ __forceinline__ void load_low(const int * __restrict__ xs0, const int & stride) {
+        ((mma_int_A_I16K4 *) x)[0].load(xs0, stride);
+    }
+};
+
+struct mma_int_B_J8K4 {
+    static constexpr int J  = 8;
+    static constexpr int K  = 4;
+    static constexpr int ne = 1;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_j(const int /* l */) {
+        const int ret = threadIdx.x / K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int /* l */) {
+        const int ret = threadIdx.x % K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+
+    __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
+#if defined(INT8_MMA_AVAILABLE) && false // Loading as 4 byte values is faster
+        const int * xs = xs0 + (threadIdx.x%J)*stride;
+        asm("ldmatrix.sync.aligned.m8n8.x1.b16 {%0}, [%1];"
+            : "+r"(x[0])
+            : "l"(xs));
+#else
+#pragma unroll
+        for (int l = 0; l < ne; ++l) {
+            x[l] = xs0[get_j(l)*stride + get_k(l)];
+        }
+#endif // defined(INT8_MMA_AVAILABLE)
+    }
+};
+
+struct mma_int_B_J8K8 {
+    static constexpr int J  = 8;
+    static constexpr int K  = 8;
+    static constexpr int ne = 2;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_j(const int /* l */) {
+        const int ret = threadIdx.x / (K/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int l) {
+        const int ret = l * (K/2) + threadIdx.x % (K/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+
+    __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
+#if defined(INT8_MMA_AVAILABLE) && false // Loading as 4 byte values is faster
+        const int * xs = xs0 + (threadIdx.x%J)*stride + ((threadIdx.x/J)*(K/2)) % K;
+        asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
+            : "+r"(x[0]), "+r"(x[1])
+            : "l"(xs));
+#else
+#pragma unroll
+        for (int l = 0; l < ne; ++l) {
+            x[l] = xs0[get_j(l)*stride + get_k(l)];
+        }
+#endif // defined(INT8_MMA_AVAILABLE)
+    }
+};
+
+struct mma_int_C_I16J8 {
+    static constexpr int I  = 16;
+    static constexpr int J  = 8;
+    static constexpr int ne = 4;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = (l/2) * (I/2) + threadIdx.x / (J/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_j(const int l) {
+        const int ret = 2 * (threadIdx.x % (J/2)) + l%2;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    __device__ __forceinline__ void mma_K4(const mma_int_A_I16K4 & mma_A, const mma_int_B_J8K4 & mma_B) {
+#ifdef INT8_MMA_AVAILABLE
+#if __CUDA_ARCH__ >= CC_AMPERE
+        asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
+            : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_B.x[0]));
+#else
+        // On Turing m16n8k16 mma is not available, use 2x m8n8k16 mma instead:
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[0]), "+r"(x[1])
+            : "r"(mma_A.x[0]), "r"(mma_B.x[0]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[1]), "r"(mma_B.x[0]));
+#endif // __CUDA_ARCH__ >= CC_AMPERE
+#else
+        GGML_UNUSED(mma_A);
+        GGML_UNUSED(mma_B);
+        NO_DEVICE_CODE;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    __device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) {
+#ifdef INT8_MMA_AVAILABLE
+#if __CUDA_ARCH__ >= CC_AMPERE
+        asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
+            : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_A.x[2]), "r"(mma_A.x[3]), "r"(mma_B.x[0]), "r"(mma_B.x[1]));
+#else
+        // On Turing m16n8k32 mma is not available, use 4x m8n8k16 mma instead:
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[0]), "+r"(x[1])
+            : "r"(mma_A.x[0]), "r"(mma_B.x[0]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[1]), "r"(mma_B.x[0]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[0]), "+r"(x[1])
+            : "r"(mma_A.x[2]), "r"(mma_B.x[1]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[3]), "r"(mma_B.x[1]));
+#endif // __CUDA_ARCH__ >= CC_AMPERE
+#else
+        GGML_UNUSED(mma_A);
+        GGML_UNUSED(mma_B);
+        NO_DEVICE_CODE;
+#endif // INT8_MMA_AVAILABLE
+    }
+};
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
new file mode 100644
index 0000000000000..78d70cd7a4e64
--- /dev/null
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -0,0 +1,150 @@
+#include "mmq.cuh"
+
+void ggml_cuda_op_mul_mat_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream) {
+
+    const int64_t ne00 = src0->ne[0];
+
+    const int64_t nb01 = src0->nb[1];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t row_diff = row_high - row_low;
+    const int64_t stride00 = nb01 / ggml_type_size(src0->type);
+
+    int id = ggml_cuda_get_device();
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the kernel writes into
+    const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
+
+    const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst};
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            mul_mat_q_case<GGML_TYPE_Q4_0>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            mul_mat_q_case<GGML_TYPE_Q4_1>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            mul_mat_q_case<GGML_TYPE_Q5_0>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            mul_mat_q_case<GGML_TYPE_Q5_1>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            mul_mat_q_case<GGML_TYPE_Q8_0>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            mul_mat_q_case<GGML_TYPE_Q2_K>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            mul_mat_q_case<GGML_TYPE_Q3_K>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            mul_mat_q_case<GGML_TYPE_Q4_K>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            mul_mat_q_case<GGML_TYPE_Q5_K>(ctx, args, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            mul_mat_q_case<GGML_TYPE_Q6_K>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ2_XXS:
+            mul_mat_q_case<GGML_TYPE_IQ2_XXS>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            mul_mat_q_case<GGML_TYPE_IQ2_XS>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ2_S:
+            mul_mat_q_case<GGML_TYPE_IQ2_S>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            mul_mat_q_case<GGML_TYPE_IQ3_XXS>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            mul_mat_q_case<GGML_TYPE_IQ3_S>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ1_S:
+            mul_mat_q_case<GGML_TYPE_IQ1_S>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ4_XS:
+            mul_mat_q_case<GGML_TYPE_IQ4_XS>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ4_NL:
+            mul_mat_q_case<GGML_TYPE_IQ4_NL>(ctx, args, stream);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddf_i);
+}
+
+bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
+#ifdef GGML_CUDA_FORCE_CUBLAS
+    return false;
+#endif // GGML_CUDA_FORCE_CUBLAS
+
+    bool mmq_supported;
+
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ4_NL:
+            mmq_supported = true;
+            break;
+        default:
+            mmq_supported = false;
+            break;
+    }
+
+    if (!mmq_supported) {
+        return false;
+    }
+
+    if (int8_mma_available(cc)) {
+        return true;
+    }
+
+    if (cc < MIN_CC_DP4A) {
+        return false;
+    }
+
+#ifdef GGML_CUDA_FORCE_MMQ
+    return true;
+#endif //GGML_CUDA_FORCE_MMQ
+
+    if (cc < CC_OFFSET_AMD) {
+        return cc < CC_VOLTA || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
+    }
+
+    return cc < CC_RDNA3 || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
+}
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
new file mode 100644
index 0000000000000..e8a957447de9b
--- /dev/null
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -0,0 +1,2936 @@
+#pragma once
+
+#include "common.cuh"
+#include "vecdotq.cuh"
+#include "mma.cuh"
+
+#include <climits>
+#include <cstdint>
+
+#define MMQ_DP4A_MAX_BATCH_SIZE 64 // Max. batch size to use for dp4a MMQ kernels when FP16 tensor cores are available.
+#define MMQ_ITER_K 256
+#define MMQ_NWARPS 8
+
+typedef void (*load_tiles_mmq_t)(const char * __restrict__ x, int * x_tile, const int & kbx0, const int & i_max, const int & stride);
+typedef void (*vec_dot_mmq_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00);
+typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max);
+
+enum mmq_q8_1_ds_layout {
+    MMQ_Q8_1_DS_LAYOUT_D4,
+    MMQ_Q8_1_DS_LAYOUT_DS4,
+    MMQ_Q8_1_DS_LAYOUT_D2S6,
+};
+
+struct block_q8_1_mmq {
+    // The y float data is converted to a data layout that can simply be copied to shared memory as a contiguous block.
+    // The y float data is first grouped as blocks of 128 values.
+    // These blocks are then treated as individual data values and transposed.
+    //
+    // To avoid shared memory bank conflicts each block is padded with 16 bytes.
+    // This padding is also used to store block scales/partial sums.
+    // The scales multiplied with the quantized data are equal to the unquantized values.
+    // The partial sums are obtained by summing up a subgroup of the contained values (prior to quantization)
+    //     and are only needed for performance reasons.
+    //
+    // The exact data stored depends on the x data type.
+    union {
+        float d4[4];    // 1 32 bit scale per 32 values, stored as d0,d1,d2,d3
+        half2 ds4[4];   // 1 16 bit scale + 1 16 bit partial sum per 32 values, stored as d0,s0,d1,s1,d2,s2,d3,s3
+        half  d2s6[8];  // 1 16 bit scale per 64 values + 1 16 bit partial sum per 16 values for the first 96 values,
+                        //     stored as d0,d1,s1,s2,s3,s4,s5
+    };
+    int8_t qs[4*QK8_1]; // 128 values quantized to 8 bit each
+};
+static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size");
+static_assert(sizeof(block_q8_1_mmq) == 4*sizeof(block_q8_1),      "Unexpected block_q8_1_mmq size");
+
+static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
+    switch (type_x) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            return MMQ_Q8_1_DS_LAYOUT_DS4;
+        case GGML_TYPE_Q5_0:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_Q5_1:
+            return MMQ_Q8_1_DS_LAYOUT_DS4;
+        case GGML_TYPE_Q8_0:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_Q2_K:
+            return MMQ_Q8_1_DS_LAYOUT_D2S6;
+        case GGML_TYPE_Q3_K:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+            return MMQ_Q8_1_DS_LAYOUT_DS4;
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_IQ1_S:
+            return MMQ_Q8_1_DS_LAYOUT_DS4;
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ4_NL:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+
+struct tile_x_sizes {
+    int qs;
+    int dm;
+    int sc;
+};
+
+static constexpr int get_mmq_x_max_host(const int cc) {
+    return int8_mma_available(cc) ? 128 :
+#ifdef GGML_CUDA_FORCE_MMQ
+        cc >= CC_VOLTA && cc < CC_OFFSET_AMD ? 128                     : 64;
+#else
+        cc >= CC_VOLTA && cc < CC_OFFSET_AMD ? MMQ_DP4A_MAX_BATCH_SIZE : 64;
+#endif // GGML_CUDA_FORCE_MMQ
+}
+
+static constexpr __device__ int get_mmq_x_max_device() {
+#ifdef INT8_MMA_AVAILABLE
+    return 128;
+#else // INT8_MMA_AVAILABLE
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    return 128;
+#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+
+#if __CUDA_ARCH__ >= CC_VOLTA
+#ifdef GGML_CUDA_FORCE_MMQ
+    return MMQ_DP4A_MAX_BATCH_SIZE;
+#else // GGML_CUDA_FORCE_MMQ
+    return 128;
+#endif // GGML_CUDA_FORCE_MMQ
+#else // __CUDA_ARCH__ >= CC_VOLTA
+
+    return 64;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#endif // INT8_MMA_AVAILABLE
+}
+
+static constexpr int get_mmq_y_host(const int cc) {
+    return cc >= CC_OFFSET_AMD ? (cc == CC_RDNA1 ? 64 : 128) : (cc >= CC_VOLTA ? 128 : 64);
+}
+
+static constexpr __device__ int get_mmq_y_device() {
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA1)
+    return 64;
+#else
+    return 128;
+#endif // defined RDNA1
+#else
+#if __CUDA_ARCH__ >= CC_VOLTA
+    return 128;
+#else
+    return 64;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+}
+
+#define MMQ_DP4A_TXS_Q4_0    tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_0   + mmq_y/QI4_0,     0}
+#define MMQ_DP4A_TXS_Q4_1    tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_1   + mmq_y/QI4_1,     0}
+#define MMQ_DP4A_TXS_Q8_0    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*2/QI8_0 + mmq_y/(QI8_0/2), 0}
+#define MMQ_DP4A_TXS_Q8_0_16 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*4/QI8_0 + mmq_y/(QI8_0/4), 0}
+#define MMQ_DP4A_TXS_Q8_1    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*2/QI8_1 + mmq_y/(QI8_1/2), 0}
+#define MMQ_DP4A_TXS_Q2_K    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE         + mmq_y,           0}
+#define MMQ_DP4A_TXS_Q3_K    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y,                                     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q4_K    tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_K,                     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q5_K    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_K   + mmq_y/QI5_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q6_K    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI6_K   + mmq_y/QI6_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
+
+static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int mmq_y) {
+    return type == GGML_TYPE_Q4_0 ? MMQ_DP4A_TXS_Q4_0 :
+        type == GGML_TYPE_Q4_1    ? MMQ_DP4A_TXS_Q4_1 :
+        type == GGML_TYPE_Q5_0    ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_Q5_1    ? MMQ_DP4A_TXS_Q8_1 :
+        type == GGML_TYPE_Q8_0    ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_Q2_K    ? MMQ_DP4A_TXS_Q2_K :
+        type == GGML_TYPE_Q3_K    ? MMQ_DP4A_TXS_Q3_K :
+        type == GGML_TYPE_Q4_K    ? MMQ_DP4A_TXS_Q4_K :
+        type == GGML_TYPE_Q5_K    ? MMQ_DP4A_TXS_Q5_K :
+        type == GGML_TYPE_Q6_K    ? MMQ_DP4A_TXS_Q6_K :
+        type == GGML_TYPE_IQ2_XXS ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ2_XS  ? MMQ_DP4A_TXS_Q8_0_16 :
+        type == GGML_TYPE_IQ2_S   ? MMQ_DP4A_TXS_Q8_0_16 :
+        type == GGML_TYPE_IQ3_XXS ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ3_S   ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ1_S   ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ4_XS  ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ4_NL  ? MMQ_DP4A_TXS_Q8_0 :
+        tile_x_sizes{0, 0, 0};
+}
+
+#define MMQ_MMA_TILE_X_K_Q8_0 (2*WARP_SIZE + 2*WARP_SIZE/QI8_0                 + 4)
+#define MMQ_MMA_TILE_X_K_Q8_1 (2*WARP_SIZE + 2*WARP_SIZE/QI8_0                 + 4)
+#define MMQ_MMA_TILE_X_K_Q2_K (2*WARP_SIZE + WARP_SIZE                         + 4)
+#define MMQ_MMA_TILE_X_K_Q3_K (2*WARP_SIZE + WARP_SIZE/2                       + 4)
+#define MMQ_MMA_TILE_X_K_Q6_K (2*WARP_SIZE + WARP_SIZE/QI6_K     + WARP_SIZE/8 + 7)
+
+static_assert(MMQ_MMA_TILE_X_K_Q8_0 % 8 == 4, "Wrong padding.");
+static_assert(MMQ_MMA_TILE_X_K_Q8_1 % 8 == 4, "Wrong padding.");
+static_assert(MMQ_MMA_TILE_X_K_Q2_K % 8 == 4, "Wrong padding.");
+static_assert(MMQ_MMA_TILE_X_K_Q3_K % 8 == 4, "Wrong padding.");
+static_assert(MMQ_MMA_TILE_X_K_Q6_K % 8 == 4, "Wrong padding.");
+
+static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
+    return type == GGML_TYPE_Q4_0 ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_Q4_1    ? MMQ_MMA_TILE_X_K_Q8_1 :
+        type == GGML_TYPE_Q5_0    ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_Q5_1    ? MMQ_MMA_TILE_X_K_Q8_1 :
+        type == GGML_TYPE_Q8_0    ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_Q2_K    ? MMQ_MMA_TILE_X_K_Q2_K :
+        type == GGML_TYPE_Q3_K    ? MMQ_MMA_TILE_X_K_Q3_K :
+        type == GGML_TYPE_Q4_K    ? MMQ_MMA_TILE_X_K_Q8_1 :
+        type == GGML_TYPE_Q5_K    ? MMQ_MMA_TILE_X_K_Q8_1 :
+        type == GGML_TYPE_Q6_K    ? MMQ_MMA_TILE_X_K_Q6_K :
+        type == GGML_TYPE_IQ2_XXS ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ2_XS  ? MMQ_MMA_TILE_X_K_Q3_K :
+        type == GGML_TYPE_IQ2_S   ? MMQ_MMA_TILE_X_K_Q3_K :
+        type == GGML_TYPE_IQ3_XXS ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ3_S   ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ1_S   ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ4_XS  ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ4_NL  ? MMQ_MMA_TILE_X_K_Q8_0 :
+        0;
+}
+
+#define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1)
+
+static int mmq_get_granularity_host(const int mmq_x, const int cc) {
+    return int8_mma_available(cc) && mmq_x >= 48 ? 16 : 8;
+}
+
+#ifdef INT8_MMA_AVAILABLE
+static constexpr __device__ int mmq_get_granularity_device(const int mmq_x) {
+    return mmq_x >= 48 ? 16 : 8;
+}
+#else
+static constexpr __device__ int mmq_get_granularity_device(const int /* mmq_x */) {
+    return 8;
+}
+#endif // INT8_MMA_AVAILABLE
+
+// ------------------------------------------------------------
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + 2*WARP_SIZE);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kbx  = threadIdx.x / QI4_0;
+    const int kqsx = threadIdx.x % QI4_0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx;
+        const int qs0 = get_int_b2(bxi->qs, kqsx);
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + 0]     = __vsubss4((qs0 >> 0) & 0x0F0F0F0F, 0x08080808);
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + QI4_0] = __vsubss4((qs0 >> 4) & 0x0F0F0F0F, 0x08080808);
+#else
+        x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
+        int i = i0 + threadIdx.y * QI4_0 + threadIdx.x / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbxd;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0       + kbxd] = bxi->d;
+#else
+        x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + kbxd] = bxi->d;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + txs.qs;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_0*VDR_Q4_0_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                const int kyqs = QI8_1 * ((k01/2) / (QI8_1/2)) + (k01/2) % (QI8_1/2);
+
+                int u[2*VDR_Q4_0_Q8_1_MMQ];
+
+#pragma unroll
+                for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
+                    u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + kyqs +  l];
+                    u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + kyqs + (l + QI4_0)];
+                }
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
+                    (&x_qs[i*(WARP_SIZE + 1) + k0/QR4_0], u,
+                     x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/(QR4_0*QI4_0)], y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kbx  = threadIdx.x / QI4_1;
+    const int kqsx = threadIdx.x % QI4_1;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx;
+        const int qs0 = get_int_b4(bxi->qs, kqsx);
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + 0]     = (qs0 >> 0) & 0x0F0F0F0F;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + QI4_1] = (qs0 >> 4) & 0x0F0F0F0F;
+#else
+        x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
+        int i = i0 + threadIdx.y * QI4_1 + threadIdx.x / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbxd;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_dm[i*MMQ_MMA_TILE_X_K_Q8_1       + kbxd] = bxi->dm;
+#else
+        x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + kbxd] = bxi->dm;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const half2 * x_dm = (const half2 *) x_qs + txs.qs;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_1*VDR_Q4_1_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                const int kyqs = QI8_1 * ((k01/2) / (QI8_1/2)) + (k01/2) % (QI8_1/2);
+
+                int u[2*VDR_Q4_1_Q8_1_MMQ];
+
+#pragma unroll
+                for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
+                    u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + kyqs +  l];
+                    u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + kyqs + (l + QI4_1)];
+                }
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
+                    (&x_qs[i*(WARP_SIZE + 1) + k0/QR4_1], u,
+                     x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + k0/(QR4_1*QI4_1)], y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_0, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kbx  = threadIdx.x / QI5_0;
+    const int kqsx = threadIdx.x % QI5_0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbx;
+
+        const int ql = get_int_b2(bxi->qs, kqsx);
+        const int qh = get_int_b2(bxi->qh, 0) >> (4 * (threadIdx.x % QI5_0));
+
+        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
+        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
+        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
+        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
+        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
+        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16
+
+        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
+        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
+        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
+        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
+        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
+        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
+#else
+        x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
+        x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
+        int i = i0 + threadIdx.y * QI5_0 + threadIdx.x / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbxd;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0       + kbxd] = bxi->d;
+#else
+        x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + kbxd] = bxi->d;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kbx  = threadIdx.x / QI5_1;
+    const int kqsx = threadIdx.x % QI5_1;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbx;
+
+        const int ql = get_int_b4(bxi->qs, kqsx);
+        const int qh = get_int_b4(bxi->qh, 0) >> (4 * (threadIdx.x % QI5_1));
+
+        int qs0 = (ql >>  0) & 0x0F0F0F0F;
+        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
+        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
+        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
+        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
+
+        int qs1 = (ql >>  4) & 0x0F0F0F0F;
+        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
+        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
+        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
+        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
+#else
+        x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
+        x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
+        int i = i0 + threadIdx.y * QI5_1 + threadIdx.x / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbxd;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_dm[i*MMQ_MMA_TILE_X_K_Q8_1       + kbxd] = bxi->dm;
+#else
+        x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + kbxd] = bxi->dm;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_tile + 2*WARP_SIZE);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kbx  = threadIdx.x / QI8_0;
+    const int kqsx = threadIdx.x % QI8_0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 0         + threadIdx.x] = get_int_b2(bxi[0].qs,               kqsx);
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + WARP_SIZE + threadIdx.x] = get_int_b2(bxi[WARP_SIZE/QI8_0].qs, kqsx);
+#else
+        x_qs[i*(2*WARP_SIZE + 1)     + 0         + threadIdx.x] = get_int_b2(bxi[0].qs,               kqsx);
+        x_qs[i*(2*WARP_SIZE + 1)     + WARP_SIZE + threadIdx.x] = get_int_b2(bxi[WARP_SIZE/QI8_0].qs, kqsx);
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    const int blocks_per_tile_x_row = 2*WARP_SIZE / QI8_0;
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0/2) {
+        int i = i0 + threadIdx.y * (QI8_0/2) + threadIdx.x / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0             + kbxd] = bxi->d;
+#else
+        x_df[i*(2*WARP_SIZE/QI8_0) + i/(QI8_0/2) + kbxd] = bxi->d;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + txs.qs;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += VDR_Q8_0_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl<float, VDR_Q8_0_Q8_1_MMQ>
+                    (&x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k0 % WARP_SIZE],
+                     x_df[i*(2*WARP_SIZE/QI8_0) + i/(QI8_0/2) + k0/QI8_0], y_df[j*MMQ_TILE_Y_K + (k0/QI8_1) % (WARP_SIZE/QI8_1)]);
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps, mmq_q8_1_ds_layout ds_layout>
+static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    constexpr int granularity = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
+
+    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
+
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + 2*WARP_SIZE;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+    const half2 * y_ds = (const half2 *) y;
+
+    mma_A A[ntx][WARP_SIZE/QI8_0];
+    float dA[ntx][mma_C::ne/2][WARP_SIZE/QI8_0];
+
+    const int i0 = (threadIdx.y/ntx)*rows_per_warp;
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
+            const int k0 = k00 + k01;
+
+            A[n][k01/QI8_0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_0 + k0, MMQ_MMA_TILE_X_K_Q8_0);
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + n*mma_A::I + mma_C::get_i(2*l);
+
+#pragma unroll
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
+                const int k0 = k00 + k01;
+
+                dA[n][l][k01/QI8_0] = x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + k0/QI8_0];
+            }
+        }
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
+            mma_B  B;
+            float dB[mma_C::ne/2];
+
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D4) {
+                    dB[l] =             y_df[j*MMQ_TILE_Y_K + k01/QI8_1];
+                } else {
+                    dB[l] = __low2float(y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+                }
+            }
+
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C;
+                C.mma_K8(A[n][k01/QI8_0], B);
+
+#pragma unroll
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += C.x[l]*dA[n][l/2][k01/QI8_0]*dB[l%2];
+                }
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const half2 * x_dm = (const half2 *) x_qs + txs.qs;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += VDR_Q8_0_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
+                    (&x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01],
+                    x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI8_1], y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    constexpr int granularity = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
+
+    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
+
+    const int   * x_qs = (const int   *) x;
+    const half2 * x_dm = (const half2 *) x_qs + 2*WARP_SIZE;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_dm = (const half2 *) y;
+
+    mma_A    A[ntx][WARP_SIZE/QI8_1];
+    float2 dmA[ntx][mma_C::ne/2][WARP_SIZE/QI8_1];
+
+    const int i0 = (threadIdx.y/ntx)*rows_per_warp;
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+            const int k0 = k00 + k01;
+
+            A[n][k01/QI8_1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_1 + k0, MMQ_MMA_TILE_X_K_Q8_1);
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + n*mma_A::I + mma_C::get_i(2*l);
+
+#pragma unroll
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+                const int k0 = k00 + k01;
+
+                dmA[n][l][k01/QI8_1] = __half22float2(x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + k0/QI8_1]);
+            }
+        }
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+            mma_B    B;
+            float2 dsB[mma_C::ne/2];
+
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dsB[l] = __half22float2(y_dm[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C;
+                C.mma_K8(A[n][k01/QI8_1], B);
+
+#pragma unroll
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dmA[n][l/2][k01/QI8_1].x*dsB[l%2].x*C.x[l];
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dmA[n][l/2][k01/QI8_1].y*dsB[l%2].y;
+                }
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16;
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + txs.qs;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_16_q8_1_impl<QI8_0>(
+                    &x_qs[i*(2*WARP_SIZE + 1) + k0],
+                    &y_qs[j*MMQ_TILE_Y_K + k01],
+                    &x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + k0/(QI8_0/2)],
+                    y_df[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+#ifdef INT8_MMA_AVAILABLE
+
+    typedef mma_int_A_I16K4 mma_A;
+    typedef mma_int_A_I16K8 mma_A_K8;
+    typedef mma_int_B_J8K4  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    constexpr int granularity = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
+
+    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
+
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + WARP_SIZE*2;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
+
+    mma_A   A[ntx][8];
+    float  dA[ntx][mma_C::ne/2][8];
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
+            const int k0 = k00 + k01;
+
+            ((mma_A_K8 *) A[n])[k01/8].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K);
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+
+#pragma unroll
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += 4) {
+                const int k0 = k00 + k01;
+
+                dA[n][l][k01/4] = x_df[i*MMQ_MMA_TILE_X_K_Q3_K + k0/4];
+            }
+        }
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR3_K*VDR_Q3_K_Q8_1_MMQ) {
+            mma_B B[2];
+            float dB[mma_C::ne/2];
+
+            B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
+            B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dB[l] = y_df[j*MMQ_TILE_Y_K + k01/QI8_1];
+            }
+
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C[2];
+                C[0].mma_K4(A[n][k01/4 + 0], B[0]);
+                C[1].mma_K4(A[n][k01/4 + 1], B[1]);
+
+#pragma unroll
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dB[l%2]*(C[0].x[l]*dA[n][l/2][k01/4 + 0] + C[1].x[l]*dA[n][l/2][k01/4 + 1]);
+                }
+            }
+        }
+    }
+#else
+    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
+    NO_DEVICE_CODE;
+#endif // INT8_MMA_AVAILABLE
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % QI2_K;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/QI2_K) {
+        int i = i0 + threadIdx.y*(WARP_SIZE/QI2_K) + threadIdx.x/QI2_K;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = (const block_q2_K *) x + kbx0 + i*stride;
+
+        const int x_ql_0 = get_int_b2(bxi->qs, kqsx);
+
+#pragma unroll
+        for (int l = 0; l < QR2_K; ++l) {
+            const int k = (kqsx/8)*32 + l*8 + kqsx % 8;
+
+            const int x_qs_k = (x_ql_0 >> (2*l)) & 0x03030303;
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q2_K + k] = x_qs_k;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + k] = x_qs_k;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int sc_m = bxi->scales[kqsx];
+#ifdef FAST_FP16_AVAILABLE
+        const half2 x_dm_ik = __hmul2(bxi->dm, make_half2(sc_m & 0x0F, sc_m >> 4));
+#else
+        const float2 bxi_dmf = __half22float2(bxi->dm);
+        const half2 x_dm_ik = make_half2(bxi_dmf.x*(sc_m & 0x0F), bxi_dmf.y*(sc_m >> 4));
+#endif // FAST_FP16_AVAILABLE
+
+#ifdef INT8_MMA_AVAILABLE
+        x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + kqsx] = x_dm_ik;
+#else
+        x_dm[i*(WARP_SIZE + 1)       + kqsx] = x_dm_ik;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q2_K_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const half2 * x_dm = (const half2 *) x_qs + txs.qs;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    float2 y_df[mmq_x/nwarps];
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+
+        y_df[j0/nwarps] = __half22float2(y_ds[j*MMQ_TILE_Y_K]);
+    }
+
+#pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR2_K*VDR_Q2_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                if (k01 < WARP_SIZE/2) {
+                    constexpr int ns = 2;
+                    sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q2_K_q8_1_impl_mmq<ns>(
+                        &x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01],
+                        &x_dm[i*(WARP_SIZE + 1) + k0/4], k01 < WARP_SIZE/2 ? y_df[j0/nwarps].x : y_df[j0/nwarps].y,
+                        &y_ds[j*MMQ_TILE_Y_K + (1 + k01/QI8_1)]);
+                } else {
+                    constexpr int ns = 1;
+                    sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q2_K_q8_1_impl_mmq<ns>(
+                        &x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01],
+                        &x_dm[i*(WARP_SIZE + 1) + k0/4], k01 < WARP_SIZE/2 ? y_df[j0/nwarps].x : y_df[j0/nwarps].y,
+                        &y_ds[j*MMQ_TILE_Y_K + (1 + k01/QI8_1)]);
+                }
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+#ifdef INT8_MMA_AVAILABLE
+
+    typedef mma_int_A_I16K4 mma_A;
+    typedef mma_int_A_I16K8 mma_A_K8;
+    typedef mma_int_B_J8K4  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    constexpr int granularity = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
+
+    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
+
+    const int   * x_qs = (const int   *) x;
+    const half2 * x_dm = (const half2 *) x_qs + WARP_SIZE*2;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
+
+    mma_A   A[ntx][8];
+    float  dA[ntx][mma_C::ne/2][8];
+    float  mA[ntx][mma_C::ne/2][8];
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+            const int k0 = k00 + k01;
+
+            ((mma_A_K8 *) A[n])[k01/QI8_1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q2_K + k0, MMQ_MMA_TILE_X_K_Q2_K);
+        }
+    }
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+
+#pragma unroll
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1/2) {
+                const int k0 = k00 + k01;
+
+                const float2 dm = __half22float2(x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + k0/(QI8_1/2)]);
+
+                dA[n][l][k01/(QI8_1/2)] = dm.x;
+                mA[n][l][k01/(QI8_1/2)] = dm.y;
+            }
+        }
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
+        float2 dB[mma_C::ne/2];
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int j = j0 + mma_C::get_j(l);
+
+            dB[l] = __half22float2(y_ds[j*MMQ_TILE_Y_K]);
+        }
+
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+            mma_B B[2];
+
+            B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
+            B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
+
+            mma_C Cm[2];
+            if (k01 >= WARP_SIZE * 3/4) {
+                mma_A A1;
+                A1.x[0] = 0x01010101;
+                A1.x[1] = 0x01010101;
+                Cm[0].mma_K4(A1, B[0]);
+                Cm[1].mma_K4(A1, B[1]);
+            }
+
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+                mma_C Cd[2];
+
+                Cd[0].mma_K4(A[n][k01/4 + 0], B[0]);
+                Cd[1].mma_K4(A[n][k01/4 + 1], B[1]);
+
+#pragma unroll
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    float tmp = Cd[0].x[l]*dA[n][l/2][k01/4 + 0] + Cd[1].x[l]*dA[n][l/2][k01/4 + 1];
+                    if (k01 >= WARP_SIZE * 3/4) {
+                        tmp -= Cm[0].x[l]*mA[n][l/2][k01/4 + 0] + Cm[1].x[l]*mA[n][l/2][k01/4 + 1];
+                    }
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += tmp*(k01 < WARP_SIZE/2 ? dB[l%2].x : dB[l%2].y);
+                }
+            }
+        }
+
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE * 3/4; k01 += QI8_1) {
+            float2 sB[mma_C::ne/2];
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                sB[l] = __half22float2(y_ds[j*MMQ_TILE_Y_K + (1 + k01/QI8_1)]);
+            }
+
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] -= mA[n][l/2][k01/4 + 0]*sB[l%2].x;
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] -= mA[n][l/2][k01/4 + 1]*sB[l%2].y;
+                }
+            }
+        }
+    }
+#else
+    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
+    NO_DEVICE_CODE;
+#endif // INT8_MMA_AVAILABLE
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q3_K, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+    int   * x_sc = (int   *) (x_df + txs.dm);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % QI3_K;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/QI3_K) {
+        int i = i0 + threadIdx.y * (WARP_SIZE/QI3_K) + threadIdx.x / QI3_K;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride;
+
+        const int x_ql_0 = get_int_b2(bxi->qs,    kqsx);
+        const int x_qh_0 = get_int_b2(bxi->hmask, kqsx % (QI3_K/2)) >> (4 * (kqsx / (QI3_K/2)));
+
+#pragma unroll
+        for (int l = 0; l < QR3_K; ++l) {
+            const int k = (kqsx/8)*32 + l*8 + kqsx % 8;
+
+            const int x_ql_k =  (x_ql_0 >> (2*l))       & 0x03030303;
+            const int x_qh_k = ((x_qh_0 >>    l)  << 2) & 0x04040404;
+
+            const int x_qs_k = __vsubss4(x_ql_k | x_qh_k, 0x04040404);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k] = x_qs_k;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + k] = x_qs_k;
+#endif // INT8_MMA_AVAILABLE
+        }
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*8) {
+        int i = i0 + threadIdx.y*8 + threadIdx.x/(WARP_SIZE/8);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride;
+
+        const int ksc = threadIdx.x % (WARP_SIZE/8);
+
+        const int ksc_low = ksc % (QI3_K/8);
+        const int shift_low = 4 * (ksc / (QI3_K/8));
+        const int sc_low = (get_int_b2(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
+
+        const int ksc_high = QI3_K/8;
+        const int shift_high = 2 * ksc;
+        const int sc_high = ((get_int_b2(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
+
+        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
+
+#ifdef INT8_MMA_AVAILABLE
+        const int8_t * sc8 = (const int8_t *) &sc;
+        const float d = bxi->d;
+
+#pragma unroll
+        for (int l = 0; l < sizeof(int); ++l) {
+            x_df[i*MMQ_MMA_TILE_X_K_Q3_K + sizeof(int)*(threadIdx.x % (WARP_SIZE/8)) + l] = d*sc8[l];
+        }
+#else
+        x_sc[i*(WARP_SIZE/8) + i/8 + threadIdx.x % (WARP_SIZE/8)] = sc;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+#ifndef INT8_MMA_AVAILABLE
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*WARP_SIZE) {
+        int i = (i0 + threadIdx.y*WARP_SIZE + threadIdx.x) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride;
+
+        x_df[i] = bxi->d;
+    }
+#endif // INT8_MMA_AVAILABLE
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q3_K_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q3_K, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + txs.qs;
+    const int   * x_sc = (const int   *) x_df + txs.dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR3_K*VDR_Q3_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                const int8_t * scales = ((const int8_t *) (x_sc + i*(WARP_SIZE/8) + i/8)) + k0/4;
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q3_K_q8_1_impl_mmq(
+                    &x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01], scales,
+                    x_df[i], y_df[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+static __device__ __forceinline__ int unpack_scales_q45_K(const int * scales, const int ksc) {
+    // scale arrangement after the following two lines:
+    //   - ksc == 0: sc0, sc1, sc2, sc3
+    //   - ksc == 1: sc4, sc5, sc6, sc7
+    //   - ksc == 2:  m0,  m1,  m2,  m3
+    //   - ksc == 3:  m4,  m5,  m6,  m7
+    return ((scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F) | // lower 4 bits
+           ((scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030);  // upper 2 bits
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_K, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + txs.qs);
+    int   * x_sc = (int   *) (x_dm + txs.dm);
+#endif // INT8_MMA_AVAILABLE
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
+        const int qs0 = get_int_b4(bxi->qs, threadIdx.x);
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(threadIdx.x/8) + threadIdx.x % 8 + 0] = (qs0 >> 0) & 0x0F0F0F0F;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(threadIdx.x/8) + threadIdx.x % 8 + 8] = (qs0 >> 4) & 0x0F0F0F0F;
+#else
+        x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+#ifdef INT8_MMA_AVAILABLE
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*16) {
+        int i = (i0 + threadIdx.y*16 + threadIdx.x/(WARP_SIZE/16)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
+
+        const int * scales = (const int *) bxi->scales;
+        const int ksc = threadIdx.x % (WARP_SIZE/16);
+
+        const int sc32 = unpack_scales_q45_K(scales, ksc + 0);
+        const int  m32 = unpack_scales_q45_K(scales, ksc + 2);
+
+        const uint8_t * sc8 = (const uint8_t *) &sc32;
+        const uint8_t *  m8 = (const uint8_t *)  &m32;
+
+        const half2 dm = bxi->dm * make_half2(1.0f, -1.0f);
+
+#pragma unroll
+        for (int l = 0; l < sizeof(int); ++l) {
+            x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + sizeof(int)*ksc + l] = dm*make_half2(sc8[l], m8[l]);
+        }
+    }
+
+#else
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*QI4_K) {
+        int i = (i0 + threadIdx.y*QI4_K + threadIdx.x) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
+
+        x_dm[i] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / (QI4_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = threadIdx.x % (WARP_SIZE/8);
+        const int scales8 = unpack_scales_q45_K(scales, ksc);
+
+        x_sc[i*(WARP_SIZE/8) + i/8 + ksc] = scales8;
+    }
+#endif // INT8_MMA_AVAILABLE
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_K, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const half2 * x_dm = (const half2 *) x_qs + txs.qs;
+    const int   * x_sc = (const int   *) x_dm + txs.dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_K*VDR_Q4_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                const uint8_t * sc = (const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/32] + 2*(k01/16);
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_K_q8_1_impl_mmq(
+                    &x_qs[i*(WARP_SIZE + 1) + k0/2], &y_qs[j*MMQ_TILE_Y_K + k01], sc, sc+8,
+                    x_dm[i], &y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_K, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_dm = (half2 *) (x_qs + txs.qs);
+    int   * x_sc = (int   *) (x_dm + txs.dm);
+#endif // INT8_MMA_AVAILABLE
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride;
+        const int ky = QR5_K*threadIdx.x;
+
+        const int ql = get_int_b4(bxi->qs, threadIdx.x);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_b4(bxi->qh, threadIdx.x % (QI5_K/4));
+        const int qh0 = ((qh >> (2 * (threadIdx.x / (QI5_K/4)) + 0)) << 4) & 0x10101010;
+        const int qh1 = ((qh >> (2 * (threadIdx.x / (QI5_K/4)) + 1)) << 4) & 0x10101010;
+
+        const int kq0 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + 0;
+        const int kq1 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + QI5_K/4;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq0] = ql0 | qh0;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq1] = ql1 | qh1;
+#else
+        x_qs[i*(2*WARP_SIZE + 1)     + kq0] = ql0 | qh0;
+        x_qs[i*(2*WARP_SIZE + 1)     + kq1] = ql1 | qh1;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+#ifdef INT8_MMA_AVAILABLE
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*16) {
+        int i = (i0 + threadIdx.y*16 + threadIdx.x/(WARP_SIZE/16)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride;
+
+        const int * scales = (const int *) bxi->scales;
+        const int ksc = threadIdx.x % (WARP_SIZE/16);
+
+        const int sc32 = unpack_scales_q45_K(scales, ksc + 0);
+        const int  m32 = unpack_scales_q45_K(scales, ksc + 2);
+
+        const uint8_t * sc8 = (const uint8_t *) &sc32;
+        const uint8_t *  m8 = (const uint8_t *)  &m32;
+
+        const half2 dm = bxi->dm * make_half2(1.0f, -1.0f);
+
+#pragma unroll
+        for (int l = 0; l < sizeof(int); ++l) {
+            x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + sizeof(int)*ksc + l] = dm*make_half2(sc8[l], m8[l]);
+        }
+    }
+
+#else
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*QI5_K) {
+        int i = (i0 + threadIdx.y*QI5_K + threadIdx.x) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride;
+
+        x_dm[i] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*8) {
+        int i = (i0 + threadIdx.y*8 + threadIdx.x/(WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride;
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = threadIdx.x % (WARP_SIZE/8);
+        const int scales8 = unpack_scales_q45_K(scales, ksc);
+
+        x_sc[i*(WARP_SIZE/8) + i/8 + ksc] = scales8;
+    }
+#endif // INT8_MMA_AVAILABLE
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_K, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const half2 * x_dm = (const half2 *) x_qs + txs.qs;
+    const int   * x_sc = (const int   *) x_dm + txs.dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR5_K*VDR_Q5_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k00/32]) + 2*(k01/16);
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q5_K_q8_1_impl_mmq(
+                    &x_qs[i*(QR5_K*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01], sc, sc+8,
+                    x_dm[i], &y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+    int   * x_sc = (int   *) (x_df + WARP_SIZE/QI6_K);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q6_K, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+    int   * x_sc = (int   *) (x_df + txs.dm);
+#endif // INT8_MMA_AVAILABLE
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride;
+
+        const int ql = get_int_b2(bxi->ql, threadIdx.x);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_b2(bxi->qh, (QI6_K/4) * (threadIdx.x / (QI6_K/2)) + threadIdx.x % (QI6_K/4));
+        const int qh0 = ((qh >> ((threadIdx.x & 0x08) >> 2)) << 4) & 0x30303030;
+        const int qh1 =  (qh >> ((threadIdx.x & 0x08) >> 2))       & 0x30303030;
+
+        const int kq0 = 2*threadIdx.x - threadIdx.x % (QI6_K/2) + 0;
+        const int kq1 = 2*threadIdx.x - threadIdx.x % (QI6_K/2) + QI6_K/2;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
+        x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
+#else
+        x_qs[i*(2*WARP_SIZE + 1)     + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
+        x_qs[i*(2*WARP_SIZE + 1)     + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K;  // == 1 if QK_K == 256
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
+        int i = (i0 + threadIdx.y * QI6_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbxd;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q6_K       + kbxd] = bxi->d;
+#else
+        x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K + kbxd] = bxi->d;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / 4;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + threadIdx.x % (WARP_SIZE/8)] = get_int_b2(bxi->scales, threadIdx.x % (QI6_K/8));
+#else
+        x_sc[i*(WARP_SIZE/8) + i/8   + threadIdx.x % (WARP_SIZE/8)] = get_int_b2(bxi->scales, threadIdx.x % (QI6_K/8));
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q6_K, mmq_y);
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + txs.qs;
+    const int   * x_sc = (const int   *) x_df + txs.dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR6_K*VDR_Q6_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]);
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q6_K_q8_1_impl_mmq(
+                    &x_qs[i*(QR6_K*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01], sc,
+                    x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K], &y_df[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+#ifdef INT8_MMA_AVAILABLE
+
+    typedef mma_int_A_I16K4 mma_A;
+    typedef mma_int_B_J8K4  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    constexpr int granularity = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
+
+    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
+
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + WARP_SIZE*2;
+    const int   * x_sc = (const int   *) x_df + WARP_SIZE/QI6_K;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
+
+    mma_A   A[ntx][8];
+    int   scA[ntx][mma_C::ne/2][8];
+    float  dA[ntx][mma_C::ne/2];
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
+            const int k0 = k00 + k01;
+
+            A[n][k01/4 + 0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + 0),        MMQ_MMA_TILE_X_K_Q6_K);
+            A[n][k01/4 + 1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + mma_A::K), MMQ_MMA_TILE_X_K_Q6_K);
+        }
+
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 16) {
+            const int k0 = k00 + k01;
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+
+                const int      sc_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + k0/16];
+                const int8_t * sc        = (const int8_t *) &sc_packed;
+
+#pragma unroll
+                for (int ksc = 0; ksc < sizeof(int); ++ksc) {
+                    scA[n][l][k01/4 + ksc] = sc[ksc];
+                }
+            }
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+
+            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q6_K];
+        }
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
+        float tmp[ntx][mma_C::ne] = {{0.0f}};
+
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
+            mma_B B[2];
+            float dB[mma_C::ne/2];
+
+            B[0].load(y_qs + j0*MMQ_TILE_Y_K + 0        + k01, MMQ_TILE_Y_K);
+            B[1].load(y_qs + j0*MMQ_TILE_Y_K + mma_B::K + k01, MMQ_TILE_Y_K);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dB[l] = y_df[j*MMQ_TILE_Y_K + k01/QI8_1];
+            }
+
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C[2];
+                C[0].mma_K4(A[n][k01/4 + 0], B[0]);
+                C[1].mma_K4(A[n][k01/4 + 1], B[1]);
+
+#pragma unroll
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    tmp[n][l] += (C[0].x[l]*scA[n][l/2][k01/4 + 0] + C[1].x[l]*scA[n][l/2][k01/4 + 1])*dB[l%2];
+                }
+            }
+        }
+
+#pragma unroll
+        for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+            for (int l = 0; l < mma_C::ne; ++l) {
+                sum[(j0/mma_C::J + n)*mma_C::ne + l] += tmp[n][l]*dA[n][l/2];
+            }
+        }
+    }
+#else
+    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
+    NO_DEVICE_CODE;
+#endif // INT8_MMA_AVAILABLE
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq4_nl(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_NL, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kbx  = threadIdx.x / QI4_NL;
+    const int kqsx = threadIdx.x % QI4_NL;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbx;
+
+        const int aux_q4 = get_int_b2(bxi->qs, kqsx);
+        const int2 v = get_int_from_table_16(aux_q4);
+        const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4;
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
+#else
+        x_qs[i*(2*WARP_SIZE + 1)     + k0 + 0] = v.x;
+        x_qs[i*(2*WARP_SIZE + 1)     + k0 + 4] = v.y;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_NL;
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_NL) {
+        int i = i0 + threadIdx.y * QI4_NL + threadIdx.x / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbxd;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = __half2float(bxi->d);
+#else
+        x_df[i*(WARP_SIZE/4) + i/4   + kbxd] = __half2float(bxi->d);
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_xxs(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_XXS, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI2_XXS/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI2_XXS/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI2_XXS) + threadIdx.x/(QI2_XXS/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq2_xxs * bxi = (const block_iq2_xxs *) x + kbx0 + i*stride;
+
+        const int q2 = get_int_b2(bxi->qs, 2*kqsx+0);
+        const uint8_t * aux8 = (const uint8_t *) &q2;
+        const uint32_t aux32 = get_int_b2(bxi->qs, 2*kqsx+1);
+
+#pragma unroll
+        for (int l = 0; l < QR2_XXS; ++l) {
+            const int * grid_pos = (const int *) (iq2xxs_grid + aux8[l]);
+            const int signs_packed = ksigns_iq2xs[(aux32 >> (7*l)) & 0x7F];
+
+            const int signs0 = __vcmpne4(((signs_packed & 0x03) << 7) | ((signs_packed & 0x0C) << 21), 0x00000000);
+            const int grid0 = __vsub4(grid_pos[0] ^ signs0, signs0);
+
+            const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000);
+            const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid0;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid1;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid0;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid1;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = aux32 >> 28;
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/4;
+#else
+        x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = (ls*d + d/2)/4;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_xs(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16;
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI2_XS/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI2_XS/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI2_XS) + threadIdx.x/(QI2_XS/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq2_xs * bxi = (const block_iq2_xs *) x + kbx0 + i*stride;
+
+        const int2 q2_packed = make_int2(get_int_b2(bxi->qs, 2*kqsx+0), get_int_b2(bxi->qs, 2*kqsx+1));
+        const uint16_t * q2 = (const uint16_t *) &q2_packed;
+
+    #pragma unroll
+        for (int l = 0; l < QR2_XS; ++l) {
+            const uint32_t * grid_pos = (const uint32_t *)(iq2xs_grid + (q2[l] & 0x000001FF));
+            const uint32_t * signs    = (const uint32_t *)(ksigns64   + (q2[l] >> 9));
+
+            const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]);
+            const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = bxi->scales[kqsx];
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
+#else
+        x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
+        x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_s(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_S, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI2_S/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI2_S/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI2_S) + threadIdx.x/(QI2_S/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq2_s * bxi = (const block_iq2_s *) x + kbx0 + i*stride;
+
+        const int       qs_packed = get_int_b2(bxi->qs, kqsx);
+        const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+        const int qh = bxi->qh[kqsx];
+
+        const int       signs_packed_32 = get_int_b2(bxi->qs, QK_K/32 + kqsx);
+        const uint8_t * signs_packed_8  = (const uint8_t *) &signs_packed_32;
+
+#pragma unroll
+        for (int l = 0; l < QR2_S; ++l) {
+            const int * grid_pos = (const int *)(iq2s_grid + (qs[l] | ((qh << (8-2*l)) & 0x300)));
+
+            const int signs0 = __vcmpne4(((signs_packed_8[l] & 0x03) << 7) | ((signs_packed_8[l] & 0x0C) << 21), 0x00000000);
+            const int signs1 = __vcmpne4(((signs_packed_8[l] & 0x30) << 3) | ((signs_packed_8[l] & 0xC0) << 17), 0x00000000);
+
+            const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0);
+            const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = bxi->scales[kqsx];
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
+#else
+        x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
+        x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq3_xxs(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_XXS, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI3_XXS/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI3_XXS/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI3_XXS) + threadIdx.x/(QI3_XXS/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq3_xxs * bxi = (const block_iq3_xxs *) x + kbx0 + i*stride;
+
+        const int2 q3_packed = make_int2(get_int_b2(bxi->qs, 2*kqsx+0), get_int_b2(bxi->qs, 2*kqsx+1));
+        const uint8_t * q3 = (const uint8_t *) &q3_packed;
+        const uint32_t aux32 = get_int_b2(bxi->qs, QK_K/16 + kqsx);
+
+#pragma unroll
+        for (int l = 0; l < QR3_XXS; ++l) {
+            const int2 grid_pos = make_int2(iq3xxs_grid[q3[2*l+0]], iq3xxs_grid[q3[2*l+1]]);
+
+            const int * signs = (const int *)(ksigns64 + ((aux32 >> (7*l)) & 0x7F));
+
+            const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]);
+            const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid_h;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = aux32 >> 28;
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/2;
+#else
+        x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = (ls*d + d/2)/2;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq3_s(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI3_S/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI3_S/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI3_S) + threadIdx.x/(QI3_S/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq3_s * bxi = (const block_iq3_s *) x + kbx0 + i*stride;
+
+        const int2      qs_packed = make_int2(get_int_b2(bxi->qs, 2*kqsx+0), get_int_b2(bxi->qs, 2*kqsx+1));
+        const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+        const int qh = bxi->qh[kqsx];
+
+        const int       signs_packed_32 = get_int_b2(bxi->signs, kqsx);
+        const uint8_t * signs_packed_8  = (const uint8_t *) &signs_packed_32;
+
+#pragma unroll
+        for (int l = 0; l < QR3_S; ++l) {
+            const int2 grid_pos = make_int2(
+                iq3s_grid[qs[2*l+0] | ((qh << (8 - 2*l)) & 0x100)],
+                iq3s_grid[qs[2*l+1] | ((qh << (7 - 2*l)) & 0x100)]);
+
+            const int signs0 = __vcmpne4(((signs_packed_8[l] & 0x03) << 7) | ((signs_packed_8[l] & 0x0C) << 21), 0x00000000);
+            const int signs1 = __vcmpne4(((signs_packed_8[l] & 0x30) << 3) | ((signs_packed_8[l] & 0xC0) << 17), 0x00000000);
+
+            const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0);
+            const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+0)] = grid_l;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+1)] = grid_h;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+0)] = grid_l;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+1)] = grid_h;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = 1 + 2*((bxi->scales[kqsx/2] >> (((2*kqsx) << 1) & 0x04)) & 0x0F);
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = ls*d;
+#else
+        x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = ls*d;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq1_s(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_ds = (half2 *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_ds = (half2 *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % QI1_S;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/QI1_S) {
+        int i = i0 + threadIdx.y*(WARP_SIZE/QI1_S) + threadIdx.x/QI1_S;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq1_s * bxi = (const block_iq1_s *) x + kbx0 + i*stride;
+
+        const int       qs_packed = get_int_b2(bxi->qs, kqsx);
+        const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+        const int qh = bxi->qh[kqsx];
+
+    #pragma unroll
+        for (int l = 0; l < QR1_S/2; ++l) {
+            const int grid = iq1s_grid_gpu[qs[l] | (((qh >> (3*l)) & 0x07) << 8)];
+
+            const int grid0 = (grid >> 0) & 0x0F0F0F0F;
+            const int grid1 = (grid >> 4) & 0x0F0F0F0F;
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+0)] = grid0;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+1)] = grid1;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+0)] = grid0;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+1)] = grid1;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const float  d1q   = __half2float(bxi->d) * (((qh >> 11) & 0x0E) + 1);
+        const float  delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000);
+
+#ifdef INT8_MMA_AVAILABLE
+        x_ds[i*MMQ_MMA_TILE_X_K_Q8_1 + kqsx] = make_half2(d1q, d1q*delta);
+#else
+        x_ds[i*(WARP_SIZE/4) + i/4   + kqsx] = make_half2(d1q, d1q*delta);
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq4_xs(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_XS, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kbx  = 0;           // threadIdx.x / QI4_XS
+    const int kqsx = threadIdx.x; // threadIdx.x % QI4_XS
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq4_xs * bxi = (const block_iq4_xs *) x + kbx0 + i*stride + kbx;
+
+        const int aux_q4 = get_int_b4(bxi->qs, kqsx);
+        const int2 v = get_int_from_table_16(aux_q4);
+        const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4;
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
+#else
+        x_qs[i*(2*WARP_SIZE + 1)     + k0 + 0] = v.x;
+        x_qs[i*(2*WARP_SIZE + 1)     + k0 + 4] = v.y;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + threadIdx.y * 4 + threadIdx.x / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq4_xs * bxi = (const block_iq4_xs *) x + kbx0 + i*stride;
+
+        const float d = __half2float(bxi->d);
+
+        const int ls = ((bxi->scales_l[(threadIdx.x % 8)/2] >> (4*(threadIdx.x % 2))) & 0x0F)
+            | (((bxi->scales_h >> (2*(threadIdx.x % 8))) & 0x03) << 4);
+
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + threadIdx.x % 8] = d * (ls - 32);
+#else
+        x_df[i*(WARP_SIZE/4) + i/4   + threadIdx.x % 8] = d * (ls - 32);
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template<int mmq_x, int mmq_y, int nwarps, bool need_check>
+static __device__ __forceinline__ void mmq_write_back_dp4a(
+    const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) {
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+
+        if (j > j_max) {
+            return;
+        }
+
+#pragma unroll
+        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+
+            if (need_check && i > i_max) {
+                continue;
+            }
+
+            dst[j*stride + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE];
+        }
+    }
+}
+
+template<int mmq_x, int mmq_y, int nwarps, bool need_check>
+static __device__ __forceinline__ void mmq_write_back_mma(
+    const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) {
+
+    typedef mma_int_C_I16J8 mma_C;
+
+    constexpr int granularity = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
+
+    const int i0 = (threadIdx.y / ntx) * (ntx*mma_C::I);
+#ifdef INT8_MMA_AVAILABLE
+    static_assert(nwarps*mma_C::I == mmq_y, "nwarps*mma_C::I != mmq_y");
+#endif // INT8_MMA_AVAILABLE
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
+#pragma unroll
+        for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+            for (int l = 0; l < mma_C::ne; ++l) {
+                const int j = j0 + (threadIdx.y % ntx) * mma_C::J + mma_C::get_j(l);
+
+                if (j > j_max) {
+                    continue;
+                }
+
+                const int i = i0 + n*mma_C::I + mma_C::get_i(l);
+
+                if (need_check && i > i_max) {
+                    continue;
+                }
+
+                dst[j*stride + i] = sum[(j0/mma_C::J + n)*mma_C::ne + l];
+            }
+        }
+    }
+}
+
+// -------------------------------------------------------------------------------------------------------------------------------------
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check, ggml_type type>
+struct mmq_type_traits;
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_0> {
+    static constexpr int              vdr          = VDR_Q4_0_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q4_0<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_DS4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q4_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_1> {
+    static constexpr int              vdr          = VDR_Q4_1_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q4_1<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q4_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_0> {
+    static constexpr int              vdr          = VDR_Q5_0_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q5_0<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_1> {
+    static constexpr int              vdr          = VDR_Q5_1_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q5_1<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q8_0> {
+    static constexpr int              vdr          = VDR_Q8_0_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q8_0<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q2_K> {
+    static constexpr int              vdr          = VDR_Q2_K_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q2_K<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q2_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q2_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q3_K> {
+    static constexpr int              vdr          = VDR_Q3_K_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q3_K<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q3_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_K> {
+    static constexpr int              vdr          = VDR_Q4_K_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q4_K<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q4_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_K> {
+    static constexpr int              vdr          = VDR_Q5_K_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q5_K<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q5_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q6_K> {
+    static constexpr int              vdr          = VDR_Q6_K_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q6_K<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q6_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q6_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ2_XXS> {
+    static constexpr int              vdr          = VDR_IQ2_XXS_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq2_xxs<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ2_XS> {
+    static constexpr int              vdr          = VDR_IQ2_XS_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq2_xs<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_16_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ2_S> {
+    static constexpr int              vdr          = VDR_IQ2_S_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq2_s<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_16_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ3_XXS> {
+    static constexpr int              vdr          = VDR_IQ3_XXS_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq3_xxs<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ3_S> {
+    static constexpr int              vdr          = VDR_IQ3_S_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq3_s<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ1_S> {
+    static constexpr int              vdr          = VDR_IQ1_S_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq1_s<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ4_NL> {
+    static constexpr int              vdr          = VDR_IQ4_NL_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq4_nl<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ4_XS> {
+    static constexpr int              vdr          = VDR_IQ4_XS_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq4_xs<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <ggml_type type, int mmq_x, int nwarps, bool need_check, bool fixup>
+static __device__ void mul_mat_q_process_tile(
+    const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup,
+    const int & ne00, const int & ne01, const int & stride01, const int & ne10, const int & ne11, const int & stride11, const int & ne0,
+    const int & it, const int & jt, const int & kb0_start, const int & kb0_stop) {
+
+    constexpr int              qk         = ggml_cuda_type_traits<type>::qk;
+    constexpr int              mmq_y      = get_mmq_y_device();
+    constexpr load_tiles_mmq_t load_tiles = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::load_tiles;
+
+    extern __shared__ char data_mul_mat_q[];
+    int * tile_y = (int *) data_mul_mat_q;
+    int * tile_x = tile_y + GGML_PAD(mmq_x*(WARP_SIZE + WARP_SIZE/QI8_1), nwarps*WARP_SIZE);
+
+#ifdef INT8_MMA_AVAILABLE
+    constexpr vec_dot_mmq_t    vec_dot    = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot_mma;
+    constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    constexpr vec_dot_mmq_t    vec_dot    = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot_dp4a;
+    constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
+
+    constexpr int blocks_per_iter = MMQ_ITER_K / qk;
+
+    float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f};
+
+    const int tile_x_max_i = ne01 - it*mmq_y - 1;
+    const int tile_y_max_j = ne11 - jt*mmq_x - 1;
+
+    const int * y = (const int *) yc + jt*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int));
+
+    for (int kb0 = kb0_start; kb0 < kb0_stop; kb0 += blocks_per_iter) {
+        load_tiles(x, tile_x, stride01*it*mmq_y + kb0, tile_x_max_i, stride01);
+
+        {
+            const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + 0*sizeof(block_q8_1_mmq)/sizeof(int));
+#pragma unroll
+            for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) {
+                int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x;
+
+                tile_y[l] = by0[l];
+            }
+        }
+
+        __syncthreads();
+
+        vec_dot(tile_x, tile_y, sum, 0);
+
+        __syncthreads();
+
+        {
+            const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + 1*sizeof(block_q8_1_mmq)/sizeof(int));
+#pragma unroll
+            for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) {
+                int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x;
+
+                tile_y[l] = by0[l];
+            }
+        }
+
+        __syncthreads();
+
+        vec_dot(tile_x, tile_y, sum, WARP_SIZE);
+
+        __syncthreads();
+    }
+
+    if (fixup) {
+        write_back(sum, tmp_fixup + blockIdx.x*(mmq_x*mmq_y), mmq_y, mmq_y, mmq_x);
+    } else {
+        write_back(sum, dst + jt*mmq_x*ne0 + it*mmq_y, ne0, tile_x_max_i, tile_y_max_j);
+    }
+}
+
+
+// The mul_mat_q kernel implements "stream-k" work partitioning as described in https://arxiv.org/abs/2301.03598
+
+template <ggml_type type, int mmq_x, int nwarps, bool need_check>
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*nwarps, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#else
+#if __CUDA_ARCH__ >= CC_VOLTA
+    __launch_bounds__(WARP_SIZE*nwarps, 1)
+#else
+    __launch_bounds__(WARP_SIZE*nwarps, 2)
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+static __global__ void mul_mat_q(
+    const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup,
+    const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) {
+
+    // Skip unused template specializations for faster compilation:
+    if (mmq_x > get_mmq_x_max_device() || mmq_x % mmq_get_granularity_device(mmq_x) != 0) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
+    constexpr int qk    = ggml_cuda_type_traits<type>::qk;
+    constexpr int mmq_y = get_mmq_y_device();
+
+    // On AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead:
+#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA
+    {
+        constexpr bool fixup = false;
+        mul_mat_q_process_tile<type, mmq_x, nwarps, need_check, fixup>
+            (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0,
+                blockIdx.x, blockIdx.y, 0, ne00/qk);
+        return;
+    }
+#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA
+
+    const     int64_t blocks_per_ne00 = ne00 / qk;
+    constexpr int     blocks_per_iter = MMQ_ITER_K / qk;
+
+    const int ntx = (ne11 + mmq_x - 1) / mmq_x; // Number of tiles x
+    const int nty = (ne01 + mmq_y - 1) / mmq_y; // Number of tiles y
+
+    // kbc == k block continuous, current index in continuous ijk space.
+    int64_t kbc      = (int64_t) blockIdx.x     *blocks_per_ne00*ntx*nty / gridDim.x;
+    int64_t kbc_stop = (int64_t)(blockIdx.x + 1)*blocks_per_ne00*ntx*nty / gridDim.x;
+
+    kbc      -= (kbc      % blocks_per_ne00) % blocks_per_iter;
+    kbc_stop -= (kbc_stop % blocks_per_ne00) % blocks_per_iter;
+
+    // kb0 == k index when doing the matrix multiplication for an output tile.
+    int kb0_start = kbc % blocks_per_ne00;
+    int kb0_stop  = min(blocks_per_ne00, kb0_start + kbc_stop - kbc);
+    while (kbc < kbc_stop && kb0_stop == blocks_per_ne00) {
+        const int jt =  kbc /    (blocks_per_ne00*nty);                    // j index of current tile.
+        const int it = (kbc - jt*(blocks_per_ne00*nty)) / blocks_per_ne00; // i index of current tile.
+
+        constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
+        mul_mat_q_process_tile<type, mmq_x, nwarps, need_check, fixup>
+            (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0,
+             it, jt, kb0_start, kb0_stop);
+
+        kbc += blocks_per_ne00;
+        kbc -= kbc % blocks_per_ne00;
+
+        kb0_start = 0;
+        kb0_stop  = min(blocks_per_ne00, kbc_stop - kbc);
+    }
+
+    if (kbc >= kbc_stop) {
+        return;
+    }
+
+    const int jt =  kbc /    (blocks_per_ne00*nty);
+    const int it = (kbc - jt*(blocks_per_ne00*nty)) / blocks_per_ne00;
+
+    constexpr bool fixup = true; // Last index writes it data to fixup buffer to avoid data races with other blocks.
+    mul_mat_q_process_tile<type, mmq_x, nwarps, need_check, fixup>
+        (x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0,
+            it, jt, kb0_start, kb0_stop);
+}
+
+
+template <ggml_type type, int mmq_x, int nwarps, bool need_check>
+static __global__ void mul_mat_q_stream_k_fixup(
+    float * __restrict__ dst, const float * __restrict__ tmp_last_tile, const int ne00, const int ne01, const int ne11, const int ne0, const int block_num_mmq) {
+
+    constexpr int     mmq_y           = get_mmq_y_device();
+    constexpr int     qk              = ggml_cuda_type_traits<type>::qk;
+    constexpr int     blocks_per_iter = MMQ_ITER_K / qk;
+    const     int64_t blocks_per_ne00 = ne00 / qk;
+
+    float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f};
+
+    const int ntx = (ne11 + mmq_x - 1) / mmq_x;
+    const int nty = (ne01 + mmq_y - 1) / mmq_y;
+
+    bool any_fixup = false;
+
+    const int bidx_start = ((blockIdx.y*nty + blockIdx.x)     * block_num_mmq)                           / (gridDim.y*gridDim.x);
+    const int bidx_stop  = ((blockIdx.y*nty + blockIdx.x + 1) * block_num_mmq + gridDim.y*gridDim.x - 1) / (gridDim.y*gridDim.x);
+
+    int64_t kbc_0;
+    int64_t kbc_stop_0 = (int64_t) bidx_start*blocks_per_ne00*ntx*nty / block_num_mmq;
+
+    for (int bidx = bidx_start; bidx < bidx_stop; ++bidx) {
+        kbc_0 = kbc_stop_0;
+        kbc_stop_0 = (int64_t) (bidx + 1)*blocks_per_ne00*ntx*nty / block_num_mmq;
+
+        const int64_t kbc      = kbc_0      - (kbc_0      % blocks_per_ne00) % blocks_per_iter;
+        const int64_t kbc_stop = kbc_stop_0 - (kbc_stop_0 % blocks_per_ne00) % blocks_per_iter;
+
+        // Skip fixup tile if the MMQ CUDA block never wrote anything to it:
+        if (kbc == kbc_stop || kbc_stop % blocks_per_ne00 == 0) {
+            continue;
+        }
+
+        const int jt =  kbc_stop /    (blocks_per_ne00*nty);
+        const int it = (kbc_stop - jt*(blocks_per_ne00*nty)) / blocks_per_ne00;
+
+        // Skip fixup tile if it's unrelated to the output tile assigned to this CUDA block:
+        if (it != blockIdx.x || jt != blockIdx.y) {
+            continue;
+        }
+
+        any_fixup = true;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE] += tmp_last_tile[bidx*(mmq_x*mmq_y) + j*mmq_y + i];
+            }
+        }
+    }
+
+    if (!any_fixup) {
+        return;
+    }
+
+    dst += blockIdx.y*mmq_x*ne0 + blockIdx.x*mmq_y;
+
+    const int i_max = ne01 - blockIdx.x*mmq_y - 1;
+    const int j_max = ne11 - blockIdx.y*mmq_x - 1;
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+
+        if (j > j_max) {
+            return;
+        }
+
+#pragma unroll
+        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+
+            if (need_check && i > i_max) {
+                continue;
+            }
+
+            dst[j*ne0 + i] += sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE];
+        }
+    }
+}
+
+struct mmq_args {
+    const char * x; const char * y; float * dst;
+    int64_t ne00; int64_t ne01; int64_t stride01;
+    int64_t ne10; int64_t ne11; int64_t stride11;
+    int64_t ne0;
+};
+
+template<ggml_type type>
+static int mmq_get_shmem(const int mmq_x, const int mmq_y, const int cc) {
+    const tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(type, mmq_y);
+    const int mmq_tile_x_k = mmq_get_mma_tile_x_k(type);
+    const int shmem_x = int8_mma_available(cc) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int);
+    const int shmem_y = mmq_x*sizeof(block_q8_1_mmq);
+    return shmem_x + GGML_PAD(shmem_y, MMQ_NWARPS*WARP_SIZE*sizeof(int));
+}
+
+template <ggml_type type, int mmq_x>
+static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
+    const int id = ggml_cuda_get_device();
+    const int cc = ggml_cuda_info().devices[id].cc;
+    const int nsm = ggml_cuda_info().devices[id].nsm;
+    const int mmq_y = get_mmq_y_host(cc);
+
+    const dim3 block_dims(WARP_SIZE, MMQ_NWARPS, 1);
+
+    const int shmem = mmq_get_shmem<type>(mmq_x, mmq_y, cc);
+
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+    static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
+    if (!shmem_limit_raised[id]) {
+        CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, MMQ_NWARPS, false>, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
+        CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, MMQ_NWARPS, true>,  cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
+        shmem_limit_raised[id] = true;
+    }
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+
+    const int nty = (args.ne01 + mmq_y - 1) / mmq_y;
+    const int ntx = (args.ne11 + mmq_x - 1) / mmq_x;
+    const dim3 block_nums_xy_tiling(nty, ntx, 1);
+
+    const bool use_stream_k = cc >= CC_VOLTA && cc < CC_OFFSET_AMD;
+    if (!use_stream_k) {
+        if (args.ne01 % mmq_y == 0) {
+            constexpr bool need_check = false;
+            mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, shmem, stream>>>
+                (args.x, args.y, args.dst, nullptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+        } else {
+            constexpr bool need_check = true;
+            mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, shmem, stream>>>
+                (args.x, args.y, args.dst, nullptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+        }
+        return;
+    }
+
+    const dim3 block_nums_mmq(nsm, 1, 1);
+
+    ggml_cuda_pool & pool = ctx.pool(id);
+    ggml_cuda_pool_alloc<float> tmp_fixup(pool, block_nums_mmq.x * mmq_x*mmq_y);
+
+    if (args.ne01 % mmq_y == 0) {
+        constexpr bool need_check = false;
+
+        mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_mmq, block_dims, shmem, stream>>>
+            (args.x, args.y, args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+
+        mul_mat_q_stream_k_fixup<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, 0, stream>>>
+            (args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.ne11, args.ne0, block_nums_mmq.x);
+    } else {
+        constexpr bool need_check = true;
+
+        mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_mmq, block_dims, shmem, stream>>>
+            (args.x, args.y, args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
+
+        mul_mat_q_stream_k_fixup<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, 0, stream>>>
+            (args.dst, tmp_fixup.ptr, args.ne00, args.ne01, args.ne11, args.ne0, block_nums_mmq.x);
+    }
+}
+
+template <ggml_type type>
+void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
+    const int id    = ggml_cuda_get_device();
+    const int nsm   = ggml_cuda_info().devices[id].nsm;
+    const int cc    = ggml_cuda_info().devices[id].cc;
+    const int smpbo = ggml_cuda_info().devices[id].smpbo;
+
+    const int mmq_x_max = get_mmq_x_max_host(cc);
+    const int mmq_y = get_mmq_y_host(cc);
+    const int block_num_y = (args.ne01 + mmq_y - 1) / mmq_y;
+    const bool use_stream_k = cc >= CC_VOLTA && cc < CC_OFFSET_AMD;
+
+    int mmq_x_best  = 0;
+    int nparts_best = INT_MAX;
+
+    for (int mmq_x = 8; mmq_x <= mmq_x_max && nparts_best > 1; mmq_x += 8) {
+        const int granularity = mmq_get_granularity_host(mmq_x, cc);
+
+        if (mmq_x % granularity != 0 || mmq_get_shmem<type>(mmq_x, mmq_y, cc) > smpbo) {
+            continue;
+        }
+
+        const int ntiles_x = (args.ne11 + mmq_x - 1) / mmq_x;
+        const int nwaves_xy_tiling = ntiles_x*block_num_y;
+        const int nparts = use_stream_k ? ntiles_x : nwaves_xy_tiling;
+
+        if (nparts < nparts_best) {
+            mmq_x_best  = mmq_x;
+            nparts_best = nparts;
+        }
+    }
+
+    switch (mmq_x_best) {
+        case   8:
+            launch_mul_mat_q<type,   8>(ctx, args, stream);
+            break;
+        case  16:
+            launch_mul_mat_q<type,  16>(ctx, args, stream);
+            break;
+        case  24:
+            launch_mul_mat_q<type,  24>(ctx, args, stream);
+            break;
+        case  32:
+            launch_mul_mat_q<type,  32>(ctx, args, stream);
+            break;
+        case  40:
+            launch_mul_mat_q<type,  40>(ctx, args, stream);
+            break;
+        case  48:
+            launch_mul_mat_q<type,  48>(ctx, args, stream);
+            break;
+        case  56:
+            launch_mul_mat_q<type,  56>(ctx, args, stream);
+            break;
+        case  64:
+            launch_mul_mat_q<type,  64>(ctx, args, stream);
+            break;
+        case  72:
+            launch_mul_mat_q<type,  72>(ctx, args, stream);
+            break;
+        case  80:
+            launch_mul_mat_q<type,  80>(ctx, args, stream);
+            break;
+        case  88:
+            launch_mul_mat_q<type,  88>(ctx, args, stream);
+            break;
+        case  96:
+            launch_mul_mat_q<type,  96>(ctx, args, stream);
+            break;
+        case 104:
+            launch_mul_mat_q<type, 104>(ctx, args, stream);
+            break;
+        case 112:
+            launch_mul_mat_q<type, 112>(ctx, args, stream);
+            break;
+        case 120:
+            launch_mul_mat_q<type, 120>(ctx, args, stream);
+            break;
+        case 128:
+            launch_mul_mat_q<type, 128>(ctx, args, stream);
+            break;
+        default:
+            fprintf(stderr, "mmq_x_best=%d\n", mmq_x_best);
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+
+#define DECL_MMQ_CASE(type)                                                        \
+    template void mul_mat_q_case<type>(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) \
+
+extern DECL_MMQ_CASE(GGML_TYPE_Q4_0);
+extern DECL_MMQ_CASE(GGML_TYPE_Q4_1);
+extern DECL_MMQ_CASE(GGML_TYPE_Q5_0);
+extern DECL_MMQ_CASE(GGML_TYPE_Q5_1);
+extern DECL_MMQ_CASE(GGML_TYPE_Q8_0);
+extern DECL_MMQ_CASE(GGML_TYPE_Q2_K);
+extern DECL_MMQ_CASE(GGML_TYPE_Q3_K);
+extern DECL_MMQ_CASE(GGML_TYPE_Q4_K);
+extern DECL_MMQ_CASE(GGML_TYPE_Q5_K);
+extern DECL_MMQ_CASE(GGML_TYPE_Q6_K);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XXS);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XS);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ2_S);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ3_XXS);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ3_S);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ1_S);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ4_NL);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ4_XS);
+
+// -------------------------------------------------------------------------------------------------------------------------
+
+void ggml_cuda_op_mul_mat_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
+
+bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11);
diff --git a/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu
similarity index 68%
rename from ggml-cuda/mmvq.cu
rename to ggml/src/ggml-cuda/mmvq.cu
index 65cc1bcaad697..7dbbc993903c3 100644
--- a/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -1,9 +1,53 @@
 #include "mmvq.cuh"
 #include "vecdotq.cuh"
 
-typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
+typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs);
+
+static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) {
+    return type == GGML_TYPE_Q4_0 ? vec_dot_q4_0_q8_1 :
+        type == GGML_TYPE_Q4_1 ? vec_dot_q4_1_q8_1 :
+        type == GGML_TYPE_Q5_0 ? vec_dot_q5_0_q8_1 :
+        type == GGML_TYPE_Q5_1 ? vec_dot_q5_1_q8_1 :
+        type == GGML_TYPE_Q8_0 ? vec_dot_q8_0_q8_1 :
+        type == GGML_TYPE_Q2_K ? vec_dot_q2_K_q8_1 :
+        type == GGML_TYPE_Q3_K ? vec_dot_q3_K_q8_1 :
+        type == GGML_TYPE_Q4_K ? vec_dot_q4_K_q8_1 :
+        type == GGML_TYPE_Q5_K ? vec_dot_q5_K_q8_1 :
+        type == GGML_TYPE_Q6_K ? vec_dot_q6_K_q8_1 :
+        type == GGML_TYPE_IQ2_XXS ? vec_dot_iq2_xxs_q8_1 :
+        type == GGML_TYPE_IQ2_XS ? vec_dot_iq2_xs_q8_1 :
+        type == GGML_TYPE_IQ2_S ? vec_dot_iq2_s_q8_1 :
+        type == GGML_TYPE_IQ3_XXS ? vec_dot_iq3_xxs_q8_1 :
+        type == GGML_TYPE_IQ1_S ? vec_dot_iq1_s_q8_1 :
+        type == GGML_TYPE_IQ1_M ? vec_dot_iq1_m_q8_1 :
+        type == GGML_TYPE_IQ4_NL ? vec_dot_iq4_nl_q8_1 :
+        type == GGML_TYPE_IQ4_XS ? vec_dot_iq4_xs_q8_1 :
+        type == GGML_TYPE_IQ3_S ? vec_dot_iq3_s_q8_1 :
+        nullptr;
+}
+
+static constexpr __device__ int get_vdr_mmvq(ggml_type type) {
+    return type == GGML_TYPE_Q4_0 ? VDR_Q4_0_Q8_1_MMVQ :
+        type == GGML_TYPE_Q4_1    ? VDR_Q4_1_Q8_1_MMVQ :
+        type == GGML_TYPE_Q5_0    ? VDR_Q5_0_Q8_1_MMVQ :
+        type == GGML_TYPE_Q5_1    ? VDR_Q5_1_Q8_1_MMVQ :
+        type == GGML_TYPE_Q8_0    ? VDR_Q8_0_Q8_1_MMVQ :
+        type == GGML_TYPE_Q2_K    ? VDR_Q2_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q3_K    ? VDR_Q3_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q4_K    ? VDR_Q4_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q5_K    ? VDR_Q5_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q6_K    ? VDR_Q6_K_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ2_XXS ? VDR_IQ2_XXS_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ2_XS  ? VDR_IQ2_XS_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ2_S   ? VDR_IQ2_S_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ3_XXS ? VDR_IQ3_XXS_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ3_S   ? VDR_IQ3_S_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ4_NL  ? VDR_IQ4_NL_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ4_XS  ? VDR_IQ4_XS_Q8_1_MMVQ :
+        1;
+}
 
-template <int ncols_y, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
+template <ggml_type type, int ncols_y>
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
 // tell the compiler to use as many registers as it wants, see nwarps definition below
 __launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1)
@@ -12,6 +56,12 @@ static __global__ void mul_mat_vec_q(
     const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) {
 
+    constexpr int qk  = ggml_cuda_type_traits<type>::qk;
+    constexpr int qi  = ggml_cuda_type_traits<type>::qi;
+    constexpr int vdr = get_vdr_mmvq(type);
+
+    constexpr vec_dot_q_cuda_t vec_dot_q_cuda = get_vec_dot_q_cuda(type);
+
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3))
     constexpr int nwarps              = 1;
     constexpr int rows_per_cuda_block = 1;
@@ -29,7 +79,6 @@ static __global__ void mul_mat_vec_q(
 // partial sum for each thread
     float tmp[ncols_y][rows_per_cuda_block] = {0.0f};
 
-    const block_q_t  * x = (const block_q_t  *) vx;
     const block_q8_1 * y = (const block_q8_1 *) vy;
 
     for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
@@ -42,8 +91,7 @@ static __global__ void mul_mat_vec_q(
         for (int j = 0; j < ncols_y; ++j) {
 #pragma unroll
             for (int i = 0; i < rows_per_cuda_block; ++i) {
-                tmp[j][i] += vec_dot_q_cuda(
-                    &x[kbx + (row0 + i)*blocks_per_row_x], &y[j*blocks_per_col_y + kby], kqs);
+                tmp[j][i] += vec_dot_q_cuda(vx, &y[j*blocks_per_col_y + kby], (row0 + i)*blocks_per_row_x + kbx, kqs);
             }
         }
     }
@@ -75,18 +123,18 @@ static __global__ void mul_mat_vec_q(
             tmp[j][i] = warp_reduce_sum(tmp[j][i]);
         }
 
-        if (threadIdx.x < rows_per_cuda_block) {
+        if (threadIdx.x < rows_per_cuda_block && (rows_per_cuda_block == 1 || row0 + threadIdx.x < nrows_dst)) {
             dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x];
         }
     }
 }
 
-template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot>
+template <ggml_type type>
 static void mul_mat_vec_q_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    GGML_ASSERT(ncols_x % qk == 0);
+    GGML_ASSERT(ncols_x % ggml_blck_size(type) == 0);
     GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE);
 
     int id = ggml_cuda_get_device();
@@ -114,7 +162,7 @@ static void mul_mat_vec_q_cuda(
                 rows_per_cuda_block = 2;
                 break;
             default:
-                GGML_ASSERT(false);
+                GGML_ABORT("fatal error");
                 break;
         }
     }
@@ -124,39 +172,31 @@ static void mul_mat_vec_q_cuda(
 
     switch (ncols_y) {
         case 1:
-            mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            mul_mat_vec_q<type, 1><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
             break;
         case 2:
-            mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            mul_mat_vec_q<type, 2><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
             break;
         case 3:
-            mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            mul_mat_vec_q<type, 3><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
             break;
         case 4:
-            mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            mul_mat_vec_q<type, 4><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
             break;
         case 5:
-            mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            mul_mat_vec_q<type, 5><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
             break;
         case 6:
-            mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            mul_mat_vec_q<type, 6><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
             break;
         case 7:
-            mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            mul_mat_vec_q<type, 7><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
             break;
         case 8:
-            mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
-                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            mul_mat_vec_q<type, 8><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
             break;
         default:
-            GGML_ASSERT(false);
+            GGML_ABORT("fatal error");
             break;
     }
 }
@@ -165,152 +205,133 @@ static void mul_mat_vec_q4_0_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q4_0>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q4_1_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q4_1>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q5_0_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q5_0>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q5_1_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q5_1>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q8_0_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q8_0>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q2_K_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q2_K>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q3_K_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q3_K>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q4_K_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q4_K>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q5_K_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q5_K>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_q6_K_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_Q6_K>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq2_xxs_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ2_XXS>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq2_xs_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ2_XS>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq2_s_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_s_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ2_S>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq3_xxs_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ3_XXS>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq1_s_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ1_S>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq1_m_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ1_M>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq4_nl_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ4_NL>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq4_xs_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ4_XS>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 static void mul_mat_vec_iq3_s_q8_1_cuda(
     const void * vx, const void * vy, float * dst,
     const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
 
-    mul_mat_vec_q_cuda<QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
-        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+    mul_mat_vec_q_cuda<GGML_TYPE_IQ3_S>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
 }
 
 void ggml_cuda_op_mul_mat_vec_q(
@@ -392,7 +413,7 @@ void ggml_cuda_op_mul_mat_vec_q(
             mul_mat_vec_iq3_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
             break;
         default:
-            GGML_ASSERT(false);
+            GGML_ABORT("fatal error");
             break;
     }
 
diff --git a/ggml-cuda/mmvq.cuh b/ggml/src/ggml-cuda/mmvq.cuh
similarity index 83%
rename from ggml-cuda/mmvq.cuh
rename to ggml/src/ggml-cuda/mmvq.cuh
index 88c42c4b7a8fb..d9e42fdd6d16c 100644
--- a/ggml-cuda/mmvq.cuh
+++ b/ggml/src/ggml-cuda/mmvq.cuh
@@ -1,5 +1,7 @@
 #include "common.cuh"
 
+#define MMVQ_MAX_BATCH_SIZE 8 // Max. batch size for which to use MMVQ kernels.
+
 void ggml_cuda_op_mul_mat_vec_q(
     ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
diff --git a/ggml-cuda/norm.cu b/ggml/src/ggml-cuda/norm.cu
similarity index 95%
rename from ggml-cuda/norm.cu
rename to ggml/src/ggml-cuda/norm.cu
index 86f7745344994..133e219f0aeda 100644
--- a/ggml-cuda/norm.cu
+++ b/ggml/src/ggml-cuda/norm.cu
@@ -142,8 +142,7 @@ static void norm_f32_cuda(const float * x, float * dst, const int ncols, const i
     }
 }
 
-static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) {
-    static const float eps = 1e-6f;
+static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const float eps, const int group_size, const int ne_elements, cudaStream_t stream) {
     if (group_size < 1024) {
         const dim3 block_dims(WARP_SIZE, 1, 1);
         group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
@@ -170,6 +169,8 @@ void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -188,12 +189,18 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
     int num_groups = dst->op_params[0];
+
+    float eps;
+    memcpy(&eps, dst->op_params + 1, sizeof(float));
+
     int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
-    group_norm_f32_cuda(src0_d, dst_d, num_groups * src0->ne[3], group_size, ggml_nelements(src0), stream);
+    group_norm_f32_cuda(src0_d, dst_d, num_groups * src0->ne[3], eps, group_size, ggml_nelements(src0), stream);
 }
 
 void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -202,6 +209,8 @@ void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
diff --git a/ggml-cuda/norm.cuh b/ggml/src/ggml-cuda/norm.cuh
similarity index 100%
rename from ggml-cuda/norm.cuh
rename to ggml/src/ggml-cuda/norm.cuh
diff --git a/ggml-cuda/pad.cu b/ggml/src/ggml-cuda/pad.cu
similarity index 100%
rename from ggml-cuda/pad.cu
rename to ggml/src/ggml-cuda/pad.cu
diff --git a/ggml-cuda/pad.cuh b/ggml/src/ggml-cuda/pad.cuh
similarity index 100%
rename from ggml-cuda/pad.cuh
rename to ggml/src/ggml-cuda/pad.cuh
diff --git a/ggml-cuda/pool2d.cu b/ggml/src/ggml-cuda/pool2d.cu
similarity index 100%
rename from ggml-cuda/pool2d.cu
rename to ggml/src/ggml-cuda/pool2d.cu
diff --git a/ggml-cuda/pool2d.cuh b/ggml/src/ggml-cuda/pool2d.cuh
similarity index 100%
rename from ggml-cuda/pool2d.cuh
rename to ggml/src/ggml-cuda/pool2d.cuh
diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu
new file mode 100644
index 0000000000000..45408ce8684e4
--- /dev/null
+++ b/ggml/src/ggml-cuda/quantize.cu
@@ -0,0 +1,169 @@
+#include "quantize.cuh"
+#include <cstdint>
+
+static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx0_padded) {
+    const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (ix0 >= kx0_padded) {
+        return;
+    }
+
+    const int64_t ix1 = blockIdx.y;
+
+    const int64_t i_padded = ix1*kx0_padded + ix0;
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int64_t ib = i_padded / QK8_1; // block index
+    const int64_t iqs = i_padded % QK8_1; // quant index
+
+    const float xi = ix0 < kx ? x[ix1*kx + ix0] : 0.0f;
+    float amax = fabsf(xi);
+    float sum = xi;
+
+    amax = warp_reduce_max(amax);
+    sum = warp_reduce_sum(sum);
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    reinterpret_cast<half&>(y[ib].ds.x) = d;
+    reinterpret_cast<half&>(y[ib].ds.y) = sum;
+}
+
+template <mmq_q8_1_ds_layout ds_layout>
+static __global__ void quantize_mmq_q8_1(
+    const float * __restrict__ x, void * __restrict__ vy, const int64_t kx0, const int64_t kx1, const int64_t kx0_padded) {
+
+    constexpr int vals_per_scale = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 64 : 32;
+    constexpr int vals_per_sum   = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 16 : 32;
+
+    const int64_t ix0 = ((int64_t)blockDim.x*blockIdx.x + threadIdx.x)*4;
+
+    if (ix0 >= kx0_padded) {
+        return;
+    }
+
+    const float4 * x4 = (const float4 *) x;
+
+    const int64_t ix1 = kx1*blockIdx.z + blockIdx.y;
+
+    block_q8_1_mmq * y = (block_q8_1_mmq *) vy;
+
+    const int64_t ib0 = blockIdx.z*((int64_t)gridDim.y*gridDim.x*blockDim.x/QK8_1); // first block of channel
+    const int64_t ib  = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y;                   // block index in channel
+    const int64_t iqs = ix0 % (4*QK8_1);                                            // quant index in block
+
+    // Load 4 floats per thread and calculate max. abs. value between them:
+    const float4 xi = ix0 < kx0 ? x4[(ix1*kx0 + ix0)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f);
+    float amax = fabsf(xi.x);
+    amax = fmaxf(amax, fabsf(xi.y));
+    amax = fmaxf(amax, fabsf(xi.z));
+    amax = fmaxf(amax, fabsf(xi.w));
+
+    // Exchange max. abs. value between vals_per_scale/4 threads.
+#pragma unroll
+    for (int mask = vals_per_scale/8; mask > 0; mask >>= 1) {
+        amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, WARP_SIZE));
+    }
+
+    float sum;
+    if (ds_layout != MMQ_Q8_1_DS_LAYOUT_D4) {
+        sum = xi.x + xi.y + xi.z + xi.w;
+
+        // Exchange calculate sum across vals_per_sum/4 threads.
+#pragma unroll
+        for (int mask = vals_per_sum/8; mask > 0; mask >>= 1) {
+            sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, WARP_SIZE);
+        }
+    }
+
+    const float d_inv = 127.0f / amax;
+    char4 q;
+    q.x = roundf(xi.x*d_inv);
+    q.y = roundf(xi.y*d_inv);
+    q.z = roundf(xi.z*d_inv);
+    q.w = roundf(xi.w*d_inv);
+
+    // Write back 4 int8 values as a single 32 bit value for better memroy bandwidth:
+    char4 * yqs4 = (char4 *) y[ib].qs;
+    yqs4[iqs/4] = q;
+
+    if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6) {
+        if (iqs % 16 != 0 || iqs >= 96) {
+            return;
+        }
+
+        y[ib].d2s6[2 + iqs/16] = sum;
+
+        if (iqs % 64 != 0) {
+            return;
+        }
+
+        const float d = 1.0f / d_inv;
+
+        y[ib].d2s6[iqs/64] = d;
+
+        return;
+    }
+
+    if (iqs % 32 != 0) {
+        return;
+    }
+
+    const float d = 1.0f / d_inv;
+
+    if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) {
+        y[ib].ds4[iqs/32] = make_half2(d, sum);
+    } else {
+        y[ib].d4[iqs/32]  = d;
+    }
+}
+
+void quantize_row_q8_1_cuda(
+    const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels,
+    const int64_t kx0_padded, const ggml_type type_x, cudaStream_t stream) {
+
+    GGML_ASSERT(kx0_padded % QK8_1 == 0);
+
+    const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    const dim3 num_blocks(block_num_x, kx1*channels, 1);
+    const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
+    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx0_padded);
+
+    GGML_UNUSED(type_x);
+}
+
+void quantize_mmq_q8_1_cuda(
+    const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels,
+    const int64_t kx0_padded, const ggml_type type_x, cudaStream_t stream) {
+
+    GGML_ASSERT(kx0_padded % (4*QK8_1) == 0);
+
+    const int64_t block_num_x = (kx0_padded + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ);
+    const dim3 num_blocks(block_num_x, kx1, channels);
+    const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
+    switch (mmq_get_q8_1_ds_layout(type_x)) {
+        case MMQ_Q8_1_DS_LAYOUT_D4:
+            quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D4>
+                <<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+            break;
+        case MMQ_Q8_1_DS_LAYOUT_DS4:
+            quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_DS4>
+                <<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+            break;
+        case MMQ_Q8_1_DS_LAYOUT_D2S6:
+            quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D2S6>
+                <<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
diff --git a/ggml/src/ggml-cuda/quantize.cuh b/ggml/src/ggml-cuda/quantize.cuh
new file mode 100644
index 0000000000000..03bf322b95873
--- /dev/null
+++ b/ggml/src/ggml-cuda/quantize.cuh
@@ -0,0 +1,24 @@
+#pragma once
+
+#include "common.cuh"
+#include "mmq.cuh"
+
+#include <cstdint>
+
+#define CUDA_QUANTIZE_BLOCK_SIZE     256
+#define CUDA_QUANTIZE_BLOCK_SIZE_MMQ 128
+
+static_assert(MATRIX_ROW_PADDING %    CUDA_QUANTIZE_BLOCK_SIZE      == 0, "Risk of out-of-bounds access.");
+static_assert(MATRIX_ROW_PADDING % (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ) == 0, "Risk of out-of-bounds access.");
+
+typedef void (*quantize_cuda_t)(
+    const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded,
+    const ggml_type type_x, cudaStream_t stream);
+
+void quantize_row_q8_1_cuda(
+    const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded,
+    const ggml_type type_x, cudaStream_t stream);
+
+void quantize_mmq_q8_1_cuda(
+    const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded,
+    const ggml_type type_x, cudaStream_t stream);
diff --git a/ggml/src/ggml-cuda/rope.cu b/ggml/src/ggml-cuda/rope.cu
new file mode 100644
index 0000000000000..88f586d689cfd
--- /dev/null
+++ b/ggml/src/ggml-cuda/rope.cu
@@ -0,0 +1,271 @@
+#include "rope.cuh"
+
+struct rope_corr_dims {
+    float v[2];
+};
+
+static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static __device__ void rope_yarn(
+    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
+    *cos_theta = cosf(theta) * mscale;
+    *sin_theta = sinf(theta) * mscale;
+}
+
+template<typename T, bool has_ff>
+static __global__ void rope_norm(
+    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors) {
+    const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i  = row*ne0 + i0;
+    const int i2 = row/p_delta_rows;
+
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+    float cos_theta;
+    float sin_theta;
+
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + 1];
+
+    dst[i + 0] = x0*cos_theta - x1*sin_theta;
+    dst[i + 1] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T, bool has_ff>
+static __global__ void rope_neox(
+    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors) {
+    const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i  = row*ne0 + i0/2;
+    const int i2 = row/p_delta_rows;
+
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+    float cos_theta;
+    float sin_theta;
+
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + n_dims/2];
+
+    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T>
+static void rope_norm_cuda(
+    const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nr, n_blocks_x, 1);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    if (freq_factors == nullptr) {
+        rope_norm<T, false><<<block_nums, block_dims, 0, stream>>>(
+                x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+                theta_scale, freq_factors
+                );
+    } else {
+        rope_norm<T, true><<<block_nums, block_dims, 0, stream>>>(
+                x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+                theta_scale, freq_factors
+                );
+    }
+}
+
+template<typename T>
+static void rope_neox_cuda(
+    const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nr, n_blocks_x, 1);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    if (freq_factors == nullptr) {
+        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
+                x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+                theta_scale, freq_factors
+                );
+    } else {
+        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
+                x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+                theta_scale, freq_factors
+                );
+    }
+}
+
+static void rope_norm_cuda_f16(
+    const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+
+    rope_norm_cuda<half>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
+}
+
+static void rope_norm_cuda_f32(
+    const float * x, float * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+
+    rope_norm_cuda<float>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
+}
+
+static void rope_neox_cuda_f16(
+    const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+
+    rope_neox_cuda<half>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
+}
+
+static void rope_neox_cuda_f32(
+    const float * x, float * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream
+) {
+
+    rope_neox_cuda<float>(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
+}
+
+void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];
+
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == dst->type);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t nr = ggml_nrows(src0);
+
+    //const int n_past     = ((int32_t *) dst->op_params)[0];
+    const int n_dims     = ((int32_t *) dst->op_params)[1];
+    const int mode       = ((int32_t *) dst->op_params)[2];
+    //const int n_ctx      = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
+
+    // RoPE alteration for extended context
+    float freq_base;
+    float freq_scale;
+    float ext_factor;
+    float attn_factor;
+    float beta_fast;
+    float beta_slow;
+
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+
+    const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+
+    const int32_t * pos = (const int32_t *) src1_d;
+
+    const float * freq_factors = nullptr;
+    if (src2 != nullptr) {
+        freq_factors = (const float *) src2->data;
+    }
+
+    rope_corr_dims corr_dims;
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v);
+
+    // compute
+    if (is_neox) {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_neox_cuda_f32(
+                (const float *)src0_d, (float *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_neox_cuda_f16(
+                (const half *)src0_d, (half *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, stream
+            );
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    } else {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_norm_cuda_f32(
+                (const float *)src0_d, (float *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_norm_cuda_f16(
+                (const half *)src0_d, (half *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, stream
+            );
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    }
+}
diff --git a/ggml-cuda/rope.cuh b/ggml/src/ggml-cuda/rope.cuh
similarity index 100%
rename from ggml-cuda/rope.cuh
rename to ggml/src/ggml-cuda/rope.cuh
diff --git a/ggml-cuda/scale.cu b/ggml/src/ggml-cuda/scale.cu
similarity index 100%
rename from ggml-cuda/scale.cu
rename to ggml/src/ggml-cuda/scale.cu
diff --git a/ggml-cuda/scale.cuh b/ggml/src/ggml-cuda/scale.cuh
similarity index 100%
rename from ggml-cuda/scale.cuh
rename to ggml/src/ggml-cuda/scale.cuh
diff --git a/ggml-cuda/softmax.cu b/ggml/src/ggml-cuda/softmax.cu
similarity index 99%
rename from ggml-cuda/softmax.cu
rename to ggml/src/ggml-cuda/softmax.cu
index ce64f2f2ce28b..c24abae1f138c 100644
--- a/ggml-cuda/softmax.cu
+++ b/ggml/src/ggml-cuda/softmax.cu
@@ -130,6 +130,7 @@ static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, cons
     const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
+    // FIXME: this limit could be raised by ~2-4x on Ampere or newer
     if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
         switch (ncols_x) {
             case 32:
diff --git a/ggml-cuda/softmax.cuh b/ggml/src/ggml-cuda/softmax.cuh
similarity index 100%
rename from ggml-cuda/softmax.cuh
rename to ggml/src/ggml-cuda/softmax.cuh
diff --git a/ggml-cuda/sumrows.cu b/ggml/src/ggml-cuda/sumrows.cu
similarity index 100%
rename from ggml-cuda/sumrows.cu
rename to ggml/src/ggml-cuda/sumrows.cu
diff --git a/ggml-cuda/sumrows.cuh b/ggml/src/ggml-cuda/sumrows.cuh
similarity index 100%
rename from ggml-cuda/sumrows.cuh
rename to ggml/src/ggml-cuda/sumrows.cuh
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu
new file mode 100644
index 0000000000000..6696a238476d8
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu
new file mode 100644
index 0000000000000..dd070db2853f5
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu
new file mode 100644
index 0000000000000..54dcde6f52324
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu
new file mode 100644
index 0000000000000..4ec22f791912d
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu
new file mode 100644
index 0000000000000..3c15bf7f0ef16
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu
new file mode 100644
index 0000000000000..7e61b5fdcdbca
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu
new file mode 100644
index 0000000000000..fdb15b580cff8
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu
new file mode 100644
index 0000000000000..0f7c417d2c0c8
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu
new file mode 100644
index 0000000000000..851f33c43f040
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu
new file mode 100644
index 0000000000000..763809cbeb44c
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu
new file mode 100644
index 0000000000000..f2a276e50e5fa
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu
new file mode 100644
index 0000000000000..cb227f6f5ce1f
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu
new file mode 100644
index 0000000000000..97ac0520c71d1
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu
new file mode 100644
index 0000000000000..c772b42634fe6
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu
new file mode 100644
index 0000000000000..5cb7430819e4e
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu
new file mode 100644
index 0000000000000..98a709d171446
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu
new file mode 100644
index 0000000000000..4f2f947ae81e6
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu
new file mode 100644
index 0000000000000..11f96b6f65cee
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu
new file mode 100644
index 0000000000000..b39bdc0611c0d
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu
new file mode 100644
index 0000000000000..bbd6a2c7f491c
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu
new file mode 100644
index 0000000000000..9d84ff2b19175
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu
new file mode 100644
index 0000000000000..bc8a5bff684ff
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu
new file mode 100644
index 0000000000000..a679100c83807
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu
new file mode 100644
index 0000000000000..8f21bccf7f8da
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu
new file mode 100644
index 0000000000000..858b00fd74191
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu
new file mode 100644
index 0000000000000..0fc8011fac5fc
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu
new file mode 100644
index 0000000000000..261fdf623e098
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu
new file mode 100644
index 0000000000000..0fb8247383063
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu
new file mode 100644
index 0000000000000..a9d9d089bd314
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu
new file mode 100644
index 0000000000000..7d7b27920aa3e
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu
new file mode 100644
index 0000000000000..a092ee2d50957
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu
new file mode 100644
index 0000000000000..db55927a19457
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu
new file mode 100644
index 0000000000000..c3c21cefae047
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu
new file mode 100644
index 0000000000000..35dd9f520802c
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu
new file mode 100644
index 0000000000000..050c22ac7c6c7
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu
new file mode 100644
index 0000000000000..de4866c5e65ce
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu
new file mode 100644
index 0000000000000..57a10bc4be4a3
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu
new file mode 100644
index 0000000000000..e0f08b46a7e35
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu
new file mode 100644
index 0000000000000..1c8e8a467a8aa
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu
new file mode 100644
index 0000000000000..cefed83fb9562
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu
new file mode 100644
index 0000000000000..aede6e3588195
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu
new file mode 100644
index 0000000000000..1a1a92c788fbd
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu
new file mode 100644
index 0000000000000..ad667473d110b
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f16.cuh"
+
+DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu
new file mode 100644
index 0000000000000..c499f455da971
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu
new file mode 100644
index 0000000000000..8286ebf373627
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu
new file mode 100644
index 0000000000000..4587868825d21
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu
new file mode 100644
index 0000000000000..d89103ce0c68f
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu
new file mode 100644
index 0000000000000..bb75fd42ff17d
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu
new file mode 100644
index 0000000000000..b1629817e79e3
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu
new file mode 100644
index 0000000000000..d8657604dab80
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu
new file mode 100644
index 0000000000000..2e5bd2f1a3acc
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu
new file mode 100644
index 0000000000000..be5f302d9f1d4
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu
new file mode 100644
index 0000000000000..8dd91cd72eb60
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu
new file mode 100644
index 0000000000000..4cb791502a157
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu
new file mode 100644
index 0000000000000..09dea426736e9
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu
new file mode 100644
index 0000000000000..0fbb607694f25
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu
new file mode 100644
index 0000000000000..2aeab83b20d21
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu
new file mode 100644
index 0000000000000..599415b494741
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu
new file mode 100644
index 0000000000000..e4f8e3083bb6b
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu
new file mode 100644
index 0000000000000..34d166527e93a
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu
new file mode 100644
index 0000000000000..4bebef45a37cb
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu
new file mode 100644
index 0000000000000..326468da2fb24
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu
new file mode 100644
index 0000000000000..511b58f4ecc72
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu
new file mode 100644
index 0000000000000..d9906d142e159
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu
new file mode 100644
index 0000000000000..f61c183abbaf7
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu
new file mode 100644
index 0000000000000..c10450fd29e76
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu
new file mode 100644
index 0000000000000..2d5cb195c41dc
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu
new file mode 100644
index 0000000000000..b384f34d7d921
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu
new file mode 100644
index 0000000000000..446e293b16edc
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu
new file mode 100644
index 0000000000000..6f430298899c7
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu
new file mode 100644
index 0000000000000..1cd8ba88fd650
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu
new file mode 100644
index 0000000000000..1ee2eab65a1c9
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu
new file mode 100644
index 0000000000000..2bc77816a5d4e
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu
new file mode 100644
index 0000000000000..d55ced08bc940
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu
new file mode 100644
index 0000000000000..8361e99c4e4a4
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu
new file mode 100644
index 0000000000000..7507a67c4c5e9
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu
new file mode 100644
index 0000000000000..61f050b235ff2
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu
new file mode 100644
index 0000000000000..d4a49d9c9912a
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu
new file mode 100644
index 0000000000000..d146278976211
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu
new file mode 100644
index 0000000000000..e73f917a1f186
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu
new file mode 100644
index 0000000000000..d40825dfc21f0
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu
new file mode 100644
index 0000000000000..b5c6869f4ec42
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu
new file mode 100644
index 0000000000000..4e21b0ccaef16
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu
new file mode 100644
index 0000000000000..2eac321b370df
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu
new file mode 100644
index 0000000000000..f7d2c3b4e0a12
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu
new file mode 100644
index 0000000000000..a013f400bd33b
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f32.cuh"
+
+DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu
new file mode 100644
index 0000000000000..2d94e65c28c29
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu
@@ -0,0 +1,10 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-wmma-f16.cuh"
+
+DECL_FATTN_WMMA_F16_CASE(64, 16, float);
+DECL_FATTN_WMMA_F16_CASE(80, 16, float);
+DECL_FATTN_WMMA_F16_CASE(96, 16, float);
+DECL_FATTN_WMMA_F16_CASE(112, 16, float);
+DECL_FATTN_WMMA_F16_CASE(128, 16, float);
+DECL_FATTN_WMMA_F16_CASE(256, 16, float);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu
new file mode 100644
index 0000000000000..c3d9df3c44313
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu
@@ -0,0 +1,9 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-wmma-f16.cuh"
+
+DECL_FATTN_WMMA_F16_CASE(64, 32, float);
+DECL_FATTN_WMMA_F16_CASE(80, 32, float);
+DECL_FATTN_WMMA_F16_CASE(96, 32, float);
+DECL_FATTN_WMMA_F16_CASE(112, 32, float);
+DECL_FATTN_WMMA_F16_CASE(128, 32, float);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu
new file mode 100644
index 0000000000000..bb680e401f7da
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu
@@ -0,0 +1,10 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-wmma-f16.cuh"
+
+DECL_FATTN_WMMA_F16_CASE(64, 16, half);
+DECL_FATTN_WMMA_F16_CASE(80, 16, half);
+DECL_FATTN_WMMA_F16_CASE(96, 16, half);
+DECL_FATTN_WMMA_F16_CASE(112, 16, half);
+DECL_FATTN_WMMA_F16_CASE(128, 16, half);
+DECL_FATTN_WMMA_F16_CASE(256, 16, half);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu
new file mode 100644
index 0000000000000..073f71b1f3e26
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu
@@ -0,0 +1,10 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-wmma-f16.cuh"
+
+DECL_FATTN_WMMA_F16_CASE(64, 32, half);
+DECL_FATTN_WMMA_F16_CASE(80, 32, half);
+DECL_FATTN_WMMA_F16_CASE(96, 32, half);
+DECL_FATTN_WMMA_F16_CASE(112, 32, half);
+DECL_FATTN_WMMA_F16_CASE(128, 32, half);
+DECL_FATTN_WMMA_F16_CASE(256, 32, half);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu
new file mode 100644
index 0000000000000..d30710c5fa21b
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu
@@ -0,0 +1,8 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-wmma-f16.cuh"
+
+DECL_FATTN_WMMA_F16_CASE(64, 8, half);
+DECL_FATTN_WMMA_F16_CASE(96, 8, half);
+DECL_FATTN_WMMA_F16_CASE(128, 8, half);
+DECL_FATTN_WMMA_F16_CASE(256, 8, half);
diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
new file mode 100755
index 0000000000000..d7874e6eaf832
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+
+from glob import glob
+import os
+
+TYPES_KV = ["GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_F16"]
+
+SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-vec-f{vkq_size}.cuh"
+
+DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v});
+"""
+
+SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-wmma-f16.cuh"
+
+"""
+
+SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n"
+
+TYPES_MMQ = [
+    "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
+    "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K",
+    "GGML_TYPE_IQ2_XXS", "GGML_TYPE_IQ2_XS", "GGML_TYPE_IQ2_S", "GGML_TYPE_IQ3_XXS", "GGML_TYPE_IQ3_S",
+    "GGML_TYPE_IQ1_S", "GGML_TYPE_IQ4_NL", "GGML_TYPE_IQ4_XS"
+]
+
+SOURCE_MMQ = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE({type});
+"""
+
+
+def get_short_name(long_quant_name):
+    return long_quant_name.replace("GGML_TYPE_", "").lower()
+
+
+def get_head_sizes(type_k, type_v):
+    if type_k == "GGML_TYPE_F16" and type_v == "GGML_TYPE_F16":
+        return [64, 128, 256]
+    if type_k == "GGML_TYPE_F16":
+        return [64, 128]
+    return [128]
+
+
+for filename in glob("*.cu"):
+    os.remove(filename)
+
+for vkq_size in [16, 32]:
+    for type_k in TYPES_KV:
+        for type_v in TYPES_KV:
+            for head_size in get_head_sizes(type_k, type_v):
+                with open(f"fattn-vec-f{vkq_size}-instance-hs{head_size}-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f:
+                    f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v))
+
+for kq_acc_t in ["half", "float"]:
+    for cols_per_block in [8, 16, 32]:
+        if kq_acc_t == "float" and cols_per_block == 8:
+            continue
+
+        with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f:
+            f.write(SOURCE_FATTN_WMMA_START)
+
+            for head_size in [64, 80, 96, 112, 128, 256]:
+                if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32
+                    continue
+                if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance
+                    continue
+                f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size))
+
+for type in TYPES_MMQ:
+    with open(f"mmq-instance-{get_short_name(type)}.cu", "w") as f:
+        f.write(SOURCE_MMQ.format(type=type))
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq1_s.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq1_s.cu
new file mode 100644
index 0000000000000..84ec850294731
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq1_s.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ1_S);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_s.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_s.cu
new file mode 100644
index 0000000000000..583c4e5a51501
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_s.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ2_S);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xs.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xs.cu
new file mode 100644
index 0000000000000..edaf1560defdd
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xs.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ2_XS);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xxs.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xxs.cu
new file mode 100644
index 0000000000000..233d9342c9941
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xxs.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ2_XXS);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_s.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_s.cu
new file mode 100644
index 0000000000000..6092dc7136341
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_s.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ3_S);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_xxs.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_xxs.cu
new file mode 100644
index 0000000000000..1d5bd201fb5a9
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_xxs.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ3_XXS);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_nl.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_nl.cu
new file mode 100644
index 0000000000000..eb02fab002ecc
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_nl.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ4_NL);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_xs.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_xs.cu
new file mode 100644
index 0000000000000..1eb3b7430726f
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq4_xs.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ4_XS);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q2_k.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q2_k.cu
new file mode 100644
index 0000000000000..6415369dc1d95
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q2_k.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q2_K);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q3_k.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q3_k.cu
new file mode 100644
index 0000000000000..ffb6213af83ea
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q3_k.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q3_K);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_0.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_0.cu
new file mode 100644
index 0000000000000..0c0b0c8a8ed22
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q4_0);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_1.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_1.cu
new file mode 100644
index 0000000000000..ee67f6942a8fc
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q4_1);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_k.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_k.cu
new file mode 100644
index 0000000000000..9eeb3cd7f3cc1
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q4_k.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q4_K);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_0.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_0.cu
new file mode 100644
index 0000000000000..cc57fb9753c9e
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q5_0);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_1.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_1.cu
new file mode 100644
index 0000000000000..721ac790c44f4
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_1.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q5_1);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_k.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_k.cu
new file mode 100644
index 0000000000000..a2e90ffd5d0aa
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q5_k.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q5_K);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q6_k.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q6_k.cu
new file mode 100644
index 0000000000000..470938fef8a05
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q6_k.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q6_K);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q8_0.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q8_0.cu
new file mode 100644
index 0000000000000..974477bbb73a8
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q8_0.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q8_0);
diff --git a/ggml-cuda/tsembd.cu b/ggml/src/ggml-cuda/tsembd.cu
similarity index 100%
rename from ggml-cuda/tsembd.cu
rename to ggml/src/ggml-cuda/tsembd.cu
diff --git a/ggml-cuda/tsembd.cuh b/ggml/src/ggml-cuda/tsembd.cuh
similarity index 100%
rename from ggml-cuda/tsembd.cuh
rename to ggml/src/ggml-cuda/tsembd.cuh
diff --git a/ggml-cuda/unary.cu b/ggml/src/ggml-cuda/unary.cu
similarity index 87%
rename from ggml-cuda/unary.cu
rename to ggml/src/ggml-cuda/unary.cu
index ac03d5c6fce54..f9e208011e2a8 100644
--- a/ggml-cuda/unary.cu
+++ b/ggml/src/ggml-cuda/unary.cu
@@ -92,6 +92,15 @@ static __global__ void sqr_f32(const float * x, float * dst, const int k) {
     dst[i] = x[i] * x[i];
 }
 
+static __global__ void sqrt_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sqrtf(x[i]);
+}
+
 static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
     gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
@@ -142,12 +151,19 @@ static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t
     sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
 }
 
+static void sqrt_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SQRT_BLOCK_SIZE - 1) / CUDA_SQRT_BLOCK_SIZE;
+    sqrt_f32<<<num_blocks, CUDA_SQRT_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
 void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const float * src0_d = (const float *)src0->data;
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -160,6 +176,8 @@ void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -172,6 +190,8 @@ void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -184,6 +204,8 @@ void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -196,6 +218,8 @@ void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -208,6 +232,8 @@ void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -220,6 +246,8 @@ void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -232,6 +260,8 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -244,6 +274,8 @@ void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -259,8 +291,24 @@ void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
     sqr_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
 }
+
+void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    sqrt_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
diff --git a/ggml-cuda/unary.cuh b/ggml/src/ggml-cuda/unary.cuh
similarity index 90%
rename from ggml-cuda/unary.cuh
rename to ggml/src/ggml-cuda/unary.cuh
index a1d07c04fcd43..4cfb0479e7169 100644
--- a/ggml-cuda/unary.cuh
+++ b/ggml/src/ggml-cuda/unary.cuh
@@ -8,6 +8,7 @@
 #define CUDA_HARDSIGMOID_BLOCK_SIZE 256
 #define CUDA_HARDSWISH_BLOCK_SIZE 256
 #define CUDA_SQR_BLOCK_SIZE 256
+#define CUDA_SQRT_BLOCK_SIZE 256
 
 void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
@@ -28,3 +29,5 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/upscale.cu b/ggml/src/ggml-cuda/upscale.cu
similarity index 100%
rename from ggml-cuda/upscale.cu
rename to ggml/src/ggml-cuda/upscale.cu
diff --git a/ggml-cuda/upscale.cuh b/ggml/src/ggml-cuda/upscale.cuh
similarity index 100%
rename from ggml-cuda/upscale.cuh
rename to ggml/src/ggml-cuda/upscale.cuh
diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh
new file mode 100644
index 0000000000000..40091a0ef07b4
--- /dev/null
+++ b/ggml/src/ggml-cuda/vecdotq.cuh
@@ -0,0 +1,1133 @@
+#include "common.cuh"
+#include <cstdint>
+
+static __device__ __forceinline__ int get_int_b2(const void * x, const int & i32) {
+    const uint16_t * x16 = (const uint16_t *) x; // assume at least 2 byte alignment
+
+    int x32  = x16[2*i32 + 0] <<  0;
+    x32     |= x16[2*i32 + 1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32) {
+    return ((const int *) x)[i32]; // assume at least 4 byte alignment
+}
+
+// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
+// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
+
+#define VDR_Q4_0_Q8_1_MMVQ 2
+#define VDR_Q4_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
+    const int * v, const int * u, const float & d4, const half2 & ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi);
+        sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi);
+    }
+
+    const float2 ds8f = __half22float2(ds8);
+
+    // second part effectively subtracts 8 from each quant value
+    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
+}
+
+#define VDR_Q4_1_Q8_1_MMVQ 2
+#define VDR_Q4_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi);
+        sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi);
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
+    const float d4d8 = tmp.x;
+    const float m4s8 = tmp.y;
+#else
+    const float2 dm4f = __half22float2(dm4);
+    const float2 ds8f = __half22float2(ds8);
+    const float d4d8 = dm4f.x * ds8f.x;
+    const float m4s8 = dm4f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
+    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
+}
+
+#define VDR_Q5_0_Q8_1_MMVQ 2
+#define VDR_Q5_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+    const float2 ds8f = __half22float2(ds8);
+
+    // second part effectively subtracts 16 from each quant value
+    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
+}
+
+#define VDR_Q5_1_Q8_1_MMVQ 2
+#define VDR_Q5_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
+    const float d5d8 = tmp.x;
+    const float m5s8 = tmp.y;
+#else
+    const float2 dm5f = __half22float2(dm5);
+    const float2 ds8f = __half22float2(ds8);
+    const float d5d8 = dm5f.x * ds8f.x;
+    const float m5s8 = dm5f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
+    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
+}
+
+#define VDR_Q8_0_Q8_1_MMVQ 2
+#define VDR_Q8_0_Q8_1_MMQ 8
+
+template <typename T, int vdr> static __device__ __forceinline__ T vec_dot_q8_0_q8_1_impl(
+    const int * v, const int * u, const T & d8_0, const T & d8_1) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = ggml_cuda_dp4a(v[i], u[i], sumi);
+    }
+
+    return d8_0*d8_1 * ((T) sumi);
+}
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = ggml_cuda_dp4a(v[i], u[i], sumi);
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
+    const float d8d8 = tmp.x;
+    const float m8s8 = tmp.y;
+#else
+    const float2 dm8f = __half22float2(dm8);
+    const float2 ds8f = __half22float2(ds8);
+    const float d8d8 = dm8f.x * ds8f.x;
+    const float m8s8 = dm8f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
+    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
+}
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_16_q8_1_impl(
+    const int * v, const int * u, const float * d8_0, const float & d8_1) {
+
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < vdr; i0 += QI8_0/2) {
+        int sumi = 0;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_0/2; ++i) {
+            // SIMD dot product of quantized values
+            sumi = ggml_cuda_dp4a(v[i], u[i], sumi);
+        }
+
+        sumf += d8_0[i0/(QI8_0/2)]*sumi;
+    }
+
+    return d8_1*sumf;
+}
+
+#define VDR_Q2_K_Q8_1_MMVQ 1
+#define VDR_Q2_K_Q8_1_MMQ  4
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
+    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float * __restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++i) {
+        const int sc = scales[2*i];
+
+        const int vi = (v >> (2*i)) & 0x03030303;
+
+        sumf_d += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+        sumf_m += d8[i] * ggml_cuda_dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
+    }
+
+    const float2 dm2f = __half22float2(dm2);
+
+    return dm2f.x*sumf_d - dm2f.y*sumf_m;
+}
+
+// contiguous v/x + u/y values
+template <int ns8>
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const half2 * dm2, const float & d8, const half2 * s8) {
+
+    float sumf    = 0.0f;
+    float sumf_d8 = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QR2_K*VDR_Q2_K_Q8_1_MMQ; i0 += QI8_1) {
+        const float2 dm2f0 = __half22float2(dm2[i0/(QI8_1/2) + 0]);
+        int sumi_d0 = 0;
+
+        const float2 dm2f1 = __half22float2(dm2[i0/(QI8_1/2) + 1]);
+        int sumi_d1 = 0;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_d0 = ggml_cuda_dp4a(v[i], u[i], sumi_d0);
+        }
+        sumf_d8 += dm2f0.x * sumi_d0;
+
+#pragma unroll
+        for (int i = i0 + QI8_1/2; i < i0 + QI8_1; ++i) {
+            sumi_d1 = ggml_cuda_dp4a(v[i], u[i], sumi_d1);
+        }
+        sumf_d8 += dm2f1.x * sumi_d1;
+
+        if (i0/QI8_1 < ns8) {
+            const float2 s8f = __half22float2(s8[i0/QI8_1]);
+            sumf -= dm2f0.y*s8f.x;
+            sumf -= dm2f1.y*s8f.y;
+        } else {
+            int sumi_m0 = 0;
+#pragma unroll
+            for (int i = i0; i < i0 + QI8_1/2; ++i) {
+                sumi_m0 = ggml_cuda_dp4a(0x01010101, u[i], sumi_m0);
+            }
+            sumf_d8 -= dm2f0.y * sumi_m0;
+
+            int sumi_m1 = 0;
+#pragma unroll
+            for (int i = i0 + QI8_1/2; i < i0 + QI8_1; ++i) {
+                sumi_m1 = ggml_cuda_dp4a(0x01010101, u[i], sumi_m1);
+            }
+            sumf_d8 -= dm2f1.y * sumi_m1;
+        }
+    }
+
+    return sumf + d8*sumf_d8;
+}
+
+#define VDR_Q3_K_Q8_1_MMVQ 1
+#define VDR_Q3_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
+
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        const int isc = scale_offset + 2*i;
+
+        const int isc_low = isc % (QK_K/32);
+        const int sc_shift_low = 4 * (isc / (QK_K/32));
+        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
+
+        const int isc_high = isc % (QK_K/64);
+        const int sc_shift_high = 2 * (isc / (QK_K/64));
+        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
+
+        const int sc = (sc_low | sc_high) - 32;
+
+        const int vil = (vl >> (2*i)) & 0x03030303;
+
+        const int vih = ((vh >> i) << 2) & 0x04040404;
+
+        const int vi = __vsubss4(vil, vih);
+
+        sumf += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d3 * sumf;
+}
+
+// contiguous v/x + u/y values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d3, const float & d8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
+        int sumi_sc = 0;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_sc = ggml_cuda_dp4a(v[i], u[i], sumi_sc); // SIMD dot product
+        }
+
+        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
+    }
+
+    return d3*d8 * sumi;
+}
+
+#define VDR_Q4_K_Q8_1_MMVQ 2
+#define VDR_Q4_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K; ++i) {
+        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
+        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int dot1 = ggml_cuda_dp4a(v1i, u[2*i+1], ggml_cuda_dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
+        const int dot2 = ggml_cuda_dp4a(0x01010101, u[2*i+1], ggml_cuda_dp4a(0x01010101, u[2*i+0], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+}
+
+// contiguous v/x + u/y values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = ggml_cuda_dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i]);
+
+        sumf_d += ds8f.x * (sc[i] * sumi_d);
+        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+}
+
+#define VDR_Q5_K_Q8_1_MMVQ 2
+#define VDR_Q5_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
+    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
+        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
+        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
+
+        const int v0i = vl0i | vh0i;
+        const int v1i = vl1i | vh1i;
+
+        const int dot1 = ggml_cuda_dp4a(v0i, u[2*i+0], ggml_cuda_dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
+        const int dot2 = ggml_cuda_dp4a(0x01010101, u[2*i+0], ggml_cuda_dp4a(0x01010101, u[2*i+1], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);
+
+    }
+
+    const float2 dm5f = __half22float2(dm5);
+
+    return dm5f.x*sumf_d - dm5f.y*sumf_m;
+}
+
+// contiguous v/x + u/y values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = ggml_cuda_dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i]);
+
+        sumf_d += ds8f.x * (sc[i] * sumi_d);
+        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+}
+
+#define VDR_Q6_K_Q8_1_MMVQ 1
+#define VDR_Q6_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d, const float * __restrict__ d8) {
+
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        const int sc = scales[4*i];
+
+        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
+
+        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
+
+        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
+
+        sumf += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d*sumf;
+}
+
+// contiguous v/x + u/y values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
+    const float & d6, const float * __restrict__ d8) {
+
+    float sumf_d = 0.0f;
+
+    const int      sc_packed = get_int_b4(sc, 0);
+    const int8_t * sc_reg    = (const int8_t *) &sc_packed;
+
+#pragma unroll
+    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
+        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
+
+#pragma unroll
+        for (int i = i0; i < i0 + 2; ++i) {
+            sumi_d.x = ggml_cuda_dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
+            sumi_d.x = ggml_cuda_dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
+
+            sumi_d.y = ggml_cuda_dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
+            sumi_d.y = ggml_cuda_dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
+        }
+
+        sumf_d += d8[i0/4] * (sc_reg[i0/2+0]*sumi_d.x + sc_reg[i0/2+1]*sumi_d.y);
+    }
+
+    return d6 * sumf_d;
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq + kbx;
+
+    int v[VDR_Q4_0_Q8_1_MMVQ];
+    int u[2*VDR_Q4_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
+        v[i]     = get_int_b2(bq4_0->qs, iqs + i);
+        u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI4_0);
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
+}
+
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq + kbx;
+
+    int v[VDR_Q4_1_Q8_1_MMVQ];
+    int u[2*VDR_Q4_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
+        v[i]     = get_int_b4(bq4_1->qs, iqs + i);
+        u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI4_1);
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq + kbx;
+
+    int vl[VDR_Q5_0_Q8_1_MMVQ];
+    int vh[VDR_Q5_0_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
+        vl[i]    = get_int_b2(bq5_0->qs, iqs + i);
+        vh[i]    = get_int_b2(bq5_0->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI5_0);
+    }
+
+    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq + kbx;
+
+    int vl[VDR_Q5_1_Q8_1_MMVQ];
+    int vh[VDR_Q5_1_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
+        vl[i]    = get_int_b4(bq5_1->qs, iqs + i);
+        vh[i]    = get_int_b4(bq5_1->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI5_1);
+    }
+
+    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq + kbx;
+
+    int v[VDR_Q8_0_Q8_1_MMVQ];
+    int u[VDR_Q8_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
+        v[i] = get_int_b2(bq8_0->qs, iqs + i);
+        u[i] = get_int_b4(bq8_1->qs, iqs + i);
+    }
+
+    return vec_dot_q8_0_q8_1_impl<float, VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q2_K * bq2_K = (const block_q2_K *) vbq + kbx;
+
+    const int bq8_offset = QR2_K * (iqs / QI8_1);
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const uint8_t * scales = bq2_K->scales + scale_offset;
+
+    const int v = get_int_b4(bq2_K->qs, iqs);
+    int    u[QR2_K];
+    float d8[QR2_K];
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++ i) {
+        u[i]  = get_int_b4(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
+    }
+
+    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q3_K * bq3_K = (const block_q3_K *) vbq + kbx;
+
+    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const float d = bq3_K->d;
+
+    const int vl = get_int_b2(bq3_K->qs, iqs);
+
+    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+    const int vh = ~get_int_b2(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
+
+    int    u[QR3_K];
+    float d8[QR3_K];
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        u[i]  = get_int_b4(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
+    }
+
+    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq + kbx;
+
+    int    v[2];
+    int    u[2*QR4_K];
+    float d8[QR4_K];
+
+    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
+    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
+
+    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
+    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
+    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
+    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
+
+    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    v[0] = q4[0];
+    v[1] = q4[4];
+
+    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+    for (int i = 0; i < QR4_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = __low2float(bq8i->ds);
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq + kbx;
+
+    int   vl[2];
+    int   vh[2];
+    int    u[2*QR5_K];
+    float d8[QR5_K];
+
+    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
+    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
+
+    vl[0] = ql[0];
+    vl[1] = ql[4];
+
+    vh[0] = qh[0] >> bq8_offset;
+    vh[1] = qh[4] >> bq8_offset;
+
+    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = __low2float(bq8i->ds);
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q6_K * bq6_K = (const block_q6_K *) vbq + kbx;
+
+    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
+    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
+    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
+
+    const int vl = get_int_b2(bq6_K->ql, iqs);
+    const int vh = get_int_b2(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
+
+    const int8_t * scales = bq6_K->scales + scale_offset;
+
+    int    u[QR6_K];
+    float d8[QR6_K];
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        u[i]  = get_int_b4(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds);
+    }
+
+    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
+}
+
+#define VDR_IQ2_XXS_Q8_1_MMVQ 2
+#define VDR_IQ2_XXS_Q8_1_MMQ  2
+
+static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq + kbx;
+
+    const int q2 = get_int_b2(bq2->qs, iqs);
+    const uint8_t * aux8 = (const uint8_t *) &q2;
+    const uint32_t aux32 = get_int_b2(bq2->qs, iqs + 1);
+
+    int sumi = 0;
+#pragma unroll
+    for (int k0 = 0; k0 < 8; k0 += 2) {
+        const int * grid_pos = (const int *) (iq2xxs_grid + aux8[k0/2]);
+        const int signs_packed = ksigns_iq2xs[(aux32 >> (7*k0/2)) & 0x7F];
+
+        const int signs0 = __vcmpne4(((signs_packed & 0x03) << 7) | ((signs_packed & 0x0C) << 21), 0x00000000);
+        const int grid0 = __vsub4(grid_pos[0] ^ signs0, signs0);
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, k0 + 0);
+        sumi = ggml_cuda_dp4a(grid0, u0, sumi);
+
+        const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000);
+        const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, k0 + 1);
+        sumi = ggml_cuda_dp4a(grid1, u1, sumi);
+    }
+
+    const int ls = aux32 >> 28;
+    sumi = (ls*sumi + sumi/2)/4;
+    const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds);
+    return d * sumi;
+}
+
+#define VDR_IQ2_XS_Q8_1_MMVQ 2
+#define VDR_IQ2_XS_Q8_1_MMQ  2
+
+static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq + kbx;
+
+    const int2 q2_packed = make_int2(get_int_b2(bq2->qs, iqs + 0), get_int_b2(bq2->qs, iqs + 1));
+    const uint16_t * q2 = (const uint16_t *) &q2_packed;
+    const int ls0 = bq2->scales[iqs/2] & 0x0F;
+    const int ls1 = bq2->scales[iqs/2] >> 4;
+
+    int sumi0 = 0;
+    int sumi1 = 0;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const uint32_t * grid_pos = (const uint32_t *)(iq2xs_grid + (q2[l0/2] & 0x000001FF));
+        const uint32_t * signs    = (const uint32_t *)(ksigns64   + (q2[l0/2] >> 9));
+
+        const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]);
+
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1);
+
+        if (l0 < 4) {
+            sumi0 = ggml_cuda_dp4a(grid_l, u0, sumi0);
+            sumi0 = ggml_cuda_dp4a(grid_h, u1, sumi0);
+        } else {
+            sumi1 = ggml_cuda_dp4a(grid_l, u0, sumi1);
+            sumi1 = ggml_cuda_dp4a(grid_h, u1, sumi1);
+        }
+    }
+    const int sumi = (sumi0*ls0 + sumi1*ls1 + (sumi0 + sumi1)/2)/4;
+    const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds);
+    return d * sumi;
+}
+
+#define VDR_IQ2_S_Q8_1_MMVQ 2
+#define VDR_IQ2_S_Q8_1_MMQ  2
+
+static __device__ __forceinline__ float vec_dot_iq2_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_iq2_s * bq2 = (const block_iq2_s *) vbq + kbx;
+
+    const int       qs_packed = get_int_b2(bq2->qs, iqs/2);
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    const int qh = bq2->qh[iqs/2];
+
+    const int       signs_packed_32 = get_int_b2(bq2->qs, QK_K/32 + iqs/2);
+    const uint8_t * signs_packed_8  = (const uint8_t *) &signs_packed_32;
+
+    const int ls0 = bq2->scales[iqs/2] & 0x0F;
+    const int ls1 = bq2->scales[iqs/2] >> 4;
+
+    int sumi0 = 0;
+    int sumi1 = 0;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int * grid_pos = (const int *)(iq2s_grid + (qs[l0/2] | ((qh << (8-l0)) & 0x300)));
+
+        const int signs0 = __vcmpne4(((signs_packed_8[l0/2] & 0x03) << 7) | ((signs_packed_8[l0/2] & 0x0C) << 21), 0x00000000);
+        const int signs1 = __vcmpne4(((signs_packed_8[l0/2] & 0x30) << 3) | ((signs_packed_8[l0/2] & 0xC0) << 17), 0x00000000);
+
+        const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0);
+        const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1);
+
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1);
+
+        if (l0 < 4) {
+            sumi0 = ggml_cuda_dp4a(grid_l, u0, sumi0);
+            sumi0 = ggml_cuda_dp4a(grid_h, u1, sumi0);
+        } else {
+            sumi1 = ggml_cuda_dp4a(grid_l, u0, sumi1);
+            sumi1 = ggml_cuda_dp4a(grid_h, u1, sumi1);
+        }
+    }
+    const int sumi = (sumi0*ls0 + sumi1*ls1 + (sumi0 + sumi1)/2)/4;
+
+    const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds);
+    return d * sumi;
+}
+
+#define VDR_IQ3_XXS_Q8_1_MMVQ 2
+#define VDR_IQ3_XXS_Q8_1_MMQ  2
+
+static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_iq3_xxs * bq3 = (const block_iq3_xxs *) vbq + kbx;
+
+    const int2 q3_packed = make_int2(get_int_b2(bq3->qs, iqs), get_int_b2(bq3->qs, iqs+1));
+    const uint8_t * q3 = (const uint8_t *) &q3_packed;
+    const uint32_t aux32 = get_int_b2(bq3->qs, QK_K/16 + iqs/2);
+
+    int sumi = 0;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int2 grid_pos = make_int2(iq3xxs_grid[q3[l0 + 0]], iq3xxs_grid[q3[l0 + 1]]);
+
+        const int * signs = (const int *)(ksigns64 + ((aux32 >> (7*l0/2)) & 0x7F));
+
+        const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]);
+
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1);
+
+        sumi = ggml_cuda_dp4a(grid_l, u0, sumi);
+        sumi = ggml_cuda_dp4a(grid_h, u1, sumi);
+    }
+
+    const int ls = aux32 >> 28;
+    sumi = (ls*sumi + sumi/2)/2;
+    const float d = __half2float(bq3->d) * __low2float(bq8_1[iqs/2].ds);
+    return d * sumi;
+}
+
+#define VDR_IQ3_S_Q8_1_MMVQ 2
+#define VDR_IQ3_S_Q8_1_MMQ  2
+
+// TODO: don't use lookup table for signs
+static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_iq3_s * bq3 = (const block_iq3_s *) vbq + kbx;
+
+    const int2      qs_packed = make_int2(get_int_b2(bq3->qs, iqs + 0), get_int_b2(bq3->qs, iqs + 1));
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    const int qh = bq3->qh[iqs/2];
+
+    const int       signs_packed_32 = get_int_b2(bq3->signs, iqs/2);
+    const uint8_t * signs_packed_8  = (const uint8_t *) &signs_packed_32;
+
+    int sumi = 0;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int2 grid_pos = make_int2(
+            iq3s_grid[qs[l0 + 0] | ((qh << (8 - l0)) & 0x100)],
+            iq3s_grid[qs[l0 + 1] | ((qh << (7 - l0)) & 0x100)]);
+
+        const int signs0 = __vcmpne4(((signs_packed_8[l0/2] & 0x03) << 7) | ((signs_packed_8[l0/2] & 0x0C) << 21), 0x00000000);
+        const int signs1 = __vcmpne4(((signs_packed_8[l0/2] & 0x30) << 3) | ((signs_packed_8[l0/2] & 0xC0) << 17), 0x00000000);
+
+        const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0);
+        const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1);
+
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1);
+
+        sumi = ggml_cuda_dp4a(grid_l, u0, sumi);
+        sumi = ggml_cuda_dp4a(grid_h, u1, sumi);
+    }
+
+    sumi *= 1 + 2*((bq3->scales[iqs/4] >> ((iqs << 1) & 0x04)) & 0x0F);
+
+    const float d = __half2float(bq3->d) * __low2float(bq8_1[iqs/2].ds);
+    return d * sumi;
+}
+
+#define VDR_IQ1_S_Q8_1_MMVQ 1
+#define VDR_IQ1_S_Q8_1_MMQ  1
+
+static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+    const block_iq1_s * bq1 = (const block_iq1_s *) vbq + kbx;
+
+    const int       qs_packed = get_int_b2(bq1->qs, iqs);
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    const int qh = bq1->qh[iqs];
+
+    int sumi = 0;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int grid = iq1s_grid_gpu[qs[l0/2] | (((qh >> 3*(l0/2)) & 0x07) << 8)];
+
+        const int grid0 = (grid >> 0) & 0x0F0F0F0F;
+        const int grid1 = (grid >> 4) & 0x0F0F0F0F;
+
+        const int u0 = get_int_b4(bq8_1[iqs].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs].qs, l0 + 1);
+
+        sumi = ggml_cuda_dp4a(grid0, u0, sumi);
+        sumi = ggml_cuda_dp4a(grid1, u1, sumi);
+    }
+
+    const float  d1q   = __half2float(bq1->d) * (((qh >> 11) & 0x0E) + 1);
+    const float  delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000);
+    const float2 ds    = __half22float2(bq8_1[iqs].ds);
+    return d1q * (ds.x*sumi + ds.y*delta);
+}
+
+#define VDR_IQ1_M_Q8_1_MMVQ 1
+#define VDR_IQ1_M_Q8_1_MMQ  1
+
+static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_iq1_m * bq1 = (const block_iq1_m *) vbq + kbx;
+
+    const int       qs_packed = get_int_b4(bq1->qs, iqs);
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    int   sumi[2] = {0};
+    float sumf[2] = {0.0f};
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int qhl = bq1->qh[2*iqs + l0/4] >> (4 * ((l0/2) % 2));
+
+        const int grid = iq1s_grid_gpu[qs[l0/2] | ((qhl & 0x07) << 8)];
+
+        const int grid0 = (grid >> 0) & 0x0F0F0F0F;
+        const int grid1 = (grid >> 4) & 0x0F0F0F0F;
+
+        const int u0 = get_int_b4(bq8_1[iqs].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs].qs, l0 + 1);
+
+        sumi[l0/4] = ggml_cuda_dp4a(grid0, u0, sumi[l0/4]);
+        sumi[l0/4] = ggml_cuda_dp4a(grid1, u1, sumi[l0/4]);
+
+        const float delta = -1.0f + IQ1M_DELTA - (qhl & 0x08) * (2.0f*IQ1M_DELTA/0x08);
+        int sumy = 0;
+        sumy = ggml_cuda_dp4a(u0, 0x01010101, sumy);
+        sumy = ggml_cuda_dp4a(u1, 0x01010101, sumy);
+        sumf[l0/4] += delta*sumy;
+    }
+
+    const uint16_t * sc = (const uint16_t *) bq1->scales;
+
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00F0) | ((sc[2] >> 4) & 0x0F00) | (sc[3] & 0xF000);
+    const float d = __half2float(scale.f16) * __low2float(bq8_1[iqs].ds);
+
+    const int tmp = sc[iqs/2] >> (6*(iqs%2));
+    const int sc0 = 2*((tmp >> 0) & 0x07) + 1;
+    const int sc1 = 2*((tmp >> 3) & 0x07) + 1;
+    return d * ((sumi[0] + sumf[0]) * sc0 + (sumi[1] + sumf[1]) * sc1);
+}
+
+static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4) {
+    const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
+    const int8_t * q0_8   = (const int8_t *) &q0_32;
+    const char4    val0_8 = make_char4(
+        kvalues_iq4nl[q0_8[0]], kvalues_iq4nl[q0_8[1]], kvalues_iq4nl[q0_8[2]], kvalues_iq4nl[q0_8[3]]);
+
+    const int      q1_32  = (q4 >> 4) & 0x0F0F0F0F;
+    const int8_t * q1_8   = (const int8_t *) &q1_32;
+    const char4    val1_8 = make_char4(
+        kvalues_iq4nl[q1_8[0]], kvalues_iq4nl[q1_8[1]], kvalues_iq4nl[q1_8[2]], kvalues_iq4nl[q1_8[3]]);
+
+    return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
+}
+
+#define VDR_IQ4_NL_Q8_1_MMVQ 2
+#define VDR_IQ4_NL_Q8_1_MMQ  4
+
+static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_iq4_nl * bq4 = (const block_iq4_nl *) vbq + kbx;
+
+    const int * q8 = (const int *) bq8_1->qs + iqs;
+
+    int sumi = 0;
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
+        const int aux_q4 = get_int_b2(bq4->qs, iqs + l);
+        const int2 v = get_int_from_table_16(aux_q4);
+
+        sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
+        sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
+    }
+
+    const float d = __half2float(bq4->d) * __low2float(bq8_1->ds);
+    return d * sumi;
+}
+
+#define VDR_IQ4_XS_Q8_1_MMVQ 4
+#define VDR_IQ4_XS_Q8_1_MMQ  4
+
+static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq + kbx;
+
+    int sumi = 0;
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        const int aux_q4 = get_int_b4(bq4->qs, iqs + j);
+        const int2 v = get_int_from_table_16(aux_q4);
+
+        const int u0 = get_int_b4(bq8_1[iqs/4].qs, j + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/4].qs, j + 4);
+
+        sumi = ggml_cuda_dp4a(v.x, u0, sumi);
+        sumi = ggml_cuda_dp4a(v.y, u1, sumi);
+    }
+
+    const int ls = ((bq4->scales_l[iqs/8] >> (iqs & 0x04)) & 0x0F) | (((bq4->scales_h >> (iqs/2)) & 0x03) << 4);
+    sumi *= ls - 32;
+
+    const float d = __half2float(bq4->d) * __low2float(bq8_1[iqs/4].ds);
+    return d * sumi;
+}
diff --git a/ggml/src/ggml-cuda/vendors/cuda.h b/ggml/src/ggml-cuda/vendors/cuda.h
new file mode 100644
index 0000000000000..db9f6a165d07c
--- /dev/null
+++ b/ggml/src/ggml-cuda/vendors/cuda.h
@@ -0,0 +1,14 @@
+#pragma once
+
+#include <cuda_runtime.h>
+#include <cuda.h>
+#include <cublas_v2.h>
+#include <cuda_fp16.h>
+
+#if CUDART_VERSION < 11020
+#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
+#define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH
+#define CUBLAS_COMPUTE_16F CUDA_R_16F
+#define CUBLAS_COMPUTE_32F CUDA_R_32F
+#define cublasComputeType_t cudaDataType_t
+#endif // CUDART_VERSION < 11020
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
new file mode 100644
index 0000000000000..d0c377255968c
--- /dev/null
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -0,0 +1,177 @@
+#pragma once
+
+#include <hip/hip_runtime.h>
+#include <hipblas/hipblas.h>
+#include <hip/hip_fp16.h>
+#ifdef __HIP_PLATFORM_AMD__
+// for rocblas_initialize()
+#include "rocblas/rocblas.h"
+#endif // __HIP_PLATFORM_AMD__
+#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
+#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
+#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
+#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_OP_N HIPBLAS_OP_N
+#define CUBLAS_OP_T HIPBLAS_OP_T
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_TF32_TENSOR_OP_MATH 0
+#define CUDA_R_16F  HIPBLAS_R_16F
+#define CUDA_R_32F  HIPBLAS_R_32F
+#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
+#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
+#define cublasCreate hipblasCreate
+#define cublasDestroy hipblasDestroy
+#define cublasGemmEx hipblasGemmEx
+#define cublasGemmBatchedEx hipblasGemmBatchedEx
+#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
+#define cublasHandle_t hipblasHandle_t
+#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
+#define cublasSetStream hipblasSetStream
+#define cublasSgemm hipblasSgemm
+#define cublasStatus_t hipblasStatus_t
+#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6
+#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
+#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
+#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
+#define cudaDeviceProp hipDeviceProp_t
+#define cudaDeviceSynchronize hipDeviceSynchronize
+#define cudaError_t hipError_t
+#define cudaErrorPeerAccessAlreadyEnabled hipErrorPeerAccessAlreadyEnabled
+#define cudaErrorPeerAccessNotEnabled hipErrorPeerAccessNotEnabled
+#define cudaEventCreateWithFlags hipEventCreateWithFlags
+#define cudaEventDisableTiming hipEventDisableTiming
+#define cudaEventRecord hipEventRecord
+#define cudaEventSynchronize hipEventSynchronize
+#define cudaEvent_t hipEvent_t
+#define cudaEventDestroy hipEventDestroy
+#define cudaFree hipFree
+#define cudaFreeHost hipHostFree
+#define cudaGetDevice hipGetDevice
+#define cudaGetDeviceCount hipGetDeviceCount
+#define cudaGetDeviceProperties hipGetDeviceProperties
+#define cudaGetErrorString hipGetErrorString
+#define cudaGetLastError hipGetLastError
+#define cudaHostRegister hipHostRegister
+#define cudaHostRegisterPortable hipHostRegisterPortable
+#define cudaHostRegisterReadOnly hipHostRegisterReadOnly
+#define cudaHostUnregister hipHostUnregister
+#define cudaLaunchHostFunc hipLaunchHostFunc
+#define cudaMalloc hipMalloc
+#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
+#define cudaMemcpy hipMemcpy
+#define cudaMemcpyAsync hipMemcpyAsync
+#define cudaMemcpyPeerAsync hipMemcpyPeerAsync
+#define cudaMemcpy2DAsync hipMemcpy2DAsync
+#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
+#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
+#define cudaMemcpyKind hipMemcpyKind
+#define cudaMemset hipMemset
+#define cudaMemsetAsync hipMemsetAsync
+#define cudaMemGetInfo hipMemGetInfo
+#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
+#define cudaSetDevice hipSetDevice
+#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
+#define cudaStreamDestroy hipStreamDestroy
+#define cudaStreamFireAndForget hipStreamFireAndForget
+#define cudaStreamNonBlocking hipStreamNonBlocking
+#define cudaStreamPerThread hipStreamPerThread
+#define cudaStreamSynchronize hipStreamSynchronize
+#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
+#define cudaStream_t hipStream_t
+#define cudaSuccess hipSuccess
+#define __trap() do { abort(); __builtin_unreachable(); } while(0)
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED
+#define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED
+#define CUBLAS_STATUS_INVALID_VALUE HIPBLAS_STATUS_INVALID_VALUE
+#define CUBLAS_STATUS_ARCH_MISMATCH HIPBLAS_STATUS_ARCH_MISMATCH
+#define CUBLAS_STATUS_MAPPING_ERROR HIPBLAS_STATUS_MAPPING_ERROR
+#define CUBLAS_STATUS_EXECUTION_FAILED HIPBLAS_STATUS_EXECUTION_FAILED
+#define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
+#define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
+
+#define __CUDA_ARCH__ 1300
+
+#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
+    defined(__gfx1150__) || defined(__gfx1151__)
+#define RDNA3
+#endif
+
+#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
+    defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
+#define RDNA2
+#endif
+
+#if defined(__gfx1010__) || defined(__gfx1012__)
+#define RDNA1
+#endif
+
+#ifndef __has_builtin
+    #define __has_builtin(x) 0
+#endif
+
+typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
+typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
+static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+#if __has_builtin(__builtin_elementwise_sub_sat)
+    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
+    return reinterpret_cast<const int &>(c);
+#else
+    int8x4_t c;
+    int16_t tmp;
+#pragma unroll
+    for (int i = 0; i < 4; i++) {
+        tmp = va[i] - vb[i];
+        if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
+        if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
+        c[i] = tmp;
+    }
+    return reinterpret_cast<int &>(c);
+#endif // __has_builtin(__builtin_elementwise_sub_sat)
+}
+
+static __device__ __forceinline__ int __vsub4(const int a, const int b) {
+    return __vsubss4(a, b);
+}
+
+static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigned int b) {
+    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
+    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
+    unsigned int c;
+    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
+#pragma unroll
+    for (int i = 0; i < 4; ++i) {
+        vc[i] = va[i] == vb[i] ? 0xff : 0x00;
+    }
+    return c;
+}
+
+static __device__ __forceinline__ unsigned int __vcmpne4(unsigned int a, unsigned int b) {
+    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
+    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
+    unsigned int c;
+    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
+#pragma unroll
+    for (int i = 0; i < 4; ++i) {
+        vc[i] = va[i] == vb[i] ? 0x00 : 0xff;
+    }
+    return c;
+}
+
+#if defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000
+// __shfl_xor() for half2 was added in ROCm 5.6
+static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int width) {
+    typedef union half2_b32 {
+        half2 val;
+        int   b32;
+    } half2_b32_t;
+    half2_b32_t tmp;
+    tmp.val = var;
+    tmp.b32 = __shfl_xor(tmp.b32, laneMask, width);
+    return tmp.val;
+}
+#endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000
diff --git a/ggml/src/ggml-cuda/vendors/musa.h b/ggml/src/ggml-cuda/vendors/musa.h
new file mode 100644
index 0000000000000..e50a103ac07a8
--- /dev/null
+++ b/ggml/src/ggml-cuda/vendors/musa.h
@@ -0,0 +1,171 @@
+#pragma once
+
+#include <musa_runtime.h>
+#include <musa.h>
+#include <mublas.h>
+#include <musa_fp16.h>
+#define CUBLAS_COMPUTE_16F CUDA_R_16F
+#define CUBLAS_COMPUTE_32F CUDA_R_32F
+#define CUBLAS_COMPUTE_32F_FAST_16F MUBLAS_COMPUTE_32F_FAST_16F
+#define CUBLAS_GEMM_DEFAULT MUBLAS_GEMM_DEFAULT
+#define CUBLAS_GEMM_DEFAULT_TENSOR_OP MUBLAS_GEMM_DEFAULT
+#define CUBLAS_OP_N MUBLAS_OP_N
+#define CUBLAS_OP_T MUBLAS_OP_T
+#define CUBLAS_STATUS_SUCCESS MUBLAS_STATUS_SUCCESS
+#define CUBLAS_TF32_TENSOR_OP_MATH MUBLAS_MATH_MODE_DEFAULT
+#define CUDA_R_16F  MUSA_R_16F
+#define CUDA_R_32F  MUSA_R_32F
+#define cublasComputeType_t cudaDataType_t
+#define cublasCreate mublasCreate
+#define cublasDestroy mublasDestroy
+#define cublasGemmEx mublasGemmEx
+#define cublasGemmBatchedEx mublasGemmBatchedEx
+#define cublasGemmStridedBatchedEx mublasGemmStridedBatchedEx
+#define cublasHandle_t mublasHandle_t
+#define cublasSetMathMode mublasSetMathMode
+#define cublasSetStream mublasSetStream
+#define cublasSgemm mublasSgemm
+#define cublasStatus_t mublasStatus_t
+#define cublasGetStatusString mublasStatus_to_string
+#define cudaDataType_t musaDataType_t
+#define cudaDeviceCanAccessPeer musaDeviceCanAccessPeer
+#define cudaDeviceDisablePeerAccess musaDeviceDisablePeerAccess
+#define cudaDeviceEnablePeerAccess musaDeviceEnablePeerAccess
+#define cudaDeviceProp musaDeviceProp
+#define cudaDeviceSynchronize musaDeviceSynchronize
+#define cudaError_t musaError_t
+#define cudaErrorPeerAccessAlreadyEnabled musaErrorPeerAccessAlreadyEnabled
+#define cudaErrorPeerAccessNotEnabled musaErrorPeerAccessNotEnabled
+#define cudaEventCreateWithFlags musaEventCreateWithFlags
+#define cudaEventDisableTiming musaEventDisableTiming
+#define cudaEventRecord musaEventRecord
+#define cudaEventSynchronize musaEventSynchronize
+#define cudaEvent_t musaEvent_t
+#define cudaEventDestroy musaEventDestroy
+#define cudaFree musaFree
+#define cudaFreeHost musaFreeHost
+#define cudaGetDevice musaGetDevice
+#define cudaGetDeviceCount musaGetDeviceCount
+#define cudaGetDeviceProperties musaGetDeviceProperties
+#define cudaGetErrorString musaGetErrorString
+#define cudaGetLastError musaGetLastError
+#define cudaHostRegister musaHostRegister
+#define cudaHostRegisterPortable musaHostRegisterPortable
+#define cudaHostRegisterReadOnly musaHostRegisterReadOnly
+#define cudaHostUnregister musaHostUnregister
+#define cudaLaunchHostFunc musaLaunchHostFunc
+#define cudaMalloc musaMalloc
+#define cudaMallocHost musaMallocHost
+#define cudaMemcpy musaMemcpy
+#define cudaMemcpyAsync musaMemcpyAsync
+#define cudaMemcpyPeerAsync musaMemcpyPeerAsync
+#define cudaMemcpy2DAsync musaMemcpy2DAsync
+#define cudaMemcpyDeviceToDevice musaMemcpyDeviceToDevice
+#define cudaMemcpyDeviceToHost musaMemcpyDeviceToHost
+#define cudaMemcpyHostToDevice musaMemcpyHostToDevice
+#define cudaMemcpyKind musaMemcpyKind
+#define cudaMemset musaMemset
+#define cudaMemsetAsync musaMemsetAsync
+#define cudaMemGetInfo musaMemGetInfo
+#define cudaOccupancyMaxPotentialBlockSize musaOccupancyMaxPotentialBlockSize
+#define cudaSetDevice musaSetDevice
+#define cudaStreamCreateWithFlags musaStreamCreateWithFlags
+#define cudaStreamDestroy musaStreamDestroy
+#define cudaStreamFireAndForget musaStreamFireAndForget
+#define cudaStreamNonBlocking musaStreamNonBlocking
+#define cudaStreamPerThread musaStreamPerThread
+#define cudaStreamSynchronize musaStreamSynchronize
+#define cudaStreamWaitEvent musaStreamWaitEvent
+#define cudaStream_t musaStream_t
+#define cudaSuccess musaSuccess
+
+// Additional mappings for MUSA virtual memory pool
+#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED MU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
+#define CU_MEM_ACCESS_FLAGS_PROT_READWRITE MU_MEM_ACCESS_FLAGS_PROT_READWRITE
+#define CU_MEM_ALLOC_GRANULARITY_RECOMMENDED MU_MEM_ALLOC_GRANULARITY_RECOMMENDED
+#define CU_MEM_ALLOCATION_TYPE_PINNED MU_MEM_ALLOCATION_TYPE_PINNED
+#define CU_MEM_LOCATION_TYPE_DEVICE MU_MEM_LOCATION_TYPE_DEVICE
+#define CUdevice MUdevice
+#define CUdeviceptr MUdeviceptr
+#define CUmemAccessDesc MUmemAccessDesc
+#define CUmemAllocationProp MUmemAllocationProp
+#define CUmemGenericAllocationHandle MUmemGenericAllocationHandle
+#define cuDeviceGet muDeviceGet
+#define cuDeviceGetAttribute muDeviceGetAttribute
+#define cuMemAddressFree muMemAddressFree
+#define cuMemAddressReserve muMemAddressReserve
+#define cuMemCreate muMemCreate
+#define cuMemGetAllocationGranularity muMemGetAllocationGranularity
+#define cuMemMap muMemMap
+#define cuMemRelease muMemRelease
+#define cuMemSetAccess muMemSetAccess
+#define cuMemUnmap muMemUnmap
+#define cudaFuncAttributeMaxDynamicSharedMemorySize musaFuncAttributeMaxDynamicSharedMemorySize
+#define cudaFuncSetAttribute musaFuncSetAttribute
+#define cudaMemcpy3DPeerParms musaMemcpy3DPeerParms
+#define make_cudaExtent make_musaExtent
+#define make_cudaPitchedPtr make_musaPitchedPtr
+
+// Additional mappings for MUSA graphs
+#define CUDA_SUCCESS MUSA_SUCCESS
+#define CUresult MUresult
+#define cuGetErrorString muGetErrorString
+#define cudaErrorGraphExecUpdateFailure musaErrorGraphExecUpdateFailure
+#define cudaErrorInvalidDeviceFunction musaErrorInvalidDeviceFunction
+#define cudaGraphDestroy musaGraphDestroy
+#define cudaGraphExecDestroy musaGraphExecDestroy
+#define cudaGraphExec_t musaGraphExec_t
+#define cudaGraphExecUpdate musaGraphExecUpdate
+#define cudaGraphExecUpdateResultInfo musaGraphExecUpdateResult
+#define cudaGraphGetNodes musaGraphGetNodes
+#define cudaGraphInstantiate musaGraphInstantiate
+#define cudaGraphKernelNodeGetParams musaGraphKernelNodeGetParams
+#define cudaGraphKernelNodeSetParams musaGraphKernelNodeSetParams
+#define cudaGraphLaunch musaGraphLaunch
+#define cudaGraphNodeGetType musaGraphNodeGetType
+#define cudaGraphNode_t musaGraphNode_t
+#define cudaGraphNodeType musaGraphNodeType
+#define cudaGraphNodeTypeKernel musaGraphNodeTypeKernel
+#define cudaGraph_t musaGraph_t
+#define cudaKernelNodeParams musaKernelNodeParams
+#define cudaStreamCaptureModeRelaxed musaStreamCaptureModeRelaxed
+#define cudaStreamEndCapture musaStreamEndCapture
+
+// XXX: Clang builtins mapping
+#define __vsub4   __vsub4_musa
+#define __vcmpeq4 __vcmpeq4_musa
+#define __vcmpne4 __vcmpne4_musa
+
+#ifndef __has_builtin
+    #define __has_builtin(x) 0
+#endif
+
+typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
+
+static __device__ __forceinline__ int __vsub4_musa(const int a, const int b) {
+    return __vsubss4(a, b);
+}
+
+static __device__ __forceinline__ unsigned int __vcmpeq4_musa(unsigned int a, unsigned int b) {
+    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
+    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
+    unsigned int c;
+    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
+#pragma unroll
+    for (int i = 0; i < 4; ++i) {
+        vc[i] = va[i] == vb[i] ? 0xff : 0x00;
+    }
+    return c;
+}
+
+static __device__ __forceinline__ unsigned int __vcmpne4_musa(unsigned int a, unsigned int b) {
+    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
+    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
+    unsigned int c;
+    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
+#pragma unroll
+    for (int i = 0; i < 4; ++i) {
+        vc[i] = va[i] == vb[i] ? 0x00 : 0xff;
+    }
+    return c;
+}
diff --git a/ggml-impl.h b/ggml/src/ggml-impl.h
similarity index 77%
rename from ggml-impl.h
rename to ggml/src/ggml-impl.h
index 5ff014fe3f339..190af081031da 100644
--- a/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -17,7 +17,7 @@
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 
-#if defined(_WIN32)
+#if defined(_MSC_VER)
 
 #define m512bh(p) p
 #define m512i(p) p
@@ -80,8 +80,9 @@ static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
 /**
  * Converts float32 to brain16.
  *
- * This function is binary identical to AMD Zen4 VCVTNEPS2BF16.
- * Subnormals shall be flushed to zero, and NANs will be quiet.
+ * This is binary identical with Google Brain float conversion.
+ * Floats shall round to nearest even, and NANs shall be quiet.
+ * Subnormals aren't flushed to zero, except perhaps when used.
  * This code should vectorize nicely if using modern compilers.
  */
 static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
@@ -95,10 +96,6 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
         h.bits = (u.i >> 16) | 64; /* force to quiet */
         return h;
     }
-    if (!(u.i & 0x7f800000)) { /* subnormal */
-        h.bits = (u.i & 0x80000000) >> 16; /* flush to zero */
-        return h;
-    }
     h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16;
     return h;
 }
@@ -144,6 +141,11 @@ extern "C" {
 #endif
 #endif
 
+#if defined(__ARM_FEATURE_SVE)
+#include <arm_sve.h>
+#include <sys/prctl.h>
+#endif
+
 // 16-bit float
 // on Arm, we use __fp16
 // on x86, we use uint16_t
@@ -455,6 +457,34 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
 #include <riscv_vector.h>
 #endif
 
+#if defined(__loongarch64)
+#if defined(__loongarch_asx)
+#include <lasxintrin.h>
+#endif
+#if defined(__loongarch_sx)
+#include <lsxintrin.h>
+#endif
+#endif
+
+#if defined(__loongarch_asx)
+
+typedef union {
+    int32_t i;
+    float f;
+} ft_union;
+
+/* float type data load instructions */
+static __m128 __lsx_vreplfr2vr_s(float val) {
+    ft_union fi_tmpval = {.f = val};
+    return (__m128)__lsx_vreplgr2vr_w(fi_tmpval.i);
+}
+
+static __m256 __lasx_xvreplfr2vr_s(float val) {
+    ft_union fi_tmpval = {.f = val};
+    return (__m256)__lasx_xvreplgr2vr_w(fi_tmpval.i);
+}
+#endif
+
 #ifdef __F16C__
 
 #ifdef _MSC_VER
@@ -577,6 +607,10 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
 
 #endif // defined(__ARM_NEON) && (!defined(__MSC_VER)
 
+#ifdef __ARM_FEATURE_SVE
+#include <arm_sve.h>
+#endif // __ARM_FEATURE_SVE
+
 // precomputed f32 table for f16 (256 KB)
 // defined in ggml.c, initialized in ggml_init()
 extern float ggml_table_f32_f16[1 << 16];
@@ -598,21 +632,121 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
 #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
 #endif
 
-#define GGML_HASHTABLE_FULL ((size_t)-1)
-#define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2)
+// bitset
+
+static_assert(sizeof(ggml_bitset_t) == 4, "bitset_t constants must be updated");
+#define BITSET_SHR 5 // log2(sizeof(ggml_bitset_t)*8)
+#define BITSET_MASK (sizeof(ggml_bitset_t)*8 - 1)
+
+static size_t ggml_bitset_size(size_t n) {
+    return (n + BITSET_MASK) >> BITSET_SHR;
+}
+
+static inline bool ggml_bitset_get(const ggml_bitset_t * bitset, size_t i) {
+    return !!(bitset[i >> BITSET_SHR] & (1u << (i & BITSET_MASK)));
+}
+
+static inline void ggml_bitset_set(ggml_bitset_t * bitset, size_t i) {
+    bitset[i >> BITSET_SHR] |= (1u << (i & BITSET_MASK));
+}
+
+static inline void ggml_bitset_clear(ggml_bitset_t * bitset, size_t i) {
+    bitset[i >> BITSET_SHR] &= ~(1u << (i & BITSET_MASK));
+}
+
+// hash set
+
+#define GGML_HASHSET_FULL ((size_t)-1)
+#define GGML_HASHSET_ALREADY_EXISTS ((size_t)-2)
 
 struct ggml_hash_set ggml_hash_set_new(size_t size);
+void                 ggml_hash_set_free(struct ggml_hash_set * hash_set);
+
+// returns the minimum size for a hash set that can hold min_sz elements
+size_t ggml_hash_size(size_t min_sz);
 
-bool   ggml_hash_contains      (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
+// remove all elements from the hash set
+void ggml_hash_set_reset(struct ggml_hash_set * hash_set);
 
-// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
-size_t ggml_hash_find          (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
+// returns true if key is in the hash set
+static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key);
 
-// returns GGML_HASHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
-size_t ggml_hash_insert        (      struct ggml_hash_set hash_set, struct ggml_tensor * key);
+// returns GGML_HASHSET_FULL if table is full, otherwise the current index of the key or where it should be inserted
+static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, struct ggml_tensor * key);
+
+// returns GGML_HASHSET_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
+static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key);
 
 // return index, asserts if table is full
-size_t ggml_hash_find_or_insert(      struct ggml_hash_set hash_set, struct ggml_tensor * key);
+static size_t ggml_hash_find_or_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key);
+
+// hash function for ggml_tensor
+static inline size_t ggml_hash(const struct ggml_tensor * p) {
+    // the last 4 bits are always zero due to alignment
+    return (size_t)(uintptr_t)p >> 4;
+}
+
+static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
+    size_t h = ggml_hash(key) % hash_set->size;
+
+    // linear probing
+    size_t i = h;
+    while (ggml_bitset_get(hash_set->used, i) && hash_set->keys[i] != key) {
+        i = (i + 1) % hash_set->size;
+        if (i == h) {
+            // visited all hash table entries -> not found
+            return GGML_HASHSET_FULL;
+        }
+    }
+    return i;
+}
+
+static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
+    size_t i = ggml_hash_find(hash_set, key);
+    return i != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, i);
+}
+
+static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
+    size_t h = ggml_hash(key) % hash_set->size;
+
+    // linear probing
+    size_t i = h;
+    do {
+        if (!ggml_bitset_get(hash_set->used, i)) {
+            ggml_bitset_set(hash_set->used, i);
+            hash_set->keys[i] = key;
+            return i;
+        }
+        if (hash_set->keys[i] == key) {
+            return GGML_HASHSET_ALREADY_EXISTS;
+        }
+        i = (i + 1) % hash_set->size;
+    } while (i != h);
+
+    // visited all hash table entries -> not found
+    GGML_ABORT("fatal error");
+}
+
+static size_t ggml_hash_find_or_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
+    size_t h = ggml_hash(key) % hash_set->size;
+
+    // linear probing
+    size_t i = h;
+    do {
+        if (!ggml_bitset_get(hash_set->used, i)) {
+            ggml_bitset_set(hash_set->used, i);
+            hash_set->keys[i] = key;
+            return i;
+        }
+        if (hash_set->keys[i] == key) {
+            return i;
+        }
+        i = (i + 1) % hash_set->size;
+    } while (i != h);
+
+    // visited all hash table entries -> not found
+    GGML_ABORT("fatal error");
+}
 
 #ifdef __cplusplus
 }
diff --git a/ggml-kompute.cpp b/ggml/src/ggml-kompute.cpp
similarity index 97%
rename from ggml-kompute.cpp
rename to ggml/src/ggml-kompute.cpp
index 3f033d58be481..41ac63fa48e0f 100644
--- a/ggml-kompute.cpp
+++ b/ggml/src/ggml-kompute.cpp
@@ -22,6 +22,7 @@
 #include "shaderop_mul_mat_q4_1.h"
 #include "shaderop_mul_mat_q6_k.h"
 #include "shaderop_mul_mat_mat_f32.h"
+#include "shaderop_getrows_f32.h"
 #include "shaderop_getrows_f16.h"
 #include "shaderop_getrows_q4_0.h"
 #include "shaderop_getrows_q4_1.h"
@@ -565,7 +566,7 @@ uint32_t safe_divide(uint32_t a, uint32_t b) {
     }
     if ((a % b) != 0) {
         fprintf(stderr, "((%u %% %u) == %u) != 0\n", a, b, a % b);
-        GGML_ASSERT(!"safe_divide result would've had remainder");
+        GGML_ABORT("safe_divide result would've had remainder");
     }
     return a / b;
 }
@@ -1146,6 +1147,14 @@ static void ggml_vk_get_rows(
     seq.record<kp::OpAlgoDispatch>(s_algo);
 }
 
+template <typename... Args>
+static void ggml_vk_get_rows_f32(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f32_comp_spv,
+        kp::shader_data::op_getrows_f32_comp_spv_len);
+
+    ggml_vk_get_rows(spirv, "f32", sizeof(float), 0, std::forward<Args>(args)...);
+}
+
 template <typename... Args>
 static void ggml_vk_get_rows_f16(Args&&... args) {
     const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f16_comp_spv,
@@ -1183,7 +1192,7 @@ static void ggml_vk_rope(
     const std::shared_ptr<kp::Tensor>& inB,
     const std::shared_ptr<kp::Tensor>& out,
     uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
-    ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_orig_ctx,
+    ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_ctx_orig,
     float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow,
     int32_t ne01, int32_t ne02, int32_t ne03,
     uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03,
@@ -1212,14 +1221,14 @@ static void ggml_vk_rope(
 
     struct PushConstants {
         uint32_t inAOff, inBOff, outOff;
-        int32_t n_dims, mode, n_orig_ctx;
+        int32_t n_dims, mode, n_ctx_orig;
         float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
         uint32_t nb00, nb01, nb02, nb03;
         int32_t ne0;
         uint32_t nb0, nb1, nb2, nb3;
     } pushConsts {
         safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(outOff, type_size),
-        n_dims, mode, n_orig_ctx,
+        n_dims, mode, n_ctx_orig,
         freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow,
         nb00, nb01, nb02, nb03,
         ne0,
@@ -1331,7 +1340,7 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) {
                 case GGML_UNARY_OP_RELU:
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_SILU:
-                    return true;
+                    return ggml_is_contiguous(op->src[0]);
                 default:
                     ;
             }
@@ -1371,6 +1380,7 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) {
             return op->ne[3] == 1;
         case GGML_OP_GET_ROWS:
             switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
                 case GGML_TYPE_F16:
                 case GGML_TYPE_Q4_0:
                 case GGML_TYPE_Q4_1:
@@ -1450,7 +1460,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
 
             if (!ggml_vk_supports_op(dst)) {
                  fprintf(stderr, "%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
-                 GGML_ASSERT(!"unsupported op");
+                 GGML_ABORT("unsupported op");
             }
 
             const int32_t ne00 = src0 ? src0->ne[0] : 0;
@@ -1552,7 +1562,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
                             default:
                                 {
                                     fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
-                                    GGML_ASSERT(false);
+                                    GGML_ABORT("fatal error");
                                 }
                         }
                     } break;
@@ -1597,7 +1607,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
                     {
                         GGML_ASSERT(ne00 == ne10);
 
-                        // TODO: assert that dim2 and dim3 are contiguous
                         GGML_ASSERT(ne12 % ne02 == 0);
                         GGML_ASSERT(ne13 % ne03 == 0);
 
@@ -1662,7 +1671,9 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
                     } break;
                 case GGML_OP_GET_ROWS:
                     {
-                        if (src0t == GGML_TYPE_F16) {
+                        if (src0t == GGML_TYPE_F32) {
+                            ggml_vk_get_rows_f32(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else if (src0t == GGML_TYPE_F16) {
                             ggml_vk_get_rows_f16(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
                         } else if (src0t == GGML_TYPE_Q4_0) {
                             ggml_vk_get_rows_q4_0(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
@@ -1677,13 +1688,20 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
                     } break;
                 case GGML_OP_ROPE:
                     {
+#pragma message("TODO: implement phi3 frequency factors support")
+#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7225")
+                        GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet");
+
+#pragma message("TODO: update rope NORM mode to match NEOX mode")
+#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7634")
+
                         GGML_ASSERT(ne10 == ne02);
                         GGML_ASSERT(src0t == dstt);
                         // const int n_past = ((int32_t *) dst->op_params)[0];
                         const int n_dims     = ((int32_t *) dst->op_params)[1];
                         const int mode       = ((int32_t *) dst->op_params)[2];
                         // skip 3, n_ctx used in GLM RoPE, unimplemented in Vulkan
-                        const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+                        const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
 
                         float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
                         memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
@@ -1693,7 +1711,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
                         memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
                         memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
                         ggml_vk_rope(
-                            seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_orig_ctx,
+                            seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_ctx_orig,
                             freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow,
                             ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3
                         );
@@ -1727,7 +1745,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
             continue;
             not_implemented: {}
             fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
-            //GGML_ASSERT(false);
+            //GGML_ABORT("fatal error");
         }
 
         // Evaluate sequence
@@ -1884,18 +1902,12 @@ static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_
     return ctx->max_alloc;
 }
 
-static bool ggml_backend_kompute_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    GGML_UNUSED(buft);
-    return ggml_backend_is_kompute(backend);
-}
-
 static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = {
     /* .get_name         = */ ggml_backend_kompute_buffer_type_get_name,
     /* .alloc_buffer     = */ ggml_backend_kompute_buffer_type_alloc_buffer,
     /* .get_alignment    = */ ggml_backend_kompute_buffer_type_get_alignment,
     /* .get_max_size     = */ ggml_backend_vk_buffer_type_get_max_size,
     /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
-    /* .supports_backend = */ ggml_backend_kompute_buffer_type_supports_backend,
     /* .is_host          = */ NULL,
 };
 
@@ -1955,6 +1967,11 @@ static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struc
     return ggml_vk_supports_op(op);
 }
 
+static bool ggml_backend_kompute_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    GGML_UNUSED(backend);
+    return buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name;
+}
+
 static struct ggml_backend_i kompute_backend_i = {
     /* .get_name                = */ ggml_backend_kompute_name,
     /* .free                    = */ ggml_backend_kompute_free,
@@ -1965,9 +1982,11 @@ static struct ggml_backend_i kompute_backend_i = {
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_kompute_graph_compute,
     /* .supports_op             = */ ggml_backend_kompute_supports_op,
+    /* .supports_buft           = */ ggml_backend_kompute_supports_buft,
     /* .offload_op              = */ NULL,
     /* .event_new               = */ NULL,
     /* .event_free              = */ NULL,
diff --git a/ggml-metal.m b/ggml/src/ggml-metal.m
similarity index 93%
rename from ggml-metal.m
rename to ggml/src/ggml-metal.m
index b0b16dbf77160..995f1934bc73b 100644
--- a/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -35,6 +35,10 @@
     GGML_METAL_KERNEL_TYPE_MUL_ROW,
     GGML_METAL_KERNEL_TYPE_DIV,
     GGML_METAL_KERNEL_TYPE_DIV_ROW,
+    GGML_METAL_KERNEL_TYPE_REPEAT_F32,
+    GGML_METAL_KERNEL_TYPE_REPEAT_F16,
+    GGML_METAL_KERNEL_TYPE_REPEAT_I32,
+    GGML_METAL_KERNEL_TYPE_REPEAT_I16,
     GGML_METAL_KERNEL_TYPE_SCALE,
     GGML_METAL_KERNEL_TYPE_SCALE_4,
     GGML_METAL_KERNEL_TYPE_CLAMP,
@@ -168,8 +172,10 @@
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,
     GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,
-    GGML_METAL_KERNEL_TYPE_ROPE_F32,
-    GGML_METAL_KERNEL_TYPE_ROPE_F16,
+    GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32,
+    GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16,
+    GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32,
+    GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16,
     GGML_METAL_KERNEL_TYPE_IM2COL_F16,
     GGML_METAL_KERNEL_TYPE_IM2COL_F32,
     GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
@@ -184,19 +190,19 @@
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,
+  //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,     // https://github.com/ggerganov/llama.cpp/issues/7261
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
-    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,
-    GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
+  //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261
     GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
+    GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
+    GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,
     GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,
-    GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
-    GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
     GGML_METAL_KERNEL_TYPE_CONCAT,
     GGML_METAL_KERNEL_TYPE_SQR,
     GGML_METAL_KERNEL_TYPE_SUM_ROWS,
@@ -204,7 +210,7 @@
     GGML_METAL_KERNEL_TYPE_COUNT
 };
 
-struct ggml_metal_context {
+struct ggml_backend_metal_context {
     int n_cb;
 
     id<MTLDevice>       device;
@@ -218,6 +224,10 @@
     bool support_simdgroup_mm;
 
     bool should_capture_next_compute;
+
+    // abort ggml_metal_graph_compute if callback returns true
+    ggml_abort_callback abort_callback;
+    void *              abort_callback_data;
 };
 
 // MSL code
@@ -283,7 +293,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
     return data;
 }
 
-static struct ggml_metal_context * ggml_metal_init(int n_cb) {
+static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
     GGML_METAL_LOG_INFO("%s: allocating\n", __func__);
 
 #if TARGET_OS_OSX && !GGML_METAL_NDEBUG
@@ -300,7 +310,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
     GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]);
 
     // Configure context
-    struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
+    struct ggml_backend_metal_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_context));
     ctx->device = device;
     ctx->n_cb   = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
     ctx->queue  = [ctx->device newCommandQueue];
@@ -381,10 +391,6 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
                 // dictionary of preprocessor macros
                 NSMutableDictionary * prep = [NSMutableDictionary dictionary];
 
-#ifdef GGML_QKK_64
-                prep[@"GGML_QKK_64"] = @(1);
-#endif
-
                 MTLCompileOptions* options = [MTLCompileOptions new];
                 options.preprocessorMacros = prep;
 
@@ -489,6 +495,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW,                       mul_row,                        true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                           div,                            true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW,                       div_row,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32,                    repeat_f32,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16,                    repeat_f16,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32,                    repeat_i32,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I16,                    repeat_i16,                     true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE,                         scale,                          true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4,                       scale_4,                        true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP,                         clamp,                          true);
@@ -622,8 +632,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,           mul_mm_id_iq1_m_f32,            ctx->support_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,          mul_mm_id_iq4_nl_f32,           ctx->support_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,          mul_mm_id_iq4_xs_f32,           ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32,                      rope_f32,                       true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16,                      rope_f16,                       true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32,                 rope_norm_f32,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16,                 rope_norm_f16,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32,                 rope_neox_f32,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16,                 rope_neox_f16,                  true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16,                    im2col_f16,                     true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32,                    im2col_f32,                     true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32,                   upscale_f32,                    true);
@@ -638,19 +650,19 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,        flash_attn_ext_f16_h96,         ctx->support_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,       flash_attn_ext_f16_h112,        ctx->support_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,       flash_attn_ext_f16_h128,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,       flash_attn_ext_f16_h256,        ctx->support_simdgroup_mm);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,       flash_attn_ext_f16_h256,        ctx->support_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,   flash_attn_ext_vec_f16_h128,    ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,   flash_attn_ext_vec_f16_h256,    ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,   flash_attn_ext_vec_f16_h256,    ctx->support_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16,                   cpy_f32_f16,                    true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,                   cpy_f32_f32,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,                   cpy_f16_f16,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,                   cpy_f16_f32,                    true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,                  cpy_f32_q8_0,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,                  cpy_f32_q4_0,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,                  cpy_f32_q4_1,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,                  cpy_f32_q5_0,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,                  cpy_f32_q5_1,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,                cpy_f32_iq4_nl,                 true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,                   cpy_f16_f16,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,                   cpy_f16_f32,                    true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT,                        concat,                         true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR,                           sqr,                            true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                      sum_rows,                       true);
@@ -660,7 +672,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
     return ctx;
 }
 
-static void ggml_metal_free(struct ggml_metal_context * ctx) {
+static void ggml_metal_free(struct ggml_backend_metal_context * ctx) {
     GGML_METAL_LOG_INFO("%s: deallocating\n", __func__);
 
     for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) {
@@ -726,7 +738,13 @@ static void ggml_metal_free(struct ggml_metal_context * ctx) {
     return nil;
 }
 
-static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) {
+static bool ggml_metal_supports_op(const struct ggml_backend_metal_context * ctx, const struct ggml_tensor * op) {
+    for (size_t i = 0, n = 3; i < n; ++i) {
+        if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) {
+            return false;
+        }
+    }
+
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
@@ -736,7 +754,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_SILU:
-                    return true;
+                    return ggml_is_contiguous(op->src[0]);
                 default:
                     return false;
             }
@@ -750,6 +768,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
         case GGML_OP_ACC:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
+        case GGML_OP_REPEAT:
         case GGML_OP_SCALE:
         case GGML_OP_CLAMP:
         case GGML_OP_SQR:
@@ -774,6 +793,15 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
         case GGML_OP_LEAKY_RELU:
             return true;
         case GGML_OP_FLASH_ATTN_EXT:
+            if (op->src[1]->type != GGML_TYPE_F16) {
+                return false;
+            }
+            if (op->src[2]->type != GGML_TYPE_F16) {
+                return false;
+            }
+            if (op->src[0]->ne[0] == 256) {
+                return false;
+            }
             return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
@@ -786,8 +814,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
                 switch (op->src[0]->type) {
                     case GGML_TYPE_F32:
                         switch (op->type) {
-                           case GGML_TYPE_F16:
                            case GGML_TYPE_F32:
+                           case GGML_TYPE_F16:
                            case GGML_TYPE_Q8_0:
                            case GGML_TYPE_Q4_0:
                            case GGML_TYPE_Q4_1:
@@ -800,8 +828,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
                         }
                     case GGML_TYPE_F16:
                         switch (op->type) {
-                           case GGML_TYPE_F16:
                            case GGML_TYPE_F32:
+                           case GGML_TYPE_F16:
                                 return true;
                            default:
                                 return false;
@@ -813,7 +841,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_GET_ROWS:
             {
-                return op->src[0]->type != GGML_TYPE_BF16 && op->ne[3] == 1;
+                return op->ne[3] == 1;
             }
         default:
             return false;
@@ -821,7 +849,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
 }
 
 static enum ggml_status ggml_metal_graph_compute(
-        struct ggml_metal_context * ctx,
+        struct ggml_backend_metal_context * ctx,
                struct ggml_cgraph * gf) {
 
     @autoreleasepool {
@@ -845,7 +873,7 @@ static enum ggml_status ggml_metal_graph_compute(
         NSError * error = nil;
         if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) {
             GGML_METAL_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]);
-            GGML_ASSERT(!"capture failed");
+            GGML_ABORT("capture failed");
         }
     }
 
@@ -854,8 +882,11 @@ static enum ggml_status ggml_metal_graph_compute(
         id<MTLCommandBuffer> command_buffer  = [ctx->queue commandBufferWithUnretainedReferences];
         command_buffer_builder[cb_idx] = command_buffer;
 
-        // enqueue the command buffers in order to specify their execution order
-        [command_buffer enqueue];
+        // always enqueue the first two command buffers
+        // enqueue all of the command buffers if we don't need to abort
+        if (cb_idx < 2 || ctx->abort_callback == NULL) {
+            [command_buffer enqueue];
+        }
     }
 
     const id<MTLCommandBuffer> *command_buffers = command_buffer_builder;
@@ -907,7 +938,7 @@ static enum ggml_status ggml_metal_graph_compute(
 
             if (!ggml_metal_supports_op(ctx, dst)) {
                 GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
-                GGML_ASSERT(!"unsupported op");
+                GGML_ABORT("unsupported op");
             }
 
             if (should_capture) {
@@ -927,22 +958,32 @@ static enum ggml_status ggml_metal_graph_compute(
             const int64_t  ne10 = src1 ? src1->ne[0] : 0;
             const int64_t  ne11 = src1 ? src1->ne[1] : 0;
             const int64_t  ne12 = src1 ? src1->ne[2] : 0;
-            const int64_t  ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
+            const int64_t  ne13 = src1 ? src1->ne[3] : 0;
 
             const uint64_t nb10 = src1 ? src1->nb[0] : 0;
             const uint64_t nb11 = src1 ? src1->nb[1] : 0;
             const uint64_t nb12 = src1 ? src1->nb[2] : 0;
-            const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
+            const uint64_t nb13 = src1 ? src1->nb[3] : 0;
+
+            const int64_t  ne20 = src2 ? src2->ne[0] : 0;
+            const int64_t  ne21 = src2 ? src2->ne[1] : 0;
+            const int64_t  ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22);
+            const int64_t  ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23);
+
+            const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20);
+            const uint64_t nb21 = src2 ? src2->nb[1] : 0;
+            const uint64_t nb22 = src2 ? src2->nb[2] : 0;
+            const uint64_t nb23 = src2 ? src2->nb[3] : 0;
 
-            const int64_t  ne0  = dst ? dst->ne[0] : 0;
-            const int64_t  ne1  = dst ? dst->ne[1] : 0;
-            const int64_t  ne2  = dst ? dst->ne[2] : 0;
-            const int64_t  ne3  = dst ? dst->ne[3] : 0;
+            const int64_t  ne0  =  dst ?  dst->ne[0] : 0;
+            const int64_t  ne1  =  dst ?  dst->ne[1] : 0;
+            const int64_t  ne2  =  dst ?  dst->ne[2] : 0;
+            const int64_t  ne3  =  dst ?  dst->ne[3] : 0;
 
-            const uint64_t nb0  = dst ? dst->nb[0] : 0;
-            const uint64_t nb1  = dst ? dst->nb[1] : 0;
-            const uint64_t nb2  = dst ? dst->nb[2] : 0;
-            const uint64_t nb3  = dst ? dst->nb[3] : 0;
+            const uint64_t nb0  =  dst ?  dst->nb[0] : 0;
+            const uint64_t nb1  =  dst ?  dst->nb[1] : 0;
+            const uint64_t nb2  =  dst ?  dst->nb[2] : 0;
+            const uint64_t nb3  =  dst ?  dst->nb[3] : 0;
 
             const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
             const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
@@ -970,10 +1011,10 @@ static enum ggml_status ggml_metal_graph_compute(
             switch (dst->op) {
                 case GGML_OP_CONCAT:
                     {
-                        const int64_t nb = ne00;
-
                         id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline;
 
+                        const int32_t dim = ((int32_t *) dst->op_params)[0];
+
                         [encoder setComputePipelineState:pipeline];
                         [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                         [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
@@ -1002,7 +1043,7 @@ static enum ggml_status ggml_metal_graph_compute(
                         [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:24];
                         [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:25];
                         [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:26];
-                        [encoder setBytes:&nb   length:sizeof(nb)   atIndex:27];
+                        [encoder setBytes:&dim  length:sizeof(dim)  atIndex:27];
 
                         const int nth = MIN(1024, ne0);
 
@@ -1012,11 +1053,14 @@ static enum ggml_status ggml_metal_graph_compute(
                 case GGML_OP_MUL:
                 case GGML_OP_DIV:
                     {
+                        GGML_ASSERT(src0t == GGML_TYPE_F32);
+                        GGML_ASSERT(src1t == GGML_TYPE_F32);
+
                         const size_t offs = 0;
 
                         bool bcast_row = false;
 
-                        int64_t nb = ne00;
+                        int64_t nb = ne00; // used by the "row" kernels
 
                         id<MTLComputePipelineState> pipeline = nil;
 
@@ -1031,7 +1075,7 @@ static enum ggml_status ggml_metal_graph_compute(
                                 case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break;
                                 case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break;
                                 case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break;
-                                default: GGML_ASSERT(false);
+                                default: GGML_ABORT("fatal error");
                             }
 
                             bcast_row = true;
@@ -1040,7 +1084,7 @@ static enum ggml_status ggml_metal_graph_compute(
                                 case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break;
                                 case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break;
                                 case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break;
-                                default: GGML_ASSERT(false);
+                                default: GGML_ABORT("fatal error");
                             }
                         }
 
@@ -1085,6 +1129,42 @@ static enum ggml_status ggml_metal_graph_compute(
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         }
                     } break;
+                case GGML_OP_REPEAT:
+                    {
+                        id<MTLComputePipelineState> pipeline;
+
+                        switch (src0t) {
+                            case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break;
+                            case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break;
+                            case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break;
+                            case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break;
+                            default: GGML_ABORT("fatal error");
+                        }
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
+                        [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
+                        [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
+                        [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
+                        [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
+                        [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
+                        [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
+                        [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
+                        [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:10];
+                        [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:11];
+                        [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:12];
+                        [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:13];
+                        [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:14];
+                        [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:15];
+                        [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:16];
+                        [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:17];
+
+                        const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
                 case GGML_OP_ACC:
                     {
                         GGML_ASSERT(src0t == GGML_TYPE_F32);
@@ -1314,7 +1394,7 @@ static enum ggml_status ggml_metal_graph_compute(
                         default:
                             {
                                 GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
-                                GGML_ASSERT(false);
+                                GGML_ABORT("fatal error");
                             }
                     } break;
                 case GGML_OP_SQR:
@@ -1462,7 +1542,6 @@ static enum ggml_status ggml_metal_graph_compute(
                     {
                         GGML_ASSERT(ne00 == ne10);
 
-                        // TODO: assert that dim2 and dim3 are contiguous
                         GGML_ASSERT(ne12 % ne02 == 0);
                         GGML_ASSERT(ne13 % ne03 == 0);
 
@@ -1508,8 +1587,8 @@ static enum ggml_status ggml_metal_graph_compute(
                             // some Metal matrix data types require aligned pointers
                             // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
                             switch (src0->type) {
-                                case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break;
-                                case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8  == 0); break;
+                                case GGML_TYPE_F32:  GGML_ASSERT(nb01 % 16 == 0); break;
+                                case GGML_TYPE_F16:  GGML_ASSERT(nb01 % 8  == 0); break;
                                 default: break;
                             }
 
@@ -1537,7 +1616,7 @@ static enum ggml_status ggml_metal_graph_compute(
                                 case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32  ].pipeline; break;
                                 case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
                                 case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break;
-                                default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
+                                default: GGML_ABORT("MUL MAT-MAT not implemented");
                             }
 
                             [encoder setComputePipelineState:pipeline];
@@ -1710,14 +1789,10 @@ static enum ggml_status ggml_metal_graph_compute(
                                 default:
                                     {
                                         GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
-                                        GGML_ASSERT(false && "not implemented");
+                                        GGML_ABORT("not implemented");
                                     }
                             };
 
-                            if (ggml_is_quantized(src0t)) {
-                                GGML_ASSERT(ne00 >= nth0*nth1);
-                            }
-
                             [encoder setComputePipelineState:pipeline];
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
@@ -1763,11 +1838,7 @@ static enum ggml_status ggml_metal_graph_compute(
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
                             else if (src0t == GGML_TYPE_Q3_K) {
-#ifdef GGML_QKK_64
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-#else
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-#endif
                             }
                             else if (src0t == GGML_TYPE_Q5_K) {
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@@ -1785,16 +1856,6 @@ static enum ggml_status ggml_metal_graph_compute(
                         const int n_as = src0->ne[2];
 
                         // src2 = ids
-                        const int64_t  ne20 = src2->ne[0];
-                        const int64_t  ne21 = src2->ne[1];
-                        const int64_t  ne22 = src2->ne[2]; GGML_UNUSED(ne22);
-                        const int64_t  ne23 = src2->ne[3]; GGML_UNUSED(ne23);
-
-                        const uint64_t nb20 = src2->nb[0]; GGML_UNUSED(nb20);
-                        const uint64_t nb21 = src2->nb[1];
-                        const uint64_t nb22 = src2->nb[2]; GGML_UNUSED(nb22);
-                        const uint64_t nb23 = src2->nb[3]; GGML_UNUSED(nb23);
-
                         const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t);
 
                         GGML_ASSERT(src2t == GGML_TYPE_I32);
@@ -1810,9 +1871,10 @@ static enum ggml_status ggml_metal_graph_compute(
                         // ne21 = n_rows
                         const int dst_rows = ne20*ne21;
                         const int dst_rows_min = n_as;
+                        const int dst_rows_max = (ctx->device.maxThreadgroupMemoryLength - 32 - 8192)/4;
 
                         // max size of the rowids array in the kernel shared buffer
-                        GGML_ASSERT(dst_rows <= 2048);
+                        GGML_ASSERT(dst_rows <= dst_rows_max);
 
                         // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
                         // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
@@ -1856,7 +1918,7 @@ static enum ggml_status ggml_metal_graph_compute(
                                 case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32  ].pipeline; break;
                                 case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break;
                                 case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break;
-                                default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
+                                default: GGML_ABORT("MUL_MAT_ID not implemented");
                             }
 
                             [encoder setComputePipelineState:pipeline];
@@ -2018,17 +2080,12 @@ static enum ggml_status ggml_metal_graph_compute(
                                     {
                                         nth0 = 4;
                                         nth1 = 16;
-                                    #if QK_K == 64
-                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
-                                    #else
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
-                                    #endif
-
                                     } break;
                                 default:
                                     {
                                         GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
-                                        GGML_ASSERT(false && "not implemented");
+                                        GGML_ABORT("not implemented");
                                     }
                             };
 
@@ -2088,11 +2145,7 @@ static enum ggml_status ggml_metal_graph_compute(
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                             }
                             else if (src0t == GGML_TYPE_Q3_K) {
-#ifdef GGML_QKK_64
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-#else
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-#endif
                             }
                             else if (src0t == GGML_TYPE_Q5_K) {
                                 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@@ -2132,7 +2185,7 @@ static enum ggml_status ggml_metal_graph_compute(
                             case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
                             case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break;
                             case GGML_TYPE_I32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32    ].pipeline; break;
-                            default: GGML_ASSERT(false && "not implemented");
+                            default: GGML_ABORT("not implemented");
                         }
 
                         [encoder setComputePipelineState:pipeline];
@@ -2153,6 +2206,7 @@ static enum ggml_status ggml_metal_graph_compute(
                 case GGML_OP_RMS_NORM:
                     {
                         GGML_ASSERT(ne00 % 4 == 0);
+                        GGML_ASSERT(ggml_is_contiguous_1(src0));
 
                         float eps;
                         memcpy(&eps, dst->op_params, sizeof(float));
@@ -2180,11 +2234,10 @@ static enum ggml_status ggml_metal_graph_compute(
                 case GGML_OP_GROUP_NORM:
                     {
                         GGML_ASSERT(ne00 % 4 == 0);
+                        GGML_ASSERT(ggml_is_contiguous(src0));
 
-                        //float eps;
-                        //memcpy(&eps, dst->op_params, sizeof(float));
-
-                        const float eps = 1e-6f; // TODO: temporarily hardcoded
+                        float eps;
+                        memcpy(&eps, dst->op_params + 1, sizeof(float));
 
                         const int32_t n_groups = ((int32_t *) dst->op_params)[0];
 
@@ -2213,6 +2266,8 @@ static enum ggml_status ggml_metal_graph_compute(
                     } break;
                 case GGML_OP_NORM:
                     {
+                        GGML_ASSERT(ggml_is_contiguous_1(src0));
+
                         float eps;
                         memcpy(&eps, dst->op_params, sizeof(float));
 
@@ -2242,9 +2297,15 @@ static enum ggml_status ggml_metal_graph_compute(
                         const int n_dims     = ((int32_t *) dst->op_params)[1];
                         const int mode       = ((int32_t *) dst->op_params)[2];
                         // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
-                        const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+                        const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
+
+                        float freq_base;
+                        float freq_scale;
+                        float ext_factor;
+                        float attn_factor;
+                        float beta_fast;
+                        float beta_slow;
 
-                        float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
                         memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
                         memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
                         memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
@@ -2252,38 +2313,52 @@ static enum ggml_status ggml_metal_graph_compute(
                         memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
                         memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
 
+                        const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+
                         id<MTLComputePipelineState> pipeline = nil;
 
-                        switch (src0->type) {
-                            case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break;
-                            case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break;
-                            default: GGML_ASSERT(false);
-                        };
+                        if (!is_neox) {
+                            switch (src0->type) {
+                                case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break;
+                                case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break;
+                                default: GGML_ABORT("fatal error");
+                            };
+                        } else {
+                            switch (src0->type) {
+                                case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break;
+                                case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break;
+                                default: GGML_ABORT("fatal error");
+                            };
+                        }
 
                         [encoder setComputePipelineState:pipeline];
                         [encoder setBuffer:id_src0     offset:offs_src0        atIndex:0];
                         [encoder setBuffer:id_src1     offset:offs_src1        atIndex:1];
-                        [encoder setBuffer:id_dst      offset:offs_dst         atIndex:2];
-                        [encoder setBytes:&ne00        length:sizeof( int64_t) atIndex:3];
-                        [encoder setBytes:&ne01        length:sizeof( int64_t) atIndex:4];
-                        [encoder setBytes:&ne02        length:sizeof( int64_t) atIndex:5];
-                        [encoder setBytes:&ne03        length:sizeof( int64_t) atIndex:6];
-                        [encoder setBytes:&nb00        length:sizeof(uint64_t) atIndex:7];
-                        [encoder setBytes:&nb01        length:sizeof(uint64_t) atIndex:8];
-                        [encoder setBytes:&nb02        length:sizeof(uint64_t) atIndex:9];
-                        [encoder setBytes:&nb03        length:sizeof(uint64_t) atIndex:10];
-                        [encoder setBytes:&ne0         length:sizeof( int64_t) atIndex:11];
-                        [encoder setBytes:&ne1         length:sizeof( int64_t) atIndex:12];
-                        [encoder setBytes:&ne2         length:sizeof( int64_t) atIndex:13];
-                        [encoder setBytes:&ne3         length:sizeof( int64_t) atIndex:14];
-                        [encoder setBytes:&nb0         length:sizeof(uint64_t) atIndex:15];
-                        [encoder setBytes:&nb1         length:sizeof(uint64_t) atIndex:16];
-                        [encoder setBytes:&nb2         length:sizeof(uint64_t) atIndex:17];
-                        [encoder setBytes:&nb3         length:sizeof(uint64_t) atIndex:18];
-                        [encoder setBytes:&n_past      length:sizeof(     int) atIndex:19];
-                        [encoder setBytes:&n_dims      length:sizeof(     int) atIndex:20];
-                        [encoder setBytes:&mode        length:sizeof(     int) atIndex:21];
-                        [encoder setBytes:&n_orig_ctx  length:sizeof(     int) atIndex:22];
+                        if (id_src2 != nil) {
+                            [encoder setBuffer:id_src2 offset:offs_src2        atIndex:2];
+                        } else {
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:2];
+                        }
+                        [encoder setBuffer:id_dst      offset:offs_dst         atIndex:3];
+                        [encoder setBytes:&ne00        length:sizeof( int64_t) atIndex:4];
+                        [encoder setBytes:&ne01        length:sizeof( int64_t) atIndex:5];
+                        [encoder setBytes:&ne02        length:sizeof( int64_t) atIndex:6];
+                        [encoder setBytes:&ne03        length:sizeof( int64_t) atIndex:7];
+                        [encoder setBytes:&nb00        length:sizeof(uint64_t) atIndex:8];
+                        [encoder setBytes:&nb01        length:sizeof(uint64_t) atIndex:9];
+                        [encoder setBytes:&nb02        length:sizeof(uint64_t) atIndex:10];
+                        [encoder setBytes:&nb03        length:sizeof(uint64_t) atIndex:11];
+                        [encoder setBytes:&ne0         length:sizeof( int64_t) atIndex:12];
+                        [encoder setBytes:&ne1         length:sizeof( int64_t) atIndex:13];
+                        [encoder setBytes:&ne2         length:sizeof( int64_t) atIndex:14];
+                        [encoder setBytes:&ne3         length:sizeof( int64_t) atIndex:15];
+                        [encoder setBytes:&nb0         length:sizeof(uint64_t) atIndex:16];
+                        [encoder setBytes:&nb1         length:sizeof(uint64_t) atIndex:17];
+                        [encoder setBytes:&nb2         length:sizeof(uint64_t) atIndex:18];
+                        [encoder setBytes:&nb3         length:sizeof(uint64_t) atIndex:19];
+                        [encoder setBytes:&n_past      length:sizeof(     int) atIndex:20];
+                        [encoder setBytes:&n_dims      length:sizeof(     int) atIndex:21];
+                        [encoder setBytes:&n_ctx_orig  length:sizeof(     int) atIndex:22];
                         [encoder setBytes:&freq_base   length:sizeof(   float) atIndex:23];
                         [encoder setBytes:&freq_scale  length:sizeof(   float) atIndex:24];
                         [encoder setBytes:&ext_factor  length:sizeof(   float) atIndex:25];
@@ -2329,7 +2404,7 @@ static enum ggml_status ggml_metal_graph_compute(
                         switch (dst->type) {
                             case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break;
                             case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break;
-                            default: GGML_ASSERT(false);
+                            default: GGML_ABORT("fatal error");
                         };
 
                         [encoder setComputePipelineState:pipeline];
@@ -2486,7 +2561,7 @@ static enum ggml_status ggml_metal_graph_compute(
                         switch (order) {
                             case GGML_SORT_ORDER_ASC:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline;  break;
                             case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
-                            default: GGML_ASSERT(false);
+                            default: GGML_ABORT("fatal error");
                         };
 
                         [encoder setComputePipelineState:pipeline];
@@ -2535,11 +2610,6 @@ static enum ggml_status ggml_metal_graph_compute(
                         GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) &&
                                 "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big");
 
-                        const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20);
-                        const uint64_t nb21 = src2 ? src2->nb[1] : 0;
-                        const uint64_t nb22 = src2 ? src2->nb[2] : 0;
-                        const uint64_t nb23 = src2 ? src2->nb[3] : 0;
-
                         const int64_t  ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30);
                       //const int64_t  ne31 = src3 ? src3->ne[1] : 0;
                         const int64_t  ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32);
@@ -2575,12 +2645,12 @@ static enum ggml_status ggml_metal_graph_compute(
                                 case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break;
                                 case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break;
                                 case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break;
-                                case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
+                              //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
                                 default:
                                           {
                                               GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
                                               GGML_METAL_LOG_ERROR("add template specialization for this size\n");
-                                              GGML_ASSERT(false && "add template specialization for this size");
+                                              GGML_ABORT("add template specialization for this size");
                                           }
                             }
                         } else {
@@ -2588,12 +2658,12 @@ static enum ggml_status ggml_metal_graph_compute(
 
                             switch (ne00) {
                                 case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break;
-                                case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
+                              //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
                                 default:
                                           {
                                               GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
                                               GGML_METAL_LOG_ERROR("add template specialization for this size\n");
-                                              GGML_ASSERT(false && "add template specialization for this size");
+                                              GGML_ABORT("add template specialization for this size");
                                           }
                             }
                         }
@@ -2706,26 +2776,26 @@ static enum ggml_status ggml_metal_graph_compute(
                                     GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0);
 
                                     switch (dstt) {
-                                        case GGML_TYPE_F16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline;  break;
-                                        case GGML_TYPE_F32:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline;  break;
+                                        case GGML_TYPE_F32:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break;
+                                        case GGML_TYPE_F16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break;
                                         case GGML_TYPE_Q8_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break;
                                         case GGML_TYPE_Q4_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break;
                                         case GGML_TYPE_Q4_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break;
                                         case GGML_TYPE_Q5_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break;
                                         case GGML_TYPE_Q5_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break;
                                         case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break;
-                                        default: GGML_ASSERT(false && "not implemented");
+                                        default: GGML_ABORT("not implemented");
                                     };
                                 } break;
                             case GGML_TYPE_F16:
                                 {
                                     switch (dstt) {
-                                        case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break;
-                                        case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break;
-                                        default: GGML_ASSERT(false && "not implemented");
+                                        case GGML_TYPE_F32:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break;
+                                        case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break;
+                                        default: GGML_ABORT("not implemented");
                                     };
                                 } break;
-                            default: GGML_ASSERT(false && "not implemented");
+                            default: GGML_ABORT("not implemented");
                         }
 
                         [encoder setComputePipelineState:pipeline];
@@ -2753,7 +2823,7 @@ static enum ggml_status ggml_metal_graph_compute(
                 default:
                     {
                         GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
-                        GGML_ASSERT(false);
+                        GGML_ABORT("fatal error");
                     }
             }
 
@@ -2764,7 +2834,9 @@ static enum ggml_status ggml_metal_graph_compute(
 
         [encoder endEncoding];
 
-        [command_buffer commit];
+        if (cb_idx < 2 || ctx->abort_callback == NULL) {
+            [command_buffer commit];
+        }
     });
 
     // Wait for completion and check status of each command buffer
@@ -2784,6 +2856,23 @@ static enum ggml_status ggml_metal_graph_compute(
 
             return GGML_STATUS_FAILED;
         }
+
+        id<MTLCommandBuffer> next_buffer = (i + 1 < n_cb ? command_buffers[i + 1] : nil);
+        if (!next_buffer) {
+            continue;
+        }
+
+        bool next_queued = ([next_buffer status] != MTLCommandBufferStatusNotEnqueued);
+        if (next_queued) {
+            continue;
+        }
+
+        if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) {
+            GGML_METAL_LOG_INFO("%s: command buffer %d aborted", __func__, i);
+            return GGML_STATUS_ABORTED;
+        }
+
+        [next_buffer commit];
     }
 
     if (should_capture) {
@@ -2982,12 +3071,6 @@ GGML_CALL static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend
     UNUSED(buft);
 }
 
-GGML_CALL static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend);
-
-    UNUSED(buft);
-}
-
 GGML_CALL static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
     return true;
 
@@ -3002,7 +3085,6 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
             /* .get_alignment    = */ ggml_backend_metal_buffer_type_get_alignment,
             /* .get_max_size     = */ ggml_backend_metal_buffer_type_get_max_size,
             /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
-            /* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend,
             /* .is_host          = */ ggml_backend_metal_buffer_type_is_host,
         },
         /* .context = */ NULL,
@@ -3094,7 +3176,7 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
 }
 
 GGML_CALL static void ggml_backend_metal_free(ggml_backend_t backend) {
-    struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
+    struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context;
     ggml_metal_free(ctx);
     free(backend);
 }
@@ -3106,17 +3188,23 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffe
 }
 
 GGML_CALL static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
+    struct ggml_backend_metal_context * metal_ctx = (struct ggml_backend_metal_context *)backend->context;
 
     return ggml_metal_graph_compute(metal_ctx, cgraph);
 }
 
 GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
+    struct ggml_backend_metal_context * metal_ctx = (struct ggml_backend_metal_context *)backend->context;
 
     return ggml_metal_supports_op(metal_ctx, op);
 }
 
+GGML_CALL static bool ggml_backend_metal_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name;
+
+    UNUSED(backend);
+}
+
 static struct ggml_backend_i ggml_backend_metal_i = {
     /* .get_name                = */ ggml_backend_metal_name,
     /* .free                    = */ ggml_backend_metal_free,
@@ -3127,9 +3215,11 @@ GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, con
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_metal_graph_compute,
     /* .supports_op             = */ ggml_backend_metal_supports_op,
+    /* .supports_buft           = */ ggml_backend_metal_supports_buft,
     /* .offload_op              = */ NULL,
     /* .event_new               = */ NULL,
     /* .event_free              = */ NULL,
@@ -3149,9 +3239,9 @@ static ggml_guid_t ggml_backend_metal_guid(void) {
 }
 
 ggml_backend_t ggml_backend_metal_init(void) {
-    struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
-
+    struct ggml_backend_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
     if (ctx == NULL) {
+        GGML_METAL_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
         return NULL;
     }
 
@@ -3173,15 +3263,24 @@ bool ggml_backend_is_metal(ggml_backend_t backend) {
 void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
     GGML_ASSERT(ggml_backend_is_metal(backend));
 
-    struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
+    struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context;
 
     ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
 }
 
+void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data) {
+    GGML_ASSERT(ggml_backend_is_metal(backend));
+
+    struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context;
+
+    ctx->abort_callback = abort_callback;
+    ctx->abort_callback_data = user_data;
+}
+
 bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
     GGML_ASSERT(ggml_backend_is_metal(backend));
 
-    struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
+    struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context;
 
     return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)];
 }
@@ -3189,7 +3288,7 @@ bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
 void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) {
     GGML_ASSERT(ggml_backend_is_metal(backend));
 
-    struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
+    struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context;
     ctx->should_capture_next_compute = true;
 }
 
diff --git a/ggml-metal.metal b/ggml/src/ggml-metal.metal
similarity index 84%
rename from ggml-metal.metal
rename to ggml/src/ggml-metal.metal
index 386e9195fcffa..3bb37d32aced0 100644
--- a/ggml-metal.metal
+++ b/ggml/src/ggml-metal.metal
@@ -168,6 +168,53 @@ kernel void kernel_div(
     }
 }
 
+template<typename T>
+kernel void kernel_repeat(
+        device const char * src0,
+        device       char * dst,
+        constant  int64_t & ne00,
+        constant  int64_t & ne01,
+        constant  int64_t & ne02,
+        constant  int64_t & ne03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
+        constant  int64_t & ne0,
+        constant  int64_t & ne1,
+        constant  int64_t & ne2,
+        constant  int64_t & ne3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    const int64_t i3 = tgpig.z;
+    const int64_t i2 = tgpig.y;
+    const int64_t i1 = tgpig.x;
+
+    const int64_t i03 = i3 % ne03;
+    const int64_t i02 = i2 % ne02;
+    const int64_t i01 = i1 % ne01;
+
+    device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    device       char * dst_ptr  = dst  +  i3*nb3  +  i2*nb2  +  i1*nb1 ;
+
+    for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
+        const int i00 = i0 % ne00;
+        *((device T *)(dst_ptr + i0*nb0)) = *((device T *)(src0_ptr + i00*nb00));
+    }
+}
+
+typedef decltype(kernel_repeat<float>) kernel_repeat_t;
+
+template [[host_name("kernel_repeat_f32")]] kernel kernel_repeat_t kernel_repeat<float>;
+template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat<half>;
+template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat<int>;
+template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat<short>;
+
 // assumption: src1 is a row
 // broadcast src1 into src0
 kernel void kernel_add_row(
@@ -1172,9 +1219,10 @@ kernel void kernel_mul_mv_q8_0_f32(
     kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg);
 }
 
-#define N_F32_F32 4
+#define N_MV_T_T 4
 
-void kernel_mul_mv_f32_f32_impl(
+template<typename T0, typename T04, typename T1, typename T14>
+void kernel_mul_mv_impl(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
@@ -1192,13 +1240,12 @@ void kernel_mul_mv_f32_f32_impl(
                   uint64_t   nb12,
                    int64_t   ne0,
                    int64_t   ne1,
-                     uint    r2,
-                     uint    r3,
-                     uint3   tgpig,
-                     uint    tiisg) {
-
+                   uint      r2,
+                   uint      r3,
+                   uint3     tgpig,
+                   uint      tiisg) {
     const int64_t r0 = tgpig.x;
-    const int64_t rb = tgpig.y*N_F32_F32;
+    const int64_t rb = tgpig.y*N_MV_T_T;
     const int64_t im = tgpig.z;
 
     const uint i12 = im%ne12;
@@ -1206,20 +1253,20 @@ void kernel_mul_mv_f32_f32_impl(
 
     const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
 
-    device const float * x = (device const float *) (src0 + offset0);
+    device const T0 * x = (device const T0 *) (src0 + offset0);
 
     if (ne00 < 128) {
-        for (int row = 0; row < N_F32_F32; ++row) {
+        for (int row = 0; row < N_MV_T_T; ++row) {
             int r1 = rb + row;
             if (r1 >= ne11) {
                 break;
             }
 
-            device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
+            device const T1 * y = (device const T1 *) (src1 + r1*nb11 + im*nb12);
 
             float sumf = 0;
             for (int i = tiisg; i < ne00; i += 32) {
-                sumf += (float) x[i] * (float) y[i];
+                sumf += (T0) x[i] * (T1) y[i];
             }
 
             float all_sum = simd_sum(sumf);
@@ -1228,32 +1275,32 @@ void kernel_mul_mv_f32_f32_impl(
             }
         }
     } else {
-        device const float4 * x4 = (device const float4 *)x;
-        for (int row = 0; row < N_F32_F32; ++row) {
+        device const T04 * x4 = (device const T04 *) x;
+        for (int row = 0; row < N_MV_T_T; ++row) {
             int r1 = rb + row;
             if (r1 >= ne11) {
                 break;
             }
 
-            device const float  * y  = (device const float  *) (src1 + r1*nb11 + im*nb12);
-            device const float4 * y4 = (device const float4 *) y;
+            device const T1  * y  = (device const T1  *) (src1 + r1*nb11 + im*nb12);
+            device const T14 * y4 = (device const T14 *) y;
 
             float sumf = 0;
             for (int i = tiisg; i < ne00/4; i += 32) {
-                for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
+                for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
             }
 
             float all_sum = simd_sum(sumf);
             if (tiisg == 0) {
-                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
+                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]);
                 dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
             }
         }
     }
 }
 
-[[host_name("kernel_mul_mv_f32_f32")]]
-kernel void kernel_mul_mv_f32_f32(
+template<typename T0, typename T04, typename T1, typename T14>
+kernel void kernel_mul_mv(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
@@ -1275,90 +1322,38 @@ kernel void kernel_mul_mv_f32_f32(
         constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]]) {
-    kernel_mul_mv_f32_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
+    kernel_mul_mv_impl<T0, T04, T1, T14>(
+        src0,
+        src1,
+        dst,
+        ne00,
+        ne01,
+        ne02,
+        nb00,
+        nb01,
+        nb02,
+        ne10,
+        ne11,
+        ne12,
+        nb10,
+        nb11,
+        nb12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        tgpig,
+        tiisg);
 }
 
-#define N_F16_F16 4
+typedef decltype(kernel_mul_mv<half, half4, half, half4>) mul_mv_t;
 
-kernel void kernel_mul_mv_f16_f16(
-        device const  char * src0,
-        device const  char * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne11,
-        constant   int64_t & ne12,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]]) {
-
-    const int64_t r0 = tgpig.x;
-    const int64_t rb = tgpig.y*N_F16_F16;
-    const int64_t im = tgpig.z;
-
-    const uint i12 = im%ne12;
-    const uint i13 = im/ne12;
-
-    const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
-
-    device const half * x = (device const half *) (src0 + offset0);
-
-    if (ne00 < 128) {
-        for (int row = 0; row < N_F16_F16; ++row) {
-            int r1 = rb + row;
-            if (r1 >= ne11) {
-                break;
-            }
-
-            device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12);
+template [[host_name("kernel_mul_mv_f32_f32")]]   kernel mul_mv_t kernel_mul_mv<float,  float4,  float,  float4>;
+template [[host_name("kernel_mul_mv_f16_f32")]]   kernel mul_mv_t kernel_mul_mv<half,   half4,   float,  float4>;
+template [[host_name("kernel_mul_mv_f16_f16")]]   kernel mul_mv_t kernel_mul_mv<half,   half4,   half,   half4>;
 
-            float sumf = 0;
-            for (int i = tiisg; i < ne00; i += 32) {
-                sumf += (half) x[i] * (half) y[i];
-            }
-
-            float all_sum = simd_sum(sumf);
-            if (tiisg == 0) {
-                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
-            }
-        }
-    } else {
-        device const half4 * x4 = (device const half4 *)x;
-        for (int row = 0; row < N_F16_F16; ++row) {
-            int r1 = rb + row;
-            if (r1 >= ne11) {
-                break;
-            }
-
-            device const half  * y  = (device const half  *) (src1 + r1*nb11 + im*nb12);
-            device const half4 * y4 = (device const half4 *) y;
-
-            float sumf = 0;
-            for (int i = tiisg; i < ne00/4; i += 32) {
-                for (int k = 0; k < 4; ++k) sumf += (half) x4[i][k] * y4[i][k];
-            }
-
-            float all_sum = simd_sum(sumf);
-            if (tiisg == 0) {
-                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (half) x[i] * y[i];
-                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
-            }
-        }
-    }
-}
-
-void kernel_mul_mv_f16_f32_1row_impl(
+template<typename T, typename T4>
+kernel void kernel_mul_mv_1row(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
@@ -1390,7 +1385,7 @@ void kernel_mul_mv_f16_f32_1row_impl(
 
     const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
 
-    device const half  * x = (device const half  *) (src0 + offset0);
+    device const T     * x = (device const T     *) (src0 + offset0);
     device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
 
     float sumf = 0;
@@ -1403,153 +1398,29 @@ void kernel_mul_mv_f16_f32_1row_impl(
             dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
         }
     } else {
-        device const half4  * x4 = (device const half4  *) x;
+        device const T4     * x4 = (device const T4     *) x;
         device const float4 * y4 = (device const float4 *) y;
+
         for (int i = tiisg; i < ne00/4; i += 32) {
-            for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k];
+            for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
         }
+
         float all_sum = simd_sum(sumf);
+
         if (tiisg == 0) {
-            for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
+            for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]);
             dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
         }
     }
 }
 
-[[host_name("kernel_mul_mv_f16_f32_1row")]]
-kernel void kernel_mul_mv_f16_f32_1row(
-        device const  char * src0,
-        device const  char * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne11,
-        constant   int64_t & ne12,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]]) {
-    kernel_mul_mv_f16_f32_1row_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
-}
-
-#define N_F16_F32 4
-
-void kernel_mul_mv_f16_f32_impl(
-        device const  char * src0,
-        device const  char * src1,
-        device       float * dst,
-                   int64_t   ne00,
-                   int64_t   ne01,
-                   int64_t   ne02,
-                  uint64_t   nb00,
-                  uint64_t   nb01,
-                  uint64_t   nb02,
-                   int64_t   ne10,
-                   int64_t   ne11,
-                   int64_t   ne12,
-                  uint64_t   nb10,
-                  uint64_t   nb11,
-                  uint64_t   nb12,
-                   int64_t   ne0,
-                   int64_t   ne1,
-                   uint      r2,
-                   uint      r3,
-                   uint3     tgpig,
-                   uint      tiisg) {
-
-    const int64_t r0 = tgpig.x;
-    const int64_t rb = tgpig.y*N_F16_F32;
-    const int64_t im = tgpig.z;
-
-    const uint i12 = im%ne12;
-    const uint i13 = im/ne12;
-
-    const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
-
-    device const half * x = (device const half *) (src0 + offset0);
-
-    if (ne00 < 128) {
-        for (int row = 0; row < N_F16_F32; ++row) {
-            int r1 = rb + row;
-            if (r1 >= ne11) {
-                break;
-            }
-
-            device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
+typedef decltype(kernel_mul_mv_1row<half, half4>) mul_mv_1row_t;
 
-            float sumf = 0;
-            for (int i = tiisg; i < ne00; i += 32) {
-                sumf += (float) x[i] * (float) y[i];
-            }
-
-            float all_sum = simd_sum(sumf);
-            if (tiisg == 0) {
-                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
-            }
-        }
-    } else {
-        device const half4 * x4 = (device const half4 *)x;
-        for (int row = 0; row < N_F16_F32; ++row) {
-            int r1 = rb + row;
-            if (r1 >= ne11) {
-                break;
-            }
-
-            device const float  * y  = (device const float  *) (src1 + r1*nb11 + im*nb12);
-            device const float4 * y4 = (device const float4 *) y;
-
-            float sumf = 0;
-            for (int i = tiisg; i < ne00/4; i += 32) {
-                for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
-            }
-
-            float all_sum = simd_sum(sumf);
-            if (tiisg == 0) {
-                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
-                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
-            }
-        }
-    }
-}
-
-[[host_name("kernel_mul_mv_f16_f32")]]
-kernel void kernel_mul_mv_f16_f32(
-        device const  char * src0,
-        device const  char * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne11,
-        constant   int64_t & ne12,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint tiisg[[thread_index_in_simdgroup]]) {
-    kernel_mul_mv_f16_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
-}
+template [[host_name("kernel_mul_mv_f16_f32_1row")]]  kernel mul_mv_1row_t kernel_mul_mv_1row<half,   half4>;
 
 // Assumes row size (ne00) is a multiple of 4
-kernel void kernel_mul_mv_f16_f32_l4(
+template<typename T, typename T4>
+kernel void kernel_mul_mv_l4(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
@@ -1581,14 +1452,14 @@ kernel void kernel_mul_mv_f16_f32_l4(
 
     const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
 
-    device const half4 * x4 = (device const half4 *) (src0 + offset0);
+    device const T4 * x4 = (device const T4 *) (src0 + offset0);
 
     for (int r1 = 0; r1 < nrows; ++r1) {
         device const float4 * y4 = (device const float4 *) (src1 + r1*nb11 + im*nb12);
 
         float sumf = 0;
         for (int i = tiisg; i < ne00/4; i += 32) {
-            for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
+            for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
         }
 
         float all_sum = simd_sum(sumf);
@@ -1598,6 +1469,10 @@ kernel void kernel_mul_mv_f16_f32_l4(
     }
 }
 
+typedef decltype(kernel_mul_mv_l4<half, half4>) mul_mv_l4_t;
+
+template [[host_name("kernel_mul_mv_f16_f32_l4")]]  kernel mul_mv_l4_t kernel_mul_mv_l4<half, half4>;
+
 static float rope_yarn_ramp(const float low, const float high, const int i0) {
     const float y = (i0 / 2 - low) / max(0.001f, high - low);
     return 1.0f - min(1.0f, max(0.0f, y));
@@ -1607,8 +1482,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) {
 // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
 static void rope_yarn(
     float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale,
-    thread float * cos_theta, thread float * sin_theta
-) {
+    thread float * cos_theta, thread float * sin_theta) {
     // Get n-d rotational scaling corrected for extrapolation
     float theta_interp = freq_scale * theta_extrap;
     float theta = theta_interp;
@@ -1625,21 +1499,23 @@ static void rope_yarn(
 
 // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
 // `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
-static float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) {
-    return n_dims * log(n_orig_ctx / (n_rot * 2 * M_PI_F)) / (2 * log(base));
+static float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) {
+    return n_dims * log(n_ctx_orig / (n_rot * 2 * M_PI_F)) / (2 * log(base));
 }
 
 static void rope_yarn_corr_dims(
-    int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
+    int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]
 ) {
     // start and end correction dims
-    dims[0] = max(0.0f,         floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base)));
-    dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base)));
+    dims[0] = max(0.0f,         floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base)));
+    dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base)));
 }
 
-typedef void (rope_t)(
+template<typename T>
+kernel void kernel_rope_norm(
         device const    void * src0,
         device const int32_t * src1,
+        device const   float * src2,
         device         float * dst,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -1659,8 +1535,7 @@ typedef void (rope_t)(
         constant    uint64_t & nb3,
         constant         int & n_past,
         constant         int & n_dims,
-        constant         int & mode,
-        constant         int & n_orig_ctx,
+        constant         int & n_ctx_orig,
         constant       float & freq_base,
         constant       float & freq_scale,
         constant       float & ext_factor,
@@ -1669,12 +1544,55 @@ typedef void (rope_t)(
         constant       float & beta_slow,
         uint  tiitg[[thread_index_in_threadgroup]],
         uint3 tptg[[threads_per_threadgroup]],
-        uint3 tgpig[[threadgroup_position_in_grid]]);
+        uint3 tgpig[[threadgroup_position_in_grid]]) {
+    const int64_t i3 = tgpig[2];
+    const int64_t i2 = tgpig[1];
+    const int64_t i1 = tgpig[0];
+
+    float corr_dims[2];
+    rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
+
+    device const int32_t * pos = src1;
+
+    const float theta_base = (float) pos[i2];
+    const float inv_ndims = -1.f/n_dims;
+
+    float cos_theta;
+    float sin_theta;
+
+    for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) {
+        if (i0 < n_dims) {
+            const int64_t ic = i0/2;
+
+            const float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+            device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            device       T * dst_data  = (device T *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            const float x0 = src[0];
+            const float x1 = src[1];
+
+            dst_data[0] = x0*cos_theta - x1*sin_theta;
+            dst_data[1] = x0*sin_theta + x1*cos_theta;
+        } else {
+            device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            device       T * dst_data  = (device T *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
 
 template<typename T>
-kernel void kernel_rope(
+kernel void kernel_rope_neox(
         device const    void * src0,
         device const int32_t * src1,
+        device const   float * src2,
         device         float * dst,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -1694,8 +1612,7 @@ kernel void kernel_rope(
         constant    uint64_t & nb3,
         constant         int & n_past,
         constant         int & n_dims,
-        constant         int & mode,
-        constant         int & n_orig_ctx,
+        constant         int & n_ctx_orig,
         constant       float & freq_base,
         constant       float & freq_scale,
         constant       float & ext_factor,
@@ -1709,71 +1626,53 @@ kernel void kernel_rope(
     const int64_t i2 = tgpig[1];
     const int64_t i1 = tgpig[0];
 
-    const bool is_neox = mode & 2;
-
     float corr_dims[2];
-    rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
+    rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
 
     device const int32_t * pos = src1;
 
-    const int64_t p = pos[i2];
-
-    const float theta_0 = (float)p;
+    const float theta_base = (float) pos[i2];
     const float inv_ndims = -1.f/n_dims;
 
-    if (!is_neox) {
-        for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) {
-
-            const float theta = theta_0 * pow(freq_base, inv_ndims*i0);
-            float cos_theta, sin_theta;
-            rope_yarn(theta, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-            device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-            device       T * dst_data  = (device T *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
-
-            const T x0 = src[0];
-            const T x1 = src[1];
-
-            dst_data[0] = x0*cos_theta - x1*sin_theta;
-            dst_data[1] = x0*sin_theta + x1*cos_theta;
-        }
-    } else {
-        for (int64_t ic = 2*tiitg; ic < ne0; ic += 2*tptg.x) {
-            if (ic < n_dims) {
-                const int64_t ib = 0;
+    float cos_theta;
+    float sin_theta;
 
-                // simplified from `(ib * n_dims + ic) * inv_ndims`
-                const float cur_rot = inv_ndims*ic - ib;
+    for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) {
+        if (i0 < n_dims) {
+            const int64_t ic = i0/2;
 
-                const float theta = theta_0 * pow(freq_base, cur_rot);
-                float cos_theta, sin_theta;
-                rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
+            const float theta = theta_base * pow(freq_base, inv_ndims*i0);
 
-                const int64_t i0 = ib*n_dims + ic/2;
+            const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
 
-                device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                device       T * dst_data  = (device T *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+            rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
 
-                const float x0 = src[0];
-                const float x1 = src[n_dims/2];
+            device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+            device       T * dst_data  = (device T *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
 
-                dst_data[0]        = x0*cos_theta - x1*sin_theta;
-                dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
-            } else {
-                const int64_t i0 = ic;
+            const float x0 = src[0];
+            const float x1 = src[n_dims/2];
 
-                device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                device       T * dst_data  = (device T *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+            dst_data[0]        = x0*cos_theta - x1*sin_theta;
+            dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
+        } else {
+            device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            device       T * dst_data  = (device T *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
 
-                dst_data[0] = src[0];
-                dst_data[1] = src[1];
-            }
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
         }
     }
 }
 
-template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope<float>;
-template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope<half>;
+typedef decltype(kernel_rope_norm<float>) kernel_rope_norm_t;
+typedef decltype(kernel_rope_neox<float>) kernel_rope_neox_t;
+
+template [[host_name("kernel_rope_norm_f32")]] kernel kernel_rope_norm_t kernel_rope_norm<float>;
+template [[host_name("kernel_rope_norm_f16")]] kernel kernel_rope_norm_t kernel_rope_norm<half>;
+
+template [[host_name("kernel_rope_neox_f32")]] kernel kernel_rope_neox_t kernel_rope_neox<float>;
+template [[host_name("kernel_rope_neox_f16")]] kernel kernel_rope_neox_t kernel_rope_neox<half>;
 
 typedef void (im2col_t)(
         device const float * x,
@@ -2204,11 +2103,7 @@ kernel void kernel_flash_attn_ext_f16(
         // pointer to the mask
         device const half * mp = (device const half *) (mask + iq1*nb31);
 
-        // prepare diagonal scale matrix
-        simdgroup_float8x8 mscale(scale);
-
-        // prepare diagonal slope matrix
-        simdgroup_float8x8 mslope(1.0f);
+        float slope = 1.0f;
 
         // ALiBi
         if (max_bias > 0.0f) {
@@ -2217,7 +2112,7 @@ kernel void kernel_flash_attn_ext_f16(
             const float base = h < n_head_log2 ? m0 : m1;
             const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
 
-            mslope = simdgroup_float8x8(pow(base, exph));
+            slope = pow(base, exph);
         }
 
         // loop over the KV cache
@@ -2242,18 +2137,20 @@ kernel void kernel_flash_attn_ext_f16(
                         simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk);
                     }
 
+                    simdgroup_store(mqk, ss + 8*cc, TF, 0, false);
+
+                    const short tx = tiisg%4;
+                    const short ty = tiisg/4;
+
                     if (mask != q) {
                         // mqk = mqk*scale + mask*slope
-                        simdgroup_half8x8 mm;
-                        simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false);
-                        simdgroup_multiply(mm, mslope, mm);
-                        simdgroup_multiply_accumulate(mqk, mqk, mscale, mm);
+                        ss[8*cc + ty*TF + 2*tx + 0] = scale*ss[8*cc + ty*TF + 2*tx + 0] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 0];
+                        ss[8*cc + ty*TF + 2*tx + 1] = scale*ss[8*cc + ty*TF + 2*tx + 1] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 1];
                     } else {
                         // mqk = mqk*scale
-                        simdgroup_multiply(mqk, mscale, mqk);
+                        ss[8*cc + ty*TF + 2*tx + 0] *= scale;
+                        ss[8*cc + ty*TF + 2*tx + 1] *= scale;
                     }
-
-                    simdgroup_store(mqk, ss + 8*cc, TF, 0, false);
                 }
             }
 
@@ -2416,7 +2313,7 @@ template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f
 template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>;
 template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>;
 template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>;
-template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>;
+//template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>;
 
 template<int64_t D, int64_t Q = 1, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup
 kernel void kernel_flash_attn_ext_vec_f16(
@@ -2694,93 +2591,12 @@ kernel void kernel_flash_attn_ext_vec_f16(
 }
 
 template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>;
-template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>;
-
-kernel void kernel_cpy_f16_f16(
-        device  const half * src0,
-        device        half * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne03,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant  uint64_t & nb03,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   int64_t & ne2,
-        constant   int64_t & ne3,
-        constant  uint64_t & nb0,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        constant  uint64_t & nb3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
-
-    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
-
-    const int64_t i3 = n / (ne2*ne1*ne0);
-    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
-    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
-
-    device half * dst_data = (device half *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-
-    for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        device const half * src = (device half *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
-        dst_data[i00] = src[0];
-    }
-}
-
-kernel void kernel_cpy_f16_f32(
-        device  const half * src0,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne03,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant  uint64_t & nb03,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   int64_t & ne2,
-        constant   int64_t & ne3,
-        constant  uint64_t & nb0,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        constant  uint64_t & nb3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
-
-    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
-
-    const int64_t i3 = n / (ne2*ne1*ne0);
-    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
-    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
-
-    device float * dst_data = (device float *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-
-    for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        device const half * src = (device half *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
-        dst_data[i00] = src[0];
-    }
-}
+//template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>;
 
-kernel void kernel_cpy_f32_f16(
-        device const float * src0,
-        device        half * dst,
+template<typename T0, typename T1>
+kernel void kernel_cpy(
+        device  const void * src0,
+        device        void * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
@@ -2811,57 +2627,20 @@ kernel void kernel_cpy_f32_f16(
     const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
     const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
 
-    device half * dst_data = (device half *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+    device T1 * dst_data = (device T1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
 
     for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
-
-        // TODO: is there a better way to handle -INFINITY?
-        dst_data[i00] = src[0] == -INFINITY ? -MAXHALF : src[0];
+        device const T0 * src = (device T0 *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+        dst_data[i00] = (T1) src[0];
     }
 }
 
-kernel void kernel_cpy_f32_f32(
-        device const float * src0,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne03,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant  uint64_t & nb03,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   int64_t & ne2,
-        constant   int64_t & ne3,
-        constant  uint64_t & nb0,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        constant  uint64_t & nb3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
-
-    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
-
-    const int64_t i3 = n / (ne2*ne1*ne0);
-    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
-    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
-
-    device float * dst_data = (device float *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-
-    for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+typedef decltype(kernel_cpy<float, float>) kernel_cpy_t;
 
-        dst_data[i00] = src[0];
-    }
-}
+template [[host_name("kernel_cpy_f32_f32")]]  kernel kernel_cpy_t kernel_cpy<float,  float>;
+template [[host_name("kernel_cpy_f32_f16")]]  kernel kernel_cpy_t kernel_cpy<float,  half>;
+template [[host_name("kernel_cpy_f16_f16")]]  kernel kernel_cpy_t kernel_cpy<half,   half>;
+template [[host_name("kernel_cpy_f16_f32")]]  kernel kernel_cpy_t kernel_cpy<half,   float>;
 
 kernel void kernel_cpy_f32_q8_0(
         device const float * src0,
@@ -3318,31 +3097,30 @@ kernel void kernel_concat(
     constant  uint64_t & nb1,
     constant  uint64_t & nb2,
     constant  uint64_t & nb3,
+    constant   int32_t & dim,
     uint3 tgpig[[threadgroup_position_in_grid]],
     uint3 tpitg[[thread_position_in_threadgroup]],
     uint3   ntg[[threads_per_threadgroup]]) {
 
-    const int64_t i03 = tgpig.z;
-    const int64_t i02 = tgpig.y;
-    const int64_t i01 = tgpig.x;
+    const int64_t i3 = tgpig.z;
+    const int64_t i2 = tgpig.y;
+    const int64_t i1 = tgpig.x;
 
-    const int64_t i13 = i03 % ne13;
-    const int64_t i12 = i02 % ne12;
-    const int64_t i11 = i01 % ne11;
+    int64_t o[4] = {0, 0, 0, 0};
+    o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
 
-    device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + tpitg.x*nb00;
-    device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11 + tpitg.x*nb10;
-    device       char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1  + tpitg.x*nb0;
+    device const float * x;
 
     for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
-        if (i02 < ne02) {
-            ((device float *)dst_ptr)[0] = ((device float *)src0_ptr)[0];
-            src0_ptr += ntg.x*nb00;
+        if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+            x = (device const float *)(src0 + (i3       )*nb03 + (i2       )*nb02 + (i1       )*nb01 + (i0       )*nb00);
         } else {
-            ((device float *)dst_ptr)[0] = ((device float *)src1_ptr)[0];
-            src1_ptr += ntg.x*nb10;
+            x = (device const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10);
         }
-        dst_ptr += ntg.x*nb0;
+
+        device float * y = (device float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+        *y = *x;
     }
 }
 
@@ -3385,7 +3163,6 @@ void kernel_mul_mv_q2_K_f32_impl(
 
     const int step = sizeof(block_q2_K) * nb;
 
-#if QK_K == 256
     const int ix = tiisg/8;  // 0...3
     const int it = tiisg%8;  // 0...7
     const int iq = it/4;     // 0 or 1
@@ -3437,57 +3214,6 @@ void kernel_mul_mv_q2_K_f32_impl(
 
         y4 += 4 * QK_K;
     }
-#else
-    const int ix = tiisg/2;  // 0...15
-    const int it = tiisg%2;  // 0...1
-
-    device const float * y4 = y + ix * QK_K + 8 * it;
-
-    for (int ib = ix; ib < nb; ib += 16) {
-
-        float4 sumy = {0.f, 0.f, 0.f, 0.f};
-        for (int i = 0; i < 8; ++i) {
-            yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0];
-            yl[i+ 8] = y4[i+16]; sumy[1] += yl[i+ 8];
-            yl[i+16] = y4[i+32]; sumy[2] += yl[i+16];
-            yl[i+24] = y4[i+48]; sumy[3] += yl[i+24];
-        }
-
-        device const uint8_t  * sc = (device const uint8_t  *)x[ib].scales;
-        device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 4 * it;
-        device const half     * dh = &x[ib].d;
-
-        for (int row = 0; row < N_DST; row++) {
-
-            float4 acc1 = {0.f, 0.f, 0.f, 0.f};
-            float4 acc2 = {0.f, 0.f, 0.f, 0.f};
-            for (int i = 0; i < 8; i += 2) {
-                acc1[0] += yl[i+ 0] * (qs[i/2] & 0x0003);
-                acc2[0] += yl[i+ 1] * (qs[i/2] & 0x0300);
-                acc1[1] += yl[i+ 8] * (qs[i/2] & 0x000c);
-                acc2[1] += yl[i+ 9] * (qs[i/2] & 0x0c00);
-                acc1[2] += yl[i+16] * (qs[i/2] & 0x0030);
-                acc2[2] += yl[i+17] * (qs[i/2] & 0x3000);
-                acc1[3] += yl[i+24] * (qs[i/2] & 0x00c0);
-                acc2[3] += yl[i+25] * (qs[i/2] & 0xc000);
-            }
-
-            float dall = dh[0];
-            float dmin = dh[1];
-            sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc2[0]) * (sc[0] & 0xF) * 1.f/ 1.f +
-                                 (acc1[1] + 1.f/256.f * acc2[1]) * (sc[1] & 0xF) * 1.f/ 4.f +
-                                 (acc1[2] + 1.f/256.f * acc2[2]) * (sc[2] & 0xF) * 1.f/16.f +
-                                 (acc1[3] + 1.f/256.f * acc2[3]) * (sc[3] & 0xF) * 1.f/64.f) -
-                         dmin * (sumy[0] * (sc[0] >> 4) + sumy[1] * (sc[1] >> 4) + sumy[2] * (sc[2] >> 4) + sumy[3] * (sc[3] >> 4));
-
-            qs += step/2;
-            sc += step;
-            dh += step/2;
-        }
-
-        y4 += 16 * QK_K;
-    }
-#endif
 
     for (int row = 0; row < N_DST; ++row) {
         all_sum = simd_sum(sumf[row]);
@@ -3525,7 +3251,6 @@ kernel void kernel_mul_mv_q2_K_f32(
     kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
 }
 
-#if QK_K == 256
 void kernel_mul_mv_q3_K_f32_impl(
         device const  void * src0,
         device const float * src1,
@@ -3640,128 +3365,50 @@ void kernel_mul_mv_q3_K_f32_impl(
                 s2 += yl[l+1] * (qs & qm[il/2][1]);
                 s3 += ((h[l/2] & hm[0]) ? 0.f : yl[l+0]) + ((h[l/2] & hm[1]) ? 0.f : yl[l+1]);
                 s4 += yl[l+16] * (qs & qm[il/2][2]);
-                s5 += yl[l+17] * (qs & qm[il/2][3]);
-                s6 += ((h[l/2] & hm[2]) ? 0.f : yl[l+16]) + ((h[l/2] & hm[3]) ? 0.f : yl[l+17]);
-            }
-            float d1 = d_all * (s1 + 1.f/256.f * s2 - s3*v1);
-            float d2 = d_all * (s4 + 1.f/256.f * s5 - s6*v2);
-            sumf1[row] += d1 * (scales[0] - 32);
-            sumf2[row] += d2 * (scales[2] - 32);
-
-            s1 = s2 = s3 = s4 = s5 = s6 = 0;
-            for (int l = 0; l < n; l += 2) {
-                const int32_t qs = q[l/2+8];
-                s1 += yl[l+8] * (qs & qm[il/2][0]);
-                s2 += yl[l+9] * (qs & qm[il/2][1]);
-                s3 += ((h[l/2+8] & hm[0]) ? 0.f : yl[l+8]) + ((h[l/2+8] & hm[1]) ? 0.f : yl[l+9]);
-                s4 += yl[l+24] * (qs & qm[il/2][2]);
-                s5 += yl[l+25] * (qs & qm[il/2][3]);
-                s6 += ((h[l/2+8] & hm[2]) ? 0.f : yl[l+24]) + ((h[l/2+8] & hm[3]) ? 0.f : yl[l+25]);
-            }
-            d1 = d_all * (s1 + 1.f/256.f * s2 - s3*v1);
-            d2 = d_all * (s4 + 1.f/256.f * s5 - s6*v2);
-            sumf1[row] += d1 * (scales[1] - 32);
-            sumf2[row] += d2 * (scales[3] - 32);
-
-            q  += step;
-            h  += step;
-            a  += step;
-            dh += step;
-
-        }
-
-        y1 += 4 * QK_K;
-
-    }
-
-    for (int row = 0; row < 2; ++row) {
-        const float sumf = (sumf1[row] + 0.25f * sumf2[row]) / (1 << shift);
-        sumf1[row] = simd_sum(sumf);
-    }
-    if (tiisg == 0) {
-        for (int row = 0; row < 2; ++row) {
-            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = sumf1[row];
-        }
-    }
-}
-#else
-void kernel_mul_mv_q3_K_f32_impl(
-        device const  void * src0,
-        device const float * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        threadgroup int8_t * shared_values [[threadgroup(0)]],
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
-
-    const int nb = ne00/QK_K;
-
-    const int64_t r0 = tgpig.x;
-    const int64_t r1 = tgpig.y;
-    const int64_t im = tgpig.z;
-
-    const int row = 2 * r0 + sgitg;
-
-    const uint i12 = im%ne12;
-    const uint i13 = im/ne12;
+                s5 += yl[l+17] * (qs & qm[il/2][3]);
+                s6 += ((h[l/2] & hm[2]) ? 0.f : yl[l+16]) + ((h[l/2] & hm[3]) ? 0.f : yl[l+17]);
+            }
+            float d1 = d_all * (s1 + 1.f/256.f * s2 - s3*v1);
+            float d2 = d_all * (s4 + 1.f/256.f * s5 - s6*v2);
+            sumf1[row] += d1 * (scales[0] - 32);
+            sumf2[row] += d2 * (scales[2] - 32);
 
-    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+            s1 = s2 = s3 = s4 = s5 = s6 = 0;
+            for (int l = 0; l < n; l += 2) {
+                const int32_t qs = q[l/2+8];
+                s1 += yl[l+8] * (qs & qm[il/2][0]);
+                s2 += yl[l+9] * (qs & qm[il/2][1]);
+                s3 += ((h[l/2+8] & hm[0]) ? 0.f : yl[l+8]) + ((h[l/2+8] & hm[1]) ? 0.f : yl[l+9]);
+                s4 += yl[l+24] * (qs & qm[il/2][2]);
+                s5 += yl[l+25] * (qs & qm[il/2][3]);
+                s6 += ((h[l/2+8] & hm[2]) ? 0.f : yl[l+24]) + ((h[l/2+8] & hm[3]) ? 0.f : yl[l+25]);
+            }
+            d1 = d_all * (s1 + 1.f/256.f * s2 - s3*v1);
+            d2 = d_all * (s4 + 1.f/256.f * s5 - s6*v2);
+            sumf1[row] += d1 * (scales[1] - 32);
+            sumf2[row] += d2 * (scales[3] - 32);
 
-    device const block_q3_K * x = (device const block_q3_K *) src0 + row*nb + offset0;
-    device const float     * yy = (device const float      *) src1 + r1*ne10 + im*ne00*ne1;
+            q  += step;
+            h  += step;
+            a  += step;
+            dh += step;
 
-    const int ix = tiisg/4;
-    const int il = 4 * (tiisg%4);// 0, 4, 8, 12
-    const int iq = il/8;         // 0, 0, 1, 1
-    const int in = il%8;         // 0, 4, 0, 4
-
-    float2 sum = {0.f, 0.f};
-
-    for (int i = ix; i < nb; i += 8) {
-
-        const float d_all = (float)(x[i].d);
-
-        device const uint16_t * q = (device const uint16_t *)(x[i].qs + il);
-        device const uint16_t * h = (device const uint16_t *)(x[i].hmask + in);
-        device const uint16_t * s = (device const uint16_t *)(x[i].scales);
-        device const float    * y = yy + i * QK_K + il;
-
-        const float d1 = d_all * ((int32_t)(s[0] & 0x000F) - 8);
-        const float d2 = d_all * ((int32_t)(s[0] & 0x00F0) - 128) * 1.f/64.f;
-        const float d3 = d_all * ((int32_t)(s[0] & 0x0F00) - 2048) * 1.f/4096.f;
-        const float d4 = d_all * ((int32_t)(s[0] & 0xF000) - 32768) * 1.f/262144.f;
-
-        for (int l = 0; l < 4; l += 2) {
-            const uint16_t hm = h[l/2] >> iq;
-            sum[0] += y[l+ 0] * d1 * ((int32_t)(q[l/2] & 0x0003) - ((hm & 0x0001) ? 0 :  4))
-                    + y[l+16] * d2 * ((int32_t)(q[l/2] & 0x000c) - ((hm & 0x0004) ? 0 : 16))
-                    + y[l+32] * d3 * ((int32_t)(q[l/2] & 0x0030) - ((hm & 0x0010) ? 0 : 64))
-                    + y[l+48] * d4 * ((int32_t)(q[l/2] & 0x00c0) - ((hm & 0x0040) ? 0 : 256));
-            sum[1] += y[l+ 1] * d1 * ((int32_t)(q[l/2] & 0x0300) - ((hm & 0x0100) ? 0 : 1024))
-                    + y[l+17] * d2 * ((int32_t)(q[l/2] & 0x0c00) - ((hm & 0x0400) ? 0 : 4096))
-                    + y[l+33] * d3 * ((int32_t)(q[l/2] & 0x3000) - ((hm & 0x1000) ? 0 : 16384))
-                    + y[l+49] * d4 * ((int32_t)(q[l/2] & 0xc000) - ((hm & 0x4000) ? 0 : 65536));
         }
 
+        y1 += 4 * QK_K;
+
     }
-    const float sumf = sum[0] + sum[1] * 1.f/256.f;
 
-    const float tot = simd_sum(sumf);
+    for (int row = 0; row < 2; ++row) {
+        const float sumf = (sumf1[row] + 0.25f * sumf2[row]) / (1 << shift);
+        sumf1[row] = simd_sum(sumf);
+    }
     if (tiisg == 0) {
-        dst[r1*ne0 + im*ne0*ne1 + row] = tot;
+        for (int row = 0; row < 2; ++row) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = sumf1[row];
+        }
     }
-
 }
-#endif
 
 [[host_name("kernel_mul_mv_q3_K_f32")]]
 kernel void kernel_mul_mv_q3_K_f32(
@@ -3791,7 +3438,6 @@ kernel void kernel_mul_mv_q3_K_f32(
     kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg);
 }
 
-#if QK_K == 256
 void kernel_mul_mv_q4_K_f32_impl(
         device const  void * src0,
         device const float * src1,
@@ -3905,103 +3551,6 @@ void kernel_mul_mv_q4_K_f32_impl(
         }
     }
 }
-#else
-void kernel_mul_mv_q4_K_f32_impl(
-        device const  void * src0,
-        device const float * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        threadgroup int8_t * shared_values [[threadgroup(0)]],
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]],
-        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
-
-    const int ix = tiisg/4;  // 0...7
-    const int it = tiisg%4;  // 0...3
-
-    const int nb = ne00/QK_K;
-    const int r0 = tgpig.x;
-    const int r1 = tgpig.y;
-    const int im = tgpig.z;
-    const int first_row = r0 * N_DST;
-    const int ib_row = first_row * nb;
-
-    const uint i12 = im%ne12;
-    const uint i13 = im/ne12;
-
-    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
-
-    device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0;
-    device const float      * y = (device const float      *) src1 + r1*ne10 + im*ne00*ne1;
-
-    float yl[8];
-    float yh[8];
-    float sumf[N_DST]={0.f}, all_sum;
-
-    const int step = sizeof(block_q4_K) * nb / 2;
-
-    device const float * y4 = y + ix * QK_K + 8 * it;
-
-    uint16_t sc16[4];
-
-    for (int ib = ix; ib < nb; ib += 8) {
-
-        float2 sumy = {0.f, 0.f};
-        for (int i = 0; i < 8; ++i) {
-            yl[i] = y4[i+ 0]; sumy[0] += yl[i];
-            yh[i] = y4[i+32]; sumy[1] += yh[i];
-        }
-
-        device const uint16_t * sc = (device const uint16_t *)x[ib].scales;
-        device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 4 * it;
-        device const half     * dh = x[ib].d;
-
-        for (int row = 0; row < N_DST; row++) {
-
-            sc16[0] = sc[0] & 0x000f;
-            sc16[1] = sc[0] & 0x0f00;
-            sc16[2] = sc[0] & 0x00f0;
-            sc16[3] = sc[0] & 0xf000;
-
-            float2 acc1 = {0.f, 0.f};
-            float2 acc2 = {0.f, 0.f};
-            for (int i = 0; i < 8; i += 2) {
-                acc1[0] += yl[i+0] * (qs[i/2] & 0x000F);
-                acc1[1] += yl[i+1] * (qs[i/2] & 0x0F00);
-                acc2[0] += yh[i+0] * (qs[i/2] & 0x00F0);
-                acc2[1] += yh[i+1] * (qs[i/2] & 0xF000);
-            }
-
-            float dall = dh[0];
-            float dmin = dh[1];
-            sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc16[0] +
-                                 (acc2[0] + 1.f/256.f * acc2[1]) * sc16[1] * 1.f/4096.f) -
-                         dmin * 1.f/16.f * (sumy[0] * sc16[2] + sumy[1] * sc16[3] * 1.f/256.f);
-
-            qs += step;
-            sc += step;
-            dh += step;
-        }
-
-        y4 += 8 * QK_K;
-    }
-
-    for (int row = 0; row < N_DST; ++row) {
-        all_sum = simd_sum(sumf[row]);
-        if (tiisg == 0) {
-            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
-        }
-    }
-}
-#endif
 
 [[host_name("kernel_mul_mv_q4_K_f32")]]
 kernel void kernel_mul_mv_q4_K_f32(
@@ -4069,8 +3618,6 @@ void kernel_mul_mv_q5_K_f32_impl(
 
     const int step = sizeof(block_q5_K) * nb;
 
-#if QK_K == 256
-#
     float yl[16], yh[16];
 
     const uint16_t kmask1 = 0x3f3f;
@@ -4153,54 +3700,6 @@ void kernel_mul_mv_q5_K_f32_impl(
         y1 += 4 * QK_K;
 
     }
-#else
-    float yl[8], yh[8];
-
-    const int il = 4 * (tiisg/8);  // 0, 4, 8, 12
-    const int ix = tiisg%8;
-    const int iq = il/8;         // 0, 0, 1, 1
-    const int in = il%8;         // 0, 4, 0, 4
-
-    device const float * y = yy + ix*QK_K + il;
-
-    for (int i = ix; i < nb; i += 8) {
-
-        for (int l = 0; l < 4; ++l) {
-            yl[l+0] = y[l+ 0];
-            yl[l+4] = y[l+16];
-            yh[l+0] = y[l+32];
-            yh[l+4] = y[l+48];
-        }
-
-        device const half * dh = &x[i].d;
-        device const uint8_t * q = x[i].qs + il;
-        device const uint8_t * h = x[i].qh + in;
-        device const int8_t  * s = x[i].scales;
-
-        for (int row = 0; row < 2; ++row) {
-
-            const float d = dh[0];
-
-            float2 acc = {0.f, 0.f};
-            for (int l = 0; l < 4; ++l) {
-                const uint8_t hl = h[l] >> iq;
-                acc[0] += yl[l+0] * s[0] * ((int16_t)(q[l+ 0] & 0x0F) - (hl & 0x01 ? 0 : 16))
-                        + yl[l+4] * s[1] * ((int16_t)(q[l+16] & 0x0F) - (hl & 0x04 ? 0 : 16));
-                acc[1] += yh[l+0] * s[2] * ((int16_t)(q[l+ 0] & 0xF0) - (hl & 0x10 ? 0 : 256))
-                        + yh[l+4] * s[3] * ((int16_t)(q[l+16] & 0xF0) - (hl & 0x40 ? 0 : 256));
-            }
-            sumf[row] += d * (acc[0] + 1.f/16.f * acc[1]);
-
-            q += step;
-            h += step;
-            s += step;
-            dh += step/2;
-
-        }
-
-        y += 8 * QK_K;
-    }
-#endif
 
     for (int row = 0; row < 2; ++row) {
         const float tot = simd_sum(sumf[row]);
@@ -4279,7 +3778,6 @@ void kernel_mul_mv_q6_K_f32_impl(
 
     float sumf = 0;
 
-#if QK_K == 256
     const int tid  = tiisg/2;
     const int ix   = tiisg%2;
     const int ip   = tid/8;         // 0 or 1
@@ -4315,30 +3813,6 @@ void kernel_mul_mv_q6_K_f32_impl(
 
     }
 
-#else
-    const int ix  = tiisg/4;
-    const int il  = 4*(tiisg%4);
-
-    for (int i = ix; i < nb; i += 8) {
-        device const float * y = yy + i * QK_K + il;
-        device const uint8_t * ql = x[i].ql + il;
-        device const uint8_t * qh = x[i].qh + il;
-        device const int8_t  * s  = x[i].scales;
-
-        const float d = x[i].d;
-
-        float4 sums = {0.f, 0.f, 0.f, 0.f};
-        for (int l = 0; l < 4; ++l) {
-            sums[0] += y[l+ 0] * ((int8_t)((ql[l+ 0] & 0xF) | ((qh[l] & kmask1) << 4)) - 32);
-            sums[1] += y[l+16] * ((int8_t)((ql[l+16] & 0xF) | ((qh[l] & kmask2) << 2)) - 32);
-            sums[2] += y[l+32] * ((int8_t)((ql[l+ 0] >>  4) | ((qh[l] & kmask3) >> 0)) - 32);
-            sums[3] += y[l+48] * ((int8_t)((ql[l+16] >>  4) | ((qh[l] & kmask4) >> 2)) - 32);
-        }
-        sumf += d * (sums[0] * s[0] + sums[1] * s[1] + sums[2] * s[2] + sums[3] * s[3]);
-    }
-
-#endif
-
     const float tot = simd_sum(sumf);
     if (tiisg == 0) {
         dst[r1*ne0 + im*ne0*ne1 + row] = tot;
@@ -5172,9 +4646,7 @@ void kernel_mul_mv_iq1_m_f32_impl(
 
     device const float * y4 = y + 32 * ix;
 
-#if QK_K != 64
     iq1m_scale_t scale;
-#endif
 
     for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
 
@@ -5195,10 +4667,7 @@ void kernel_mul_mv_iq1_m_f32_impl(
         device const uint16_t * sc = (device const uint16_t *)xr->scales;
 
         for (int row = 0; row < N_DST; row++) {
-
-#if QK_K != 64
             scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-#endif
 
             constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
             constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
@@ -5214,14 +4683,9 @@ void kernel_mul_mv_iq1_m_f32_impl(
             }
             const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
             const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
-#if QK_K == 64
-            const float d = (float) *((device const half *)(sc - 1));
-            sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) +
-                              (sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1));
-#else
+
             sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) +
                                              (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1));
-#endif
 
             sc += nb*sizeof(block_iq1_m)/2;
             qs += nb*sizeof(block_iq1_m);
@@ -5293,7 +4757,7 @@ void kernel_mul_mv_iq4_nl_f32_impl(
         device const float4 * y4 = (device const float4 *)yb;
         yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
 
-        for (int row = 0; row < 2; ++row) {
+        for (int row = 0; row < 2 && first_row + row < ne01; ++row) {
 
             device const block_iq4_nl & xb = x[row*nb + ib];
             device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it);
@@ -5325,7 +4789,7 @@ void kernel_mul_mv_iq4_nl_f32_impl(
         yb += 16 * QK4_NL;
     }
 
-    for (int row = 0; row < 2; ++row) {
+    for (int row = 0; row < 2 && first_row + row < ne01; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
@@ -5333,7 +4797,6 @@ void kernel_mul_mv_iq4_nl_f32_impl(
     }
 }
 
-#if QK_K != 64
 void kernel_mul_mv_iq4_xs_f32_impl(
         device const  void * src0,
         device const float * src1,
@@ -5428,7 +4891,6 @@ void kernel_mul_mv_iq4_xs_f32_impl(
         }
     }
 }
-#endif
 
 [[host_name("kernel_mul_mv_iq1_s_f32")]]
 kernel void kernel_mul_mv_iq1_s_f32(
@@ -5541,11 +5003,7 @@ kernel void kernel_mul_mv_iq4_xs_f32(
         uint tiisg[[thread_index_in_simdgroup]],
         uint sgitg[[simdgroup_index_in_threadgroup]]) {
 
-#if QK_K == 64
-    kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
-#else
     kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
-#endif
 }
 
 //============================= templates and their specializations =============================
@@ -5671,10 +5129,9 @@ void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg
     float dl, ml;
     uint8_t sc = xb->scales[il];
 
-#if QK_K == 256
     q = q + 32*(il/8) + 16*(il&1);
     il = (il/2)%4;
-#endif
+
     half  coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
     uchar mask = il>1 ? (il>2 ? 192    : 48)     : (il>0 ? 12    : 3);
     dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4);
@@ -5690,7 +5147,6 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg
     device const uint8_t * h = (device const uint8_t *)xb->hmask;
     device const int8_t * scales = (device const int8_t *)xb->scales;
 
-#if QK_K == 256
     q = q + 32 * (il/8) + 16 * (il&1);
     h = h + 16 * (il&1);
     uint8_t m = 1 << (il/2);
@@ -5711,17 +5167,6 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg
     for (int i = 0; i < 16; ++i) {
         reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml);
     }
-#else
-    float    kcoef = il&1 ? 1.f/16.f : 1.f;
-    uint16_t kmask = il&1 ? 0xF0     : 0x0F;
-    float    dl = d_all * ((scales[il/2] & kmask) * kcoef - 8);
-    float    coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
-    uint8_t  mask = il>1 ? (il>2 ? 192    : 48)     : (il>0 ? 12    : 3);
-    uint8_t  m = 1<<(il*2);
-    for (int i = 0; i < 16; ++i) {
-        reg[i/4][i%4] = coef * dl * ((q[i] & mask) - ((h[i%8] & (m * (1 + i/8))) ? 0 : 4.f/coef));
-    }
-#endif
 }
 
 static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) {
@@ -5733,7 +5178,6 @@ template <typename type4x4>
 void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) {
     device const uchar * q = xb->qs;
 
-#if QK_K == 256
     short is = (il/4) * 2;
     q = q + (il/4) * 32 + 16 * (il&1);
     il = il & 3;
@@ -5742,16 +5186,7 @@ void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg
     const float min = xb->dmin;
     const float dl = d * sc[0];
     const float ml = min * sc[1];
-#else
-    (void) get_scale_min_k4_just2;
-
-    q = q + 16 * (il&1);
-    device const uint8_t * s = xb->scales;
-    device const half2 * dh = (device const half2 *)xb->d;
-    const float2 d = (float2)dh[0];
-    const float dl = il<2 ? d[0] * (s[0]&0xF) : d[0] * (s[1]&0xF)/16.h;
-    const float ml = il<2 ? d[1] * (s[0]>>4)  : d[1] * (s[1]>>4);
-#endif
+
     const ushort mask = il<2 ? 0x0F : 0xF0;
     for (int i = 0; i < 16; ++i) {
         reg[i/4][i%4] = dl * (q[i] & mask) - ml;
@@ -5763,7 +5198,6 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg
     device const uint8_t * q  = xb->qs;
     device const uint8_t * qh = xb->qh;
 
-#if QK_K == 256
     short is = (il/4) * 2;
     q  = q + 32 * (il/4) + 16 * (il&1);
     qh = qh + 16 * (il&1);
@@ -5780,17 +5214,6 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg
     for (int i = 0; i < 16; ++i) {
         reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml;
     }
-#else
-    q = q + 16 * (il&1);
-    device const int8_t * s = xb->scales;
-    const float dl = xb->d * s[il];
-    uint8_t m = 1<<(il*2);
-    const float  coef = il<2 ? 1.f  : 1.f/16.f;
-    const ushort mask = il<2 ? 0x0F : 0xF0;
-    for (int i = 0; i < 16; ++i) {
-        reg[i/4][i%4] = coef * dl * ((q[i] & mask) - (qh[i%8] & (m*(1+i/8)) ? 0.f : 16.f/coef));
-    }
-#endif
 }
 
 template <typename type4x4>
@@ -5800,15 +5223,11 @@ void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg
     device const uint8_t * qh = (device const uint8_t *)xb->qh;
     device const int8_t * scales = (device const int8_t *)xb->scales;
 
-#if QK_K == 256
     ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1);
     qh = qh + 32*(il/8) + 16*(il&1);
     float sc = scales[(il%2) + 2 * ((il/2))];
     il = (il/2) & 3;
-#else
-    ql = ql + 16 * (il&1);
-    float sc = scales[il];
-#endif
+
     const uint16_t  kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
     const uint16_t  kmask2 = il>1 ? 0xF0              : 0x0F;
     const float       coef = il>1 ? 1.f/16.f          : 1.f;
@@ -5965,20 +5384,15 @@ void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 &
     const int ib32 = il/2;
     il = il%2;
     device const uint16_t * sc = (device const uint16_t *)xb->scales;
-#if QK_K == 64
-    const float d = xb->d;
-#else
+
     iq1m_scale_t scale;
     scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
     const float d = scale.f16;
-#endif
+
     device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
     device const uint8_t * qh = xb->qh + 2*ib32 + il;
-#if QK_K == 64
-    const float dl  = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1);
-#else
+
     const float dl  = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
-#endif
     const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
     const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
     constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
@@ -6008,9 +5422,6 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4
 
 template <typename type4x4>
 void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
-#if QK_K == 64
-    dequantize_iq4_nl(xb, il, reg);
-#else
     // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
     const int ib32 = il/2;
     il = il%2;
@@ -6027,13 +5438,12 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4
         reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
         reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
     }
-#endif
 }
 
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
-kernel void kernel_get_rows(
+kernel void kernel_get_rows_q(
         device const  void * src0,
-        device const  char * src1,
+        device const  void * src1,
         device       float * dst,
         constant   int64_t & ne00,
         constant  uint64_t & nb01,
@@ -6046,55 +5456,24 @@ kernel void kernel_get_rows(
         uint3                tgpig[[threadgroup_position_in_grid]],
         uint                 tiitg[[thread_index_in_threadgroup]],
         uint3                tptg [[threads_per_threadgroup]]) {
-    //const int64_t i = tgpig;
-    //const int64_t r = ((device int32_t *) src1)[i];
-
     const int64_t i10 = tgpig.x;
     const int64_t i11 = tgpig.y;
 
-    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
+    const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
 
     const int64_t i02 = i11;
 
     for (int64_t ind = tiitg; ind < ne00/16; ind += tptg.x) {
         float4x4 temp;
-        dequantize_func(
-            ((device const block_q *) ((device char *) src0 + r*nb01 + i02*nb02)) + ind/nl, ind%nl, temp);
+        dequantize_func(((device const block_q *) ((const device char *) src0 + r*nb01 + i02*nb02)) + ind/nl, ind%nl, temp);
         *(((device float4x4 *) ((device char *) dst + i11*nb2 + i10*nb1)) + ind) = temp;
     }
 }
 
-kernel void kernel_get_rows_f32(
-        device const  void * src0,
-        device const  char * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        uint3                tgpig[[threadgroup_position_in_grid]],
-        uint                 tiitg[[thread_index_in_threadgroup]],
-        uint3                tptg [[threads_per_threadgroup]]) {
-    const int64_t i10 = tgpig.x;
-    const int64_t i11 = tgpig.y;
-
-    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
-
-    const int64_t i02 = i11;
-
-    for (int ind = tiitg; ind < ne00; ind += tptg.x) {
-        ((device float *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
-            ((device float *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
-    }
-}
-
-kernel void kernel_get_rows_f16(
+template<typename T>
+kernel void kernel_get_rows_f(
         device const  void * src0,
-        device const  char * src1,
+        device const  void * src1,
         device       float * dst,
         constant   int64_t & ne00,
         constant  uint64_t & nb01,
@@ -6110,19 +5489,19 @@ kernel void kernel_get_rows_f16(
     const int64_t i10 = tgpig.x;
     const int64_t i11 = tgpig.y;
 
-    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
+    const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
 
     const int64_t i02 = i11;
 
     for (int ind = tiitg; ind < ne00; ind += tptg.x) {
-        ((device float *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
-            ((device half *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
+        ((      device float *) ((      device char *)  dst + i11*nb2  + i10*nb1))[ind] =
+        ((const device T     *) ((const device char *) src0 + i02*nb02 +  r*nb01))[ind];
     }
 }
 
 kernel void kernel_get_rows_i32(
         device const  void * src0,
-        device const  char * src1,
+        device const  void * src1,
         device     int32_t * dst,
         constant   int64_t & ne00,
         constant  uint64_t & nb01,
@@ -6138,13 +5517,13 @@ kernel void kernel_get_rows_i32(
     const int64_t i10 = tgpig.x;
     const int64_t i11 = tgpig.y;
 
-    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
+    const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
 
     const int64_t i02 = i11;
 
     for (int ind = tiitg; ind < ne00; ind += tptg.x) {
-        ((device int32_t *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
-            ((device int32_t *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
+        ((      device int32_t *) ((      device char *) dst  + i11*nb2 + i10*nb1))[ind] =
+        ((const device int32_t *) ((const device char *) src0 + i02*nb02 + r*nb01))[ind];
     }
 }
 
@@ -6161,28 +5540,28 @@ kernel void kernel_get_rows_i32(
 #define SG_MAT_ROW 8
 
 // each block_q contains 16*nl weights
-template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
-void kernel_mul_mm_impl(device const  uchar * src0,
-                        device const  uchar * src1,
-                        device        float * dst,
-                        constant    int64_t & ne00,
-                        constant    int64_t & ne02,
-                        constant   uint64_t & nb01,
-                        constant   uint64_t & nb02,
-                        constant    int64_t & ne12,
-                        constant   uint64_t & nb10,
-                        constant   uint64_t & nb11,
-                        constant   uint64_t & nb12,
-                        constant    int64_t & ne0,
-                        constant    int64_t & ne1,
-                        constant       uint & r2,
-                        constant       uint & r3,
-                        threadgroup   uchar * shared_memory [[threadgroup(0)]],
-                        uint3                 tgpig[[threadgroup_position_in_grid]],
-                        uint                  tiitg[[thread_index_in_threadgroup]],
-                        uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
+template<typename T, typename T4x4, typename simdgroup_T8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>
+kernel void kernel_mul_mm(device const  uchar * src0,
+                          device const  uchar * src1,
+                          device        float * dst,
+                          constant    int64_t & ne00,
+                          constant    int64_t & ne02,
+                          constant   uint64_t & nb01,
+                          constant   uint64_t & nb02,
+                          constant    int64_t & ne12,
+                          constant   uint64_t & nb10,
+                          constant   uint64_t & nb11,
+                          constant   uint64_t & nb12,
+                          constant    int64_t & ne0,
+                          constant    int64_t & ne1,
+                          constant       uint & r2,
+                          constant       uint & r3,
+                          threadgroup   uchar * shared_memory [[threadgroup(0)]],
+                          uint3                 tgpig[[threadgroup_position_in_grid]],
+                          uint                  tiitg[[thread_index_in_threadgroup]],
+                          uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    threadgroup half  * sa = (threadgroup half  *)(shared_memory);
+    threadgroup T     * sa = (threadgroup T     *)(shared_memory);
     threadgroup float * sb = (threadgroup float *)(shared_memory + 4096);
 
     const uint r0 = tgpig.y;
@@ -6197,7 +5576,7 @@ void kernel_mul_mm_impl(device const  uchar * src0,
     short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
     short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1;
 
-    simdgroup_half8x8  ma[4];
+    simdgroup_T8x8     ma[4];
     simdgroup_float8x8 mb[2];
     simdgroup_float8x8 c_res[8];
     for (int i = 0; i < 8; i++){
@@ -6220,7 +5599,7 @@ void kernel_mul_mm_impl(device const  uchar * src0,
 
     for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
         // load data and store to threadgroup memory
-        half4x4 temp_a;
+        T4x4 temp_a;
         dequantize_func(x, il, temp_a);
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
@@ -6240,7 +5619,7 @@ void kernel_mul_mm_impl(device const  uchar * src0,
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
         // load matrices from threadgroup memory and conduct outer products
-        threadgroup half  * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
+        threadgroup T     * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
         threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2));
 
         #pragma unroll(4)
@@ -6416,48 +5795,6 @@ void kernel_mul_mm_id_impl(
     }
 }
 
-template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
-kernel void kernel_mul_mm(device const  uchar * src0,
-                          device const  uchar * src1,
-                          device        float * dst,
-                          constant    int64_t & ne00,
-                          constant    int64_t & ne02,
-                          constant   uint64_t & nb01,
-                          constant   uint64_t & nb02,
-                          constant    int64_t & ne12,
-                          constant   uint64_t & nb10,
-                          constant   uint64_t & nb11,
-                          constant   uint64_t & nb12,
-                          constant    int64_t & ne0,
-                          constant    int64_t & ne1,
-                          constant       uint & r2,
-                          constant       uint & r3,
-                          threadgroup   uchar * shared_memory [[threadgroup(0)]],
-                          uint3                 tgpig[[threadgroup_position_in_grid]],
-                          uint                  tiitg[[thread_index_in_threadgroup]],
-                          uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
-    kernel_mul_mm_impl<block_q, nl, dequantize_func>(
-        src0,
-        src1,
-        dst,
-        ne00,
-        ne02,
-        nb01,
-        nb02,
-        ne12,
-        nb10,
-        nb11,
-        nb12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        shared_memory,
-        tgpig,
-        tiitg,
-        sgitg);
-}
-
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
 kernel void kernel_mul_mm_id(
         device const   uchar * src0s,
@@ -6532,87 +5869,66 @@ kernel void kernel_mul_mm_id(
         sgitg);
 }
 
-#if QK_K == 256
 #define QK_NL 16
-#else
-#define QK_NL 4
-#endif
 
 //
 // get rows
 //
 
-typedef void (get_rows_t)(
-        device const void * src0,
-        device const char * src1,
-        device      float * dst,
-        constant  int64_t & ne00,
-        constant uint64_t & nb01,
-        constant uint64_t & nb02,
-        constant  int64_t & ne10,
-        constant uint64_t & nb10,
-        constant uint64_t & nb11,
-        constant uint64_t & nb1,
-        constant uint64_t & nb2,
-        uint3, uint, uint3);
-
-//template [[host_name("kernel_get_rows_f32")]]  kernel get_rows_t kernel_get_rows<float4x4,   1, dequantize_f32>;
-//template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_t kernel_get_rows<half4x4,    1, dequantize_f16>;
-template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_t kernel_get_rows<block_q4_0, 2, dequantize_q4_0>;
-template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_t kernel_get_rows<block_q4_1, 2, dequantize_q4_1>;
-template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_t kernel_get_rows<block_q5_0, 2, dequantize_q5_0>;
-template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_t kernel_get_rows<block_q5_1, 2, dequantize_q5_1>;
-template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_t kernel_get_rows<block_q8_0, 2, dequantize_q8_0>;
-template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_t kernel_get_rows<block_q2_K, QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_t kernel_get_rows<block_q3_K, QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows<block_q4_K, QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows<block_q5_K, QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows<block_q6_K, QK_NL, dequantize_q6_K>;
-template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
-template [[host_name("kernel_get_rows_iq2_xs")]]  kernel get_rows_t kernel_get_rows<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
-template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
-template [[host_name("kernel_get_rows_iq3_s")]]   kernel get_rows_t kernel_get_rows<block_iq3_s,   QK_NL, dequantize_iq3_s>;
-template [[host_name("kernel_get_rows_iq2_s")]]   kernel get_rows_t kernel_get_rows<block_iq2_s,   QK_NL, dequantize_iq2_s>;
-template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_t kernel_get_rows<block_iq1_s,   QK_NL, dequantize_iq1_s>;
-template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_t kernel_get_rows<block_iq1_m,   QK_NL, dequantize_iq1_m>;
-template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_t kernel_get_rows<block_iq4_nl,  2,     dequantize_iq4_nl>;
-#if QK_K == 64
-template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_t kernel_get_rows<block_iq4_xs,  2,     dequantize_iq4_xs>;
-#else
-template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_t kernel_get_rows<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
-#endif
+typedef decltype(kernel_get_rows_f<float>) get_rows_f_t;
+
+template [[host_name("kernel_get_rows_f32")]]  kernel get_rows_f_t kernel_get_rows_f<float>;
+template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_f_t kernel_get_rows_f<half>;
+
+typedef decltype(kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>) get_rows_q_t;
+
+template [[host_name("kernel_get_rows_q4_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_0,    2, dequantize_q4_0>;
+template [[host_name("kernel_get_rows_q4_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_1,    2, dequantize_q4_1>;
+template [[host_name("kernel_get_rows_q5_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_0,    2, dequantize_q5_0>;
+template [[host_name("kernel_get_rows_q5_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_1,    2, dequantize_q5_1>;
+template [[host_name("kernel_get_rows_q8_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q8_0,    2, dequantize_q8_0>;
+template [[host_name("kernel_get_rows_q2_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_get_rows_q3_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_get_rows_q4_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_get_rows_q5_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_get_rows_q6_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q6_K,    QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
+template [[host_name("kernel_get_rows_iq2_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
+template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_get_rows_iq3_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_get_rows_iq2_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq2_s,   QK_NL, dequantize_iq2_s>;
+template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl,  2,     dequantize_iq4_nl>;
+template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
 
 //
 // matrix-matrix multiplication
 //
 
-typedef decltype(kernel_mul_mm<float4x4, 1, dequantize_f32>) mat_mm_t;
-
-template [[host_name("kernel_mul_mm_f32_f32")]]     kernel mat_mm_t kernel_mul_mm<float4x4,      1,     dequantize_f32>;
-template [[host_name("kernel_mul_mm_f16_f32")]]     kernel mat_mm_t kernel_mul_mm<half4x4,       1,     dequantize_f16>;
-template [[host_name("kernel_mul_mm_q4_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_0,    2,     dequantize_q4_0>;
-template [[host_name("kernel_mul_mm_q4_1_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_1,    2,     dequantize_q4_1>;
-template [[host_name("kernel_mul_mm_q5_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_0,    2,     dequantize_q5_0>;
-template [[host_name("kernel_mul_mm_q5_1_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_1,    2,     dequantize_q5_1>;
-template [[host_name("kernel_mul_mm_q8_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q8_0,    2,     dequantize_q8_0>;
-template [[host_name("kernel_mul_mm_q2_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q2_K,    QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_mul_mm_q3_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q3_K,    QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_mul_mm_q4_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_K,    QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_mul_mm_q5_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_K,    QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_mul_mm_q6_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q6_K,    QK_NL, dequantize_q6_K>;
-template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
-template [[host_name("kernel_mul_mm_iq2_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
-template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
-template [[host_name("kernel_mul_mm_iq3_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq3_s,   QK_NL, dequantize_iq3_s>;
-template [[host_name("kernel_mul_mm_iq2_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq2_s,   QK_NL, dequantize_iq2_s>;
-template [[host_name("kernel_mul_mm_iq1_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq1_s,   QK_NL, dequantize_iq1_s>;
-template [[host_name("kernel_mul_mm_iq1_m_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq1_m,   QK_NL, dequantize_iq1_m>;
-template [[host_name("kernel_mul_mm_iq4_nl_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_nl,  2,     dequantize_iq4_nl>;
-#if QK_K == 64
-template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_nl,  2,     dequantize_iq4_xs>;
-#else
-template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
-#endif
+typedef decltype(kernel_mul_mm<half, half4x4, simdgroup_half8x8, float4x4, 1, dequantize_f32>) mat_mm_t;
+
+template [[host_name("kernel_mul_mm_f32_f32")]]     kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   float4x4,      1,     dequantize_f32>;
+template [[host_name("kernel_mul_mm_f16_f32")]]     kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half4x4,       1,     dequantize_f16>;
+template [[host_name("kernel_mul_mm_q4_0_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q4_0,    2,     dequantize_q4_0>;
+template [[host_name("kernel_mul_mm_q4_1_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q4_1,    2,     dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_q5_0_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_0,    2,     dequantize_q5_0>;
+template [[host_name("kernel_mul_mm_q5_1_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_1,    2,     dequantize_q5_1>;
+template [[host_name("kernel_mul_mm_q8_0_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q8_0,    2,     dequantize_q8_0>;
+template [[host_name("kernel_mul_mm_q2_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_mul_mm_q3_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_mul_mm_q4_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_mul_mm_q5_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_mul_mm_q6_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q6_K,    QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
+template [[host_name("kernel_mul_mm_iq2_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
+template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_mul_mm_iq3_s_f32")]]   kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_mul_mm_iq2_s_f32")]]   kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq2_s,   QK_NL, dequantize_iq2_s>;
+template [[host_name("kernel_mul_mm_iq1_s_f32")]]   kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_mul_mm_iq1_m_f32")]]   kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_mul_mm_iq4_nl_f32")]]  kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl>;
+template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
 
 //
 // indirect matrix-matrix multiplication
@@ -6640,11 +5956,7 @@ template [[host_name("kernel_mul_mm_id_iq2_s_f32")]]   kernel mat_mm_id_t kernel
 template [[host_name("kernel_mul_mm_id_iq1_s_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_s,   QK_NL, dequantize_iq1_s>;
 template [[host_name("kernel_mul_mm_id_iq1_m_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_m,   QK_NL, dequantize_iq1_m>;
 template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_nl,  2,     dequantize_iq4_nl>;
-#if QK_K == 64
-template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs,  2,     dequantize_iq4_xs>;
-#else
 template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
-#endif
 
 //
 // matrix-vector multiplication
@@ -6753,7 +6065,7 @@ void mmv_fn(
     impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg);
 }
 
-typedef decltype(mmv_fn<kernel_mul_mv_f32_f32_impl>) mul_mv_impl_fn_t;
+typedef decltype(mmv_fn<kernel_mul_mv_impl<half, half4, half, half4>>) mul_mv_impl_fn_t;
 
 template<mul_mv_impl_fn_t impl_fn>
 kernel void kernel_mul_mv_id(
@@ -6831,20 +6143,20 @@ kernel void kernel_mul_mv_id(
         sgitg);
 }
 
-typedef decltype(kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f32_f32_impl>>) kernel_mul_mv_id_t;
-
-template [[host_name("kernel_mul_mv_id_f32_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f32_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_f16_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f16_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q8_0_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
-template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
-template [[host_name("kernel_mul_mv_id_q5_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
-template [[host_name("kernel_mul_mv_id_q5_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
-template [[host_name("kernel_mul_mv_id_q2_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q3_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q4_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q5_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q5_K_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q6_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q6_K_f32_impl>>;
+typedef decltype(kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<float, float4, float, float4>>>) kernel_mul_mv_id_t;
+
+template [[host_name("kernel_mul_mv_id_f32_f32")]]     kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<float, float4, float, float4>>>;
+template [[host_name("kernel_mul_mv_id_f16_f32")]]     kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<half, half4, float, float4>>>;
+template [[host_name("kernel_mul_mv_id_q8_0_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q8_0_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q4_0_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q4_1_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q5_0_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q5_1_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q2_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q3_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q4_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q5_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q5_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q6_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q6_K_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq1_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq1_s_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq1_m_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq1_m_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_xxs_f32_impl>>;
@@ -6853,7 +6165,4 @@ template [[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] kernel kernel_mul_mv_id_t
 template [[host_name("kernel_mul_mv_id_iq3_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq3_s_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq2_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_s_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_nl_f32_impl>>;
-#if QK_K != 64
 template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_xs_f32_impl>>;
-#endif
-
diff --git a/ggml-quants.c b/ggml/src/ggml-quants.c
similarity index 78%
rename from ggml-quants.c
rename to ggml/src/ggml-quants.c
index ff10598638169..d5b91c2dbc0c1 100644
--- a/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -4,8 +4,6 @@
 #include "ggml-quants.h"
 #include "ggml-impl.h"
 
-#define GGML_COMMON_IMPL_C
-#include "ggml-common.h"
 
 #include <math.h>
 #include <string.h>
@@ -262,8 +260,405 @@ static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4
 static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4
 #endif
 
+#if defined(__loongarch_asx)
+
+#ifdef __clang__
+#define VREGS_PREFIX "$vr"
+#define XREGS_PREFIX "$xr"
+#else // GCC
+#define VREGS_PREFIX "$f"
+#define XREGS_PREFIX "$f"
+#endif
+#define __ALL_REGS "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31"
+// Convert __m128i to __m256i
+static inline __m256i ____m256i(__m128i in) {
+    __m256i out = __lasx_xvldi(0);
+    __asm__ volatile (
+        ".irp i," __ALL_REGS                "\n\t"
+        " .ifc %[out], " XREGS_PREFIX"\\i    \n\t"
+        "  .irp j," __ALL_REGS              "\n\t"
+        "   .ifc %[in], " VREGS_PREFIX "\\j  \n\t"
+        "    xvpermi.q $xr\\i, $xr\\j, 0x20  \n\t"
+        "   .endif                           \n\t"
+        "  .endr                             \n\t"
+        " .endif                             \n\t"
+        ".endr                               \n\t"
+        : [out] "+f" (out) : [in] "f" (in)
+    );
+    return out;
+}
+// Convert two __m128i to __m256i
+static inline __m256i lasx_set_q(__m128i inhi, __m128i inlo) {
+    __m256i out;
+    __asm__ volatile (
+        ".irp i," __ALL_REGS                "\n\t"
+        " .ifc %[hi], " VREGS_PREFIX "\\i    \n\t"
+        "  .irp j," __ALL_REGS              "\n\t"
+        "   .ifc %[lo], " VREGS_PREFIX "\\j  \n\t"
+        "    xvpermi.q $xr\\i, $xr\\j, 0x20  \n\t"
+        "   .endif                           \n\t"
+        "  .endr                             \n\t"
+        " .endif                             \n\t"
+        ".endr                               \n\t"
+        ".ifnc %[out], %[hi]                 \n\t"
+        ".irp i," __ALL_REGS                "\n\t"
+        " .ifc %[out], " XREGS_PREFIX "\\i   \n\t"
+        "  .irp j," __ALL_REGS              "\n\t"
+        "   .ifc %[hi], " VREGS_PREFIX "\\j  \n\t"
+        "    xvori.b $xr\\i, $xr\\j, 0       \n\t"
+        "   .endif                           \n\t"
+        "  .endr                             \n\t"
+        " .endif                             \n\t"
+        ".endr                               \n\t"
+        ".endif                              \n\t"
+        : [out] "=f" (out), [hi] "+f" (inhi)
+        : [lo] "f" (inlo)
+    );
+    return out;
+}
+// Convert __m256i low part to __m128i
+static inline __m128i lasx_extracti128_lo(__m256i in) {
+    __m128i out;
+    __asm__ volatile (
+        ".ifnc %[out], %[in]                 \n\t"
+        ".irp i," __ALL_REGS                "\n\t"
+        " .ifc %[out], " VREGS_PREFIX "\\i   \n\t"
+        "  .irp j," __ALL_REGS              "\n\t"
+        "   .ifc %[in], " XREGS_PREFIX "\\j  \n\t"
+        "    vori.b $vr\\i, $vr\\j, 0        \n\t"
+        "   .endif                           \n\t"
+        "  .endr                             \n\t"
+        " .endif                             \n\t"
+        ".endr                               \n\t"
+        ".endif                              \n\t"
+        : [out] "=f" (out) : [in] "f" (in)
+    );
+    return out;
+}
+// Convert __m256i high part to __m128i
+static inline __m128i lasx_extracti128_hi(__m256i in) {
+    __m128i out;
+    __asm__ volatile (
+        ".irp i," __ALL_REGS                "\n\t"
+        " .ifc %[out], " VREGS_PREFIX "\\i   \n\t"
+        "  .irp j," __ALL_REGS              "\n\t"
+        "   .ifc %[in], " XREGS_PREFIX "\\j  \n\t"
+        "    xvpermi.q $xr\\i, $xr\\j, 0x11  \n\t"
+        "   .endif                           \n\t"
+        "  .endr                             \n\t"
+        " .endif                             \n\t"
+        ".endr                               \n\t"
+        : [out] "=f" (out) : [in] "f" (in)
+    );
+    return out;
+}
+
+static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1, int e0) {
+    v8i32 __ret = {e0, e1, e2, e3, e4, e5, e6, e7};
+    return (__m256i)__ret;
+}
+
+static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) {
+    v4i32 __ret = {d, c, b, a};
+    return (__m128i)__ret;
+}
+
+static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) {
+    v4i64 __ret = {d, c, b, a};
+    return (__m256i)__ret;
+}
+
+static __m256i lasx_insertf128( __m128i x, __m128i y) {
+    return lasx_set_q(x, y);
+}
+
+static __m128i lsx_shuffle_b(__m128i a, __m128i b) {
+    __m128i mask_f, zero, tmp0, tmp2, mask;
+    int f = 0x8f;
+    mask_f = __lsx_vreplgr2vr_b(f);
+    zero = __lsx_vldi(0);
+    tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits
+    tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or  with 0x10 prepare for positive
+    mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask
+    tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones
+    return __lsx_vshuf_b(a, zero, tmp2);
+}
+
+static __m256i lasx_shuffle_b(__m256i a, __m256i b) {
+    __m256i mask_f, zero, tmp0, tmp2, mask;
+    int f = 0x8f;
+    mask_f = __lasx_xvreplgr2vr_b(f);
+    zero = __lasx_xvldi(0);
+    tmp0 = __lasx_xvand_v(b, mask_f); // get mask with low 4 bit and sign bits
+    tmp0 = __lasx_xvori_b(tmp0, 0x10); // make each mask or  with 0x10 prepare for positive
+    mask = __lasx_xvsle_b(zero, tmp0); // if mask >= 0, set mask
+    tmp2 = __lasx_xvand_v(tmp0, mask); // maskout the in2 < ones
+    return __lasx_xvshuf_b(a, zero, tmp2);
+}
+
+static __m256i lasx_extu8_16(__m128i a) {
+    __m128i zero = __lsx_vldi(0);
+    __m128i vlo = __lsx_vilvl_b(zero, a);
+    __m128i vhi = __lsx_vilvh_b(zero, a);
+    return lasx_set_q(vhi, vlo);
+}
+
+static __m256i lasx_ext8_16(__m128i a) {
+     __m128i sign = __lsx_vslti_b(a, 0);
+     __m128i vlo = __lsx_vilvl_b(sign, a);
+     __m128i vhi = __lsx_vilvh_b(sign, a);
+     return lasx_set_q(vhi, vlo);
+}
+
+static __m256i lasx_ext16_32(__m128i a) {
+    __m256i tmp1;
+    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0);
+    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1);
+    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2);
+    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3);
+    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4);
+    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5);
+    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6);
+    tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7);
+    return tmp1;
+}
+
+static __m128i lasx_extracti128( __m256i a, int pos) {
+    __m128i ret;
+    if( pos == 0)
+    {
+       ret = lasx_extracti128_lo(a);
+    } else {
+       ret = lasx_extracti128_hi(a);
+    }
+    return ret;
+}
+
+static __m128 lasx_extractf128( __m256 a, int pos) {
+    __m128 ret;
+    if( pos == 0)
+    {
+       ret = (__m128)lasx_extracti128_lo((__m256i)a);
+    } else {
+       ret = (__m128)lasx_extracti128_hi((__m256i)a);
+    }
+    return ret;
+}
+
+static __m128i lsx_hadd_h(__m128i a, __m128i b) {
+    __m128i tmp1 = __lsx_vpickev_h(b, a);
+    __m128i tmp2 = __lsx_vpickod_h(b, a);
+    return __lsx_vadd_h(tmp1, tmp2);
+}
+
+static __m128i lsx_hadd_w(__m128i a, __m128i b) {
+    __m128i tmp1 = __lsx_vpickev_w(b, a);
+    __m128i tmp2 = __lsx_vpickod_w(b, a);
+    return __lsx_vadd_w(tmp1, tmp2);
+}
+
+static __m128 lsx_hadd_s(__m128 a, __m128 b) {
+    __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a);
+    __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a);
+
+    return __lsx_vfadd_s(tmp1, tmp2);
+}
+
+static __m256i lasx_maddubs_h(__m256i a, __m256i b) {
+    __m256i tmp1, tmp2;
+    tmp1 = __lasx_xvmulwev_h_b(a, b);
+    tmp2 = __lasx_xvmulwod_h_b(a, b);
+    return __lasx_xvsadd_h(tmp1, tmp2);
+}
+
+static __m256i lasx_madd_h(__m256i a, __m256i b) {
+    __m256i tmp1, tmp2;
+    tmp1 = __lasx_xvmulwev_w_h(a, b);
+    tmp2 = __lasx_xvmulwod_w_h(a, b);
+    return __lasx_xvadd_w(tmp1, tmp2);
+}
+
+static __m256i lasx_packs_w(__m256i a, __m256i b) {
+    __m256i tmp, tmp1;
+    tmp = __lasx_xvsat_w(a, 15);
+    tmp1 = __lasx_xvsat_w(b, 15);
+    return __lasx_xvpickev_h(tmp1, tmp);
+}
+
+static __m256i lasx_packs_h(__m256i a, __m256i b) {
+    __m256i tmp, tmp1;
+    tmp = __lasx_xvsat_h(a, 7);
+    tmp1 = __lasx_xvsat_h(b, 7);
+    return __lasx_xvpickev_b(tmp1, tmp);
+}
+
+static __m128i lsx_packs_w(__m128i a, __m128i b) {
+    __m128i tmp, tmp1;
+    tmp = __lsx_vsat_w(a, 15);
+    tmp1 = __lsx_vsat_w(b, 15);
+    return __lsx_vpickev_h(tmp1, tmp);
+}
+
+static __m128i lsx_packs_h(__m128i a, __m128i b) {
+    __m128i tmp, tmp1;
+    tmp = __lsx_vsat_h(a, 7);
+    tmp1 = __lsx_vsat_h(b, 7);
+    return __lsx_vpickev_b(tmp1, tmp);
+}
+
+static __m128i lsx_packus_h(__m128i a, __m128i b) {
+    __m128i tmp, tmp1;
+    tmp = __lsx_vsat_hu(a, 7);
+    tmp1 = __lsx_vsat_hu(b, 7);
+    return __lsx_vpickev_b(tmp1, tmp);
+}
+
+
+static __m128i lsx_maddubs_h(__m128i a, __m128i b) {
+    __m128i tmp1, tmp2;
+    tmp1 = __lsx_vmulwev_h_b(a, b);
+    tmp2 = __lsx_vmulwod_h_b(a, b);
+    return __lsx_vsadd_h(tmp1, tmp2);
+}
+
+static __m128i lsx_madd_h(__m128i a, __m128i b) {
+    __m128i tmp1, tmp2;
+    tmp1 = __lsx_vmulwev_w_h(a, b);
+    tmp2 = __lsx_vmulwod_w_h(a, b);
+    return __lsx_vadd_w(tmp1, tmp2);
+}
+
+// multiply int8_t, add results pairwise twice
+static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
+    // Get absolute values of x vectors
+    const __m128i ax = __lsx_vsigncov_b(x, x);
+    // Sign the values of the y vectors
+    const __m128i sy = __lsx_vsigncov_b(x, y);
+    // Perform multiplication and create 16-bit values
+    const __m128i dot = lsx_maddubs_h(ax, sy);
+    const __m128i ones = __lsx_vreplgr2vr_h(1);
+    return lsx_madd_h(ones, dot);
+}
+
+// horizontally add 8 floats
+static inline float hsum_float_8(const __m256 x) {
+    __m128 res = lasx_extractf128(x, 1);
+    ft_union tmp;
+    res = __lsx_vfadd_s(res, lasx_extractf128(x, 0));
+    res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res));
+    res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0));
+    tmp.i = __lsx_vpickve2gr_w(res, 0);
+    return tmp.f;
+}
+
+// horizontally add 8 int32_t
+static inline int hsum_i32_8(const __m256i a) {
+
+    __m256i tmp1 = __lasx_xvpermi_q(a, a, 0x11);
+    __m256i tmp2 = __lasx_xvpermi_q(a, a, 0x00);
+
+    __m128i  tmp1_128 = lasx_extracti128_lo(tmp1);
+    __m128i  tmp2_128 = lasx_extracti128_lo(tmp2);
+
+    __m128i sum128 = __lsx_vadd_w(tmp1_128, tmp2_128);
+
+    __m128i ev = __lsx_vpickev_w(sum128, sum128);
+    __m128i od = __lsx_vpickod_w(sum128, sum128);
+    __m128i sum64 = __lsx_vadd_w(ev, od);
+
+    int sum64_1, sum64_2;
+    sum64_1 = __lsx_vpickve2gr_w(sum64, 0);
+    sum64_2 = __lsx_vpickve2gr_w(sum64, 1);
+
+    return  sum64_1 + sum64_2;
+}
+
+// horizontally add 4 int32_t
+static inline int hsum_i32_4(const __m128i a) {
+    __m128i ev = __lsx_vpickev_w(a, a);
+    __m128i od = __lsx_vpickod_w(a, a);
+    __m128i sum64 = __lsx_vadd_w(ev, od);
+
+    int sum64_1, sum64_2;
+    sum64_1 = __lsx_vpickve2gr_w(sum64, 0);
+    sum64_2 = __lsx_vpickve2gr_w(sum64, 1);
+
+    return  sum64_1 + sum64_2;
+}
+
+// spread 32 bits to 32 bytes { 0x00, 0xFF }
+static inline __m256i bytes_from_bits_32(const uint8_t * x) {
+
+    uint32_t x32;
+    memcpy(&x32, x, sizeof(uint32_t));
+    const __m256i shuf_mask = lasx_set_d(
+            0x0303030303030303, 0x0202020202020202,
+            0x0101010101010101, 0x0000000000000000);
+
+    __m256i bytes = lasx_shuffle_b(__lasx_xvreplgr2vr_w(x32), shuf_mask);
+    const __m256i bit_mask = __lasx_xvreplgr2vr_d(0x7fbfdfeff7fbfdfe);
+    bytes = __lasx_xvor_v(bytes, bit_mask);
+    return __lasx_xvseq_b(bytes, __lasx_xvreplgr2vr_d(-1));
+}
+
+// Unpack 32 4-bit fields into 32 bytes
+// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
+static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) {
+    const __m128i lo = __lsx_vld((const __m128i *)rsi, 0);
+    __m128i hi = __lsx_vsrli_h(lo, 4);
+    return __lasx_xvandi_b(lasx_insertf128(hi, lo), 0xf);
+}
+
+// add int16_t pairwise and return as float vector
+static inline __m256 sum_i16_pairs_float(const __m256i x) {
+    __m256i v = __lasx_xvpackod_h(x, x);
+    __m256i summed_pairs = __lasx_xvaddwev_w_h(x, v);
+    return __lasx_xvffint_s_w(summed_pairs);
+}
+
+static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
+    // Perform multiplication and create 16-bit values
+    const __m256i dot = lasx_maddubs_h(ax, sy);
+    return sum_i16_pairs_float(dot);
+}
+
+// multiply int8_t, add results pairwise twice and return as float vector
+static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
+
+    // Get absolute values of x vectors
+    const __m256i ax = __lasx_xvsigncov_b(x, x);
+    // Sign the values of the y vectors
+    const __m256i sy = __lasx_xvsigncov_b(x, y);
+
+    return mul_sum_us8_pairs_float(ax, sy);
+}
+
+static inline __m128i packNibbles( __m256i bytes ) {
+    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
+    const __m256i lowByte = __lasx_xvreplgr2vr_h(0xFF);
+     __m256i high = __lasx_xvandn_v(lowByte, bytes);
+    __m256i low = __lasx_xvand_v(lowByte, bytes);
+    high = __lasx_xvsrli_h(high, 4);
+    bytes = __lasx_xvor_v(low, high);
+    // Compress uint16_t lanes into bytes
+    __m128i *r0 = (__m128i *)&bytes;
+    __m256i tmp_h128 = __lasx_xvpermi_q(bytes, bytes, 0x11);
+    __m128i *r1 = (__m128i *)&tmp_h128;
+
+    __m128i zero = __lsx_vldi(0);
+    __m128i tmp, tmp2, tmp3;
+
+    tmp = __lsx_vmax_h(zero, *r0);
+    tmp2 = __lsx_vsat_hu(tmp, 7);
+
+    tmp = __lsx_vmax_h(zero, *r1);
+    tmp3 = __lsx_vsat_hu(tmp, 7);
+    return  __lsx_vpickev_b(tmp3, tmp2);
+}
+#endif  //__loongarch_asx
+
 // reference implementation for deterministic creation of model files
-void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int64_t k) {
+void quantize_row_q4_0_ref(const float * restrict x, block_q4_0 * restrict y, int64_t k) {
     static const int qk = QK4_0;
 
     assert(k % qk == 0);
@@ -301,11 +696,11 @@ void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict
 }
 
 void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q4_0_reference(x, y, k);
+    quantize_row_q4_0_ref(x, y, k);
 }
 
 
-void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int64_t k) {
+void quantize_row_q4_1_ref(const float * restrict x, block_q4_1 * restrict y, int64_t k) {
     const int qk = QK4_1;
 
     assert(k % qk == 0);
@@ -343,10 +738,10 @@ void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict
 }
 
 void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q4_1_reference(x, y, k);
+    quantize_row_q4_1_ref(x, y, k);
 }
 
-void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int64_t k) {
+void quantize_row_q5_0_ref(const float * restrict x, block_q5_0 * restrict y, int64_t k) {
     static const int qk = QK5_0;
 
     assert(k % qk == 0);
@@ -391,10 +786,10 @@ void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict
 }
 
 void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q5_0_reference(x, y, k);
+    quantize_row_q5_0_ref(x, y, k);
 }
 
-void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int64_t k) {
+void quantize_row_q5_1_ref(const float * restrict x, block_q5_1 * restrict y, int64_t k) {
     const int qk = QK5_1;
 
     assert(k % qk == 0);
@@ -439,11 +834,11 @@ void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict
 }
 
 void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q5_1_reference(x, y, k);
+    quantize_row_q5_1_ref(x, y, k);
 }
 
 // reference implementation for deterministic creation of model files
-void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int64_t k) {
+void quantize_row_q8_0_ref(const float * restrict x, block_q8_0 * restrict y, int64_t k) {
     assert(k % QK8_0 == 0);
     const int nb = k / QK8_0;
 
@@ -649,6 +1044,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k)
         // store result
         __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
     }
+
 #elif defined(__POWER9_VECTOR__)
     for (int i = 0; i < nb; i++) {
         vector float srcv [8];
@@ -681,15 +1077,79 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k)
         vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])),  0, &y[i].qs[0]);
         vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]);
     }
+
+#elif defined(__loongarch_asx)
+    for (int i = 0; i < nb; i++) {
+        ft_union fi;
+        __m256 v0 = (__m256)__lasx_xvld( x , 0);
+        __m256 v1 = (__m256)__lasx_xvld( x , 32);
+        __m256 v2 = (__m256)__lasx_xvld( x , 64);
+        __m256 v3 = (__m256)__lasx_xvld( x , 96);
+        x += 32;
+
+        // Compute max(abs(e)) for the block
+        const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f );
+        __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 );
+        max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) );
+        max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) );
+        max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) );
+
+        __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs , 0) );
+        max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) );
+        __m128 tmp = max4;
+        max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 ));
+        fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 );
+        const float max_scalar = fi.f;
+
+        // Quantize these floats
+        const float d = max_scalar / 127.f;
+        y[i].d = GGML_FP32_TO_FP16(d);
+        const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f;
+        const __m256 mul = (__m256)__lasx_xvreplfr2vr_s( id );
+
+        // Apply the multiplier
+        v0 = __lasx_xvfmul_s( v0, mul );
+        v1 = __lasx_xvfmul_s( v1, mul );
+        v2 = __lasx_xvfmul_s( v2, mul );
+        v3 = __lasx_xvfmul_s( v3, mul );
+
+        // Round to nearest integer
+        __m256i i0 = __lasx_xvftintrne_w_s( v0 );
+        __m256i i1 = __lasx_xvftintrne_w_s( v1 );
+        __m256i i2 = __lasx_xvftintrne_w_s( v2 );
+        __m256i i3 = __lasx_xvftintrne_w_s( v3 );
+
+        __m128i ni0 = lasx_extracti128( i0, 0 );
+        __m128i ni1 = lasx_extracti128( i0, 1);
+        __m128i ni2 = lasx_extracti128( i1, 0);
+        __m128i ni3 = lasx_extracti128( i1, 1);
+        __m128i ni4 = lasx_extracti128( i2, 0);
+        __m128i ni5 = lasx_extracti128( i2, 1);
+        __m128i ni6 = lasx_extracti128( i3, 0);
+        __m128i ni7 = lasx_extracti128( i3, 1);
+
+        // Convert int32 to int16
+        ni0 = lsx_packs_w( ni0, ni1 );
+        ni2 = lsx_packs_w( ni2, ni3 );
+        ni4 = lsx_packs_w( ni4, ni5 );
+        ni6 = lsx_packs_w( ni6, ni7 );
+        // Convert int16 to int8
+        ni0 = lsx_packs_h( ni0, ni2 );
+        ni4 = lsx_packs_h( ni4, ni6 );
+
+        __lsx_vst(ni0, (__m128i *)(y[i].qs +  0), 0);
+        __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0);
+
+    }
 #else
     GGML_UNUSED(nb);
     // scalar
-    quantize_row_q8_0_reference(x, y, k);
+    quantize_row_q8_0_ref(x, y, k);
 #endif
 }
 
 // reference implementation for deterministic creation of model files
-void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int64_t k) {
+void quantize_row_q8_1_ref(const float * restrict x, block_q8_1 * restrict y, int64_t k) {
     assert(QK8_1 == 32);
     assert(k % QK8_1 == 0);
     const int nb = k / QK8_1;
@@ -828,12 +1288,12 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
         __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) );
         max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) );
         max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) );
-        const float maxScalar = _mm_cvtss_f32( max4 );
+        const float max_scalar = _mm_cvtss_f32( max4 );
 
         // Quantize these floats
-        const float d = maxScalar / 127.f;
+        const float d = max_scalar / 127.f;
         y[i].d = GGML_FP32_TO_FP16(d);
-        const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
+        const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f;
         const __m256 mul = _mm256_set1_ps( id );
 
         // Apply the multiplier
@@ -936,6 +1396,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
         int sum = __riscv_vmv_x_s_i16m1_i16(vwrs);
         y[i].s = GGML_FP32_TO_FP16(sum*d);
     }
+
 #elif defined(__POWER9_VECTOR__)
     for (int i = 0; i < nb; i++) {
         vector float srcv [8];
@@ -976,10 +1437,78 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
         accv = vec_add(accv, vec_sld(accv, accv, 8));
         y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0));
     }
+
+#elif defined(__loongarch_asx)
+    for (int i = 0; i < nb; i++) {
+        ft_union ft;
+        __m256 v0 = (__m256)__lasx_xvld( x , 0 );
+        __m256 v1 = (__m256)__lasx_xvld( x , 32 );
+        __m256 v2 = (__m256)__lasx_xvld( x , 64 );
+        __m256 v3 = (__m256)__lasx_xvld( x , 96 );
+        x += 32;
+
+        // Compute max(abs(e)) for the block
+        const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f );
+        __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 );
+        max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) );
+        max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) );
+        max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) );
+
+        __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs, 0) );
+        max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) );
+        __m128 tmp = max4;
+        max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 ));
+        ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 );
+        const float max_scalar = ft.f;
+
+        // Quantize these floats
+        const float d = max_scalar / 127.f;
+        y[i].d = GGML_FP32_TO_FP16(d);
+        const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f;
+        const __m256 mul = __lasx_xvreplfr2vr_s( id );
+
+        // Apply the multiplier
+        v0 = __lasx_xvfmul_s( v0, mul );
+        v1 = __lasx_xvfmul_s( v1, mul );
+        v2 = __lasx_xvfmul_s( v2, mul );
+        v3 = __lasx_xvfmul_s( v3, mul );
+
+        // Round to nearest integer
+        __m256i i0 = __lasx_xvftintrne_w_s( v0 );
+        __m256i i1 = __lasx_xvftintrne_w_s( v1 );
+        __m256i i2 = __lasx_xvftintrne_w_s( v2 );
+        __m256i i3 = __lasx_xvftintrne_w_s( v3 );
+
+        __m128i ni0 = lasx_extracti128(i0, 0);
+        __m128i ni1 = lasx_extracti128( i0, 1);
+        __m128i ni2 = lasx_extracti128( i1, 0);
+        __m128i ni3 = lasx_extracti128( i1, 1);
+        __m128i ni4 = lasx_extracti128( i2, 0 );
+        __m128i ni5 = lasx_extracti128( i2, 1);
+        __m128i ni6 = lasx_extracti128( i3, 0);
+        __m128i ni7 = lasx_extracti128( i3, 1);
+
+        // Compute the sum of the quants and set y[i].s
+        const __m128i s0 = __lsx_vadd_w(__lsx_vadd_w(ni0, ni1), __lsx_vadd_w(ni2, ni3));
+        const __m128i s1 = __lsx_vadd_w(__lsx_vadd_w(ni4, ni5), __lsx_vadd_w(ni6, ni7));
+        y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1)));
+
+        // Convert int32 to int16
+        ni0 = lsx_packs_w( ni0, ni1 );
+        ni2 = lsx_packs_w( ni2, ni3 );
+        ni4 = lsx_packs_w( ni4, ni5 );
+        ni6 = lsx_packs_w( ni6, ni7 );
+        // Convert int16 to int8
+        ni0 = lsx_packs_h( ni0, ni2 );
+        ni4 = lsx_packs_h( ni4, ni6 );
+
+        __lsx_vst(ni0, (__m128i *)(y[i].qs +  0), 0);
+        __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0);
+    }
 #else
     GGML_UNUSED(nb);
     // scalar
-    quantize_row_q8_1_reference(x, y, k);
+    quantize_row_q8_1_ref(x, y, k);
 #endif
 }
 
@@ -1359,7 +1888,6 @@ static float make_qkx2_quants(int n, int nmax, const float * restrict x, const f
     return scale;
 }
 
-#if QK_K == 256
 static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) {
     if (j < 4) {
         *d = q[j] & 63; *m = q[j + 4] & 63;
@@ -1368,11 +1896,10 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t *
         *m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
     }
 }
-#endif
 
 //========================- 2-bit (de)-quantization
 
-void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int64_t k) {
+void quantize_row_q2_K_ref(const float * restrict x, block_q2_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -1432,20 +1959,13 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
             }
         }
 
-#if QK_K == 256
         for (int j = 0; j < QK_K; j += 128) {
             for (int l = 0; l < 32; ++l) {
                 y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
             }
         }
-#else
-        for (int l = 0; l < 16; ++l) {
-            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
-        }
-#endif
 
         x += QK_K;
-
     }
 }
 
@@ -1460,7 +1980,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6
 
         const uint8_t * q = x[i].qs;
 
-#if QK_K == 256
         int is = 0;
         float dl, ml;
         for (int n = 0; n < QK_K; n += 128) {
@@ -1479,24 +1998,11 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6
             }
             q += 32;
         }
-#else
-        float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4);
-        float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4);
-        float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4);
-        float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4);
-        for (int l = 0; l < 16; ++l) {
-            y[l+ 0] = dl1 * ((int8_t)((q[l] >> 0) & 3)) - ml1;
-            y[l+16] = dl2 * ((int8_t)((q[l] >> 2) & 3)) - ml2;
-            y[l+32] = dl3 * ((int8_t)((q[l] >> 4) & 3)) - ml3;
-            y[l+48] = dl4 * ((int8_t)((q[l] >> 6) & 3)) - ml4;
-        }
-        y += QK_K;
-#endif
     }
 }
 
 void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) {
-    quantize_row_q2_K_reference(x, vy, k);
+    quantize_row_q2_K_ref(x, vy, k);
 }
 
 static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
@@ -1682,36 +2188,9 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri
         }
 
         float dm, mm;
-#if QK_K == 64
-        float max_scale = 0, max_min = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            max_scale = MAX(max_scale, scales[j]);
-            max_min   = MAX(max_min,   mins[j]);
-        }
-        dm = max_scale/15;
-        mm = max_min/15;
-        if (max_scale) {
-            float id = 1/dm;
-            for (int j = 0; j < QK_K/16; ++j) {
-                int l = nearest_int(id*scales[j]);
-                Ls[j] = MAX(0, MIN(15, l));
-            }
-        } else {
-            memset(Ls, 0, QK_K/16);
-        }
-        if (max_min) {
-            float id = 1/mm;
-            for (int j = 0; j < QK_K/16; ++j) {
-                int l = nearest_int(id*mins[j]);
-                Lm[j] = MAX(0, MIN(15, l));
-            }
-        } else {
-            memset(Lm, 0, QK_K/16);
-        }
-#else
         dm  = make_qp_quants(QK_K/16, 15, scales, Ls, sw);
         mm  = make_qp_quants(QK_K/16, 15, mins,   Lm, sw);
-#endif
+
         y[i].d    = GGML_FP32_TO_FP16(dm);
         y[i].dmin = GGML_FP32_TO_FP16(mm);
         dm        = GGML_FP16_TO_FP32(y[i].d);
@@ -1734,27 +2213,20 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri
             }
         }
 
-#if QK_K == 256
         for (int j = 0; j < QK_K; j += 128) {
             for (int l = 0; l < 32; ++l) {
                 y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
             }
         }
-#else
-        for (int l = 0; l < 16; ++l) {
-            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
-        }
-#endif
 
         x += QK_K;
-
     }
 }
 
 size_t quantize_q2_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q2_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q2_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -1769,7 +2241,7 @@ size_t quantize_q2_K(const float * restrict src, void * restrict dst, int64_t nr
 
 //========================= 3-bit (de)-quantization
 
-void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int64_t k) {
+void quantize_row_q3_K_ref(const float * restrict x, block_q3_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -1788,7 +2260,6 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict
             }
         }
 
-#if QK_K == 256
         memset(y[i].scales, 0, 12);
         if (max_scale) {
             float iscale = -32.f/max_scale;
@@ -1822,36 +2293,6 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict
                 L[16*j + ii] = l + 4;
             }
         }
-#else
-        if (max_scale) {
-            float iscale = -8.f/max_scale;
-            for (int j = 0; j < QK_K/16; j+=2) {
-                int l1 = nearest_int(iscale*scales[j]);
-                l1 = 8 + MAX(-8, MIN(7, l1));
-                int l2 = nearest_int(iscale*scales[j+1]);
-                l2 = 8 + MAX(-8, MIN(7, l2));
-                y[i].scales[j/2] = l1 | (l2 << 4);
-            }
-            y[i].d = GGML_FP32_TO_FP16(1/iscale);
-        } else {
-            for (int j = 0; j < QK_K/16; j+=2) {
-                y[i].scales[j/2] = 0;
-            }
-            y[i].d = GGML_FP32_TO_FP16(0.f);
-        }
-        for (int j = 0; j < QK_K/16; ++j) {
-            int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4;
-            float d = GGML_FP16_TO_FP32(y[i].d) * (s - 8);
-            if (!d) {
-                continue;
-            }
-            for (int ii = 0; ii < 16; ++ii) {
-                int l = nearest_int(x[16*j + ii]/d);
-                l = MAX(-4, MIN(3, l));
-                L[16*j + ii] = l + 4;
-            }
-        }
-#endif
 
         memset(y[i].hmask, 0, QK_K/8);
         // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc.
@@ -1866,23 +2307,16 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict
                 m = 0; hm <<= 1;
             }
         }
-#if QK_K == 256
         for (int j = 0; j < QK_K; j += 128) {
             for (int l = 0; l < 32; ++l) {
                 y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
             }
         }
-#else
-        for (int l = 0; l < 16; ++l) {
-            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
-        }
-#endif
 
         x += QK_K;
     }
 }
 
-#if QK_K == 256
 void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
@@ -1932,49 +2366,12 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int6
 
     }
 }
-#else
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) {
-    assert(k % QK_K == 0);
-    assert(QK_K == 64);
-    const int nb = k / QK_K;
-
-    for (int i = 0; i < nb; i++) {
-
-        const float d_all = GGML_FP16_TO_FP32(x[i].d);
-
-        const uint8_t * restrict q = x[i].qs;
-        const uint8_t * restrict hm = x[i].hmask;
-
-        const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8);
-        const float d2 = d_all * ((x[i].scales[0] >>  4) - 8);
-        const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8);
-        const float d4 = d_all * ((x[i].scales[1] >>  4) - 8);
-
-        for (int l=0; l<8; ++l) {
-            uint8_t h = hm[l];
-            y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4));
-            y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4));
-            y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4));
-            y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4));
-            y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4));
-            y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4));
-            y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4));
-            y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4));
-        }
-        y += QK_K;
-    }
-}
-#endif
 
 void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) {
-    quantize_row_q3_K_reference(x, vy, k);
+    quantize_row_q3_K_ref(x, vy, k);
 }
 
 static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) {
-#if QK_K != 256
-    (void)quant_weights;
-    quantize_row_q3_K_reference(x, y, n_per_row);
-#else
     assert(n_per_row % QK_K == 0);
     const int nb = n_per_row / QK_K;
 
@@ -2056,13 +2453,12 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri
 
         x += QK_K;
     }
-#endif
 }
 
 size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q3_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q3_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -2077,7 +2473,7 @@ size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nr
 
 // ====================== 4-bit (de)-quantization
 
-void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int64_t k) {
+void quantize_row_q4_K_ref(const float * restrict x, block_q4_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -2088,7 +2484,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
     float scales[QK_K/32];
 
     for (int i = 0; i < nb; i++) {
-
         float max_scale = 0; // as we are deducting the min, scales are always positive
         float max_min = 0;
         for (int j = 0; j < QK_K/32; ++j) {
@@ -2108,7 +2503,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
             }
         }
 
-#if QK_K == 256
         float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
         float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
         for (int j = 0; j < QK_K/32; ++j) {
@@ -2140,39 +2534,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
                 L[32*j + ii] = l;
             }
         }
-#else
-        const float s_factor = 15.f;
-        float inv_scale = max_scale > 0 ? s_factor/max_scale : 0.f;
-        float inv_min   = max_min   > 0 ? s_factor/max_min   : 0.f;
-        int d1 = nearest_int(inv_scale*scales[0]);
-        int m1 = nearest_int(inv_min*mins[0]);
-        int d2 = nearest_int(inv_scale*scales[1]);
-        int m2 = nearest_int(inv_min*mins[1]);
-        y[i].scales[0] = d1 | (m1 << 4);
-        y[i].scales[1] = d2 | (m2 << 4);
-        y[i].d[0] = GGML_FP32_TO_FP16(max_scale/s_factor);
-        y[i].d[1] = GGML_FP32_TO_FP16(max_min/s_factor);
 
-        float sumlx = 0;
-        int   suml2 = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            const uint8_t sd = y[i].scales[j] & 0xF;
-            const uint8_t sm = y[i].scales[j] >>  4;
-            const float d = GGML_FP16_TO_FP32(y[i].d[0]) * sd;
-            if (!d) continue;
-            const float m = GGML_FP16_TO_FP32(y[i].d[1]) * sm;
-            for (int ii = 0; ii < 32; ++ii) {
-                int l = nearest_int((x[32*j + ii] + m)/d);
-                l = MAX(0, MIN(15, l));
-                L[32*j + ii] = l;
-                sumlx += (x[32*j + ii] + m)*l*sd;
-                suml2 += l*l*sd*sd;
-            }
-        }
-        if (suml2) {
-            y[i].d[0] = GGML_FP32_TO_FP16(sumlx/suml2);
-        }
-#endif
         uint8_t * q = y[i].qs;
         for (int j = 0; j < QK_K; j += 64) {
             for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4);
@@ -2180,7 +2542,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
         }
 
         x += QK_K;
-
     }
 }
 
@@ -2189,11 +2550,8 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6
     const int nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
-
         const uint8_t * q = x[i].qs;
 
-#if QK_K == 256
-
         const float d   = GGML_FP16_TO_FP32(x[i].d);
         const float min = GGML_FP16_TO_FP32(x[i].dmin);
 
@@ -2208,32 +2566,16 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6
             for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l]  >> 4) - m2;
             q += 32; is += 2;
         }
-#else
-        const float dall = GGML_FP16_TO_FP32(x[i].d[0]);
-        const float mall = GGML_FP16_TO_FP32(x[i].d[1]);
-        const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4);
-        const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4);
-        for (int l = 0; l < 32; ++l) {
-            y[l+ 0] = d1 * (q[l] & 0xF) - m1;
-            y[l+32] = d2 * (q[l] >>  4) - m2;
-        }
-        y += QK_K;
-#endif
-
     }
 }
 
 void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q4_K * restrict y = vy;
-    quantize_row_q4_K_reference(x, y, k);
+    quantize_row_q4_K_ref(x, y, k);
 }
 
 static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) {
-#if QK_K != 256
-    (void)quant_weights;
-    quantize_row_q4_K_reference(x, y, n_per_row);
-#else
     assert(n_per_row % QK_K == 0);
     const int64_t nb = n_per_row / QK_K;
 
@@ -2304,13 +2646,12 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
         x += QK_K;
 
     }
-#endif
 }
 
 size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q4_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q4_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -2325,25 +2666,17 @@ size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nr
 
 // ====================== 5-bit (de)-quantization
 
-void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int64_t k) {
+void quantize_row_q5_K_ref(const float * restrict x, block_q5_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int64_t nb = k / QK_K;
 
-#if QK_K == 256
     uint8_t L[QK_K];
     float mins[QK_K/32];
     float scales[QK_K/32];
     float weights[32];
     uint8_t Laux[32];
-#else
-    int8_t L[QK_K];
-    float scales[QK_K/16];
-#endif
 
     for (int i = 0; i < nb; i++) {
-
-#if QK_K == 256
-
         float max_scale = 0; // as we are deducting the min, scales are always positive
         float max_min = 0;
         for (int j = 0; j < QK_K/32; ++j) {
@@ -2415,69 +2748,19 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
             m1 <<= 2; m2 <<= 2;
             ql += 32;
         }
-#else
-        float max_scale = 0, amax = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1, NULL);
-            float abs_scale = fabsf(scales[j]);
-            if (abs_scale > amax) {
-                amax = abs_scale;
-                max_scale = scales[j];
-            }
-        }
 
-        float iscale = -128.f/max_scale;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int l = nearest_int(iscale*scales[j]);
-            y[i].scales[j] = MAX(-128, MIN(127, l));
-        }
-        y[i].d = GGML_FP32_TO_FP16(1/iscale);
-
-        for (int j = 0; j < QK_K/16; ++j) {
-            const float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j];
-            if (!d) continue;
-            for (int ii = 0; ii < 16; ++ii) {
-                int l = nearest_int(x[16*j + ii]/d);
-                l = MAX(-16, MIN(15, l));
-                L[16*j + ii] = l + 16;
-            }
-        }
-
-        uint8_t * restrict qh = y[i].qh;
-        uint8_t * restrict ql = y[i].qs;
-        memset(qh, 0, QK_K/8);
-
-        for (int j = 0; j < 32; ++j) {
-            int jm = j%8;
-            int is = j/8;
-            int l1 = L[j];
-            if (l1 > 15) {
-                l1 -= 16; qh[jm] |= (1 << is);
-            }
-            int l2 = L[j + 32];
-            if (l2 > 15) {
-                l2 -= 16; qh[jm] |= (1 << (4 + is));
-            }
-            ql[j] = l1 | (l2 << 4);
-        }
-#endif
-
-        x += QK_K;
-
-    }
-}
+        x += QK_K;
+    }
+}
 
 void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int64_t nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
-
         const uint8_t * ql = x[i].qs;
         const uint8_t * qh = x[i].qh;
 
-#if QK_K == 256
-
         const float d = GGML_FP16_TO_FP32(x[i].d);
         const float min = GGML_FP16_TO_FP32(x[i].dmin);
 
@@ -2494,35 +2777,16 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6
             ql += 32; is += 2;
             u1 <<= 2; u2 <<= 2;
         }
-#else
-        float d = GGML_FP16_TO_FP32(x[i].d);
-        const int8_t * restrict s = x[i].scales;
-        for (int l = 0; l < 8; ++l) {
-            y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16));
-            y[l+ 8] = d * s[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16));
-            y[l+16] = d * s[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16));
-            y[l+24] = d * s[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16));
-            y[l+32] = d * s[2] * ((ql[l+ 0] >>  4) - (qh[l] & 0x10 ? 0 : 16));
-            y[l+40] = d * s[2] * ((ql[l+ 8] >>  4) - (qh[l] & 0x20 ? 0 : 16));
-            y[l+48] = d * s[3] * ((ql[l+16] >>  4) - (qh[l] & 0x40 ? 0 : 16));
-            y[l+56] = d * s[3] * ((ql[l+24] >>  4) - (qh[l] & 0x80 ? 0 : 16));
-        }
-        y += QK_K;
-#endif
     }
 }
 
 void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q5_K * restrict y = vy;
-    quantize_row_q5_K_reference(x, y, k);
+    quantize_row_q5_K_ref(x, y, k);
 }
 
 static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) {
-#if QK_K != 256
-    (void)quant_weights;
-    quantize_row_q5_K_reference(x, y, n_per_row);
-#else
     assert(n_per_row % QK_K == 0);
     const int64_t nb = n_per_row / QK_K;
 
@@ -2613,13 +2877,12 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
         x += QK_K;
 
     }
-#endif
 }
 
 size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q5_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q5_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -2634,7 +2897,7 @@ size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nr
 
 // ====================== 6-bit (de)-quantization
 
-void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int64_t k) {
+void quantize_row_q6_K_ref(const float * restrict x, block_q6_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int64_t nb = k / QK_K;
 
@@ -2686,7 +2949,6 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict
 
         uint8_t * restrict ql = y[i].ql;
         uint8_t * restrict qh = y[i].qh;
-#if QK_K == 256
         for (int j = 0; j < QK_K; j += 128) {
             for (int l = 0; l < 32; ++l) {
                 const uint8_t q1 = L[j + l +  0] & 0xF;
@@ -2700,19 +2962,8 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict
             ql += 64;
             qh += 32;
         }
-#else
-        for (int l = 0; l < 32; ++l) {
-            const uint8_t q1 = L[l +  0] & 0xF;
-            const uint8_t q2 = L[l + 32] & 0xF;
-            ql[l] = q1 | (q2 << 4);
-        }
-        for (int l = 0; l < 16; ++l) {
-            qh[l] = (L[l] >> 4) | ((L[l + 16] >> 4) << 2) | ((L[l + 32] >> 4) << 4) | ((L[l + 48] >> 4) << 6);
-        }
-#endif
 
         x += QK_K;
-
     }
 }
 
@@ -2721,14 +2972,12 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6
     const int64_t nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
-
         const float d = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict ql = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
         const int8_t  * restrict sc = x[i].scales;
 
-#if QK_K == 256
         for (int n = 0; n < QK_K; n += 128) {
             for (int l = 0; l < 32; ++l) {
                 int is = l/16;
@@ -2746,34 +2995,16 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6
             qh += 32;
             sc += 8;
         }
-#else
-        for (int l = 0; l < 16; ++l) {
-            const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-            const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-            const int8_t q3 = (int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-            const int8_t q4 = (int8_t)((ql[l+16]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            y[l+ 0] = d * sc[0] * q1;
-            y[l+16] = d * sc[1] * q2;
-            y[l+32] = d * sc[2] * q3;
-            y[l+48] = d * sc[3] * q4;
-        }
-        y  += 64;
-#endif
-
     }
 }
 
 void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q6_K * restrict y = vy;
-    quantize_row_q6_K_reference(x, y, k);
+    quantize_row_q6_K_ref(x, y, k);
 }
 
 static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) {
-#if QK_K != 256
-    (void)quant_weights;
-    quantize_row_q6_K_reference(x, y, n_per_row);
-#else
     assert(n_per_row % QK_K == 0);
     const int64_t nb = n_per_row / QK_K;
 
@@ -2855,13 +3086,12 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri
         x += QK_K;
 
     }
-#endif
 }
 
 size_t quantize_q6_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q6_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q6_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -2878,7 +3108,7 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri
     static_assert(QK4_0 == 32, "QK4_0 must be 32");
 
     if (!quant_weights) {
-        quantize_row_q4_0_reference(x, y, n_per_row);
+        quantize_row_q4_0_ref(x, y, n_per_row);
         return;
     }
 
@@ -2904,7 +3134,7 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri
 
 size_t quantize_q4_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        quantize_row_q4_0_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q4_0_ref(src, dst, (int64_t)nrow*n_per_row);
         return nrow * ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
@@ -2921,7 +3151,7 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri
     static_assert(QK4_1 == 32, "QK4_1 must be 32");
 
     if (!quant_weights) {
-        quantize_row_q4_1_reference(x, y, n_per_row);
+        quantize_row_q4_1_ref(x, y, n_per_row);
         return;
     }
 
@@ -2949,7 +3179,7 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri
 
 size_t quantize_q4_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        quantize_row_q4_1_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q4_1_ref(src, dst, (int64_t)nrow*n_per_row);
         return nrow * ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
@@ -2966,7 +3196,7 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri
     static_assert(QK5_0 == 32, "QK5_0 must be 32");
 
     if (!quant_weights) {
-        quantize_row_q5_0_reference(x, y, n_per_row);
+        quantize_row_q5_0_ref(x, y, n_per_row);
         return;
     }
 
@@ -3003,7 +3233,7 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri
 
 size_t quantize_q5_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        quantize_row_q5_0_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q5_0_ref(src, dst, (int64_t)nrow*n_per_row);
         return nrow * ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
@@ -3020,7 +3250,7 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri
     static_assert(QK5_1 == 32, "QK5_1 must be 32");
 
     if (!quant_weights) {
-        quantize_row_q5_1_reference(x, y, n_per_row);
+        quantize_row_q5_1_ref(x, y, n_per_row);
         return;
     }
 
@@ -3056,7 +3286,7 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri
 
 size_t quantize_q5_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        quantize_row_q5_1_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q5_1_ref(src, dst, (int64_t)nrow*n_per_row);
         return nrow * ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
@@ -3072,7 +3302,7 @@ size_t quantize_q5_1(const float * restrict src, void * restrict dst, int64_t nr
 size_t quantize_q8_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     (void)quant_weights; // not used
     const size_t row_size = ggml_row_size(GGML_TYPE_Q8_0, n_per_row);
-    quantize_row_q8_0_reference(src, dst, (int64_t)nrow*n_per_row);
+    quantize_row_q8_0_ref(src, dst, (int64_t)nrow*n_per_row);
     return nrow * row_size;
 }
 
@@ -3272,30 +3502,21 @@ void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, in
     float delta[4];
     uint16_t idx[4];
 
-#if QK_K != 64
     iq1m_scale_t scale;
-#endif
 
     for (int i = 0; i < nb; i++) {
 
         const uint16_t * sc = (const uint16_t *)x[i].scales;
-#if QK_K == 64
-        const float d = GGML_FP16_TO_FP32(x[i].d);
-#else
         scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
         const float d = GGML_FP16_TO_FP32(scale.f16);
-#endif
+
         const uint8_t * qs = x[i].qs;
         const uint8_t * qh = x[i].qh;
 
         for (int ib = 0; ib < QK_K/32; ++ib) {
-#if QK_K == 64
-            const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1);
-            const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1);
-#else
             const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1);
             const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1);
-#endif
+
             idx[0] = qs[0] | ((qh[0] << 8) & 0x700);
             idx[1] = qs[1] | ((qh[0] << 4) & 0x700);
             idx[2] = qs[2] | ((qh[1] << 8) & 0x700);
@@ -3346,9 +3567,6 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y,
 
 void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int64_t k) {
     assert(k % QK_K == 0);
-#if QK_K == 64
-    dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k);
-#else
     const int64_t nb = k / QK_K;
 
     for (int i = 0; i < nb; i++) {
@@ -3368,12 +3586,11 @@ void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y,
             qs += 16;
         }
     }
-#endif
 }
 
 //===================================== Q8_K ==============================================
 
-void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int64_t k) {
+void quantize_row_q8_K_ref(const float * restrict x, block_q8_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int64_t nb = k / QK_K;
 
@@ -3424,7 +3641,7 @@ void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int6
 }
 
 void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q8_K_reference(x, y, k);
+    quantize_row_q8_K_ref(x, y, k);
 }
 
 //===================================== Dot ptoducts =================================
@@ -3470,6 +3687,43 @@ static inline __m128i get_scale_shuffle(int i) {
     };
     return _mm_loadu_si128((const __m128i*)k_shuffle + i);
 }
+#elif defined(__loongarch_asx)
+// shuffles to pick the required scales in dot products
+static inline __m256i get_scale_shuffle_q3k(int i) {
+    static const uint8_t k_shuffle[128] = {
+         0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,     2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
+         4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,     6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
+         8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,    10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,
+        12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,    14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,
+    };
+    return __lasx_xvld((const __m256i*)k_shuffle + i, 0);
+}
+static inline __m256i get_scale_shuffle_k4(int i) {
+    static const uint8_t k_shuffle[256] = {
+         0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
+         2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
+         4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,
+         6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
+         8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,
+        10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,
+        12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,
+        14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15
+    };
+    return __lasx_xvld((const __m256i*)k_shuffle + i, 0);
+}
+static inline __m128i get_scale_shuffle(int i) {
+    static const uint8_t k_shuffle[128] = {
+         0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+         2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
+         4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5,
+         6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
+         8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9,
+        10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11,
+        12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13,
+        14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15
+    };
+    return __lsx_vld((const __m128i*)k_shuffle + i, 0);
+}
 #endif
 
 void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
@@ -3554,22 +3808,61 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2);
         float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1);
 
-        vst1_f32(s, vget_low_f32(sumv2));
+        vst1_f32(s,      vget_low_f32(sumv2));
         vst1_f32(s + bs, vget_high_f32(sumv2));
         return;
     }
 #endif
-#if defined(__ARM_NEON)
+
+    int ib = 0;
+    float sumf = 0;
+
+#if defined(__ARM_FEATURE_SVE)
+    if (ggml_sve_cnt_b == QK8_0) {
+        const svbool_t ptrueh = svptrue_pat_b8(SV_VL16);
+        const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh);
+
+        svfloat32_t sumv0 = svdup_n_f32(0.0f);
+        svfloat32_t sumv1 = svdup_n_f32(0.0f);
+
+        for (; ib + 1 < nb; ib += 2) {
+            const block_q4_0 * restrict x0 = &x[ib + 0];
+            const block_q4_0 * restrict x1 = &x[ib + 1];
+            const block_q8_0 * restrict y0 = &y[ib + 0];
+            const block_q8_0 * restrict y1 = &y[ib + 1];
+
+            // load x
+            const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs);
+            const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs);
+
+            // 4-bit -> 8-bit
+            const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx0r, 0x0F), 0x04));
+            const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx1r, 0x0F), 0x04));
+
+            // sub 8
+            const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8);
+            const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8);
+
+            // load y
+            const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
+            const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
+
+            // dot product
+            sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+            sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+        }
+
+        sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
+    }
+#elif defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q4_0 * restrict x0 = &x[i + 0];
-        const block_q4_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i + 0];
-        const block_q8_0 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q4_0 * restrict x0 = &x[ib + 0];
+        const block_q4_0 * restrict x1 = &x[ib + 1];
+        const block_q8_0 * restrict y0 = &y[ib + 0];
+        const block_q8_0 * restrict y1 = &y[ib + 1];
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
         const int8x16_t  s8b = vdupq_n_s8(0x8);
@@ -3603,23 +3896,23 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
 #elif defined(__AVX2__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
+    for (; ib < nb; ++ib) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
 
         // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
         const __m256i off = _mm256_set1_epi8( 8 );
         qx = _mm256_sub_epi8( qx, off );
 
-        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -3627,28 +3920,28 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = _mm256_fmadd_ps( d, q, acc );
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
+    for (; ib < nb; ++ib) {
         // Compute combined scale for the block
-        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
         const __m128i lowMask = _mm_set1_epi8(0xF);
         const __m128i off = _mm_set1_epi8(8);
 
-        const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs);
+        const __m128i tmp = _mm_loadu_si128((const __m128i *)x[ib].qs);
 
         __m128i bx_0 = _mm_and_si128(lowMask, tmp);
-        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[ib].qs);
         bx_0 = _mm_sub_epi8(bx_0, off);
         const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
 
         bx_0 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
-        by_0 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
+        by_0 = _mm_loadu_si128((const __m128i *)(y[ib].qs + 16));
         bx_0 = _mm_sub_epi8(bx_0, off);
         const __m128i i32_1 = mul_sum_i8_pairs(bx_0, by_0);
 
@@ -3659,7 +3952,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc);
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__SSSE3__)
     // set constants
     const __m128i lowMask = _mm_set1_epi8(0xF);
@@ -3671,94 +3964,40 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     __m128 acc_2 = _mm_setzero_ps();
     __m128 acc_3 = _mm_setzero_ps();
 
-    // First round without accumulation
-    {
-        _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) );
-
-        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
-
-        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
-        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs);
-        bx_0 = _mm_sub_epi8(bx_0, off);
-        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
-
-        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
-        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16));
-        bx_1 = _mm_sub_epi8(bx_1, off);
-        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
-
-        _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) );
-
-        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
-
-        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
-        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs);
-        bx_2 = _mm_sub_epi8(bx_2, off);
-        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
-
-        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
-        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16));
-        bx_3 = _mm_sub_epi8(bx_3, off);
-        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
-
-        // Convert int32_t to float
-        __m128 p0 = _mm_cvtepi32_ps(i32_0);
-        __m128 p1 = _mm_cvtepi32_ps(i32_1);
-        __m128 p2 = _mm_cvtepi32_ps(i32_2);
-        __m128 p3 = _mm_cvtepi32_ps(i32_3);
-
-        // Apply the scale
-        acc_0 = _mm_mul_ps( d_0_1, p0 );
-        acc_1 = _mm_mul_ps( d_0_1, p1 );
-        acc_2 = _mm_mul_ps( d_2_3, p2 );
-        acc_3 = _mm_mul_ps( d_2_3, p3 );
-    }
-
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    // Main loop
-    for (int i = 2; i < nb; i+=2) {
-        _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
+    for (; ib + 1 < nb; ib += 2) {
+        _mm_prefetch(&x[ib] + sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[ib] + sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
-        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
+        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[ib].qs);
 
         __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
-        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[ib].qs);
         bx_0 = _mm_sub_epi8(bx_0, off);
         const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
 
         __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
-        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
+        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[ib].qs + 16));
         bx_1 = _mm_sub_epi8(bx_1, off);
         const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
 
-        _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
+        _mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) );
+        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
 
-        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
+        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs);
 
         __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
-        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs);
+        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs);
         bx_2 = _mm_sub_epi8(bx_2, off);
         const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
 
         __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
-        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16));
+        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[ib + 1].qs + 16));
         bx_3 = _mm_sub_epi8(bx_3, off);
         const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
 
@@ -3781,18 +4020,16 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc_3 = _mm_add_ps(p3_d, acc_3);
     }
 
-    *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
+    sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
 
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         // load elements
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl);
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl);
 
         // mask and store lower part of x, and then upper part
         vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
@@ -3815,29 +4052,29 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
+        sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d);
     }
 
-    *s = sumf;
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector signed int v0 = vec_splats((int32_t)0);
     const vector unsigned char v4 = vec_splats((unsigned char)0x4);
     const vector signed char v8 = vec_splats((signed char)0x8);
 
     vector float vsumf0 = vec_splats(0.0f);
 
-#pragma GCC unroll 4
-    for (int i = 0; i < nb; i++) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+#pragma GCC unroll 8
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs);
-        vector signed char q8y0 = vec_xl( 0, y[i].qs);
-        vector signed char q8y1 = vec_xl(16, y[i].qs);
+        vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs);
+        vector signed char q8y0 = vec_xl( 0, y[ib].qs);
+        vector signed char q8y1 = vec_xl(16, y[ib].qs);
 
         vector signed char q4x0 = vec_and(qxs, lowMask);
         vector signed char q4x1 = vec_sr(qxs, v4);
@@ -3848,9 +4085,10 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0));
         vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1));
 
-        qv0 = vec_add(qv0, qv1);
+        vector signed int vsumi0 = v0;
 
-        vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0));
+        vsumi0 = vec_sum4s(qv0, vsumi0);
+        vsumi0 = vec_sum4s(qv1, vsumi0);
 
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
     }
@@ -3858,26 +4096,116 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
-#else
-    // scalar
-    float sumf = 0.0;
+    sumf = vec_extract(vsumf0, 0);
 
-    for (int i = 0; i < nb; i++) {
-        int sumi = 0;
+#elif defined(__loongarch_asx)
+    // Initialize accumulator with zeros
+    __m256 acc = (__m256)__lasx_xvldi(0);
+
+    // Main loop
+    for (; ib < nb; ++ib) {
+        /* Compute combined scale for the block */
+        const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
+
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+
+        // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
+        const __m256i off = __lasx_xvreplgr2vr_b( 8 );
+        qx = __lasx_xvsub_b( qx, off );
+
+        __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
+
+        const __m256 q = mul_sum_i8_pairs_float(qx, qy);
+
+        /* Multiply q with scale and accumulate */
+        acc = __lasx_xvfmadd_s( d, q, acc );
+    }
+
+    sumf = hsum_float_8(acc);
+#elif defined(__loongarch_sx)
+    // set constants
+    const __m128i low_mask = __lsx_vreplgr2vr_b(0xF);
+    const __m128i off = __lsx_vreplgr2vr_b(8);
+
+    // Initialize accumulator with zeros
+    __m128 acc_0 = __lsx_vldi(0);
+    __m128 acc_1 = __lsx_vldi(0);
+    __m128 acc_2 = __lsx_vldi(0);
+    __m128 acc_3 = __lsx_vldi(0);
+
+    for (; ib + 1 < nb; ib += 2) {
+
+        // Compute combined scale for the block 0 and 1
+        const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
+
+        const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0);
+
+        __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1);
+        __m128i by_0 = __lsx_vld((const __m128i *)y[ib].qs, 0);
+        bx_0 = __lsx_vsub_b(bx_0, off);
+        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
+
+        __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4));
+        __m128i by_1 = __lsx_vld((const __m128i *)(y[ib].qs + 16), 0);
+        bx_1 = __lsx_vsub_b(bx_1, off);
+        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
+
+        //_mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
+        //_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
+
+        // Compute combined scale for the block 2 and 3
+        const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
+
+        const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0);
+
+        __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3);
+        __m128i by_2 = __lsx_vld((const __m128i *)y[ib + 1].qs, 0);
+        bx_2 = __lsx_vsub_b(bx_2, off);
+        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
+
+        __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4));
+        __m128i by_3 = __lsx_vld((const __m128i *)(y[ib + 1].qs + 16), 0);
+        bx_3 = __lsx_vsub_b(bx_3, off);
+        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
+
+        // Convert int32_t to float
+        __m128 p0 = __lsx_vffint_s_w(i32_0);
+        __m128 p1 = __lsx_vffint_s_w(i32_1);
+        __m128 p2 = __lsx_vffint_s_w(i32_2);
+        __m128 p3 = __lsx_vffint_s_w(i32_3);
+
+        // Apply the scale
+        __m128 p0_d = __lsx_vfmul_s( d_0_1, p0 );
+        __m128 p1_d = __lsx_vfmul_s( d_0_1, p1 );
+        __m128 p2_d = __lsx_vfmul_s( d_2_3, p2 );
+        __m128 p3_d = __lsx_vfmul_s( d_2_3, p3 );
+
+        // Acummulate
+        acc_0 = __lsx_vfadd_s(p0_d, acc_0);
+        acc_1 = __lsx_vfadd_s(p1_d, acc_1);
+        acc_2 = __lsx_vfadd_s(p2_d, acc_2);
+        acc_3 = __lsx_vfadd_s(p3_d, acc_3);
+    }
+
+    sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
+#endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
 
         for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[i].qs[j] & 0x0F) - 8;
-            const int v1 = (x[i].qs[j] >>   4) - 8;
+            const int v0 = (x[ib].qs[j] & 0x0F) - 8;
+            const int v1 = (x[ib].qs[j] >>   4) - 8;
 
-            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
         }
 
-        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
+        int sumi = sumi0 + sumi1;
+        sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d);
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
@@ -3963,11 +4291,15 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1);
         sumv2 = vaddq_f32(sumv2, summs0);
 
-        vst1_f32(s, vget_low_f32(sumv2));
+        vst1_f32(s,      vget_low_f32 (sumv2));
         vst1_f32(s + bs, vget_high_f32(sumv2));
         return;
     }
 #endif
+
+    int ib = 0;
+    float sumf = 0;
+
     // TODO: add WASM SIMD
 #if defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
@@ -3975,13 +4307,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
     float summs = 0;
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q4_1 * restrict x0 = &x[i + 0];
-        const block_q4_1 * restrict x1 = &x[i + 1];
-        const block_q8_1 * restrict y0 = &y[i + 0];
-        const block_q8_1 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q4_1 * restrict x0 = &x[ib + 0];
+        const block_q4_1 * restrict x1 = &x[ib + 1];
+        const block_q8_1 * restrict y0 = &y[ib + 0];
+        const block_q8_1 * restrict y1 = &y[ib + 1];
 
         summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s);
 
@@ -4010,7 +4340,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
 #elif defined(__AVX2__) || defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
@@ -4018,11 +4348,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     float summs = 0;
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
-        const float d0 = GGML_FP16_TO_FP32(x[i].d);
-        const float d1 = GGML_FP16_TO_FP32(y[i].d);
+    for (; ib < nb; ++ib) {
+        const float d0 = GGML_FP16_TO_FP32(x[ib].d);
+        const float d1 = GGML_FP16_TO_FP32(y[ib].d);
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
 
         const __m256 d0v = _mm256_set1_ps( d0 );
         const __m256 d1v = _mm256_set1_ps( d1 );
@@ -4031,8 +4361,8 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
 
         // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
-        const __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[i].qs );
+        const __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[ib].qs );
 
         const __m256 xy = mul_sum_us8_pairs_float(qx, qy);
 
@@ -4044,18 +4374,16 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 #endif
     }
 
-    *s = hsum_float_8(acc) + summs;
+    sumf = hsum_float_8(acc) + summs;
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
 
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         // load elements
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl);
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl);
 
         // mask and store lower part of x, and then upper part
         vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
@@ -4074,42 +4402,40 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
     }
 
-    *s = sumf;
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector signed int v0 = vec_splats((int32_t)0);
     const vector unsigned char v4 = vec_splats((unsigned char)0x4);
 
     vector float vsumf0 = vec_splats(0.0f);
 
 #pragma GCC unroll 4
-    for (int i = 0; i < nb; i++) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].m));
-        vector float vys = {GGML_FP16_TO_FP32(y[i].s), 0.0f, 0.0f, 0.0f};
+        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m));
+        vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.0f, 0.0f, 0.0f};
         vsumf0 = vec_madd(vxmin, vys, vsumf0);
 
-        vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs);
-        vector signed char q8y0 = vec_xl( 0, y[i].qs);
-        vector signed char q8y1 = vec_xl(16, y[i].qs);
-
-        vector signed char q4x0 = vec_and(qxs, lowMask);
-        vector signed char q4x1 = vec_sr(qxs, v4);
+        vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs);
+        vector signed char q8y0 = vec_xl( 0, y[ib].qs);
+        vector signed char q8y1 = vec_xl(16, y[ib].qs);
 
-        vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0));
-        vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1));
+        vector unsigned char q4x0 = (vector unsigned char)vec_and(qxs, lowMask);
+        vector unsigned char q4x1 = (vector unsigned char)vec_sr(qxs, v4);
 
-        qv0 = vec_add(qv0, qv1);
+        vector signed int vsumi0 = v0;
 
-        vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0));
+        vsumi0 = vec_msum(q8y0, q4x0, vsumi0);
+        vsumi0 = vec_msum(q8y1, q4x1, vsumi0);
 
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
     }
@@ -4117,32 +4443,65 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
-#else
-    // scalar
-    float sumf = 0.0;
+    sumf = vec_extract(vsumf0, 0);
 
-    for (int i = 0; i < nb; i++) {
-        int sumi = 0;
+#elif defined(__loongarch_asx)
+    // Initialize accumulator with zeros
+    __m256 acc = (__m256)__lasx_xvldi(0);
+
+    float summs = 0;
+
+    // Main loop
+    for (; ib < nb; ++ib) {
+        const float d0 = GGML_FP16_TO_FP32(x[ib].d);
+        const float d1 = GGML_FP16_TO_FP32(y[ib].d);
+
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
+
+        const __m256 d0v = __lasx_xvreplfr2vr_s( d0 );
+        const __m256 d1v = __lasx_xvreplfr2vr_s( d1 );
+
+        // Compute combined scales
+        const __m256 d0d1 = __lasx_xvfmul_s( d0v, d1v );
+
+        // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
+        const __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        const __m256i qy = __lasx_xvld( (const __m256i *)y[ib].qs, 0);
+
+        const __m256 xy = mul_sum_us8_pairs_float(qx, qy);
+
+        // Accumulate d0*d1*x*y
+        acc = __lasx_xvfmadd_s( d0d1, xy, acc );
+    }
+
+    sumf = hsum_float_8(acc) + summs;
+#endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
 
         for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[i].qs[j] & 0x0F);
-            const int v1 = (x[i].qs[j] >>   4);
+            const int v0 = (x[ib].qs[j] & 0x0F);
+            const int v1 = (x[ib].qs[j] >>   4);
 
-            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
 
+    int ib = 0;
+    float sumf = 0;
+
     assert(n % qk == 0);
     assert(qk == QK5_0);
     assert(nrc == 1);
@@ -4164,13 +4523,11 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     uint64_t tmp0[4];
     uint64_t tmp1[4];
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q5_0 * restrict x0 = &x[i];
-        const block_q5_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i];
-        const block_q8_0 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q5_0 * restrict x0 = &x[ib];
+        const block_q5_0 * restrict x1 = &x[ib + 1];
+        const block_q8_0 * restrict y0 = &y[ib];
+        const block_q8_0 * restrict y1 = &y[ib + 1];
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
@@ -4222,7 +4579,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
                         ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
 #elif defined(__wasm_simd128__)
     v128_t sumv = wasm_f32x4_splat(0.0f);
 
@@ -4230,9 +4587,9 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     uint64_t tmp[4];
 
     // TODO: check if unrolling this is better
-    for (int i = 0; i < nb; ++i) {
-        const block_q5_0 * restrict x0 = &x[i];
-        const block_q8_0 * restrict y0 = &y[i];
+    for (; ib < nb; ++ib) {
+        const block_q5_0 * restrict x0 = &x[ib];
+        const block_q8_0 * restrict y0 = &y[ib];
 
         const v128_t m4b  = wasm_i8x16_splat(0x0F);
 
@@ -4282,23 +4639,23 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
                     wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
     }
 
-    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
-         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
+    sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
+           wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
 #elif defined(__AVX2__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0));
         qx = _mm256_or_si256(qx, bxhi);
 
-        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -4306,19 +4663,19 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = _mm256_fmadd_ps(d, q, acc);
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
     __m128i mask = _mm_set1_epi8((char)0xF0);
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
 
-        __m256i bx_0 = bytes_from_nibbles_32(x[i].qs);
-        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs);
+        const __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         __m128i bxhil = _mm256_castsi256_si128(bxhi);
         __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
         bxhil = _mm_andnot_si128(bxhil, mask);
@@ -4329,7 +4686,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         bxh = _mm_or_si128(bxh, bxhih);
         bx_0 = MM256_SET_M128I(bxh, bxl);
 
-        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_i8_pairs_float(bx_0, by_0);
 
@@ -4337,10 +4694,8 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc);
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
     uint32_t qh;
 
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
@@ -4352,8 +4707,8 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl);
     vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
 
-    for (int i = 0; i < nb; i++) {
-        memcpy(&qh, x[i].qh, sizeof(uint32_t));
+    for (; ib < nb; ++ib) {
+        memcpy(&qh, x[ib].qh, sizeof(uint32_t));
 
         // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
         vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl);
@@ -4372,10 +4727,10 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
 
         // load
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl);
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl);
 
         vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
         vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
@@ -4399,10 +4754,9 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi;
     }
 
-    *s = sumf;
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
     const vector unsigned char v4 = vec_splats((unsigned char)4);
@@ -4410,27 +4764,27 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vector float vsumf0 = vec_splats(0.0f);
 
 #pragma GCC unroll 4
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[i].qh[0]]), (uint64_t)(table_b2b_1[x[i].qh[1]])};
-        vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[i].qh[2]]), (uint64_t)(table_b2b_1[x[i].qh[3]])};
+        vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[ib].qh[0]]), (uint64_t)(table_b2b_1[x[ib].qh[1]])};
+        vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[ib].qh[2]]), (uint64_t)(table_b2b_1[x[ib].qh[3]])};
 
         vector signed char qh0 = (vector signed char)aux64x2_0;
         vector signed char qh1 = (vector signed char)aux64x2_1;
 
-        vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs);
+        vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs);
 
         vector signed char q5x0 = vec_sub(vec_and (qxs, lowMask), qh0);
         vector signed char q5x1 = vec_sub(vec_sr(qxs, v4), qh1);
 
-        vector signed char q8y0 = vec_xl(  0, y[i].qs);
-        vector signed char q8y1 = vec_xl( 16, y[i].qs);
+        vector signed char q8y0 = vec_xl(  0, y[ib].qs);
+        vector signed char q8y1 = vec_xl( 16, y[ib].qs);
 
         vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0));
         vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1));
@@ -4445,38 +4799,64 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
-#else
-    // scalar
-    float sumf = 0.0;
+    sumf = vec_extract(vsumf0, 0);
 
-    for (int i = 0; i < nb; i++) {
+#elif defined(__loongarch_asx)
+    // Initialize accumulator with zeros
+    __m256 acc = (__m256)__lasx_xvldi(0);
+
+    // Main loop
+    for (; ib < nb; ++ib) {
+        /* Compute combined scale for the block */
+        const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); //FIXME
+
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        __m256i bxhi = bytes_from_bits_32(x[ib].qh);
+        bxhi = __lasx_xvandn_v(bxhi, __lasx_xvreplgr2vr_b((char)0xF0));
+        qx = __lasx_xvor_v(qx, bxhi);
+
+        __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
+
+        const __m256 q = mul_sum_i8_pairs_float(qx, qy);
+
+        /* Multiply q with scale and accumulate */
+        acc = __lasx_xvfmadd_s(d, q, acc);
+    }
+
+    sumf = hsum_float_8(acc);
+#endif
+    for (; ib < nb; ++ib) {
         uint32_t qh;
-        memcpy(&qh, x[i].qh, sizeof(qh));
+        memcpy(&qh, x[ib].qh, sizeof(qh));
 
-        int sumi = 0;
+        int sumi0 = 0;
+        int sumi1 = 0;
 
         for (int j = 0; j < qk/2; ++j) {
             const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
             const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
 
-            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
-            const int32_t x1 = ((x[i].qs[j] >>   4) | xh_1) - 16;
+            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
+            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
 
-            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi;
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_1;
     const int nb = n / qk;
 
+    int ib = 0;
+    float sumf = 0;
+
     assert(n % qk == 0);
     assert(qk == QK5_1);
     assert(nrc == 1);
@@ -4501,13 +4881,11 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     uint64_t tmp0[4];
     uint64_t tmp1[4];
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q5_1 * restrict x0 = &x[i];
-        const block_q5_1 * restrict x1 = &x[i + 1];
-        const block_q8_1 * restrict y0 = &y[i];
-        const block_q8_1 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q5_1 * restrict x0 = &x[ib];
+        const block_q5_1 * restrict x1 = &x[ib + 1];
+        const block_q8_1 * restrict y0 = &y[ib];
+        const block_q8_1 * restrict y1 = &y[ib + 1];
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
@@ -4562,7 +4940,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
                         ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
 #elif defined(__wasm_simd128__)
     v128_t sumv = wasm_f32x4_splat(0.0f);
 
@@ -4572,9 +4950,9 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     uint64_t tmp[4];
 
     // TODO: check if unrolling this is better
-    for (int i = 0; i < nb; ++i) {
-        const block_q5_1 * restrict x0 = &x[i];
-        const block_q8_1 * restrict y0 = &y[i];
+    for (; ib < nb; ++ib) {
+        const block_q5_1 * restrict x0 = &x[ib];
+        const block_q8_1 * restrict y0 = &y[ib];
 
         summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s);
 
@@ -4626,8 +5004,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
                     wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
     }
 
-    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
-         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
+    sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
+           wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
 #elif defined(__AVX2__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
@@ -4635,25 +5013,25 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     float summs = 0.0f;
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
-        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
+    for (; ib < nb; ++ib) {
+        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
         qx = _mm256_or_si256(qx, bxhi);
 
-        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d));
-        const __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d));
+        const __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_us8_pairs_float(qx, qy);
 
         acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
     }
 
-    *s = hsum_float_8(acc) + summs;
+    sumf = hsum_float_8(acc) + summs;
 #elif defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
@@ -4662,13 +5040,13 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     float summs = 0.0f;
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
-        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
+    for (; ib < nb; ++ib) {
+        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
 
-        __m256i bx_0 = bytes_from_nibbles_32(x[i].qs);
-        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs);
+        const __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         __m128i bxhil = _mm256_castsi256_si128(bxhi);
         __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
         bxhil = _mm_and_si128(bxhil, mask);
@@ -4679,18 +5057,16 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         bxh = _mm_or_si128(bxh, bxhih);
         bx_0 = MM256_SET_M128I(bxh, bxl);
 
-        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d));
-        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d));
+        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0);
 
         acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
     }
 
-    *s = hsum_float_8(acc) + summs;
+    sumf = hsum_float_8(acc) + summs;
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
     uint32_t qh;
 
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
@@ -4699,8 +5075,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
     vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
 
-    for (int i = 0; i < nb; i++) {
-        memcpy(&qh, x[i].qh, sizeof(uint32_t));
+    for (; ib < nb; ++ib) {
+        memcpy(&qh, x[ib].qh, sizeof(uint32_t));
 
         // load qh
         vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl);
@@ -4722,10 +5098,10 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
 
         // load
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl);
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl);
 
         vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
         vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
@@ -4746,49 +5122,47 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
     }
 
-    *s = sumf;
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector signed int v0 = vec_splats((int32_t)0);
     const vector unsigned char v4 = vec_splats((unsigned char)0x4);
 
     vector float vsumf0 = vec_splats(0.0f);
 
 #pragma GCC unroll 4
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].m));
-        vector float vys = {GGML_FP16_TO_FP32(y[i].s), 0.f, 0.f, 0.f};
+        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m));
+        vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.f, 0.f, 0.f};
         vsumf0 = vec_madd(vxmin, vys, vsumf0);
 
-        vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[i].qh[0]]), (uint64_t)(table_b2b_0[x[i].qh[1]])};
-        vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[i].qh[2]]), (uint64_t)(table_b2b_0[x[i].qh[3]])};
+        vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[ib].qh[0]]), (uint64_t)(table_b2b_0[x[ib].qh[1]])};
+        vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[ib].qh[2]]), (uint64_t)(table_b2b_0[x[ib].qh[3]])};
 
         vector signed char qh0 = (vector signed char)aux64x2_0;
         vector signed char qh1 = (vector signed char)aux64x2_1;
 
-        vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs);
-
-        vector signed char q5x0 = vec_or(vec_and(qxs, lowMask), qh0);
-        vector signed char q5x1 = vec_or(vec_sr(qxs, v4), qh1);
+        vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs);
 
-        vector signed char q8y0 = vec_xl(  0, y[i].qs);
-        vector signed char q8y1 = vec_xl( 16, y[i].qs);
+        vector unsigned char q5x0 = (vector unsigned char)vec_or(vec_and(qxs, lowMask), qh0);
+        vector unsigned char q5x1 = (vector unsigned char)vec_or(vec_sr(qxs, v4), qh1);
 
-        vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0));
-        vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1));
+        vector signed char q8y0 = vec_xl(  0, y[ib].qs);
+        vector signed char q8y1 = vec_xl( 16, y[ib].qs);
 
-        qv0 = vec_add(qv0, qv1);
+        vector signed int vsumi0 = v0;
 
-        vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0));
+        vsumi0 = vec_msum(q8y0, q5x0, vsumi0);
+        vsumi0 = vec_msum(q8y1, q5x1, vsumi0);
 
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
     }
@@ -4796,32 +5170,58 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
-#else
-    // scalar
-    float sumf = 0.0;
+    sumf = vec_extract(vsumf0, 0);
 
-    for (int i = 0; i < nb; i++) {
+#elif defined(__loongarch_asx)
+    // Initialize accumulator with zeros
+    __m256 acc = (__m256)__lasx_xvldi(0);
+
+    float summs = 0.0f;
+
+    // Main loop
+    for (; ib < nb; ++ib) {
+        const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d));
+
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
+
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        __m256i bxhi = bytes_from_bits_32(x[ib].qh);
+        bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10));
+        qx = __lasx_xvor_v(qx, bxhi);
+
+        const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib].d));
+        const __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
+
+        const __m256 q = mul_sum_us8_pairs_float(qx, qy);
+
+        acc = __lasx_xvfmadd_s(q, __lasx_xvfmul_s(dx, dy), acc);
+    }
+
+    sumf = hsum_float_8(acc) + summs;
+#endif
+    for (; ib < nb; ++ib) {
         uint32_t qh;
-        memcpy(&qh, x[i].qh, sizeof(qh));
+        memcpy(&qh, x[ib].qh, sizeof(qh));
 
-        int sumi = 0;
+        int sumi0 = 0;
+        int sumi1 = 0;
 
         for (int j = 0; j < qk/2; ++j) {
             const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
             const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
 
-            const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0;
-            const int32_t x1 = (x[i].qs[j] >>  4) | xh_1;
+            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
+            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
 
-            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
@@ -4898,17 +5298,44 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         return;
     }
 #endif
-#if defined(__ARM_NEON)
+
+    int ib = 0;
+    float sumf = 0;
+
+#if defined(__ARM_FEATURE_SVE)
+    if (ggml_sve_cnt_b == QK8_0) {
+        svfloat32_t sumv0 = svdup_n_f32(0.0f);
+        svfloat32_t sumv1 = svdup_n_f32(0.0f);
+
+        for (; ib + 1 < nb; ib += 2) {
+            const block_q8_0 * restrict x0 = &x[ib + 0];
+            const block_q8_0 * restrict x1 = &x[ib + 1];
+            const block_q8_0 * restrict y0 = &y[ib + 0];
+            const block_q8_0 * restrict y1 = &y[ib + 1];
+
+            // load x
+            const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs);
+            const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs);
+
+            // load y
+            const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
+            const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
+
+            sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+            sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+        }
+
+        sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
+    }
+#elif defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q8_0 * restrict x0 = &x[i + 0];
-        const block_q8_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i + 0];
-        const block_q8_0 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q8_0 * restrict x0 = &x[ib + 0];
+        const block_q8_0 * restrict x1 = &x[ib + 1];
+        const block_q8_0 * restrict y0 = &y[ib + 0];
+        const block_q8_0 * restrict y1 = &y[ib + 1];
 
         const int8x16_t x0_0 = vld1q_s8(x0->qs);
         const int8x16_t x0_1 = vld1q_s8(x0->qs + 16);
@@ -4930,17 +5357,17 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
                         ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
 #elif defined(__AVX2__) || defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
+    for (; ib < nb; ++ib) {
         // Compute combined scale for the block
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
-        __m256i qx = _mm256_loadu_si256((const __m256i *)x[i].qs);
-        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
+        __m256i qx = _mm256_loadu_si256((const __m256i *)x[ib].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -4952,15 +5379,14 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
 #endif
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
     size_t vl = __riscv_vsetvl_e8m1(qk);
 
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         // load elements
-        vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[i].qs, vl);
-        vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[i].qs, vl);
+        vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[ib].qs, vl);
+        vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[ib].qs, vl);
 
         vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx_0, by_0, vl);
 
@@ -4969,39 +5395,38 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum);
 
-        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
+        sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d));
     }
-
-    *s = sumf;
 #elif defined(__POWER9_VECTOR__)
+    const vector signed int v0 = vec_splats((int32_t)0);
     vector float vsumf0 = vec_splats(0.0f);
 
-#pragma GCC unroll 4
-    for (int i = 0; i < nb; i++) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+#pragma GCC unroll 8
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed char q8x0 = vec_xl( 0, x[i].qs);
-        vector signed char q8x1 = vec_xl(16, x[i].qs);
-        vector signed char q8y0 = vec_xl( 0, y[i].qs);
-        vector signed char q8y1 = vec_xl(16, y[i].qs);
+        vector signed char q8x0 = vec_xl( 0, x[ib].qs);
+        vector signed char q8x1 = vec_xl(16, x[ib].qs);
+        vector signed char q8y0 = vec_xl( 0, y[ib].qs);
+        vector signed char q8y1 = vec_xl(16, y[ib].qs);
 
         vector signed short qv0 = vec_mule(q8x0, q8y0);
         vector signed short qv1 = vec_mulo(q8x0, q8y0);
         vector signed short qv2 = vec_mule(q8x1, q8y1);
         vector signed short qv3 = vec_mulo(q8x1, q8y1);
 
-        vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackh(qv1));
-        vector signed int vsumi1 = vec_add(vec_unpackl(qv0), vec_unpackl(qv1));
-        vector signed int vsumi2 = vec_add(vec_unpackh(qv2), vec_unpackh(qv3));
-        vector signed int vsumi3 = vec_add(vec_unpackl(qv2), vec_unpackl(qv3));
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
 
-        vsumi0 = vec_add(vsumi0, vsumi2);
-        vsumi1 = vec_add(vsumi1, vsumi3);
+        vsumi0 = vec_sum4s(qv0, vsumi0);
+        vsumi1 = vec_sum4s(qv1, vsumi1);
+        vsumi0 = vec_sum4s(qv2, vsumi0);
+        vsumi1 = vec_sum4s(qv3, vsumi1);
 
         vsumi0 = vec_add(vsumi0, vsumi1);
 
@@ -5011,26 +5436,40 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
-#else
-    // scalar
-    float sumf = 0.0;
+    sumf = vec_extract(vsumf0, 0);
 
-    for (int i = 0; i < nb; i++) {
+#elif defined(__loongarch_asx)
+    // Initialize accumulator with zeros
+    __m256 acc = (__m256)__lasx_xvldi(0);
+
+    // Main loop
+    for (; ib < nb; ++ib) {
+        // Compute combined scale for the block
+        const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
+        __m256i qx = __lasx_xvld((const __m256i *)x[ib].qs, 0);
+        __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
+
+        const __m256 q = mul_sum_i8_pairs_float(qx, qy);
+
+        // Multiply q with scale and accumulate
+        acc = __lasx_xvfmadd_s( d, q, acc );
+    }
+
+    sumf = hsum_float_8(acc);
+#endif
+    for (; ib < nb; ++ib) {
         int sumi = 0;
 
         for (int j = 0; j < qk; j++) {
-            sumi += x[i].qs[j]*y[i].qs[j];
+            sumi += x[ib].qs[j]*y[ib].qs[j];
         }
 
-        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
+        sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d));
     }
 
     *s = sumf;
-#endif
 }
 
-#if QK_K == 256
 void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(nrc == 1);
     UNUSED(nrc);
@@ -5370,6 +5809,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0x3);
     const vector signed char lowScaleMask = vec_splats((signed char)0xF);
+    const vector int v0 = vec_splats((int32_t)0);
     const vector unsigned char v2 = vec_splats((unsigned char)0x2);
     const vector unsigned char v6 = vec_splats((unsigned char)0x6);
     const vector unsigned char v4 = vec_splats((unsigned char)0x4);
@@ -5407,14 +5847,14 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2);
         vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
+        vector signed int vsumi4 = v0;
+        vector signed int vsumi5 = v0;
+        vector signed int vsumi6 = v0;
+        vector signed int vsumi7 = v0;
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -5427,14 +5867,14 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             vector signed char qxs1 = (vector signed char)vec_xl(16, q2);
             q2 += 32;
 
-            vector signed char q2x00 = vec_and(qxs0, lowMask);
-            vector signed char q2x01 = vec_and(vec_sr(qxs0, v2), lowMask);
-            vector signed char q2x02 = vec_and(vec_sr(qxs0, v4), lowMask);
-            vector signed char q2x03 = vec_and(vec_sr(qxs0, v6), lowMask);
-            vector signed char q2x10 = vec_and(qxs1, lowMask);
-            vector signed char q2x11 = vec_and(vec_sr(qxs1, v2), lowMask);
-            vector signed char q2x12 = vec_and(vec_sr(qxs1, v4), lowMask);
-            vector signed char q2x13 = vec_and(vec_sr(qxs1, v6), lowMask);
+            vector unsigned char q2x00 = (vector unsigned char)vec_and(qxs0, lowMask);
+            vector unsigned char q2x01 = (vector unsigned char)vec_and(vec_sr(qxs0, v2), lowMask);
+            vector unsigned char q2x02 = (vector unsigned char)vec_and(vec_sr(qxs0, v4), lowMask);
+            vector unsigned char q2x03 = (vector unsigned char)vec_and(vec_sr(qxs0, v6), lowMask);
+            vector unsigned char q2x10 = (vector unsigned char)vec_and(qxs1, lowMask);
+            vector unsigned char q2x11 = (vector unsigned char)vec_and(vec_sr(qxs1, v2), lowMask);
+            vector unsigned char q2x12 = (vector unsigned char)vec_and(vec_sr(qxs1, v4), lowMask);
+            vector unsigned char q2x13 = (vector unsigned char)vec_and(vec_sr(qxs1, v6), lowMask);
 
             vector signed char q8y00 = vec_xl(  0, q8);
             vector signed char q8y10 = vec_xl( 16, q8);
@@ -5446,45 +5886,36 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             vector signed char q8y13 = vec_xl(112, q8);
             q8 += 128;
 
-            vector signed short qv0 = vec_add(vec_mule(q2x00, q8y00), vec_mulo(q2x00, q8y00));
-            vector signed short qv1 = vec_add(vec_mule(q2x01, q8y01), vec_mulo(q2x01, q8y01));
-            vector signed short qv2 = vec_add(vec_mule(q2x02, q8y02), vec_mulo(q2x02, q8y02));
-            vector signed short qv3 = vec_add(vec_mule(q2x03, q8y03), vec_mulo(q2x03, q8y03));
-            vector signed short qv4 = vec_add(vec_mule(q2x10, q8y10), vec_mulo(q2x10, q8y10));
-            vector signed short qv5 = vec_add(vec_mule(q2x11, q8y11), vec_mulo(q2x11, q8y11));
-            vector signed short qv6 = vec_add(vec_mule(q2x12, q8y12), vec_mulo(q2x12, q8y12));
-            vector signed short qv7 = vec_add(vec_mule(q2x13, q8y13), vec_mulo(q2x13, q8y13));
-
-            vector signed short vscales_h = vec_unpackh(vscales);
-            vector signed short vs0 = vec_splat(vscales_h, 0);
-            vector signed short vs1 = vec_splat(vscales_h, 1);
-            vector signed short vs2 = vec_splat(vscales_h, 2);
-            vector signed short vs3 = vec_splat(vscales_h, 3);
-            vector signed short vs4 = vec_splat(vscales_h, 4);
-            vector signed short vs5 = vec_splat(vscales_h, 5);
-            vector signed short vs6 = vec_splat(vscales_h, 6);
-            vector signed short vs7 = vec_splat(vscales_h, 7);
+            vector signed int qv0 = vec_msum(q8y00, q2x00, v0);
+            vector signed int qv1 = vec_msum(q8y01, q2x01, v0);
+            vector signed int qv2 = vec_msum(q8y02, q2x02, v0);
+            vector signed int qv3 = vec_msum(q8y03, q2x03, v0);
+            vector signed int qv4 = vec_msum(q8y10, q2x10, v0);
+            vector signed int qv5 = vec_msum(q8y11, q2x11, v0);
+            vector signed int qv6 = vec_msum(q8y12, q2x12, v0);
+            vector signed int qv7 = vec_msum(q8y13, q2x13, v0);
+
+            vector signed short vscales_07 = vec_unpackh(vscales);
+            vector signed int vscales_03 = vec_unpackh(vscales_07);
+            vector signed int vscales_47 = vec_unpackl(vscales_07);
+            vector signed int vs0 = vec_splat(vscales_03, 0);
+            vector signed int vs1 = vec_splat(vscales_03, 1);
+            vector signed int vs2 = vec_splat(vscales_03, 2);
+            vector signed int vs3 = vec_splat(vscales_03, 3);
+            vector signed int vs4 = vec_splat(vscales_47, 0);
+            vector signed int vs5 = vec_splat(vscales_47, 1);
+            vector signed int vs6 = vec_splat(vscales_47, 2);
+            vector signed int vs7 = vec_splat(vscales_47, 3);
             vscales = vec_sld(vscales, vscales, 8);
 
-            qv0 = vec_mul(qv0, vs0);
-            qv1 = vec_mul(qv1, vs2);
-            qv2 = vec_mul(qv2, vs4);
-            qv3 = vec_mul(qv3, vs6);
-
-            qv0 = vec_madd(qv4, vs1, qv0);
-            qv1 = vec_madd(qv5, vs3, qv1);
-            qv2 = vec_madd(qv6, vs5, qv2);
-            qv3 = vec_madd(qv7, vs7, qv3);
-
-            vsumi0 = vec_add(vec_unpackh(qv0), vsumi0);
-            vsumi1 = vec_add(vec_unpackh(qv1), vsumi1);
-            vsumi2 = vec_add(vec_unpackh(qv2), vsumi2);
-            vsumi3 = vec_add(vec_unpackh(qv3), vsumi3);
-
-            vsumi4 = vec_add(vec_unpackl(qv0), vsumi4);
-            vsumi5 = vec_add(vec_unpackl(qv1), vsumi5);
-            vsumi6 = vec_add(vec_unpackl(qv2), vsumi6);
-            vsumi7 = vec_add(vec_unpackl(qv3), vsumi7);
+            vsumi0 = vec_add(vec_mul(qv0, vs0), vsumi0);
+            vsumi1 = vec_add(vec_mul(qv1, vs2), vsumi1);
+            vsumi2 = vec_add(vec_mul(qv2, vs4), vsumi2);
+            vsumi3 = vec_add(vec_mul(qv3, vs6), vsumi3);
+            vsumi4 = vec_add(vec_mul(qv4, vs1), vsumi4);
+            vsumi5 = vec_add(vec_mul(qv5, vs3), vsumi5);
+            vsumi6 = vec_add(vec_mul(qv6, vs5), vsumi6);
+            vsumi7 = vec_add(vec_mul(qv7, vs7), vsumi7);
         }
 
         vsumi0 = vec_add(vsumi0, vsumi4);
@@ -5508,6 +5939,72 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     *s = vec_extract(vsumf0, 0);
 
+#elif defined __loongarch_asx
+
+    const __m256i m3 = __lasx_xvreplgr2vr_b(3);
+    const __m128i m4 = __lsx_vreplgr2vr_b(0xF);
+
+    __m256 acc = (__m256)__lasx_xvldi(0);
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+
+        const uint8_t * restrict q2 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0);
+        const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4);
+        const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4);
+        const __m256i mins = lasx_ext8_16(mins8);
+        const __m256i prod = lasx_madd_h(mins, __lasx_xvld((const __m256i*)y[i].bsums, 0));
+
+        acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(dmin), __lasx_xvffint_s_w(prod), acc);
+
+        const __m256i all_scales = lasx_ext8_16(scales8);
+        const __m128i l_scales = lasx_extracti128(all_scales, 0);
+        const __m128i h_scales = lasx_extracti128(all_scales, 1);
+        const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)};
+
+        __m256i sumi = __lasx_xvldi(0);
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            const __m256i q2bits = __lasx_xvld((const __m256i*)q2, 0); q2 += 32;
+
+            const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+
+            const __m256i q2_0 = __lasx_xvand_v(q2bits, m3);
+            const __m256i q2_1 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 2), m3);
+            const __m256i q2_2 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 4), m3);
+            const __m256i q2_3 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 6), m3);
+
+            __m256i p0 = lasx_maddubs_h(q2_0, q8_0);
+            __m256i p1 = lasx_maddubs_h(q2_1, q8_1);
+            __m256i p2 = lasx_maddubs_h(q2_2, q8_2);
+            __m256i p3 = lasx_maddubs_h(q2_3, q8_3);
+
+            p0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(0)), p0);
+            p1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(1)), p1);
+            p2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(2)), p2);
+            p3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(3)), p3);
+
+            p0 = __lasx_xvadd_w(p0, p1);
+            p2 = __lasx_xvadd_w(p2, p3);
+
+            sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p0, p2));
+        }
+
+        acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);
+
+    }
+
+    *s = hsum_float_8(acc);
+
 #else
 
     float sumf = 0;
@@ -5551,454 +6048,108 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 #endif
 }
 
-#else
-
-void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
     assert(nrc == 1);
     UNUSED(nrc);
     UNUSED(bx);
     UNUSED(by);
     UNUSED(bs);
 
-    const block_q2_K * restrict x = vx;
+    const uint32_t kmask1 = 0x03030303;
+    const uint32_t kmask2 = 0x0f0f0f0f;
+
+    const block_q3_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
 
     const int nb = n / QK_K;
 
 #ifdef __ARM_NEON
-    const uint8x16_t m3 = vdupq_n_u8(0x3);
 
-    const int32x4_t vzero = vdupq_n_s32(0);
+    uint32_t aux[3];
+    uint32_t utmp[4];
 
-    ggml_int8x16x4_t q2bytes;
+    const uint8x16_t m3b = vdupq_n_u8(0x3);
+    const int32x4_t  vzero = vdupq_n_s32(0);
 
-    uint32_t aux32[2];
-    const uint8_t * scales = (const uint8_t *)aux32;
+    const uint8x16_t m0 = vdupq_n_u8(1);
+    const uint8x16_t m1 = vshlq_n_u8(m0, 1);
+    const uint8x16_t m2 = vshlq_n_u8(m0, 2);
+    const uint8x16_t m3 = vshlq_n_u8(m0, 3);
+    const int8_t m32 = 32;
+
+    ggml_int8x16x4_t q3bytes;
 
     float sum = 0;
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d    =  y[i].d * GGML_FP16_TO_FP32(x[i].d);
-        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
-        const uint8_t * restrict q2 = x[i].qs;
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict qh = x[i].hmask;
         const int8_t  * restrict q8 = y[i].qs;
-        const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
-
-        aux32[0] = sc[0] & 0x0f0f0f0f;
-        aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f;
 
-        sum += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]);
+        ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh);
 
-        int isum1 = 0, isum2 = 0;
+        ggml_uint8x16x4_t q3h;
 
-        const uint8x16_t q2bits = vld1q_u8(q2);
+        int32_t isum = 0;
 
-        const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8);
+        // Set up scales
+        memcpy(aux, x[i].scales, 12);
+        utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
+        utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
+        utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
+        utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
 
-        q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3));
-        q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3));
-        q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3));
-        q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3));
+        int8_t * scale = (int8_t *)utmp;
+        for (int j = 0; j < 16; ++j) scale[j] -= m32;
 
-        isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0];
-        isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1];
-        isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2];
-        isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3];
+        for (int j = 0; j < QK_K/128; ++j) {
 
-        sum += d * (isum1 + isum2);
-    }
+            const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32;
+            const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64;
+            const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64;
 
-    *s = sum;
+            q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2);
+            q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2);
+            q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1);
+            q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1);
 
-#elif defined __AVX2__
+            q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0]));
+            q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1]));
+            q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
+            q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
 
-    const __m256i m3 = _mm256_set1_epi8(3);
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3];
 
-    __m256 acc = _mm256_setzero_ps();
+            scale += 4;
 
-    uint32_t ud, um;
-    const uint8_t * restrict db = (const uint8_t *)&ud;
-    const uint8_t * restrict mb = (const uint8_t *)&um;
+            q3h.val[0] = vbicq_u8(m2, qhbits.val[0]);
+            q3h.val[1] = vbicq_u8(m2, qhbits.val[1]);
+            q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1);
+            q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1);
 
-    float summs = 0;
+            q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0]));
+            q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1]));
+            q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
+            q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
 
-    // TODO: optimize this
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2];
+            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3];
 
-    for (int i = 0; i < nb; ++i) {
+            scale += 4;
 
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
-
-        const uint8_t * restrict q2 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
-        ud = (sc[0] >> 0) & 0x0f0f0f0f;
-        um = (sc[0] >> 4) & 0x0f0f0f0f;
-
-        int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3];
-        summs += dmin * smin;
-
-        const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
-        const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3);
-        const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
-
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        const __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0);
-        const __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1);
-
-        const __m256i p_0 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 0));
-        const __m256i p_1 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 1));
-        const __m256i p_2 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 0));
-        const __m256i p_3 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 1));
-
-        acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0), acc);
-        acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1), acc);
-        acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2), acc);
-        acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3), acc);
-    }
-
-    *s = hsum_float_8(acc) + summs;
-
-#elif defined __AVX__
-
-    const __m128i m3 = _mm_set1_epi8(3);
-
-    __m256 acc = _mm256_setzero_ps();
-
-    uint32_t ud, um;
-    const uint8_t * restrict db = (const uint8_t *)&ud;
-    const uint8_t * restrict mb = (const uint8_t *)&um;
-
-    float summs = 0;
-
-    // TODO: optimize this
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
-
-        const uint8_t * restrict q2 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
-        ud = (sc[0] >> 0) & 0x0f0f0f0f;
-        um = (sc[0] >> 4) & 0x0f0f0f0f;
-
-        int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3];
-        summs += dmin * smin;
-
-        const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
-        const __m128i q2_0 = _mm_and_si128(q2bits, m3);
-        const __m128i q2_1 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3);
-        const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3);
-        const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3);
-
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        const __m128i p0 = _mm_maddubs_epi16(q2_0, _mm256_extractf128_si256(q8_0, 0));
-        const __m128i p1 = _mm_maddubs_epi16(q2_1, _mm256_extractf128_si256(q8_0, 1));
-        const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0));
-        const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1));
-
-        const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
-        const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
-        const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
-        const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
-
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc);
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc);
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2)), acc);
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3)), acc);
-    }
-
-    *s = hsum_float_8(acc) + summs;
-
-#elif defined __riscv_v_intrinsic
-
-    uint32_t aux32[2];
-    const uint8_t * scales = (const uint8_t *)aux32;
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d    =  y[i].d * GGML_FP16_TO_FP32(x[i].d);
-        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
-
-        const uint8_t * restrict q2 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-        const uint32_t * restrict sc = (const uint32_t *)x[i].scales;
-
-        aux32[0] = sc[0] & 0x0f0f0f0f;
-        aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f;
-
-        sumf += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]);
-
-        int isum1 = 0;
-        int isum2 = 0;
-
-        size_t vl = 16;
-
-        vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1);
-
-        // load Q2
-        vuint8mf2_t q2_x = __riscv_vle8_v_u8mf2(q2, vl);
-
-        vint8mf2_t q2_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q2_x, 0x03, vl));
-        vint8mf2_t q2_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x2, vl), 0x03 , vl));
-        vint8mf2_t q2_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x4, vl), 0x03 , vl));
-        vint8mf2_t q2_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x6, vl), 0x03 , vl));
-
-        // load Q8, and take product with Q2
-        vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q2_0, __riscv_vle8_v_i8mf2(q8, vl), vl);
-        vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q2_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl);
-        vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q2_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl);
-        vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q2_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl);
-
-        vint16m1_t vs_0 = __riscv_vredsum_vs_i16m1_i16m1(p0, vzero, vl);
-        vint16m1_t vs_1 = __riscv_vredsum_vs_i16m1_i16m1(p1, vzero, vl);
-        vint16m1_t vs_2 = __riscv_vredsum_vs_i16m1_i16m1(p2, vzero, vl);
-        vint16m1_t vs_3 = __riscv_vredsum_vs_i16m1_i16m1(p3, vzero, vl);
-
-        isum1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[0];
-        isum2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[1];
-        isum1 += __riscv_vmv_x_s_i16m1_i16(vs_2) * scales[2];
-        isum2 += __riscv_vmv_x_s_i16m1_i16(vs_3) * scales[3];
-
-        sumf += d * (isum1 + isum2);
-
-    }
-
-    *s = sumf;
-
-#elif defined(__POWER9_VECTOR__)
-    const vector signed char lowMask = vec_splats((signed char)0x3);
-    const vector signed char lowScaleMask = vec_splats((signed char)0xF);
-    const vector unsigned char v2 = vec_splats((unsigned char)0x2);
-    const vector unsigned char v4 = vec_splats((unsigned char)0x4);
-    const vector unsigned char v6 = vec_splats((unsigned char)0x6);
-
-    vector float vsumf0 = vec_splats(0.0f);
-    vector float vsumf1 = vec_splats(0.0f);
-    vector float vsumf2 = vec_splats(0.0f);
-    vector float vsumf3 = vec_splats(0.0f);
-
-#pragma GCC unroll 2
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
-
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(y[i].d);
-        vector float vd = vec_mul(vxd, vyd);
-
-        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin));
-        vector float vdmin = vec_mul(vxmin, vyd);
-
-        vector signed short q8ysums0 = vec_xl_len(y[i].bsums, 8);
-
-        vector signed char q2xmins = (vector signed char)vec_xl_len(x[i].scales, 4);
-        vector signed char vscales = vec_and(q2xmins, lowScaleMask);
-
-        q2xmins = vec_sr(q2xmins, v4);
-        vector signed short q2xmins0 = vec_unpackh((vector signed char)q2xmins);
-
-        vector signed int prod0 = vec_mule(q2xmins0, q8ysums0);
-        vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0);
-
-        vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0);
-        vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1);
-
-        vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs);
-        vector signed char q2x00 = vec_and(qxs0, lowMask);
-        vector signed char q2x01 = vec_and(vec_sr(qxs0, v2), lowMask);
-        vector signed char q2x02 = vec_and(vec_sr(qxs0, v4), lowMask);
-        vector signed char q2x03 = vec_and(vec_sr(qxs0, v6), lowMask);
-
-        vector signed char q8y00 = vec_xl(  0, y[i].qs);
-        vector signed char q8y01 = vec_xl( 16, y[i].qs);
-        vector signed char q8y02 = vec_xl( 32, y[i].qs);
-        vector signed char q8y03 = vec_xl( 48, y[i].qs);
-
-        vector signed short qv0 = vec_add(vec_mule(q2x00, q8y00), vec_mulo(q2x00, q8y00));
-        vector signed short qv1 = vec_add(vec_mule(q2x01, q8y01), vec_mulo(q2x01, q8y01));
-        vector signed short qv2 = vec_add(vec_mule(q2x02, q8y02), vec_mulo(q2x02, q8y02));
-        vector signed short qv3 = vec_add(vec_mule(q2x03, q8y03), vec_mulo(q2x03, q8y03));
-
-        vector signed short vscales_h = vec_unpackh(vscales);
-        vector signed short vs0 = vec_splat(vscales_h, 0);
-        vector signed short vs1 = vec_splat(vscales_h, 1);
-        vector signed short vs2 = vec_splat(vscales_h, 2);
-        vector signed short vs3 = vec_splat(vscales_h, 3);
-
-        vector signed int vsumi0 = vec_add(vec_mule(qv0, vs0), vec_mulo(qv0, vs0));
-        vector signed int vsumi1 = vec_add(vec_mule(qv1, vs1), vec_mulo(qv1, vs1));
-        vector signed int vsumi2 = vec_add(vec_mule(qv2, vs2), vec_mulo(qv2, vs2));
-        vector signed int vsumi3 = vec_add(vec_mule(qv3, vs3), vec_mulo(qv3, vs3));
-
-        vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
-        vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
-        vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
-        vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3);
-    }
-
-    vsumf0 = vec_add(vsumf0, vsumf2);
-    vsumf1 = vec_add(vsumf1, vsumf3);
-
-    vsumf0 = vec_add(vsumf0, vsumf1);
-
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
-
-    *s = vec_extract(vsumf0, 0);
-
-#else
-
-    float sumf = 0;
-
-    int isum[QK_K/16];
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * q2 = x[i].qs;
-        const  int8_t * q8 = y[i].qs;
-        const uint8_t * sc = x[i].scales;
-
-        int summs = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            summs += y[i].bsums[j] * (sc[j] >> 4);
-        }
-
-        const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
-
-        memset(isum, 0, (QK_K/16)*sizeof(int));
-        for (int l =  0; l < 16; ++l) {
-            isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3);
-            isum[1] += q8[l+16] * ((q2[l] >> 2) & 3);
-            isum[2] += q8[l+32] * ((q2[l] >> 4) & 3);
-            isum[3] += q8[l+48] * ((q2[l] >> 6) & 3);
-        }
-        for (int l = 0; l < QK_K/16; ++l) {
-            isum[l] *= (sc[l] & 0xF);
-        }
-        sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs;
-    }
-    *s = sumf;
-#endif
-}
-#endif
-
-#if QK_K == 256
-void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
-    assert(n % QK_K == 0);
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-
-    const uint32_t kmask1 = 0x03030303;
-    const uint32_t kmask2 = 0x0f0f0f0f;
-
-    const block_q3_K * restrict x = vx;
-    const block_q8_K * restrict y = vy;
-
-    const int nb = n / QK_K;
-
-#ifdef __ARM_NEON
-
-    uint32_t aux[3];
-    uint32_t utmp[4];
-
-    const uint8x16_t m3b = vdupq_n_u8(0x3);
-    const int32x4_t  vzero = vdupq_n_s32(0);
-
-    const uint8x16_t m0 = vdupq_n_u8(1);
-    const uint8x16_t m1 = vshlq_n_u8(m0, 1);
-    const uint8x16_t m2 = vshlq_n_u8(m0, 2);
-    const uint8x16_t m3 = vshlq_n_u8(m0, 3);
-    const int8_t m32 = 32;
-
-    ggml_int8x16x4_t q3bytes;
-
-    float sum = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-
-        const uint8_t * restrict q3 = x[i].qs;
-        const uint8_t * restrict qh = x[i].hmask;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh);
-
-        ggml_uint8x16x4_t q3h;
-
-        int32_t isum = 0;
-
-        // Set up scales
-        memcpy(aux, x[i].scales, 12);
-        utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
-        utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
-        utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
-        utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
-
-        int8_t * scale = (int8_t *)utmp;
-        for (int j = 0; j < 16; ++j) scale[j] -= m32;
-
-        for (int j = 0; j < QK_K/128; ++j) {
-
-            const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32;
-            const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64;
-            const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64;
-
-            q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2);
-            q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2);
-            q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1);
-            q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1);
-
-            q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0]));
-            q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1]));
-            q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
-            q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
-
-            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0];
-            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1];
-            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2];
-            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3];
-
-            scale += 4;
-
-            q3h.val[0] = vbicq_u8(m2, qhbits.val[0]);
-            q3h.val[1] = vbicq_u8(m2, qhbits.val[1]);
-            q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1);
-            q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1);
-
-            q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0]));
-            q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1]));
-            q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2]));
-            q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3]));
-
-            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0];
-            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1];
-            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2];
-            isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3];
-
-            scale += 4;
-
-            if (j == 0) {
-                qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4);
-                qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4);
-            }
+            if (j == 0) {
+                qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4);
+                qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4);
+            }
 
         }
         sum += d * isum;
@@ -6298,22 +6449,22 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             // compute mask for subtraction
             vuint8m1_t qh_m0 = __riscv_vand_vx_u8m1(vqh, m, vl);
             vbool8_t vmask_0 = __riscv_vmseq_vx_u8m1_b8(qh_m0, 0, vl);
-            vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_m(vmask_0, q3_0, 0x4, vl);
+            vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_mu(vmask_0, q3_0, q3_0, 0x4, vl);
             m <<= 1;
 
             vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl);
             vbool8_t vmask_1 = __riscv_vmseq_vx_u8m1_b8(qh_m1, 0, vl);
-            vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_m(vmask_1, q3_1, 0x4, vl);
+            vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_mu(vmask_1, q3_1, q3_1, 0x4, vl);
             m <<= 1;
 
             vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl);
             vbool8_t vmask_2 = __riscv_vmseq_vx_u8m1_b8(qh_m2, 0, vl);
-            vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_m(vmask_2, q3_2, 0x4, vl);
+            vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_mu(vmask_2, q3_2, q3_2, 0x4, vl);
             m <<= 1;
 
             vuint8m1_t qh_m3 = __riscv_vand_vx_u8m1(vqh, m, vl);
             vbool8_t vmask_3 = __riscv_vmseq_vx_u8m1_b8(qh_m3, 0, vl);
-            vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_m(vmask_3, q3_3, 0x4, vl);
+            vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_mu(vmask_3, q3_3, q3_3, 0x4, vl);
             m <<= 1;
 
             // load Q8 and take product with Q3
@@ -6355,6 +6506,9 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0x3);
+    const vector signed char lowMask1 = vec_splats((int8_t)0xf);
+    const vector signed char lowMask2 = vec_splats((int8_t)0x30);
+    const vector int v0 = vec_splats((int32_t)0);
     const vector signed char v1 = vec_splats((signed char)0x1);
     const vector unsigned char v2 = vec_splats((unsigned char)0x2);
     const vector unsigned char v3 = vec_splats((unsigned char)0x3);
@@ -6372,29 +6526,33 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         vector float vyd = vec_splats(y[i].d);
         vector float vd = vec_mul(vxd, vyd);
 
-        uint32_t aux[3];
-        uint32_t utmp[4];
+        UNUSED(kmask1);
+        UNUSED(kmask2);
 
-        memcpy(aux, x[i].scales, 12);
-        utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
-        utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
-        utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
-        utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
+        vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8);
+        vector signed char u1 = vec_and(u0, lowMask1);
+        vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4);
+        vector signed char u3 = (vector signed char)vec_mergeh((vector signed int)u2, (vector signed int)vec_sr(u2, v2));
+        vector signed char u30 = vec_sl(vec_and(u3, lowMask), v4);
+        vector signed char u31 = vec_and(u3, lowMask2);
+
+        u1 = vec_or(u1, u30);
+        u2 = vec_or(vec_sr(u0, v4), u31);
 
-        vector signed char vscales = (vector signed char)vec_xl( 0, utmp);
+        vector signed char vscales = (vector signed char)vec_mergeh((vector signed long long)u1, (vector signed long long)u2);
         vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].hmask);
         vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].hmask);
 
         vscales = vec_sub(vscales, off);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
+        vector signed int vsumi4 = v0;
+        vector signed int vsumi5 = v0;
+        vector signed int vsumi6 = v0;
+        vector signed int vsumi7 = v0;
 
         const uint8_t * restrict q3 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -6468,23 +6626,14 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             vector signed short qv12 = vec_add(vec_mule(q3x12, q8y12), vec_mulo(q3x12, q8y12));
             vector signed short qv13 = vec_add(vec_mule(q3x13, q8y13), vec_mulo(q3x13, q8y13));
 
-            vector signed int vsum0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0));
-            vector signed int vsum1 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2));
-            vector signed int vsum2 = vec_add(vec_mule(qv02, vs4), vec_mulo(qv02, vs4));
-            vector signed int vsum3 = vec_add(vec_mule(qv03, vs6), vec_mulo(qv03, vs6));
-            vector signed int vsum4 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1));
-            vector signed int vsum5 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3));
-            vector signed int vsum6 = vec_add(vec_mule(qv12, vs5), vec_mulo(qv12, vs5));
-            vector signed int vsum7 = vec_add(vec_mule(qv13, vs7), vec_mulo(qv13, vs7));
-
-            vsumi0 = vec_add(vsum0, vsumi0);
-            vsumi1 = vec_add(vsum1, vsumi1);
-            vsumi2 = vec_add(vsum2, vsumi2);
-            vsumi3 = vec_add(vsum3, vsumi3);
-            vsumi4 = vec_add(vsum4, vsumi4);
-            vsumi5 = vec_add(vsum5, vsumi5);
-            vsumi6 = vec_add(vsum6, vsumi6);
-            vsumi7 = vec_add(vsum7, vsumi7);
+            vsumi0 = vec_msum(qv00, vs0, vsumi0);
+            vsumi1 = vec_msum(qv01, vs2, vsumi1);
+            vsumi2 = vec_msum(qv02, vs4, vsumi2);
+            vsumi3 = vec_msum(qv03, vs6, vsumi3);
+            vsumi4 = vec_msum(qv10, vs1, vsumi4);
+            vsumi5 = vec_msum(qv11, vs3, vsumi5);
+            vsumi6 = vec_msum(qv12, vs5, vsumi6);
+            vsumi7 = vec_msum(qv13, vs7, vsumi7);
         }
 
         vsumi0 = vec_add(vsumi0, vsumi4);
@@ -6507,499 +6656,168 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
     *s = vec_extract(vsumf0, 0);
-#else
-    // scalar version
-    // This function is written like this so the compiler can manage to vectorize most of it
-    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
-    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
-    // The ideal situation would be if we could just write the code once, and the compiler would
-    // automatically produce the best possible set of machine instructions, instead of us having to manually
-    // write vectorized versions for AVX, ARM_NEON, etc.
 
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
+#elif defined __loongarch_asx
 
-    uint32_t auxs[4];
-    const int8_t * scales = (const int8_t*)auxs;
+    const __m256i m3 = __lasx_xvreplgr2vr_b(3);
+    const __m256i mone = __lasx_xvreplgr2vr_b(1);
+    const __m128i m32 = __lsx_vreplgr2vr_b(32);
 
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * restrict q3 = x[i].qs;
-        const uint8_t * restrict hm = x[i].hmask;
-        const  int8_t * restrict q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * restrict a = aux8;
-        uint8_t m = 1;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
-            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
-            a += 32; m <<= 1;
-            q3 += 32;
-        }
-        a = aux8;
-
-        memcpy(auxs, x[i].scales, 12);
-        uint32_t tmp = auxs[2];
-        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
-        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
-        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
-        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
-        for (int j = 0; j < QK_K/16; ++j) {
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-
-#endif
-
-}
+    __m256 acc = (__m256)__lasx_xvldi(0);
 
-#else
-
-void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
-    assert(n % QK_K == 0);
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-
-    const block_q3_K * restrict x = vx;
-    const block_q8_K * restrict y = vy;
-
-    const int nb = n / QK_K;
-
-#ifdef __ARM_NEON
-    const int32x4_t vzero = vdupq_n_s32(0);
-
-    const uint8x16_t m3b = vdupq_n_u8(0x3);
-    const uint8x16_t mh  = vdupq_n_u8(4);
-
-    ggml_int8x16x4_t q3bytes;
-
-    uint16_t aux16[2];
-    int8_t * scales = (int8_t *)aux16;
-
-    float sum = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        ggml_uint8x16x4_t q3h;
-
-        const uint8x8_t  hbits    = vld1_u8(x[i].hmask);
-        const uint8x16_t q3bits   = vld1q_u8(x[i].qs);
-        const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(y[i].qs);
-
-        const uint16_t a = *(const uint16_t *)x[i].scales;
-        aux16[0] = a & 0x0f0f;
-        aux16[1] = (a >> 4) & 0x0f0f;
-
-        for (int j = 0; j < 4; ++j) scales[j] -= 8;
-
-        int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-
-        const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1));
-        q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2));
-        q3h.val[1] = vandq_u8(mh, htmp);
-        q3h.val[2] = vandq_u8(mh, vshrq_n_u8(htmp, 2));
-        q3h.val[3] = vandq_u8(mh, vshrq_n_u8(htmp, 4));
-
-        q3bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q3bits, m3b),                q3h.val[0]));
-        q3bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 2), m3b), q3h.val[1]));
-        q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2]));
-        q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6),                q3h.val[3]));
-
-        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0];
-        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2];
-        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1];
-        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3];
-
-        sum += d * isum;
-
-    }
-
-    *s = sum;
-
-#elif defined __AVX2__
-
-    const __m256i m3 = _mm256_set1_epi8(3);
-    const __m256i m1 = _mm256_set1_epi8(1);
-
-    __m256 acc = _mm256_setzero_ps();
-
-    uint64_t aux64;
-
-    uint16_t aux16[2];
-    const int8_t * aux8 = (const int8_t *)aux16;
+    uint32_t aux[3];
 
     for (int i = 0; i < nb; ++i) {
 
         const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-
         const uint8_t * restrict q3 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
+        // Set up scales
+        memcpy(aux, x[i].scales, 12);
+        __m128i scales128 = lsx_set_w(
+                ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4),
+                ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4),
+                (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4),
+                (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4));
+        scales128 = __lsx_vsub_b(scales128, m32);
+        const __m256i all_scales = lasx_ext8_16(scales128);
+        const __m128i l_scales = lasx_extracti128(all_scales, 0);
+        const __m128i h_scales = lasx_extracti128(all_scales, 1);
+        const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)};
 
-        const uint16_t a = *(const uint16_t *)x[i].scales;
-        aux16[0] = a & 0x0f0f;
-        aux16[1] = (a >> 4) & 0x0f0f;
-
-        const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
-        const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
-
-        memcpy(&aux64, x[i].hmask, 8);
-
-        const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
-        __m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux);
-        __m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4);
-        q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2);
-        q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2);
-
-        // load low 2 bits
-        const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
-
-        // prepare low and high bits
-        const __m256i q3aux  = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits);
-        const __m256i q3l_0 = _mm256_and_si256(q3aux, m3);
-        const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3);
-
-        // load Q8 quants
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16,
-        // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set,
-        // and 2 if the high bit was set)
-        const __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0);
-        const __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1);
-
-        __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0);
-        __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1);
-
-        p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
-        p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
-
-        // multiply with scales
-        p16_0 = _mm256_madd_epi16(scale_0, p16_0);
-        p16_1 = _mm256_madd_epi16(scale_1, p16_1);
+        // high bit
+        const __m256i hbits = __lasx_xvld((const __m256i*)x[i].hmask, 0);
 
-        p16_0 = _mm256_add_epi32(p16_0, p16_1);
+        // integer accumulator
+        __m256i sumi = __lasx_xvldi(0);
 
-        // multiply with block scale and accumulate
-        acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16_0), acc);
+        int bit = 0;
+        int is  = 0;
+        __m256i xvbit;
 
-    }
 
-    *s = hsum_float_8(acc);
+        for (int j = 0; j < QK_K/128; ++j) {
+            // load low 2 bits
+            const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32;
 
-#elif defined __AVX__
+            xvbit = __lasx_xvreplgr2vr_h(bit);
+            // prepare low and high bits
+            const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3);
+            const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2);
+            ++bit;
 
-    const __m128i m3 = _mm_set1_epi8(3);
-    const __m128i m1 = _mm_set1_epi8(1);
+            xvbit = __lasx_xvreplgr2vr_h(bit);
+            const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3);
+            const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2);
+            ++bit;
 
-    __m256 acc = _mm256_setzero_ps();
+            xvbit = __lasx_xvreplgr2vr_h(bit);
+            const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3);
+            const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2);
+            ++bit;
 
-    uint64_t aux64;
+            xvbit = __lasx_xvreplgr2vr_h(bit);
+            const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3);
+            const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2);
+            ++bit;
 
-    uint16_t aux16[2];
-    const int8_t * aux8 = (const int8_t *)aux16;
+            // load Q8 quants
+            const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
 
-    for (int i = 0; i < nb; ++i) {
+            // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h,
+            // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set,
+            // and 2 if the high bit was set)
+            __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0);
+            __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1);
+            __m256i q8s_2 = lasx_maddubs_h(q3h_2, q8_2);
+            __m256i q8s_3 = lasx_maddubs_h(q3h_3, q8_3);
 
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+            __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0);
+            __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1);
+            __m256i p16_2 = lasx_maddubs_h(q3l_2, q8_2);
+            __m256i p16_3 = lasx_maddubs_h(q3l_3, q8_3);
 
-        const uint8_t * restrict q3 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
+            p16_0 = __lasx_xvsub_h(p16_0, q8s_0);
+            p16_1 = __lasx_xvsub_h(p16_1, q8s_1);
+            p16_2 = __lasx_xvsub_h(p16_2, q8s_2);
+            p16_3 = __lasx_xvsub_h(p16_3, q8s_3);
 
-        const uint16_t a = *(const uint16_t *)x[i].scales;
-        aux16[0] = a & 0x0f0f;
-        aux16[1] = (a >> 4) & 0x0f0f;
-
-        const __m128i scale_0 = _mm_set1_epi16(aux8[0] - 8);
-        const __m128i scale_1 = _mm_set1_epi16(aux8[2] - 8);
-        const __m128i scale_2 = _mm_set1_epi16(aux8[1] - 8);
-        const __m128i scale_3 = _mm_set1_epi16(aux8[3] - 8);
-
-        memcpy(&aux64, x[i].hmask, 8);
-
-        __m128i q3h_0 = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
-        __m128i q3h_1 = _mm_srli_epi16(q3h_0, 2);
-        __m128i q3h_2 = _mm_srli_epi16(q3h_0, 4);
-        __m128i q3h_3 = _mm_srli_epi16(q3h_0, 6);
-        q3h_0 = _mm_slli_epi16(_mm_andnot_si128(q3h_0, m1), 2);
-        q3h_1 = _mm_slli_epi16(_mm_andnot_si128(q3h_1, m1), 2);
-        q3h_2 = _mm_slli_epi16(_mm_andnot_si128(q3h_2, m1), 2);
-        q3h_3 = _mm_slli_epi16(_mm_andnot_si128(q3h_3, m1), 2);
-
-        // load low 2 bits
-        const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
-
-        // prepare low and high bits
-        const __m128i q3l_0 = _mm_and_si128(q3bits, m3);
-        const __m128i q3l_1 = _mm_and_si128(_mm_srli_epi16(q3bits, 2), m3);
-        const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits, 4), m3);
-        const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits, 6), m3);
-
-        // load Q8 quants
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm_maddubs_epi16,
-        // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set,
-        // and 2 if the high bit was set)
-        const __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, _mm256_extractf128_si256(q8_0, 0));
-        const __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, _mm256_extractf128_si256(q8_0, 1));
-        const __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, _mm256_extractf128_si256(q8_1, 0));
-        const __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, _mm256_extractf128_si256(q8_1, 1));
-
-        __m128i p16_0 = _mm_maddubs_epi16(q3l_0, _mm256_extractf128_si256(q8_0, 0));
-        __m128i p16_1 = _mm_maddubs_epi16(q3l_1, _mm256_extractf128_si256(q8_0, 1));
-        __m128i p16_2 = _mm_maddubs_epi16(q3l_2, _mm256_extractf128_si256(q8_1, 0));
-        __m128i p16_3 = _mm_maddubs_epi16(q3l_3, _mm256_extractf128_si256(q8_1, 1));
-
-        p16_0 = _mm_sub_epi16(p16_0, q8s_0);
-        p16_1 = _mm_sub_epi16(p16_1, q8s_1);
-        p16_2 = _mm_sub_epi16(p16_2, q8s_2);
-        p16_3 = _mm_sub_epi16(p16_3, q8s_3);
-
-        // multiply with scales
-        p16_0 = _mm_madd_epi16(scale_0, p16_0);
-        p16_1 = _mm_madd_epi16(scale_1, p16_1);
-        p16_2 = _mm_madd_epi16(scale_2, p16_2);
-        p16_3 = _mm_madd_epi16(scale_3, p16_3);
-
-        p16_0 = _mm_add_epi32(p16_0, p16_2);
-        p16_1 = _mm_add_epi32(p16_1, p16_3);
-        __m256i p16 = MM256_SET_M128I(p16_1, p16_0);
+            // multiply with scales
+            p16_0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0);
+            p16_1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1);
+            p16_2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2);
+            p16_3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3);
 
+            // accumulate
+            p16_0 = __lasx_xvadd_w(p16_0, p16_1);
+            p16_2 = __lasx_xvadd_w(p16_2, p16_3);
+            sumi  = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_2));
+        }
         // multiply with block scale and accumulate
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc);
-
+        acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);//FIXME
     }
 
     *s = hsum_float_8(acc);
 
-#elif defined __riscv_v_intrinsic
-
-    uint16_t aux16[2];
-    int8_t * scales = (int8_t *)aux16;
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * restrict q3 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const uint16_t a = *(const uint16_t *)x[i].scales;
-        aux16[0] = a & 0x0f0f;
-        aux16[1] = (a >> 4) & 0x0f0f;
-
-        for (int j = 0; j < 4; ++j) scales[j] -= 8;
-
-        int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-
-        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
-
-        // load qh
-        vuint8mf4_t qh_x1   = __riscv_vle8_v_u8mf4(x[i].hmask, 8);
-        vuint8mf2_t qh_x2   = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8));
-
-        size_t vl = 16;
-
-        // extend and combine both qh_x1 and qh_x2
-        vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl);
-
-        vuint8mf2_t qh_0 = __riscv_vand_vx_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl);
-        vuint8mf2_t qh_1 = __riscv_vand_vx_u8mf2(qh_x, 0x4, vl);
-        vuint8mf2_t qh_2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl);
-        vuint8mf2_t qh_3 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), 0x4, vl);
-
-        // load Q3
-        vuint8mf2_t q3_x  = __riscv_vle8_v_u8mf2(q3, vl);
-
-        vuint8mf2_t q3h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q3_x, 0x3, vl), qh_0, vl);
-        vuint8mf2_t q3h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 2, vl), 0x3, vl), qh_1, vl);
-        vuint8mf2_t q3h_2 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 4, vl), 0x3, vl), qh_2, vl);
-        vuint8mf2_t q3h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 0x6, vl), qh_3, vl);
-
-        vint8mf2_t q3_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_0);
-        vint8mf2_t q3_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_1);
-        vint8mf2_t q3_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_2);
-        vint8mf2_t q3_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_3);
-
-        // load Q8 and take product with Q3
-        vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q3_0, __riscv_vle8_v_i8mf2(q8, vl), vl);
-        vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q3_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl);
-        vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q3_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl);
-        vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q3_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl);
-
-        vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl);
-        vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl);
-        vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl);
-        vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl);
-
-        isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scales[0];
-        isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scales[2];
-        isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scales[1];
-        isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scales[3];
-
-        sumf += d * isum;
-
-    }
-
-    *s = sumf;
-
-#elif defined(__POWER9_VECTOR__)
-    const vector signed char lowMask = vec_splats((signed char)0x3);
-    const vector signed char v1 = vec_splats((signed char)0x1);
-    const vector unsigned char v2 = vec_splats((unsigned char)0x2);
-    const vector unsigned char v4 = vec_splats((unsigned char)0x4);
-    const vector unsigned char v6 = vec_splats((unsigned char)0x6);
-    const vector signed char off = vec_splats((signed char)0x8);
-
-    vector float vsumf0 = vec_splats(0.0f);
-    vector float vsumf1 = vec_splats(0.0f);
-    vector float vsumf2 = vec_splats(0.0f);
-    vector float vsumf3 = vec_splats(0.0f);
-
-#pragma GCC unroll 2
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
-
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(y[i].d);
-        vector float vd = vec_mul(vxd, vyd);
-
-        uint16_t aux16[2];
-        int8_t * scales = (int8_t *)aux16;
-
-        const uint16_t a = *(const uint16_t *)x[i].scales;
-        aux16[0] = a & 0x0f0f;
-        aux16[1] = (a >> 4) & 0x0f0f;
-
-        vector signed char vscales = (vector signed char)vec_xl_len(scales, 8);
-        vector signed char qxhs0 = (vector signed char)vec_xl_len(x[i].hmask, 8);
-        qxhs0 = vec_or(qxhs0, vec_sr(vec_sld(qxhs0, qxhs0, 8), (vector unsigned char)v1));
-
-        vscales = vec_sub(vscales, off);
-
-        vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs);
-        vector signed char qxs00 = vec_and(qxs0, lowMask);
-        vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask);
-        vector signed char qxs10 = vec_and(vec_sr(qxs0, v4), lowMask);
-        vector signed char qxs11 = vec_and(vec_sr(qxs0, v6), lowMask);
-
-        //the 3rd bit
-        vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2);
-        vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2);
-        vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v4)), v2);
-        vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v6)), v2);
-        qxhs0 = vec_sr(qxhs0, v4);
-
-        vector signed char q3x00 = vec_sub(qxs00, qxh00);
-        vector signed char q3x01 = vec_sub(qxs01, qxh01);
-        vector signed char q3x10 = vec_sub(qxs10, qxh02);
-        vector signed char q3x11 = vec_sub(qxs11, qxh03);
-
-        vector signed char q8y00 = vec_xl(  0, y[i].qs);
-        vector signed char q8y01 = vec_xl( 16, y[i].qs);
-        vector signed char q8y10 = vec_xl( 32, y[i].qs);
-        vector signed char q8y11 = vec_xl( 48, y[i].qs);
-
-        vector signed short vscales_h = vec_unpackh(vscales);
-        vector signed short vs0 = vec_splat(vscales_h, 0);
-        vector signed short vs1 = vec_splat(vscales_h, 1);
-        vector signed short vs2 = vec_splat(vscales_h, 2);
-        vector signed short vs3 = vec_splat(vscales_h, 3);
-
-        vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00));
-        vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10));
-        vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01));
-        vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11));
-
-        vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0));
-        vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1));
-        vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2));
-        vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3));
-
-        vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
-        vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
-        vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
-        vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3);
-    }
-
-    vsumf0 = vec_add(vsumf0, vsumf2);
-    vsumf1 = vec_add(vsumf1, vsumf3);
-
-    vsumf0 = vec_add(vsumf0, vsumf1);
-
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
-
-    *s = vec_extract(vsumf0, 0);
 #else
+    // scalar version
+    // This function is written like this so the compiler can manage to vectorize most of it
+    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
+    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
+    // The ideal situation would be if we could just write the code once, and the compiler would
+    // automatically produce the best possible set of machine instructions, instead of us having to manually
+    // write vectorized versions for AVX, ARM_NEON, etc.
 
     int8_t  aux8[QK_K];
     int16_t aux16[8];
     float   sums [8];
     int32_t aux32[8];
-    int32_t scales[4];
     memset(sums, 0, 8*sizeof(float));
 
+    uint32_t auxs[4];
+    const int8_t * scales = (const int8_t*)auxs;
+
     float sumf = 0;
     for (int i = 0; i < nb; ++i) {
         const uint8_t * restrict q3 = x[i].qs;
         const uint8_t * restrict hm = x[i].hmask;
         const  int8_t * restrict q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
         int8_t * restrict a = aux8;
-        for (int l = 0; l < 8; ++l) {
-            a[l+ 0] = (int8_t)((q3[l+0] >> 0) & 3) - (hm[l] & 0x01 ? 0 : 4);
-            a[l+ 8] = (int8_t)((q3[l+8] >> 0) & 3) - (hm[l] & 0x02 ? 0 : 4);
-            a[l+16] = (int8_t)((q3[l+0] >> 2) & 3) - (hm[l] & 0x04 ? 0 : 4);
-            a[l+24] = (int8_t)((q3[l+8] >> 2) & 3) - (hm[l] & 0x08 ? 0 : 4);
-            a[l+32] = (int8_t)((q3[l+0] >> 4) & 3) - (hm[l] & 0x10 ? 0 : 4);
-            a[l+40] = (int8_t)((q3[l+8] >> 4) & 3) - (hm[l] & 0x20 ? 0 : 4);
-            a[l+48] = (int8_t)((q3[l+0] >> 6) & 3) - (hm[l] & 0x40 ? 0 : 4);
-            a[l+56] = (int8_t)((q3[l+8] >> 6) & 3) - (hm[l] & 0x80 ? 0 : 4);
-        }
-
-        scales[0] = (x[i].scales[0] & 0xF) - 8;
-        scales[1] = (x[i].scales[0] >>  4) - 8;
-        scales[2] = (x[i].scales[1] & 0xF) - 8;
-        scales[3] = (x[i].scales[1] >>  4) - 8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            q3 += 32;
+        }
+        a = aux8;
 
-        memset(aux32, 0, 8*sizeof(int32_t));
+        memcpy(auxs, x[i].scales, 12);
+        uint32_t tmp = auxs[2];
+        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
         for (int j = 0; j < QK_K/16; ++j) {
             for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
             q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] += q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
             q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux32[l] += scales[j] * aux16[l];
         }
         const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
         for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
@@ -7010,9 +6828,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 #endif
 
 }
-#endif
 
-#if QK_K == 256
 void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
     assert(nrc == 1);
@@ -7316,6 +7132,10 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector signed char lowMask1 = vec_splats((int8_t)0x3f);
+    const vector signed char lowMask2 = vec_splats((int8_t)0x30);
+    const vector int v0 = vec_splats((int32_t)0);
+    const vector unsigned char v2 = vec_splats((uint8_t)2);
     const vector unsigned char v4 = vec_splats((unsigned char)0x4);
 
     vector float vsumf0 = vec_splats(0.0f);
@@ -7334,15 +7154,24 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         vector signed short q8ysums0 = vec_xl( 0, y[i].bsums);
         vector signed short q8ysums1 = vec_xl(16, y[i].bsums);
 
-        memcpy(utmp, x[i].scales, 12);
+        UNUSED(kmask1);
+        UNUSED(kmask2);
+        UNUSED(kmask3);
+        UNUSED(utmp);
 
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
+        vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8);
+        vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2);
+        vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4);
+        vector signed char u3 = vec_sr(u2, v4);
+
+        vector signed char u30 = u1;
+        vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3);
+
+        u1 = vec_and(u0, lowMask1);
+        u2 = vec_or(u30, u31);
+
+        vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2);
 
-        vector signed char utmps = (vector signed char)vec_xl( 0, utmp);
         vector signed short vscales = vec_unpackh(utmps);
         vector signed short q4xmins = vec_unpackl(utmps);
         vector signed short q4xmins0 = vec_mergeh(q4xmins, q4xmins);
@@ -7358,14 +7187,10 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2);
         vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
 
         const uint8_t * restrict q4 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -7380,14 +7205,14 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             vector signed char qxs3 = (vector signed char)vec_xl(48, q4);
             q4 += 64;
 
-            vector signed char q4x00 = vec_and(qxs0, lowMask);
-            vector signed char q4x01 = vec_sr(qxs0, v4);
-            vector signed char q4x10 = vec_and(qxs1, lowMask);
-            vector signed char q4x11 = vec_sr(qxs1, v4);
-            vector signed char q4x20 = vec_and(qxs2, lowMask);
-            vector signed char q4x21 = vec_sr(qxs2, v4);
-            vector signed char q4x30 = vec_and(qxs3, lowMask);
-            vector signed char q4x31 = vec_sr(qxs3, v4);
+            vector unsigned char q4x00 = (vector unsigned char)vec_and(qxs0, lowMask);
+            vector unsigned char q4x01 = (vector unsigned char)vec_sr(qxs0, v4);
+            vector unsigned char q4x10 = (vector unsigned char)vec_and(qxs1, lowMask);
+            vector unsigned char q4x11 = (vector unsigned char)vec_sr(qxs1, v4);
+            vector unsigned char q4x20 = (vector unsigned char)vec_and(qxs2, lowMask);
+            vector unsigned char q4x21 = (vector unsigned char)vec_sr(qxs2, v4);
+            vector unsigned char q4x30 = (vector unsigned char)vec_and(qxs3, lowMask);
+            vector unsigned char q4x31 = (vector unsigned char)vec_sr(qxs3, v4);
 
             vector signed char q8y00 = vec_xl(  0, q8);
             vector signed char q8y10 = vec_xl( 16, q8);
@@ -7399,41 +7224,33 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             vector signed char q8y31 = vec_xl(112, q8);
             q8 += 128;
 
-            vector signed short qv00 = vec_add(vec_mule(q4x00, q8y00), vec_mulo(q4x00, q8y00));
-            vector signed short qv01 = vec_add(vec_mule(q4x01, q8y01), vec_mulo(q4x01, q8y01));
-            vector signed short qv10 = vec_add(vec_mule(q4x10, q8y10), vec_mulo(q4x10, q8y10));
-            vector signed short qv11 = vec_add(vec_mule(q4x11, q8y11), vec_mulo(q4x11, q8y11));
-            vector signed short qv20 = vec_add(vec_mule(q4x20, q8y20), vec_mulo(q4x20, q8y20));
-            vector signed short qv21 = vec_add(vec_mule(q4x21, q8y21), vec_mulo(q4x21, q8y21));
-            vector signed short qv30 = vec_add(vec_mule(q4x30, q8y30), vec_mulo(q4x30, q8y30));
-            vector signed short qv31 = vec_add(vec_mule(q4x31, q8y31), vec_mulo(q4x31, q8y31));
-
-            vector signed short vs0 = vec_splat(vscales, 0);
-            vector signed short vs1 = vec_splat(vscales, 1);
-            vector signed short vs2 = vec_splat(vscales, 2);
-            vector signed short vs3 = vec_splat(vscales, 3);
+            vector signed int qv00 = vec_msum(q8y00, q4x00, v0);
+            vector signed int qv01 = vec_msum(q8y01, q4x01, v0);
+            vector signed int qv10 = vec_msum(q8y10, q4x10, v0);
+            vector signed int qv11 = vec_msum(q8y11, q4x11, v0);
+            vector signed int qv20 = vec_msum(q8y20, q4x20, v0);
+            vector signed int qv21 = vec_msum(q8y21, q4x21, v0);
+            vector signed int qv30 = vec_msum(q8y30, q4x30, v0);
+            vector signed int qv31 = vec_msum(q8y31, q4x31, v0);
+
+            vector signed int vscales_h = vec_unpackh(vscales);
+            vector signed int vs0 = vec_splat(vscales_h, 0);
+            vector signed int vs1 = vec_splat(vscales_h, 1);
+            vector signed int vs2 = vec_splat(vscales_h, 2);
+            vector signed int vs3 = vec_splat(vscales_h, 3);
             vscales = vec_sld(vscales, vscales, 8);
 
-            qv00 = vec_add(qv00, qv10);
-            qv10 = vec_add(qv01, qv11);
-            qv20 = vec_add(qv20, qv30);
-            qv30 = vec_add(qv21, qv31);
+            vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0);
+            vsumi1 = vec_add(vec_mul(qv01, vs1), vsumi1);
+            vsumi2 = vec_add(vec_mul(qv20, vs2), vsumi2);
+            vsumi3 = vec_add(vec_mul(qv21, vs3), vsumi3);
 
-            vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0);
-            vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv10, vs1), vsumi2);
-            vsumi3 = vec_add(vec_mulo(qv10, vs1), vsumi3);
-            vsumi4 = vec_add(vec_mule(qv20, vs2), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv20, vs2), vsumi5);
-            vsumi6 = vec_add(vec_mule(qv30, vs3), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv30, vs3), vsumi7);
+            vsumi0 = vec_add(vec_mul(qv10, vs0), vsumi0);
+            vsumi1 = vec_add(vec_mul(qv11, vs1), vsumi1);
+            vsumi2 = vec_add(vec_mul(qv30, vs2), vsumi2);
+            vsumi3 = vec_add(vec_mul(qv31, vs3), vsumi3);
         }
 
-        vsumi0 = vec_add(vsumi0, vsumi4);
-        vsumi1 = vec_add(vsumi1, vsumi5);
-        vsumi2 = vec_add(vsumi2, vsumi6);
-        vsumi3 = vec_add(vsumi3, vsumi7);
-
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
         vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
         vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
@@ -7450,8 +7267,78 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     *s = vec_extract(vsumf0, 0);
 
-#else
+#elif defined __loongarch_asx
+    GGML_UNUSED(kmask1);
+    GGML_UNUSED(kmask2);
+    GGML_UNUSED(kmask3);
+
+    const __m256i m4 = __lasx_xvreplgr2vr_b(0xF);
+
+    __m256 acc = (__m256)__lasx_xvldi(0);
+    __m128 acc_m = (__m128)__lsx_vldi(0);
+
+   for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0]));
+
+        const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0);
+        const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1));
+        const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s);
+        acc_m = __lsx_vfmadd_s(__lsx_vreplfr2vr_s(dmin), __lsx_vffint_s_w(prod), acc_m);
+
+        const __m128i sc128  = lasx_extracti128(mins_and_scales, 0);
+        const __m256i scales = lasx_insertf128(sc128, sc128);
+
+        __m256i sumi = __lasx_xvldi(0);
+
+        for (int j = 0; j < QK_K/64; ++j) {
+
+            const __m256i scale_l = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0));
+            const __m256i scale_h = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1));
+
+            const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); q4 += 32;
+            const __m256i q4l = __lasx_xvand_v(q4bits, m4);
+            const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4);
+
+            const __m256i q8l = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            __m256i p16l = lasx_maddubs_h(q4l, q8l);
+            p16l = lasx_madd_h(scale_l, p16l);
+
+            const __m256i q8h = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            __m256i p16h = lasx_maddubs_h(q4h, q8h);
+            p16h = lasx_madd_h(scale_h, p16h);
+            const __m256i sumj = __lasx_xvadd_w(p16l, p16h);
 
+            sumi = __lasx_xvadd_w(sumi, sumj);
+        }
+
+        __m256 vd = __lasx_xvreplfr2vr_s(d);
+        acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc);
+
+    }
+
+    acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee));
+    __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0);
+    acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1);
+
+
+    ft_union fi;
+    fi.i = __lsx_vpickve2gr_w(acc_m, 0);
+    *s = hsum_float_8(acc) + fi.f ;
+#else
 
     const uint8_t * scales = (const uint8_t*)&utmp[0];
     const uint8_t * mins   = (const uint8_t*)&utmp[2];
@@ -7509,336 +7396,7 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
     *s = sumf;
 #endif
 }
-#else
-void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
-    assert(n % QK_K == 0);
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-
-    const block_q4_K * restrict x = vx;
-    const block_q8_K * restrict y = vy;
-
-    const int nb = n / QK_K;
-
-#ifdef __ARM_NEON
-    const uint8x16_t m4b = vdupq_n_u8(0xf);
-
-    const int32x4_t mzero = vdupq_n_s32(0);
-
-    float sumf = 0;
-
-    ggml_int8x16x2_t q4bytes;
-    ggml_int8x16x4_t q8bytes;
-
-    float sum_mins = 0.f;
-
-    uint16_t aux16[2];
-    const uint8_t * restrict scales = (const uint8_t *)aux16;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * restrict q4 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const uint16_t * restrict a = (const uint16_t *)x[i].scales;
-        aux16[0] = a[0] & 0x0f0f;
-        aux16[1] = (a[0] >> 4) & 0x0f0f;
-
-        const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]);
-        sum_mins += y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * summi;
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]);
-
-        const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4);
-
-        q8bytes = ggml_vld1q_s8_x4(q8);
-        q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[0], m4b));
-        q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8  (q4bits.val[1], m4b));
-
-        const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
-        const int32_t sumi1 = vaddvq_s32(p1) * scales[0];
-
-        q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
-        q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
-
-        const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]);
-        const int32_t sumi2 = vaddvq_s32(p2) * scales[1];
-
-        sumf += d * (sumi1 + sumi2);
-    }
-
-    *s = sumf - sum_mins;
-
-#elif defined __AVX2__
-
-    const __m256i m4 = _mm256_set1_epi8(0xF);
-
-    __m256 acc = _mm256_setzero_ps();
-
-    float summs = 0;
-
-    uint16_t aux16[2];
-    const uint8_t * scales = (const uint8_t *)aux16;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d;
-        const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d;
-        const __m256 vd = _mm256_set1_ps(d);
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux16[0] = a[0] & 0x0f0f;
-        aux16[1] = (a[0] >> 4) & 0x0f0f;
-
-        summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
-
-        const uint8_t * restrict q4 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4);
-        const __m256i q4l = _mm256_and_si256(q4bits, m4);
-        const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4);
-
-        const __m256i q8l = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8h = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        const __m256i p16l = _mm256_maddubs_epi16(q4l, q8l);
-        const __m256i p16h = _mm256_maddubs_epi16(q4h, q8h);
-
-        const __m256i p32l = _mm256_madd_epi16(_mm256_set1_epi16(scales[0]), p16l);
-        acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32l), acc);
-
-        const __m256i p32h = _mm256_madd_epi16(_mm256_set1_epi16(scales[1]), p16h);
-        acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32h), acc);
-
-    }
-
-    *s = hsum_float_8(acc) - summs;
-
-#elif defined __AVX__
-
-    const __m128i m4 = _mm_set1_epi8(0xF);
-
-    __m256 acc = _mm256_setzero_ps();
-
-    float summs = 0;
-
-    uint16_t aux16[2];
-    const uint8_t * scales = (const uint8_t *)aux16;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d;
-        const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d;
-        const __m256 vd = _mm256_set1_ps(d);
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux16[0] = a[0] & 0x0f0f;
-        aux16[1] = (a[0] >> 4) & 0x0f0f;
-
-        summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
-
-        const uint8_t * restrict q4 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4);
-        const __m128i q4bits_0 = _mm256_extractf128_si256(q4bits, 0);
-        const __m128i q4bits_1 = _mm256_extractf128_si256(q4bits, 1);
-        const __m128i q4_0 = _mm_and_si128(q4bits_0, m4);
-        const __m128i q4_1 = _mm_and_si128(q4bits_1, m4);
-        const __m128i q4_2 = _mm_and_si128(_mm_srli_epi16(q4bits_0, 4), m4);
-        const __m128i q4_3 = _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4);
-
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        const __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0));
-        const __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1));
-        const __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0));
-        const __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1));
-
-        const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0);
-        const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1);
-        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc);
-
-        const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2);
-        const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3);
-        acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc);
-
-    }
-
-    *s = hsum_float_8(acc) - summs;
-
-#elif defined __riscv_v_intrinsic
-
-    uint16_t s16[2];
-    const uint8_t * restrict scales = (const uint8_t *)s16;
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * restrict q4 = x[i].qs;
-        const  int8_t * restrict q8 = y[i].qs;
-
-        const uint16_t * restrict b = (const uint16_t *)x[i].scales;
-        s16[0] = b[0] & 0x0f0f;
-        s16[1] = (b[0] >> 4) & 0x0f0f;
-
-        sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]);
-
-        size_t vl = 32;
-
-        vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1);
-
-        // load Q4
-        vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl);
-
-        // load Q8 and multiply it with lower Q4 nibble
-        vint8m1_t  q4_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl));
-        vint16m2_t va_0 = __riscv_vwmul_vv_i16m2(q4_a, __riscv_vle8_v_i8m1(q8, vl), vl);
-        vint16m1_t aux1 = __riscv_vredsum_vs_i16m2_i16m1(va_0, vzero, vl);
-
-        sumf += d*scales[0]*__riscv_vmv_x_s_i16m1_i16(aux1);
-
-        // load Q8 and multiply it with upper Q4 nibble
-        vint8m1_t  q4_s = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl));
-        vint16m2_t va_1 = __riscv_vwmul_vv_i16m2(q4_s, __riscv_vle8_v_i8m1(q8+32, vl), vl);
-        vint16m1_t aux2 = __riscv_vredsum_vs_i16m2_i16m1(va_1, vzero, vl);
-
-        sumf += d*scales[1]*__riscv_vmv_x_s_i16m1_i16(aux2);
-
-    }
-
-    *s = sumf;
-
-#elif defined(__POWER9_VECTOR__)
-    const vector signed char lowMask = vec_splats((signed char)0xF);
-    const vector unsigned char v4 = vec_splats((unsigned char)0x4);
-
-    vector float vsumf0 = vec_splats(0.0f);
-    vector float vsumf1 = vec_splats(0.0f);
-    vector float vsumf2 = vec_splats(0.0f);
-    vector float vsumf3 = vec_splats(0.0f);
-
-#pragma GCC unroll 2
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
-
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d[1]));
-        vector float vyd = vec_splats(y[i].d);
-        vector float vd= vec_mul(vxd, vyd);
-
-        uint16_t s16[2];
-        const uint8_t * scales = (const uint8_t *)s16;
-
-        const uint16_t * restrict b = (const uint16_t *)x[i].scales;
-        s16[0] = b[0] & 0x0f0f;
-        s16[1] = (b[0] >> 4) & 0x0f0f;
-
-        vector signed char utmps = (vector signed char)vec_xl_len(scales, 4);
-        vector signed short vscales = (vector signed short)vec_unpackh(utmps);
-        vector signed short q4xmins0 = vec_mergeh(vscales, vscales);
-        q4xmins0 = vec_sld(q4xmins0, q4xmins0, 8);
-
-        vector signed short q8ysums0 = vec_xl_len((const int16_t *)(y[i].bsums), 8);
-
-        vector signed int prod0 = vec_mule(q4xmins0, q8ysums0);
-        vector signed int prod1 = vec_mulo(q4xmins0, q8ysums0);
-
-        vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vd, vsumf0);
-        vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vd, vsumf1);
-
-        vd = vec_mul(vyd, vec_splats(GGML_FP16_TO_FP32(x[i].d[0])));
-
-        vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs);
-        vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].qs);
-        vector signed char q4x00 = vec_and(qxs0, lowMask);
-        vector signed char q4x01 = vec_sr(qxs0, v4);
-        vector signed char q4x10 = vec_and(qxs1, lowMask);
-        vector signed char q4x11 = vec_sr(qxs1, v4);
-
-        vector signed char q8y00 = vec_xl( 0, y[i].qs);
-        vector signed char q8y10 = vec_xl(16, y[i].qs);
-        vector signed char q8y01 = vec_xl(32, y[i].qs);
-        vector signed char q8y11 = vec_xl(48, y[i].qs);
-
-        vector signed short qv00 = vec_add(vec_mule(q4x00, q8y00), vec_mulo(q4x00, q8y00));
-        vector signed short qv01 = vec_add(vec_mule(q4x01, q8y01), vec_mulo(q4x01, q8y01));
-        vector signed short qv10 = vec_add(vec_mule(q4x10, q8y10), vec_mulo(q4x10, q8y10));
-        vector signed short qv11 = vec_add(vec_mule(q4x11, q8y11), vec_mulo(q4x11, q8y11));
-
-        vector signed short vs0 = vec_splat(vscales, 0);
-        vector signed short vs1 = vec_splat(vscales, 1);
-
-        vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0));
-        vector signed int vsumi1 = vec_add(vec_mule(qv10, vs0), vec_mulo(qv10, vs0));
-        vector signed int vsumi2 = vec_add(vec_mule(qv01, vs1), vec_mulo(qv01, vs1));
-        vector signed int vsumi3 = vec_add(vec_mule(qv11, vs1), vec_mulo(qv11, vs1));
-
-        vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
-        vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
-        vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
-        vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3);
-    }
-
-    vsumf0 = vec_add(vsumf0, vsumf2);
-    vsumf1 = vec_add(vsumf1, vsumf3);
-
-    vsumf0 = vec_add(vsumf0, vsumf1);
-
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
-
-    *s = vec_extract(vsumf0, 0);
-
-#else
-
-    uint8_t aux8[QK_K];
-    int16_t aux16[16];
-    float   sums [8];
-    memset(sums, 0, 8*sizeof(float));
-
-    uint16_t s16[2];
-    const uint8_t * restrict scales = (const uint8_t *)s16;
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * restrict q4 = x[i].qs;
-        const  int8_t * restrict q8 = y[i].qs;
-        uint8_t * restrict a = aux8;
-        for (int l = 0; l < 32; ++l) a[l+ 0] = q4[l] & 0xF;
-        for (int l = 0; l < 32; ++l) a[l+32] = q4[l]  >> 4;
-
-        const uint16_t * restrict b = (const uint16_t *)x[i].scales;
-        s16[0] = b[0] & 0x0f0f;
-        s16[1] = (b[0] >> 4) & 0x0f0f;
-
-        sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]);
-
-        for (int j = 0; j < QK_K/32; ++j) {
-            for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l];
-            q8 += 16; a += 16;
-            for (int l = 0; l < 16; ++l) aux16[l] += q8[l] * a[l];
-            q8 += 16; a += 16;
-            const float dl = d * scales[j];
-            for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[l+8]);
-        }
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-#endif
-}
-#endif
 
-#if QK_K == 256
 void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy,  size_t by, int nrc) {
     assert(n % QK_K == 0);
     assert(nrc == 1);
@@ -7934,14 +7492,12 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     __m256 acc = _mm256_setzero_ps();
 
-    float summs = 0.f;
-
-   for (int i = 0; i < nb; ++i) {
+    float summs = 0.f;
 
+    for (int i = 0; i < nb; ++i) {
         const uint8_t * restrict q5 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
 
-#if QK_K == 256
         const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
         const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
@@ -7951,10 +7507,6 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
         utmp[2] = uaux;
         utmp[0] &= kmask1;
-#else
-        // TODO
-        const float d = 0, dmin = 0;
-#endif
 
         const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]));
 
@@ -8168,13 +7720,13 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             vint8m1_t q5_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q5_x, 0x0F, vl));
             vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl);
             vbool8_t vmask_1 = __riscv_vmsne_vx_u8m1_b8(qh_m1, 0, vl);
-            vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_m(vmask_1, q5_a, 16, vl);
+            vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_mu(vmask_1, q5_a, q5_a, 16, vl);
             m <<= 1;
 
             vint8m1_t q5_l = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q5_x, 0x04, vl));
             vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl);
             vbool8_t vmask_2 = __riscv_vmsne_vx_u8m1_b8(qh_m2, 0, vl);
-            vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_m(vmask_2, q5_l, 16, vl);
+            vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_mu(vmask_2, q5_l, q5_l, 16, vl);
             m <<= 1;
 
             vint16m2_t v0 = __riscv_vwmul_vv_i16m2(q5_m1, q8_y1, vl);
@@ -8200,6 +7752,9 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector signed char lowMask1 = vec_splats((int8_t)0x3f);
+    const vector signed char lowMask2 = vec_splats((int8_t)0x30);
+    const vector int v0 = vec_splats((int32_t)0);
     const vector unsigned char v1 = vec_splats((unsigned char)0x1);
     const vector unsigned char v2 = vec_splats((unsigned char)0x2);
     const vector unsigned char v3 = vec_splats((unsigned char)0x3);
@@ -8218,18 +7773,27 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin));
         vector float vdmin = vec_mul(vxmin, vyd);
 
-        memcpy(utmp, x[i].scales, 12);
+        UNUSED(kmask1);
+        UNUSED(kmask2);
+        UNUSED(kmask3);
+        UNUSED(utmp);
 
-        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
-        const uint32_t uaux = utmp[1] & kmask1;
-        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
-        utmp[2] = uaux;
-        utmp[0] &= kmask1;
+        vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8);
+        vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2);
+        vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4);
+        vector signed char u3 = vec_sr(u2, v4);
+
+        vector signed char u30 = u1;
+        vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3);
+
+        u1 = vec_and(u0, lowMask1);
+        u2 = vec_or(u30, u31);
+
+        vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2);
 
         vector signed short q8ysums0 = vec_xl( 0, y[i].bsums);
         vector signed short q8ysums1 = vec_xl(16, y[i].bsums);
 
-        vector signed char utmps = (vector signed char)vec_xl( 0, utmp);
         vector signed short vscales = vec_unpackh(utmps);
 
         vector signed short q5xmins = vec_unpackl(utmps);
@@ -8249,10 +7813,10 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh);
         vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].qh);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
 
         const uint8_t * restrict q5 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -8277,10 +7841,10 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             qxhs0 = vec_sr(qxhs0, v2);
             qxhs1 = vec_sr(qxhs1, v2);
 
-            vector signed char q5x00 = vec_or(q5h00, qxs00);
-            vector signed char q5x01 = vec_or(q5h01, qxs01);
-            vector signed char q5x10 = vec_or(q5h10, qxs10);
-            vector signed char q5x11 = vec_or(q5h11, qxs11);
+            vector unsigned char q5x00 = (vector unsigned char)vec_or(q5h00, qxs00);
+            vector unsigned char q5x01 = (vector unsigned char)vec_or(q5h01, qxs01);
+            vector unsigned char q5x10 = (vector unsigned char)vec_or(q5h10, qxs10);
+            vector unsigned char q5x11 = (vector unsigned char)vec_or(q5h11, qxs11);
 
             vector signed char q8y00 = vec_xl( 0, q8);
             vector signed char q8y10 = vec_xl(16, q8);
@@ -8288,22 +7852,20 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             vector signed char q8y11 = vec_xl(48, q8);
             q8 += 64;
 
-            vector signed short qv00 = vec_add(vec_mule(q5x00, q8y00), vec_mulo(q5x00, q8y00));
-            vector signed short qv01 = vec_add(vec_mule(q5x01, q8y01), vec_mulo(q5x01, q8y01));
-            vector signed short qv10 = vec_add(vec_mule(q5x10, q8y10), vec_mulo(q5x10, q8y10));
-            vector signed short qv11 = vec_add(vec_mule(q5x11, q8y11), vec_mulo(q5x11, q8y11));
+            vector signed int qv00 = vec_msum(q8y00, q5x00, v0);
+            vector signed int qv01 = vec_msum(q8y01, q5x01, v0);
+            vector signed int qv10 = vec_msum(q8y10, q5x10, v0);
+            vector signed int qv11 = vec_msum(q8y11, q5x11, v0);
 
-            vector signed short vs0 = vec_splat(vscales, 0);
-            vector signed short vs1 = vec_splat(vscales, 1);
+            vector signed int vscales_h = vec_unpackh(vscales);
+            vector signed int vs0 = vec_splat(vscales_h, 0);
+            vector signed int vs1 = vec_splat(vscales_h, 1);
             vscales = vec_sld(vscales, vscales, 12);
 
-            qv00 = vec_add(qv00, qv10);
-            qv01 = vec_add(qv01, qv11);
-
-            vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0);
-            vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv01, vs1), vsumi2);
-            vsumi3 = vec_add(vec_mulo(qv01, vs1), vsumi3);
+            vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0);
+            vsumi1 = vec_add(vec_mul(qv10, vs0), vsumi1);
+            vsumi2 = vec_add(vec_mul(qv01, vs1), vsumi2);
+            vsumi3 = vec_add(vec_mul(qv11, vs1), vsumi3);
         }
 
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
@@ -8322,6 +7884,92 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     *s = vec_extract(vsumf0, 0);
 
+#elif defined __loongarch_asx
+    GGML_UNUSED(kmask1);
+    GGML_UNUSED(kmask2);
+    GGML_UNUSED(kmask3);
+
+    const __m256i m4 = __lasx_xvreplgr2vr_b(0xF);
+    const __m128i mzero = __lsx_vldi(0);
+    const __m256i mone  = __lasx_xvreplgr2vr_b(1);
+
+    __m256 acc = (__m256)__lasx_xvldi(0);
+
+    float summs = 0.f;
+
+   for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * restrict q5 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0]));
+
+        const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0);
+        const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1));
+        const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s);
+        const __m128i hsum = lsx_hadd_w(lsx_hadd_w(prod, mzero), mzero);
+        summs += dmin * __lsx_vpickve2gr_w(hsum, 0);    //TODO check
+
+        const __m128i sc128  = lasx_extracti128(mins_and_scales, 0);
+        const __m256i scales = lasx_insertf128(sc128, sc128);
+
+        const __m256i hbits = __lasx_xvld((const __m256i*)x[i].qh, 0);
+        __m256i hmask = mone;
+
+        __m256i sumi = __lasx_xvldi(0);
+
+        int bit = 0;
+        __m256i xvbit;
+
+        for (int j = 0; j < QK_K/64; ++j) {
+
+            const __m256i scale_0 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0));
+            const __m256i scale_1 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1));
+
+            const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32;
+
+            xvbit = __lasx_xvreplgr2vr_h(bit++);
+            const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4);
+            const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4);
+            const __m256i q5_0  = __lasx_xvadd_b(q5l_0, q5h_0);
+            hmask = __lasx_xvslli_h(hmask, 1);
+
+            xvbit = __lasx_xvreplgr2vr_h(bit++);
+            const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4);
+            const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4);
+            const __m256i q5_1  = __lasx_xvadd_b(q5l_1, q5h_1);
+            hmask = __lasx_xvslli_h(hmask, 1);
+
+            const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+
+            __m256i p16_0 = lasx_maddubs_h(q5_0, q8_0);
+            __m256i p16_1 = lasx_maddubs_h(q5_1, q8_1);
+
+            p16_0 = lasx_madd_h(scale_0, p16_0);
+            p16_1 = lasx_madd_h(scale_1, p16_1);
+
+            sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1));
+
+        }
+
+        __m256 vd = __lasx_xvreplfr2vr_s(d);
+        acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc);
+
+    }
+
+    *s = hsum_float_8(acc) + summs;
+
 #else
 
     const uint8_t * scales = (const uint8_t*)&utmp[0];
@@ -8369,373 +8017,23 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
             for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
             q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d;
-        sumf -= dmin * sumi;
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-#endif
-}
-
-#else
-
-void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
-    assert(n % QK_K == 0);
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-
-    const block_q5_K * restrict x = vx;
-    const block_q8_K * restrict y = vy;
-
-    const int nb = n / QK_K;
-
-#ifdef __ARM_NEON
-    const uint8x16_t m4b = vdupq_n_u8(0xf);
-    const uint8x16_t mh = vdupq_n_u8(16);
-    const int32x4_t mzero = vdupq_n_s32(0);
-
-    ggml_int8x16x4_t q5bytes;
-    ggml_uint8x16x4_t q5h;
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-        const int8_t * sc = x[i].scales;
-
-        const uint8_t * restrict q5 = x[i].qs;
-        const uint8_t * restrict qh = x[i].qh;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const uint8x8_t qhbits = vld1_u8(qh);
-
-        const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5);
-        const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8);
-
-        const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1));
-        q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4));
-        q5h.val[1] = vbicq_u8(mh, vshlq_n_u8(htmp, 2));
-        q5h.val[2] = vbicq_u8(mh, htmp);
-        q5h.val[3] = vbicq_u8(mh, vshrq_n_u8(htmp, 2));
-
-        q5bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[0], m4b)), vreinterpretq_s8_u8(q5h.val[0]));
-        q5bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[1], m4b)), vreinterpretq_s8_u8(q5h.val[1]));
-        q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2]));
-        q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3]));
-
-        int32_t sumi1 = sc[0] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]));
-        int32_t sumi2 = sc[1] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1]));
-        int32_t sumi3 = sc[2] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]));
-        int32_t sumi4 = sc[3] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3]));
-
-        sumf += d * (sumi1 + sumi2 + sumi3 + sumi4);
-    }
-
-    *s = sumf;
-
-#elif defined __AVX2__
-
-    const __m256i m4 = _mm256_set1_epi8(0xF);
-    const __m256i mone  = _mm256_set1_epi8(1);
-
-    __m256 acc = _mm256_setzero_ps();
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * restrict q5 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-
-        const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
-
-        const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
-        const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
-
-        int64_t aux64;
-        memcpy(&aux64, x[i].qh, 8);
-        const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64);
-        const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128);
-
-        const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4);
-        const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4);
-
-        const __m256i q5l_0 = _mm256_and_si256(q5bits, m4);
-        const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4);
-
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        const __m256i p16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5l_0, q8_0));
-        const __m256i p16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5l_1, q8_1));
-        const __m256i s16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5h_0, q8_0));
-        const __m256i s16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5h_1, q8_1));
-
-        const __m256i dot = _mm256_sub_epi32(_mm256_add_epi32(p16_0, p16_1), _mm256_add_epi32(s16_0, s16_1));
-
-        acc = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(dot), acc);
-
-    }
-
-    *s = hsum_float_8(acc);
-
-#elif defined __AVX__
-
-    const __m128i m4 = _mm_set1_epi8(0xF);
-    const __m128i mone  = _mm_set1_epi8(1);
-
-    __m256 acc = _mm256_setzero_ps();
-
-    for (int i = 0; i < nb; ++i) {
-
-        const uint8_t * restrict q5 = x[i].qs;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-
-        const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
-
-        const __m128i scale_0 = _mm_set1_epi16(x[i].scales[0]);
-        const __m128i scale_1 = _mm_set1_epi16(x[i].scales[1]);
-        const __m128i scale_2 = _mm_set1_epi16(x[i].scales[2]);
-        const __m128i scale_3 = _mm_set1_epi16(x[i].scales[3]);
-
-        int64_t aux64;
-        memcpy(&aux64, x[i].qh, 8);
-        const __m128i haux128_0 = _mm_set_epi64x(aux64 >> 1, aux64);
-        const __m128i haux128_1 = _mm_srli_epi16(haux128_0, 2);
-
-        const __m128i q5h_0 = _mm_slli_epi16(_mm_andnot_si128(haux128_0, mone), 4);
-        const __m128i q5h_1 = _mm_slli_epi16(_mm_andnot_si128(haux128_1, mone), 4);
-        const __m128i q5h_2 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_0, 4), mone), 4);
-        const __m128i q5h_3 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_1, 4), mone), 4);
-
-        const __m128i q5l_0 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 0), m4);
-        const __m128i q5l_1 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 1), m4);
-        const __m128i q5l_2 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 0), 4), m4);
-        const __m128i q5l_3 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 1), 4), m4);
-
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        const __m128i p16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5l_0, _mm256_extractf128_si256(q8_0, 0)));
-        const __m128i p16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5l_1, _mm256_extractf128_si256(q8_0, 1)));
-        const __m128i p16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5l_2, _mm256_extractf128_si256(q8_1, 0)));
-        const __m128i p16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5l_3, _mm256_extractf128_si256(q8_1, 1)));
-        const __m128i s16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5h_0, _mm256_extractf128_si256(q8_0, 0)));
-        const __m128i s16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5h_1, _mm256_extractf128_si256(q8_0, 1)));
-        const __m128i s16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5h_2, _mm256_extractf128_si256(q8_1, 0)));
-        const __m128i s16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5h_3, _mm256_extractf128_si256(q8_1, 1)));
-
-        const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2));
-        const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3));
-
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc);
-
-    }
-
-    *s = hsum_float_8(acc);
-
-#elif defined __riscv_v_intrinsic
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-        const int8_t * sc = x[i].scales;
-
-        const uint8_t * restrict q5 = x[i].qs;
-        const uint8_t * restrict qh = x[i].qh;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
-
-        // load qh
-        vuint8mf4_t qh_x1   = __riscv_vle8_v_u8mf4(qh, 8);
-        vuint8mf2_t qh_x2   = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8));
-
-        size_t vl = 16;
-
-        // combine both qh_1 and qh_2
-        vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl);
-
-        vuint8mf2_t qh_h0 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl);
-        vuint8mf2_t qh_h1 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), vl), 16, vl);
-        vuint8mf2_t qh_h2 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(qh_x, vl), 16, vl);
-        vuint8mf2_t qh_h3 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl);
-
-        vint8mf2_t qh_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h0);
-        vint8mf2_t qh_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h1);
-        vint8mf2_t qh_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h2);
-        vint8mf2_t qh_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h3);
-
-        // load q5
-        vuint8mf2_t q5_x1  = __riscv_vle8_v_u8mf2(q5, vl);
-        vuint8mf2_t q5_x2  = __riscv_vle8_v_u8mf2(q5+16, vl);
-
-        vint8mf2_t q5s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x1, 0xF, vl));
-        vint8mf2_t q5s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x2, 0xF, vl));
-        vint8mf2_t q5s_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x1, 0x4, vl));
-        vint8mf2_t q5s_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x2, 0x4, vl));
-
-        vint8mf2_t q5_0 = __riscv_vsub_vv_i8mf2(q5s_0, qh_0, vl);
-        vint8mf2_t q5_1 = __riscv_vsub_vv_i8mf2(q5s_1, qh_1, vl);
-        vint8mf2_t q5_2 = __riscv_vsub_vv_i8mf2(q5s_2, qh_2, vl);
-        vint8mf2_t q5_3 = __riscv_vsub_vv_i8mf2(q5s_3, qh_3, vl);
-
-        // load Q8 and multiply it with Q5
-        vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q5_0, __riscv_vle8_v_i8mf2(q8, vl), vl);
-        vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q5_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl);
-        vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q5_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl);
-        vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q5_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl);
-
-        vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl);
-        vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl);
-        vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl);
-        vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl);
-
-        int32_t sumi1 = sc[0] * __riscv_vmv_x_s_i32m1_i32(vs_0);
-        int32_t sumi2 = sc[1] * __riscv_vmv_x_s_i32m1_i32(vs_1);
-        int32_t sumi3 = sc[2] * __riscv_vmv_x_s_i32m1_i32(vs_2);
-        int32_t sumi4 = sc[3] * __riscv_vmv_x_s_i32m1_i32(vs_3);
-
-        sumf += d * (sumi1 + sumi2 + sumi3 + sumi4);
-
-    }
-
-    *s = sumf;
-
-#elif defined(__POWER9_VECTOR__)
-    const vector signed char lowMask = vec_splats((signed char)0xF);
-    const vector unsigned char v1 = vec_splats((unsigned char)0x1);
-    const vector unsigned char v2 = vec_splats((unsigned char)0x2);
-    const vector unsigned char v4 = vec_splats((unsigned char)0x4);
-
-    vector float vsumf0 = vec_splats(0.0f);
-    vector float vsumf1 = vec_splats(0.0f);
-    vector float vsumf2 = vec_splats(0.0f);
-    vector float vsumf3 = vec_splats(0.0f);
-
-#pragma GCC unroll 2
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
-
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(y[i].d);
-        vector float vd= vec_mul(vxd, vyd);
-
-        vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs);
-        vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].qs);
-        vector signed char qxs00 = (vector signed char)vec_and(qxs0, lowMask);
-        vector signed char qxs01 = (vector signed char)vec_sr(qxs0, v4);
-        vector signed char qxs10 = (vector signed char)vec_and(qxs1, lowMask);
-        vector signed char qxs11 = (vector signed char)vec_sr(qxs1, v4);
-
-        vector signed char qxhs = (vector signed char)vec_xl_len(x[i].qh, 8);
-        vector signed char qxhs0 = vec_or(qxhs, vec_sr(vec_sld(qxhs, qxhs, 8), v1));
-        vector signed char qxhs1 = vec_sr(qxhs0, v2);
-        vector signed char qxh00 = vec_sl(vec_andc((vector signed char)v1, qxhs0), v4);
-        vector signed char qxh10 = vec_sl(vec_andc((vector signed char)v1, qxhs1), v4);
-        vector signed char qxh01 = vec_sl(vec_andc((vector signed char)v1, vec_sr(qxhs0, v4)), v4);
-        vector signed char qxh11 = vec_sl(vec_andc((vector signed char)v1, vec_sr(qxhs1, v4)), v4);
-
-        vector signed char q5x00 = vec_sub(qxs00, qxh00);
-        vector signed char q5x10 = vec_sub(qxs10, qxh10);
-        vector signed char q5x01 = vec_sub(qxs01, qxh01);
-        vector signed char q5x11 = vec_sub(qxs11, qxh11);
-
-        vector signed char q8y00 = vec_xl( 0, y[i].qs);
-        vector signed char q8y10 = vec_xl(16, y[i].qs);
-        vector signed char q8y01 = vec_xl(32, y[i].qs);
-        vector signed char q8y11 = vec_xl(48, y[i].qs);
-
-        vector signed short qv00 = vec_add(vec_mule(q5x00, q8y00), vec_mulo(q5x00, q8y00));
-        vector signed short qv01 = vec_add(vec_mule(q5x01, q8y01), vec_mulo(q5x01, q8y01));
-        vector signed short qv10 = vec_add(vec_mule(q5x10, q8y10), vec_mulo(q5x10, q8y10));
-        vector signed short qv11 = vec_add(vec_mule(q5x11, q8y11), vec_mulo(q5x11, q8y11));
-
-        vector signed short vs = (vector signed short)vec_unpackh(vec_xl_len(x[i].scales, 4));
-        vector signed short vs0 = vec_splat(vs, 0);
-        vector signed short vs1 = vec_splat(vs, 1);
-        vector signed short vs2 = vec_splat(vs, 2);
-        vector signed short vs3 = vec_splat(vs, 3);
-
-        vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0));
-        vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1));
-        vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2));
-        vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3));
-
-        vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
-        vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
-        vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
-        vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3);
-    }
-
-    vsumf0 = vec_add(vsumf0, vsumf2);
-    vsumf1 = vec_add(vsumf1, vsumf3);
-
-    vsumf0 = vec_add(vsumf0, vsumf1);
-
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
-
-    *s = vec_extract(vsumf0, 0);
-
-#else
-
-    int8_t aux8[QK_K];
-    int16_t aux16[16];
-    float   sums [8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * restrict q4 = x[i].qs;
-        const uint8_t * restrict hm = x[i].qh;
-        const  int8_t * restrict q8 = y[i].qs;
-        int8_t * restrict a = aux8;
-        for (int l = 0; l < 32; ++l) {
-            a[l+ 0] = q4[l] & 0xF;
-            a[l+32] = q4[l]  >> 4;
-        }
-        for (int is = 0; is < 8; ++is) {
-            uint8_t m = 1 << is;
-            for (int l = 0; l < 8; ++l) a[8*is + l] -= (hm[l] & m ? 0 : 16);
-        }
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-        const int8_t * restrict sc = x[i].scales;
-
-        for (int j = 0; j < QK_K/16; ++j) {
-            const float dl = d * sc[j];
-            for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l <  8; ++l) sums[l] += dl * (aux16[l] + aux16[8+l]);
-            q8 += 16; a += 16;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
         }
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
     }
     for (int l = 0; l < 8; ++l) sumf += sums[l];
     *s = sumf;
 #endif
 }
-#endif
-
 
-#if QK_K == 256
 void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
     assert(nrc == 1);
@@ -9128,6 +8426,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector int v0 = vec_splats((int32_t)0);
     const vector unsigned char v2 = vec_splats((unsigned char)0x2);
     const vector unsigned char v3 = vec_splats((unsigned char)0x3);
     const vector unsigned char v4 = vec_splats((unsigned char)0x4);
@@ -9144,14 +8443,14 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         vector float vyd = vec_splats(y[i].d);
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
+        vector signed int vsumi4 = v0;
+        vector signed int vsumi5 = v0;
+        vector signed int vsumi6 = v0;
+        vector signed int vsumi7 = v0;
 
         const uint8_t * restrict q6 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -9231,23 +8530,14 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
             vector signed short vs6 = vec_splat(vscales, 6);
             vector signed short vs7 = vec_splat(vscales, 7);
 
-            vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0);
-            vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv01, vs4), vsumi2);
-            vsumi3 = vec_add(vec_mulo(qv01, vs4), vsumi3);
-            vsumi4 = vec_add(vec_mule(qv10, vs1), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv10, vs1), vsumi5);
-            vsumi6 = vec_add(vec_mule(qv11, vs5), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv11, vs5), vsumi7);
-
-            vsumi0 = vec_add(vec_mule(qv20, vs2), vsumi0);
-            vsumi1 = vec_add(vec_mulo(qv20, vs2), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv21, vs6), vsumi2);
-            vsumi3 = vec_add(vec_mulo(qv21, vs6), vsumi3);
-            vsumi4 = vec_add(vec_mule(qv30, vs3), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv30, vs3), vsumi5);
-            vsumi6 = vec_add(vec_mule(qv31, vs7), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv31, vs7), vsumi7);
+            vsumi0 = vec_msum(qv00, vs0, vsumi0);
+            vsumi1 = vec_msum(qv01, vs4, vsumi1);
+            vsumi2 = vec_msum(qv10, vs1, vsumi2);
+            vsumi3 = vec_msum(qv11, vs5, vsumi3);
+            vsumi4 = vec_msum(qv20, vs2, vsumi4);
+            vsumi5 = vec_msum(qv21, vs6, vsumi5);
+            vsumi6 = vec_msum(qv30, vs3, vsumi6);
+            vsumi7 = vec_msum(qv31, vs7, vsumi7);
         }
 
         vsumi0 = vec_add(vsumi0, vsumi4);
@@ -9271,124 +8561,13 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 
     *s = vec_extract(vsumf0, 0);
 
-#else
-
-    int8_t  aux8[QK_K];
-    int16_t aux16[8];
-    float   sums [8];
-    int32_t aux32[8];
-    memset(sums, 0, 8*sizeof(float));
-
-    float sumf = 0;
-    for (int i = 0; i < nb; ++i) {
-        const uint8_t * restrict q4 = x[i].ql;
-        const uint8_t * restrict qh = x[i].qh;
-        const  int8_t * restrict q8 = y[i].qs;
-        memset(aux32, 0, 8*sizeof(int32_t));
-        int8_t * restrict a = aux8;
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            }
-            a  += 128;
-            q4 += 64;
-            qh += 32;
-        }
-        a = aux8;
-        int is = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int scale = x[i].scales[is++];
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
-            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
-            q8 += 8; a += 8;
-        }
-        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
-        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-    }
-    for (int l = 0; l < 8; ++l) sumf += sums[l];
-    *s = sumf;
-#endif
-}
-
-#else
-
-void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
-    assert(n % QK_K == 0);
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-
-    const block_q6_K * restrict x = vx;
-    const block_q8_K * restrict y = vy;
-
-    const int nb = n / QK_K;
-
-#ifdef __ARM_NEON
-    float sum = 0;
-
-    const uint8x16_t m4b = vdupq_n_u8(0xF);
-    const int8x16_t  m32s = vdupq_n_s8(32);
-    const int32x4_t  vzero = vdupq_n_s32(0);
-
-    const uint8x16_t mone = vdupq_n_u8(3);
-
-    ggml_int8x16x4_t q6bytes;
-    ggml_uint8x16x4_t q6h;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d_all = GGML_FP16_TO_FP32(x[i].d);
-
-        const uint8_t * restrict q6 = x[i].ql;
-        const uint8_t * restrict qh = x[i].qh;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const int8_t * restrict scale = x[i].scales;
-
-        int32_t isum = 0;
-
-        uint8x16_t qhbits = vld1q_u8(qh);
-        ggml_uint8x16x2_t q6bits = ggml_vld1q_u8_x2(q6);
-        ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8);
-
-        q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4);
-        uint8x16_t shifted = vshrq_n_u8(qhbits, 2);
-        q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
-        shifted = vshrq_n_u8(qhbits, 4);
-        q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
-        shifted = vshrq_n_u8(qhbits, 6);
-        q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
-
-        q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s);
-        q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s);
-        q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s);
-        q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s);
-
-        isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] +
-                vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] +
-                vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] +
-                vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3];
-
-        sum += isum * d_all * y[i].d;
-
-    }
-    *s = sum;
-
-#elif defined __AVX2__
+#elif defined __loongarch_asx
 
-    const __m256i m4 = _mm256_set1_epi8(0xF);
-    const __m256i m2 = _mm256_set1_epi8(3);
-    const __m256i m32s = _mm256_set1_epi8(32);
+    const __m256i m4 = __lasx_xvreplgr2vr_b(0xF);
+    const __m256i m2 = __lasx_xvreplgr2vr_b(3);
+    const __m256i m32s = __lasx_xvreplgr2vr_b(32);
 
-    __m256 acc = _mm256_setzero_ps();
+    __m256 acc = (__m256)__lasx_xvldi(0);
 
     for (int i = 0; i < nb; ++i) {
 
@@ -9398,264 +8577,68 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         const uint8_t * restrict qh = x[i].qh;
         const int8_t  * restrict q8 = y[i].qs;
 
-        const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]);
-        const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]);
-        const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]);
-        const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]);
-
-        __m256i sumi = _mm256_setzero_si256();
-
-        const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1);
-        const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3);
-
-        const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
-        const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
-
-        const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
-        const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
-
-        const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
-        const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1);
-
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
-
-        __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0);
-        __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1);
-
-        __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0);
-        __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1);
-
-        p16_0 = _mm256_sub_epi16(p16_0, q8s_0);
-        p16_1 = _mm256_sub_epi16(p16_1, q8s_1);
-
-        p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0);
-        p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1);
-
-        sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1));
-
-        acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc);
-    }
-
-    *s = hsum_float_8(acc);
-
-#elif defined __AVX__
-
-    const __m128i m4 = _mm_set1_epi8(0xF);
-    const __m128i m2 = _mm_set1_epi8(3);
-    const __m128i m32s = _mm_set1_epi8(32);
-
-    __m256 acc = _mm256_setzero_ps();
+        const __m128i scales = __lsx_vld((const __m128i*)x[i].scales, 0);
 
-    for (int i = 0; i < nb; ++i) {
-
-        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
-
-        const uint8_t * restrict q4 = x[i].ql;
-        const uint8_t * restrict qh = x[i].qh;
-        const int8_t  * restrict q8 = y[i].qs;
+        __m256i sumi = __lasx_xvldi(0);
 
-        const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]);
-        const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]);
-        const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]);
-        const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]);
+        int is = 0;
 
-        __m128i sumi_0 = _mm_setzero_si128();
-        __m128i sumi_1 = _mm_setzero_si128();
+        for (int j = 0; j < QK_K/128; ++j) {
 
-        const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1);
-        const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3);
+            const __m128i scale_0 = lsx_shuffle_b(scales, get_scale_shuffle(is + 0));
+            const __m128i scale_1 = lsx_shuffle_b(scales, get_scale_shuffle(is + 1));
+            const __m128i scale_2 = lsx_shuffle_b(scales, get_scale_shuffle(is + 2));
+            const __m128i scale_3 = lsx_shuffle_b(scales, get_scale_shuffle(is + 3));
+            is += 4;
 
-        const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
-        const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
+            const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32;
+            const __m256i q4bits2 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32;
+            const __m256i q4bitsH = __lasx_xvld((const __m256i*)qh, 0); qh += 32;
 
-        const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH, m2), 4);
-        const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 2), m2), 4);
-        const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 4), m2), 4);
-        const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 6), m2), 4);
+            const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(q4bitsH, m2), 4);
+            const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 2), m2), 4);
+            const __m256i q4h_2 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 4), m2), 4);
+            const __m256i q4h_3 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 6), m2), 4);
 
-        const __m128i q4_0 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 0), m4), q4h_0);
-        const __m128i q4_1 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 1), m4), q4h_1);
-        const __m128i q4_2 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 0), 4), m4), q4h_2);
-        const __m128i q4_3 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 1), 4), m4), q4h_3);
+            const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0);
+            const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(q4bits2, m4), q4h_1);
+            const __m256i q4_2 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_2);
+            const __m256i q4_3 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits2, 4), m4), q4h_3);
 
-        const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
+            const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
 
-        __m128i q8s_0 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 0));
-        __m128i q8s_1 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 1));
-        __m128i q8s_2 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 0));
-        __m128i q8s_3 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 1));
+            __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0);
+            __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1);
+            __m256i q8s_2 = lasx_maddubs_h(m32s, q8_2);
+            __m256i q8s_3 = lasx_maddubs_h(m32s, q8_3);
 
-        __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0));
-        __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1));
-        __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0));
-        __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1));
+            __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0);
+            __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1);
+            __m256i p16_2 = lasx_maddubs_h(q4_2, q8_2);
+            __m256i p16_3 = lasx_maddubs_h(q4_3, q8_3);
 
-        p16_0 = _mm_sub_epi16(p16_0, q8s_0);
-        p16_1 = _mm_sub_epi16(p16_1, q8s_1);
-        p16_2 = _mm_sub_epi16(p16_2, q8s_2);
-        p16_3 = _mm_sub_epi16(p16_3, q8s_3);
+            p16_0 = __lasx_xvsub_h(p16_0, q8s_0);
+            p16_1 = __lasx_xvsub_h(p16_1, q8s_1);
+            p16_2 = __lasx_xvsub_h(p16_2, q8s_2);
+            p16_3 = __lasx_xvsub_h(p16_3, q8s_3);
 
-        p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0);
-        p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1);
-        p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2);
-        p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3);
+            p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0);
+            p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1);
+            p16_2 = lasx_madd_h(lasx_ext8_16(scale_2), p16_2);
+            p16_3 = lasx_madd_h(lasx_ext8_16(scale_3), p16_3);
 
-        sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
-        sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3));
+            sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1));
+            sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_2, p16_3));
+        }
 
-        acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc);
+        acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);
     }
 
     *s = hsum_float_8(acc);
 
-#elif defined __riscv_v_intrinsic
-
-    float sumf = 0;
-
-    for (int i = 0; i < nb; ++i) {
-
-        const float d_all = GGML_FP16_TO_FP32(x[i].d);
-
-        const uint8_t * restrict q6 = x[i].ql;
-        const uint8_t * restrict qh = x[i].qh;
-        const int8_t  * restrict q8 = y[i].qs;
-
-        const int8_t * restrict scale = x[i].scales;
-
-        int32_t isum = 0;
-
-        size_t vl = 16;
-
-        vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
-
-        // load Q6
-        vuint8mf2_t q6_0 = __riscv_vle8_v_u8mf2(q6, vl);
-        vuint8mf2_t q6_1 = __riscv_vle8_v_u8mf2(q6+16, vl);
-
-        // load qh
-        vuint8mf2_t qh_x = __riscv_vle8_v_u8mf2(qh, vl);
-
-        vuint8mf2_t qh0 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl);
-        qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl);
-        vuint8mf2_t qh1 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl);
-        qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl);
-        vuint8mf2_t qh2 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl);
-        qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl);
-        vuint8mf2_t qh3 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl);
-
-        vuint8mf2_t q6h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_0, 0xF, vl), qh0, vl);
-        vuint8mf2_t q6h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_1, 0xF, vl), qh1, vl);
-        vuint8mf2_t q6h_2 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_0, 0x4, vl), qh2, vl);
-        vuint8mf2_t q6h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_1, 0x4, vl), qh3, vl);
-
-        vint8mf2_t q6v_0 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_0), 32, vl);
-        vint8mf2_t q6v_1 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_1), 32, vl);
-        vint8mf2_t q6v_2 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_2), 32, vl);
-        vint8mf2_t q6v_3 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_3), 32, vl);
-
-        // load Q8 and take product
-        vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q6v_0, __riscv_vle8_v_i8mf2(q8, vl), vl);
-        vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q6v_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl);
-        vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q6v_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl);
-        vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q6v_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl);
-
-        vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl);
-        vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl);
-        vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl);
-        vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl);
-
-        isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scale[0];
-        isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scale[1];
-        isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scale[2];
-        isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scale[3];
-
-        sumf += isum * d_all * y[i].d;
-
-    }
-
-    *s = sumf;
-
-#elif defined(__POWER9_VECTOR__)
-    const vector signed char lowMask = vec_splats((signed char)0xF);
-    const vector unsigned char v2 = vec_splats((unsigned char)0x2);
-    const vector unsigned char v3 = vec_splats((unsigned char)0x3);
-    const vector unsigned char v4 = vec_splats((unsigned char)0x4);
-    const vector unsigned char v6 = vec_splats((unsigned char)0x6);
-    const vector signed char off = vec_splats((signed char)0x20);
-
-    vector float vsumf0 = vec_splats(0.0f);
-    vector float vsumf1 = vec_splats(0.0f);
-    vector float vsumf2 = vec_splats(0.0f);
-    vector float vsumf3 = vec_splats(0.0f);
-
-#pragma GCC unroll 2
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].ql, 0, 1);
-        __builtin_prefetch(x[i].qh, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
-
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(y[i].d);
-        vector float vd= vec_mul(vxd, vyd);
-
-        vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].ql);
-        vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].ql);
-        vector signed char qxs00 = vec_and(qxs0, lowMask);
-        vector signed char qxs01 = vec_sr(qxs0, v4);
-        vector signed char qxs10 = vec_and(qxs1, lowMask);
-        vector signed char qxs11 = vec_sr(qxs1, v4);
-
-        vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh);
-
-        vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4);
-        vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4);
-        vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4);
-        vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4);
-
-        vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off);
-        vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off);
-        vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off);
-        vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off);
-
-        vector signed char q8y00 = vec_xl( 0, y[i].qs);
-        vector signed char q8y10 = vec_xl(16, y[i].qs);
-        vector signed char q8y01 = vec_xl(32, y[i].qs);
-        vector signed char q8y11 = vec_xl(48, y[i].qs);
-
-        vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00));
-        vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10));
-        vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01));
-        vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11));
-
-        vector signed short vs = (vector signed short)vec_unpackh(vec_xl_len(x[i].scales, 4));
-        vector signed short vs0 = vec_splat(vs, 0);
-        vector signed short vs1 = vec_splat(vs, 1);
-        vector signed short vs2 = vec_splat(vs, 2);
-        vector signed short vs3 = vec_splat(vs, 3);
-
-        vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0));
-        vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1));
-        vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2));
-        vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3));
-
-        vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
-        vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
-        vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
-        vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3);
-    }
-
-    vsumf0 = vec_add(vsumf0, vsumf2);
-    vsumf1 = vec_add(vsumf1, vsumf3);
-
-    vsumf0 = vec_add(vsumf0, vsumf1);
-
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
-
-    *s = vec_extract(vsumf0, 0);
-
 #else
 
     int8_t  aux8[QK_K];
@@ -9671,12 +8654,18 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
         const  int8_t * restrict q8 = y[i].qs;
         memset(aux32, 0, 8*sizeof(int32_t));
         int8_t * restrict a = aux8;
-        for (int l = 0; l < 16; ++l) {
-            a[l+ 0] = (int8_t)((q4[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-            a[l+16] = (int8_t)((q4[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-            a[l+32] = (int8_t)((q4[l+ 0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-            a[l+48] = (int8_t)((q4[l+16] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            }
+            a  += 128;
+            q4 += 64;
+            qh += 32;
         }
+        a = aux8;
         int is = 0;
         for (int j = 0; j < QK_K/16; ++j) {
             int scale = x[i].scales[is++];
@@ -9695,9 +8684,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
 #endif
 }
 
-#endif
-
-#if defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__)
+#if defined (__AVX__) || defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx)
 static const int8_t keven_signs_q2xs[1024] = {
      1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,  1,  1,  1, -1, -1, -1,  1,  1,  1,  1,  1,  1,
      1,  1, -1,  1,  1,  1,  1, -1, -1,  1, -1,  1,  1,  1,  1,  1,  1, -1, -1,  1,  1,  1,  1,  1, -1, -1, -1,  1,  1,  1,  1, -1,
@@ -9818,19 +8805,75 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void
             const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2);
             const uint16_t ls1 = aux32[1] >> 28;
             const uint16_t ls2 = aux32[3] >> 28;
-            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1));
-            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1));
-            sumi1 = _mm256_add_epi32(sumi1, p1);
-            sumi2 = _mm256_add_epi32(sumi2, p2);
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1));
+            sumi1 = _mm256_add_epi32(sumi1, p1);
+            sumi2 = _mm256_add_epi32(sumi2, p2);
+        }
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+
+#elif defined(__AVX__)
+    const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
+
+    uint32_t aux32[4];
+    const uint8_t * aux8 = (const uint8_t *)aux32;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * restrict q2 = x[i].qs;
+        const int8_t   * restrict q8 = y[i].qs;
+        __m128i sumi1_0 = _mm_setzero_si128();
+        __m128i sumi1_1 = _mm_setzero_si128();
+        __m128i sumi2_0 = _mm_setzero_si128();
+        __m128i sumi2_1 = _mm_setzero_si128();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8;
+            const __m128i q2_1_0 = _mm_set_epi64x(iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]);
+            const __m128i q2_1_1 = _mm_set_epi64x(iq2xxs_grid[aux8[3]], iq2xxs_grid[aux8[2]]);
+            const __m128i q2_2_0 = _mm_set_epi64x(iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]);
+            const __m128i q2_2_1 = _mm_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]]);
+            const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[1] >>  7) & 127], signs64[(aux32[1] >>  0) & 127]);
+            const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]);
+            const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[3] >>  7) & 127], signs64[(aux32[3] >>  0) & 127]);
+            const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127]);
+            const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0);
+            const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1);
+            const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0);
+            const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1);
+            const __m128i dot1_0  = _mm_maddubs_epi16(q2_1_0, q8s_1_0);
+            const __m128i dot1_1  = _mm_maddubs_epi16(q2_1_1, q8s_1_1);
+            const __m128i dot2_0  = _mm_maddubs_epi16(q2_2_0, q8s_2_0);
+            const __m128i dot2_1  = _mm_maddubs_epi16(q2_2_1, q8s_2_1);
+            const uint16_t ls1 = aux32[1] >> 28;
+            const uint16_t ls2 = aux32[3] >> 28;
+            const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1));
+            const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1));
+            const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1));
+            const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1));
+            sumi1_0 = _mm_add_epi32(sumi1_0, p1_0);
+            sumi1_1 = _mm_add_epi32(sumi1_1, p1_1);
+            sumi2_0 = _mm_add_epi32(sumi2_0, p2_0);
+            sumi2_1 = _mm_add_epi32(sumi2_1, p2_1);
         }
 
-        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+        accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf);
 
     }
 
     *s = 0.125f * hsum_float_8(accumf);
 
 #elif defined(__POWER9_VECTOR__)
+    const vector int v0 = vec_splats((int32_t)0);
     vector float vsumf0 = vec_splats(0.0f);
     vector float vsumf1 = vec_splats(0.0f);
     vector float vsumf2 = vec_splats(0.0f);
@@ -9843,14 +8886,10 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void
         vector float vyd = vec_splats(y[i].d);
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
 
         const uint16_t * restrict q2 = x[i].qs;
         const int8_t  *  restrict q8 = y[i].qs;
@@ -9897,21 +8936,12 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void
             vector signed short vscales01 = vec_splats((int16_t)(2*ls0+1));
             vector signed short vscales23 = vec_splats((int16_t)(2*ls1+1));
 
-            vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0);
-            vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2);
-            vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3);
-            vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5);
-            vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7);
+            vsumi0 = vec_msum(qv0, vscales01, vsumi0);
+            vsumi1 = vec_msum(qv1, vscales01, vsumi1);
+            vsumi2 = vec_msum(qv2, vscales23, vsumi2);
+            vsumi3 = vec_msum(qv3, vscales23, vsumi3);
         }
 
-        vsumi0 = vec_add(vsumi0, vsumi4);
-        vsumi1 = vec_add(vsumi1, vsumi5);
-        vsumi2 = vec_add(vsumi2, vsumi6);
-        vsumi3 = vec_add(vsumi3, vsumi7);
-
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
         vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
         vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
@@ -9927,6 +8957,49 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
     *s = 0.125f * vec_extract(vsumf0, 0);
+
+#elif defined(__loongarch_asx)
+
+    const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
+
+    uint32_t aux32[4];
+    const uint8_t * aux8 = (const uint8_t *)aux32;
+
+    __m256 accumf = (__m256)__lasx_xvldi(0);
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * restrict q2 = x[i].qs;
+        const int8_t   * restrict q8 = y[i].qs;
+        __m256i sumi1 = __lasx_xvldi(0);
+        __m256i sumi2 = __lasx_xvldi(0);
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8;
+
+            const __m256i q2_1 = lasx_set_d(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]);
+            const __m256i q2_2 = lasx_set_d(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]);
+            const __m256i s2_1 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127],
+                                                   signs64[(aux32[1] >>  7) & 127], signs64[(aux32[1] >>  0) & 127]);
+            const __m256i s2_2 = lasx_set_d(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127],
+                                                   signs64[(aux32[3] >>  7) & 127], signs64[(aux32[3] >>  0) & 127]);
+            const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1);
+            const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2);
+            const __m256i dot1  = lasx_maddubs_h(q2_1, q8s_1);
+            const __m256i dot2  = lasx_maddubs_h(q2_2, q8s_2);
+            const uint16_t ls1 = aux32[1] >> 28;
+            const uint16_t ls2 = aux32[3] >> 28;
+            const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1));
+            const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1));
+            sumi1 = __lasx_xvadd_w(sumi1, p1);
+            sumi2 = __lasx_xvadd_w(sumi2, p2);
+        }
+
+        accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf);
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+
 #else
 
     uint32_t aux32[2];
@@ -10045,64 +9118,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
     const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1);
     const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2);
 
-#if QK_K == 64
-    static const uint8_t k_bit_helper[16] = {
-        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
-    };
-    const __m128i bit_helper = _mm_loadu_si128((const __m128i*)k_bit_helper);
-    const __m128i m511 = _mm_set1_epi16(511);
-    typedef union {
-        __m128i vec_index;
-        uint16_t index[8];
-    } index_t;
-
-    index_t idx;
-    __m256 accumf = _mm256_setzero_ps();
-    for (int i = 0; i < nb; ++i) {
-        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
-        const __m128i q2_data = _mm_loadu_si128((const __m128i*)x[i].qs);
-        idx.vec_index = _mm_and_si128(q2_data, m511);
-
-        const __m128i partial_sign_bits = _mm_srli_epi16(q2_data, 9);
-        const __m128i partial_sign_bits_upper = _mm_srli_epi16(q2_data, 13);
-        const __m128i partial_sign_bits_for_counting = _mm_xor_si128(partial_sign_bits, partial_sign_bits_upper);
-
-        const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting);
-        const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits);
-        const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits);
-
-        const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs);
-        const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32));
-
-        const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]],
-                                               iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]);
-        const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]],
-                                               iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]);
-
-        __m256i signs;
-        signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_1);
-        signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
-        const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone));
-
-        signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_2);
-        signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
-        const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone));
-
-        const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1);
-        const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2);
-
-        const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1));
-        const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1));
-
-        const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2));
-
-        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sum), accumf);
-
-    }
-
-    *s = 0.125f * hsum_float_8(accumf);
-#else
-
     static const uint8_t k_bit_helper[32] = {
         0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
         0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
@@ -10200,9 +9215,284 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
     }
 
     *s = 0.125f * hsum_float_8(accumf);
-#endif
 
+#elif defined(__AVX__)
+    const __m128i mone = _mm_set1_epi8(1);
+    static const char block_sign_shuffle_mask_1[32] = {
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02,
+        0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06,
+    };
+    static const char block_sign_shuffle_mask_2[32] = {
+        0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a,
+        0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e,
+    };
+    static const uint8_t bit_selector_mask_bytes[32] = {
+        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m128i bit_selector_mask_0 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes);
+    const __m128i bit_selector_mask_1 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes + 1);
+    const __m128i block_sign_shuffle_1_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1);
+    const __m128i block_sign_shuffle_1_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1 + 1);
+    const __m128i block_sign_shuffle_2_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2);
+    const __m128i block_sign_shuffle_2_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2 + 1);
+
+    static const uint8_t k_bit_helper[32] = {
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+    };
+    const __m128i bit_helper_0 = _mm_loadu_si128((const __m128i*)k_bit_helper);
+    const __m128i bit_helper_1 = _mm_loadu_si128((const __m128i*)k_bit_helper + 1);
+    const __m128i m511 = _mm_set1_epi16(511);
+    const __m128i m4 = _mm_set1_epi8(0xf);
+    const __m128i m1 = _mm_set1_epi8(1);
+
+    uint64_t aux64;
+
+    // somewhat hacky, but gives a significant boost in performance
+    __m256i aux_gindex;
+    const uint16_t * gindex = (const uint16_t *)&aux_gindex;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * restrict q2 = x[i].qs;
+        const int8_t   * restrict q8 = y[i].qs;
+
+        memcpy(&aux64, x[i].scales, 8);
+        __m128i stmp = _mm_set1_epi64x(aux64);
+        stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4));
+        const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1);
+
+        __m128i sumi1_0 = _mm_setzero_si128();
+        __m128i sumi1_1 = _mm_setzero_si128();
+        __m128i sumi2_0 = _mm_setzero_si128();
+        __m128i sumi2_1 = _mm_setzero_si128();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) {
+
+            const __m128i q2_data_0 = _mm_loadu_si128((const __m128i*)q2);
+            const __m128i q2_data_1 = _mm_loadu_si128((const __m128i*)q2 + 1);  q2 += 16;
+            aux_gindex = MM256_SET_M128I(_mm_and_si128(q2_data_1, m511), _mm_and_si128(q2_data_0, m511));
+
+            const __m128i partial_sign_bits_0 = _mm_srli_epi16(q2_data_0, 9);
+            const __m128i partial_sign_bits_1 = _mm_srli_epi16(q2_data_1, 9);
+            const __m128i partial_sign_bits_upper_0 = _mm_srli_epi16(q2_data_0, 13);
+            const __m128i partial_sign_bits_upper_1 = _mm_srli_epi16(q2_data_1, 13);
+            const __m128i partial_sign_bits_for_counting_0 = _mm_xor_si128(partial_sign_bits_0, partial_sign_bits_upper_0);
+            const __m128i partial_sign_bits_for_counting_1 = _mm_xor_si128(partial_sign_bits_1, partial_sign_bits_upper_1);
+
+            const __m128i odd_bits_0 = _mm_shuffle_epi8(bit_helper_0, partial_sign_bits_for_counting_0);
+            const __m128i odd_bits_1 = _mm_shuffle_epi8(bit_helper_1, partial_sign_bits_for_counting_1);
+            const __m128i full_sign_bits_0 = _mm_or_si128(partial_sign_bits_0, odd_bits_0);
+            const __m128i full_sign_bits_1 = _mm_or_si128(partial_sign_bits_1, odd_bits_1);
+
+            const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_3_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_3_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_4_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_4_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+
+            const __m128i q2_1_0 = _mm_set_epi64x(iq2xs_grid[gindex[1]], iq2xs_grid[gindex[0]]);
+            const __m128i q2_1_1 = _mm_set_epi64x(iq2xs_grid[gindex[3]], iq2xs_grid[gindex[2]]);
+            const __m128i q2_2_0 = _mm_set_epi64x(iq2xs_grid[gindex[5]], iq2xs_grid[gindex[4]]);
+            const __m128i q2_2_1 = _mm_set_epi64x(iq2xs_grid[gindex[7]], iq2xs_grid[gindex[6]]);
+            const __m128i q2_3_0 = _mm_set_epi64x(iq2xs_grid[gindex[9]], iq2xs_grid[gindex[8]]);
+            const __m128i q2_3_1 = _mm_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]]);
+            const __m128i q2_4_0 = _mm_set_epi64x(iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]);
+            const __m128i q2_4_1 = _mm_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]]);
+
+            // AVX2 full_signs_1 is full_sign_bits_0 here
+            // AVX2 full_signs_2 is full_sign_bits_1 here
+            __m128i signs_0, signs_1;
+            signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_0);
+            signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_1);
+            signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0);
+            signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1);
+            const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, _mm_or_si128(signs_0, mone));
+            const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, _mm_or_si128(signs_1, mone));
+
+            signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_0);
+            signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_1);
+            signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0);
+            signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1);
+            const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, _mm_or_si128(signs_0, mone));
+            const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, _mm_or_si128(signs_1, mone));
+
+            signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_0);
+            signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_1);
+            signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0);
+            signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1);
+            const __m128i q8s_3_0 = _mm_sign_epi8(q8_3_0, _mm_or_si128(signs_0, mone));
+            const __m128i q8s_3_1 = _mm_sign_epi8(q8_3_1, _mm_or_si128(signs_1, mone));
+
+            signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_0);
+            signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_1);
+            signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0);
+            signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1);
+            const __m128i q8s_4_0 = _mm_sign_epi8(q8_4_0, _mm_or_si128(signs_0, mone));
+            const __m128i q8s_4_1 = _mm_sign_epi8(q8_4_1, _mm_or_si128(signs_1, mone));
+
+            const __m128i dot1_0  = _mm_maddubs_epi16(q2_1_0, q8s_1_0);
+            const __m128i dot1_1  = _mm_maddubs_epi16(q2_1_1, q8s_1_1);
+            const __m128i dot2_0  = _mm_maddubs_epi16(q2_2_0, q8s_2_0);
+            const __m128i dot2_1  = _mm_maddubs_epi16(q2_2_1, q8s_2_1);
+            const __m128i dot3_0  = _mm_maddubs_epi16(q2_3_0, q8s_3_0);
+            const __m128i dot3_1  = _mm_maddubs_epi16(q2_3_1, q8s_3_1);
+            const __m128i dot4_0  = _mm_maddubs_epi16(q2_4_0, q8s_4_0);
+            const __m128i dot4_1  = _mm_maddubs_epi16(q2_4_1, q8s_4_1);
+
+            __m128i sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0));
+            const __m128i sc1_0 = _mm_cvtepi8_epi16(sc_tmp);
+            const __m128i sc1_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8));
+            sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1));
+            const __m128i sc2_0 = _mm_cvtepi8_epi16(sc_tmp);
+            const __m128i sc2_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8));
+            sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2));
+            const __m128i sc3_0 = _mm_cvtepi8_epi16(sc_tmp);
+            const __m128i sc3_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8));
+            sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3));
+            const __m128i sc4_0 = _mm_cvtepi8_epi16(sc_tmp);
+            const __m128i sc4_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8));
+
+            sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot1_0, sc1_0));
+            sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot1_1, sc1_1));
+            sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot2_0, sc2_0));
+            sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot2_1, sc2_1));
+            sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot3_0, sc3_0));
+            sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot3_1, sc3_1));
+            sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot4_0, sc4_0));
+            sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot4_1, sc4_1));
+        }
+
+        accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+
+#elif defined(__loongarch_asx)
+
+    const __m256i mone = __lasx_xvreplgr2vr_b(1);
+    static const char block_sign_shuffle_mask_1[32] = {
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02,
+        0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06,
+    };
+    static const char block_sign_shuffle_mask_2[32] = {
+        0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a,
+        0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e,
+    };
+    static const uint8_t bit_selector_mask_bytes[32] = {
+        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m256i bit_selector_mask = __lasx_xvld((const __m256i*)bit_selector_mask_bytes, 0);
+    const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0);
+    const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0);
+
+    static const uint8_t k_bit_helper[32] = {
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+    };
+    const __m256i bit_helper = __lasx_xvld((const __m256i*)k_bit_helper, 0);
+    const __m256i m511 = __lasx_xvreplgr2vr_h(511);
+    const __m128i m4 = __lsx_vreplgr2vr_b(0xf);
+    const __m128i m1 = __lsx_vreplgr2vr_b(1);
+
+    uint64_t aux64;
+
+    // somewhat hacky, but gives a significant boost in performance
+    __m256i aux_gindex;
+    const uint16_t * gindex = (const uint16_t *)&aux_gindex;
+
+    __m256 accumf = (__m256)__lasx_xvldi(0);
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * restrict q2 = x[i].qs;
+        const int8_t   * restrict q8 = y[i].qs;
+
+        memcpy(&aux64, x[i].scales, 8);
+        __m128i stmp = __lsx_vreplgr2vr_d(aux64);
+        stmp = __lsx_vilvl_b( __lsx_vand_v(__lsx_vsrli_h(stmp, 4), m4), __lsx_vand_v(stmp, m4));
+        const __m128i scales = __lsx_vadd_b(__lsx_vslli_h(stmp, 1), m1);
+
+        __m256i sumi1 = __lasx_xvldi(0);
+        __m256i sumi2 = __lasx_xvldi(0);
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) {
+
+            const __m256i q2_data = __lasx_xvld((const __m256i*)q2, 0);  q2 += 16;
+            aux_gindex = __lasx_xvand_v(q2_data, m511);
+
+            const __m256i partial_sign_bits = __lasx_xvsrli_h(q2_data, 9);
+            const __m256i partial_sign_bits_upper = __lasx_xvsrli_h(q2_data, 13);
+            const __m256i partial_sign_bits_for_counting = __lasx_xvxor_v(partial_sign_bits, partial_sign_bits_upper);
+
+            const __m256i odd_bits = lasx_shuffle_b(bit_helper, partial_sign_bits_for_counting);
+            const __m256i full_sign_bits = __lasx_xvor_v(partial_sign_bits, odd_bits);
+
+            const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q8_3 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q8_4 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+
+            const __m256i q2_1 = lasx_set_d(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]],
+                                                   iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]);
+            const __m256i q2_2 = lasx_set_d(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]],
+                                                   iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]);
+            const __m256i q2_3 = lasx_set_d(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]],
+                                                   iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]);
+            const __m256i q2_4 = lasx_set_d(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]],
+                                                   iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]);
+
+            const __m128i full_signs_l = lasx_extracti128(full_sign_bits, 0);
+            const __m128i full_signs_h = lasx_extracti128(full_sign_bits, 1);
+            const __m256i full_signs_1 = lasx_insertf128(full_signs_l, full_signs_l);
+            const __m256i full_signs_2 = lasx_insertf128(full_signs_h, full_signs_h);
+
+            __m256i signs;
+            signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_1);
+            signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask);
+            const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1);
+
+            signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_2);
+            signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask);
+            const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2);
+
+            signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_1);
+            signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask);
+            const __m256i q8s_3 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_3);
+
+            signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_2);
+            signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask);
+            const __m256i q8s_4 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_4);
+
+            const __m256i dot1  = lasx_maddubs_h(q2_1, q8s_1);
+            const __m256i dot2  = lasx_maddubs_h(q2_2, q8s_2);
+            const __m256i dot3  = lasx_maddubs_h(q2_3, q8s_3);
+            const __m256i dot4  = lasx_maddubs_h(q2_4, q8s_4);
+
+            const __m256i sc1 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+0)));
+            const __m256i sc2 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+1)));
+            const __m256i sc3 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+2)));
+            const __m256i sc4 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+3)));
+
+            sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot1, sc1));
+            sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot2, sc2));
+            sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot3, sc3));
+            sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot4, sc4));
+        }
+
+        accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
 #elif defined(__POWER9_VECTOR__)
+    const vector int v0 = vec_splats((int32_t)0);
     vector float vsumf0 = vec_splats(0.0f);
     vector float vsumf1 = vec_splats(0.0f);
     vector float vsumf2 = vec_splats(0.0f);
@@ -10215,14 +9505,10 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
         vector float vyd = vec_splats(y[i].d);
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
 
         const uint16_t * restrict q2 = x[i].qs;
         const uint8_t  * restrict sc = x[i].scales;
@@ -10270,21 +9556,12 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
             vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1));
             vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1));
 
-            vsumi0 = vec_add(vec_mule(qv0, vscales0), vsumi0);
-            vsumi1 = vec_add(vec_mule(qv1, vscales1), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv2, vscales2), vsumi2);
-            vsumi3 = vec_add(vec_mule(qv3, vscales3), vsumi3);
-            vsumi4 = vec_add(vec_mulo(qv0, vscales0), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv1, vscales1), vsumi5);
-            vsumi6 = vec_add(vec_mulo(qv2, vscales2), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv3, vscales3), vsumi7);
+            vsumi0 = vec_msum(qv0, vscales0, vsumi0);
+            vsumi1 = vec_msum(qv1, vscales1, vsumi1);
+            vsumi2 = vec_msum(qv2, vscales2, vsumi2);
+            vsumi3 = vec_msum(qv3, vscales3, vsumi3);
         }
 
-        vsumi0 = vec_add(vsumi0, vsumi4);
-        vsumi1 = vec_add(vsumi1, vsumi5);
-        vsumi2 = vec_add(vsumi2, vsumi6);
-        vsumi3 = vec_add(vsumi3, vsumi7);
-
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
         vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
         vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
@@ -10441,8 +9718,81 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void *
     const __m128i m4 = _mm_set1_epi8(0xf);
     const __m128i m1 = _mm_set1_epi8(1);
 
-    const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1);
-    const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2);
+    const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1);
+    const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2);
+
+    uint64_t aux64;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8);
+        const int8_t  * restrict q8 = y[i].qs;
+
+        memcpy(&aux64, x[i].scales, 8);
+        const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1);
+        const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15
+
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)],
+                                                   iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)],
+                                                   iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)],
+                                                   iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]);
+            const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)],
+                                                   iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)],
+                                                   iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)],
+                                                   iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]);
+            qs += 8;
+
+            __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1);
+
+            aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2);
+
+            signs += 4;
+
+            const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1
+            const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3
+
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0)));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1)));
+            sumi1 = _mm256_add_epi32(sumi1, p1);
+            sumi2 = _mm256_add_epi32(sumi2, p2);
+        }
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+
+#elif defined(__AVX__)
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+                                        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m128i m4 = _mm_set1_epi8(0xf);
+    const __m128i m1 = _mm_set1_epi8(1);
+
+    const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1);
+    const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1);
+    const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2);
+    const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1);
 
     uint64_t aux64;
 
@@ -10456,45 +9806,64 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void *
 
         memcpy(&aux64, x[i].scales, 8);
         const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1);
-        const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15
+        const __m128i scales16_0 = _mm_cvtepi8_epi16(scales8);
+        const __m128i scales16_1 = _mm_cvtepi8_epi16(_mm_srli_si128(scales8, 8));
 
-        __m256i sumi1 = _mm256_setzero_si256();
-        __m256i sumi2 = _mm256_setzero_si256();
+        __m128i sumi1_0 = _mm_setzero_si128();
+        __m128i sumi1_1 = _mm_setzero_si128();
+        __m128i sumi2_0 = _mm_setzero_si128();
+        __m128i sumi2_1 = _mm_setzero_si128();
         for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
-            const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
-            const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
-            const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)],
-                                                   iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)],
-                                                   iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)],
-                                                   iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]);
-            const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)],
-                                                   iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)],
-                                                   iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)],
-                                                   iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]);
+            const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q2_1_0 = _mm_set_epi64x(iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)],
+                                                  iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]);
+            const __m128i q2_1_1 = _mm_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)],
+                                                  iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)]);
+            const __m128i q2_2_0 = _mm_set_epi64x(iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)],
+                                                  iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]);
+            const __m128i q2_2_1 = _mm_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)],
+                                                  iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)]);
             qs += 8;
 
-            __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16));
-            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
-            const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2);
-            const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1);
-
-            aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16));
-            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
-            const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2);
-            const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2);
+            __m128i aux128_0 = _mm_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16));
+            __m128i aux128_1 = aux128_0;
+            aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0);
+            aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1);
+            const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0);
+            const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1);
+            const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0);
+            const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1);
+
+            aux128_0 = _mm_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16));
+            aux128_1 = aux128_0;
+            aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0);
+            aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1);
+            const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0);
+            const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1);
+            const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0);
+            const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1);
 
             signs += 4;
 
-            const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1
-            const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3
+            const __m128i dot1_0  = _mm_maddubs_epi16(q2_1_0, q8s_1_0);
+            const __m128i dot1_1  = _mm_maddubs_epi16(q2_1_1, q8s_1_1);
+            const __m128i dot2_0  = _mm_maddubs_epi16(q2_2_0, q8s_2_0);
+            const __m128i dot2_1  = _mm_maddubs_epi16(q2_2_1, q8s_2_1);
 
-            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0)));
-            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1)));
-            sumi1 = _mm256_add_epi32(sumi1, p1);
-            sumi2 = _mm256_add_epi32(sumi2, p2);
+            const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 0)));
+            const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 1)));
+            const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 0)));
+            const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 1)));
+            sumi1_0 = _mm_add_epi32(sumi1_0, p1_0);
+            sumi1_1 = _mm_add_epi32(sumi1_1, p1_1);
+            sumi2_0 = _mm_add_epi32(sumi2_0, p2_0);
+            sumi2_1 = _mm_add_epi32(sumi2_1, p2_1);
         }
 
-        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+        accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf);
 
     }
 
@@ -10507,6 +9876,8 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void *
 
     static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,};
 
+    const vector int v0 = vec_splats((int32_t)0);
+
     vector float vsumf0 = vec_splats(0.0f);
     vector float vsumf1 = vec_splats(0.0f);
     vector float vsumf2 = vec_splats(0.0f);
@@ -10521,14 +9892,10 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void *
         vector float vyd = vec_splats(y[i].d);
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
 
         const uint8_t *  restrict q2 = x[i].qs;
         const uint8_t *  restrict qh = x[i].qh;
@@ -10588,21 +9955,12 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void *
             vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1));
             vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1));
 
-            vsumi0 = vec_add(vec_mule(qv0, vscales0), vsumi0);
-            vsumi1 = vec_add(vec_mule(qv1, vscales1), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv2, vscales2), vsumi2);
-            vsumi3 = vec_add(vec_mule(qv3, vscales3), vsumi3);
-            vsumi4 = vec_add(vec_mulo(qv0, vscales0), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv1, vscales1), vsumi5);
-            vsumi6 = vec_add(vec_mulo(qv2, vscales2), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv3, vscales3), vsumi7);
+            vsumi0 = vec_msum(qv0, vscales0, vsumi0);
+            vsumi1 = vec_msum(qv1, vscales1, vsumi1);
+            vsumi2 = vec_msum(qv2, vscales2, vsumi2);
+            vsumi3 = vec_msum(qv3, vscales3, vsumi3);
         }
 
-        vsumi0 = vec_add(vsumi0, vsumi4);
-        vsumi1 = vec_add(vsumi1, vsumi5);
-        vsumi2 = vec_add(vsumi2, vsumi6);
-        vsumi3 = vec_add(vsumi3, vsumi7);
-
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
         vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
         vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
@@ -10618,6 +9976,81 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void *
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
     *s = 0.125f * vec_extract(vsumf0, 0);
+
+#elif defined(__loongarch_asx)
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+                                        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+
+    const __m128i m4 = __lsx_vreplgr2vr_b(0xf);
+    const __m128i m1 = __lsx_vreplgr2vr_b(1);
+
+    const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0);
+    const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0);
+    uint64_t aux64;
+
+    __m256 accumf = (__m256)__lasx_xvldi(0);
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8);
+        const int8_t  * restrict q8 = y[i].qs;
+
+        __m128i tmp1;
+        memcpy(&aux64, x[i].scales, 8);
+        tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64, 0);
+        tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64 >> 4, 1);
+        const __m128i scales8 = __lsx_vadd_b(__lsx_vslli_h(__lsx_vand_v(tmp1, m4), 1), m1);
+        const __m256i scales16 = lasx_ext8_16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15
+
+        __m256i sumi1 = __lasx_xvldi(0);
+        __m256i sumi2 = __lasx_xvldi(0);
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q2_1 = lasx_set_d(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)],
+                                                   iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)],
+                                                   iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)],
+                                                   iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]);
+            const __m256i q2_2 = lasx_set_d(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)],
+                                                   iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)],
+                                                   iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)],
+                                                   iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]);
+            qs += 8;
+
+            __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | ((uint32_t) signs[1] << 16));
+            aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2);
+            const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2);
+            const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1);
+
+            aux256 = __lasx_xvreplgr2vr_w(signs[2] | ((uint32_t) signs[3] << 16));
+            aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2);
+            const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2);
+            const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2);
+
+            signs += 4;
+
+            const __m256i dot1  = lasx_maddubs_h(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1
+            const __m256i dot2  = lasx_maddubs_h(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3
+
+            const __m256i p1 = lasx_madd_h(dot1, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+0)));
+            const __m256i p2 = lasx_madd_h(dot2, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+1)));
+            sumi1 = __lasx_xvadd_w(sumi1, p1);
+            sumi2 = __lasx_xvadd_w(sumi2, p2);
+        }
+
+        accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf);
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+
 #else
 
     float sumf = 0;
@@ -10762,9 +10195,68 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void
 
     *s = 0.25f * hsum_float_8(accumf);
 
+#elif defined(__AVX__)
+    const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
+
+    uint32_t aux32[2];
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict gas = x[i].qs + QK_K/4;
+        const int8_t  * restrict q8 = y[i].qs;
+        __m128i sumi1_0 = _mm_setzero_si128();
+        __m128i sumi1_1 = _mm_setzero_si128();
+        __m128i sumi2_0 = _mm_setzero_si128();
+        __m128i sumi2_1 = _mm_setzero_si128();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q2_1_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]);
+            const __m128i q2_1_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]);
+            q3 += 8;
+            const __m128i q2_2_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]);
+            const __m128i q2_2_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]);
+            q3 += 8;
+            memcpy(aux32, gas, 8); gas += 8;
+            const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[0] >>  7) & 127], signs64[(aux32[0] >>  0) & 127]);
+            const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127]);
+            const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[1] >>  7) & 127], signs64[(aux32[1] >>  0) & 127]);
+            const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]);
+            const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0);
+            const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1);
+            const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0);
+            const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1);
+            const __m128i dot1_0  = _mm_maddubs_epi16(q2_1_0, q8s_1_0);
+            const __m128i dot1_1  = _mm_maddubs_epi16(q2_1_1, q8s_1_1);
+            const __m128i dot2_0  = _mm_maddubs_epi16(q2_2_0, q8s_2_0);
+            const __m128i dot2_1  = _mm_maddubs_epi16(q2_2_1, q8s_2_1);
+            const uint16_t ls1 = aux32[0] >> 28;
+            const uint16_t ls2 = aux32[1] >> 28;
+            const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1));
+            const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1));
+            const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1));
+            const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1));
+            sumi1_0 = _mm_add_epi32(sumi1_0, p1_0);
+            sumi1_1 = _mm_add_epi32(sumi1_1, p1_1);
+            sumi2_0 = _mm_add_epi32(sumi2_0, p2_0);
+            sumi2_1 = _mm_add_epi32(sumi2_1, p2_1);
+        }
+
+        accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf);
+
+    }
+
+    *s = 0.25f * hsum_float_8(accumf);
+
 #elif defined(__POWER9_VECTOR__)
     const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
 
+    const vector int v0 = vec_splats((int32_t)0);
+
     vector float vsumf0 = vec_splats(0.0f);
     vector float vsumf1 = vec_splats(0.0f);
     vector float vsumf2 = vec_splats(0.0f);
@@ -10775,14 +10267,10 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void
         vector float vyd = vec_splats(y[i].d);
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
 
         const uint8_t * restrict q3 = x[i].qs;
         const uint32_t * restrict signs = (const uint32_t *)(x[i].qs + QK_K/4);
@@ -10827,21 +10315,12 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void
             vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1));
             vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1));
 
-            vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0);
-            vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2);
-            vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3);
-            vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5);
-            vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7);
+            vsumi0 = vec_msum(qv0, vscales01, vsumi0);
+            vsumi1 = vec_msum(qv1, vscales01, vsumi1);
+            vsumi2 = vec_msum(qv2, vscales23, vsumi2);
+            vsumi3 = vec_msum(qv3, vscales23, vsumi3);
         }
 
-        vsumi0 = vec_add(vsumi0, vsumi4);
-        vsumi1 = vec_add(vsumi1, vsumi5);
-        vsumi2 = vec_add(vsumi2, vsumi6);
-        vsumi3 = vec_add(vsumi3, vsumi7);
-
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
         vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
         vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
@@ -10857,6 +10336,54 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
     *s = 0.25f * vec_extract(vsumf0, 0);
+
+#elif defined(__loongarch_asx)
+
+    const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
+
+    uint32_t aux32[2];
+
+    __m256 accumf = (__m256)__lasx_xvldi(0);
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict gas = x[i].qs + QK_K/4;
+        const int8_t  * restrict q8 = y[i].qs;
+        __m256i sumi1 = __lasx_xvldi(0);
+        __m256i sumi2 = __lasx_xvldi(0);
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q2_1 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]],
+                                                iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]);
+            q3 += 8;
+            const __m256i q2_2 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]],
+                                                iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]);
+            q3 += 8;
+            memcpy(aux32, gas, 8); gas += 8;
+
+            const __m256i s2_1 = lasx_set_d(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127],
+                                                   signs64[(aux32[0] >>  7) & 127], signs64[(aux32[0] >>  0) & 127]);
+            const __m256i s2_2 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127],
+                                                   signs64[(aux32[1] >>  7) & 127], signs64[(aux32[1] >>  0) & 127]);
+            const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1);
+            const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2);
+            const __m256i dot1  = lasx_maddubs_h(q2_1, q8s_1);
+            const __m256i dot2  = lasx_maddubs_h(q2_2, q8s_2);
+            const uint16_t ls1 = aux32[0] >> 28;
+            const uint16_t ls2 = aux32[1] >> 28;
+
+            const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1));
+            const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1));
+            sumi1 = __lasx_xvadd_w(sumi1, p1);
+            sumi2 = __lasx_xvadd_w(sumi2, p2);
+        }
+
+        accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf);
+    }
+
+    *s = 0.25f * hsum_float_8(accumf);
+
 #else
 
     uint32_t aux32;
@@ -10931,10 +10458,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void *
     ggml_int8x16x4_t q8b;
     vec_index_t idx;
 
-#if QK_K == 256
     uint32_t scales32[2];
     const uint8_t * scales8 = (const uint8_t *)scales32;
-#endif
 
     float sumf = 0;
     for (int i = 0; i < nb; ++i) {
@@ -10944,11 +10469,9 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void *
         const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
         const int8_t   * restrict q8 = y[i].qs;
 
-#if QK_K == 256
         memcpy(scales32, x[i].scales, 4);
         scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101;
         scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101;
-#endif
 
         int sumi1 = 0, sumi2 = 0;
         for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
@@ -10989,13 +10512,9 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void *
 
             const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]);
             const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]);
-#if QK_K == 256
+
             sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0];
             sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4];
-#else
-            sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf));
-            sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >>  4));
-#endif
         }
         sumf += d*(sumi1 + sumi2);
     }
@@ -11084,6 +10603,112 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void *
 
     *s = hsum_float_8(accumf);
 
+#elif defined(__AVX__)
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+                                        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1);
+    const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1);
+    const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2);
+    const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1);
+
+    const __m128i idx_mul_0 = _mm_set_epi32(32, 64, 128, 256);
+    const __m128i idx_mul_1 = _mm_set_epi32(2, 4, 8, 16);
+    const __m128i idx_mask  = _mm_set1_epi32(256);
+
+    typedef union {
+        __m128i  vec[4];
+        uint32_t index[16];
+    } index_t;
+
+    index_t idx;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
+        const int8_t  * restrict q8 = y[i].qs;
+        __m128i sumi1_0 = _mm_setzero_si128();
+        __m128i sumi1_1 = _mm_setzero_si128();
+        __m128i sumi2_0 = _mm_setzero_si128();
+        __m128i sumi2_1 = _mm_setzero_si128();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i qs_tmp = _mm_loadu_si128((const __m128i *)qs);
+            const __m128i idx_l_0 = _mm_cvtepu8_epi16(qs_tmp);
+            const __m128i idx_l_1 = _mm_cvtepu8_epi16(_mm_srli_si128(qs_tmp, 8)); qs += 16;
+            idx.vec[0] = _mm_set1_epi32(qh[ib32+0]);
+            idx.vec[1] = idx.vec[0];
+            idx.vec[2] = _mm_set1_epi32(qh[ib32+1]);
+            idx.vec[3] = idx.vec[2];
+
+            idx.vec[0] = _mm_and_si128(_mm_mullo_epi32(idx.vec[0], idx_mul_0), idx_mask);
+            idx.vec[1] = _mm_and_si128(_mm_mullo_epi32(idx.vec[1], idx_mul_1), idx_mask);
+            idx.vec[2] = _mm_and_si128(_mm_mullo_epi32(idx.vec[2], idx_mul_0), idx_mask);
+            idx.vec[3] = _mm_and_si128(_mm_mullo_epi32(idx.vec[3], idx_mul_1), idx_mask);
+
+            idx.vec[0] = _mm_or_si128(idx.vec[0], _mm_cvtepi16_epi32(idx_l_0));
+            idx.vec[1] = _mm_or_si128(idx.vec[1], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_0, 8)));
+            idx.vec[2] = _mm_or_si128(idx.vec[2], _mm_cvtepi16_epi32(idx_l_1));
+            idx.vec[3] = _mm_or_si128(idx.vec[3], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_1, 8)));
+
+            const __m128i q2_1_0 = _mm_set_epi32(iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]);
+            const __m128i q2_1_1 = _mm_set_epi32(iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]]);
+            const __m128i q2_2_0 = _mm_set_epi32(iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[9]], iq3s_grid[idx.index[8]]);
+            const __m128i q2_2_1 = _mm_set_epi32(iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]]);
+
+            __m128i aux128_0 = _mm_set1_epi32(signs[0] | (signs[1] << 16));
+            __m128i aux128_1 = aux128_0;
+            aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0);
+            aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1);
+            const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0);
+            const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1);
+            const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0);
+            const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1);
+
+            aux128_0 = _mm_set1_epi32(signs[2] | (signs[3] << 16));
+            aux128_1 = aux128_0;
+            aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0);
+            aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1);
+            const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0);
+            const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1);
+            const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0);
+            const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1);
+
+            signs += 4;
+
+            const __m128i dot1_0  = _mm_maddubs_epi16(q2_1_0, q8s_1_0);
+            const __m128i dot1_1  = _mm_maddubs_epi16(q2_1_1, q8s_1_1);
+            const __m128i dot2_0  = _mm_maddubs_epi16(q2_2_0, q8s_2_0);
+            const __m128i dot2_1  = _mm_maddubs_epi16(q2_2_1, q8s_2_1);
+            const uint16_t ls1 = x[i].scales[ib32/2] & 0xf;
+            const uint16_t ls2 = x[i].scales[ib32/2] >>  4;
+            const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1));
+            const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1));
+            const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1));
+            const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1));
+            sumi1_0 = _mm_add_epi32(sumi1_0, p1_0);
+            sumi1_1 = _mm_add_epi32(sumi1_1, p1_1);
+            sumi2_0 = _mm_add_epi32(sumi2_0, p2_0);
+            sumi2_1 = _mm_add_epi32(sumi2_1, p2_1);
+        }
+
+        accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf);
+
+    }
+
+    *s = hsum_float_8(accumf);
+
 #elif defined(__POWER9_VECTOR__)
     static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
                                         0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
@@ -11091,6 +10716,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void *
 
     static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,};
 
+    const vector int v0 = vec_splats((int32_t)0);
+
     vector float vsumf0 = vec_splats(0.0f);
     vector float vsumf1 = vec_splats(0.0f);
     vector float vsumf2 = vec_splats(0.0f);
@@ -11111,14 +10738,10 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void *
         const uint8_t *  restrict sc = x[i].scales;
         const int8_t  *  restrict q8 = y[i].qs;
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
 
         for (int j = 0; j < QK_K/32; j += 2) {
             __builtin_prefetch(q3, 0, 1);
@@ -11160,48 +10783,122 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void *
             vector signed char q8y3 = vec_xl(48, q8);
             q8 += 64;
 
-            vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0));
-            vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1));
-            vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2));
-            vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3));
+            vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0));
+            vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1));
+            vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2));
+            vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3));
+
+            const uint16_t ls0 = (uint16_t)(sc[0] & 0xf);
+            const uint16_t ls1 = (uint16_t)(sc[0] >>  4);
+            sc ++;
+
+            vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1));
+            vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1));
+
+            vsumi0 = vec_msum(qv0, vscales01, vsumi0);
+            vsumi1 = vec_msum(qv1, vscales01, vsumi1);
+            vsumi2 = vec_msum(qv2, vscales23, vsumi2);
+            vsumi3 = vec_msum(qv3, vscales23, vsumi3);
+        }
+
+        vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
+        vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
+        vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
+        vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3);
+    }
+
+    vsumf0 = vec_add(vsumf0, vsumf2);
+    vsumf1 = vec_add(vsumf1, vsumf3);
+
+    vsumf0 = vec_add(vsumf0, vsumf1);
+
+    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
+    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
+
+    *s = vec_extract(vsumf0, 0);
+
+#elif defined(__loongarch_asx)
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+                                        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0);
+    const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0);
+
+    __m256i idx_shift = lasx_set_w(1, 2, 3, 4, 5, 6, 7, 8);
+    const __m256i idx_mask  = __lasx_xvreplgr2vr_w(256);
+
+    typedef union {
+        __m256i  vec[2];
+        uint32_t index[16];
+    } index_t;
+
+    index_t idx;
+
+    __m256 accumf = (__m256)__lasx_xvldi(0);
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
+        const int8_t  * restrict q8 = y[i].qs;
+        __m256i sumi1 = __lasx_xvldi(0);
+        __m256i sumi2 = __lasx_xvldi(0);
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i idx_l = lasx_extu8_16(__lsx_vld(qs, 0)); qs += 16;
+            idx.vec[0] = __lasx_xvreplgr2vr_w(qh[ib32+0]);
+            idx.vec[1] = __lasx_xvreplgr2vr_w(qh[ib32+1]);
+            idx.vec[0] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[0], idx_shift), idx_mask);
+            idx.vec[1] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[1], idx_shift), idx_mask);
+            idx.vec[0] = __lasx_xvor_v(idx.vec[0], lasx_ext16_32(lasx_extracti128(idx_l, 0)));
+            idx.vec[1] = __lasx_xvor_v(idx.vec[1], lasx_ext16_32(lasx_extracti128(idx_l, 1)));
+
+            // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange.
+            //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4);
+            //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4);
+            const __m256i q2_1 = lasx_set_w(
+                    iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]],
+                    iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]
+            );
+            const __m256i q2_2 = lasx_set_w(
+                    iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]],
+                    iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]]
+            );
+
+            __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | (signs[1] << 16));
+            aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2);
+            const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2);
+            const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1);
 
-            const uint16_t ls0 = (uint16_t)(sc[0] & 0xf);
-            const uint16_t ls1 = (uint16_t)(sc[0] >>  4);
-            sc ++;
+            aux256 = __lasx_xvreplgr2vr_w(signs[2] | (signs[3] << 16));
+            aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2);
+            const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2);
+            const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2);
 
-            vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1));
-            vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1));
+            signs += 4;
 
-            vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0);
-            vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2);
-            vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3);
-            vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5);
-            vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7);
+            const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1);
+            const __m256i dot2  = lasx_maddubs_h(q2_2, q8s_2);
+            const uint16_t ls1 = x[i].scales[ib32/2] & 0xf;
+            const uint16_t ls2 = x[i].scales[ib32/2] >>  4;
+            const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1));
+            const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1));
+            sumi1 = __lasx_xvadd_w(sumi1, p1);
+            sumi2 = __lasx_xvadd_w(sumi2, p2);
         }
 
-        vsumi0 = vec_add(vsumi0, vsumi4);
-        vsumi1 = vec_add(vsumi1, vsumi5);
-        vsumi2 = vec_add(vsumi2, vsumi6);
-        vsumi3 = vec_add(vsumi3, vsumi7);
-
-        vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
-        vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
-        vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
-        vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3);
+        accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf);
     }
 
-    vsumf0 = vec_add(vsumf0, vsumf2);
-    vsumf1 = vec_add(vsumf1, vsumf3);
-
-    vsumf0 = vec_add(vsumf0, vsumf1);
-
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
-    vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
+    *s = hsum_float_8(accumf);
 
-    *s = vec_extract(vsumf0, 0);
 #else
 
     float sumf = 0.f;
@@ -11249,12 +10946,30 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void *
 }
 
 
-#ifdef __AVX2__
+#if defined(__AVX__)
+static inline __m128i mul_add_epi8_sse(const __m128i x, const __m128i y) {
+    const __m128i ax = _mm_sign_epi8(x, x);
+    const __m128i sy = _mm_sign_epi8(y, x);
+    return _mm_maddubs_epi16(ax, sy);
+}
+#endif
+
+#if defined(__AVX2__)
 static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
     const __m256i ax = _mm256_sign_epi8(x, x);
     const __m256i sy = _mm256_sign_epi8(y, x);
     return _mm256_maddubs_epi16(ax, sy);
 }
+#elif defined(__loongarch_asx)
+static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
+    const __m256i ax = __lasx_xvsigncov_b(x, x);
+    const __m256i sy = __lasx_xvsigncov_b(x, y);
+    __m256i tmp1, tmp2, tmp3;
+    tmp1 = __lasx_xvmulwev_h_bu_b(ax, sy);
+    tmp2 = __lasx_xvmulwod_h_bu_b(ax, sy);
+    tmp3 = __lasx_xvadd_h(tmp1, tmp2);
+    return __lasx_xvsat_h(tmp3, 15);
+}
 #endif
 
 void ggml_vec_dot_iq1_s_q8_K  (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
@@ -11356,6 +11071,54 @@ void ggml_vec_dot_iq1_s_q8_K  (int n, float * restrict s, size_t bs, const void
 
     *s = hsum_float_8(accum) + IQ1S_DELTA * accum1;
 
+#elif defined __AVX__
+    __m256 accum = _mm256_setzero_ps();
+    float accum1 = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        __m128i sumi1_0 = _mm_setzero_si128();
+        __m128i sumi1_1 = _mm_setzero_si128();
+        int sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m128i q1b_1_0 = _mm_set_epi64x(iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]);
+            const __m128i q1b_1_1 = _mm_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)]);
+            const __m128i q1b_2_0 = _mm_set_epi64x(iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]);
+            const __m128i q1b_2_1 = _mm_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)]);
+            qs += 8;
+            const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+
+            const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0);
+            const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1);
+            const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0);
+            const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1);
+            const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1;
+            const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1;
+            const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(ls1));
+            const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(ls1));
+            const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(ls2));
+            const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(ls2));
+
+            sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0));
+            sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1));
+            sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1
+                   + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2;
+        }
+
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum);
+        accum1 += d * sumi1;
+
+    }
+
+    *s = hsum_float_8(accum) + IQ1S_DELTA * accum1;
+
 #elif defined(__POWER9_VECTOR__)
     const vector unsigned char v0 = vec_splats((unsigned char)0x0);
     const vector unsigned short vsign = vec_splats((unsigned short)0x8000);
@@ -11374,10 +11137,6 @@ void ggml_vec_dot_iq1_s_q8_K  (int n, float * restrict s, size_t bs, const void
         vector signed int vsumi1 = vec_splats((int32_t)0);
         vector signed int vsumi2 = vec_splats((int32_t)0);
         vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
         vector signed int vsumi8 = vec_splats((int32_t)0);
 
         const uint8_t  * restrict q1 = x[i].qs;
@@ -11419,14 +11178,10 @@ void ggml_vec_dot_iq1_s_q8_K  (int n, float * restrict s, size_t bs, const void
             vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1));
             vector signed short vscales = vec_sld(vscales23, vscales01, 8);
 
-            vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0);
-            vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2);
-            vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3);
-            vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5);
-            vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7);
+            vsumi0 = vec_msum(qv0, vscales01, vsumi0);
+            vsumi1 = vec_msum(qv1, vscales01, vsumi1);
+            vsumi2 = vec_msum(qv2, vscales23, vsumi2);
+            vsumi3 = vec_msum(qv3, vscales23, vsumi3);
 
             vector signed short q8ysums = vec_xl_len(qs, 8);
             qs += 4;
@@ -11441,11 +11196,6 @@ void ggml_vec_dot_iq1_s_q8_K  (int n, float * restrict s, size_t bs, const void
             vsumi8 = vec_add(vec_mule(q8ysum, vscales), vsumi8);
         }
 
-        vsumi0 = vec_add(vsumi0, vsumi4);
-        vsumi1 = vec_add(vsumi1, vsumi5);
-        vsumi2 = vec_add(vsumi2, vsumi6);
-        vsumi3 = vec_add(vsumi3, vsumi7);
-
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
         vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
         vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
@@ -11463,6 +11213,62 @@ void ggml_vec_dot_iq1_s_q8_K  (int n, float * restrict s, size_t bs, const void
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
     *s = vec_extract(vsumf0, 0);
+
+#elif defined(__loongarch_asx)
+
+    __m256 accum = (__m256)__lasx_xvldi(0);
+    float accum1 = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        __m256i sumi = __lasx_xvldi(0);
+        int sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            __m256i q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)], 0);
+            q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], 1);
+            q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], 2);
+            q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], 3);
+
+            __m256i q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)], 0);
+            q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], 1);
+            q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], 2);
+            q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], 3);
+
+            qs += 8;
+            const __m256i q8b_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+            const __m256i q8b_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32;
+
+            const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
+            const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
+            const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1;
+            const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1;
+
+            __m256i tmp1, tmp5, tmp6;
+            tmp1 = __lasx_xvreplgr2vr_h(ls1);
+            tmp5 = __lasx_xvmulwev_w_h(dot1, tmp1);
+            tmp6 = __lasx_xvmulwod_w_h(dot1, tmp1);
+            const __m256i p1 = __lasx_xvadd_w(tmp5, tmp6);
+
+            tmp1 = __lasx_xvreplgr2vr_h(ls2);
+            tmp5 = __lasx_xvmulwev_w_h(dot2, tmp1);
+            tmp6 = __lasx_xvmulwod_w_h(dot2, tmp1);
+            const __m256i p2 = __lasx_xvadd_w(tmp5, tmp6);
+
+            sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p1, p2));
+            sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1
+                   + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2;
+        }
+
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), accum);
+        accum1 += d * sumi1;
+    }
+
+    *s = hsum_float_8(accum) + IQ1S_DELTA * accum1;
+
 #else
 
     float sumf = 0;
@@ -11510,17 +11316,10 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
 
     const int nb = n / QK_K;
 
-#if QK_K != 64
     iq1m_scale_t scale;
-#endif
 
 #if defined __ARM_NEON
-
-#if QK_K == 64
-    const int32x4_t mask  = vdupq_n_s32(0xf);
-#else
     const int32x4_t mask  = vdupq_n_s32(0x7);
-#endif
     const int32x4_t mone  = vdupq_n_s32(1);
     const int32x4_t mzero = vdupq_n_s32(0);
 
@@ -11544,9 +11343,7 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
         const uint8_t  * qh = x[i].qh;
         const uint16_t * sc = (const uint16_t *)x[i].scales;
 
-#if QK_K != 64
         scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-#endif
 
         int32x4_t sumi1 = mzero;
         int32x4_t sumi2 = mzero;
@@ -11575,11 +11372,8 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
             const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3]));
             const int32x4_t p34 = vpaddq_s32(p3, p4);
 
-#if QK_K == 64
-            int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12);
-#else
             int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9);
-#endif
+
             scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone);
 
             sumi1 = vmlaq_s32(sumi1, scales_4, p12);
@@ -11589,22 +11383,14 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
 
         }
 
-#if QK_K == 64
-        sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
-#else
         sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
-#endif
     }
 
     *s = sumf;
 
 #elif defined __AVX2__
 
-#if QK_K == 64
-    const __m256i mask = _mm256_set1_epi16(0xf);
-#else
     const __m256i mask = _mm256_set1_epi16(0x7);
-#endif
     const __m256i mone = _mm256_set1_epi16(1);
 
     __m256 accum1 = _mm256_setzero_ps();
@@ -11616,9 +11402,7 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
         const uint8_t  * qh = x[i].qh;
         const uint16_t * sc = (const uint16_t *)x[i].scales;
 
-#if QK_K != 64
         scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-#endif
 
         __m256i sumi1 = _mm256_setzero_si256();
         __m256i sumi2 = _mm256_setzero_si256();
@@ -11648,13 +11432,10 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
 
             const __m256i dot3 = mul_add_epi8(delta1, q8b_1);
             const __m256i dot4 = mul_add_epi8(delta2, q8b_2);
-#if QK_K == 64
-            __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >>  4), _mm_set1_epi16(sc[0] >> 0));
-            __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8));
-#else
+
             __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0));
             __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6));
-#endif
+
             scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone);
             scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone);
             const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
@@ -11668,14 +11449,96 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
             qs += 8; qh += 4;
         }
 
-#if QK_K == 64
-        const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d));
-#else
         const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16));
-#endif
+
         accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1);
         accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2);
+    }
+
+    *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2);
+
+#elif defined __AVX__
+    const __m128i mask = _mm_set1_epi16(0x7);
+    const __m128i mone = _mm_set1_epi16(1);
 
+    __m256 accum1 = _mm256_setzero_ps();
+    __m256 accum2 = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint8_t  * qh = x[i].qh;
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+
+        __m128i sumi1_0 = _mm_setzero_si128();
+        __m128i sumi1_1 = _mm_setzero_si128();
+        __m128i sumi2_0 = _mm_setzero_si128();
+        __m128i sumi2_1 = _mm_setzero_si128();
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m128i q1b_1_0 = _mm_set_epi64x(
+                    iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]);
+            const __m128i q1b_1_1 = _mm_set_epi64x(
+                    iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)]);
+            const __m128i q1b_2_0 = _mm_set_epi64x(
+                    iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]);
+            const __m128i q1b_2_1 = _mm_set_epi64x(
+                    iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)]);
+            const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+
+            const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0);
+            const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1);
+            const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0);
+            const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1);
+
+            const __m128i delta1_0 = _mm_set_epi64x(qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
+            const __m128i delta1_1 = _mm_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
+            const __m128i delta2_0 = _mm_set_epi64x(qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
+            const __m128i delta2_1 = _mm_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
+
+            const __m128i dot3_0 = mul_add_epi8_sse(delta1_0, q8b_1_0);
+            const __m128i dot3_1 = mul_add_epi8_sse(delta1_1, q8b_1_1);
+            const __m128i dot4_0 = mul_add_epi8_sse(delta2_0, q8b_2_0);
+            const __m128i dot4_1 = mul_add_epi8_sse(delta2_1, q8b_2_1);
+
+            __m128i scale1_0 = _mm_set1_epi16(sc[ib/2] >> 0);
+            __m128i scale1_1 = _mm_set1_epi16(sc[ib/2] >> 3);
+            __m128i scale2_0 = _mm_set1_epi16(sc[ib/2] >> 6);
+            __m128i scale2_1 = _mm_set1_epi16(sc[ib/2] >> 9);
+
+            scale1_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_0, mask), 1), mone);
+            scale1_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_1, mask), 1), mone);
+            scale2_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_0, mask), 1), mone);
+            scale2_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_1, mask), 1), mone);
+            const __m128i p1_0 = _mm_madd_epi16(dot1_0, scale1_0);
+            const __m128i p1_1 = _mm_madd_epi16(dot1_1, scale1_1);
+            const __m128i p2_0 = _mm_madd_epi16(dot2_0, scale2_0);
+            const __m128i p2_1 = _mm_madd_epi16(dot2_1, scale2_1);
+            const __m128i p3_0 = _mm_madd_epi16(dot3_0, scale1_0);
+            const __m128i p3_1 = _mm_madd_epi16(dot3_1, scale1_1);
+            const __m128i p4_0 = _mm_madd_epi16(dot4_0, scale2_0);
+            const __m128i p4_1 = _mm_madd_epi16(dot4_1, scale2_1);
+
+            sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0));
+            sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1));
+            sumi2_0 = _mm_add_epi32(sumi2_0, _mm_add_epi32(p3_0, p4_0));
+            sumi2_1 = _mm_add_epi32(sumi2_1, _mm_add_epi32(p3_1, p4_1));
+
+            qs += 8; qh += 4;
+        }
+
+        const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16));
+
+        accum1 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum1);
+        accum2 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi2_1, sumi2_0))), accum2);
     }
 
     *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2);
@@ -11692,9 +11555,7 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
         const uint8_t  * qh = x[i].qh;
         const uint16_t * sc = (const uint16_t *)x[i].scales;
 
-#if QK_K != 64
         scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-#endif
 
         int sumi1 = 0, sumi2 = 0;
         for (int ib = 0; ib < QK_K/32; ++ib) {
@@ -11714,24 +11575,17 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
                 sum1[l/2] += lsum1;
                 sum2[l/2] += lsum2*delta[l];
             }
-#if QK_K == 64
-            const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1;
-            const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1;
-#else
+
             const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
             const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
-#endif
+
             sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
             sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
             qs += 4;
             qh += 2;
         }
 
-#if QK_K == 64
-        sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
-#else
         sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
-#endif
     }
 
     *s = sumf;
@@ -11753,6 +11607,9 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
 
     const int nb = n / QK4_NL;
 
+    int ib = 0;
+    float sumf = 0;
+
 #if defined __ARM_NEON
     const int8x16_t values = vld1q_s8(kvalues_iq4nl);
     const uint8x16_t m4b = vdupq_n_u8(0x0f);
@@ -11761,16 +11618,14 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
     int8x16x4_t q8b;
     int32x4_t prod_1, prod_2;
 
-    float sumf = 0;
-
-    for (int ib = 0; ib < nb; ib += 2) {
+    for (; ib + 1 < nb; ib += 2) {
 
-        q4bits.val[0] = vld1q_u8(x[ib+0].qs);
-        q4bits.val[1] = vld1q_u8(x[ib+1].qs);
-        q8b.val[0]    = vld1q_s8(y[ib+0].qs);
-        q8b.val[1]    = vld1q_s8(y[ib+0].qs + 16);
-        q8b.val[2]    = vld1q_s8(y[ib+1].qs);
-        q8b.val[3]    = vld1q_s8(y[ib+1].qs + 16);
+        q4bits.val[0] = vld1q_u8(x[ib + 0].qs);
+        q4bits.val[1] = vld1q_u8(x[ib + 1].qs);
+        q8b.val[0]    = vld1q_s8(y[ib + 0].qs);
+        q8b.val[1]    = vld1q_s8(y[ib + 0].qs + 16);
+        q8b.val[2]    = vld1q_s8(y[ib + 1].qs);
+        q8b.val[3]    = vld1q_s8(y[ib + 1].qs + 16);
 
         q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
         q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
@@ -11781,12 +11636,10 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
 
         sumf +=
-            GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib+0].d) * vaddvq_s32(prod_1) +
-            GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib+1].d) * vaddvq_s32(prod_2);
+            GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) +
+            GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2);
     }
 
-    *s = sumf;
-
 #elif defined __AVX2__
 
     const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
@@ -11795,11 +11648,11 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
 
     __m256 accum1 = _mm256_setzero_ps();
     __m256 accum2 = _mm256_setzero_ps();
-    for (int ib = 0; ib < nb; ib += 2) {
-        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[0].qs);
-        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[1].qs);
-        const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[0].qs);
-        const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[1].qs);
+    for (; ib + 1 < nb; ib += 2) {
+        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs);
+        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs);
+        const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[ib + 0].qs);
+        const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[ib + 1].qs);
         const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
                                               _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
         const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
@@ -11808,19 +11661,52 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
         const __m256i p_1 = _mm256_madd_epi16(p16_1, mone);
         const __m256i p_2 = _mm256_madd_epi16(p16_2, mone);
-        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)),
+        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)),
                 _mm256_cvtepi32_ps(p_1), accum1);
-        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)),
+        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)),
                 _mm256_cvtepi32_ps(p_2), accum2);
-
-        y += 2;
-        x += 2;
     }
 
-    *s = hsum_float_8(_mm256_add_ps(accum1, accum2));
+    sumf = hsum_float_8(_mm256_add_ps(accum1, accum2));
+
+#elif defined __AVX__
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+    const __m128i mone = _mm_set1_epi16(1);
+
+    __m256 accum1 = _mm256_setzero_ps();
+    __m256 accum2 = _mm256_setzero_ps();
+    for (; ib + 1 < nb; ib += 2) {
+        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs);
+        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs);
+        const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs);
+        const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1);
+        const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs);
+        const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1);
+
+        const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b));
+        const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b));
+        const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b));
+        const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b));
+        const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0);
+        const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1);
+        const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0);
+        const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1);
+        const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, mone);
+        const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, mone);
+        const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, mone);
+        const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, mone);
+        accum1 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)),
+                _mm256_cvtepi32_ps(MM256_SET_M128I(p_1_1, p_1_0))), accum1);
+        accum2 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)),
+                _mm256_cvtepi32_ps(MM256_SET_M128I(p_2_1, p_2_0))), accum2);
+    }
+
+    sumf = hsum_float_8(_mm256_add_ps(accum1, accum2));
 
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector signed int v0 = vec_splats((int32_t)0);
     const vector unsigned char v4 = vec_splats((unsigned char)0x4);
 
     vector float vsumf0 = vec_splats(0.0f);
@@ -11829,7 +11715,7 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
     const vector signed char values = vec_xl( 0, kvalues_iq4nl);
 
 #pragma GCC unroll 4
-    for (int ib = 0; ib < nb; ++ib) {
+    for (; ib < nb; ++ib) {
         __builtin_prefetch(x[ib].qs, 0, 1);
         __builtin_prefetch(y[ib].qs, 0, 1);
 
@@ -11851,8 +11737,11 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0));
         vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1));
 
-        vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0));
-        vector signed int vsumi1 = vec_add(vec_unpackh(qv1), vec_unpackl(qv1));
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+
+        vsumi0 = vec_sum4s(qv0, vsumi0);
+        vsumi1 = vec_sum4s(qv1, vsumi1);
 
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
         vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
@@ -11863,10 +11752,39 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
-#else
-    float sumf = 0;
-    for (int ib = 0; ib < nb; ++ib) {
+    sumf = vec_extract(vsumf0, 0);
+
+#elif defined (__loongarch_asx)
+
+    const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0);
+    const __m128i m4b  = __lsx_vreplgr2vr_b(0x0f);
+    const __m256i mone = __lasx_xvreplgr2vr_h(1);
+
+    __m256 accum1 = (__m256)__lasx_xvldi(0);
+    __m256 accum2 = (__m256)__lasx_xvldi(0);
+    for (; ib + 1 < nb; ib += 2) {
+        const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[ib + 0].qs, 0);
+        const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[ib + 1].qs, 0);
+        const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[ib + 0].qs, 0);
+        const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[ib + 1].qs, 0);
+        const __m256i q4b_1 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b)),
+                                              lsx_shuffle_b(values128, __lsx_vand_v(q4bits_1, m4b)));
+        const __m256i q4b_2 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b)),
+                                              lsx_shuffle_b(values128, __lsx_vand_v(q4bits_2, m4b)));
+        const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
+        const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
+        const __m256i p_1 = lasx_madd_h(p16_1, mone);
+        const __m256i p_2 = lasx_madd_h(p16_2, mone);
+        accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)),
+                __lasx_xvffint_s_w(p_1), accum1);
+        accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)),
+                __lasx_xvffint_s_w(p_2), accum2);
+    }
+
+    sumf = hsum_float_8(__lasx_xvfadd_s(accum1, accum2));
+
+#endif
+    for (; ib < nb; ++ib) {
         const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d);
         int sumi1 = 0, sumi2 = 0;
         for (int j = 0; j < QK4_NL/2; ++j) {
@@ -11876,7 +11794,6 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         sumf += d * (sumi1 + sumi2);
     }
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
@@ -11886,9 +11803,6 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void *
     UNUSED(by);
     UNUSED(bs);
     assert(n % QK_K == 0);
-#if QK_K == 64
-    ggml_vec_dot_iq4_nl_q8_0(n, s, bs, vx, bx, vy, by, nrc);
-#else
 
     const block_iq4_xs * restrict x = vx;
     const block_q8_K   * restrict y = vy;
@@ -11975,8 +11889,57 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void *
 
     *s = hsum_float_8(accum);
 
+#elif defined __AVX__
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+
+    __m256 accum = _mm256_setzero_ps();
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        uint16_t sh = x[ibl].scales_h;
+        __m128i sumi1_0 = _mm_setzero_si128();
+        __m128i sumi1_1 = _mm_setzero_si128();
+        __m128i sumi2_0 = _mm_setzero_si128();
+        __m128i sumi2_1 = _mm_setzero_si128();
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)qs); qs += 16;
+            const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)qs); qs += 16;
+            const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16;
+            const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b));
+            const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b));
+            const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b));
+            const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b));
+            const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0);
+            const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1);
+            const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0);
+            const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1);
+            const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32;
+            const int16_t ls2 = ((x[ibl].scales_l[ib/2] >>  4) | ((sh << 2) & 0x30)) - 32;
+            sh >>= 4;
+            const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, _mm_set1_epi16(ls1));
+            const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, _mm_set1_epi16(ls1));
+            const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, _mm_set1_epi16(ls2));
+            const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, _mm_set1_epi16(ls2));
+            sumi1_0 = _mm_add_epi32(p_1_0, sumi1_0);
+            sumi1_1 = _mm_add_epi32(p_1_1, sumi1_1);
+            sumi2_0 = _mm_add_epi32(p_2_0, sumi2_0);
+            sumi2_1 = _mm_add_epi32(p_2_1, sumi2_1);
+        }
+        __m128i sumi12_0 = _mm_add_epi32(sumi1_0, sumi2_0);
+        __m128i sumi12_1 = _mm_add_epi32(sumi1_1, sumi2_1);
+        accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d),
+                _mm256_cvtepi32_ps(MM256_SET_M128I(sumi12_1, sumi12_0))), accum);
+    }
+
+    *s = hsum_float_8(accum);
+
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
+    const vector int v0 = vec_splats((int32_t)0);
     const vector unsigned char v4 = vec_splats((unsigned char)0x4);
 
     vector float vsumf0 = vec_splats(0.0f);
@@ -11992,14 +11955,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void *
         vector float vyd = vec_splats(y[ibl].d);
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed int vsumi0 = vec_splats((int32_t)0);
-        vector signed int vsumi1 = vec_splats((int32_t)0);
-        vector signed int vsumi2 = vec_splats((int32_t)0);
-        vector signed int vsumi3 = vec_splats((int32_t)0);
-        vector signed int vsumi4 = vec_splats((int32_t)0);
-        vector signed int vsumi5 = vec_splats((int32_t)0);
-        vector signed int vsumi6 = vec_splats((int32_t)0);
-        vector signed int vsumi7 = vec_splats((int32_t)0);
+        vector signed int vsumi0 = v0;
+        vector signed int vsumi1 = v0;
+        vector signed int vsumi2 = v0;
+        vector signed int vsumi3 = v0;
 
         uint16_t h = x[ibl].scales_h;
 
@@ -12044,21 +12003,12 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void *
             vector signed short vscales01 = vec_splats((int16_t)ls0);
             vector signed short vscales23 = vec_splats((int16_t)ls1);
 
-            vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0);
-            vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1);
-            vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2);
-            vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3);
-            vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4);
-            vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5);
-            vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6);
-            vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7);
+            vsumi0 = vec_msum(qv0, vscales01, vsumi0);
+            vsumi1 = vec_msum(qv1, vscales01, vsumi1);
+            vsumi2 = vec_msum(qv2, vscales23, vsumi2);
+            vsumi3 = vec_msum(qv3, vscales23, vsumi3);
         }
 
-        vsumi0 = vec_add(vsumi0, vsumi4);
-        vsumi1 = vec_add(vsumi1, vsumi5);
-        vsumi2 = vec_add(vsumi2, vsumi6);
-        vsumi3 = vec_add(vsumi3, vsumi7);
-
         vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0);
         vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1);
         vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2);
@@ -12074,6 +12024,80 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void *
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
     *s = vec_extract(vsumf0, 0);
+
+#elif defined(__loongarch_asx)
+
+    const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0);
+    const __m128i m4b  = __lsx_vreplgr2vr_b(0x0f);
+
+    __m256 accum = (__m256)__lasx_xvldi(0);
+    __m256i tmp1;
+    __m128i tmp0, tmp2, tmp3, tmp4, mask_8f, mask;
+
+    mask_8f = __lsx_vreplgr2vr_b(0x8f);
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        uint16_t sh = x[ibl].scales_h;
+        __m256i sumi1 = __lasx_xvldi(0);
+        __m256i sumi2 = __lasx_xvldi(0);
+        __m128i zero = __lsx_vldi(0);
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m128i q4bits_1 = __lsx_vld((const __m128i*)qs, 0);  qs += 16;
+            const __m128i q4bits_2 = __lsx_vld((const __m128i*)qs, 0);  qs += 16;
+            const __m256i q8b_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            const __m256i q8b_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32;
+            tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b), mask_8f);
+            tmp0 = __lsx_vori_b(tmp2, 0x10);
+            mask = __lsx_vsle_b(zero, tmp2);
+            tmp3 = __lsx_vand_v(tmp0, mask);
+            tmp3 = __lsx_vshuf_b(values128, zero, tmp3);
+
+            tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_1, m4b), mask_8f);
+            tmp0 = __lsx_vori_b(tmp2, 0x10);
+            mask = __lsx_vsle_b(zero, tmp2);
+            tmp4 = __lsx_vand_v(tmp0, mask);
+            tmp4 = __lsx_vshuf_b(values128, zero, tmp4);
+
+            const __m256i q4b_1 = lasx_insertf128(tmp3, tmp4);
+
+            tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b), mask_8f);
+            tmp0 = __lsx_vori_b(tmp2, 0x10);
+            mask = __lsx_vsle_b(zero, tmp2);
+            tmp3 = __lsx_vand_v(tmp0, mask);
+            tmp3 = __lsx_vshuf_b(values128, zero, tmp3);
+
+            tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_2, m4b), mask_8f);
+            tmp0 = __lsx_vori_b(tmp2, 0x10);
+            mask = __lsx_vsle_b(zero, tmp2);
+            tmp4 = __lsx_vand_v(tmp0, mask);
+            tmp4 = __lsx_vshuf_b(values128, zero, tmp4);
+
+            const __m256i q4b_2 = lasx_insertf128(tmp3, tmp4);
+
+            const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
+            const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
+            const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32;
+            const int16_t ls2 = ((x[ibl].scales_l[ib/2] >>  4) | ((sh << 2) & 0x30)) - 32;
+            sh >>= 4;
+            __m256i tmp5, tmp6;
+            tmp1 = __lasx_xvreplgr2vr_h(ls1);
+            tmp5 = __lasx_xvmulwev_w_h(p16_1, tmp1);
+            tmp6 = __lasx_xvmulwod_w_h(p16_1, tmp1);
+            const __m256i p_1 = __lasx_xvadd_w(tmp5, tmp6);
+            tmp1 = __lasx_xvreplgr2vr_h(ls2);
+            tmp5 = __lasx_xvmulwev_w_h(p16_2, tmp1);
+            tmp6 = __lasx_xvmulwod_w_h(p16_2, tmp1);
+            const __m256i p_2 = __lasx_xvadd_w(tmp5, tmp6);
+            sumi1 = __lasx_xvadd_w(p_1, sumi1);
+            sumi2 = __lasx_xvadd_w(p_2, sumi2);
+        }
+        accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d),
+                __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accum);
+    }
+
+    *s = hsum_float_8(accum);
+
 #else
     float sumf = 0;
     for (int ibl = 0; ibl < nb; ++ibl) {
@@ -12107,7 +12131,6 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void *
     }
     *s = sumf;
 #endif
-#endif
 }
 
 // ================================ IQ2 quantization =============================================
@@ -12681,9 +12704,9 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict
                     printf("Oops: found point %u not on grid:", u);
                     for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]);
                     printf("\n");
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
-                q2[2*ib+0] |= (grid_index << 8*k);
+                q2[2*ib+0] |= ((uint32_t) grid_index << 8*k);
                 q2[2*ib+1] |= (block_signs[k] << 7*k);
             }
             GGML_ASSERT(scale >= 0);
@@ -12860,7 +12883,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v
                     printf("Oops: found point %u not on grid:", u);
                     for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]);
                     printf("\n");
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
                 q2[2*ib+k] = grid_index | (block_signs[k] << 9);
             }
@@ -13303,7 +13326,7 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, v
                     printf("Oops: found point %u not on grid:", u);
                     for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]);
                     printf("\n");
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
                 if (grid_size == 256) {
                     q3[8*ib+k] = grid_index;
@@ -13357,10 +13380,10 @@ size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int64_t
 void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq3_xxs * restrict y = vy;
-    quantize_row_iq3_xxs_reference(x, y, k);
+    quantize_row_iq3_xxs_ref(x, y, k);
 }
 
-void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) {
+void quantize_row_iq3_xxs_ref(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_row_iq3_xxs_impl(256, x, y, k, NULL);
 }
@@ -13516,7 +13539,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo
                     printf("Oops: found point %u not on grid:", u);
                     for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]);
                     printf("\n");
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
                 qs[k] = grid_index & 255;
                 qh[(ib*bs4+k)/8] |= ((grid_index >> 8) << ((ib*bs4+k)%8));
@@ -13573,10 +13596,10 @@ size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int64_t n
 void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq3_s * restrict y = vy;
-    quantize_row_iq3_s_reference(x, y, k);
+    quantize_row_iq3_s_ref(x, y, k);
 }
 
-void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int64_t k) {
+void quantize_row_iq3_s_ref(const float * restrict x, block_iq3_s * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_iq3_s(x, y, 1, k, NULL);
 }
@@ -13588,7 +13611,7 @@ static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const u
         const float * restrict xval, const float * restrict weight, float * scale, int8_t * restrict L, int ngrid) {
     int num_neighbors = neighbours[0];
     GGML_ASSERT(num_neighbors > 0);
-    float best_score = 0;
+    float best_score = -FLT_MAX;
     int grid_index = -1;
     for (int j = 1; j <= num_neighbors; ++j) {
         const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
@@ -13786,7 +13809,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
                     sumw[j+1] = sumw[j] + weight[i];
                 }
             }
-            float best_score = 0, scale = max;
+            float best_score = -FLT_MIN, scale = max;
             int besti1 = -1, besti2 = -1, best_shift = 0;
             for (int i1 = 0; i1 <= block_size; ++i1) {
                 for (int i2 = i1; i2 <= block_size; ++i2) {
@@ -13925,10 +13948,6 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
     const float * xx;
 
     for (int ibl = 0; ibl < nbl; ++ibl) {
-
-#if QK_K == 64
-        y[ibl].d = GGML_FP32_TO_FP16(0.f);
-#endif
         memset(y[ibl].qs, 0, QK_K/8);
         memset(y[ibl].qh, 0, QK_K/16);
         memset(y[ibl].scales, 0, QK_K/32);
@@ -13966,7 +13985,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
                 idx[2*j] = j;
             }
             qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
-            float best_score = 0, scale = max;
+            float best_score = -FLT_MIN, scale = max;
             int besti1 = -1, besti2 = -1, best_k = -1;
             // 0: +, +
             // 1: +, -
@@ -14103,22 +14122,13 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
         }
 
         uint16_t * sc = (uint16_t *)y[ibl].scales;
-#if QK_K == 64
-        float d = max_scale/31;
-#else
         float d = max_scale/15;
-#endif
         float id = 1/d;
         float sumqx_f = 0, sumq2_f = 0;
         for (int ib = 0; ib < QK_K/block_size; ++ib) {
             int l = nearest_int(0.5f*(id*scales[ib+0]-1));
-#if QK_K == 64
-            l = MAX(0, MIN(15, l));
-            sc[ib/4] |= (l << 4*(ib%4));
-#else
             l = MAX(0, MIN(7, l));
             sc[ib/4] |= (l << 3*(ib%4));
-#endif
             y[ibl].qh[ib] |= masks[shifts[ib]];
             const float * xb = xbl + block_size*ib;
             if (quant_weights) {
@@ -14141,14 +14151,10 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
         }
         if (sumq2_f > 0) d = sumqx_f/sumq2_f;
         s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed.
-#if QK_K == 64
-        y[ibl].d = s.f16;
-#else
         sc[0] |= ((s.u16 & 0x000f) << 12);
         sc[1] |= ((s.u16 & 0x00f0) <<  8);
         sc[2] |= ((s.u16 & 0x0f00) <<  4);
         sc[3] |= ((s.u16 & 0xf000) <<  0);
-#endif
     }
 }
 
@@ -14331,15 +14337,12 @@ void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k
     }
 }
 
-void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int64_t k) {
+void quantize_row_iq4_nl_ref(const float * restrict x, block_iq4_nl * restrict y, int64_t k) {
     assert(k % QK4_NL == 0);
     quantize_row_iq4_nl(x, y, k);
 }
 
 size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
-#if QK_K == 64
-    return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights);
-#else
     GGML_ASSERT(n_per_row%QK_K == 0);
     int64_t nblock = n_per_row/QK_K;
     char * qrow = (char *)dst;
@@ -14357,16 +14360,15 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t
         qrow += nblock*sizeof(block_iq4_xs);
     }
     return nrow * nblock * sizeof(block_iq4_xs);
-#endif
 }
 
 void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq4_xs * restrict y = vy;
-    quantize_row_iq4_xs_reference(x, y, k);
+    quantize_row_iq4_xs_ref(x, y, k);
 }
 
-void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int64_t k) {
+void quantize_row_iq4_xs_ref(const float * restrict x, block_iq4_xs * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_iq4_xs(x, y, 1, k, NULL);
 }
@@ -14513,7 +14515,7 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy
                     printf("Oops: found point %u not on grid:", u);
                     for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]);
                     printf("\n");
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
                 const int i8 = 2*ib + k;
                 y[ibl].qs[i8] = grid_index & 255;
@@ -14553,7 +14555,7 @@ size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int64_t n
     return nrow * nblock * sizeof(block_iq2_s);
 }
 
-void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int64_t k) {
+void quantize_row_iq2_s_ref(const float * restrict x, block_iq2_s * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_iq2_s(x, y, 1, k, NULL);
 }
@@ -14561,7 +14563,7 @@ void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restri
 void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq2_s * restrict y = vy;
-    quantize_row_iq2_s_reference(x, y, k);
+    quantize_row_iq2_s_ref(x, y, k);
 }
 
 static bool validate_float(float f, size_t i) {
@@ -14616,6 +14618,16 @@ static bool validate_fp16(ggml_fp16_t f, size_t i) {
         } \
     }
 
+#define VALIDATE_ROW_DATA_DVEC_F16_IMPL(type, data, nb, nr) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        for (size_t j = 0; j < (nr); ++j) { \
+            if (!validate_fp16(q[i].d[j], i)) { \
+                return false; \
+            } \
+        } \
+    }
+
 bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
     if (type < 0 || type >= GGML_TYPE_COUNT) {
         fprintf(stderr, "%s: invalid type %d\n", __func__, type);
@@ -14623,7 +14635,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
     }
 
     if (nbytes % ggml_type_size(type) != 0) {
-        fprintf(stderr, "%s: invalid size %zu for type %d\n", __func__, nbytes, type);
+        fprintf(stderr, "%s: invalid size %zu for type %s (type size = %zu)\n", __func__, nbytes, ggml_type_name(type), ggml_type_size(type));
         return false;
     }
 
@@ -14769,19 +14781,11 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
             } break;
         case GGML_TYPE_Q4_K:
             {
-            #ifdef GGML_QKK_64
-                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]);
-            #else
                 VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin);
-            #endif
             } break;
         case GGML_TYPE_Q5_K:
             {
-            #ifdef GGML_QKK_64
-                VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb);
-            #else
                 VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin);
-            #endif
             } break;
         case GGML_TYPE_Q6_K:
             {
@@ -14804,18 +14808,12 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
             {
                 const block_iq1_m * q = (const block_iq1_m *) data;
                 for (size_t i = 0; i < nb; ++i) {
-                #if QK_K == 64
-                    if (!validate_fp16(q[i].d, i)) {
-                        return false;
-                    }
-                #else
                     iq1m_scale_t scale;
                     const uint16_t * sc = (const uint16_t *)q[i].scales;
                     scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
                     if (!validate_fp16(scale.f16, i)) {
                         return false;
                     }
-                #endif
                 }
             } break;
         case GGML_TYPE_IQ2_XXS:
@@ -14840,16 +14838,23 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
                 VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb);
             } break;
         case GGML_TYPE_IQ4_XS:
-        #if QK_K != 64
             {
                 VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb);
             } break;
-        #endif
-        // with QK_K == 64, iq4_xs is iq4_nl
         case GGML_TYPE_IQ4_NL:
             {
                 VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
             } break;
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+            {
+                VALIDATE_ROW_DATA_DVEC_F16_IMPL(block_q4_0x4, data, nbytes / sizeof(block_q4_0x4), 4);
+            } break;
+        case GGML_TYPE_Q4_0_8_8:
+            {
+                VALIDATE_ROW_DATA_DVEC_F16_IMPL(block_q4_0x8, data, nbytes / sizeof(block_q4_0x8), 8);
+            } break;
+
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
diff --git a/ggml-quants.h b/ggml/src/ggml-quants.h
similarity index 84%
rename from ggml-quants.h
rename to ggml/src/ggml-quants.h
index 4d436a8f06b3e..525d5ee30d8de 100644
--- a/ggml-quants.h
+++ b/ggml/src/ggml-quants.h
@@ -12,25 +12,25 @@ extern "C" {
 #endif
 
 // Quantization
-void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
-
-void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
-
-void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
-void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl  * GGML_RESTRICT y, int64_t k);
-void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs  * GGML_RESTRICT y, int64_t k);
-void quantize_row_iq3_s_reference  (const float * GGML_RESTRICT x, block_iq3_s   * GGML_RESTRICT y, int64_t k);
-void quantize_row_iq2_s_reference  (const float * GGML_RESTRICT x, block_iq2_s   * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_1_ref(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_0_ref(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
+
+void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_K_ref(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q6_K_ref(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_K_ref(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
+
+void quantize_row_iq3_xxs_ref(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_nl_ref (const float * GGML_RESTRICT x, block_iq4_nl  * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_xs_ref (const float * GGML_RESTRICT x, block_iq4_xs  * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq3_s_ref  (const float * GGML_RESTRICT x, block_iq3_s   * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq2_s_ref  (const float * GGML_RESTRICT x, block_iq2_s   * GGML_RESTRICT y, int64_t k);
 
 void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@@ -127,7 +127,10 @@ void iq2xs_free_impl(enum ggml_type type);
 void iq3xs_init_impl(int grid_size);
 void iq3xs_free_impl(int grid_size);
 
+#if defined(__ARM_FEATURE_SVE)
+extern int ggml_sve_cnt_b;
+#endif
+
 #ifdef __cplusplus
 }
 #endif
-
diff --git a/ggml-rpc.cpp b/ggml/src/ggml-rpc.cpp
similarity index 76%
rename from ggml-rpc.cpp
rename to ggml/src/ggml-rpc.cpp
index 4a9bfa52d87b1..7757615f5a24b 100644
--- a/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc.cpp
@@ -6,6 +6,7 @@
 #include <string>
 #include <vector>
 #include <memory>
+#include <mutex>
 #include <unordered_map>
 #include <unordered_set>
 #ifdef _WIN32
@@ -47,6 +48,7 @@ struct socket_t {
     sockfd_t fd;
     socket_t(sockfd_t fd) : fd(fd) {}
     ~socket_t() {
+        GGML_PRINT_DEBUG("[%s] closing socket %d\n", __func__, this->fd);
 #ifdef _WIN32
         closesocket(this->fd);
 #else
@@ -56,6 +58,7 @@ struct socket_t {
 };
 
 // ggml_tensor is serialized into rpc_tensor
+#pragma pack(push, 1)
 struct rpc_tensor {
     uint64_t id;
     uint32_t type;
@@ -70,7 +73,12 @@ struct rpc_tensor {
     uint64_t view_offs;
     uint64_t data;
     char name[GGML_MAX_NAME];
+
+    char padding[4];
 };
+#pragma pack(pop)
+
+static_assert(sizeof(rpc_tensor) % 8 == 0, "rpc_tensor size must be multiple of 8");
 
 // RPC commands
 enum rpc_cmd {
@@ -95,7 +103,7 @@ static ggml_guid_t ggml_backend_rpc_guid() {
 }
 
 struct ggml_backend_rpc_buffer_type_context {
-    std::shared_ptr<socket_t> sock;
+    std::string endpoint;
     std::string name;
     size_t alignment;
     size_t max_size;
@@ -104,8 +112,6 @@ struct ggml_backend_rpc_buffer_type_context {
 struct ggml_backend_rpc_context {
     std::string endpoint;
     std::string name;
-    std::shared_ptr<socket_t> sock;
-    ggml_backend_buffer_type_t buft;
 };
 
 struct ggml_backend_rpc_buffer_context {
@@ -191,6 +197,10 @@ static std::shared_ptr<socket_t> create_server_socket(const char * host, int por
         fprintf(stderr, "Failed to set SO_REUSEADDR\n");
         return nullptr;
     }
+    if (inet_addr(host) == INADDR_NONE) {
+        fprintf(stderr, "Invalid host address: %s\n", host);
+        return nullptr;
+    }
     struct sockaddr_in serv_addr;
     serv_addr.sin_family = AF_INET;
     serv_addr.sin_addr.s_addr = inet_addr(host);
@@ -229,14 +239,13 @@ static bool recv_data(sockfd_t sockfd, void * data, size_t size) {
     return true;
 }
 
-static bool parse_endpoint(const char * endpoint, std::string & host, int & port) {
-    std::string str(endpoint);
-    size_t pos = str.find(':');
+static bool parse_endpoint(const std::string & endpoint, std::string & host, int & port) {
+    size_t pos = endpoint.find(':');
     if (pos == std::string::npos) {
         return false;
     }
-    host = str.substr(0, pos);
-    port = std::stoi(str.substr(pos + 1));
+    host = endpoint.substr(0, pos);
+    port = std::stoi(endpoint.substr(pos + 1));
     return true;
 }
 
@@ -271,6 +280,44 @@ static bool send_rpc_cmd(const std::shared_ptr<socket_t> & sock, enum rpc_cmd cm
 
 // RPC client-side implementation
 
+static std::shared_ptr<socket_t> get_socket(const std::string & endpoint) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+    static std::unordered_map<std::string, std::weak_ptr<socket_t>> sockets;
+    static bool initialized = false;
+
+    auto it = sockets.find(endpoint);
+    if (it != sockets.end()) {
+        if (auto sock = it->second.lock()) {
+            return sock;
+        }
+    }
+    std::string host;
+    int port;
+    if (!parse_endpoint(endpoint, host, port)) {
+        return nullptr;
+    }
+#ifdef _WIN32
+    if (!initialized) {
+        WSADATA wsaData;
+        int res = WSAStartup(MAKEWORD(2, 2), &wsaData);
+        if (res != 0) {
+            return nullptr;
+        }
+        initialized = true;
+    }
+#else
+    UNUSED(initialized);
+#endif
+    auto sock = socket_connect(host.c_str(), port);
+    if (sock == nullptr) {
+        return nullptr;
+    }
+    GGML_PRINT_DEBUG("[%s] connected to %s, sockfd=%d\n", __func__, endpoint.c_str(), sock->fd);
+    sockets[endpoint] = sock;
+    return sock;
+}
+
 GGML_CALL static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) {
     ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
     return ctx->name.c_str();
@@ -340,23 +387,6 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) {
     return result;
 }
 
-static ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) {
-    ggml_tensor * result = ggml_new_tensor_4d(ctx, (ggml_type) tensor->type,
-        tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
-    for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) {
-        result->nb[i] = tensor->nb[i];
-    }
-    result->buffer = reinterpret_cast<ggml_backend_buffer_t>(tensor->buffer);
-    result->op = (ggml_op) tensor->op;
-    for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) {
-        result->op_params[i] = tensor->op_params[i];
-    }
-    result->flags = tensor->flags;
-    result->data = reinterpret_cast<void *>(tensor->data);
-    ggml_set_name(result, tensor->name);
-    return result;
-}
-
 GGML_CALL static void ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
     UNUSED(buffer);
     if (ggml_is_quantized(tensor->type)) {
@@ -457,7 +487,8 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer
     std::vector<uint8_t> input(input_size, 0);
     memcpy(input.data(), &size, sizeof(size));
     std::vector<uint8_t> output;
-    bool status = send_rpc_cmd(buft_ctx->sock, ALLOC_BUFFER, input, output);
+    auto sock = get_socket(buft_ctx->endpoint);
+    bool status = send_rpc_cmd(sock, ALLOC_BUFFER, input, output);
     GGML_ASSERT(status);
     GGML_ASSERT(output.size() == 2*sizeof(uint64_t));
     // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) |
@@ -465,13 +496,15 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer
     memcpy(&remote_ptr, output.data(), sizeof(remote_ptr));
     size_t remote_size;
     memcpy(&remote_size, output.data() + sizeof(uint64_t), sizeof(remote_size));
-
-    ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft,
-        ggml_backend_rpc_buffer_interface,
-        new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"},
-        remote_size);
-
-    return buffer;
+    if (remote_ptr != 0) {
+        ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft,
+            ggml_backend_rpc_buffer_interface,
+            new ggml_backend_rpc_buffer_context{sock, {}, remote_ptr, "RPC[" + std::string(buft_ctx->endpoint) + "]"},
+            remote_size);
+        return buffer;
+    } else {
+        return nullptr;
+    }
 }
 
 static size_t get_alignment(const std::shared_ptr<socket_t> & sock) {
@@ -515,26 +548,15 @@ GGML_CALL static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend
     return ggml_nbytes(tensor);
 }
 
-GGML_CALL static bool ggml_backend_rpc_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    if (!ggml_backend_is_rpc(backend)) {
-        return false;
-    }
-    ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context;
-    ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
-    return buft_ctx->sock == rpc_ctx->sock;
-}
-
 static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = {
     /* .get_name         = */ ggml_backend_rpc_buffer_type_name,
     /* .alloc_buffer     = */ ggml_backend_rpc_buffer_type_alloc_buffer,
     /* .get_alignment    = */ ggml_backend_rpc_buffer_type_get_alignment,
     /* .get_max_size     = */ ggml_backend_rpc_get_max_size,
     /* .get_alloc_size   = */ ggml_backend_rpc_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_rpc_buffer_type_supports_backend,
     /* .is_host          = */ NULL,
 };
 
-
 GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) {
     ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
 
@@ -543,16 +565,13 @@ GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) {
 
 GGML_CALL static void ggml_backend_rpc_free(ggml_backend_t backend) {
     ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
-    ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)rpc_ctx->buft->context;
-    delete buft_ctx;
-    delete rpc_ctx->buft;
     delete rpc_ctx;
     delete backend;
 }
 
 GGML_CALL static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) {
     ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context;
-    return ctx->buft;
+    return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str());
 }
 
 GGML_CALL static void ggml_backend_rpc_synchronize(ggml_backend_t backend) {
@@ -588,9 +607,8 @@ static void serialize_graph(const ggml_cgraph * cgraph, std::vector<uint8_t> & o
     int output_size = sizeof(uint32_t) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t) + n_tensors * sizeof(rpc_tensor);
     output.resize(output_size, 0);
     memcpy(output.data(), &n_nodes, sizeof(n_nodes));
-    uint64_t * out_nodes = (uint64_t *)(output.data() + sizeof(n_nodes));
     for (uint32_t i = 0; i < n_nodes; i++) {
-        out_nodes[i] = reinterpret_cast<uint64_t>(cgraph->nodes[i]);
+        memcpy(output.data() + sizeof(n_nodes) + i * sizeof(uint64_t), &cgraph->nodes[i], sizeof(uint64_t));
     }
     uint32_t * out_ntensors = (uint32_t *)(output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t));
     *out_ntensors = n_tensors;
@@ -603,7 +621,8 @@ GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t
     std::vector<uint8_t> input;
     serialize_graph(cgraph, input);
     std::vector<uint8_t> output;
-    bool status = send_rpc_cmd(rpc_ctx->sock, GRAPH_COMPUTE, input, output);
+    auto sock = get_socket(rpc_ctx->endpoint);
+    bool status = send_rpc_cmd(sock, GRAPH_COMPUTE, input, output);
     GGML_ASSERT(status);
     GGML_ASSERT(output.size() == 1);
     return (enum ggml_status)output[0];
@@ -612,8 +631,17 @@ GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t
 GGML_CALL static bool ggml_backend_rpc_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
     UNUSED(backend);
     UNUSED(op);
-    GGML_ASSERT(false && "not implemented");
-    return false;
+    //TODO: call the remote backend and cache the results
+    return true;
+}
+
+GGML_CALL static bool ggml_backend_rpc_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    if (buft->iface.get_name != ggml_backend_rpc_buffer_type_name) {
+        return false;
+    }
+    ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context;
+    ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
+    return buft_ctx->endpoint == rpc_ctx->endpoint;
 }
 
 static ggml_backend_i ggml_backend_rpc_interface = {
@@ -626,9 +654,11 @@ static ggml_backend_i ggml_backend_rpc_interface = {
     /* .synchronize             = */ ggml_backend_rpc_synchronize,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_rpc_graph_compute,
     /* .supports_op             = */ ggml_backend_rpc_supports_op,
+    /* .supports_buft           = */ ggml_backend_rpc_supports_buft,
     /* .offload_op              = */ NULL,
     /* .event_new               = */ NULL,
     /* .event_free              = */ NULL,
@@ -637,65 +667,48 @@ static ggml_backend_i ggml_backend_rpc_interface = {
     /* .event_synchronize       = */ NULL,
 };
 
-static std::unordered_map<std::string, ggml_backend_t> instances;
-
 GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) {
-    ggml_backend_t backend = ggml_backend_rpc_init(endpoint);
-    return backend != nullptr ? ggml_backend_rpc_get_default_buffer_type(backend) : nullptr;
-}
-
-GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) {
-    std::string endpoint_str(endpoint);
-    if (instances.find(endpoint_str) != instances.end()) {
-        return instances[endpoint_str];
-    }
-#ifdef _WIN32
-    {
-        WSADATA wsaData;
-        int res = WSAStartup(MAKEWORD(2, 2), &wsaData);
-        if (res != 0) {
-            return nullptr;
-        }
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+    // NOTE: buffer types are allocated and never freed; this is by design
+    static std::unordered_map<std::string, ggml_backend_buffer_type_t> buft_map;
+    auto it = buft_map.find(endpoint);
+    if (it != buft_map.end()) {
+        return it->second;
     }
-#endif
-    GGML_PRINT_DEBUG("Connecting to %s\n", endpoint);
-    std::string host;
-    int port;
-    if (!parse_endpoint(endpoint, host, port)) {
-        return nullptr;
-    }
-    auto sock = socket_connect(host.c_str(), port);
+    auto sock = get_socket(endpoint);
     if (sock == nullptr) {
         return nullptr;
     }
     size_t alignment = get_alignment(sock);
     size_t max_size = get_max_size(sock);
     ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context {
-        /* .sock   = */ sock,
-        /* .name   = */ "RPC" + std::to_string(sock->fd),
+        /* .endpoint  = */ endpoint,
+        /* .name      = */ "RPC[" + std::string(endpoint) + "]",
         /* .alignment = */ alignment,
-        /* .max_size = */ max_size
+        /* .max_size  = */ max_size
     };
 
     ggml_backend_buffer_type_t buft = new ggml_backend_buffer_type {
         /* .iface   = */ ggml_backend_rpc_buffer_type_interface,
         /* .context = */ buft_ctx
     };
+    buft_map[endpoint] = buft;
+    return buft;
+}
 
+GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) {
     ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context {
-        /* .endpoint = */ endpoint,
-        /* .name     = */ "RPC" + std::to_string(sock->fd),
-        /* .sock     = */ sock,
-        /* .buft     = */ buft
+        /* .endpoint  = */ endpoint,
+        /* .name      = */ "RPC[" + std::string(endpoint) + "]",
     };
 
-    instances[endpoint] = new ggml_backend {
+    ggml_backend_t backend = new ggml_backend {
         /* .guid      = */ ggml_backend_rpc_guid(),
         /* .interface = */ ggml_backend_rpc_interface,
         /* .context   = */ ctx
     };
-
-    return instances[endpoint];
+    return backend;
 }
 
 GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend) {
@@ -719,34 +732,72 @@ static void get_device_memory(const std::shared_ptr<socket_t> & sock, size_t * f
 }
 
 GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) {
-    ggml_backend_t backend = ggml_backend_rpc_init(endpoint);
-    if (backend == nullptr) {
+    auto sock = get_socket(endpoint);
+    if (sock == nullptr) {
         *free = 0;
         *total = 0;
         return;
     }
-    ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context;
-    get_device_memory(ctx->sock, free, total);
+    get_device_memory(sock, free, total);
 }
 
 // RPC server-side implementation
 
-static void rpc_alloc_buffer(ggml_backend_t backend, const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
+class rpc_server {
+public:
+    rpc_server(ggml_backend_t backend) : backend(backend) {}
+    ~rpc_server();
+
+    bool alloc_buffer(const std::vector<uint8_t> & input, std::vector<uint8_t> & output);
+    void get_alignment(std::vector<uint8_t> & output);
+    void get_max_size(std::vector<uint8_t> & output);
+    bool buffer_get_base(const std::vector<uint8_t> & input, std::vector<uint8_t> & output);
+    bool free_buffer(const std::vector<uint8_t> & input);
+    bool buffer_clear(const std::vector<uint8_t> & input);
+    bool set_tensor(const std::vector<uint8_t> & input);
+    bool get_tensor(const std::vector<uint8_t> & input, std::vector<uint8_t> & output);
+    bool copy_tensor(const std::vector<uint8_t> & input, std::vector<uint8_t> & output);
+    bool graph_compute(const std::vector<uint8_t> & input, std::vector<uint8_t> & output);
+
+private:
+    ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor);
+    ggml_tensor * create_node(uint64_t id,
+                              struct ggml_context * ctx,
+                              const std::unordered_map<uint64_t, const rpc_tensor*> & tensor_ptrs,
+                              std::unordered_map<uint64_t, struct ggml_tensor*> & tensor_map);
+
+
+    ggml_backend_t backend;
+    std::unordered_set<ggml_backend_buffer_t> buffers;
+};
+
+bool rpc_server::alloc_buffer(const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
     // input serialization format: | size (8 bytes) |
+    if (input.size() != sizeof(uint64_t)) {
+        return false;
+    }
     uint64_t size;
     memcpy(&size, input.data(), sizeof(size));
     ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend);
     ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size);
-    uint64_t remote_ptr = reinterpret_cast<uint64_t>(buffer);
-    uint64_t remote_size = buffer->size;
-    GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, size, remote_ptr, remote_size);
+    uint64_t remote_ptr = 0;
+    uint64_t remote_size = 0;
+    if (buffer != nullptr) {
+        remote_ptr = reinterpret_cast<uint64_t>(buffer);
+        remote_size = buffer->size;
+        GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, size, remote_ptr, remote_size);
+        buffers.insert(buffer);
+    } else {
+        GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, size);
+    }
     // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) |
     output.resize(2*sizeof(uint64_t), 0);
     memcpy(output.data(), &remote_ptr, sizeof(remote_ptr));
     memcpy(output.data() + sizeof(uint64_t), &remote_size, sizeof(remote_size));
+    return true;
 }
 
-static void rpc_get_alignment(ggml_backend_t backend, std::vector<uint8_t> & output) {
+void rpc_server::get_alignment(std::vector<uint8_t> & output) {
     ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend);
     size_t alignment = ggml_backend_buft_get_alignment(buft);
     GGML_PRINT_DEBUG("[%s] alignment: %lu\n", __func__, alignment);
@@ -755,7 +806,7 @@ static void rpc_get_alignment(ggml_backend_t backend, std::vector<uint8_t> & out
     memcpy(output.data(), &alignment, sizeof(alignment));
 }
 
-static void rpc_get_max_size(ggml_backend_t backend, std::vector<uint8_t> & output) {
+void rpc_server::get_max_size(std::vector<uint8_t> & output) {
     ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend);
     size_t max_size = ggml_backend_buft_get_max_size(buft);
     GGML_PRINT_DEBUG("[%s] max_size: %lu\n", __func__, max_size);
@@ -764,45 +815,102 @@ static void rpc_get_max_size(ggml_backend_t backend, std::vector<uint8_t> & outp
     memcpy(output.data(), &max_size, sizeof(max_size));
 }
 
-static void rpc_buffer_get_base(const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
+bool rpc_server::buffer_get_base(const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
     // input serialization format: | remote_ptr (8 bytes) |
+    if (input.size() != sizeof(uint64_t)) {
+        return false;
+    }
     uint64_t remote_ptr;
     memcpy(&remote_ptr, input.data(), sizeof(remote_ptr));
     GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr);
     ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(remote_ptr);
+    if (buffers.find(buffer) == buffers.end()) {
+        GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
+        return false;
+    }
     void * base = ggml_backend_buffer_get_base(buffer);
     // output serialization format: | base_ptr (8 bytes) |
     uint64_t base_ptr = reinterpret_cast<uint64_t>(base);
     output.resize(sizeof(uint64_t), 0);
     memcpy(output.data(), &base_ptr, sizeof(base_ptr));
+    return true;
 }
 
-static void rpc_free_buffer(const std::vector<uint8_t> & input) {
+bool rpc_server::free_buffer(const std::vector<uint8_t> & input) {
     // input serialization format: | remote_ptr (8 bytes) |
+    if (input.size() != sizeof(uint64_t)) {
+        return false;
+    }
     uint64_t remote_ptr;
     memcpy(&remote_ptr, input.data(), sizeof(remote_ptr));
     GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr);
     ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(remote_ptr);
+    if (buffers.find(buffer) == buffers.end()) {
+        GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
+        return false;
+    }
     ggml_backend_buffer_free(buffer);
+    buffers.erase(buffer);
+    return true;
 }
 
-static void rpc_buffer_clear(const std::vector<uint8_t> & input) {
+bool rpc_server::buffer_clear(const std::vector<uint8_t> & input) {
     // input serialization format: | remote_ptr (8 bytes) | value (1 byte) |
+    if (input.size() != sizeof(uint64_t) + sizeof(uint8_t)) {
+        return false;
+    }
     uint64_t remote_ptr;
     memcpy(&remote_ptr, input.data(), sizeof(remote_ptr));
     uint8_t value;
     memcpy(&value, input.data() + sizeof(uint64_t), sizeof(value));
     GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, remote_ptr, value);
     ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(remote_ptr);
+    if (buffers.find(buffer) == buffers.end()) {
+        GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
+        return false;
+    }
     ggml_backend_buffer_clear(buffer, value);
+    return true;
+}
+
+ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) {
+    ggml_tensor * result = ggml_new_tensor_4d(ctx, (ggml_type) tensor->type,
+        tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
+    for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) {
+        result->nb[i] = tensor->nb[i];
+    }
+    result->buffer = reinterpret_cast<ggml_backend_buffer_t>(tensor->buffer);
+    if (result->buffer && buffers.find(result->buffer) == buffers.end()) {
+        return nullptr;
+    }
+
+    // require that the tensor data does not go beyond the buffer end
+    uint64_t tensor_size = (uint64_t) ggml_nbytes(result);
+    uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(result->buffer);
+    uint64_t buffer_size = (uint64_t) ggml_backend_buffer_get_size(result->buffer);
+    GGML_ASSERT(tensor->data + tensor_size >= tensor->data); // check for overflow
+    GGML_ASSERT(tensor->data >= buffer_start && tensor->data + tensor_size <= buffer_start + buffer_size);
+
+    result->op = (ggml_op) tensor->op;
+    for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) {
+        result->op_params[i] = tensor->op_params[i];
+    }
+    result->flags = tensor->flags;
+    result->data = reinterpret_cast<void *>(tensor->data);
+    ggml_set_name(result, tensor->name);
+    return result;
 }
 
-static void rpc_set_tensor(const std::vector<uint8_t> & input) {
+
+bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
     // serialization format: | rpc_tensor | offset (8 bytes) | data (size bytes) |
+    if (input.size() < sizeof(rpc_tensor) + sizeof(uint64_t)) {
+        return false;
+    }
     const rpc_tensor * in_tensor = (const rpc_tensor *)input.data();
     uint64_t offset;
     memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset));
-    size_t size = input.size() - sizeof(rpc_tensor) - sizeof(offset);
+    const size_t size = input.size() - sizeof(rpc_tensor) - sizeof(offset);
 
     struct ggml_init_params params {
         /*.mem_size   =*/ ggml_tensor_overhead(),
@@ -811,14 +919,34 @@ static void rpc_set_tensor(const std::vector<uint8_t> & input) {
     };
     struct ggml_context * ctx = ggml_init(params);
     ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor);
+    if (tensor == nullptr) {
+        GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__);
+        ggml_free(ctx);
+        return false;
+    }
     GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size);
+
+    // sanitize tensor->data
+    {
+        const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer);
+        const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer);
+
+        if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) {
+            GGML_ABORT("[%s] tensor->data out of bounds\n", __func__);
+        }
+    }
+
     const void * data = input.data() + sizeof(rpc_tensor) + sizeof(offset);
     ggml_backend_tensor_set(tensor, data, offset, size);
     ggml_free(ctx);
+    return true;
 }
 
-static void rpc_get_tensor(const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
+bool rpc_server::get_tensor(const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
     // serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) |
+    if (input.size() != sizeof(rpc_tensor) + 2*sizeof(uint64_t)) {
+        return false;
+    }
     const rpc_tensor * in_tensor = (const rpc_tensor *)input.data();
     uint64_t offset;
     memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset));
@@ -832,15 +960,35 @@ static void rpc_get_tensor(const std::vector<uint8_t> & input, std::vector<uint8
     };
     struct ggml_context * ctx = ggml_init(params);
     ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor);
+    if (tensor == nullptr) {
+        GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__);
+        ggml_free(ctx);
+        return false;
+    }
     GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, offset, size);
+
+    // sanitize tensor->data
+    {
+        const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer);
+        const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer);
+
+        if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) {
+            GGML_ABORT("[%s] tensor->data out of bounds\n", __func__);
+        }
+    }
+
     // output serialization format: | data (size bytes) |
     output.resize(size, 0);
     ggml_backend_tensor_get(tensor, output.data(), offset, size);
     ggml_free(ctx);
+    return true;
 }
 
-static void rpc_copy_tensor(const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
+bool rpc_server::copy_tensor(const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
     // serialization format: | rpc_tensor src | rpc_tensor dst |
+    if (input.size() != 2*sizeof(rpc_tensor)) {
+        return false;
+    }
     const rpc_tensor * rpc_src = (const rpc_tensor *)input.data();
     const rpc_tensor * rpc_dst = (const rpc_tensor *)(input.data() + sizeof(rpc_src));
 
@@ -852,18 +1000,24 @@ static void rpc_copy_tensor(const std::vector<uint8_t> & input, std::vector<uint
     struct ggml_context * ctx = ggml_init(params);
     ggml_tensor * src = deserialize_tensor(ctx, rpc_src);
     ggml_tensor * dst = deserialize_tensor(ctx, rpc_dst);
+    if (src == nullptr || dst == nullptr) {
+        GGML_PRINT_DEBUG("[%s] error deserializing tensors\n", __func__);
+        ggml_free(ctx);
+        return false;
+    }
     GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n", __func__, (void*)src->buffer, (void*)dst->buffer);
     bool result = ggml_backend_buffer_copy_tensor(src, dst);
     // output serialization format: | result (1 byte) |
     output.resize(1, 0);
     output[0] = result;
     ggml_free(ctx);
+    return true;
 }
 
-static struct ggml_tensor * create_node(uint64_t id,
-                                        struct ggml_context * ctx,
-                                        const std::unordered_map<uint64_t, const rpc_tensor*> & tensor_ptrs,
-                                        std::unordered_map<uint64_t, struct ggml_tensor*> & tensor_map) {
+ggml_tensor * rpc_server::create_node(uint64_t id,
+                                      struct ggml_context * ctx,
+                                      const std::unordered_map<uint64_t, const rpc_tensor*> & tensor_ptrs,
+                                      std::unordered_map<uint64_t, struct ggml_tensor*> & tensor_map) {
     if (id == 0) {
         return nullptr;
     }
@@ -872,6 +1026,9 @@ static struct ggml_tensor * create_node(uint64_t id,
     }
     const rpc_tensor * tensor = tensor_ptrs.at(id);
     struct ggml_tensor * result = deserialize_tensor(ctx, tensor);
+    if (result == nullptr) {
+        return nullptr;
+    }
     tensor_map[id] = result;
     for (int i = 0; i < GGML_MAX_SRC; i++) {
         result->src[i] = create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map);
@@ -881,14 +1038,23 @@ static struct ggml_tensor * create_node(uint64_t id,
     return result;
 }
 
-static void rpc_graph_compute(ggml_backend_t backend, const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
+bool rpc_server::graph_compute(const std::vector<uint8_t> & input, std::vector<uint8_t> & output) {
     // serialization format:
     // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) |
+    if (input.size() < sizeof(uint32_t)) {
+        return false;
+    }
     uint32_t n_nodes;
     memcpy(&n_nodes, input.data(), sizeof(n_nodes));
+    if (input.size() < sizeof(uint32_t) + n_nodes*sizeof(uint64_t) + sizeof(uint32_t)) {
+        return false;
+    }
     const uint64_t * nodes = (const uint64_t *)(input.data() + sizeof(n_nodes));
     uint32_t n_tensors;
     memcpy(&n_tensors, input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t), sizeof(n_tensors));
+    if (input.size() < sizeof(uint32_t) + n_nodes*sizeof(uint64_t) + sizeof(uint32_t) + n_tensors*sizeof(rpc_tensor)) {
+        return false;
+    }
     const rpc_tensor * tensors = (const rpc_tensor *)(input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t) + sizeof(n_tensors));
     GGML_PRINT_DEBUG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors);
 
@@ -907,16 +1073,26 @@ static void rpc_graph_compute(ggml_backend_t backend, const std::vector<uint8_t>
     }
     std::unordered_map<uint64_t, ggml_tensor*> tensor_map;
     for (uint32_t i = 0; i < n_nodes; i++) {
-        graph->nodes[i] = create_node(nodes[i], ctx, tensor_ptrs, tensor_map);
+        int64_t id;
+        memcpy(&id, &nodes[i], sizeof(id));
+        graph->nodes[i] = create_node(id, ctx, tensor_ptrs, tensor_map);
     }
     ggml_status status = ggml_backend_graph_compute(backend, graph);
     // output serialization format: | status (1 byte) |
     output.resize(1, 0);
     output[0] = status;
     ggml_free(ctx);
+    return true;
+}
+
+rpc_server::~rpc_server() {
+    for (auto buffer : buffers) {
+        ggml_backend_buffer_free(buffer);
+    }
 }
 
 static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t free_mem, size_t total_mem) {
+    rpc_server server(backend);
     while (true) {
         uint8_t cmd;
         if (!recv_data(sockfd, &cmd, 1)) {
@@ -932,45 +1108,46 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre
         if (!recv_data(sockfd, input.data(), input_size)) {
             break;
         }
+        bool ok = true;
         switch (cmd) {
             case ALLOC_BUFFER: {
-                rpc_alloc_buffer(backend, input, output);
+                ok = server.alloc_buffer(input, output);
                 break;
             }
             case GET_ALIGNMENT: {
-                rpc_get_alignment(backend, output);
+                server.get_alignment(output);
                 break;
             }
             case GET_MAX_SIZE: {
-                rpc_get_max_size(backend, output);
+                server.get_max_size(output);
                 break;
             }
             case BUFFER_GET_BASE: {
-                rpc_buffer_get_base(input, output);
+                ok = server.buffer_get_base(input, output);
                 break;
             }
             case FREE_BUFFER: {
-                rpc_free_buffer(input);
+                ok = server.free_buffer(input);
                 break;
             }
             case BUFFER_CLEAR: {
-                rpc_buffer_clear(input);
+                ok = server.buffer_clear(input);
                 break;
             }
             case SET_TENSOR: {
-                rpc_set_tensor(input);
+                ok = server.set_tensor(input);
                 break;
             }
             case GET_TENSOR: {
-                rpc_get_tensor(input, output);
+                ok = server.get_tensor(input, output);
                 break;
             }
             case COPY_TENSOR: {
-                rpc_copy_tensor(input, output);
+                ok = server.copy_tensor(input, output);
                 break;
             }
             case GRAPH_COMPUTE: {
-                rpc_graph_compute(backend, input, output);
+                ok = server.graph_compute(input, output);
                 break;
             }
             case GET_DEVICE_MEMORY: {
@@ -982,9 +1159,12 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre
             }
             default: {
                 fprintf(stderr, "Unknown command: %d\n", cmd);
-                return;
+                ok = false;
             }
         }
+        if (!ok) {
+            break;
+        }
         uint64_t output_size = output.size();
         if (!send_data(sockfd, &output_size, sizeof(output_size))) {
             break;
diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp
new file mode 100644
index 0000000000000..d8eb86c2c1862
--- /dev/null
+++ b/ggml/src/ggml-sycl.cpp
@@ -0,0 +1,5330 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include <algorithm>
+#include <assert.h>
+#include <atomic>
+#include <cinttypes>
+#include <cstddef>
+#include <cstdint>
+#include <cstdlib>
+#include <float.h>
+#include <limits>
+#include <stdint.h>
+#include <stdio.h>
+#include <vector>
+#include <cmath>
+#include <iostream>
+#include <fstream>
+#include <stdio.h>
+#include <stdlib.h>
+#include <regex>
+
+#include <sycl/sycl.hpp>
+#include <sycl/half_type.hpp>
+
+#include "ggml-sycl.h"
+#include "ggml.h"
+#include "ggml-backend-impl.h"
+
+#include "ggml-sycl/backend.hpp"
+#include "ggml-sycl/presets.hpp"
+
+bool   ggml_sycl_loaded(void);
+void   ggml_sycl_free_data(struct ggml_tensor * tensor);
+void   ggml_sycl_copy_to_device(struct ggml_tensor * tensor);
+void   ggml_sycl_set_main_device(int main_device);
+void   ggml_sycl_set_mul_mat_q(bool mul_mat_q);
+void   ggml_sycl_get_device_description(int device, char * description, size_t description_size);
+bool   ggml_backend_is_sycl(ggml_backend_t backend);
+int    ggml_backend_sycl_get_device(ggml_backend_t backend);
+static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer);
+static inline int get_sycl_env(const char *env_name, int default_val);
+
+
+void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst,
+                    const void *ptr_src, size_t size) {
+    char *host_buf = (char *)malloc(size);
+    q_src.memcpy(host_buf, (const char *)ptr_src, size).wait();
+    q_dst.memcpy((char *)ptr_dst, host_buf, size).wait();
+    free(host_buf);
+}
+
+typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
+typedef void (*ggml_sycl_func_t)(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+typedef void (*ggml_sycl_op_mul_mat_t)(
+    ggml_backend_sycl_context & ctx,
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const queue_ptr &stream);
+typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                       const ggml_tensor *src1,
+                                       ggml_tensor *dst, const float *src0_dd,
+                                       const float *src1_dd, float *dst_dd,
+                                       const queue_ptr &main_stream);
+
+static __dpct_inline__ float op_repeat(const float a, const float b) {
+    return b;
+    GGML_UNUSED(a);
+}
+
+static __dpct_inline__ float op_add(const float a, const float b) {
+    return a + b;
+}
+
+static __dpct_inline__ float op_mul(const float a, const float b) {
+    return a * b;
+}
+
+static __dpct_inline__ float op_div(const float a, const float b) {
+    return a / b;
+}
+
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s10,*/ int s11, int s12, int s13,
+        const sycl::nd_item<3> &item_ct1) {
+    const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+    const int i1 = (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1));
+    const int i2 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) +
+                    item_ct1.get_local_id(0)) /
+                   ne3;
+    const int i3 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) +
+                    item_ct1.get_local_id(0)) %
+                   ne3;
+
+    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
+
+    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
+    const size_t i_dst  = i_src0;
+
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
+    dst_t * dst_row = dst + i_dst;
+
+    for (int i0 = i0s; i0 < ne0;
+         i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) {
+        const int i10 = i0 % ne10;
+        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+    }
+}
+
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s10,*/ int s11, int s12, int s13,
+        const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    const int i3 = i/(ne2*ne1*ne0);
+    const int i2 = (i/(ne1*ne0)) % ne2;
+    const int i1 = (i/ne0) % ne1;
+    const int i0 = i % ne0;
+
+    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
+
+    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
+    const size_t i_dst  = i_src0;
+
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
+    dst_t * dst_row = dst + i_dst;
+
+    const int i10 = i0 % ne10;
+    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+}
+
+static void acc_f32(const float * x, const float * y, float * dst, const int ne,
+    const int ne10, const int ne11, const int ne12,
+    const int nb1, const int nb2, int offset, const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+    if (i >= ne) {
+        return;
+    }
+    int src1_idx = i - offset;
+    int oz = src1_idx / nb2;
+    int oy = (src1_idx - (oz * nb2)) / nb1;
+    int ox = src1_idx % nb1;
+    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
+        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
+    } else {
+        dst[i] = x[i];
+    }
+}
+
+static void gelu_f32(const float * x, float * dst, const int k,
+                     const sycl::nd_item<3> &item_ct1) {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    float xi = x[i];
+    dst[i] = 0.5f * xi *
+             (1.0f +
+              sycl::tanh(SQRT_2_OVER_PI * xi * (1.0f + GELU_COEF_A * xi * xi)));
+}
+
+static void silu_f32(const float * x, float * dst, const int k,
+                     const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] / (1.0f + sycl::native::exp(-x[i]));
+}
+
+static void gelu_quick_f32(const float *x, float *dst, int k,
+                           const sycl::nd_item<3> &item_ct1) {
+    const float GELU_QUICK_COEF = -1.702f;
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * (1.0f / (1.0f + sycl::native::exp(GELU_QUICK_COEF * x[i])));
+}
+
+static void tanh_f32(const float *x, float *dst, int k,
+                     const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::tanh((float)(x[i]));
+}
+
+static void relu_f32(const float * x, float * dst, const int k,
+                     const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::fmax((float)(x[i]), (float)0);
+}
+
+static void hardsigmoid_f32(const float * x, float * dst, const int k,
+                            const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static void hardswish_f32(const float * x, float * dst, const int k,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope,
+                           const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::fmax((float)(x[i]), (float)0) +
+             sycl::fmin((float)(x[i]), 0.0f) * negative_slope;
+}
+
+static void sqr_f32(const float * x, float * dst, const int k,
+                    const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * x[i];
+}
+
+static void upscale_f32(const float  *x, float *dst, const int nb00, const int nb01,
+                        const int nb02, const int nb03, const int ne10, const int ne11,
+                        const int ne12, const int ne13, const float sf0, const float sf1,
+                        const float sf2, const float sf3, const sycl::nd_item<1> &item_ct1) {
+    int index = item_ct1.get_local_id(0) +
+               item_ct1.get_group(0) * item_ct1.get_local_range(0);
+    if (index >= ne10 * ne11 * ne12 * ne13) {
+        return;
+    }
+    // operation
+    int i10 = index % ne10;
+    int i11 = (index / ne10) % ne11;
+    int i12 = (index / (ne10 * ne11)) % ne12;
+    int i13 = (index / (ne10 * ne11 * ne12)) % ne13;
+
+    int i00 = i10 / sf0;
+    int i01 = i11 / sf1;
+    int i02 = i12 / sf2;
+    int i03 = i13 / sf3;
+
+    dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00);
+}
+
+static void pad_f32(const float  *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02,
+                    const sycl::nd_item<3> &item_ct1) {
+    int nidx = item_ct1.get_local_id(2) +
+               item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (nidx >= ne0) {
+        return;
+    }
+
+    // operation
+    int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+    if (nidx < ne00 && item_ct1.get_group(1) < ne01 &&
+        item_ct1.get_group(0) < ne02) {
+        int offset_src = nidx + item_ct1.get_group(1) * ne00 +
+                         item_ct1.get_group(0) * ne00 * ne01;
+            dst[offset_dst] = x[offset_src];
+    } else {
+        dst[offset_dst] = 0.0f;
+    }
+}
+
+template<int QUANT_BLOCK_TILE>
+static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int ix = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2)) * QUANT_BLOCK_TILE;
+
+    if (ix >= kx_padded) {
+        return;
+    }
+
+    const int iy = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                   item_ct1.get_local_id(1);
+
+    const int i_padded = iy*kx_padded + ix;
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int ib = i_padded / QK8_1; // block index
+    const int iqs = i_padded % QK8_1; // quant index
+    typedef  sycl::vec<float, QUANT_BLOCK_TILE> TC;
+    typedef  sycl::vec<int8_t, QUANT_BLOCK_TILE> TQ;
+    TC zeros;
+    TQ qzeros;
+#pragma unroll
+    for (int i = 0; i < QUANT_BLOCK_TILE; i++)
+    {
+        zeros[i] = 0.f;
+        qzeros[i] = 0;
+    }
+    const TC xi = ix < kx ? *(TC *)&x[iy * kx + ix] : zeros;
+    float sum = xi[0];
+    float amax = sycl::fabs(xi[0]);
+#pragma unroll
+    for (int i = 1; i < QUANT_BLOCK_TILE; i++)
+    {
+        sum += xi[i];
+        amax = sycl::fmax(sycl::fabs(xi[i]), amax);
+    }
+    sum = warp_reduce_sum(sum, item_ct1);
+    amax = warp_reduce_max(amax, item_ct1);
+
+    const float d = amax / 127;
+    TQ q = qzeros;
+    if (amax != 0.0f)
+    {
+#pragma unroll
+        for (int i = 0; i < QUANT_BLOCK_TILE; i++) {
+            q[i] = sycl::round(xi[i] / d);
+        }
+    }
+
+    *(TQ *)&y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    reinterpret_cast<sycl::half &>(y[ib].ds.x()) = d;
+    reinterpret_cast<sycl::half &>(y[ib].ds.y()) = sum;
+}
+
+template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void k_get_rows(
+            const void * src0, const int32_t * src1, dst_t * dst,
+            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
+            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
+            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
+            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
+            size_t s10, size_t s11, size_t s12,
+            const sycl::nd_item<3> &item_ct1/*, size_t s13*/) {
+
+    const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) +
+                     item_ct1.get_local_id(2)) *
+                    2;
+    const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1);
+    const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
+                     item_ct1.get_local_id(0)) /
+                    ne12;
+    const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
+                     item_ct1.get_local_id(0)) %
+                    ne12;
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+    const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
+
+    const int ib = i00/qk; // block index
+    const int iqs = (i00%qk)/qr; // quant index
+    const int iybs = i00 - i00%qk; // dst block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(src0_row, ib, iqs, v);
+
+    dst_row[iybs + iqs + 0] = v.x();
+    dst_row[iybs + iqs + y_offset] = v.y();
+}
+
+template<typename src0_t, typename dst_t>
+static void k_get_rows_float(
+            const src0_t * src0, const int32_t * src1, dst_t * dst,
+            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
+            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
+            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
+            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
+            size_t s10, size_t s11, size_t s12,
+            const sycl::nd_item<3> &item_ct1/*, size_t s13*/) {
+
+    const int i00 = item_ct1.get_group(2) * item_ct1.get_local_range(2) +
+                    item_ct1.get_local_id(2);
+    const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1);
+    const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
+                     item_ct1.get_local_id(0)) /
+                    ne12;
+    const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
+                     item_ct1.get_local_id(0)) %
+                    ne12;
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+    const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
+
+    dst_row[i00] = src0_row[i00];
+}
+
+static void mul_mat_p021_f16_f32(
+    const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y,
+    const sycl::nd_item<3> &item_ct1) {
+
+    const sycl::half *x = (const sycl::half *)vx;
+
+    const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                      item_ct1.get_local_id(1);
+    const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) +
+                        item_ct1.get_local_id(0);
+    const int channel_x = channel / (nchannels_y / nchannels_x);
+
+    const int nrows_y = ncols_x;
+    const int nrows_dst = nrows_x;
+    const int row_dst = row_x;
+
+    float tmp = 0.0f;
+
+    for (int col_x0 = 0; col_x0 < ncols_x;
+         col_x0 += item_ct1.get_local_range(2)) {
+        const int col_x = col_x0 + item_ct1.get_local_id(2);
+
+        if (col_x >= ncols_x) {
+            break;
+        }
+
+        // x is transposed and permuted
+        const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x;
+        const float xi =
+            sycl::vec<sycl::half, 1>(x[ix])
+                .convert<float, sycl::rounding_mode::automatic>()[0];
+
+        const int row_y = col_x;
+
+
+        // y is not transposed but permuted
+        const int iy = channel*nrows_y + row_y;
+
+        tmp += xi * y[iy];
+    }
+
+    // dst is not transposed and not permuted
+    const int idst = channel*nrows_dst + row_dst;
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[idst] = tmp;
+    }
+}
+
+static void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
+    const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x,
+    const int row_stride_x, const int channel_stride_x, const int channel_x_divisor,
+    const sycl::nd_item<3> &item_ct1) {
+
+    const sycl::half *x = (const sycl::half *)vx;
+
+    const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                      item_ct1.get_local_id(1);
+    const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) +
+                        item_ct1.get_local_id(0);
+    const int channel_x = channel / channel_x_divisor;
+
+    const int nrows_y   = ncols_x;
+    const int nrows_dst = nrows_x;
+    const int row_dst   = row_x;
+
+    const int idst = channel*nrows_dst + row_dst;
+
+    float tmp = 0.0f;
+
+    for (int col_x0 = 0; col_x0 < ncols_x;
+         col_x0 += item_ct1.get_local_range(2)) {
+        const int col_x = col_x0 + item_ct1.get_local_id(2);
+
+        if (col_x >= ncols_x) {
+            break;
+        }
+
+        const int row_y = col_x;
+
+        const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x;
+        const int iy = channel*nrows_y + row_y;
+
+        const float xi =
+            sycl::vec<sycl::half, 1>(x[ix])
+                .convert<float, sycl::rounding_mode::automatic>()[0];
+
+        tmp += xi * y[iy];
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[idst] = tmp;
+    }
+}
+
+static void cpy_1_f32_f32(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_f32_f16(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    sycl::half *dsti = (sycl::half *)cdsti;
+
+    *dsti = sycl::vec<float, 1>(*xi)
+                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+}
+
+static void cpy_1_f16_f16(const char * cxi, char * cdsti) {
+    const sycl::half *xi = (const sycl::half *)cxi;
+    sycl::half *dsti = (sycl::half *)cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_f16_f32(const char * cxi, char * cdsti) {
+    const sycl::half *xi = (const sycl::half *)cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_i16_i16(const char * cxi, char * cdsti) {
+    const int16_t *xi = (const int16_t *)cxi;
+    int16_t *dsti = (int16_t *)cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_i32_i32(const char * cxi, char * cdsti) {
+    const int32_t *xi = (const int32_t *)cxi;
+    int32_t *dsti = (int32_t *)cdsti;
+
+    *dsti = *xi;
+}
+
+template <cpy_kernel_t cpy_1>
+static void cpy_f32_f16(const char * cx, char * cdst, const int ne,
+                        const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                        const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                        const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= ne) {
+        return;
+    }
+
+    // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
+    // then combine those indices with the corresponding byte offsets to get the total offsets
+    const int i03 = i/(ne00 * ne01 * ne02);
+    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+    const int i13 = i/(ne10 * ne11 * ne12);
+    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
+
+    cpy_1(cx + x_offset, cdst + dst_offset);
+}
+
+static void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q8_0 * dsti = (block_q8_0 *) cdsti;
+
+    float amax = 0.0f; // absolute max
+
+    for (int j = 0; j < QK8_0; j++) {
+        const float v = xi[j];
+        amax = sycl::fmax(amax, sycl::fabs((float)v));
+    }
+
+    const float d = amax / ((1 << 7) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK8_0; ++j) {
+        const float x0 = xi[j]*id;
+
+        dsti->qs[j] = sycl::round((float)x0);
+    }
+}
+
+static void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q4_0 * dsti = (block_q4_0 *) cdsti;
+
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK4_0; ++j) {
+        const float v = xi[j];
+        if (amax < sycl::fabs((float)v)) {
+            amax = sycl::fabs((float)v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -8;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK4_0/2; ++j) {
+        const float x0 = xi[0       + j]*id;
+        const float x1 = xi[QK4_0/2 + j]*id;
+
+        const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 8.5f));
+        const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 8.5f));
+
+        dsti->qs[j]  = xi0;
+        dsti->qs[j] |= xi1 << 4;
+    }
+}
+
+static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q4_1 * dsti = (block_q4_1 *) cdsti;
+
+    float vmin = FLT_MAX;
+    float vmax = -FLT_MAX;
+
+    for (int j = 0; j < QK4_1; ++j) {
+        const float v = xi[j];
+
+        if (v < vmin) vmin = v;
+        if (v > vmax) vmax = v;
+    }
+
+    const float d  = (vmax - vmin) / ((1 << 4) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->dm.x() = d;
+    dsti->dm.y() = vmin;
+
+    for (int j = 0; j < QK4_1/2; ++j) {
+        const float x0 = (xi[0       + j] - vmin)*id;
+        const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
+
+        const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 0.5f));
+        const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 0.5f));
+
+        dsti->qs[j]  = xi0;
+        dsti->qs[j] |= xi1 << 4;
+    }
+}
+
+template <cpy_kernel_t cpy_blck, int qk>
+static void cpy_f32_q(const char * cx, char * cdst, const int ne,
+                      const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                      const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                      const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
+    const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                   item_ct1.get_local_id(2)) *
+                  qk;
+
+    if (i >= ne) {
+        return;
+    }
+
+    const int i03 = i/(ne00 * ne01 * ne02);
+    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+    const int i13 = i/(ne10 * ne11 * ne12);
+    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
+
+    cpy_blck(cx + x_offset, cdst + dst_offset);
+}
+
+static void k_sum_rows_f32(const float * x, float * dst, const int ncols,
+                           const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(1);
+    const int col = item_ct1.get_local_id(2);
+
+    float sum = 0.0f;
+    for (int i = col; i < ncols; i += item_ct1.get_local_range(2)) {
+        sum += x[row * ncols + i];
+    }
+
+    sum = warp_reduce_sum(sum, item_ct1);
+
+    if (col == 0) {
+        dst[row] = sum;
+    }
+}
+
+
+template<typename T>
+static inline void ggml_sycl_swap(T & a, T & b) {
+    T tmp = a;
+    a = b;
+    b = tmp;
+}
+
+template <ggml_sort_order order>
+__dpct_inline__ static void
+k_argsort_f32_i32(const float *x, int *dst, const int ncols, int ncols_pad,
+                  const sycl::nd_item<3> &item_ct1, uint8_t *dpct_local) {
+    // bitonic sort
+    int col = item_ct1.get_local_id(2);
+    int row = item_ct1.get_group(1);
+
+    if (col >= ncols_pad) {
+        return;
+    }
+
+    const float * x_row = x + row * ncols;
+    auto dst_row = (int *)dpct_local;
+
+    // initialize indices
+    dst_row[col] = col;
+
+    item_ct1.barrier(sycl::access::fence_space::local_space);
+
+    for (int k = 2; k <= ncols_pad; k *= 2) {
+        for (int j = k / 2; j > 0; j /= 2) {
+            int ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (dst_row[col] >= ncols ||
+                        (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    ) {
+                        ggml_sycl_swap(dst_row[col], dst_row[ixj]);
+                    }
+                } else {
+                    if (dst_row[ixj] >= ncols ||
+                        (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    ) {
+                        ggml_sycl_swap(dst_row[col], dst_row[ixj]);
+                    }
+                }
+            }
+            /*
+            DPCT1118:1: SYCL group functions and algorithms must be encountered
+            in converged control flow. You may need to adjust the code.
+            */
+            item_ct1.barrier(sycl::access::fence_space::local_space);
+        }
+    }
+
+    // copy the result to dst without the padding
+    if (col < ncols) {
+        dst[row * ncols + col] = dst_row[col];
+    }
+}
+
+
+static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past,
+                              const sycl::nd_item<3> &item_ct1) {
+    const int col = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1);
+    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int i = row*ncols + col;
+    //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i];
+    //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
+    dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
+}
+
+static void scale_f32(const float * x, float * dst, const float scale, const int k,
+                      const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = scale * x[i];
+}
+
+static void clamp_f32(const float * x, float * dst, const float min, const float max, const int k,
+                      const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
+}
+
+template <typename T>
+static void im2col_kernel(const float *x, T *dst, int offset_delta,
+                           int IW, int IH, int OW, int KW, int KH,
+                           int pelements, int CHW, int s0, int s1, int p0,
+                           int p1, int d0, int d1,
+                           const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_id(2) +
+                  item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (i >= pelements) {
+        return;
+    }
+
+    const int ksize = OW * (KH > 1 ? KW : 1);
+    const int kx = i / ksize;
+    const int kd = kx * ksize;
+    const int ky = (i - kd) / OW;
+    const int ix = i % OW;
+
+    const int64_t iiw = ix * s0 + kx * d0 - p0;
+    const int64_t iih = item_ct1.get_group(1) * s1 + ky * d1 - p1;
+
+    const int64_t offset_dst =
+        (item_ct1.get_group(1) * OW + ix) * CHW +
+        (item_ct1.get_group(0) * (KW * KH) + ky * KW + kx);
+
+    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+        dst[offset_dst] =
+            sycl::vec<float, 1>(0.0f)
+                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+    } else {
+        const int64_t offset_src = item_ct1.get_group(0) * offset_delta;
+        dst[offset_dst] =
+            sycl::vec<float, 1>(x[offset_src + iih * IW + iiw])
+                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+    }
+}
+
+template <typename Ti, typename To>
+static  void pool2d_nchw_kernel(
+        const int ih, const int iw, const int oh, const int ow,
+        const int kh, const int kw, const int sh, const int sw,
+        const int ph, const int pw, const int parallel_elements,
+        const Ti* src, To* dst, const enum ggml_op_pool op,
+        const sycl::nd_item<3> &item_ct1) {
+        int idx = item_ct1.get_local_id(2) +
+                  item_ct1.get_group(2) * item_ct1.get_local_range(2);
+        if (idx >= parallel_elements) {
+            return;
+        }
+
+        const int I_HW = ih * iw;
+        const int O_HW = oh * ow;
+        const int nc = idx / O_HW;
+        const int cur_oh = idx % O_HW / ow;
+        const int cur_ow = idx % O_HW % ow;
+        const Ti* i_ptr = src + nc * I_HW;
+        To* o_ptr = dst + nc * O_HW;
+        const int start_h = cur_oh * sh - ph;
+        const int bh = sycl::max(0, start_h);
+        const int eh = sycl::min(ih, start_h + kh);
+        const int start_w = cur_ow * sw - pw;
+        const int bw = sycl::max(0, start_w);
+        const int ew = sycl::min(iw, start_w + kw);
+
+        To res = 0;
+
+        switch (op) {
+            case GGML_OP_POOL_AVG: res = 0; break;
+            case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
+        }
+
+        for (int i = bh; i < eh; i += 1) {
+            for (int j = bw; j < ew; j += 1) {
+#if DPCT_COMPATIBILITY_TEMP >= 350
+                /*
+                DPCT1098:106: The '*' expression is used instead of the __ldg
+                call. These two expressions do not provide the exact same
+                functionality. Check the generated code for potential precision
+                and/or performance issues.
+                */
+                Ti cur = *(i_ptr + i * iw + j);
+#else
+                Ti cur = i_ptr[i * iw + j];
+#endif
+                switch (op) {
+                    case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break;
+                    case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break;
+                }
+            }
+        }
+        o_ptr[cur_oh * ow + cur_ow] = res;
+}
+
+template <int qk, int qr, dequantize_kernel_t dq>
+static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                          ggml_tensor *dst, const void *src0_dd,
+                          const int32_t *src1_dd, float *dst_dd,
+                          queue_ptr stream) {
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE);
+    const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE);
+    const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x);
+
+    // strides in elements
+    //const size_t s0 = nb0 / ggml_element_size(dst);
+    const size_t s1 = nb1 / ggml_element_size(dst);
+    const size_t s2 = nb2 / ggml_element_size(dst);
+    const size_t s3 = nb3 / ggml_element_size(dst);
+
+    const size_t s10 = nb10 / ggml_element_size(src1);
+    const size_t s11 = nb11 / ggml_element_size(src1);
+    const size_t s12 = nb12 / ggml_element_size(src1);
+    //const size_t s13 = nb13 / ggml_element_size(src1);
+
+    GGML_ASSERT(ne00 % 2 == 0);
+
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             k_get_rows<qk, qr, dq>(
+                                 src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
+                                 s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
+                         });
+
+    (void) dst;
+}
+
+template <typename src0_t>
+static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const src0_t *src0_dd, const int32_t *src1_dd,
+                                float *dst_dd, queue_ptr stream) {
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE);
+    const int block_num_x = (ne00 + SYCL_GET_ROWS_BLOCK_SIZE - 1) / SYCL_GET_ROWS_BLOCK_SIZE;
+    const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x);
+
+    // strides in elements
+    //const size_t s0 = nb0 / ggml_element_size(dst);
+    const size_t s1 = nb1 / ggml_element_size(dst);
+    const size_t s2 = nb2 / ggml_element_size(dst);
+    const size_t s3 = nb3 / ggml_element_size(dst);
+
+    const size_t s10 = nb10 / ggml_element_size(src1);
+    const size_t s11 = nb11 / ggml_element_size(src1);
+    const size_t s12 = nb12 / ggml_element_size(src1);
+    //const size_t s13 = nb13 / ggml_element_size(src1);
+
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                k_get_rows_float(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
+                                 s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
+            });
+    }
+
+    (void) dst;
+}
+
+template<float (*bin_op)(const float, const float)>
+struct bin_bcast_sycl {
+    template <typename src0_t, typename src1_t, typename dst_t>
+    void operator()(ggml_backend_sycl_context & ctx,
+                    const struct ggml_tensor *src0,
+                    const struct ggml_tensor *src1, struct ggml_tensor *dst,
+                    const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd,
+                    queue_ptr stream) {
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        int nr0 = ne10/ne0;
+        int nr1 = ne11/ne1;
+        int nr2 = ne12/ne2;
+        int nr3 = ne13/ne3;
+
+        int nr[4] = { nr0, nr1, nr2, nr3 };
+
+        // collapse dimensions until first broadcast dimension
+        int64_t cne0[] = {ne0, ne1, ne2, ne3};
+        int64_t cne1[] = {ne10, ne11, ne12, ne13};
+        size_t cnb0[] = {nb0, nb1, nb2, nb3};
+        size_t cnb1[] = {nb10, nb11, nb12, nb13};
+        auto collapse = [](int64_t cne[]) {
+            cne[0] *= cne[1];
+            cne[1] = cne[2];
+            cne[2] = cne[3];
+            cne[3] = 1;
+        };
+
+        auto collapse_nb = [](size_t cnb[], int64_t cne[]) {
+            cnb[1] *= cne[1];
+            cnb[2] *= cne[2];
+            cnb[3] *= cne[3];
+        };
+
+        for (int i = 0; i < 4; i++) {
+            if (nr[i] != 1) {
+                break;
+            }
+            if (i > 0) {
+                collapse_nb(cnb0, cne0);
+                collapse_nb(cnb1, cne1);
+                collapse(cne0);
+                collapse(cne1);
+            }
+        }
+        {
+            int64_t ne0 = cne0[0];
+            int64_t ne1 = cne0[1];
+            int64_t ne2 = cne0[2];
+            int64_t ne3 = cne0[3];
+
+            int64_t ne10 = cne1[0];
+            int64_t ne11 = cne1[1];
+            int64_t ne12 = cne1[2];
+            int64_t ne13 = cne1[3];
+
+            size_t nb0 = cnb0[0];
+            size_t nb1 = cnb0[1];
+            size_t nb2 = cnb0[2];
+            size_t nb3 = cnb0[3];
+
+            size_t nb10 = cnb1[0];
+            size_t nb11 = cnb1[1];
+            size_t nb12 = cnb1[2];
+            size_t nb13 = cnb1[3];
+
+            size_t s0 = nb0 / sizeof(dst_t);
+            size_t s1 = nb1 / sizeof(dst_t);
+            size_t s2 = nb2 / sizeof(dst_t);
+            size_t s3 = nb3 / sizeof(dst_t);
+
+            size_t s10 = nb10 / sizeof(src1_t);
+            size_t s11 = nb11 / sizeof(src1_t);
+            size_t s12 = nb12 / sizeof(src1_t);
+            size_t s13 = nb13 / sizeof(src1_t);
+
+            GGML_ASSERT(s0 == 1);
+            GGML_ASSERT(s10 == 1);
+
+            const int block_size = 128;
+
+            int64_t hne0 = std::max(ne0/2LL, 1LL);
+
+            sycl::range<3> block_dims(1, 1, 1);
+            block_dims[2] = std::min<unsigned int>(hne0, block_size);
+            block_dims[1] = std::min<unsigned int>(
+                ne1, block_size / (unsigned int)block_dims[2]);
+            block_dims[0] = std::min(
+                std::min<unsigned int>(
+                    ne2 * ne3, block_size / (unsigned int)block_dims[2] /
+                                   (unsigned int)block_dims[1]),
+                64U);
+
+            sycl::range<3> block_nums(
+                (ne2 * ne3 + block_dims[0] - 1) / block_dims[0],
+                (ne1 + block_dims[1] - 1) / block_dims[1],
+                (hne0 + block_dims[2] - 1) / block_dims[2]);
+
+            if (block_nums[0] > 65535) {
+                // this is the maximum number of blocks in z direction, fallback to 1D grid kernel
+                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
+                {
+                    dpct::has_capability_or_fail(stream->get_device(),
+                                                 {sycl::aspect::fp16});
+
+                    stream->parallel_for(
+                        sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) *
+                                              sycl::range<3>(1, 1, block_size),
+                                          sycl::range<3>(1, 1, block_size)),
+                        [=](sycl::nd_item<3> item_ct1) {
+                            k_bin_bcast_unravel<bin_op>(
+                                src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3,
+                                ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12,
+                                s13, item_ct1);
+                        });
+                }
+            } else {
+                /*
+                DPCT1049:16: The work-group size passed to the SYCL kernel may
+                exceed the limit. To get the device limit, query
+                info::device::max_work_group_size. Adjust the work-group size if
+                needed.
+                */
+                dpct::has_capability_or_fail(stream->get_device(),
+                                             {sycl::aspect::fp16});
+
+                stream->parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        k_bin_bcast<bin_op>(src0_dd, src1_dd, dst_dd, ne0, ne1,
+                                            ne2, ne3, ne10, ne11, ne12, ne13,
+                                            s1, s2, s3, s11, s12, s13,
+                                            item_ct1);
+                    });
+            }
+        }
+    }
+};
+
+static void acc_f32_sycl(const float *x, const float *y, float *dst,
+                         const int n_elements, const int ne10, const int ne11,
+                         const int ne12, const int nb1, const int nb2,
+                         const int offset, queue_ptr stream) {
+    int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset,
+                    item_ct1);
+        });
+}
+
+static void gelu_f32_sycl(const float *x, float *dst, const int k,
+                          queue_ptr stream) {
+    const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            gelu_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void silu_f32_sycl(const float *x, float *dst, const int k,
+                          queue_ptr stream) {
+    const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            silu_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void gelu_quick_f32_sycl(const float *x, float *dst, const int k,
+                                queue_ptr stream) {
+    const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            gelu_quick_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void tanh_f32_sycl(const float *x, float *dst, const int k,
+                          queue_ptr stream) {
+    const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            tanh_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void relu_f32_sycl(const float *x, float *dst, const int k,
+                          queue_ptr stream) {
+    const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            relu_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k,
+                                 queue_ptr stream) {
+    const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            hardsigmoid_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void hardswish_f32_sycl(const float *x, float *dst, const int k,
+                               queue_ptr stream) {
+    const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            hardswish_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void leaky_relu_f32_sycl(const float *x, float *dst, const int k,
+                                const float negative_slope,
+                                queue_ptr stream) {
+    const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            leaky_relu_f32(x, dst, k, negative_slope, item_ct1);
+        });
+}
+
+static void sqr_f32_sycl(const float *x, float *dst, const int k,
+                         queue_ptr stream) {
+    const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            sqr_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01,
+                             const int nb02, const int nb03, const int ne10, const int ne11,
+                             const int ne12, const int ne13, const float sf0, const float sf1,
+                             const float sf2, const float sf3, queue_ptr stream) {
+    int dst_size = ne10 * ne11 * ne12 * ne13;
+    int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE;
+    sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)),
+        [=](sycl::nd_item<1> item_ct1) {
+            upscale_f32(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1);
+        });
+}
+
+static void pad_f32_sycl(const float *x, float *dst, const int ne00,
+                         const int ne01, const int ne02, const int ne0,
+                         const int ne1, const int ne2, queue_ptr stream) {
+    int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE;
+    sycl::range<3> gridDim(ne2, ne1, num_blocks);
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            pad_f32(x, dst, ne0, ne00, ne01, ne02, item_ct1);
+        });
+}
+
+static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx,
+                                   const int ky, const int kx_padded,
+                                   queue_ptr stream) {
+    const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE;
+    const sycl::range<3> num_blocks(1, ky, block_num_x);
+    int constexpr QUANT_BLOCK_TILE = QK8_1 / WARP_SIZE;
+    static_assert(QK8_1 % WARP_SIZE == 0);
+    const sycl::range<3> block_size(1, 1, SYCL_QUANTIZE_BLOCK_SIZE / QUANT_BLOCK_TILE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(num_blocks * block_size, block_size),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                quantize_q8_1<QUANT_BLOCK_TILE>(x, vy, kx, kx_padded, item_ct1);
+            });
+    }
+}
+
+static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y,
+                                           float *dst, const int ncols_x,
+                                           const int nrows_x,
+                                           const int nchannels_x,
+                                           const int nchannels_y,
+                                           queue_ptr stream) {
+
+    const sycl::range<3> block_nums(nchannels_y, nrows_x, 1);
+    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                mul_mat_p021_f16_f32(vx, y, dst, ncols_x, nrows_x, nchannels_x,
+                                     nchannels_y, item_ct1);
+            });
+    }
+}
+
+static void ggml_mul_mat_vec_nc_f16_f32_sycl(
+    const void *vx, const float *y, float *dst, const int ncols_x,
+    const int nrows_x, const int row_stride_x, const int nchannels_x,
+    const int nchannels_y, const int channel_stride_x, queue_ptr stream) {
+
+    const sycl::range<3> block_nums(nchannels_y, nrows_x, 1);
+    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                mul_mat_vec_nc_f16_f32(vx, y, dst, ncols_x, nrows_x,
+                                       row_stride_x, channel_stride_x,
+                                       nchannels_y / nchannels_x, item_ct1);
+            });
+    }
+}
+
+static void
+ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00,
+                      const int ne01, const int ne02, const int nb00,
+                      const int nb01, const int nb02, const int nb03,
+                      const int ne10, const int ne11, const int ne12,
+                      const int nb10, const int nb11, const int nb12,
+                      const int nb13, queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f16_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00,
+                                           nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f32_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f32_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne,
+                                   const int ne00, const int ne01,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
+                                   queue_ptr stream) {
+
+    GGML_ASSERT(ne % QK8_0 == 0);
+    const int num_blocks = ne / QK8_0;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
+                                           sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
+                         });
+}
+
+static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne,
+                                   const int ne00, const int ne01,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
+                                   queue_ptr stream) {
+
+    GGML_ASSERT(ne % QK4_0 == 0);
+    const int num_blocks = ne / QK4_0;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
+                                           sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
+                         });
+}
+
+static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne,
+                                   const int ne00, const int ne01,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
+                                   queue_ptr stream) {
+
+    GGML_ASSERT(ne % QK4_1 == 0);
+    const int num_blocks = ne / QK4_1;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
+                                           sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
+                         });
+}
+
+static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f16_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        // dpct::has_capability_or_fail(stream->get_device(),
+        //                              {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_i16_i16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        // dpct::has_capability_or_fail(stream->get_device(),
+        //                              {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_i32_i32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void scale_f32_sycl(const float *x, float *dst, const float scale,
+                           const int k, queue_ptr stream) {
+    const int num_blocks = (k + SYCL_SCALE_BLOCK_SIZE - 1) / SYCL_SCALE_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            scale_f32(x, dst, scale, k, item_ct1);
+        });
+}
+
+static void clamp_f32_sycl(const float *x, float *dst, const float min,
+                           const float max, const int k,
+                           queue_ptr stream) {
+    const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            clamp_f32(x, dst, min, max, k, item_ct1);
+        });
+}
+
+static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols,
+                              const int nrows, queue_ptr stream) {
+    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+    const sycl::range<3> block_nums(1, nrows, 1);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1)
+                             [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                                 k_sum_rows_f32(x, dst, ncols, item_ct1);
+                             });
+}
+
+static int next_power_of_2(int x) {
+    int n = 1;
+    while (n < x) {
+        n *= 2;
+    }
+    return n;
+}
+
+static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
+                                 const int nrows, ggml_sort_order order,
+                                 queue_ptr stream) {
+    // bitonic sort requires ncols to be power of 2
+    const int ncols_pad = next_power_of_2(ncols);
+
+    const sycl::range<3> block_dims(1, 1, ncols_pad);
+    const sycl::range<3> block_nums(1, nrows, 1);
+    const size_t shared_mem = ncols_pad * sizeof(int);
+
+    if (order == GGML_SORT_ORDER_ASC) {
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
+                sycl::range<1>(shared_mem), cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
+                    k_argsort_f32_i32<GGML_SORT_ORDER_ASC>(
+                        x, dst, ncols, ncols_pad, item_ct1,
+                        dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
+                            .get());
+                });
+        });
+    } else if (order == GGML_SORT_ORDER_DESC) {
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
+                sycl::range<1>(shared_mem), cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
+                    k_argsort_f32_i32<GGML_SORT_ORDER_DESC>(
+                        x, dst, ncols, ncols_pad, item_ct1,
+                        dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
+                            .get());
+                });
+        });
+    } else {
+        GGML_ABORT("fatal error");
+    }
+}
+
+static void diag_mask_inf_f32_sycl(const float *x, float *dst,
+                                   const int ncols_x, const int nrows_x,
+                                   const int rows_per_channel, const int n_past,
+                                   queue_ptr stream) {
+    const sycl::range<3> block_dims(1, SYCL_DIAG_MASK_INF_BLOCK_SIZE, 1);
+    const int block_num_x = (ncols_x + SYCL_DIAG_MASK_INF_BLOCK_SIZE - 1) / SYCL_DIAG_MASK_INF_BLOCK_SIZE;
+    const sycl::range<3> block_nums(1, block_num_x, nrows_x);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             diag_mask_inf_f32(x, dst, ncols_x,
+                                               rows_per_channel, n_past,
+                                               item_ct1);
+                         });
+}
+
+template <typename T>
+static void im2col_sycl(const float *x, T *dst, int IW, int IH,
+                                int OW, int OH, int KW, int KH, int IC,
+                                int offset_delta, int s0, int s1, int p0,
+                                int p1, int d0, int d1,
+                                queue_ptr stream) {
+    const int parallel_elements = OW * KW * KH;
+    const int num_blocks = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE;
+    sycl::range<3> block_nums(IC, OH, num_blocks);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums *
+                                  sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH,
+                               parallel_elements, (IC * KH * KW), s0, s1, p0,
+                               p1, d0, d1, item_ct1);
+            });
+    }
+}
+
+
+static bool g_sycl_loaded = false;
+
+bool ggml_sycl_loaded(void) {
+    return g_sycl_loaded;
+}
+
+void print_device_detail(int id, sycl::device &device, std::string device_type) {
+
+    dpct::device_info prop;
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        dpct::get_device_info(prop, device)));
+
+    std::string version;
+    version += std::to_string(prop.get_major_version());
+    version += ".";
+    version += std::to_string(prop.get_minor_version());
+
+    device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), "");
+    std::string name = std::string(prop.get_name());
+    name = std::regex_replace(name, std::regex("\\(R\\)"), "");
+    name = std::regex_replace(name, std::regex("\\(TM\\)"), "");
+
+    auto global_mem_size = prop.get_global_mem_size()/1000000;
+
+    fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(),
+            name.c_str(), version.c_str(), prop.get_max_compute_units(),
+            prop.get_max_work_group_size(), prop.get_max_sub_group_size(),
+            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str());
+}
+
+void ggml_backend_sycl_print_sycl_devices() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n");
+    int device_count = dpct::dev_mgr::instance().device_count();
+    std::map<std::string, size_t> DeviceNums;
+    fprintf(stderr, "found %d SYCL devices:\n", device_count);
+    fprintf(stderr, "|  |                   |                                       |       |Max    |        |Max  |Global |                     |\n");
+    fprintf(stderr, "|  |                   |                                       |       |compute|Max work|sub  |mem    |                     |\n");
+    fprintf(stderr, "|ID|        Device Type|                                   Name|Version|units  |group   |group|size   |       Driver version|\n");
+    fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n");
+    for (int id = 0; id < device_count; ++id) {
+        sycl::device device = dpct::dev_mgr::instance().get_device(id);
+        sycl::backend backend = device.get_backend();
+        std::string backend_type = get_device_backend_and_type(device);
+        int type_id=DeviceNums[backend_type]++;
+        std::stringstream device_type;
+        device_type << "[" <<  backend_type << ":" << std::to_string(type_id) << "]";
+        print_device_detail(id, device, device_type.str());
+    }
+}
+
+static inline int get_sycl_env(const char *env_name, int default_val) {
+    char *user_device_string = getenv(env_name);
+    int user_number = default_val;
+
+    unsigned n;
+    if (user_device_string != NULL &&
+        sscanf(user_device_string, " %u", &n) == 1) {
+        user_number = (int)n;
+    } else {
+        user_number = default_val;
+    }
+    return user_number;
+}
+
+static void ggml_check_sycl() try {
+    static bool initialized = false;
+
+    if (!initialized) {
+        fprintf(stderr, "[SYCL] call ggml_check_sycl\n");
+        g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0);
+
+        fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug);
+
+#if defined(GGML_SYCL_F16)
+        fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__);
+#else
+        fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__);
+#endif
+
+/* NOT REMOVE, keep it for next optimize for XMX.
+#if defined(SYCL_USE_XMX)
+        fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__);
+#else
+        fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__);
+#endif
+*/
+
+        if (CHECK_TRY_ERROR(g_all_sycl_device_count =
+                            dpct::dev_mgr::instance().device_count()) != 0) {
+            initialized = true;
+            g_sycl_loaded = false;
+            return;
+        }
+        GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES);
+        ggml_backend_sycl_print_sycl_devices();
+        initialized = true;
+        g_sycl_loaded = true;
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static ggml_sycl_device_info ggml_sycl_init() {
+    ggml_sycl_device_info info = {};
+
+    info.device_count = dpct::dev_mgr::instance().device_count();
+    if (info.device_count == 0) {
+        fprintf(stderr, "%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__);
+        return info;
+    }
+
+    GGML_ASSERT(info.device_count <= GGML_SYCL_MAX_DEVICES);
+
+    int64_t total_vram = 0;
+#if defined(GGML_SYCL_FORCE_MMQ)
+    fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ:   yes\n", __func__);
+#else
+    fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ:   no\n", __func__);
+#endif
+#if defined(SYCL_USE_XMX)
+    fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__);
+#else
+    fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__);
+#endif
+    fprintf(stderr, "%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count);
+
+    for (int i = 0; i < info.device_count; ++i) {
+        info.devices[i].vmm = 0;
+        dpct::device_info prop;
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
+            prop, dpct::dev_mgr::instance().get_device(i))));
+
+        info.default_tensor_split[i] = total_vram;
+        total_vram += prop.get_global_mem_size();
+
+        info.devices[i].cc =
+            100 * prop.get_major_version() + 10 * prop.get_minor_version();
+
+        info.max_work_group_sizes[i] = prop.get_max_work_group_size();
+    }
+
+    for (int id = 0; id < info.device_count; ++id) {
+        info.default_tensor_split[id] /= total_vram;
+    }
+    return info;
+}
+
+const ggml_sycl_device_info & ggml_sycl_info() {
+    static ggml_sycl_device_info info = ggml_sycl_init();
+    return info;
+}
+
+/*
+device_index: device index from 0 to n (continue numbers).
+    It is used for device select/set in SYCL backend internal data structure.
+*/
+inline void check_allow_gpu_index(const int device_index) {
+  if (device_index >= ggml_sycl_info().device_count) {
+    char error_buf[256];
+    snprintf(
+        error_buf,
+        sizeof(error_buf),
+        "%s error: device_index:%d is out of range: [0-%d]",
+        __func__,
+        device_index,
+        ggml_sycl_info().device_count - 1);
+    fprintf(stderr, "%s\n", error_buf);
+    assert(false);
+  }
+}
+
+// buffer pool for sycl (legacy)
+struct ggml_sycl_pool_leg : public ggml_sycl_pool {
+    static const int MAX_SYCL_BUFFERS = 256;
+
+    int device;
+    queue_ptr qptr;
+    struct ggml_sycl_buffer {
+        void * ptr = nullptr;
+        size_t size = 0;
+    };
+
+    ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {};
+    size_t pool_size = 0;
+
+    explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) :
+        qptr(qptr_),
+        device(device_) {
+    }
+
+    ~ggml_sycl_pool_leg() {
+        for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
+            ggml_sycl_buffer & b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+                SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr)));
+                pool_size -= b.size;
+            }
+        }
+        GGML_ASSERT(pool_size == 0);
+    }
+
+    void * alloc(size_t size, size_t * actual_size) override {
+#ifdef DEBUG_sycl_MALLOC
+        int nnz = 0;
+        size_t max_size = 0;
+#endif
+        size_t best_diff = 1ull << 36;
+        int ibest = -1;
+        for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
+            ggml_sycl_buffer& b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+#ifdef DEBUG_sycl_MALLOC
+                ++nnz;
+                if (b.size > max_size) max_size = b.size;
+#endif
+                if (b.size >= size) {
+                    size_t diff = b.size - size;
+                    if (diff < best_diff) {
+                        best_diff = diff;
+                        ibest = i;
+                        if (!best_diff) {
+                            void * ptr = b.ptr;
+                            *actual_size = b.size;
+                            b.ptr = nullptr;
+                            b.size = 0;
+                            return ptr;
+                        }
+                    }
+                }
+            }
+        }
+        if (ibest >= 0) {
+            ggml_sycl_buffer& b = buffer_pool[ibest];
+            void * ptr = b.ptr;
+            *actual_size = b.size;
+            b.ptr = nullptr;
+            b.size = 0;
+            return ptr;
+        }
+        void * ptr;
+        size_t look_ahead_size = (size_t) (1.05 * size);
+
+        SYCL_CHECK(
+            CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device(
+                                look_ahead_size, *qptr)));
+        *actual_size = look_ahead_size;
+        pool_size += look_ahead_size;
+
+    #ifdef DEBUG_SYCL_MALLOC
+        fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz,
+                (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024));
+    #endif
+        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr);
+        return ptr;
+    }
+
+    void free(void * ptr, size_t size) override {
+        for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
+            ggml_sycl_buffer& b = buffer_pool[i];
+            if (b.ptr == nullptr) {
+                b.ptr = ptr;
+                b.size = size;
+                return;
+            }
+        }
+        fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n");
+        SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr)));
+        pool_size -= size;
+    }
+};
+
+std::unique_ptr<ggml_sycl_pool> ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) {
+    // TBD: NO VMM support
+    // if (ggml_sycl_info().devices[device].vmm) {
+    //     return std::unique_ptr<ggml_sycl_pool>(new ggml_sycl_pool_vmm(device));
+    // }
+   return std::unique_ptr<ggml_sycl_pool>(new ggml_sycl_pool_leg(qptr, device));
+}
+
+// TBD pool with virtual memory management
+// struct ggml_sycl_pool_vmm : public ggml_sycl_pool
+
+static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst,
+                                          const struct ggml_tensor *src,
+                                          int64_t i3, int64_t i2,
+                                          int64_t i1_low, int64_t i1_high,
+                                          queue_ptr stream) try {
+
+    dpct::memcpy_direction kind;
+    char * src_ptr;
+    if (src->backend == GGML_BACKEND_TYPE_CPU) {
+        kind = dpct::host_to_device;
+        src_ptr = (char *) src->data;
+        // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d  GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr);
+    } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
+        GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
+        kind = dpct::device_to_device;
+        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
+        int id;
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            id = get_current_device_id()));
+        // GGML_SYCL_DEBUG("current device index %d\n", id);
+        src_ptr = (char *) extra->data_device[id];
+    } else {
+        // GGML_SYCL_DEBUG("GGML_ABORT("fatal error")\n");
+        GGML_ABORT("fatal error");
+    }
+    char * dst_ptr = (char *) dst;
+
+    GGML_TENSOR_LOCALS_1(int64_t, ne, src, ne);
+    GGML_TENSOR_LOCALS(int64_t, nb, src, nb);
+    const enum ggml_type type = src->type;
+    const int64_t ts = ggml_type_size(type);
+    const int64_t bs = ggml_blck_size(type);
+    int64_t i1_diff = i1_high - i1_low;
+
+    const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
+    if (nb0 == ts && nb1 == ts*ne0/bs) {
+        // GGML_SYCL_DEBUG("stream->memcpy: dst_ptr=%p, x=%p, size=%lu\n", dst_ptr, x, i1_diff * nb1);
+        // return CHECK_TRY_ERROR(stream->memcpy(dst_ptr, x, i1_diff * nb1));
+        return CHECK_TRY_ERROR(dpct::async_dpct_memcpy(dst_ptr, x, i1_diff * nb1,
+                                    kind, *stream));
+
+    } else if (nb0 == ts) {
+        return CHECK_TRY_ERROR(
+            dpct::async_dpct_memcpy(dst_ptr, ts * ne0 / bs, x, nb1,
+                                    ts * ne0 / bs, i1_diff, kind, *stream));
+    } else {
+        for (int64_t i1 = 0; i1 < i1_diff; i1++) {
+            const void * rx = (const void *) ((const char *) x + i1*nb1);
+            void * rd = (void *) (dst_ptr + i1*ts*ne0/bs);
+            // pretend the row is a matrix with cols=1
+            dpct::err0 r = CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                rd, ts / bs, rx, nb0, ts / bs, ne0, kind, *stream));
+            /*
+            DPCT1001:85: The statement could not be removed.
+            */
+            /*
+            DPCT1000:86: Error handling if-stmt was detected but could not be
+            rewritten.
+            */
+            if (r != 0) return r;
+        }
+        return 0;
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                  const ggml_tensor *src1, ggml_tensor *dst,
+                                  const float *src0_d, const float *src1_d,
+                                  float *dst_d, const queue_ptr &stream) {
+
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
+    GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
+    GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
+
+    const int32_t * src1_i32 = (const int32_t *) src1_d;
+
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            get_rows_sycl_float(ctx, src0, src1, dst, (const sycl::half *)src0_d,
+                                src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_F32:
+            get_rows_sycl_float(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q4_0:
+            get_rows_sycl<QK4_0, QR4_0, dequantize_q4_0>(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            get_rows_sycl<QK4_1, QR4_1, dequantize_q4_1>(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            get_rows_sycl<QK5_0, QR5_0, dequantize_q5_0>(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            get_rows_sycl<QK5_1, QR5_1, dequantize_q5_1>(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            get_rows_sycl<QK8_0, QR8_0, dequantize_q8_0>(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        default:
+            // TODO: k-quants
+            fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+
+template <class op>
+inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                   const ggml_tensor *src1, ggml_tensor *dst,
+                                   const float *src0_dd, const float *src1_dd,
+                                   float *dst_dd,
+                                   const queue_ptr &main_stream) {
+
+    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+        op()(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+        op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd,
+             (sycl::half *)dst_dd, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+        op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd,
+             main_stream);
+    } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) {
+        op()(ctx, src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd,
+             main_stream);
+    } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) {
+        op()(ctx, src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd,
+             main_stream);
+    } else {
+        fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
+            ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ABORT("fatal error");
+    }
+}
+
+static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const float *src0_d, const float *src1_d,
+                                float *dst_d,
+                                const queue_ptr &main_stream) {
+
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_repeat>>(ctx, dst, src0, dst, nullptr, src0_d, dst_d, main_stream);
+
+    (void) src1;
+    (void) src1_d;
+}
+
+inline void ggml_sycl_op_add(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const queue_ptr &main_stream) {
+
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_add>>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
+}
+
+inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
+
+    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
+    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
+    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
+    int offset = dst->op_params[3] / 4; // offset in bytes
+
+    acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream);
+
+    (void) dst;
+}
+
+inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const queue_ptr &main_stream) {
+
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_mul>>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
+}
+
+inline void ggml_sycl_op_div(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const queue_ptr &main_stream) {
+
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_div>>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
+}
+
+inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                    const ggml_tensor *src1, ggml_tensor *dst,
+                                    const float *src0_dd, const float *src1_dd,
+                                    float *dst_dd,
+                                    const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                     const ggml_tensor *src1, ggml_tensor *dst,
+                                     const float *src0_dd, const float *src1_dd,
+                                     float *dst_dd,
+                                     const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                   const ggml_tensor *src1, ggml_tensor *dst,
+                                   const float *src0_dd, const float *src1_dd,
+                                   float *dst_dd, const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                    const ggml_tensor *src1, ggml_tensor *dst,
+                                    const float *src0_dd, const float *src1_dd,
+                                    float *dst_dd,
+                                    const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float negative_slope;
+    memcpy(&negative_slope, dst->op_params, sizeof(float));
+
+    leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                 const ggml_tensor *src1, ggml_tensor *dst,
+                                 const float *src0_dd, const float *src1_dd,
+                                 float *dst_dd,
+                                 const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const float sf0 = (float)dst->ne[0]/src0->ne[0];
+    const float sf1 = (float)dst->ne[1]/src0->ne[1];
+    const float sf2 = (float)dst->ne[2]/src0->ne[2];
+    const float sf3 = (float)dst->ne[3]/src0->ne[3];
+
+    upscale_f32_sycl(src0_dd, dst_dd, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+                     dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3,
+                     main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+
+    pad_f32_sycl(src0_dd, dst_dd,
+        src0->ne[0], src0->ne[1], src0->ne[2],
+        dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split) {
+    int64_t min_compute_capability = INT_MAX;
+    int64_t max_compute_capability = INT_MIN;
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        if (tensor_split[i] < (i + 1 < ggml_sycl_info().device_count ? tensor_split[i + 1] : 1.0f)) {
+            if (min_compute_capability > ggml_sycl_info().devices[i].cc) {
+                min_compute_capability = ggml_sycl_info().devices[i].cc;
+            }
+            if (max_compute_capability < ggml_sycl_info().devices[i].cc) {
+                max_compute_capability = ggml_sycl_info().devices[i].cc;
+            }
+        }
+    }
+
+    switch(type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            return max_compute_capability >= VER_GEN9 ? 128 : 64;
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+            return 64;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+            return 1;
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ4_NL:
+            return max_compute_capability >= VER_GEN9 ? 128 : 64;
+        case GGML_TYPE_IQ3_S:
+            return max_compute_capability >= VER_GEN9 ? 128 : 64;
+        case GGML_TYPE_Q6_K:
+            return 64;
+        default:
+            GGML_ABORT("fatal error");
+    }
+
+}
+
+inline void ggml_sycl_op_mul_mat_sycl(
+    ggml_backend_sycl_context & ctx,
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const queue_ptr &stream) try {
+
+    GGML_ASSERT(src0_dd_i  != nullptr);
+    GGML_ASSERT(src1_ddf_i != nullptr);
+    GGML_ASSERT(dst_dd_i   != nullptr);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t row_diff = row_high - row_low;
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // ldc == nrows of the matrix that cuBLAS writes into
+    int ldc = id == ctx.device ? ne0 : row_diff;
+
+#ifdef GGML_SYCL_F16
+    bool use_fp16 = true;  // TODO(Yu) SYCL capability check
+#else
+    bool use_fp16 = false;
+#endif
+    if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
+        use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] &&
+        dst->op_params[0] == GGML_PREC_DEFAULT) {
+
+        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n");
+        ggml_sycl_pool_alloc<sycl::half> src0_as_f16(ctx.pool());
+        if (src0->type != GGML_TYPE_F16) {
+            const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src0->type);
+            GGML_ASSERT(to_fp16_sycl != nullptr);
+            size_t ne = row_diff*ne00;
+            src0_as_f16.alloc(ne);
+            to_fp16_sycl(src0_dd_i, src0_as_f16.get(), ne, stream);
+        }
+        const sycl::half *src0_ptr = src0->type == GGML_TYPE_F16
+                                         ? (const sycl::half *)src0_dd_i
+                                         : src0_as_f16.get();
+
+        ggml_sycl_pool_alloc<sycl::half> src1_as_f16(ctx.pool());
+        if (src1->type != GGML_TYPE_F16) {
+            const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+            GGML_ASSERT(to_fp16_sycl != nullptr);
+            size_t ne = src1_ncols*ne10;
+            src1_as_f16.alloc(ne);
+            to_fp16_sycl(src1_ddf_i, src1_as_f16.get(), ne, stream);
+        }
+        const sycl::half *src1_ptr = src1->type == GGML_TYPE_F16
+                ? (const sycl::half *)src1->data + src1_padded_row_size
+                                         : src1_as_f16.get();
+        ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool(), row_diff * src1_ncols);
+
+        const sycl::half alpha_f16 = 1.0f;
+        const sycl::half beta_f16 = 0.0f;
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
+            *stream, oneapi::mkl::transpose::trans,
+            oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
+            &alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00,
+            src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16,
+            dst_f16.get(), dpct::library_data_t::real_half, ldc,
+            dpct::library_data_t::real_half)));
+        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
+        to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
+    }
+    else {
+        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n");
+        ggml_sycl_pool_alloc<float> src0_ddq_as_f32(ctx.pool());
+        ggml_sycl_pool_alloc<float> src1_ddq_as_f32(ctx.pool());
+        if (src0->type != GGML_TYPE_F32) {
+            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type);
+            GGML_ASSERT(to_fp32_sycl != nullptr);
+            src0_ddq_as_f32.alloc(row_diff*ne00);
+            to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream);
+        }
+        if (src1->type != GGML_TYPE_F32) {
+            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type);
+            GGML_ASSERT(to_fp32_sycl != nullptr);
+            src1_ddq_as_f32.alloc(src1_ncols*ne10);
+            to_fp32_sycl(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream);
+        }
+        const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get();
+        const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get();
+
+        const float alpha = 1.0f;
+        const float beta = 0.0f;
+
+        SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
+            *stream, oneapi::mkl::transpose::trans,
+            oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
+            dpct::get_value(&alpha, *stream), src0_ddf_i, ne00,
+            src1_ddf1_i, ne10, dpct::get_value(&beta, *stream),
+            dst_dd_i, ldc)));
+    }
+    (void) dst;
+    (void) src1_ddq_i;
+    (void) src1_padded_row_size;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const float *src0_dd, const float *src1_dd,
+                                float *dst_dd, const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+
+    const int64_t IH = src0->ne[1];
+    const int64_t IW = src0->ne[0];
+
+    const int64_t N = dst->ne[3];
+    const int64_t OC = dst->ne[2];
+    const int64_t OH = dst->ne[1];
+    const int64_t OW = dst->ne[0];
+
+    const int parallel_elements = N * OC * OH * OW;
+    const int num_blocks = (parallel_elements + SYCL_POOL2D_BLOCK_SIZE - 1) / SYCL_POOL2D_BLOCK_SIZE;
+    sycl::range<3> block_nums(1, 1, num_blocks);
+    main_stream->parallel_for(
+        sycl::nd_range<3>(block_nums *
+                              sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            pool2d_nchw_kernel(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0,
+                               parallel_elements, src0_dd, dst_dd, op,
+                               item_ct1);
+        });
+
+    (void) src1;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_im2col(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const float *src0_dd, const float *src1_dd,
+                                float *dst_dd,
+                                const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
+
+    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
+
+    const int64_t IC = src1->ne[is_2D ? 2 : 1];
+    const int64_t IH = is_2D ? src1->ne[1] : 1;
+    const int64_t IW =         src1->ne[0];
+
+    const int64_t KH = is_2D ? src0->ne[1] : 1;
+    const int64_t KW =         src0->ne[0];
+
+    const int64_t OH = is_2D ? dst->ne[2] : 1;
+    const int64_t OW =         dst->ne[1];
+
+    const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+
+    if (dst->type == GGML_TYPE_F16) {
+        im2col_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
+    } else {
+        im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
+    }
+
+    (void) src0;
+    (void) src0_dd;
+}
+
+inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                  const ggml_tensor *src1, ggml_tensor *dst,
+                                  const float *src0_dd, const float *src1_dd,
+                                  float *dst_dd,
+                                  const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                 const ggml_tensor *src1, ggml_tensor *dst,
+                                 const float *src0_dd, const float *src1_dd,
+                                 float *dst_dd,
+                                 const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_I32);
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
+
+    argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                       const ggml_tensor *src1,
+                                       ggml_tensor *dst, const float *src0_dd,
+                                       const float *src1_dd, float *dst_dd,
+                                       const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int nrows0 = ggml_nrows(src0);
+
+    const int n_past = ((int32_t *) dst->op_params)[0];
+
+    diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                               ggml_tensor *dst, const float *src0_dd,
+                               const float *src1_dd, float *dst_dd,
+                               const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float scale;
+    memcpy(&scale, dst->op_params, sizeof(float));
+
+    scale_f32_sycl(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream);
+    /*
+    DPCT1010:87: SYCL uses exceptions to report errors and does not use the
+    error codes. The call was replaced with 0. You need to rewrite this code.
+    */
+    SYCL_CHECK(0);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                               ggml_tensor *dst, const float *src0_dd,
+                               const float *src1_dd, float *dst_dd,
+                               const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float min;
+    float max;
+    memcpy(&min, dst->op_params, sizeof(float));
+    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
+
+    clamp_f32_sycl(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
+    /*
+    DPCT1010:88: SYCL uses exceptions to report errors and does not use the
+    error codes. The call was replaced with 0. You need to rewrite this code.
+    */
+    SYCL_CHECK(0);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                 const ggml_tensor *src1, ggml_tensor *dst,
+                                 const ggml_sycl_op_flatten_t op) try {
+    const int64_t nrows0 = ggml_nrows(src0);
+
+    const bool use_src1 = src1 != nullptr;
+    const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
+
+    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+
+    ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
+    ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
+
+    // dd = data device
+    float * src0_ddf = (float *) src0->data;
+    float * src1_ddf = use_src1 ? (float *) src1->data : nullptr;
+    float *  dst_ddf = (float *) dst->data;
+
+    ggml_sycl_pool_alloc<float> src0_f(ctx.pool());
+    ggml_sycl_pool_alloc<float> src1_f(ctx.pool());
+    ggml_sycl_pool_alloc<float>  dst_f(ctx.pool());
+
+    ggml_sycl_set_device(ctx.device);
+    queue_ptr main_stream = ctx.stream();
+    // GGML_SYCL_DEBUG("ctx.device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n",
+        // ctx.device, main_stream, src0_on_device, src1_on_device, dst_on_device);
+
+    // do the computation
+    op(ctx, src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
+    // print_ggml_tensor("tensor", dst);
+}
+catch (sycl::exception const &exc) {
+
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) {
+    static bool peer_access_enabled = false;
+
+    const bool enable_peer_access = n_tokens <= GGML_SYCL_PEER_MAX_BATCH_SIZE;
+
+    if (peer_access_enabled == enable_peer_access) {
+        return;
+    }
+
+#ifdef NDEBUG
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        SYCL_CHECK(ggml_sycl_set_device(i));
+    }
+
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        SYCL_CHECK(ggml_sycl_set_device(i));
+
+        for (int id_other = 0; id_other < ggml_sycl_info().device_count; ++id_other) {
+            if (i == id_other) {
+                continue;
+            }
+            if (i != main_device && id_other != main_device) {
+                continue;
+            }
+
+            // int can_access_peer;
+            // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other));
+            // if (can_access_peer) {
+            //     if (enable_peer_access) {
+            //         SYCL_CHECK(syclDeviceEnablePeerAccess(id_other, 0));
+            //     } else {
+            //         SYCL_CHECK(syclDeviceDisablePeerAccess(id_other));
+            //     }
+            // }
+        }
+    }
+#endif // NDEBUG
+
+    peer_access_enabled = enable_peer_access;
+}
+
+struct ggml_backend_sycl_split_buffer_type_context {
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split;
+};
+
+static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                 const ggml_tensor *src1, ggml_tensor *dst,
+                                 ggml_sycl_op_mul_mat_t op,
+                                 const bool convert_src1_to_q8_1) try {
+
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
+
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+    const int64_t nrows1 = ggml_nrows(src1);
+
+    GGML_ASSERT(ne03 == ne13);
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    const int nb2 = dst->nb[2];
+    const int nb3 = dst->nb[3];
+
+    GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
+
+    GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
+
+    const int64_t i02_divisor = ne12 / ne02;
+
+    const size_t src0_ts = ggml_type_size(src0->type);
+    const size_t src0_bs = ggml_blck_size(src0->type);
+    const size_t q8_1_ts = sizeof(block_q8_1);
+    const size_t q8_1_bs = QK8_1;
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
+
+    const bool src0_is_contiguous = ggml_is_contiguous(src0);
+    const bool src1_is_contiguous = ggml_is_contiguous(src1);
+
+    int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
+
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    GGML_ASSERT(!(split && ne02 > 1));
+    GGML_ASSERT(!(split && ne03 > 1));
+    GGML_ASSERT(!(split && ne02 < ne12));
+
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split;
+    if (split) {
+        // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check
+        // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...);
+        ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context;
+        tensor_split = buft_ctx->tensor_split;
+    }
+
+    struct dev_data {
+        ggml_sycl_pool_alloc<char> src0_dd_alloc;
+        ggml_sycl_pool_alloc<float> src1_ddf_alloc;
+        ggml_sycl_pool_alloc<char> src1_ddq_alloc;
+        ggml_sycl_pool_alloc<float> dst_dd_alloc;
+
+        char *src0_dd = nullptr;
+        float *src1_ddf = nullptr; // float
+        char *src1_ddq = nullptr;  // q8_1
+        float *dst_dd = nullptr;
+
+        int64_t row_low;
+        int64_t row_high;
+    };
+
+    dev_data dev[GGML_SYCL_MAX_DEVICES];
+
+    int used_devices = 0;
+    queue_ptr main_stream = ctx.stream();
+
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        // by default, use all rows
+        dev[i].row_low  = 0;
+        dev[i].row_high = ne01;
+
+        // for multi GPU, get the row boundaries from tensor split
+        // and round to mul_mat_q tile sizes
+        if (split) {
+            const int64_t rounding = get_row_rounding(src0->type, tensor_split);
+
+            if (i != 0) {
+                dev[i].row_low  = ne01*tensor_split[i];
+                if (dev[i].row_low < ne01) {
+                    dev[i].row_low -= dev[i].row_low % rounding;
+                }
+            }
+
+            if (i != ggml_sycl_info().device_count - 1) {
+                dev[i].row_high  = ne01*tensor_split[i + 1];
+                if (dev[i].row_high < ne01) {
+                    dev[i].row_high -= dev[i].row_high % rounding;
+                }
+            }
+        }
+    }
+
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        if ((!split && i != ctx.device) || dev[i].row_low == dev[i].row_high) {
+            continue;
+        }
+
+        used_devices++;
+
+        const bool src1_on_device = i == ctx.device;
+        const bool  dst_on_device = i == ctx.device;
+
+        ggml_sycl_set_device(i);
+        queue_ptr stream = ctx.stream(i, 0);
+
+        if (src0_is_contiguous) {
+            dev[i].src0_dd = (char *) src0->data;
+        } else {
+            dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ctx.pool(i), ggml_nbytes(src0));
+        }
+
+        if (src1_on_device && src1_is_contiguous) {
+            dev[i].src1_ddf = (float *) src1->data;
+        } else {
+            dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ctx.pool(i), ggml_nelements(src1));
+        }
+
+        if (convert_src1_to_q8_1) {
+            dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(ctx.pool(i), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
+
+            if (src1_on_device && src1_is_contiguous) {
+                quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
+                /*
+                DPCT1010:90: SYCL uses exceptions to report errors and does not
+                use the error codes. The call was replaced with 0. You need to
+                rewrite this code.
+                */
+                SYCL_CHECK(0);
+            }
+        }
+
+        if (dst_on_device) {
+            dev[i].dst_dd = (float *) dst->data;
+        } else {
+            const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst);
+            dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(ctx.pool(i), size_dst_ddf);
+        }
+    }
+
+    // if multiple devices are used they need to wait for the main device
+    // here an event is recorded that signals that the main device has finished calculating the input data
+    if (split && used_devices > 1) {
+        ggml_sycl_set_device(ctx.device);
+        /*
+        DPCT1024:91: The original code returned the error code that was further
+        consumed by the program logic. This original code was replaced with 0.
+        You may need to rewrite the program logic consuming the error code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            *src0_extra->events[ctx.device][0] =
+                ctx.stream()->ext_oneapi_submit_barrier()));
+    }
+
+    const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
+    for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
+        const int64_t is = split ? (src1_col_0/src1_col_stride) % GGML_SYCL_MAX_STREAMS : 0;
+        const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
+
+        for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+            if ((!split && i != ctx.device) || dev[i].row_low == dev[i].row_high) {
+                continue;
+            }
+
+            const bool src1_on_device = i == ctx.device;
+            const bool  dst_on_device = i == ctx.device;
+            const int64_t row_diff = dev[i].row_high - dev[i].row_low;
+
+            ggml_sycl_set_device(i);
+            queue_ptr stream = ctx.stream(i, is);
+
+            // wait for main GPU data if necessary
+            if (split && (i != ctx.device || is != 0)) {
+                /*
+                DPCT1009:163: SYCL uses exceptions to report errors and does not
+                use the error codes. The original code was commented out and a
+                warning string was inserted. You need to rewrite this code.
+                */
+                SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier(
+                    {*src0_extra->events[ctx.device][0]})));
+            }
+
+            for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
+                const int64_t i03 = i0 / ne12;
+                const int64_t i02 = i0 % ne12;
+
+                const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs;
+
+                // for split tensors the data begins at i0 == i0_offset_low
+                char  *  src0_dd_i =  dev[i].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
+                float * src1_ddf_i = dev[i].src1_ddf + (i0*ne11 + src1_col_0) * ne10;
+                char  * src1_ddq_i = dev[i].src1_ddq +  src1_ddq_i_offset;
+                float *   dst_dd_i =   dev[i].dst_dd + (i0*ne1  + src1_col_0) * (dst_on_device ? ne0 : row_diff);
+
+                // the main device memory buffer can be on VRAM scratch, with space for all partial results
+                // in that case an offset on dst_ddf_i is needed
+                if (i == ctx.device) {
+                    dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split
+                }
+
+                // copy src0, src1 to device if necessary
+                if (src1_is_contiguous) {
+                    if (i != ctx.device) {
+                        if (convert_src1_to_q8_1) {
+                            char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset;
+                          SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
+                                src1_ddq_i, src1_ddq_i_source,
+                                src1_ncols * src1_padded_col_size * q8_1_ts /
+                                    q8_1_bs).wait()));
+                        } else {
+
+                            float * src1_ddf_i_source = (float *) src1_extra->data_device[ctx.device];
+                            src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
+
+                            SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream,
+                                src1_ddf_i, src1_ddf_i_source,
+                                src1_ncols * ne10 * sizeof(float))));
+                        }
+                    }
+                } else if (src1_on_device && !src1_is_contiguous) {
+                    SYCL_CHECK(ggml_sycl_cpy_tensor_2d(
+                                   src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
+                } else {
+                    GGML_ABORT("fatal error");
+                }
+
+                if (convert_src1_to_q8_1 && !src1_is_contiguous) {
+                    quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
+                    /*
+                    DPCT1010:92: SYCL uses exceptions to report errors and does
+                    not use the error codes. The call was replaced with 0. You
+                    need to rewrite this code.
+                    */
+                    SYCL_CHECK(0);
+                }
+
+                if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) {
+                    SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream));
+                }
+                if (src1->type == GGML_TYPE_F16) {
+                    src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10;
+                }
+                // do the computation
+                SYCL_CHECK(CHECK_TRY_ERROR(op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
+                    dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream)));
+                /*
+                DPCT1010:93: SYCL uses exceptions to report errors and does not
+                use the error codes. The call was replaced with 0. You need to
+                rewrite this code.
+                */
+                SYCL_CHECK(0);
+
+                // copy dst to host or other device if necessary
+                if (!dst_on_device) {
+                    void * dst_off_device = dst->data;
+                    if (split) {
+                        // src0 = weight matrix is saved as a transposed matrix for better memory layout.
+                        // dst is NOT transposed.
+                        // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU.
+                        // Instead they need to be copied to the correct slice in ne0 = dst row index.
+                        // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results.
+                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
+                        GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
+                        dhf_dst_i += src1_col_0*ne0 + dev[i].row_low;
+
+                        SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                            dhf_dst_i, ne0 * sizeof(float), dst_dd_i,
+                            row_diff * sizeof(float), row_diff * sizeof(float),
+                            src1_ncols, dpct::device_to_device, *stream)));
+                    } else {
+                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
+                        GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
+                        dhf_dst_i += src1_col_0*ne0;
+                        SYCL_CHECK(CHECK_TRY_ERROR(
+                            stream->memcpy(dhf_dst_i, dst_dd_i,
+                                           src1_ncols * ne0 * sizeof(float)).wait()));
+                    }
+                }
+
+                // add event for the main device to wait on until other device is done
+                if (split && (i != ctx.device || is != 0)) {
+                    /*
+                    DPCT1024:94: The original code returned the error code that
+                    was further consumed by the program logic. This original
+                    code was replaced with 0. You may need to rewrite the
+                    program logic consuming the error code.
+                    */
+                    SYCL_CHECK(CHECK_TRY_ERROR(
+                        *src0_extra->events[i][is] =
+                            stream->ext_oneapi_submit_barrier()));
+                }
+            }
+        }
+    }
+
+    // main device waits for all other devices to be finished
+    if (split && ggml_sycl_info().device_count > 1) {
+        int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
+        is_max = is_max <= GGML_SYCL_MAX_STREAMS ? is_max : GGML_SYCL_MAX_STREAMS;
+
+        ggml_sycl_set_device(ctx.device);
+        for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+            if (dev[i].row_low == dev[i].row_high) {
+                continue;
+            }
+            for (int64_t is = 0; is < is_max; ++is) {
+                SYCL_CHECK(CHECK_TRY_ERROR(
+                    ctx.stream()->ext_oneapi_submit_barrier(
+                        {*src0_extra->events[i][is]})));
+            }
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+
+static void ggml_sycl_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_repeat);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_get_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_get_rows);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_add);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_acc);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_mul);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_div);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_silu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu_quick);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_tanh);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_relu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardsigmoid);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardswish);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_leaky_relu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqr);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_norm);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_group_norm);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_upscale);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pad);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+
+static void ggml_sycl_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rms_norm);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                       const ggml_tensor *src1,
+                                       ggml_tensor *dst) try {
+    GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
+    GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    const int64_t ne12 = src1->ne[2];
+
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+    queue_ptr main_stream = ctx.stream();
+
+    void  * src0_ddq = src0->data;
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf  = (float *) dst->data;
+
+    ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_mul_mat_vec_nc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                     const ggml_tensor *src1,
+                                     ggml_tensor *dst) try {
+    GGML_ASSERT(!ggml_is_transposed(src0));
+    GGML_ASSERT(!ggml_is_transposed(src1));
+    GGML_ASSERT(!ggml_is_permuted(src0));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    const int64_t nb01 = src0->nb[1];
+    const int64_t nb02 = src0->nb[2];
+
+    const int64_t ne12 = src1->ne[2];
+
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+    queue_ptr main_stream = ctx.stream();
+
+    void  * src0_ddq = src0->data;
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf  = (float *) dst->data;
+
+    const int64_t row_stride_x = nb01 / sizeof(sycl::half);
+    const int64_t channel_stride_x = nb02 / sizeof(sycl::half);
+
+    ggml_mul_mat_vec_nc_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void k_compute_batched_ptrs(const sycl::half *src0_as_f16,
+                                   const sycl::half *src1_as_f16, char *dst,
+                                   const void **ptrs_src, void **ptrs_dst,
+                                   int64_t ne12, int64_t ne13, int64_t ne23,
+                                   size_t nb02, size_t nb03, size_t nb12,
+                                   size_t nb13, size_t nbd2, size_t nbd3,
+                                   int64_t r2, int64_t r3,
+                                   const sycl::nd_item<3> &item_ct1) {
+    int64_t i13 = item_ct1.get_group(2) * item_ct1.get_local_range(2) +
+                  item_ct1.get_local_id(2);
+    int64_t i12 = item_ct1.get_group(1) * item_ct1.get_local_range(1) +
+                  item_ct1.get_local_id(1);
+
+    if (i13 >= ne13 || i12 >= ne12) {
+        return;
+    }
+
+    int64_t i03 = i13 / r3;
+    int64_t i02 = i12 / r2;
+
+    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
+    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13;
+    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)         dst + i12*nbd2 + i13*nbd3;
+}
+
+static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
+                                             const ggml_tensor *src0,
+                                             const ggml_tensor *src1,
+                                             ggml_tensor *dst) try {
+    GGML_ASSERT(!ggml_is_transposed(src0));
+    GGML_ASSERT(!ggml_is_transposed(src1));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int64_t ne_dst = ggml_nelements(dst);
+
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+    queue_ptr main_stream = ctx.stream();;
+
+    void * src0_ddq = src0->data;
+    sycl::half *src0_as_f16 = (sycl::half *)src0_ddq;
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf = (float *) dst->data;
+
+    // convert src1 to fp16
+    ggml_sycl_pool_alloc<sycl::half> src1_f16_alloc(ctx.pool());
+    if (src1->type != GGML_TYPE_F16) {
+        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+        const int64_t ne_src1 = ggml_nelements(src1);
+        src1_f16_alloc.alloc(ne_src1);
+        GGML_ASSERT(to_fp16_sycl != nullptr);
+        to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream);
+    }
+    sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf
+                                                       : src1_f16_alloc.get();
+
+    char * dst_t;
+
+    dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float;
+    dpct::library_data_t cu_data_type = dpct::library_data_t::real_float;
+
+    // dst strides
+    size_t nbd2 = dst->nb[2];
+    size_t nbd3 = dst->nb[3];
+
+    const float alpha_f32 = 1.0f;
+    const float beta_f32 = 0.0f;
+
+    const void * alpha = &alpha_f32;
+    const void * beta  = &beta_f32;
+
+    dst_t = (char *) dst_ddf;
+
+    GGML_ASSERT(ne12 % ne02 == 0);
+    GGML_ASSERT(ne13 % ne03 == 0);
+
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
+
+    if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
+        // there is no broadcast and src0, src1 are contiguous across dims 2, 3
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
+            *main_stream, oneapi::mkl::transpose::trans,
+            oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
+            (const char *)src0_as_f16, dpct::library_data_t::real_half,
+            nb01 / nb00, nb02 / nb00,
+            (const char *)src1_f16, dpct::library_data_t::real_half,
+            nb11 / nb10, nb12 / nb10, beta,
+            (char *)dst_t, cu_data_type, ne01, nb2 / nb0,
+            ne12 * ne13, cu_compute_type)));
+    } else {
+        const int ne23 = ne12*ne13;
+
+        ggml_sycl_pool_alloc<const void *> ptrs_src(ctx.pool(), 2*ne23);
+        ggml_sycl_pool_alloc<      void *> ptrs_dst(ctx.pool(), 1*ne23);
+
+        sycl::range<3> block_dims(1, ne12, ne13);
+        /*
+        DPCT1049:47: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(main_stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            main_stream->submit([&](sycl::handler &cgh) {
+                const void **ptrs_src_get = ptrs_src.get();
+                void **ptrs_dst_get = ptrs_dst.get();
+                size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2;
+                size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2;
+                cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims),
+                                 [=](sycl::nd_item<3> item_ct1) {
+                                     k_compute_batched_ptrs(
+                                         src0_as_f16, src1_f16,
+                                         dst_t, ptrs_src_get,
+                                         ptrs_dst_get, ne12, ne13, ne23,
+                                         nb02, nb03, nb12_scaled, nb13_scaled,
+                                         nbd2, nbd3, r2, r3, item_ct1);
+                                 });
+            });
+        }
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
+            *main_stream, oneapi::mkl::transpose::trans,
+            oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
+            (const void **)(ptrs_src.get() + 0 * ne23),
+            dpct::library_data_t::real_half, nb01 / nb00,
+            (const void **)(ptrs_src.get() + 1 * ne23),
+            dpct::library_data_t::real_half, nb11 / nb10, beta,
+            (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23,
+            cu_compute_type)));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
+    // TODO: accuracy issues in MMQ
+    return false;
+}
+
+bool ggml_sycl_supports_dmmv(enum ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_F16:
+            return true;
+        default:
+            return false;
+    }
+}
+
+static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer);
+    int64_t min_compute_capability = INT_MAX;
+
+    if (split) {
+        ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context;
+        auto & tensor_split = buft_ctx->tensor_split;
+        for (int id = 0; id < ggml_sycl_info().device_count; ++id) {
+            // skip devices that are not going to do any work:
+            if (tensor_split[id] >= (id + 1 < ggml_sycl_info().device_count ? tensor_split[id + 1] : 1.0f)) {
+                continue;
+            }
+
+            if (min_compute_capability > ggml_sycl_info().devices[id].cc) {
+                min_compute_capability = ggml_sycl_info().devices[id].cc;
+            }
+        }
+    } else {
+        min_compute_capability    = ggml_sycl_info().devices[ctx.device].cc;
+    }
+
+    // check data types and tensor shapes for custom matrix multiplication kernels:
+    bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+        && src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1;
+
+    bool use_mul_mat_vec_q =  ggml_is_quantized(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+        && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
+
+    bool use_mul_mat_q =  ggml_sycl_supports_mmq(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+    // mmvq and mmq need the __dp4a instruction which is available for gen12+
+    // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
+    use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
+#ifdef SYCL_USE_XMX
+    use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
+#endif // SYCL_USE_XMX
+
+    // mmvq path is faster in the CUDA backend.
+    if (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda)
+        use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
+
+    if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+        // KQ single-batch
+        ggml_sycl_mul_mat_vec_p021(ctx, src0, src1, dst);
+    } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+        // KQV single-batch
+        ggml_sycl_mul_mat_vec_nc(ctx, src0, src1, dst);
+    } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
+        // KQ + KQV multi-batch
+        ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst);
+    } else if (use_dequantize_mul_mat_vec) {
+        ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false);
+    } else if (use_mul_mat_vec_q) {
+        ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
+    } else if (use_mul_mat_q) {
+        ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_q, true);
+    } else {
+        ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
+    }
+}
+
+
+struct mmid_row_mapping {
+    int32_t i1;
+    int32_t i2;
+};
+
+__dpct_inline__ static void k_copy_src1_to_contiguous(
+    const char *__restrict__ src1_original, char *__restrict__ src1_contiguous,
+    int *__restrict__ cur_src1_row, mmid_row_mapping *__restrict__ row_mapping,
+    const char *__restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0,
+    int64_t ne11, int64_t ne10, size_t nb11, size_t nb12,
+    const sycl::nd_item<3> &item_ct1, int &src1_row) {
+    int32_t iid1 = item_ct1.get_group(2);
+    int32_t id = item_ct1.get_group(1);
+
+    const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0);
+
+    if (row_id_i != i02) {
+        return;
+    }
+
+    const int64_t i11 = id % ne11;
+    const int64_t i12 = iid1;
+
+    if (item_ct1.get_local_id(2) == 0) {
+        src1_row =
+            dpct::atomic_fetch_add<sycl::access::address_space::generic_space>(
+                cur_src1_row, 1);
+        row_mapping[src1_row] = {id, iid1};
+    }
+    /*
+    DPCT1065:194: Consider replacing sycl::nd_item::barrier() with
+    sycl::nd_item::barrier(sycl::access::fence_space::local_space) for better
+    performance if there is no access to global memory.
+    */
+    item_ct1.barrier();
+
+    const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12);
+    float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11);
+
+#pragma unroll
+    for (int i = item_ct1.get_local_id(2); i < ne10;
+         i += item_ct1.get_local_range(2)) {
+        src1_row_contiguous[i] = src1_row_original[i];
+    }
+}
+
+__dpct_inline__ static void k_copy_dst_from_contiguous(
+    char *__restrict__ dst_original, const char *__restrict__ dst_contiguous,
+    const mmid_row_mapping *__restrict__ row_mapping, int64_t ne0, size_t nb1,
+    size_t nb2, const sycl::nd_item<3> &item_ct1) {
+    int32_t i = item_ct1.get_group(2);
+
+    const int32_t i1 = row_mapping[i].i1;
+    const int32_t i2 = row_mapping[i].i2;
+
+    const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1);
+    float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2);
+
+#pragma unroll
+    for (int j = item_ct1.get_local_id(2); j < ne0;
+         j += item_ct1.get_local_range(2)) {
+        dst_row_original[j] = dst_row_contiguous[j];
+    }
+}
+
+static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                 const ggml_tensor *src1,
+                                 ggml_tensor *dst) try {
+    GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer) && "mul_mat_id does not support split buffers");
+
+    const ggml_tensor *ids = dst->src[2];
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const queue_ptr stream = ctx.stream();
+
+    const int64_t n_as = ne02;
+    const int64_t n_ids = ids->ne[0];
+
+    std::vector<char> ids_host(ggml_nbytes(ids));
+    const char * ids_dev = (const char *) ids->data;
+
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
+    SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
+
+    ggml_tensor src0_row = *src0;
+    ggml_tensor src1_row = *src1;
+    ggml_tensor dst_row = *dst;
+
+    char *src0_original = (char *)src0->data;
+    char *src1_original = (char *)src1->data;
+    char *dst_original = (char *)dst->data;
+
+    src0_row.ne[2] = 1;
+    src0_row.ne[3] = 1;
+    src0_row.nb[3] = nb02;
+
+    src1_row.ne[1] = 1;
+    src1_row.ne[2] = 1;
+    src1_row.ne[3] = 1;
+    src1_row.nb[2] = nb11;
+    src1_row.nb[3] = nb11;
+
+    dst_row.ne[1] = 1;
+    dst_row.ne[2] = 1;
+    dst_row.ne[3] = 1;
+    dst_row.nb[2] = nb1;
+    dst_row.nb[3] = nb1;
+    if (ne12 == 1) {
+        for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+            for (int64_t id = 0; id < n_ids; id++) {
+                const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+                GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+                const int64_t i11 = id % ne11;
+                const int64_t i12 = iid1;
+
+                const int64_t i1 = id;
+                const int64_t i2 = i12;
+
+            src0_row.data = src0_original + i02*nb02;
+            src1_row.data = src1_original + + i11*nb11 + i12*nb12;
+            dst_row.data = dst_original + i1*nb1   + i2*nb2;
+
+            ggml_sycl_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
+            }
+        }
+    } else {
+        ggml_sycl_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
+        ggml_sycl_pool_alloc<char>  dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst));
+
+        src1_row.data = src1_contiguous.get();
+        dst_row.data  =  dst_contiguous.get();
+
+        for (int64_t i02 = 0; i02 < n_as; i02++) {
+            int64_t num_src1_rows = 0;
+            for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+                for (int64_t id = 0; id < n_ids; id++) {
+                    const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+
+                    GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as);
+
+                    if (row_id_i != i02) {
+                        continue;
+                    }
+
+                    num_src1_rows++;
+                }
+            }
+
+            if (num_src1_rows == 0) {
+                continue;
+            }
+
+
+            ggml_sycl_pool_alloc<int> dev_cur_src1_row(ctx.pool(), 1);
+            ggml_sycl_pool_alloc<mmid_row_mapping> dev_row_mapping(ctx.pool(), num_src1_rows);
+            SYCL_CHECK(CHECK_TRY_ERROR(
+                stream->memset(dev_cur_src1_row.get(), 0, sizeof(int))));
+
+            {
+                sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, 768u));
+                sycl::range<3> grid_dims(1, n_ids, ids->ne[1]);
+                stream->submit([&](sycl::handler &cgh) {
+                    sycl::local_accessor<int, 0> src1_row_acc(cgh);
+
+                    char *__restrict src1_contiguous_get =
+                        src1_contiguous.get();
+                    int *__restrict dev_cur_src1_row_get =
+                        dev_cur_src1_row.get();
+                    mmid_row_mapping *__restrict dev_row_mapping_get =
+                        dev_row_mapping.get();
+                    size_t ids_nb_ct6 = ids->nb[1];
+                    size_t ids_nb_ct7 = ids->nb[0];
+
+                    cgh.parallel_for(
+                        sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                        [=](sycl::nd_item<3> item_ct1) {
+                            k_copy_src1_to_contiguous(
+                                src1_original, src1_contiguous_get,
+                                dev_cur_src1_row_get,
+                                dev_row_mapping_get, ids_dev, i02,
+                                ids_nb_ct6, ids_nb_ct7, ne11, ne10, nb11, nb12,
+                                item_ct1, src1_row_acc);
+                        });
+                });
+            }
+
+            src0_row.data = src0_original + i02*nb02;
+
+            GGML_ASSERT(nb11 == sizeof(float)*ne10);
+            GGML_ASSERT(nb1 == sizeof(float)*ne0);
+            src1_row.ne[1] = num_src1_rows;
+
+            src1_row.nb[1] = nb11;
+            src1_row.nb[2] = num_src1_rows*nb11;
+            src1_row.nb[3] = num_src1_rows*nb11;
+
+            dst_row.ne[1] = num_src1_rows;
+            dst_row.nb[1] = nb1;
+            dst_row.nb[2] = num_src1_rows*nb1;
+            dst_row.nb[3] = num_src1_rows*nb1;
+
+            ggml_sycl_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
+
+            {
+                sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, 768u));
+                sycl::range<3> grid_dims(1, 1, num_src1_rows);
+                stream->submit([&](sycl::handler &cgh) {
+                    const char *__restrict dst_contiguous_get =
+                        dst_contiguous.get();
+                    const mmid_row_mapping *__restrict dev_row_mapping_get =
+                        dev_row_mapping.get();
+
+                    cgh.parallel_for(
+                        sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                        [=](sycl::nd_item<3> item_ct1) {
+                            k_copy_dst_from_contiguous(dst_original,
+                                                       dst_contiguous_get,
+                                                       dev_row_mapping_get,
+                                                       ne0, nb1, nb2, item_ct1);
+                        });
+                });
+            }
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_scale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_scale);
+}
+
+static void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_clamp);
+}
+
+static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
+                          ggml_tensor *dst) try {
+    const int64_t ne = ggml_nelements(src0);
+    GGML_ASSERT(ne == ggml_nelements(src1));
+
+    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
+    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
+
+    GGML_TENSOR_BINARY_OP_LOCALS01;
+
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+    queue_ptr main_stream = ctx.stream();
+
+    char * src0_ddc = (char *) src0->data;
+    char * src1_ddc = (char *) src1->data;
+
+    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
+        ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
+        ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
+        ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f16_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) {
+        ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) {
+        ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else {
+        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
+                ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ABORT("fatal error");
+    }
+
+    (void) dst;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_dup(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    // TODO: why do we pass dst as src1 here?
+    ggml_sycl_cpy(ctx, src0, dst, nullptr);
+    (void) src1;
+}
+
+static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_diag_mask_inf);
+}
+
+static void ggml_sycl_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_soft_max);
+}
+
+static void ggml_sycl_rope(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rope);
+}
+
+static void ggml_sycl_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pool2d);
+}
+
+static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_im2col);
+}
+
+static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum_rows);
+}
+
+static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argsort);
+}
+
+static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    (void) src0;
+    (void) src1;
+    (void) dst;
+}
+
+static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
+}
+
+void ggml_sycl_set_main_device(const int main_device) try {
+    if (dpct::get_current_device_id() == main_device) return;
+    check_allow_gpu_index(main_device);
+    dpct::select_device(main_device);
+
+    if (g_ggml_sycl_debug) {
+        dpct::device_info prop;
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
+            prop, dpct::dev_mgr::instance().get_device(main_device))));
+        fprintf(stderr, "Using device %d (%s) as main device\n",
+                main_device, prop.get_name());
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tensor * tensor) {
+    if (!g_sycl_loaded) return false;
+
+    ggml_sycl_func_t func;
+
+    switch (tensor->op) {
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            func = ggml_sycl_op_conv_transpose_1d;
+            break;
+        case GGML_OP_REPEAT:
+            func = ggml_sycl_repeat;
+            break;
+        case GGML_OP_GET_ROWS:
+            func = ggml_sycl_get_rows;
+            break;
+        case GGML_OP_DUP:
+            func = ggml_sycl_dup;
+            break;
+        case GGML_OP_ADD:
+            func = ggml_sycl_add;
+            break;
+        case GGML_OP_ACC:
+            func = ggml_sycl_acc;
+            break;
+        case GGML_OP_MUL:
+            func = ggml_sycl_mul;
+            break;
+        case GGML_OP_DIV:
+            func = ggml_sycl_div;
+            break;
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(tensor)) {
+                case GGML_UNARY_OP_GELU:
+                    func = ggml_sycl_gelu;
+                    break;
+                case GGML_UNARY_OP_SILU:
+                    func = ggml_sycl_silu;
+                    break;
+                case GGML_UNARY_OP_GELU_QUICK:
+                    func = ggml_sycl_gelu_quick;
+                    break;
+                case GGML_UNARY_OP_TANH:
+                    func = ggml_sycl_tanh;
+                    break;
+                case GGML_UNARY_OP_RELU:
+                    func = ggml_sycl_relu;
+                    break;
+                case GGML_UNARY_OP_HARDSIGMOID:
+                    func = ggml_sycl_hardsigmoid;
+                    break;
+                case GGML_UNARY_OP_HARDSWISH:
+                    func = ggml_sycl_hardswish;
+                    break;
+                default:
+                    return false;
+            }
+            break;
+        case GGML_OP_NORM:
+            func = ggml_sycl_norm;
+            break;
+        case GGML_OP_GROUP_NORM:
+            func = ggml_sycl_group_norm;
+            break;
+        case GGML_OP_CONCAT:
+            func = ggml_sycl_op_concat;
+            break;
+        case GGML_OP_UPSCALE:
+            func = ggml_sycl_upscale;
+            break;
+        case GGML_OP_PAD:
+            func = ggml_sycl_pad;
+            break;
+        case GGML_OP_LEAKY_RELU:
+            func = ggml_sycl_leaky_relu;
+            break;
+        case GGML_OP_RMS_NORM:
+            func = ggml_sycl_rms_norm;
+            break;
+        case GGML_OP_MUL_MAT:
+            if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
+                return false;
+            }
+            func = ggml_sycl_mul_mat;
+            break;
+        case GGML_OP_MUL_MAT_ID:
+            if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
+                return false;
+            }
+            func = ggml_sycl_mul_mat_id;
+            break;
+        case GGML_OP_SCALE:
+            func = ggml_sycl_scale;
+            break;
+        case GGML_OP_SQR:
+            func = ggml_sycl_sqr;
+            break;
+        case GGML_OP_CLAMP:
+            func = ggml_sycl_clamp;
+            break;
+        case GGML_OP_CPY:
+            func = ggml_sycl_cpy;
+            break;
+        case GGML_OP_CONT:
+            func = ggml_sycl_dup;
+            break;
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+            func = ggml_sycl_nop;
+            break;
+        case GGML_OP_DIAG_MASK_INF:
+            func = ggml_sycl_diag_mask_inf;
+            break;
+        case GGML_OP_SOFT_MAX:
+            func = ggml_sycl_soft_max;
+            break;
+        case GGML_OP_ROPE:
+            func = ggml_sycl_rope;
+            break;
+        case GGML_OP_IM2COL:
+            func = ggml_sycl_im2col;
+            break;
+        case GGML_OP_POOL_2D:
+            func = ggml_sycl_pool2d;
+            break;
+        case GGML_OP_SUM_ROWS:
+            func = ggml_sycl_sum_rows;
+            break;
+        case GGML_OP_ARGSORT:
+            func = ggml_sycl_argsort;
+            break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            func = ggml_sycl_op_timestep_embedding;
+            break;
+        default:
+            return false;
+    }
+
+    if (tensor->src[0] != nullptr && ggml_backend_buffer_is_sycl_split(tensor->src[0]->buffer)) {
+        ggml_sycl_set_peer_access(tensor->src[1]->ne[1], ctx.device);
+    }
+
+    func(ctx, tensor->src[0], tensor->src[1], tensor);
+    return true;
+}
+
+GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n");
+    for(int i=0;i<max_len;i++) id_list[i] = -1;
+
+    for (int i=0;i< ggml_sycl_info().device_count;i++){
+        if (i>=max_len) break;
+        id_list[i] = i;
+    }
+    return;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+int ggml_sycl_get_device_count() try {
+    int device_count;
+    if (CHECK_TRY_ERROR(device_count =
+                             dpct::dev_mgr::instance().device_count()) != 0) {
+        return 0;
+    }
+    return device_count;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description,
+                                      size_t description_size) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n");
+    dpct::device_info prop;
+    SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
+        prop, dpct::dev_mgr::instance().get_device(device))));
+    snprintf(description, description_size, "%s", prop.get_name());
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free,
+                                                   size_t *total) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_memory\n");
+    ggml_sycl_set_device(device);
+
+    /*
+    DPCT1009:218: SYCL uses exceptions to report errors and does not use the
+    error codes. The original code was commented out and a warning string was
+    inserted. You need to rewrite this code.
+    */
+    /*
+    DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for
+    device information which may not be supported by all compilers or runtimes.
+    You may need to adjust the code.
+    */
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        dpct::dev_mgr::instance().get_device(device).get_memory_info(*free, *total)));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+// backend interface
+
+#define UNUSED GGML_UNUSED
+
+// sycl buffer
+
+struct ggml_backend_sycl_buffer_context {
+    int device;
+    void * dev_ptr = nullptr;
+    queue_ptr stream;
+    std::string name;
+
+     ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream) :
+        device(device), dev_ptr(dev_ptr), stream(stream) {
+            check_allow_gpu_index(device);
+            name = (GGML_SYCL_NAME + std::to_string(device));
+        }
+
+
+    ~ggml_backend_sycl_buffer_context() {
+        if (dev_ptr != nullptr) {
+            ggml_sycl_set_device(device);
+            SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(dev_ptr, *stream)));
+        }
+    }
+};
+
+GGML_CALL static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) {
+    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
+    return ctx->name.c_str();
+}
+
+GGML_CALL static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name;
+}
+
+static void
+ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try {
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    ggml_sycl_set_device(ctx->device);
+
+    delete ctx;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) {
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    return ctx->dev_ptr;
+}
+
+GGML_CALL static void
+ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
+                                     ggml_tensor *tensor) try {
+    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
+
+    if (tensor->view_src != NULL && tensor->view_offs == 0) {
+        assert(tensor->view_src->buffer->buft == buffer->buft);
+        tensor->backend = tensor->view_src->backend;
+        tensor->extra = tensor->view_src->extra;
+        return;
+    }
+
+
+    if (ggml_is_quantized(tensor->type)) {
+        // initialize padding to 0 to avoid possible NaN values
+        size_t original_size = ggml_nbytes(tensor);
+        size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
+
+        if (padded_size > original_size && tensor->view_src == nullptr) {
+            SYCL_CHECK(CHECK_TRY_ERROR(ctx->stream->memset(
+                (char *)tensor->data + original_size, 0,
+                padded_size - original_size).wait()));
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
+                                                ggml_tensor *tensor,
+                                                const void *data, size_t offset,
+                                                size_t size) try {
+
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+
+    ggml_sycl_set_device(ctx->device);
+    auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue());
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
+    char* host_buf = (char*)malloc(size);
+    memcpy(host_buf, data, size);
+    SYCL_CHECK(
+        CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size)
+                             .wait()));
+    free(host_buf);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer,
+                                                const ggml_tensor *tensor,
+                                                void *data, size_t offset,
+                                                size_t size) try {
+
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+
+    ggml_sycl_set_device(ctx->device);
+    auto stream = dpct::dev_mgr::instance().get_device(ctx->device).default_queue();
+
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        stream.memcpy(data, (const char *)tensor->data + offset, size)
+            .wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static bool
+ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
+                                    const ggml_tensor *src,
+                                    ggml_tensor *dst) try {
+    if (ggml_backend_buffer_is_sycl(src->buffer)) {
+        ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context;
+        ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)dst->buffer->context;
+
+        ggml_sycl_set_device(src_ctx->device);
+        /*
+        DPCT1009:198: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw()));
+        ggml_sycl_set_device(dst_ctx->device);
+        /*
+        DPCT1009:199: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw()));
+        /*
+        DPCT1009:200: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+
+        queue_ptr stream_dst = dst_ctx->stream;
+        queue_ptr stream_src = src_ctx->stream;
+        size_t size = ggml_nbytes(src);
+
+        //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs.
+        dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size);
+
+//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove
+#if 0
+        SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy(
+            (char *)dst->data, (const char *)src->data, size).wait()));
+
+        /*
+        DPCT1009:201: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw()));
+#endif
+        return true;
+    }
+    return false;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+
+static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer,
+                                           uint8_t value) try {
+     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+
+    ggml_sycl_set_device(ctx->device);
+    queue_ptr stream = ctx->stream;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw()));
+
+    SYCL_CHECK(CHECK_TRY_ERROR((*stream)
+                                    .memset(ctx->dev_ptr, value, buffer->size)
+                                    .wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = {
+    /* .get_name        = */ ggml_backend_sycl_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_sycl_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_sycl_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_sycl_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_sycl_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_sycl_buffer_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_sycl_buffer_cpy_tensor,
+    /* .clear           = */ ggml_backend_sycl_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// sycl buffer type
+struct ggml_backend_sycl_buffer_type_context {
+    int device;
+    std::string name;
+
+    // each buffer type has its own stream
+    queue_ptr stream = nullptr;
+};
+
+GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
+
+    return ctx->name.c_str();
+}
+GGML_CALL static ggml_backend_buffer_t
+ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
+                                           size_t size) try {
+    ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
+    ggml_sycl_set_device(buft_ctx->device);
+    const queue_ptr stream = buft_ctx->stream;
+    size = std::max(size, (size_t)1); // syclMalloc returns null for size 0
+
+    void * dev_ptr;
+    SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device(
+                                    size, *stream)));
+    ggml_backend_sycl_buffer_context * ctx = new  ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream);
+    return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
+    UNUSED(buft);
+}
+
+static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+    return dpct::get_current_device().get_max_mem_alloc_size();
+
+    UNUSED(buft);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    size_t size = ggml_nbytes(tensor);
+    int64_t ne0 = tensor->ne[0];
+
+    if (ggml_is_quantized(tensor->type)) {
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+    }
+
+    return size;
+
+    UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_sycl_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_sycl_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_sycl_buffer_type_get_alignment,
+    /* .get_max_size     = */ ggml_backend_sycl_buffer_type_get_max_size,
+    /* .get_alloc_size   = */ ggml_backend_sycl_buffer_type_get_alloc_size,
+    /* .is_host          = */ nullptr,
+};
+
+ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n");
+
+    if (device>=ggml_sycl_info().device_count or device<0) {
+        printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
+            device, ggml_sycl_info().device_count-1);
+        GGML_ASSERT(device<ggml_sycl_info().device_count);
+    }
+    static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_types[GGML_SYCL_MAX_DEVICES];
+
+    static bool ggml_backend_sycl_buffer_type_initialized = false;
+
+    if (!ggml_backend_sycl_buffer_type_initialized) {
+        for (int i = 0; i < ggml_sycl_info().device_count; i++) {
+            auto & device_i = dpct::dev_mgr::instance().get_device(i);
+            queue_ptr stream = &(device_i.default_queue());
+            ggml_backend_sycl_buffer_types[i] = {
+                /* .iface    = */ ggml_backend_sycl_buffer_type_interface,
+                /* .context  = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i), stream},
+            };
+        }
+        ggml_backend_sycl_buffer_type_initialized = true;
+    }
+    return &ggml_backend_sycl_buffer_types[device];
+}
+
+ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(ggml_backend_sycl_context * ctx) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n");
+
+    int device = ctx->device;
+    if (device>=ggml_sycl_info().device_count or device<0) {
+        printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
+            device, ggml_sycl_info().device_count-1);
+        GGML_ASSERT(device<ggml_sycl_info().device_count);
+    }
+    static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_types[GGML_SYCL_MAX_DEVICES];
+
+    static bool ggml_backend_sycl_buffer_type_initialized = false;
+
+    if (!ggml_backend_sycl_buffer_type_initialized) {
+        for (int i = 0; i < ggml_sycl_info().device_count; i++) {
+            ggml_backend_sycl_buffer_types[i] = {
+                /* .iface    = */ ggml_backend_sycl_buffer_type_interface,
+                /* .context  = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i), ctx->stream(i, 0)},
+            };
+        }
+        ggml_backend_sycl_buffer_type_initialized = true;
+    }
+    return &ggml_backend_sycl_buffer_types[device];
+}
+
+// sycl split buffer type
+static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split, int id) {
+    const int64_t nrows = ggml_nrows(tensor);
+    const int64_t rounding = get_row_rounding(tensor->type, tensor_split);
+
+    *row_low = id == 0 ? 0 : nrows*tensor_split[id];
+    *row_low -= *row_low % rounding;
+    if (id == ggml_sycl_info().device_count - 1) {
+        *row_high = nrows;
+    } else {
+        *row_high = nrows*tensor_split[id + 1];
+        *row_high -= *row_high % rounding;
+    }
+}
+
+struct ggml_backend_sycl_split_buffer_context {
+    ~ggml_backend_sycl_split_buffer_context() try {
+        for (ggml_tensor_extra_gpu * extra : tensor_extras) {
+            for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+                for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) {
+                    if (extra->events[i][is] != nullptr) {
+                        /*
+                        DPCT1009:206: SYCL uses exceptions to report errors and
+                        does not use the error codes. The original code was
+                        commented out and a warning string was inserted. You
+                        need to rewrite this code.
+                        */
+                        SYCL_CHECK(CHECK_TRY_ERROR(
+                            dpct::destroy_event(extra->events[i][is])));
+                    }
+                }
+                if (extra->data_device[i] != nullptr) {
+                    /*
+                    DPCT1009:207: SYCL uses exceptions to report errors and does
+                    not use the error codes. The original code was commented out
+                    and a warning string was inserted. You need to rewrite this
+                    code.
+                    */
+                    ggml_sycl_set_device(i);
+                    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(
+                        extra->data_device[i], *(streams[i]))));
+                }
+            }
+            delete extra;
+        }
+    }
+    catch (sycl::exception const &exc) {
+      std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+                << ", line:" << __LINE__ << std::endl;
+      std::exit(1);
+    }
+
+    std::vector<ggml_tensor_extra_gpu *> tensor_extras;
+    std::vector<queue_ptr> streams;
+};
+
+GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) {
+    return GGML_SYCL_NAME "_Split";
+
+    UNUSED(buffer);
+}
+
+static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) {
+   return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name;
+}
+
+GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
+    delete ctx;
+}
+
+GGML_CALL static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) {
+    // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
+    return (void *)0x1000;
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
+                                           ggml_tensor *tensor) try {
+    GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
+
+    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
+
+    ctx->tensor_extras.push_back(extra);
+        ctx->streams.push_back(&(dpct::get_current_device().default_queue()));
+
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        // FIXME: do not crash if cudaMalloc fails
+        // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
+        ggml_sycl_set_device(i);
+        const queue_ptr stream = ctx->streams[i];
+        char * buf;
+        /*
+        DPCT1009:208: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device(
+                                        size, *stream)));
+
+        // set padding to 0 to avoid possible NaN values
+        if (size > original_size) {
+            /*
+            DPCT1009:209: SYCL uses exceptions to report errors and does not use
+            the error codes. The original code was commented out and a warning
+            string was inserted. You need to rewrite this code.
+            */
+            SYCL_CHECK(CHECK_TRY_ERROR(
+                (*stream)
+                    .memset(buf + original_size, 0, size - original_size)
+                    .wait()));
+        }
+
+        extra->data_device[i] = buf;
+
+        for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) {
+            /*
+            DPCT1009:210: SYCL uses exceptions to report errors and does not use
+            the error codes. The original code was commented out and a warning
+            string was inserted. You need to rewrite this code.
+            */
+            SYCL_CHECK(
+                CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event()));
+        }
+    }
+    tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT;
+    tensor->extra = extra;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer,
+                                          ggml_tensor *tensor, const void *data,
+                                          size_t offset, size_t size) try {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+
+    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
+
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        const char * buf_host = (const char *)data + offset_split;
+        /*
+        DPCT1009:211: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        ggml_sycl_set_device(i);
+        const queue_ptr stream = ctx->streams[i];
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            (*stream)
+                .memcpy(extra->data_device[i], buf_host, original_size)
+                .wait()));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer,
+                                          const ggml_tensor *tensor, void *data,
+                                          size_t offset, size_t size) try {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+
+    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
+
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        char * buf_host = (char *)data + offset_split;
+        /*
+        DPCT1009:212: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        ggml_sycl_set_device(i);
+        const queue_ptr stream = ctx->streams[i];
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            (*stream)
+                .memcpy(buf_host, extra->data_device[i], original_size)
+                .wait()));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    UNUSED(buffer);
+    UNUSED(value);
+}
+
+static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = {
+    /* .get_name        = */ ggml_backend_sycl_split_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_sycl_split_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_sycl_split_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_sycl_split_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_sycl_split_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_sycl_split_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_sycl_split_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+GGML_CALL static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_SYCL_NAME "_Split";
+
+    UNUSED(buft);
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
+    // instead, we allocate them for each tensor separately in init_tensor
+    // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
+    // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct.
+    ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context();
+
+    return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
+    UNUSED(buft);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context;
+
+    size_t total_size = 0;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        total_size += ggml_nbytes_split(tensor, nrows_split);
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+    }
+
+    return total_size;
+}
+
+GGML_CALL static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+    return false;
+
+    UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_sycl_split_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_sycl_split_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_sycl_split_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_sycl_split_buffer_type_get_alloc_size,
+    /* .is_host          = */ ggml_backend_sycl_split_buffer_type_is_host,
+};
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n");
+    ggml_check_sycl();
+    // FIXME: this is not thread safe
+    static std::map<std::array<float, GGML_SYCL_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
+
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split_arr = {};
+
+    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; });
+    if (all_zero) {
+        tensor_split_arr = ggml_sycl_info().default_tensor_split;
+    } else {
+        float split_sum = 0.0f;
+        for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+            tensor_split_arr[i] = split_sum;
+            split_sum += tensor_split[i];
+        }
+        for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
+            tensor_split_arr[i] /= split_sum;
+        }
+    }
+
+    auto it = buft_map.find(tensor_split_arr);
+    if (it != buft_map.end()) {
+        return &it->second;
+    }
+
+    struct ggml_backend_buffer_type buft {
+        /* .iface   = */ ggml_backend_sycl_split_buffer_type_interface,
+        /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr},
+    };
+
+    auto result = buft_map.emplace(tensor_split_arr, buft);
+    return &result.first->second;
+}
+
+// host buffer type
+
+GGML_CALL static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_SYCL_NAME "_Host";
+
+    UNUSED(buft);
+}
+
+GGML_CALL static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) {
+    return GGML_SYCL_NAME "_Host";
+
+    UNUSED(buffer);
+}
+
+static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_sycl_host_free(buffer->context);
+}
+
+static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    void * ptr = ggml_sycl_host_malloc(size);
+
+    if (ptr == nullptr) {
+        // fallback to cpu buffer
+        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+    }
+
+    // FIXME: this is a hack to avoid having to implement a new buffer type
+    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
+    buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_sycl_host_buffer_name;
+    buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer;
+
+    return buffer;
+}
+
+ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n");
+    static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = {
+        /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_sycl_host_buffer_type_name,
+            /* .alloc_buffer     = */ ggml_backend_sycl_host_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
+            /* .get_max_size     = */ NULL, // TODO: return device.maxBufferLength
+            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
+        },
+        /* .context  = */ nullptr,
+    };
+
+    return &ggml_backend_sycl_buffer_type_host;
+}
+
+// backend
+
+GGML_CALL static const char * ggml_backend_sycl_name(ggml_backend_t backend) {
+
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+
+    return sycl_ctx->name.c_str();
+}
+
+GGML_CALL static void ggml_backend_sycl_free(ggml_backend_t backend) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+
+    delete sycl_ctx;
+    delete backend;
+}
+
+
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    return ggml_backend_sycl_buffer_type(sycl_ctx->device);
+}
+
+GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
+                                               ggml_tensor *tensor,
+                                               const void *data, size_t offset,
+                                               size_t size) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+    GGML_ASSERT(buf->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
+    const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0);
+    SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy(
+        (char *)tensor->data + offset, data, size).wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
+                                               const ggml_tensor *tensor,
+                                               void *data, size_t offset,
+                                               size_t size) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+    GGML_ASSERT(buf->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
+    const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0);
+    SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy(
+        data, (const char *)tensor->data + offset, size).wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend,
+                                                         const ggml_tensor *src,
+                                                         ggml_tensor *dst) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) {
+        /*
+        DPCT1009:215: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0);
+        SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy(
+            dst->data, src->data, ggml_nbytes(dst)).wait()));
+        return true;
+    }
+
+    return false;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0);
+    SYCL_CHECK(CHECK_TRY_ERROR((stream)->wait()));
+
+    UNUSED(backend);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    ggml_sycl_set_main_device(sycl_ctx->device);
+
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+            continue;
+        }
+#ifndef NDEBUG
+        assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            if (node->src[j] != nullptr) {
+                assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
+            }
+        }
+#endif
+        bool ok = ggml_sycl_compute_forward(*sycl_ctx, node);
+        if (!ok) {
+            fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
+        }
+        GGML_ASSERT(ok);
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
+GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+    switch (op->op) {
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                ggml_type src1_type = op->src[1]->type;
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                return false;
+            } break;
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_SILU:
+                case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_HARDSIGMOID:
+                case GGML_UNARY_OP_HARDSWISH:
+                case GGML_UNARY_OP_GELU_QUICK:
+                case GGML_UNARY_OP_TANH:
+                    return ggml_is_contiguous(op->src[0]);
+                default:
+                    return false;
+            }
+            break;
+        case GGML_OP_MUL_MAT:
+        case GGML_OP_MUL_MAT_ID:
+            {
+                struct ggml_tensor * a;
+                struct ggml_tensor * b;
+                if (op->op == GGML_OP_MUL_MAT) {
+                    a = op->src[0];
+                    b = op->src[1];
+                } else {
+                    a = op->src[2];
+                    b = op->src[1];
+                }
+                if (a->ne[3] != b->ne[3]) {
+                    return false;
+                }
+                ggml_type a_type = a->type;
+                if (a_type == GGML_TYPE_IQ4_NL  || a_type == GGML_TYPE_IQ4_XS ||
+                    a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ3_S  ||
+                    a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ2_S ||
+                    a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ1_M
+                    ) {
+                    if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
+                        return false;
+                    }
+                }
+                ggml_type src0_type = op->src[0]->type;
+                if (src0_type == GGML_TYPE_BF16) {
+                    return false;
+                }
+                return true;
+            } break;
+        case GGML_OP_GET_ROWS:
+            {
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                        return true;
+                    default:
+                        return false;
+                }
+            } break;
+        case GGML_OP_CPY:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                ggml_type src1_type = op->src[1]->type;
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                return false;
+            } break;
+        case GGML_OP_CONCAT:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                int dim = op->op_params[0];
+                return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16 && dim == 2;
+            } break;
+        case GGML_OP_DUP:
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_REPEAT:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_NORM:
+        case GGML_OP_ADD:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SCALE:
+        case GGML_OP_SQR:
+        case GGML_OP_CLAMP:
+        case GGML_OP_CONT:
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_SOFT_MAX:
+            return true;
+        case GGML_OP_ROPE:
+            return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_IM2COL:
+        case GGML_OP_POOL_2D:
+        case GGML_OP_SUM_ROWS:
+        case GGML_OP_ARGSORT:
+        case GGML_OP_ACC:
+        case GGML_OP_GROUP_NORM:
+        case GGML_OP_UPSCALE:
+        case GGML_OP_PAD:
+        case GGML_OP_LEAKY_RELU:
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            return true;
+        default:
+            return false;
+    }
+
+    UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+    const int min_batch_size = 32;
+    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID;
+    GGML_UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_sycl_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    if (buft->iface.get_name != ggml_backend_sycl_buffer_type_name) {
+        return false;
+    }
+    ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    return buft_ctx->device == sycl_ctx->device;
+}
+
+static ggml_backend_i ggml_backend_sycl_interface = {
+    /* .get_name                = */ ggml_backend_sycl_name,
+    /* .free                    = */ ggml_backend_sycl_free,
+    /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type,
+    /* .set_tensor_async        = */ ggml_backend_sycl_set_tensor_async,
+    /* .get_tensor_async        = */ ggml_backend_sycl_get_tensor_async,
+    /* .cpy_tensor_async        = */ NULL, //ggml_backend_sycl_cpy_tensor_async, // TODO: update for the new interface
+    /* .synchronize             = */ ggml_backend_sycl_synchronize,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_sycl_graph_compute,
+    /* .supports_op             = */ ggml_backend_sycl_supports_op,
+    /* .supports_buft           = */ ggml_backend_sycl_supports_buft,
+    /* .offload_op              = */ ggml_backend_sycl_offload_op,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_sycl_guid() {
+    static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 };
+    return &guid;
+}
+
+GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_init\n");
+    ggml_check_sycl();
+
+    check_allow_gpu_index(device);
+
+    ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context(device);
+    if (ctx == nullptr) {
+        fprintf(stderr, "%s: error: failed to allocate context\n", __func__);
+        return nullptr;
+    };
+
+    ggml_backend_t sycl_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_sycl_guid(),
+        /* .interface = */ ggml_backend_sycl_interface,
+        /* .context   = */ ctx
+    };
+
+    return sycl_backend;
+}
+
+bool ggml_backend_is_sycl(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid());
+}
+
+GGML_CALL int ggml_backend_sycl_get_device_count() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n");
+    return ggml_sycl_info().device_count;
+}
+
+GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) {
+    ggml_backend_t sycl_backend = ggml_backend_sycl_init((int) (intptr_t) user_data);
+    return sycl_backend;
+
+    UNUSED(params);
+}
+
+extern "C" int ggml_backend_sycl_reg_devices();
+
+int ggml_backend_sycl_reg_devices() {
+    assert(ggml_sycl_info().device_count>0);
+    for (int i = 0; i < ggml_sycl_info().device_count; i++) {
+        char name[128];
+        snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, i);
+        ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i);
+    }
+    return ggml_sycl_info().device_count;
+}
diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp
new file mode 100644
index 0000000000000..58dd9c9a60e7d
--- /dev/null
+++ b/ggml/src/ggml-sycl/backend.hpp
@@ -0,0 +1,29 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_BACKEND_HPP
+#define GGML_SYCL_BACKEND_HPP
+
+#include "concat.hpp"
+#include "common.hpp"
+#include "conv.hpp"
+#include "convert.hpp"
+#include "dequantize.hpp"
+#include "dmmv.hpp"
+#include "mmq.hpp"
+#include "mmvq.hpp"
+#include "rope.hpp"
+#include "norm.hpp"
+#include "softmax.hpp"
+#include "tsembd.hpp"
+
+#endif // GGML_SYCL_BACKEND_HPP
diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp
new file mode 100644
index 0000000000000..e878f4f50f09e
--- /dev/null
+++ b/ggml/src/ggml-sycl/common.cpp
@@ -0,0 +1,53 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include "common.hpp"
+
+int get_current_device_id() {
+  return dpct::dev_mgr::instance().current_device_id();
+}
+
+void* ggml_sycl_host_malloc(size_t size) try {
+  if (getenv("GGML_SYCL_NO_PINNED") != nullptr) {
+    return nullptr;
+  }
+
+  void* ptr = nullptr;
+  // allow to use dpct::get_in_order_queue() for host malloc
+  dpct::err0 err = CHECK_TRY_ERROR(
+      ptr = (void*)sycl::malloc_host(size, dpct::get_in_order_queue()));
+
+  if (err != 0) {
+    // clear the error
+    fprintf(
+        stderr,
+        "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
+        size / 1024.0 / 1024.0,
+        "syclGetErrorString is not supported");
+    return nullptr;
+  }
+
+  return ptr;
+} catch (sycl::exception const& exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+void ggml_sycl_host_free(void* ptr) try {
+  // allow to use dpct::get_in_order_queue() for host malloc
+  SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, dpct::get_in_order_queue())));
+} catch (sycl::exception const& exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
new file mode 100644
index 0000000000000..86d8b40e8b013
--- /dev/null
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -0,0 +1,355 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_COMMON_HPP
+#define GGML_SYCL_COMMON_HPP
+
+#include <fstream>
+#include <iostream>
+
+#include "dpct/helper.hpp"
+#include "ggml-sycl.h"
+#include "presets.hpp"
+
+#define GGML_COMMON_DECL_SYCL
+#define GGML_COMMON_IMPL_SYCL
+#include "ggml-common.h"
+
+void* ggml_sycl_host_malloc(size_t size);
+void ggml_sycl_host_free(void* ptr);
+
+static int g_ggml_sycl_debug = 0;
+#define GGML_SYCL_DEBUG(...)        \
+  do {                              \
+    if (g_ggml_sycl_debug)          \
+      fprintf(stderr, __VA_ARGS__); \
+  } while (0)
+
+#define CHECK_TRY_ERROR(expr)                                            \
+  [&]() {                                                                \
+    try {                                                                \
+      expr;                                                              \
+      return dpct::success;                                              \
+    } catch (std::exception const& e) {                                  \
+      std::cerr << e.what() << "\nException caught at file:" << __FILE__ \
+                << ", line:" << __LINE__ << ", func:" << __func__        \
+                << std::endl;                                            \
+      return dpct::default_error;                                        \
+    }                                                                    \
+  }()
+
+
+#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP
+#define VER_4VEC 610 // todo for hardward optimize.
+#define VER_GEN9 700 // todo for hardward optimize.
+#define VER_GEN12 1000000 // todo for hardward optimize.
+#define VER_GEN13 (VER_GEN12 + 1030) // todo for hardward optimize.
+
+#define GGML_SYCL_MAX_NODES 8192 // TODO: adapt to hardwares
+
+// define for XMX in Intel GPU
+// TODO: currently, it's not used for XMX really.
+#if !defined(GGML_SYCL_FORCE_MMQ)
+    #define SYCL_USE_XMX
+#endif
+
+// max batch size to use MMQ kernels when tensor cores are available
+#define MMQ_MAX_BATCH_SIZE 32
+
+#if defined(_MSC_VER)
+#pragma warning(disable : 4244 4267) // possible loss of data
+#endif
+
+// dmmv = dequantize_mul_mat_vec
+#ifndef GGML_SYCL_DMMV_X
+#define GGML_SYCL_DMMV_X 32
+#endif
+#ifndef GGML_SYCL_MMV_Y
+#define GGML_SYCL_MMV_Y 1
+#endif
+
+typedef sycl::queue *queue_ptr;
+
+enum ggml_sycl_backend_gpu_mode {
+  SYCL_UNSET_GPU_MODE = -1,
+  SYCL_SINGLE_GPU_MODE = 0,
+  SYCL_MUL_GPU_MODE
+};
+
+static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size");
+
+static void crash() {
+  int* ptr = NULL;
+  *ptr = 0;
+}
+
+[[noreturn]] static void ggml_sycl_error(
+    const char* stmt,
+    const char* func,
+    const char* file,
+    const int line,
+    const char* msg) {
+  fprintf(stderr, "SYCL error: %s: %s\n", stmt, msg);
+  fprintf(stderr, "  in function %s at %s:%d\n", func, file, line);
+  GGML_ABORT("SYCL error");
+}
+
+#define SYCL_CHECK(err)                     \
+  do {                                      \
+    auto err_ = (err);                      \
+    if (err_ != 0)                          \
+      ggml_sycl_error(                      \
+          #err,                             \
+          __func__,                         \
+          __FILE__,                         \
+          __LINE__,                         \
+          "Meet error in this line code!"); \
+  } while (0)
+
+#if DPCT_COMPAT_RT_VERSION >= 11100
+#define GGML_SYCL_ASSUME(x) __builtin_assume(x)
+#else
+#define GGML_SYCL_ASSUME(x)
+#endif // DPCT_COMPAT_RT_VERSION >= 11100
+
+#ifdef GGML_SYCL_F16
+typedef sycl::half dfloat; // dequantize float
+typedef sycl::half2 dfloat2;
+#else
+typedef float dfloat; // dequantize float
+typedef sycl::float2 dfloat2;
+#endif // GGML_SYCL_F16
+
+#define MMVQ_MAX_BATCH_SIZE  8
+
+static const int8_t kvalues_iq4nl[16]={-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+
+static int g_all_sycl_device_count = -1;
+static bool g_ggml_backend_sycl_buffer_type_initialized = false;
+
+static ggml_sycl_backend_gpu_mode g_ggml_sycl_backend_gpu_mode =
+    SYCL_UNSET_GPU_MODE;
+
+static void* g_scratch_buffer = nullptr;
+static size_t g_scratch_size = 0; // disabled by default
+static size_t g_scratch_offset = 0;
+
+[[noreturn]] static inline void bad_arch(const sycl::stream& stream_ct1) {
+  stream_ct1 << "ERROR: ggml-sycl was compiled without support for the "
+                "current GPU architecture.\n";
+  // __trap();
+  std::exit(1);
+
+  (void)bad_arch; // suppress unused function warning
+}
+
+int get_current_device_id();
+
+inline dpct::err0 ggml_sycl_set_device(const int device) try {
+
+  int current_device_id;
+  SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id()));
+
+  // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d,
+  // current_device_id=%d\n", device, current_device);
+  if (device == current_device_id) {
+    return 0;
+  }
+
+  return CHECK_TRY_ERROR(dpct::select_device(device));
+} catch (sycl::exception const& exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  crash();
+  std::exit(1);
+}
+
+//////////////////////
+
+struct ggml_sycl_device_info {
+    int device_count;
+
+    struct sycl_device_info {
+        int     cc;                 // compute capability
+        // int     nsm;                // number of streaming multiprocessors
+        // size_t  smpb;               // max. shared memory per block
+        bool    vmm;                // virtual memory support
+        size_t  total_vram;
+    };
+
+    sycl_device_info devices[GGML_SYCL_MAX_DEVICES] = {};
+
+    std::array<float, GGML_SYCL_MAX_DEVICES> default_tensor_split = {};
+
+    int max_work_group_sizes[GGML_SYCL_MAX_DEVICES] = {0};
+};
+
+const ggml_sycl_device_info & ggml_sycl_info();
+
+struct ggml_sycl_pool {
+    virtual ~ggml_sycl_pool() = default;
+
+    virtual void * alloc(size_t size, size_t * actual_size) = 0;
+    virtual void free(void * ptr, size_t size) = 0;
+};
+
+template<typename T>
+struct ggml_sycl_pool_alloc {
+    ggml_sycl_pool * pool = nullptr;
+    T * ptr = nullptr;
+    size_t actual_size = 0;
+
+    explicit ggml_sycl_pool_alloc(ggml_sycl_pool & pool) : pool(&pool) {
+    }
+
+    ggml_sycl_pool_alloc(ggml_sycl_pool & pool, size_t size) : pool(&pool) {
+        alloc(size);
+    }
+
+    ~ggml_sycl_pool_alloc() {
+        if (ptr != nullptr) {
+            pool->free(ptr, actual_size);
+        }
+    }
+
+    // size is in number of elements
+    T * alloc(size_t size) {
+        GGML_ASSERT(pool != nullptr);
+        GGML_ASSERT(ptr == nullptr);
+        ptr = (T *) pool->alloc(size * sizeof(T), &this->actual_size);
+        return ptr;
+    }
+
+    T * alloc(ggml_sycl_pool & pool, size_t size) {
+        this->pool = &pool;
+        return alloc(size);
+    }
+
+    T * get() {
+        return ptr;
+    }
+
+    ggml_sycl_pool_alloc() = default;
+    ggml_sycl_pool_alloc(const ggml_sycl_pool_alloc &) = delete;
+    ggml_sycl_pool_alloc(ggml_sycl_pool_alloc &&) = delete;
+    ggml_sycl_pool_alloc& operator=(const ggml_sycl_pool_alloc &) = delete;
+    ggml_sycl_pool_alloc& operator=(ggml_sycl_pool_alloc &&) = delete;
+};
+
+// backend interface
+
+struct ggml_tensor_extra_gpu {
+  void* data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split
+                                       // tensors
+  dpct::event_ptr events[GGML_SYCL_MAX_DEVICES]
+                        [GGML_SYCL_MAX_STREAMS]; // events for synchronizing multiple GPUs
+};
+
+struct ggml_backend_sycl_context {
+    int device;
+    std::string name;
+
+    queue_ptr qptrs[GGML_SYCL_MAX_DEVICES][GGML_SYCL_MAX_STREAMS] = { { nullptr } };
+
+    explicit ggml_backend_sycl_context(int device) :
+        device(device),
+        name(GGML_SYCL_NAME + std::to_string(device)) {
+    }
+
+    queue_ptr stream(int device, int stream) {
+        if (qptrs[device][stream] == nullptr) {
+            qptrs[device][stream] = &(dpct::get_device(device).default_queue());
+        }
+        return qptrs[device][stream];
+    }
+
+    queue_ptr stream() {
+        return stream(device, 0);
+    }
+
+    // pool
+    std::unique_ptr<ggml_sycl_pool> pools[GGML_SYCL_MAX_DEVICES];
+
+    static std::unique_ptr<ggml_sycl_pool> new_pool_for_device(queue_ptr qptr, int device);
+
+    ggml_sycl_pool & pool(int device) {
+        if (pools[device] == nullptr) {
+            pools[device] = new_pool_for_device(stream(device,0), device);
+        }
+        return *pools[device];
+    }
+
+    ggml_sycl_pool & pool() {
+        return pool(device);
+    }
+};
+
+// common device functions
+
+static __dpct_inline__ float warp_reduce_sum(float x,
+    const sycl::nd_item<3>& item_ct1) {
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        /*
+        DPCT1096:98: The right-most dimension of the work-group used in the SYCL
+        kernel that calls this function may be less than "32". The function
+        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
+        CPU device. Modify the size of the work-group to ensure that the value
+        of the right-most dimension is a multiple of "32".
+        */
+        x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask);
+    }
+    return x;
+}
+
+static __dpct_inline__ sycl::float2
+warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3>& item_ct1) {
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(),
+            mask);
+        a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(),
+            mask);
+    }
+    return a;
+}
+
+static __dpct_inline__ float warp_reduce_max(float x,
+    const sycl::nd_item<3>& item_ct1) {
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        /*
+        DPCT1096:97: The right-most dimension of the work-group used in the SYCL
+        kernel that calls this function may be less than "32". The function
+        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
+        CPU device. Modify the size of the work-group to ensure that the value
+        of the right-most dimension is a multiple of "32".
+        */
+        x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
+            item_ct1.get_sub_group(), x, mask));
+    }
+    return x;
+}
+
+// Helper for vec loading aligned data
+template <typename Tp, int n>
+inline sycl::vec<Tp, n> vec_aligned_load(const Tp* aligned_ptr) {
+    return *reinterpret_cast<const sycl::vec<Tp, n>*>(aligned_ptr);
+}
+
+// Helper for accessing pointers with no warnings
+template <typename Tp, int dim>
+static __dpct_inline__ Tp* get_pointer(sycl::local_accessor<Tp, dim> acc) {
+    return acc.template get_multi_ptr<sycl::access::decorated::no>().get();
+}
+
+#endif // GGML_SYCL_COMMON_HPP
diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp
new file mode 100644
index 0000000000000..632eedb9d42b8
--- /dev/null
+++ b/ggml/src/ggml-sycl/concat.cpp
@@ -0,0 +1,195 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include "concat.hpp"
+#include "common.hpp"
+
+static void concat_f32_dim0(const float *x, const float *y, float *dst,
+                            const int ne0, const int ne00,
+                            const sycl::nd_item<3> &item_ct1) {
+  int nidx = item_ct1.get_local_id(2) +
+             item_ct1.get_group(2) * item_ct1.get_local_range(2);
+  if (nidx >= ne0) {
+    return;
+  }
+  // operation
+  int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                   item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+  if (nidx < ne00) { // src0
+    int offset_src = nidx + item_ct1.get_group(1) * ne00 +
+                     item_ct1.get_group(0) * ne00 * item_ct1.get_group_range(1);
+    dst[offset_dst] = x[offset_src];
+  } else {
+    int offset_src =
+        nidx - ne00 + item_ct1.get_group(1) * (ne0 - ne00) +
+        item_ct1.get_group(0) * (ne0 - ne00) * item_ct1.get_group_range(1);
+    dst[offset_dst] = y[offset_src];
+  }
+}
+
+static void concat_f32_dim1(const float *x, const float *y, float *dst,
+                            const int ne0, const int ne01,
+                            const sycl::nd_item<3> &item_ct1) {
+  int nidx = item_ct1.get_local_id(2) +
+             item_ct1.get_group(2) * item_ct1.get_local_range(2);
+  if (nidx >= ne0) {
+    return;
+  }
+  // operation
+  int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                   item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+  if (item_ct1.get_group(1) < ne01) { // src0
+    int offset_src =
+        nidx + item_ct1.get_group(1) * ne0 + item_ct1.get_group(0) * ne0 * ne01;
+    dst[offset_dst] = x[offset_src];
+  } else {
+    int offset_src =
+        nidx + (item_ct1.get_group(1) - ne01) * ne0 +
+        item_ct1.get_group(0) * ne0 * (item_ct1.get_group_range(1) - ne01);
+    dst[offset_dst] = y[offset_src];
+  }
+}
+
+static void concat_f32_dim2(const float *x, const float *y, float *dst,
+                            const int ne0, const int ne02,
+                            const sycl::nd_item<3> &item_ct1) {
+  int nidx = item_ct1.get_local_id(2) +
+             item_ct1.get_group(2) * item_ct1.get_local_range(2);
+  if (nidx >= ne0) {
+    return;
+  }
+  // operation
+  int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                   item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+  if (item_ct1.get_group(0) < ne02) { // src0
+    int offset_src = nidx + item_ct1.get_group(1) * ne0 +
+                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+    dst[offset_dst] = x[offset_src];
+  } else {
+    int offset_src =
+        nidx + item_ct1.get_group(1) * ne0 +
+        (item_ct1.get_group(0) - ne02) * ne0 * item_ct1.get_group_range(1);
+    dst[offset_dst] = y[offset_src];
+  }
+}
+
+static void concat_f32_sycl(const float *x, const float *y, float *dst,
+                            int ne00, int ne01, int ne02, int ne0, int ne1,
+                            int ne2, int dim, queue_ptr stream) {
+  int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE;
+  sycl::range<3> gridDim(ne2, ne1, num_blocks);
+  switch (dim) {
+  case 0:
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1);
+        });
+    break;
+  case 1:
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1);
+        });
+    break;
+  default:
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1);
+        });
+    break;
+  }
+}
+
+// non-contiguous kernel (slow)
+static void concat_f32_sycl_non_cont(
+    queue_ptr stream, const char *src0, const char *src1, char *dst,
+    int64_t ne00, int64_t ne01, int64_t ne02, int64_t ne03, uint64_t nb00,
+    uint64_t nb01, uint64_t nb02, uint64_t nb03, int64_t /*ne10*/,
+    int64_t /*ne11*/, int64_t /*ne12*/, int64_t /*ne13*/, uint64_t nb10,
+    uint64_t nb11, uint64_t nb12, uint64_t nb13, int64_t ne0, int64_t ne1,
+    int64_t ne2, int64_t ne3, uint64_t nb0, uint64_t nb1, uint64_t nb2,
+    uint64_t nb3, int32_t dim) {
+  sycl::range<3> gridDim(ne3, ne2, ne1);
+  stream->parallel_for(
+      sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)),
+      [=](sycl::nd_item<3> item_ct1) {
+        int64_t i3 = item_ct1.get_group(0);
+        int64_t i2 = item_ct1.get_group(1);
+        int64_t i1 = item_ct1.get_group(2);
+
+        int64_t o[4] = {0, 0, 0, 0};
+        o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
+
+        const float *x;
+
+        for (int i0 = item_ct1.get_local_id(2); i0 < ne0;
+             i0 += item_ct1.get_local_range(2)) {
+          if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+            x = (const float *)(src0 + (i3)*nb03 + (i2)*nb02 + (i1)*nb01 +
+                                (i0)*nb00);
+          } else {
+            x = (const float *)(src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 +
+                                (i1 - o[1]) * nb11 + (i0 - o[0]) * nb10);
+          }
+
+          float *y = (float *)(dst + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);
+
+          *y = *x;
+        }
+      });
+}
+
+void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst) {
+  queue_ptr stream = ctx.stream();
+
+  const int32_t dim = ((int32_t *)dst->op_params)[0];
+
+  if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+    const float *src0_d = (const float *)src0->data;
+    const float *src1_d = (const float *)src1->data;
+
+    float *dst_d = (float *)dst->data;
+
+    if (dim != 3) {
+      for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+        concat_f32_sycl(
+            src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4),
+            dst_d + i3 * (dst->nb[3] / 4), src0->ne[0], src0->ne[1],
+            src0->ne[2], dst->ne[0], dst->ne[1], dst->ne[2], dim, stream);
+      }
+    } else {
+      const size_t size0 = ggml_nbytes(src0);
+      const size_t size1 = ggml_nbytes(src1);
+
+      SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0).wait()));
+      SYCL_CHECK(CHECK_TRY_ERROR(
+          stream->memcpy(dst_d + size0 / 4, src1_d, size1).wait()));
+    }
+  } else
+    concat_f32_sycl_non_cont(
+        stream, (const char *)src0->data, (const char *)src1->data,
+        (char *)dst->data, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+        src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], src1->ne[0],
+        src1->ne[1], src1->ne[2], src1->ne[3], src1->nb[0], src1->nb[1],
+        src1->nb[2], src1->nb[3], dst->ne[0], dst->ne[1], dst->ne[2],
+        dst->ne[3], dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
+}
diff --git a/ggml/src/ggml-sycl/concat.hpp b/ggml/src/ggml-sycl/concat.hpp
new file mode 100644
index 0000000000000..5a04feaab6b0a
--- /dev/null
+++ b/ggml/src/ggml-sycl/concat.hpp
@@ -0,0 +1,21 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_CONCAT_HPP
+#define GGML_SYCL_CONCAT_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst);
+
+#endif // GGML_SYCL_CONCAT_HPP
diff --git a/ggml/src/ggml-sycl/conv.cpp b/ggml/src/ggml-sycl/conv.cpp
new file mode 100644
index 0000000000000..bc4ab1ddbadf0
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv.cpp
@@ -0,0 +1,99 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include "conv.hpp"
+
+static  void conv_transpose_1d_kernel(
+        const int s0, const int output_size,
+        const int src0_ne0, const int src0_ne1, const int src0_ne2,
+        const int src1_ne0, const int dst_ne0,
+        const float * src0, const float * src1,  float * dst,
+        const sycl::nd_item<3> &item_ct1) {
+    int global_index = item_ct1.get_local_id(2) +
+                       item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (global_index >= output_size) {
+        return;
+    }
+
+    int out_index = global_index / dst_ne0;
+
+    float accumulator = 0;
+
+    for (int c = 0; c < src0_ne2; c++) {
+        int idx = global_index % dst_ne0;
+
+        int kernel_offset = (src0_ne0 * src0_ne1 * c) + (out_index * src0_ne0);
+        int input_offset = src1_ne0 * c;
+
+        for (int i = 0; i < src1_ne0; i++) {
+            if (!(idx >= i*s0 && idx < i*s0 + src0_ne0)) {
+                continue;
+            }
+            int weight_idx = idx - i*s0;
+
+            float kernel_weight = src0[kernel_offset + weight_idx];
+            float input_value =  src1[input_offset+i];
+
+            accumulator += kernel_weight * input_value;
+        }
+    }
+    dst[global_index] = accumulator;
+}
+
+static void conv_transpose_1d_f32_f32_sycl(
+    const int s0, const int output_size,
+    const int src0_ne0, const int src0_ne1, const int src0_ne2,
+    const int src1_ne0, const int dst_ne0,
+    const float *src0, const float *src1, float *dst,
+    const queue_ptr& stream) {
+
+    const int num_blocks = (output_size + SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE - 1) / SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE;
+    const sycl::range<3> block_dims(1, 1, SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, 1, num_blocks);
+    stream->parallel_for(
+        sycl::nd_range<3>(
+            block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) {
+            conv_transpose_1d_kernel(
+                s0, output_size,
+                src0_ne0, src0_ne1, src0_ne2,
+                src1_ne0, dst_ne0,
+                src0, src1, dst, item_ct1);
+        });
+}
+
+void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+    const ggml_tensor *src1, ggml_tensor *dst) {
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+
+    float * dst_d = (float *)dst->data;
+    dpct::queue_ptr stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+
+    const int s0 = opts[0];
+
+    const int64_t output_size = ggml_nelements(dst);
+
+    conv_transpose_1d_f32_f32_sycl(s0, output_size,
+        src0->ne[0], src0->ne[1], src0->ne[2],
+        src1->ne[0], dst->ne[0],
+        src0_d, src1_d, dst_d, stream);
+}
+
diff --git a/ggml/src/ggml-sycl/conv.hpp b/ggml/src/ggml-sycl/conv.hpp
new file mode 100644
index 0000000000000..eb20730f904a6
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv.hpp
@@ -0,0 +1,21 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_CONV_HPP
+#define GGML_SYCL_CONV_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+  const ggml_tensor *src1, ggml_tensor *dst);
+
+#endif // GGML_SYCL_CONV_HPP
diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp
new file mode 100644
index 0000000000000..39c28753cf85c
--- /dev/null
+++ b/ggml/src/ggml-sycl/convert.cpp
@@ -0,0 +1,547 @@
+#include "convert.hpp"
+#include "dequantize.hpp"
+#include "presets.hpp"
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k,
+                             const sycl::nd_item<3> &item_ct1) {
+    const int i = 2 * (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                       item_ct1.get_local_id(2));
+
+    if (i >= k) {
+        return;
+    }
+
+    const int ib = i/qk; // block index
+    const int iqs = (i%qk)/qr; // quant index
+    const int iybs = i - i%qk; // y block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(vx, ib, iqs, v);
+
+    y[iybs + iqs + 0] = v.x();
+    y[iybs + iqs + y_offset] = v.y();
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block_sycl(const void *__restrict__ vx,
+                                  dst_t *__restrict__ y, const int k,
+                                  dpct::queue_ptr stream) {
+    const int num_blocks = (k + 2*SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2*SYCL_DEQUANTIZE_BLOCK_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+        stream->parallel_for(
+            sycl::nd_range<3>(
+                sycl::range<3>(1, 1, num_blocks) *
+                    sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
+                sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                dequantize_block<qk, qr, dequantize_kernel>(vx, y, k, item_ct1);
+            });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q2_K(vx, y, item_ct1);
+                             });
+    }
+#else
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q2_K(vx, y, item_ct1);
+                             });
+    }
+
+#endif
+}
+
+template <typename dst_t>
+static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q3_K(vx, y, item_ct1);
+                             });
+    }
+#else
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q3_K(vx, y, item_ct1);
+                             });
+    }
+#endif
+}
+
+template <typename dst_t>
+static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_0(vx, y, nb32, item_ct1);
+                             });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_1(vx, y, nb32, item_ct1);
+                             });
+    }
+}
+
+
+template <typename dst_t>
+static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(12), cgh);
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_K(vx, y, get_pointer(scale_local_acc), item_ct1);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q5_K(vx, y, item_ct1);
+                             });
+    }
+#else
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q5_K(vx, y, item_ct1);
+                             });
+    }
+
+#endif
+}
+
+template <typename dst_t>
+static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q6_K(vx, y, item_ct1);
+                             });
+    }
+#else
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q6_K(vx, y, item_ct1);
+                             });
+    }
+
+#endif
+}
+
+template <typename dst_t>
+static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq1_s(
+                                     vx, y, item_ct1, iq1s_grid_gpu
+                                     );
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq1_m_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq1_m(
+                                     vx, y, item_ct1, iq1s_grid_gpu
+                                     );
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_xxs(
+                                     vx, y, item_ct1, iq2xxs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int k,
+                                       dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_xs(
+                                     vx, y, item_ct1, iq2xs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq2_s_sycl(const void *vx, dst_t *y, const int k,
+                                      dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_s(vx, y, item_ct1);
+                             });
+        });
+    }
+}
+
+
+template <typename dst_t>
+static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq3_xxs(
+                                     vx, y, item_ct1, iq3xxs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq3_s(
+                                     vx, y, item_ct1, kmask_iq2xs, iq3s_grid);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k,
+                                       dpct::queue_ptr stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+#if QK_K == 64
+    dequantize_row_iq4_nl_sycl(vx, y, k, stream);
+#else
+      {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                  cgh.parallel_for(
+                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                            sycl::range<3>(1, 1, 32),
+                                        sycl::range<3>(1, 1, 32)),
+                      [=](sycl::nd_item<3> item_ct1) {
+                            dequantize_block_iq4_xs(vx, y, item_ct1);
+                      });
+            });
+      }
+#endif
+}
+
+template <typename dst_t>
+static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int k,
+                                       dpct::queue_ptr stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+      {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                  cgh.parallel_for(
+                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                            sycl::range<3>(1, 1, 32),
+                                        sycl::range<3>(1, 1, 32)),
+                      [=](sycl::nd_item<3> item_ct1) {
+                            dequantize_block_iq4_nl(vx, y, item_ct1);
+                      });
+            });
+      }
+}
+
+template <typename src_t, typename dst_t>
+static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    const src_t * x = (src_t *) vx;
+
+    y[i] = x[i];
+}
+
+template <typename src_t, typename dst_t>
+static void convert_unary_sycl(const void *__restrict__ vx,
+                               dst_t *__restrict__ y, const int k,
+                               dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(
+                sycl::range<3>(1, 1, num_blocks) *
+                    sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
+                sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                convert_unary<src_t>(vx, y, k, item_ct1);
+            });
+    }
+}
+
+to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_block_sycl<QK4_0, QR4_0, dequantize_q4_0>;
+        case GGML_TYPE_Q4_1:
+            return dequantize_block_sycl<QK4_1, QR4_1, dequantize_q4_1>;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_sycl<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_sycl<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_sycl<QK8_0, QR8_0, dequantize_q8_0>;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_sycl;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_sycl;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_sycl;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_sycl;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_sycl;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_sycl;
+        case GGML_TYPE_IQ1_M:
+            return dequantize_row_iq1_m_sycl;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_sycl;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_sycl;
+        case GGML_TYPE_IQ2_S:
+            return dequantize_row_iq2_s_sycl;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_sycl;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_sycl;
+        case GGML_TYPE_IQ4_XS:
+            return dequantize_row_iq4_xs_sycl;
+        case GGML_TYPE_IQ4_NL:
+            return dequantize_row_iq4_nl_sycl;
+        case GGML_TYPE_F32:
+            return convert_unary_sycl<float>;
+        default:
+            return nullptr;
+    }
+}
+
+to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_row_q4_0_sycl;
+        case GGML_TYPE_Q4_1:
+            return dequantize_row_q4_1_sycl;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_sycl<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_sycl<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_sycl<QK8_0, QR8_0, dequantize_q8_0>;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_sycl;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_sycl;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_sycl;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_sycl;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_sycl;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_sycl;
+        case GGML_TYPE_IQ1_M:
+            return dequantize_row_iq1_m_sycl;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_sycl;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_sycl;
+        case GGML_TYPE_IQ2_S:
+            return dequantize_row_iq2_s_sycl;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_sycl;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_sycl;
+        case GGML_TYPE_IQ4_XS:
+            return dequantize_row_iq4_xs_sycl;
+        case GGML_TYPE_IQ4_NL:
+            return dequantize_row_iq4_nl_sycl;
+        case GGML_TYPE_F16:
+            return convert_unary_sycl<sycl::half>;
+        default:
+            return nullptr;
+    }
+}
diff --git a/ggml/src/ggml-sycl/convert.hpp b/ggml/src/ggml-sycl/convert.hpp
new file mode 100644
index 0000000000000..b1f10d6355535
--- /dev/null
+++ b/ggml/src/ggml-sycl/convert.hpp
@@ -0,0 +1,27 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_CONVERT_HPP
+#define GGML_SYCL_CONVERT_HPP
+
+#include "common.hpp"
+
+template <typename T>
+using to_t_sycl_t = void (*)(const void *__restrict__ x, T *__restrict__ y,
+                             int k, dpct::queue_ptr stream);
+typedef to_t_sycl_t<float> to_fp32_sycl_t;
+typedef to_t_sycl_t<sycl::half> to_fp16_sycl_t;
+
+to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type);
+to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type);
+
+#endif // GGML_SYCL_CONVERT_HPP
diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp
new file mode 100644
index 0000000000000..ed8ad098bcb2f
--- /dev/null
+++ b/ggml/src/ggml-sycl/dequantize.hpp
@@ -0,0 +1,698 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_DEQUANTIZE_HPP
+#define GGML_SYCL_DEQUANTIZE_HPP
+
+#include "common.hpp"
+
+typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
+
+static __dpct_inline__ void dequantize_q4_0(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q4_0 * x = (const block_q4_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x() = vui & 0xF;
+    v.y() = vui >> 4;
+
+#ifdef GGML_SYCL_F16
+    // v = v - {8.0f, 8.0f};
+    // v = v * {d, d};
+    v.s0() = (v.s0() - 8.0f) * d;
+    v.s1() = (v.s1() - 8.0f) * d;
+
+#else
+    v.x() = (v.x() - 8.0f) * d;
+    v.y() = (v.y() - 8.0f) * d;
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void dequantize_q4_1(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q4_1 * x = (const block_q4_1 *) vx;
+
+    const dfloat d = x[ib].dm[0];
+    const dfloat m = x[ib].dm[1];
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x() = vui & 0xF;
+    v.y() = vui >> 4;
+
+#ifdef GGML_SYCL_F16
+    // v = v * {d, d};
+    // v = v + {m, m};
+    v.s0() = (v.s0() * d) + m;
+    v.s1() = (v.s1() * d) + m;
+
+#else
+    v.x() = (v.x() * d) + m;
+    v.y() = (v.y() * d) + m;
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void dequantize_q5_0(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q5_0 * x = (const block_q5_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x() = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y() = ((x[ib].qs[iqs] >> 4) | xh_1);
+
+#ifdef GGML_SYCL_F16
+    // v = v - {16.0f, 16.0f};
+    // v = v * {d, d};
+    v.s0() = (v.s0() - 16.0f) * d;
+    v.s1() = (v.s1() - 16.0f) * d;
+
+#else
+    v.x() = (v.x() - 16.0f) * d;
+    v.y() = (v.y() - 16.0f) * d;
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void dequantize_q5_1(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q5_1 * x = (const block_q5_1 *) vx;
+
+    const dfloat d = x[ib].dm[0];
+    const dfloat m = x[ib].dm[1];
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x() = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y() = ((x[ib].qs[iqs] >> 4) | xh_1);
+
+#ifdef GGML_SYCL_F16
+    // v = v * {d, d};
+    // v = v + {m, m};
+    v.s0() = (v.s0() * d) + m;
+    v.s1() = (v.s1() * d) + m;
+#else
+    v.x() = (v.x() * d) + m;
+    v.y() = (v.y() * d) + m;
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void dequantize_q8_0(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q8_0 * x = (const block_q8_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    v.x() = x[ib].qs[iqs + 0];
+    v.y() = x[ib].qs[iqs + 1];
+
+#ifdef GGML_SYCL_F16
+    // v = v * {d, d};
+    v.s0() *= d;
+    v.s1() *= d;
+#else
+    v.x() *= d;
+    v.y() *= d;
+#endif // GGML_SYCL_F16
+}
+
+template<typename dst_t>
+static void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_0 * x = (const block_q4_0 *)vx + ib;
+    const float d = sycl::vec<sycl::half, 1>(x->d)
+                        .convert<float, sycl::rounding_mode::automatic>()[0];
+    const float dm = -8*d;
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d * (q[l] & 0xF) + dm;
+        y[l+16] = d * (q[l] >>  4) + dm;
+    }
+}
+
+template<typename dst_t>
+static void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_1 * x = (const block_q4_1 *)vx + ib;
+    const sycl::float2 d =
+        x->dm.convert<float, sycl::rounding_mode::automatic>();
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l + 0] = d.x() * (q[l] & 0xF) + d.y();
+        y[l + 16] = d.x() * (q[l] >> 4) + d.y();
+    }
+}
+
+
+//================================== k-quants
+
+template<typename dst_t>
+static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+    const block_q2_K * x = (const block_q2_K *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int n   = tid/32;
+    const int l   = tid - 32*n;
+    const int is  = 8*n + l/16;
+
+    const uint8_t q = x[i].qs[32*n + l];
+    dst_t * y = yy + i*QK_K + 128*n;
+
+    float dall = x[i].dm[0];
+    float dmin = x[i].dm[1];
+    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
+    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
+    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
+#else
+    const int is = tid/16;  // 0 or 1
+    const int il = tid%16;  // 0...15
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    dst_t * y = yy + i*QK_K + 16*is + il;
+
+    float dall = x[i].dm[0];
+    float dmin = x[i].dm[1];
+    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+    const block_q3_K * x = (const block_q3_K *) vx;
+
+#if QK_K == 256
+    const int r = item_ct1.get_local_id(2) / 4;
+    const int tid = r/2;
+    const int is0 = r%2;
+    const int l0 = 16 * is0 + 4 * (item_ct1.get_local_id(2) % 4);
+    const int n = tid / 4;
+    const int j = tid - 4*n;
+
+    uint8_t m = 1 << (4*n + j);
+    int is = 8*n + 2*j + is0;
+    int shift = 2*j;
+
+    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
+                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
+                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
+                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
+    float d_all = x[i].d;
+    float dl = d_all * (us - 32);
+
+    dst_t * y = yy + i*QK_K + 128*n + 32*j;
+    const uint8_t * q = x[i].qs + 32*n;
+    const uint8_t * hm = x[i].hmask;
+
+    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
+#else
+    const int tid = item_ct1.get_local_id(2);
+    const int is  = tid/16;  // 0 or 1
+    const int il  = tid%16;  // 0...15
+    const int im  = il/8;    // 0...1
+    const int in  = il%8;    // 0...7
+
+    dst_t * y = yy + i*QK_K + 16*is + il;
+
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    const uint8_t h = x[i].hmask[in] >> (2*is + im);
+    const float   d = (float)x[i].d;
+
+    if (is == 0) {
+        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    } else {
+        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    }
+#endif
+
+}
+
+#if QK_K == 256
+static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
+    if (j < 4) {
+        d = q[j] & 63;
+        m = q[j + 4] & 63;
+    } else {
+        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
+        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+    }
+}
+#endif
+
+template<typename dst_t>
+static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  uint8_t* scales_local, const sycl::nd_item<3> &item_ct1) {
+    const block_q4_K * x = (const block_q4_K *) vx;
+
+    const int i = item_ct1.get_group(2);
+
+#if QK_K == 256
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int is  = 2*il;
+    const int n   = 4;
+
+    dst_t * y = yy + i*QK_K + 64*il + n*ir;
+
+    const sycl::half2 dm = x[i].dm;
+    const float dall = dm[0];
+    const float dmin = dm[1];
+
+    if (tid < 12)
+        scales_local[tid] = x[i].scales[tid];
+    item_ct1.barrier(sycl::access::fence_space::local_space);
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, scales_local, sc, m);
+    const float d1 = dall * sc;
+    const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, scales_local, sc, m);
+    const float d2 = dall * sc;
+    const float m2 = dmin * m;
+
+    sycl::vec<uint8_t, n> q_vec = vec_aligned_load<uint8_t, n>(x[i].qs + 32*il + n*ir);
+    for (int l = 0; l < n; ++l) {
+        y[l + 0] = d1 * (q_vec[l] & 0xF) - m1;
+        y[l +32] = d2 * (q_vec[l] >>  4) - m2;
+    }
+#else
+    const int tid = item_ct1.get_local_id(2);
+    const uint8_t * q = x[i].qs;
+    dst_t * y = yy + i*QK_K;
+    const float d = (float)x[i].dm[0];
+    const float m = (float)x[i].dm[1];
+    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
+    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+    const block_q5_K * x = (const block_q5_K *) vx;
+
+    const int i = item_ct1.get_group(2);
+
+#if QK_K == 256
+    // assume 64 threads - this is very slightly better than the one below
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/16;   // il is in 0...3
+    const int ir  = tid%16;   // ir is in 0...15
+    const int is  = 2*il;     // is is in 0...6
+
+    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
+
+    const float dall = x[i].dm[0];
+    const float dmin = x[i].dm[1];
+
+    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
+    const uint8_t * qh = x[i].qh + 2*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+
+    uint8_t   hm  = 1 << (2*il);
+    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
+    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
+    hm <<= 1;
+    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
+    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
+#else
+    const int tid = item_ct1.get_local_id(2);
+    const uint8_t q = x[i].qs[tid];
+    const int im = tid/8;  // 0...3
+    const int in = tid%8;  // 0...7
+    const int is = tid/16; // 0 or 1
+    const uint8_t h = x[i].qh[in] >> im;
+    const float d = x[i].d;
+    dst_t * y = yy + i*QK_K + tid;
+    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
+    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+    const block_q6_K * x = (const block_q6_K *) vx;
+
+    const int i = item_ct1.get_group(2);
+#if QK_K == 256
+
+    // assume 64 threads - this is very slightly better than the one below
+    const int tid = item_ct1.get_local_id(2);
+    const int ip  = tid/32;   // ip is 0 or 1
+    const int il  = tid - 32*ip; // 0...32
+    const int is  = 8*ip + il/16;
+
+    dst_t * y = yy + i*QK_K + 128*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t * ql = x[i].ql + 64*ip + il;
+    const uint8_t   qh = x[i].qh[32*ip + il];
+    const int8_t  * sc = x[i].scales + is;
+
+    y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
+    y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+    y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
+#else
+
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int ip  = tid/16;         // 0 or 1
+    const int il  = tid - 16*ip;    // 0...15
+
+    dst_t * y = yy + i*QK_K + 16*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t   ql = x[i].ql[16*ip + il];
+    const uint8_t   qh = x[i].qh[il] >> (2*ip);
+    const int8_t  * sc = x[i].scales;
+
+    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                     const sycl::nd_item<3> &item_ct1,
+                                     const uint64_t *iq2xxs_grid_ptr,
+                                     const uint8_t *ksigns_iq2xs_ptr,
+                                     const uint8_t *kmask_iq2xs_ptr) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid_ptr + aux8[il]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f);
+#else
+    assert(false);
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                    const sycl::nd_item<3> &item_ct1,
+                                    const uint64_t *iq2xs_grid,
+                                    const uint8_t *ksigns_iq2xs,
+                                    const uint8_t *kmask_iq2xs) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq2_xs * x = (const block_iq2_xs *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                       const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq2_s * x = (const block_iq2_s *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
+#pragma unroll
+    for (int j = 0; j < 8; ++j)
+        y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    assert(false);
+
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                     const sycl::nd_item<3> &item_ct1,
+                                     const uint32_t *iq3xxs_grid,
+                                     const uint8_t *ksigns_iq2xs,
+                                     const uint8_t *kmask_iq2xs) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t  * q3 = x[i].qs + 8*ib;
+    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
+    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
+    const uint32_t aux32 = gas[0] | (gas[1] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                       const sycl::nd_item<3> &item_ct1,
+                       const uint8_t *kmask_iq2xs, const uint32_t *iq3s_grid) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq3_s * x = (const block_iq3_s *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t * qs = x[i].qs + 8*ib;
+    const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
+    const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
+    const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf));
+    const uint8_t signs = x[i].signs[4*ib + il];
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                       const sycl::nd_item<3> &item_ct1,
+                       const uint32_t *iq1s_grid_gpu) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq1_s * x = (const block_iq1_s  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
+    const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1);
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        y[j] = d * (q[j] + delta);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                       const sycl::nd_item<3> &item_ct1,
+                       const uint32_t *iq1s_grid_gpu) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq1_m * x = (const block_iq1_m  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * sc = (const uint16_t *)x[i].scales;
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
+    const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
+    const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        y[j] = d * (q[j] + delta);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq4_nl(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                        const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);
+
+    const int tid = item_ct1.get_local_id(2);
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = (float)x[ib].d;
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
+        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+    }
+
+}
+
+
+template <typename dst_t>
+__dpct_inline__ static void
+dequantize_block_iq4_xs(const void *__restrict__ vx, dst_t *__restrict__ yy,
+                        const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_group(2);
+    const block_iq4_xs * x = (const block_iq4_xs *)vx;
+
+    const int tid = item_ct1.get_local_id(2);
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
+    const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
+        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+    }
+}
+
+
+#endif // GGML_SYCL_DEQUANTIZE_HPP
diff --git a/ggml/src/ggml-sycl/dmmv.cpp b/ggml/src/ggml-sycl/dmmv.cpp
new file mode 100644
index 0000000000000..ae45630e1173d
--- /dev/null
+++ b/ggml/src/ggml-sycl/dmmv.cpp
@@ -0,0 +1,1023 @@
+#include "convert.hpp"
+#include "dmmv.hpp"
+#include "dequantize.hpp"
+#include "presets.hpp"
+
+
+static void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const sycl::half *x = (const sycl::half *)vx;
+
+    // automatic half -> float type cast if dfloat == float
+    v.x() = x[ib + iqs + 0];
+    v.y() = x[ib + iqs + 1];
+}
+
+static void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const float * x = (const float *) vx;
+
+    // automatic half -> float type cast if dfloat == float
+    v.x() = x[ib + iqs + 0];
+    v.y() = x[ib + iqs + 1];
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
+static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows,
+                                   const sycl::nd_item<3> &item_ct1) {
+    // qk = quantized weights per x block
+    // qr = number of quantized weights per data value in x block
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int tid = item_ct1.get_local_id(2);
+
+    const int iter_stride = 2*GGML_SYCL_DMMV_X;
+    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+// partial sum for each thread
+#ifdef GGML_SYCL_F16
+    sycl::half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
+#else
+    float tmp = 0.0f;
+#endif // GGML_SYCL_F16
+
+    for (int i = 0; i < ncols; i += iter_stride) {
+        const int col = i + vals_per_iter*tid;
+        const int ib = (row*ncols + col)/qk; // x block index
+        const int iqs = (col%qk)/qr; // x quant index
+        const int iybs = col - col%qk; // y block start index
+
+// processing >2 values per i iter is faster for fast GPUs
+#pragma unroll
+        for (int j = 0; j < vals_per_iter; j += 2) {
+            // process 2 vals per j iter
+
+            // dequantize
+            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
+            dfloat2 v;
+            dequantize_kernel(vx, ib, iqs + j/qr, v);
+
+            // matrix multiplication
+            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
+#ifdef GGML_SYCL_F16
+            dfloat2 t1{y[iybs + iqs + j / qr + 0],
+                        y[iybs + iqs + j / qr + y_offset]};
+
+            tmp += v * t1;
+#else
+            tmp += v.x() * y[iybs + iqs + j / qr + 0];
+            tmp += v.y() * y[iybs + iqs + j / qr + y_offset];
+#endif // GGML_SYCL_F16
+        }
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (tid == 0) {
+#ifdef GGML_SYCL_F16
+        dst[row] = tmp.x() + tmp.y();
+#else
+        dst[row] = tmp;
+#endif // GGML_SYCL_F16
+    }
+}
+
+static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
+                                         float *dst, const int ncols,
+                                         const int nrows,
+                                         dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols,
+                                                          nrows, item_ct1);
+            });
+    }
+}
+
+/*
+DPCT1110:4: The total declared local variable size in device function
+dequantize_mul_mat_vec_q2_k exceeds 128 bytes and may cause high register
+pressure. Consult with your hardware vendor to find the total register size
+available and adjust the code, or use smaller sub-group size to avoid high
+register pressure.
+*/
+static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
+                                        const float *__restrict__ yy,
+                                        float *__restrict__ dst,
+                                        const int ncols, int nrows,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q2_K * x = (const block_q2_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const int tid =
+        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...15
+    const int ix =
+        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
+    const int q_offset = 32*im + l0;
+    const int s_offset = 8*im;
+    const int y_offset = 128*im + l0;
+
+    uint32_t aux[4];
+    const uint8_t * d = (const uint8_t *)aux;
+    const uint8_t * m = (const uint8_t *)(aux + 2);
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+
+        const float dall = x[i].dm[0];
+        const float dmin = x[i].dm[1];
+
+        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
+        aux[0] = a[0] & 0x0f0f0f0f;
+        aux[1] = a[1] & 0x0f0f0f0f;
+        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
+        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
+            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
+
+        }
+        tmp += dall * sum1 - dmin * sum2;
+
+    }
+#else
+    const int tid = item_ct1.get_local_id(2) /
+                    (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7
+    const int ix = item_ct1.get_local_id(2) %
+                   (2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;
+
+    uint32_t uaux[2];
+    const uint8_t * d = (const uint8_t *)uaux;
+
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint32_t * s = (const uint32_t *)x[i].scales;
+
+        uaux[0] = s[0] & 0x0f0f0f0f;
+        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
+
+        const sycl::float2 dall =
+            x[i].dm.convert<float, sycl::rounding_mode::automatic>();
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t ql = q[l];
+            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
+                  + y[l+16] * d[1] * ((ql >> 2) & 3)
+                  + y[l+32] * d[2] * ((ql >> 4) & 3)
+                  + y[l+48] * d[3] * ((ql >> 6) & 3);
+            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
+        }
+        tmp += dall.x() * sum1 - dall.y() * sum2;
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+/*
+DPCT1110:5: The total declared local variable size in device function
+dequantize_mul_mat_vec_q3_k exceeds 128 bytes and may cause high register
+pressure. Consult with your hardware vendor to find the total register size
+available and adjust the code, or use smaller sub-group size to avoid high
+register pressure.
+*/
+static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx,
+                                        const float *__restrict__ yy,
+                                        float *__restrict__ dst,
+                                        const int ncols, int nrows,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q3_K * x = (const block_q3_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+
+    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask2 = 0x0f0f;
+
+    const int tid =
+        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
+    const int ix =
+        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
+
+    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
+    const int step = 16/K_QUANTS_PER_ITERATION;
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0....15 or 0...7
+
+    const uint8_t m = 1 << (4*im);
+
+    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
+    const int q_offset =  32*im + l0;
+    const int y_offset = 128*im + l0;
+
+    uint16_t utmp[4];
+    const int8_t * s = (const int8_t *)utmp;
+
+    const uint16_t s_shift = 4*im;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+        const uint8_t * h = x[i].hmask + l0;
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
+        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
+
+        const float d = x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < n; ++l) {
+            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
+            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
+        }
+        tmp += d * sum;
+
+    }
+#else
+
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
+    const int in = offset/8;                                 // 0 or 1
+    const int im = offset%8;                                 // 0...7
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint8_t * s = x[i].scales;
+
+        const float dall = (float)x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t hl = x[i].hmask[im+l] >> in;
+            const uint8_t ql = q[l];
+            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
+                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
+                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
+                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+/*
+DPCT1110:6: The total declared local variable size in device function
+dequantize_mul_mat_vec_q4_k exceeds 128 bytes and may cause high register
+pressure. Consult with your hardware vendor to find the total register size
+available and adjust the code, or use smaller sub-group size to avoid high
+register pressure.
+*/
+static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
+                                        const float *__restrict__ yy,
+                                        float *__restrict__ dst,
+                                        const int ncols, int nrows,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    if (row > nrows) return;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q4_K * x = (const block_q4_K *)vx + ib0;
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid =
+        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
+    const int ix =
+        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
+
+    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4
+
+    const int il  = tid/step;                            // 0...3
+    const int ir  = tid - step*il;                       // 0...7 or 0...3
+    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+#if K_QUANTS_PER_ITERATION == 2
+    uint32_t q32[4];
+    const uint8_t * q4 = (const uint8_t *)q32;
+#else
+    uint16_t q16[4];
+    const uint8_t * q4 = (const uint8_t *)q16;
+#endif
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y1 = yy + i*QK_K + y_offset;
+        const float   * y2 = y1 + 128;
+
+        const float dall = x[i].dm[0];
+        const float dmin = x[i].dm[1];
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+#if K_QUANTS_PER_ITERATION == 2
+        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
+        const uint32_t * q2 = q1 + 16;
+
+        q32[0] = q1[0] & 0x0f0f0f0f;
+        q32[1] = q1[0] & 0xf0f0f0f0;
+        q32[2] = q2[0] & 0x0f0f0f0f;
+        q32[3] = q2[0] & 0xf0f0f0f0;
+
+        sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 4; ++l) {
+            s.x() += y1[l] * q4[l + 0]; s.y() += y1[l + 32] * q4[l + 4];
+            s.z() += y2[l] * q4[l + 8]; s.w() += y2[l + 32] * q4[l + 12];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f / 16.f +
+                       s.z() * sc[4] + s.w() * sc[5] * 1.f / 16.f) -
+               dmin * smin;
+#else
+        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
+        const uint16_t * q2 = q1 + 32;
+
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[0] & 0xf0f0;
+        q16[2] = q2[0] & 0x0f0f;
+        q16[3] = q2[0] & 0xf0f0;
+
+        float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 2; ++l) {
+            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
+            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+#endif
+
+    }
+#else
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    float tmp = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const float   * y = yy + i*QK_K + step;
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+        const float d = (float)x[i].dm[0];
+        const float m = (float)x[i].dm[1];
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
+                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
+        }
+        tmp += sum;
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+/*
+DPCT1110:7: The total declared local variable size in device function
+dequantize_mul_mat_vec_q5_k exceeds 128 bytes and may cause high register
+pressure. Consult with your hardware vendor to find the total register size
+available and adjust the code, or use smaller sub-group size to avoid high
+register pressure.
+*/
+static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx,
+                                        const float *__restrict__ yy,
+                                        float *__restrict__ dst,
+                                        const int ncols,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    const int row = item_ct1.get_group(2);
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q5_K * x = (const block_q5_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid = item_ct1.get_local_id(2) / 2; // 0...15
+    const int ix = item_ct1.get_local_id(2) % 2;
+
+    const int il  = tid/4;     // 0...3
+    const int ir  = tid - 4*il;// 0...3
+    const int n   = 2;
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    const uint8_t hm1  = 1 << (2*im);
+    const uint8_t hm2  = hm1 << 4;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    uint16_t q16[8];
+    const uint8_t * q4 = (const uint8_t *)q16;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2) {
+
+        const uint8_t * ql1 = x[i].qs + q_offset;
+        const uint8_t * qh  = x[i].qh + l0;
+        const float   * y1  = yy + i*QK_K + y_offset;
+        const float   * y2  = y1 + 128;
+
+        const float dall = x[i].dm[0];
+        const float dmin = x[i].dm[1];
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        sycl::float4 sum = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        const uint16_t * q1 = (const uint16_t *)ql1;
+        const uint16_t * q2 = q1 + 32;
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[8] & 0x0f0f;
+        q16[2] = (q1[0] >> 4) & 0x0f0f;
+        q16[3] = (q1[8] >> 4) & 0x0f0f;
+        q16[4] = q2[0] & 0x0f0f;
+        q16[5] = q2[8] & 0x0f0f;
+        q16[6] = (q2[0] >> 4) & 0x0f0f;
+        q16[7] = (q2[8] >> 4) & 0x0f0f;
+        for (int l = 0; l < n; ++l) {
+            sum.x() +=
+                y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) +
+                y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0));
+            sum.y() +=
+                y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) +
+                y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0));
+            sum.z() +=
+                y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) +
+                y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0));
+            sum.w() +=
+                y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) +
+                y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0));
+            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
+        }
+        tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] +
+                       sum.w() * sc[5]) -
+               dmin * smin;
+    }
+
+#else
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
+    const int step = tid * K_QUANTS_PER_ITERATION;
+    const int im = step/8;
+    const int in = step%8;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const int8_t  * s = x[i].scales;
+        const float   * y = yy + i*QK_K + step;
+        const float     d = x[i].d;
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            const uint8_t h = x[i].qh[in+j] >> im;
+            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
+                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
+                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
+                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q6_K * x = (const block_q6_K *)vx + ib0;
+
+#if QK_K == 256
+
+    const int tid =
+        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
+    const int ix =
+        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0, 1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
+    const int is = 0;
+#else
+    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
+    const int is = in / 4;
+#endif
+    const int ql_offset = 64*im + l0;
+    const int qh_offset = 32*im + l0;
+    const int s_offset  =  8*im + is;
+    const int y_offset = 128*im + l0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * ql = x[i].ql + ql_offset;
+        const uint8_t * qh = x[i].qh + qh_offset;
+        const int8_t  * s  = x[i].scales + s_offset;
+
+        const float d = x[i].d;
+
+#if K_QUANTS_PER_ITERATION == 1
+        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
+        tmp += sum;
+#else
+        float sum = 0;
+        for (int l = 0; l < 4; ++l) {
+            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp += sum;
+#endif
+
+    }
+
+#else
+
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...7
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);  // 0...3
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + step;
+        const uint8_t * ql = x[i].ql + step;
+        const uint8_t * qh = x[i].qh + step;
+        const int8_t  * s  = x[i].scales;
+
+        const float d = x[i+0].d;
+
+        float sum = 0;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
+                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
+        }
+        tmp += sum;
+
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+
+static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
+}
+
+static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
+}
+
+static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
+}
+
+static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const sycl::range<3> block_dims(1, 1, QK_WARP_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1);
+        });
+}
+
+static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
+}
+
+void ggml_sycl_op_dequantize_mul_mat_vec(
+    ggml_backend_sycl_context & ctx,
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream) {
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
+#ifdef GGML_SYCL_F16
+    ggml_sycl_pool_alloc<sycl::half> src1_dfloat_a(ctx.pool());
+    sycl::half *src1_dfloat = nullptr; // dfloat == half
+
+    bool src1_convert_f16 =
+        src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
+        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
+        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+
+    if (src1_convert_f16) {
+        src1_dfloat = src1_dfloat_a.alloc(ne00);
+        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+        GGML_ASSERT(to_fp16_sycl != nullptr);
+        to_fp16_sycl(src1_ddf_i, src1_dfloat, ne00, stream);
+    }
+#else
+    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
+#endif // GGML_SYCL_F16
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            dequantize_mul_mat_vec_q4_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            dequantize_mul_mat_vec_q4_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            dequantize_mul_mat_vec_q5_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            dequantize_mul_mat_vec_q5_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            dequantize_mul_mat_vec_q8_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            dequantize_mul_mat_vec_q2_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            dequantize_mul_mat_vec_q3_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            dequantize_mul_mat_vec_q4_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            dequantize_mul_mat_vec_q5_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            dequantize_mul_mat_vec_q6_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_F16:
+            convert_mul_mat_vec_f16_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        default:
+            printf("ggml_sycl_op_dequantize_mul_mat_vec unsupported GGML_TYPE %d\n", src0->type);
+            GGML_ABORT("fatal error");
+            break;
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_ddq_i;
+    (void) src1_ncols;
+    (void) src1_padded_row_size;
+}
diff --git a/ggml/src/ggml-sycl/dmmv.hpp b/ggml/src/ggml-sycl/dmmv.hpp
new file mode 100644
index 0000000000000..bd83735641533
--- /dev/null
+++ b/ggml/src/ggml-sycl/dmmv.hpp
@@ -0,0 +1,27 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_DMMV_HPP
+#define GGML_SYCL_DMMV_HPP
+
+#include "common.hpp"
+
+
+void ggml_sycl_op_dequantize_mul_mat_vec(
+    ggml_backend_sycl_context & ctx,
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream);
+
+#endif // GGML_SYCL_DMMV_HPP
diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp
new file mode 100644
index 0000000000000..fe4a8f744e2e0
--- /dev/null
+++ b/ggml/src/ggml-sycl/dpct/helper.hpp
@@ -0,0 +1,3024 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_DPCT_HELPER_HPP
+#define GGML_SYCL_DPCT_HELPER_HPP
+
+#include <sycl/sycl.hpp>
+#include <sycl/half_type.hpp>
+#include <oneapi/mkl.hpp>
+#include <map>
+
+#include "ggml.h"
+
+#if defined(__linux__)
+#include <sys/mman.h>
+#elif defined(_WIN64)
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#else
+#error "Only support Windows and Linux."
+#endif
+
+#if defined(__linux__)
+#include <unistd.h>
+#include <sys/syscall.h>
+#endif
+#if defined(_WIN64)
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#endif
+
+#define DPCT_COMPATIBILITY_TEMP (900)
+
+#if defined(_MSC_VER)
+#define __dpct_align__(n) __declspec(align(n))
+#define __dpct_inline__ __forceinline
+#else
+#define __dpct_align__(n) __attribute__((aligned(n)))
+#define __dpct_inline__ __inline__ __attribute__((always_inline))
+#endif
+
+#if defined(_MSC_VER)
+#define __dpct_noinline__ __declspec(noinline)
+#else
+#define __dpct_noinline__ __attribute__((noinline))
+#endif
+
+inline std::string get_device_type_name(const sycl::device &Device) {
+    auto DeviceType = Device.get_info<sycl::info::device::device_type>();
+    switch (DeviceType) {
+    case sycl::info::device_type::cpu:
+        return "cpu";
+    case sycl::info::device_type::gpu:
+        return "gpu";
+    case sycl::info::device_type::host:
+        return "host";
+    case sycl::info::device_type::accelerator:
+        return "acc";
+    default:
+        return "unknown";
+    }
+}
+
+inline std::string get_device_backend_and_type(const sycl::device &device) {
+    std::stringstream device_type;
+    sycl::backend backend = device.get_backend();
+    device_type <<  backend << ":" << get_device_type_name(device);
+    return device_type.str();
+}
+
+namespace dpct
+{
+    typedef sycl::queue *queue_ptr;
+    typedef sycl::event *event_ptr;
+    typedef char *device_ptr;
+    typedef uint8_t byte_t;
+    typedef sycl::buffer<byte_t> buffer_t;
+
+    /// SYCL default exception handler
+    inline auto exception_handler = [](sycl::exception_list exceptions)
+    {
+        for (std::exception_ptr const &e : exceptions)
+        {
+            try
+            {
+                std::rethrow_exception(e);
+            }
+            catch (sycl::exception const &e)
+            {
+                std::cerr << "Caught asynchronous SYCL exception:" << std::endl
+                          << e.what() << std::endl
+                          << "Exception caught at file:" << __FILE__
+                          << ", line:" << __LINE__ << std::endl;
+            }
+        }
+    };
+
+    enum error_code
+    {
+        success = 0,
+        default_error = 999
+    };
+
+    enum memcpy_direction
+    {
+        host_to_host,
+        host_to_device,
+        device_to_host,
+        device_to_device,
+        automatic
+    };
+
+    enum memory_region
+    {
+        global = 0, // device global memory
+        constant,   // device constant memory
+        local,      // device local memory
+        shared,     // memory which can be accessed by host and device
+    };
+
+    enum class library_data_t : unsigned char
+    {
+        real_float = 0,
+        complex_float,
+        real_double,
+        complex_double,
+        real_half,
+        complex_half,
+        real_bfloat16,
+        complex_bfloat16,
+        real_int4,
+        complex_int4,
+        real_uint4,
+        complex_uint4,
+        real_int8,
+        complex_int8,
+        real_uint8,
+        complex_uint8,
+        real_int16,
+        complex_int16,
+        real_uint16,
+        complex_uint16,
+        real_int32,
+        complex_int32,
+        real_uint32,
+        complex_uint32,
+        real_int64,
+        complex_int64,
+        real_uint64,
+        complex_uint64,
+        real_int8_4,
+        real_int8_32,
+        real_uint8_4,
+        library_data_t_size
+    };
+
+    template <typename T>
+    struct DataType
+    {
+        using T2 = T;
+    };
+    template <typename T>
+    struct DataType<sycl::vec<T, 2>>
+    {
+        using T2 = std::complex<T>;
+    };
+
+    static void destroy_event(event_ptr event)
+    {
+        delete event;
+    }
+
+    static inline unsigned int get_tid()
+    {
+#if defined(__linux__)
+        return syscall(SYS_gettid);
+#elif defined(_WIN64)
+        return GetCurrentThreadId();
+#else
+#error "Only support Windows and Linux."
+#endif
+    }
+
+    namespace detail
+    {
+        static void get_version(const sycl::device &dev, int &major, int &minor)
+        {
+            // Version string has the following format:
+            // a. OpenCL<space><major.minor><space><vendor-specific-information>
+            // b. <major.minor>
+            // c. <AmdGcnArchName> e.g gfx1030
+            std::string ver;
+            ver = dev.get_info<sycl::info::device::version>();
+            std::string::size_type i = 0;
+            while (i < ver.size()) {
+              if (isdigit(ver[i]))
+                break;
+              i++;
+            }
+            major = std::stoi(&(ver[i]));
+            while (i < ver.size()) {
+              if (ver[i] == '.')
+                break;
+              i++;
+            }
+            if (i < ver.size()) {
+              // a. and b.
+              i++;
+              minor = std::stoi(&(ver[i]));
+            } else {
+              // c.
+              minor = 0;
+            }
+        }
+
+        template <typename tag, typename T>
+        class generic_error_type
+        {
+        public:
+            generic_error_type() = default;
+            generic_error_type(T value) : value{value} {}
+            operator T() const { return value; }
+
+        private:
+            T value;
+        };
+
+    } // namespace detail
+
+    /// Pitched 2D/3D memory data.
+    class pitched_data
+    {
+    public:
+        pitched_data() : pitched_data(nullptr, 0, 0, 0) {}
+        pitched_data(void *data, size_t pitch, size_t x, size_t y)
+            : _data(data), _pitch(pitch), _x(x), _y(y) {}
+
+        void *get_data_ptr() { return _data; }
+        void set_data_ptr(void *data) { _data = data; }
+
+        size_t get_pitch() { return _pitch; }
+        void set_pitch(size_t pitch) { _pitch = pitch; }
+
+        size_t get_x() { return _x; }
+        void set_x(size_t x) { _x = x; }
+
+        size_t get_y() { return _y; }
+        void set_y(size_t y) { _y = y; }
+
+    private:
+        void *_data;
+        size_t _pitch, _x, _y;
+    };
+
+    class device_info
+    {
+    public:
+        // get interface
+        const char *get_name() const { return _name; }
+        char *get_name() { return _name; }
+        template <typename WorkItemSizesTy = sycl::range<3>,
+                  std::enable_if_t<std::is_same_v<WorkItemSizesTy, sycl::range<3>> ||
+                                       std::is_same_v<WorkItemSizesTy, int *>,
+                                   int> = 0>
+        auto get_max_work_item_sizes() const
+        {
+            if constexpr (std::is_same_v<WorkItemSizesTy, sycl::range<3>>)
+                return sycl::range<3>(_max_work_item_sizes_i[0],
+                                      _max_work_item_sizes_i[1],
+                                      _max_work_item_sizes_i[2]);
+            else
+            {
+                return _max_work_item_sizes_i;
+            }
+        }
+        template <typename WorkItemSizesTy = sycl::range<3>,
+                  std::enable_if_t<std::is_same_v<WorkItemSizesTy, sycl::range<3>> ||
+                                       std::is_same_v<WorkItemSizesTy, int *>,
+                                   int> = 0>
+        auto get_max_work_item_sizes()
+        {
+            if constexpr (std::is_same_v<WorkItemSizesTy, sycl::range<3>>)
+                return sycl::range<3>(_max_work_item_sizes_i[0],
+                                      _max_work_item_sizes_i[1],
+                                      _max_work_item_sizes_i[2]);
+            else
+            {
+                return _max_work_item_sizes_i;
+            }
+        }
+        bool get_host_unified_memory() const { return _host_unified_memory; }
+        int get_major_version() const { return _major; }
+        int get_minor_version() const { return _minor; }
+        int get_integrated() const { return _integrated; }
+        int get_max_clock_frequency() const { return _frequency; }
+        int get_max_compute_units() const { return _max_compute_units; }
+        int get_max_work_group_size() const { return _max_work_group_size; }
+        int get_max_sub_group_size() const { return _max_sub_group_size; }
+        int get_max_work_items_per_compute_unit() const
+        {
+            return _max_work_items_per_compute_unit;
+        }
+        int get_max_register_size_per_work_group() const
+        {
+            return _max_register_size_per_work_group;
+        }
+        template <typename NDRangeSizeTy = size_t *,
+                  std::enable_if_t<std::is_same_v<NDRangeSizeTy, size_t *> ||
+                                       std::is_same_v<NDRangeSizeTy, int *>,
+                                   int> = 0>
+        auto get_max_nd_range_size() const
+        {
+            if constexpr (std::is_same_v<NDRangeSizeTy, size_t *>)
+                return _max_nd_range_size;
+            else
+                return _max_nd_range_size_i;
+        }
+        template <typename NDRangeSizeTy = size_t *,
+                  std::enable_if_t<std::is_same_v<NDRangeSizeTy, size_t *> ||
+                                       std::is_same_v<NDRangeSizeTy, int *>,
+                                   int> = 0>
+        auto get_max_nd_range_size()
+        {
+            if constexpr (std::is_same_v<NDRangeSizeTy, size_t *>)
+                return _max_nd_range_size;
+            else
+                return _max_nd_range_size_i;
+        }
+        size_t get_global_mem_size() const { return _global_mem_size; }
+        size_t get_local_mem_size() const { return _local_mem_size; }
+        size_t get_max_mem_alloc_size() const { return _max_mem_alloc_size; }
+        /// Returns the maximum clock rate of device's global memory in kHz. If
+        /// compiler does not support this API then returns default value 3200000 kHz.
+        unsigned int get_memory_clock_rate() const { return _memory_clock_rate; }
+        /// Returns the maximum bus width between device and memory in bits. If
+        /// compiler does not support this API then returns default value 64 bits.
+        unsigned int get_memory_bus_width() const { return _memory_bus_width; }
+        uint32_t get_device_id() const { return _device_id; }
+        std::array<unsigned char, 16> get_uuid() const { return _uuid; }
+        /// Returns global memory cache size in bytes.
+        unsigned int get_global_mem_cache_size() const
+        {
+            return _global_mem_cache_size;
+        }
+
+        // set interface
+        void set_name(const char *name)
+        {
+            size_t length = strlen(name);
+            if (length < 256)
+            {
+                std::memcpy(_name, name, length + 1);
+            }
+            else
+            {
+                std::memcpy(_name, name, 255);
+                _name[255] = '\0';
+            }
+        }
+        void set_max_work_item_sizes(const sycl::range<3> max_work_item_sizes)
+        {
+            for (int i = 0; i < 3; ++i)
+                _max_work_item_sizes_i[i] = max_work_item_sizes[i];
+        }
+        [[deprecated]] void
+        set_max_work_item_sizes(const sycl::id<3> max_work_item_sizes)
+        {
+            for (int i = 0; i < 3; ++i)
+            {
+                _max_work_item_sizes_i[i] = max_work_item_sizes[i];
+            }
+        }
+        void set_host_unified_memory(bool host_unified_memory)
+        {
+            _host_unified_memory = host_unified_memory;
+        }
+        void set_major_version(int major) { _major = major; }
+        void set_minor_version(int minor) { _minor = minor; }
+        void set_integrated(int integrated) { _integrated = integrated; }
+        void set_max_clock_frequency(int frequency) { _frequency = frequency; }
+        void set_max_compute_units(int max_compute_units)
+        {
+            _max_compute_units = max_compute_units;
+        }
+        void set_global_mem_size(size_t global_mem_size)
+        {
+            _global_mem_size = global_mem_size;
+        }
+        void set_local_mem_size(size_t local_mem_size)
+        {
+            _local_mem_size = local_mem_size;
+        }
+        void set_max_mem_alloc_size(size_t max_mem_alloc_size)
+        {
+            _max_mem_alloc_size = max_mem_alloc_size;
+        }
+        void set_max_work_group_size(int max_work_group_size)
+        {
+            _max_work_group_size = max_work_group_size;
+        }
+        void set_max_sub_group_size(int max_sub_group_size)
+        {
+            _max_sub_group_size = max_sub_group_size;
+        }
+        void
+        set_max_work_items_per_compute_unit(int max_work_items_per_compute_unit)
+        {
+            _max_work_items_per_compute_unit = max_work_items_per_compute_unit;
+        }
+        void set_max_nd_range_size(int max_nd_range_size[])
+        {
+            for (int i = 0; i < 3; i++)
+            {
+                _max_nd_range_size[i] = max_nd_range_size[i];
+                _max_nd_range_size_i[i] = max_nd_range_size[i];
+            }
+        }
+        void set_memory_clock_rate(unsigned int memory_clock_rate)
+        {
+            _memory_clock_rate = memory_clock_rate;
+        }
+        void set_memory_bus_width(unsigned int memory_bus_width)
+        {
+            _memory_bus_width = memory_bus_width;
+        }
+        void
+        set_max_register_size_per_work_group(int max_register_size_per_work_group)
+        {
+            _max_register_size_per_work_group = max_register_size_per_work_group;
+        }
+        void set_device_id(uint32_t device_id)
+        {
+            _device_id = device_id;
+        }
+        void set_uuid(std::array<unsigned char, 16> uuid)
+        {
+            _uuid = std::move(uuid);
+        }
+        void set_global_mem_cache_size(unsigned int global_mem_cache_size)
+        {
+            _global_mem_cache_size = global_mem_cache_size;
+        }
+
+    private:
+        char _name[256];
+        int _max_work_item_sizes_i[3];
+        bool _host_unified_memory = false;
+        int _major;
+        int _minor;
+        int _integrated = 0;
+        int _frequency;
+        // Set estimated value 3200000 kHz as default value.
+        unsigned int _memory_clock_rate = 3200000;
+        // Set estimated value 64 bits as default value.
+        unsigned int _memory_bus_width = 64;
+        unsigned int _global_mem_cache_size;
+        int _max_compute_units;
+        int _max_work_group_size;
+        int _max_sub_group_size;
+        int _max_work_items_per_compute_unit;
+        int _max_register_size_per_work_group;
+        size_t _global_mem_size;
+        size_t _local_mem_size;
+        size_t _max_mem_alloc_size;
+        size_t _max_nd_range_size[3];
+        int _max_nd_range_size_i[3];
+        uint32_t _device_id;
+        std::array<unsigned char, 16> _uuid;
+    };
+
+    static int get_major_version(const sycl::device &dev)
+    {
+        int major, minor;
+        detail::get_version(dev, major, minor);
+        return major;
+    }
+
+    static int get_minor_version(const sycl::device &dev)
+    {
+        int major, minor;
+        detail::get_version(dev, major, minor);
+        return minor;
+    }
+
+    static void get_device_info(device_info &out, const sycl::device &dev)
+    {
+        device_info prop;
+        prop.set_name(dev.get_info<sycl::info::device::name>().c_str());
+
+        int major, minor;
+        detail::get_version(dev, major, minor);
+        prop.set_major_version(major);
+        prop.set_minor_version(minor);
+
+        prop.set_max_work_item_sizes(
+#if (__SYCL_COMPILER_VERSION && __SYCL_COMPILER_VERSION < 20220902)
+            // oneAPI DPC++ compiler older than 2022/09/02, where max_work_item_sizes
+            // is an enum class element
+            dev.get_info<sycl::info::device::max_work_item_sizes>());
+#else
+            // SYCL 2020-conformant code, max_work_item_sizes is a struct templated by
+            // an int
+            dev.get_info<sycl::info::device::max_work_item_sizes<3>>());
+#endif
+        prop.set_host_unified_memory(dev.has(sycl::aspect::usm_host_allocations));
+
+        prop.set_max_clock_frequency(
+            dev.get_info<sycl::info::device::max_clock_frequency>() * 1000);
+
+        prop.set_max_compute_units(
+            dev.get_info<sycl::info::device::max_compute_units>());
+        prop.set_max_work_group_size(
+            dev.get_info<sycl::info::device::max_work_group_size>());
+        prop.set_global_mem_size(dev.get_info<sycl::info::device::global_mem_size>());
+        prop.set_local_mem_size(dev.get_info<sycl::info::device::local_mem_size>());
+        prop.set_max_mem_alloc_size(dev.get_info<sycl::info::device::max_mem_alloc_size>());
+
+#if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6)
+        if (dev.has(sycl::aspect::ext_intel_memory_clock_rate))
+        {
+            unsigned int tmp =
+                dev.get_info<sycl::ext::intel::info::device::memory_clock_rate>();
+            if (tmp != 0)
+                prop.set_memory_clock_rate(1000 * tmp);
+        }
+        if (dev.has(sycl::aspect::ext_intel_memory_bus_width))
+        {
+            prop.set_memory_bus_width(
+                dev.get_info<sycl::ext::intel::info::device::memory_bus_width>());
+        }
+        if (dev.has(sycl::aspect::ext_intel_device_id))
+        {
+            prop.set_device_id(
+                dev.get_info<sycl::ext::intel::info::device::device_id>());
+        }
+        if (dev.has(sycl::aspect::ext_intel_device_info_uuid))
+        {
+            prop.set_uuid(dev.get_info<sycl::ext::intel::info::device::uuid>());
+        }
+#elif defined(_MSC_VER) && !defined(__clang__)
+#pragma message("get_device_info: querying memory_clock_rate and \
+        memory_bus_width are not supported by the compiler used. \
+        Use 3200000 kHz as memory_clock_rate default value. \
+        Use 64 bits as memory_bus_width default value.")
+#else
+#warning "get_device_info: querying memory_clock_rate and \
+        memory_bus_width are not supported by the compiler used. \
+        Use 3200000 kHz as memory_clock_rate default value. \
+        Use 64 bits as memory_bus_width default value."
+#endif
+
+        size_t max_sub_group_size = 1;
+        std::vector<size_t> sub_group_sizes =
+            dev.get_info<sycl::info::device::sub_group_sizes>();
+
+        for (const auto &sub_group_size : sub_group_sizes)
+        {
+            if (max_sub_group_size < sub_group_size)
+                max_sub_group_size = sub_group_size;
+        }
+
+        prop.set_max_sub_group_size(max_sub_group_size);
+
+        prop.set_max_work_items_per_compute_unit(
+            dev.get_info<sycl::info::device::max_work_group_size>());
+        int max_nd_range_size[] = {0x7FFFFFFF, 0x7FFFFFFF, 0x7FFFFFFF};
+        prop.set_max_nd_range_size(max_nd_range_size);
+
+        // Estimates max register size per work group, feel free to update the value
+        // according to device properties.
+        prop.set_max_register_size_per_work_group(65536);
+
+        prop.set_global_mem_cache_size(
+            dev.get_info<sycl::info::device::global_mem_cache_size>());
+        out = prop;
+    }
+
+    /// dpct device extension
+    class device_ext : public sycl::device {
+      typedef std::mutex mutex_type;
+
+     public:
+      device_ext() : sycl::device() {}
+      ~device_ext() {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        clear_queues();
+      }
+      device_ext(const sycl::device &base) : sycl::device(base) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        init_queues();
+      }
+
+      int is_native_atomic_supported() { return 0; }
+      int get_major_version() const { return dpct::get_major_version(*this); }
+
+      int get_minor_version() const { return dpct::get_minor_version(*this); }
+
+      int get_max_compute_units() const {
+        return get_device_info().get_max_compute_units();
+      }
+
+      /// Return the maximum clock frequency of this device in KHz.
+      int get_max_clock_frequency() const {
+        return get_device_info().get_max_clock_frequency();
+      }
+
+      int get_integrated() const { return get_device_info().get_integrated(); }
+
+      int get_max_sub_group_size() const {
+        return get_device_info().get_max_sub_group_size();
+      }
+
+      int get_max_register_size_per_work_group() const {
+        return get_device_info().get_max_register_size_per_work_group();
+      }
+
+      int get_max_work_group_size() const {
+        return get_device_info().get_max_work_group_size();
+      }
+
+      int get_mem_base_addr_align() const {
+        return get_info<sycl::info::device::mem_base_addr_align>();
+      }
+
+      size_t get_global_mem_size() const {
+        return get_device_info().get_global_mem_size();
+      }
+
+      size_t get_max_mem_alloc_size() const {
+        return get_device_info().get_max_mem_alloc_size();
+      }
+
+      /// Get the number of bytes of free and total memory on the SYCL device.
+      /// \param [out] free_memory The number of bytes of free memory on the
+      /// SYCL device. \param [out] total_memory The number of bytes of total
+      /// memory on the SYCL device.
+      void get_memory_info(size_t &free_memory, size_t &total_memory) {
+        total_memory = get_device_info().get_global_mem_size();
+        const char *warning_info =
+            "get_memory_info: [warning] ext_intel_free_memory is not "
+            "supported (export/set ZES_ENABLE_SYSMAN=1 to support), "
+            "use total memory as free memory";
+#if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105)
+        if (!has(sycl::aspect::ext_intel_free_memory)) {
+          std::cerr << warning_info << std::endl;
+          free_memory = total_memory;
+        } else {
+          free_memory = get_info<sycl::ext::intel::info::device::free_memory>();
+        }
+#else
+        std::cerr << warning_info << std::endl;
+        free_memory = total_memory;
+#if defined(_MSC_VER) && !defined(__clang__)
+#pragma message("Querying the number of bytes of free memory is not supported")
+#else
+#warning "Querying the number of bytes of free memory is not supported"
+#endif
+#endif
+      }
+
+      void get_device_info(device_info &out) const {
+        dpct::get_device_info(out, *this);
+      }
+
+      device_info get_device_info() const {
+        device_info prop;
+        dpct::get_device_info(prop, *this);
+        return prop;
+      }
+
+      void reset() {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        clear_queues();
+        init_queues();
+      }
+
+      sycl::queue &in_order_queue() { return _q_in_order; }
+
+      sycl::queue &out_of_order_queue() { return _q_out_of_order; }
+
+      sycl::queue &default_queue() { return in_order_queue(); }
+
+      void queues_wait_and_throw() {
+        std::unique_lock<mutex_type> lock(m_mutex);
+        lock.unlock();
+        for (auto &q : _queues) {
+            q.wait_and_throw();
+        }
+        // Guard the destruct of current_queues to make sure the ref count is
+        // safe.
+        lock.lock();
+      }
+
+      sycl::queue create_queue(bool enable_exception_handler = false) {
+        return create_in_order_queue(enable_exception_handler);
+      }
+
+      sycl::queue create_queue(sycl::device device,
+                               bool enable_exception_handler = false) {
+        return create_in_order_queue(device, enable_exception_handler);
+      }
+
+      sycl::queue create_in_order_queue(bool enable_exception_handler = false) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        return create_queue_impl(enable_exception_handler,
+                                 sycl::property::queue::in_order());
+      }
+
+      sycl::queue create_in_order_queue(sycl::device device,
+                                        bool enable_exception_handler = false) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        return create_queue_impl(device, enable_exception_handler,
+                                 sycl::property::queue::in_order());
+      }
+
+      sycl::queue create_out_of_order_queue(
+          bool enable_exception_handler = false) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        return create_queue_impl(enable_exception_handler);
+      }
+
+      void destroy_queue(sycl::queue queue) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        _queues.erase(std::remove_if(_queues.begin(), _queues.end(),
+                                    [=](const sycl::queue &q) -> bool
+                                    {
+                                        return q == queue;
+                                    }),
+                    _queues.end());
+      }
+      void set_saved_queue(sycl::queue q) {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        _saved_queue = q;
+      }
+      sycl::queue get_saved_queue() const {
+        std::lock_guard<mutex_type> lock(m_mutex);
+        return _saved_queue;
+      }
+
+     private:
+      void clear_queues() { _queues.clear(); }
+
+      void init_queues() {
+        _q_in_order =
+            create_queue_impl(true, sycl::property::queue::in_order());
+        _q_out_of_order = create_queue_impl(true);
+        _saved_queue = default_queue();
+      }
+
+      /// Caller should acquire resource \p m_mutex before calling this
+      /// function.
+      template <class... Properties>
+      sycl::queue create_queue_impl(bool enable_exception_handler,
+                                    Properties... properties) {
+        sycl::async_handler eh = {};
+        if (enable_exception_handler) {
+          eh = exception_handler;
+        }
+        _queues.push_back(sycl::queue(
+            *this, eh,
+            sycl::property_list(
+#ifdef DPCT_PROFILING_ENABLED
+                sycl::property::queue::enable_profiling(),
+#endif
+                properties...)));
+
+        return _queues.back();
+      }
+
+      template <class... Properties>
+      sycl::queue create_queue_impl(sycl::device device,
+                                    bool enable_exception_handler,
+                                    Properties... properties) {
+        sycl::async_handler eh = {};
+        if (enable_exception_handler) {
+          eh = exception_handler;
+        }
+        _queues.push_back(sycl::queue(
+            device, eh,
+                        sycl::property_list(
+#ifdef DPCT_PROFILING_ENABLED
+                            sycl::property::queue::enable_profiling(),
+#endif
+                            properties...)));
+
+        return _queues.back();
+      }
+
+      void get_version(int &major, int &minor) const {
+        detail::get_version(*this, major, minor);
+      }
+      sycl::queue _q_in_order, _q_out_of_order;
+      sycl::queue _saved_queue;
+      std::vector<sycl::queue> _queues;
+      mutable mutex_type m_mutex;
+    };
+
+
+    /// device manager
+    class dev_mgr
+    {
+    public:
+        device_ext &current_device()
+        {
+            unsigned int dev_id = current_device_id();
+            check_id(dev_id);
+            return *_devs[dev_id];
+        }
+        device_ext &cpu_device() const
+        {
+            std::lock_guard<std::recursive_mutex> lock(m_mutex);
+            if (_cpu_device == -1)
+            {
+                throw std::runtime_error("no valid cpu device");
+            }
+            else
+            {
+                return *_devs[_cpu_device];
+            }
+        }
+        device_ext &get_device(unsigned int id) const
+        {
+            std::lock_guard<std::recursive_mutex> lock(m_mutex);
+            check_id(id);
+            return *_devs[id];
+        }
+        unsigned int current_device_id() const
+        {
+            std::lock_guard<std::recursive_mutex> lock(m_mutex);
+            auto it = _thread2dev_map.find(get_tid());
+            if (it != _thread2dev_map.end())
+                return it->second;
+            return DEFAULT_DEVICE_ID;
+        }
+
+        /// Select device with a device ID.
+        /// \param [in] id The id of the device which can
+        /// be obtained through get_device_id(const sycl::device).
+        void select_device(unsigned int id)
+        {
+            std::lock_guard<std::recursive_mutex> lock(m_mutex);
+            check_id(id);
+            _thread2dev_map[get_tid()] = id;
+        }
+        unsigned int device_count() { return _devs.size(); }
+
+        unsigned int get_device_id(const sycl::device &dev)
+        {
+            unsigned int id = 0;
+            for (auto &dev_item : _devs)
+            {
+                if (*dev_item == dev)
+                {
+                    return id;
+                }
+                id++;
+            }
+            return -1;
+        }
+
+        inline std::string get_preferred_gpu_platform_name() {
+            std::string result;
+
+            std::string filter = "";
+            char* env = getenv("ONEAPI_DEVICE_SELECTOR");
+            if (env) {
+                if (std::strstr(env, "level_zero")) {
+                    filter = "level-zero";
+                }
+                else if (std::strstr(env, "opencl")) {
+                    filter = "opencl";
+                }
+                else if (std::strstr(env, "cuda")) {
+                    filter = "cuda";
+                }
+                else if (std::strstr(env, "hip")) {
+                    filter = "hip";
+                }
+                else {
+                    throw std::runtime_error("invalid device filter: " + std::string(env));
+                }
+            } else {
+                auto default_device = sycl::device(sycl::default_selector_v);
+                auto default_platform_name = default_device.get_platform().get_info<sycl::info::platform::name>();
+
+                if (std::strstr(default_platform_name.c_str(), "Level-Zero") || default_device.is_cpu()) {
+                    filter = "level-zero";
+                }
+                else if (std::strstr(default_platform_name.c_str(), "CUDA")) {
+                    filter = "cuda";
+                }
+                else if (std::strstr(default_platform_name.c_str(), "HIP")) {
+                    filter = "hip";
+                }
+            }
+
+            auto platform_list = sycl::platform::get_platforms();
+
+            for (const auto& platform : platform_list) {
+                auto devices = platform.get_devices();
+                auto gpu_dev = std::find_if(devices.begin(), devices.end(), [](const sycl::device& d) {
+                    return d.is_gpu();
+                });
+
+                if (gpu_dev == devices.end()) {
+                    // cout << "platform [" << platform_name
+                    //      << "] does not contain GPU devices, skipping\n";
+                    continue;
+                }
+
+                auto platform_name = platform.get_info<sycl::info::platform::name>();
+                std::string platform_name_low_case;
+                platform_name_low_case.resize(platform_name.size());
+
+                std::transform(
+                    platform_name.begin(), platform_name.end(), platform_name_low_case.begin(), ::tolower);
+
+                if (platform_name_low_case.find(filter) == std::string::npos) {
+                    // cout << "platform [" << platform_name
+                    //      << "] does not match with requested "
+                    //      << filter << ", skipping\n";
+                    continue;
+                }
+
+                result = platform_name;
+            }
+
+            if (result.empty())
+                throw std::runtime_error("can not find preferred GPU platform");
+
+            return result;
+        }
+
+        template <class DeviceSelector>
+        std::enable_if_t<
+            std::is_invocable_r_v<int, DeviceSelector, const sycl::device &>>
+        select_device(const DeviceSelector &selector = sycl::gpu_selector_v)
+        {
+            sycl::device selected_device = sycl::device(selector);
+            unsigned int selected_device_id = get_device_id(selected_device);
+            select_device(selected_device_id);
+        }
+
+        /// Returns the instance of device manager singleton.
+        static dev_mgr &instance()
+        {
+            static dev_mgr d_m;
+            return d_m;
+        }
+        dev_mgr(const dev_mgr &) = delete;
+        dev_mgr &operator=(const dev_mgr &) = delete;
+        dev_mgr(dev_mgr &&) = delete;
+        dev_mgr &operator=(dev_mgr &&) = delete;
+
+    private:
+        mutable std::recursive_mutex m_mutex;
+        static bool compare_dev(sycl::device &device1, sycl::device &device2)
+        {
+            sycl::backend backend1 = device1.get_backend();
+            sycl::backend backend2 = device2.get_backend();
+            // levelzero backends always come first
+            if(backend1 == sycl::backend::ext_oneapi_level_zero && backend2 != sycl::backend::ext_oneapi_level_zero) return true;
+            if(backend1 != sycl::backend::ext_oneapi_level_zero && backend2 == sycl::backend::ext_oneapi_level_zero) return false;
+            dpct::device_info prop1;
+            dpct::get_device_info(prop1, device1);
+            dpct::device_info prop2;
+            dpct::get_device_info(prop2, device2);
+            return prop1.get_max_compute_units() > prop2.get_max_compute_units();
+        }
+        static int convert_backend_index(std::string & backend) {
+            if (backend == "ext_oneapi_level_zero:gpu") return 0;
+            if (backend == "opencl:gpu") return 1;
+            if (backend == "ext_oneapi_cuda:gpu") return 2;
+            if (backend == "ext_oneapi_hip:gpu") return 3;
+            if (backend == "opencl:cpu") return 4;
+            if (backend == "opencl:acc") return 5;
+            printf("convert_backend_index: can't handle backend=%s\n", backend.c_str());
+            GGML_ABORT("fatal error");
+        }
+        static bool compare_backend(std::string &backend1, std::string &backend2) {
+            return convert_backend_index(backend1) < convert_backend_index(backend2);
+        }
+        dev_mgr()
+        {
+            sycl::device default_device =
+                sycl::device(sycl::default_selector_v);
+            _devs.push_back(std::make_shared<device_ext>(default_device));
+
+            std::vector<sycl::device> sycl_all_devs;
+            // Collect other devices except for the default device.
+            if (default_device.is_cpu())
+                _cpu_device = 0;
+
+            auto Platforms = sycl::platform::get_platforms();
+            // Keep track of the number of devices per backend
+            std::map<sycl::backend, size_t> DeviceNums;
+            std::map<std::string, std::vector<sycl::device>> backend_devices;
+            auto preferred_platform_name = get_preferred_gpu_platform_name();
+
+            while (!Platforms.empty()) {
+                auto Platform = Platforms.back();
+                Platforms.pop_back();
+                auto platform_name = Platform.get_info<sycl::info::platform::name>();
+                if (platform_name.compare(preferred_platform_name) != 0) {
+                    continue;
+                }
+                auto devices = Platform.get_devices();
+                std::string backend_type = get_device_backend_and_type(devices[0]);
+                for (const auto &device : devices) {
+                    backend_devices[backend_type].push_back(device);
+                }
+            }
+
+            std::vector<std::string> keys;
+            for(auto it = backend_devices.begin(); it != backend_devices.end(); ++it) {
+                keys.push_back(it->first);
+            }
+            std::sort(keys.begin(), keys.end(), compare_backend);
+
+            for (auto &key : keys) {
+                std::vector<sycl::device> devs = backend_devices[key];
+                std::sort(devs.begin(), devs.end(), compare_dev);
+                for (const auto &dev : devs) {
+                    sycl_all_devs.push_back(dev);
+                }
+            }
+
+            for (auto &dev : sycl_all_devs)
+            {
+                if (dev == default_device)
+                {
+                    continue;
+                }
+                _devs.push_back(std::make_shared<device_ext>(dev));
+                if (_cpu_device == -1 && dev.is_cpu())
+                {
+                    _cpu_device = _devs.size() - 1;
+                }
+            }
+        }
+        void check_id(unsigned int id) const
+        {
+            if (id >= _devs.size())
+            {
+                throw std::runtime_error("invalid device id");
+            }
+        }
+        std::vector<std::shared_ptr<device_ext>> _devs;
+        /// DEFAULT_DEVICE_ID is used, if current_device_id() can not find current
+        /// thread id in _thread2dev_map, which means default device should be used
+        /// for the current thread.
+        const unsigned int DEFAULT_DEVICE_ID = 0;
+        /// thread-id to device-id map.
+        std::map<unsigned int, unsigned int> _thread2dev_map;
+        int _cpu_device = -1;
+    };
+
+    static inline sycl::queue &get_default_queue()
+    {
+        return dev_mgr::instance().current_device().default_queue();
+    }
+
+    namespace detail
+    {
+        enum class pointer_access_attribute
+        {
+            host_only = 0,
+            device_only,
+            host_device,
+            end
+        };
+
+        static pointer_access_attribute get_pointer_attribute(sycl::queue &q,
+                                                              const void *ptr)
+        {
+            switch (sycl::get_pointer_type(ptr, q.get_context()))
+            {
+            case sycl::usm::alloc::unknown:
+                return pointer_access_attribute::host_only;
+            case sycl::usm::alloc::device:
+                return pointer_access_attribute::device_only;
+            case sycl::usm::alloc::shared:
+            case sycl::usm::alloc::host:
+                return pointer_access_attribute::host_device;
+            }
+        }
+
+        template <typename ArgT>
+        inline constexpr std::uint64_t get_type_combination_id(ArgT Val)
+        {
+            static_assert((unsigned char)library_data_t::library_data_t_size <=
+                              std::numeric_limits<unsigned char>::max() &&
+                          "library_data_t size exceeds limit.");
+            static_assert(std::is_same_v<ArgT, library_data_t>, "Unsupported ArgT");
+            return (std::uint64_t)Val;
+        }
+
+        template <typename FirstT, typename... RestT>
+        inline constexpr std::uint64_t get_type_combination_id(FirstT FirstVal,
+                                                               RestT... RestVal)
+        {
+            static_assert((std::uint8_t)library_data_t::library_data_t_size <=
+                              std::numeric_limits<unsigned char>::max() &&
+                          "library_data_t size exceeds limit.");
+            static_assert(sizeof...(RestT) <= 8 && "Too many parameters");
+            static_assert(std::is_same_v<FirstT, library_data_t>, "Unsupported FirstT");
+            return get_type_combination_id(RestVal...) << 8 | ((std::uint64_t)FirstVal);
+        }
+
+        class mem_mgr
+        {
+            mem_mgr()
+            {
+                // Reserved address space, no real memory allocation happens here.
+#if defined(__linux__)
+                mapped_address_space =
+                    (byte_t *)mmap(nullptr, mapped_region_size, PROT_NONE,
+                                   MAP_PRIVATE | MAP_ANONYMOUS, -1, 0);
+#elif defined(_WIN64)
+                mapped_address_space = (byte_t *)VirtualAlloc(
+                    NULL,               // NULL specified as the base address parameter
+                    mapped_region_size, // Size of allocation
+                    MEM_RESERVE,        // Allocate reserved pages
+                    PAGE_NOACCESS);     // Protection = no access
+#else
+#error "Only support Windows and Linux."
+#endif
+                next_free = mapped_address_space;
+            }
+
+        public:
+            using buffer_id_t = int;
+
+            struct allocation
+            {
+                buffer_t buffer;
+                byte_t *alloc_ptr;
+                size_t size;
+            };
+
+            ~mem_mgr()
+            {
+#if defined(__linux__)
+                munmap(mapped_address_space, mapped_region_size);
+#elif defined(_WIN64)
+                VirtualFree(mapped_address_space, 0, MEM_RELEASE);
+#else
+#error "Only support Windows and Linux."
+#endif
+            }
+
+            mem_mgr(const mem_mgr &) = delete;
+            mem_mgr &operator=(const mem_mgr &) = delete;
+            mem_mgr(mem_mgr &&) = delete;
+            mem_mgr &operator=(mem_mgr &&) = delete;
+
+            /// Allocate
+            void *mem_alloc(size_t size)
+            {
+                if (!size)
+                    return nullptr;
+                std::lock_guard<std::mutex> lock(m_mutex);
+                if (next_free + size > mapped_address_space + mapped_region_size)
+                {
+                    throw std::runtime_error("dpct_malloc: out of memory for virtual memory pool");
+                }
+                // Allocation
+                sycl::range<1> r(size);
+                buffer_t buf(r);
+                allocation A{buf, next_free, size};
+                // Map allocation to device pointer
+                void *result = next_free;
+                m_map.emplace(next_free + size, A);
+                // Update pointer to the next free space.
+                next_free += (size + extra_padding + alignment - 1) & ~(alignment - 1);
+
+                return result;
+            }
+
+            /// Deallocate
+            void mem_free(const void *ptr)
+            {
+                if (!ptr)
+                    return;
+                std::lock_guard<std::mutex> lock(m_mutex);
+                auto it = get_map_iterator(ptr);
+                m_map.erase(it);
+            }
+
+            /// map: device pointer -> allocation(buffer, alloc_ptr, size)
+            allocation translate_ptr(const void *ptr)
+            {
+                std::lock_guard<std::mutex> lock(m_mutex);
+                auto it = get_map_iterator(ptr);
+                return it->second;
+            }
+
+            /// Check if the pointer represents device pointer or not.
+            bool is_device_ptr(const void *ptr) const
+            {
+                std::lock_guard<std::mutex> lock(m_mutex);
+                return (mapped_address_space <= ptr) &&
+                       (ptr < mapped_address_space + mapped_region_size);
+            }
+
+            /// Returns the instance of memory manager singleton.
+            static mem_mgr &instance()
+            {
+                static mem_mgr m;
+                return m;
+            }
+
+        private:
+            std::map<byte_t *, allocation> m_map;
+            mutable std::mutex m_mutex;
+            byte_t *mapped_address_space;
+            byte_t *next_free;
+            const size_t mapped_region_size = 128ull * 1024 * 1024 * 1024;
+            const size_t alignment = 256;
+            /// This padding may be defined to some positive value to debug
+            /// out of bound accesses.
+            const size_t extra_padding = 0;
+
+            std::map<byte_t *, allocation>::iterator get_map_iterator(const void *ptr)
+            {
+                auto it = m_map.upper_bound((byte_t *)ptr);
+                if (it == m_map.end())
+                {
+                    // Not a virtual pointer.
+                    throw std::runtime_error("can not get buffer from non-virtual pointer");
+                }
+                const allocation &alloc = it->second;
+                if (ptr < alloc.alloc_ptr)
+                {
+                    // Out of bound.
+                    // This may happen if there's a gap between allocations due to alignment
+                    // or extra padding and pointer points to this gap.
+                    throw std::runtime_error("invalid virtual pointer");
+                }
+                return it;
+            }
+        };
+
+        template <class T, memory_region Memory, size_t Dimension>
+        class accessor;
+        template <memory_region Memory, class T = byte_t>
+        class memory_traits
+        {
+        public:
+            static constexpr sycl::access::target target =
+                sycl::access::target::device;
+            static constexpr sycl::access_mode mode =
+                (Memory == constant) ? sycl::access_mode::read
+                                     : sycl::access_mode::read_write;
+            static constexpr size_t type_size = sizeof(T);
+            using element_t =
+                typename std::conditional<Memory == constant, const T, T>::type;
+            using value_t = typename std::remove_cv<T>::type;
+            template <size_t Dimension = 1>
+            using accessor_t = typename std::conditional<
+                Memory == local, sycl::local_accessor<value_t, Dimension>,
+                sycl::accessor<T, Dimension, mode, target>>::type;
+            using pointer_t = T *;
+        };
+
+        static inline void *dpct_malloc(size_t size, sycl::queue &q)
+        {
+            return sycl::malloc_device(size, q.get_device(), q.get_context());
+        }
+
+#define PITCH_DEFAULT_ALIGN(x) (((x) + 31) & ~(0x1F))
+        static inline void *dpct_malloc(size_t &pitch, size_t x, size_t y, size_t z,
+                                        sycl::queue &q)
+        {
+            pitch = PITCH_DEFAULT_ALIGN(x);
+            return dpct_malloc(pitch * y * z, q);
+        }
+
+        /**
+         * @brief Sets \p value to the first \p size elements starting from \p dev_ptr in \p q.
+         * @tparam valueT The type of the element to be set.
+         * @param [in] q The queue in which the operation is done.
+         * @param [in] dev_ptr Pointer to the virtual device memory address.
+         * @param [in] value The value to be set.
+         * @param [in] size Number of elements to be set to the value.
+         * @return An event representing the memset operation.
+         */
+        template <typename valueT>
+        static inline sycl::event dpct_memset(sycl::queue &q, void *dev_ptr,
+                                              valueT value, size_t size)
+        {
+            return q.fill(dev_ptr, value, size);
+        }
+
+        /**
+         * @brief Sets \p value to the 3D memory region pointed by \p data in \p q.
+         * @tparam valueT The type of the element to be set.
+         * @param [in] q The queue in which the operation is done.
+         * @param [in] data Pointer to the pitched device memory region.
+         * @param [in] value The value to be set.
+         * @param [in] size 3D memory region by number of elements.
+         * @return An event list representing the memset operations.
+         */
+        template <typename valueT>
+        static inline std::vector<sycl::event>
+        dpct_memset(sycl::queue &q, pitched_data data, valueT value,
+                    sycl::range<3> size)
+        {
+            std::vector<sycl::event> event_list;
+            size_t slice = data.get_pitch() * data.get_y();
+            unsigned char *data_surface = (unsigned char *)data.get_data_ptr();
+            for (size_t z = 0; z < size.get(2); ++z)
+            {
+                unsigned char *data_ptr = data_surface;
+                for (size_t y = 0; y < size.get(1); ++y)
+                {
+                    event_list.push_back(dpct_memset(q, data_ptr, value, size.get(0)));
+                    data_ptr += data.get_pitch();
+                }
+                data_surface += slice;
+            }
+            return event_list;
+        }
+
+        /**
+         * @brief Sets \p val to the pitched 2D memory region pointed by \p ptr in \p q.
+         * @tparam valueT The type of the element to be set.
+         * @param [in] q The queue in which the operation is done.
+         * @param [in] ptr Pointer to the virtual device memory.
+         * @param [in] pitch The pitch size by number of elements, including padding.
+         * @param [in] val The value to be set.
+         * @param [in] x The width of memory region by number of elements.
+         * @param [in] y The height of memory region by number of elements.
+         * @return An event list representing the memset operations.
+         */
+        template <typename valueT>
+        static inline std::vector<sycl::event>
+        dpct_memset(sycl::queue &q, void *ptr, size_t pitch, valueT val, size_t x,
+                    size_t y)
+        {
+            return dpct_memset(q, pitched_data(ptr, pitch, x, 1), val,
+                               sycl::range<3>(x, y, 1));
+        }
+
+        static memcpy_direction deduce_memcpy_direction(sycl::queue &q, void *to_ptr,
+                                                        const void *from_ptr,
+                                                        memcpy_direction dir)
+        {
+            switch (dir)
+            {
+            case memcpy_direction::host_to_host:
+            case memcpy_direction::host_to_device:
+            case memcpy_direction::device_to_host:
+            case memcpy_direction::device_to_device:
+                return dir;
+            case memcpy_direction::automatic:
+            {
+                // table[to_attribute][from_attribute]
+                static const memcpy_direction
+                    direction_table[static_cast<unsigned>(pointer_access_attribute::end)]
+                                   [static_cast<unsigned>(pointer_access_attribute::end)] =
+                                       {{memcpy_direction::host_to_host,
+                                         memcpy_direction::device_to_host,
+                                         memcpy_direction::host_to_host},
+                                        {memcpy_direction::host_to_device,
+                                         memcpy_direction::device_to_device,
+                                         memcpy_direction::device_to_device},
+                                        {memcpy_direction::host_to_host,
+                                         memcpy_direction::device_to_device,
+                                         memcpy_direction::device_to_device}};
+                return direction_table[static_cast<unsigned>(get_pointer_attribute(
+                    q, to_ptr))][static_cast<unsigned>(get_pointer_attribute(q, from_ptr))];
+            }
+            default:
+                throw std::runtime_error("dpct_memcpy: invalid direction value");
+            }
+        }
+
+        static sycl::event
+        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size,
+                    memcpy_direction direction,
+                    const std::vector<sycl::event> &dep_events = {})
+        {
+            if (!size)
+                return sycl::event{};
+            return q.memcpy(to_ptr, from_ptr, size, dep_events);
+            GGML_UNUSED(direction);
+        }
+
+        // Get actual copy range and make sure it will not exceed range.
+        static inline size_t get_copy_range(sycl::range<3> size, size_t slice,
+                                            size_t pitch)
+        {
+            return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0);
+        }
+
+        static inline size_t get_offset(sycl::id<3> id, size_t slice,
+                                        size_t pitch)
+        {
+            return slice * id.get(2) + pitch * id.get(1) + id.get(0);
+        }
+
+        /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr
+        /// and \p from_range to another specified by \p to_ptr and \p to_range.
+        static inline std::vector<sycl::event>
+        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
+                    sycl::range<3> to_range, sycl::range<3> from_range,
+                    sycl::id<3> to_id, sycl::id<3> from_id,
+                    sycl::range<3> size, memcpy_direction direction,
+                    const std::vector<sycl::event> &dep_events = {})
+        {
+            // RAII for host pointer
+            class host_buffer
+            {
+                void *_buf;
+                size_t _size;
+                sycl::queue &_q;
+                const std::vector<sycl::event> &_deps; // free operation depends
+
+            public:
+                host_buffer(size_t size, sycl::queue &q,
+                            const std::vector<sycl::event> &deps)
+                    : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {}
+                void *get_ptr() const { return _buf; }
+                size_t get_size() const { return _size; }
+                ~host_buffer()
+                {
+                    if (_buf)
+                    {
+                        _q.submit([&](sycl::handler &cgh)
+                                  {
+        cgh.depends_on(_deps);
+        cgh.host_task([buf = _buf] { std::free(buf); }); });
+                    }
+                }
+            };
+            std::vector<sycl::event> event_list;
+
+            size_t to_slice = to_range.get(1) * to_range.get(0),
+                   from_slice = from_range.get(1) * from_range.get(0);
+            unsigned char *to_surface =
+                (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0));
+            const unsigned char *from_surface =
+                (const unsigned char *)from_ptr +
+                get_offset(from_id, from_slice, from_range.get(0));
+
+            if (to_slice == from_slice && to_slice == size.get(1) * size.get(0))
+            {
+                return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2),
+                                    direction, dep_events)};
+            }
+            direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction);
+            size_t size_slice = size.get(1) * size.get(0);
+            switch (direction)
+            {
+            case host_to_host:
+                for (size_t z = 0; z < size.get(2); ++z)
+                {
+                    unsigned char *to_ptr = to_surface;
+                    const unsigned char *from_ptr = from_surface;
+                    if (to_range.get(0) == from_range.get(0) &&
+                        to_range.get(0) == size.get(0))
+                    {
+                        event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice,
+                                                         direction, dep_events));
+                    }
+                    else
+                    {
+                        for (size_t y = 0; y < size.get(1); ++y)
+                        {
+                            event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0),
+                                                             direction, dep_events));
+                            to_ptr += to_range.get(0);
+                            from_ptr += from_range.get(0);
+                        }
+                    }
+                    to_surface += to_slice;
+                    from_surface += from_slice;
+                }
+                break;
+            case host_to_device:
+            {
+                host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q,
+                                event_list);
+                std::vector<sycl::event> host_events;
+                if (to_slice == size_slice)
+                {
+                    // Copy host data to a temp host buffer with the shape of target.
+                    host_events =
+                        dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range,
+                                    sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size,
+                                    host_to_host, dep_events);
+                }
+                else
+                {
+                    // Copy host data to a temp host buffer with the shape of target.
+                    host_events = dpct_memcpy(
+                        q, buf.get_ptr(), from_surface, to_range, from_range,
+                        sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host,
+                        // If has padding data, not sure whether it is useless. So fill temp
+                        // buffer with it.
+                        std::vector<sycl::event>{
+                            dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(),
+                                        device_to_host, dep_events)});
+                }
+                // Copy from temp host buffer to device with only one submit.
+                event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(),
+                                                 buf.get_size(), host_to_device,
+                                                 host_events));
+                break;
+            }
+            case device_to_host:
+            {
+                host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q,
+                                event_list);
+                // Copy from host temp buffer to host target with reshaping.
+                event_list = dpct_memcpy(
+                    q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0),
+                    sycl::id<3>(0, 0, 0), size, host_to_host,
+                    // Copy from device to temp host buffer with only one submit.
+                    std::vector<sycl::event>{dpct_memcpy(q, buf.get_ptr(), from_surface,
+                                                         buf.get_size(),
+                                                         device_to_host, dep_events)});
+                break;
+            }
+            case device_to_device:
+                event_list.push_back(q.submit([&](sycl::handler &cgh){
+                cgh.depends_on(dep_events);
+                cgh.parallel_for<class dpct_memcpy_3d_detail>(
+                    size,
+                    [=](sycl::id<3> id) {
+                        to_surface[get_offset(id, to_slice, to_range.get(0))] =
+                            from_surface[get_offset(id, from_slice, from_range.get(0))];
+                    }); }));
+                break;
+            default:
+                throw std::runtime_error("dpct_memcpy: invalid direction value");
+            }
+            return event_list;
+        }
+
+        /// memcpy 2D/3D matrix specified by pitched_data.
+        static inline std::vector<sycl::event>
+        dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id,
+                    pitched_data from, sycl::id<3> from_id, sycl::range<3> size,
+                    memcpy_direction direction = automatic)
+        {
+            return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(),
+                               sycl::range<3>(to.get_pitch(), to.get_y(), 1),
+                               sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id,
+                               size, direction);
+        }
+
+        /// memcpy 2D matrix with pitch.
+        static inline std::vector<sycl::event>
+        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
+                    size_t to_pitch, size_t from_pitch, size_t x, size_t y,
+                    memcpy_direction direction = automatic)
+        {
+            return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1),
+                               sycl::range<3>(from_pitch, y, 1),
+                               sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0),
+                               sycl::range<3>(x, y, 1), direction);
+        }
+
+        namespace deprecated
+        {
+
+            template <typename T, sycl::usm::alloc AllocKind>
+            class usm_allocator
+            {
+            private:
+                using Alloc = sycl::usm_allocator<T, AllocKind>;
+                Alloc _impl;
+
+            public:
+                using value_type = typename std::allocator_traits<Alloc>::value_type;
+                using pointer = typename std::allocator_traits<Alloc>::pointer;
+                using const_pointer = typename std::allocator_traits<Alloc>::const_pointer;
+                using void_pointer = typename std::allocator_traits<Alloc>::void_pointer;
+                using const_void_pointer =
+                    typename std::allocator_traits<Alloc>::const_void_pointer;
+                using reference = typename std::allocator_traits<Alloc>::value_type &;
+                using const_reference =
+                    const typename std::allocator_traits<Alloc>::value_type &;
+                using difference_type =
+                    typename std::allocator_traits<Alloc>::difference_type;
+                using size_type = typename std::allocator_traits<Alloc>::size_type;
+                using propagate_on_container_copy_assignment = typename std::allocator_traits<
+                    Alloc>::propagate_on_container_copy_assignment;
+                using propagate_on_container_move_assignment = typename std::allocator_traits<
+                    Alloc>::propagate_on_container_move_assignment;
+                using propagate_on_container_swap =
+                    typename std::allocator_traits<Alloc>::propagate_on_container_swap;
+                using is_always_equal =
+                    typename std::allocator_traits<Alloc>::is_always_equal;
+
+                template <typename U>
+                struct rebind
+                {
+                    typedef usm_allocator<U, AllocKind> other;
+                };
+
+                usm_allocator() : _impl(dpct::get_default_queue()) {}
+                ~usm_allocator() {}
+                usm_allocator(const usm_allocator &other) : _impl(other._impl) {}
+                usm_allocator(usm_allocator &&other) : _impl(std::move(other._impl)) {}
+                pointer address(reference r) { return &r; }
+                const_pointer address(const_reference r) { return &r; }
+                pointer allocate(size_type cnt, const_void_pointer hint = nullptr)
+                {
+                    return std::allocator_traits<Alloc>::allocate(_impl, cnt, hint);
+                }
+                void deallocate(pointer p, size_type cnt)
+                {
+                    std::allocator_traits<Alloc>::deallocate(_impl, p, cnt);
+                }
+                size_type max_size() const
+                {
+                    return std::allocator_traits<Alloc>::max_size(_impl);
+                }
+                bool operator==(const usm_allocator &other) const { return _impl == other._impl; }
+                bool operator!=(const usm_allocator &other) const { return _impl != other._impl; }
+            };
+
+        } // namespace deprecated
+
+        inline void dpct_free(void *ptr,
+                              const sycl::queue &q)
+        {
+            if (ptr)
+            {
+                sycl::free(ptr, q.get_context());
+            }
+        }
+
+        template <typename T>
+        inline auto get_memory(const void *x)
+        {
+            T *new_x = reinterpret_cast<T *>(const_cast<void *>(x));
+            return new_x;
+        }
+
+        template <typename T>
+        inline typename DataType<T>::T2 get_value(const T *s, sycl::queue &q)
+        {
+            using Ty = typename DataType<T>::T2;
+            Ty s_h;
+            if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only)
+                detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host)
+                    .wait();
+            else
+                s_h = *reinterpret_cast<const Ty *>(s);
+            return s_h;
+        }
+
+    } // namespace detail
+
+    template <typename T>
+    inline auto get_value(const T *s, sycl::queue &q)
+    {
+        return detail::get_value(s, q);
+    }
+
+    namespace detail
+    {
+        template <class Ta, class Tb, class Tc, class Ts>
+        inline void gemm_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                              oneapi::mkl::transpose b_trans, int m, int n, int k,
+                              const void *alpha, const void *a, int lda, const void *b,
+                              int ldb, const void *beta, void *c, int ldc)
+        {
+            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
+            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
+            auto data_a = get_memory<const Ta>(a);
+            auto data_b = get_memory<const Tb>(b);
+            auto data_c = get_memory<Tc>(c);
+            oneapi::mkl::blas::column_major::gemm(
+                q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda,
+                data_b, ldb, beta_value, data_c, ldc);
+        }
+
+        template <typename VecT, class BinaryOperation, class = void>
+        class vectorized_binary
+        {
+        public:
+            inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op)
+            {
+                VecT v4;
+                for (size_t i = 0; i < v4.size(); ++i)
+                {
+                    v4[i] = binary_op(a[i], b[i]);
+                }
+                return v4;
+            }
+        };
+
+        template <typename VecT, class BinaryOperation>
+        class vectorized_binary<
+            VecT, BinaryOperation,
+            std::void_t<std::invoke_result_t<BinaryOperation, VecT, VecT>>>
+        {
+        public:
+            inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op)
+            {
+                return binary_op(a, b).template as<VecT>();
+            }
+        };
+
+        template <class Ta, class Tb, class Tc, class Ts>
+        inline void gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                                    oneapi::mkl::transpose b_trans, int m, int n, int k,
+                                    const void *alpha, const void **a, int lda,
+                                    const void **b, int ldb, const void *beta, void **c,
+                                    int ldc, int batch_size)
+        {
+            struct matrix_info_t
+            {
+                oneapi::mkl::transpose transpose_info[2];
+                Ts value_info[2];
+                std::int64_t size_info[3];
+                std::int64_t ld_info[3];
+                std::int64_t groupsize_info;
+            };
+
+            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
+            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
+
+            matrix_info_t *matrix_info =
+                (matrix_info_t *)std::malloc(sizeof(matrix_info_t));
+            matrix_info->transpose_info[0] = a_trans;
+            matrix_info->transpose_info[1] = b_trans;
+            matrix_info->value_info[0] = alpha_value;
+            matrix_info->value_info[1] = beta_value;
+            matrix_info->size_info[0] = m;
+            matrix_info->size_info[1] = n;
+            matrix_info->size_info[2] = k;
+            matrix_info->ld_info[0] = lda;
+            matrix_info->ld_info[1] = ldb;
+            matrix_info->ld_info[2] = ldc;
+            matrix_info->groupsize_info = batch_size;
+
+            sycl::event e = oneapi::mkl::blas::column_major::gemm_batch(
+                q, matrix_info->transpose_info, matrix_info->transpose_info + 1,
+                matrix_info->size_info, matrix_info->size_info + 1,
+                matrix_info->size_info + 2, matrix_info->value_info,
+                reinterpret_cast<const Ta **>(a), matrix_info->ld_info,
+                reinterpret_cast<const Tb **>(b), matrix_info->ld_info + 1,
+                matrix_info->value_info + 1, reinterpret_cast<Tc **>(c),
+                matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
+
+            q.submit([&](sycl::handler &cgh)
+                     {
+    cgh.depends_on(e);
+    cgh.host_task([=] { std::free(matrix_info); }); });
+        }
+
+        template <class Ta, class Tb, class Tc, class Ts>
+        inline void
+        gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                        oneapi::mkl::transpose b_trans, int m, int n,
+                        int k, const void *alpha, const void *a, int lda,
+                        long long int stride_a, const void *b, int ldb,
+                        long long int stride_b, const void *beta, void *c,
+                        int ldc, long long int stride_c, int batch_size)
+        {
+            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
+            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
+            auto data_a = get_memory<const Ta>(a);
+            auto data_b = get_memory<const Tb>(b);
+            auto data_c = get_memory<Tc>(c);
+            oneapi::mkl::blas::column_major::gemm_batch(
+                q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda,
+                stride_a, data_b, ldb, stride_b, beta_value,
+                data_c, ldc, stride_c, batch_size);
+        }
+
+    } // namespace detail
+
+    template <typename VecT, class BinaryOperation>
+    inline unsigned vectorized_binary(unsigned a, unsigned b,
+                                      const BinaryOperation binary_op)
+    {
+        sycl::vec<unsigned, 1> v0{a}, v1{b};
+        auto v2 = v0.as<VecT>();
+        auto v3 = v1.as<VecT>();
+        auto v4 =
+            detail::vectorized_binary<VecT, BinaryOperation>()(v2, v3, binary_op);
+        v0 = v4.template as<sycl::vec<unsigned, 1>>();
+        return v0;
+    }
+
+    static void async_dpct_memcpy(void *to_ptr, const void *from_ptr, size_t size,
+                                  memcpy_direction direction = automatic,
+                                  sycl::queue &q = dpct::get_default_queue())
+    {
+        detail::dpct_memcpy(q, to_ptr, from_ptr, size, direction);
+    }
+
+    static inline unsigned int select_device(unsigned int id)
+    {
+        dev_mgr::instance().select_device(id);
+        return id;
+    }
+
+    template <typename T>
+    T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask,
+                               unsigned int logical_sub_group_size = 32)
+    {
+        unsigned int id = g.get_local_linear_id();
+        unsigned int start_index =
+            id / logical_sub_group_size * logical_sub_group_size;
+        unsigned int target_offset = (id % logical_sub_group_size) ^ mask;
+        return sycl::select_from_group(g, x,
+                                       target_offset < logical_sub_group_size
+                                           ? start_index + target_offset
+                                           : id);
+    }
+
+    template <typename T>
+    sycl::vec<T, 4> extract_and_sign_or_zero_extend4(T val)
+    {
+        return sycl::vec<T, 1>(val)
+            .template as<sycl::vec<
+                std::conditional_t<std::is_signed_v<T>, int8_t, uint8_t>, 4>>()
+            .template convert<T>();
+    }
+
+    template <typename T1, typename T2>
+    using dot_product_acc_t =
+        std::conditional_t<std::is_unsigned_v<T1> && std::is_unsigned_v<T2>,
+                           uint32_t, int32_t>;
+
+    template <typename T1, typename T2, typename T3>
+    inline auto dp4a(T1 a, T2 b, T3 c)
+    {
+        dot_product_acc_t<T1, T2> res = c;
+        auto va = extract_and_sign_or_zero_extend4(a);
+        auto vb = extract_and_sign_or_zero_extend4(b);
+        res += va[0] * vb[0];
+        res += va[1] * vb[1];
+        res += va[2] * vb[2];
+        res += va[3] * vb[3];
+        return res;
+    }
+
+    struct sub_sat
+    {
+        template <typename T>
+        auto operator()(const T x, const T y) const
+        {
+            return sycl::sub_sat(x, y);
+        }
+    };
+
+    template <typename S, typename T>
+    inline T vectorized_min(T a, T b)
+    {
+        sycl::vec<T, 1> v0{a}, v1{b};
+        auto v2 = v0.template as<S>();
+        auto v3 = v1.template as<S>();
+        auto v4 = sycl::min(v2, v3);
+        v0 = v4.template as<sycl::vec<T, 1>>();
+        return v0;
+    }
+
+    inline float pow(const float a, const int b) { return sycl::pown(a, b); }
+    inline double pow(const double a, const int b) { return sycl::pown(a, b); }
+    inline float pow(const float a, const float b) { return sycl::pow(a, b); }
+    inline double pow(const double a, const double b) { return sycl::pow(a, b); }
+    template <typename T, typename U>
+    inline typename std::enable_if_t<std::is_floating_point_v<T>, T>
+    pow(const T a, const U b)
+    {
+        return sycl::pow(a, static_cast<T>(b));
+    }
+    template <typename T, typename U>
+    inline typename std::enable_if_t<!std::is_floating_point_v<T>, double>
+    pow(const T a, const U b)
+    {
+        return sycl::pow(static_cast<double>(a), static_cast<double>(b));
+    }
+
+    inline double min(const double a, const float b)
+    {
+        return sycl::fmin(a, static_cast<double>(b));
+    }
+    inline double min(const float a, const double b)
+    {
+        return sycl::fmin(static_cast<double>(a), b);
+    }
+    inline float min(const float a, const float b) { return sycl::fmin(a, b); }
+    inline double min(const double a, const double b) { return sycl::fmin(a, b); }
+    inline std::uint32_t min(const std::uint32_t a, const std::int32_t b)
+    {
+        return sycl::min(a, static_cast<std::uint32_t>(b));
+    }
+    inline std::uint32_t min(const std::int32_t a, const std::uint32_t b)
+    {
+        return sycl::min(static_cast<std::uint32_t>(a), b);
+    }
+    inline std::int32_t min(const std::int32_t a, const std::int32_t b)
+    {
+        return sycl::min(a, b);
+    }
+    inline std::uint32_t min(const std::uint32_t a, const std::uint32_t b)
+    {
+        return sycl::min(a, b);
+    }
+    inline std::uint64_t min(const std::uint64_t a, const std::int64_t b)
+    {
+        return sycl::min(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t min(const std::int64_t a, const std::uint64_t b)
+    {
+        return sycl::min(static_cast<std::uint64_t>(a), b);
+    }
+    inline std::int64_t min(const std::int64_t a, const std::int64_t b)
+    {
+        return sycl::min(a, b);
+    }
+    inline std::uint64_t min(const std::uint64_t a, const std::uint64_t b)
+    {
+        return sycl::min(a, b);
+    }
+    inline std::uint64_t min(const std::uint64_t a, const std::int32_t b)
+    {
+        return sycl::min(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t min(const std::int32_t a, const std::uint64_t b)
+    {
+        return sycl::min(static_cast<std::uint64_t>(a), b);
+    }
+    inline std::uint64_t min(const std::uint64_t a, const std::uint32_t b)
+    {
+        return sycl::min(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t min(const std::uint32_t a, const std::uint64_t b)
+    {
+        return sycl::min(static_cast<std::uint64_t>(a), b);
+    }
+    // max function overloads.
+    // For floating-point types, `float` or `double` arguments are acceptable.
+    // For integer types, `std::uint32_t`, `std::int32_t`, `std::uint64_t` or
+    // `std::int64_t` type arguments are acceptable.
+    inline double max(const double a, const float b)
+    {
+        return sycl::fmax(a, static_cast<double>(b));
+    }
+    inline double max(const float a, const double b)
+    {
+        return sycl::fmax(static_cast<double>(a), b);
+    }
+    inline float max(const float a, const float b) { return sycl::fmax(a, b); }
+    inline double max(const double a, const double b) { return sycl::fmax(a, b); }
+    inline std::uint32_t max(const std::uint32_t a, const std::int32_t b)
+    {
+        return sycl::max(a, static_cast<std::uint32_t>(b));
+    }
+    inline std::uint32_t max(const std::int32_t a, const std::uint32_t b)
+    {
+        return sycl::max(static_cast<std::uint32_t>(a), b);
+    }
+    inline std::int32_t max(const std::int32_t a, const std::int32_t b)
+    {
+        return sycl::max(a, b);
+    }
+    inline std::uint32_t max(const std::uint32_t a, const std::uint32_t b)
+    {
+        return sycl::max(a, b);
+    }
+    inline std::uint64_t max(const std::uint64_t a, const std::int64_t b)
+    {
+        return sycl::max(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t max(const std::int64_t a, const std::uint64_t b)
+    {
+        return sycl::max(static_cast<std::uint64_t>(a), b);
+    }
+    inline std::int64_t max(const std::int64_t a, const std::int64_t b)
+    {
+        return sycl::max(a, b);
+    }
+    inline std::uint64_t max(const std::uint64_t a, const std::uint64_t b)
+    {
+        return sycl::max(a, b);
+    }
+    inline std::uint64_t max(const std::uint64_t a, const std::int32_t b)
+    {
+        return sycl::max(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t max(const std::int32_t a, const std::uint64_t b)
+    {
+        return sycl::max(static_cast<std::uint64_t>(a), b);
+    }
+    inline std::uint64_t max(const std::uint64_t a, const std::uint32_t b)
+    {
+        return sycl::max(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t max(const std::uint32_t a, const std::uint64_t b)
+    {
+        return sycl::max(static_cast<std::uint64_t>(a), b);
+    }
+
+    inline void
+    has_capability_or_fail(const sycl::device &dev,
+                           const std::initializer_list<sycl::aspect> &props)
+    {
+        for (const auto &it : props)
+        {
+            if (dev.has(it))
+                continue;
+            switch (it)
+            {
+            case sycl::aspect::fp64:
+                throw std::runtime_error("'double' is not supported in '" +
+                                         dev.get_info<sycl::info::device::name>() +
+                                         "' device");
+                break;
+            case sycl::aspect::fp16:
+                throw std::runtime_error("'half' is not supported in '" +
+                                         dev.get_info<sycl::info::device::name>() +
+                                         "' device");
+                break;
+            default:
+#define __SYCL_ASPECT(ASPECT, ID) \
+    case sycl::aspect::ASPECT:    \
+        return #ASPECT;
+#define __SYCL_ASPECT_DEPRECATED(ASPECT, ID, MESSAGE) __SYCL_ASPECT(ASPECT, ID)
+#define __SYCL_ASPECT_DEPRECATED_ALIAS(ASPECT, ID, MESSAGE)
+                auto getAspectNameStr = [](sycl::aspect AspectNum) -> std::string
+                {
+                    switch (AspectNum)
+                    {
+#include <sycl/info/aspects.def>
+#include <sycl/info/aspects_deprecated.def>
+                    default:
+                        return "unknown aspect";
+                    }
+                };
+#undef __SYCL_ASPECT_DEPRECATED_ALIAS
+#undef __SYCL_ASPECT_DEPRECATED
+#undef __SYCL_ASPECT
+                throw std::runtime_error(
+                    "'" + getAspectNameStr(it) + "' is not supported in '" +
+                    dev.get_info<sycl::info::device::name>() + "' device");
+            }
+            break;
+        }
+    }
+
+    static inline unsigned int get_current_device_id()
+    {
+        return dev_mgr::instance().current_device_id();
+    }
+
+    static inline device_ext &get_current_device()
+    {
+        return dev_mgr::instance().current_device();
+    }
+
+    static inline device_ext &get_device(unsigned int id)
+    {
+        return dev_mgr::instance().get_device(id);
+    }
+
+    static inline sycl::queue &get_in_order_queue()
+    {
+        return dev_mgr::instance().current_device().in_order_queue();
+    }
+
+    static sycl::event
+    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size,
+                memcpy_direction direction,
+                const std::vector<sycl::event> &dep_events = {})
+    {
+        if (!size)
+            return sycl::event{};
+        return q.memcpy(to_ptr, from_ptr, size, dep_events);
+        GGML_UNUSED(direction);
+    }
+
+    // Get actual copy range and make sure it will not exceed range.
+    static inline size_t get_copy_range(sycl::range<3> size, size_t slice,
+                                        size_t pitch)
+    {
+        return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0);
+    }
+
+    static inline size_t get_offset(sycl::id<3> id, size_t slice,
+                                    size_t pitch)
+    {
+        return slice * id.get(2) + pitch * id.get(1) + id.get(0);
+    }
+
+    /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr
+    /// and \p from_range to another specified by \p to_ptr and \p to_range.
+    static inline std::vector<sycl::event>
+    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
+                sycl::range<3> to_range, sycl::range<3> from_range,
+                sycl::id<3> to_id, sycl::id<3> from_id,
+                sycl::range<3> size, memcpy_direction direction,
+                const std::vector<sycl::event> &dep_events = {})
+    {
+        // RAII for host pointer
+        class host_buffer
+        {
+            void *_buf;
+            size_t _size;
+            sycl::queue &_q;
+            const std::vector<sycl::event> &_deps; // free operation depends
+
+        public:
+            host_buffer(size_t size, sycl::queue &q,
+                        const std::vector<sycl::event> &deps)
+                : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {}
+            void *get_ptr() const { return _buf; }
+            size_t get_size() const { return _size; }
+            ~host_buffer()
+            {
+                if (_buf)
+                {
+                    _q.submit([&](sycl::handler &cgh)
+                              {
+            cgh.depends_on(_deps);
+            cgh.host_task([buf = _buf] { std::free(buf); }); });
+                }
+            }
+        };
+        std::vector<sycl::event> event_list;
+
+        size_t to_slice = to_range.get(1) * to_range.get(0),
+               from_slice = from_range.get(1) * from_range.get(0);
+        unsigned char *to_surface =
+            (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0));
+        const unsigned char *from_surface =
+            (const unsigned char *)from_ptr +
+            get_offset(from_id, from_slice, from_range.get(0));
+
+        if (to_slice == from_slice && to_slice == size.get(1) * size.get(0))
+        {
+            return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2),
+                                direction, dep_events)};
+        }
+        direction = detail::deduce_memcpy_direction(q, to_ptr, from_ptr, direction);
+        size_t size_slice = size.get(1) * size.get(0);
+        switch (direction)
+        {
+        case host_to_host:
+            for (size_t z = 0; z < size.get(2); ++z)
+            {
+                unsigned char *to_ptr = to_surface;
+                const unsigned char *from_ptr = from_surface;
+                if (to_range.get(0) == from_range.get(0) &&
+                    to_range.get(0) == size.get(0))
+                {
+                    event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice,
+                                                     direction, dep_events));
+                }
+                else
+                {
+                    for (size_t y = 0; y < size.get(1); ++y)
+                    {
+                        event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0),
+                                                         direction, dep_events));
+                        to_ptr += to_range.get(0);
+                        from_ptr += from_range.get(0);
+                    }
+                }
+                to_surface += to_slice;
+                from_surface += from_slice;
+            }
+            break;
+        case host_to_device:
+        {
+            host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q,
+                            event_list);
+            std::vector<sycl::event> host_events;
+            if (to_slice == size_slice)
+            {
+                // Copy host data to a temp host buffer with the shape of target.
+                host_events =
+                    dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range,
+                                sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size,
+                                host_to_host, dep_events);
+            }
+            else
+            {
+                // Copy host data to a temp host buffer with the shape of target.
+                host_events = dpct_memcpy(
+                    q, buf.get_ptr(), from_surface, to_range, from_range,
+                    sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host,
+                    // If has padding data, not sure whether it is useless. So fill temp
+                    // buffer with it.
+                    std::vector<sycl::event>{
+                        dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(),
+                                    device_to_host, dep_events)});
+            }
+            // Copy from temp host buffer to device with only one submit.
+            event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(),
+                                             buf.get_size(), host_to_device,
+                                             host_events));
+            break;
+        }
+        case device_to_host:
+        {
+            host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q,
+                            event_list);
+            // Copy from host temp buffer to host target with reshaping.
+            event_list = dpct_memcpy(
+                q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0),
+                sycl::id<3>(0, 0, 0), size, host_to_host,
+                // Copy from device to temp host buffer with only one submit.
+                std::vector<sycl::event>{dpct_memcpy(q, buf.get_ptr(), from_surface,
+                                                     buf.get_size(),
+                                                     device_to_host, dep_events)});
+            break;
+        }
+        case device_to_device:
+            event_list.push_back(q.submit([&](sycl::handler &cgh)
+                                          {
+        cgh.depends_on(dep_events);
+        cgh.parallel_for<class dpct_memcpy_3d_detail>(
+            size,
+            [=](sycl::id<3> id) {
+                to_surface[get_offset(id, to_slice, to_range.get(0))] =
+                    from_surface[get_offset(id, from_slice, from_range.get(0))];
+            }); }));
+        break;
+        default:
+            throw std::runtime_error("dpct_memcpy: invalid direction value");
+        }
+        return event_list;
+    }
+
+    /// memcpy 2D/3D matrix specified by pitched_data.
+    static inline std::vector<sycl::event>
+    dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id,
+                pitched_data from, sycl::id<3> from_id, sycl::range<3> size,
+                memcpy_direction direction = automatic)
+    {
+        return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(),
+                           sycl::range<3>(to.get_pitch(), to.get_y(), 1),
+                           sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id,
+                           size, direction);
+    }
+
+    /// memcpy 2D matrix with pitch.
+    static inline std::vector<sycl::event>
+    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
+                size_t to_pitch, size_t from_pitch, size_t x, size_t y,
+                memcpy_direction direction = automatic)
+    {
+        return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1),
+                           sycl::range<3>(from_pitch, y, 1),
+                           sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0),
+                           sycl::range<3>(x, y, 1), direction);
+    }
+
+    inline void gemm(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                     oneapi::mkl::transpose b_trans, int m, int n, int k,
+                     const void *alpha, const void *a, library_data_t a_type,
+                     int lda, const void *b, library_data_t b_type, int ldb,
+                     const void *beta, void *c, library_data_t c_type, int ldc,
+                     library_data_t scaling_type)
+    {
+        if (scaling_type == library_data_t::real_float &&
+            c_type == library_data_t::complex_float)
+        {
+            scaling_type = library_data_t::complex_float;
+        }
+        else if (scaling_type == library_data_t::real_double &&
+                 c_type == library_data_t::complex_double)
+        {
+            scaling_type = library_data_t::complex_double;
+        }
+
+        std::uint64_t key =
+            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
+        switch (key)
+        {
+        case detail::get_type_combination_id(
+            library_data_t::real_float, library_data_t::real_float,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_impl<float, float, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_double, library_data_t::real_double,
+            library_data_t::real_double, library_data_t::real_double):
+        {
+            detail::gemm_impl<double, double, double, double>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_float, library_data_t::complex_float,
+            library_data_t::complex_float, library_data_t::complex_float):
+        {
+            detail::gemm_impl<std::complex<float>, std::complex<float>,
+                              std::complex<float>, std::complex<float>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_double, library_data_t::complex_double,
+            library_data_t::complex_double, library_data_t::complex_double):
+        {
+            detail::gemm_impl<std::complex<double>, std::complex<double>,
+                              std::complex<double>, std::complex<double>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_half):
+        {
+            detail::gemm_impl<sycl::half, sycl::half, sycl::half,
+                              sycl::half>(q, a_trans, b_trans, m, n, k, alpha, a,
+                                          lda, b, ldb, beta, c, ldc);
+            break;
+        }
+#ifdef __INTEL_MKL__
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
+                              float>(q, a_trans, b_trans, m, n, k, alpha, a, lda, b,
+                                     ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_impl<sycl::half, sycl::half, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_float):
+        {
+            float alpha_value =
+                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
+            float beta_value =
+                dpct::get_value(reinterpret_cast<const float *>(beta), q);
+            sycl::half alpha_half(alpha_value);
+            sycl::half beta_half(beta_value);
+            detail::gemm_impl<sycl::half, sycl::half, sycl::half,
+                              sycl::half>(q, a_trans, b_trans, m, n, k, &alpha_half,
+                                          a, lda, b, ldb, &beta_half, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_impl<std::int8_t, std::int8_t, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_bfloat16, library_data_t::real_float):
+        {
+            detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
+                              oneapi::mkl::bfloat16, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_int32, library_data_t::real_int32):
+        {
+            float alpha_float =
+                dpct::get_value(reinterpret_cast<const std::int32_t *>(alpha), q);
+            float beta_float =
+                dpct::get_value(reinterpret_cast<const std::int32_t *>(beta), q);
+            detail::gemm_impl<std::int8_t, std::int8_t, std::int32_t, float>(
+                q, a_trans, b_trans, m, n, k, &alpha_float, a, lda, b, ldb, &beta_float, c, ldc);
+            break;
+        }
+#endif // __INTEL_MKL__
+        default:
+            throw std::runtime_error("the combination of data type is unsupported");
+        }
+    } // gemm()
+
+    /// Computes a batch of matrix-matrix product with general matrices.
+    /// \param [in] q The queue where the routine should be executed.
+    /// \param [in] a_trans Specifies the operation applied to A.
+    /// \param [in] b_trans Specifies the operation applied to B.
+    /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C.
+    /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C.
+    /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B).
+    /// \param [in] alpha Scaling factor for the matrix-matrix product.
+    /// \param [in] a Input matrix A.
+    /// \param [in] a_type Data type of the matrix A.
+    /// \param [in] lda Leading dimension of A.
+    /// \param [in] b Input matrix B.
+    /// \param [in] b_type Data type of the matrix B.
+    /// \param [in] ldb Leading dimension of B.
+    /// \param [in] beta Scaling factor for matrix C.
+    /// \param [in, out] c Input/Output matrix C.
+    /// \param [in] c_type Data type of the matrix C.
+    /// \param [in] ldc Leading dimension of C.
+    /// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
+    /// \param [in] scaling_type Data type of the scaling factors.
+    inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                           oneapi::mkl::transpose b_trans, int m, int n, int k,
+                           const void *alpha, const void *a[],
+                           library_data_t a_type, int lda, const void *b[],
+                           library_data_t b_type, int ldb, const void *beta,
+                           void *c[], library_data_t c_type, int ldc,
+                           int batch_size, library_data_t scaling_type)
+    {
+        if (scaling_type == library_data_t::real_float &&
+            c_type == library_data_t::complex_float)
+        {
+            scaling_type = library_data_t::complex_float;
+        }
+        else if (scaling_type == library_data_t::real_double &&
+                 c_type == library_data_t::complex_double)
+        {
+            scaling_type = library_data_t::complex_double;
+        }
+
+        std::uint64_t key =
+            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
+        switch (key)
+        {
+        case detail::get_type_combination_id(
+            library_data_t::real_float, library_data_t::real_float,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<float, float, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_double, library_data_t::real_double,
+            library_data_t::real_double, library_data_t::real_double):
+        {
+            detail::gemm_batch_impl<double, double, double, double>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_float, library_data_t::complex_float,
+            library_data_t::complex_float, library_data_t::complex_float):
+        {
+            detail::gemm_batch_impl<std::complex<float>, std::complex<float>,
+                                    std::complex<float>, std::complex<float>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_double, library_data_t::complex_double,
+            library_data_t::complex_double, library_data_t::complex_double):
+        {
+            detail::gemm_batch_impl<std::complex<double>, std::complex<double>,
+                                    std::complex<double>, std::complex<double>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_half):
+        {
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half,
+                                    sycl::half>(q, a_trans, b_trans, m, n, k, alpha,
+                                                a, lda, b, ldb, beta, c, ldc,
+                                                batch_size);
+            break;
+        }
+#ifdef __INTEL_MKL__
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_bfloat16, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
+                                    oneapi::mkl::bfloat16, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
+                                    float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
+                                           b, ldb, beta, c, ldc, batch_size);
+            break;
+        }
+#endif
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_int32, library_data_t::real_int32):
+        {
+            float alpha_float =
+                dpct::get_value(reinterpret_cast<const std::int32_t *>(alpha), q);
+            float beta_float =
+                dpct::get_value(reinterpret_cast<const std::int32_t *>(beta), q);
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, std::int32_t,
+                                    float>(q, a_trans, b_trans, m, n, k, &alpha_float,
+                                           a, lda, b, ldb, &beta_float, c, ldc,
+                                           batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<sycl::half, sycl::half, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_float):
+        {
+            float alpha_value =
+                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
+            float beta_value =
+                dpct::get_value(reinterpret_cast<const float *>(beta), q);
+            sycl::half alpha_half(alpha_value);
+            sycl::half beta_half(beta_value);
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half, sycl::half>(
+                q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, b, ldb, &beta_half, c, ldc,
+                batch_size);
+            break;
+        }
+        default:
+            throw std::runtime_error("the combination of data type is unsupported");
+        }
+    }
+
+    /// Computes a batch of matrix-matrix product with general matrices.
+    /// \param [in] q The queue where the routine should be executed.
+    /// \param [in] a_trans Specifies the operation applied to A.
+    /// \param [in] b_trans Specifies the operation applied to B.
+    /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C.
+    /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C.
+    /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B).
+    /// \param [in] alpha Scaling factor for the matrix-matrix product.
+    /// \param [in] a Input matrix A.
+    /// \param [in] a_type Data type of the matrix A.
+    /// \param [in] lda Leading dimension of A.
+    /// \param [in] stride_a Stride between the different A matrices.
+    /// \param [in] b Input matrix B.
+    /// \param [in] b_type Data type of the matrix B.
+    /// \param [in] ldb Leading dimension of B.
+    /// \param [in] stride_b Stride between the different B matrices.
+    /// \param [in] beta Scaling factor for matrix C.
+    /// \param [in, out] c Input/Output matrix C.
+    /// \param [in] c_type Data type of the matrix C.
+    /// \param [in] ldc Leading dimension of C.
+    /// \param [in] stride_c Stride between the different C matrices.
+    /// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
+    /// \param [in] scaling_type Data type of the scaling factors.
+    inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                           oneapi::mkl::transpose b_trans, int m, int n, int k,
+                           const void *alpha, const void *a, library_data_t a_type,
+                           int lda, long long int stride_a, const void *b,
+                           library_data_t b_type, int ldb, long long int stride_b,
+                           const void *beta, void *c, library_data_t c_type,
+                           int ldc, long long int stride_c, int batch_size,
+                           library_data_t scaling_type)
+    {
+        if (scaling_type == library_data_t::real_float &&
+            c_type == library_data_t::complex_float)
+        {
+            scaling_type = library_data_t::complex_float;
+        }
+        else if (scaling_type == library_data_t::real_double &&
+                 c_type == library_data_t::complex_double)
+        {
+            scaling_type = library_data_t::complex_double;
+        }
+
+        std::uint64_t key =
+            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
+        switch (key)
+        {
+        case detail::get_type_combination_id(
+            library_data_t::real_float, library_data_t::real_float,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<float, float, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_double, library_data_t::real_double,
+            library_data_t::real_double, library_data_t::real_double):
+        {
+            detail::gemm_batch_impl<double, double, double, double>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_float, library_data_t::complex_float,
+            library_data_t::complex_float, library_data_t::complex_float):
+        {
+            detail::gemm_batch_impl<std::complex<float>, std::complex<float>,
+                                    std::complex<float>, std::complex<float>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_double, library_data_t::complex_double,
+            library_data_t::complex_double, library_data_t::complex_double):
+        {
+            detail::gemm_batch_impl<std::complex<double>, std::complex<double>,
+                                    std::complex<double>, std::complex<double>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_half):
+        {
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half,
+                                    sycl::half>(q, a_trans, b_trans, m, n, k, alpha,
+                                                a, lda, stride_a, b, ldb, stride_b,
+                                                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+#ifdef __INTEL_MKL__
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_bfloat16, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
+                                    oneapi::mkl::bfloat16, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
+                                    float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
+                                           stride_a, b, ldb, stride_b, beta, c, ldc,
+                                           stride_c, batch_size);
+            break;
+        }
+#endif
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_int32, library_data_t::real_int32):
+        {
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, std::int32_t,
+                                    std::int32_t>(q, a_trans, b_trans, m, n, k, alpha,
+                                                  a, lda, stride_a, b, ldb, stride_b,
+                                                  beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<sycl::half, sycl::half, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_float):
+        {
+            float alpha_value =
+                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
+            float beta_value =
+                dpct::get_value(reinterpret_cast<const float *>(beta), q);
+            sycl::half alpha_half(alpha_value);
+            sycl::half beta_half(beta_value);
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half, sycl::half>(
+                q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, stride_a, b, ldb, stride_b,
+                &beta_half, c, ldc, stride_c, batch_size);
+            break;
+        }
+        default:
+            throw std::runtime_error("the combination of data type is unsupported");
+        }
+    }
+
+    static inline void
+    async_dpct_memcpy(void *to_ptr, size_t to_pitch, const void *from_ptr,
+                      size_t from_pitch, size_t x, size_t y,
+                      memcpy_direction direction = automatic,
+                      sycl::queue &q = get_default_queue())
+    {
+        detail::dpct_memcpy(q, to_ptr, from_ptr, to_pitch, from_pitch, x, y,
+                            direction);
+    }
+
+    using err0 = detail::generic_error_type<struct err0_tag, int>;
+    using err1 = detail::generic_error_type<struct err1_tag, int>;
+
+    static inline void dpct_free(void *ptr, sycl::queue &q = get_default_queue()) {
+        detail::dpct_free(ptr, q);
+    }
+
+    /// dpct accessor used as device function parameter.
+    template <class T, memory_region Memory, size_t Dimension> class accessor;
+    template <class T, memory_region Memory> class accessor<T, Memory, 3> {
+    public:
+        using memory_t = detail::memory_traits<Memory, T>;
+        using element_t = typename memory_t::element_t;
+        using pointer_t = typename memory_t::pointer_t;
+        using accessor_t = typename memory_t::template accessor_t<3>;
+        accessor(pointer_t data, const sycl::range<3> &in_range)
+            : _data(data), _range(in_range) {}
+        template <memory_region M = Memory>
+        accessor(typename std::enable_if<M != local, const accessor_t>::type &acc)
+            : accessor(acc, acc.get_range()) {}
+        accessor(const accessor_t &acc, const sycl::range<3> &in_range)
+            : accessor(acc.get_pointer(), in_range) {}
+        accessor<T, Memory, 2> operator[](size_t index) const {
+            sycl::range<2> sub(_range.get(1), _range.get(2));
+            return accessor<T, Memory, 2>(_data + index * sub.size(), sub);
+        }
+
+        pointer_t get_ptr() const { return _data; }
+
+    private:
+        pointer_t _data;
+        sycl::range<3> _range;
+    };
+    template <class T, memory_region Memory> class accessor<T, Memory, 2> {
+    public:
+        using memory_t = detail::memory_traits<Memory, T>;
+        using element_t = typename memory_t::element_t;
+        using pointer_t = typename memory_t::pointer_t;
+        using accessor_t = typename memory_t::template accessor_t<2>;
+        accessor(pointer_t data, const sycl::range<2> &in_range)
+            : _data(data), _range(in_range) {}
+        template <memory_region M = Memory>
+        accessor(typename std::enable_if<M != local, const accessor_t>::type &acc)
+            : accessor(acc, acc.get_range()) {}
+        accessor(const accessor_t &acc, const sycl::range<2> &in_range)
+            : accessor(acc.get_pointer(), in_range) {}
+
+        pointer_t operator[](size_t index) const {
+            return _data + _range.get(1) * index;
+        }
+
+        pointer_t get_ptr() const { return _data; }
+
+    private:
+        pointer_t _data;
+        sycl::range<2> _range;
+    };
+
+    namespace detail {
+        /// Device variable with address space of shared, global or constant.
+        template <class T, memory_region Memory, size_t Dimension> class device_memory {
+        public:
+            using accessor_t =
+                typename detail::memory_traits<Memory,
+                                            T>::template accessor_t<Dimension>;
+            using value_t = typename detail::memory_traits<Memory, T>::value_t;
+            using dpct_accessor_t = dpct::accessor<T, Memory, Dimension>;
+
+            device_memory() : device_memory(sycl::range<Dimension>(1)) {}
+
+            /// Constructor of 1-D array with initializer list
+            device_memory(const sycl::range<Dimension> &in_range,
+                        std::initializer_list<value_t> &&init_list)
+                : device_memory(in_range) {
+                assert(init_list.size() <= in_range.size());
+                _host_ptr = (value_t *)std::malloc(_size);
+                std::memset(_host_ptr, 0, _size);
+                std::memcpy(_host_ptr, init_list.begin(), init_list.size() * sizeof(T));
+            }
+
+            /// Constructor of 2-D array with initializer list
+            template <size_t D = Dimension>
+            device_memory(
+                const typename std::enable_if<D == 2, sycl::range<2>>::type &in_range,
+                std::initializer_list<std::initializer_list<value_t>> &&init_list)
+                : device_memory(in_range) {
+                assert(init_list.size() <= in_range[0]);
+                _host_ptr = (value_t *)std::malloc(_size);
+                std::memset(_host_ptr, 0, _size);
+                auto tmp_data = _host_ptr;
+                for (auto sub_list : init_list) {
+                    assert(sub_list.size() <= in_range[1]);
+                    std::memcpy(tmp_data, sub_list.begin(),
+                                sub_list.size() * sizeof(T));
+                    tmp_data += in_range[1];
+                }
+            }
+
+            /// Constructor with range
+            device_memory(const sycl::range<Dimension> &range_in)
+                : _size(range_in.size() * sizeof(T)), _range(range_in),
+                _reference(false), _host_ptr(nullptr), _device_ptr(nullptr) {
+                static_assert(
+                    (Memory == global) || (Memory == constant) || (Memory == shared),
+                    "device memory region should be global, constant or shared");
+                // Make sure that singleton class mem_mgr and dev_mgr will destruct
+                // later than this.
+                detail::mem_mgr::instance();
+                dev_mgr::instance();
+            }
+
+            /// Constructor with range
+            template <class... Args>
+            device_memory(Args... Arguments)
+                : device_memory(sycl::range<Dimension>(Arguments...)) {}
+
+            ~device_memory() {
+                if (_device_ptr && !_reference)
+                    dpct::dpct_free(_device_ptr);
+                if (_host_ptr)
+                    std::free(_host_ptr);
+            }
+
+            /// Allocate memory with default queue, and init memory if has initial
+            /// value.
+            void init() { init(dpct::get_default_queue()); }
+            /// Allocate memory with specified queue, and init memory if has initial
+            /// value.
+            void init(sycl::queue &q) {
+                if (_device_ptr)
+                    return;
+                if (!_size)
+                    return;
+                allocate_device(q);
+                if (_host_ptr)
+                    detail::dpct_memcpy(q, _device_ptr, _host_ptr, _size,
+                                        host_to_device);
+            }
+
+            /// The variable is assigned to a device pointer.
+            void assign(value_t *src, size_t size) {
+                this->~device_memory();
+                new (this) device_memory(src, size);
+            }
+
+            /// Get memory pointer of the memory object, which is virtual pointer when
+            /// usm is not used, and device pointer when usm is used.
+            value_t *get_ptr() { return get_ptr(get_default_queue()); }
+            /// Get memory pointer of the memory object, which is virtual pointer when
+            /// usm is not used, and device pointer when usm is used.
+            value_t *get_ptr(sycl::queue &q) {
+                init(q);
+                return _device_ptr;
+            }
+
+            /// Get the device memory object size in bytes.
+            size_t get_size() { return _size; }
+
+            template <size_t D = Dimension>
+            typename std::enable_if<D == 1, T>::type &operator[](size_t index) {
+                init();
+                return _device_ptr[index];
+            }
+
+            /// Get dpct::accessor with dimension info for the device memory object
+            /// when usm is used and dimension is greater than 1.
+            template <size_t D = Dimension>
+            typename std::enable_if<D != 1, dpct_accessor_t>::type
+            get_access([[maybe_unused]] sycl::handler &cgh) {
+                return dpct_accessor_t((T *)_device_ptr, _range);
+            }
+
+        private:
+            device_memory(value_t *memory_ptr, size_t size)
+                : _size(size), _range(size / sizeof(T)), _reference(true),
+                _device_ptr(memory_ptr) {}
+
+            void allocate_device(sycl::queue &q) {
+        #ifndef DPCT_USM_LEVEL_NONE
+                if (Memory == shared) {
+                    _device_ptr = (value_t *)sycl::malloc_shared(_size, q.get_device(),
+                                                                q.get_context());
+                    return;
+                }
+        #ifdef SYCL_EXT_ONEAPI_USM_DEVICE_READ_ONLY
+                if (Memory == constant) {
+                    _device_ptr = (value_t *)sycl::malloc_device(
+                        _size, q.get_device(), q.get_context(),
+                        sycl::ext::oneapi::property::usm::device_read_only());
+                    return;
+                }
+        #endif
+        #endif
+                _device_ptr = (value_t *)detail::dpct_malloc(_size, q);
+            }
+
+            size_t _size;
+            sycl::range<Dimension> _range;
+            bool _reference;
+            value_t *_host_ptr;
+            value_t *_device_ptr;
+        };
+        template <class T, memory_region Memory>
+        class device_memory<T, Memory, 0> : public device_memory<T, Memory, 1> {
+        public:
+            using base = device_memory<T, Memory, 1>;
+            using value_t = typename base::value_t;
+            using accessor_t =
+                typename detail::memory_traits<Memory, T>::template accessor_t<0>;
+
+            /// Constructor with initial value.
+            device_memory(const value_t &val) : base(sycl::range<1>(1), {val}) {}
+
+            /// Default constructor
+            device_memory() : base(1) {}
+        };
+        } // namespace detail
+
+    template <class T, size_t Dimension>
+    using global_memory = detail::device_memory<T, global, Dimension>;
+    template <class T, size_t Dimension>
+    using constant_memory = detail::device_memory<T, constant, Dimension>;
+    template <class T, size_t Dimension>
+    using shared_memory = detail::device_memory<T, shared, Dimension>;
+
+
+    template <typename T,
+            sycl::access::address_space addressSpace =
+                sycl::access::address_space::global_space,
+            sycl::memory_order memoryOrder = sycl::memory_order::relaxed,
+            sycl::memory_scope memoryScope = sycl::memory_scope::device>
+    inline T atomic_fetch_add(T *addr, T operand) {
+    auto atm =
+        sycl::atomic_ref<T, memoryOrder, memoryScope, addressSpace>(addr[0]);
+    return atm.fetch_add(operand);
+    }
+
+    template <sycl::access::address_space addressSpace =
+                sycl::access::address_space::global_space,
+            sycl::memory_order memoryOrder = sycl::memory_order::relaxed,
+            sycl::memory_scope memoryScope = sycl::memory_scope::device,
+            typename T1, typename T2>
+    inline T1 atomic_fetch_add(T1 *addr, T2 operand) {
+    auto atm =
+        sycl::atomic_ref<T1, memoryOrder, memoryScope, addressSpace>(addr[0]);
+    return atm.fetch_add(operand);
+    }
+
+    template <typename T, sycl::access::address_space addressSpace =
+                            sycl::access::address_space::global_space>
+    inline T atomic_fetch_add(T *addr, T operand,
+                            sycl::memory_order memoryOrder) {
+    switch (memoryOrder) {
+        case sycl::memory_order::relaxed:
+            return atomic_fetch_add<T, addressSpace, sycl::memory_order::relaxed,
+                                    sycl::memory_scope::device>(addr, operand);
+        case sycl::memory_order::acq_rel:
+            return atomic_fetch_add<T, addressSpace, sycl::memory_order::acq_rel,
+                                    sycl::memory_scope::device>(addr, operand);
+        case sycl::memory_order::seq_cst:
+            return atomic_fetch_add<T, addressSpace, sycl::memory_order::seq_cst,
+                                    sycl::memory_scope::device>(addr, operand);
+        default:
+            assert(false && "Invalid memory_order for atomics. Valid memory_order for "
+                            "atomics are: sycl::memory_order::relaxed, "
+                            "sycl::memory_order::acq_rel, sycl::memory_order::seq_cst!");
+        }
+    }
+
+    template <sycl::access::address_space addressSpace =
+                sycl::access::address_space::global_space,
+            typename T1, typename T2>
+    inline T1 atomic_fetch_add(T1 *addr, T2 operand,
+                            sycl::memory_order memoryOrder) {
+    atomic_fetch_add<T1, addressSpace>(addr, operand, memoryOrder);
+    }
+
+} // COPY from DPCT head files
+
+#endif // GGML_SYCL_DPCT_HELPER_HPP
diff --git a/ggml/src/ggml-sycl/mmq.cpp b/ggml/src/ggml-sycl/mmq.cpp
new file mode 100644
index 0000000000000..e952533d310ec
--- /dev/null
+++ b/ggml/src/ggml-sycl/mmq.cpp
@@ -0,0 +1,3031 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include "mmq.hpp"
+#include "vecdotq.hpp"
+
+typedef void (*allocate_tiles_sycl_t)(
+    int** x_ql,
+    sycl::half2** x_dm,
+    int** x_qh,
+    int** x_sc);
+typedef void (*load_tiles_sycl_t)(
+    const void* __restrict__ vx,
+    int* __restrict__ x_ql,
+    sycl::half2* __restrict__ x_dm,
+    int* __restrict__ x_qh,
+    int* __restrict__ x_sc,
+    const int& i_offset,
+    const int& i_max,
+    const int& k,
+    const int& blocks_per_row);
+typedef float (*vec_dot_q_mul_mat_sycl_t)(
+    const int* __restrict__ x_ql,
+    const sycl::half2* __restrict__ x_dm,
+    const int* __restrict__ x_qh,
+    const int* __restrict__ x_sc,
+    const int* __restrict__ y_qs,
+    const sycl::half2* __restrict__ y_ms,
+    const int& i,
+    const int& j,
+    const int& k);
+
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q4_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_qs_q4_0, float *tile_x_d_q4_0) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_qs_q4_0;
+    *x_dm = (sycl::half2 *)tile_x_d_q4_0;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q4_0(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_0;
+    const int kqsx = k % QI4_0;
+
+    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
+
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
+        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q4_0_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const float * x_dmf = (const float *) x_dm;
+
+    int u[2*VDR_Q4_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q4_1(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_qs_q4_1, sycl::half2 *tile_x_dm_q4_1) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_qs_q4_1;
+    *x_dm = tile_x_dm_q4_1;
+}
+
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q4_1(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_1;
+    const int kqsx = k % QI4_1;
+
+    const block_q4_1 * bx0 = (const block_q4_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
+        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q4_1_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+
+    int u[2*VDR_Q4_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q5_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q5_0, float *tile_x_d_q5_0) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_ql_q5_0;
+    *x_dm = (sycl::half2 *)tile_x_d_q5_0;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q5_0(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_0;
+    const int kqsx = k % QI5_0;
+
+    const block_q5_0 * bx0 = (const block_q5_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
+
+        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
+        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
+        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
+        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
+        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
+        qs0 = dpct::vectorized_binary<sycl::char4>(
+            qs0, 0x10101010, dpct::sub_sat()); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
+        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
+        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
+        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
+        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
+        qs1 = dpct::vectorized_binary<sycl::char4>(
+            qs1, 0x10101010, dpct::sub_sat()); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
+        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q5_0_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    int u[2*VDR_Q5_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q5_1(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q5_1, sycl::half2 *tile_x_dm_q5_1) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_ql_q5_1;
+    *x_dm = tile_x_dm_q5_1;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q5_1(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset < nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_1;
+    const int kqsx = k % QI5_1;
+
+    const block_q5_1 * bx0 = (const block_q5_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
+
+        int qs0 = (ql >>  0) & 0x0F0F0F0F;
+        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
+        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
+        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
+        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4) & 0x0F0F0F0F;
+        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
+        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
+        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
+        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
+        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q5_1_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
+
+    int u[2*VDR_Q5_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q8_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_qs_q8_0, float *tile_x_d_q8_0) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_qs_q8_0;
+    *x_dm = (sycl::half2 *)tile_x_d_q8_0;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q8_0(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI8_0;
+    const int kqsx = k % QI8_0;
+    float * x_dmf = (float *) x_dm;
+
+    const block_q8_0 * bx0 = (const block_q8_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
+        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q8_0_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
+         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q2_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q2_K, sycl::half2 *tile_x_dm_q2_K,
+                    int *tile_x_sc_q2_K) {
+    (void)x_qh;
+
+    *x_ql = tile_x_ql_q2_K;
+    *x_dm = tile_x_dm_q2_K;
+    *x_sc = tile_x_sc_q2_K;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q2_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI2_K;
+    const int kqsx = k % QI2_K;
+
+    const block_q2_K * bx0 = (const block_q2_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
+        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
+    }
+}
+
+#define VDR_Q2_K_Q8_1_MMQ  2
+// contiguous u/y values
+static __dpct_inline__ float
+vec_dot_q2_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
+                           const uint8_t *__restrict__ scales,
+                           const sycl::half2 &dm2, const float &d8) {
+
+    int sumi_d = 0;
+    int sumi_m = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
+        int sumi_d_sc = 0;
+
+        const int sc = scales[i0 / (QI8_1/2)];
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_d_sc = dpct::dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
+            sumi_m = dpct::dp4a(m, u[i],
+                                sumi_m); // multiply sum of q8_1 values with m
+        }
+
+        sumi_d += sumi_d_sc * (sc & 0xF);
+    }
+
+    const sycl::float2 dm2f =
+        dm2.convert<float, sycl::rounding_mode::automatic>();
+
+    return d8 * (dm2f.x() * sumi_d - dm2f.y() * sumi_m);
+}
+
+static __dpct_inline__ float vec_dot_q2_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh;
+
+    const int kbx = k / QI2_K;
+    const int ky  = (k % QI2_K) * QR2_K;
+    const float * y_df = (const float *) y_ds;
+
+    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
+
+    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
+    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
+
+#pragma unroll
+    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
+        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
+    }
+
+    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
+
+    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
+    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q3_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q3_K, sycl::half2 *tile_x_dm_q3_K,
+                    int *tile_x_qh_q3_K, int *tile_x_sc_q3_K) {
+
+    *x_ql = tile_x_ql_q3_K;
+    *x_dm = tile_x_dm_q3_K;
+    *x_qh = tile_x_qh_q3_K;
+    *x_sc = tile_x_sc_q3_K;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q3_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI3_K;
+    const int kqsx = k % QI3_K;
+
+    const block_q3_K * bx0 = (const block_q3_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
+        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
+        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
+
+        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
+
+        const int ksc = k % (QI3_K/4);
+
+        const int ksc_low = ksc % (QI3_K/8);
+        const int shift_low = 4 * (ksc / (QI3_K/8));
+        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
+
+        const int ksc_high = QI3_K/8;
+        const int shift_high = 2 * ksc;
+        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
+
+        const int sc = dpct::vectorized_binary<sycl::char4>(
+            sc_low | sc_high, 0x20202020, dpct::sub_sat());
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
+    }
+}
+
+#define VDR_Q3_K_Q8_1_MMQ  2
+// contiguous u/y values
+static __dpct_inline__ float
+vec_dot_q3_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
+                           const int8_t *__restrict__ scales, const float &d3,
+                           const float &d8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
+        int sumi_sc = 0;
+
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_sc = dpct::dp4a(v[i], u[i], sumi_sc); // SIMD dot product
+        }
+
+        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
+    }
+
+    return d3*d8 * sumi;
+}
+
+static __dpct_inline__ float vec_dot_q3_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+
+    const int kbx  = k / QI3_K;
+    const int ky  = (k % QI3_K) * QR3_K;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
+
+    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
+        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
+        const int shift = 2 * ((ky % 32) / 8);
+        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
+
+        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
+        const int vlh = (vh << 2) & 0x04040404;
+
+        v[l] = dpct::vectorized_binary<sycl::char4>(vll, vlh, dpct::sub_sat());
+    }
+
+    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
+    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q4_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q4_K, sycl::half2 *tile_x_dm_q4_K,
+                    int *tile_x_sc_q4_K) {
+    (void)x_qh;
+
+    *x_ql = tile_x_ql_q4_K;
+    *x_dm = tile_x_dm_q4_K;
+    *x_sc = tile_x_sc_q4_K;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI4_K; // == k if QK_K == 256
+
+    const block_q4_K * bx0 = (const block_q4_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
+        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
+#else
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+
+#define VDR_Q4_K_Q8_1_MMQ  8
+
+// contiguous u/y values
+static __dpct_inline__ float vec_dot_q4_K_q8_1_impl_mmq(
+    const int *__restrict__ v, const int *__restrict__ u,
+    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
+    const sycl::half2 &dm4, const sycl::half2 *__restrict__ ds8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = dpct::dp4a((v[j] >> (4 * i)) & 0x0F0F0F0F,
+                                u[i * QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const sycl::float2 ds8f =
+            ds8[i].convert<float, sycl::rounding_mode::automatic>();
+
+        sumf_d += ds8f.x() * (sc[i] * sumi_d);
+        sumf_m += ds8f.y() * m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const sycl::float2 dm4f =
+        dm4.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
+}
+
+
+static __dpct_inline__ float vec_dot_q4_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh;
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
+
+    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
+    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q5_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q5_K, sycl::half2 *tile_x_dm_q5_K,
+                    int *tile_x_sc_q5_K) {
+    (void)x_qh;
+
+    *x_ql = tile_x_ql_q5_K;
+    *x_dm = tile_x_dm_q5_K;
+    *x_sc = tile_x_sc_q5_K;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI5_K; // == k if QK_K == 256
+
+    const block_q5_K * bx0 = (const block_q5_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR5_K*kqsx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
+        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
+        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
+
+        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
+        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
+        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+#define VDR_Q5_K_Q8_1_MMQ  8
+
+// contiguous u/y values
+static __dpct_inline__ float vec_dot_q5_K_q8_1_impl_mmq(
+    const int *__restrict__ v, const int *__restrict__ u,
+    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
+    const sycl::half2 &dm4, const sycl::half2 *__restrict__ ds8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = dpct::dp4a(v[i * QI8_1 + j], u[i * QI8_1 + j],
+                                sumi_d); // SIMD dot product
+        }
+
+        const sycl::float2 ds8f =
+            ds8[i].convert<float, sycl::rounding_mode::automatic>();
+
+        sumf_d += ds8f.x() * (sc[i] * sumi_d);
+        sumf_m += ds8f.y() * m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const sycl::float2 dm4f =
+        dm4.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
+}
+
+static __dpct_inline__ float vec_dot_q5_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh;
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
+
+    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
+    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
+    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q6_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql, sycl::half2 *tile_x_dm, int *tile_x_sc) {
+    (void)x_qh;
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q6_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI6_K; // == k if QK_K == 256
+
+    const block_q6_K * bx0 = (const block_q6_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR6_K*kqsx;
+
+        const int ql = get_int_from_uint8(bxi->ql, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
+        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
+        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
+
+        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
+        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
+
+        x_ql[i * (2 * WARP_SIZE + 1) + kq0] =
+            dpct::vectorized_binary<sycl::char4>(ql0 | qh0, 0x20202020,
+                                                 dpct::sub_sat());
+        x_ql[i * (2 * WARP_SIZE + 1) + kq1] =
+            dpct::vectorized_binary<sycl::char4>(ql1 | qh1, 0x20202020,
+                                                 dpct::sub_sat());
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
+        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
+    }
+}
+
+#define VDR_Q6_K_Q8_1_MMQ  8
+
+// contiguous u/y values
+static __dpct_inline__ float
+vec_dot_q6_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
+                           const int8_t *__restrict__ sc, const float &d6,
+                           const float *__restrict__ d8) {
+
+    float sumf_d = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
+        sycl::int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
+
+#pragma unroll
+        for (int i = i0; i < i0 + 2; ++i) {
+            sumi_d.x() = dpct::dp4a(v[2 * i + 0], u[2 * i + 0],
+                                    sumi_d.x()); // SIMD dot product
+            sumi_d.x() = dpct::dp4a(v[2 * i + 1], u[2 * i + 1],
+                                    sumi_d.x()); // SIMD dot product
+
+            sumi_d.y() = dpct::dp4a(v[2 * i + 4], u[2 * i + 4],
+                                    sumi_d.y()); // SIMD dot product
+            sumi_d.y() = dpct::dp4a(v[2 * i + 5], u[2 * i + 5],
+                                    sumi_d.y()); // SIMD dot product
+        }
+
+        sumf_d += d8[i0 / 4] *
+                  (sc[i0 / 2 + 0] * sumi_d.x() + sc[i0 / 2 + 1] * sumi_d.y());
+    }
+
+    return d6 * sumf_d;
+}
+
+static __dpct_inline__ float vec_dot_q6_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh;
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
+
+    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
+    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
+    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
+}
+
+template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x,
+          int mmq_y, int nwarps, load_tiles_sycl_t load_tiles, int vdr,
+          vec_dot_q_mul_mat_sycl_t vec_dot>
+/*
+DPCT1110:8: The total declared local variable size in device function mul_mat_q
+exceeds 128 bytes and may cause high register pressure. Consult with your
+hardware vendor to find the total register size available and adjust the code,
+or use smaller sub-group size to avoid high register pressure.
+*/
+static __dpct_inline__ void
+mul_mat_q(const void *__restrict__ vx, const void *__restrict__ vy,
+          float *__restrict__ dst, const int ncols_x, const int nrows_x,
+          const int ncols_y, const int nrows_y, const int nrows_dst,
+          int *tile_x_ql, sycl::half2 *tile_x_dm, int *tile_x_qh,
+          int *tile_x_sc, const sycl::nd_item<3> &item_ct1, int *tile_y_qs,
+          sycl::half2 *tile_y_ds) {
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    const int blocks_per_row_x = ncols_x / qk;
+    const int blocks_per_col_y = nrows_y / QK8_1;
+    const int blocks_per_warp = WARP_SIZE / qi;
+
+    const int & ncols_dst = ncols_y;
+
+    const int row_dst_0 = item_ct1.get_group(2) * mmq_y;
+    const int & row_x_0 = row_dst_0;
+
+    const int col_dst_0 = item_ct1.get_group(1) * mmq_x;
+    const int & col_y_0 = col_dst_0;
+
+    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
+
+    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
+
+        load_tiles(x + row_x_0 * blocks_per_row_x + ib0, tile_x_ql, tile_x_dm,
+                   tile_x_qh, tile_x_sc, item_ct1.get_local_id(1),
+                   nrows_x - row_x_0 - 1, item_ct1.get_local_id(2),
+                   blocks_per_row_x);
+
+#pragma unroll
+        for (int ir = 0; ir < qr; ++ir) {
+            const int kqs = ir * WARP_SIZE + item_ct1.get_local_id(2);
+            const int kbxd = kqs / QI8_1;
+
+#pragma unroll
+            for (int i = 0; i < mmq_x; i += nwarps) {
+                const int col_y_eff = dpct::min(
+                    (unsigned int)(col_y_0 + item_ct1.get_local_id(1) + i),
+                    ncols_y - 1); // to prevent out-of-bounds memory accesses
+
+                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
+
+                const int index_y = (item_ct1.get_local_id(1) + i) * WARP_SIZE +
+                                    kqs % WARP_SIZE;
+                tile_y_qs[index_y] = get_int_from_int8_aligned(
+                    by0->qs, item_ct1.get_local_id(2) % QI8_1);
+            }
+
+#pragma unroll
+            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
+                const int ids =
+                    (ids0 + item_ct1.get_local_id(1) * QI8_1 +
+                     item_ct1.get_local_id(2) / (WARP_SIZE / QI8_1)) %
+                    mmq_x;
+                const int kby = item_ct1.get_local_id(2) % (WARP_SIZE / QI8_1);
+                const int col_y_eff = sycl::min(col_y_0 + ids, ncols_y - 1);
+
+                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
+                const sycl::half2 *dsi_src =
+                    &y[col_y_eff * blocks_per_col_y + ib0 * (qk / QK8_1) +
+                       ir * (WARP_SIZE / QI8_1) + kby]
+                         .ds;
+                sycl::half2 *dsi_dst =
+                    &tile_y_ds[ids * (WARP_SIZE / QI8_1) + kby];
+                if (need_sum) {
+                    *dsi_dst = *dsi_src;
+                } else {
+                    float * dfi_dst = (float *) dsi_dst;
+                    *dfi_dst = (*dsi_src)[0];
+                }
+            }
+
+            /*
+            DPCT1118:9: SYCL group functions and algorithms must be encountered
+            in converged control flow. You may need to adjust the code.
+            */
+            /*
+            DPCT1065:56: Consider replacing sycl::nd_item::barrier() with
+            sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+            better performance if there is no access to global memory.
+            */
+            item_ct1.barrier();
+
+// #pragma unroll // unrolling this loop causes too much register pressure
+            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
+#pragma unroll
+                for (int j = 0; j < mmq_x; j += nwarps) {
+#pragma unroll
+                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+                        sum[i / WARP_SIZE][j / nwarps] += vec_dot(
+                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
+                            tile_y_qs, tile_y_ds, item_ct1.get_local_id(2) + i,
+                            item_ct1.get_local_id(1) + j, k);
+                    }
+                }
+            }
+
+            /*
+            DPCT1118:10: SYCL group functions and algorithms must be encountered
+            in converged control flow. You may need to adjust the code.
+            */
+            /*
+            DPCT1065:57: Consider replacing sycl::nd_item::barrier() with
+            sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+            better performance if there is no access to global memory.
+            */
+            item_ct1.barrier();
+        }
+    }
+
+#pragma unroll
+    for (int j = 0; j < mmq_x; j += nwarps) {
+        const int col_dst = col_dst_0 + j + item_ct1.get_local_id(1);
+
+        if (col_dst >= ncols_dst) {
+            return;
+        }
+
+#pragma unroll
+        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+            const int row_dst = row_dst_0 + item_ct1.get_local_id(2) + i;
+
+            if (row_dst >= nrows_dst) {
+                continue;
+            }
+
+            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
+        }
+    }
+}
+
+#define  MMQ_X_Q4_0_RDNA2  64
+#define  MMQ_Y_Q4_0_RDNA2  128
+#define NWARPS_Q4_0_RDNA2  8
+#define  MMQ_X_Q4_0_RDNA1  64
+#define  MMQ_Y_Q4_0_RDNA1  64
+#define NWARPS_Q4_0_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q4_0_AMPERE 4
+#define  MMQ_Y_Q4_0_AMPERE 32
+#define NWARPS_Q4_0_AMPERE 4
+#else
+#define  MMQ_X_Q4_0_AMPERE 64
+#define  MMQ_Y_Q4_0_AMPERE 128
+#define NWARPS_Q4_0_AMPERE 4
+#endif
+#define  MMQ_X_Q4_0_PASCAL 64
+#define  MMQ_Y_Q4_0_PASCAL 64
+#define NWARPS_Q4_0_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q4_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q4_0, float *tile_x_d_q4_0,
+    int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+
+    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+    const int nwarps = NWARPS_Q4_0_AMPERE;
+    allocate_tiles_q4_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_qs_q4_0, tile_x_d_q4_0);
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps,
+              load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ,
+              vec_dot_q4_0_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q4_1_RDNA2  64
+#define  MMQ_Y_Q4_1_RDNA2  128
+#define NWARPS_Q4_1_RDNA2  8
+#define  MMQ_X_Q4_1_RDNA1  64
+#define  MMQ_Y_Q4_1_RDNA1  64
+#define NWARPS_Q4_1_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q4_1_AMPERE 4
+#define  MMQ_Y_Q4_1_AMPERE 32
+#define NWARPS_Q4_1_AMPERE 4
+#else
+#define  MMQ_X_Q4_1_AMPERE 64
+#define  MMQ_Y_Q4_1_AMPERE 128
+#define NWARPS_Q4_1_AMPERE 4
+#endif
+#define  MMQ_X_Q4_1_PASCAL 64
+#define  MMQ_Y_Q4_1_PASCAL 64
+#define NWARPS_Q4_1_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q4_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q4_1,
+    sycl::half2 *tile_x_dm_q4_1, int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+    const int nwarps = NWARPS_Q4_1_AMPERE;
+    allocate_tiles_q4_1<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_qs_q4_1, tile_x_dm_q4_1);
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps,
+              load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ,
+              vec_dot_q4_1_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q5_0_RDNA2  64
+#define  MMQ_Y_Q5_0_RDNA2  128
+#define NWARPS_Q5_0_RDNA2  8
+#define  MMQ_X_Q5_0_RDNA1  64
+#define  MMQ_Y_Q5_0_RDNA1  64
+#define NWARPS_Q5_0_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q5_0_AMPERE 4
+#define  MMQ_Y_Q5_0_AMPERE 32
+#define NWARPS_Q5_0_AMPERE 4
+#else
+#define  MMQ_X_Q5_0_AMPERE 128
+#define  MMQ_Y_Q5_0_AMPERE 64
+#define NWARPS_Q5_0_AMPERE 4
+#endif
+#define  MMQ_X_Q5_0_PASCAL 64
+#define  MMQ_Y_Q5_0_PASCAL 64
+#define NWARPS_Q5_0_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q5_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_0, float *tile_x_d_q5_0,
+    int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+    const int nwarps = NWARPS_Q5_0_AMPERE;
+    allocate_tiles_q5_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q5_0, tile_x_d_q5_0);
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps,
+              load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ,
+              vec_dot_q5_0_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q5_1_RDNA2  64
+#define  MMQ_Y_Q5_1_RDNA2  128
+#define NWARPS_Q5_1_RDNA2  8
+#define  MMQ_X_Q5_1_RDNA1  64
+#define  MMQ_Y_Q5_1_RDNA1  64
+#define NWARPS_Q5_1_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q5_1_AMPERE 4
+#define  MMQ_Y_Q5_1_AMPERE 32
+#define NWARPS_Q5_1_AMPERE 4
+#else
+#define  MMQ_X_Q5_1_AMPERE 128
+#define  MMQ_Y_Q5_1_AMPERE 64
+#define NWARPS_Q5_1_AMPERE 4
+#endif
+#define  MMQ_X_Q5_1_PASCAL 64
+#define  MMQ_Y_Q5_1_PASCAL 64
+#define NWARPS_Q5_1_PASCAL 8
+
+template <bool need_check> static void
+mul_mat_q5_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_1,
+    sycl::half2 *tile_x_dm_q5_1, int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+    const int nwarps = NWARPS_Q5_1_AMPERE;
+    allocate_tiles_q5_1<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q5_1, tile_x_dm_q5_1);
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps,
+              load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ,
+              vec_dot_q5_1_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q8_0_RDNA2  64
+#define  MMQ_Y_Q8_0_RDNA2  128
+#define NWARPS_Q8_0_RDNA2  8
+#define  MMQ_X_Q8_0_RDNA1  64
+#define  MMQ_Y_Q8_0_RDNA1  64
+#define NWARPS_Q8_0_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q8_0_AMPERE 4
+#define  MMQ_Y_Q8_0_AMPERE 32
+#define NWARPS_Q8_0_AMPERE 4
+#else
+#define  MMQ_X_Q8_0_AMPERE 128
+#define  MMQ_Y_Q8_0_AMPERE 64
+#define NWARPS_Q8_0_AMPERE 4
+#endif
+#define  MMQ_X_Q8_0_PASCAL 64
+#define  MMQ_Y_Q8_0_PASCAL 64
+#define NWARPS_Q8_0_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q8_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q8_0, float *tile_x_d_q8_0,
+    int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+    const int nwarps = NWARPS_Q8_0_AMPERE;
+    allocate_tiles_q8_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_qs_q8_0, tile_x_d_q8_0);
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps,
+              load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ,
+              vec_dot_q8_0_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q2_K_RDNA2  64
+#define  MMQ_Y_Q2_K_RDNA2  128
+#define NWARPS_Q2_K_RDNA2  8
+#define  MMQ_X_Q2_K_RDNA1  128
+#define  MMQ_Y_Q2_K_RDNA1  32
+#define NWARPS_Q2_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q2_K_AMPERE 4
+#define  MMQ_Y_Q2_K_AMPERE 32
+#define NWARPS_Q2_K_AMPERE 4
+#else
+#define  MMQ_X_Q2_K_AMPERE 64
+#define  MMQ_Y_Q2_K_AMPERE 128
+#define NWARPS_Q2_K_AMPERE 4
+#endif
+#define  MMQ_X_Q2_K_PASCAL 64
+#define  MMQ_Y_Q2_K_PASCAL 64
+#define NWARPS_Q2_K_PASCAL 8
+
+template <bool need_check> static void
+mul_mat_q2_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q2_K,
+    sycl::half2 *tile_x_dm_q2_K, int *tile_x_sc_q2_K, int *tile_y_qs,
+    sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+    const int nwarps = NWARPS_Q2_K_AMPERE;
+    allocate_tiles_q2_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q2_K, tile_x_dm_q2_K, tile_x_sc_q2_K);
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ,
+              vec_dot_q2_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q3_K_RDNA2  128
+#define  MMQ_Y_Q3_K_RDNA2  64
+#define NWARPS_Q3_K_RDNA2  8
+#define  MMQ_X_Q3_K_RDNA1  32
+#define  MMQ_Y_Q3_K_RDNA1  128
+#define NWARPS_Q3_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q3_K_AMPERE 4
+#define  MMQ_Y_Q3_K_AMPERE 32
+#define NWARPS_Q3_K_AMPERE 4
+#else
+#define  MMQ_X_Q3_K_AMPERE 128
+#define  MMQ_Y_Q3_K_AMPERE 128
+#define NWARPS_Q3_K_AMPERE 4
+#endif
+#define  MMQ_X_Q3_K_PASCAL 64
+#define  MMQ_Y_Q3_K_PASCAL 64
+#define NWARPS_Q3_K_PASCAL 8
+
+template <bool need_check> static void
+mul_mat_q3_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q3_K,
+    sycl::half2 *tile_x_dm_q3_K, int *tile_x_qh_q3_K, int *tile_x_sc_q3_K,
+    int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+    const int nwarps = NWARPS_Q3_K_AMPERE;
+    allocate_tiles_q3_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q3_K, tile_x_dm_q3_K, tile_x_qh_q3_K,
+                               tile_x_sc_q3_K);
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ,
+              vec_dot_q3_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q4_K_RDNA2  64
+#define  MMQ_Y_Q4_K_RDNA2  128
+#define NWARPS_Q4_K_RDNA2  8
+#define  MMQ_X_Q4_K_RDNA1  32
+#define  MMQ_Y_Q4_K_RDNA1  64
+#define NWARPS_Q4_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q4_K_AMPERE 4
+#define  MMQ_Y_Q4_K_AMPERE 32
+#define NWARPS_Q4_K_AMPERE 4
+#else
+#define  MMQ_X_Q4_K_AMPERE 64
+#define  MMQ_Y_Q4_K_AMPERE 128
+#define NWARPS_Q4_K_AMPERE 4
+#endif
+#define  MMQ_X_Q4_K_PASCAL 64
+#define  MMQ_Y_Q4_K_PASCAL 64
+#define NWARPS_Q4_K_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q4_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q4_K,
+    sycl::half2 *tile_x_dm_q4_K, int *tile_x_sc_q4_K, int *tile_y_qs,
+    sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+    const int nwarps = NWARPS_Q4_K_AMPERE;
+    allocate_tiles_q4_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q4_K, tile_x_dm_q4_K, tile_x_sc_q4_K);
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ,
+              vec_dot_q4_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q5_K_RDNA2  64
+#define  MMQ_Y_Q5_K_RDNA2  128
+#define NWARPS_Q5_K_RDNA2  8
+#define  MMQ_X_Q5_K_RDNA1  32
+#define  MMQ_Y_Q5_K_RDNA1  64
+#define NWARPS_Q5_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q5_K_AMPERE 4
+#define  MMQ_Y_Q5_K_AMPERE 32
+#define NWARPS_Q5_K_AMPERE 4
+#else
+#define  MMQ_X_Q5_K_AMPERE 64
+#define  MMQ_Y_Q5_K_AMPERE 128
+#define NWARPS_Q5_K_AMPERE 4
+#endif
+#define  MMQ_X_Q5_K_PASCAL 64
+#define  MMQ_Y_Q5_K_PASCAL 64
+#define NWARPS_Q5_K_PASCAL 8
+
+template <bool need_check> static void
+mul_mat_q5_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_K,
+    sycl::half2 *tile_x_dm_q5_K, int *tile_x_sc_q5_K, int *tile_y_qs,
+    sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+    const int nwarps = NWARPS_Q5_K_AMPERE;
+    allocate_tiles_q5_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q5_K, tile_x_dm_q5_K, tile_x_sc_q5_K);
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ,
+              vec_dot_q5_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q6_K_RDNA2  64
+#define  MMQ_Y_Q6_K_RDNA2  128
+#define NWARPS_Q6_K_RDNA2  8
+#define  MMQ_X_Q6_K_RDNA1  32
+#define  MMQ_Y_Q6_K_RDNA1  64
+#define NWARPS_Q6_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q6_K_AMPERE 4
+#define  MMQ_Y_Q6_K_AMPERE 32
+#define NWARPS_Q6_K_AMPERE 4
+#else
+#define  MMQ_X_Q6_K_AMPERE 64
+#define  MMQ_Y_Q6_K_AMPERE 64
+#define NWARPS_Q6_K_AMPERE 4
+#endif
+#define  MMQ_X_Q6_K_PASCAL 64
+#define  MMQ_Y_Q6_K_PASCAL 64
+#define NWARPS_Q6_K_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q6_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql, sycl::half2 *tile_x_dm,
+    int *tile_x_sc, int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    // int   * tile_x_ql = nullptr;
+    // sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    // int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+    const int nwarps = NWARPS_Q6_K_AMPERE;
+    allocate_tiles_q6_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql, tile_x_dm, tile_x_sc);
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ,
+              vec_dot_q6_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
+        nwarps = NWARPS_Q4_0_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
+        nwarps = NWARPS_Q4_0_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q4_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+        nwarps = NWARPS_Q4_0_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q4_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
+        nwarps = NWARPS_Q4_0_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:20: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_qs_q4_0_acc_ct1),
+                            get_pointer(tile_x_d_q4_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:21: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_qs_q4_0_acc_ct1),
+                            get_pointer(tile_x_d_q4_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
+        nwarps = NWARPS_Q4_1_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
+        nwarps = NWARPS_Q4_1_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q4_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+        nwarps = NWARPS_Q4_1_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q4_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
+        nwarps = NWARPS_Q4_1_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:22: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_1<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_qs_q4_1_acc_ct1),
+                            get_pointer(tile_x_dm_q4_1_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:23: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_1<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_qs_q4_1_acc_ct1),
+                            get_pointer(tile_x_dm_q4_1_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
+        nwarps = NWARPS_Q5_0_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
+        nwarps = NWARPS_Q5_0_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q5_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+        nwarps = NWARPS_Q5_0_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q5_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
+        nwarps = NWARPS_Q5_0_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:24: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q5_0_acc_ct1),
+                            get_pointer(tile_x_d_q5_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:25: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q5_0_acc_ct1),
+                            get_pointer(tile_x_d_q5_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
+        nwarps = NWARPS_Q5_1_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
+        nwarps = NWARPS_Q5_1_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q5_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+        nwarps = NWARPS_Q5_1_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q5_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
+        nwarps = NWARPS_Q5_1_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:26: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_1<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q5_1_acc_ct1),
+                            get_pointer(tile_x_dm_q5_1_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:27: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_1<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q5_1_acc_ct1),
+                            get_pointer(tile_x_dm_q5_1_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
+        nwarps = NWARPS_Q8_0_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
+        nwarps = NWARPS_Q8_0_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q8_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+        nwarps = NWARPS_Q8_0_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q8_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
+        nwarps = NWARPS_Q8_0_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:28: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q8_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_qs_q8_0_acc_ct1),
+                            get_pointer(tile_x_d_q8_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:29: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q8_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_qs_q8_0_acc_ct1),
+                            get_pointer(tile_x_d_q8_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
+        nwarps = NWARPS_Q2_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
+        nwarps = NWARPS_Q2_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q2_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+        nwarps = NWARPS_Q2_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q2_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
+        nwarps = NWARPS_Q2_K_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:30: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q2_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q2_K_acc_ct1),
+                            get_pointer(tile_x_dm_q2_K_acc_ct1),
+                            get_pointer(tile_x_sc_q2_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:31: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q2_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q2_K_acc_ct1),
+                            get_pointer(tile_x_dm_q2_K_acc_ct1),
+                            get_pointer(tile_x_sc_q2_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+#if QK_K == 256
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
+        nwarps = NWARPS_Q3_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
+        nwarps = NWARPS_Q3_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q3_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+        nwarps = NWARPS_Q3_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q3_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
+        nwarps = NWARPS_Q3_K_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:32: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_qh_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q3_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q3_K_acc_ct1),
+                            get_pointer(tile_x_dm_q3_K_acc_ct1),
+                            get_pointer(tile_x_qh_q3_K_acc_ct1),
+                            get_pointer(tile_x_sc_q3_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:33: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_qh_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q3_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q3_K_acc_ct1),
+                            get_pointer(tile_x_dm_q3_K_acc_ct1),
+                            get_pointer(tile_x_qh_q3_K_acc_ct1),
+                            get_pointer(tile_x_sc_q3_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+#endif
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
+        nwarps = NWARPS_Q4_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
+        nwarps = NWARPS_Q4_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q4_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+        nwarps = NWARPS_Q4_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q4_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
+        nwarps = NWARPS_Q4_K_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:34: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q4_K_acc_ct1),
+                            get_pointer(tile_x_dm_q4_K_acc_ct1),
+                            get_pointer(tile_x_sc_q4_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:35: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q4_K_acc_ct1),
+                            get_pointer(tile_x_dm_q4_K_acc_ct1),
+                            get_pointer(tile_x_sc_q4_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
+        nwarps = NWARPS_Q5_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
+        nwarps = NWARPS_Q5_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q5_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+        nwarps = NWARPS_Q5_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q5_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
+        nwarps = NWARPS_Q5_K_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:36: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q5_K_acc_ct1),
+                            get_pointer(tile_x_dm_q5_K_acc_ct1),
+                            get_pointer(tile_x_sc_q5_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:37: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_q5_K_acc_ct1),
+                            get_pointer(tile_x_dm_q5_K_acc_ct1),
+                            get_pointer(tile_x_sc_q5_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = ggml_sycl_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
+        nwarps = NWARPS_Q6_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
+        nwarps = NWARPS_Q6_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q6_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+        nwarps = NWARPS_Q6_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q6_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
+        nwarps = NWARPS_Q6_K_PASCAL;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:38: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q6_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_acc_ct1),
+                            get_pointer(tile_x_dm_acc_ct1),
+                            get_pointer(tile_x_sc_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:39: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q6_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            get_pointer(tile_x_ql_acc_ct1),
+                            get_pointer(tile_x_dm_acc_ct1),
+                            get_pointer(tile_x_sc_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+void ggml_sycl_op_mul_mat_q(
+    ggml_backend_sycl_context & ctx,
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream) try {
+
+    const int64_t ne00 = src0->ne[0];
+
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t row_diff = row_high - row_low;
+
+    int device_id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(device_id = get_current_device_id()));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
+    const int64_t nrows_dst = device_id == ctx.device ? ne0 : row_diff;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_mul_mat_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            ggml_mul_mat_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            ggml_mul_mat_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            ggml_mul_mat_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_mul_mat_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            ggml_mul_mat_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            ggml_mul_mat_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            ggml_mul_mat_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            ggml_mul_mat_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            ggml_mul_mat_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_ddf_i;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
diff --git a/ggml/src/ggml-sycl/mmq.hpp b/ggml/src/ggml-sycl/mmq.hpp
new file mode 100644
index 0000000000000..3f5297aaa5373
--- /dev/null
+++ b/ggml/src/ggml-sycl/mmq.hpp
@@ -0,0 +1,33 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_MMQ_HPP
+#define GGML_SYCL_MMQ_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_mul_mat_q(
+    ggml_backend_sycl_context & ctx,
+    const ggml_tensor* src0,
+    const ggml_tensor* src1,
+    ggml_tensor* dst,
+    const char* src0_dd_i,
+    const float* src1_ddf_i,
+    const char* src1_ddq_i,
+    float* dst_dd_i,
+    const int64_t row_low,
+    const int64_t row_high,
+    const int64_t src1_ncols,
+    const int64_t src1_padded_row_size,
+    const dpct::queue_ptr& stream);
+
+#endif // GGML_SYCL_MMQ_HPP
diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp
new file mode 100644
index 0000000000000..1b96925e14eba
--- /dev/null
+++ b/ggml/src/ggml-sycl/mmvq.cpp
@@ -0,0 +1,1027 @@
+#include "mmvq.hpp"
+#include "vecdotq.hpp"
+
+
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_sycl_t vec_dot_q_sycl>
+static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx,
+                                       const void *__restrict__ vy,
+                                       float *__restrict__ dst, const int ncols,
+                                       const int nrows,
+                                       const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq2_xxs_q8_1(&x[ibx], &y[iby], iqs, iq2xxs_grid, ksigns_iq2xs, kmask_iq2xs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx,
+                                      const void *__restrict__ vy,
+                                      float *__restrict__ dst, const int ncols,
+                                      const int nrows,
+                                      const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq2_xs_q8_1(&x[ibx], &y[iby], iqs, iq2xs_grid, ksigns64);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx,
+                                     const void *__restrict__ vy,
+                                     float *__restrict__ dst, const int ncols,
+                                     const int nrows,
+                                     const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq2_s_q8_1(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx,
+                                       const void *__restrict__ vy,
+                                       float *__restrict__ dst, const int ncols,
+                                       const int nrows,
+                                       const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq3_xxs_q8_1(&x[ibx], &y[iby], iqs, iq3xxs_grid, ksigns64);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx,
+                                     const void *__restrict__ vy,
+                                     float *__restrict__ dst, const int ncols,
+                                     const int nrows,
+                                     const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq3_s_q8_1(&x[ibx], &y[iby], iqs, iq3s_grid);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx,
+                                     const void *__restrict__ vy,
+                                     float *__restrict__ dst, const int ncols,
+                                     const int nrows,
+                                     const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq1_s_q8_1(&x[ibx], &y[iby], iqs, iq1s_grid_gpu);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx,
+                                     const void *__restrict__ vy,
+                                     float *__restrict__ dst, const int ncols,
+                                     const int nrows,
+                                     const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq1_m_q8_1(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx,
+                                      const void *__restrict__ vy,
+                                      float *__restrict__ dst, const int ncols,
+                                      const int nrows,
+                                      const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq4_nl_q8_1(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx,
+                                      const void *__restrict__ vy,
+                                      float *__restrict__ dst, const int ncols,
+                                      const int nrows,
+                                      const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq4_xs_q8_1(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK4_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK4_0, QI4_0, block_q4_0,
+                                      VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK4_1 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
+                                      VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK5_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
+                                      VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK5_1 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
+                                      VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK8_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
+                                      VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
+                                      VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
+                                      VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
+                                      VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
+                                      VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
+                                      VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+
+static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS/2, block_iq2_xxs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
+                                         float *dst, const int ncols,
+                                         const int nrows,
+                                         dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS/2, block_iq2_xs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
+                                         float *dst, const int ncols,
+                                         const int nrows,
+                                         dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S/2, block_iq2_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq3xxs_grid_ptr_ct1 = &iq3xxs_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS/2, block_iq3_xxs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq3s_grid_ptr_ct1 = &iq3s_grid[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S/2, block_iq3_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq1s_grid_ptr_ct1 = &iq1s_grid_gpu[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK4_NL == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 2>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS/4, block_iq4_xs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+void ggml_sycl_op_mul_mat_vec_q(
+    ggml_backend_sycl_context & ctx,
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_col_size,
+    const dpct::queue_ptr &stream) {
+
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const size_t q8_1_ts = sizeof(block_q8_1);
+    const size_t q8_1_bs = QK8_1;
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the kernel writes into
+    const int64_t nrows_dst = id == ctx.device ? ne00 : row_diff;
+    for (int i = 0; i < src1_ncols; i++)
+    {
+        const size_t src1_ddq_i_offset = i * src1_padded_col_size * q8_1_ts / q8_1_bs;
+        const char* src1_ddq_i_bs = src1_ddq_i + src1_ddq_i_offset;
+        float* dst_dd_i_bs = dst_dd_i + i * dst->ne[0];
+        switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ1_S:
+            mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ1_M:
+            mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ2_XXS:
+            mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ2_S:
+            mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ4_NL:
+            mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ4_XS:
+            mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+            break;
+        }
+    }
+    (void) src1;
+    (void) dst;
+    (void) src1_ddf_i;
+}
diff --git a/ggml/src/ggml-sycl/mmvq.hpp b/ggml/src/ggml-sycl/mmvq.hpp
new file mode 100644
index 0000000000000..049b43d453532
--- /dev/null
+++ b/ggml/src/ggml-sycl/mmvq.hpp
@@ -0,0 +1,27 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_MMVQ_HPP
+#define GGML_SYCL_MMVQ_HPP
+
+#include "common.hpp"
+
+
+void ggml_sycl_op_mul_mat_vec_q(
+    ggml_backend_sycl_context & ctx,
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream);
+
+#endif // GGML_SYCL_MMVQ_HPP
diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp
new file mode 100644
index 0000000000000..b3159b9d1b94d
--- /dev/null
+++ b/ggml/src/ggml-sycl/norm.cpp
@@ -0,0 +1,377 @@
+#include "norm.hpp"
+
+static void norm_f32(const float* x, float* dst, const int ncols, const float eps,
+    const sycl::nd_item<3>& item_ct1, sycl::float2* s_sum, int block_size) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+        item_ct1.get_local_id(1);
+    const int tid = item_ct1.get_local_id(2);
+
+    const int nthreads = item_ct1.get_local_range(2);
+    const int nwarps = nthreads / WARP_SIZE;
+    assert(nwarps % WARP_SIZE == 0);
+    sycl::float2 mean_var = sycl::float2(0.f, 0.f);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row * ncols + col];
+        mean_var.x() += xi;
+        mean_var.y() += xi * xi;
+    }
+
+    // sum up partial sums
+    mean_var = warp_reduce_sum(mean_var, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = mean_var;
+        }
+        /*
+        DPCT1118:0: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+        mean_var = 0.f;
+        int nreduce = nwarps / WARP_SIZE;
+        for (size_t i = 0; i < nreduce; i += 1)
+        {
+            mean_var += s_sum[lane_id + i * WARP_SIZE];
+        }
+        mean_var = warp_reduce_sum(mean_var, item_ct1);
+    }
+
+    const float mean = mean_var.x() / ncols;
+    const float var = mean_var.y() / ncols - mean * mean;
+    const float inv_std = sycl::rsqrt(var + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row * ncols + col] = (x[row * ncols + col] - mean) * inv_std;
+    }
+}
+
+static void group_norm_f32(const float* x, float* dst, const int group_size, const int ne_elements, const float eps,
+    const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
+    int start = item_ct1.get_group(2) * group_size;
+    int end = start + group_size;
+    const int nthreads = item_ct1.get_local_range(2);
+    const int nwarps = nthreads / WARP_SIZE;
+    assert(nwarps % WARP_SIZE == 0);
+    start += item_ct1.get_local_id(2);
+    int nreduce = nwarps / WARP_SIZE;
+
+    if (end >= ne_elements) {
+        end = ne_elements;
+    }
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int j = start; j < end; j += block_size) {
+        tmp += x[j];
+    }
+
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:1: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        /*
+        DPCT1065:54: Consider replacing sycl::nd_item::barrier() with
+        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+        better performance if there is no access to global memory.
+        */
+        item_ct1.barrier();
+        tmp = 0.f;
+        for (size_t i = 0; i < nreduce; i += 1)
+        {
+            tmp += s_sum[lane_id + i * WARP_SIZE];
+        }
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    float mean = tmp / group_size;
+    tmp = 0.0f;
+
+    for (int j = start; j < end; j += block_size) {
+        float xi = x[j] - mean;
+        dst[j] = xi;
+        tmp += xi * xi;
+    }
+
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:2: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        /*
+        DPCT1065:55: Consider replacing sycl::nd_item::barrier() with
+        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+        better performance if there is no access to global memory.
+        */
+        item_ct1.barrier();
+        tmp = 0.f;
+        for (size_t i = 0; i < nreduce; i += 1)
+        {
+            tmp += s_sum[lane_id + i * WARP_SIZE];
+        }
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    float variance = tmp / group_size;
+    float scale = sycl::rsqrt(variance + eps);
+    for (int j = start; j < end; j += block_size) {
+        dst[j] *= scale;
+    }
+}
+
+static void rms_norm_f32(const float* x, float* dst, const int ncols, const float eps,
+    const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+        item_ct1.get_local_id(1);
+    const int tid = item_ct1.get_local_id(2);
+    const int nthreads = item_ct1.get_local_range(2);
+    const int nwarps = nthreads / WARP_SIZE;
+    assert(nwarps % WARP_SIZE == 0);
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row * ncols + col];
+        tmp += xi * xi;
+    }
+
+    // sum up partial sums
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:3: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+        int nreduce = nwarps / WARP_SIZE;
+        tmp = 0.f;
+        for (size_t i = 0; i < nreduce; i += 1)
+        {
+            tmp += s_sum[lane_id + i * WARP_SIZE];
+        }
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    const float mean = tmp / ncols;
+    const float scale = sycl::rsqrt(mean + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row * ncols + col] = scale * x[row * ncols + col];
+    }
+}
+
+static void norm_f32_sycl(const float* x, float* dst, const int ncols,
+    const int nrows, const float eps,
+    queue_ptr stream, int device) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler& cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    norm_f32(x, dst, ncols, eps, item_ct1,
+                        nullptr, WARP_SIZE);
+                });
+            });
+    }
+    else {
+        const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:17: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->submit([&](sycl::handler& cgh) {
+            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
+                sycl::range<1>(work_group_size / WARP_SIZE), cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    norm_f32(x, dst, ncols, eps, item_ct1,
+                        get_pointer(s_sum_acc_ct1), work_group_size);
+                });
+            });
+    }
+}
+
+static void group_norm_f32_sycl(const float* x, float* dst,
+    const int num_groups, const float eps, const int group_size,
+    const int ne_elements, queue_ptr stream, int device) {
+    if (group_size < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler& cgh) {
+            const float eps_ct4 = eps;
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    group_norm_f32(
+                        x, dst, group_size, ne_elements, eps_ct4, item_ct1,
+                        nullptr, WARP_SIZE);
+                });
+            });
+    }
+    else {
+        const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:18: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+
+        stream->submit([&](sycl::handler& cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
+                cgh);
+
+            const float eps_ct4 = eps;
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    group_norm_f32(x, dst, group_size, ne_elements,
+                        eps_ct4, item_ct1,
+                        get_pointer(s_sum_acc_ct1), work_group_size);
+                });
+            });
+    }
+}
+
+static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols,
+    const int nrows, const float eps,
+    queue_ptr stream, int device) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
+    if (ncols < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler& cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    rms_norm_f32(x, dst, ncols, eps, item_ct1,
+                        nullptr, WARP_SIZE);
+                });
+            });
+    }
+    else {
+        const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->submit([&](sycl::handler& cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
+                cgh);
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    rms_norm_f32(x, dst, ncols, eps, item_ct1,
+                        get_pointer(s_sum_acc_ct1), work_group_size);
+                });
+            });
+    }
+}
+
+void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1,
+    ggml_tensor* dst, const float* src0_dd,
+    const float* src1_dd, float* dst_dd,
+    const queue_ptr& main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device);
+
+    (void)src1;
+    (void)dst;
+    (void)src1_dd;
+}
+
+void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
+    const ggml_tensor* src1, ggml_tensor* dst,
+    const float* src0_dd, const float* src1_dd,
+    float* dst_dd,
+    const queue_ptr& main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    int num_groups = dst->op_params[0];
+
+    float eps;
+    memcpy(&eps, dst->op_params + 1, sizeof(float));
+
+    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
+    group_norm_f32_sycl(src0_dd, dst_dd, num_groups, eps, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream, ctx.device);
+
+    (void)src1;
+    (void)dst;
+    (void)src1_dd;
+}
+
+void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
+    const ggml_tensor* src1, ggml_tensor* dst,
+    const float* src0_dd, const float* src1_dd,
+    float* dst_dd,
+    const queue_ptr& main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device);
+
+    (void)src1;
+    (void)dst;
+    (void)src1_dd;
+}
diff --git a/ggml/src/ggml-sycl/norm.hpp b/ggml/src/ggml-sycl/norm.hpp
new file mode 100644
index 0000000000000..a9ad9156fa33e
--- /dev/null
+++ b/ggml/src/ggml-sycl/norm.hpp
@@ -0,0 +1,35 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_NORM_HPP
+#define GGML_SYCL_NORM_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1,
+    ggml_tensor* dst, const float* src0_dd,
+    const float* src1_dd, float* dst_dd,
+    const queue_ptr& main_stream);
+
+void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
+    const ggml_tensor* src1, ggml_tensor* dst,
+    const float* src0_dd, const float* src1_dd,
+    float* dst_dd,
+    const queue_ptr& main_stream);
+
+void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
+    const ggml_tensor* src1, ggml_tensor* dst,
+    const float* src0_dd, const float* src1_dd,
+    float* dst_dd,
+    const queue_ptr& main_stream);
+
+#endif // GGML_SYCL_NORM_HPP
diff --git a/ggml/src/ggml-sycl/presets.hpp b/ggml/src/ggml-sycl/presets.hpp
new file mode 100644
index 0000000000000..340ab8e932bcf
--- /dev/null
+++ b/ggml/src/ggml-sycl/presets.hpp
@@ -0,0 +1,68 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_PRESETS_HPP
+#define GGML_SYCL_PRESETS_HPP
+
+#define GGML_SYCL_MAX_STREAMS       8
+#define GGML_SYCL_MAX_BUFFERS       256
+
+#define WARP_SIZE GGML_SYCL_WARP_SIZE
+#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
+
+#define SYCL_GELU_BLOCK_SIZE 256
+#define SYCL_SILU_BLOCK_SIZE 256
+#define SYCL_TANH_BLOCK_SIZE 256
+#define SYCL_RELU_BLOCK_SIZE 256
+#define SYCL_HARDSIGMOID_BLOCK_SIZE 256
+#define SYCL_HARDSWISH_BLOCK_SIZE 256
+#define SYCL_SQR_BLOCK_SIZE 256
+#define SYCL_CPY_BLOCK_SIZE 32
+#define SYCL_SCALE_BLOCK_SIZE 256
+#define SYCL_CLAMP_BLOCK_SIZE 256
+#define SYCL_ROPE_BLOCK_SIZE 256
+#define SYCL_ALIBI_BLOCK_SIZE 32
+#define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32
+#define SYCL_QUANTIZE_BLOCK_SIZE 256
+#define SYCL_DEQUANTIZE_BLOCK_SIZE 256
+#define SYCL_GET_ROWS_BLOCK_SIZE 256
+#define SYCL_UPSCALE_BLOCK_SIZE 256
+#define SYCL_CONCAT_BLOCK_SIZE 256
+#define SYCL_PAD_BLOCK_SIZE 256
+#define SYCL_ACC_BLOCK_SIZE 256
+#define SYCL_IM2COL_BLOCK_SIZE 256
+#define SYCL_POOL2D_BLOCK_SIZE 256
+#define SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE 256
+#define SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE 256
+
+// dmmv = dequantize_mul_mat_vec
+#ifndef GGML_SYCL_DMMV_X
+#define GGML_SYCL_DMMV_X 32
+#endif
+#ifndef GGML_SYCL_MMV_Y
+#define GGML_SYCL_MMV_Y 1
+#endif
+
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 2
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
+#ifndef GGML_SYCL_PEER_MAX_BATCH_SIZE
+#define GGML_SYCL_PEER_MAX_BATCH_SIZE 128
+#endif // GGML_SYCL_PEER_MAX_BATCH_SIZE
+
+#define MUL_MAT_SRC1_COL_STRIDE 128
+
+#define QK_WARP_SIZE 32
+#endif // GGML_SYCL_PRESETS_HPP
diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp
new file mode 100644
index 0000000000000..1f06f78fa3d91
--- /dev/null
+++ b/ggml/src/ggml-sycl/rope.cpp
@@ -0,0 +1,275 @@
+#include "rope.hpp"
+
+struct rope_corr_dims {
+    float v[2];
+};
+
+static float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low);
+    return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static void rope_yarn(
+    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale);
+    }
+    *cos_theta = sycl::cos(theta) * mscale;
+    *sin_theta = sycl::sin(theta) * mscale;
+}
+
+template<typename T, bool has_ff>
+static void rope_norm(
+    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors,
+    const sycl::nd_item<3> &item_ct1) {
+    const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                         item_ct1.get_local_id(1));
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i = row*ne0 + i0;
+    const int i2 = row/p_delta_rows;
+
+    const float theta_base = pos[i2] * sycl::pow(theta_scale, i0 / 2.0f);
+
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+    float cos_theta;
+    float sin_theta;
+
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + 1];
+
+    dst[i + 0] = x0*cos_theta - x1*sin_theta;
+    dst[i + 1] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T, bool has_ff>
+static void rope_neox(
+    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors,
+    const sycl::nd_item<3> &item_ct1) {
+    const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                         item_ct1.get_local_id(1));
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i  = row*ne0 + i0/2;
+    const int i2 = row/p_delta_rows;
+
+    const float theta_base = pos[i2] * sycl::pow(theta_scale, i0 / 2.0f);
+
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+    float cos_theta;
+    float sin_theta;
+
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + n_dims/2];
+
+    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+}
+
+template <typename T>
+static void rope_norm_sycl(
+    const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, num_blocks_x, nr);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+    if (freq_factors == nullptr) {
+        /*
+        DPCT1049:40: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_norm<T, false>(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows,
+                               ext_factor, attn_factor, corr_dims, theta_scale, freq_factors,
+                               item_ct1);
+            });
+    } else {
+        /*
+        DPCT1049:41: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_norm<T, true>(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows,
+                              ext_factor, attn_factor, corr_dims, theta_scale, freq_factors,
+                              item_ct1);
+            });
+    }
+}
+
+template <typename T>
+static void rope_neox_sycl(
+    const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, num_blocks_x, nr);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    dpct::has_capability_or_fail(stream->get_device(),
+                                    {sycl::aspect::fp16});
+
+    if (freq_factors == nullptr) {
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_neox<T, false>(x, dst, ne0, n_dims, pos, freq_scale,
+                                    p_delta_rows, ext_factor, attn_factor,
+                                    corr_dims, theta_scale, freq_factors,
+                                    item_ct1);
+            });
+    } else {
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_neox<T, true>(x, dst, ne0, n_dims, pos, freq_scale,
+                                    p_delta_rows, ext_factor, attn_factor,
+                                    corr_dims, theta_scale, freq_factors,
+                                    item_ct1);
+            });
+    }
+}
+
+void ggml_sycl_op_rope(
+    ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const float *src0_dd, const float *src1_dd, float *dst_dd, const queue_ptr &main_stream) {
+    const ggml_tensor * src2 = dst->src[2];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == dst->type);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t nr = ggml_nrows(src0);
+
+    //const int n_past      = ((int32_t *) dst->op_params)[0];
+    const int n_dims      = ((int32_t *) dst->op_params)[1];
+    const int mode        = ((int32_t *) dst->op_params)[2];
+    //const int n_ctx       = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig  = ((int32_t *) dst->op_params)[4];
+
+    // RoPE alteration for extended context
+    float freq_base;
+    float freq_scale;
+    float ext_factor;
+    float attn_factor;
+    float beta_fast;
+    float beta_slow;
+
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+
+    const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+
+    const int32_t * pos = (const int32_t *) src1_dd;
+
+    const float * freq_factors = nullptr;
+    if (src2 != nullptr) {
+        freq_factors = (const float *) src2->data;
+    }
+
+    rope_corr_dims corr_dims;
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v);
+
+    // compute
+    if (is_neox) {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_neox_sycl(
+                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_neox_sycl(
+                (const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
+            );
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    } else {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_norm_sycl(
+                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_norm_sycl(
+                (const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
+            );
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
diff --git a/ggml/src/ggml-sycl/rope.hpp b/ggml/src/ggml-sycl/rope.hpp
new file mode 100644
index 0000000000000..00354c3131bd7
--- /dev/null
+++ b/ggml/src/ggml-sycl/rope.hpp
@@ -0,0 +1,22 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_ROPE_HPP
+#define GGML_SYCL_ROPE_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_rope(
+    ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const float *src0_dd, const float *src1_dd, float *dst_dd, const queue_ptr &main_stream);
+
+#endif // GGML_SYCL_ROPE_HPP
diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp
new file mode 100644
index 0000000000000..17a542e490362
--- /dev/null
+++ b/ggml/src/ggml-sycl/softmax.cpp
@@ -0,0 +1,251 @@
+#include "norm.hpp"
+
+template <bool vals_smem, int ncols_template, int block_size_template>
+static void soft_max_f32(const float * x, const float * mask, float * dst, const int ncols_par,
+                         const int nrows_y, const float scale, const float max_bias, const float m0,
+                         const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) {
+    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
+
+    const int tid = item_ct1.get_local_id(2);
+    const int rowx = item_ct1.get_group(2);
+    const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
+
+    const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template;
+
+    const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+    const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+    const int nthreads = block_size;
+    const int nwarps = nthreads / WARP_SIZE;
+    int nreduce = nwarps / WARP_SIZE;
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const uint32_t h = rowx/nrows_y; // head index
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = sycl::pow(base, float(exp));
+    }
+
+    float *vals = vals_smem ? buf + std::max(nwarps, WARP_SIZE) : dst + rowx * ncols;
+    float max_val = -INFINITY;
+
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const int ix = rowx*ncols + col;
+        const int iy = rowy*ncols + col;
+
+        const float val = x[ix]*scale + (mask ? slope*mask[iy] : 0.0f);
+
+        vals[col] = val;
+        max_val = sycl::max(max_val, val);
+    }
+
+    // find the max value in the block
+    max_val = warp_reduce_max(max_val, item_ct1);
+    if (block_size > WARP_SIZE) {
+        if (warp_id == 0) {
+            buf[lane_id] = -INFINITY;
+            for (size_t i = 1; i < nreduce; i += 1)
+                buf[lane_id + i * WARP_SIZE] = -INFINITY;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+        if (lane_id == 0) {
+            buf[warp_id] = max_val;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+        max_val = buf[lane_id];
+        for (size_t i = 1; i < nreduce; i += 1)
+        {
+            max_val = std::max(max_val, buf[lane_id + i * WARP_SIZE]);
+        }
+        max_val = warp_reduce_max(max_val, item_ct1);
+    }
+
+    float tmp = 0.f;
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+                if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const float val = sycl::native::exp(vals[col] - max_val);
+        tmp += val;
+        vals[col] = val;
+    }
+
+    // find the sum of exps in the block
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+        if (warp_id == 0) {
+            buf[lane_id] = 0.f;
+            for (size_t i = 1; i < nreduce; i += 1)
+                buf[lane_id + i * WARP_SIZE] = 0.f;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+        if (lane_id == 0) {
+            buf[warp_id] = tmp;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+        tmp = buf[lane_id];
+        for (size_t i = 1; i < nreduce; i += 1)
+        {
+            tmp += buf[lane_id + i * WARP_SIZE];
+        }
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    const float inv_sum = 1.f / tmp;
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            return;
+        }
+
+        const int idst = rowx*ncols + col;
+        dst[idst] = vals[col] * inv_sum;
+    }
+}
+
+template <bool vals_smem, int ncols_template, int block_size_template>
+static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par,
+                                   const int nrows_y, const float scale, const float max_bias, const float m0,
+                                   const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
+                                   const size_t n_local_scratch, queue_ptr stream) {
+    stream->submit([&](sycl::handler &cgh) {
+        sycl::local_accessor<float, 1> local_buf_acc(n_local_scratch, cgh);
+
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
+                                                                             nrows_y, scale, max_bias, m0,
+                                                                             m1, n_head_log2, item_ct1,
+                                                                             get_pointer(local_buf_acc));
+            });
+    });
+}
+
+static void soft_max_f32_sycl(const float * x, const float * mask,
+                              float * dst, const int ncols_x, const int nrows_x,
+                              const int nrows_y, const float scale, const float max_bias,
+                              queue_ptr stream, int device) {
+    int nth = WARP_SIZE;
+    int max_block_size = ggml_sycl_info().max_work_group_sizes[device];
+    while (nth < ncols_x && nth < max_block_size) nth *= 2;
+    if (nth>max_block_size) nth = max_block_size;
+
+    const sycl::range<3> block_dims(1, 1, nth);
+    const sycl::range<3> block_nums(1, 1, nrows_x);
+    const size_t n_val_tmp = nth / WARP_SIZE;
+    const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + n_val_tmp);
+
+    const uint32_t n_head_kv   = nrows_x/nrows_y;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    const size_t local_mem_size = stream->get_device().get_info<sycl::info::device::local_mem_size>();
+    if (n_local_scratch*sizeof(float) < local_mem_size) {
+        if (ncols_x > max_block_size) {
+            soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
+                                               max_bias, m0, m1, n_head_log2, block_nums,
+                                               block_dims, n_local_scratch, stream);
+            return;
+        }
+        switch (ncols_x) {
+            case 32:
+                soft_max_f32_submitter<true, 32, 32>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                     max_bias, m0, m1, n_head_log2, block_nums,
+                                                     block_dims, n_local_scratch, stream);
+                break;
+            case 64:
+                soft_max_f32_submitter<true, 64, 64>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                     max_bias, m0, m1, n_head_log2, block_nums,
+                                                     block_dims, n_local_scratch, stream);
+                break;
+            case 128:
+                soft_max_f32_submitter<true, 128, 128>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 256:
+                soft_max_f32_submitter<true, 256, 256>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 512:
+                soft_max_f32_submitter<true, 512, 512>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 1024:
+                soft_max_f32_submitter<true, 1024, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            case 2048:
+                soft_max_f32_submitter<true, 2048, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            case 4096:
+                soft_max_f32_submitter<true, 4096, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            default:
+                soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
+                                                   max_bias, m0, m1, n_head_log2, block_nums,
+                                                   block_dims, n_local_scratch, stream);
+                break;
+        }
+    } else {
+        soft_max_f32_submitter<false, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
+                                            max_bias, m0, m1, n_head_log2, block_nums,
+                                            block_dims, WARP_SIZE, stream);
+    }
+}
+
+void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                  const ggml_tensor *src1, ggml_tensor *dst,
+                                  const float *src0_dd, const float *src1_dd,
+                                  float *dst_dd,
+                                  const queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
+    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows_x = ggml_nrows(src0);
+    const int64_t nrows_y = src0->ne[1];
+
+    float scale = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale, dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, dst->op_params + 1, sizeof(float));
+
+    soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00,
+        nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device);
+}
diff --git a/ggml/src/ggml-sycl/softmax.hpp b/ggml/src/ggml-sycl/softmax.hpp
new file mode 100644
index 0000000000000..bdb8f712e32f8
--- /dev/null
+++ b/ggml/src/ggml-sycl/softmax.hpp
@@ -0,0 +1,24 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_SOFTMAX_HPP
+#define GGML_SYCL_SOFTMAX_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_soft_max(ggml_backend_sycl_context &ctx, const ggml_tensor *src0,
+    const ggml_tensor *src1, ggml_tensor *dst,
+    const float *src0_dd, const float *src1_dd,
+    float *dst_dd,
+    const queue_ptr &main_stream);
+
+#endif // GGML_SYCL_SOFTMAX_HPP
diff --git a/ggml/src/ggml-sycl/tsembd.cpp b/ggml/src/ggml-sycl/tsembd.cpp
new file mode 100644
index 0000000000000..d5c227cd1abcd
--- /dev/null
+++ b/ggml/src/ggml-sycl/tsembd.cpp
@@ -0,0 +1,71 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include "tsembd.hpp"
+
+static void timestep_embedding_f32(
+        const float * timesteps, float * dst, const int nb1,
+        const int dim, const int max_period, const sycl::nd_item<3> &item_ct1) {
+    // item_ct1.get_group(1)(blockIDx.y): idx of timesteps->ne[0]
+    // item_ct1.get_group(2) (blockIDx.x): idx of ((dim + 1) / 2) / BLOCK_SIZE
+    int i = item_ct1.get_group(1);
+    int j = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    float * embed_data = (float *)((char *)dst +  i*nb1);
+
+    if (dim % 2 != 0 && j == ((dim + 1) / 2)) {
+        embed_data[dim] = 0.f;
+    }
+
+    int half = dim / 2;
+    if (j >= half) {
+        return;
+    }
+
+    float timestep = timesteps[i];
+    float freq = (float)sycl::native::exp(-(sycl::log((float)max_period)) * j / half);
+    float arg = timestep * freq;
+    embed_data[j] = sycl::cos(arg);
+    embed_data[j + half] = sycl::sin(arg);
+}
+
+static void timestep_embedding_f32_sycl(
+        const float * x, float * dst, const int ne00, const int nb1,
+        const int dim, const int max_period, const queue_ptr& stream) {
+    // As the kernel returns when thread.idx is larger than dim/2, the half_ceil does not need to pad
+    int half_ceil = dim / 2;
+    int num_blocks = (half_ceil + SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE;
+    sycl::range<3> block_dims(1, 1, SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE);
+    sycl::range<3> gridDim(1, ne00, num_blocks);
+    stream->parallel_for(
+        sycl::nd_range<3>(
+            gridDim * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) {
+            timestep_embedding_f32(
+                x, dst, nb1, dim, max_period, item_ct1
+            );
+        });
+}
+
+void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+    const ggml_tensor *src1, ggml_tensor * dst) {
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    dpct::queue_ptr stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int dim = dst->op_params[0];
+    const int max_period = dst->op_params[1];
+
+    timestep_embedding_f32_sycl(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
+}
diff --git a/ggml/src/ggml-sycl/tsembd.hpp b/ggml/src/ggml-sycl/tsembd.hpp
new file mode 100644
index 0000000000000..ff854c337c344
--- /dev/null
+++ b/ggml/src/ggml-sycl/tsembd.hpp
@@ -0,0 +1,21 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_TSEMBD_HPP
+#define GGML_SYCL_TSEMBD_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+    const ggml_tensor *src1, ggml_tensor * dst);
+
+#endif // GGML_SYCL_TSEMBD_HPP
diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp
new file mode 100644
index 0000000000000..d2dccade20bfd
--- /dev/null
+++ b/ggml/src/ggml-sycl/vecdotq.hpp
@@ -0,0 +1,1140 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_VECDOTQ_HPP
+#define GGML_SYCL_VECDOTQ_HPP
+
+#include "dpct/helper.hpp"
+
+typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
+
+static __dpct_inline__ int get_int_from_int8(const int8_t* x8, const int& i32) {
+  const uint16_t* x16 =
+      (const uint16_t*)(x8 + sizeof(int) * i32); // assume at least 2 byte
+                                                 // alignment
+
+  int x32 = 0;
+  x32 |= x16[0] << 0;
+  x32 |= x16[1] << 16;
+
+  return x32;
+}
+
+static __dpct_inline__ int get_int_from_uint8(
+    const uint8_t* x8,
+    const int& i32) {
+  const uint16_t* x16 =
+      (const uint16_t*)(x8 + sizeof(int) * i32); // assume at least 2 byte
+                                                 // alignment
+
+  int x32 = 0;
+  x32 |= x16[0] << 0;
+  x32 |= x16[1] << 16;
+
+  return x32;
+}
+
+static __dpct_inline__ int get_int_from_int8_aligned(
+    const int8_t* x8,
+    const int& i32) {
+  return *(
+      (const int*)(x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+static __dpct_inline__ int get_int_from_uint8_aligned(
+    const uint8_t* x8,
+    const int& i32) {
+  return *(
+      (const int*)(x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4,
+                                                  const uint8_t *values,
+                                                  int &val1, int &val2) {
+
+    uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32;
+    aux32 = q4 & 0x0f0f0f0f;
+    uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8);
+    uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8);
+    val1 = v1 | (v2 << 16);
+    aux32 = (q4 >> 4) & 0x0f0f0f0f;
+    v1 = values[q8[0]] | (values[q8[1]] << 8);
+    v2 = values[q8[2]] | (values[q8[3]] << 8);
+    val2 = v1 | (v2 << 16);
+}
+
+#define VDR_Q2_K_Q8_1_MMVQ 1
+
+// contiguous v/x values
+static __dpct_inline__ float vec_dot_q2_K_q8_1_impl_mmvq(
+    const int &v, const int *__restrict__ u, const uint8_t *__restrict__ scales,
+    const sycl::half2 &dm2, const float *__restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++i) {
+        const int sc = scales[2*i];
+
+        const int vi = (v >> (2*i)) & 0x03030303;
+
+        sumf_d +=
+            d8[i] * (dpct::dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+        sumf_m += d8[i] *
+                  dpct::dp4a(
+                      m, u[i],
+                      0); // multiply constant q2_K part with sum of q8_1 values
+    }
+
+    const sycl::float2 dm2f =
+        dm2.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm2f.x() * sumf_d - dm2f.y() * sumf_m;
+}
+
+
+#define VDR_Q3_K_Q8_1_MMVQ 1
+
+// contiguous v/x values
+static __dpct_inline__ float vec_dot_q3_K_q8_1_impl_mmvq(
+    const int &vl, const int &vh, const int *__restrict__ u,
+    const uint8_t *__restrict__ scales, const int &scale_offset,
+    const float &d3, const float *__restrict__ d8) {
+
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        const int isc = scale_offset + 2*i;
+
+        const int isc_low = isc % (QK_K/32);
+        const int sc_shift_low = 4 * (isc / (QK_K/32));
+        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
+
+        const int isc_high = isc % (QK_K/64);
+        const int sc_shift_high = 2 * (isc / (QK_K/64));
+        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
+
+        const int sc = (sc_low | sc_high) - 32;
+
+        const int vil = (vl >> (2*i)) & 0x03030303;
+
+        const int vih = ((vh >> i) << 2) & 0x04040404;
+
+        const int vi =
+            dpct::vectorized_binary<sycl::char4>(vil, vih, dpct::sub_sat());
+
+        sumf += d8[i] * (dpct::dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d3 * sumf;
+}
+
+#define VDR_Q4_K_Q8_1_MMVQ 2
+
+// contiguous v/x values
+static __dpct_inline__ float vec_dot_q4_K_q8_1_impl_vmmq(
+    const int *__restrict__ v, const int *__restrict__ u,
+    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
+    const sycl::half2 &dm4, const float *__restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K; ++i) {
+        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
+        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int dot1 =
+            dpct::dp4a(v1i, u[2 * i + 1],
+                       dpct::dp4a(v0i, u[2 * i + 0], 0)); // SIMD dot product
+        const int dot2 =
+            dpct::dp4a(0x01010101, u[2 * i + 1],
+                       dpct::dp4a(0x01010101, u[2 * i + 0], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
+    }
+
+    const sycl::float2 dm4f =
+        dm4.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
+}
+
+
+#define VDR_Q5_K_Q8_1_MMVQ 2
+
+// contiguous v/x values
+static __dpct_inline__ float vec_dot_q5_K_q8_1_impl_vmmq(
+    const int *__restrict__ vl, const int *__restrict__ vh,
+    const int *__restrict__ u, const uint8_t *__restrict__ sc,
+    const uint8_t *__restrict__ m, const sycl::half2 &dm5,
+    const float *__restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
+        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
+        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
+
+        const int v0i = vl0i | vh0i;
+        const int v1i = vl1i | vh1i;
+
+        const int dot1 =
+            dpct::dp4a(v0i, u[2 * i + 0],
+                       dpct::dp4a(v1i, u[2 * i + 1], 0)); // SIMD dot product
+        const int dot2 =
+            dpct::dp4a(0x01010101, u[2 * i + 0],
+                       dpct::dp4a(0x01010101, u[2 * i + 1], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);
+
+    }
+
+    const sycl::float2 dm5f =
+        dm5.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm5f.x() * sumf_d - dm5f.y() * sumf_m;
+}
+
+
+#define VDR_Q6_K_Q8_1_MMVQ 1
+
+// contiguous v/x values
+static __dpct_inline__ float
+vec_dot_q6_K_q8_1_impl_mmvq(const int &vl, const int &vh,
+                            const int *__restrict__ u,
+                            const int8_t *__restrict__ scales, const float &d,
+                            const float *__restrict__ d8) {
+
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        const int sc = scales[4*i];
+
+        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
+
+        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
+
+        const int vi = dpct::vectorized_binary<sycl::char4>(
+            (vil | vih), 0x20202020, dpct::sub_sat()); // vi = (vil | vih) - 32
+
+        sumf += d8[i] * (dpct::dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d*sumf;
+}
+
+// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
+// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
+
+#define VDR_Q4_0_Q8_1_MMVQ 2
+#define VDR_Q4_0_Q8_1_MMQ  4
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q4_0_q8_1_impl(const int *v, const int *u,
+                                                    const float &d4,
+                                                    const sycl::half2 &ds8) {
+    int sumi = 0;
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = dpct::dp4a(vi0, u[2 * i + 0], sumi);
+        sumi = dpct::dp4a(vi1, u[2 * i + 1], sumi);
+    }
+
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+
+    // second part effectively subtracts 8 from each quant value
+    return d4 * (sumi * ds8f.x() - (8 * vdr / QI4_0) * ds8f.y());
+}
+
+#define VDR_Q4_1_Q8_1_MMVQ 2
+#define VDR_Q4_1_Q8_1_MMQ  4
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q4_1_q8_1_impl(const int *v, const int *u,
+                                                    const sycl::half2 &dm4,
+                                                    const sycl::half2 &ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = dpct::dp4a(vi0, u[2 * i + 0], sumi);
+        sumi = dpct::dp4a(vi1, u[2 * i + 1], sumi);
+    }
+
+#ifdef GGML_SYCL_F16
+    const sycl::float2 tmp =
+        (dm4 * ds8).convert<float, sycl::rounding_mode::automatic>();
+    const float d4d8 = tmp.x();
+    const float m4s8 = tmp.y();
+#else
+    const sycl::float2 dm4f =
+        dm4.convert<float, sycl::rounding_mode::automatic>();
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+    const float d4d8 = dm4f.x() * ds8f.x();
+    const float m4s8 = dm4f.y() * ds8f.y();
+#endif // GGML_SYCL_F16
+
+    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
+    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
+}
+
+#define VDR_Q5_0_Q8_1_MMVQ 2
+#define VDR_Q5_0_Q8_1_MMQ  4
+
+template <int vdr>
+static __dpct_inline__ float
+vec_dot_q5_0_q8_1_impl(const int *vl, const int *vh, const int *u,
+                       const float &d5, const sycl::half2 &ds8) {
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = dpct::dp4a(vi0, u[2 * i + 0],
+                          sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = dpct::dp4a(vi1, u[2 * i + 1],
+                          sumi); // SIMD dot product of quantized values
+    }
+
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+
+    // second part effectively subtracts 16 from each quant value
+    return d5 * (sumi * ds8f.x() - (16 * vdr / QI5_0) * ds8f.y());
+}
+
+#define VDR_Q5_1_Q8_1_MMVQ 2
+#define VDR_Q5_1_Q8_1_MMQ  4
+
+template <int vdr>
+static __dpct_inline__ float
+vec_dot_q5_1_q8_1_impl(const int *vl, const int *vh, const int *u,
+                       const sycl::half2 &dm5, const sycl::half2 &ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = dpct::dp4a(vi0, u[2 * i + 0],
+                          sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = dpct::dp4a(vi1, u[2 * i + 1],
+                          sumi); // SIMD dot product of quantized values
+    }
+
+#ifdef GGML_SYCL_F16
+     const sycl::float2 tmp =
+        (dm5 * ds8).convert<float, sycl::rounding_mode::automatic>();
+    const float d5d8 = tmp.x();
+    const float m5s8 = tmp.y();
+
+
+#else
+    const sycl::float2 dm5f =
+        dm5.convert<float, sycl::rounding_mode::automatic>();
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+    const float d5d8 = dm5f.x() * ds8f.x();
+    const float m5s8 = dm5f.y() * ds8f.y();
+#endif // GGML_SYCL_F16
+
+    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
+    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
+}
+
+#define VDR_Q8_0_Q8_1_MMVQ 2
+#define VDR_Q8_0_Q8_1_MMQ 8
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q8_0_q8_1_impl(const int *v, const int *u,
+                                                    const float &d8_0,
+                                                    const float &d8_1) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = dpct::dp4a(v[i], u[i], sumi);
+    }
+
+    return d8_0*d8_1 * sumi;
+}
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q8_1_q8_1_impl(const int *v, const int *u,
+                                                    const sycl::half2 &dm8,
+                                                    const sycl::half2 &ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = dpct::dp4a(v[i], u[i], sumi);
+    }
+
+#ifdef GGML_SYCL_F16
+    const sycl::float2 tmp =
+        (dm8 * ds8).convert<float, sycl::rounding_mode::automatic>();
+    const float d8d8 = tmp.x();
+    const float m8s8 = tmp.y();
+#else
+    const sycl::float2 dm8f =
+        dm8.convert<float, sycl::rounding_mode::automatic>();
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+    const float d8d8 = dm8f.x() * ds8f.x();
+    const float m8s8 = dm8f.y() * ds8f.y();
+#endif // GGML_SYCL_F16
+
+    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
+    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
+}
+
+static __dpct_inline__ float
+vec_dot_q4_0_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int v[VDR_Q4_0_Q8_1_MMVQ];
+    int u[2*VDR_Q4_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
+        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
+}
+
+static __dpct_inline__ float
+vec_dot_q4_1_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    int v[VDR_Q4_1_Q8_1_MMVQ];
+    int u[2*VDR_Q4_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
+        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
+}
+
+static __dpct_inline__ float
+vec_dot_q5_0_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int vl[VDR_Q5_0_Q8_1_MMVQ];
+    int vh[VDR_Q5_0_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
+        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
+        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
+    }
+
+    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
+}
+
+static __dpct_inline__ float
+vec_dot_q5_1_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    int vl[VDR_Q5_1_Q8_1_MMVQ];
+    int vh[VDR_Q5_1_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
+        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
+        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
+    }
+
+    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
+}
+
+static __dpct_inline__ float
+vec_dot_q8_0_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int v[VDR_Q8_0_Q8_1_MMVQ];
+    int u[VDR_Q8_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
+        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
+        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+    }
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d,
+                                                      bq8_1->ds[0]);
+}
+
+static __dpct_inline__ float
+vec_dot_q2_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
+
+    const int bq8_offset = QR2_K * (iqs / QI8_1);
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const uint8_t * scales = bq2_K->scales + scale_offset;
+
+    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
+    int    u[QR2_K];
+    float d8[QR2_K];
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++ i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + i].ds[0];
+    }
+
+    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
+}
+
+static __dpct_inline__ float
+vec_dot_q3_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
+
+    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const float d = bq3_K->d;
+
+    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
+
+    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
+
+    int    u[QR3_K];
+    float d8[QR3_K];
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + i].ds[0];
+    }
+
+    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
+}
+
+static __dpct_inline__ float
+vec_dot_q4_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    int    v[2];
+    int    u[2*QR4_K];
+    float d8[QR4_K];
+
+    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
+    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
+
+    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
+    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
+    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
+    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
+
+    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    v[0] = q4[0];
+    v[1] = q4[4];
+
+    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+    for (int i = 0; i < QR4_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = bq8i->ds[0];
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
+
+#else
+
+#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    const uint16_t * a = (const uint16_t *)bq4_K->scales;
+    aux16[0] = a[0] & 0x0f0f;
+    aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+    const float dall = bq4_K->dm[0];
+    const float dmin = bq4_K->dm[1];
+
+    const float d8_1 = bq8_1[0].ds[0];
+    const float d8_2 = bq8_1[1].ds[1];
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
+    const int v1 = q4[0];
+    const int v2 = q4[4];
+
+    const int dot1 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0));
+    const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
+    const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0));
+    const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::dp4a(0x01010101, ui3, 0));
+
+    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
+    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
+
+    return dall * sumf_d - dmin * sumf_m;
+
+#else
+    bad_arch();
+#endif // __SYCL_ARCH__ >= VER_4VEC
+
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_q5_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    int   vl[2];
+    int   vh[2];
+    int    u[2*QR5_K];
+    float d8[QR5_K];
+
+    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
+    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
+
+    vl[0] = ql[0];
+    vl[1] = ql[4];
+
+    vh[0] = qh[0] >> bq8_offset;
+    vh[1] = qh[4] >> bq8_offset;
+
+    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = bq8i->ds[0];
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
+
+#else
+
+#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    const int8_t * s = bq5_K->scales;
+
+    const float d = bq5_K->d;
+
+    const float d8_1 = bq8_1[0].ds[0];
+    const float d8_2 = bq8_1[1].ds[1];
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * ql = (const int *)bq5_K->qs + (iqs/2);
+    const int vl1 = ql[0];
+    const int vl2 = ql[4];
+
+    const int step = 4 * (iqs/2); // 0, 4, 8, 12
+    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
+    const int in = step%8; // 0, 4, 0, 4
+    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
+
+    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
+    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
+    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
+    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
+
+    const float sumf_d = d8_1 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1])
+                       + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]);
+
+    return d * sumf_d;
+
+#else
+    bad_arch();
+#endif // __SYCL_ARCH__ >= VER_4VEC
+
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_q6_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
+
+    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
+    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
+    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
+
+    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
+    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
+
+    const int8_t * scales = bq6_K->scales + scale_offset;
+
+    int    u[QR6_K];
+    float d8[QR6_K];
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + 2 * i].ds[0];
+    }
+
+    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
+}
+
+
+static __dpct_inline__ float
+vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq,
+                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                     const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs,
+                     const uint8_t *kmask_iq2xs) {
+#if QK_K == 256
+    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
+
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = q2[2] | (q2[3] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
+        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
+        for (int j = 0; j < 8; ++j) {
+            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+        }
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f;
+    return d * sumi;
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq,
+                    const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                    const uint64_t *iq2xs_grid, const uint64_t *ksigns64) {
+#if DPCT_COMPATIBILITY_TEMP >=                                                 \
+    MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
+
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs[1], signs[1], std::minus<>());
+        sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs[1], signs[1], std::minus<>());
+        sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    assert(false);
+    return 0.f;
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq2_s_q8_1(const void *__restrict__ vbq,
+                   const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+#if QK_K == 256
+    const block_iq2_s * bq2 = (const block_iq2_s *) vbq;
+
+    const int ib32 = iqs;
+    const int8_t  * q8 = bq8_1[ib32].qs;
+    const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
+        const uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
+            ((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201,
+            std::equal_to<>());
+        const uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
+            ((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201,
+            std::equal_to<>());
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs0, signs0, std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs1, signs1, std::minus<>());
+        sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
+        const uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
+            ((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201,
+            std::equal_to<>());
+        const uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
+            ((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201,
+            std::equal_to<>());
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs0, signs0, std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs1, signs1, std::minus<>());
+        sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    assert(false);
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq,
+                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                     const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) {
+#if DPCT_COMPATIBILITY_TEMP >=                                                 \
+    MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * q3 = bq2->qs + 8*ib32;
+    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = gas[0] | (gas[1] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
+        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid1[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid2[0] ^ signs[1], signs[1], std::minus<>());
+        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
+        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.5f;
+    return d * sumi;
+#else
+    assert(false);
+    return 0.f;
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq3_s_q8_1(const void *__restrict__ vbq,
+                   const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                   const uint32_t *iq3s_grid) {
+#if QK_K == 256
+    const block_iq3_s * bq2 = (const block_iq3_s *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * qs = bq2->qs + 8*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
+        const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
+        uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
+            ((bq2->signs[4 * ib32 + l] & 0xf) * 0x01010101) & 0x08040201,
+            0x08040201, std::equal_to<>());
+        uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
+            ((bq2->signs[4 * ib32 + l] >> 4) * 0x01010101) & 0x08040201,
+            0x08040201, std::equal_to<>());
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid1[0] ^ signs0, signs0, std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid2[0] ^ signs1, signs1, std::minus<>());
+        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
+        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
+        q8 += 8;
+    }
+    const float d =
+        (float)bq2->d *
+        (1 + 2 * ((bq2->scales[ib32 / 2] >> 4 * (ib32 % 2)) & 0xf)) *
+        bq8_1[ib32].ds[0];
+    return d * sumi;
+#else
+    assert(false);
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq1_s_q8_1(const void *__restrict__ vbq,
+                   const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                   const uint32_t *iq1s_grid_gpu) {
+#if QK_K == 256
+    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
+
+    const int ib32 = iqs;
+    int sumi = 0;
+    const int * q8 = (const int *)bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+        int grid0 = grid[0] & 0x0f0f0f0f;
+        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
+        sumi = dpct::dp4a(q8[2 * l + 1], grid1,
+                          dpct::dp4a(q8[2 * l + 0], grid0, sumi));
+    }
+
+    const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA;
+    const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1);
+    const float d = d1q * bq8_1[ib32].ds[0];
+    const float m = d1q * bq8_1[ib32].ds[1];
+    return d * sumi + m * delta;
+#else
+    assert(false);
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq1_m_q8_1(const void *__restrict__ vbq,
+                   const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+#if QK_K == 256
+    const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
+
+    const int ib32 = iqs;
+    int   sumi[2] = {0, 0};
+    float sumf[2] = {0.f, 0.f};
+
+    const int * q8 = (const int *)bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
+        int grid0 = grid[0] & 0x0f0f0f0f;
+        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
+        sumi[l / 2] = dpct::dp4a(q8[2 * l + 1], grid1,
+                                 dpct::dp4a(q8[2 * l + 0], grid0, sumi[l / 2]));
+        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
+        const int sumy = dpct::dp4a(q8[2 * l + 1], 0x01010101,
+                                    dpct::dp4a(q8[2 * l + 0], 0x01010101, 0));
+        sumf[l/2] += delta*sumy;
+    }
+
+    iq1m_scale_t scale;
+    const uint16_t * sc = (const uint16_t *)bq1->scales;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const float d = (float)scale.f16 * bq8_1[ib32].ds[0];
+    return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
+#else
+    assert(false);
+#endif
+}
+
+
+static __dpct_inline__ float
+vec_dot_iq4_nl_q8_1(const void *__restrict__ vbq,
+                    const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_iq4_nl * bq = (const block_iq4_nl *) vbq;
+
+    const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs;
+    const int32_t  * q8 = (const int32_t  *)bq8_1->qs + iqs;
+
+    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
+
+    int v1, v2;
+    int sumi1 = 0, sumi2 = 0;
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
+        const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16);
+        get_int_from_table_16(aux, values, v1, v2);
+        sumi1 = dpct::dp4a(v1, q8[l + 0], sumi1);
+        sumi2 = dpct::dp4a(v2, q8[l + 4], sumi2);
+    }
+
+    const float d = (float)bq->d * bq8_1->ds[0];
+    return d * (sumi1 + sumi2);
+}
+
+
+static __dpct_inline__ float
+vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq,
+                    const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+#if QK_K == 256
+    const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
+    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
+
+    // iqs is 0...7
+    const int ib32 = iqs;
+    const int32_t  * q8 = (const int *)bq8_1[ib32].qs;
+    const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
+    const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
+    const float d = (float)bq4->d * (ls - 32) * bq8_1[ib32].ds[0];
+    int v1, v2;
+    int sumi1 = 0, sumi2 = 0;
+    for (int j = 0; j < 4; ++j) {
+        get_int_from_table_16(q4[j], values, v1, v2);
+        sumi1 = dpct::dp4a(v1, q8[j + 0], sumi1);
+        sumi2 = dpct::dp4a(v2, q8[j + 4], sumi2);
+    }
+    return d * (sumi1 + sumi2);
+#else
+    assert(false);
+#endif
+}
+
+#endif // GGML_SYCL_VECDOTQ_HPP
diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp
new file mode 100644
index 0000000000000..7a0ec706f4c31
--- /dev/null
+++ b/ggml/src/ggml-vulkan.cpp
@@ -0,0 +1,7281 @@
+#include "ggml-vulkan.h"
+#include <vulkan/vulkan_core.h>
+#if defined(GGML_VULKAN_RUN_TESTS) || defined(GGML_VULKAN_PERF)
+#include <chrono>
+#endif
+
+#include <vulkan/vulkan.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <iomanip>
+#include <iostream>
+#include <tuple>
+#include <vector>
+#include <sstream>
+#include <utility>
+#include <memory>
+#include <limits>
+#include <map>
+#include <unordered_map>
+#include <memory>
+#include <mutex>
+
+#include "ggml.h"
+#include "ggml-backend-impl.h"
+
+#include "ggml-vulkan-shaders.hpp"
+
+#define VK_API_VERSION VK_API_VERSION_1_2
+
+#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
+
+#define VK_VENDOR_ID_AMD 0x1002
+#define VK_VENDOR_ID_APPLE 0x106b
+#define VK_VENDOR_ID_INTEL 0x8086
+#define VK_VENDOR_ID_NVIDIA 0x10de
+
+#define VK_DEVICE_DESCRIPTOR_POOL_SIZE 32
+
+#define GGML_VK_MAX_NODES 8192
+
+#define MAX_VK_BUFFERS 256
+
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 1
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
+#define VK_CHECK(err, msg)                                          \
+    do {                                                            \
+        vk::Result err_ = (err);                                    \
+        if (err_ != vk::Result::eSuccess) {                         \
+            fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n",  \
+                #err, to_string(err_).c_str(), __FILE__, __LINE__); \
+            exit(1);                                                \
+        }                                                           \
+    } while (0)
+
+#ifdef GGML_VULKAN_DEBUG
+#define VK_LOG_DEBUG(msg) std::cerr << msg << std::endl
+#else
+#define VK_LOG_DEBUG(msg) ((void) 0)
+#endif // GGML_VULKAN_DEBUG
+
+struct ggml_backend_vk_context;
+
+struct vk_queue {
+    uint32_t queue_family_index;
+    vk::Queue queue;
+    vk::CommandPool pool;
+    uint32_t cmd_buffer_idx;
+    std::vector<vk::CommandBuffer> cmd_buffers;
+
+    vk::PipelineStageFlags stage_flags;
+
+    bool transfer_only;
+};
+
+struct vk_pipeline_struct {
+    std::string name;
+    vk::ShaderModule shader_module;
+    vk::DescriptorSetLayout dsl;
+    std::vector<vk::DescriptorPool> descriptor_pools;
+    std::vector<vk::DescriptorSet> descriptor_sets;
+    uint32_t descriptor_set_idx;
+    vk::PipelineLayout layout;
+    vk::Pipeline pipeline;
+    uint32_t push_constant_size;
+    uint32_t parameter_count;
+    std::array<uint32_t, 3> wg_denoms;
+    uint32_t align;
+};
+
+typedef std::shared_ptr<vk_pipeline_struct> vk_pipeline;
+typedef std::weak_ptr<vk_pipeline_struct> vk_pipeline_ref;
+
+static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
+
+struct vk_matmul_pipeline_struct {
+    vk_pipeline l, m, s;
+    vk_pipeline a_l, a_m, a_s;
+};
+
+typedef std::shared_ptr<vk_matmul_pipeline_struct> vk_matmul_pipeline;
+
+struct vk_device_struct;
+typedef std::shared_ptr<vk_device_struct> vk_device;
+typedef std::weak_ptr<vk_device_struct> vk_device_ref;
+
+struct vk_buffer_struct;
+typedef std::shared_ptr<vk_buffer_struct> vk_buffer;
+typedef std::weak_ptr<vk_buffer_struct> vk_buffer_ref;
+
+struct ggml_backend_vk_buffer_type_context {
+    std::string name;
+    vk_device device;
+};
+
+GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft);
+GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft);
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft);
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor);
+static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_vk_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_vk_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_vk_buffer_type_get_alignment,
+    /* .get_max_size     = */ ggml_backend_vk_buffer_type_get_max_size,
+    /* .get_alloc_size   = */ ggml_backend_vk_buffer_type_get_alloc_size,
+    /* .is_host          = */ NULL,
+};
+
+#ifdef GGML_VULKAN_MEMORY_DEBUG
+class vk_memory_logger;
+#endif
+#ifdef GGML_VULKAN_PERF
+class vk_perf_logger;
+#endif
+static void ggml_vk_destroy_buffer(vk_buffer& buf);
+
+struct vk_device_struct {
+    std::mutex mutex;
+
+    vk::PhysicalDevice physical_device;
+    vk::PhysicalDeviceProperties properties;
+    std::string name;
+    uint64_t max_memory_allocation_size;
+    bool fp16;
+    vk::Device device;
+    uint32_t vendor_id;
+    vk_queue compute_queue;
+    vk_queue transfer_queue;
+    bool single_queue;
+    uint32_t subgroup_size;
+    bool uma;
+
+    size_t idx;
+
+    vk_matmul_pipeline pipeline_matmul_f32;
+    vk_matmul_pipeline pipeline_matmul_f32_f16;
+    vk_matmul_pipeline pipeline_matmul_f16;
+    vk_matmul_pipeline pipeline_matmul_f16_f32;
+    vk_pipeline pipeline_matmul_split_k_reduce;
+
+    vk_matmul_pipeline pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT];
+
+    vk_matmul_pipeline pipeline_matmul_id_f32;
+    vk_matmul_pipeline pipeline_matmul_id_f16;
+    vk_matmul_pipeline pipeline_matmul_id_f16_f32;
+
+    vk_matmul_pipeline pipeline_dequant_mul_mat_mat_id[GGML_TYPE_COUNT];
+
+    vk_pipeline pipeline_dequant[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_COUNT];
+
+    vk_pipeline pipeline_mul_mat_vec_p021_f16_f32;
+    vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
+    vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_add_f32, pipeline_add_f16_f32_f16;
+    vk_pipeline pipeline_mul_f32;
+    vk_pipeline pipeline_div_f32;
+    vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32;
+    vk_pipeline pipeline_upscale_f32;
+    vk_pipeline pipeline_scale_f32;
+    vk_pipeline pipeline_sqr_f32;
+    vk_pipeline pipeline_clamp_f32;
+    vk_pipeline pipeline_pad_f32;
+    vk_pipeline pipeline_repeat_f32;
+    vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16;
+    vk_pipeline pipeline_norm_f32;
+    vk_pipeline pipeline_group_norm_f32;
+    vk_pipeline pipeline_rms_norm_f32;
+    vk_pipeline pipeline_gelu_f32;
+    vk_pipeline pipeline_gelu_quick_f32;
+    vk_pipeline pipeline_silu_f32;
+    vk_pipeline pipeline_relu_f32;
+    vk_pipeline pipeline_leaky_relu_f32;
+    vk_pipeline pipeline_tanh_f32;
+    vk_pipeline pipeline_diag_mask_inf_f32;
+    vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
+    vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16;
+    vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
+    vk_pipeline pipeline_argsort_f32;
+    vk_pipeline pipeline_sum_rows_f32;
+    vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
+    vk_pipeline pipeline_timestep_embedding_f32;
+
+    std::unordered_map<std::string, vk_pipeline_ref> pipelines;
+    std::unordered_map<std::string, uint64_t> pipeline_descriptor_set_requirements;
+
+    std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;
+
+    vk::Fence fence;
+    vk_buffer sync_staging;
+
+    ggml_backend_buffer_type buffer_type;
+
+#ifdef GGML_VULKAN_MEMORY_DEBUG
+    std::unique_ptr<vk_memory_logger> memory_logger;
+#endif
+#ifdef GGML_VULKAN_PERF
+    std::unique_ptr<vk_perf_logger> perf_logger;
+#endif
+
+    ~vk_device_struct() {
+        VK_LOG_DEBUG("destroy device " << name);
+
+        device.destroyFence(fence);
+
+        ggml_vk_destroy_buffer(sync_staging);
+
+        device.destroyCommandPool(compute_queue.pool);
+        if (!single_queue) {
+            device.destroyCommandPool(transfer_queue.pool);
+        }
+
+        for (auto& pipeline : pipelines) {
+            if (pipeline.second.expired()) {
+                continue;
+            }
+
+            vk_pipeline pl = pipeline.second.lock();
+            ggml_vk_destroy_pipeline(device, pl);
+        }
+        pipelines.clear();
+
+        device.destroy();
+    }
+};
+
+struct vk_buffer_struct {
+    vk::Buffer buffer = VK_NULL_HANDLE;
+    vk::DeviceMemory device_memory = VK_NULL_HANDLE;
+    vk::MemoryPropertyFlags memory_property_flags;
+    void * ptr;
+    size_t size = 0;
+
+    vk_device device;
+
+    ~vk_buffer_struct() {
+        if (size == 0) {
+            return;
+        }
+        VK_LOG_DEBUG("~vk_buffer_struct(" << buffer << ", " << size << ")");
+
+        device->device.freeMemory(device_memory);
+        device->device.destroyBuffer(buffer);
+    }
+};
+
+struct vk_subbuffer {
+    vk_buffer buffer;
+    uint64_t offset;
+    uint64_t size;
+
+    operator vk::DescriptorBufferInfo() const {
+        return { buffer->buffer, offset, size };
+    }
+};
+
+struct vk_semaphore {
+    vk::Semaphore s;
+    uint64_t value;
+};
+
+struct vk_submission {
+    vk::CommandBuffer buffer;
+    std::vector<vk_semaphore> wait_semaphores;
+    std::vector<vk_semaphore> signal_semaphores;
+};
+
+typedef std::vector<vk_submission> vk_sequence;
+
+struct vk_mat_mat_push_constants {
+    uint32_t M; uint32_t N; uint32_t K;
+    uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
+    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
+    uint32_t k_split;
+    uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
+};
+struct vk_mat_vec_push_constants {
+    uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
+    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
+    uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
+};
+
+struct vk_mat_mat_id_push_constants {
+    uint32_t M; uint32_t N; uint32_t K;
+    uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
+    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
+    uint32_t nei0; uint32_t nei1; uint32_t nbi1; uint32_t ne11;
+};
+struct vk_mat_vec_id_push_constants {
+    uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
+    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
+    uint32_t nei0; uint32_t ne11;
+};
+
+struct vk_op_push_constants {
+    uint32_t KX;
+    uint32_t KY;
+    float param1;
+    float param2;
+};
+
+struct vk_op_unary_push_constants {
+    uint32_t ne;
+    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
+    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
+    uint32_t d_offset;
+    float param1; float param2;
+};
+
+struct vk_op_binary_push_constants {
+    uint32_t ne;
+    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
+    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
+    uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23;
+    uint32_t d_offset;
+    float param1; float param2; int32_t param3;
+};
+
+struct vk_op_diag_mask_push_constants {
+    uint32_t ncols;
+    uint32_t rows_per_channel;
+    int32_t n_past;
+};
+
+struct vk_op_rope_push_constants {
+    uint32_t ncols;
+    uint32_t n_dims;
+    float freq_scale;
+    uint32_t p_delta_rows;
+    float freq_base;
+    float ext_factor;
+    float attn_factor;
+    float corr_dims[2];
+    float theta_scale;
+    uint32_t has_ff;
+};
+
+struct vk_op_soft_max_push_constants {
+    uint32_t KX;
+    uint32_t KY;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint32_t n_head_log2;
+};
+
+struct vk_op_argsort_push_constants {
+    uint32_t ncols;
+    uint32_t ncols_pad;
+    int32_t order;
+};
+
+struct vk_op_im2col_push_constants {
+    uint32_t batch_offset; uint32_t offset_delta;
+    uint32_t IC;
+    uint32_t IW; uint32_t IH;
+    uint32_t OW; uint32_t OH;
+    uint32_t KW; uint32_t KH;
+    uint32_t pelements;
+    uint32_t CHW;
+    int32_t s0; int32_t s1;
+    int32_t p0; int32_t p1;
+    int32_t d0; int32_t d1;
+};
+
+struct vk_op_timestep_embedding_push_constants {
+    uint32_t nb1;
+    uint32_t dim;
+    uint32_t max_period;
+};
+
+// Allow pre-recording command buffers
+struct vk_staging_memcpy {
+    vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
+
+    void * dst;
+    const void * src;
+    size_t n;
+};
+
+struct vk_op_upscale_push_constants {
+    uint32_t ne; uint32_t d_offset;
+    uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
+    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13;
+    float sf0; float sf1; float sf2; float sf3;
+};
+
+struct vk_context_struct {
+    vk_submission * s;
+    std::vector<vk_sequence> seqs;
+
+    int exit_tensor_idx;
+
+    std::vector<vk_staging_memcpy> in_memcpys;
+    std::vector<vk_staging_memcpy> out_memcpys;
+
+    vk_queue * q;
+};
+typedef std::shared_ptr<vk_context_struct> vk_context;
+typedef std::weak_ptr<vk_context_struct> vk_context_ref;
+
+struct ggml_tensor_extra_gpu {
+    vk_buffer_ref buffer_gpu;
+    uint64_t offset;
+
+    void reset() {
+        buffer_gpu.reset();
+        offset = 0;
+    }
+};
+
+struct ggml_vk_garbage_collector {
+    std::vector<vk_semaphore> tl_semaphores;
+    std::vector<vk_semaphore> semaphores;
+    std::vector<vk::Event> events;
+    std::vector<vk_buffer> temp_buffers;
+    std::vector<vk_context> contexts;
+};
+
+#if defined(GGML_VULKAN_MEMORY_DEBUG) || defined(GGML_VULKAN_DEBUG)
+#define VK_LOG_MEMORY(msg) std::cerr << "ggml_vulkan memory: " << msg << std::endl
+
+static std::string format_size(size_t size) {
+    const size_t kib = 1024;
+    const size_t mib = kib * 1024;
+    const size_t gib = mib * 1024;
+
+    std::ostringstream oss;
+    oss << std::fixed << std::setprecision(2);
+
+    if (size >= gib) {
+        oss << static_cast<double>(size) / gib << " GiB";
+    } else if (size >= mib) {
+        oss << static_cast<double>(size) / mib << " MiB";
+    } else if (size >= kib) {
+        oss << static_cast<double>(size) / kib << " KiB";
+    } else {
+        oss << size << " B";
+    }
+
+    return oss.str();
+}
+
+static std::mutex log_mutex;
+
+class vk_memory_logger {
+public:
+    vk_memory_logger(): total_device(0), total_host(0) {}
+    void log_allocation(vk_buffer_ref buf_ref, size_t size);
+    void log_deallocation(vk_buffer_ref buf_ref);
+
+private:
+    std::map<vk::Buffer, size_t> allocations; // Track allocations
+    size_t total_device;
+    size_t total_host;
+};
+#else
+#define VK_LOG_MEMORY(msg) ((void) 0)
+#endif // GGML_VULKAN_MEMORY_DEBUG
+
+#if defined(GGML_VULKAN_PERF)
+
+class vk_perf_logger {
+public:
+    void print_timings() {
+        std::cerr << "----------------\nVulkan Timings:" << std::endl;
+        for (const auto& t : timings) {
+            uint64_t total = 0;
+            for (const auto& time : t.second) {
+                total += time;
+            }
+            std::cerr << t.first << ": " << t.second.size() << " x " << (total / t.second.size() / 1000.0) << " ms" << std::endl;
+        }
+
+        timings.clear();
+    }
+
+    void log_timing(const ggml_tensor * node, uint64_t time) {
+        if (node->op == GGML_OP_UNARY) {
+            timings[ggml_unary_op_name(ggml_get_unary_op(node))].push_back(time);
+            return;
+        }
+        if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
+            const uint64_t m = node->src[0]->ne[1];
+            const uint64_t n = node->src[1]->ne[1];
+            const uint64_t k = node->src[1]->ne[0];
+            std::string name = ggml_op_name(node->op);
+            if (n == 1) {
+                name += "_VEC m=" + std::to_string(m) + " k=" + std::to_string(k);
+            } else {
+                name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k);
+            }
+            timings[name].push_back(time);
+            return;
+        }
+        timings[ggml_op_name(node->op)].push_back(time);
+    }
+private:
+    std::map<std::string, std::vector<uint64_t>> timings;
+};
+#endif // GGML_VULKAN_PERF
+
+struct ggml_backend_vk_context {
+    std::string name;
+
+    vk_device device;
+
+    size_t semaphore_idx, event_idx;
+    ggml_vk_garbage_collector gc;
+    size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k;
+    vk_buffer prealloc_x, prealloc_y, prealloc_split_k;
+    vk::Fence fence;
+
+    vk_buffer buffer_pool[MAX_VK_BUFFERS];
+
+    vk_context_ref compute_ctx;
+    vk_context_ref transfer_ctx;
+
+    std::vector<vk_context_ref> tensor_ctxs;
+};
+
+#ifdef GGML_VULKAN_MEMORY_DEBUG
+void vk_memory_logger::log_allocation(vk_buffer_ref buf_ref, size_t size) {
+    std::lock_guard<std::mutex> guard(log_mutex);
+    vk_buffer buf = buf_ref.lock();
+    const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal);
+    const std::string type = device ? "device" : "host";
+    allocations[buf->buffer] = size;
+    total_device += device ? size : 0;
+    total_host += device ? 0 : size;
+    VK_LOG_MEMORY(buf->device->name << ": +" << format_size(size) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host));
+}
+
+void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) {
+    if (buf_ref.expired() || buf_ref.lock()->size == 0) {
+        return;
+    }
+
+    std::lock_guard<std::mutex> guard(log_mutex);
+    vk_buffer buf = buf_ref.lock();
+    const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal);
+    std::string type = device ? "device" : "host";
+    auto it = allocations.find(buf->buffer);
+    total_device -= device ? it->second : 0;
+    total_host -= device ? 0 : it->second;
+    if (it != allocations.end()) {
+        VK_LOG_MEMORY(buf->device->name << ": -" << format_size(it->second) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host));
+        allocations.erase(it);
+    } else {
+        VK_LOG_MEMORY("ERROR " << buf->device->name << ": Attempted to deallocate unknown " << type << " memory at " << buf->buffer);
+    }
+}
+#endif // GGML_VULKAN_MEMORY_DEBUG
+
+struct vk_instance_t {
+    vk::Instance instance;
+
+    std::vector<size_t> device_indices;
+    vk_device devices[GGML_VK_MAX_DEVICES];
+};
+
+static bool vk_instance_initialized = false;
+static vk_instance_t vk_instance;
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+static size_t vk_skip_checks;
+static size_t vk_output_tensor;
+
+static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name);
+static void ggml_vk_check_results_0(ggml_tensor * tensor);
+static void ggml_vk_check_results_1(ggml_tensor * tensor);
+#endif
+
+typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+
+GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend);
+
+static void ggml_vk_create_pipeline(vk_device& device, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t>&& specialization_constants, uint32_t align) {
+    VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")");
+    GGML_ASSERT(parameter_count > 0);
+    GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
+
+    std::lock_guard<std::mutex> guard(device->mutex);
+
+    pipeline = std::make_shared<vk_pipeline_struct>();
+    pipeline->name = name;
+    pipeline->parameter_count = parameter_count;
+    pipeline->push_constant_size = push_constant_size;
+    pipeline->wg_denoms = wg_denoms;
+    pipeline->align = align;
+
+    vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast<const uint32_t *>(spv_data));
+    pipeline->shader_module = device->device.createShaderModule(shader_module_create_info);
+
+    std::vector<vk::DescriptorSetLayoutBinding> dsl_binding;
+    std::vector<vk::DescriptorBindingFlags> dsl_binding_flags;
+    for (uint32_t i = 0; i < parameter_count; i++) {
+        dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute});
+        dsl_binding_flags.push_back({});
+    }
+
+    vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags };
+
+    vk::PushConstantRange pcr(
+        vk::ShaderStageFlagBits::eCompute,
+        0,
+        pipeline->push_constant_size
+    );
+
+    vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info(
+        {},
+        dsl_binding);
+    descriptor_set_layout_create_info.setPNext(&dslbfci);
+    pipeline->dsl = device->device.createDescriptorSetLayout(descriptor_set_layout_create_info);
+
+    vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count * VK_DEVICE_DESCRIPTOR_POOL_SIZE);
+    vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size);
+    pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info));
+
+    pipeline->descriptor_set_idx = 0;
+
+    vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr);
+    pipeline->layout = device->device.createPipelineLayout(pipeline_layout_create_info);
+
+    std::vector<vk::SpecializationMapEntry> specialization_entries(specialization_constants.size());
+
+    for (size_t i = 0; i < specialization_constants.size(); i++) {
+        specialization_entries[i].constantID = i;
+        specialization_entries[i].offset = i * sizeof(uint32_t);
+        specialization_entries[i].size = sizeof(uint32_t);
+    }
+
+    vk::SpecializationInfo specialization_info(
+        specialization_entries.size(),
+        specialization_entries.data(),
+        specialization_constants.size() * sizeof(uint32_t),
+        specialization_constants.data()
+    );
+
+    vk::PipelineShaderStageCreateInfo pipeline_shader_create_info(
+            vk::PipelineShaderStageCreateFlags(),
+            vk::ShaderStageFlagBits::eCompute,
+            pipeline->shader_module,
+            entrypoint.c_str(),
+            &specialization_info);
+    vk::ComputePipelineCreateInfo compute_pipeline_create_info(
+        vk::PipelineCreateFlags(),
+        pipeline_shader_create_info,
+        pipeline->layout);
+    pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
+
+    device->pipelines.insert({ pipeline->name, pipeline });
+}
+
+static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) {
+    VK_LOG_DEBUG("ggml_pipeline_destroy_pipeline(" << pipeline->name << ")");
+    for (auto& pool : pipeline->descriptor_pools) {
+        device.destroyDescriptorPool(pool);
+    }
+    pipeline->descriptor_pools.clear();
+    pipeline->descriptor_sets.clear();
+    pipeline->descriptor_set_idx = 0;
+
+    device.destroyDescriptorSetLayout(pipeline->dsl);
+
+    device.destroyPipelineLayout(pipeline->layout);
+
+    device.destroyShaderModule(pipeline->shader_module);
+
+    device.destroyPipeline(pipeline->pipeline);
+}
+
+static void ggml_pipeline_request_descriptor_sets(vk_device& device, vk_pipeline& pipeline, uint32_t n) {
+    VK_LOG_DEBUG("ggml_pipeline_request_descriptor_sets(" << pipeline->name << ", " << n << ")");
+    device->pipeline_descriptor_set_requirements[pipeline->name] += n;
+}
+
+static void ggml_pipeline_allocate_descriptor_sets(vk_device& device) {
+    std::lock_guard<std::mutex> guard(device->mutex);
+
+    for (auto& pair : device->pipeline_descriptor_set_requirements) {
+        vk_pipeline pipeline = device->pipelines.at(pair.first).lock();
+        const uint64_t n = pair.second;
+
+        VK_LOG_DEBUG("ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")");
+
+        if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) {
+            // Enough descriptors are available
+            continue;
+        }
+
+        uint32_t to_alloc = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size();
+        uint32_t pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE - pipeline->descriptor_sets.size() % VK_DEVICE_DESCRIPTOR_POOL_SIZE;
+        uint32_t pool_idx = pipeline->descriptor_sets.size() / VK_DEVICE_DESCRIPTOR_POOL_SIZE;
+
+        while (to_alloc > 0) {
+            const uint32_t alloc_count = std::min(pool_remaining, to_alloc);
+            to_alloc -= alloc_count;
+            pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE;
+
+            if (pool_idx >= pipeline->descriptor_pools.size()) {
+                vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count * VK_DEVICE_DESCRIPTOR_POOL_SIZE);
+                vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size);
+                pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info));
+            }
+
+            std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
+            for (uint32_t i = 0; i < alloc_count; i++) {
+                layouts[i] = pipeline->dsl;
+            }
+            vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[pool_idx], alloc_count, layouts.data());
+            std::vector<vk::DescriptorSet> sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info);
+            pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end());
+
+            pool_idx++;
+        }
+    }
+}
+
+static void ggml_pipeline_cleanup(vk_pipeline& pipeline) {
+    VK_LOG_DEBUG("ggml_pipeline_cleanup(" << pipeline->name << ")");
+    pipeline->descriptor_set_idx = 0;
+}
+
+static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_device& device, vk_queue& q) {
+    VK_LOG_DEBUG("ggml_vk_create_cmd_buffer()");
+    std::lock_guard<std::mutex> guard(device->mutex);
+
+    if (q.cmd_buffers.size() > q.cmd_buffer_idx) {
+        // Reuse command buffer
+        return q.cmd_buffers[q.cmd_buffer_idx++];
+    }
+
+    vk::CommandBufferAllocateInfo command_buffer_alloc_info(
+        q.pool,
+        vk::CommandBufferLevel::ePrimary,
+        1);
+    const std::vector<vk::CommandBuffer> cmd_buffers = device->device.allocateCommandBuffers(command_buffer_alloc_info);
+    auto buf = cmd_buffers.front();
+
+    q.cmd_buffers.push_back(buf);
+    q.cmd_buffer_idx++;
+
+    return buf;
+}
+
+static vk_submission ggml_vk_create_submission(vk_device& device, vk_queue& q, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
+    VK_LOG_DEBUG("ggml_vk_create_submission()");
+    vk_submission s;
+    s.buffer = ggml_vk_create_cmd_buffer(device, q);
+    s.wait_semaphores = std::move(wait_semaphores);
+    s.signal_semaphores = std::move(signal_semaphores);
+    return s;
+}
+
+static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
+    if (ctx->seqs.empty()) {
+        return;
+    }
+    VK_LOG_DEBUG("ggml_vk_submit(" << ctx << ", " << fence << ")");
+
+    std::vector<std::vector<uint64_t>> tl_wait_vals;
+    std::vector<std::vector<uint64_t>> tl_signal_vals;
+    std::vector<std::vector<vk::Semaphore>> tl_wait_semaphores;
+    std::vector<std::vector<vk::Semaphore>> tl_signal_semaphores;
+    std::vector<vk::TimelineSemaphoreSubmitInfo> tl_submit_infos;
+    std::vector<vk::SubmitInfo> submit_infos;
+    int idx = -1;
+    std::vector<std::vector<vk::PipelineStageFlags>> stage_flags;
+
+    size_t reserve = 0;
+
+    for (const auto& sequence : ctx->seqs) {
+        reserve += sequence.size();
+    }
+
+    // Pre-reserve vectors to prevent reallocation, which invalidates pointers
+    tl_wait_semaphores.reserve(reserve);
+    tl_wait_vals.reserve(reserve);
+    tl_signal_semaphores.reserve(reserve);
+    tl_signal_vals.reserve(reserve);
+    tl_submit_infos.reserve(reserve);
+    submit_infos.reserve(reserve);
+    stage_flags.reserve(reserve);
+
+    for (const auto& sequence : ctx->seqs) {
+        for (const auto& submission : sequence) {
+            stage_flags.push_back({});
+            idx++;
+            tl_wait_vals.push_back({});
+            tl_wait_semaphores.push_back({});
+            tl_signal_vals.push_back({});
+            tl_signal_semaphores.push_back({});
+            for (size_t i = 0; i < submission.wait_semaphores.size(); i++) {
+                stage_flags[idx].push_back(ctx->q->stage_flags);
+                tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value);
+                tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s);
+            }
+            for (size_t i = 0; i < submission.signal_semaphores.size(); i++) {
+                tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value);
+                tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s);
+            }
+            tl_submit_infos.push_back({
+                (uint32_t) submission.wait_semaphores.size(),
+                tl_wait_vals[idx].data(),
+                (uint32_t) submission.signal_semaphores.size(),
+                tl_signal_vals[idx].data(),
+            });
+            tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo;
+            tl_submit_infos[idx].pNext = nullptr;
+            vk::SubmitInfo si{
+                (uint32_t) submission.wait_semaphores.size(),
+                tl_wait_semaphores[idx].data(),
+                stage_flags[idx].data(),
+                1,
+                &submission.buffer,
+                (uint32_t) submission.signal_semaphores.size(),
+                tl_signal_semaphores[idx].data(),
+            };
+            si.setPNext(&tl_submit_infos[idx]);
+            submit_infos.push_back(si);
+        }
+    }
+
+    ctx->q->queue.submit(submit_infos, fence);
+
+    ctx->seqs.clear();
+}
+
+static uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyProperties>& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) {
+    VK_LOG_DEBUG("ggml_vk_find_queue_family_index()");
+    const uint32_t qfsize = queue_family_props.size();
+
+    // Try with avoid preferences first
+    for (uint32_t i = 0; i < qfsize; i++) {
+        if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) {
+            return i;
+        }
+    }
+
+    // Fall back to only required
+    for (size_t i = 0; i < qfsize; i++) {
+        if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) {
+            return i;
+        }
+    }
+
+    // Fall back to reusing compute queue
+    for (size_t i = 0; i < qfsize; i++) {
+        if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) {
+            return i;
+        }
+    }
+
+    // Fall back to ignoring min_num_queries
+    for (size_t i = 0; i < qfsize; i++) {
+        if (queue_family_props[i].queueFlags & required) {
+            return i;
+        }
+    }
+
+    // All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
+    // Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
+    if (compute_index >= 0) {
+        return compute_index;
+    }
+
+    std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;
+
+    for(auto &q_family : queue_family_props) {
+        std::cerr << "Queue number: "  + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl;
+    }
+    abort();
+}
+
+static void ggml_vk_create_queue(vk_device& device, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags, bool transfer_only) {
+    VK_LOG_DEBUG("ggml_vk_create_queue()");
+    std::lock_guard<std::mutex> guard(device->mutex);
+
+    q.queue_family_index = queue_family_index;
+    q.transfer_only = transfer_only;
+
+    vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index);
+    q.pool = device->device.createCommandPool(command_pool_create_info_compute);
+
+    q.cmd_buffer_idx = 0;
+
+    q.queue = device->device.getQueue(queue_family_index, queue_index);
+
+    q.stage_flags = stage_flags;
+}
+
+static vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) {
+    vk_context result = std::make_shared<vk_context_struct>();
+    VK_LOG_DEBUG("ggml_vk_create_context(" << result << ")");
+    ctx->gc.contexts.emplace_back(result);
+    result->q = &q;
+    return result;
+}
+
+static vk_context ggml_vk_create_temporary_context(vk_queue& q) {
+    vk_context result = std::make_shared<vk_context_struct>();
+    VK_LOG_DEBUG("ggml_vk_create_temporary_context(" << result << ")");
+    result->q = &q;
+    return result;
+}
+
+static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) {
+    VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()");
+    vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 };
+    vk::SemaphoreCreateInfo ci{};
+    ci.setPNext(&tci);
+    vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
+    ctx->gc.semaphores.push_back({ semaphore, 0 });
+    return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1];
+}
+
+static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) {
+    VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()");
+    if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) {
+        vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
+        vk::SemaphoreCreateInfo ci{};
+        ci.setPNext(&tci);
+        vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
+        ctx->gc.tl_semaphores.push_back({ semaphore, 0 });
+    }
+    return &ctx->gc.tl_semaphores[ctx->semaphore_idx++];
+}
+
+static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) {
+    if (ctx->event_idx >= ctx->gc.events.size()) {
+        ctx->gc.events.push_back(ctx->device->device.createEvent({}));
+    }
+    return ctx->gc.events[ctx->event_idx++];
+}
+
+static void ggml_vk_queue_cleanup(vk_device& device, vk_queue& q) {
+    VK_LOG_DEBUG("ggml_vk_queue_cleanup()");
+    std::lock_guard<std::mutex> guard(device->mutex);
+
+    // Requires command buffers to be done
+    device->device.resetCommandPool(q.pool);
+    q.cmd_buffer_idx = 0;
+}
+
+static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) {
+    for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) {
+        vk::MemoryType memory_type = mem_props->memoryTypes[i];
+        if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) &&
+            (flags & memory_type.propertyFlags) == flags &&
+            mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) {
+            return static_cast<int32_t>(i);
+        }
+    }
+    return UINT32_MAX;
+}
+
+static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
+    VK_LOG_DEBUG("ggml_vk_create_buffer(" << device->name << ", " << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")");
+    if (size > device->max_memory_allocation_size) {
+        throw vk::OutOfDeviceMemoryError("Requested buffer size exceeds device memory allocation limit");
+    }
+
+    std::lock_guard<std::mutex> guard(device->mutex);
+
+    vk_buffer buf = std::make_shared<vk_buffer_struct>();
+
+    if (size == 0) {
+        buf->size = 0;
+        return buf;
+    }
+
+    buf->size = size;
+    vk::BufferCreateInfo buffer_create_info{
+        vk::BufferCreateFlags(),
+        size,
+        vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst,
+        vk::SharingMode::eExclusive,
+        0,
+        nullptr,
+    };
+
+    buf->buffer = device->device.createBuffer(buffer_create_info);
+
+    vk::MemoryRequirements mem_req = device->device.getBufferMemoryRequirements(buf->buffer);
+
+    vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties();
+
+    uint32_t memory_type_index = UINT32_MAX;
+
+    memory_type_index = find_properties(&mem_props, &mem_req, req_flags);
+    buf->memory_property_flags = req_flags;
+
+    if (memory_type_index == UINT32_MAX && fallback_flags) {
+        memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags);
+        buf->memory_property_flags = fallback_flags;
+    }
+
+    if (memory_type_index == UINT32_MAX) {
+        device->device.destroyBuffer(buf->buffer);
+        buf->size = 0;
+        throw vk::OutOfDeviceMemoryError("No suitable memory type found");
+    }
+
+    try {
+        buf->device_memory = device->device.allocateMemory({ mem_req.size, memory_type_index });
+    } catch (const vk::SystemError& e) {
+        // Out of Host/Device memory, clean up buffer
+        device->device.destroyBuffer(buf->buffer);
+        buf->size = 0;
+        throw e;
+    }
+    buf->ptr = nullptr;
+
+    if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        buf->ptr = device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
+    }
+
+    device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0);
+
+    buf->device = device;
+
+#ifdef GGML_VULKAN_MEMORY_DEBUG
+    device->memory_logger->log_allocation(buf, size);
+#endif
+
+    return buf;
+}
+
+static vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
+    try {
+        return ggml_vk_create_buffer(device, size, req_flags, fallback_flags);
+    } catch (const vk::SystemError& e) {
+        std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl;
+        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
+        throw e;
+    }
+}
+
+static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
+    vk_buffer buf;
+    try {
+        if (device->uma) {
+            // Fall back to host memory type
+            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+        } else {
+            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        }
+    } catch (const vk::SystemError& e) {
+        std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
+        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
+        throw e;
+    }
+
+    return buf;
+}
+
+static void ggml_vk_destroy_buffer(vk_buffer& buf) {
+    if (buf == nullptr) {
+        return;
+    }
+
+#ifdef GGML_VULKAN_MEMORY_DEBUG
+    if (buf->device != nullptr) {
+        buf->device->memory_logger->log_deallocation(buf);
+    }
+#endif
+
+    buf.reset();
+}
+
+static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) {
+    return { buf, 0, VK_WHOLE_SIZE };
+}
+
+static void ggml_vk_sync_buffers(vk_context& ctx) {
+    VK_LOG_DEBUG("ggml_vk_sync_buffers()");
+
+    const bool transfer_queue = ctx->q->transfer_only;
+
+    ctx->s->buffer.pipelineBarrier(
+        ctx->q->stage_flags,
+        ctx->q->stage_flags,
+        {},
+        { {
+          { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) },
+          { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) }
+        } },
+        {},
+        {}
+    );
+}
+
+static void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events) {
+    VK_LOG_DEBUG("ggml_vk_wait_events()");
+    if (events.empty()) {
+        return;
+    }
+
+    ctx->s->buffer.waitEvents(
+        events,
+        ctx->q->stage_flags,
+        ctx->q->stage_flags,
+        {},
+        {},
+        {}
+    );
+}
+
+static void ggml_vk_load_shaders(vk_device& device) {
+    VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")");
+
+    // mulmat
+    std::initializer_list<uint32_t> warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_m = { 128,  64,  64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_s = { device->subgroup_size,  32,  32, 16, 32, 32, 2, 2, 2, device->subgroup_size };
+
+    std::initializer_list<uint32_t> warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_mmq_m = { 128,  64,  64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_mmq_s = { device->subgroup_size,  32,  32, 32, 32, 32, 2, 2, 2, device->subgroup_size };
+
+    std::array<uint32_t, 3> l_wg_denoms = {128, 128, 1 };
+    std::array<uint32_t, 3> m_wg_denoms = { 64,  64, 1 };
+    std::array<uint32_t, 3> s_wg_denoms = { 32,  32, 1 };
+
+    uint32_t l_align = 128;
+    uint32_t m_align =  64;
+    uint32_t s_align =  32;
+
+    device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_matmul_f32_f16 = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_matmul_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_matmul_f16 = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL] = std::make_shared<vk_matmul_pipeline_struct>();
+
+    device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_matmul_id_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_matmul_id_f16 = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL] = std::make_shared<vk_matmul_pipeline_struct>();
+
+    if (device->fp16) {
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+    } else {
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+    }
+
+    // mul mat vec
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32",  mul_mat_vec_f32_f32_f32_len,  mul_mat_vec_f32_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32",  mul_mat_vec_f16_f32_f32_len,  mul_mat_vec_f16_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32",  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+
+    // dequant shaders
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_k", dequant_q2_k_len, dequant_q2_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_k", dequant_q3_k_len, dequant_q3_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_k", dequant_q4_k_len, dequant_q4_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_k", dequant_q5_k_len, dequant_q5_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_k", dequant_q6_k_len, dequant_q6_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL], "dequant_iq4_nl", dequant_iq4_nl_len, dequant_iq4_nl_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+
+    // get_rows
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16",  get_rows_f16_len,  get_rows_f16_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl", get_rows_iq4_nl_len, get_rows_iq4_nl_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl_f32", get_rows_iq4_nl_f32_len, get_rows_iq4_nl_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16, "add_f16_f32_f16", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_concat_f32, "concat_f32", concat_f32_len, concat_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_concat_f16, "concat_f16", concat_f16_len, concat_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_concat_i32, "concat_i32", concat_i32_len, concat_i32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_upscale_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_repeat_f32, "repeat_f32", repeat_f32_len, repeat_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_gelu_quick_f32, "gelu_quick_f32", gelu_quick_f32_len, gelu_quick_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_leaky_relu_f32, "leaky_relu_f32", leaky_relu_f32_len, leaky_relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_tanh_f32, "tanh_f32", tanh_f32_len, tanh_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32_len, im2col_f32_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_len, im2col_f32_f16_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1);
+}
+
+static vk_device ggml_vk_get_device(size_t idx) {
+    VK_LOG_DEBUG("ggml_vk_get_device(" << idx << ")");
+
+    if (vk_instance.devices[idx] == nullptr) {
+        VK_LOG_DEBUG("Initializing new vk_device");
+        vk_device device = std::make_shared<vk_device_struct>();
+        vk_instance.devices[idx] = device;
+
+#ifdef GGML_VULKAN_MEMORY_DEBUG
+        device->memory_logger = std::unique_ptr<vk_memory_logger>(new vk_memory_logger());
+#endif
+#ifdef GGML_VULKAN_PERF
+        device->perf_logger = std::unique_ptr<vk_perf_logger>(new vk_perf_logger());
+#endif
+
+        size_t dev_num = vk_instance.device_indices[idx];
+
+        std::vector<vk::PhysicalDevice> physical_devices = vk_instance.instance.enumeratePhysicalDevices();
+
+        if (dev_num >= physical_devices.size()) {
+            std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
+            throw std::runtime_error("Device not found");
+        }
+
+        device->physical_device = physical_devices[dev_num];
+        const std::vector<vk::ExtensionProperties> ext_props = device->physical_device.enumerateDeviceExtensionProperties();
+
+        bool maintenance4_support = false;
+
+        // Check if maintenance4 is supported
+        for (const auto& properties : ext_props) {
+            if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
+                maintenance4_support = true;
+            }
+        }
+
+        vk::PhysicalDeviceProperties2 props2;
+        vk::PhysicalDeviceMaintenance3Properties props3;
+        vk::PhysicalDeviceMaintenance4Properties props4;
+        vk::PhysicalDeviceSubgroupProperties subgroup_props;
+        props2.pNext = &props3;
+        props3.pNext = &subgroup_props;
+        if (maintenance4_support) {
+            subgroup_props.pNext = &props4;
+        }
+        device->physical_device.getProperties2(&props2);
+        device->properties = props2.properties;
+
+        const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
+
+        if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) {
+            device->max_memory_allocation_size = std::stoi(GGML_VK_FORCE_MAX_ALLOCATION_SIZE);
+        } else if (maintenance4_support) {
+            device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize);
+        } else {
+            device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
+        }
+
+        device->vendor_id = device->properties.vendorID;
+        device->subgroup_size = subgroup_props.subgroupSize;
+        device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
+
+        bool fp16_storage = false;
+        bool fp16_compute = false;
+
+        for (const auto& properties : ext_props) {
+            if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
+                fp16_storage = true;
+            } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
+                fp16_compute = true;
+            }
+        }
+
+        const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
+        const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
+
+        device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
+
+        std::vector<vk::QueueFamilyProperties> queue_family_props = device->physical_device.getQueueFamilyProperties();
+
+        // Try to find a non-graphics compute queue and transfer-focused queues
+        const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1);
+        const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1);
+
+        const float priorities[] = { 1.0f, 1.0f };
+        device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1;
+
+        std::vector<vk::DeviceQueueCreateInfo> device_queue_create_infos;
+        if (compute_queue_family_index != transfer_queue_family_index) {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1});
+        } else if(!device->single_queue) {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities});
+        } else {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
+        }
+        vk::DeviceCreateInfo device_create_info;
+        std::vector<const char *> device_extensions;
+        vk::PhysicalDeviceFeatures device_features = device->physical_device.getFeatures();
+
+        VkPhysicalDeviceFeatures2 device_features2;
+        device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
+        device_features2.pNext = nullptr;
+        device_features2.features = (VkPhysicalDeviceFeatures)device_features;
+
+        VkPhysicalDeviceVulkan11Features vk11_features;
+        vk11_features.pNext = nullptr;
+        vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
+        device_features2.pNext = &vk11_features;
+
+        VkPhysicalDeviceVulkan12Features vk12_features;
+        vk12_features.pNext = nullptr;
+        vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
+        vk11_features.pNext = &vk12_features;
+
+        vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2);
+
+        device->fp16 = device->fp16 && vk12_features.shaderFloat16;
+
+        if (!vk11_features.storageBuffer16BitAccess) {
+            std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
+            throw std::runtime_error("Unsupported device");
+        }
+
+        device_extensions.push_back("VK_KHR_16bit_storage");
+
+#ifdef GGML_VULKAN_VALIDATE
+        device_extensions.push_back("VK_KHR_shader_non_semantic_info");
+#endif
+
+        if (device->fp16) {
+            device_extensions.push_back("VK_KHR_shader_float16_int8");
+        }
+        device->name = device->properties.deviceName.data();
+
+        device_create_info = {
+            vk::DeviceCreateFlags(),
+            device_queue_create_infos,
+            {},
+            device_extensions
+        };
+        device_create_info.setPNext(&device_features2);
+        device->device = device->physical_device.createDevice(device_create_info);
+
+        // Queues
+        ggml_vk_create_queue(device, device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false);
+
+        // Shaders
+        ggml_vk_load_shaders(device);
+
+        if (!device->single_queue) {
+            const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0;
+            ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true);
+        } else {
+            // TODO: Use pointer or reference to avoid copy
+            device->transfer_queue = device->compute_queue;
+        }
+
+        device->buffer_type = {
+            /* .iface    = */ ggml_backend_vk_buffer_type_interface,
+            /* .context  = */ new ggml_backend_vk_buffer_type_context{ device->name, device },
+        };
+
+        device->fence = device->device.createFence({});
+
+        device->idx = idx;
+
+        return device;
+    }
+
+    return vk_instance.devices[idx];
+}
+
+
+static void ggml_vk_print_gpu_info(size_t idx) {
+    GGML_ASSERT(idx < vk_instance.device_indices.size());
+    size_t dev_num = vk_instance.device_indices[idx];
+    VK_LOG_DEBUG("ggml_vk_print_gpu_info(" << dev_num << ")");
+    GGML_ASSERT(vk_instance_initialized);
+
+    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
+    if (dev_num >= devices.size()) {
+        std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
+        throw std::runtime_error("Device not found");
+    }
+
+    vk::PhysicalDevice physical_device = devices[dev_num];
+    std::vector<vk::ExtensionProperties> ext_props = physical_device.enumerateDeviceExtensionProperties();
+
+    vk::PhysicalDeviceProperties2 props2;
+    vk::PhysicalDeviceMaintenance3Properties props3;
+    vk::PhysicalDeviceSubgroupProperties subgroup_props;
+    vk::PhysicalDeviceDriverProperties driver_props;
+    props2.pNext = &props3;
+    props3.pNext = &subgroup_props;
+    subgroup_props.pNext = &driver_props;
+    physical_device.getProperties2(&props2);
+
+    const size_t subgroup_size = subgroup_props.subgroupSize;
+    const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
+
+    bool fp16_storage = false;
+    bool fp16_compute = false;
+
+    for (auto properties : ext_props) {
+        if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
+            fp16_storage = true;
+        } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
+            fp16_compute = true;
+        }
+    }
+
+    const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
+    bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
+
+    bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
+
+    vk::PhysicalDeviceFeatures device_features = physical_device.getFeatures();
+
+    VkPhysicalDeviceFeatures2 device_features2;
+    device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
+    device_features2.pNext = nullptr;
+    device_features2.features = (VkPhysicalDeviceFeatures)device_features;
+
+    VkPhysicalDeviceVulkan11Features vk11_features;
+    vk11_features.pNext = nullptr;
+    vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
+    device_features2.pNext = &vk11_features;
+
+    VkPhysicalDeviceVulkan12Features vk12_features;
+    vk12_features.pNext = nullptr;
+    vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
+    vk11_features.pNext = &vk12_features;
+
+    vkGetPhysicalDeviceFeatures2(physical_device, &device_features2);
+
+    fp16 = fp16 && vk12_features.shaderFloat16;
+
+    std::string device_name = props2.properties.deviceName.data();
+    std::cerr << GGML_VK_NAME << idx << ": " << device_name << " (" << driver_props.driverName << ") | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl;
+
+    if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) {
+        std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl;
+    }
+}
+
+static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
+static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
+
+void ggml_vk_instance_init() {
+    if (vk_instance_initialized) {
+        return;
+    }
+    VK_LOG_DEBUG("ggml_vk_instance_init()");
+
+    vk_instance_initialized = true;
+
+    vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION };
+
+    const std::vector<vk::ExtensionProperties> instance_extensions = vk::enumerateInstanceExtensionProperties();
+    const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions);
+#ifdef __APPLE__
+    const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions);
+#endif
+
+    std::vector<const char*> layers;
+
+    if (validation_ext) {
+        layers.push_back("VK_LAYER_KHRONOS_validation");
+    }
+    std::vector<const char*> extensions;
+    if (validation_ext) {
+        extensions.push_back("VK_EXT_validation_features");
+    }
+#ifdef __APPLE__
+    if (portability_enumeration_ext) {
+        extensions.push_back("VK_KHR_portability_enumeration");
+    }
+#endif
+    vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions);
+#ifdef __APPLE__
+    if (portability_enumeration_ext) {
+        instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR;
+    }
+#endif
+
+    std::vector<vk::ValidationFeatureEnableEXT> features_enable;
+    vk::ValidationFeaturesEXT validation_features;
+
+    if (validation_ext) {
+        features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices };
+        validation_features = {
+            features_enable,
+            {},
+        };
+        validation_features.setPNext(nullptr);
+        instance_create_info.setPNext(&validation_features);
+
+        std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl;
+    }
+    vk_instance.instance = vk::createInstance(instance_create_info);
+
+    size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();
+
+    // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
+    char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES");
+    if (devices_env != nullptr) {
+        std::string devices(devices_env);
+        std::replace(devices.begin(), devices.end(), ',', ' ');
+
+        std::stringstream ss(devices);
+        size_t tmp;
+        while (ss >> tmp) {
+            if(tmp >= num_available_devices) {
+                std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl;
+                throw std::runtime_error("Invalid Vulkan device index");
+            }
+            vk_instance.device_indices.push_back(tmp);
+        }
+    } else {
+        std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
+        // Make sure at least one device exists
+        if (devices.empty()) {
+            std::cerr << "ggml_vulkan: Error: No devices found." << std::endl;
+            GGML_ABORT("fatal error");
+        }
+
+        // Default to using all dedicated GPUs
+        for (size_t i = 0; i < devices.size(); i++) {
+            vk::PhysicalDeviceProperties2 new_props;
+            vk::PhysicalDeviceDriverProperties new_driver;
+            vk::PhysicalDeviceIDProperties new_id;
+            new_props.pNext = &new_driver;
+            new_driver.pNext = &new_id;
+            devices[i].getProperties2(&new_props);
+
+            if (new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) {
+                // Check if there are two physical devices corresponding to the same GPU
+                auto old_device = std::find_if(
+                    vk_instance.device_indices.begin(),
+                    vk_instance.device_indices.end(),
+                    [&devices, &new_id](const size_t k){
+                        vk::PhysicalDeviceProperties2 old_props;
+                        vk::PhysicalDeviceIDProperties old_id;
+                        old_props.pNext = &old_id;
+                        devices[k].getProperties2(&old_props);
+                        return std::equal(std::begin(old_id.deviceUUID), std::end(old_id.deviceUUID), std::begin(new_id.deviceUUID));
+                    }
+                );
+                if (old_device == vk_instance.device_indices.end()) {
+                    vk_instance.device_indices.push_back(i);
+                } else {
+                    // There can be two physical devices corresponding to the same GPU if there are 2 different drivers
+                    // This can cause error when splitting layers aross the devices, need to keep only 1
+                    VK_LOG_DEBUG("Device " << i << " and device " << *old_device << " have the same deviceUUID");
+
+                    vk::PhysicalDeviceProperties2 old_props;
+                    vk::PhysicalDeviceDriverProperties old_driver;
+                    old_props.pNext = &old_driver;
+                    devices[*old_device].getProperties2(&old_props);
+
+                    std::map<vk::DriverId, int> driver_priorities {};
+                    int old_priority = std::numeric_limits<int>::max();
+                    int new_priority = std::numeric_limits<int>::max();
+
+                    // Check https://registry.khronos.org/vulkan/specs/1.3-extensions/man/html/VkDriverId.html for the list of driver id
+                    // Smaller number -> higher priority
+                    switch (old_props.properties.vendorID) {
+                        case VK_VENDOR_ID_AMD:
+                            driver_priorities[vk::DriverId::eMesaRadv] = 1;
+                            driver_priorities[vk::DriverId::eAmdOpenSource] = 2;
+                            driver_priorities[vk::DriverId::eAmdProprietary] = 3;
+                            break;
+                        case VK_VENDOR_ID_INTEL:
+                            driver_priorities[vk::DriverId::eIntelOpenSourceMESA] = 1;
+                            driver_priorities[vk::DriverId::eIntelProprietaryWindows] = 2;
+                            break;
+                        case VK_VENDOR_ID_NVIDIA:
+                            driver_priorities[vk::DriverId::eNvidiaProprietary] = 1;
+#if defined(VK_API_VERSION_1_3) && VK_HEADER_VERSION >= 235
+                            driver_priorities[vk::DriverId::eMesaNvk] = 2;
+#endif
+                            break;
+                    }
+
+                    if (driver_priorities.count(old_driver.driverID)) {
+                        old_priority = driver_priorities[old_driver.driverID];
+                    }
+                    if (driver_priorities.count(new_driver.driverID)) {
+                        new_priority = driver_priorities[new_driver.driverID];
+                    }
+
+                    if (new_priority < old_priority) {
+                        auto r = std::remove(vk_instance.device_indices.begin(), vk_instance.device_indices.end(), *old_device);
+                        vk_instance.device_indices.erase(r, vk_instance.device_indices.end());
+                        vk_instance.device_indices.push_back(i);
+
+                        VK_LOG_DEBUG("Prioritize device " << i << " driver " << new_driver.driverName << " over device " << *old_device << " driver " << old_driver.driverName);
+                    }
+                    else {
+                        VK_LOG_DEBUG("Prioritize device " << *old_device << " driver " << old_driver.driverName << " over device " << i << " driver " << new_driver.driverName << std::endl);
+                    }
+                }
+            }
+        }
+
+        // If no dedicated GPUs found, fall back to GPU 0
+        if (vk_instance.device_indices.empty()) {
+            vk_instance.device_indices.push_back(0);
+        }
+    }
+
+    std::cerr << "ggml_vulkan: Found " << vk_instance.device_indices.size() << " Vulkan devices:" << std::endl;
+
+    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
+        ggml_vk_print_gpu_info(i);
+    }
+}
+
+static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
+    VK_LOG_DEBUG("ggml_vk_init(" << ctx->name << ", " << idx << ")");
+    ggml_vk_instance_init();
+    GGML_ASSERT(idx < vk_instance.device_indices.size());
+
+    ctx->name = GGML_VK_NAME + std::to_string(idx);
+
+    ctx->device = ggml_vk_get_device(idx);
+
+    ctx->semaphore_idx = 0;
+    ctx->event_idx = 0;
+
+    ctx->prealloc_size_x = 0;
+    ctx->prealloc_size_y = 0;
+    ctx->prealloc_size_split_k = 0;
+
+    ctx->fence = ctx->device->device.createFence({});
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+    const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS");
+    vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks));
+    const char* output_tensor = getenv("GGML_VULKAN_OUTPUT_TENSOR");
+    vk_output_tensor = (output_tensor == NULL ? 0 : atoi(output_tensor));
+#endif
+}
+
+static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) {
+    VK_LOG_DEBUG("ggml_vk_get_to_fp16()");
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant[type];
+}
+
+static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
+    VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_pipeline(" << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");
+    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_f32;
+    }
+    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
+        return ctx->device->pipeline_matmul_f32_f16;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_f16_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+        return ctx->device->pipeline_matmul_f16;
+    }
+
+    if (src1_type != GGML_TYPE_F32) {
+        return nullptr;
+    }
+
+    switch (src0_type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant_mul_mat_mat[src0_type];
+}
+
+static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) {
+    VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()");
+    GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16);
+
+    switch (a_type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type];
+}
+
+static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
+    VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_id_pipeline()");
+    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_id_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_id_f16_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+        return ctx->device->pipeline_matmul_id_f16;
+    }
+
+    GGML_ASSERT(src1_type == GGML_TYPE_F32);
+
+    switch (src0_type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type];
+}
+
+static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) {
+    VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()");
+    GGML_ASSERT(b_type == GGML_TYPE_F32);
+
+    switch (a_type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant_mul_mat_vec_id_f32[a_type];
+}
+
+static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) {
+    VK_LOG_DEBUG("ggml_vk_pool_malloc(" << size << ")");
+    VK_LOG_MEMORY("ggml_vk_pool_malloc");
+
+    int best_i = -1;
+    size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
+    int worst_i = -1;
+    size_t worst_size = 0; //largest unused buffer seen so far
+    for (int i = 0; i < MAX_VK_BUFFERS; ++i) {
+        vk_buffer &b = ctx->buffer_pool[i];
+        if (b != nullptr && b->size >= size && b->size < best_size) {
+            best_i = i;
+            best_size = b->size;
+        }
+        if (b != nullptr && b->size > worst_size) {
+            worst_i = i;
+            worst_size = b->size;
+        }
+    }
+    if(best_i != -1) {
+        //found the smallest buffer that fits our needs
+        vk_buffer b = ctx->buffer_pool[best_i];
+        ctx->buffer_pool[best_i].reset();
+        return b;
+    }
+    if(worst_i != -1) {
+        //no buffer that fits our needs, resize largest one to save memory
+        vk_buffer& b = ctx->buffer_pool[worst_i];
+        ggml_vk_destroy_buffer(b);
+    }
+
+    return ggml_vk_create_buffer_device(ctx->device, size);
+}
+
+static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) {
+    VK_LOG_DEBUG("ggml_vk_pool_free(" << buffer->size << ")");
+    for (int i = 0; i < MAX_VK_BUFFERS; ++i) {
+        vk_buffer& b = ctx->buffer_pool[i];
+        if (b == nullptr) {
+            b = buffer;
+            return;
+        }
+    }
+    std::cerr << "ggml_vulkan: WARNING: vk buffer pool full, increase MAX_VK_BUFFERS" << std::endl;
+    ggml_vk_destroy_buffer(buffer);
+}
+
+// Returns an available temporary buffer that may only be used temporarily, it will be reused
+static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_t size) {
+    // Try to find existing temp buffer with enough capacity
+    for (auto& buffer : ctx->gc.temp_buffers) {
+        if (buffer->size >= size) {
+            return buffer;
+        }
+    }
+
+    VK_LOG_MEMORY("ggml_vk_create_buffer_temp(" << size << ")");
+
+    // Otherwise create new buffer
+    vk_buffer buf = ggml_vk_pool_malloc(ctx, size);
+    ctx->gc.temp_buffers.push_back(buf);
+
+    return buf;
+}
+
+static void * ggml_vk_host_malloc(vk_device& device, size_t size) {
+    VK_LOG_MEMORY("ggml_vk_host_malloc(" << size << ")");
+    vk_buffer buf = ggml_vk_create_buffer(device, size,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+
+    if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
+        fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
+            size/1024.0/1024.0);
+        device->device.freeMemory(buf->device_memory);
+        device->device.destroyBuffer(buf->buffer);
+        return nullptr;
+    }
+
+    device->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf));
+
+    return buf->ptr;
+}
+
+static void ggml_vk_host_free(vk_device& device, void* ptr) {
+    if (ptr == nullptr) {
+        return;
+    }
+    VK_LOG_MEMORY("ggml_vk_host_free(" << ptr << ")");
+    vk_buffer buf;
+    size_t index;
+    for (size_t i = 0; i < device->pinned_memory.size(); i++) {
+        const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]);
+        const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]);
+        if (ptr >= addr && ptr < endr) {
+            buf = std::get<2>(device->pinned_memory[i]);
+            index = i;
+            break;
+        }
+    }
+    if (buf == nullptr) {
+        fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n");
+        return;
+    }
+
+    ggml_vk_destroy_buffer(buf);
+
+    device->pinned_memory.erase(device->pinned_memory.begin() + index);
+}
+
+static void ggml_vk_host_get(vk_device& device, const void * ptr, vk_buffer& buf, size_t& buf_offset) {
+    buf = nullptr;
+    buf_offset = 0;
+    for (size_t i = 0; i < device->pinned_memory.size(); i++) {
+        const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]);
+        const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]);
+        if (ptr >= addr && ptr < endr) {
+            buf = std::get<2>(device->pinned_memory[i]);
+            buf_offset = ((const uint8_t *)ptr) - addr;
+            break;
+        }
+    }
+}
+
+static vk_submission ggml_vk_begin_submission(vk_device& device, vk_queue& q, bool one_time = true) {
+    vk_submission s;
+    s.buffer = ggml_vk_create_cmd_buffer(device, q);
+    if (one_time) {
+        s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit });
+    } else {
+        s.buffer.begin({ vk::CommandBufferUsageFlags{} });
+    }
+
+    return s;
+}
+
+
+
+static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context& subctx, vk_pipeline& pipeline, std::initializer_list<vk::DescriptorBufferInfo> const& descriptor_buffer_infos, size_t push_constant_size, const void* push_constants, std::array<uint32_t, 3> elements) {
+    const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]);
+    const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]);
+    const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]);
+    VK_LOG_DEBUG("ggml_vk_dispatch_pipeline(" << pipeline->name << ", {";
+    for (auto& buffer : descriptor_buffer_infos) {
+        std::cerr << "(" << buffer << ", " << buffer.offset << ", " << buffer.size << "), ";
+    }
+    std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))");
+    GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size());
+    GGML_ASSERT(descriptor_buffer_infos.size() == pipeline->parameter_count);
+
+    vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++];
+    vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() };
+    ctx->device->device.updateDescriptorSets({ write_descriptor_set }, {});
+
+    subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants);
+    subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline);
+    subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute,
+                                pipeline->layout,
+                                0,
+                                { descriptor_set },
+                                {});
+    subctx->s->buffer.dispatch(wg0, wg1, wg2);
+}
+
+static void ggml_vk_end_submission(vk_submission& s, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
+    s.buffer.end();
+
+    s.wait_semaphores = std::move(wait_semaphores);
+    s.signal_semaphores = std::move(signal_semaphores);
+}
+
+static void ggml_vk_ctx_end(vk_context& ctx) {
+    VK_LOG_DEBUG("ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")");
+    if (ctx->s == nullptr) {
+        return;
+    }
+
+    ctx->s->buffer.end();
+    ctx->s = nullptr;
+}
+
+static void ggml_vk_ctx_begin(vk_device& device, vk_context& subctx) {
+    VK_LOG_DEBUG("ggml_vk_ctx_begin(" << device->name << ")");
+    if (subctx->s != nullptr) {
+        ggml_vk_ctx_end(subctx);
+    }
+
+    subctx->seqs.push_back({ ggml_vk_begin_submission(device, *subctx->q) });
+    subctx->s = subctx->seqs[subctx->seqs.size() - 1].data();
+}
+
+static size_t ggml_vk_align_size(size_t width, size_t align) {
+    VK_LOG_DEBUG("ggml_vk_align_size(" << width << ", " << align << ")");
+    return CEIL_DIV(width, align) * align;
+}
+
+static void deferred_memcpy(void * dst, const void * src, size_t size, std::vector<vk_staging_memcpy>* memcpys = nullptr) {
+    if (memcpys == nullptr) {
+        memcpy(dst, src, size);
+    } else {
+        memcpys->emplace_back(dst, src, size);
+    }
+}
+
+static void ggml_vk_ensure_sync_staging_buffer(vk_device& device, size_t size) {
+    if (device->sync_staging == nullptr || device->sync_staging->size < size) {
+        VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")");
+        ggml_vk_destroy_buffer(device->sync_staging);
+        device->sync_staging = ggml_vk_create_buffer_check(device, size,
+            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+    }
+}
+
+static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context& subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) {
+    VK_LOG_DEBUG("ggml_vk_buffer_write_nc_async(" << tensor << ")");
+    GGML_ASSERT(!ggml_is_contiguous(tensor));
+    // Buffer is already mapped
+    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl;
+        GGML_ABORT("fatal error");
+    }
+    // Check if src is pinned memory
+    vk_buffer buf;
+    size_t buf_offset;
+    ggml_vk_host_get(ctx->device, tensor->data, buf, buf_offset);
+
+    const uint64_t ne0 = tensor->ne[0];
+    const uint64_t ne1 = tensor->ne[1];
+    const uint64_t ne2 = tensor->ne[2];
+    const uint64_t ne3 = tensor->ne[3];
+    const uint64_t nb0 = tensor->nb[0];
+    const uint64_t nb1 = tensor->nb[1];
+    const uint64_t nb2 = tensor->nb[2];
+    const uint64_t nb3 = tensor->nb[3];
+    const ggml_type type = tensor->type;
+    const uint64_t ts = ggml_type_size(type);
+    const uint64_t bs = ggml_blck_size(type);
+
+    const uint64_t dstnb0 = ts;
+    const uint64_t dstnb1 = dstnb0*(ne0/bs);
+    const uint64_t dstnb2 = dstnb1*ne1;
+    const uint64_t dstnb3 = dstnb2*ne2;
+
+    const uint64_t ne = ggml_nelements(tensor);
+
+    if (buf != nullptr) {
+        // Memory is pinned, use as staging buffer
+        std::vector<vk::BufferCopy> slices;
+
+        for (uint64_t i3 = 0; i3 < ne3; i3++) {
+            for (uint64_t i2 = 0; i2 < ne2; i2++) {
+                // Find longest contiguous slice
+                if (ne1*nb1 == dstnb2) {
+                    slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 });
+                } else {
+                    for (uint64_t i1 = 0; i1 < ne1; i1++) {
+                        if (ne0*nb0/bs == dstnb1) {
+                            slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 });
+                        } else {
+                            const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
+                            const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
+                            for (uint64_t i0 = 0; i0 < ne0; i0++) {
+                                slices.push_back({ s_off + i1*nb0, d_off + i0*dstnb0, dstnb0 });
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        ggml_vk_sync_buffers(subctx);
+        subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
+        return;
+    }
+
+    if (!sync_staging) {
+        GGML_ABORT("Asynchronous write to non-pinned memory not supported");
+    }
+
+    // Staging buffer required
+    vk_buffer& staging = ctx->device->sync_staging;
+    const uint64_t copy_size = ts*ne/bs;
+    ggml_vk_ensure_sync_staging_buffer(ctx->device, copy_size);
+    VkBufferCopy buf_copy{ 0, offset, copy_size };
+
+    ggml_vk_sync_buffers(subctx);
+    vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy);
+
+    for (uint64_t i3 = 0; i3 < ne3; i3++) {
+        for (uint64_t i2 = 0; i2 < ne2; i2++) {
+            // Find longest contiguous slice
+            if (ne1*nb1 == dstnb2) {
+                deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys);
+            } else {
+                for (uint64_t i1 = 0; i1 < ne1; i1++) {
+                    if (ne0*nb0/bs == dstnb1) {
+                        deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys);
+                    } else {
+                        const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
+                        const uint64_t d_off = i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
+                        for (uint64_t i0 = 0; i0 < ne0; i0++) {
+                            deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) {
+    VK_LOG_DEBUG("ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")");
+    // Buffer is already mapped
+    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl;
+        GGML_ABORT("fatal error");
+    }
+    // Check if src is pinned memory
+    vk_buffer buf = nullptr;
+    size_t buf_offset;
+    ggml_vk_host_get(dst->device, src, buf, buf_offset);
+
+    if (buf != nullptr) {
+        // Memory is pinned, use as staging buffer
+        std::vector<vk::BufferCopy> slices(1);
+        if (width == spitch) {
+            // Only do single write if stride is equal
+            slices[0].srcOffset = buf_offset;
+            slices[0].dstOffset = offset;
+            slices[0].size = width * height;
+        } else {
+            slices.resize(height);
+            for (size_t i = 0; i < height; i++) {
+                slices[i].srcOffset = buf_offset + i * spitch;
+                slices[i].dstOffset = offset + i * width;
+                slices[i].size = width;
+            }
+        }
+
+        ggml_vk_sync_buffers(subctx);
+        subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
+        return;
+    }
+    VK_LOG_DEBUG("STAGING");
+
+    if (!sync_staging) {
+        GGML_ABORT("Asynchronous write to non-pinned memory not supported");
+    }
+
+    // Staging buffer required
+    const size_t copy_size = width*height;
+    ggml_vk_ensure_sync_staging_buffer(dst->device, copy_size);
+
+    vk_buffer& staging_buffer = dst->device->sync_staging;
+
+    VkBufferCopy buf_copy = {
+        0,
+        offset,
+        copy_size};
+
+    ggml_vk_sync_buffers(subctx);
+    vkCmdCopyBuffer(subctx->s->buffer, staging_buffer->buffer, dst->buffer, 1, &buf_copy);
+
+    if (width == spitch) {
+        deferred_memcpy((uint8_t *)staging_buffer->ptr, src, width * height, &subctx->in_memcpys);
+    } else {
+        for (size_t i = 0; i < height; i++) {
+            deferred_memcpy((uint8_t *)staging_buffer->ptr + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys);
+        }
+    }
+}
+
+static void ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) {
+    VK_LOG_DEBUG("ggml_vk_buffer_write_async(" << size << ")");
+    return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, 1, sync_staging);
+}
+
+static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) {
+    VK_LOG_DEBUG("ggml_vk_buffer_write_2d(" << width << ", " << height << ")");
+    // Buffer is already mapped
+    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
+
+        for (size_t i = 0; i < height; i++) {
+            memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width);
+        }
+    } else {
+        vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue);
+        ggml_vk_ctx_begin(dst->device, subctx);
+        ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, width, height, true);
+        ggml_vk_ctx_end(subctx);
+
+        for (auto& cpy : subctx->in_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+
+        ggml_vk_submit(subctx, dst->device->fence);
+        VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
+        dst->device->device.resetFences({ dst->device->fence });
+    }
+}
+
+static void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src, size_t size) {
+    VK_LOG_DEBUG("ggml_vk_buffer_write(" << size << ")");
+    ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1);
+}
+
+static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
+    VK_LOG_DEBUG("ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")");
+    GGML_ASSERT(width > 0);
+    GGML_ASSERT(height > 0);
+    GGML_ASSERT(src != nullptr);
+
+    // TODO: staging_offset is not used
+
+    // Check if dst is pinned memory
+    vk_buffer buf = nullptr;
+    size_t buf_offset;
+    ggml_vk_host_get(src->device, dst, buf, buf_offset);
+
+    std::vector<vk::BufferCopy> slices(1);
+    if (width == spitch && width == dpitch) {
+        // Only do single write if stride is equal
+        slices[0].srcOffset = offset;
+        slices[0].dstOffset = buf_offset;
+        slices[0].size = width * height;
+    } else {
+        slices.resize(height);
+        for (size_t i = 0; i < height; i++) {
+            slices[i].srcOffset = offset + i * spitch;
+            slices[i].dstOffset = buf_offset + i * dpitch;
+            slices[i].size = width;
+        }
+    }
+
+    if (buf != nullptr) {
+        // Memory is pinned, use as staging buffer
+        ggml_vk_sync_buffers(subctx);
+        subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices);
+
+        return;
+    }
+    VK_LOG_DEBUG("STAGING");
+
+    if (!sync_staging) {
+        GGML_ABORT("Asynchronous read from non-pinned memory not supported");
+    }
+
+    // Fall back to staging buffer
+    const size_t copy_size = dpitch * height;
+    ggml_vk_ensure_sync_staging_buffer(src->device, copy_size);
+
+    vk_buffer& staging_buffer = src->device->sync_staging;
+
+    ggml_vk_sync_buffers(subctx);
+    subctx->s->buffer.copyBuffer(src->buffer, staging_buffer->buffer, slices);
+
+    deferred_memcpy(dst, staging_buffer->ptr, copy_size, &subctx->out_memcpys);
+}
+
+static void ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) {
+    return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, sync_staging);
+}
+
+static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) {
+    VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")");
+    if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
+
+        memcpy(dst, (uint8_t *) src->ptr + offset, size);
+    } else {
+        vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue);
+        ggml_vk_ctx_begin(src->device, subctx);
+        ggml_vk_buffer_read_async(subctx, src, offset, dst, size, true);
+        ggml_vk_ctx_end(subctx);
+
+        ggml_vk_submit(subctx, src->device->fence);
+        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences");
+        src->device->device.resetFences({ src->device->fence });
+
+        for (auto& cpy : subctx->out_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+    }
+}
+
+static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
+    VK_LOG_DEBUG("ggml_vk_buffer_copy_async(" << size << ")");
+    // Make sure both buffers are on same device
+    GGML_ASSERT(src->device == dst->device);
+
+    VkBufferCopy bc{ src_offset, dst_offset, size };
+
+    vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc);
+}
+
+static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
+    if (src->device == dst->device) {
+        VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")");
+        // Copy within the device
+        vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue);
+        ggml_vk_ctx_begin(src->device, subctx);
+        ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
+        ggml_vk_ctx_end(subctx);
+        ggml_vk_submit(subctx, src->device->fence);
+        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
+        src->device->device.resetFences({ src->device->fence });
+    } else {
+        VK_LOG_DEBUG("ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")");
+        // Copy device to device
+        ggml_vk_ensure_sync_staging_buffer(src->device, size);
+        ggml_vk_ensure_sync_staging_buffer(dst->device, size);
+
+        // Copy to src staging buffer
+        ggml_vk_buffer_copy(src->device->sync_staging, 0, src, src_offset, size);
+        // memcpy to dst staging buffer
+        memcpy(dst->device->sync_staging->ptr, src->device->sync_staging->ptr, size);
+        // Copy to dst buffer
+        ggml_vk_buffer_copy(dst, dst_offset, dst->device->sync_staging, 0, size);
+    }
+}
+
+static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
+    VK_LOG_DEBUG("ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")");
+
+    vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue);
+    ggml_vk_ctx_begin(dst->device, subctx);
+    subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c);
+    ggml_vk_ctx_end(subctx);
+
+    ggml_vk_submit(subctx, dst->device->fence);
+    VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_memset waitForFences");
+    dst->device->device.resetFences({ dst->device->fence });
+}
+
+static uint32_t ggml_vk_guess_split_k(int m, int n, int k) {
+    VK_LOG_DEBUG("ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")");
+    // if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) {
+    //     return 4;
+    // }
+
+    return 1;
+
+    GGML_UNUSED(m); GGML_UNUSED(n); GGML_UNUSED(k);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
+    if (m <= 32 || n <= 32) {
+        return aligned ? mmp->a_s : mmp->s;
+    }
+    return aligned ? mmp->a_m : mmp->m;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
+    return aligned ? mmp->a_m : mmp->m;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
+    return aligned ? mmp->a_s : mmp->s;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
+    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")");
+    switch (ctx->device->vendor_id) {
+    case VK_VENDOR_ID_AMD:
+        return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned);
+    case VK_VENDOR_ID_APPLE:
+        return ggml_vk_guess_matmul_pipeline_apple(ctx, mmp, aligned);
+    case VK_VENDOR_ID_INTEL:
+        return ggml_vk_guess_matmul_pipeline_intel(ctx, mmp, aligned);
+    default:
+        break;
+    }
+
+    if (m <= 32 || n <= 32) {
+        return aligned ? mmp->a_s : mmp->s;
+    }
+    if (m <= 64 || n <= 64) {
+        return aligned ? mmp->a_m : mmp->m;
+    }
+    return aligned ? mmp->a_l : mmp->l;
+}
+
+static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) {
+    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")");
+    return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true)->align;
+}
+
+static void ggml_vk_matmul(
+        ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline,
+        vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer,
+        uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d,
+        uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d,
+        uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3) {
+        VK_LOG_DEBUG("ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ")");
+    ggml_vk_sync_buffers(subctx);
+    if (split_k == 1) {
+        const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, k, ne02, ne12, broadcast2, broadcast3 };
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch });
+        return;
+    }
+
+    GGML_ASSERT(batch_stride_d == m * n);
+
+    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3 };
+    // Make sure enough workgroups get assigned for split k to work
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
+    ggml_vk_sync_buffers(subctx);
+    const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
+}
+
+static void ggml_vk_matmul_id(
+        ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline,
+        vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& ids,
+        uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d,
+        uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d,
+        uint32_t n_as, uint32_t nei0, uint32_t nei1, uint32_t nbi1, uint32_t ne11) {
+    VK_LOG_DEBUG("ggml_vk_matmul_id(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), ids: (" << ids.buffer->buffer << ", " << ids.offset << ", " << ids.size << "), " <<
+        "m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", " <<
+        "batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", " <<
+        "n_as: " << n_as << ", nei0: " << nei0 << ", nei1: " << nei1 << ", nbi1: " << nbi1 << ", ne11: " << ne11 << ")");
+    ggml_vk_sync_buffers(subctx);
+    const vk_mat_mat_id_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d,
+                                              nei0, nei1, nbi1, ne11 };
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d, ids }, sizeof(vk_mat_mat_id_push_constants), &pc, { m, nei1, n_as });
+}
+
+static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) {
+    return
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
+        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
+        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+}
+
+static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) {
+    if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) {
+        return ctx->device->pipeline_cpy_f32_f32;
+    }
+    if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) {
+        return ctx->device->pipeline_cpy_f32_f16;
+    }
+    if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) {
+        return ctx->device->pipeline_cpy_f16_f16;
+    }
+
+    std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl;
+    GGML_ABORT("fatal error");
+}
+
+static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) {
+    VK_LOG_DEBUG("ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
+    std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")");
+    const int tensor_type_size = ggml_type_size(tensor->type);
+
+    const uint32_t ne = ggml_nelements(tensor);
+
+    const vk_op_unary_push_constants pc = {
+        (uint32_t)ne,
+        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
+        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3],                       1                   , (uint32_t)tensor->ne[0]                   , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]),
+        0,
+        0.0f, 0.0f,
+    };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 });
+}
+
+static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
+    std::cerr << "), " << (dryrun ? "dryrun" : "") << ")");
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne13 = src1->ne[3];
+
+    const uint64_t ne20 = dst->ne[0];
+    const uint64_t ne21 = dst->ne[1];
+
+    const uint64_t r2 = ne12 / ne02;
+    const uint64_t r3 = ne13 / ne03;
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qx;
+    size_t qx_buf_offset = 0;
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset);
+        ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset);
+        src0_uma = d_Qx != nullptr;
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+
+    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
+
+    vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
+
+    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
+
+    if (mmp == nullptr) {
+        // Fall back to dequant + f16 mulmat
+        mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16);
+    }
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
+    const int x_ne = ne01 * ne00;
+    const int y_ne = ne11 * ne10;
+    const int d_ne = ne11 * ne01;
+
+    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11));
+    const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8;
+
+    const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
+
+    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned);
+
+    const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
+    const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
+
+    if (x_non_contig) {
+        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16);
+    } else {
+        to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
+    }
+    if (y_non_contig) {
+        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16);
+    } else {
+        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
+    }
+    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
+    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
+
+    if (dryrun) {
+        const uint64_t x_sz_upd = x_sz * ne02 * ne03;
+        const uint64_t y_sz_upd = y_sz * ne12 * ne13;
+        const uint64_t split_k_size = split_k > 1 ? d_sz * ne12 * ne13 * 4 : 0;
+        if (
+                (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) ||
+                (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size) ||
+                (split_k > 1 && split_k_size > ctx->device->max_memory_allocation_size)) {
+            GGML_ABORT("Requested preallocation size is too large");
+        }
+        if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) {
+            ctx->prealloc_size_x = x_sz_upd;
+        }
+        if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) {
+            ctx->prealloc_size_y = y_sz_upd;
+        }
+        if (split_k > 1 && ctx->prealloc_size_split_k < split_k_size) {
+            ctx->prealloc_size_split_k = split_k_size;
+        }
+
+        // Request descriptor sets
+        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        if (qx_needs_dequant) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1);
+        }
+        if (qy_needs_dequant) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1);
+        }
+        if (split_k > 1) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, 1);
+        }
+        return;
+    }
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset + dst->view_offs;
+    GGML_ASSERT(d_D != nullptr);
+    GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03);
+    vk_buffer d_X;
+    uint64_t x_buf_offset = 0;
+    vk_buffer d_Y;
+    uint64_t y_buf_offset = 0;
+    if (!src0_uma) {
+        d_Qx = extra_src0->buffer_gpu.lock();
+        qx_buf_offset = extra_src0->offset + src0->view_offs;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+    if (!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset + src1->view_offs;
+        GGML_ASSERT(d_Qy != nullptr);
+    }
+    if (qx_needs_dequant) {
+        d_X = ctx->prealloc_x;
+        GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03);
+    } else {
+        d_X = d_Qx;
+        x_buf_offset = qx_buf_offset;
+        GGML_ASSERT(qx_sz == x_sz);
+    }
+    if (qy_needs_dequant) {
+        d_Y = ctx->prealloc_y;
+        GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03);
+    } else {
+        d_Y = d_Qy;
+        y_buf_offset = qy_buf_offset;
+        GGML_ASSERT(qy_sz == y_sz);
+    }
+
+    if (x_non_contig) {
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    } else if (qx_needs_dequant) {
+        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
+    }
+    if (y_non_contig) {
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+    }
+
+    uint32_t stride_batch_x = ne00*ne01;
+    uint32_t stride_batch_y = ne10*ne11;
+
+    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
+        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
+    }
+
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
+        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
+    }
+
+    // compute
+    ggml_vk_matmul(
+        ctx, subctx, pipeline,
+        { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 },
+        { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k },
+        ne01, ne11, ne10,
+        ne10, ne10, ne01, stride_batch_x, stride_batch_y, ne20*ne21,
+        split_k, ne12*ne13, ne02, ne12, r2, r3
+    );  // NOLINT
+}
+
+static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
+    std::cerr << "), " << (dryrun ? "dryrun" : "") << "),)");
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne13 = src1->ne[3];
+
+    GGML_ASSERT(ne11 == 1);
+
+    const uint64_t ne20 = dst->ne[0];
+    const uint64_t ne21 = dst->ne[1];
+    const uint64_t ne22 = dst->ne[2];
+    const uint64_t ne23 = dst->ne[3];
+
+    const uint64_t r2 = ne12 / ne02;
+    const uint64_t r3 = ne13 / ne03;
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qx;
+    size_t qx_buf_offset = 0;
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset);
+        ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset);
+        src0_uma = d_Qx != nullptr;
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+
+    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
+
+    const bool qx_needs_dequant = x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
+    const uint64_t x_ne = ne01 * ne00;
+    const uint64_t y_ne = ne11 * ne10;
+    const uint64_t d_ne = ne11 * ne01;
+
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
+    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
+    if (x_non_contig) {
+        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type);
+    }
+    if (y_non_contig) {
+        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type);
+    } else {
+        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
+    }
+    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type);
+    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
+    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
+    GGML_ASSERT(dmmv != nullptr);
+
+    if (dryrun) {
+        const uint64_t x_sz_upd = x_sz * ne02 * ne03;
+        const uint64_t y_sz_upd = y_sz * ne12 * ne13;
+        if (
+                (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) ||
+                (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) {
+            GGML_ABORT("Requested preallocation size is too large");
+        }
+        if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) {
+            ctx->prealloc_size_x = x_sz_upd;
+        }
+        if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) {
+            ctx->prealloc_size_y = y_sz_upd;
+        }
+
+        // Request descriptor sets
+        if (qx_needs_dequant) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1);
+        }
+        if (qy_needs_dequant) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1);
+        }
+        ggml_pipeline_request_descriptor_sets(ctx->device, dmmv, 1);
+        return;
+    }
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset + dst->view_offs;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_X;
+    uint64_t x_buf_offset = 0;
+    vk_buffer d_Y;
+    uint64_t y_buf_offset = 0;
+    if(!src0_uma) {
+        d_Qx = extra_src0->buffer_gpu.lock();
+        qx_buf_offset = extra_src0->offset + src0->view_offs;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+    if(!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset + src1->view_offs;
+        GGML_ASSERT(d_Qy != nullptr);
+    }
+    if (qx_needs_dequant) {
+        d_X = ctx->prealloc_x;
+    } else {
+        d_X = d_Qx;
+        x_buf_offset = qx_buf_offset;
+        GGML_ASSERT(qx_sz == x_sz);
+    }
+    if (qy_needs_dequant) {
+        d_Y = ctx->prealloc_y;
+    } else {
+        d_Y = d_Qy;
+        y_buf_offset = qy_buf_offset;
+        GGML_ASSERT(qy_sz == y_sz);
+    }
+
+    if (x_non_contig) {
+        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    }
+    if (y_non_contig) {
+        GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+    }
+
+    uint32_t stride_batch_x = ne00*ne01;
+    uint32_t stride_batch_y = ne10*ne11;
+
+    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
+        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
+    }
+
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
+        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
+    }
+
+    const uint32_t max_groups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0];
+
+    uint32_t groups_x = ne01;
+    uint32_t groups_z = 1;
+
+    if (ne01 > max_groups_x) {
+        groups_z = 64;
+        groups_x /= groups_z;
+    }
+
+    // compute
+    const vk_mat_vec_push_constants pc = {
+        (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
+        stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21),
+        (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
+    };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
+                              { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23} },
+                              sizeof(vk_mat_vec_push_constants), &pc, { groups_x, (uint32_t)(ne12 * ne13), groups_z });
+}
+
+static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_mul_mat_p021_f16_f32(" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
+    std::cerr << "), " << (dryrun ? "dryrun" : "") << ")");
+    GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
+    GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]);  // NOLINT
+    GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]);  // NOLINT
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    // const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    // const uint64_t ne13 = src1->ne[3];
+
+    GGML_ASSERT(ne11 == 1);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset);
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const uint64_t x_ne = ne00 * ne01 * ne02;
+    const uint64_t y_ne = ne10 * ne11 * ne12;
+    const uint64_t d_ne = ne01 * ne11 * ne12;
+
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    if (dryrun) {
+        // Request descriptor sets
+        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1);
+        return;
+    }
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset + dst->view_offs;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
+    const uint64_t qx_buf_offset = extra_src0->offset + src0->view_offs;
+    GGML_ASSERT(d_Qx != nullptr);
+    if (!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset + src1->view_offs;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+
+    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
+
+    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
+
+    // compute
+    const std::array<uint32_t, 6> pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
+}
+
+static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
+    std::cerr << "), " << (dryrun ? "dryrun" : "") << ")");
+    GGML_ASSERT(!ggml_is_transposed(src0));
+    GGML_ASSERT(!ggml_is_transposed(src1));
+    GGML_ASSERT(!ggml_is_permuted(src0));
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    // const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t nb01 = src0->nb[1];
+    const uint64_t nb02 = src0->nb[2];
+
+    // const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    // const uint64_t ne13 = src1->ne[3];
+
+    GGML_ASSERT(ne11 == 1);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qy = nullptr;
+    size_t qy_buf_offset = 0;
+
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset);
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const uint64_t d_ne = ne01 * ne11 * ne12;
+
+    const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t);
+    const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t);
+
+    const uint64_t qx_sz = ggml_nbytes(src0);
+    const uint64_t qy_sz = ggml_nbytes(src1);
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    if (dryrun) {
+        // Request descriptor sets
+        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1);
+        return;
+    }
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset + dst->view_offs;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
+    const uint64_t qx_buf_offset = extra_src0->offset + src0->view_offs;
+    GGML_ASSERT(d_Qx != nullptr);
+    if (!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset + src1->view_offs;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+
+    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
+
+    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
+
+    // compute
+    const std::array<uint32_t, 7> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32,
+        { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
+}
+
+static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")");
+    if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1) {
+        ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst, dryrun);
+    } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1) {
+        ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst, dryrun);
+    } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
+        ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun);
+    } else {
+        ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, dryrun);
+    }
+}
+
+static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)");
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
+    GGML_ASSERT(ids->type == GGML_TYPE_I32);
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne13 = src1->ne[3];
+
+    const uint64_t nei0 = ids->ne[0];
+    const uint64_t nei1 = ids->ne[1];
+    GGML_ASSERT(nei0 * nei1 <= 3072);
+
+    const uint32_t nbi1 = ids->nb[1];
+    const uint32_t nbi2 = ids->nb[2];
+
+    const uint64_t ne20 = dst->ne[0];
+    const uint64_t ne21 = dst->ne[1];
+    const uint64_t ne22 = dst->ne[2];
+    const uint64_t ne23 = dst->ne[3];
+
+    const uint64_t n_as = ne02;
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+    ggml_tensor_extra_gpu * extra_ids = (ggml_tensor_extra_gpu *) ids->extra;
+
+    vk_buffer d_Qx;
+    size_t qx_buf_offset = 0;
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+    vk_buffer d_ids;
+    size_t ids_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+    bool ids_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset);
+        ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset);
+        ggml_vk_host_get(ctx->device, ids->data, d_ids, ids_buf_offset);
+        src0_uma = d_Qx != nullptr;
+        src1_uma = d_Qy != nullptr;
+        ids_uma = d_ids != nullptr;
+    }
+
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+
+    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
+
+    vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
+
+    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
+
+    if (mmp == nullptr) {
+        GGML_ABORT("fatal error");
+    }
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
+    const uint64_t x_ne = ne01 * ne00;
+    const uint64_t y_ne = ne11 * ne10;
+    const uint64_t d_ne = ne21 * ne20;
+
+    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, nei1));
+    const bool aligned = ne10 == kpad && ne01 > 8 && nei1 > 8;
+
+    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, nei1, aligned);
+
+    const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
+    const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t ids_sz = nbi2;
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
+
+    if (x_non_contig) {
+        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16);
+    } else {
+        to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
+    }
+    if (y_non_contig) {
+        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16);
+    } else {
+        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
+    }
+    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
+    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
+
+    if (dryrun) {
+        const uint64_t x_sz_upd = x_sz * ne02 * ne03;
+        const uint64_t y_sz_upd = y_sz * ne12 * ne13;
+        if (
+                (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) ||
+                (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) {
+            GGML_ABORT("Requested preallocation size is too large");
+        }
+        if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) {
+            ctx->prealloc_size_x = x_sz_upd;
+        }
+        if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) {
+            ctx->prealloc_size_y = y_sz_upd;
+        }
+
+        // Request descriptor sets
+        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        if (qx_needs_dequant) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1);
+        }
+        if (qy_needs_dequant) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1);
+        }
+        return;
+    }
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset + dst->view_offs;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_X;
+    uint64_t x_buf_offset = 0;
+    vk_buffer d_Y;
+    uint64_t y_buf_offset = 0;
+    if (!src0_uma) {
+        d_Qx = extra_src0->buffer_gpu.lock();
+        qx_buf_offset = extra_src0->offset + src0->view_offs;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+    if (!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset + src1->view_offs;
+        GGML_ASSERT(d_Qy != nullptr);
+    }
+    if (!ids_uma) {
+        d_ids = extra_ids->buffer_gpu.lock();
+        ids_buf_offset = extra_ids->offset + ids->view_offs;
+        GGML_ASSERT(d_ids != nullptr);
+    }
+    if (qx_needs_dequant) {
+        d_X = ctx->prealloc_x;
+        GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03);
+    } else {
+        d_X = d_Qx;
+        x_buf_offset = qx_buf_offset;
+        GGML_ASSERT(qx_sz == x_sz);
+    }
+    if (qy_needs_dequant) {
+        d_Y = ctx->prealloc_y;
+        GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03);
+    } else {
+        d_Y = d_Qy;
+        y_buf_offset = qy_buf_offset;
+        GGML_ASSERT(qy_sz == y_sz);
+    }
+
+    if (x_non_contig) {
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    } else if (qx_needs_dequant) {
+        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0,
+            { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
+    }
+    if (y_non_contig) {
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+    }
+
+    uint32_t stride_batch_x = ne00*ne01;
+    uint32_t stride_batch_y = ne10*ne11;
+
+    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
+        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
+    }
+
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
+        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
+    }
+
+    // compute
+    ggml_vk_matmul_id(
+        ctx, subctx, pipeline,
+        { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 },
+        { d_D, d_buf_offset, d_sz * ne22 * ne23 }, { d_ids, ids_buf_offset, ids_sz },
+        ne01, ne21, ne10, ne10, ne10, ne01,
+        stride_batch_x, stride_batch_y, ne20*ne21,
+        n_as, nei0, nei1, nbi1 / ggml_type_size(ids->type), ne11
+    );  // NOLINT
+}
+
+static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
+    std::cerr << "), " << (dryrun ? "dryrun" : "") << ")");
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
+    GGML_ASSERT(ids->type == GGML_TYPE_I32);
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne13 = src1->ne[3];
+
+    const uint64_t nei0 = ids->ne[0];
+    const uint64_t nei1 = ids->ne[1];
+
+    const uint64_t nbi2 = ids->nb[2];
+
+    GGML_ASSERT(nei1 == 1);
+
+    const uint64_t ne20 = dst->ne[0];
+    const uint64_t ne21 = dst->ne[1];
+    const uint64_t ne22 = dst->ne[2];
+    const uint64_t ne23 = dst->ne[3];
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+    ggml_tensor_extra_gpu * extra_ids = (ggml_tensor_extra_gpu *) ids->extra;
+
+    vk_buffer d_Qx;
+    size_t qx_buf_offset = 0;
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+    vk_buffer d_ids;
+    size_t ids_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+    bool ids_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset);
+        ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset);
+        ggml_vk_host_get(ctx->device, ids->data, d_ids, ids_buf_offset);
+        src0_uma = d_Qx != nullptr;
+        src1_uma = d_Qy != nullptr;
+        ids_uma = d_ids != nullptr;
+    }
+
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+
+    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
+
+    const bool qx_needs_dequant = x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
+    const uint64_t x_ne = ne01 * ne00;
+    const uint64_t y_ne = ne11 * ne10;
+    const uint64_t d_ne = ne21 * ne20;
+
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
+    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t ids_sz = nbi2;
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
+    if (x_non_contig) {
+        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type);
+    }
+    if (y_non_contig) {
+        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type);
+    } else {
+        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
+    }
+    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type);
+    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
+    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
+    GGML_ASSERT(dmmv != nullptr);
+
+    if (dryrun) {
+        const uint64_t x_sz_upd = x_sz * ne02 * ne03;
+        const uint64_t y_sz_upd = y_sz * ne12 * ne13;
+        if (
+                (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) ||
+                (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) {
+            GGML_ABORT("Requested preallocation size is too large");
+        }
+        if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) {
+            ctx->prealloc_size_x = x_sz_upd;
+        }
+        if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) {
+            ctx->prealloc_size_y = y_sz_upd;
+        }
+
+        // Request descriptor sets
+        if (qx_needs_dequant) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1);
+        }
+        if (qy_needs_dequant) {
+            ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1);
+        }
+        ggml_pipeline_request_descriptor_sets(ctx->device, dmmv, 1);
+        return;
+    }
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset + dst->view_offs;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_X;
+    uint64_t x_buf_offset = 0;
+    vk_buffer d_Y;
+    uint64_t y_buf_offset = 0;
+    if(!src0_uma) {
+        d_Qx = extra_src0->buffer_gpu.lock();
+        qx_buf_offset = extra_src0->offset + src0->view_offs;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+    if(!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset + src1->view_offs;
+        GGML_ASSERT(d_Qy != nullptr);
+    }
+    if(!ids_uma) {
+        d_ids = extra_ids->buffer_gpu.lock();
+        ids_buf_offset = extra_ids->offset + ids->view_offs;
+        GGML_ASSERT(d_ids != nullptr);
+    }
+    if (qx_needs_dequant) {
+        d_X = ctx->prealloc_x;
+    } else {
+        d_X = d_Qx;
+        x_buf_offset = qx_buf_offset;
+        GGML_ASSERT(qx_sz == x_sz);
+    }
+    if (qy_needs_dequant) {
+        d_Y = ctx->prealloc_y;
+    } else {
+        d_Y = d_Qy;
+        y_buf_offset = qy_buf_offset;
+        GGML_ASSERT(qy_sz == y_sz);
+    }
+
+    if (x_non_contig) {
+        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    }
+    if (y_non_contig) {
+        GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+    }
+
+    uint32_t stride_batch_y = ne10*ne11;
+
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
+        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
+    }
+
+    const uint32_t max_groups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0];
+
+    uint32_t groups_x = ne01;
+    uint32_t groups_z = 1;
+
+    if (ne01 > max_groups_x) {
+        groups_z = 64;
+        groups_x /= groups_z;
+    }
+
+    // compute
+    const vk_mat_vec_id_push_constants pc = {
+        (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
+        (uint32_t)x_ne, stride_batch_y, (uint32_t)(ne20*ne21),
+        (uint32_t)nei0, (uint32_t)ne11,
+    };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
+        { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 },
+        vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23}, vk_subbuffer{ d_ids, ids_buf_offset, ids_sz } },
+        sizeof(vk_mat_vec_id_push_constants), &pc, { groups_x, (uint32_t)nei0, groups_z });
+}
+
+static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_mul_mat_id(" << src0 << ", " << src1 << ", " << src2 << ", " << dst << ")");
+    if (src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
+        ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
+    } else {
+        ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
+    }
+}
+
+static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
+    switch (op) {
+    case GGML_OP_GET_ROWS:
+        GGML_ASSERT(src1->type == GGML_TYPE_I32);
+        if (dst->type == GGML_TYPE_F16) {
+            return ctx->device->pipeline_get_rows[src0->type];
+        }
+        if (dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_get_rows_f32[src0->type];
+        }
+        return nullptr;
+    case GGML_OP_ADD:
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_add_f32;
+        }
+        if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
+            return ctx->device->pipeline_add_f16_f32_f16;
+        }
+        return nullptr;
+    case GGML_OP_MUL:
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_mul_f32;
+        }
+        return nullptr;
+    case GGML_OP_DIV:
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_div_f32;
+        }
+        return nullptr;
+    case GGML_OP_CONCAT:
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_concat_f32;
+        }
+        if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+            return ctx->device->pipeline_concat_f16;
+        }
+        if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) {
+            return ctx->device->pipeline_concat_i32;
+        }
+        return nullptr;
+    case GGML_OP_UPSCALE:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_upscale_f32;
+        }
+        return nullptr;
+    case GGML_OP_SCALE:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_scale_f32;
+        }
+        return nullptr;
+    case GGML_OP_SQR:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_sqr_f32;
+        }
+        return nullptr;
+    case GGML_OP_CLAMP:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_clamp_f32;
+        }
+        return nullptr;
+    case GGML_OP_PAD:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_pad_f32;
+        }
+        return nullptr;
+    case GGML_OP_REPEAT:
+        if (ggml_type_size(src0->type) == sizeof(float) && ggml_type_size(dst->type) == sizeof(float)) {
+            return ctx->device->pipeline_repeat_f32;
+        }
+        return nullptr;
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+        return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type);
+    case GGML_OP_NORM:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_norm_f32;
+        }
+        return nullptr;
+    case GGML_OP_GROUP_NORM:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_group_norm_f32;
+        }
+        return nullptr;
+    case GGML_OP_RMS_NORM:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_rms_norm_f32;
+        }
+        return nullptr;
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(dst)) {
+            case GGML_UNARY_OP_SILU:
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_silu_f32;
+                }
+                break;
+            case GGML_UNARY_OP_GELU:
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_gelu_f32;
+                }
+                break;
+            case GGML_UNARY_OP_GELU_QUICK:
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_gelu_quick_f32;
+                }
+                break;
+            case GGML_UNARY_OP_RELU:
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_relu_f32;
+                }
+                break;
+            case GGML_UNARY_OP_TANH:
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_tanh_f32;
+                }
+                break;
+            default:
+                break;
+        }
+        return nullptr;
+    case GGML_OP_DIAG_MASK_INF:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_diag_mask_inf_f32;
+        }
+        return nullptr;
+    case GGML_OP_SOFT_MAX:
+        GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
+
+        if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_soft_max_f32;
+        }
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_soft_max_f32_f16;
+        }
+        return nullptr;
+    case GGML_OP_ROPE:
+        {
+            const int mode = ((const int32_t *) dst->op_params)[2];
+            const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+
+            if (is_neox) {
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_rope_neox_f32;
+                }
+                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+                    return ctx->device->pipeline_rope_neox_f16;
+                }
+            } else {
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_rope_norm_f32;
+                }
+                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+                    return ctx->device->pipeline_rope_norm_f16;
+                }
+            }
+            return nullptr;
+        }
+    case GGML_OP_ARGSORT:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
+            return ctx->device->pipeline_argsort_f32;
+        }
+        return nullptr;
+    case GGML_OP_SUM_ROWS:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_sum_rows_f32;
+        }
+        return nullptr;
+    case GGML_OP_IM2COL:
+        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_im2col_f32;
+        }
+        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
+            return ctx->device->pipeline_im2col_f32_f16;
+        }
+        return nullptr;
+    case GGML_OP_TIMESTEP_EMBEDDING:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_timestep_embedding_f32;
+        }
+        return nullptr;
+    case GGML_OP_LEAKY_RELU:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_leaky_relu_f32;
+        }
+        return nullptr;
+    default:
+        return nullptr;
+    }
+
+    GGML_UNUSED(src2);
+}
+
+static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
+    switch (op) {
+    case GGML_OP_CPY:
+    case GGML_OP_GET_ROWS:
+    case GGML_OP_ADD:
+    case GGML_OP_MUL:
+    case GGML_OP_DIV:
+    case GGML_OP_CONCAT:
+    case GGML_OP_UPSCALE:
+    case GGML_OP_SCALE:
+    case GGML_OP_SQR:
+    case GGML_OP_CLAMP:
+    case GGML_OP_PAD:
+    case GGML_OP_REPEAT:
+        return true;
+    default:
+        return false;
+    }
+}
+
+template<typename PC>
+static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc, bool dryrun = false) {
+    VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    if (src1 != nullptr) {
+        std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    }
+    if (src2 != nullptr) {
+        std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3];
+    }
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
+    std::cerr << "), " << ggml_op_name(op) << ", " << (dryrun ? "dryrun" : "") << ")");
+    GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
+    GGML_ASSERT(ggml_vk_op_supports_incontiguous(op) || ggml_vk_dim01_contiguous(src0));  // NOLINT
+    GGML_ASSERT(dst->extra != nullptr);
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+    const uint64_t ne0 = ne00 * ne01;
+
+    const bool use_src1 = src1 != nullptr;
+    const uint64_t ne10 = use_src1 ? src1->ne[0] : 0;
+    const uint64_t ne11 = use_src1 ? src1->ne[1] : 0;
+    const uint64_t ne12 = use_src1 ? src1->ne[2] : 0;
+    const uint64_t ne13 = use_src1 ? src1->ne[3] : 0;
+    const uint64_t ne1 = ne10 * ne11;
+    // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0;
+
+    const bool use_src2 = src2 != nullptr;
+    const uint64_t ne20 = use_src2 ? src2->ne[0] : 0;
+    const uint64_t ne21 = use_src2 ? src2->ne[1] : 0;
+    const uint64_t ne22 = use_src2 ? src2->ne[2] : 0;
+    const uint64_t ne23 = use_src2 ? src2->ne[3] : 0;
+    const uint64_t ne2 = ne20 * ne21;
+
+    const uint64_t ned0 = dst->ne[0];
+    const uint64_t ned1 = dst->ne[1];
+    const uint64_t ned2 = dst->ne[2];
+    const uint64_t ned3 = dst->ne[3];
+    const uint64_t ned = ned0 * ned1;
+
+    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op);
+
+    if (pipeline == nullptr) {
+        std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type);
+        if (src1 != nullptr) {
+            std::cerr << " and " << ggml_type_name(src1->type);
+        }
+        std::cerr << " to " << ggml_type_name(dst->type) << std::endl;
+        GGML_ABORT("fatal error");
+    }
+
+    if (dryrun) {
+        ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
+        return;
+    }
+
+    const bool op_supports_incontiguous = ggml_vk_op_supports_incontiguous(op);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
+    ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr;
+
+    vk_buffer d_X = nullptr;
+    size_t x_buf_offset = 0;
+    vk_buffer d_Y = nullptr;
+    size_t y_buf_offset = 0;
+    vk_buffer d_Z = nullptr;
+    size_t z_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+    bool src2_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx->device, src0->data, d_X, x_buf_offset);
+        src0_uma = d_X != nullptr;
+        if (use_src1) {
+            ggml_vk_host_get(ctx->device, src1->data, d_Y, y_buf_offset);
+            src1_uma = d_Y != nullptr;
+        }
+        if (use_src2) {
+            ggml_vk_host_get(ctx->device, src2->data, d_Z, z_buf_offset);
+            src2_uma = d_Z != nullptr;
+        }
+    }
+
+    uint64_t x_sz = ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0;
+    uint64_t y_sz = use_src1 ? ggml_type_size(src1->type) * ne1 : 0;
+    uint64_t z_sz = use_src2 ? ggml_type_size(src2->type) * ne2 : 0;
+    uint64_t d_sz = ggml_type_size(dst->type) * ned;
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+
+    // Workaround for tiny tensor inputs on ROPE
+    if (op == GGML_OP_ROPE && use_src1 && y_sz > d_D->size) {
+        y_sz = VK_WHOLE_SIZE;
+    }
+
+    GGML_ASSERT(d_D != nullptr);
+    uint64_t d_buf_offset = ((extra->offset + dst->view_offs) / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY);  // NOLINT
+    if(!src0_uma) {
+        d_X = extra_src0->buffer_gpu.lock();
+        x_buf_offset = extra_src0->offset + src0->view_offs;
+        GGML_ASSERT(d_X != nullptr);
+    }
+    if (use_src1 && !src1_uma) {
+        d_Y = extra_src1->buffer_gpu.lock();
+        y_buf_offset = extra_src1->offset + src1->view_offs;
+        GGML_ASSERT(d_Y != nullptr);
+    }
+    if (use_src2 && !src2_uma) {
+        d_Z = extra_src2->buffer_gpu.lock();
+        z_buf_offset = extra_src2->offset + src2->view_offs;
+        GGML_ASSERT(d_Z != nullptr);
+    }
+
+    if (op_supports_incontiguous) {
+        x_sz = ggml_nbytes(src0);
+        y_sz = use_src1 ? ggml_nbytes(src1) : 0;
+        z_sz = use_src2 ? ggml_nbytes(src2) : 0;
+        d_sz = ggml_nbytes(dst);
+
+        if (x_buf_offset + x_sz >= d_X->size) {
+            x_sz = VK_WHOLE_SIZE;
+        }
+        if (use_src1 && y_buf_offset + y_sz >= d_Y->size) {
+            y_sz = VK_WHOLE_SIZE;
+        }
+        if (use_src2 && z_buf_offset + z_sz >= d_Z->size) {
+            z_sz = VK_WHOLE_SIZE;
+        }
+        if (d_buf_offset + d_sz >= d_D->size) {
+            d_sz = VK_WHOLE_SIZE;
+        }
+    }
+
+    std::array<uint32_t, 3> elements;
+
+    // Single call if dimension 2 is contiguous
+    GGML_ASSERT(op_supports_incontiguous || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1))));
+
+    switch (op) {
+    case GGML_OP_NORM:
+    case GGML_OP_RMS_NORM:
+    case GGML_OP_SOFT_MAX:
+    case GGML_OP_SUM_ROWS:
+        {
+            const uint32_t nr = ggml_nrows(src0);
+            if (nr > 262144) {
+                elements = { 512, 512, CEIL_DIV(nr, 262144) };
+            } else if (nr > 512) {
+                elements = { 512, CEIL_DIV(nr, 512), 1 };
+            } else {
+                elements = { nr, 1, 1 };
+            }
+        } break;
+    case GGML_OP_GROUP_NORM:
+        {
+            const uint32_t num_groups = dst->op_params[0];
+            elements = { num_groups * (uint32_t)src0->ne[3], 1, 1 };
+        } break;
+    case GGML_OP_DIAG_MASK_INF:
+    case GGML_OP_ROPE:
+        elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 };
+        break;
+    case GGML_OP_GET_ROWS:
+        elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
+        break;
+    case GGML_OP_ARGSORT:
+        elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 };
+        break;
+    case GGML_OP_IM2COL:
+        {
+            const bool is_2D = dst->op_params[6] == 1;
+
+            const uint32_t IC = src1->ne[is_2D ? 2 : 1];
+
+            const uint32_t KH = is_2D ? src0->ne[1] : 1;
+            const uint32_t KW =         src0->ne[0];
+
+            const uint32_t OH = is_2D ? dst->ne[2] : 1;
+            const uint32_t OW =         dst->ne[1];
+
+            const uint32_t batch = src1->ne[3];
+
+            elements = { OW * KW * KH, OH, batch * IC };
+        } break;
+    case GGML_OP_TIMESTEP_EMBEDDING:
+        {
+            const uint32_t dim = dst->op_params[0];
+            uint32_t half_ceil = (dim + 1) / 2;
+            elements = { half_ceil, (uint32_t)src0->ne[0], 1 };
+        } break;
+    case GGML_OP_ADD:
+    case GGML_OP_DIV:
+    case GGML_OP_MUL:
+    case GGML_OP_SCALE:
+    case GGML_OP_SQR:
+    case GGML_OP_CLAMP:
+    case GGML_OP_PAD:
+    case GGML_OP_REPEAT:
+    case GGML_OP_CPY:
+    case GGML_OP_CONCAT:
+    case GGML_OP_UPSCALE:
+    case GGML_OP_UNARY:
+        {
+            const uint32_t ne = ggml_nelements(dst);
+            if (ne > 262144) {
+                elements = { 512, 512, CEIL_DIV(ne, 262144) };
+            } else if (ne > 512) {
+                elements = { 512, CEIL_DIV(ne, 512), 1 };
+            } else {
+                elements = { ne, 1, 1 };
+            }
+        } break;
+    default:
+        elements = { (uint32_t)ggml_nelements(src0), 1, 1 };
+        break;
+    }
+
+    if (!op_supports_incontiguous) {
+        if (x_sz != VK_WHOLE_SIZE) {
+            x_sz *= ne02 * ne03;
+        }
+        if (use_src1 && y_sz != VK_WHOLE_SIZE) {
+            y_sz *= ne12 * ne13;
+        }
+        if (use_src2 && z_sz != VK_WHOLE_SIZE) {
+            z_sz *= ne22 * ne23;
+        }
+        if (d_sz != VK_WHOLE_SIZE) {
+            d_sz *= ned2 * ned3;
+        }
+    }
+
+    if (op == GGML_OP_SOFT_MAX) {
+        // Empty src1 is possible in soft_max, but the shader needs a buffer
+        vk_subbuffer subbuf_y;
+        if (use_src1) {
+            subbuf_y = { d_Y, y_buf_offset, y_sz };
+        } else {
+            subbuf_y = { d_X, 0, x_sz };
+        }
+
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+    } else if (op == GGML_OP_ROPE) {
+        // Empty src2 is possible in rope, but the shader needs a buffer
+        vk_subbuffer subbuf_z;
+        if (use_src2) {
+            subbuf_z = { d_Z, z_buf_offset, z_sz };
+        } else {
+            subbuf_z = { d_X, 0, x_sz };
+        }
+
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+    } else if (op == GGML_OP_IM2COL) {
+        // im2col uses only src1 and dst buffers
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+    } else if (use_src2) {
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+    } else if (use_src1) {
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+    } else {
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+    }
+}
+
+static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f, 0,
+    }, dryrun);
+}
+
+static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f, 0,
+    }, dryrun);
+}
+
+static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f, 0,
+    }, dryrun);
+}
+
+static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_DIV, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f, 0,
+    }, dryrun);
+}
+
+static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    int * op_params = (int *)dst->op_params;
+
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_CONCAT, {
+        (uint32_t)ggml_nelements(dst),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f, op_params[0],
+    }, dryrun);
+}
+
+static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+
+    const float sf0 = (float)dst->ne[0] / src0->ne[0];
+    const float sf1 = (float)dst->ne[1] / src0->ne[1];
+    const float sf2 = (float)dst->ne[2] / src0->ne[2];
+    const float sf3 = (float)dst->ne[3] / src0->ne[3];
+
+    ggml_vk_op_f32<vk_op_upscale_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UPSCALE, {
+        (uint32_t)ggml_nelements(dst), 0,
+        (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)dst->ne[0], (uint32_t)dst->ne[1], (uint32_t)dst->ne[2],(uint32_t)dst->ne[3],
+        sf0, sf1, sf2, sf3,
+    }, dryrun);
+}
+
+static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    float * op_params = (float *)dst->op_params;
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        op_params[0], 0.0f
+    }, dryrun);
+}
+
+static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    }, dryrun);
+}
+
+static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    float * op_params = (float *)dst->op_params;
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        op_params[0], op_params[1],
+    }, dryrun);
+}
+
+static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_PAD, {
+        (uint32_t)ggml_nelements(dst),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    }, dryrun);
+}
+
+static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_REPEAT, {
+        (uint32_t)ggml_nelements(dst),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    }, dryrun);
+}
+
+static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+    const uint32_t d_offset = ((extra->offset + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        d_offset,
+        0.0f, 0.0f,
+    }, dryrun);
+}
+
+static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    float * op_params = (float *)dst->op_params;
+
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }, dryrun);
+}
+
+static void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    const int * int_op_params = (const int *)dst->op_params;
+    const float * float_op_params = (const float *)dst->op_params;
+
+    const uint32_t num_groups = int_op_params[0];
+    const float eps = float_op_params[1];
+    const uint32_t group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
+
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_GROUP_NORM, { group_size, 0, eps, 0.0f }, dryrun);
+}
+
+static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    float * op_params = (float *)dst->op_params;
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }, dryrun);
+}
+
+static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }, dryrun);
+}
+
+static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    int32_t * op_params = (int32_t *)dst->op_params;
+    ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }, dryrun);
+}
+
+static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    float * op_params = (float *)dst->op_params;
+
+    float scale = op_params[0];
+    float max_bias = op_params[1];
+
+    const uint32_t ncols =   (uint32_t)src0->ne[0];
+    const uint32_t nrows_x = (uint32_t)ggml_nrows(src0);
+    const uint32_t nrows_y = (uint32_t)src0->ne[1];
+
+    const uint32_t n_head_kv   = nrows_x/nrows_y;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX, {
+        ncols,
+        src1 != nullptr ? nrows_y : (uint32_t)0,
+        scale, max_bias,
+        m0, m1,
+        n_head_log2,
+    }, dryrun);
+}
+
+static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
+    const int n_dims        = ((int32_t *) dst->op_params)[1];
+    // const int mode          = ((int32_t *) dst->op_params)[2];
+    // const int n_ctx         = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig    = ((int32_t *) dst->op_params)[4];
+    const float freq_base   = ((float *)   dst->op_params)[5];
+    const float freq_scale  = ((float *)   dst->op_params)[6];
+    const float ext_factor  = ((float *)   dst->op_params)[7];
+    const float attn_factor = ((float *)   dst->op_params)[8];
+    const float beta_fast   = ((float *)   dst->op_params)[9];
+    const float beta_slow   = ((float *)   dst->op_params)[10];
+
+    float corr_dims[2];
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, {
+        (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1],
+        freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale,
+        src2 != nullptr,
+    }, dryrun);
+}
+
+static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    int32_t * op_params = (int32_t *)dst->op_params;
+
+    uint32_t ncols = src0->ne[0];
+
+    uint32_t ncols_pad = 1;
+    while (ncols_pad < ncols) {
+        ncols_pad *= 2;
+    }
+
+    GGML_ASSERT(ncols_pad <= 1024);
+
+    ggml_vk_op_f32<vk_op_argsort_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, {
+        ncols,
+        ncols_pad,
+        op_params[0],
+    }, dryrun);
+}
+
+static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }, dryrun);
+}
+
+static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    const int32_t s0 = dst->op_params[0];
+    const int32_t s1 = dst->op_params[1];
+    const int32_t p0 = dst->op_params[2];
+    const int32_t p1 = dst->op_params[3];
+    const int32_t d0 = dst->op_params[4];
+    const int32_t d1 = dst->op_params[5];
+
+    const bool is_2D = dst->op_params[6] == 1;
+
+    const uint32_t IC = src1->ne[is_2D ? 2 : 1];
+    const uint32_t IH = is_2D ? src1->ne[1] : 1;
+    const uint32_t IW =         src1->ne[0];
+
+    const uint32_t KH = is_2D ? src0->ne[1] : 1;
+    const uint32_t KW =         src0->ne[0];
+
+    const uint32_t OH = is_2D ? dst->ne[2] : 1;
+    const uint32_t OW =         dst->ne[1];
+
+    const uint32_t offset_delta = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+    const uint32_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32
+
+    const uint32_t pelements = OW * KW * KH;
+
+    ggml_vk_op_f32<vk_op_im2col_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_IM2COL, {
+        batch_offset, offset_delta,
+        IC, IW, IH, OW, OH, KW, KH,
+        pelements,
+        IC * KH * KW,
+        s0, s1, p0, p1, d0, d1,
+    }, dryrun);
+}
+
+static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    const uint32_t dim = dst->op_params[0];
+    const uint32_t max_period = dst->op_params[1];
+    const uint32_t nb1 = dst->nb[1] / ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_timestep_embedding_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_TIMESTEP_EMBEDDING, {
+        nb1, dim, max_period,
+    }, dryrun);
+}
+
+static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    const float * op_params = (const float *)dst->op_params;
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }, dryrun);
+}
+
+#ifdef GGML_VULKAN_RUN_TESTS
+static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) {
+    if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) {
+        return;
+    }
+    i0 = std::max(i0, 5);
+    i1 = std::max(i1, 5);
+    i2 = std::max(i2, 0);
+    fprintf(stderr, "         ");
+    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+        fprintf(stderr, "%7d ", idx1);
+    }
+    fprintf(stderr, "\n");
+    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
+        fprintf(stderr, "%7d: ", idx0);
+        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+            if (idx0 >= 0 && idx0 < ne0 && idx1 >= 0 && idx1 < ne1) {
+                float val;
+                if (type == GGML_TYPE_F32) {
+                    val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0);
+                } else if (type == GGML_TYPE_F16) {
+                    val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0));
+                } else {
+                    GGML_ABORT("fatal error");
+                }
+                fprintf(stderr, "% 7.2f ", val);
+            } else {
+                fprintf(stderr, "        ");
+            }
+        }
+        fprintf(stderr, "\n");
+    }
+}
+
+template <typename X_TYPE, typename Y_TYPE>
+static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) {
+    VK_LOG_DEBUG("ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")");
+    const size_t x_ne = m * k * batch;
+    const size_t y_ne = k * n * batch;
+    const size_t d_ne = m * n * batch;
+
+    vk_pipeline p;
+    std::string shname;
+    if (shader_size == 0) {
+        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32->a_s;
+            shname = "F32_ALIGNED_S";
+        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32_f16->a_s;
+            shname = "F32_F16_ALIGNED_S";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16_f32->a_s;
+            shname = "F16_F32_ALIGNED_S";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16->a_s;
+            shname = "F16_ALIGNED_S";
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    } else if (shader_size == 1) {
+        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32->a_m;
+            shname = "F32_ALIGNED_M";
+        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32_f16->a_m;
+            shname = "F32_F16_ALIGNED_M";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16_f32->a_m;
+            shname = "F16_F32_ALIGNED_M";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16->a_m;
+            shname = "F16_ALIGNED_M";
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    } else if (shader_size == 2) {
+        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32->a_l;
+            shname = "F32_ALIGNED_L";
+        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32_f16->a_l;
+            shname = "F32_F16_ALIGNED_L";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16_f32->a_l;
+            shname = "F16_F32_ALIGNED_L";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16->a_l;
+            shname = "F16_ALIGNED_L";
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    } else {
+        GGML_ASSERT(0);
+    }
+
+    const size_t kpad = ggml_vk_align_size(k, p->align);
+
+    if (k != kpad) {
+        if (shader_size == 0) {
+            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32->s;
+                shname = "F32_S";
+            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32_f16->s;
+                shname = "F32_F16_S";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16_f32->s;
+                shname = "F16_F32_S";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16->s;
+                shname = "F16_S";
+            }
+        } else if (shader_size == 1) {
+            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32->m;
+                shname = "F32_M";
+            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32_f16->m;
+                shname = "F32_F16_M";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16_f32->m;
+                shname = "F16_F32_M";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16->m;
+                shname = "F16_M";
+            }
+        } else if (shader_size == 2) {
+            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32->l;
+                shname = "F32_L";
+            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32_f16->l;
+                shname = "F32_F16_L";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16_f32->l;
+                shname = "F16_F32_L";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16->l;
+                shname = "F16_L";
+            }
+        }
+    }
+
+    ggml_pipeline_request_descriptor_sets(ctx->device, p, num_it);
+    if (split_k > 1) {
+        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+
+        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
+            // Resize buffer
+            if (ctx->prealloc_split_k != nullptr) {
+                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+            }
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        }
+    }
+
+    vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+
+    X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne);
+    Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne);
+    float* d = (float *) malloc(sizeof(float) * d_ne);
+
+    for (size_t i = 0; i < x_ne; i++) {
+        if (std::is_same<float, X_TYPE>()) {
+            x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
+            x[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    }
+    for (size_t i = 0; i < y_ne; i++) {
+        if (std::is_same<float, Y_TYPE>()) {
+            // y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+            y[i] = (i % k == i / k) ? 1.0f : 0.0f;
+        } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            // y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
+            y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f);
+        } else {
+            GGML_ABORT("fatal error");
+        }
+    }
+
+    ggml_vk_buffer_write(d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
+    ggml_vk_buffer_write(d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
+
+    vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    for (size_t i = 0; i < num_it; i++) {
+        ggml_vk_ctx_begin(ctx->device, subctx);
+        ggml_vk_matmul(
+            ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k),
+            m, n, k,
+            k, k, m, k*m, k*n, m*n,
+            split_k, batch, batch, batch, 1, 1
+        );
+        ggml_vk_ctx_end(subctx);
+    }
+
+    auto begin = std::chrono::high_resolution_clock::now();
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    auto end = std::chrono::high_resolution_clock::now();
+    double time = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+
+    // copy dst to host
+    ggml_vk_buffer_read(d_D, 0, d, sizeof(float) * d_ne);
+
+    float * d_chk = (float *) malloc(sizeof(float) * d_ne);
+
+    ggml_init_params iparams = {
+        /*.mem_size   =*/ 1024*1024*1024,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context * ggml_ctx = ggml_init(iparams);
+
+    ggml_type src0_type;
+    ggml_type src1_type;
+
+    if (std::is_same<float, X_TYPE>()) {
+        src0_type = GGML_TYPE_F32;
+    } else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
+        src0_type = GGML_TYPE_F16;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+    if (std::is_same<float, Y_TYPE>()) {
+        src1_type = GGML_TYPE_F32;
+    } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
+        src1_type = GGML_TYPE_F16;
+    } else {
+        GGML_ABORT("fatal error");
+    }
+
+    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, src0_type, k, m, batch);
+    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, src1_type, k, n, batch);
+    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
+
+    src0_ggml->data = x;
+    src1_ggml->data = y;
+    tensor_ggml->data = d_chk;
+
+    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
+    ggml_build_forward_expand(cgraph, tensor_ggml);
+
+    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
+
+    ggml_free(ggml_ctx);
+
+    double avg_err = 0.0;
+    int first_err_n = -1;
+    int first_err_m = -1;
+    int first_err_b = -1;
+
+    for (size_t i = 0; i < m*n*batch; i++) {
+        double err = std::fabs(d[i] - d_chk[i]);
+        avg_err += err;
+
+        if (err > 0.05f && first_err_n == -1) {
+            first_err_b = i / (m * n);
+            first_err_n = (i % (m * n)) / m;
+            first_err_m = (i % (m * n)) % m;
+        }
+    }
+
+    avg_err /= m * n;
+
+    std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.1) {
+        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+        std::cerr << std::endl;
+        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n + 15, first_err_b);
+        std::cerr << "Expected result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+        if (split_k > 1) {
+            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
+            ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
+
+            std::cerr << "d_buf0: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf1: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf2: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf3: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            free(split_k_buf);
+        }
+    }
+
+    free(d_chk);
+
+    ggml_vk_queue_cleanup(ctx->device, ctx->device->transfer_queue);
+    ggml_vk_queue_cleanup(ctx->device, ctx->device->compute_queue);
+
+    ggml_vk_destroy_buffer(d_X);
+    ggml_vk_destroy_buffer(d_Y);
+    ggml_vk_destroy_buffer(d_D);
+
+    ggml_pipeline_cleanup(p);
+    ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce);
+
+    free(x);
+    free(y);
+    free(d);
+}
+
+static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
+    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
+        return;
+    }
+    i0 = std::max(i0, 5);
+    i1 = std::max(i1, 5);
+    i2 = std::max(i2, 0);
+    i3 = std::max(i3, 0);
+    fprintf(stderr, "         ");
+    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+        fprintf(stderr, "%7d ", idx1);
+    }
+    fprintf(stderr, "\n");
+    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
+        fprintf(stderr, "%7d: ", idx0);
+        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+            if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
+                float val;
+                if (tensor->type == GGML_TYPE_F32) {
+                    val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
+                } else if (tensor->type == GGML_TYPE_F16) {
+                    val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
+                } else {
+                    GGML_ABORT("fatal error");
+                }
+                fprintf(stderr, "% 7.2f ", val);
+            } else {
+                fprintf(stderr, "        ");
+            }
+        }
+        fprintf(stderr, "\n");
+    }
+}
+
+static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
+    ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
+}
+
+static void ggml_vk_dequantize_data(const void * from, float * to, size_t ne, ggml_type quant) {
+    if (quant == GGML_TYPE_F32) {
+        memcpy(to, from, sizeof(float) * ne);
+        return;
+    }
+
+    ggml_type_traits_t tt = ggml_internal_get_type_traits(quant);
+
+    ggml_to_float_t dequant_fn = tt.to_float;
+
+    dequant_fn(from, to, ne);
+}
+
+static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
+    VK_LOG_DEBUG("ggml_vk_test_dequant(" << ne << ")");
+    const size_t x_sz = sizeof(float) * ne;
+    const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne;
+    const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant);
+    float * x = (float *) malloc(x_sz);
+    void * qx = malloc(qx_sz);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    float * x_ref = (float *) malloc(x_sz);
+    ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16);
+
+    for (size_t i = 0; i < ne; i++) {
+        x[i] = rand() / (float)RAND_MAX;
+    }
+
+    vk_pipeline p = ggml_vk_get_to_fp16(ctx, quant);
+
+    ggml_vk_quantize_data(x, qx, ne, quant);
+    ggml_vk_dequantize_data(qx, x_ref, ne, quant);
+
+    ggml_pipeline_request_descriptor_sets(ctx->device, p, 1);
+
+    ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
+
+    vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    ggml_vk_ctx_begin(ctx->device, subctx);
+    const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
+    ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1});
+    ggml_vk_ctx_end(subctx);
+
+    auto begin = std::chrono::high_resolution_clock::now();
+
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    double ms_dequant = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+    ggml_vk_buffer_read(x_buf, 0, x_chk, x_sz_f16);
+
+    int first_err = -1;
+
+    double avg_err = 0.0;
+    for (size_t i = 0; i < ne; i++) {
+        double error = std::fabs(x_ref[i] - ggml_fp16_to_fp32(x_chk[i]));
+        avg_err += error;
+
+        if (first_err < 0 && error > 0.05) {
+            first_err = i;
+        }
+    }
+
+    avg_err /= ne;
+
+    std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.1) {
+        std::cerr << "first_error = " << first_err << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
+            std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", ";
+        }
+        std::cerr << std::endl << "Expected result: " << std::endl << std::endl;
+        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
+            std::cerr << x_ref[i] << ", ";
+        }
+        std::cerr << std::endl;
+    }
+
+    ggml_vk_destroy_buffer(x_buf);
+    ggml_vk_destroy_buffer(qx_buf);
+
+    free(x);
+    free(qx);
+    free(x_ref);
+    free(x_chk);
+}
+
+static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) {
+    VK_LOG_DEBUG("ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")");
+    const size_t x_ne = m * k * batch;
+    const size_t y_ne = k * n * batch;
+    const size_t d_ne = m * n * batch;
+
+    vk_pipeline p;
+    std::string shname;
+    if (shader_size == 0) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S";
+    } else if (shader_size == 1) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M";
+    } else if (shader_size == 2) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L";
+    } else {
+        GGML_ASSERT(0);
+    }
+
+    const size_t kpad = ggml_vk_align_size(k, p->align);
+
+    if (k != kpad) {
+        if (shader_size == 0) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s;
+            shname = std::string(ggml_type_name(quant)) + "_S";
+        } else if (shader_size == 1) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m;
+            shname = std::string(ggml_type_name(quant)) + "_M";
+        } else if (shader_size == 2) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l;
+            shname = std::string(ggml_type_name(quant)) + "_L";
+        } else {
+            GGML_ASSERT(0);
+        }
+    }
+
+    const size_t x_sz = sizeof(float) * x_ne;
+    const size_t y_sz = sizeof(float) * y_ne;
+    const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant);
+    const size_t d_sz = sizeof(float) * d_ne;
+    float * x = (float *) malloc(x_sz);
+    float * y = (float *) malloc(y_sz);
+    void * qx = malloc(qx_sz);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx->device, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx->device, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    float * d = (float *) malloc(d_sz);
+    float * d_chk = (float *) malloc(d_sz);
+
+    for (size_t i = 0; i < x_ne; i++) {
+        x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+    }
+
+    ggml_vk_quantize_data(x, qx, x_ne, quant);
+
+    for (size_t i = 0; i < y_ne; i++) {
+        // y[i] = rand() / (float)RAND_MAX;
+        y[i] = (i % k == i / k) ? 1.0f : 0.0f;
+    }
+
+    ggml_pipeline_request_descriptor_sets(ctx->device, p, num_it);
+    if (split_k > 1) {
+        ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+
+        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
+            // Resize buffer
+            if (ctx->prealloc_split_k != nullptr) {
+                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+            }
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        }
+    }
+
+    ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
+    ggml_vk_buffer_write(y_buf, 0, y, y_sz);
+
+    vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    for (size_t i = 0; i < num_it; i++) {
+        ggml_vk_ctx_begin(ctx->device, subctx);
+        ggml_vk_matmul(
+            ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k),
+            m, n, k,
+            k, k, m, k*m, k*n, m*n,
+            split_k, batch, batch, batch, 1, 1
+        );
+        ggml_vk_ctx_end(subctx);
+    }
+
+    auto begin = std::chrono::high_resolution_clock::now();
+
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    double time_ms = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+    ggml_vk_buffer_read(d_buf, 0, d, d_sz);
+
+    ggml_init_params iparams = {
+        /*.mem_size   =*/ 1024*1024*1024,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context * ggml_ctx = ggml_init(iparams);
+
+    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch);
+    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch);
+    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
+
+    src0_ggml->data = qx;
+    src1_ggml->data = y;
+    tensor_ggml->data = d_chk;
+
+    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
+    ggml_build_forward_expand(cgraph, tensor_ggml);
+
+    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
+
+    ggml_free(ggml_ctx);
+
+    double avg_err = 0.0;
+    int first_err_n = -1;
+    int first_err_m = -1;
+    int first_err_b = -1;
+
+    for (size_t i = 0; i < m*n*batch; i++) {
+        double err = std::fabs(d[i] - d_chk[i]);
+        avg_err += err;
+
+        if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) {
+            first_err_b = i / (m * n);
+            first_err_n = (i % (m * n)) / m;
+            first_err_m = (i % (m * n)) % m;
+        }
+    }
+
+    avg_err /= m * n;
+
+    std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.01 || std::isnan(avg_err)) {
+        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+        std::cerr << std::endl;
+        std::cerr << "Expected result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+        if (split_k > 1) {
+            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
+            ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
+
+            std::cerr << "d_buf0: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf1: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf2: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf3: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            free(split_k_buf);
+        }
+    }
+
+    ggml_vk_destroy_buffer(qx_buf);
+    ggml_vk_destroy_buffer(y_buf);
+    ggml_vk_destroy_buffer(d_buf);
+
+    free(x);
+    free(qx);
+    free(y);
+    free(d);
+    free(d_chk);
+}
+#endif
+
+static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) {
+    VK_LOG_DEBUG("ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))");
+    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu;
+    extra->reset();
+    tensor->extra = extra;
+    return extra;
+}
+
+static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
+#if defined(GGML_VULKAN_RUN_TESTS)
+    ctx->staging = ggml_vk_create_buffer_check(ctx->device, 100ul * 1024ul * 1024ul,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_IQ4_NL);
+
+    ggml_vk_test_matmul<ggml_fp16_t, ggml_fp16_t>(ctx, 512, 512, 100, 32, 100, 1, 2);
+
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 0);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 1);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 2);
+    // ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 0);
+    // ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 1);
+    // ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 2);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q2_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q2_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q2_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q2_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q3_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q3_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q3_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q3_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q6_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q6_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q6_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q6_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_IQ4_NL);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_IQ4_NL);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_IQ4_NL);
+
+    std::cerr << std::endl;
+
+    const std::vector<size_t> vals {
+        8, 8, 8,
+        100, 46, 576,
+        623, 111, 128,
+        100, 46, 558,
+        512, 1, 256,
+        128, 110, 622,
+        511, 511, 127,
+        511, 511, 7,
+        511, 511, 17,
+        49, 49, 128,
+        128, 49, 49,
+        4096, 49, 4096,
+        11008, 49, 4096,
+        4096, 49, 11008,
+        32000, 49, 4096,
+        512, 512, 128,
+        128, 512, 512,
+        4096, 512, 4096,
+        11008, 512, 4096,
+        4096, 512, 11008,
+        32000, 512, 4096,
+    };
+    const size_t num_it = 1;
+    for (size_t i = 0; i < vals.size(); i += 3) {
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0);
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1);
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2);
+        // ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0);
+        // ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1);
+        // ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2);
+        std::cerr << std::endl;
+    }
+
+    GGML_ABORT("fatal error");
+#endif
+
+    if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) {
+        VK_LOG_MEMORY("ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << ")");
+        // Resize buffer
+        if (ctx->prealloc_x != nullptr) {
+            ggml_vk_destroy_buffer(ctx->prealloc_x);
+        }
+        ctx->prealloc_x = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_x);
+    }
+    if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) {
+        VK_LOG_MEMORY("ggml_vk_preallocate_buffers(y_size: " << ctx->prealloc_size_y << ")");
+        // Resize buffer
+        if (ctx->prealloc_y != nullptr) {
+            ggml_vk_destroy_buffer(ctx->prealloc_y);
+        }
+        ctx->prealloc_y = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_y);
+    }
+    if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) {
+        VK_LOG_MEMORY("ggml_vk_preallocate_buffers(split_k_size: " << ctx->prealloc_size_split_k << ")");
+        // Resize buffer
+        if (ctx->prealloc_split_k != nullptr) {
+            ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+        }
+        ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_split_k);
+    }
+}
+
+static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, bool last_node, bool dryrun){
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
+
+    if (ggml_is_empty(node) || extra == nullptr) {
+        return;
+    }
+
+    VK_LOG_DEBUG("ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")");
+    ctx->semaphore_idx = 0;
+
+    const ggml_tensor * src0 = node->src[0];
+    const ggml_tensor * src1 = node->src[1];
+    const ggml_tensor * src2 = node->src[2];
+
+    switch (node->op) {
+    // Return on empty ops to avoid generating a compute_ctx and setting exit_tensor
+    case GGML_OP_RESHAPE:
+    case GGML_OP_VIEW:
+    case GGML_OP_PERMUTE:
+    case GGML_OP_TRANSPOSE:
+    case GGML_OP_NONE:
+        return;
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(node)) {
+        case GGML_UNARY_OP_SILU:
+        case GGML_UNARY_OP_GELU:
+        case GGML_UNARY_OP_GELU_QUICK:
+        case GGML_UNARY_OP_RELU:
+        case GGML_UNARY_OP_TANH:
+            break;
+        default:
+            return;
+        }
+        break;
+    case GGML_OP_REPEAT:
+    case GGML_OP_GET_ROWS:
+    case GGML_OP_ADD:
+    case GGML_OP_MUL:
+    case GGML_OP_DIV:
+    case GGML_OP_CONCAT:
+    case GGML_OP_UPSCALE:
+    case GGML_OP_SCALE:
+    case GGML_OP_SQR:
+    case GGML_OP_CLAMP:
+    case GGML_OP_PAD:
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+    case GGML_OP_NORM:
+    case GGML_OP_GROUP_NORM:
+    case GGML_OP_RMS_NORM:
+    case GGML_OP_DIAG_MASK_INF:
+    case GGML_OP_SOFT_MAX:
+    case GGML_OP_ROPE:
+    case GGML_OP_MUL_MAT:
+    case GGML_OP_MUL_MAT_ID:
+    case GGML_OP_ARGSORT:
+    case GGML_OP_SUM_ROWS:
+    case GGML_OP_IM2COL:
+    case GGML_OP_TIMESTEP_EMBEDDING:
+    case GGML_OP_LEAKY_RELU:
+        break;
+    default:
+        std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
+        GGML_ABORT("fatal error");
+        return;
+    }
+
+    vk_context compute_ctx;
+
+    if (!dryrun) {
+        if (ctx->compute_ctx.expired()) {
+            compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+            ctx->compute_ctx = compute_ctx;
+            ggml_vk_ctx_begin(ctx->device, compute_ctx);
+        } else {
+            compute_ctx = ctx->compute_ctx.lock();
+        }
+    }
+
+    switch (node->op) {
+    case GGML_OP_REPEAT:
+        ggml_vk_repeat(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_GET_ROWS:
+        ggml_vk_get_rows(ctx, compute_ctx, src0, src1, node, dryrun);
+
+        break;
+    case GGML_OP_ADD:
+        ggml_vk_add(ctx, compute_ctx, src0, src1, node, dryrun);
+
+        break;
+    case GGML_OP_MUL:
+        ggml_vk_mul(ctx, compute_ctx, src0, src1, node, dryrun);
+
+        break;
+    case GGML_OP_DIV:
+        ggml_vk_div(ctx, compute_ctx, src0, src1, node, dryrun);
+
+        break;
+    case GGML_OP_CONCAT:
+        ggml_vk_concat(ctx, compute_ctx, src0, src1, node, dryrun);
+
+        break;
+    case GGML_OP_UPSCALE:
+        ggml_vk_upscale(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_SCALE:
+        ggml_vk_scale(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_SQR:
+        ggml_vk_sqr(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_CLAMP:
+        ggml_vk_clamp(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_PAD:
+        ggml_vk_pad(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+        ggml_vk_cpy(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_NORM:
+        ggml_vk_norm(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_GROUP_NORM:
+        ggml_vk_group_norm(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_RMS_NORM:
+        ggml_vk_rms_norm(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(node)) {
+        case GGML_UNARY_OP_SILU:
+        case GGML_UNARY_OP_GELU:
+        case GGML_UNARY_OP_GELU_QUICK:
+        case GGML_UNARY_OP_RELU:
+        case GGML_UNARY_OP_TANH:
+            ggml_vk_unary(ctx, compute_ctx, src0, node, dryrun);
+            break;
+        default:
+            return;
+        }
+        break;
+    case GGML_OP_DIAG_MASK_INF:
+        ggml_vk_diag_mask_inf(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_SOFT_MAX:
+        ggml_vk_soft_max(ctx, compute_ctx, src0, src1, node, dryrun);
+
+        break;
+    case GGML_OP_ROPE:
+        ggml_vk_rope(ctx, compute_ctx, src0, src1, src2, node, dryrun);
+
+        break;
+    case GGML_OP_ARGSORT:
+        ggml_vk_argsort(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_SUM_ROWS:
+        ggml_vk_sum_rows(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_IM2COL:
+        ggml_vk_im2col(ctx, compute_ctx, src0, src1, node, dryrun);
+
+        break;
+    case GGML_OP_TIMESTEP_EMBEDDING:
+        ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_LEAKY_RELU:
+        ggml_vk_leaky_relu(ctx, compute_ctx, src0, node, dryrun);
+
+        break;
+    case GGML_OP_MUL_MAT:
+        ggml_vk_mul_mat(ctx, compute_ctx, src0, src1, node, dryrun);
+
+        break;
+    case GGML_OP_MUL_MAT_ID:
+        ggml_vk_mul_mat_id(ctx, compute_ctx, src0, src1, src2, node, dryrun);
+
+        break;
+    default:
+        return;
+    }
+
+    if (dryrun) {
+        return;
+    }
+
+    ctx->tensor_ctxs[node_idx] = compute_ctx;
+
+#if defined(GGML_VULKAN_CHECK_RESULTS) || defined(GGML_VULKAN_PERF)
+    // Force context reset on each node so that each tensor ends up in its own context
+    // and can be run and compared to its CPU equivalent separately
+    last_node = true;
+#endif
+
+    if (last_node) {
+        ggml_vk_ctx_end(compute_ctx);
+        compute_ctx->exit_tensor_idx = node_idx;
+        ctx->compute_ctx.reset();
+    }
+}
+
+static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor, int tensor_idx){
+    ggml_tensor_extra_gpu * extra = nullptr;
+
+    switch (tensor->op) {
+    case GGML_OP_ADD:
+    case GGML_OP_GET_ROWS:
+    case GGML_OP_MUL:
+    case GGML_OP_DIV:
+    case GGML_OP_CONCAT:
+    case GGML_OP_UPSCALE:
+    case GGML_OP_SCALE:
+    case GGML_OP_SQR:
+    case GGML_OP_CLAMP:
+    case GGML_OP_PAD:
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+    case GGML_OP_NORM:
+    case GGML_OP_GROUP_NORM:
+    case GGML_OP_RMS_NORM:
+    case GGML_OP_DIAG_MASK_INF:
+    case GGML_OP_SOFT_MAX:
+    case GGML_OP_ROPE:
+    case GGML_OP_RESHAPE:
+    case GGML_OP_VIEW:
+    case GGML_OP_PERMUTE:
+    case GGML_OP_TRANSPOSE:
+    case GGML_OP_NONE:
+    case GGML_OP_ARGSORT:
+    case GGML_OP_SUM_ROWS:
+    case GGML_OP_IM2COL:
+    case GGML_OP_TIMESTEP_EMBEDDING:
+    case GGML_OP_LEAKY_RELU:
+    case GGML_OP_REPEAT:
+        extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+        break;
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(tensor)) {
+        case GGML_UNARY_OP_SILU:
+        case GGML_UNARY_OP_GELU:
+        case GGML_UNARY_OP_GELU_QUICK:
+        case GGML_UNARY_OP_RELU:
+        case GGML_UNARY_OP_TANH:
+            extra = (ggml_tensor_extra_gpu *) tensor->extra;
+            break;
+        default:
+            return false;
+        }
+        break;
+    case GGML_OP_MUL_MAT:
+    case GGML_OP_MUL_MAT_ID:
+        extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+        break;
+    default:
+        return false;
+    }
+
+    if (extra == nullptr) {
+        return false;
+    }
+
+    VK_LOG_DEBUG("ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")");
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+    ggml_vk_check_results_0(tensor);
+#endif
+
+    vk_context subctx = ctx->tensor_ctxs[tensor_idx].lock();
+
+#ifdef GGML_VULKAN_PERF
+    std::chrono::steady_clock::time_point start;
+#endif // GGML_VULKAN_PERF
+
+    // Only run if ctx hasn't been submitted yet
+    if (!subctx->seqs.empty()) {
+        // Do staging buffer copies
+        for (auto& cpy : subctx->in_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+
+#ifdef GGML_VULKAN_PERF
+        start = std::chrono::steady_clock::now();
+#endif // GGML_VULKAN_PERF
+
+        ggml_vk_submit(subctx, ctx->fence);
+    }
+
+    if (tensor_idx == subctx->exit_tensor_idx) {
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
+
+#ifdef GGML_VULKAN_PERF
+        auto duration = std::chrono::duration_cast<std::chrono::nanoseconds>(std::chrono::steady_clock::now() - start);
+        ctx->device->perf_logger->log_timing(tensor, duration.count());
+#endif // GGML_VULKAN_PERF
+
+        ctx->device->device.resetFences({ ctx->fence });
+
+        // Do staging buffer copies
+        for (auto& cpy : subctx->out_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+        subctx->in_memcpys.clear();
+        subctx->out_memcpys.clear();
+    }
+
+    return true;
+}
+
+// Clean up after graph processing is done
+static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
+    VK_LOG_DEBUG("ggml_vk_graph_cleanup()");
+    for (auto& buffer : ctx->gc.temp_buffers) {
+        ggml_vk_pool_free(ctx, buffer);
+    }
+    ctx->gc.temp_buffers.clear();
+
+    for (auto& dsr : ctx->device->pipeline_descriptor_set_requirements) {
+        vk_pipeline_ref plr = ctx->device->pipelines[dsr.first];
+
+        if (plr.expired()) {
+            continue;
+        }
+
+        vk_pipeline pl = plr.lock();
+        ggml_pipeline_cleanup(pl);
+    }
+
+    ggml_vk_queue_cleanup(ctx->device, ctx->device->compute_queue);
+    ggml_vk_queue_cleanup(ctx->device, ctx->device->transfer_queue);
+
+    for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) {
+        ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s });
+    }
+    ctx->gc.semaphores.clear();
+
+    for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) {
+        ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s });
+    }
+    ctx->gc.tl_semaphores.clear();
+    ctx->semaphore_idx = 0;
+
+    ctx->event_idx = 0;
+
+    for (auto& event : ctx->gc.events) {
+        ctx->device->device.resetEvent(event);
+    }
+
+    ctx->tensor_ctxs.clear();
+    ctx->gc.contexts.clear();
+    ctx->device->pipeline_descriptor_set_requirements.clear();
+}
+
+// Clean up on backend free
+static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
+    VK_LOG_DEBUG("ggml_vk_cleanup(" << ctx->name << ")");
+    ggml_vk_graph_cleanup(ctx);
+
+    ggml_vk_destroy_buffer(ctx->prealloc_x);
+    ggml_vk_destroy_buffer(ctx->prealloc_y);
+    ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+
+    for (auto& buffer : ctx->buffer_pool) {
+        ggml_vk_destroy_buffer(buffer);
+    }
+
+    ctx->prealloc_size_x = 0;
+    ctx->prealloc_size_y = 0;
+    ctx->prealloc_size_split_k = 0;
+
+    for (auto& event : ctx->gc.events) {
+        ctx->device->device.destroyEvent(event);
+    }
+    ctx->gc.events.clear();
+
+    ctx->device->device.destroyFence(ctx->fence);
+}
+
+GGML_CALL static int ggml_vk_get_device_count() {
+    ggml_vk_instance_init();
+
+    return vk_instance.device_indices.size();
+}
+
+GGML_CALL static void ggml_vk_get_device_description(int device, char * description, size_t description_size) {
+    ggml_vk_instance_init();
+
+    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
+    vk::PhysicalDeviceProperties props;
+    devices[device].getProperties(&props);
+
+    snprintf(description, description_size, "%s", props.deviceName.data());
+}
+
+// backend interface
+
+#define UNUSED GGML_UNUSED
+
+// device backend
+
+static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000;  // NOLINT
+
+struct ggml_backend_vk_buffer_context {
+    vk_device_ref device;
+    vk_buffer dev_buffer;
+    ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
+    size_t temp_tensor_extra_index = 0;
+    std::string name;
+
+    ggml_backend_vk_buffer_context(vk_device_ref device, vk_buffer&& dev_buffer, std::string& name) :
+        device(device),
+        dev_buffer(dev_buffer),
+        name(name) {
+    }
+
+    ~ggml_backend_vk_buffer_context() {
+        ggml_vk_destroy_buffer(dev_buffer);
+        if (temp_tensor_extras != nullptr) {
+            delete[] temp_tensor_extras;
+        }
+    }
+
+    ggml_tensor_extra_gpu * ggml_vk_alloc_temp_tensor_extra() {
+        if (temp_tensor_extras == nullptr) {
+            temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_VK_MAX_NODES];
+        }
+
+        size_t alloc_index = temp_tensor_extra_index;
+        temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_VK_MAX_NODES;
+        ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
+        extra->reset();
+
+        return extra;
+    }
+};
+
+GGML_CALL static const char * ggml_backend_vk_buffer_get_name(ggml_backend_buffer_t buffer) {
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+    return ctx->name.c_str();
+}
+
+GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_vk_buffer_get_name;
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    VK_LOG_MEMORY("ggml_backend_vk_buffer_free_buffer()");
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+    ggml_vk_destroy_buffer(ctx->dev_buffer);
+    delete ctx;
+}
+
+GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return vk_ptr_base;
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    VK_LOG_DEBUG("ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")");
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+
+    if (tensor->view_src != nullptr) {
+        GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
+        GGML_ASSERT(tensor->view_src->extra != nullptr);
+        tensor->extra = tensor->view_src->extra;
+    } else {
+        ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra();
+        extra->buffer_gpu = ctx->dev_buffer;
+        extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
+        tensor->extra = extra;
+    }
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    VK_LOG_DEBUG("ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")");
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    vk_buffer buf = extra->buffer_gpu.lock();
+
+    ggml_vk_buffer_write(buf, extra->offset + tensor->view_offs + offset, data, size);
+
+    GGML_UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")");
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    vk_buffer buf = extra->buffer_gpu.lock();
+
+    ggml_vk_buffer_read(buf, extra->offset + tensor->view_offs + offset, data, size);
+
+    GGML_UNUSED(buffer);
+}
+
+GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
+    if (ggml_backend_buffer_is_vk(src->buffer)) {
+        ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
+        ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+
+        vk_buffer src_buf = src_extra->buffer_gpu.lock();
+        vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
+
+        ggml_vk_buffer_copy(dst_buf, dst_extra->offset + dst->view_offs, src_buf, src_extra->offset + src->view_offs, ggml_nbytes(src));
+
+        return true;
+    }
+    return false;
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+
+    ggml_vk_buffer_memset(ctx->dev_buffer, 0, value, buffer->size);
+}
+
+static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
+    /* .get_name        = */ ggml_backend_vk_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_vk_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_vk_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_vk_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_vk_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_vk_buffer_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_vk_buffer_cpy_tensor,
+    /* .clear           = */ ggml_backend_vk_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// vk buffer type
+GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
+
+    return ctx->name.c_str();
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    VK_LOG_MEMORY("ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")");
+    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
+
+    vk_buffer dev_buffer = nullptr;
+    try {
+        dev_buffer = ggml_vk_create_buffer_device(ctx->device, size);
+    } catch (const vk::SystemError& e) {
+        return nullptr;
+    }
+
+    ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->device, std::move(dev_buffer), ctx->name);
+
+    return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size);
+}
+
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
+    return ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+}
+
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
+    return ctx->device->max_memory_allocation_size;
+}
+
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    return ggml_nbytes(tensor);
+
+    UNUSED(buft);
+}
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) {
+    ggml_vk_instance_init();
+
+    VK_LOG_DEBUG("ggml_backend_vk_buffer_type(" << dev_num << ")");
+
+    vk_device dev = ggml_vk_get_device(dev_num);
+
+    return &dev->buffer_type;
+}
+
+// host buffer type
+
+GGML_CALL static const char * ggml_backend_vk_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_VK_NAME "_Host";
+
+    UNUSED(buft);
+}
+
+GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffer) {
+    return GGML_VK_NAME "_Host";
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    VK_LOG_MEMORY("ggml_backend_vk_host_buffer_free_buffer()");
+    ggml_vk_host_free(vk_instance.devices[0], buffer->context);
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    VK_LOG_MEMORY("ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")");
+
+    size += 32;  // Behave like the CPU buffer type
+    void * ptr = nullptr;
+    try {
+        ptr = ggml_vk_host_malloc(vk_instance.devices[0], size);
+    } catch (vk::SystemError& e) {
+        std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl;
+        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
+        // fallback to cpu buffer
+        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+    }
+
+    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
+    buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_vk_host_buffer_name;
+    buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer;
+
+    return buffer;
+
+    UNUSED(buft);
+}
+
+GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return vk_instance.devices[0]->properties.limits.minMemoryMapAlignment;
+
+    UNUSED(buft);
+}
+
+// Should be changed to return device-specific host buffer type
+// but that probably requires changes in llama.cpp
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() {
+    static struct ggml_backend_buffer_type ggml_backend_vk_buffer_type_host = {
+        /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_vk_host_buffer_type_name,
+            /* .alloc_buffer     = */ ggml_backend_vk_host_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_vk_host_buffer_type_get_alignment,
+            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
+        },
+        /* .context  = */ nullptr,
+    };
+
+    // Make sure device 0 is initialized
+    ggml_vk_instance_init();
+    ggml_vk_get_device(0);
+
+    return &ggml_backend_vk_buffer_type_host;
+}
+
+
+// backend
+
+GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) {
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+
+    return ctx->name.c_str();
+}
+
+GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) {
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    VK_LOG_DEBUG("ggml_backend_vk_free(" << ctx->name << ")");
+
+    ggml_vk_cleanup(ctx);
+
+    delete ctx;
+    delete backend;
+}
+
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) {
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+
+    return &ctx->device->buffer_type;
+}
+
+GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    VK_LOG_DEBUG("ggml_backend_vk_set_tensor_async(" << size << ")");
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    vk_context transfer_ctx;
+
+    if (ctx->transfer_ctx.expired()) {
+        // Initialize new transfer context
+        transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        ctx->transfer_ctx = transfer_ctx;
+        ggml_vk_ctx_begin(ctx->device, transfer_ctx);
+    } else {
+        transfer_ctx = ctx->transfer_ctx.lock();
+    }
+
+    vk_buffer buf = extra->buffer_gpu.lock();
+
+    ggml_vk_buffer_write_async(transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size);
+}
+
+GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    VK_LOG_DEBUG("ggml_backend_vk_get_tensor_async(" << size << ")");
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    vk_context transfer_ctx;
+
+    if (ctx->transfer_ctx.expired()) {
+        // Initialize new transfer context
+        transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        ctx->transfer_ctx = transfer_ctx;
+        ggml_vk_ctx_begin(ctx->device, transfer_ctx);
+    } else {
+        transfer_ctx = ctx->transfer_ctx.lock();
+    }
+
+    vk_buffer buf = extra->buffer_gpu.lock();
+
+    ggml_vk_buffer_read_async(transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size);
+}
+
+GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
+    VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async()");
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    if ((dst->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) {
+        ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
+        ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+
+        vk_context transfer_ctx;
+
+        if (ctx->transfer_ctx.expired()) {
+            // Initialize new transfer context
+            transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+            ctx->transfer_ctx = transfer_ctx;
+            ggml_vk_ctx_begin(ctx->device, transfer_ctx);
+        } else {
+            transfer_ctx = ctx->transfer_ctx.lock();
+        }
+
+        vk_buffer src_buf = src_extra->buffer_gpu.lock();
+        vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
+
+        ggml_vk_buffer_copy_async(transfer_ctx, dst_buf, dst_extra->offset + dst->view_offs, src_buf, src_extra->offset + src->view_offs, ggml_nbytes(src));
+        return true;
+    }
+
+    return false;
+}
+
+GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) {
+    VK_LOG_DEBUG("ggml_backend_vk_synchronize()");
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    if(ctx->transfer_ctx.expired()) {
+        return;
+    }
+
+    vk_context transfer_ctx = ctx->transfer_ctx.lock();
+
+    ggml_vk_ctx_end(transfer_ctx);
+
+    for (auto& cpy : transfer_ctx->in_memcpys) {
+        memcpy(cpy.dst, cpy.src, cpy.n);
+    }
+
+    ggml_vk_submit(transfer_ctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    for (auto& cpy : transfer_ctx->out_memcpys) {
+        memcpy(cpy.dst, cpy.src, cpy.n);
+    }
+
+    ctx->transfer_ctx.reset();
+}
+
+static bool ggml_vk_is_empty(ggml_tensor * node) {
+    return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE;
+}
+
+GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)");
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_vk_build_graph(ctx, cgraph->nodes[i], i, 0, true);
+    }
+    ggml_vk_preallocate_buffers(ctx);
+    ggml_pipeline_allocate_descriptor_sets(ctx->device);
+
+    int last_node = cgraph->n_nodes - 1;
+
+    // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly
+    while (last_node > 0 && ggml_vk_is_empty(cgraph->nodes[last_node])) {
+        last_node -= 1;
+    }
+
+    // Reserve tensor context space for all nodes
+    ctx->tensor_ctxs.resize(cgraph->n_nodes);
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_vk_build_graph(ctx, cgraph->nodes[i], i, i == last_node, false);
+    }
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+
+        if (ggml_vk_is_empty(node)) {
+            continue;
+        }
+
+        bool ok = ggml_vk_compute_forward(ctx, node, i);
+        if (!ok) {
+            if (node->op == GGML_OP_UNARY) {
+                std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast<ggml_unary_op>(node->op_params[0])) << ")" << std::endl;
+            } else {
+                std::cerr << __func__ << ": error: op not supported " << node->name << " (" << ggml_op_name(node->op) << ")" << std::endl;
+            }
+        }
+#ifdef GGML_VULKAN_CHECK_RESULTS
+        else {
+            ggml_vk_check_results_1(node);
+        }
+#endif
+        GGML_ASSERT(ok);
+    }
+
+#ifdef GGML_VULKAN_PERF
+    ctx->device->perf_logger->print_timings();
+#endif
+
+    ggml_vk_graph_cleanup(ctx);
+
+    return GGML_STATUS_SUCCESS;
+
+    UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+    // ggml_backend_vk_context * ctx = (ggml_backend_vk_context *) backend->context;
+
+    switch (op->op) {
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_GELU_QUICK:
+                case GGML_UNARY_OP_SILU:
+                case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_TANH:
+                    return ggml_is_contiguous(op->src[0]);
+                default:
+                    return false;
+            }
+            break;
+        case GGML_OP_MUL_MAT:
+        case GGML_OP_MUL_MAT_ID:
+            {
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q2_K:
+                    case GGML_TYPE_Q3_K:
+                    case GGML_TYPE_Q4_K:
+                    case GGML_TYPE_Q5_K:
+                    case GGML_TYPE_Q6_K:
+                    case GGML_TYPE_IQ4_NL:
+                        break;
+                    default:
+                        return false;
+                }
+                struct ggml_tensor * a;
+                struct ggml_tensor * b;
+                if (op->op == GGML_OP_MUL_MAT) {
+                    a = op->src[0];
+                    b = op->src[1];
+                } else {
+                    a = op->src[2];
+                    b = op->src[1];
+                }
+                if (a->ne[3] != b->ne[3]) {
+                    return false;
+                }
+                return true;
+            } break;
+        case GGML_OP_GET_ROWS:
+            {
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_IQ4_NL:
+                        return true;
+                    default:
+                        return false;
+                }
+            } break;
+        case GGML_OP_CPY:
+        case GGML_OP_DUP:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                ggml_type src1_type = op->src[1] != nullptr ? op->src[1]->type : src0_type;
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+                    return true;
+                }
+                return false;
+            } break;
+        case GGML_OP_REPEAT:
+            return ggml_type_size(op->type) == sizeof(float) && ggml_type_size(op->src[0]->type) == sizeof(float);
+        case GGML_OP_ROPE:
+            return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_NORM:
+        case GGML_OP_GROUP_NORM:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_ADD:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+        case GGML_OP_CONCAT:
+        case GGML_OP_UPSCALE:
+        case GGML_OP_SCALE:
+        case GGML_OP_SQR:
+        case GGML_OP_CLAMP:
+        case GGML_OP_PAD:
+        case GGML_OP_CONT:
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_ARGSORT:
+        case GGML_OP_SUM_ROWS:
+        case GGML_OP_IM2COL:
+        case GGML_OP_TIMESTEP_EMBEDDING:
+        case GGML_OP_LEAKY_RELU:
+            return true;
+        default:
+            return false;
+    }
+
+    UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+    const int min_batch_size = 32;
+
+    return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) ||
+           (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID);
+
+    UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_vk_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) {
+        return false;
+    }
+
+    ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+
+    return buft_ctx->device == ctx->device;
+}
+
+// TODO: enable async and synchronize
+static ggml_backend_i ggml_backend_vk_interface = {
+    /* .get_name                = */ ggml_backend_vk_name,
+    /* .free                    = */ ggml_backend_vk_free,
+    /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type,
+    /* .set_tensor_async        = */ NULL,  // ggml_backend_vk_set_tensor_async,
+    /* .get_tensor_async        = */ NULL,  // ggml_backend_vk_get_tensor_async,
+    /* .cpy_tensor_async        = */ NULL,  // ggml_backend_vk_cpy_tensor_async,
+    /* .synchronize             = */ NULL,  // ggml_backend_vk_synchronize,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_vk_graph_compute,
+    /* .supports_op             = */ ggml_backend_vk_supports_op,
+    /* .supports_buft           = */ ggml_backend_vk_supports_buft,
+    /* .offload_op              = */ ggml_backend_vk_offload_op,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_vk_guid() {
+    static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
+    return &guid;
+}
+
+GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
+    VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")");
+
+    ggml_backend_vk_context * ctx = new ggml_backend_vk_context;
+    ggml_vk_init(ctx, dev_num);
+
+    ggml_backend_t vk_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_vk_guid(),
+        /* .interface = */ ggml_backend_vk_interface,
+        /* .context   = */ ctx,
+    };
+
+    return vk_backend;
+}
+
+GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid());
+}
+
+GGML_CALL int ggml_backend_vk_get_device_count() {
+    return ggml_vk_get_device_count();
+}
+
+GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) {
+    ggml_vk_get_device_description(device, description, description_size);
+}
+
+GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
+    GGML_ASSERT(device < (int) vk_instance.device_indices.size());
+
+    vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];
+
+    vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties();
+
+    for (const vk::MemoryHeap& heap : memprops.memoryHeaps) {
+        if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) {
+            *total = heap.size;
+            *free = heap.size;
+            break;
+        }
+    }
+}
+
+// backend registry
+GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, void * user_data) {
+    ggml_backend_t vk_backend = ggml_backend_vk_init((int) (intptr_t) user_data);
+    return vk_backend;
+
+    UNUSED(params);
+}
+
+extern "C" GGML_CALL int ggml_backend_vk_reg_devices();
+
+GGML_CALL int ggml_backend_vk_reg_devices() {
+    ggml_vk_instance_init();
+
+    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
+        char name[128];
+        snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, i);
+        ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(i), (void *) (intptr_t) i);  // NOLINT
+    }
+    return vk_instance.device_indices.size();
+}
+
+// Extension availability
+static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
+#ifdef GGML_VULKAN_VALIDATE
+    bool portability_enumeration_ext = false;
+    // Check for portability enumeration extension for MoltenVK support
+    for (const auto& properties : instance_extensions) {
+        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
+            return true;
+        }
+    }
+    if (!portability_enumeration_ext) {
+        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
+    }
+#endif
+    return false;
+
+    UNUSED(instance_extensions);
+}
+static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
+#ifdef __APPLE__
+    bool portability_enumeration_ext = false;
+    // Check for portability enumeration extension for MoltenVK support
+    for (const auto& properties : instance_extensions) {
+        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
+            return true;
+        }
+    }
+    if (!portability_enumeration_ext) {
+        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
+    }
+#endif
+    return false;
+
+    UNUSED(instance_extensions);
+}
+
+// checks
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector<const ggml_tensor *>& done, int level = 0) {
+    if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) {
+        return;
+    }
+    for (int j = 0; j < level; j++) {
+        std::cerr << " ";
+    }
+    std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << std::endl;
+
+    done.push_back(tensor);
+
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (tensor->src[i] != nullptr) {
+            ggml_vk_print_graph_origin(tensor->src[i], done, level + 1);
+        }
+    }
+}
+
+static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, int i0, int i1, int i2, int i3) {
+    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16 && tensor->type != GGML_TYPE_I32) {
+        return;
+    }
+    i0 = std::max(i0, 5);
+    i1 = std::max(i1, 5);
+    i2 = std::max(i2, 0);
+    i3 = std::max(i3, 0);
+    fprintf(stderr, "         ");
+    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+        fprintf(stderr, "%7d ", idx1);
+    }
+    fprintf(stderr, "\n");
+    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
+        fprintf(stderr, "%7d: ", idx0);
+        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+            if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
+                float val;
+                if (tensor->type == GGML_TYPE_F32) {
+                    val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
+                } else if (tensor->type == GGML_TYPE_F16) {
+                    val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
+                } else if (tensor->type == GGML_TYPE_I32) {
+                    val = *(const int32_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
+                } else {
+                    GGML_ABORT("fatal error");
+                }
+                fprintf(stderr, "% 7.2f ", val);
+            } else {
+                fprintf(stderr, "        ");
+            }
+        }
+        fprintf(stderr, "\n");
+    }
+}
+
+static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) {
+    void * tensor_data = tensor->data;
+
+    const bool is_gpu = tensor->buffer != nullptr && ggml_backend_buffer_is_vk(tensor->buffer);
+
+    if (is_gpu) {
+        const size_t tensor_size = ggml_nbytes(tensor);
+        tensor_data = malloc(tensor_size);
+
+        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+        vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+        ggml_vk_buffer_read(buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
+    }
+
+    std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
+    std::cerr << "tensor=" << tensor << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl;
+    if (tensor->src[0] != nullptr) {
+        std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl;
+    }
+    if (tensor->src[1] != nullptr) {
+        std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl;
+    }
+    std::cerr << std::endl << "Result:" << std::endl;
+    ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
+    std::cerr << std::endl;
+    std::vector<const ggml_tensor *> done;
+    ggml_vk_print_graph_origin(tensor, done);
+
+    if (is_gpu) {
+        free(tensor_data);
+    }
+}
+
+void * comp_result;
+size_t comp_size;
+size_t comp_nb[GGML_MAX_DIMS];
+size_t check_counter = 0;
+static void ggml_vk_check_results_0(ggml_tensor * tensor) {
+    if (tensor->op == GGML_OP_TRANSPOSE) {
+        return;
+    }
+
+    check_counter++;
+    if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
+        return;
+    }
+
+    VK_LOG_DEBUG("ggml_vk_check_results_0(" << tensor->name << ")");
+
+    ggml_tensor * src0 = tensor->src[0];
+    ggml_tensor * src1 = tensor->src[1];
+    ggml_tensor * src2 = tensor->src[2];
+
+    struct ggml_init_params iparams = {
+        /*.mem_size   =*/ 2ul*1024ul*1024ul*1024ul,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ false,
+    };
+
+    struct ggml_context * ggml_ctx = ggml_init(iparams);
+
+    struct ggml_tensor * src0_clone = nullptr;
+    struct ggml_tensor * src1_clone = nullptr;
+    struct ggml_tensor * src2_clone = nullptr;
+    struct ggml_tensor * tensor_clone = nullptr;
+
+    size_t src0_size;
+    size_t src1_size;
+    size_t src2_size;
+
+    void * src0_buffer = nullptr;
+    void * src1_buffer = nullptr;
+    void * src2_buffer = nullptr;
+
+    if (src0 != nullptr) {
+        src0_clone = ggml_dup_tensor(ggml_ctx, src0);
+
+        src0_size = ggml_nbytes(src0);
+
+        src0_buffer = malloc(src0_size);
+        src0_clone->data = src0_buffer;
+        if (ggml_backend_buffer_is_host(src0->buffer)) {
+            memcpy(src0_clone->data, src0->data, src0_size);
+            memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
+        } else if (ggml_backend_buffer_is_vk(src0->buffer)) {
+            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra;
+            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+            uint64_t offset = extra->offset + src0->view_offs;
+            if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) {
+                for (int i3 = 0; i3 < src0->ne[3]; i3++) {
+                    for (int i2 = 0; i2 < src0->ne[2]; i2++) {
+                        const int idx = i3*src0->ne[2] + i2;
+                        ggml_vk_buffer_read(buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
+                    }
+                }
+
+                src0_clone->nb[0] = src0->nb[0];
+                src0_clone->nb[1] = src0->nb[1];
+                for (int i = 2; i < GGML_MAX_DIMS; i++) {
+                    src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1];
+                }
+            } else {
+                if (offset + src0_size >= buffer_gpu->size) {
+                    src0_size = buffer_gpu->size - offset;
+                }
+                ggml_vk_buffer_read(buffer_gpu, offset, src0_clone->data, src0_size);
+                memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
+            }
+        } else {
+            GGML_ABORT("fatal error");
+        }
+
+        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+            ggml_vk_print_tensor(src0, "src0");
+        }
+    }
+    if (src1 != nullptr) {
+        src1_clone = ggml_dup_tensor(ggml_ctx, src1);
+
+        src1_size = ggml_nbytes(src1);
+
+        src1_buffer = malloc(src1_size);
+        src1_clone->data = src1_buffer;
+        if (ggml_backend_buffer_is_host(src1->buffer)) {
+            memcpy(src1_clone->data, src1->data, src1_size);
+            memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
+        } else if (ggml_backend_buffer_is_vk(src1->buffer)) {
+            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra;
+            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+            uint64_t offset = extra->offset + src1->view_offs;
+            if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) {
+                for (int i3 = 0; i3 < src1->ne[3]; i3++) {
+                    for (int i2 = 0; i2 < src1->ne[2]; i2++) {
+                        const int idx = i3*src1->ne[2] + i2;
+                        ggml_vk_buffer_read(buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
+                    }
+                }
+
+                src1_clone->nb[0] = src1->nb[0];
+                src1_clone->nb[1] = src1->nb[1];
+                for (int i = 2; i < GGML_MAX_DIMS; i++) {
+                    src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1];
+                }
+            } else {
+                if (offset + src1_size >= buffer_gpu->size) {
+                    src1_size = buffer_gpu->size - offset;
+                }
+                ggml_vk_buffer_read(buffer_gpu, offset, src1_clone->data, src1_size);
+                memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
+            }
+        } else {
+            GGML_ABORT("fatal error");
+        }
+
+        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+            ggml_vk_print_tensor(src1, "src1");
+        }
+    }
+    if (src2 != nullptr) {
+        src2_clone = ggml_dup_tensor(ggml_ctx, src2);
+
+        src2_size = ggml_nbytes(src2);
+
+        src2_buffer = malloc(src2_size);
+        src2_clone->data = src2_buffer;
+        if (ggml_backend_buffer_is_host(src2->buffer)) {
+            memcpy(src2_clone->data, src2->data, src2_size);
+            memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
+        } else if (ggml_backend_buffer_is_vk(src2->buffer)) {
+            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra;
+            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+            uint64_t offset = extra->offset + src2->view_offs;
+            if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) {
+                for (int i3 = 0; i3 < src2->ne[3]; i3++) {
+                    for (int i2 = 0; i2 < src2->ne[2]; i2++) {
+                        const int idx = i3*src2->ne[2] + i2;
+                        ggml_vk_buffer_read(buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]);
+                    }
+                }
+
+                src2_clone->nb[0] = src2->nb[0];
+                src2_clone->nb[1] = src2->nb[1];
+                for (int i = 2; i < GGML_MAX_DIMS; i++) {
+                    src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1];
+                }
+            } else {
+                if (offset + src2_size >= buffer_gpu->size) {
+                    src2_size = buffer_gpu->size - offset;
+                }
+                ggml_vk_buffer_read(buffer_gpu, offset, src2_clone->data, src2_size);
+                memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
+            }
+        } else {
+            GGML_ABORT("fatal error");
+        }
+
+        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+            ggml_vk_print_tensor(src2, "src2");
+        }
+    }
+
+    if (tensor->op == GGML_OP_MUL_MAT) {
+        tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_MUL_MAT_ID) {
+        tensor_clone = ggml_mul_mat_id(ggml_ctx, src0_clone, src1_clone, src2_clone);
+    } else if (tensor->op == GGML_OP_MUL) {
+        tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_DIV) {
+        tensor_clone = ggml_div(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_CONCAT) {
+        tensor_clone = ggml_concat(ggml_ctx, src0_clone, src1_clone, *(int *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_UPSCALE) {
+        tensor_clone = ggml_upscale_ext(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
+    } else if (tensor->op == GGML_OP_SCALE) {
+        tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]);
+    } else if (tensor->op == GGML_OP_SQR) {
+        tensor_clone = ggml_sqr(ggml_ctx, src0_clone);
+    } else if (tensor->op == GGML_OP_CLAMP) {
+        tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
+    } else if (tensor->op == GGML_OP_PAD) {
+        tensor_clone = ggml_pad(ggml_ctx, src0_clone, tensor->ne[0] - src0_clone->ne[0], tensor->ne[1] - src0_clone->ne[1], tensor->ne[2] - src0_clone->ne[2], tensor->ne[3] - src0_clone->ne[3]);
+    } else if (tensor->op == GGML_OP_REPEAT) {
+        tensor_clone = ggml_repeat(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_ADD) {
+        tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_NORM) {
+        tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_GROUP_NORM) {
+        tensor_clone = ggml_group_norm(ggml_ctx, src0_clone, *(int *)tensor->op_params, ((float *)tensor->op_params)[1]);
+    } else if (tensor->op == GGML_OP_RMS_NORM) {
+        tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_SOFT_MAX) {
+        if (src1 != nullptr) {
+            tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
+        } else {
+            tensor_clone = ggml_soft_max(ggml_ctx, src0_clone);
+        }
+    } else if (tensor->op == GGML_OP_DIAG_MASK_INF) {
+        tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(int *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_ROPE) {
+        const int n_dims      = ((int32_t *) tensor->op_params)[1];
+        const int mode        = ((int32_t *) tensor->op_params)[2];
+        //const int n_ctx_ggml       = ((int32_t *) tensor->op_params)[3];
+        const int n_ctx_orig_ggml  = ((int32_t *) tensor->op_params)[4];
+        const float freq_base       = ((float *) tensor->op_params)[5];
+        const float freq_scale      = ((float *) tensor->op_params)[6];
+        const float ext_factor      = ((float *) tensor->op_params)[7];
+        const float attn_factor     = ((float *) tensor->op_params)[8];
+        const float beta_fast       = ((float *) tensor->op_params)[9];
+        const float beta_slow       = ((float *) tensor->op_params)[10];
+        tensor_clone = ggml_rope_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, n_dims, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+    } else if (tensor->op == GGML_OP_UNARY) {
+        switch (ggml_get_unary_op(tensor)) {
+        case GGML_UNARY_OP_SILU:
+            tensor_clone = ggml_silu(ggml_ctx, src0_clone);
+            break;
+        case GGML_UNARY_OP_GELU:
+            tensor_clone = ggml_gelu(ggml_ctx, src0_clone);
+            break;
+        case GGML_UNARY_OP_GELU_QUICK:
+            tensor_clone = ggml_gelu_quick(ggml_ctx, src0_clone);
+            break;
+        case GGML_UNARY_OP_RELU:
+            tensor_clone = ggml_relu(ggml_ctx, src0_clone);
+            break;
+        case GGML_UNARY_OP_TANH:
+            tensor_clone = ggml_tanh(ggml_ctx, src0_clone);
+            break;
+        default:
+            std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
+            GGML_ABORT("fatal error");
+        }
+    } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) {
+        if (src1 == nullptr) {
+            tensor_clone = ggml_dup(ggml_ctx, src0_clone);
+            tensor_clone->type = tensor->type;
+        } else {
+            tensor_clone = ggml_cpy(ggml_ctx, src0_clone, src1_clone);
+        }
+    } else if (tensor->op == GGML_OP_CONT) {
+        tensor_clone = ggml_cont_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
+    } else if (tensor->op == GGML_OP_RESHAPE) {
+        tensor_clone = ggml_reshape_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
+    } else if (tensor->op == GGML_OP_VIEW) {
+        tensor_clone = ggml_view_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]);
+    } else if (tensor->op == GGML_OP_PERMUTE) {
+        int32_t * params = (int32_t *)tensor->op_params;
+        tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]);
+    } else if (tensor->op == GGML_OP_TRANSPOSE) {
+        tensor_clone = ggml_transpose(ggml_ctx, src0_clone);
+    } else if (tensor->op == GGML_OP_GET_ROWS) {
+        tensor_clone = ggml_get_rows(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_ARGSORT) {
+        tensor_clone = ggml_argsort(ggml_ctx, src0_clone, (ggml_sort_order) *(int *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_SUM_ROWS) {
+        tensor_clone = ggml_sum_rows(ggml_ctx, src0_clone);
+    } else if (tensor->op == GGML_OP_IM2COL) {
+        const int32_t s0 = tensor->op_params[0];
+        const int32_t s1 = tensor->op_params[1];
+        const int32_t p0 = tensor->op_params[2];
+        const int32_t p1 = tensor->op_params[3];
+        const int32_t d0 = tensor->op_params[4];
+        const int32_t d1 = tensor->op_params[5];
+
+        const bool is_2D = tensor->op_params[6] == 1;
+        tensor_clone = ggml_im2col(ggml_ctx, src0_clone, src1_clone, s0, s1, p0, p1, d0, d1, is_2D, tensor->type);
+    } else if (tensor->op == GGML_OP_TIMESTEP_EMBEDDING) {
+        const int32_t dim = tensor->op_params[0];
+        const int32_t max_period = tensor->op_params[1];
+        tensor_clone = ggml_timestep_embedding(ggml_ctx, src0_clone, dim, max_period);
+    } else if (tensor->op == GGML_OP_LEAKY_RELU) {
+        const float * op_params = (const float *)tensor->op_params;
+        tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false);
+    } else {
+        std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
+        GGML_ABORT("fatal error");
+    }
+
+    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
+    ggml_build_forward_expand(cgraph, tensor_clone);
+
+    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8);
+
+    if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+        ggml_vk_print_tensor(tensor_clone, "tensor_clone");
+    }
+
+    comp_size = ggml_nbytes(tensor_clone);
+
+    comp_result = malloc(comp_size);
+    memcpy(comp_result, tensor_clone->data, comp_size);
+    memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS);
+
+    if (src0 != nullptr) {
+        free(src0_buffer);
+    }
+    if (src1 != nullptr) {
+        free(src1_buffer);
+    }
+
+    ggml_free(ggml_ctx);
+
+    VK_LOG_DEBUG("END ggml_vk_check_results_0(" << tensor->name << ")");
+}
+
+static void ggml_vk_check_results_1(ggml_tensor * tensor) {
+    if (tensor->op == GGML_OP_TRANSPOSE) {
+        return;
+    }
+    if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
+        return;
+    }
+
+    VK_LOG_DEBUG("ggml_vk_check_results_1(" << tensor->name << ")");
+
+    ggml_tensor * src0 = tensor->src[0];
+    ggml_tensor * src1 = tensor->src[1];
+    ggml_tensor * src2 = tensor->src[2];
+
+    void * tensor_data = tensor->data;
+
+    if (ggml_backend_buffer_is_vk(tensor->buffer)) {
+        size_t tensor_size = ggml_nbytes(tensor);
+        tensor_data = malloc(tensor_size);
+
+        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+        vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+        if (extra->offset + tensor->view_offs + tensor_size >= buffer_gpu->size) {
+            tensor_size = buffer_gpu->size - (extra->offset + tensor->view_offs);
+        }
+
+        ggml_vk_buffer_read(buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
+    }
+
+    float first_error_result = -1.0f;
+    float first_error_correct = -1.0f;
+    std::array<int, 4> first_error = { -1, -1, -1, -1 };
+    double avg_err = 0.0;
+    size_t counter = 0;
+
+    for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
+        for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
+            for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
+                for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
+                    const bool buffer_size_fit = i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0] < comp_size;
+                    float correct = 0.0f;
+                    float result = 0.0f;
+
+                    if (buffer_size_fit) {
+                        if (tensor->type == GGML_TYPE_F32) {
+                            correct = *(float *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
+                            result  = *(float *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
+                        } else if (tensor->type == GGML_TYPE_F16) {
+                            correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]));
+                            result  = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
+                        } else if (tensor->type == GGML_TYPE_I32) {
+                            correct = *(int32_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
+                            result  = *(int32_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
+                        } else {
+                            std::cerr << "Results check not implemented for type " << ggml_type_name(tensor->type) << std::endl;
+                        }
+                    } else {
+                        std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl;
+                        GGML_ABORT("fatal error");
+                    }
+
+                    if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) {
+                        std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl;
+                        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
+                        if (src0 != nullptr) {
+                            std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
+                        }
+                        if (src1 != nullptr) {
+                            std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
+                        }
+                        if (src2 != nullptr) {
+                            std::cerr << "src2=" << src2 << " src2->name=" << src2->name << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
+                        }
+                        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
+                        std::cerr << std::endl << "Result:" << std::endl;
+                        ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3);
+                        std::cerr << std::endl << "Correct:" << std::endl;
+                        ggml_vk_print_tensor_area(tensor, comp_result, i0, i1, i2, i3);
+                        std::cerr << std::endl;
+                        std::vector<const ggml_tensor *> done;
+                        ggml_vk_print_graph_origin(tensor, done);
+                        GGML_ABORT("fatal error");
+                    }
+                    if (first_error[0] == -1 && std::fabs(correct - result) > 0.1f) {
+                        first_error[0] = i0;
+                        first_error[1] = i1;
+                        first_error[2] = i2;
+                        first_error[3] = i3;
+                        first_error_result = result;
+                        first_error_correct = correct;
+                    }
+
+                    // Special case, value is infinite, avoid NaN result in avg_err
+                    // NaN also appears in results, if both are nan error is 0
+                    if (!std::isinf(correct) && !std::isinf(result) && !std::isnan(correct) && !std::isnan(result)) {
+                        avg_err += std::fabs(correct - result);
+                    }
+                    counter++;
+                }
+            }
+        }
+    }
+
+    avg_err /= counter;
+
+    if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+        std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
+        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
+        if (src0 != nullptr) {
+            std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
+        }
+        if (src1 != nullptr) {
+            std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
+        }
+        if (src2 != nullptr) {
+            std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
+        }
+        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
+        std::cerr << std::endl << "Result:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
+        std::cerr << std::endl << "Correct:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 0, 0);
+        std::cerr << std::endl;
+        std::vector<const ggml_tensor *> done;
+        ggml_vk_print_graph_origin(tensor, done);
+    }
+
+    if (avg_err > 0.05 || std::isnan(avg_err)) {
+        std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
+        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
+        if (src0 != nullptr) {
+            std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
+        }
+        if (src1 != nullptr) {
+            std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
+        }
+        if (src2 != nullptr) {
+            std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
+        }
+        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
+        std::cerr << std::endl << "Result:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]);
+        std::cerr << std::endl << "Correct:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, comp_result, first_error[0], first_error[1], first_error[2], first_error[3]);
+        std::cerr << std::endl;
+        std::vector<const ggml_tensor *> done;
+        ggml_vk_print_graph_origin(tensor, done);
+        GGML_ABORT("fatal error");
+    } else {
+        std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " avg_err=" << avg_err << std::endl;
+    }
+
+    free(comp_result);
+    comp_result = nullptr;
+    comp_size = 0;
+
+    if (ggml_backend_buffer_is_vk(tensor->buffer)) {
+        free(tensor_data);
+    }
+
+    VK_LOG_DEBUG("END ggml_vk_check_results_1(" << tensor->name << ")");
+}
+#endif
diff --git a/ggml.c b/ggml/src/ggml.c
similarity index 87%
rename from ggml.c
rename to ggml/src/ggml.c
index 53da231ee061c..88e4fb7325dd9 100644
--- a/ggml.c
+++ b/ggml/src/ggml.c
@@ -4,6 +4,7 @@
 #include "ggml-impl.h"
 #include "ggml-quants.h"
 #include "ggml.h"
+#include "ggml-aarch64.h"
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <malloc.h> // using malloc.h with MSC/MINGW
@@ -28,16 +29,23 @@
 #include <syscall.h>
 #endif
 
+#ifdef GGML_USE_OPENMP
+#include <omp.h>
+#endif
+
 #ifdef GGML_USE_METAL
 #include <unistd.h>
 #endif
 
-#ifdef __ARM_FEATURE_MATMUL_INT8
+#if defined(__ARM_FEATURE_SVE)
+int ggml_sve_cnt_b = 0;
+#endif
+#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8)
 #undef GGML_USE_LLAMAFILE
 #endif
 
 #ifdef GGML_USE_LLAMAFILE
-#include "sgemm.h"
+#include <llamafile/sgemm.h>
 #endif
 
 #if defined(_MSC_VER)
@@ -48,6 +56,9 @@
 // disable POSIX deprecation warnings
 // these functions are never going away, anyway
 #pragma warning(disable: 4996)
+
+// unreachable code because of multiple instances of code after GGML_ABORT
+#pragma warning(disable: 4702)
 #endif
 
 #if defined(_WIN32)
@@ -60,6 +71,9 @@
 
 typedef volatile LONG atomic_int;
 typedef atomic_int atomic_bool;
+typedef atomic_int atomic_flag;
+
+#define ATOMIC_FLAG_INIT 0
 
 static void atomic_store(atomic_int * ptr, LONG val) {
     InterlockedExchange(ptr, val);
@@ -73,6 +87,12 @@ static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) {
 static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) {
     return atomic_fetch_add(ptr, -(dec));
 }
+static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) {
+    return InterlockedExchange(ptr, 1);
+}
+static void atomic_flag_clear(atomic_flag * ptr) {
+    InterlockedExchange(ptr, 0);
+}
 
 typedef HANDLE pthread_t;
 
@@ -127,23 +147,69 @@ typedef pthread_t ggml_thread_t;
 
 #include <sys/wait.h>
 
-void ggml_print_backtrace(void) {
-    /*
-    #include <execinfo.h>
-    #include <dlfcn.h>
+#if defined(__ANDROID__)
+#include <unwind.h>
+#include <dlfcn.h>
+#include <stdio.h>
+
+struct backtrace_state {
+    void ** current;
+    void ** end;
+};
 
-    void * trace[100];
+static _Unwind_Reason_Code unwind_callback(struct _Unwind_Context* context, void* arg) {
+    struct backtrace_state * state = (struct backtrace_state *)arg;
+    uintptr_t pc = _Unwind_GetIP(context);
+    if (pc) {
+        if (state->current == state->end) {
+            return _URC_END_OF_STACK;
+        } else {
+            *state->current++ = (void*)pc;
+        }
+    }
+    return _URC_NO_REASON;
+}
 
-    int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0]));
+static void ggml_print_backtrace_symbols(void) {
+    const int max = 100;
+    void* buffer[max];
 
+    struct backtrace_state state = {buffer, buffer + max};
+    _Unwind_Backtrace(unwind_callback, &state);
+
+    int count = state.current - buffer;
+
+    for (int idx = 0; idx < count; ++idx) {
+        const void * addr = buffer[idx];
+        const char * symbol = "";
+
+        Dl_info info;
+        if (dladdr(addr, &info) && info.dli_sname) {
+            symbol = info.dli_sname;
+        }
+
+        fprintf(stderr, "%d: %p %s\n", idx, addr, symbol);
+    }
+}
+#elif defined(__linux__) && defined(__GLIBC__)
+#include <execinfo.h>
+static void ggml_print_backtrace_symbols(void) {
+    void * trace[100];
+    int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0]));
     backtrace_symbols_fd(trace, nptrs, STDERR_FILENO);
-    */
+}
+#else
+static void ggml_print_backtrace_symbols(void) {
+    // platform not supported
+}
+#endif
 
-    // backtrack_symbols does not show line numbers, use gdb instead
+static void ggml_print_backtrace(void) {
     char attach[32];
     snprintf(attach, sizeof(attach), "attach %d", getpid());
     int pid = fork();
     if (pid == 0) {
+        // try gdb
         execlp("gdb", "gdb", "--batch",
             "-ex", "set style enabled on",
             "-ex", attach,
@@ -151,17 +217,46 @@ void ggml_print_backtrace(void) {
             "-ex", "detach",
             "-ex", "quit",
             (char *) NULL);
+        // try lldb
+        execlp("lldb", "lldb", "--batch",
+            "-o", "bt",
+            "-o", "quit",
+            "-p", attach,
+            (char *) NULL);
+        exit(EXIT_FAILURE);
     } else {
-        waitpid(pid, NULL, 0);
+        int wstatus;
+        waitpid(pid, &wstatus, 0);
+        if (WIFEXITED(wstatus)) {
+            if (WEXITSTATUS(wstatus) == EXIT_FAILURE) {
+                // gdb failed, fallback to backtrace_symbols
+                ggml_print_backtrace_symbols();
+            }
+        }
     }
 }
 #else
-void ggml_print_backtrace(void) {
+static void ggml_print_backtrace(void) {
     // platform not supported
 }
 #endif
 
-/*#define GGML_PERF*/
+void ggml_abort(const char * file, int line, const char * fmt, ...) {
+    fflush(stdout);
+
+    fprintf(stderr, "%s:%d: ", file, line);
+
+    va_list args;
+    va_start(args, fmt);
+    vfprintf(stderr, fmt, args);
+    va_end(args);
+
+    fprintf(stderr, "\n");
+
+    ggml_print_backtrace();
+    abort();
+}
+
 #define GGML_DEBUG 0
 #define GGML_GELU_FP16
 #define GGML_GELU_QUICK_FP16
@@ -233,7 +328,7 @@ inline static void * ggml_aligned_malloc(size_t size) {
                 break;
         }
         GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0));
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
         return NULL;
     }
     return aligned_memory;
@@ -254,7 +349,7 @@ inline static void * ggml_malloc(size_t size) {
     void * result = malloc(size);
     if (result == NULL) {
         GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0));
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
     return result;
 }
@@ -268,7 +363,7 @@ inline static void * ggml_calloc(size_t num, size_t size) {
     void * result = calloc(num, size);
     if (result == NULL) {
         GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0));
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
     return result;
 }
@@ -279,21 +374,10 @@ inline static void * ggml_calloc(size_t num, size_t size) {
 #define GGML_FREE(ptr) free(ptr)
 
 #define UNUSED GGML_UNUSED
-#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
+#define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0)
 
 #if defined(GGML_USE_ACCELERATE)
 #include <Accelerate/Accelerate.h>
-#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions
-#include "ggml-opencl.h"
-#endif
-#elif defined(GGML_USE_OPENBLAS)
-#if defined(GGML_BLAS_USE_MKL)
-#include <mkl.h>
-#else
-#include <cblas.h>
-#endif
-#elif defined(GGML_USE_CLBLAST)
-#include "ggml-opencl.h"
 #endif
 
 // floating point type used to accumulate sums
@@ -402,9 +486,16 @@ void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) {
     }
 }
 
+void ggml_fp32_to_bf16_row_ref(const float * x, ggml_bf16_t * y, int64_t n) {
+    for (int i = 0; i < n; i++) {
+        y[i] = ggml_compute_fp32_to_bf16(x[i]);
+    }
+}
+
 void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) {
   int i = 0;
 #if defined(__AVX512BF16__)
+  // subnormals are flushed to zero on this platform
   for (; i + 32 <= n; i += 32) {
         _mm512_storeu_si512(
             (__m512i *)(y + i),
@@ -471,18 +562,6 @@ int64_t ggml_cycles_per_ms(void) {
     return CLOCKS_PER_SEC/1000;
 }
 
-#ifdef GGML_PERF
-#define ggml_perf_time_ms()       ggml_time_ms()
-#define ggml_perf_time_us()       ggml_time_us()
-#define ggml_perf_cycles()        ggml_cycles()
-#define ggml_perf_cycles_per_ms() ggml_cycles_per_ms()
-#else
-#define ggml_perf_time_ms()       0
-#define ggml_perf_time_us()       0
-#define ggml_perf_cycles()        0
-#define ggml_perf_cycles_per_ms() 0
-#endif
-
 //
 // cross-platform UTF-8 file paths
 //
@@ -602,7 +681,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
         .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
-        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .from_float_ref           = (ggml_from_float_t) ggml_fp32_to_fp16_row,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
         .vec_dot_type             = GGML_TYPE_F16,
         .nrows                    = 1,
@@ -614,7 +693,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
         .from_float               = quantize_row_q4_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q4_0_ref,
         .vec_dot                  = ggml_vec_dot_q4_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
 #if defined (__ARM_FEATURE_MATMUL_INT8)
@@ -630,7 +709,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
         .from_float               = quantize_row_q4_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q4_1_ref,
         .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
 #if defined (__ARM_FEATURE_MATMUL_INT8)
@@ -646,7 +725,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = NULL,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_COUNT,
         .nrows                    = 1,
@@ -658,7 +737,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = NULL,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_COUNT,
         .nrows                    = 1,
@@ -670,7 +749,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
         .from_float               = quantize_row_q5_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q5_0_ref,
         .vec_dot                  = ggml_vec_dot_q5_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
@@ -682,7 +761,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
         .from_float               = quantize_row_q5_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q5_1_ref,
         .vec_dot                  = ggml_vec_dot_q5_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
         .nrows                    = 1,
@@ -694,7 +773,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q8_0,
         .from_float               = quantize_row_q8_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q8_0_ref,
+        .from_float_to_mat        = quantize_mat_q8_0,
         .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
 #if defined (__ARM_FEATURE_MATMUL_INT8)
@@ -709,7 +789,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(block_q8_1),
         .is_quantized             = true,
         .from_float               = quantize_row_q8_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q8_1_ref,
         .vec_dot_type             = GGML_TYPE_Q8_1,
         .nrows                    = 1,
     },
@@ -720,7 +800,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
         .from_float               = quantize_row_q2_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q2_K_ref,
         .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -732,7 +812,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
         .from_float               = quantize_row_q3_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q3_K_ref,
         .vec_dot                  = ggml_vec_dot_q3_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -744,7 +824,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
         .from_float               = quantize_row_q4_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q4_K_ref,
         .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -756,7 +836,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
         .from_float               = quantize_row_q5_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q5_K_ref,
         .vec_dot                  = ggml_vec_dot_q5_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -768,7 +848,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
         .from_float               = quantize_row_q6_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q6_K_ref,
         .vec_dot                  = ggml_vec_dot_q6_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -780,7 +860,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_xxs,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = ggml_vec_dot_iq2_xxs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -792,7 +872,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_xs,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = ggml_vec_dot_iq2_xs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -804,7 +884,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq3_xxs,
         .from_float               = quantize_row_iq3_xxs,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq3_xxs_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq3_xxs_ref,
         .vec_dot                  = ggml_vec_dot_iq3_xxs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -816,7 +896,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq3_s,
         .from_float               = quantize_row_iq3_s,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq3_s_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq3_s_ref,
         .vec_dot                  = ggml_vec_dot_iq3_s_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -828,7 +908,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_s,
         .from_float               = quantize_row_iq2_s,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq2_s_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq2_s_ref,
         .vec_dot                  = ggml_vec_dot_iq2_s_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -840,7 +920,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq1_s,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = ggml_vec_dot_iq1_s_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -852,7 +932,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq1_m,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = ggml_vec_dot_iq1_m_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -864,29 +944,21 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq4_nl,
         .from_float               = quantize_row_iq4_nl,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq4_nl_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq4_nl_ref,
         .vec_dot                  = ggml_vec_dot_iq4_nl_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
     },
     [GGML_TYPE_IQ4_XS] = {
         .type_name                = "iq4_xs",
-#if QK_K == 64
-        .blck_size                = QK4_NL,
-#else
         .blck_size                = QK_K,
-#endif
         .type_size                = sizeof(block_iq4_xs),
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq4_xs,
         .from_float               = quantize_row_iq4_xs,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq4_xs_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq4_xs_ref,
         .vec_dot                  = ggml_vec_dot_iq4_xs_q8_K,
-#if QK_K == 64
-        .vec_dot_type             = GGML_TYPE_Q8_0,
-#else
         .vec_dot_type             = GGML_TYPE_Q8_K,
-#endif
         .nrows                    = 1,
     },
     [GGML_TYPE_Q8_K] = {
@@ -903,10 +975,58 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = (ggml_to_float_t) ggml_bf16_to_fp32_row,
         .from_float               = (ggml_from_float_t) ggml_fp32_to_bf16_row,
-        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_bf16_row,
+        .from_float_ref           = (ggml_from_float_t) ggml_fp32_to_bf16_row_ref,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_bf16,
         .vec_dot_type             = GGML_TYPE_BF16,
         .nrows                    = 1,
+    },
+    [GGML_TYPE_Q4_0_4_4] = {
+        .type_name                = "q4_0_4x4",
+        .blck_size                = QK4_0,
+        .blck_size_interleave     = 4,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
+        .to_float                 = NULL,
+        .from_float               = NULL,
+        .from_float_ref           = NULL,
+        .vec_dot                  = NULL,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+        .ncols                    = 4,
+        .gemv                     = ggml_gemv_q4_0_4x4_q8_0,
+        .gemm                     = ggml_gemm_q4_0_4x4_q8_0,
+    },
+    [GGML_TYPE_Q4_0_4_8] = {
+        .type_name                = "q4_0_4x8",
+        .blck_size                = QK4_0,
+        .blck_size_interleave     = 8,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
+        .to_float                 = NULL,
+        .from_float               = NULL,
+        .from_float_ref           = NULL,
+        .vec_dot                  = NULL,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+        .ncols                    = 4,
+        .gemv                     = ggml_gemv_q4_0_4x8_q8_0,
+        .gemm                     = ggml_gemm_q4_0_4x8_q8_0,
+    },
+    [GGML_TYPE_Q4_0_8_8] = {
+        .type_name                = "q4_0_8x8",
+        .blck_size                = QK4_0,
+        .blck_size_interleave     = 8,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
+        .to_float                 = NULL,
+        .from_float               = NULL,
+        .from_float_ref           = NULL,
+        .vec_dot                  = NULL,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+        .ncols                    = 8,
+        .gemv                     = ggml_gemv_q4_0_8x8_q8_0,
+        .gemm                     = ggml_gemm_q4_0_8x8_q8_0,
     }
 };
 
@@ -1523,6 +1643,196 @@ static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) {
 #define GGML_F16_VEC_MUL             GGML_F32Cx4_MUL
 #define GGML_F16_VEC_REDUCE          GGML_F32Cx4_REDUCE
 
+#elif defined(__loongarch_asx)
+
+#define GGML_SIMD
+
+// F32 LASX
+#define GGML_F32_STEP 32
+#define GGML_F32_EPR  8
+
+#define GGML_F32x8         __m256
+#define GGML_F32x8_ZERO    (__m256)__lasx_xvldi(0)
+#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x))
+#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0)
+#define GGML_F32x8_STORE(x,y)   __lasx_xvst((y), (x), 0)
+#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a)
+#define GGML_F32x8_ADD     __lasx_xvfadd_s
+#define GGML_F32x8_MUL     __lasx_xvfmul_s
+#define GGML_F32x8_REDUCE(res, x)                                 \
+do {                                                              \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = __lasx_xvfadd_s(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = __lasx_xvfadd_s(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = __lasx_xvfadd_s(x[i], x[offset+i]);                  \
+    }                                                             \
+    float *tmp_p = (float *)&x[0]; \
+    res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7];  \
+} while (0)
+// TODO: is this optimal ?
+
+#define GGML_F32_VEC        GGML_F32x8
+#define GGML_F32_VEC_ZERO   GGML_F32x8_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x8_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x8_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x8_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x8_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x8_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x8_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE
+
+// F16 LASX
+
+#define GGML_F16_STEP 32
+#define GGML_F16_EPR  8
+
+// F16 arithmetic is not supported by AVX, so we use F32 instead
+
+#define GGML_F32Cx8          __m256
+#define GGML_F32Cx8_ZERO    (__m256)__lasx_xvldi(0)
+#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x))
+
+static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) {
+    float tmp[8];
+
+    for (int i = 0; i < 8; i++) {
+        tmp[i] = GGML_FP16_TO_FP32(x[i]);
+    }
+
+    return (__m256)__lasx_xvld(tmp, 0);
+}
+static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
+    float arr[8];
+
+    __lasx_xvst(y, arr, 0);
+
+    for (int i = 0; i < 8; i++) {
+        x[i] = GGML_FP32_TO_FP16(arr[i]);
+    }
+}
+#define GGML_F32Cx8_LOAD(x)     __lasx_f32cx8_load(x)
+#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y)
+
+#define GGML_F32Cx8_FMA         GGML_F32x8_FMA
+#define GGML_F32Cx8_ADD         __lasx_xvfadd_s
+#define GGML_F32Cx8_MUL         __lasx_xvfmul_s
+#define GGML_F32Cx8_REDUCE      GGML_F32x8_REDUCE
+
+#define GGML_F16_VEC                GGML_F32Cx8
+#define GGML_F16_VEC_ZERO           GGML_F32Cx8_ZERO
+#define GGML_F16_VEC_SET1           GGML_F32Cx8_SET1
+#define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx8_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i])
+#define GGML_F16_VEC_FMA            GGML_F32Cx8_FMA
+#define GGML_F16_VEC_ADD            GGML_F32Cx8_ADD
+#define GGML_F16_VEC_MUL            GGML_F32Cx8_MUL
+#define GGML_F16_VEC_REDUCE         GGML_F32Cx8_REDUCE
+
+#elif defined(__loongarch_sx)
+
+#define GGML_SIMD
+
+// F32 LSX
+
+#define GGML_F32_STEP 32
+#define GGML_F32_EPR  4
+
+#define GGML_F32x4         __m128
+#define GGML_F32x4_ZERO    __lsx_vldi(0)
+#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0)
+#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0)
+#define GGML_F32x4_STORE((x),(y))   __lsx_vst((y), (x), 0)
+#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a)
+#define GGML_F32x4_ADD     __lsx_vfadd_s
+#define GGML_F32x4_MUL     __lsx_vfmul_s
+#define GGML_F32x4_REDUCE(res, x)                                 \
+{                                                                 \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = __lsx_vfadd_s(x[i], x[offset+i]);                     \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = __lsx_vfadd_s(x[i], x[offset+i]);                     \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = __lsx_vfadd_s(x[i], x[offset+i]);                     \
+    }                                                             \
+    __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \
+    tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \
+    tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \
+    const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \
+    tmp = __lsx_vsrli_d((__m128i)t0, 32); \
+    tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \
+    tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \
+    res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0);        \
+}
+
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
+
+// F16 LSX
+
+#define GGML_F16_STEP 32
+#define GGML_F16_EPR  4
+
+static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) {
+    float tmp[4];
+
+    tmp[0] = GGML_FP16_TO_FP32(x[0]);
+    tmp[1] = GGML_FP16_TO_FP32(x[1]);
+    tmp[2] = GGML_FP16_TO_FP32(x[2]);
+    tmp[3] = GGML_FP16_TO_FP32(x[3]);
+
+    return __lsx_vld(tmp, 0);
+}
+
+static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
+    float arr[4];
+
+    __lsx_vst(y, arr, 0);
+
+    x[0] = GGML_FP32_TO_FP16(arr[0]);
+    x[1] = GGML_FP32_TO_FP16(arr[1]);
+    x[2] = GGML_FP32_TO_FP16(arr[2]);
+    x[3] = GGML_FP32_TO_FP16(arr[3]);
+}
+
+#define GGML_F32Cx4             __m128
+#define GGML_F32Cx4_ZERO        __lsx_vldi(0)
+#define GGML_F32Cx4_SET1(x)     __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0)
+#define GGML_F32Cx4_LOAD(x)     __lsx_f16x4_load(x)
+#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y)
+#define GGML_F32Cx4_FMA         GGML_F32x4_FMA
+#define GGML_F32Cx4_ADD         __lsx_vfadd_s
+#define GGML_F32Cx4_MUL         __lsx_vfmul_s
+#define GGML_F32Cx4_REDUCE      GGML_F32x4_REDUCE
+
+#define GGML_F16_VEC                 GGML_F32Cx4
+#define GGML_F16_VEC_ZERO            GGML_F32Cx4_ZERO
+#define GGML_F16_VEC_SET1            GGML_F32Cx4_SET1
+#define GGML_F16_VEC_LOAD(p, i)      GGML_F32Cx4_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i)  GGML_F32Cx4_STORE(p, r[i])
+#define GGML_F16_VEC_FMA             GGML_F32Cx4_FMA
+#define GGML_F16_VEC_ADD             GGML_F32Cx4_ADD
+#define GGML_F16_VEC_MUL             GGML_F32Cx4_MUL
+#define GGML_F16_VEC_REDUCE          GGML_F32Cx4_REDUCE
+
 #endif
 
 // GGML_F32_ARR / GGML_F16_ARR
@@ -1545,8 +1855,8 @@ struct ggml_context {
 
     int    n_objects;
 
-    struct ggml_object* objects_begin;
-    struct ggml_object* objects_end;
+    struct ggml_object * objects_begin;
+    struct ggml_object * objects_end;
 
     struct ggml_scratch scratch;
     struct ggml_scratch scratch_save;
@@ -1559,30 +1869,38 @@ struct ggml_context_container {
 };
 
 struct ggml_compute_state_shared {
-    const struct ggml_cgraph* cgraph;
-    const struct ggml_cplan* cplan;
+    const struct ggml_cgraph * cgraph;
+    const struct ggml_cplan * cplan;
 
-    int64_t perf_node_start_cycles;
-    int64_t perf_node_start_time_us;
-
-    const int n_threads;
+    int n_threads;
 
     // synchronization primitives
-    atomic_int n_active;  // num active threads
-    atomic_int node_n;    // active graph node
-    atomic_int node_task; // active graph node task phase
+    atomic_int n_barrier;
+    atomic_int n_barrier_passed;
 
     ggml_abort_callback abort_callback; // abort ggml_graph_compute when true
-    void* abort_callback_data;
+    void * abort_callback_data;
+
+    atomic_int current_chunk; // currently processing chunk during mul_mat, shared between all the threads
 
-    atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
+    enum ggml_status ec;
 };
 
 struct ggml_compute_state {
     ggml_thread_t thrd;
     int ith;
-    struct ggml_compute_state_shared* shared;
-    enum ggml_status ec;
+    struct ggml_compute_state_shared * shared;
+};
+
+struct ggml_compute_params {
+    // ith = thread index, nth = number of threads
+    int ith, nth;
+
+    // work buffer for all threads
+    size_t wsize;
+    void * wdata;
+
+    struct ggml_compute_state_shared * shared;
 };
 
 //
@@ -1997,7 +2315,7 @@ inline static void ggml_vec_abs_f32  (const int n, float * y, const float * x) {
 inline static void ggml_vec_sgn_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); }
 inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; }
 inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]);  }
-inline static void ggml_vec_elu_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
+inline static void ggml_vec_elu_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); }
 inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
 inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); }
 inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); }
@@ -2076,6 +2394,11 @@ inline static float ggml_silu_f32(float x) {
     return x/(1.0f + expf(-x));
 }
 
+#if __FINITE_MATH_ONLY__
+#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix"
+#error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461"
+#endif
+
 #if defined(__ARM_NEON) && defined(__aarch64__)
 
 // adapted from arm limited optimized routine
@@ -2125,32 +2448,27 @@ inline static __m512 ggml_v_expf(__m512 x) {
   const __m512 r = _mm512_set1_ps(0x1.8p23f);
   const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r);
   const __m512 n = _mm512_sub_ps(z, r);
-  const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f),
-                                    _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x));
-  const __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23);
-  const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1))));
-  const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _CMP_GT_OQ);
-  const __m512 u = _mm512_mul_ps(b, b);
-  const __m512 j = _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b,
-                                                                   _mm512_set1_ps(0x1.573e2ep-5f)), u,
-                                                   _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b,
-                                                                   _mm512_set1_ps(0x1.fffdb6p-2f))),
-                                   u, _mm512_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b));
-  if (_mm512_kortestz(c, c))
-    return _mm512_fmadd_ps(j, k, k);
-  const __m512i g = _mm512_and_si512(
-      _mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)),
-      _mm512_set1_epi32(0x82000000u));
-  const __m512 s1 =
-      _mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u)));
-  const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g));
+  const __m512 b =
+      _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f),
+                       _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x));
   const __mmask16 d =
       _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ);
-  return _mm512_mask_blend_ps(
-      d, _mm512_mask_blend_ps(
-          c, _mm512_fmadd_ps(k, j, k),
-          _mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)),
-      _mm512_mul_ps(s1, s1));
+  const __m512 u = _mm512_mul_ps(b, b);
+  const __m512 j = _mm512_fmadd_ps(
+      _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b,
+                                      _mm512_set1_ps(0x1.573e2ep-5f)),
+                      u,
+                      _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b,
+                                      _mm512_set1_ps(0x1.fffdb6p-2f))),
+      u,
+      _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F)));
+  const __m512 res = _mm512_scalef_ps(j, n);
+  if (_mm512_kortestz(d, d))
+    return res;
+  const __m512 zero = _mm512_setzero_ps();
+  const __m512 alt = _mm512_mask_blend_ps(
+      _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero);
+  return _mm512_mask_blend_ps(d, res, alt);
 }
 
 // computes silu x/(1+exp(-x)) in single precision vector
@@ -2489,9 +2807,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "ARGSORT",
     "LEAKY_RELU",
 
-    "FLASH_ATTN",
     "FLASH_ATTN_EXT",
-    "FLASH_FF",
     "FLASH_ATTN_BACK",
     "SSM_CONV",
     "SSM_SCAN",
@@ -2517,7 +2833,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS_BACK",
 };
 
-static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
+static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -2579,9 +2895,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "argsort(x)",
     "leaky_relu(x)",
 
-    "flash_attn(x)",
     "flash_attn_ext(x)",
-    "flash_ff(x)",
     "flash_attn_back(x)",
     "ssm_conv(x)",
     "ssm_scan(x)",
@@ -2607,7 +2921,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss_back(x,y)",
 };
 
-static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
+static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -2634,42 +2948,6 @@ static_assert(GGML_UNARY_OP_COUNT == 13, "GGML_UNARY_OP_COUNT != 13");
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
 static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
 
-// WARN:
-// Mis-configuration can lead to problem that's hard to reason about:
-// * At best  it crash or talks nosense.
-// * At worst it talks slightly difference but hard to perceive.
-//
-// An op has to enable INIT or FINALIZE when any of it's branch needs that pass.
-// Take care about compile options (e.g., GGML_USE_xxx).
-static bool GGML_OP_HAS_INIT    [GGML_OP_COUNT] = { 0 };
-static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 };
-
-static void ggml_setup_op_has_task_pass(void) {
-    {   // INIT
-        bool * p = GGML_OP_HAS_INIT;
-
-        p[GGML_OP_ACC                    ] = true;
-        p[GGML_OP_MUL_MAT                ] = true;
-        p[GGML_OP_MUL_MAT_ID             ] = true;
-        p[GGML_OP_OUT_PROD               ] = true;
-        p[GGML_OP_SET                    ] = true;
-        p[GGML_OP_GET_ROWS_BACK          ] = true;
-        p[GGML_OP_DIAG_MASK_INF          ] = true;
-        p[GGML_OP_DIAG_MASK_ZERO         ] = true;
-        p[GGML_OP_CONV_TRANSPOSE_1D      ] = true;
-        p[GGML_OP_CONV_TRANSPOSE_2D      ] = true;
-        p[GGML_OP_FLASH_ATTN_BACK        ] = true;
-        p[GGML_OP_CROSS_ENTROPY_LOSS     ] = true;
-        p[GGML_OP_ADD_REL_POS            ] = true;
-    }
-
-    {   // FINALIZE
-        bool * p = GGML_OP_HAS_FINALIZE;
-
-        p[GGML_OP_CROSS_ENTROPY_LOSS     ] = true;
-    }
-}
-
 //
 // NUMA support
 //
@@ -2706,38 +2984,76 @@ struct ggml_state {
 
 // global state
 static struct ggml_state g_state;
-static atomic_int g_state_barrier = 0;
+static atomic_flag g_state_critical = ATOMIC_FLAG_INIT;
 
-// barrier via spin lock
+// critical section via spin lock
 inline static void ggml_critical_section_start(void) {
-    int processing = atomic_fetch_add(&g_state_barrier, 1);
-
-    while (processing > 0) {
-        // wait for other threads to finish
-        atomic_fetch_sub(&g_state_barrier, 1);
-        sched_yield(); // TODO: reconsider this
-        processing = atomic_fetch_add(&g_state_barrier, 1);
+    while (atomic_flag_test_and_set(&g_state_critical)) {
+        // spin
+        sched_yield();
     }
 }
 
-// TODO: make this somehow automatically executed
-//       some sort of "sentry" mechanism
-inline static void ggml_critical_section_end(void) {
-    atomic_fetch_sub(&g_state_barrier, 1);
-}
+#ifdef GGML_USE_OPENMP
+static void ggml_barrier(struct ggml_compute_state_shared * shared) {
+    if (shared->n_threads == 1) {
+        return;
+    }
 
-#if defined(__gnu_linux__)
-static cpu_set_t ggml_get_numa_affinity(void) {
-    cpu_set_t cpuset;
-    pthread_t thread;
-    thread = pthread_self();
-    CPU_ZERO(&cpuset);
-    pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset);
-    return cpuset;
+    #pragma omp barrier
 }
 #else
-static uint32_t ggml_get_numa_affinity(void) {
-    return 0; // no NUMA support
+static void ggml_barrier(struct ggml_compute_state_shared * shared) {
+    if (shared->n_threads == 1) {
+        return;
+    }
+
+    atomic_int * n_barrier = &shared->n_barrier;
+    atomic_int * n_barrier_passed = &shared->n_barrier_passed;
+
+    int n_threads = shared->n_threads;
+    int passed_old = atomic_load(n_barrier_passed);
+
+    if (atomic_fetch_add(n_barrier, 1) == n_threads - 1) {
+        // last thread
+        atomic_store(n_barrier, 0);
+        atomic_fetch_add(n_barrier_passed, 1);
+    } else {
+        // wait for other threads
+        const int n_spin_before_sleep = 100000;
+        while (true) {
+            for (int i = 0; i < n_spin_before_sleep; i++) {
+                if (atomic_load(n_barrier_passed) != passed_old) {
+                    return;
+                }
+            #if defined(__SSE3__)
+                _mm_pause();
+            #endif
+            }
+            sched_yield();
+        }
+    }
+}
+#endif
+
+// TODO: make this somehow automatically executed
+//       some sort of "sentry" mechanism
+inline static void ggml_critical_section_end(void) {
+    atomic_flag_clear(&g_state_critical);
+}
+
+#if defined(__gnu_linux__)
+static cpu_set_t ggml_get_numa_affinity(void) {
+    cpu_set_t cpuset;
+    pthread_t thread;
+    thread = pthread_self();
+    CPU_ZERO(&cpuset);
+    pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset);
+    return cpuset;
+}
+#else
+static uint32_t ggml_get_numa_affinity(void) {
+    return 0; // no NUMA support
 }
 #endif
 
@@ -2824,7 +3140,7 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) {
         }
     }
 #else
-    GGML_UNUSED(numa_flag);
+    UNUSED(numa_flag);
     // TODO
 #endif
 }
@@ -2888,7 +3204,7 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
     return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN);
 }
 
-GGML_CALL int ggml_blck_size(enum ggml_type type) {
+GGML_CALL int64_t ggml_blck_size(enum ggml_type type) {
     return type_traits[type].blck_size;
 }
 
@@ -2930,9 +3246,7 @@ GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t) {
         enum ggml_unary_op uop = ggml_get_unary_op(t);
         return ggml_unary_op_name(uop);
     }
-    else {
-        return ggml_op_name(t->op);
-    }
+    return ggml_op_name(t->op);
 }
 
 GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor) {
@@ -3012,6 +3326,9 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_IQ4_XS:        wtype = GGML_TYPE_IQ4_XS;   break;
         case GGML_FTYPE_MOSTLY_IQ3_S:         wtype = GGML_TYPE_IQ3_S;    break;
         case GGML_FTYPE_MOSTLY_IQ2_S:         wtype = GGML_TYPE_IQ2_S;    break;
+        case GGML_FTYPE_MOSTLY_Q4_0_4_4:      wtype = GGML_TYPE_Q4_0_4_4; break;
+        case GGML_FTYPE_MOSTLY_Q4_0_4_8:      wtype = GGML_TYPE_Q4_0_4_8; break;
+        case GGML_FTYPE_MOSTLY_Q4_0_8_8:      wtype = GGML_TYPE_Q4_0_8_8; break;
         case GGML_FTYPE_UNKNOWN:              wtype = GGML_TYPE_COUNT; break;
         case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
     }
@@ -3029,23 +3346,42 @@ GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) {
     return tensor->nb[0] > tensor->nb[1];
 }
 
+static bool ggml_is_contiguous_n(const struct ggml_tensor * tensor, int n) {
+    size_t next_nb = ggml_type_size(tensor->type);
+    if (tensor->ne[0] != ggml_blck_size(tensor->type) && tensor->nb[0] != next_nb) {
+        return false;
+    }
+    next_nb *= tensor->ne[0]/ggml_blck_size(tensor->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        if (tensor->ne[i] != 1) {
+            if (i > n) {
+                if (tensor->nb[i] != next_nb) {
+                    return false;
+                }
+                next_nb *= tensor->ne[i];
+            } else {
+                // this dimension does not need to be contiguous
+                next_nb = tensor->ne[i]*tensor->nb[i];
+            }
+        }
+    }
+    return true;
+}
+
 GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
-    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+    return ggml_is_contiguous_0(tensor);
+}
 
-    return
-        tensor->nb[0] == ggml_type_size(tensor->type) &&
-        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
-        tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
-        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor) {
+    return ggml_is_contiguous_n(tensor, 0);
 }
 
-static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) {
-    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor) {
+    return ggml_is_contiguous_n(tensor, 1);
+}
 
-    return
-        tensor->nb[0] == ggml_type_size(tensor->type) &&
-        tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
-        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) {
+    return ggml_is_contiguous_n(tensor, 2);
 }
 
 GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) {
@@ -3077,24 +3413,24 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        (t0->ne[0] == t1->ne[0] ) &&
-        (t0->ne[1] == t1->ne[1] ) &&
-        (t0->ne[2] == t1->ne[2] ) &&
-        (t0->ne[3] == t1->ne[3] );
+        (t0->ne[0] == t1->ne[0]) &&
+        (t0->ne[1] == t1->ne[1]) &&
+        (t0->ne[2] == t1->ne[2]) &&
+        (t0->ne[3] == t1->ne[3]);
 }
 
 bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        (t0->nb[0] == t1->nb[0] ) &&
-        (t0->nb[1] == t1->nb[1] ) &&
-        (t0->nb[2] == t1->nb[2] ) &&
-        (t0->nb[3] == t1->nb[3] );
+        (t0->nb[0] == t1->nb[0]) &&
+        (t0->nb[1] == t1->nb[1]) &&
+        (t0->nb[2] == t1->nb[2]) &&
+        (t0->nb[3] == t1->nb[3]);
 }
 
 // check if t1 can be represented as a repeatition of t0
-static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return ggml_is_empty(t0) ? ggml_is_empty(t1) :
@@ -3125,7 +3461,7 @@ static inline int ggml_up(int n, int m) {
 }
 
 // assert that pointer is aligned to GGML_MEM_ALIGN
-#define ggml_assert_aligned(ptr) \
+#define GGML_ASSERT_ALIGNED(ptr) \
     GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0)
 
 ////////////////////////////////////////////////////////////////////////////////
@@ -3180,12 +3516,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
             GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
         }
 
-#if defined(GGML_USE_CLBLAST)
-        ggml_cl_init();
-#endif
-
-        ggml_setup_op_has_task_pass();
-
         is_first_call = false;
     }
 
@@ -3232,7 +3562,13 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
 
     GGML_ASSERT(ctx->mem_buffer != NULL);
 
-    ggml_assert_aligned(ctx->mem_buffer);
+    GGML_ASSERT_ALIGNED(ctx->mem_buffer);
+
+#if defined(__ARM_FEATURE_SVE)
+    if (!ggml_sve_cnt_b) {
+        ggml_sve_cnt_b = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL);
+    }
+#endif
 
     GGML_PRINT_DEBUG("%s: context initialized\n", __func__);
 
@@ -3364,7 +3700,7 @@ static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml
         .type = type,
     };
 
-    ggml_assert_aligned(mem_buffer + obj_new->offs);
+    GGML_ASSERT_ALIGNED(mem_buffer + obj_new->offs);
 
     if (obj_cur != NULL) {
         obj_cur->next = obj_new;
@@ -3388,7 +3724,8 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         struct ggml_tensor  * view_src,
         size_t                view_offs) {
 
-    assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);
+    GGML_ASSERT(type >= 0 && type < GGML_TYPE_COUNT);
+    GGML_ASSERT(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);
 
     // find the base tensor and absolute offset
     if (view_src != NULL && view_src->view_src != NULL) {
@@ -3452,15 +3789,12 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         /*.flags        =*/ 0,
         /*.grad         =*/ NULL,
         /*.src          =*/ { NULL },
-        /*.perf_runs    =*/ 0,
-        /*.perf_cycles  =*/ 0,
-        /*.perf_time_us =*/ 0,
         /*.view_src     =*/ view_src,
         /*.view_offs    =*/ view_offs,
         /*.data         =*/ obj_alloc_size > 0 ? (void *)(result + 1) : data,
         /*.name         =*/ { 0 },
         /*.extra        =*/ NULL,
-        /*.padding      =*/ { 0 },
+        ///*.padding      =*/ { 0 },
     };
 
 #ifdef __clang__
@@ -3468,7 +3802,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
 #endif
 
     // TODO: this should not be needed as long as we don't rely on aligned SIMD loads
-    //ggml_assert_aligned(result->data);
+    //GGML_ASSERT_ALIGNED(result->data);
 
     for (int i = 0; i < n_dims; i++) {
         result->ne[i] = ne[i];
@@ -3641,8 +3975,8 @@ struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 
     return tensor;
@@ -3700,8 +4034,8 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 
     return tensor;
@@ -3770,11 +4104,9 @@ int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) {
             }
         default:
             {
-                GGML_ASSERT(false);
+                GGML_ABORT("fatal error");
             }
     }
-
-    return 0.0f;
 }
 
 void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
@@ -3817,8 +4149,8 @@ void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -3838,10 +4170,8 @@ int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i
         case GGML_TYPE_F32:
             return ((float *) data)[0];
         default:
-            GGML_ASSERT(false);
+            GGML_ABORT("fatal error");
     }
-
-    return 0.0f;
 }
 
 void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) {
@@ -3873,8 +4203,8 @@ void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -3887,41 +4217,33 @@ float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
     switch (tensor->type) {
         case GGML_TYPE_I8:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
                 return ((int8_t *)(tensor->data))[i];
             }
         case GGML_TYPE_I16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
                 return ((int16_t *)(tensor->data))[i];
             }
         case GGML_TYPE_I32:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
                 return ((int32_t *)(tensor->data))[i];
             }
         case GGML_TYPE_F16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
                 return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
             }
         case GGML_TYPE_BF16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
                 return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]);
             }
         case GGML_TYPE_F32:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(float));
                 return ((float *)(tensor->data))[i];
             }
         default:
             {
-                GGML_ASSERT(false);
+                GGML_ABORT("fatal error");
             }
     }
-
-    return 0.0f;
 }
 
 void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
@@ -3934,38 +4256,32 @@ void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
     switch (tensor->type) {
         case GGML_TYPE_I8:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
                 ((int8_t *)(tensor->data))[i] = value;
             } break;
         case GGML_TYPE_I16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
                 ((int16_t *)(tensor->data))[i] = value;
             } break;
         case GGML_TYPE_I32:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
                 ((int32_t *)(tensor->data))[i] = value;
             } break;
         case GGML_TYPE_F16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
                 ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
             } break;
         case GGML_TYPE_BF16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
                 ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value);
             } break;
         case GGML_TYPE_F32:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(float));
                 ((float *)(tensor->data))[i] = value;
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -3985,10 +4301,8 @@ float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
         case GGML_TYPE_F32:
             return ((float *) data)[0];
         default:
-            GGML_ASSERT(false);
+            GGML_ABORT("fatal error");
     }
-
-    return 0.0f;
 }
 
 void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) {
@@ -4020,8 +4334,8 @@ void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2,
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -4044,8 +4358,11 @@ const char * ggml_get_name(const struct ggml_tensor * tensor) {
 }
 
 struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) {
-    strncpy(tensor->name, name, sizeof(tensor->name) - 1);
-    tensor->name[sizeof(tensor->name) - 1] = '\0';
+    size_t i;
+    for (i = 0; i < sizeof(tensor->name) - 1 && name[i] != '\0'; i++) {
+        tensor->name[i] = name[i];
+    }
+    tensor->name[i] = '\0';
     return tensor;
 }
 
@@ -4616,7 +4933,7 @@ struct ggml_tensor * ggml_mean(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement
+        GGML_ABORT("fatal error"); // TODO: implement
         is_node = true;
     }
 
@@ -4639,7 +4956,7 @@ struct ggml_tensor * ggml_argmax(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
         is_node = true;
     }
 
@@ -4705,10 +5022,21 @@ struct ggml_tensor * ggml_repeat_back(
 // ggml_concat
 
 struct ggml_tensor * ggml_concat(
-    struct ggml_context* ctx,
-    struct ggml_tensor* a,
-    struct ggml_tensor* b) {
-    GGML_ASSERT(a->ne[0] == b->ne[0] && a->ne[1] == b->ne[1] && a->ne[3] == b->ne[3]);
+    struct ggml_context * ctx,
+    struct ggml_tensor * a,
+    struct ggml_tensor * b,
+    int dim) {
+    GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS);
+
+    int64_t ne[GGML_MAX_DIMS];
+    for (int d = 0; d < GGML_MAX_DIMS; ++d) {
+        if (d == dim) {
+            ne[d] = a->ne[d] + b->ne[d];
+            continue;
+        }
+        GGML_ASSERT(a->ne[d] == b->ne[d]);
+        ne[d] = a->ne[d];
+    }
 
     bool is_node = false;
 
@@ -4716,7 +5044,9 @@ struct ggml_tensor * ggml_concat(
         is_node = true;
     }
 
-    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0], a->ne[1], a->ne[2] + b->ne[2], a->ne[3]);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne);
+
+    ggml_set_op_params_i32(result, 0, dim);
 
     result->op = GGML_OP_CONCAT;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -4836,6 +5166,7 @@ struct ggml_tensor * ggml_leaky_relu(
     }
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
     ggml_set_op_params(result, &negative_slope, sizeof(negative_slope));
 
     result->op   = GGML_OP_LEAKY_RELU;
@@ -4948,7 +5279,7 @@ static struct ggml_tensor * ggml_norm_impl(
     bool is_node = false;
 
     if (!inplace && (a->grad)) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -5047,17 +5378,19 @@ static struct ggml_tensor * ggml_group_norm_impl(
     struct ggml_context * ctx,
     struct ggml_tensor * a,
     int n_groups,
+    float eps,
     bool inplace) {
 
     bool is_node = false;
     if (!inplace && (a->grad)) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
-    result->op_params[0] = n_groups;
+    ggml_set_op_params_i32(result, 0, n_groups);
+    ggml_set_op_params_f32(result, 1, eps);
 
     result->op = GGML_OP_GROUP_NORM;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -5069,15 +5402,17 @@ static struct ggml_tensor * ggml_group_norm_impl(
 struct ggml_tensor * ggml_group_norm(
     struct ggml_context * ctx,
     struct ggml_tensor * a,
-    int n_groups) {
-    return ggml_group_norm_impl(ctx, a, n_groups, false);
+    int n_groups,
+    float eps) {
+    return ggml_group_norm_impl(ctx, a, n_groups, eps, false);
 }
 
 struct ggml_tensor * ggml_group_norm_inplace(
     struct ggml_context * ctx,
     struct ggml_tensor * a,
-    int n_groups) {
-    return ggml_group_norm_impl(ctx, a, n_groups, true);
+    int n_groups,
+    float eps) {
+    return ggml_group_norm_impl(ctx, a, n_groups, eps, true);
 }
 
 // ggml_mul_mat
@@ -5124,7 +5459,7 @@ void ggml_mul_mat_set_prec(
     as  -> [cols, rows, n_expert]
     ids -> [n_experts_used, n_tokens] (i32)
     b   -> [cols, n_expert_used, n_tokens]
-    c   -> [cols, n_expert_used, n_tokens]
+    c   -> [rows, n_expert_used, n_tokens]
 
     in b, n_experts_used can be broadcasted to match the n_expert_used of ids
 
@@ -5465,7 +5800,7 @@ struct ggml_tensor * ggml_reshape(
 
     if (b->grad) {
         // gradient propagation is not supported
-        //GGML_ASSERT(false);
+        //GGML_ABORT("fatal error");
     }
 
     struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, GGML_MAX_DIMS, b->ne, a, 0);
@@ -6042,23 +6377,28 @@ static struct ggml_tensor * ggml_rope_impl(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
+        struct ggml_tensor  * c,
         int                   n_dims,
         int                   mode,
-        int                   n_ctx,
-        int                   n_orig_ctx,
+        int                   n_ctx_orig,
         float                 freq_base,
         float                 freq_scale,
         float                 ext_factor,
         float                 attn_factor,
         float                 beta_fast,
         float                 beta_slow,
-        float                 xpos_base,
-        bool                  xpos_down,
         bool                  inplace) {
+    GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported");
+
     GGML_ASSERT(ggml_is_vector(b));
     GGML_ASSERT(b->type == GGML_TYPE_I32);
     GGML_ASSERT(a->ne[2] == b->ne[0]);
 
+    if (c) {
+        GGML_ASSERT(c->type == GGML_TYPE_F32);
+        GGML_ASSERT(c->ne[0] >= n_dims / 2);
+    }
+
     bool is_node = false;
 
     if (a->grad) {
@@ -6067,21 +6407,20 @@ static struct ggml_tensor * ggml_rope_impl(
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
-    int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
+    int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
     memcpy(params +  5, &freq_base,    sizeof(float));
     memcpy(params +  6, &freq_scale,   sizeof(float));
     memcpy(params +  7, &ext_factor,   sizeof(float));
     memcpy(params +  8, &attn_factor,  sizeof(float));
     memcpy(params +  9, &beta_fast,    sizeof(float));
     memcpy(params + 10, &beta_slow,    sizeof(float));
-    memcpy(params + 11, &xpos_base,    sizeof(float));
-    memcpy(params + 12, &xpos_down,    sizeof(bool));
     ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_ROPE;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
     result->src[1] = b;
+    result->src[2] = c;
 
     return result;
 }
@@ -6091,10 +6430,9 @@ struct ggml_tensor * ggml_rope(
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
         int                   n_dims,
-        int                   mode,
-        int                   n_ctx) {
+        int                   mode) {
     return ggml_rope_impl(
-        ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false
+        ctx, a, b, NULL, n_dims, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, false
     );
 }
 
@@ -6103,21 +6441,40 @@ struct ggml_tensor * ggml_rope_inplace(
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
         int                   n_dims,
+        int                   mode) {
+    return ggml_rope_impl(
+        ctx, a, b, NULL, n_dims, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, true
+    );
+}
+
+struct ggml_tensor * ggml_rope_ext(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        struct ggml_tensor  * c,
+        int                   n_dims,
         int                   mode,
-        int                   n_ctx) {
+        int                   n_ctx_orig,
+        float                 freq_base,
+        float                 freq_scale,
+        float                 ext_factor,
+        float                 attn_factor,
+        float                 beta_fast,
+        float                 beta_slow) {
     return ggml_rope_impl(
-        ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true
+        ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+        ext_factor, attn_factor, beta_fast, beta_slow, false
     );
 }
 
-struct ggml_tensor * ggml_rope_custom(
+struct ggml_tensor * ggml_rope_ext_inplace(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
+        struct ggml_tensor  * c,
         int                   n_dims,
         int                   mode,
-        int                   n_ctx,
-        int                   n_orig_ctx,
+        int                   n_ctx_orig,
         float                 freq_base,
         float                 freq_scale,
         float                 ext_factor,
@@ -6125,19 +6482,18 @@ struct ggml_tensor * ggml_rope_custom(
         float                 beta_fast,
         float                 beta_slow) {
     return ggml_rope_impl(
-        ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
-        ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false
+        ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+        ext_factor, attn_factor, beta_fast, beta_slow, true
     );
 }
 
-struct ggml_tensor * ggml_rope_custom_inplace(
+struct ggml_tensor * ggml_rope_custom(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
         int                   n_dims,
         int                   mode,
-        int                   n_ctx,
-        int                   n_orig_ctx,
+        int                   n_ctx_orig,
         float                 freq_base,
         float                 freq_scale,
         float                 ext_factor,
@@ -6145,19 +6501,28 @@ struct ggml_tensor * ggml_rope_custom_inplace(
         float                 beta_fast,
         float                 beta_slow) {
     return ggml_rope_impl(
-        ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
-        ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true
+        ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+        ext_factor, attn_factor, beta_fast, beta_slow, false
     );
 }
 
-struct ggml_tensor * ggml_rope_xpos_inplace(
+struct ggml_tensor * ggml_rope_custom_inplace(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
         int                   n_dims,
-        float                 base,
-        bool                  down) {
-    return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true);
+        int                   mode,
+        int                   n_ctx_orig,
+        float                 freq_base,
+        float                 freq_scale,
+        float                 ext_factor,
+        float                 attn_factor,
+        float                 beta_fast,
+        float                 beta_slow) {
+    return ggml_rope_impl(
+        ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
+        ext_factor, attn_factor, beta_fast, beta_slow, true
+    );
 }
 
 // ggml_rope_back
@@ -6166,21 +6531,20 @@ struct ggml_tensor * ggml_rope_back(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * b,
+        struct ggml_tensor  * c,
         int                   n_dims,
         int                   mode,
-        int                   n_ctx,
-        int                   n_orig_ctx,
+        int                   n_ctx_orig,
         float                 freq_base,
         float                 freq_scale,
         float                 ext_factor,
         float                 attn_factor,
         float                 beta_fast,
-        float                 beta_slow,
-        float                 xpos_base,
-        bool                  xpos_down) {
+        float                 beta_slow) {
     GGML_ASSERT(ggml_is_vector(b));
     GGML_ASSERT(b->type == GGML_TYPE_I32);
     GGML_ASSERT(a->ne[2] == b->ne[0]);
+    GGML_ASSERT(c == NULL && "freq factors not implemented yet");
 
     GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet");
 
@@ -6192,15 +6556,13 @@ struct ggml_tensor * ggml_rope_back(
 
     struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
 
-    int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
+    int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig };
     memcpy(params +  5, &freq_base,    sizeof(float));
     memcpy(params +  6, &freq_scale,   sizeof(float));
     memcpy(params +  7, &ext_factor,   sizeof(float));
     memcpy(params +  8, &attn_factor,  sizeof(float));
     memcpy(params +  9, &beta_fast,    sizeof(float));
     memcpy(params + 10, &beta_slow,    sizeof(float));
-    memcpy(params + 11, &xpos_base,    sizeof(float));
-    memcpy(params + 12, &xpos_down,    sizeof(bool));
     ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_ROPE_BACK;
@@ -6221,7 +6583,7 @@ struct ggml_tensor * ggml_clamp(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6297,7 +6659,7 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
     bool is_node = false;
 
     if (a->grad || b->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6369,7 +6731,7 @@ struct ggml_tensor * ggml_im2col(
     bool is_node = false;
 
     if (a->grad || b->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6455,7 +6817,7 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0(
     bool is_node = false;
 
     if (a->grad || b->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6496,7 +6858,7 @@ struct ggml_tensor * ggml_pool_1d(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6534,7 +6896,7 @@ struct ggml_tensor * ggml_pool_2d(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6567,7 +6929,7 @@ static struct ggml_tensor * ggml_upscale_impl(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6617,7 +6979,7 @@ struct ggml_tensor * ggml_pad(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6666,7 +7028,7 @@ struct ggml_tensor * ggml_timestep_embedding(
     bool is_node = false;
 
     if (timesteps->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -6724,38 +7086,6 @@ struct ggml_tensor * ggml_top_k(
     return result;
 }
 
-// ggml_flash_attn
-
-struct ggml_tensor * ggml_flash_attn(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * q,
-        struct ggml_tensor  * k,
-        struct ggml_tensor  * v,
-        bool                  masked) {
-    GGML_ASSERT(ggml_can_mul_mat(k, q));
-    // TODO: check if vT can be multiplied by (k*qT)
-
-    bool is_node = false;
-
-    if (q->grad || k->grad || v->grad) {
-        is_node = true;
-    }
-
-    //struct ggml_tensor * result = ggml_dup_tensor(ctx, q);
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, q->ne);
-
-    int32_t t = masked ? 1 : 0;
-    ggml_set_op_params(result, &t, sizeof(t));
-
-    result->op   = GGML_OP_FLASH_ATTN;
-    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
-    result->src[0] = q;
-    result->src[1] = k;
-    result->src[2] = v;
-
-    return result;
-}
-
 // ggml_flash_attn_ext
 
 struct ggml_tensor * ggml_flash_attn_ext(
@@ -6815,38 +7145,6 @@ void ggml_flash_attn_ext_set_prec(
     ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second
 }
 
-// ggml_flash_ff
-
-struct ggml_tensor * ggml_flash_ff(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * b0,
-        struct ggml_tensor  * b1,
-        struct ggml_tensor  * c0,
-        struct ggml_tensor  * c1) {
-    GGML_ASSERT(ggml_can_mul_mat(b0, a));
-    // TODO: more checks
-
-    bool is_node = false;
-
-    if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) {
-        is_node = true;
-    }
-
-    //struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, a->ne);
-
-    result->op   = GGML_OP_FLASH_FF;
-    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
-    result->src[0] = a;
-    result->src[1] = b0;
-    result->src[2] = b1;
-    result->src[3] = c0;
-    result->src[4] = c1;
-
-    return result;
-}
-
 // ggml_flash_attn_back
 
 struct ggml_tensor * ggml_flash_attn_back(
@@ -6856,6 +7154,8 @@ struct ggml_tensor * ggml_flash_attn_back(
         struct ggml_tensor  * v,
         struct ggml_tensor  * d,
         bool                  masked) {
+    GGML_ABORT("TODO: adapt to ggml_flash_attn_ext() changes");
+
     GGML_ASSERT(ggml_can_mul_mat(k, q));
     // TODO: check if vT can be multiplied by (k*qT)
 
@@ -6953,7 +7253,7 @@ struct ggml_tensor * ggml_ssm_conv(
     bool is_node = false;
 
     if (s->grad || x->grad || c->grad || sq->grad) {
-        GGML_ASSERT(false); // TODO: implement
+        GGML_ABORT("fatal error"); // TODO: implement
         is_node = true;
     }
 
@@ -7007,7 +7307,7 @@ struct ggml_tensor * ggml_ssm_scan(
     bool is_node = false;
 
     if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) {
-        GGML_ASSERT(false); // TODO: implement
+        GGML_ABORT("fatal error"); // TODO: implement
         is_node = true;
     }
 
@@ -7039,7 +7339,7 @@ struct ggml_tensor * ggml_win_part(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -7077,7 +7377,7 @@ struct ggml_tensor * ggml_win_unpart(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -7107,7 +7407,7 @@ struct ggml_tensor * ggml_get_rel_pos(
     bool is_node = false;
 
     if (a->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
+        GGML_ABORT("fatal error"); // TODO: implement backward
         is_node = true;
     }
 
@@ -7173,13 +7473,15 @@ struct ggml_tensor * ggml_add_rel_pos_inplace(
     return ggml_add_rel_pos_impl(ctx, a, pw, ph, true);
 }
 
-// gmml_unary
+// ggml_unary
 
 static struct ggml_tensor * ggml_unary_impl(
         struct ggml_context * ctx,
         struct ggml_tensor * a,
         enum ggml_unary_op op,
         bool inplace) {
+    GGML_ASSERT(ggml_is_contiguous_1(a));
+
     bool is_node = false;
 
     if (!inplace && (a->grad)) {
@@ -7669,10 +7971,6 @@ static void ggml_compute_forward_dup_same_cont(
     GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
     GGML_ASSERT(src0->type == dst->type);
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const size_t nb00 = src0->nb[0];
     const size_t nb0 = dst->nb[0];
 
@@ -7701,10 +7999,6 @@ static void ggml_compute_forward_dup_f16(
 
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_UNARY_OP_LOCALS
 
     const int ith = params->ith; // thread index
@@ -7803,7 +8097,7 @@ static void ggml_compute_forward_dup_f16(
                     }
                 }
             } else {
-                GGML_ASSERT(false); // TODO: implement
+                GGML_ABORT("fatal error"); // TODO: implement
             }
         } else {
             //printf("%s: this is not optimal - fix me\n", __func__);
@@ -7845,7 +8139,7 @@ static void ggml_compute_forward_dup_f16(
                     }
                 }
             } else {
-                GGML_ASSERT(false); // TODO: implement
+                GGML_ABORT("fatal error"); // TODO: implement
             }
         }
         return;
@@ -7962,7 +8256,7 @@ static void ggml_compute_forward_dup_f16(
             }
         }
     } else {
-        GGML_ASSERT(false); // TODO: implement
+        GGML_ABORT("fatal error"); // TODO: implement
     }
 }
 
@@ -7974,10 +8268,6 @@ static void ggml_compute_forward_dup_bf16(
 
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_UNARY_OP_LOCALS
 
     const int ith = params->ith; // thread index
@@ -8093,7 +8383,7 @@ static void ggml_compute_forward_dup_bf16(
                     }
                 }
             } else {
-                GGML_ASSERT(false); // TODO: implement
+                GGML_ABORT("fatal error"); // TODO: implement
             }
         } else {
             //printf("%s: this is not optimal - fix me\n", __func__);
@@ -8153,7 +8443,7 @@ static void ggml_compute_forward_dup_bf16(
                     }
                 }
             } else {
-                GGML_ASSERT(false); // TODO: implement
+                GGML_ABORT("fatal error"); // TODO: implement
             }
         }
         return;
@@ -8322,7 +8612,7 @@ static void ggml_compute_forward_dup_bf16(
             }
         }
     } else {
-        GGML_ASSERT(false); // TODO: implement
+        GGML_ABORT("fatal error"); // TODO: implement
     }
 }
 
@@ -8334,10 +8624,6 @@ static void ggml_compute_forward_dup_f32(
 
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_UNARY_OP_LOCALS
 
     const int ith = params->ith; // thread index
@@ -8412,7 +8698,7 @@ static void ggml_compute_forward_dup_f32(
                     }
                 }
             } else {
-                GGML_ASSERT(false); // TODO: implement
+                GGML_ABORT("fatal error"); // TODO: implement
             }
         } else {
             //printf("%s: this is not optimal - fix me\n", __func__);
@@ -8472,7 +8758,7 @@ static void ggml_compute_forward_dup_f32(
                     }
                 }
             } else {
-                GGML_ASSERT(false); // TODO: implement
+                GGML_ABORT("fatal error"); // TODO: implement
             }
         }
 
@@ -8643,7 +8929,7 @@ static void ggml_compute_forward_dup_f32(
             }
         }
     } else {
-        GGML_ASSERT(false); // TODO: implement
+        GGML_ABORT("fatal error"); // TODO: implement
     }
 }
 
@@ -8657,10 +8943,6 @@ static void ggml_compute_forward_dup_bytes(
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
     GGML_ASSERT(src0->type == dst->type);
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
         ggml_compute_forward_dup_same_cont(params, dst);
         return;
@@ -8825,8 +9107,8 @@ static void ggml_compute_forward_dup(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -8841,24 +9123,9 @@ static void ggml_compute_forward_add_f32(
 
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const int ith = params->ith;
     const int nth = params->nth;
 
-#ifdef GGML_USE_CLBLAST
-    if (src1->backend == GGML_BACKEND_TYPE_GPU) {
-        // TODO: OpenCL kernel support full broadcast
-        GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
-        if (ith == 0) {
-            ggml_cl_add(src0, src1, dst);
-        }
-        return;
-    }
-#endif
-
     const int nr  = ggml_nrows(src0);
 
     GGML_TENSOR_BINARY_OP_LOCALS
@@ -8931,10 +9198,6 @@ static void ggml_compute_forward_add_f16_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -8997,7 +9260,7 @@ static void ggml_compute_forward_add_f16_f32(
     }
     else {
         // src1 is not contiguous
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
@@ -9010,10 +9273,6 @@ static void ggml_compute_forward_add_bf16_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -9076,7 +9335,7 @@ static void ggml_compute_forward_add_bf16_f32(
     }
     else {
         // src1 is not contiguous
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
@@ -9089,10 +9348,6 @@ static void ggml_compute_forward_add_f16_f16(
 
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -9132,7 +9387,7 @@ static void ggml_compute_forward_add_f16_f16(
     }
     else {
         // src1 is not contiguous
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
@@ -9145,10 +9400,6 @@ static void ggml_compute_forward_add_bf16_bf16(
 
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -9188,7 +9439,7 @@ static void ggml_compute_forward_add_bf16_bf16(
     }
     else {
         // src1 is not contiguous
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
@@ -9201,10 +9452,6 @@ static void ggml_compute_forward_add_q_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const int nr  = ggml_nrows(src0);
 
     GGML_TENSOR_BINARY_OP_LOCALS
@@ -9286,7 +9533,7 @@ static void ggml_compute_forward_add(
                     ggml_compute_forward_add_f32(params, dst);
                 }
                 else {
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
             } break;
         case GGML_TYPE_F16:
@@ -9298,7 +9545,7 @@ static void ggml_compute_forward_add(
                     ggml_compute_forward_add_f16_f32(params, dst);
                 }
                 else {
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
             } break;
         case GGML_TYPE_BF16:
@@ -9310,7 +9557,7 @@ static void ggml_compute_forward_add(
                     ggml_compute_forward_add_bf16_f32(params, dst);
                 }
                 else {
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
             } break;
         case GGML_TYPE_Q4_0:
@@ -9332,13 +9579,16 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
             {
                 ggml_compute_forward_add_q_f32(params, dst);
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -9354,10 +9604,6 @@ static void ggml_compute_forward_add1_f32(
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -9408,10 +9654,6 @@ static void ggml_compute_forward_add1_f16_f32(
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // scalar to add
     const float v = *(float *) src1->data;
 
@@ -9460,10 +9702,6 @@ static void ggml_compute_forward_add1_f16_f16(
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // scalar to add
     const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data);
 
@@ -9512,10 +9750,6 @@ static void ggml_compute_forward_add1_q_f32(
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // scalar to add
     const float v = *(float *) src1->data;
 
@@ -9581,10 +9815,6 @@ static void ggml_compute_forward_add1_bf16_f32(
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // scalar to add
     const float v = *(float *) src1->data;
 
@@ -9633,10 +9863,6 @@ static void ggml_compute_forward_add1_bf16_bf16(
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // scalar to add
     const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data);
 
@@ -9696,7 +9922,7 @@ static void ggml_compute_forward_add1(
                     ggml_compute_forward_add1_f16_f32(params, dst);
                 }
                 else {
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
             } break;
         case GGML_TYPE_BF16:
@@ -9708,7 +9934,7 @@ static void ggml_compute_forward_add1(
                     ggml_compute_forward_add1_bf16_f32(params, dst);
                 }
                 else {
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
                 }
             } break;
         case GGML_TYPE_Q4_0:
@@ -9731,13 +9957,16 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
             {
                 ggml_compute_forward_add1_q_f32(params, dst);
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -9761,20 +9990,16 @@ static void ggml_compute_forward_acc_f32(
     size_t offset  = ((int32_t *) dst->op_params)[3];
     bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
 
-    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
-        if (params->ith != 0) {
-            return;
+    if (!inplace) {
+        if (params->ith == 0) {
+            // memcpy needs to be synchronized across threads to avoid race conditions.
+            // => do it in INIT phase
+            memcpy(
+                ((char *)  dst->data),
+                ((char *) src0->data),
+                ggml_nbytes(dst));
         }
-        // memcpy needs to be synchronized across threads to avoid race conditions.
-        // => do it in INIT phase
-        memcpy(
-            ((char *)  dst->data),
-            ((char *) src0->data),
-            ggml_nbytes(dst));
-    }
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
+        ggml_barrier(params->shared);
     }
 
     const int ith = params->ith;
@@ -9860,10 +10085,13 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -9876,14 +10104,13 @@ static void ggml_compute_forward_sub_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
-    const int nr  = ggml_nrows(src0);
+    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    const int nr  = ggml_nrows(src0);
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
@@ -9944,8 +10171,8 @@ static void ggml_compute_forward_sub(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -9960,23 +10187,9 @@ static void ggml_compute_forward_mul_f32(
 
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
     const int ith = params->ith;
     const int nth = params->nth;
 
-#if defined(GGML_USE_CLBLAST)
-    if (src1->backend == GGML_BACKEND_TYPE_GPU) {
-        // TODO: OpenCL kernel support full broadcast
-        GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
-        if (ith == 0) {
-            ggml_cl_mul(src0, src1, dst);
-        }
-        return;
-    }
-#endif
-
     const int64_t nr = ggml_nrows(src0);
 
     GGML_TENSOR_BINARY_OP_LOCALS
@@ -10052,8 +10265,8 @@ static void ggml_compute_forward_mul(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10068,10 +10281,6 @@ static void ggml_compute_forward_div_f32(
 
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -10147,8 +10356,8 @@ static void ggml_compute_forward_div(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10160,13 +10369,12 @@ static void ggml_compute_forward_sqr_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n     = ggml_nrows(src0);
     const int nc    = src0->ne[0];
 
@@ -10193,8 +10401,8 @@ static void ggml_compute_forward_sqr(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10206,13 +10414,12 @@ static void ggml_compute_forward_sqrt_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
@@ -10239,8 +10446,8 @@ static void ggml_compute_forward_sqrt(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10252,13 +10459,12 @@ static void ggml_compute_forward_log_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(params->ith == 0);
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
@@ -10285,8 +10491,8 @@ static void ggml_compute_forward_log(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10298,13 +10504,13 @@ static void ggml_compute_forward_sum_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_is_scalar(dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_scalar(dst));
+
+
     assert(ggml_is_scalar(dst));
     assert(src0->nb[0] == sizeof(float));
 
@@ -10333,13 +10539,12 @@ static void ggml_compute_forward_sum_f16(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_is_scalar(dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_scalar(dst));
+
     assert(src0->nb[0] == sizeof(ggml_fp16_t));
 
     GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
@@ -10367,13 +10572,12 @@ static void ggml_compute_forward_sum_bf16(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_is_scalar(dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_scalar(dst));
+
     assert(src0->nb[0] == sizeof(ggml_bf16_t));
 
     GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
@@ -10416,8 +10620,8 @@ static void ggml_compute_forward_sum(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10429,9 +10633,7 @@ static void ggml_compute_forward_sum_rows_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -10471,8 +10673,8 @@ static void ggml_compute_forward_sum_rows(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10484,9 +10686,7 @@ static void ggml_compute_forward_mean_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -10530,8 +10730,8 @@ static void ggml_compute_forward_mean(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10543,9 +10743,7 @@ static void ggml_compute_forward_argmax_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -10580,8 +10778,8 @@ static void ggml_compute_forward_argmax(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10593,13 +10791,12 @@ static void ggml_compute_forward_repeat_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(params->ith == 0);
-    GGML_ASSERT(ggml_can_repeat(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    GGML_ASSERT(ggml_can_repeat(src0, dst));
+
     GGML_TENSOR_UNARY_OP_LOCALS
 
     // guaranteed to be an integer due to the check in ggml_can_repeat
@@ -10638,13 +10835,12 @@ static void ggml_compute_forward_repeat_f16(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(params->ith == 0);
-    GGML_ASSERT(ggml_can_repeat(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    GGML_ASSERT(ggml_can_repeat(src0, dst));
+
     GGML_TENSOR_UNARY_OP_LOCALS
 
     // guaranteed to be an integer due to the check in ggml_can_repeat
@@ -10700,8 +10896,8 @@ static void ggml_compute_forward_repeat(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10713,13 +10909,12 @@ static void ggml_compute_forward_repeat_back_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(params->ith == 0);
-    GGML_ASSERT(ggml_can_repeat(dst, src0));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    GGML_ASSERT(ggml_can_repeat(dst, src0));
+
     GGML_TENSOR_UNARY_OP_LOCALS
 
     // guaranteed to be an integer due to the check in ggml_can_repeat
@@ -10779,8 +10974,8 @@ static void ggml_compute_forward_repeat_back(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10793,10 +10988,6 @@ static void ggml_compute_forward_concat_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_ASSERT(src0->nb[0] == sizeof(float));
 
     const int ith = params->ith;
@@ -10809,26 +11000,29 @@ static void ggml_compute_forward_concat_f32(
     GGML_ASSERT(nb00 == sizeof(float));
     GGML_ASSERT(nb10 == sizeof(float));
 
+    const int32_t dim = ggml_get_op_params_i32(dst, 0);
+
+    GGML_ASSERT(dim >= 0 && dim < 4);
+
+    int64_t o[4] = {0, 0, 0, 0};
+    o[dim] = src0->ne[dim];
+
+    const float * x;
+
+    // TODO: smarter multi-theading
     for (int i3 = 0; i3 < ne3; i3++) {
         for (int i2 = ith; i2 < ne2; i2 += nth) {
-            if (i2 < ne02) { // src0
-                for (int i1 = 0; i1 < ne1; i1++) {
-                    for (int i0 = 0; i0 < ne0; i0++) {
-                        const float * x = (float *)((char *) src0->data + i0 * nb00 + i1 * nb01 + i2 * nb02 + i3 * nb03);
-
-                        float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3);
-                        *y = *x;
-                    }
-                }
-            } // src1
-            else {
-                for (int i1 = 0; i1 < ne1; i1++) {
-                    for (int i0 = 0; i0 < ne0; i0++) {
-                        const float * x = (float *)((char *) src1->data + i0 * nb10 + i1 * nb11 + (i2 - ne02) * nb12 + i3 * nb13);
-
-                        float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3);
-                        *y = *x;
+            for (int i1 = 0; i1 < ne1; i1++) {
+                for (int i0 = 0; i0 < ne0; i0++) {
+                    if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+                        x = (const float *) ((const char *)src0->data + (i0       )*nb00 + (i1       )*nb01 + (i2       )*nb02 + (i3       )*nb03);
+                    } else {
+                        x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13);
                     }
+
+                    float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
+
+                    *y = *x;
                 }
             }
         }
@@ -10836,8 +11030,8 @@ static void ggml_compute_forward_concat_f32(
 }
 
 static void ggml_compute_forward_concat(
-    const struct ggml_compute_params* params,
-    struct ggml_tensor* dst) {
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
 
     const struct ggml_tensor * src0 = dst->src[0];
 
@@ -10849,8 +11043,8 @@ static void ggml_compute_forward_concat(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10862,19 +11056,17 @@ static void ggml_compute_forward_abs_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_abs_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -10895,8 +11087,8 @@ static void ggml_compute_forward_abs(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10908,19 +11100,17 @@ static void ggml_compute_forward_sgn_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_sgn_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -10941,8 +11131,8 @@ static void ggml_compute_forward_sgn(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -10954,19 +11144,17 @@ static void ggml_compute_forward_neg_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_neg_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -10987,8 +11175,8 @@ static void ggml_compute_forward_neg(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11000,19 +11188,17 @@ static void ggml_compute_forward_step_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_step_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11033,8 +11219,8 @@ static void ggml_compute_forward_step(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11046,19 +11232,17 @@ static void ggml_compute_forward_tanh_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_tanh_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11079,8 +11263,8 @@ static void ggml_compute_forward_tanh(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11092,19 +11276,17 @@ static void ggml_compute_forward_elu_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_elu_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11125,8 +11307,8 @@ static void ggml_compute_forward_elu(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11138,19 +11320,17 @@ static void ggml_compute_forward_relu_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_relu_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11171,8 +11351,8 @@ static void ggml_compute_forward_relu(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11184,19 +11364,17 @@ static void ggml_compute_forward_sigmoid_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_sigmoid_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11217,8 +11395,8 @@ static void ggml_compute_forward_sigmoid(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11230,13 +11408,9 @@ static void ggml_compute_forward_gelu_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -11280,8 +11454,8 @@ static void ggml_compute_forward_gelu(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11293,13 +11467,9 @@ static void ggml_compute_forward_gelu_quick_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -11343,8 +11513,8 @@ static void ggml_compute_forward_gelu_quick(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11356,13 +11526,9 @@ static void ggml_compute_forward_silu_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -11406,8 +11572,8 @@ static void ggml_compute_forward_silu(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 // ggml_compute_forward_leaky_relu
@@ -11418,13 +11584,14 @@ static void ggml_compute_forward_leaky_relu_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
@@ -11454,8 +11621,8 @@ static void ggml_compute_forward_leaky_relu(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11468,15 +11635,11 @@ static void ggml_compute_forward_silu_back_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * grad = dst->src[1];
 
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad));
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, grad));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    assert(ggml_is_contiguous_1(grad));
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+    assert(ggml_are_same_shape(src0, grad));
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -11521,8 +11684,8 @@ static void ggml_compute_forward_silu_back(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11533,19 +11696,17 @@ static void ggml_compute_forward_hardswish_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_hardswish_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11565,8 +11726,8 @@ static void ggml_compute_forward_hardswish(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11576,19 +11737,17 @@ static void ggml_compute_forward_hardsigmoid_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_hardsigmoid_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11609,8 +11768,8 @@ static void ggml_compute_forward_hardsigmoid(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11625,10 +11784,6 @@ static void ggml_compute_forward_norm_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_ASSERT(src0->nb[0] == sizeof(float));
 
     const int ith = params->ith;
@@ -11685,8 +11840,8 @@ static void ggml_compute_forward_norm(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11700,10 +11855,6 @@ static void ggml_compute_forward_rms_norm_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_ASSERT(src0->nb[0] == sizeof(float));
 
     const int ith = params->ith;
@@ -11757,8 +11908,8 @@ static void ggml_compute_forward_rms_norm(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11771,10 +11922,6 @@ static void ggml_compute_forward_rms_norm_back_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_ASSERT(src0->nb[0] == sizeof(float));
 
     const int ith = params->ith;
@@ -11934,8 +12081,8 @@ static void ggml_compute_forward_rms_norm_back(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -11949,10 +12096,6 @@ static void ggml_compute_forward_group_norm_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_ASSERT(src0->nb[0] == sizeof(float));
 
     const int ith = params->ith;
@@ -11960,10 +12103,11 @@ static void ggml_compute_forward_group_norm_f32(
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
-    const float eps = 1e-6f; // TODO: make this a parameter
-
     // TODO: optimize
 
+    float eps;
+    memcpy(&eps, dst->op_params + 1, sizeof(float));
+
     int n_channels = src0->ne[2];
     int n_groups = dst->op_params[0];
     int n_channels_per_group = (n_channels + n_groups - 1) / n_groups;
@@ -12032,66 +12176,33 @@ static void ggml_compute_forward_group_norm(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
 // ggml_compute_forward_mul_mat
 
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-// helper function to determine if it is better to use BLAS or not
-// for large matrices, BLAS is faster
-static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) {
+static void ggml_compute_forward_mul_mat_one_chunk(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst,
+    const int64_t num_rows_per_vec_dot,
+    const int64_t ir0_start,
+    const int64_t ir0_end,
+    const int64_t ir1_start,
+    const int64_t ir1_end) {
+
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    //const int64_t ne00 = src0->ne[0];
-    //const int64_t ne01 = src0->ne[1];
-
-    const int64_t ne10 = src1->ne[0];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-
-    // NOTE: with GGML_OP_MUL_MAT_ID we don't want to go through the BLAS branch because it will dequantize (to_float)
-    //       all the experts for each batch element and the processing would become incredibly slow
-    // TODO: find the optimal values for these
-    if (dst->op != GGML_OP_MUL_MAT_ID &&
-        ggml_is_contiguous(src0) &&
-        ggml_is_contiguous(src1) &&
-      //src0->type == GGML_TYPE_F32 &&
-        src1->type == GGML_TYPE_F32 &&
-        (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
-
-        /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
-        return true;
-    }
-
-    return false;
-}
-#endif
-
-static void ggml_compute_forward_mul_mat_one_chunk(
-    const struct ggml_compute_params * params,
-    struct ggml_tensor * dst,
-    const int64_t num_rows_per_vec_dot,
-    const int64_t ir0_start,
-    const int64_t ir0_end,
-    const int64_t ir1_start,
-    const int64_t ir1_end) {
-
-    const struct ggml_tensor * src0 = dst->src[0];
-    const struct ggml_tensor * src1 = dst->src[1];
-
-    GGML_TENSOR_BINARY_OP_LOCALS
+    GGML_TENSOR_BINARY_OP_LOCALS
 
     const enum ggml_type type = src0->type;
 
     const bool src1_cont = ggml_is_contiguous(src1);
 
-    ggml_vec_dot_t    const vec_dot = type_traits[type].vec_dot;
-    enum ggml_type    const vec_dot_type = type_traits[type].vec_dot_type;
+    ggml_vec_dot_t const vec_dot      = type_traits[type].vec_dot;
+    enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
 
     // broadcast factors
     const int64_t r2 = ne12 / ne02;
@@ -12165,15 +12276,11 @@ static void ggml_compute_forward_mul_mat_one_chunk(
 
 static void ggml_compute_forward_mul_mat(
         const struct ggml_compute_params * params,
-              struct ggml_tensor * dst,
-              struct ggml_compute_state * state) {
+              struct ggml_tensor * dst) {
 
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int ith = params->ith;
@@ -12181,9 +12288,14 @@ static void ggml_compute_forward_mul_mat(
 
     const enum ggml_type type = src0->type;
 
-    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
-    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
-    int64_t           const vec_dot_num_rows      = type_traits[type].nrows;
+    enum ggml_type           const vec_dot_type         = type_traits[type].vec_dot_type;
+    ggml_from_float_t        const from_float           = type_traits[vec_dot_type].from_float;
+    ggml_from_float_to_mat_t const from_float_to_mat    = type_traits[vec_dot_type].from_float_to_mat;
+    int64_t                  const vec_dot_num_rows     = type_traits[type].nrows;
+    int64_t                  const matmul_num_cols      = type_traits[type].ncols;
+    int64_t                  const blck_size_interleave = type_traits[type].blck_size_interleave;
+    ggml_gemv_t              const gemv                 = type_traits[type].gemv;
+    ggml_gemm_t              const gemm                 = type_traits[type].gemm;
 
     GGML_ASSERT(ne0 == ne01);
     GGML_ASSERT(ne1 == ne11);
@@ -12200,92 +12312,14 @@ static void ggml_compute_forward_mul_mat(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    // broadcast factors
-    const int64_t r2 = ne12 / ne02;
-    const int64_t r3 = ne13 / ne03;
-    UNUSED(r2);
-    UNUSED(r3);
-
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-#if defined(GGML_USE_CLBLAST)
-    if (ggml_cl_can_mul_mat(src0, src1, dst)) {
-        if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) {
-            ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
-        }
-        return;
-    }
-#endif
-
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    if (ggml_compute_forward_mul_mat_use_blas(dst)) {
-        const int64_t ne_plane      = ne01*ne00;
-        const size_t  desired_wsize = ne13*ne12*ne_plane*sizeof(float);
-        UNUSED(desired_wsize);
-
-        if (params->type == GGML_TASK_TYPE_INIT) {
-            if (type != GGML_TYPE_F32) {
-                assert(params->wsize >= desired_wsize);
-                // parallelize by src0 rows
-                for (int64_t i13 = 0; i13 < ne13; i13++) {
-                    for (int64_t i12 = 0; i12 < ne12; i12++) {
-                        // broadcast src0 into src1 across 2nd,3rd dimension
-                        const int64_t i03 = i13/r3;
-                        const int64_t i02 = i12/r2;
-
-                        const void           *       x        = (char *)  src0->data    + i02*nb02          + i03*nb03;
-                              float          * const wdata    = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
-                              ggml_to_float_t  const to_float = type_traits[type].to_float;
-
-                        for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
-                            to_float((const char *) x + i01*nb01, wdata + i01*ne00, ne00);
-                        }
-                    }
-                }
-            }
-            return;
-        }
-
-        if (params->type == GGML_TASK_TYPE_FINALIZE) {
-            return;
-        }
-
-        // perform sgemm, parallelization controlled by blas lib
-        if (ith != 0) {
-            return;
-        }
-
-        //const int64_t tgemm0 = ggml_perf_time_us();
-        for (int64_t i13 = 0; i13 < ne13; i13++) {
-            for (int64_t i12 = 0; i12 < ne12; i12++) {
-                const int64_t i03 = i13/r3;
-                const int64_t i02 = i12/r2;
-
-                const void  * x = (char *)            src0->data + i02*nb02 + i03*nb03;
-                const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
-                      float * d = (float *) ((char *)  dst->data + i12*nb2  + i13*nb3);
-
-                if (type != GGML_TYPE_F32) {
-                    x = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
-                }
-
-                cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
-                          ne1, ne01, ne10,
-                         1.0f,    y, ne10,
-                                  x, ne00,
-                         0.0f,    d, ne01);
-            }
-        }
-        //printf("cblas_sgemm = %.3f ms, %lld flops\n", (ggml_perf_time_us() - tgemm0)/1000.0, ne13*ne12*ne1*ne01*ne10*2);
-
-        //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
-
-        return;
-    }
-#endif
-
 #if GGML_USE_LLAMAFILE
+    // broadcast factors
+    const int64_t r2 = ne12 / ne02;
+    const int64_t r3 = ne13 / ne03;
+
     const bool src1_cont = ggml_is_contiguous(src1);
 
     if (src1_cont) {
@@ -12299,7 +12333,6 @@ static void ggml_compute_forward_mul_mat(
                                      (char *)dst->data + i12*nb2 + i13*nb3,
                                      nb1/ggml_type_size(dst->type),
                                      ith, nth,
-                                     params->type,
                                      src0->type,
                                      src1->type,
                                      dst->type))
@@ -12309,36 +12342,43 @@ static void ggml_compute_forward_mul_mat(
 UseGgmlGemm1:;
 #endif
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (ith != 0) {
-            return;
-        }
-        // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
-        atomic_store(&state->shared->current_chunk, nth);
-        if (src1->type != vec_dot_type) {
-            char * wdata = params->wdata;
-            const size_t row_size = ggml_row_size(vec_dot_type, ne10);
-
-            assert(params->wsize >= ne11*ne12*ne13*row_size);
-            GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-            for (int64_t i13 = 0; i13 < ne13; ++i13) {
-                for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                    for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                        from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
-                        wdata += row_size;
+    if (src1->type != vec_dot_type) {
+        char * wdata = params->wdata;
+
+        const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
+        const size_t nbw2 = nbw1*ne11;
+        const size_t nbw3 = nbw2*ne12;
+
+        assert(params->wsize >= ne13*nbw3);
+        GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+        for (int64_t i13 = 0; i13 < ne13; ++i13) {
+            for (int64_t i12 = 0; i12 < ne12; ++i12) {
+                int64_t i11_processed = 0;
+                if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) {
+                    for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
+                        from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
+                                          (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
+                                          4, ne10, blck_size_interleave);
                     }
+                    i11_processed = ne11 - ne11 % 4;
+                }
+                for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
+                    from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
+                           (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
+                           ne10);
                 }
             }
         }
-
-        return;
     }
 
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
+    if (ith == 0) {
+        // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
+        atomic_store(&params->shared->current_chunk, nth);
     }
 
+    ggml_barrier(params->shared);
+
 #if GGML_USE_LLAMAFILE
     if (src1->type != vec_dot_type) {
         const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
@@ -12354,7 +12394,6 @@ UseGgmlGemm1:;
                                      (char *)dst->data + i12*nb2 + i13*nb3,
                                      nb1/ggml_type_size(dst->type),
                                      ith, nth,
-                                     params->type,
                                      src0->type,
                                      vec_dot_type,
                                      dst->type))
@@ -12364,11 +12403,6 @@ UseGgmlGemm1:;
 UseGgmlGemm2:;
 #endif
 
-#ifdef GGML_PERF
-    int chunks_executed = 0;
-    UNUSED(chunks_executed);
-#endif
-
     // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers)
     const int64_t nr0 = ne0;
 
@@ -12410,8 +12444,27 @@ UseGgmlGemm2:;
     const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
     const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;
 
-    //if (ith == 0)
-    //    printf("MUL_MAT = [%d, %d, %d, %d] x [%d, %d, %d, %d] = %d x %d = %d.  Fp Ops/Ch %d\n", ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nchunk0, nchunk1, nchunk0 * nchunk1, ne00 * nr0 * nr1 / nchunk0 / nchunk1);
+    if ((ggml_n_dims(src0) == 2) && gemv) {
+        const void * src1_wdata      = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+        const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11;
+        int64_t src0_start = (ith * ne01) / nth;
+        int64_t src0_end   = ((ith + 1) * ne01) / nth;
+        src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start;
+        src0_end   = (src0_end   % matmul_num_cols) ? src0_end   + matmul_num_cols - (src0_end   % matmul_num_cols): src0_end;
+        if (src0_start >= src0_end) return;
+
+        // If there are more than three rows in src1, use gemm; otherwise, use gemv.
+        if (gemm && (ne11 > 3)) {
+            gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01,
+                 (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
+        }
+        for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) {
+            gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01,
+                 (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1,
+                 src0_end - src0_start);
+        }
+        return;
+    }
 
     // The first chunk comes from our thread_id, the rest will get auto-assigned.
     int current_chunk = ith;
@@ -12428,23 +12481,12 @@ UseGgmlGemm2:;
 
         ggml_compute_forward_mul_mat_one_chunk(params, dst, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end);
 
-#ifdef GGML_PERF
-        chunks_executed++;
-#endif
-
         if (nth >= nchunk0 * nchunk1) {
             break;
         }
 
-        current_chunk = atomic_fetch_add(&state->shared->current_chunk, 1);
+        current_chunk = atomic_fetch_add(&params->shared->current_chunk, 1);
     }
-
-#ifdef GGML_PERF
-    // These numbers are useful when trying to measure how well the threading scheduling works.
-    //int64_t workSize = (ne01 * ne11 * ne12 * ne13 * ne00) / nchunk0 / nchunk1;
-    //float time = (ggml_perf_time_us() - t0);
-    //printf("MUL_MAT = %f ms, [%d, %d, %d, %d] x [%d, %d, %d, %d] = %I64u, %f ops/usec in %d chunks.\n", time / 1000.0, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, workSize, (float)workSize/time, chunks_executed);
-#endif
 }
 
 // ggml_compute_forward_mul_mat_id
@@ -12466,9 +12508,11 @@ static void ggml_compute_forward_mul_mat_id(
 
     const bool src1_cont = ggml_is_contiguous(src1);
 
-    ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
-    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
-    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
+    ggml_vec_dot_t    const vec_dot         = type_traits[type].vec_dot;
+    enum ggml_type    const vec_dot_type    = type_traits[type].vec_dot_type;
+    ggml_from_float_t const from_float      = type_traits[vec_dot_type].from_float;
+    int64_t           const matmul_num_cols = type_traits[type].ncols;
+    ggml_gemv_t       const gemv            = type_traits[type].gemv;
 
     // we don't support permuted src0 or src1
     GGML_ASSERT(nb00 == ggml_type_size(type));
@@ -12496,32 +12540,33 @@ static void ggml_compute_forward_mul_mat_id(
     int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
     struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11]
 
-   if (params->type == GGML_TASK_TYPE_INIT) {
-        if (ith != 0) {
-            return;
-        }
+    if (src1->type != vec_dot_type) {
         char * wdata = params->wdata;
-        if (src1->type != vec_dot_type) {
-            const size_t row_size = ggml_row_size(vec_dot_type, ne10);
 
-            assert(params->wsize >= ne11*ne12*ne13*row_size);
-            assert(src1->type == GGML_TYPE_F32);
+        const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
+        const size_t nbw2 = nbw1*ne11;
+        const size_t nbw3 = nbw2*ne12;
 
-            for (int64_t i13 = 0; i13 < ne13; ++i13) {
-                for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                    for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                        from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
-                        wdata += row_size;
-                    }
+        assert(params->wsize >= ne13*nbw3);
+        GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+        for (int64_t i13 = 0; i13 < ne13; ++i13) {
+            for (int64_t i12 = 0; i12 < ne12; ++i12) {
+                for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
+                    from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
+                               (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
+                               ne10);
                 }
             }
         }
+    }
+
+#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)]
 
+    if (ith == 0) {
         // initialize matrix_row_counts
         memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
 
-#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)]
-
         // group rows by src0 matrix
         for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
             for (int id = 0; id < n_ids; ++id) {
@@ -12533,13 +12578,9 @@ static void ggml_compute_forward_mul_mat_id(
                 matrix_row_counts[i02] += 1;
             }
         }
-
-        return;
     }
 
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    ggml_barrier(params->shared);
 
     // compute each matrix multiplication in sequence
     for (int cur_a = 0; cur_a < n_as; ++cur_a) {
@@ -12557,6 +12598,34 @@ static void ggml_compute_forward_mul_mat_id(
         const int64_t nr0 = ne01; // src0 rows
         const int64_t nr1 = cne1; // src1 rows
 
+        if (((ggml_n_dims(src0) - 1) == 2) && gemv) {
+            int64_t src0_cur_start = (ith * ne01) / nth;
+            int64_t src0_cur_end   = ((ith + 1) * ne01) / nth;
+            src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start;
+            src0_cur_end   = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end;
+            if (src0_cur_start >= src0_cur_end) return;
+
+            for (int ir1 = 0; ir1 < nr1; ir1++) {
+                struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1);
+                const int id       = row_mapping.i1; // selected expert index
+
+                const int64_t  i11 = id % ne11;
+                const int64_t  i12 = row_mapping.i2; // row index in src1
+
+                const int64_t  i1 = id;  // selected expert index
+                const int64_t  i2 = i12; // row
+
+                const char * src1_col = (const char *) wdata +
+                    (src1_cont || src1->type != vec_dot_type
+                    ? (i11        + i12 * ne11) * row_size
+                    : (i11 * nb11 + i12 * nb12));
+
+                gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01,
+                     (const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start);
+            }
+            continue;
+        }
+
         // distribute the thread work across the inner or outer loop based on which one is larger
 
         const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
@@ -12638,9 +12707,6 @@ static void ggml_compute_forward_out_prod_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    // int64_t t0 = ggml_perf_time_us();
-    // UNUSED(t0);
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int ith = params->ith;
@@ -12665,75 +12731,10 @@ static void ggml_compute_forward_out_prod_f32(
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-    // TODO: #if defined(GGML_USE_CLBLAST)
-
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    bool use_blas = ggml_is_matrix(src0) &&
-        ggml_is_matrix(src1) &&
-        ggml_is_contiguous(src0) &&
-        (ggml_is_contiguous(src1) || ggml_is_transposed(src1));
-#endif
-
-    if (params->type == GGML_TASK_TYPE_INIT) {
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // gemm beta will zero dst
-        if (use_blas) {
-            return;
-        }
-#endif
-        if (ith != 0) {
-            return;
-        }
+    if (ith == 0) {
         ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    if (use_blas) {
-        if (params->ith != 0) { // All threads other than the first do no work.
-            return;
-        }
-        // Arguments to ggml_compute_forward_out_prod (expressed as major,minor)
-        // src0: (k,n)
-        // src1: (k,m)
-        // dst:  (m,n)
-        //
-        // Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f)
-        // Also expressed as (major,minor)
-        // a: (m,k): so src1 transposed
-        // b: (k,n): so src0
-        // c: (m,n)
-        //
-        // However, if ggml_is_transposed(src1) is true, then
-        // src1->data already contains a transposed version, so sgemm mustn't
-        // transpose it further.
-
-        int n = src0->ne[0];
-        int k = src0->ne[1];
-        int m = src1->ne[0];
-
-        int transposeA, lda;
-
-        if (!ggml_is_transposed(src1)) {
-            transposeA = CblasTrans;
-            lda = m;
-        } else {
-            transposeA = CblasNoTrans;
-            lda = k;
-        }
-
-        float * a = (float *) ((char *) src1->data);
-        float * b = (float *) ((char *) src0->data);
-        float * c = (float *) ((char *) dst->data);
-
-        cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n);
-
-        return;
     }
-#endif
+    ggml_barrier(params->shared);
 
     // dst[:,:,:,:] = 0
     // for i2,i3:
@@ -12809,19 +12810,6 @@ static void ggml_compute_forward_out_prod_f32(
             }
         }
     }
-
-    //int64_t t1 = ggml_perf_time_us();
-    //static int64_t acc = 0;
-    //acc += t1 - t0;
-    //if (t1 - t0 > 10) {
-    //    printf("\n");
-    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
-    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
-    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
-    //    printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);
-
-    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
-    //}
 }
 
 static void ggml_compute_forward_out_prod_q_f32(
@@ -12831,9 +12819,6 @@ static void ggml_compute_forward_out_prod_q_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    // int64_t t0 = ggml_perf_time_us();
-    // UNUSED(t0);
-
     GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
@@ -12864,19 +12849,10 @@ static void ggml_compute_forward_out_prod_q_f32(
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-    // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
-
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (ith != 0) {
-            return;
-        }
+    if (ith == 0) {
         ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
     }
+    ggml_barrier(params->shared);
 
     // parallelize by last three dimensions
 
@@ -12923,19 +12899,6 @@ static void ggml_compute_forward_out_prod_q_f32(
             ggml_vec_mad_f32(ne0, d, wdata, *s1);
         }
     }
-
-    //int64_t t1 = ggml_perf_time_us();
-    //static int64_t acc = 0;
-    //acc += t1 - t0;
-    //if (t1 - t0 > 10) {
-    //    printf("\n");
-    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
-    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
-    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
-    //    printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);
-
-    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
-    //}
 }
 
 static void ggml_compute_forward_out_prod(
@@ -12964,22 +12927,25 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
             {
                 ggml_compute_forward_out_prod_q_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
-                GGML_ASSERT(false); // todo
+                GGML_ABORT("fatal error"); // todo
                 // ggml_compute_forward_out_prod_f16_f32(params, dst);
-            } break;
+            }
         case GGML_TYPE_F32:
             {
                 ggml_compute_forward_out_prod_f32(params, dst);
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -12995,10 +12961,6 @@ static void ggml_compute_forward_scale_f32(
     GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // scale factor
     float v;
     memcpy(&v, dst->op_params, sizeof(float));
@@ -13042,8 +13004,8 @@ static void ggml_compute_forward_scale(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -13067,20 +13029,16 @@ static void ggml_compute_forward_set_f32(
     size_t offset  = ((int32_t *) dst->op_params)[3];
     bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
 
-    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
-        if (params->ith != 0) {
-            return;
+    if (!inplace) {
+        if (params->ith == 0) {
+            // memcpy needs to be synchronized across threads to avoid race conditions.
+            // => do it in INIT phase
+            memcpy(
+                ((char *)  dst->data),
+                ((char *) src0->data),
+                ggml_nbytes(dst));
         }
-        // memcpy needs to be synchronized across threads to avoid race conditions.
-        // => do it in INIT phase
-        memcpy(
-            ((char *)  dst->data),
-            ((char *) src0->data),
-            ggml_nbytes(dst));
-    }
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
+        ggml_barrier(params->shared);
     }
 
     const int ith = params->ith;
@@ -13157,10 +13115,13 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -13229,10 +13190,6 @@ static void ggml_compute_forward_get_rows_q(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
@@ -13262,6 +13219,8 @@ static void ggml_compute_forward_get_rows_q(
         const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
         const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
 
+        assert(i01 >= 0 && i01 < ne01);
+
         dequantize_row_q(
                 (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
                      (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
@@ -13275,10 +13234,6 @@ static void ggml_compute_forward_get_rows_f16(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
@@ -13305,6 +13260,8 @@ static void ggml_compute_forward_get_rows_f16(
         const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
         const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
 
+        assert(i01 >= 0 && i01 < ne01);
+
         ggml_fp16_to_fp32_row(
                 (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
                      (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
@@ -13318,10 +13275,6 @@ static void ggml_compute_forward_get_rows_bf16(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
@@ -13348,7 +13301,9 @@ static void ggml_compute_forward_get_rows_bf16(
         const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
         const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
 
-       ggml_bf16_to_fp32_row(
+        assert(i01 >= 0 && i01 < ne01);
+
+        ggml_bf16_to_fp32_row(
                 (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
                      (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
     }
@@ -13361,10 +13316,6 @@ static void ggml_compute_forward_get_rows_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
@@ -13391,6 +13342,8 @@ static void ggml_compute_forward_get_rows_f32(
         const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
         const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
 
+        assert(i01 >= 0 && i01 < ne01);
+
         ggml_vec_cpy_f32(nc,
                 (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3),
                 (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
@@ -13424,6 +13377,9 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
             {
                 ggml_compute_forward_get_rows_q(params, dst);
             } break;
@@ -13442,8 +13398,8 @@ static void ggml_compute_forward_get_rows(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 
     //static bool first = true;
@@ -13474,21 +13430,15 @@ static void ggml_compute_forward_get_rows_back_f32_f16(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    GGML_ASSERT(params->ith == 0);
+    if (params->ith != 0) {
+        return;
+    }
+
     GGML_ASSERT(ggml_is_contiguous(dst));
 
     // ggml_compute_forward_dup_same_cont(params, opt0, dst);
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (params->ith != 0) {
-            return;
-        }
-        memset(dst->data, 0, ggml_nbytes(dst));
-    }
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    memset(dst->data, 0, ggml_nbytes(dst));
 
     const int nc = src0->ne[0];
     const int nr = ggml_nelements(src1);
@@ -13513,21 +13463,15 @@ static void ggml_compute_forward_get_rows_back_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    GGML_ASSERT(params->ith == 0);
+    if (params->ith != 0) {
+        return;
+    }
+
     GGML_ASSERT(ggml_is_contiguous(dst));
 
     // ggml_compute_forward_dup_same_cont(params, opt0, dst);
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (params->ith != 0) {
-            return;
-        }
-        memset(dst->data, 0, ggml_nbytes(dst));
-    }
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    memset(dst->data, 0, ggml_nbytes(dst));
 
     const int nc = src0->ne[0];
     const int nr = ggml_nelements(src1);
@@ -13562,8 +13506,8 @@ static void ggml_compute_forward_get_rows_back(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 
     //static bool first = true;
@@ -13593,9 +13537,7 @@ static void ggml_compute_forward_diag_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -13642,8 +13584,8 @@ static void ggml_compute_forward_diag(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -13664,22 +13606,18 @@ static void ggml_compute_forward_diag_mask_f32(
 
     GGML_ASSERT(n_past >= 0);
 
-    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
-        if (ith != 0) {
-            return;
+    if (!inplace) {
+        if (ith == 0) {
+            // memcpy needs to be synchronized across threads to avoid race conditions.
+            // => do it in INIT phase
+            GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
+            GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
+            memcpy(
+                ((char *)  dst->data),
+                ((char *) src0->data),
+                ggml_nbytes(dst));
         }
-        // memcpy needs to be synchronized across threads to avoid race conditions.
-        // => do it in INIT phase
-        GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
-        GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
-        memcpy(
-            ((char *)  dst->data),
-            ((char *) src0->data),
-            ggml_nbytes(dst));
-    }
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
+        ggml_barrier(params->shared);
     }
 
     // TODO: handle transposed/permuted matrices
@@ -13716,8 +13654,8 @@ static void ggml_compute_forward_diag_mask_inf(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -13734,8 +13672,8 @@ static void ggml_compute_forward_diag_mask_zero(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -13751,10 +13689,6 @@ static void ggml_compute_forward_soft_max_f32(
     assert(ggml_is_contiguous(dst));
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     float scale    = 1.0f;
     float max_bias = 0.0f;
 
@@ -13856,11 +13790,12 @@ static void ggml_compute_forward_soft_max(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
+
 // ggml_compute_forward_soft_max_back
 
 static void ggml_compute_forward_soft_max_back_f32(
@@ -13876,10 +13811,6 @@ static void ggml_compute_forward_soft_max_back_f32(
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_are_same_shape(src1, dst));
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // TODO: handle transposed/permuted matrices
 
     const int ith = params->ith;
@@ -13955,8 +13886,8 @@ static void ggml_compute_forward_soft_max_back(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -13968,9 +13899,7 @@ static void ggml_compute_forward_clamp_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -14038,6 +13967,9 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
         case GGML_TYPE_Q8_K:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
@@ -14045,8 +13977,8 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_F64:
         case GGML_TYPE_COUNT:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -14061,8 +13993,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) {
 // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
 static void rope_yarn(
     float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale,
-    float * cos_theta, float * sin_theta
-) {
+    float * cos_theta, float * sin_theta) {
     // Get n-d rotational scaling corrected for extrapolation
     float theta_interp = freq_scale * theta_extrap;
     float theta = theta_interp;
@@ -14079,18 +14010,19 @@ static void rope_yarn(
 
 // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
 // `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
-static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, float base) {
-    return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base));
+static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) {
+    return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base));
 }
 
 static void ggml_rope_cache_init(
-     float theta_base, float freq_scale, float corr_dims[2], int64_t ne0, float ext_factor, float mscale,
-     float * cache, float sin_sign, float theta_scale
-) {
+     float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale,
+     float * cache, float sin_sign, float theta_scale) {
+    // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
     float theta = theta_base;
     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+        const float ff = freq_factors ? freq_factors[i0/2] : 1.0f;
         rope_yarn(
-            theta, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1]
+            theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1]
         );
         cache[i0 + 1] *= sin_sign;
 
@@ -14099,11 +14031,11 @@ static void ggml_rope_cache_init(
 }
 
 GGML_CALL void ggml_rope_yarn_corr_dims(
-    int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
+    int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]
 ) {
     // start and end correction dims
-    float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base));
-    float end   =  ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base));
+    float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base));
+    float end   =  ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base));
     dims[0] = MAX(0, start);
     dims[1] = MIN(n_dims - 1, end);
 }
@@ -14115,22 +14047,15 @@ static void ggml_compute_forward_rope_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    const struct ggml_tensor * src2 = dst->src[2];
 
     float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
 
-    // these two only relevant for xPos RoPE:
-    float xpos_base;
-    bool  xpos_down;
-
     //const int n_past     = ((int32_t *) dst->op_params)[0];
     const int n_dims     = ((int32_t *) dst->op_params)[1];
     const int mode       = ((int32_t *) dst->op_params)[2];
-    const int n_ctx      = ((int32_t *) dst->op_params)[3];
-    const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+    //const int n_ctx      = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
 
     memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
     memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
@@ -14138,8 +14063,6 @@ static void ggml_compute_forward_rope_f32(
     memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
     memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
     memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
-    memcpy(&xpos_base,   (int32_t *) dst->op_params + 11, sizeof(float));
-    memcpy(&xpos_down,   (int32_t *) dst->op_params + 12, sizeof(bool));
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
@@ -14167,12 +14090,18 @@ static void ggml_compute_forward_rope_f32(
     int ir = 0;
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.f/n_dims;
+
     float corr_dims[2];
-    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
+
+    const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
 
-    const bool is_neox = mode & 2;
-    const bool is_glm  = mode & 4;
+    const float * freq_factors = NULL;
+    if (src2 != NULL) {
+        GGML_ASSERT(src2->type == GGML_TYPE_F32);
+        GGML_ASSERT(src2->ne[0] >= n_dims / 2);
+        freq_factors = (const float *) src2->data;
+    }
 
     // backward process uses inverse rotation by cos and sin.
     // cos and sin build a rotation matrix, where the inverse is the transpose.
@@ -14186,106 +14115,57 @@ static void ggml_compute_forward_rope_f32(
             const int64_t p = pos[i2];
 
             float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
-            if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
-                ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
-            }
+            ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
 
             for (int64_t i1 = 0; i1 < ne1; i1++) {
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                float theta_base = (float)p;
-
-                if (is_glm) {
-                    theta_base = MIN(p, n_ctx - 2);
-                    float block_theta = MAX(p - (n_ctx - 2), 0);
-                    for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
-                        const float cos_theta = cosf(theta_base);
-                        const float sin_theta = sinf(theta_base) * sin_sign;
-                        const float cos_block_theta = cosf(block_theta);
-                        const float sin_block_theta = sinf(block_theta) * sin_sign;
-
-                        theta_base *= theta_scale;
-                        block_theta *= theta_scale;
+                if (!is_neox) {
+                    for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                        const float cos_theta = cache[i0 + 0];
+                        const float sin_theta = cache[i0 + 1];
 
                         const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                              float * dst_data  = (float *)((char *)  dst->data +  i3*nb3 + i2*nb2  + i1*nb1  + i0*nb0);
+                              float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
 
                         const float x0 = src[0];
-                        const float x1 = src[n_dims/2];
-                        const float x2 = src[n_dims];
-                        const float x3 = src[n_dims/2*3];
+                        const float x1 = src[1];
 
-                        dst_data[0]          = x0*cos_theta - x1*sin_theta;
-                        dst_data[n_dims/2]   = x0*sin_theta + x1*cos_theta;
-                        dst_data[n_dims]     = x2*cos_block_theta - x3*sin_block_theta;
-                        dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta;
+                        dst_data[0] = x0*cos_theta - x1*sin_theta;
+                        dst_data[1] = x0*sin_theta + x1*cos_theta;
                     }
-                } else if (!is_neox) {
-                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
-                        const float cos_theta = cache[i0 + 0];
-                        const float sin_theta = cache[i0 + 1];
+                } else {
+                    for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                        const int64_t ic = i0/2;
 
-                        // zeta scaling for xPos only:
-                        float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
-                        if (xpos_down) zeta = 1.0f / zeta;
+                        const float cos_theta = cache[i0 + 0];
+                        const float sin_theta = cache[i0 + 1];
 
-                        const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                              float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+                        const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+                        float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
 
                         const float x0 = src[0];
-                        const float x1 = src[1];
+                        const float x1 = src[n_dims/2];
 
-                        dst_data[0] = x0*cos_theta*zeta - x1*sin_theta*zeta;
-                        dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta;
+                        dst_data[0]        = x0*cos_theta - x1*sin_theta;
+                        dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
                     }
-                } else {
-                    // TODO: this might be wrong for ne0 != n_dims - need double check
-                    //       it seems we have to rope just the first n_dims elements and do nothing with the rest
-                    // ref:  https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26
-                    theta_base *= freq_scale;
-                    for (int64_t ic = 0; ic < ne0; ic += 2) {
-                        if (ic < n_dims) {
-                            const int64_t ib = 0;
-
-                            // simplified from `(ib * n_dims + ic) * inv_ndims`
-                            float cur_rot = inv_ndims * ic - ib;
-
-                            float cos_theta, sin_theta;
-                            rope_yarn(
-                                theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
-                                &cos_theta, &sin_theta
-                            );
-                            sin_theta *= sin_sign;
-
-                            theta_base *= theta_scale;
-
-                            const int64_t i0 = ib*n_dims + ic/2;
-
-                            const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                                  float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
-
-                            const float x0 = src[0];
-                            const float x1 = src[n_dims/2];
-
-                            dst_data[0]        = x0*cos_theta - x1*sin_theta;
-                            dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
-                        } else {
-                            const int64_t i0 = ic;
+                }
 
-                            const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                                  float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+                for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
+                    const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                    float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
 
-                            dst_data[0] = src[0];
-                            dst_data[1] = src[1];
-                        }
-                    }
+                    dst_data[0] = src[0];
+                    dst_data[1] = src[1];
                 }
             }
         }
     }
 }
 
+// TODO: deduplicate f16/f32 code
 static void ggml_compute_forward_rope_f16(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst,
@@ -14293,18 +14173,15 @@ static void ggml_compute_forward_rope_f16(
 
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
+    const struct ggml_tensor * src2 = dst->src[2];
 
     float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
 
     //const int n_past     = ((int32_t *) dst->op_params)[0];
     const int n_dims     = ((int32_t *) dst->op_params)[1];
     const int mode       = ((int32_t *) dst->op_params)[2];
-    const int n_ctx      = ((int32_t *) dst->op_params)[3];
-    const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+    //const int n_ctx      = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
     memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
     memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
     memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
@@ -14338,12 +14215,18 @@ static void ggml_compute_forward_rope_f16(
     int ir = 0;
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.f/n_dims;
+
     float corr_dims[2];
-    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
 
-    const bool is_neox = mode & 2;
-    const bool is_glm  = mode & 4;
+    const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+
+    const float * freq_factors = NULL;
+    if (src2 != NULL) {
+        GGML_ASSERT(src2->type == GGML_TYPE_F32);
+        GGML_ASSERT(src2->ne[0] >= n_dims / 2);
+        freq_factors = (const float *) src2->data;
+    }
 
     // backward process uses inverse rotation by cos and sin.
     // cos and sin build a rotation matrix, where the inverse is the transpose.
@@ -14357,43 +14240,14 @@ static void ggml_compute_forward_rope_f16(
             const int64_t p = pos[i2];
 
             float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
-            if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
-                ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
-            }
+            ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
 
             for (int64_t i1 = 0; i1 < ne1; i1++) {
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                float theta_base = (float)p;
-
-                if (is_glm) {
-                    theta_base = MIN(p, n_ctx - 2);
-                    float block_theta = MAX(p - (n_ctx - 2), 0);
-                    for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
-                        const float cos_theta = cosf(theta_base);
-                        const float sin_theta = sinf(theta_base) * sin_sign;
-                        const float cos_block_theta = cosf(block_theta);
-                        const float sin_block_theta = sinf(block_theta) * sin_sign;
-
-                        theta_base *= theta_scale;
-                        block_theta *= theta_scale;
-
-                        const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                              ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data +  i3*nb3 + i2*nb2  + i1*nb1  + i0*nb0);
-
-                        const float x0 = GGML_FP16_TO_FP32(src[0]);
-                        const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
-                        const float x2 = GGML_FP16_TO_FP32(src[n_dims]);
-                        const float x3 = GGML_FP16_TO_FP32(src[n_dims/2*3]);
-
-                        dst_data[0]          = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
-                        dst_data[n_dims/2]   = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
-                        dst_data[n_dims]     = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta);
-                        dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta);
-                    }
-                } else if (!is_neox) {
-                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+                if (!is_neox) {
+                    for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
                         const float cos_theta = cache[i0 + 0];
                         const float sin_theta = cache[i0 + 1];
 
@@ -14407,46 +14261,29 @@ static void ggml_compute_forward_rope_f16(
                         dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
                     }
                 } else {
-                    // TODO: this might be wrong for ne0 != n_dims - need double check
-                    //       it seems we have to rope just the first n_dims elements and do nothing with the rest
-                    // ref:  https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26
-                    theta_base *= freq_scale;
-                    for (int64_t ic = 0; ic < ne0; ic += 2) {
-                        if (ic < n_dims) {
-                            const int64_t ib = 0;
-
-                            // simplified from `(ib * n_dims + ic) * inv_ndims`
-                            float cur_rot = inv_ndims * ic - ib;
+                    for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
+                        const int64_t ic = i0/2;
 
-                            float cos_theta, sin_theta;
-                            rope_yarn(
-                                theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
-                                &cos_theta, &sin_theta
-                            );
-                            sin_theta *= sin_sign;
-
-                            theta_base *= theta_scale;
-
-                            const int64_t i0 = ib*n_dims + ic/2;
+                        const float cos_theta = cache[i0 + 0];
+                        const float sin_theta = cache[i0 + 1];
 
-                            const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                                  ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+                        const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+                        ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
 
-                            const float x0 = GGML_FP16_TO_FP32(src[0]);
-                            const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
+                        const float x0 = GGML_FP16_TO_FP32(src[0]);
+                        const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
 
-                            dst_data[0]        = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
-                            dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
-                        } else {
-                            const int64_t i0 = ic;
+                        dst_data[0]        = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                        dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
+                    }
+                }
 
-                            const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
-                                  ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+                for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
+                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                    ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
 
-                            dst_data[0] = src[0];
-                            dst_data[1] = src[1];
-                        }
-                    }
+                    dst_data[0] = src[0];
+                    dst_data[1] = src[1];
                 }
             }
         }
@@ -14470,8 +14307,8 @@ static void ggml_compute_forward_rope(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -14494,8 +14331,8 @@ static void ggml_compute_forward_rope_back(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -14512,9 +14349,6 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int ith = params->ith;
@@ -14525,10 +14359,7 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (ith != 0) {
-            return;
-        }
+    if (ith == 0) {
         memset(params->wdata, 0, params->wsize);
 
         // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
@@ -14561,13 +14392,8 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
 
         // need to zero dst since we are accumulating into it
         memset(dst->data, 0, ggml_nbytes(dst));
-
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
     }
+    ggml_barrier(params->shared);
 
     const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
 
@@ -14611,9 +14437,6 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int ith = params->ith;
@@ -14624,10 +14447,7 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
     GGML_ASSERT(nb00 == sizeof(float));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (ith != 0) {
-            return;
-        }
+    if (ith == 0) {
         memset(params->wdata, 0, params->wsize);
 
         // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
@@ -14660,13 +14480,8 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
 
         // need to zero dst since we are accumulating into it
         memset(dst->data, 0, ggml_nbytes(dst));
-
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
     }
+    ggml_barrier(params->shared);
 
     const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
 
@@ -14716,8 +14531,8 @@ static void ggml_compute_forward_conv_transpose_1d(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -14735,9 +14550,6 @@ static void ggml_compute_forward_im2col_f32(
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
     GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
@@ -14768,14 +14580,6 @@ static void ggml_compute_forward_im2col_f32(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
     {
         float * const wdata = (float *) dst->data;
@@ -14823,9 +14627,6 @@ static void ggml_compute_forward_im2col_f16(
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F16);
 
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
     GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
@@ -14856,14 +14657,6 @@ static void ggml_compute_forward_im2col_f16(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
     {
         ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data;
@@ -14910,8 +14703,8 @@ static void ggml_compute_forward_im2col(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -14929,9 +14722,6 @@ static void ggml_compute_forward_conv_transpose_2d(
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int ith = params->ith;
@@ -14942,10 +14732,7 @@ static void ggml_compute_forward_conv_transpose_2d(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (ith != 0) {
-            return;
-        }
+    if (ith == 0) {
         memset(params->wdata, 0, params->wsize);
 
         // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
@@ -14980,13 +14767,8 @@ static void ggml_compute_forward_conv_transpose_2d(
         }
 
         memset(dst->data, 0, ggml_nbytes(dst));
-
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
     }
+    ggml_barrier(params->shared);
 
     const int32_t stride = ggml_get_op_params_i32(dst, 0);
 
@@ -15033,10 +14815,9 @@ static void ggml_compute_forward_pool_1d_sk_p0(
 
     const struct ggml_tensor * src = dst->src[0];
 
-    assert(src->type == GGML_TYPE_F32);
-    assert(params->ith == 0);
+    assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16);
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -15047,28 +14828,27 @@ static void ggml_compute_forward_pool_1d_sk_p0(
     const int64_t rs = dst->ne[0];
 
     while (cdata < data_end) {
-        const float * const srow = (const float *)cdata;
-
+        const void * srow = (const void *)cdata;
         int j = 0;
-
         for (int64_t i = 0; i < rs; ++i) {
             switch (op) {
                 case GGML_OP_POOL_AVG:   drow[i] = 0;        break;
                 case GGML_OP_POOL_MAX:   drow[i] = -FLT_MAX; break;
-                case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
+                case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error");
             }
             for (int ki = 0; ki < k; ++ki) {
+                const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]);
                 switch (op) {
-                    case GGML_OP_POOL_AVG:                          drow[i] += srow[j]; break;
-                    case GGML_OP_POOL_MAX:   if (srow[j] > drow[i]) drow[i]  = srow[j]; break;
-                    case GGML_OP_POOL_COUNT:                        GGML_ASSERT(false); break;
+                    case GGML_OP_POOL_AVG:                         drow[i] += srow_j; break;
+                    case GGML_OP_POOL_MAX:   if (srow_j > drow[i]) drow[i]  = srow_j; break;
+                    case GGML_OP_POOL_COUNT:                       GGML_ABORT("fatal error");
                 }
                 ++j;
             }
             switch (op) {
                 case GGML_OP_POOL_AVG:         drow[i] /= k; break;
                 case GGML_OP_POOL_MAX:                       break;
-                case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
+                case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error");
             }
         }
 
@@ -15102,10 +14882,9 @@ static void ggml_compute_forward_pool_2d(
 
     const struct ggml_tensor * src = dst->src[0];
 
-    GGML_ASSERT(src->type == GGML_TYPE_F32);
-    GGML_ASSERT(params->ith == 0);
+    assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16);
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -15138,7 +14917,7 @@ static void ggml_compute_forward_pool_2d(
                 switch (op) {
                     case GGML_OP_POOL_AVG:     *out = 0;        break;
                     case GGML_OP_POOL_MAX:     *out = -FLT_MAX; break;
-                    case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
+                    case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error");
                 }
 
                 const int ix = offset0 + ox * s0;
@@ -15146,21 +14925,22 @@ static void ggml_compute_forward_pool_2d(
 
                 for (int ky = 0; ky < k1; ++ky) {
                     if (iy + ky < 0 || iy + ky >= src->ne[1]) continue;
-                    const float * const srow = (const float *)(cdata + src->nb[1] * (iy + ky));
+                    const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky));
                     for (int kx = 0; kx < k0; ++kx) {
                         int j = ix + kx;
                         if (j < 0 || j >= src->ne[0]) continue;
+                        const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]);
                         switch (op) {
-                            case GGML_OP_POOL_AVG:                     *out += srow[j]; break;
-                            case GGML_OP_POOL_MAX: if (srow[j] > *out) *out  = srow[j]; break;
-                            case GGML_OP_POOL_COUNT:                GGML_ASSERT(false); break;
+                            case GGML_OP_POOL_AVG:                     *out += srow_j; break;
+                            case GGML_OP_POOL_MAX: if (srow_j > *out)  *out  = srow_j; break;
+                            case GGML_OP_POOL_COUNT:               GGML_ABORT("fatal error");
                         }
                     }
                 }
                 switch (op) {
                     case GGML_OP_POOL_AVG:           *out /= ka; break;
                     case GGML_OP_POOL_MAX:                       break;
-                    case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
+                    case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error");
                 }
             }
         }
@@ -15178,10 +14958,6 @@ static void ggml_compute_forward_upscale_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
 
     const int ith = params->ith;
@@ -15228,8 +15004,8 @@ static void ggml_compute_forward_upscale(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -15242,10 +15018,6 @@ static void ggml_compute_forward_pad_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_ASSERT(src0->nb[0] == sizeof(float));
     GGML_ASSERT( dst->nb[0] == sizeof(float));
 
@@ -15290,8 +15062,8 @@ static void ggml_compute_forward_pad(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -15302,10 +15074,6 @@ static void ggml_compute_forward_arange_f32(
     const struct ggml_compute_params * params,
     struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_ASSERT(dst->nb[0] == sizeof(float));
 
     const int ith = params->ith;
@@ -15335,8 +15103,8 @@ static void ggml_compute_forward_arange(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -15344,10 +15112,6 @@ static void ggml_compute_forward_timestep_embedding_f32(
     const struct ggml_compute_params * params,
     struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const struct ggml_tensor * src0 = dst->src[0];
 
     GGML_ASSERT(src0->nb[0] == sizeof(float));
@@ -15390,8 +15154,8 @@ static void ggml_compute_forward_timestep_embedding(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -15403,10 +15167,6 @@ static void ggml_compute_forward_argsort_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_UNARY_OP_LOCALS
 
     GGML_ASSERT(nb0 == sizeof(float));
@@ -15453,402 +15213,8 @@ static void ggml_compute_forward_argsort(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
-    }
-}
-
-// ggml_compute_forward_flash_attn
-
-static void ggml_compute_forward_flash_attn_f32(
-        const struct ggml_compute_params * params,
-        const bool masked,
-        struct ggml_tensor * dst) {
-
-    const struct ggml_tensor * q = dst->src[0];
-    const struct ggml_tensor * k = dst->src[1];
-    const struct ggml_tensor * v = dst->src[2];
-
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
-    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
-    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
-    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
-    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb)
-    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne)
-    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb)
-    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
-    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    const int64_t D = neq0;
-    const int64_t N = neq1;
-    const int64_t P = nek1 - N;
-    const int64_t M = P + N;
-
-    const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);
-
-    GGML_ASSERT(ne0 == D);
-    GGML_ASSERT(ne1 == N);
-    GGML_ASSERT(P >= 0);
-
-    GGML_ASSERT(nbq0 == sizeof(float));
-    GGML_ASSERT(nbk0 == sizeof(float));
-    GGML_ASSERT(nbv0 == sizeof(float));
-
-    GGML_ASSERT(neq0 == D);
-    GGML_ASSERT(nek0 == D);
-    GGML_ASSERT(nev1 == D);
-
-    GGML_ASSERT(neq1 == N);
-    GGML_ASSERT(nek1 == N + P);
-    GGML_ASSERT(nev1 == D);
-
-    // dst cannot be transposed or permuted
-    GGML_ASSERT(nb0 == sizeof(float));
-    GGML_ASSERT(nb0 <= nb1);
-    GGML_ASSERT(nb1 <= nb2);
-    GGML_ASSERT(nb2 <= nb3);
-
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
-    // parallelize by q rows using ggml_vec_dot_f32
-
-    // total rows in q
-    const int nr = neq1*neq2*neq3;
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    const float scale = 1.0f/sqrtf(D);
-
-    //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // q indices
-        const int iq3 = ir/(neq2*neq1);
-        const int iq2 = (ir - iq3*neq2*neq1)/neq1;
-        const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
-
-        float * S = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32);
-
-        for (int i = M; i < Mup; ++i) {
-            S[i] = -INFINITY;
-        }
-
-        const int64_t masked_begin = masked ? (P + iq1 + 1) : M;
-        for (int64_t ic = 0; ic < masked_begin; ++ic) {
-            // k indices
-            const int ik3 = iq3;
-            const int ik2 = iq2 % nek2;
-            const int ik1 = ic;
-
-            // S indices
-            const int i1 = ik1;
-
-            ggml_vec_dot_f32(neq0,
-                    S + i1, 0,
-                    (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0,
-                    (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1);
-        }
-
-        // scale
-        ggml_vec_scale_f32(masked_begin, S, scale);
-
-        for (int64_t i = masked_begin; i < M; i++) {
-            S[i] = -INFINITY;
-        }
-
-        // softmax
-        // exclude known -INF S[..] values from max and loop
-        // dont forget to set their SW values to zero
-        {
-            float max = -INFINITY;
-            ggml_vec_max_f32(masked_begin, &max, S);
-
-            ggml_float sum = 0.0;
-            {
-#ifdef GGML_SOFT_MAX_ACCELERATE
-                max = -max;
-                vDSP_vsadd(S, 1, &max, S, 1, Mup);
-                vvexpf(S, S, &Mup);
-                ggml_vec_sum_f32(Mup, &sum, S);
-#else
-                sum = ggml_vec_soft_max_f32(Mup, S, S, max);
-#endif
-            }
-
-            assert(sum > 0.0);
-
-            sum = 1.0/sum;
-            ggml_vec_scale_f32(masked_begin, S, sum);
-
-#ifndef NDEBUG
-            for (int i = 0; i < masked_begin; ++i) {
-                assert(!isnan(S[i]));
-                assert(!isinf(S[i]));
+                GGML_ABORT("fatal error");
             }
-#endif
-        }
-
-        for (int64_t ic = 0; ic < nev1; ++ic) {
-            // dst indices
-            const int i1 = iq1;
-            const int i2 = iq2;
-            const int i3 = iq3;
-
-            // v indices
-            const int iv2 = iq2 % nev2;
-            const int iv3 = iq3;
-
-            ggml_vec_dot_f32(masked_begin,
-                    (float *) ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)), 0,
-                    (float *) ((char *) v->data   + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0,
-                    S, 0, 1);
-        }
-    }
-}
-
-static void ggml_compute_forward_flash_attn_f16(
-        const struct ggml_compute_params * params,
-        const bool masked,
-        struct ggml_tensor * dst) {
-
-    const struct ggml_tensor * q = dst->src[0];
-    const struct ggml_tensor * k = dst->src[1];
-    const struct ggml_tensor * v = dst->src[2];
-
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
-    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
-    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
-    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
-    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb)
-    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne)
-    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb)
-    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
-    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    const int64_t D = neq0;
-    const int64_t N = neq1;
-    const int64_t P = nek1 - N;
-    const int64_t M = P + N;
-
-    const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);
-
-    GGML_ASSERT(ne0 == D);
-    GGML_ASSERT(ne1 == N);
-    GGML_ASSERT(P >= 0);
-
-    GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t));
-    GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t));
-    GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t));
-
-    GGML_ASSERT(neq0 == D);
-    GGML_ASSERT(nek0 == D);
-    GGML_ASSERT(nev1 == D);
-
-    GGML_ASSERT(neq1 == N);
-    GGML_ASSERT(nek1 == N + P);
-    GGML_ASSERT(nev1 == D);
-
-    // dst cannot be transposed or permuted
-    GGML_ASSERT(nb0 == sizeof(float));
-    GGML_ASSERT(nb0 <= nb1);
-    GGML_ASSERT(nb1 <= nb2);
-    GGML_ASSERT(nb2 <= nb3);
-
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
-    // parallelize by q rows using ggml_vec_dot_f32
-
-    // total rows in q
-    const int nr = neq1*neq2*neq3;
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    const float scale = 1.0f/sqrtf(D);
-
-    //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // q indices
-        const int iq3 = ir/(neq2*neq1);
-        const int iq2 = (ir - iq3*neq2*neq1)/neq1;
-        const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
-
-        float * S = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32);
-
-        for (int i = M; i < Mup; ++i) {
-            S[i] = -INFINITY;
-        }
-
-        if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) {
-            for (int64_t ic = 0; ic < nek1; ++ic) {
-                // k indices
-                const int ik3 = iq3;
-                const int ik2 = iq2 % nek2;
-                const int ik1 = ic;
-
-                // S indices
-                const int i1 = ik1;
-
-                ggml_vec_dot_f16(neq0,
-                        S + i1, 0,
-                        (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0,
-                        (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1);
-            }
-        } else {
-            for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) {
-                // k indices
-                const int ik3 = iq3;
-                const int ik2 = iq2 % nek2;
-                const int ik1 = ic;
-
-                // S indices
-                const int i1 = ik1;
-
-                ggml_vec_dot_f16_unroll(neq0, nbk1,
-                        S + i1,
-                        ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
-                        (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
-            }
-        }
-
-        // scale
-        ggml_vec_scale_f32(nek1, S, scale);
-
-        if (masked) {
-            for (int64_t i = P; i < M; i++) {
-                if (i > P + iq1) {
-                    S[i] = -INFINITY;
-                }
-            }
-        }
-
-        // softmax
-        // todo: exclude known -INF S[..] values from max and loop, assuming their results to be zero.
-        // dont forget to set their S values to zero
-        {
-            float max = -INFINITY;
-            ggml_vec_max_f32(M, &max, S);
-
-            ggml_float sum = 0.0;
-            {
-#ifdef GGML_SOFT_MAX_ACCELERATE
-                max = -max;
-                vDSP_vsadd(S, 1, &max, S, 1, Mup);
-                vvexpf(S, S, &Mup);
-                ggml_vec_sum_f32(Mup, &sum, S);
-#else
-                sum = ggml_vec_soft_max_f32(Mup, S, S, max);
-#endif
-            }
-
-            assert(sum > 0.0);
-
-            sum = 1.0/sum;
-            ggml_vec_scale_f32(M, S, sum);
-
-#ifndef NDEBUG
-            for (int i = 0; i < M; ++i) {
-                assert(!isnan(S[i]));
-                assert(!isinf(S[i]));
-            }
-#endif
-        }
-
-        ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup);
-
-        for (int64_t i = 0; i < M; i++) {
-            S16[i] = GGML_FP32_TO_FP16(S[i]);
-        }
-
-        // todo: exclude known zero S[..] values from dot (reducing nev0 and increasing begin of v and S16).
-        if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) {
-            for (int64_t ic = 0; ic < nev1; ++ic) {
-                // dst indices
-                const int i1 = iq1;
-                const int i2 = iq2;
-                const int i3 = iq3;
-
-                // v indices
-                const int iv2 = iq2 % nev2;
-                const int iv3 = iq3;
-
-                ggml_vec_dot_f16(nev0,
-                        (float *)       ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)), 0,
-                        (ggml_fp16_t *) ((char *) v->data   + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0,
-                        S16, 0, 1);
-            }
-        } else {
-            for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) {
-                // dst indices
-                const int i1 = iq1;
-                const int i2 = iq2;
-                const int i3 = iq3;
-
-                // v indices
-                const int iv2 = iq2 % nev2;
-                const int iv3 = iq3;
-
-                ggml_vec_dot_f16_unroll(nev0, nbv1,
-                        (float *) ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)),
-                        ((char *)             v->data + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)),
-                        S16);
-            }
-        }
-    }
-}
-
-static void ggml_compute_forward_flash_attn(
-        const struct ggml_compute_params * params,
-        const bool masked,
-        struct ggml_tensor * dst) {
-
-    const struct ggml_tensor * q = dst->src[0];
-
-    switch (q->type) {
-        case GGML_TYPE_F16:
-            {
-                ggml_compute_forward_flash_attn_f16(params, masked, dst);
-            } break;
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_flash_attn_f32(params, masked, dst);
-            } break;
-        default:
-            {
-                GGML_ASSERT(false);
-            } break;
     }
 }
 
@@ -15861,8 +15227,6 @@ static void ggml_compute_forward_flash_attn_ext_f16(
         const struct ggml_tensor * v,
         const struct ggml_tensor * mask,
         struct ggml_tensor * dst) {
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
 
     GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
     GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
@@ -15907,14 +15271,6 @@ static void ggml_compute_forward_flash_attn_ext_f16(
     const int64_t rv2 = neq2/nev2;
     const int64_t rv3 = neq3/nev3;
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // parallelize by q rows using ggml_vec_dot_f32
 
     // total rows in q
@@ -16023,224 +15379,65 @@ static void ggml_compute_forward_flash_attn_ext_f16(
                 if (s > M) {
                     // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f
                     M = s;
-                    ms = expf(Mold - M);
-
-                    // V = V*expf(Mold - M)
-                    ggml_vec_scale_f32(D, VKQ32, ms);
-                } else {
-                    // no new maximum, ms == 1.0f, vs != 1.0f
-                    vs = expf(s - M);
-                }
-
-                v_to_float(v_data, V32, D);
-
-                // V += v*expf(s - M)
-                ggml_vec_mad_f32(D, VKQ32, V32, vs);
-            }
-
-            S = S*ms + vs; // scale and increment sum with partial sum
-        }
-
-        if (v->type == GGML_TYPE_F16) {
-            for (int64_t d = 0; d < D; ++d) {
-                VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]);
-            }
-        }
-
-        // V /= S
-        const float S_inv = 1.0f/S;
-        ggml_vec_scale_f32(D, VKQ32, S_inv);
-
-        // dst indices
-        const int i1 = iq1;
-        const int i2 = iq2;
-        const int i3 = iq3;
-
-        // original
-        //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float));
-
-        // permute(0, 2, 1, 3)
-        memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1);
-    }
-}
-
-static void ggml_compute_forward_flash_attn_ext(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
-        const struct ggml_tensor * mask,
-        struct ggml_tensor * dst) {
-    switch (dst->op_params[2]) {
-        case GGML_PREC_DEFAULT:
-        case GGML_PREC_F32:
-            {
-                // uses F32 accumulators
-                ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
-            } break;
-        default:
-            {
-                GGML_ASSERT(false);
-            } break;
-    }
-}
-
-// ggml_compute_forward_flash_ff
-
-static void ggml_compute_forward_flash_ff_f16(
-        const struct ggml_compute_params * params,
-        struct ggml_tensor * dst) {
-
-    const struct ggml_tensor * a = dst->src[0];  // F16
-    const struct ggml_tensor * b0 = dst->src[1]; // F16 fc_w
-    const struct ggml_tensor * b1 = dst->src[2]; // F32 fc_b
-    const struct ggml_tensor * c0 = dst->src[3]; // F16 proj_w
-    const struct ggml_tensor * c1 = dst->src[4]; // F32 proj_b
-
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
-    GGML_TENSOR_LOCALS(int64_t, nea,  a,   ne)
-    GGML_TENSOR_LOCALS(size_t,  nba,  a,   nb)
-    GGML_TENSOR_LOCALS(int64_t, neb0, b0,  ne)
-    GGML_TENSOR_LOCALS(size_t,  nbb0, b0,  nb)
-    GGML_TENSOR_LOCALS(int64_t, neb1, b1,  ne)
-    GGML_TENSOR_LOCALS(size_t,  nbb1, b1,  nb)
-    GGML_TENSOR_LOCALS(int64_t, nec0, c0,  ne)
-    GGML_TENSOR_LOCALS(size_t,  nbc0, c0,  nb)
-    GGML_TENSOR_LOCALS(int64_t, nec1, c1,  ne)
-    GGML_TENSOR_LOCALS(size_t,  nbc1, c1,  nb)
-    GGML_TENSOR_LOCALS(int64_t, ne,   dst, ne)
-    GGML_TENSOR_LOCALS(size_t,  nb,   dst, nb)
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    const int64_t D = nea0;
-    //const int64_t N = nea1;
-    const int64_t M = neb01;
-
-    GGML_ASSERT(ne0 == nea0);
-    GGML_ASSERT(ne1 == nea1);
-    GGML_ASSERT(ne2 == nea2);
-
-    GGML_ASSERT(nba0  == sizeof(ggml_fp16_t));
-    GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t));
-    GGML_ASSERT(nbb10 == sizeof(float));
-    GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t));
-    GGML_ASSERT(nbc10 == sizeof(float));
-
-    GGML_ASSERT(neb00 == D);
-    GGML_ASSERT(neb01 == M);
-    GGML_ASSERT(neb10 == M);
-    GGML_ASSERT(neb11 == 1);
-
-    GGML_ASSERT(nec00 == M);
-    GGML_ASSERT(nec01 == D);
-    GGML_ASSERT(nec10 == D);
-    GGML_ASSERT(nec11 == 1);
-
-    // dst cannot be transposed or permuted
-    GGML_ASSERT(nb0 == sizeof(float));
-    GGML_ASSERT(nb0 <= nb1);
-    GGML_ASSERT(nb1 <= nb2);
-    GGML_ASSERT(nb2 <= nb3);
-
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
-    // parallelize by a rows using ggml_vec_dot_f32
-
-    // total rows in a
-    const int nr = nea1*nea2*nea3;
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // a indices
-        const int ia3 = ir/(nea2*nea1);
-        const int ia2 = (ir - ia3*nea2*nea1)/nea1;
-        const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1);
+                    ms = expf(Mold - M);
 
-        float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32);
+                    // V = V*expf(Mold - M)
+                    ggml_vec_scale_f32(D, VKQ32, ms);
+                } else {
+                    // no new maximum, ms == 1.0f, vs != 1.0f
+                    vs = expf(s - M);
+                }
 
-        for (int64_t ic = 0; ic < neb01; ++ic) {
-            // b0 indices
-            const int ib03 = ia3;
-            const int ib02 = ia2;
-            const int ib01 = ic;
+                v_to_float(v_data, V32, D);
 
-            // S indices
-            const int i1 = ib01;
+                // V += v*expf(s - M)
+                ggml_vec_mad_f32(D, VKQ32, V32, vs);
+            }
 
-            ggml_vec_dot_f16(nea0,
-                    S + i1, 0,
-                    (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), 0,
-                    (ggml_fp16_t *) ((char *)  a->data + ( ia1*nba1  +  ia2*nba2  +  ia3*nba3)), 0, 1);
+            S = S*ms + vs; // scale and increment sum with partial sum
         }
 
-        ggml_vec_add_f32(neb01, S, S, (float *) b1->data);
-        //ggml_vec_gelu_f32(neb01, S, S);
-
-        ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M);
-
-        for (int64_t i = 0; i < M; i++) {
-            S16[i] = GGML_FP32_TO_FP16(S[i]);
+        if (v->type == GGML_TYPE_F16) {
+            for (int64_t d = 0; d < D; ++d) {
+                VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]);
+            }
         }
 
-        ggml_vec_gelu_f16(neb01, S16, S16);
-
-        {
-            // dst indices
-            const int i1 = ia1;
-            const int i2 = ia2;
-            const int i3 = ia3;
+        // V /= S
+        const float S_inv = 1.0f/S;
+        ggml_vec_scale_f32(D, VKQ32, S_inv);
 
-            for (int64_t ic = 0; ic < nec01; ++ic) {
+        // dst indices
+        const int i1 = iq1;
+        const int i2 = iq2;
+        const int i3 = iq3;
 
-                ggml_vec_dot_f16(neb01,
-                        (float *)       ((char *) dst->data + (ic*nb0 + i1*nb1   + i2*nb2   + i3*nb3)), 0,
-                        (ggml_fp16_t *) ((char *) c0->data  + (         ic*nbc01 + i2*nbc02 + i3*nbc03)), 0,
-                        S16, 0, 1);
-            }
+        // original
+        //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float));
 
-            ggml_vec_add_f32(nec01,
-                    (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)),
-                    (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)),
-                    (float *) c1->data);
-        }
+        // permute(0, 2, 1, 3)
+        memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1);
     }
 }
 
-static void ggml_compute_forward_flash_ff(
+static void ggml_compute_forward_flash_attn_ext(
         const struct ggml_compute_params * params,
+        const struct ggml_tensor * q,
+        const struct ggml_tensor * k,
+        const struct ggml_tensor * v,
+        const struct ggml_tensor * mask,
         struct ggml_tensor * dst) {
-
-    const struct ggml_tensor * b0 = dst->src[1];
-
-    switch (b0->type) {
-        case GGML_TYPE_F16:
-            {
-                ggml_compute_forward_flash_ff_f16(params, dst);
-            } break;
-        case GGML_TYPE_F32:
+    switch (dst->op_params[2]) {
+        case GGML_PREC_DEFAULT:
+        case GGML_PREC_F32:
             {
-                GGML_ASSERT(false); // TODO
+                // uses F32 accumulators
+                ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -16256,9 +15453,6 @@ static void ggml_compute_forward_flash_attn_back_f32(
     const struct ggml_tensor * v = dst->src[2];
     const struct ggml_tensor * d = dst->src[3];
 
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
     GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
     GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
     GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
@@ -16305,16 +15499,10 @@ static void ggml_compute_forward_flash_attn_back_f32(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (ith == 0) {
-            memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3);
-        }
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
+    if (ith == 0) {
+        memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3);
     }
+    ggml_barrier(params->shared);
 
     const int64_t elem_q = ggml_nelements(q);
     const int64_t elem_k = ggml_nelements(k);
@@ -16584,8 +15772,8 @@ static void ggml_compute_forward_flash_attn_back(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -16594,10 +15782,6 @@ static void ggml_compute_forward_flash_attn_back(
 static void ggml_compute_forward_ssm_conv_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const struct ggml_tensor * src0 = dst->src[0]; // conv_state
     const struct ggml_tensor * src1 = dst->src[1]; // x
     const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight
@@ -16710,8 +15894,8 @@ static void ggml_compute_forward_ssm_conv(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -16720,10 +15904,6 @@ static void ggml_compute_forward_ssm_conv(
 static void ggml_compute_forward_ssm_scan_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const struct ggml_tensor * src0 = dst->src[0]; // s
     const struct ggml_tensor * src1 = dst->src[1]; // x
     const struct ggml_tensor * src2 = dst->src[2]; // dt
@@ -16835,8 +16015,8 @@ static void ggml_compute_forward_ssm_scan(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -16845,13 +16025,10 @@ static void ggml_compute_forward_ssm_scan(
 static void ggml_compute_forward_win_part_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
+    UNUSED(params);
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
     GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
 
@@ -16901,8 +16078,8 @@ static void ggml_compute_forward_win_part(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -16911,13 +16088,10 @@ static void ggml_compute_forward_win_part(
 static void ggml_compute_forward_win_unpart_f32(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
+    UNUSED(params);
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne)
     GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne)
 
@@ -16965,8 +16139,8 @@ static void ggml_compute_forward_win_unpart(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -17033,8 +16207,8 @@ static void ggml_compute_forward_unary(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -17043,13 +16217,10 @@ static void ggml_compute_forward_unary(
 static void ggml_compute_forward_get_rel_pos_f16(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
+    UNUSED(params);
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322
 
     GGML_TENSOR_UNARY_OP_LOCALS
@@ -17083,8 +16254,8 @@ static void ggml_compute_forward_get_rel_pos(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -17099,20 +16270,12 @@ static void ggml_compute_forward_add_rel_pos_f32(
     const struct ggml_tensor * src2 = dst->src[2];
 
     const bool inplace = (bool) ((int32_t *) dst->op_params)[0];
-    if (!inplace && params->type == GGML_TASK_TYPE_INIT) {
-        if (params->ith != 0) {
-            return;
+    if (!inplace) {
+        if (params->ith == 0) {
+            memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst));
         }
-        memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst));
-        return;
+        ggml_barrier(params->shared);
     }
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
     // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359
 
     float * src1_data = (float *) src1->data;
@@ -17172,8 +16335,8 @@ static void ggml_compute_forward_add_rel_pos(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -17186,18 +16349,17 @@ static void ggml_compute_forward_map_unary_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert( dst->nb[0] == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         fun(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -17219,8 +16381,8 @@ static void ggml_compute_forward_map_unary(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -17234,20 +16396,18 @@ static void ggml_compute_forward_map_binary_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    assert(params->ith == 0);
-    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(src1));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert( dst->nb[0] == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-    assert(src1->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         fun(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -17270,8 +16430,8 @@ static void ggml_compute_forward_map_binary(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -17284,9 +16444,7 @@ static void ggml_compute_forward_map_custom1_f32(
 
     const struct ggml_tensor * a = dst->src[0];
 
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -17303,9 +16461,7 @@ static void ggml_compute_forward_map_custom2_f32(
     const struct ggml_tensor * a = dst->src[0];
     const struct ggml_tensor * b = dst->src[1];
 
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -17323,9 +16479,7 @@ static void ggml_compute_forward_map_custom3_f32(
     const struct ggml_tensor * b = dst->src[1];
     const struct ggml_tensor * c = dst->src[1];
 
-    assert(params->ith == 0);
-
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+    if (params->ith != 0) {
         return;
     }
 
@@ -17340,10 +16494,6 @@ static void ggml_compute_forward_map_custom1(
 
     const struct ggml_tensor * a = dst->src[0];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     struct ggml_map_custom1_op_params p;
     memcpy(&p, dst->op_params, sizeof(p));
 
@@ -17359,10 +16509,6 @@ static void ggml_compute_forward_map_custom2(
     const struct ggml_tensor * a = dst->src[0];
     const struct ggml_tensor * b = dst->src[1];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     struct ggml_map_custom2_op_params p;
     memcpy(&p, dst->op_params, sizeof(p));
 
@@ -17379,10 +16525,6 @@ static void ggml_compute_forward_map_custom3(
     const struct ggml_tensor * b = dst->src[1];
     const struct ggml_tensor * c = dst->src[2];
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     struct ggml_map_custom3_op_params p;
     memcpy(&p, dst->op_params, sizeof(p));
 
@@ -17414,21 +16556,10 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
 
     GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc));
 
-    if (params->type == GGML_TASK_TYPE_INIT) {
-        if (ith == 0) {
-            memset(sums, 0, sizeof(float) * (nth + nth * nc));
-        }
-        return;
-    }
-
-    if (params->type == GGML_TASK_TYPE_FINALIZE) {
-        if (ith == 0) {
-            float * dp = (float *) dst->data;
-            ggml_vec_sum_f32(nth, dp, sums);
-            dp[0] *= -1.0f / (float) nr;
-        }
-        return;
+    if (ith == 0) {
+        memset(sums, 0, sizeof(float) * (nth + nth * nc));
     }
+    ggml_barrier(params->shared);
 
     const double eps = 1e-9;
 
@@ -17476,7 +16607,13 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
         }
 #endif
     }
+    ggml_barrier(params->shared);
 
+    if (ith == 0) {
+        float * dp = (float *) dst->data;
+        ggml_vec_sum_f32(nth, dp, sums);
+        dp[0] *= -1.0f / (float) nr;
+    }
 }
 
 static void ggml_compute_forward_cross_entropy_loss(
@@ -17492,8 +16629,8 @@ static void ggml_compute_forward_cross_entropy_loss(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
@@ -17516,10 +16653,6 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
     const int64_t ith = params->ith;
     const int64_t nth = params->nth;
 
-    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
-        return;
-    }
-
     const double eps = 1e-9;
 
     // TODO: handle transposed/permuted matrices
@@ -17583,14 +16716,14 @@ static void ggml_compute_forward_cross_entropy_loss_back(
             } break;
         default:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
 /////////////////////////////////
 
-static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor, struct ggml_compute_state * state) {
+static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
     GGML_ASSERT(params);
 
     if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) {
@@ -17688,7 +16821,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_MUL_MAT:
             {
-                ggml_compute_forward_mul_mat(params, tensor, state);
+                ggml_compute_forward_mul_mat(params, tensor);
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
@@ -17814,21 +16947,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_leaky_relu(params, tensor);
             } break;
-        case GGML_OP_FLASH_ATTN:
-            {
-                const int32_t t = ggml_get_op_params_i32(tensor, 0);
-                GGML_ASSERT(t == 0 || t == 1);
-                const bool masked = t != 0;
-                ggml_compute_forward_flash_attn(params, masked, tensor);
-            } break;
         case GGML_OP_FLASH_ATTN_EXT:
             {
                 ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor);
             } break;
-        case GGML_OP_FLASH_FF:
-            {
-                ggml_compute_forward_flash_ff(params, tensor);
-            } break;
         case GGML_OP_FLASH_ATTN_BACK:
             {
                 int32_t t = ggml_get_op_params_i32(tensor, 0);
@@ -17930,14 +17052,32 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_COUNT:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 }
 
 ////////////////////////////////////////////////////////////////////////////////
 
-static size_t ggml_hash_size(size_t min_sz) {
+struct ggml_hash_set ggml_hash_set_new(size_t size) {
+    size = ggml_hash_size(size);
+    struct ggml_hash_set result;
+    result.size = size;
+    result.keys = GGML_MALLOC(sizeof(struct ggml_tensor *) * size);
+    result.used = GGML_CALLOC(ggml_bitset_size(size), sizeof(ggml_bitset_t));
+    return result;
+}
+
+void ggml_hash_set_reset(struct ggml_hash_set * hash_set) {
+    memset(hash_set->used, 0, sizeof(ggml_bitset_t) * ggml_bitset_size(hash_set->size));
+}
+
+void ggml_hash_set_free(struct ggml_hash_set * hash_set) {
+    GGML_FREE(hash_set->used);
+    GGML_FREE(hash_set->keys);
+}
+
+size_t ggml_hash_size(size_t min_sz) {
     // next primes after powers of two
     static const size_t primes[] = {
         2, 3, 5, 11, 17, 37, 67, 131, 257, 521, 1031,
@@ -17948,7 +17088,7 @@ static size_t ggml_hash_size(size_t min_sz) {
     };
     static const size_t n_primes = sizeof(primes)/sizeof(primes[0]);
 
-    // find the smallest prime that is larger or equal to min_sz
+    // find the smallest prime that is larger or equal than min_sz
     size_t l = 0;
     size_t r = n_primes;
     while (l < r) {
@@ -17963,67 +17103,6 @@ static size_t ggml_hash_size(size_t min_sz) {
     return sz;
 }
 
-static size_t ggml_hash(const void * p) {
-    return (size_t)p;
-}
-
-size_t ggml_hash_find(const struct ggml_hash_set hash_set, struct ggml_tensor * key) {
-    size_t h = ggml_hash(key) % hash_set.size;
-
-    // linear probing
-    size_t i = h;
-    while (hash_set.keys[i] != NULL && hash_set.keys[i] != key) {
-        i = (i + 1) % hash_set.size;
-        if (i == h) {
-            // visited all hash table entries -> not found
-            return GGML_HASHTABLE_FULL;
-        }
-    }
-    return i;
-}
-
-bool ggml_hash_contains(struct ggml_hash_set hash_set, struct ggml_tensor * key) {
-    size_t i = ggml_hash_find(hash_set, key);
-    return i != GGML_HASHTABLE_FULL && hash_set.keys[i] == key;
-}
-
-size_t ggml_hash_insert(struct ggml_hash_set hash_set, struct ggml_tensor * key) {
-    size_t i = ggml_hash_find(hash_set, key);
-
-    GGML_ASSERT(i != GGML_HASHTABLE_FULL);
-
-    if (hash_set.keys[i] == key) {
-        return GGML_HASHTABLE_ALREADY_EXISTS;
-    }
-
-    // insert
-    GGML_ASSERT(hash_set.keys[i] == NULL);
-    hash_set.keys[i] = key;
-    return i;
-}
-
-size_t ggml_hash_find_or_insert(struct ggml_hash_set hash_set, struct ggml_tensor * key) {
-    size_t i = ggml_hash_find(hash_set, key);
-
-    GGML_ASSERT(i != GGML_HASHTABLE_FULL);
-
-    hash_set.keys[i] = key;
-    return i;
-}
-
-struct ggml_hash_set ggml_hash_set_new(size_t size) {
-    size = ggml_hash_size(size);
-    struct ggml_hash_set result;
-    result.size = size;
-    result.keys = GGML_MALLOC(sizeof(struct ggml_tensor *) * size);
-    memset(result.keys, 0, sizeof(struct ggml_tensor *) * size);
-    return result;
-}
-
-static void ggml_hash_set_free(struct ggml_hash_set hash_set) {
-    GGML_FREE(hash_set.keys);
-}
-
 struct hash_map {
     struct ggml_hash_set set;
     struct ggml_tensor ** vals;
@@ -18032,13 +17111,12 @@ struct hash_map {
 static struct hash_map * ggml_new_hash_map(size_t size) {
     struct hash_map * result = GGML_MALLOC(sizeof(struct hash_map));
     result->set = ggml_hash_set_new(size);
-    result->vals = GGML_MALLOC(sizeof(struct ggml_tensor *) * result->set.size);
-    memset(result->vals, 0, sizeof(struct ggml_tensor *) * result->set.size);
+    result->vals = GGML_CALLOC(result->set.size, sizeof(struct ggml_tensor *));
     return result;
 }
 
 static void ggml_hash_map_free(struct hash_map * map) {
-    ggml_hash_set_free(map->set);
+    ggml_hash_set_free(&map->set);
     GGML_FREE(map->vals);
     GGML_FREE(map);
 }
@@ -18059,7 +17137,7 @@ static struct ggml_tensor * ggml_recompute_graph_node(
         return node;
     }
 
-    if (!ggml_hash_contains(graph->visited_hash_table, node)) {
+    if (!ggml_hash_contains(&graph->visited_hash_set, node)) {
         return node;
     }
 
@@ -18074,8 +17152,8 @@ static struct ggml_tensor * ggml_recompute_graph_node(
         return node;
     }
 
-    size_t i = ggml_hash_find(replacements->set, node);
-    GGML_ASSERT(i != GGML_HASHTABLE_FULL); // assert that not full
+    size_t i = ggml_hash_find(&replacements->set, node);
+    GGML_ASSERT(i != GGML_HASHSET_FULL); // assert that not full
     if (replacements->set.keys[i] == node) {
         return replacements->vals[i];
     }
@@ -18133,8 +17211,8 @@ void ggml_build_backward_gradient_checkpointing(
 
     // insert checkpoints in replacements
     for (int i = 0; i < n_checkpoints; ++i) {
-        size_t k = ggml_hash_find(replacements->set, checkpoints[i]);
-        GGML_ASSERT(k != GGML_HASHTABLE_FULL); // assert that not full
+        size_t k = ggml_hash_find(&replacements->set, checkpoints[i]);
+        GGML_ASSERT(k != GGML_HASHSET_FULL); // assert that not full
         GGML_ASSERT(replacements->set.keys[k] == NULL); // assert that we don't overwrite
         replacements->set.keys[k] = checkpoints[i];
         replacements->vals[k]     = checkpoints[i];
@@ -18162,7 +17240,7 @@ void ggml_build_backward_gradient_checkpointing(
 
 // functions to change gradients considering the case that input a might be initial gradient with zero value
 
-static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) {
+static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set * zero_table) {
     if (ggml_hash_contains(zero_table, a)) {
         return b;
     } else {
@@ -18170,7 +17248,7 @@ static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct gg
     }
 }
 
-static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, struct ggml_hash_set zero_table) {
+static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, struct ggml_hash_set * zero_table) {
     if (ggml_hash_contains(zero_table, a)) {
         struct ggml_tensor * a_zero = ggml_scale(ctx, a, 0.0f);
         return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false);
@@ -18179,7 +17257,7 @@ static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct gg
     }
 }
 
-static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) {
+static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set * zero_table) {
     if (ggml_hash_contains(zero_table, a)) {
         return ggml_repeat(ctx, b, a);
     } else {
@@ -18187,7 +17265,7 @@ static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct g
     }
 }
 
-static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) {
+static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set * zero_table) {
     if (ggml_hash_contains(zero_table, a)) {
         return ggml_neg(ctx, b);
     } else {
@@ -18195,9 +17273,10 @@ static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct gg
     }
 }
 
-static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set zero_table) {
+static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set * zero_table) {
     struct ggml_tensor * src0 = tensor->src[0];
     struct ggml_tensor * src1 = tensor->src[1];
+    struct ggml_tensor * src2 = tensor->src[2];
 
     switch (tensor->op) {
         case GGML_OP_DUP:
@@ -18363,8 +17442,8 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
         case GGML_OP_MEAN:
         case GGML_OP_ARGMAX:
             {
-                GGML_ASSERT(false); // TODO: implement
-            } break;
+                GGML_ABORT("fatal error"); // TODO: implement
+            }
         case GGML_OP_REPEAT:
             {
                 // necessary for llama
@@ -18387,16 +17466,16 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             } break;
         case GGML_OP_CONCAT:
             {
-                GGML_ASSERT(false); // TODO: implement
-            } break;
+                GGML_ABORT("fatal error"); // TODO: implement
+            }
         case GGML_OP_SILU_BACK:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_NORM:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_RMS_NORM:
             {
                 // necessary for llama
@@ -18412,12 +17491,12 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             } break;
         case GGML_OP_RMS_NORM_BACK:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_GROUP_NORM:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_MUL_MAT:
             {
                 // https://cs231n.github.io/optimization-2/#staged
@@ -18478,12 +17557,12 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_OUT_PROD:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_SCALE:
             {
                 // necessary for llama
@@ -18659,12 +17738,12 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             } break;
         case GGML_OP_GET_ROWS_BACK:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_DIAG:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_DIAG_MASK_INF:
             {
                 // necessary for llama
@@ -18702,8 +17781,8 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             } break;
         case GGML_OP_SOFT_MAX_BACK:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_ROPE:
             {
                 // necessary for llama
@@ -18711,9 +17790,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                     //const int n_past = ((int32_t *) tensor->op_params)[0];
                     const int n_dims     = ((int32_t *) tensor->op_params)[1];
                     const int mode       = ((int32_t *) tensor->op_params)[2];
-                    const int n_ctx      = ((int32_t *) tensor->op_params)[3];
-                    const int n_orig_ctx = ((int32_t *) tensor->op_params)[4];
-                    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down;
+                    //const int n_ctx      = ((int32_t *) tensor->op_params)[3];
+                    const int n_ctx_orig = ((int32_t *) tensor->op_params)[4];
+                    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
 
                     memcpy(&freq_base,   (int32_t *) tensor->op_params +  5, sizeof(float));
                     memcpy(&freq_scale,  (int32_t *) tensor->op_params +  6, sizeof(float));
@@ -18721,26 +17800,22 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                     memcpy(&attn_factor, (int32_t *) tensor->op_params +  8, sizeof(float));
                     memcpy(&beta_fast,   (int32_t *) tensor->op_params +  9, sizeof(float));
                     memcpy(&beta_slow,   (int32_t *) tensor->op_params + 10, sizeof(float));
-                    memcpy(&xpos_base,   (int32_t *) tensor->op_params + 11, sizeof(float));
-                    memcpy(&xpos_down,   (int32_t *) tensor->op_params + 12, sizeof(bool));
 
                     src0->grad = ggml_add_or_set(ctx,
                             src0->grad,
                             ggml_rope_back(ctx,
                                 tensor->grad,
                                 src1,
+                                src2,
                                 n_dims,
                                 mode,
-                                n_ctx,
-                                n_orig_ctx,
+                                n_ctx_orig,
                                 freq_base,
                                 freq_scale,
                                 ext_factor,
                                 attn_factor,
                                 beta_fast,
-                                beta_slow,
-                                xpos_base,
-                                xpos_down),
+                                beta_slow),
                             zero_table);
                 }
             } break;
@@ -18750,9 +17825,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                     //const int n_past = ((int32_t *) tensor->op_params)[0];
                     const int n_dims     = ((int32_t *) tensor->op_params)[1];
                     const int mode       = ((int32_t *) tensor->op_params)[2];
-                    const int n_ctx      = ((int32_t *) tensor->op_params)[3];
-                    const int n_orig_ctx = ((int32_t *) tensor->op_params)[4];
-                    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down;
+                    //const int n_ctx      = ((int32_t *) tensor->op_params)[3];
+                    const int n_ctx_orig = ((int32_t *) tensor->op_params)[4];
+                    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
 
                     memcpy(&freq_base,   (int32_t *) tensor->op_params +  5, sizeof(float));
                     memcpy(&freq_scale,  (int32_t *) tensor->op_params +  6, sizeof(float));
@@ -18760,79 +17835,74 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                     memcpy(&attn_factor, (int32_t *) tensor->op_params +  8, sizeof(float));
                     memcpy(&beta_fast,   (int32_t *) tensor->op_params +  9, sizeof(float));
                     memcpy(&beta_slow,   (int32_t *) tensor->op_params + 10, sizeof(float));
-                    memcpy(&xpos_base,   (int32_t *) tensor->op_params + 11, sizeof(float));
-                    memcpy(&xpos_down,   (int32_t *) tensor->op_params + 12, sizeof(bool));
 
                     src0->grad = ggml_add_or_set(ctx,
                             src0->grad,
                             ggml_rope_impl(ctx,
                                 tensor->grad,
                                 src1,
+                                src2,
                                 n_dims,
                                 mode,
-                                n_ctx,
-                                n_orig_ctx,
+                                n_ctx_orig,
                                 freq_base,
                                 freq_scale,
                                 ext_factor,
                                 attn_factor,
                                 beta_fast,
                                 beta_slow,
-                                xpos_base,
-                                xpos_down,
                                 false),
                             zero_table);
                 }
             } break;
         case GGML_OP_CLAMP:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_CONV_TRANSPOSE_1D:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_IM2COL:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_CONV_TRANSPOSE_2D:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_POOL_1D:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_POOL_2D:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_UPSCALE:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_PAD:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_ARANGE:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_TIMESTEP_EMBEDDING:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_ARGSORT:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_LEAKY_RELU:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
-        case GGML_OP_FLASH_ATTN:
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_FLASH_ATTN_EXT:
             {
                 struct ggml_tensor * flash_grad = NULL;
@@ -18849,7 +17919,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                             masked);
                 }
 
-                struct ggml_tensor * src2 = tensor->src[2];
                 const int64_t elem_q = ggml_nelements(src0);
                 const int64_t elem_k = ggml_nelements(src1);
                 const int64_t elem_v = ggml_nelements(src2);
@@ -18887,19 +17956,15 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                             zero_table);
                 }
             } break;
-        case GGML_OP_FLASH_FF:
-            {
-                GGML_ASSERT(false); // not supported
-            } break;
         case GGML_OP_FLASH_ATTN_BACK:
             {
-                GGML_ASSERT(false); // not supported
-            } break;
+                GGML_ABORT("fatal error"); // not supported
+            }
         case GGML_OP_SSM_CONV:
         case GGML_OP_SSM_SCAN:
             {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
+                GGML_ABORT("fatal error"); // TODO: not implemented
+            }
         case GGML_OP_WIN_PART:
         case GGML_OP_WIN_UNPART:
         case GGML_OP_UNARY:
@@ -18937,12 +18002,12 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                         } break;
                     case GGML_UNARY_OP_TANH:
                         {
-                            GGML_ASSERT(false); // TODO: not implemented
-                        } break;
+                            GGML_ABORT("fatal error"); // TODO: not implemented
+                        }
                     case GGML_UNARY_OP_ELU:
                         {
-                            GGML_ASSERT(false); // TODO: not implemented
-                        } break;
+                            GGML_ABORT("fatal error"); // TODO: not implemented
+                        }
                     case GGML_UNARY_OP_RELU:
                         {
                             if (src0->grad) {
@@ -18956,16 +18021,16 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                         } break;
                     case GGML_UNARY_OP_SIGMOID:
                         {
-                            GGML_ASSERT(false); // TODO: not implemented
-                        } break;
+                            GGML_ABORT("fatal error"); // TODO: not implemented
+                        }
                     case GGML_UNARY_OP_GELU:
                         {
-                            GGML_ASSERT(false); // TODO: not implemented
-                        } break;
+                            GGML_ABORT("fatal error"); // TODO: not implemented
+                        }
                     case GGML_UNARY_OP_GELU_QUICK:
                         {
-                            GGML_ASSERT(false); // TODO: not implemented
-                        } break;
+                            GGML_ABORT("fatal error"); // TODO: not implemented
+                        }
                     case GGML_UNARY_OP_SILU:
                         {
                             // necessary for llama
@@ -18977,7 +18042,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                             }
                         } break;
                     default:
-                        GGML_ASSERT(false);
+                        GGML_ABORT("fatal error");
                 }
             } break;
         case GGML_OP_GET_REL_POS:
@@ -18991,8 +18056,8 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
         case GGML_OP_MAP_CUSTOM2:
         case GGML_OP_MAP_CUSTOM3:
             {
-                GGML_ASSERT(false); // not supported
-            } break;
+                GGML_ABORT("fatal error"); // not supported
+            }
         case GGML_OP_CROSS_ENTROPY_LOSS:
             {
                 if (src0->grad) {
@@ -19007,16 +18072,16 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             } break;
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
             {
-                GGML_ASSERT(false); // not supported
-            } break;
+                GGML_ABORT("fatal error"); // not supported
+            }
         case GGML_OP_NONE:
             {
                 // nop
             } break;
         case GGML_OP_COUNT:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 
     for (int i = 0; i < GGML_MAX_SRC; ++i) {
@@ -19036,7 +18101,7 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor *
     }
 
     // check if already visited
-    if (ggml_hash_insert(cgraph->visited_hash_table, node) == GGML_HASHTABLE_ALREADY_EXISTS) {
+    if (ggml_hash_insert(&cgraph->visited_hash_set, node) == GGML_HASHSET_ALREADY_EXISTS) {
         return;
     }
 
@@ -19082,7 +18147,6 @@ static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_ten
     }
 
     const int n0 = cgraph->n_nodes;
-    UNUSED(n0);
 
     ggml_visit_parents(cgraph, tensor);
 
@@ -19118,7 +18182,7 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph *
     struct ggml_hash_set zero_table = ggml_hash_set_new(gf->size);
     for (int i = 0; i < gf->n_nodes; i++) {
         if (gf->grads[i]) {
-            ggml_hash_insert(zero_table, gf->grads[i]);
+            ggml_hash_insert(&zero_table, gf->grads[i]);
         }
     }
 
@@ -19128,7 +18192,7 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph *
         // inplace operations to add gradients are not created by ggml_compute_backward
         // use allocator to automatically make inplace operations
         if (node->grad) {
-            ggml_compute_backward(ctx, node, zero_table);
+            ggml_compute_backward(ctx, node, &zero_table);
         }
     }
 
@@ -19141,16 +18205,29 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph *
         }
     }
 
-    ggml_hash_set_free(zero_table);
+    ggml_hash_set_free(&zero_table);
+}
+
+static void * incr_ptr_aligned(void ** p, size_t size, size_t align) {
+    void * ptr = *p;
+    ptr = (void *) GGML_PAD((uintptr_t) ptr, align);
+    *p = (void *) ((char *) ptr + size);
+    return ptr;
 }
 
 static size_t ggml_graph_nbytes(size_t size, bool grads) {
-    size_t nbytes = sizeof(struct ggml_cgraph);
-    nbytes += size * sizeof(struct ggml_tensor *) * 2; // leafs + nodes
+    size_t hash_size = ggml_hash_size(size * 2);
+    void * p = 0;
+    incr_ptr_aligned(&p, sizeof(struct ggml_cgraph), 1);
+    incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // nodes
+    incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // leafs
+    incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // hash keys
     if (grads) {
-        nbytes += size * sizeof(struct ggml_tensor *); // grads
+        incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grads
     }
-    nbytes += ggml_hash_size(size * 2) * sizeof(struct ggml_tensor *); // hash set
+    incr_ptr_aligned(&p, ggml_bitset_size(hash_size) * sizeof(ggml_bitset_t), sizeof(ggml_bitset_t));
+
+    size_t nbytes = (size_t) p;
     return nbytes;
 }
 
@@ -19167,19 +18244,19 @@ struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t siz
     struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_GRAPH, obj_size);
     struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs);
 
-    struct ggml_tensor ** data_start = (struct ggml_tensor **) (cgraph + 1);
-
+    // the size of the hash table is doubled since it needs to hold both nodes and leafs
     size_t hash_size = ggml_hash_size(size * 2);
-    struct ggml_tensor ** nodes_ptr = data_start;
-    struct ggml_tensor ** leafs_ptr = nodes_ptr + size;
-    struct ggml_tensor ** hash_keys_ptr = leafs_ptr + size;
-    struct ggml_tensor ** grads_ptr = grads ? hash_keys_ptr + hash_size : NULL;
 
-    // check that we allocated the correct amount of memory
-    assert(obj_size == (size_t) (
-        (grads ? (char *)(grads_ptr + size) : (char *)(hash_keys_ptr + hash_size)) - (char *)cgraph));
+    void * p = cgraph + 1;
+
+    struct ggml_tensor ** nodes_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *));
+    struct ggml_tensor ** leafs_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *));
+    struct ggml_tensor ** hash_keys_ptr = incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *));
+    struct ggml_tensor ** grads_ptr = grads ? incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL;
+    ggml_bitset_t * hash_used = incr_ptr_aligned(&p, ggml_bitset_size(hash_size) * sizeof(ggml_bitset_t), sizeof(ggml_bitset_t));
 
-    memset(hash_keys_ptr, 0, hash_size * sizeof(struct ggml_tensor *));
+    // check that we allocated the correct amount of memory
+    assert(obj_size == (size_t)((char *)p - (char *)cgraph));
 
     *cgraph = (struct ggml_cgraph) {
         /*.size         =*/ size,
@@ -19188,13 +18265,12 @@ struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t siz
         /*.nodes        =*/ nodes_ptr,
         /*.grads        =*/ grads_ptr,
         /*.leafs        =*/ leafs_ptr,
-        /*.hash_table   =*/ { hash_size, hash_keys_ptr },
+        /*.hash_table   =*/ { hash_size, hash_used, hash_keys_ptr },
         /*.order        =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT,
-        /*.perf_runs    =*/ 0,
-        /*.perf_cycles  =*/ 0,
-        /*.perf_time_us =*/ 0,
     };
 
+    ggml_hash_set_reset(&cgraph->visited_hash_set);
+
     return cgraph;
 }
 
@@ -19210,11 +18286,8 @@ struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1)
         /*.nodes        =*/ cgraph0->nodes + i0,
         /*.grads        =*/ cgraph0->grads ? cgraph0->grads + i0 : NULL,
         /*.leafs        =*/ NULL,
-        /*.hash_table   =*/ { 0, NULL },
+        /*.hash_table   =*/ { 0, NULL, NULL },
         /*.order        =*/ cgraph0->order,
-        /*.perf_runs    =*/ 0,
-        /*.perf_cycles  =*/ 0,
-        /*.perf_time_us =*/ 0,
     };
 
     return cgraph;
@@ -19223,7 +18296,7 @@ struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1)
 void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) {
     GGML_ASSERT(dst->size >= src->n_leafs);
     GGML_ASSERT(dst->size >= src->n_nodes);
-    GGML_ASSERT(dst->visited_hash_table.size >= src->visited_hash_table.size);
+    GGML_ASSERT(dst->visited_hash_set.size >= src->visited_hash_set.size);
 
     dst->n_leafs = src->n_leafs;
     dst->n_nodes = src->n_nodes;
@@ -19244,9 +18317,9 @@ void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) {
         }
     }
 
-    for (size_t i = 0; i < src->visited_hash_table.size; ++i) {
-        if (src->visited_hash_table.keys[i]) {
-            ggml_hash_insert(dst->visited_hash_table, src->visited_hash_table.keys[i]);
+    for (size_t i = 0; i < src->visited_hash_set.size; ++i) {
+        if (src->visited_hash_set.keys[i]) {
+            ggml_hash_insert(&dst->visited_hash_set, src->visited_hash_set.keys[i]);
         }
     }
 }
@@ -19272,7 +18345,7 @@ void ggml_graph_reset(struct ggml_cgraph * cgraph) {
 void ggml_graph_clear(struct ggml_cgraph * cgraph) {
     cgraph->n_leafs = 0;
     cgraph->n_nodes = 0;
-    memset(cgraph->visited_hash_table.keys, 0, cgraph->visited_hash_table.size * sizeof(struct ggml_tensor *));
+    ggml_hash_set_reset(&cgraph->visited_hash_set);
 }
 
 //
@@ -19408,16 +18481,7 @@ static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n);  }
 static void clear_numa_thread_affinity(void) {}
 #endif
 
-static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) {
-    int64_t cycles_cur  = ggml_perf_cycles()  - st->perf_node_start_cycles;
-    int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us;
-
-    node->perf_runs++;
-    node->perf_cycles  += cycles_cur;
-    node->perf_time_us += time_us_cur;
-}
-
-static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) {
+static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
     int n_tasks = 0;
 
     if (ggml_is_empty(node)) {
@@ -19429,6 +18493,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
     switch (node->op) {
         case GGML_OP_CPY:
         case GGML_OP_DUP:
+        case GGML_OP_CONT:
         case GGML_OP_ADD:
         case GGML_OP_ADD1:
         case GGML_OP_ACC:
@@ -19459,8 +18524,8 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
                 case GGML_UNARY_OP_ELU:
                 case GGML_UNARY_OP_RELU:
                 case GGML_UNARY_OP_SIGMOID:
-                case GGML_UNARY_OP_HARDSWISH: // to opt for multiple threads
-                case GGML_UNARY_OP_HARDSIGMOID: // to opt for multiple threads
+                case GGML_UNARY_OP_HARDSWISH:
+                case GGML_UNARY_OP_HARDSIGMOID:
                     {
                         n_tasks = 1;
                     } break;
@@ -19472,7 +18537,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
                         n_tasks = n_threads;
                     } break;
                 default:
-                    GGML_ASSERT(false);
+                    GGML_ABORT("fatal error");
             }
             break;
         case GGML_OP_SILU_BACK:
@@ -19483,37 +18548,21 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
         case GGML_OP_RMS_NORM_BACK:
         case GGML_OP_GROUP_NORM:
         case GGML_OP_CONCAT:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_MUL_MAT:
-            {
-                n_tasks = n_threads;
-
-                // TODO: use different scheduling for different matrix sizes
-                //const int nr0 = ggml_nrows(node->src[0]);
-                //const int nr1 = ggml_nrows(node->src[1]);
-
-                //n_tasks = MIN(n_threads, MAX(1, nr0/128));
-                //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks);
-            } break;
         case GGML_OP_MUL_MAT_ID:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_OUT_PROD:
             {
                 n_tasks = n_threads;
             } break;
         case GGML_OP_GET_ROWS:
             {
-                // FIXME: the cost of launching additional threads decreases performance with GPU offloading
-                //n_tasks = MIN(n_threads, ggml_nelements(node->src[1]));
-                n_tasks = MIN(n_cur_threads, ggml_nelements(node->src[1]));
+                // FIXME: get_rows can use additional threads, but the cost of launching additional threads
+                // decreases performance with GPU offloading
+                //n_tasks = n_threads;
+                n_tasks = 1;
             } break;
         case GGML_OP_SCALE:
         case GGML_OP_SET:
-        case GGML_OP_CONT:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
@@ -19540,14 +18589,8 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
             {
                 n_tasks = MIN(n_threads, ggml_nrows(node->src[0]));
             } break;
-        case GGML_OP_CONV_TRANSPOSE_1D:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_IM2COL:
-            {
-                n_tasks = n_threads;
-            } break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
         case GGML_OP_CONV_TRANSPOSE_2D:
             {
                 n_tasks = n_threads;
@@ -19558,38 +18601,12 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
                 n_tasks = 1;
             } break;
         case GGML_OP_UPSCALE:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_PAD:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_ARANGE:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_TIMESTEP_EMBEDDING:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_ARGSORT:
-            {
-                n_tasks = n_threads;
-            } break;
-        case GGML_OP_FLASH_ATTN:
         case GGML_OP_FLASH_ATTN_EXT:
-            {
-                n_tasks = n_threads;
-            } break;
-        case GGML_OP_FLASH_FF:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_FLASH_ATTN_BACK:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_SSM_CONV:
         case GGML_OP_SSM_SCAN:
             {
@@ -19637,9 +18654,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
                 }
             } break;
         case GGML_OP_CROSS_ENTROPY_LOSS:
-            {
-                n_tasks = n_threads;
-            } break;
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
             {
                 n_tasks = n_threads;
@@ -19650,8 +18664,8 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
             } break;
         case GGML_OP_COUNT:
             {
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
         default:
             {
                 fprintf(stderr, "%s: op not implemented: ", __func__);
@@ -19660,8 +18674,8 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
                 } else {
                     fprintf(stderr, "%d\n", node->op);
                 }
-                GGML_ASSERT(false);
-            } break;
+                GGML_ABORT("fatal error");
+            }
     }
 
     assert(n_tasks > 0);
@@ -19669,184 +18683,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
     return n_tasks;
 }
 
-static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_compute_state * state, const bool do_yield) {
-    // wait for other threads to finish
-    const int last_node_n = * node_n;
-
-    while (true) {
-        if (do_yield) {
-            sched_yield();
-        }
-
-        * node_n = atomic_load(&state->shared->node_n);
-        if (* node_n != last_node_n) break;
-#if defined(__SSE3__)
-        // Tell the processor we're spinning.  It's a processor hint for spinlocks.
-        _mm_pause();
-#endif
-    }
-}
-
-static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) {
-    // wait for other threads to finish
-    const int last_task_phase = * task_phase;
-
-    while (true) {
-        if (do_yield) {
-            sched_yield();
-        }
-
-        * task_phase = atomic_load(&state->shared->node_task);
-        if (* task_phase != last_task_phase) break;
-#if defined(__SSE3__)
-        // Tell the processor we're spinning.  It's a processor hint for spinlocks.
-        _mm_pause();
-#endif
-    }
-}
-
-static thread_ret_t ggml_graph_compute_thread(void * data) {
-    struct ggml_compute_state * state = (struct ggml_compute_state *) data;
-
-    const struct ggml_cgraph * cgraph = state->shared->cgraph;
-    const struct ggml_cplan  * cplan  = state->shared->cplan;
-
-    const int   n_threads   = state->shared->n_threads;
-
-    set_numa_thread_affinity(state->ith);
-
-    int node_n     = -1;
-    int task_phase = GGML_TASK_TYPE_FINALIZE;
-
-    while (true) {
-        if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
-            state->shared->node_n += 1;
-            state->ec = GGML_STATUS_ABORTED;
-            return 0;
-        }
-
-        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
-            // all other threads are finished and spinning
-            // do finalize and init here so we don't have synchronize again
-            struct ggml_compute_params params = {
-                /*.type  =*/ GGML_TASK_TYPE_FINALIZE,
-                /*.ith   =*/ 0,
-                /*.nth   =*/ 0,
-                /*.wsize =*/ cplan->work_size,
-                /*.wdata =*/ cplan->work_data,
-            };
-
-            if (node_n != -1) {
-                /* FINALIZE */
-                struct ggml_tensor * node = cgraph->nodes[node_n];
-                if (GGML_OP_HAS_FINALIZE[node->op]) {
-                    params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
-                    ggml_compute_forward(&params, node, state);
-                }
-                ggml_graph_compute_perf_stats_node(node, state->shared);
-            }
-
-            // distribute new work or execute it direct if 1T
-            while (++node_n < cgraph->n_nodes) {
-                GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes);
-                struct ggml_tensor * node = cgraph->nodes[node_n];
-                const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
-
-                state->shared->perf_node_start_cycles  = ggml_perf_cycles();
-                state->shared->perf_node_start_time_us = ggml_perf_time_us();
-
-                params.nth = n_tasks;
-
-                if (n_tasks == 1) {
-                    /* INIT */
-                    if (GGML_OP_HAS_INIT[node->op]) {
-                        params.type = GGML_TASK_TYPE_INIT;
-                        ggml_compute_forward(&params, node, state);
-                    }
-
-                    // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
-                    // they do something more efficient than spinning (?)
-                    params.type = GGML_TASK_TYPE_COMPUTE;
-                    ggml_compute_forward(&params, node, state);
-
-                    if (GGML_OP_HAS_FINALIZE[node->op]) {
-                        params.type = GGML_TASK_TYPE_FINALIZE;
-                        ggml_compute_forward(&params, node, state);
-                    }
-
-                    ggml_graph_compute_perf_stats_node(node, state->shared);
-                } else {
-                    break;
-                }
-
-                if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
-                    break;
-                }
-            }
-
-            task_phase = GGML_TASK_TYPE_INIT;
-            atomic_store(&state->shared->n_active,  n_threads);
-            atomic_store(&state->shared->node_n,    node_n);
-            atomic_store(&state->shared->node_task, task_phase);
-        } else {
-            ggml_graph_compute_thread_sync_node(&node_n,     state, false);
-            ggml_graph_compute_thread_sync_task(&task_phase, state, false);
-        }
-
-        // check if we should stop
-        if (node_n >= cgraph->n_nodes) break;
-
-        /* INIT & COMPUTE */
-        struct ggml_tensor * node = cgraph->nodes[node_n];
-        const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
-
-        struct ggml_compute_params params = {
-            /*.type  =*/ GGML_TASK_TYPE_INIT,
-            /*.ith   =*/ state->ith,
-            /*.nth   =*/ n_tasks,
-            /*.wsize =*/ cplan->work_size,
-            /*.wdata =*/ cplan->work_data,
-        };
-
-        if (state->ith < n_tasks) {
-            if (GGML_OP_HAS_INIT[node->op]) {
-                ggml_compute_forward(&params, node, state);
-            }
-        }
-
-        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
-            task_phase = GGML_TASK_TYPE_COMPUTE;
-            atomic_store(&state->shared->n_active,  n_threads);
-            atomic_store(&state->shared->node_task, task_phase);
-        }
-        else {
-            // TODO: this sched_yield can have significant impact on the performance - either positive or negative
-            //       depending on the workload and the operating system.
-            //       since it is not clear what is the best approach, it should potentially become user-configurable
-            //       ref: https://github.com/ggerganov/ggml/issues/291
-            // UPD:  adding the do_yield flag seems to resolve the issue universally
-            const bool do_yield = node_n < 0 || cgraph->nodes[node_n]->op == GGML_OP_MUL_MAT;
-            ggml_graph_compute_thread_sync_task(&task_phase, state, do_yield);
-        }
-
-        if (state->ith < n_tasks) {
-            params.type = GGML_TASK_TYPE_COMPUTE;
-            ggml_compute_forward(&params, node, state);
-        }
-
-        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
-            task_phase = GGML_TASK_TYPE_FINALIZE;
-            atomic_store(&state->shared->n_active,  n_threads);
-            atomic_store(&state->shared->node_task, task_phase);
-        }
-        else {
-            ggml_graph_compute_thread_sync_task(&task_phase, state, false);
-        }
-    }
-
-    return 0;
-}
-
 struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) {
     if (n_threads <= 0) {
         n_threads = GGML_DEFAULT_N_THREADS;
@@ -19863,7 +18699,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
     for (int i = 0; i < cgraph->n_nodes; i++) {
         struct ggml_tensor * node = cgraph->nodes[i];
 
-        const int n_tasks = ggml_get_n_tasks(node, n_threads, 1);
+        const int n_tasks = ggml_get_n_tasks(node, n_threads);
 
         max_tasks = MAX(max_tasks, n_tasks);
 
@@ -19897,22 +18733,6 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                 {
                     const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type;
 
-#if defined(GGML_USE_CLBLAST)
-                    if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) {
-                        cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node);
-                    } else
-#endif
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                    if (ggml_compute_forward_mul_mat_use_blas(node)) {
-                        if (node->src[0]->type != GGML_TYPE_F32) {
-                            // here we need memory for fully dequantized matrix from src0
-                            // take into account that src0 can be broadcasted into src1[2,3]
-                            cur = ggml_type_size(GGML_TYPE_F32)
-                                * node->src[0]->ne[0]*node->src[0]->ne[1]
-                                * node->src[1]->ne[2]*node->src[1]->ne[3];
-                        }
-                    } else
-#endif
                     if (node->src[1]->type != vec_dot_type) {
                         cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1]));
                     }
@@ -19965,7 +18785,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                         cur += sizeof(float)*ne00*ne01*ne02;
                         cur += sizeof(float)*ne10*ne11;
                     } else {
-                        GGML_ASSERT(false);
+                        GGML_ABORT("fatal error");
                     }
                 } break;
             case GGML_OP_CONV_TRANSPOSE_2D:
@@ -19982,40 +18802,12 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                     cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
                     cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
                 } break;
-            case GGML_OP_FLASH_ATTN:
-                {
-                    const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
-
-                    if (node->src[1]->type == GGML_TYPE_F32) {
-                        cur  = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1)
-                        cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
-                    } else if (node->src[1]->type == GGML_TYPE_F16) {
-                        cur  = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1)
-                        cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
-                    } else if (node->src[1]->type == GGML_TYPE_BF16) {
-                        cur  = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1)
-                        cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
-                    }
-                } break;
             case GGML_OP_FLASH_ATTN_EXT:
                 {
                     const int64_t ne00 = node->src[0]->ne[0]; // D
 
                     cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread
                 } break;
-            case GGML_OP_FLASH_FF:
-                {
-                    if (node->src[1]->type == GGML_TYPE_F32) {
-                        cur  = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
-                        cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
-                    } else if (node->src[1]->type == GGML_TYPE_F16) {
-                        cur  = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
-                        cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
-                    } else if (node->src[1]->type == GGML_TYPE_BF16) {
-                        cur  = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
-                        cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
-                    }
-                } break;
             case GGML_OP_FLASH_ATTN_BACK:
                 {
                     const int64_t    D = node->src[0]->ne[0];
@@ -20039,8 +18831,8 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                 } break;
             case GGML_OP_COUNT:
                 {
-                    GGML_ASSERT(false);
-                } break;
+                    GGML_ABORT("fatal error");
+                }
             default:
                 break;
         }
@@ -20059,91 +18851,121 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
     return cplan;
 }
 
-enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
-    {
-        GGML_ASSERT(cplan);
-        GGML_ASSERT(cplan->n_threads > 0);
+static thread_ret_t ggml_graph_compute_thread(void * data) {
+    struct ggml_compute_state * state = (struct ggml_compute_state *) data;
+
+    const struct ggml_cgraph * cgraph = state->shared->cgraph;
+    const struct ggml_cplan  * cplan  = state->shared->cplan;
+
+    set_numa_thread_affinity(state->ith);
+
+    struct ggml_compute_params params = {
+        /*.ith   =*/ state->ith,
+        /*.nth   =*/ state->shared->n_threads,
+        /*.wsize =*/ cplan->work_size,
+        /*.wdata =*/ cplan->work_data,
+        /*.shared=*/ state->shared,
+    };
+
+    for (int node_n = 0; node_n < cgraph->n_nodes; node_n++) {
+        struct ggml_tensor * node = cgraph->nodes[node_n];
+
+        ggml_compute_forward(&params, node);
+
+        if (state->ith == 0 && cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
+            state->shared->ec = GGML_STATUS_ABORTED;
+        }
+
+        ggml_barrier(state->shared);
 
-        if (cplan->work_size > 0) {
-            GGML_ASSERT(cplan->work_data);
+        if (state->shared->ec != GGML_STATUS_SUCCESS) {
+            break;
         }
     }
 
-    const int n_threads = cplan->n_threads;
+    return 0;
+}
+
+enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
+    GGML_ASSERT(cplan);
+    GGML_ASSERT(cplan->n_threads > 0);
+    GGML_ASSERT(cplan->work_size == 0 || cplan->work_data != NULL);
+
+    int n_threads = cplan->n_threads;
 
     struct ggml_compute_state_shared state_shared = {
         /*.cgraph                  =*/ cgraph,
         /*.cgraph_plan             =*/ cplan,
-        /*.perf_node_start_cycles  =*/ 0,
-        /*.perf_node_start_time_us =*/ 0,
         /*.n_threads               =*/ n_threads,
-        /*.n_active                =*/ n_threads,
-        /*.node_n                  =*/ -1,
-        /*.node_task               =*/ GGML_TASK_TYPE_FINALIZE,
+        /*.n_barrier               =*/ 0,
+        /*.n_barrier_passed        =*/ 0,
         /*.abort_callback          =*/ NULL,
         /*.abort_callback_data     =*/ NULL,
-        /*.current_chunk;          =*/ 0,
+        /*.current_chunk           =*/ 0,
+        /*.ec                      =*/ GGML_STATUS_SUCCESS,
     };
-    struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads);
 
-    // create thread pool
+#ifdef GGML_USE_OPENMP
     if (n_threads > 1) {
-        for (int j = 1; j < n_threads; ++j) {
-            workers[j] = (struct ggml_compute_state) {
+        #pragma omp parallel num_threads(n_threads)
+        {
+            #pragma omp single
+            {
+                // update the number of threads from the actual number of threads that we got from OpenMP
+                n_threads = omp_get_num_threads();
+                state_shared.n_threads = n_threads;
+            }
+
+            struct ggml_compute_state worker = {
                 .thrd   = 0,
-                .ith = j,
+                .ith    = omp_get_thread_num(),
                 .shared = &state_shared,
-                .ec = GGML_STATUS_SUCCESS,
             };
-
-            const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
-            GGML_ASSERT(rc == 0);
-            UNUSED(rc);
+            ggml_graph_compute_thread(&worker);
         }
+    } else {
+        struct ggml_compute_state worker = {
+            .thrd   = 0,
+            .ith    = 0,
+            .shared = &state_shared,
+        };
+        ggml_graph_compute_thread(&worker);
     }
+#else
+    struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads);
 
-    workers[0].ith = 0;
-    workers[0].shared = &state_shared;
-    workers[0].ec = GGML_STATUS_SUCCESS;
+    for (int j = 0; j < n_threads; ++j) {
+        workers[j] = (struct ggml_compute_state) {
+            .thrd   = 0,
+            .ith    = j,
+            .shared = &state_shared,
+        };
+    }
 
-    const int64_t perf_start_cycles  = ggml_perf_cycles();
-    const int64_t perf_start_time_us = ggml_perf_time_us();
+    // create thread pool
+    for (int j = 1; j < n_threads; ++j) {
+        const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
+        GGML_ASSERT(rc == 0);
+        UNUSED(rc);
+    }
 
     // this is a work thread too
     ggml_graph_compute_thread(&workers[0]);
-    enum ggml_status compute_status = workers[0].ec;
-
-    // don't leave affinity set on the main thread
-    clear_numa_thread_affinity();
 
     // join or kill thread pool
     if (n_threads > 1) {
         for (int j = 1; j < n_threads; j++) {
             const int rc = ggml_thread_join(workers[j].thrd, NULL);
             GGML_ASSERT(rc == 0);
-            if (workers[j].ec != GGML_STATUS_SUCCESS)
-                compute_status = workers[j].ec;
+            UNUSED(rc);
         }
     }
+#endif
 
-    // performance stats (graph)
-    {
-        int64_t perf_cycles_cur  = ggml_perf_cycles()  - perf_start_cycles;
-        int64_t perf_time_us_cur = ggml_perf_time_us() - perf_start_time_us;
-
-        cgraph->perf_runs++;
-        cgraph->perf_cycles  += perf_cycles_cur;
-        cgraph->perf_time_us += perf_time_us_cur;
-
-        GGML_PRINT_DEBUG("%s: perf (%d) - cpu = %.3f / %.3f ms, wall = %.3f / %.3f ms\n",
-                __func__, cgraph->perf_runs,
-                (double) perf_cycles_cur      / (double) ggml_cycles_per_ms(),
-                (double) cgraph->perf_cycles  / (double) ggml_cycles_per_ms() / (double) cgraph->perf_runs,
-                (double) perf_time_us_cur     / 1000.0,
-                (double) cgraph->perf_time_us / 1000.0 / cgraph->perf_runs);
-    }
+    // don't leave affinity set on the main thread
+    clear_numa_thread_affinity();
 
-    return compute_status;
+    return state_shared.ec;
 }
 
 enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
@@ -20263,7 +19085,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
         FILE * fout = ggml_fopen(fname, "wb");
 
         if (!fout) {
-            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
+            fprintf(stderr, "%s: failed to open %s: %s\n", __func__, fname, strerror(errno));
             return;
         }
 
@@ -20400,7 +19222,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
     {
         FILE * fin = ggml_fopen(fname, "rb");
         if (!fin) {
-            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
+            fprintf(stderr, "%s: failed to open %s: %s\n", __func__, fname, strerror(errno));
             return result;
         }
 
@@ -20642,24 +19464,16 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
 }
 
 void ggml_graph_print(const struct ggml_cgraph * cgraph) {
-    int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0};
-
     GGML_PRINT("=== GRAPH ===\n");
 
     GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes);
     for (int i = 0; i < cgraph->n_nodes; i++) {
         struct ggml_tensor * node = cgraph->nodes[i];
 
-        perf_total_per_op_us[node->op] += MAX(1, node->perf_time_us);
-
-        GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n",
+        GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s\n",
                 i,
                 node->ne[0], node->ne[1], node->ne[2],
-                ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : node->grad ? "g" : " ", node->perf_runs,
-                (double) node->perf_cycles  / (double) ggml_cycles_per_ms(),
-                (double) node->perf_cycles  / (double) ggml_cycles_per_ms() / (double) node->perf_runs,
-                (double) node->perf_time_us / 1000.0,
-                (double) node->perf_time_us / 1000.0 / node->perf_runs);
+                ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : node->grad ? "g" : " ");
     }
 
     GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs);
@@ -20673,14 +19487,6 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
                 ggml_get_name(node));
     }
 
-    for (int i = 0; i < GGML_OP_COUNT; i++) {
-        if (perf_total_per_op_us[i] == 0) {
-            continue;
-        }
-
-        GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", ggml_op_name(i), (double) perf_total_per_op_us[i] / 1000.0);
-    }
-
     GGML_PRINT("========================================\n");
 }
 
@@ -20739,7 +19545,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
 
     fprintf(fp, "digraph G {\n");
     fprintf(fp, "  newrank = true;\n");
-    fprintf(fp, "  rankdir = LR;\n");
+    fprintf(fp, "  rankdir = TB;\n");
 
     for (int i = 0; i < gb->n_nodes; i++) {
         struct ggml_tensor * node = gb->nodes[i];
@@ -20801,7 +19607,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
         }
 
         fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]);
-        if (ggml_nelements(node) < 5) {
+        if (ggml_nelements(node) < 5 && node->data != NULL) {
             fprintf(fp, " | (");
             for (int j = 0; j < ggml_nelements(node); j++) {
                 if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) {
@@ -21260,9 +20066,9 @@ static enum ggml_opt_result linesearch_backtracking(
         (*step) *= width;
     }
 
-    GGML_ASSERT(false && "line search failed");
+    GGML_ABORT("line search failed");
 
-    return GGML_LINESEARCH_FAIL;
+    //return GGML_LINESEARCH_FAIL;
 }
 
 static enum ggml_opt_result ggml_opt_lbfgs(
@@ -21530,9 +20336,9 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         step[0] = 1.0;
     }
 
-    GGML_ASSERT(false && "lbfgs failed");
+    GGML_ABORT("lbfgs failed");
 
-    return GGML_OPT_RESULT_DID_NOT_CONVERGE;
+    //return GGML_OPT_RESULT_DID_NOT_CONVERGE;
 }
 
 struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
@@ -21856,11 +20662,10 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_IQ1_S:   result = quantize_iq1_s  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ1_M:   result = quantize_iq1_m  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_NL:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-#if QK_K == 64
-        case GGML_TYPE_IQ4_XS:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-#else
         case GGML_TYPE_IQ4_XS:  result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
-#endif
+        case GGML_TYPE_Q4_0_4_4: result = quantize_q4_0_4x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_0_4_8: result = quantize_q4_0_4x8(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_0_8_8: result = quantize_q4_0_8x8(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_F16:
             {
                 size_t elemsize = sizeof(ggml_fp16_t);
@@ -21870,7 +20675,7 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_BF16:
             {
                 size_t elemsize = sizeof(ggml_bf16_t);
-                ggml_fp32_to_bf16_row(src + start, (ggml_bf16_t *)dst + start, n);
+                ggml_fp32_to_bf16_row_ref(src + start, (ggml_bf16_t *)dst + start, n);
                 result = n * elemsize;
             } break;
         case GGML_TYPE_F32:
@@ -22091,6 +20896,7 @@ struct gguf_context * gguf_init_empty(void) {
 struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
     FILE * file = ggml_fopen(fname, "rb");
     if (!file) {
+        fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
         return NULL;
     }
 
@@ -22227,10 +21033,10 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                     }
                                 } break;
                             case GGUF_TYPE_ARRAY:
-                            default: GGML_ASSERT(false && "invalid type"); break;
+                            default: GGML_ABORT("invalid type");
                         }
                     } break;
-                default: GGML_ASSERT(false && "invalid type");
+                default: GGML_ABORT("invalid type");
             }
 
             if (!ok) {
@@ -22275,7 +21081,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             gguf_tensor_info_sanitize(info);
 
             // make sure there is no duplicated tensor names
-            for (uint64_t j = 0; j < i; ++j) {
+            for (uint64_t j = 0; j < i && ok; ++j) {
                 if (strcmp(info->name.data, ctx->infos[j].name.data) == 0) {
                     fprintf(stderr, "%s: duplicated tensor name %s\n", __func__, info->name.data);
                     ok = false;
@@ -22323,9 +21129,9 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                 (int64_t) info->ne[2] *
                 (int64_t) info->ne[3];
 
-            if (ne % ggml_blck_size(info->type) != 0) {
-                fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%d)\n",
-                        __func__, info->name.data, (int)info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
+            if (ggml_blck_size(info->type) == 0 || ne % ggml_blck_size(info->type) != 0) {
+                fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n",
+                        __func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
                 fclose(file);
                 gguf_free(ctx);
                 return NULL;
@@ -22356,6 +21162,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         };
 
         *params.ctx = ggml_init(pdata);
+        if (*params.ctx == NULL) {
+            fprintf(stderr, "%s: failed to initialize context\n", __func__);
+            fclose(file);
+            gguf_free(ctx);
+            return NULL;
+        }
 
         struct ggml_context * ctx_data = *params.ctx;
 
@@ -22805,12 +21617,12 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
                         gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n);
                         GGML_FREE((void *)data);
                     } else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) {
-                        GGML_ASSERT(false && "nested arrays not supported");
+                        GGML_ABORT("nested arrays not supported");
                     } else {
                         gguf_set_arr_data(ctx, src->kv[i].key.data, src->kv[i].value.arr.type, src->kv[i].value.arr.data, src->kv[i].value.arr.n);
                     }
                 } break;
-            default: GGML_ASSERT(false && "invalid type"); break;
+            default: GGML_ABORT("invalid type");
         }
     }
 }
@@ -22819,7 +21631,7 @@ void gguf_add_tensor(
              struct gguf_context * ctx,
         const struct ggml_tensor * tensor) {
     if (gguf_find_tensor(ctx, tensor->name) != -1) {
-        GGML_ASSERT(false && "duplicated tensor name");
+        GGML_ABORT("duplicated tensor name");
     }
 
     const int idx = ctx->header.n_tensors;
@@ -22852,7 +21664,7 @@ void gguf_add_tensor(
 void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) {
     const int idx = gguf_find_tensor(ctx, name);
     if (idx < 0) {
-        GGML_ASSERT(false && "tensor not found");
+        GGML_ABORT("tensor not found");
     }
 
     ctx->infos[idx].type = type;
@@ -22861,7 +21673,7 @@ void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggm
 void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size) {
     const int idx = gguf_find_tensor(ctx, name);
     if (idx < 0) {
-        GGML_ASSERT(false && "tensor not found");
+        GGML_ABORT("tensor not found");
     }
 
     ctx->infos[idx].data = data;
@@ -22990,10 +21802,10 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf *
                                 }
                             } break;
                         case GGUF_TYPE_ARRAY:
-                        default: GGML_ASSERT(false && "invalid type"); break;
+                        default: GGML_ABORT("invalid type");
                     }
                 } break;
-            default: GGML_ASSERT(false && "invalid type");
+            default: GGML_ABORT("invalid type");
         }
     }
 
@@ -23054,7 +21866,7 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf *
 void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
     FILE * file = ggml_fopen(fname, "wb");
     if (!file) {
-        GGML_ASSERT(false && "failed to open file for writing");
+        GGML_ABORT("failed to open file for writing");
     }
 
     struct gguf_buf buf = gguf_buf_init(16*1024);
@@ -23161,6 +21973,14 @@ int ggml_cpu_has_neon(void) {
 #endif
 }
 
+int ggml_cpu_has_sve(void) {
+#if defined(__ARM_FEATURE_SVE)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_arm_fma(void) {
 #if defined(__ARM_FEATURE_FMA)
     return 1;
@@ -23202,7 +22022,7 @@ int ggml_cpu_has_wasm_simd(void) {
 }
 
 int ggml_cpu_has_blas(void) {
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL)
+#if defined(GGML_USE_BLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL)
     return 1;
 #else
     return 0;
@@ -23217,14 +22037,6 @@ int ggml_cpu_has_cuda(void) {
 #endif
 }
 
-int ggml_cpu_has_clblast(void) {
-#if defined(GGML_USE_CLBLAST)
-    return 1;
-#else
-    return 0;
-#endif
-}
-
 int ggml_cpu_has_vulkan(void) {
 #if defined(GGML_USE_VULKAN)
     return 1;
@@ -23249,9 +22061,32 @@ int ggml_cpu_has_sycl(void) {
 #endif
 }
 
+int ggml_cpu_has_rpc(void) {
+#if defined(GGML_USE_RPC)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_cann(void) {
+#if defined(GGML_USE_CANN)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_llamafile(void) {
+#if defined(GGML_USE_LLAMAFILE)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_gpublas(void) {
-    return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() ||
-           ggml_cpu_has_sycl();
+    return ggml_cpu_has_cuda() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl();
 }
 
 int ggml_cpu_has_sse3(void) {
diff --git a/kompute b/ggml/src/kompute
similarity index 100%
rename from kompute
rename to ggml/src/kompute
diff --git a/kompute-shaders/common.comp b/ggml/src/kompute-shaders/common.comp
similarity index 100%
rename from kompute-shaders/common.comp
rename to ggml/src/kompute-shaders/common.comp
diff --git a/kompute-shaders/op_add.comp b/ggml/src/kompute-shaders/op_add.comp
similarity index 100%
rename from kompute-shaders/op_add.comp
rename to ggml/src/kompute-shaders/op_add.comp
diff --git a/kompute-shaders/op_addrow.comp b/ggml/src/kompute-shaders/op_addrow.comp
similarity index 100%
rename from kompute-shaders/op_addrow.comp
rename to ggml/src/kompute-shaders/op_addrow.comp
diff --git a/kompute-shaders/op_cpy_f16_f16.comp b/ggml/src/kompute-shaders/op_cpy_f16_f16.comp
similarity index 100%
rename from kompute-shaders/op_cpy_f16_f16.comp
rename to ggml/src/kompute-shaders/op_cpy_f16_f16.comp
diff --git a/kompute-shaders/op_cpy_f16_f32.comp b/ggml/src/kompute-shaders/op_cpy_f16_f32.comp
similarity index 100%
rename from kompute-shaders/op_cpy_f16_f32.comp
rename to ggml/src/kompute-shaders/op_cpy_f16_f32.comp
diff --git a/kompute-shaders/op_cpy_f32_f16.comp b/ggml/src/kompute-shaders/op_cpy_f32_f16.comp
similarity index 100%
rename from kompute-shaders/op_cpy_f32_f16.comp
rename to ggml/src/kompute-shaders/op_cpy_f32_f16.comp
diff --git a/kompute-shaders/op_cpy_f32_f32.comp b/ggml/src/kompute-shaders/op_cpy_f32_f32.comp
similarity index 100%
rename from kompute-shaders/op_cpy_f32_f32.comp
rename to ggml/src/kompute-shaders/op_cpy_f32_f32.comp
diff --git a/kompute-shaders/op_diagmask.comp b/ggml/src/kompute-shaders/op_diagmask.comp
similarity index 100%
rename from kompute-shaders/op_diagmask.comp
rename to ggml/src/kompute-shaders/op_diagmask.comp
diff --git a/kompute-shaders/op_gelu.comp b/ggml/src/kompute-shaders/op_gelu.comp
similarity index 100%
rename from kompute-shaders/op_gelu.comp
rename to ggml/src/kompute-shaders/op_gelu.comp
diff --git a/kompute-shaders/op_getrows.comp b/ggml/src/kompute-shaders/op_getrows.comp
similarity index 100%
rename from kompute-shaders/op_getrows.comp
rename to ggml/src/kompute-shaders/op_getrows.comp
diff --git a/kompute-shaders/op_getrows_f16.comp b/ggml/src/kompute-shaders/op_getrows_f16.comp
similarity index 100%
rename from kompute-shaders/op_getrows_f16.comp
rename to ggml/src/kompute-shaders/op_getrows_f16.comp
diff --git a/ggml/src/kompute-shaders/op_getrows_f32.comp b/ggml/src/kompute-shaders/op_getrows_f32.comp
new file mode 100644
index 0000000000000..9d7acdaf8a8e4
--- /dev/null
+++ b/ggml/src/kompute-shaders/op_getrows_f32.comp
@@ -0,0 +1,31 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout (binding = 0) readonly buffer tensorInA { float inA[]; };
+layout (binding = 1) readonly buffer tensorInB { int inB[]; };
+layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int nb01;
+    int nb1;
+} pcs;
+
+void dequantize_row_f32(uint x /*Based from inA unaligned*/, uint y /*Based from out_*/, int k) {
+    for (int j = 0; j < k; j++) {
+        out_[y + j] = inA[x + j];
+    }
+}
+
+void main() {
+    const uint i = gl_WorkGroupID.x;
+    const int r = inB[i + pcs.inBOff];
+
+    dequantize_row_f32(r*pcs.nb01/4 + pcs.inAOff, i*pcs.nb1/4 + pcs.outOff, pcs.ne00);
+}
diff --git a/kompute-shaders/op_getrows_q4_0.comp b/ggml/src/kompute-shaders/op_getrows_q4_0.comp
similarity index 100%
rename from kompute-shaders/op_getrows_q4_0.comp
rename to ggml/src/kompute-shaders/op_getrows_q4_0.comp
diff --git a/kompute-shaders/op_getrows_q4_1.comp b/ggml/src/kompute-shaders/op_getrows_q4_1.comp
similarity index 100%
rename from kompute-shaders/op_getrows_q4_1.comp
rename to ggml/src/kompute-shaders/op_getrows_q4_1.comp
diff --git a/kompute-shaders/op_getrows_q6_k.comp b/ggml/src/kompute-shaders/op_getrows_q6_k.comp
similarity index 100%
rename from kompute-shaders/op_getrows_q6_k.comp
rename to ggml/src/kompute-shaders/op_getrows_q6_k.comp
diff --git a/kompute-shaders/op_mul.comp b/ggml/src/kompute-shaders/op_mul.comp
similarity index 100%
rename from kompute-shaders/op_mul.comp
rename to ggml/src/kompute-shaders/op_mul.comp
diff --git a/kompute-shaders/op_mul_mat_f16.comp b/ggml/src/kompute-shaders/op_mul_mat_f16.comp
similarity index 100%
rename from kompute-shaders/op_mul_mat_f16.comp
rename to ggml/src/kompute-shaders/op_mul_mat_f16.comp
diff --git a/kompute-shaders/op_mul_mat_mat_f32.comp b/ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp
similarity index 100%
rename from kompute-shaders/op_mul_mat_mat_f32.comp
rename to ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp
diff --git a/kompute-shaders/op_mul_mat_q4_0.comp b/ggml/src/kompute-shaders/op_mul_mat_q4_0.comp
similarity index 100%
rename from kompute-shaders/op_mul_mat_q4_0.comp
rename to ggml/src/kompute-shaders/op_mul_mat_q4_0.comp
diff --git a/kompute-shaders/op_mul_mat_q4_1.comp b/ggml/src/kompute-shaders/op_mul_mat_q4_1.comp
similarity index 100%
rename from kompute-shaders/op_mul_mat_q4_1.comp
rename to ggml/src/kompute-shaders/op_mul_mat_q4_1.comp
diff --git a/kompute-shaders/op_mul_mat_q6_k.comp b/ggml/src/kompute-shaders/op_mul_mat_q6_k.comp
similarity index 100%
rename from kompute-shaders/op_mul_mat_q6_k.comp
rename to ggml/src/kompute-shaders/op_mul_mat_q6_k.comp
diff --git a/kompute-shaders/op_mul_mat_q8_0.comp b/ggml/src/kompute-shaders/op_mul_mat_q8_0.comp
similarity index 100%
rename from kompute-shaders/op_mul_mat_q8_0.comp
rename to ggml/src/kompute-shaders/op_mul_mat_q8_0.comp
diff --git a/kompute-shaders/op_mul_mv_q_n.comp b/ggml/src/kompute-shaders/op_mul_mv_q_n.comp
similarity index 100%
rename from kompute-shaders/op_mul_mv_q_n.comp
rename to ggml/src/kompute-shaders/op_mul_mv_q_n.comp
diff --git a/kompute-shaders/op_mul_mv_q_n_pre.comp b/ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp
similarity index 100%
rename from kompute-shaders/op_mul_mv_q_n_pre.comp
rename to ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp
diff --git a/kompute-shaders/op_norm.comp b/ggml/src/kompute-shaders/op_norm.comp
similarity index 100%
rename from kompute-shaders/op_norm.comp
rename to ggml/src/kompute-shaders/op_norm.comp
diff --git a/kompute-shaders/op_relu.comp b/ggml/src/kompute-shaders/op_relu.comp
similarity index 100%
rename from kompute-shaders/op_relu.comp
rename to ggml/src/kompute-shaders/op_relu.comp
diff --git a/kompute-shaders/op_rmsnorm.comp b/ggml/src/kompute-shaders/op_rmsnorm.comp
similarity index 100%
rename from kompute-shaders/op_rmsnorm.comp
rename to ggml/src/kompute-shaders/op_rmsnorm.comp
diff --git a/kompute-shaders/op_rope_f16.comp b/ggml/src/kompute-shaders/op_rope_f16.comp
similarity index 95%
rename from kompute-shaders/op_rope_f16.comp
rename to ggml/src/kompute-shaders/op_rope_f16.comp
index b446225849d5f..0ecfb2eab527c 100644
--- a/kompute-shaders/op_rope_f16.comp
+++ b/ggml/src/kompute-shaders/op_rope_f16.comp
@@ -11,10 +11,10 @@ void main() {
     const uint i2 = gl_WorkGroupID.y;
     const uint i1 = gl_WorkGroupID.x;
 
-    const bool is_neox = (pcs.mode & 2) != 0;
+    const bool is_neox = (pcs.mode & GGML_ROPE_TYPE_NEOX) != 0;
 
     float corr_dims[2];
-    rope_yarn_corr_dims(pcs.n_dims, pcs.n_orig_ctx, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims);
+    rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims);
 
     const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims);
 
diff --git a/kompute-shaders/op_rope_f32.comp b/ggml/src/kompute-shaders/op_rope_f32.comp
similarity index 95%
rename from kompute-shaders/op_rope_f32.comp
rename to ggml/src/kompute-shaders/op_rope_f32.comp
index 2c0235d75b6b6..cec0fd9a5d10c 100644
--- a/kompute-shaders/op_rope_f32.comp
+++ b/ggml/src/kompute-shaders/op_rope_f32.comp
@@ -11,10 +11,10 @@ void main() {
     const uint i2 = gl_WorkGroupID.y;
     const uint i1 = gl_WorkGroupID.x;
 
-    const bool is_neox = (pcs.mode & 2) != 0;
+    const bool is_neox = (pcs.mode & GGML_ROPE_TYPE_NEOX) != 0;
 
     float corr_dims[2];
-    rope_yarn_corr_dims(pcs.n_dims, pcs.n_orig_ctx, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims);
+    rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims);
 
     const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims);
 
diff --git a/kompute-shaders/op_scale.comp b/ggml/src/kompute-shaders/op_scale.comp
similarity index 100%
rename from kompute-shaders/op_scale.comp
rename to ggml/src/kompute-shaders/op_scale.comp
diff --git a/kompute-shaders/op_scale_8.comp b/ggml/src/kompute-shaders/op_scale_8.comp
similarity index 100%
rename from kompute-shaders/op_scale_8.comp
rename to ggml/src/kompute-shaders/op_scale_8.comp
diff --git a/kompute-shaders/op_silu.comp b/ggml/src/kompute-shaders/op_silu.comp
similarity index 100%
rename from kompute-shaders/op_silu.comp
rename to ggml/src/kompute-shaders/op_silu.comp
diff --git a/kompute-shaders/op_softmax.comp b/ggml/src/kompute-shaders/op_softmax.comp
similarity index 100%
rename from kompute-shaders/op_softmax.comp
rename to ggml/src/kompute-shaders/op_softmax.comp
diff --git a/kompute-shaders/rope_common.comp b/ggml/src/kompute-shaders/rope_common.comp
similarity index 86%
rename from kompute-shaders/rope_common.comp
rename to ggml/src/kompute-shaders/rope_common.comp
index 57ba6597a7eb2..df4702896d46f 100644
--- a/kompute-shaders/rope_common.comp
+++ b/ggml/src/kompute-shaders/rope_common.comp
@@ -1,5 +1,7 @@
 #include "common.comp"
 
+#define GGML_ROPE_TYPE_NEOX 2
+
 // TODO: use a local size of 32 or more (Metal uses 1024)
 layout(local_size_x = 1) in;
 
@@ -9,7 +11,7 @@ layout (push_constant) uniform parameter {
     uint outOff;
     int n_dims;
     int mode;
-    int n_orig_ctx;
+    int n_ctx_orig;
     float freq_base;
     float freq_scale;
     float ext_factor;
@@ -54,14 +56,14 @@ void rope_yarn(
 
 // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
 // `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
-float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) {
-    return n_dims * log(n_orig_ctx / (n_rot * TWOPI_F)) / (2 * log(base));
+float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) {
+    return n_dims * log(n_ctx_orig / (n_rot * TWOPI_F)) / (2 * log(base));
 }
 
 void rope_yarn_corr_dims(
-    int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, out float dims[2]
+    int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, out float dims[2]
 ) {
     // start and end correction dims
-    dims[0] = max(0.0f,         floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base)));
-    dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base)));
+    dims[0] = max(0.0f,         floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base)));
+    dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base)));
 }
diff --git a/sgemm.cpp b/ggml/src/llamafile/sgemm.cpp
similarity index 97%
rename from sgemm.cpp
rename to ggml/src/llamafile/sgemm.cpp
index 40ba9d7e9a7b7..6626ceb26213f 100644
--- a/sgemm.cpp
+++ b/ggml/src/llamafile/sgemm.cpp
@@ -43,8 +43,10 @@
 // [1] J. Tunney, ‘LLaMA Now Goes Faster on CPUs’, Mar. 2024. [Online].
 //     Available: https://justine.lol/matmul/. [Accessed: 29-Mar-2024].
 
+#if defined(__GNUC__)
 #pragma GCC diagnostic ignored "-Wpedantic"
 #pragma GCC diagnostic ignored "-Wignored-attributes"
+#endif
 
 #include "sgemm.h"
 #include "ggml-impl.h"
@@ -247,9 +249,8 @@ class tinyBLAS {
         : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
     }
 
-    void matmul(int64_t m, int64_t n, int task) {
-        if (task == GGML_TASK_TYPE_COMPUTE)
-            mnpack(0, m, 0, n);
+    void matmul(int64_t m, int64_t n) {
+        mnpack(0, m, 0, n);
     }
 
   private:
@@ -456,9 +457,8 @@ class tinyBLAS_Q0_ARM {
         : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
     }
 
-    void matmul(int64_t m, int64_t n, int task) {
-        if (task == GGML_TASK_TYPE_COMPUTE)
-            mnpack(0, m, 0, n);
+    void matmul(int64_t m, int64_t n) {
+        mnpack(0, m, 0, n);
     }
 
   private:
@@ -594,9 +594,8 @@ class tinyBLAS_Q0_AVX {
         : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
     }
 
-    void matmul(int64_t m, int64_t n, int task) {
-        if (task == GGML_TASK_TYPE_COMPUTE)
-            mnpack(0, m, 0, n);
+    void matmul(int64_t m, int64_t n) {
+        mnpack(0, m, 0, n);
     }
 
   private:
@@ -827,7 +826,7 @@ class tinyBLAS_Q0_AVX {
  * For example, for single-threaded single-precision GEMM you can say
  *
  *     llamafile_sgemm(m, n, k, A, lda, B, ldb, C, ldc,
- *                     0, 1, GGML_TASK_TYPE_COMPUTE,
+ *                     0, 1,
  *                     GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32);
  *
  * @param m is rows in `A` and `C`
@@ -841,14 +840,13 @@ class tinyBLAS_Q0_AVX {
  * @param ldc is row stride of `C`
  * @param ith is thread id (must be less than `nth`)
  * @param nth is number of threads (must be greater than zero)
- * @param task is GGML task type
  * @param Atype is GGML data type of `A`
  * @param Btype is GGML data type of `B`
  * @param Ctype is GGML data type of `C`
  * @return true if this function was able to service the matmul request
  */
 bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda, const void *B, int64_t ldb, void *C,
-                     int64_t ldc, int ith, int nth, int task, int Atype, int Btype, int Ctype) {
+                     int64_t ldc, int ith, int nth, int Atype, int Btype, int Ctype) {
 
     assert(m >= 0);
     assert(n >= 0);
@@ -875,7 +873,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const float *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #elif defined(__AVX__) || defined(__AVX2__)
         if (k % 8)
@@ -885,7 +883,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const float *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #elif defined(__ARM_NEON)
         if (n < 4)
@@ -897,7 +895,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const float *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #else
         return false;
@@ -915,7 +913,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const float *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__)
         if (k % 8)
@@ -927,7 +925,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const float *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
         if (n < 8)
@@ -941,7 +939,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const ggml_fp16_t *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #elif defined(__ARM_NEON) && !defined(_MSC_VER)
         if (k % 4)
@@ -953,7 +951,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const float *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #else
         return false;
@@ -969,7 +967,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #elif defined(__ARM_FEATURE_DOTPROD)
         tinyBLAS_Q0_ARM<block_q8_0> tb{
@@ -977,7 +975,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #else
         return false;
@@ -993,7 +991,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #elif defined(__ARM_FEATURE_DOTPROD)
         tinyBLAS_Q0_ARM<block_q4_0> tb{
@@ -1001,7 +999,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
             (const block_q8_0 *)B, ldb,
             (float *)C, ldc,
             ith, nth};
-        tb.matmul(m, n, task);
+        tb.matmul(m, n);
         return true;
 #else
         return false;
@@ -1023,7 +1021,6 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
     (void)ldc;
     (void)ith;
     (void)nth;
-    (void)task;
     (void)Atype;
     (void)Btype;
     (void)Ctype;
diff --git a/sgemm.h b/ggml/src/llamafile/sgemm.h
similarity index 86%
rename from sgemm.h
rename to ggml/src/llamafile/sgemm.h
index f29747d0a477a..caf6dd5567b3a 100644
--- a/sgemm.h
+++ b/ggml/src/llamafile/sgemm.h
@@ -7,7 +7,7 @@ extern "C" {
 
 bool llamafile_sgemm(int64_t, int64_t, int64_t, const void *, int64_t,
                      const void *, int64_t, void *, int64_t, int, int,
-                     int, int, int, int);
+                     int, int, int);
 
 #ifdef __cplusplus
 }
diff --git a/ggml/src/vulkan-shaders/CMakeLists.txt b/ggml/src/vulkan-shaders/CMakeLists.txt
new file mode 100644
index 0000000000000..10075db337737
--- /dev/null
+++ b/ggml/src/vulkan-shaders/CMakeLists.txt
@@ -0,0 +1,7 @@
+find_package (Threads REQUIRED)
+
+set(TARGET vulkan-shaders-gen)
+add_executable(${TARGET} vulkan-shaders-gen.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads)
diff --git a/ggml/src/vulkan-shaders/add.comp b/ggml/src/vulkan-shaders/add.comp
new file mode 100644
index 0000000000000..3974845d637ab
--- /dev/null
+++ b/ggml/src/vulkan-shaders/add.comp
@@ -0,0 +1,14 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) + FLOAT_TYPE(data_b[src1_idx(idx)]));
+}
diff --git a/ggml/src/vulkan-shaders/argsort.comp b/ggml/src/vulkan-shaders/argsort.comp
new file mode 100644
index 0000000000000..e55414b03c519
--- /dev/null
+++ b/ggml/src/vulkan-shaders/argsort.comp
@@ -0,0 +1,71 @@
+#version 450
+
+#include "types.comp"
+
+#define BLOCK_SIZE 1024
+#define ASC 0
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1)          buffer D {int data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ncols;
+    uint ncols_pad;
+    uint order;
+} p;
+
+shared int dst_row[BLOCK_SIZE];
+
+void swap(uint idx0, uint idx1) {
+    int tmp = dst_row[idx0];
+    dst_row[idx0] = dst_row[idx1];
+    dst_row[idx1] = tmp;
+}
+
+void main() {
+    // bitonic sort
+    const int col = int(gl_LocalInvocationID.x);
+    const uint row = gl_WorkGroupID.y;
+
+    if (col >= p.ncols_pad) {
+        return;
+    }
+
+    const uint row_offset = row * p.ncols;
+
+    // initialize indices
+    dst_row[col] = col;
+    barrier();
+
+    for (uint k = 2; k <= p.ncols_pad; k *= 2) {
+        for (uint j = k / 2; j > 0; j /= 2) {
+            const uint ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (dst_row[col] >= p.ncols ||
+                        (dst_row[ixj] < p.ncols && (p.order == ASC ?
+                            data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] :
+                            data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]]))
+                    ) {
+                        swap(col, ixj);
+                    }
+                } else {
+                    if (dst_row[ixj] >= p.ncols ||
+                        (dst_row[col] < p.ncols && (p.order == ASC ?
+                            data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] :
+                            data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]]))
+                    ) {
+                        swap(col, ixj);
+                    }
+                }
+            }
+            barrier();
+        }
+    }
+
+    if (col < p.ncols) {
+        data_d[row_offset + col] = dst_row[col];
+    }
+}
diff --git a/ggml/src/vulkan-shaders/clamp.comp b/ggml/src/vulkan-shaders/clamp.comp
new file mode 100644
index 0000000000000..7071302a4b658
--- /dev/null
+++ b/ggml/src/vulkan-shaders/clamp.comp
@@ -0,0 +1,15 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
+}
diff --git a/ggml/src/vulkan-shaders/concat.comp b/ggml/src/vulkan-shaders/concat.comp
new file mode 100644
index 0000000000000..c23b6eb1b0cd5
--- /dev/null
+++ b/ggml/src/vulkan-shaders/concat.comp
@@ -0,0 +1,39 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+    const int dim = p.param3;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i3 = idx / (p.ne22*p.ne21*p.ne20);
+    const uint i3_offset = i3 * p.ne22*p.ne21*p.ne20;
+    const uint i2 = (idx - i3_offset) / (p.ne21*p.ne20);
+    const uint i2_offset = i2*p.ne21*p.ne20;
+    const uint i1 = (idx - i3_offset - i2_offset) / p.ne20;
+    const uint i0 = idx - i3_offset - i2_offset - i1*p.ne20;
+
+    uint o[4] = {0, 0, 0, 0};
+    o[dim] = dim == 0 ? p.ne00 : (dim == 1 ? p.ne01 : (dim == 2 ? p.ne02 : p.ne03));
+
+    const uint src0_idx = i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0*p.nb00;
+    const uint src1_idx = (i3 - o[3])*p.nb13 + (i2 - o[2])*p.nb12 + (i1 - o[1])*p.nb11 + (i0 - o[0])*p.nb10;
+    const uint dst_idx = i3*p.nb23 + i2*p.nb22 + i1*p.nb21 + i0*p.nb20;
+
+    const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03;
+
+#ifndef OPTIMIZATION_ERROR_WORKAROUND
+    data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : data_b[src1_idx]);
+#else
+    if (is_src0) {
+        data_d[p.d_offset + dst_idx] = data_a[src0_idx];
+    } else {
+        data_d[p.d_offset + dst_idx] = data_b[src1_idx];
+    }
+#endif
+}
diff --git a/ggml/src/vulkan-shaders/copy.comp b/ggml/src/vulkan-shaders/copy.comp
new file mode 100644
index 0000000000000..c26917c0f9af5
--- /dev/null
+++ b/ggml/src/vulkan-shaders/copy.comp
@@ -0,0 +1,18 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+#ifndef OPTIMIZATION_ERROR_WORKAROUND
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx(idx)]);
+#else
+    data_d[p.d_offset + dst_idx(idx)] = data_a[src0_idx(idx)];
+#endif
+}
diff --git a/ggml/src/vulkan-shaders/dequant_f32.comp b/ggml/src/vulkan-shaders/dequant_f32.comp
new file mode 100644
index 0000000000000..a4d3fca556208
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_f32.comp
@@ -0,0 +1,20 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {float data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x * 16;
+
+    if (i >= p.nel) {
+        return;
+    }
+
+    [[unroll]] for (uint l = 0; l < 16; l++) {
+        data_b[i + l] = D_TYPE(data_a[i + l]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_funcs.comp b/ggml/src/vulkan-shaders/dequant_funcs.comp
new file mode 100644
index 0000000000000..d5b989735bc0b
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_funcs.comp
@@ -0,0 +1,68 @@
+#if !defined(DATA_A_F32) && !defined(DATA_A_F16)
+#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
+#endif
+
+#if defined(DATA_A_F32)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]);
+}
+#endif
+
+#if defined(DATA_A_F16)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]);
+}
+#endif
+
+#if defined(DATA_A_Q4_0)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
+}
+#endif
+
+#if defined(DATA_A_Q4_1)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const float m = float(data_a[a_offset + ib].m);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2(vui & 0xF, vui >> 4) * d + m;
+}
+#endif
+
+#if defined(DATA_A_Q5_0)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0];
+    const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
+}
+#endif
+
+#if defined(DATA_A_Q5_1)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const float m = float(data_a[a_offset + ib].m);
+    const uint uint_qh = data_a[a_offset + ib].qh;
+    const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
+}
+#endif
+
+#if defined(DATA_A_Q8_0)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d;
+}
+#endif
+
+#if defined(DATA_A_IQ4_NL)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d;
+}
+#endif
diff --git a/ggml/src/vulkan-shaders/dequant_head.comp b/ggml/src/vulkan-shaders/dequant_head.comp
new file mode 100644
index 0000000000000..8d806435b7163
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_head.comp
@@ -0,0 +1,13 @@
+#extension GL_EXT_control_flow_attributes : require
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint M;
+    uint K;
+    uint stride_a;
+    uint stride_b;
+    uint nel;
+} p;
+
+#include "types.comp"
diff --git a/ggml/src/vulkan-shaders/dequant_iq4_nl.comp b/ggml/src/vulkan-shaders/dequant_iq4_nl.comp
new file mode 100644
index 0000000000000..34ef3da30b82c
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_iq4_nl.comp
@@ -0,0 +1,30 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_iq4_nl data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint q_idx = 8*il;
+    const uint b_idx = 1024*i + 32*ir + q_idx;
+
+    const float d = float(data_a[ib].d);
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * kvalues_iq4nl[data_a[ib].qs[q_idx + l] & 0xF]);
+        data_b[b_idx + l + 16] = D_TYPE(d * kvalues_iq4nl[data_a[ib].qs[q_idx + l] >>  4]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q2_k.comp b/ggml/src/vulkan-shaders/dequant_q2_k.comp
new file mode 100644
index 0000000000000..157154af3a328
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q2_k.comp
@@ -0,0 +1,34 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;
+        const uint il = tid - 32 * ip;
+        const uint is = 8 * ip + il / 16;
+
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
+
+        const uint ql_idx = 32 * ip + il;
+        const uint8_t qs = data_a[i].qs[32 * ip + il];
+
+        FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+        data_b[y_idx +  0] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+0] & 0xF) * ((qs >> 0) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+0] >> 4));
+        data_b[y_idx + 32] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+2] & 0xF) * ((qs >> 2) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+2] >> 4));
+        data_b[y_idx + 64] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+4] & 0xF) * ((qs >> 4) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+4] >> 4));
+        data_b[y_idx + 96] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+6] & 0xF) * ((qs >> 6) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+6] >> 4));
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q3_k.comp b/ggml/src/vulkan-shaders/dequant_q3_k.comp
new file mode 100644
index 0000000000000..c17dd0d999116
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q3_k.comp
@@ -0,0 +1,42 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = uint(gl_WorkGroupID.x * 256 + wgy);
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint r = gl_LocalInvocationID.x / 4;
+        const uint tid = r / 2;
+        const uint is0 = r % 2;
+        const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4);
+        const uint n = tid / 4;
+        const uint j = tid - 4*n;
+
+        const uint8_t m = uint8_t(1 << (4*n + j));
+        const uint is = 8*n + 2*j + is0;
+        const uint shift = 2*j;
+
+        const int8_t us = int8_t(is <  4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) :
+                                 is <  8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) :
+                                 is < 12 ? (data_a[i].scales[is-8] >>  4) | (((data_a[i].scales[is+0] >> 4) & 3) << 4) :
+                                           (data_a[i].scales[is-8] >>  4) | (((data_a[i].scales[is-4] >> 6) & 3) << 4));
+        const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d);
+        const FLOAT_TYPE dl    = d_all * FLOAT_TYPE(us - 32);
+
+        const uint y_idx = i * QUANT_K + 128 * n + 32 * j;
+        const uint qs_idx = 32*n;
+
+        for (uint l = l0; l < l0 + 4; ++l) {
+            data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4)));
+        }
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q4_0.comp b/ggml/src/vulkan-shaders/dequant_q4_0.comp
new file mode 100644
index 0000000000000..408185327255b
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q4_0.comp
@@ -0,0 +1,30 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q4_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint q_idx = 8*il;
+    const uint b_idx = 1024*i + 32*ir + q_idx;
+
+    const float d = float(data_a[ib].d);
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] & 0xF) - 8.0f));
+        data_b[b_idx + l + 16] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] >>  4) - 8.0f));
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q4_1.comp b/ggml/src/vulkan-shaders/dequant_q4_1.comp
new file mode 100644
index 0000000000000..2f27eee686eb9
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q4_1.comp
@@ -0,0 +1,32 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q4_1 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float m = float(data_a[ib].m);
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m);
+        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + m);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q4_k.comp b/ggml/src/vulkan-shaders/dequant_q4_k.comp
new file mode 100644
index 0000000000000..92acb75406db3
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q4_k.comp
@@ -0,0 +1,56 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint il = tid / 8;
+        const uint ir = tid % 8;
+        const uint is = 2 * il;
+        const uint n = 4;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+
+        const uint y_idx = i * QUANT_K + 64 * il + n * ir;
+        const uint qs_idx = 32*il + n * ir;
+
+        uint8_t sc;
+        uint8_t m;
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is] & 63);
+            m  = uint8_t(data_a[i].scales[is + 4] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
+        }
+        const FLOAT_TYPE d1 = dall * sc;
+        const FLOAT_TYPE m1 = dmin * m;
+
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is + 1] & 63);
+            m  = uint8_t(data_a[i].scales[is + 5] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
+        }
+        const FLOAT_TYPE d2 = dall * sc;
+        const FLOAT_TYPE m2 = dmin * m;
+
+        [[unroll]] for (uint l = 0; l < n; ++l) {
+            data_b[y_idx + l     ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1);
+            data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >>  4) - m2);
+        }
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q5_0.comp b/ggml/src/vulkan-shaders/dequant_q5_0.comp
new file mode 100644
index 0000000000000..b20b805292174
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q5_0.comp
@@ -0,0 +1,34 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q5_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        const uint iqs = q_idx + l;
+        const uint vui = uint(data_a[ib].qs[iqs]);
+        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f));
+        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f));
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q5_1.comp b/ggml/src/vulkan-shaders/dequant_q5_1.comp
new file mode 100644
index 0000000000000..dc59fe3b77ee3
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q5_1.comp
@@ -0,0 +1,35 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q5_1 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float m = float(data_a[ib].m);
+    const uint qh = data_a[ib].qh;
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        const uint iqs = q_idx + l;
+        const uint vui = uint(data_a[ib].qs[iqs]);
+        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m);
+        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10))) + m);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q5_k.comp b/ggml/src/vulkan-shaders/dequant_q5_k.comp
new file mode 100644
index 0000000000000..f314a76d105c6
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q5_k.comp
@@ -0,0 +1,58 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint il = tid / 16;
+        const uint ir = tid % 16;
+        const uint is = 2 * il;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+
+        const uint y_idx = i * QUANT_K + 64 * il + 2 * ir;
+        const uint qs_idx = 32*il + 2 * ir;
+        const uint qh_idx = 2 * ir;
+
+        uint8_t sc;
+        uint8_t m;
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is] & 63);
+            m  = uint8_t(data_a[i].scales[is + 4] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
+        }
+        const FLOAT_TYPE d1 = dall * sc;
+        const FLOAT_TYPE m1 = dmin * m;
+
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is + 1] & 63);
+            m  = uint8_t(data_a[i].scales[is + 5] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
+        }
+        const FLOAT_TYPE d2 = dall * sc;
+        const FLOAT_TYPE m2 = dmin * m;
+
+        const uint8_t hm1 = uint8_t(1 << (2 * il    ));
+        const uint8_t hm2 = uint8_t(1 << (2 * il + 1));
+        data_b[y_idx     ] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx    ] & 0xF) + (((data_a[i].qh[qh_idx    ] & hm1) != 0) ? 16 : 0)) - m1);
+        data_b[y_idx +  1] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] & 0xF) + (((data_a[i].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1);
+        data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx    ]  >> 4) + (((data_a[i].qh[qh_idx    ] & hm2) != 0) ? 16 : 0)) - m2);
+        data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1]  >> 4) + (((data_a[i].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q6_k.comp b/ggml/src/vulkan-shaders/dequant_q6_k.comp
new file mode 100644
index 0000000000000..0b91317550f97
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q6_k.comp
@@ -0,0 +1,33 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;
+        const uint il = tid - 32 * ip;
+        const uint is = 8 * ip + il / 16;
+
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
+
+        const uint ql_idx = 64 * ip + il;
+        const uint8_t qh = data_a[i].qh[32 * ip + il];
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d);
+
+        data_b[y_idx +  0] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 0] * (int8_t((data_a[i].ql[ql_idx +  0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)));
+        data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 2] * (int8_t((data_a[i].ql[ql_idx + 32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)));
+        data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 4] * (int8_t((data_a[i].ql[ql_idx +  0] >>  4) | (((qh >> 4) & 3) << 4)) - 32)));
+        data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 6] * (int8_t((data_a[i].ql[ql_idx + 32] >>  4) | (((qh >> 6) & 3) << 4)) - 32)));
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q8_0.comp b/ggml/src/vulkan-shaders/dequant_q8_0.comp
new file mode 100644
index 0000000000000..bd1344a88d129
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_q8_0.comp
@@ -0,0 +1,31 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q8_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 16*il;
+
+    const float d = float(data_a[ib].d);
+
+    const uint q_idx = 16*il;
+
+    [[unroll]] for (uint l = 0; l < 16; l += 2) {
+        data_b[b_idx + l    ] = D_TYPE(d * data_a[ib].qs[q_idx + l    ]);
+        data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/diag_mask_inf.comp b/ggml/src/vulkan-shaders/diag_mask_inf.comp
new file mode 100644
index 0000000000000..4e68742b51671
--- /dev/null
+++ b/ggml/src/vulkan-shaders/diag_mask_inf.comp
@@ -0,0 +1,34 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_control_flow_attributes : enable
+
+layout (push_constant) uniform parameter
+{
+    uint ncols;
+    uint rows_per_channel;
+    uint n_past;
+} p;
+
+#include "types.comp"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    const uint i = row*p.ncols + col;
+    if (col > p.n_past + row % p.rows_per_channel) {
+        data_d[i] = D_TYPE(uintBitsToFloat(0xFF800000));
+    } else {
+        data_d[i] = D_TYPE(data_a[i]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/div.comp b/ggml/src/vulkan-shaders/div.comp
new file mode 100644
index 0000000000000..8cfce58b15016
--- /dev/null
+++ b/ggml/src/vulkan-shaders/div.comp
@@ -0,0 +1,14 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) / FLOAT_TYPE(data_b[src1_idx(idx)]));
+}
diff --git a/ggml/src/vulkan-shaders/gelu.comp b/ggml/src/vulkan-shaders/gelu.comp
new file mode 100644
index 0000000000000..4cc7a68ca18c5
--- /dev/null
+++ b/ggml/src/vulkan-shaders/gelu.comp
@@ -0,0 +1,25 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float xi = float(data_a[i]);
+    const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi);
+    data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1)));
+}
diff --git a/ggml/src/vulkan-shaders/gelu_quick.comp b/ggml/src/vulkan-shaders/gelu_quick.comp
new file mode 100644
index 0000000000000..e6e6fcfd20e26
--- /dev/null
+++ b/ggml/src/vulkan-shaders/gelu_quick.comp
@@ -0,0 +1,23 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const float GELU_QUICK_COEF = -1.702f;
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float x = float(data_a[i]);
+    data_d[i] = D_TYPE(x * (1.0f / (1.0f + exp(GELU_QUICK_COEF * x))));
+}
diff --git a/ggml/src/vulkan-shaders/generic_binary_head.comp b/ggml/src/vulkan-shaders/generic_binary_head.comp
new file mode 100644
index 0000000000000..b6beaff1cf65a
--- /dev/null
+++ b/ggml/src/vulkan-shaders/generic_binary_head.comp
@@ -0,0 +1,52 @@
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ne;
+    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
+    uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
+    uint d_offset;
+    float param1; float param2; int param3;
+} p;
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+uint get_idx() {
+    return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+}
+
+uint src0_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
+}
+
+uint src1_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+
+    return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10;
+}
+
+uint dst_idx(uint idx) {
+    const uint i23 = idx / (p.ne22*p.ne21*p.ne20);
+    const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20;
+    const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20);
+    const uint i22_offset = i22*p.ne21*p.ne20;
+    const uint i21 = (idx - i23_offset - i22_offset) / p.ne20;
+    const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20;
+    return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20;
+}
diff --git a/ggml/src/vulkan-shaders/generic_head.comp b/ggml/src/vulkan-shaders/generic_head.comp
new file mode 100644
index 0000000000000..66e46ae6796b8
--- /dev/null
+++ b/ggml/src/vulkan-shaders/generic_head.comp
@@ -0,0 +1,9 @@
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    float param1;
+    float param2;
+} p;
diff --git a/ggml/src/vulkan-shaders/generic_unary_head.comp b/ggml/src/vulkan-shaders/generic_unary_head.comp
new file mode 100644
index 0000000000000..eacdefc7d8aa7
--- /dev/null
+++ b/ggml/src/vulkan-shaders/generic_unary_head.comp
@@ -0,0 +1,39 @@
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ne;
+    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
+    uint d_offset;
+    float param1; float param2;
+} p;
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+uint get_idx() {
+    return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+}
+
+uint src0_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
+}
+
+uint dst_idx(uint idx) {
+    const uint i13 = idx / (p.ne12*p.ne11*p.ne10);
+    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
+    const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10);
+    const uint i12_offset = i12*p.ne11*p.ne10;
+    const uint i11 = (idx - i13_offset - i12_offset) / p.ne10;
+    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
+    return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
+}
diff --git a/ggml/src/vulkan-shaders/get_rows.comp b/ggml/src/vulkan-shaders/get_rows.comp
new file mode 100644
index 0000000000000..e9ff22efa9c7a
--- /dev/null
+++ b/ggml/src/vulkan-shaders/get_rows.comp
@@ -0,0 +1,26 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    const uint i00 = gl_GlobalInvocationID.x;
+    const uint i10 = gl_GlobalInvocationID.y;
+    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
+    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
+
+    if (i00 >= p.ne00) {
+        return;
+    }
+
+    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+
+    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+
+#ifndef OPTIMIZATION_ERROR_WORKAROUND
+    data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]);
+#else
+    data_d[d_offset + i00] = data_a[a_offset + i00];
+#endif
+}
diff --git a/ggml/src/vulkan-shaders/get_rows_quant.comp b/ggml/src/vulkan-shaders/get_rows_quant.comp
new file mode 100644
index 0000000000000..53a9a96f2360a
--- /dev/null
+++ b/ggml/src/vulkan-shaders/get_rows_quant.comp
@@ -0,0 +1,31 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+#include "dequant_funcs.comp"
+
+void main() {
+    const uint i00 = (gl_GlobalInvocationID.x)*2;
+    const uint i10 = gl_GlobalInvocationID.y;
+    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
+    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
+
+    if (i00 >= p.ne00) {
+        return;
+    }
+
+    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+
+    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+
+    const uint ib = a_offset + i00/QUANT_K; // block index
+    const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index
+    const uint iybs = i00 - i00%QUANT_K; // dst block start index
+    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
+
+    vec2 v = dequantize(ib, iqs, 0);
+
+    data_d[d_offset + iybs + iqs           ] = D_TYPE(v.x);
+    data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y);
+}
diff --git a/ggml/src/vulkan-shaders/group_norm.comp b/ggml/src/vulkan-shaders/group_norm.comp
new file mode 100644
index 0000000000000..5ad9b28daffaa
--- /dev/null
+++ b/ggml/src/vulkan-shaders/group_norm.comp
@@ -0,0 +1,66 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared float tmp[BLOCK_SIZE];
+
+void main() {
+    const uint group_size = p.KX;
+    const float eps = p.param1;
+
+    const uint tid = gl_LocalInvocationID.x;
+    const uint start = gl_WorkGroupID.x * group_size + tid;
+    const uint end = start + group_size;
+
+    tmp[tid] = 0.0f;
+
+    // Calculate mean
+    [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
+        tmp[tid] += float(data_a[col]);
+    }
+
+    // tmp up partial tmps and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    const float mean = tmp[0] / group_size;
+    barrier();
+    tmp[tid] = 0.0f;
+
+    // Calculate variance
+    [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
+        const float xi = float(data_a[col]) - mean;
+        data_d[col] = D_TYPE(xi);
+        tmp[tid] += xi * xi;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    const float variance = tmp[0] / group_size;
+    const float scale = inversesqrt(variance + eps);
+
+    [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
+        data_d[col] *= D_TYPE(scale);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/im2col.comp b/ggml/src/vulkan-shaders/im2col.comp
new file mode 100644
index 0000000000000..4d48610a3adcb
--- /dev/null
+++ b/ggml/src/vulkan-shaders/im2col.comp
@@ -0,0 +1,57 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint batch_offset; uint offset_delta;
+    uint IC;
+    uint IW; uint IH;
+    uint OW; uint OH;
+    uint KW; uint KH;
+    uint pelements;
+    uint CHW;
+    int s0; int s1;
+    int p0; int p1;
+    int d0; int d1;
+} p;
+
+#include "types.comp"
+
+#define BLOCK_SIZE 256
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x;
+    if (i >= p.pelements) {
+        return;
+    }
+
+    const uint ksize = p.OW * (p.KH > 1 ? p.KW : 1);
+    const uint kx = i / ksize;
+    const uint kd = kx * ksize;
+    const uint ky = (i - kd) / p.OW;
+    const uint ix = i % p.OW;
+
+    const uint oh = gl_GlobalInvocationID.y;
+    const uint batch = gl_GlobalInvocationID.z / p.IC;
+    const uint ic = gl_GlobalInvocationID.z % p.IC;
+
+    const uint iiw = ix * p.s0 + kx * p.d0 - p.p0;
+    const uint iih = oh * p.s1 + ky * p.d1 - p.p1;
+
+    const uint offset_dst =
+        ((batch * p.OH + oh) * p.OW + ix) * p.CHW +
+        (ic * (p.KW * p.KH) + ky * p.KW + kx);
+
+    if (iih < 0 || iih >= p.IH || iiw < 0 || iiw >= p.IW) {
+        data_d[offset_dst] = D_TYPE(0.0f);
+    } else {
+        const uint offset_src = ic * p.offset_delta + batch * p.batch_offset;
+        data_d[offset_dst] = D_TYPE(data_a[offset_src + iih * p.IW + iiw]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/leaky_relu.comp b/ggml/src/vulkan-shaders/leaky_relu.comp
new file mode 100644
index 0000000000000..d90a99aea55d3
--- /dev/null
+++ b/ggml/src/vulkan-shaders/leaky_relu.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float val = float(data_a[i]);
+    data_d[i] = D_TYPE(max(val, 0.0f) + min(val, 0.0f) * p.param1);
+}
diff --git a/ggml/src/vulkan-shaders/mul.comp b/ggml/src/vulkan-shaders/mul.comp
new file mode 100644
index 0000000000000..bfb61c92d688e
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul.comp
@@ -0,0 +1,14 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(data_b[src1_idx(idx)]));
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp b/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp
new file mode 100644
index 0000000000000..825b91031f569
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp
@@ -0,0 +1,29 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {float data_a[];};
+layout (binding = 1) writeonly buffer D {float data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ne;
+    uint k_num;
+} p;
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.x;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    float result = 0.0f;
+
+    [[unroll]] for (uint i = 0; i < p.k_num; i++) {
+        result += data_a[i * p.ne + idx];
+    }
+
+    data_d[idx] = result;
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec.comp b/ggml/src/vulkan-shaders/mul_mat_vec.comp
new file mode 100644
index 0000000000000..d3ccba7fcb3fd
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec.comp
@@ -0,0 +1,56 @@
+#version 450
+
+#ifdef FLOAT16
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#endif
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
+    const uint tid = gl_LocalInvocationID.x;
+
+    // There are not enough cols to use all threads
+    if (tid >= p.ncols) {
+        return;
+    }
+
+    const uint block_size = min(p.ncols, BLOCK_SIZE);
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
+
+    tmp[tid] = FLOAT_TYPE(0.0f);
+
+    [[unroll]] for (uint i = 0; i < p.ncols/block_size; i += 2) {
+        const uint col = i*block_size + 2*tid;
+        const uint ib = (row*p.ncols + col)/QUANT_K; // block index
+        const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
+        const uint iybs = col - col%QUANT_K; // y block start index
+
+        vec2 v = dequantize(ib, iqs, a_offset / QUANT_K);
+
+        // matrix multiplication
+        tmp[tid] = fma(FLOAT_TYPE(v.x), FLOAT_TYPE(data_b[b_offset + iybs + iqs]), fma(FLOAT_TYPE(v.y), FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]), tmp[tid]));
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = block_size/2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_base.comp b/ggml/src/vulkan-shaders/mul_mat_vec_base.comp
new file mode 100644
index 0000000000000..5920bc93641c8
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec_base.comp
@@ -0,0 +1,81 @@
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_shader_8bit_storage : require
+
+#define K_QUANTS_PER_ITERATION 2
+
+#ifdef MUL_MAT_ID
+#define EXPERT_COUNT 8
+#endif
+
+#include "types.comp"
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+#ifdef MUL_MAT_ID
+layout (binding = 3) readonly buffer IDS {int data_ids[];};
+#endif
+
+#include "dequant_funcs.comp"
+
+layout (push_constant) uniform parameter
+{
+    uint ncols;
+    uint stride_a;
+    uint stride_b;
+    uint stride_d;
+
+    uint batch_stride_a;
+    uint batch_stride_b;
+    uint batch_stride_d;
+
+#ifdef MUL_MAT_ID
+    uint nei0;
+    uint ne11;
+#else
+    uint ne02;
+    uint ne12;
+    uint broadcast2;
+    uint broadcast3;
+#endif
+} p;
+
+void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) {
+#ifdef MUL_MAT_ID
+    const uint expert_idx = gl_GlobalInvocationID.y;
+#else
+    const uint batch_idx = gl_GlobalInvocationID.y;
+#endif
+
+#ifndef MUL_MAT_ID
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
+
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
+
+    const uint batch_idx_a = i03 * p.ne02 + i02;
+#else
+    const uint expert_id = data_ids[expert_idx];
+#endif
+
+    a_offset =
+#ifdef MUL_MAT_ID
+            expert_id * p.batch_stride_a;
+#else
+            batch_idx_a * p.batch_stride_a;
+#endif
+    b_offset =
+#ifdef MUL_MAT_ID
+            (expert_idx % p.ne11) * p.stride_b;
+#else
+            batch_idx * p.batch_stride_b;
+#endif
+    d_offset =
+#ifdef MUL_MAT_ID
+            expert_idx * p.stride_d;
+#else
+            batch_idx * p.batch_stride_d;
+#endif
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp b/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp
new file mode 100644
index 0000000000000..1cc4996d393a2
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp
@@ -0,0 +1,71 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#define BLOCK_SIZE 32
+#define FLOAT_TYPE float
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+layout (push_constant) uniform parameter
+{
+    uint ncols_x;
+    uint nrows_x;
+    uint row_stride_x;
+    uint channel_stride_x;
+    uint channel_x_divisor;
+    uint b_offset;
+    uint d_offset;
+} p;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint tid       = gl_LocalInvocationID.x;
+    const uint row_x     = gl_GlobalInvocationID.y;
+    const uint channel   = gl_GlobalInvocationID.z;
+    const uint channel_x = channel / p.channel_x_divisor;
+
+    const uint nrows_y   = p.ncols_x;
+    const uint nrows_dst = p.nrows_x;
+    const uint row_dst   = row_x;
+
+    const uint idst = channel*nrows_dst + row_dst;
+
+    tmp[tid] = 0.0f;
+
+    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
+        const uint col_x = col_x0 + tid;
+
+        if (col_x >= p.ncols_x) {
+            break;
+        }
+
+        const uint row_y = col_x;
+
+        const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+        const uint iy = channel*nrows_y + row_y;
+
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
+
+        tmp[tid] = fma(xi, FLOAT_TYPE(data_b[iy]), tmp[tid]);
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        dst[idst] = tmp[0];
+    }
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp b/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp
new file mode 100644
index 0000000000000..9b443807d8781
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp
@@ -0,0 +1,73 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#define BLOCK_SIZE 32
+#define FLOAT_TYPE float
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+layout (push_constant) uniform parameter
+{
+    uint ncols_x;
+    uint nrows_x;
+    uint nchannels_x;
+    uint nchannels_y;
+    uint b_offset;
+    uint d_offset;
+} p;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint row_x = gl_GlobalInvocationID.y;
+    const uint channel = gl_GlobalInvocationID.z;
+    const uint channel_x = channel / (p.nchannels_y / p.nchannels_x);
+
+    const uint nrows_y = p.ncols_x;
+    const uint nrows_dst = p.nrows_x;
+    const uint row_dst = row_x;
+
+    tmp[tid] = FLOAT_TYPE(0.0f);
+
+    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
+        const uint col_x = col_x0 + tid;
+
+        if (col_x >= p.ncols_x) {
+            break;
+        }
+
+        // x is transposed and permuted
+        const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x;
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
+
+        const uint row_y = col_x;
+
+        // y is not transposed but permuted
+        const uint iy = channel*nrows_y + row_y;
+
+        tmp[tid] = fma(xi, FLOAT_TYPE(data_b[iy]), tmp[tid]);
+    }
+
+    // dst is not transposed and not permuted
+    const uint idst = channel*nrows_dst + row_dst;
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        dst[idst] = tmp[0];
+    }
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp
new file mode 100644
index 0000000000000..ec8eadcd5828a
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp
@@ -0,0 +1,74 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint s_offset = 8*v_im;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
+        FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3), sum1))))))));
+            sum2 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF),
+                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF), sum2))))))));
+        }
+        const uint tmp_idx = 16 * ix + tid;
+        tmp[tmp_idx] = fma(dall, sum1, fma(-dmin, sum2, tmp[tmp_idx]));
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp
new file mode 100644
index 0000000000000..3ca4ad85a5ca0
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp
@@ -0,0 +1,67 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+    const uint8_t m = uint8_t(1 << (4 * v_im));
+
+    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    const uint s_shift = 4 * v_im;
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4)),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4)),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4)),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4)),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum))))))));
+        }
+        const uint tmp_idx = 16 * ix + tid;
+        tmp[tmp_idx] = fma(d, sum, tmp[tmp_idx]);
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp
new file mode 100644
index 0000000000000..d91e00e10061a
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp
@@ -0,0 +1,118 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 8/K_QUANTS_PER_ITERATION;             // 8 or 4
+
+    const uint il = tid/step;                               // 0...3
+    const uint ir = tid - step*il;                          // 0...7 or 0...3
+    const uint n =  2 * K_QUANTS_PER_ITERATION;             // 2 or 4
+
+    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const uint v_in = il % 2;
+
+    const uint l0 = n * (2 * ir + v_in);            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 64*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y1_idx = i * QUANT_K + y_offset;
+        const uint y2_idx = y1_idx + 128;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        const uint8_t sc0 = uint8_t(  data_a[ib0 + i].scales[v_im * 2    ]       & 0x3f);
+        const uint8_t sc1 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 1]       & 0x3f);
+        const uint8_t sc2 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 4]       & 0x3f);
+        const uint8_t sc3 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 5]       & 0x3f);
+        const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2    ] & 0xc0) >> 2));
+        const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
+        const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
+        const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
+
+#if K_QUANTS_PER_ITERATION == 2
+        const uint8_t q4_0  = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1  = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2  = uint8_t(data_a[ib0 + i].qs[q_offset +  2] & 0xf);
+        const uint8_t q4_3  = uint8_t(data_a[ib0 + i].qs[q_offset +  3] & 0xf);
+        const uint8_t q4_4  = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_5  = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_6  = uint8_t(data_a[ib0 + i].qs[q_offset +  2]  >> 4);
+        const uint8_t q4_7  = uint8_t(data_a[ib0 + i].qs[q_offset +  3]  >> 4);
+        const uint8_t q4_8  = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_9  = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf);
+        const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf);
+        const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+        const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66]  >> 4);
+        const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67]  >> 4);
+
+        const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx]),      q4_0,  fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]),  q4_1,  fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]),  q4_2,  FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) *  q4_3)));
+        const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_4,  fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), q4_5,  fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), q4_6,  FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7)));
+        const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx]),      q4_8,  fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]),  q4_9,  fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]),  q4_10, FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) *  q4_11)));
+        const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_12, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), q4_13, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), q4_14, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15)));
+        const FLOAT_TYPE smin =
+            fma(FLOAT_TYPE(data_b[b_offset + y1_idx    ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx    ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7,
+            fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), sc7,
+            fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), sc7,
+            fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 3]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 35]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 3]), sc6,     FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7)))))))))))))));
+        const uint tmp_idx = 16 * ix + tid;
+        tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx]));
+#else
+        const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+
+        const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx     ]), q4_0, FLOAT_TYPE(data_b[b_offset + y1_idx +  1]) * q4_1);
+        const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3);
+        const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx     ]), q4_4, FLOAT_TYPE(data_b[b_offset + y2_idx +  1]) * q4_5);
+        const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7);
+        const FLOAT_TYPE smin =
+            fma(FLOAT_TYPE(data_b[b_offset + y1_idx    ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx    ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7,
+          + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7)))))));
+
+        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) +
+                        sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin);
+        const uint tmp_idx = 16 * ix + tid;
+        tmp[tmp_idx] = fma(dall, (fma(sx, FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f), fma(sy, FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f),
+                       fma(sz, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)), fma(sw, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))))))), fma(-dmin, smin, tmp[tmp_idx]));
+#endif
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp
new file mode 100644
index 0000000000000..2306785af4226
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp
@@ -0,0 +1,109 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/2;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%2;  // 0 or 0, 1
+
+    const uint il = tid/4;                           // 0...3
+    const uint ir = tid - 4*il;                      // 0...7 or 0...3
+
+    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const uint v_in = il % 2;
+
+    const uint l0 = 4*ir + 2*v_in;                   // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 64*v_im + l0;
+
+    const uint8_t hm1 = uint8_t(1 << (2*v_im));
+    const uint8_t hm2 = uint8_t(hm1 << 4);
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) {
+        const uint y1_idx = i * QUANT_K + y_offset;
+        const uint y2_idx = y1_idx + 128;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        const uint8_t sc0 = uint8_t(  data_a[ib0 + i].scales[v_im * 2    ]       & 0x3f);
+        const uint8_t sc1 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 1]       & 0x3f);
+        const uint8_t sc2 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 4]       & 0x3f);
+        const uint8_t sc3 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 5]       & 0x3f);
+        const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2    ] & 0xc0) >> 2));
+        const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
+        const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
+        const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
+
+        const uint8_t q4_0  = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1  = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2  = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf);
+        const uint8_t q4_3  = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf);
+        const uint8_t q4_4  = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_5  = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_6  = uint8_t(data_a[ib0 + i].qs[q_offset + 16]  >> 4);
+        const uint8_t q4_7  = uint8_t(data_a[ib0 + i].qs[q_offset + 17]  >> 4);
+        const uint8_t q4_8  = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_9  = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf);
+        const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf);
+        const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+        const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80]  >> 4);
+        const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81]  >> 4);
+
+        const FLOAT_TYPE sx =
+          fma(FLOAT_TYPE(data_b[b_offset + y1_idx     ]), (q4_0 + (((data_a[ib0 + i].qh[l0     ] & hm1) != 0) ? 16 : 0)),
+          fma(FLOAT_TYPE(data_b[b_offset + y1_idx +  1]), (q4_1 + (((data_a[ib0 + i].qh[l0 +  1] & hm1) != 0) ? 16 : 0)),
+          fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 16]), (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)),
+             FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0)))));
+        const FLOAT_TYPE sy =
+          fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), (q4_4 + (((data_a[ib0 + i].qh[l0     ] & (hm1 << 1)) != 0) ? 16 : 0)),
+          fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), (q4_5 + (((data_a[ib0 + i].qh[l0 +  1] & (hm1 << 1)) != 0) ? 16 : 0)),
+          fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 48]), (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)),
+             FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0)))));
+        const FLOAT_TYPE sz =
+          fma(FLOAT_TYPE(data_b[b_offset + y2_idx     ]), (q4_8  + (((data_a[ib0 + i].qh[l0     ] & hm2) != 0) ? 16 : 0)),
+          fma(FLOAT_TYPE(data_b[b_offset + y2_idx +  1]), (q4_9  + (((data_a[ib0 + i].qh[l0 +  1] & hm2) != 0) ? 16 : 0)),
+          fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 16]), (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)),
+             FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0)))));
+        const FLOAT_TYPE sw =
+          fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), (q4_12 + (((data_a[ib0 + i].qh[l0     ] & (hm2 << 1)) != 0) ? 16 : 0)),
+          fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), (q4_13 + (((data_a[ib0 + i].qh[l0 +  1] & (hm2 << 1)) != 0) ? 16 : 0)),
+          fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 48]), (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)),
+             FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0)))));
+        const FLOAT_TYPE smin =
+          fma(FLOAT_TYPE(data_b[b_offset + y1_idx     ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]), sc2,
+          fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]), sc3,
+          fma(FLOAT_TYPE(data_b[b_offset + y2_idx     ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]), sc6,
+              (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7)));
+        const uint tmp_idx = 16 * ix + tid;
+        tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx]));
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp
new file mode 100644
index 0000000000000..95c286eeb17e1
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp
@@ -0,0 +1,79 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const uint l0 = v_in;                                   // 0...15
+    const uint is = 0;
+#else
+    const uint l0 = 4 * v_in;                               // 0, 4, 8, ..., 28
+    const uint is = v_in / 4;
+#endif
+
+    const uint ql_offset = 64*v_im + l0;
+    const uint qh_offset = 32*v_im + l0;
+    const uint s_offset  =  8*v_im + is;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx   = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+#if K_QUANTS_PER_ITERATION == 1
+        const uint tmp_idx = 16 * ix + tid;
+        tmp[tmp_idx] = fma(FLOAT_TYPE(data_b[b_offset + y_idx +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x03) << 4)) - 32),
+                       fma(FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32),
+                       fma(FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x0c) << 2)) - 32),
+                       fma(FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32),
+                       fma(FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x30) >> 0)) - 32),
+                       fma(FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32),
+                       fma(FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0xc0) >> 2)) - 32),
+                       fma(FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32), tmp[tmp_idx]))))))));
+#else
+        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+        [[unroll]] for (int l = 0; l < 4; ++l) {
+            sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32),
+                  fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32), sum))));
+        }
+        tmp[16 * ix + tid] += sum;
+#endif
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+       }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/mul_mm.comp b/ggml/src/vulkan-shaders/mul_mm.comp
new file mode 100644
index 0000000000000..fffdd18189d55
--- /dev/null
+++ b/ggml/src/vulkan-shaders/mul_mm.comp
@@ -0,0 +1,508 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#ifdef FLOAT16
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#endif
+
+#ifdef MUL_MAT_ID
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#endif
+
+#include "types.comp"
+
+#ifndef LOAD_VEC_A
+#define LOAD_VEC_A 1
+#endif
+#ifndef LOAD_VEC_B
+#define LOAD_VEC_B 1
+#endif
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+#ifdef MUL_MAT_ID
+layout (binding = 3) readonly buffer IDS {int data_ids[];};
+#endif
+
+layout (push_constant) uniform parameter
+{
+    uint M;
+    uint N;
+    uint K;
+    uint stride_a;
+    uint stride_b;
+    uint stride_d;
+
+    uint batch_stride_a;
+    uint batch_stride_b;
+    uint batch_stride_d;
+
+#ifdef MUL_MAT_ID
+    uint nei0;
+    uint nei1;
+    uint nbi1;
+    uint ne11;
+#else
+    uint k_split;
+    uint ne02;
+    uint ne12;
+    uint broadcast2;
+    uint broadcast3;
+#endif
+} p;
+
+layout (constant_id = 1) const uint BM = 64;
+layout (constant_id = 2) const uint BN = 64;
+layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working with a quant
+layout (constant_id = 4) const uint WM = 32;
+layout (constant_id = 5) const uint WN = 32;
+layout (constant_id = 6) const uint WMITER = 2;
+layout (constant_id = 7) const uint TM = 4;
+layout (constant_id = 8) const uint TN = 2;
+layout (constant_id = 9) const uint WARP = 32;
+
+shared FLOAT_TYPE buf_a[BM * (BK+1)];
+shared FLOAT_TYPE buf_b[BN * (BK+1)];
+
+#ifdef MUL_MAT_ID
+shared u16vec2 row_ids[3072];
+#endif
+
+void main() {
+#ifdef MUL_MAT_ID
+    const uint expert_idx = gl_GlobalInvocationID.z;
+#else
+    const uint batch_idx = gl_GlobalInvocationID.z;
+
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
+
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
+
+    const uint batch_idx_a = i03 * p.ne02 + i02;
+#endif
+
+    const uint blocks_m = (p.M + BM - 1) / BM;
+    const uint ir = gl_WorkGroupID.x % blocks_m;
+    const uint ik = gl_WorkGroupID.x / blocks_m;
+    const uint ic = gl_WorkGroupID.y;
+
+    const uint warp_i = gl_LocalInvocationID.x / WARP;
+    const uint warp_r = warp_i % (BM / WM);
+    const uint warp_c = warp_i / (BM / WM);
+
+    const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER);
+    const uint WSUBM = WM / WMITER;
+    const uint WSUBN = WN / WNITER;
+
+    const uint tiw = gl_LocalInvocationID.x % WARP;
+    const uint tiwr = tiw % (WSUBM / TM);
+    const uint tiwc = tiw / (WSUBM / TM);
+
+    const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A);
+    const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A);
+    const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B);
+    const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B);
+
+    const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK;
+    const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK;
+
+#ifdef MUL_MAT_ID
+    uint _ne1 = 0;
+    for (uint ii1 = 0; ii1 < p.nei1; ii1++) {
+        for (uint ii0 = 0; ii0 < p.nei0; ii0++) {
+            if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) {
+                row_ids[_ne1] = u16vec2(ii0, ii1);
+                _ne1++;
+            }
+        }
+    }
+
+    barrier();
+
+    // Workgroup has no work
+    if (ic * BN >= _ne1) return;
+#endif
+
+#ifdef MUL_MAT_ID
+    const uint start_k = 0;
+    const uint end_k = p.K;
+#else
+    const uint start_k = ik * p.k_split;
+    const uint end_k = min(p.K, (ik + 1) * p.k_split);
+#endif
+
+    uint pos_a = (
+#ifdef MUL_MAT_ID
+        expert_idx * p.batch_stride_a +
+#else
+        batch_idx_a * p.batch_stride_a +
+#endif
+        ir * BM * p.stride_a + start_k) / LOAD_VEC_A;
+#ifdef MUL_MAT_ID
+    uint pos_b = 0;
+#else
+    uint pos_b = (batch_idx * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B;
+#endif
+
+    float sums[WMITER * TM * WNITER * TN];
+    FLOAT_TYPE cache_a[WMITER * TM];
+    FLOAT_TYPE cache_b[WNITER * TN];
+
+    [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    [[unroll]] for (uint block = start_k; block < end_k; block += BK) {
+        [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {
+
+#if defined(DATA_A_F32) || defined(DATA_A_F16)
+#if LOAD_VEC_A == 8
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx][0].x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w);
+            buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x);
+            buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y);
+            buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z);
+            buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w);
+#elif LOAD_VEC_A == 4
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx].x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w);
+#else
+            if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) {
+                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]);
+            } else {
+                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f);
+            }
+#endif
+#elif defined(DATA_A_Q4_0)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q4_1)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const float m = float(data_a[ib].m);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q5_0)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
+            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q5_1)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const float m = float(data_a[ib].m);
+            const uint uint_qh = data_a[ib].qh;
+            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q8_0)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 16;
+            const uint iqs = (idx & 0xF) * 2;
+
+            const float d = float(data_a[ib].d);
+            const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q2_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                         // 2 values per idx
+            const uint iqs = idx % 128;                        // 0..127
+
+            const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30
+            const uint scalesi = iqs / 8;                      // 0..15
+            const uint qsshift = ((iqs % 64) / 16) * 2;        // 0,2,4,6
+
+            const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]);
+            const uint scales = data_a[ib].scales[scalesi];
+            const vec2 d = vec2(data_a[ib].d);
+
+            const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q3_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                   // 2 values per idx
+            const uint iqs = idx % 128;                  // 0..127
+
+            const uint n = iqs / 64;                     // 0,1
+            const uint qsi = n * 32 + (iqs % 16) * 2;    // 0,2,4..62
+            const uint hmi =          (iqs % 16) * 2;    // 0,2,4..30
+            const uint j = (iqs % 64) / 4;               // 0..3
+            const uint is = iqs / 8;                     // 0..15
+            const uint halfsplit = ((iqs % 64) / 16);    // 0,1,2,3
+            const uint qsshift = halfsplit * 2;          // 0,2,4,6
+            const uint m = 1 << (4 * n + halfsplit);     // 1,2,4,8,16,32,64,128
+
+            const int8_t us = int8_t(is <  4 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+8] >> 0) & 3) << 4) :
+                                    is <  8 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+4] >> 2) & 3) << 4) :
+                                    is < 12 ? (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is+0] >> 4) & 3) << 4) :
+                                            (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is-4] >> 6) & 3) << 4));
+            const float dl = float(data_a[ib].d) * float(us - 32);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi    ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi    ] & m) != 0) ? 0 : 4)));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4)));
+#elif defined(DATA_A_Q4_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            uint8_t sc;
+            uint8_t mbyte;
+            if (is < 4) {
+                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
+                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
+            } else {
+                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
+                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
+            }
+            const float d = loadd.x * sc;
+            const float m = -loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF), m));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF), m));
+#elif defined(DATA_A_Q5_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+            const uint qhi = (iqs % 16) * 2;           // 0,2,4..30
+
+            const uint8_t hm = uint8_t(1 << (iqs / 16));
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            uint8_t sc;
+            uint8_t mbyte;
+            if (is < 4) {
+                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
+                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
+            } else {
+                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
+                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
+            }
+            const float d = loadd.x * sc;
+            const float m = -loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi    ] & hm) != 0 ? 16 : 0), m));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0), m));
+#elif defined(DATA_A_Q6_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint iqs = idx % 128;                 // 0..127
+
+            const uint n = iqs / 64;                    // 0,1
+            const uint b = (iqs % 64) / 32;             // 0,1
+            const uint is_b = (iqs % 16) / 8;           // 0,1
+            const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
+            const uint is = 8 * n + qhshift + is_b;     // 0..15
+            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
+            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+
+            const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));
+#elif defined(DATA_A_IQ4_NL)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#endif
+        }
+        [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
+#if LOAD_VEC_B == 8
+#ifdef MUL_MAT_ID
+            const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l];
+            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b;
+#else
+            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
+#endif
+            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
+            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x);
+            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y);
+            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z);
+            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w);
+            buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x);
+            buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y);
+            buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z);
+            buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w);
+#elif LOAD_VEC_B == 4
+#ifdef MUL_MAT_ID
+            const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l];
+            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b;
+#else
+            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
+#endif
+            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
+            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x);
+            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y);
+            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z);
+            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w);
+#elif !MUL_MAT_ID
+            if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]);
+            } else {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f);
+            }
+#else
+            const uint row_i = ic * BN + loadc_b + l;
+            if (row_i < _ne1) {
+                const u16vec2 row_idx = row_ids[row_i];
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]);
+            } else {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f);
+            }
+#endif
+        }
+
+        barrier();
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (uint i = 0; i < BK; i++) {
+            // Load from shared into cache
+            [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+                [[unroll]] for (uint j = 0; j < TM; j++) {
+                    cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * (BK+1) + i];
+                }
+            }
+            [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+                [[unroll]] for (uint j = 0; j < TN; j++) {
+                    cache_b[wsic * TN + j] = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + j) * (BK+1) + i];
+                }
+            }
+
+            [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+                [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+                    [[unroll]] for (uint cc = 0; cc < TN; cc++) {
+                        [[unroll]] for (uint cr = 0; cr < TM; cr++) {
+                            const uint sums_idx = (wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr;
+                            sums[sums_idx] = fma(float(cache_a[wsir * TM + cr]), float(cache_b[wsic * TN + cc]), sums[sums_idx]);
+                        }
+                    }
+                }
+            }
+        }
+
+        barrier();
+    }
+
+    const uint dr = ir * BM + warp_r * WM;
+    const uint dc = ic * BN + warp_c * WN;
+
+#ifndef MUL_MAT_ID
+    const uint offsets = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
+#endif
+
+    [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+        [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+
+            const uint dr_warp = dr + wsir * WSUBM + tiwr * TM;
+            const uint dc_warp = dc + wsic * WSUBN + tiwc * TN;
+            [[unroll]] for (uint cc = 0; cc < TN; cc++) {
+#ifdef MUL_MAT_ID
+                const uint row_i = dc_warp + cc;
+                if (row_i >= _ne1) break;
+
+                const u16vec2 row_idx = row_ids[row_i];
+#endif
+                [[unroll]] for (uint cr = 0; cr < TM; cr++) {
+#ifdef MUL_MAT_ID
+                    data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
+#else
+                    if (dr_warp + cr < p.M && dc_warp + cc < p.N) {
+                        data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
+                    }
+#endif
+                }
+            }
+        }
+    }
+}
diff --git a/ggml/src/vulkan-shaders/norm.comp b/ggml/src/vulkan-shaders/norm.comp
new file mode 100644
index 0000000000000..6627a50bd949a
--- /dev/null
+++ b/ggml/src/vulkan-shaders/norm.comp
@@ -0,0 +1,44 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared vec2 sum[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    sum[tid] = vec2(0.0f, 0.0f);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        const float xi = float(data_a[row*p.KX + col]);
+        sum[tid].x += xi;
+        sum[tid].y += xi * xi;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            sum[tid] += sum[tid + s];
+        }
+        barrier();
+    }
+
+    const float mean = sum[0].x / p.KX;
+    const float var = sum[0].y / p.KX - mean * mean;
+    const float inv_std = inversesqrt(var + p.param1);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/pad.comp b/ggml/src/vulkan-shaders/pad.comp
new file mode 100644
index 0000000000000..a465cd52bcfa8
--- /dev/null
+++ b/ggml/src/vulkan-shaders/pad.comp
@@ -0,0 +1,26 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i3 = idx / (p.ne12*p.ne11*p.ne10);
+    const uint i3_offset = i3 * p.ne12*p.ne11*p.ne10;
+    const uint i2 = (idx - i3_offset) / (p.ne11*p.ne10);
+    const uint i2_offset = i2*p.ne11*p.ne10;
+    const uint i1 = (idx - i3_offset - i2_offset) / p.ne10;
+    const uint i0 = idx - i3_offset - i2_offset - i1*p.ne10;
+
+    const uint src0_idx = i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0*p.nb00;
+    const uint dst_idx = i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0*p.nb10;
+
+    const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03;
+
+    data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : 0.0f);
+}
diff --git a/ggml/src/vulkan-shaders/relu.comp b/ggml/src/vulkan-shaders/relu.comp
new file mode 100644
index 0000000000000..52a19b62a67db
--- /dev/null
+++ b/ggml/src/vulkan-shaders/relu.comp
@@ -0,0 +1,21 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    data_d[i] = max(float(data_a[i]), 0);
+}
diff --git a/ggml/src/vulkan-shaders/repeat.comp b/ggml/src/vulkan-shaders/repeat.comp
new file mode 100644
index 0000000000000..a86af87e7b7f9
--- /dev/null
+++ b/ggml/src/vulkan-shaders/repeat.comp
@@ -0,0 +1,24 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+uint src0_idx_mod(uint idx) {
+    const uint i13 = idx / (p.ne12*p.ne11*p.ne10);
+    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
+    const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10);
+    const uint i12_offset = i12*p.ne11*p.ne10;
+    const uint i11 = (idx - i13_offset - i12_offset) / p.ne10;
+    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
+    return (i13 % p.ne03)*p.nb03 + (i12 % p.ne02)*p.nb02 + (i11 % p.ne01)*p.nb01 + (i10 % p.ne00)*p.nb00;
+}
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx_mod(idx)]);
+}
diff --git a/ggml/src/vulkan-shaders/rms_norm.comp b/ggml/src/vulkan-shaders/rms_norm.comp
new file mode 100644
index 0000000000000..b554400ba393f
--- /dev/null
+++ b/ggml/src/vulkan-shaders/rms_norm.comp
@@ -0,0 +1,42 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared FLOAT_TYPE sum[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[row*p.KX + col]);
+        sum[tid] += xi * xi;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            sum[tid] += sum[tid + s];
+        }
+        barrier();
+    }
+
+    const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(p.KX);
+    const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1));
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        data_d[row*p.KX + col] = D_TYPE(scale * FLOAT_TYPE(data_a[row*p.KX + col]));
+    }
+}
diff --git a/ggml/src/vulkan-shaders/rope_head.comp b/ggml/src/vulkan-shaders/rope_head.comp
new file mode 100644
index 0000000000000..ea89542261cc4
--- /dev/null
+++ b/ggml/src/vulkan-shaders/rope_head.comp
@@ -0,0 +1,44 @@
+#include "types.comp"
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {int data_pos[];};
+layout (binding = 2) readonly buffer Z {float data_ff[];};
+layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ncols;
+    uint n_dims;
+    float freq_scale;
+    uint p_delta_rows;
+    float freq_base;
+    float ext_factor;
+    float attn_factor;
+    float corr_dims[2];
+    float theta_scale;
+    uint has_ff;
+} p;
+
+float rope_yarn_ramp(const float low, const float high, const uint i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) {
+    float mscale = p.attn_factor;
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = p.freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (p.ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
+    }
+    cos_theta = cos(theta) * mscale;
+    sin_theta = sin(theta) * mscale;
+}
diff --git a/ggml/src/vulkan-shaders/rope_neox.comp b/ggml/src/vulkan-shaders/rope_neox.comp
new file mode 100644
index 0000000000000..83b46b69b2a7f
--- /dev/null
+++ b/ggml/src/vulkan-shaders/rope_neox.comp
@@ -0,0 +1,37 @@
+#version 450
+
+#include "rope_head.comp"
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y * 2;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    if (col >= p.n_dims) {
+        const uint i = row*p.ncols + col;
+
+        data_d[i + 0] = data_a[i + 0];
+        data_d[i + 1] = data_a[i + 1];
+
+        return;
+    }
+
+    const uint i  = row*p.ncols + col/2;
+    const uint i2 = row/p.p_delta_rows;
+
+    const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f);
+
+    const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f;
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta);
+
+    const float x0 = float(data_a[i + 0]);
+    const float x1 = float(data_a[i + p.n_dims/2]);
+
+    data_d[i + 0]        = D_TYPE(x0*cos_theta - x1*sin_theta);
+    data_d[i + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
+}
diff --git a/ggml/src/vulkan-shaders/rope_norm.comp b/ggml/src/vulkan-shaders/rope_norm.comp
new file mode 100644
index 0000000000000..e416ad9389706
--- /dev/null
+++ b/ggml/src/vulkan-shaders/rope_norm.comp
@@ -0,0 +1,37 @@
+#version 450
+
+#include "rope_head.comp"
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y * 2;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    if (col >= p.n_dims) {
+        const uint i = row*p.ncols + col;
+
+        data_d[i + 0] = data_a[i + 0];
+        data_d[i + 1] = data_a[i + 1];
+
+        return;
+    }
+
+    const uint i = row*p.ncols + col;
+    const uint i2 = row/p.p_delta_rows;
+
+    const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f);
+
+    const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f;
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta);
+
+    const float x0 = float(data_a[i + 0]);
+    const float x1 = float(data_a[i + 1]);
+
+    data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
+    data_d[i + 1] = D_TYPE(x0*sin_theta + x1*cos_theta);
+}
diff --git a/ggml/src/vulkan-shaders/scale.comp b/ggml/src/vulkan-shaders/scale.comp
new file mode 100644
index 0000000000000..5cd2f668d01f3
--- /dev/null
+++ b/ggml/src/vulkan-shaders/scale.comp
@@ -0,0 +1,14 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(p.param1));
+}
diff --git a/ggml/src/vulkan-shaders/silu.comp b/ggml/src/vulkan-shaders/silu.comp
new file mode 100644
index 0000000000000..4d36f88e089bc
--- /dev/null
+++ b/ggml/src/vulkan-shaders/silu.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float xi = float(data_a[i]);
+    data_d[i] = D_TYPE(xi / (1.0f + exp(-xi)));
+}
diff --git a/ggml/src/vulkan-shaders/soft_max.comp b/ggml/src/vulkan-shaders/soft_max.comp
new file mode 100644
index 0000000000000..0bd51ecab5870
--- /dev/null
+++ b/ggml/src/vulkan-shaders/soft_max.comp
@@ -0,0 +1,106 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint n_head_log2;
+} p;
+
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
+layout (binding = 2) buffer D {D_TYPE data_d[];};
+
+shared FLOAT_TYPE vals[BLOCK_SIZE];
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
+    const uint rowy = rowx % p.KY;
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (p.max_bias > 0.0f) {
+        const uint h = rowx/p.KY; // head index
+
+        const float base = h < p.n_head_log2 ? p.m0 : p.m1;
+        const uint   exp  = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // Find max
+    FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000);
+
+    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            break;
+        }
+
+        max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)));
+    }
+    vals[tid] = max_val;
+
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(vals[tid], vals[tid + s]);
+        }
+        barrier();
+    }
+
+    max_val = vals[0];
+    barrier();
+
+    // Sum up values
+    vals[tid] = FLOAT_TYPE(0.0f);
+
+    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            break;
+        }
+
+        const uint i = rowx * p.KX + col;
+        const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
+        vals[tid] += val;
+        data_d[i] = D_TYPE(val);
+    }
+
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] += vals[tid + s];
+        }
+        barrier();
+    }
+
+    const D_TYPE divisor = D_TYPE(vals[0]);
+
+    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            break;
+        }
+
+        data_d[rowx*p.KX + col] /= divisor;
+    }
+}
diff --git a/ggml/src/vulkan-shaders/square.comp b/ggml/src/vulkan-shaders/square.comp
new file mode 100644
index 0000000000000..1fa118c996e04
--- /dev/null
+++ b/ggml/src/vulkan-shaders/square.comp
@@ -0,0 +1,15 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val * val);
+}
diff --git a/ggml/src/vulkan-shaders/sum_rows.comp b/ggml/src/vulkan-shaders/sum_rows.comp
new file mode 100644
index 0000000000000..961e5ffa1f56f
--- /dev/null
+++ b/ggml/src/vulkan-shaders/sum_rows.comp
@@ -0,0 +1,37 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
+    const uint col = gl_LocalInvocationID.x;
+
+    tmp[col] = FLOAT_TYPE(0.0f);
+
+    for (uint i = col; i < p.KX; i += BLOCK_SIZE) {
+        tmp[col] += FLOAT_TYPE(data_a[row*p.KX + i]);
+    }
+
+    barrier();
+    [[unroll]] for (int s = int(BLOCK_SIZE) / 2; s > 0; s >>= 1) {
+        if (col < s) {
+            tmp[col] += tmp[col + s];
+        }
+        barrier();
+    }
+
+    if (col == 0) {
+        data_d[row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/tanh.comp b/ggml/src/vulkan-shaders/tanh.comp
new file mode 100644
index 0000000000000..74630dc7fef12
--- /dev/null
+++ b/ggml/src/vulkan-shaders/tanh.comp
@@ -0,0 +1,21 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    data_d[i] = D_TYPE(tanh(data_a[i]));
+}
diff --git a/ggml/src/vulkan-shaders/timestep_embedding.comp b/ggml/src/vulkan-shaders/timestep_embedding.comp
new file mode 100644
index 0000000000000..79e065a9313aa
--- /dev/null
+++ b/ggml/src/vulkan-shaders/timestep_embedding.comp
@@ -0,0 +1,41 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint nb1;
+    uint dim;
+    uint max_period;
+} p;
+
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 256
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.y;
+    const uint j = gl_GlobalInvocationID.x;
+    const uint d_offset = i * p.nb1;
+
+    if (p.dim % 2 != 0 && j == ((p.dim + 1) / 2)) {
+        data_d[d_offset + p.dim] = 0.f;
+    }
+
+    const uint half_dim = p.dim / 2;
+    if (j >= half_dim) {
+        return;
+    }
+
+    const float timestep = float(data_a[i]);
+    const float freq = float(exp(-log(p.max_period) * j / half_dim));
+    const float arg = timestep * freq;
+    data_d[d_offset + j] = D_TYPE(cos(arg));
+    data_d[d_offset + j + half_dim] = D_TYPE(sin(arg));
+}
diff --git a/ggml/src/vulkan-shaders/types.comp b/ggml/src/vulkan-shaders/types.comp
new file mode 100644
index 0000000000000..21dce72fc7dfb
--- /dev/null
+++ b/ggml/src/vulkan-shaders/types.comp
@@ -0,0 +1,200 @@
+#if !defined(DATA_A_F32) && !defined(DATA_A_F16)
+#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
+#endif
+
+#if defined(DATA_A_F32)
+#define QUANT_K 1
+#define QUANT_R 1
+
+#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1
+#define A_TYPE float
+#elif LOAD_VEC_A == 4
+#define A_TYPE vec4
+#elif LOAD_VEC_A == 8
+#define A_TYPE mat2x4
+#endif
+#endif
+
+#if defined(DATA_A_F16)
+#define QUANT_K 1
+#define QUANT_R 1
+
+#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1
+#define A_TYPE float16_t
+#elif LOAD_VEC_A == 4
+#define A_TYPE f16vec4
+#elif LOAD_VEC_A == 8
+#define A_TYPE f16mat2x4
+#endif
+#endif
+
+#if defined(DATA_A_Q4_0)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q4_0
+{
+    float16_t d;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q4_0
+#endif
+
+#if defined(DATA_A_Q4_1)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q4_1
+{
+    float16_t d;
+    float16_t m;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q4_1
+#endif
+
+#if defined(DATA_A_Q5_0)
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q5_0
+{
+    float16_t d;
+    uint16_t qh[2];
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q5_0
+#endif
+
+#if defined(DATA_A_Q5_1)
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q5_1
+{
+    float16_t d;
+    float16_t m;
+    uint qh;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q5_1
+#endif
+
+#if defined(DATA_A_Q8_0)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 32
+#define QUANT_R 1
+
+struct block_q8_0
+{
+    float16_t d;
+    int8_t qs[32];
+};
+
+#define A_TYPE block_q8_0
+#endif
+
+// K-quants
+#if defined(DATA_A_Q2_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q2_K
+{
+    uint8_t scales[QUANT_K/16];
+    uint8_t qs[QUANT_K/4];
+    f16vec2 d;
+};
+
+#define A_TYPE block_q2_K
+#endif
+
+#if defined(DATA_A_Q3_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q3_K
+{
+    uint8_t hmask[QUANT_K/8];
+    uint8_t qs[QUANT_K/4];
+    uint8_t scales[12];
+    float16_t d;
+};
+
+#define A_TYPE block_q3_K
+#endif
+
+#if defined(DATA_A_Q4_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q4_K
+{
+    f16vec2 d;
+    uint8_t scales[3*QUANT_K/64];
+    uint8_t qs[QUANT_K/2];
+};
+
+#define A_TYPE block_q4_K
+#endif
+
+#if defined(DATA_A_Q5_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q5_K
+{
+    f16vec2 d;
+    uint8_t scales[12];
+    uint8_t qh[QUANT_K/8];
+    uint8_t qs[QUANT_K/2];
+};
+
+#define A_TYPE block_q5_K
+#endif
+
+#if defined(DATA_A_Q6_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q6_K
+{
+    uint8_t ql[QUANT_K/2];
+    uint8_t qh[QUANT_K/4];
+    int8_t scales[QUANT_K/16];
+    float16_t d;
+};
+
+#define A_TYPE block_q6_K
+#endif
+
+// IQuants
+
+#if defined(DATA_A_IQ4_NL)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_iq4_nl
+{
+    float16_t d;
+    uint8_t qs[QUANT_K/2];
+};
+
+#define A_TYPE block_iq4_nl
+
+const int8_t kvalues_iq4nl[16] = {
+    int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10),
+    int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113)
+};
+#endif
diff --git a/ggml/src/vulkan-shaders/upscale.comp b/ggml/src/vulkan-shaders/upscale.comp
new file mode 100644
index 0000000000000..511a086ea5314
--- /dev/null
+++ b/ggml/src/vulkan-shaders/upscale.comp
@@ -0,0 +1,36 @@
+#version 450
+
+layout (push_constant) uniform parameter
+{
+    uint ne; uint d_offset;
+    uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13;
+    float sf0; float sf1; float sf2; float sf3;
+} p;
+
+#include "types.comp"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i10 = idx % p.ne10;
+    const uint i11 = (idx / p.ne10) % p.ne11;
+    const uint i12 = (idx / (p.ne10 * p.ne11)) % p.ne12;
+    const uint i13 = (idx / (p.ne10 * p.ne11 * p.ne12)) % p.ne13;
+
+    const uint i00 = uint(i10 / p.sf0);
+    const uint i01 = uint(i11 / p.sf1);
+    const uint i02 = uint(i12 / p.sf2);
+    const uint i03 = uint(i13 / p.sf3);
+
+    data_d[p.d_offset + idx] = D_TYPE(data_a[i03 * p.nb03 + i02 * p.nb02 + i01 * p.nb01 + i00 * p.nb00]);
+}
diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
new file mode 100644
index 0000000000000..53ceb13d30fde
--- /dev/null
+++ b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -0,0 +1,583 @@
+
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <string>
+#include <stdexcept>
+#include <array>
+#include <vector>
+#include <map>
+#include <thread>
+#include <mutex>
+#include <future>
+#include <queue>
+#include <condition_variable>
+#include <cstdio>
+#include <cstring>
+#include <cstdlib>
+#include <sys/stat.h>
+#include <sys/types.h>
+
+#ifdef _WIN32
+    #include <windows.h>
+    #include <direct.h> // For _mkdir on Windows
+    #include <algorithm> // For std::replace on w64devkit
+#else
+    #include <unistd.h>
+    #include <sys/wait.h>
+    #include <fcntl.h>
+#endif
+
+#define ASYNCIO_CONCURRENCY 64
+
+std::mutex lock;
+std::vector<std::pair<std::string, std::string>> shader_fnames;
+
+std::string GLSLC = "glslc";
+std::string input_dir = "vulkan-shaders";
+std::string output_dir = "/tmp";
+std::string target_hpp = "ggml-vulkan-shaders.hpp";
+std::string target_cpp = "ggml-vulkan-shaders.cpp";
+bool no_clean = false;
+
+const std::vector<std::string> type_names = {
+    "f32",
+    "f16",
+    "q4_0",
+    "q4_1",
+    "q5_0",
+    "q5_1",
+    "q8_0",
+    "q2_k",
+    "q3_k",
+    "q4_k",
+    "q5_k",
+    "q6_k",
+    "iq4_nl"
+};
+
+void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) {
+#ifdef _WIN32
+    HANDLE stdout_read, stdout_write;
+    HANDLE stderr_read, stderr_write;
+    SECURITY_ATTRIBUTES sa = { sizeof(SECURITY_ATTRIBUTES), NULL, TRUE };
+
+    if (!CreatePipe(&stdout_read, &stdout_write, &sa, 0) ||
+        !SetHandleInformation(stdout_read, HANDLE_FLAG_INHERIT, 0)) {
+        throw std::runtime_error("Failed to create stdout pipe");
+    }
+
+    if (!CreatePipe(&stderr_read, &stderr_write, &sa, 0) ||
+        !SetHandleInformation(stderr_read, HANDLE_FLAG_INHERIT, 0)) {
+        throw std::runtime_error("Failed to create stderr pipe");
+    }
+
+    PROCESS_INFORMATION pi;
+    STARTUPINFOA si = { sizeof(STARTUPINFOA) };
+    si.dwFlags = STARTF_USESTDHANDLES;
+    si.hStdOutput = stdout_write;
+    si.hStdError = stderr_write;
+
+    std::vector<char> cmd(command.begin(), command.end());
+    cmd.push_back('\0');
+
+    if (!CreateProcessA(NULL, cmd.data(), NULL, NULL, TRUE, 0, NULL, NULL, &si, &pi)) {
+        throw std::runtime_error("Failed to create process");
+    }
+
+    CloseHandle(stdout_write);
+    CloseHandle(stderr_write);
+
+    std::array<char, 128> buffer;
+    DWORD bytes_read;
+
+    while (ReadFile(stdout_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) {
+        stdout_str.append(buffer.data(), bytes_read);
+    }
+
+    while (ReadFile(stderr_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) {
+        stderr_str.append(buffer.data(), bytes_read);
+    }
+
+    CloseHandle(stdout_read);
+    CloseHandle(stderr_read);
+    WaitForSingleObject(pi.hProcess, INFINITE);
+    CloseHandle(pi.hProcess);
+    CloseHandle(pi.hThread);
+#else
+int stdout_pipe[2];
+    int stderr_pipe[2];
+
+    if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) {
+        throw std::runtime_error("Failed to create pipes");
+    }
+
+    pid_t pid = fork();
+    if (pid < 0) {
+        throw std::runtime_error("Failed to fork process");
+    }
+
+    if (pid == 0) {
+        close(stdout_pipe[0]);
+        close(stderr_pipe[0]);
+        dup2(stdout_pipe[1], STDOUT_FILENO);
+        dup2(stderr_pipe[1], STDERR_FILENO);
+        close(stdout_pipe[1]);
+        close(stderr_pipe[1]);
+        execl("/bin/sh", "sh", "-c", command.c_str(), (char*) nullptr);
+        _exit(EXIT_FAILURE);
+    } else {
+        close(stdout_pipe[1]);
+        close(stderr_pipe[1]);
+
+        std::array<char, 128> buffer;
+        ssize_t bytes_read;
+
+        while ((bytes_read = read(stdout_pipe[0], buffer.data(), buffer.size())) > 0) {
+            stdout_str.append(buffer.data(), bytes_read);
+        }
+
+        while ((bytes_read = read(stderr_pipe[0], buffer.data(), buffer.size())) > 0) {
+            stderr_str.append(buffer.data(), bytes_read);
+        }
+
+        close(stdout_pipe[0]);
+        close(stderr_pipe[0]);
+        waitpid(pid, nullptr, 0);
+    }
+#endif
+}
+
+bool directory_exists(const std::string& path) {
+    struct stat info;
+    if (stat(path.c_str(), &info) != 0) {
+        return false; // Path doesn't exist or can't be accessed
+    }
+    return (info.st_mode & S_IFDIR) != 0; // Check if it is a directory
+}
+
+bool create_directory(const std::string& path) {
+#ifdef _WIN32
+    return _mkdir(path.c_str()) == 0 || errno == EEXIST; // EEXIST means the directory already exists
+#else
+    return mkdir(path.c_str(), 0755) == 0 || errno == EEXIST; // 0755 is the directory permissions
+#endif
+}
+
+std::string to_uppercase(const std::string& input) {
+    std::string result = input;
+    for (char& c : result) {
+        c = std::toupper(c);
+    }
+    return result;
+}
+
+bool string_ends_with(const std::string& str, const std::string& suffix) {
+    if (suffix.size() > str.size()) {
+        return false;
+    }
+    return std::equal(suffix.rbegin(), suffix.rend(), str.rbegin());
+}
+
+static const char path_separator = '/';
+
+std::string join_paths(const std::string& path1, const std::string& path2) {
+    return path1 + path_separator + path2;
+}
+
+std::string basename(const std::string &path) {
+    return path.substr(path.find_last_of("/\\") + 1);
+}
+
+void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16 = true) {
+    std::string name = _name + (fp16 ? "" : "_fp32");
+    std::string out_fname = join_paths(output_dir, name + ".spv");
+    std::string in_path = join_paths(input_dir, in_fname);
+
+    #ifdef _WIN32
+        std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", "\"" + in_path + "\"", "-o", "\"" + out_fname + "\""};
+    #else
+        std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o",  out_fname};
+    #endif
+    for (const auto& define : defines) {
+        cmd.push_back("-D" + define.first + "=" + define.second);
+    }
+
+    std::string command;
+    for (const auto& part : cmd) {
+        command += part + " ";
+    }
+
+    std::string stdout_str, stderr_str;
+    try {
+        // std::cout << "Executing command: ";
+        // for (const auto& part : cmd) {
+        //     std::cout << part << " ";
+        // }
+        // std::cout << std::endl;
+
+        execute_command(command, stdout_str, stderr_str);
+        if (!stderr_str.empty()) {
+            std::cerr << "cannot compile " << name << "\n\n" << command << "\n\n" << stderr_str << std::endl;
+            return;
+        }
+
+        std::lock_guard<std::mutex> guard(lock);
+        shader_fnames.push_back(std::make_pair(name, out_fname));
+    } catch (const std::exception& e) {
+        std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl;
+    }
+}
+
+std::map<std::string, std::string> merge_maps(const std::map<std::string, std::string>& a, const std::map<std::string, std::string>& b) {
+    std::map<std::string, std::string> result = a;
+    result.insert(b.begin(), b.end());
+    return result;
+}
+
+void matmul_shaders(std::vector<std::future<void>>& tasks, bool fp16, bool matmul_id) {
+    std::string load_vec = fp16 ? "8" : "4";
+    std::string aligned_b_type_f32 = fp16 ? "mat2x4" : "vec4";
+    std::string aligned_b_type_f16 = fp16 ? "f16mat2x4" : "f16vec4";
+
+    std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", fp16 ? "float16_t" : "float"}};
+    std::string shader_name = "matmul";
+
+    if (matmul_id) {
+        base_dict["MUL_MAT_ID"] = "1";
+        shader_name = "matmul_id";
+    }
+
+    if (fp16) {
+        base_dict["FLOAT16"] = "1";
+    }
+
+    // Shaders with f16 B_TYPE
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv(shader_name + "_f32_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16);
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv(shader_name + "_f32_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16);
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv(shader_name + "_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16);
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv(shader_name + "_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16);
+    }));
+
+    for (const auto& tname : type_names) {
+        std::string data_a_key = "DATA_A_" + to_uppercase(tname);
+        // For unaligned, load one at a time for f32/f16, or two at a time for quants
+        std::string load_vec_a_unaligned = (tname == "f32" || tname == "f16") ? "1" : "2";
+        // For aligned matmul loads
+        std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2";
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16);
+        }));
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16);
+        }));
+    }
+}
+
+void process_shaders(std::vector<std::future<void>>& tasks) {
+    std::cout << "ggml_vulkan: Generating and compiling shaders to SPIR-V" << std::endl;
+    std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", "float"}};
+
+    for (const auto& fp16 : {false, true}) {
+        matmul_shaders(tasks, fp16, false);
+        matmul_shaders(tasks, fp16, true);
+    }
+
+    for (const auto& tname : type_names) {
+        // mul mat vec
+        std::string data_a_key = "DATA_A_" + to_uppercase(tname);
+        std::string shader = (string_ends_with(tname, "_k")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp";
+
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+        }));
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+        }));
+
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+        }));
+
+        // Dequant shaders
+        if (tname != "f16") {
+            tasks.push_back(std::async(std::launch::async, [=] {
+                string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}}));
+            }));
+        }
+
+        if (!string_ends_with(tname, "_k")) {
+            shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp";
+
+            if (tname == "f16") {
+                tasks.push_back(std::async(std::launch::async, [=] {
+                    string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
+                }));
+            } else {
+                tasks.push_back(std::async(std::launch::async, [=] {
+                    string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}});
+                }));
+            }
+            tasks.push_back(std::async(std::launch::async, [=] {
+                string_to_spv("get_rows_" + tname + "_f32", shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}});
+            }));
+        }
+    }
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    // Norms
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("repeat_f32", "repeat.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("concat_f32", "concat.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("concat_f16", "concat.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("concat_i32", "concat.comp", {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("im2col_f32", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("im2col_f32_f16", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}));
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+}
+
+void write_output_files() {
+    FILE* hdr = fopen(target_hpp.c_str(), "w");
+    FILE* src = fopen(target_cpp.c_str(), "w");
+
+    fprintf(hdr, "#include <cstdint>\n\n");
+    fprintf(src, "#include \"%s\"\n\n", basename(target_hpp).c_str());
+
+    for (const auto& pair : shader_fnames) {
+        const std::string& name = pair.first;
+        #ifdef _WIN32
+            std::string path = pair.second;
+            std::replace(path.begin(), path.end(), '/', '\\' );
+        #else
+            const std::string& path = pair.second;
+        #endif
+
+        FILE* spv = fopen(path.c_str(), "rb");
+        if (!spv) {
+            std::cerr << "Error opening SPIR-V file: " << path << " (" << strerror(errno) << ")\n";
+            continue;
+        }
+
+        fseek(spv, 0, SEEK_END);
+        size_t size = ftell(spv);
+        fseek(spv, 0, SEEK_SET);
+
+        std::vector<unsigned char> data(size);
+        size_t read_size = fread(data.data(), 1, size, spv);
+        fclose(spv);
+        if (read_size != size) {
+            std::cerr << "Error reading SPIR-V file: " << path << " (" << strerror(errno) << ")\n";
+            continue;
+        }
+
+        fprintf(hdr, "extern unsigned char %s_data[%zu];\n", name.c_str(), size);
+        fprintf(hdr, "const uint64_t %s_len = %zu;\n\n", name.c_str(), size);
+
+        fprintf(src, "unsigned char %s_data[%zu] = {\n", name.c_str(), size);
+        for (size_t i = 0; i < size; ++i) {
+            fprintf(src, "0x%02x,", data[i]);
+            if ((i + 1) % 12 == 0) fprintf(src, "\n");
+        }
+        fprintf(src, "\n};\n\n");
+
+        if (!no_clean) {
+            std::remove(path.c_str());
+        }
+    }
+
+    fclose(hdr);
+    fclose(src);
+}
+
+int main(int argc, char** argv) {
+    std::map<std::string, std::string> args;
+    for (int i = 1; i < argc; i += 2) {
+        if (i + 1 < argc) {
+            args[argv[i]] = argv[i + 1];
+        }
+    }
+
+    if (args.find("--glslc") != args.end()) {
+        GLSLC = args["--glslc"]; // Path to glslc
+    }
+    if (args.find("--input-dir") != args.end()) {
+        input_dir = args["--input-dir"]; // Directory containing shader sources
+    }
+    if (args.find("--output-dir") != args.end()) {
+        output_dir = args["--output-dir"]; // Directory for containing SPIR-V output
+    }
+    if (args.find("--target-hpp") != args.end()) {
+        target_hpp = args["--target-hpp"]; // Path to generated header file
+    }
+    if (args.find("--target-cpp") != args.end()) {
+        target_cpp = args["--target-cpp"]; // Path to generated cpp file
+    }
+    if (args.find("--no-clean") != args.end()) {
+        no_clean = true; // Keep temporary SPIR-V files in output-dir after build
+    }
+
+    if (!directory_exists(input_dir)) {
+        std::cerr << "\"" << input_dir << "\" must be a valid directory containing shader sources" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    if (!directory_exists(output_dir)) {
+        if (!create_directory(output_dir)) {
+            std::cerr << "Error creating output directory: " << output_dir << "\n";
+            return EXIT_FAILURE;
+        }
+    }
+
+    std::vector<std::future<void>> tasks;
+    process_shaders(tasks);
+
+    for (auto& task : tasks) {
+        task.get();
+    }
+
+    write_output_files();
+
+    return EXIT_SUCCESS;
+}
diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py
deleted file mode 100644
index 8096c03b72d6d..0000000000000
--- a/ggml_vk_generate_shaders.py
+++ /dev/null
@@ -1,3184 +0,0 @@
-#!/usr/bin/env python
-
-import logging
-import argparse
-import asyncio
-import os
-import sys
-from tempfile import gettempdir, NamedTemporaryFile
-
-logger = logging.getLogger("ggml-vk-generate-shaders")
-
-shader_f32 = """
-#define FLOAT_TYPE float
-"""
-shader_f16 = """
-#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
-#define FLOAT_TYPE float16_t
-"""
-shader_int8_ext = """
-#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
-"""
-
-# Type-specific defines
-shader_f32_defines = """
-#define QUANT_K 1
-#define QUANT_R 1
-
-#define A_TYPE float
-"""
-shader_f16_defines = """
-#define QUANT_K 1
-#define QUANT_R 1
-
-#define A_TYPE float16_t
-"""
-shader_q4_0_defines = """
-#define QUANT_K 32
-#define QUANT_R 2
-
-struct block_q4_0
-{
-    float16_t d;
-    uint8_t qs[16];
-};
-
-#define A_TYPE block_q4_0
-"""
-shader_q4_1_defines = """
-#define QUANT_K 32
-#define QUANT_R 2
-
-struct block_q4_1
-{
-    float16_t d;
-    float16_t m;
-    uint8_t qs[16];
-};
-
-#define A_TYPE block_q4_1
-"""
-shader_q5_0_defines = """
-#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
-#define QUANT_K 32
-#define QUANT_R 2
-
-struct block_q5_0
-{
-    float16_t d;
-    uint16_t qh[2];
-    uint8_t qs[16];
-};
-
-#define A_TYPE block_q5_0
-"""
-shader_q5_1_defines = """
-#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
-#define QUANT_K 32
-#define QUANT_R 2
-
-struct block_q5_1
-{
-    float16_t d;
-    float16_t m;
-    uint qh;
-    uint8_t qs[16];
-};
-
-#define A_TYPE block_q5_1
-"""
-shader_q8_0_defines = """
-#define QUANT_K 32
-#define QUANT_R 1
-
-struct block_q8_0
-{
-    float16_t d;
-    int8_t qs[32];
-};
-
-#define A_TYPE block_q8_0
-"""
-
-# K-quants
-shader_q2_K_defines = """
-#define QUANT_K 256
-
-struct block_q2_K
-{
-    uint8_t scales[QUANT_K/16];
-    uint8_t qs[QUANT_K/4];
-    f16vec2 d;
-};
-
-#define A_TYPE block_q2_K
-"""
-shader_q3_K_defines = """
-#define QUANT_K 256
-
-struct block_q3_K
-{
-    uint8_t hmask[QUANT_K/8];
-    uint8_t qs[QUANT_K/4];
-    uint8_t scales[12];
-    float16_t d;
-};
-
-#define A_TYPE block_q3_K
-"""
-shader_q4_K_defines = """
-#define QUANT_K 256
-
-struct block_q4_K
-{
-    f16vec2 d;
-    uint8_t scales[3*QUANT_K/64];
-    uint8_t qs[QUANT_K/2];
-};
-
-#define A_TYPE block_q4_K
-"""
-shader_q5_K_defines = """
-#define QUANT_K 256
-
-struct block_q5_K
-{
-    f16vec2 d;
-    uint8_t scales[12];
-    uint8_t qh[QUANT_K/8];
-    uint8_t qs[QUANT_K/2];
-};
-
-#define A_TYPE block_q5_K
-"""
-shader_q6_K_defines = """
-#define QUANT_K 256
-
-struct block_q6_K
-{
-    uint8_t ql[QUANT_K/2];
-    uint8_t qh[QUANT_K/4];
-    int8_t scales[QUANT_K/16];
-    float16_t d;
-};
-
-#define A_TYPE block_q6_K
-"""
-
-# Dequant functions
-shader_float_dequant_func = """
-vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]);
-}
-"""
-
-shader_q4_0_dequant_func = """
-vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
-    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
-}
-"""
-
-shader_q4_1_dequant_func = """
-vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
-    const float m = float(data_a[a_offset + ib].m);
-    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return vec2(vui & 0xF, vui >> 4) * d + m;
-}
-"""
-
-shader_q5_0_dequant_func = """
-vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
-    const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0];
-    const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
-    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
-}
-"""
-
-shader_q5_1_dequant_func = """
-vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
-    const float m = float(data_a[a_offset + ib].m);
-    const uint uint_qh = data_a[a_offset + ib].qh;
-    const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
-    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
-    return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
-}
-"""
-
-shader_q8_0_dequant_func = """
-vec2 dequantize(uint ib, uint iqs, uint a_offset) {
-    const float d = float(data_a[a_offset + ib].d);
-    return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d;
-}
-"""
-
-# MULMAT
-
-mulmat_head = """#version 450
-
-#extension GL_EXT_control_flow_attributes : enable
-#extension GL_EXT_shader_16bit_storage : require
-
-#ifdef MUL_MAT_ID
-#extension GL_EXT_buffer_reference2 : require
-#extension GL_EXT_nonuniform_qualifier : require
-#extension GL_EXT_scalar_block_layout : require
-#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
-
-#define EXPERT_COUNT 8
-#endif
-
-#ifndef LOAD_VEC_A
-#define LOAD_VEC_A 1
-#endif
-#ifndef LOAD_VEC_B
-#define LOAD_VEC_B 1
-#endif
-"""
-
-mulmat_body1 = """
-layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
-
-#ifdef MUL_MAT_ID
-layout (binding = 3) readonly buffer IDS {int data_ids[];};
-#endif
-
-layout (push_constant) uniform parameter
-{
-    uint M;
-    uint N;
-    uint K;
-    uint stride_a;
-    uint stride_b;
-    uint stride_d;
-    uint k_split;
-
-    uint ne02;
-    uint ne12;
-    uint broadcast2;
-    uint broadcast3;
-
-    uint batch_stride_a;
-    uint batch_stride_b;
-    uint batch_stride_d;
-
-#ifdef MUL_MAT_ID
-    uint expert_stride_a;
-    uint expert_stride_b0;
-    uint expert_stride_b1;
-    uint expert_stride_d;
-
-    uint ids_stride;
-
-    uint n_as;
-    uint nei0;
-    uint nei1;
-    uint nbi1;
-    uint ne11;
-#endif
-} p;
-
-layout (constant_id = 1) const uint BM = 64;
-layout (constant_id = 2) const uint BN = 64;
-layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working with a quant
-layout (constant_id = 4) const uint WM = 32;
-layout (constant_id = 5) const uint WN = 32;
-layout (constant_id = 6) const uint WMITER = 2;
-layout (constant_id = 7) const uint TM = 4;
-layout (constant_id = 8) const uint TN = 2;
-layout (constant_id = 9) const uint WARP = 32;
-
-shared FLOAT_TYPE buf_a[BM * (BK+1)];
-shared FLOAT_TYPE buf_b[BN * (BK+1)];
-
-#ifdef MUL_MAT_ID
-shared u8vec2 rowids[2048];
-#endif
-
-void main() {
-#ifdef MUL_MAT_ID
-    const uint batch_idx = gl_GlobalInvocationID.z / p.n_as;
-    const uint expert_idx = gl_GlobalInvocationID.z % p.n_as;
-#else
-    const uint batch_idx = gl_GlobalInvocationID.z;
-#endif
-
-    const uint i13 = batch_idx / p.ne12;
-    const uint i12 = batch_idx % p.ne12;
-
-    const uint i03 = i13 / p.broadcast3;
-    const uint i02 = i12 / p.broadcast2;
-
-    const uint batch_idx_a = i03 * p.ne02 + i02;
-
-    const uint blocks_m = (p.M + BM - 1) / BM;
-    const uint ir = gl_WorkGroupID.x % blocks_m;
-    const uint ik = gl_WorkGroupID.x / blocks_m;
-    const uint ic = gl_WorkGroupID.y;
-
-    const uint warp_i = gl_LocalInvocationID.x / WARP;
-    const uint warp_r = warp_i % (BM / WM);
-    const uint warp_c = warp_i / (BM / WM);
-
-    const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER);
-    const uint WSUBM = WM / WMITER;
-    const uint WSUBN = WN / WNITER;
-
-    const uint tiw = gl_LocalInvocationID.x % WARP;
-    const uint tiwr = tiw % (WSUBM / TM);
-    const uint tiwc = tiw / (WSUBM / TM);
-
-    const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A);
-    const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A);
-    const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B);
-    const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B);
-
-    const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK;
-    const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK;
-
-#ifdef MUL_MAT_ID
-    uint _ne1 = 0;
-    for (uint ii1 = 0; ii1 < p.nei1; ii1++) {
-        for (uint ii0 = 0; ii0 < p.nei0; ii0++) {
-            if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) {
-                rowids[_ne1] = u8vec2(ii0, ii1);
-                _ne1++;
-            }
-        }
-    }
-
-    const u8vec2 id = rowids[ir * BN + ic];
-#endif
-
-    const uint start_k = ik * p.k_split;
-    const uint end_k = min(p.K, (ik + 1) * p.k_split);
-
-    uint pos_a = (
-#ifdef MUL_MAT_ID
-        expert_idx * p.expert_stride_a +
-#endif
-        batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A;
-    uint pos_b = (
-#ifdef MUL_MAT_ID
-        id.y * p.expert_stride_b1 +
-        (id.x % p.ne11) * p.expert_stride_b0 +
-#endif
-        batch_idx * p.batch_stride_b +
-        ic * BN * p.stride_b + start_k) / LOAD_VEC_B;
-
-    float sums[WMITER * TM * WNITER * TN];
-    FLOAT_TYPE cache_a[WMITER * TM];
-    FLOAT_TYPE cache_b[WNITER * TN];
-
-    [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) {
-        sums[i] = 0.0f;
-    }
-
-    [[unroll]] for (uint block = start_k; block < end_k; block += BK) {
-        [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {"""
-
-mulmat_load_scalar = """
-#if LOAD_VEC_A == 8
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
-            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx][0].x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y);
-            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z);
-            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w);
-            buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x);
-            buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y);
-            buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z);
-            buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w);
-#elif LOAD_VEC_A == 4
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
-            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx].x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y);
-            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z);
-            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w);
-#else
-            if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) {
-                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]);
-            } else {
-                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f);
-            }
-#endif
-"""
-
-mulmat_load_q4_0 = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
-
-            const uint ib = idx / 16;
-            const uint iqs = idx & 0xF;
-
-            const float d = float(data_a[ib].d);
-            const uint vui = uint(data_a[ib].qs[iqs]);
-            const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
-
-mulmat_load_q4_1 = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
-
-            const uint ib = idx / 16;
-            const uint iqs = idx & 0xF;
-
-            const float d = float(data_a[ib].d);
-            const float m = float(data_a[ib].m);
-            const uint vui = uint(data_a[ib].qs[iqs]);
-            const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m;
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
-
-mulmat_load_q5_0 = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
-
-            const uint ib = idx / 16;
-            const uint iqs = idx & 0xF;
-
-            const float d = float(data_a[ib].d);
-            const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
-            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
-            const uint vui = uint(data_a[ib].qs[iqs]);
-            const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
-
-mulmat_load_q5_1 = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
-
-            const uint ib = idx / 16;
-            const uint iqs = idx & 0xF;
-
-            const float d = float(data_a[ib].d);
-            const float m = float(data_a[ib].m);
-            const uint uint_qh = data_a[ib].qh;
-            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
-            const uint vui = uint(data_a[ib].qs[iqs]);
-            const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
-
-mulmat_load_q8_0 = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 16;
-            const uint iqs = (idx & 0xF) * 2;
-
-            const float d = float(data_a[ib].d);
-            const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d;
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);"""
-
-
-mulmat_load_q2_K = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                         // 2 values per idx
-            const uint iqs = idx % 128;                        // 0..127
-
-            const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30
-            const uint scalesi = iqs / 8;                      // 0..15
-            const uint qsshift = ((iqs % 64) / 16) * 2;        // 0,2,4,6
-
-            const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]);
-            const uint scales = data_a[ib].scales[scalesi];
-            const vec2 d = vec2(data_a[ib].d);
-
-            const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4);
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);"""
-
-mulmat_load_q3_K = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                   // 2 values per idx
-            const uint iqs = idx % 128;                  // 0..127
-
-            const uint n = iqs / 64;                     // 0,1
-            const uint qsi = n * 32 + (iqs % 16) * 2;    // 0,2,4..62
-            const uint hmi =          (iqs % 16) * 2;    // 0,2,4..30
-            const uint j = (iqs % 64) / 4;               // 0..3
-            const uint is = iqs / 8;                     // 0..15
-            const uint halfsplit = ((iqs % 64) / 16);    // 0,1,2,3
-            const uint qsshift = halfsplit * 2;          // 0,2,4,6
-            const uint m = 1 << (4 * n + halfsplit);     // 1,2,4,8,16,32,64,128
-
-            const int8_t us = int8_t(is <  4 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+8] >> 0) & 3) << 4) :
-                                    is <  8 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+4] >> 2) & 3) << 4) :
-                                    is < 12 ? (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is+0] >> 4) & 3) << 4) :
-                                            (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is-4] >> 6) & 3) << 4));
-            const float dl = float(data_a[ib].d) * float(us - 32);
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi    ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi    ] & m) != 0) ? 0 : 4)));
-            buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4)));"""
-
-mulmat_load_q4_K = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                 // 2 values per idx
-            const uint iqs = idx % 128;                // 0..127
-
-            const uint n = iqs / 32;                   // 0,1,2,3
-            const uint b = (iqs % 32) / 16;            // 0,1
-            const uint is = 2 * n + b;                 // 0..7
-            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
-
-            const vec2 loadd = vec2(data_a[ib].d);
-
-            uint8_t sc;
-            uint8_t mbyte;
-            if (is < 4) {
-                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
-                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
-            } else {
-                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
-                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
-            }
-            const float d = loadd.x * sc;
-            const float m = loadd.y * mbyte;
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) - m);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) - m);"""
-
-mulmat_load_q5_K = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                 // 2 values per idx
-            const uint iqs = idx % 128;                // 0..127
-
-            const uint n = iqs / 32;                   // 0,1,2,3
-            const uint b = (iqs % 32) / 16;            // 0,1
-            const uint is = 2 * n + b;                 // 0..7
-            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
-            const uint qhi = (iqs % 16) * 2;           // 0,2,4..30
-
-            const uint8_t hm = uint8_t(1 << (iqs / 16));
-
-            const vec2 loadd = vec2(data_a[ib].d);
-
-            uint8_t sc;
-            uint8_t mbyte;
-            if (is < 4) {
-                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
-                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
-            } else {
-                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
-                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
-            }
-            const float d = loadd.x * sc;
-            const float m = loadd.y * mbyte;
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi    ] & hm) != 0 ? 16 : 0)) - m);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0)) - m);"""
-
-mulmat_load_q6_K = """
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                  // 2 values per idx
-            const uint iqs = idx % 128;                 // 0..127
-
-            const uint n = iqs / 64;                    // 0,1
-            const uint b = (iqs % 64) / 32;             // 0,1
-            const uint is_b = (iqs % 16) / 8;           // 0,1
-            const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
-            const uint is = 8 * n + qhshift + is_b;     // 0..15
-            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
-            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
-
-            const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
-            buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));"""
-
-mulmat_body2 = """
-        }
-        [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
-#if LOAD_VEC_B == 8
-            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
-            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
-            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x);
-            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y);
-            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z);
-            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w);
-            buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x);
-            buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y);
-            buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z);
-            buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w);
-#elif LOAD_VEC_B == 4
-            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
-            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
-            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x);
-            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y);
-            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z);
-            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w);
-#else
-            if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) {
-                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]);
-            } else {
-                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f);
-            }
-#endif
-        }
-
-        barrier();
-
-        pos_a += BK / LOAD_VEC_A;
-        pos_b += BK / LOAD_VEC_B;
-
-        for (uint i = 0; i < BK; i++) {
-            // Load from shared into cache
-            [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
-                [[unroll]] for (uint j = 0; j < TM; j++) {
-                    cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * (BK+1) + i];
-                }
-            }
-            [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
-                [[unroll]] for (uint j = 0; j < TN; j++) {
-                    cache_b[wsic * TN + j] = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + j) * (BK+1) + i];
-                }
-            }
-
-            [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
-                [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
-                    [[unroll]] for (uint cc = 0; cc < TN; cc++) {
-                        [[unroll]] for (uint cr = 0; cr < TM; cr++) {
-                            sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr] += float(cache_a[wsir * TM + cr]) * float(cache_b[wsic * TN + cc]);
-                        }
-                    }
-                }
-            }
-        }
-
-        barrier();
-    }
-
-    const uint dr = ir * BM + warp_r * WM;
-    const uint dc = ic * BN + warp_c * WN;
-
-    const uint offsets =
-#ifdef MUL_MAT_ID
-            expert_idx * p.expert_stride_d +
-#endif
-            batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
-
-    [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
-        [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
-
-            const uint dr_warp = dr + wsir * WSUBM + tiwr * TM;
-            const uint dc_warp = dc + wsic * WSUBN + tiwc * TN;
-            [[unroll]] for (uint cc = 0; cc < TN; cc++) {
-                [[unroll]] for (uint cr = 0; cr < TM; cr++) {
-                    if (dr_warp + cr < p.M && dc_warp + cc < p.N) {
-                        data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
-                    }
-                }
-            }
-        }
-    }
-}
-"""
-
-mulmat_split_k_reduce_src = """#version 450
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {float data_a[];};
-layout (binding = 1) writeonly buffer D {float data_d[];};
-
-layout (push_constant) uniform parameter {
-    uint ne;
-    uint k_num;
-} p;
-
-void main() {
-    const uint idx = gl_GlobalInvocationID.x;
-
-    if (idx >= p.ne) {
-        return;
-    }
-
-    float result = 0.0f;
-
-    [[unroll]] for (uint i = 0; i < p.k_num; i++) {
-        result += data_a[i * p.ne + idx];
-    }
-
-    data_d[idx] = result;
-}
-"""
-
-# DEQUANT SHADER
-dequant_head = """#version 450
-
-#extension GL_EXT_control_flow_attributes : require
-#extension GL_EXT_shader_16bit_storage : require
-
-layout (push_constant) uniform parameter
-{
-    uint M;
-    uint K;
-    uint stride_a;
-    uint stride_b;
-    uint nel;
-} p;
-"""
-
-dequant_f32_body = """
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {float data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.x * 16;
-
-    if (i >= p.nel) {
-        return;
-    }
-
-    [[unroll]] for (uint l = 0; l < 16; l++) {
-        data_b[i + l] = D_TYPE(data_a[i + l]);
-    }
-}
-"""
-
-dequant_q4_0_body = """
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {block_q4_0 data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
-
-    const uint tid = gl_LocalInvocationID.x % 64;
-    const uint il  = tid/32;
-    const uint ir  = tid%32;
-    const uint ib = 32*i + ir;
-    if (ib >= p.nel / 32) {
-        return;
-    }
-
-    const uint b_idx = 1024*i + 32*ir + 8*il;
-
-    const float d = float(data_a[ib].d);
-    const float dm = -8.0f * d;
-
-    const uint q_idx = 8*il;
-
-    [[unroll]] for (uint l = 0; l < 8; ++l) {
-        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + dm);
-        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + dm);
-    }
-}
-"""
-
-dequant_q4_1_body = """
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {block_q4_1 data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
-
-    const uint tid = gl_LocalInvocationID.x % 64;
-    const uint il  = tid/32;
-    const uint ir  = tid%32;
-    const uint ib = 32*i + ir;
-    if (ib >= p.nel / 32) {
-        return;
-    }
-
-    const uint b_idx = 1024*i + 32*ir + 8*il;
-
-    const float d = float(data_a[ib].d);
-    const float m = float(data_a[ib].m);
-
-    const uint q_idx = 8*il;
-
-    [[unroll]] for (uint l = 0; l < 8; ++l) {
-        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m);
-        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + m);
-    }
-}
-"""
-
-dequant_q5_0_body = """
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {block_q5_0 data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
-
-    const uint tid = gl_LocalInvocationID.x % 64;
-    const uint il  = tid/32;
-    const uint ir  = tid%32;
-    const uint ib = 32*i + ir;
-    if (ib >= p.nel / 32) {
-        return;
-    }
-
-    const uint b_idx = 1024*i + 32*ir + 8*il;
-
-    const float d = float(data_a[ib].d);
-    const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
-
-    const uint q_idx = 8*il;
-
-    [[unroll]] for (uint l = 0; l < 8; ++l) {
-        const uint iqs = q_idx + l;
-        const uint vui = uint(data_a[ib].qs[iqs]);
-        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f));
-        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f));
-    }
-}
-"""
-
-dequant_q5_1_body = """
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {block_q5_1 data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
-
-    const uint tid = gl_LocalInvocationID.x % 64;
-    const uint il  = tid/32;
-    const uint ir  = tid%32;
-    const uint ib = 32*i + ir;
-    if (ib >= p.nel / 32) {
-        return;
-    }
-
-    const uint b_idx = 1024*i + 32*ir + 8*il;
-
-    const float d = float(data_a[ib].d);
-    const float m = float(data_a[ib].m);
-    const uint qh = data_a[ib].qh;
-
-    const uint q_idx = 8*il;
-
-    [[unroll]] for (uint l = 0; l < 8; ++l) {
-        const uint iqs = q_idx + l;
-        const uint vui = uint(data_a[ib].qs[iqs]);
-        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m);
-        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10))) + m);
-    }
-}
-"""
-
-dequant_q8_0_body = """
-layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {block_q8_0 data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
-
-    const uint tid = gl_LocalInvocationID.x % 64;
-    const uint il  = tid/32;
-    const uint ir  = tid%32;
-    const uint ib = 32*i + ir;
-    if (ib >= p.nel / 32) {
-        return;
-    }
-
-    const uint b_idx = 1024*i + 32*ir + 16*il;
-
-    const float d = float(data_a[ib].d);
-
-    const uint q_idx = 16*il;
-
-    [[unroll]] for (uint l = 0; l < 16; l += 2) {
-        data_b[b_idx + l    ] = D_TYPE(d * data_a[ib].qs[q_idx + l    ]);
-        data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]);
-    }
-}
-"""
-
-# K-quants
-dequant_q2_K_body = """
-layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
-        const uint i = gl_WorkGroupID.x * 256 + wgy;
-        if (i >= p.M * p.K / QUANT_K) {
-            return;
-        }
-
-        const uint tid = gl_LocalInvocationID.x;
-        const uint ip = tid / 32;
-        const uint il = tid - 32 * ip;
-        const uint is = 8 * ip + il / 16;
-
-        const uint y_idx = i * QUANT_K + 128 * ip + il;
-
-        const uint ql_idx = 32 * ip + il;
-        const uint8_t qs = data_a[i].qs[32 * ip + il];
-
-        FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
-        FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
-        data_b[y_idx +  0] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+0] & 0xF) * ((qs >> 0) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+0] >> 4));
-        data_b[y_idx + 32] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+2] & 0xF) * ((qs >> 2) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+2] >> 4));
-        data_b[y_idx + 64] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+4] & 0xF) * ((qs >> 4) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+4] >> 4));
-        data_b[y_idx + 96] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+6] & 0xF) * ((qs >> 6) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+6] >> 4));
-    }
-}
-"""
-dequant_q3_K_body = """
-layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
-        const uint i = uint(gl_WorkGroupID.x * 256 + wgy);
-        if (i >= p.M * p.K / QUANT_K) {
-            return;
-        }
-
-        const uint r = gl_LocalInvocationID.x / 4;
-        const uint tid = r / 2;
-        const uint is0 = r % 2;
-        const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4);
-        const uint n = tid / 4;
-        const uint j = tid - 4*n;
-
-        const uint8_t m = uint8_t(1 << (4*n + j));
-        const uint is = 8*n + 2*j + is0;
-        const uint shift = 2*j;
-
-        const int8_t us = int8_t(is <  4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) :
-                                 is <  8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) :
-                                 is < 12 ? (data_a[i].scales[is-8] >>  4) | (((data_a[i].scales[is+0] >> 4) & 3) << 4) :
-                                           (data_a[i].scales[is-8] >>  4) | (((data_a[i].scales[is-4] >> 6) & 3) << 4));
-        const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d);
-        const FLOAT_TYPE dl    = d_all * FLOAT_TYPE(us - 32);
-
-        const uint y_idx = i * QUANT_K + 128 * n + 32 * j;
-        const uint qs_idx = 32*n;
-
-        for (uint l = l0; l < l0 + 4; ++l) {
-            data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4)));
-        }
-    }
-}
-"""
-dequant_q4_K_body = """
-layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
-        const uint i = gl_WorkGroupID.x * 256 + wgy;
-        if (i >= p.M * p.K / QUANT_K) {
-            return;
-        }
-
-        const uint tid = gl_LocalInvocationID.x;
-        const uint il = tid / 8;
-        const uint ir = tid % 8;
-        const uint is = 2 * il;
-        const uint n = 4;
-
-        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
-
-        const uint y_idx = i * QUANT_K + 64 * il + n * ir;
-        const uint qs_idx = 32*il + n * ir;
-
-        uint8_t sc;
-        uint8_t m;
-        if (is < 4) {
-            sc = uint8_t(data_a[i].scales[is] & 63);
-            m  = uint8_t(data_a[i].scales[is + 4] & 63);
-        } else {
-            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
-            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
-        }
-        const FLOAT_TYPE d1 = dall * sc;
-        const FLOAT_TYPE m1 = dmin * m;
-
-        if (is < 4) {
-            sc = uint8_t(data_a[i].scales[is + 1] & 63);
-            m  = uint8_t(data_a[i].scales[is + 5] & 63);
-        } else {
-            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
-            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
-        }
-        const FLOAT_TYPE d2 = dall * sc;
-        const FLOAT_TYPE m2 = dmin * m;
-
-        [[unroll]] for (uint l = 0; l < n; ++l) {
-            data_b[y_idx + l     ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1);
-            data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >>  4) - m2);
-        }
-    }
-}
-"""
-dequant_q5_K_body = """
-layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
-        const uint i = gl_WorkGroupID.x * 256 + wgy;
-        if (i >= p.M * p.K / QUANT_K) {
-            return;
-        }
-
-        const uint tid = gl_LocalInvocationID.x;
-        const uint il = tid / 16;
-        const uint ir = tid % 16;
-        const uint is = 2 * il;
-
-        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
-
-        const uint y_idx = i * QUANT_K + 64 * il + 2 * ir;
-        const uint qs_idx = 32*il + 2 * ir;
-        const uint qh_idx = 2 * ir;
-
-        uint8_t sc;
-        uint8_t m;
-        if (is < 4) {
-            sc = uint8_t(data_a[i].scales[is] & 63);
-            m  = uint8_t(data_a[i].scales[is + 4] & 63);
-        } else {
-            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
-            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
-        }
-        const FLOAT_TYPE d1 = dall * sc;
-        const FLOAT_TYPE m1 = dmin * m;
-
-        if (is < 4) {
-            sc = uint8_t(data_a[i].scales[is + 1] & 63);
-            m  = uint8_t(data_a[i].scales[is + 5] & 63);
-        } else {
-            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
-            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
-        }
-        const FLOAT_TYPE d2 = dall * sc;
-        const FLOAT_TYPE m2 = dmin * m;
-
-        const uint8_t hm1 = uint8_t(1 << (2 * il    ));
-        const uint8_t hm2 = uint8_t(1 << (2 * il + 1));
-        data_b[y_idx     ] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx    ] & 0xF) + (((data_a[i].qh[qh_idx    ] & hm1) != 0) ? 16 : 0)) - m1);
-        data_b[y_idx +  1] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] & 0xF) + (((data_a[i].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1);
-        data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx    ]  >> 4) + (((data_a[i].qh[qh_idx    ] & hm2) != 0) ? 16 : 0)) - m2);
-        data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1]  >> 4) + (((data_a[i].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2);
-    }
-}
-"""
-dequant_q6_K_body = """
-layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
-
-void main() {
-    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
-        const uint i = gl_WorkGroupID.x * 256 + wgy;
-        if (i >= p.M * p.K / QUANT_K) {
-            return;
-        }
-        const uint tid = gl_LocalInvocationID.x;
-        const uint ip = tid / 32;
-        const uint il = tid - 32 * ip;
-        const uint is = 8 * ip + il / 16;
-
-        const uint y_idx = i * QUANT_K + 128 * ip + il;
-
-        const uint ql_idx = 64 * ip + il;
-        const uint8_t qh = data_a[i].qh[32 * ip + il];
-
-        const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d);
-
-        data_b[y_idx +  0] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 0] * (int8_t((data_a[i].ql[ql_idx +  0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)));
-        data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 2] * (int8_t((data_a[i].ql[ql_idx + 32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)));
-        data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 4] * (int8_t((data_a[i].ql[ql_idx +  0] >>  4) | (((qh >> 4) & 3) << 4)) - 32)));
-        data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 6] * (int8_t((data_a[i].ql[ql_idx + 32] >>  4) | (((qh >> 6) & 3) << 4)) - 32)));
-    }
-}
-"""
-
-# Mul Mat Vec
-mul_mat_vec_head = """#version 450
-
-#extension GL_EXT_control_flow_attributes : enable
-#extension GL_EXT_shader_16bit_storage : require
-#extension GL_EXT_shader_8bit_storage : require
-
-#ifdef MUL_MAT_ID
-#define EXPERT_COUNT 8
-#endif
-"""
-
-
-mul_mat_vec_layout = """
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
-#ifdef MUL_MAT_ID
-layout (binding = 3) readonly buffer IDS {int data_ids[];};
-#endif
-
-layout (push_constant) uniform parameter
-{
-    uint ncols;
-    uint stride_a;
-    uint stride_b;
-    uint stride_d;
-
-    uint ne02;
-    uint ne12;
-    uint broadcast2;
-    uint broadcast3;
-
-    uint batch_stride_a;
-    uint batch_stride_b;
-    uint batch_stride_d;
-
-#ifdef MUL_MAT_ID
-    uint expert_stride_a;
-    uint expert_stride_b0;
-    uint expert_stride_b1;
-    uint expert_stride_d0;
-    uint expert_stride_d1;
-
-    uint ne11;
-    uint nei0;
-    uint nbi1;
-    uint n_as;
-#endif
-} p;
-"""
-
-mul_mat_vec_body = """
-layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
-
-layout (constant_id = 0) const uint BLOCK_SIZE = 32;
-
-shared FLOAT_TYPE tmp[BLOCK_SIZE];
-
-void main() {
-    const uint row = gl_WorkGroupID.x;
-    const uint tid = gl_LocalInvocationID.x;
-    const uint batch_idx = gl_GlobalInvocationID.y;
-#ifdef MUL_MAT_ID
-    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
-    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
-#endif
-
-    const uint i13 = batch_idx / p.ne12;
-    const uint i12 = batch_idx % p.ne12;
-
-    const uint i03 = i13 / p.broadcast3;
-    const uint i02 = i12 / p.broadcast2;
-
-    const uint batch_idx_a = i03 * p.ne02 + i02;
-
-#ifdef MUL_MAT_ID
-    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
-#endif
-
-    const uint a_offset =
-#ifdef MUL_MAT_ID
-            expert_id * p.expert_stride_a +
-#endif
-            batch_idx_a * p.batch_stride_a;
-    const uint b_offset =
-#ifdef MUL_MAT_ID
-            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_b;
-    const uint d_offset =
-#ifdef MUL_MAT_ID
-            expert_idx0 * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_d;
-
-    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
-
-    tmp[tid] = FLOAT_TYPE(0.0f);
-
-    [[unroll]] for (uint i = 0; i < p.ncols/BLOCK_SIZE; i += 2) {
-        const uint col = i*BLOCK_SIZE + 2*tid;
-        const uint ib = (row*p.ncols + col)/QUANT_K; // block index
-        const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
-        const uint iybs = col - col%QUANT_K; // y block start index
-
-        vec2 v = dequantize(ib, iqs, a_offset / QUANT_K);
-
-        // matrix multiplication
-        tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[b_offset + iybs + iqs]) +
-                    FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]);
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
-    }
-}
-"""
-
-# K-quants
-mul_mat_vec_q2_K_body = """
-layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
-
-shared FLOAT_TYPE tmp[32];
-
-void main() {
-    const uint row = gl_WorkGroupID.x;
-    const uint batch_idx = gl_GlobalInvocationID.y;
-#ifdef MUL_MAT_ID
-    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
-    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
-#endif
-
-    const uint i13 = batch_idx / p.ne12;
-    const uint i12 = batch_idx % p.ne12;
-
-    const uint i03 = i13 / p.broadcast3;
-    const uint i02 = i12 / p.broadcast2;
-
-    const uint batch_idx_a = i03 * p.ne02 + i02;
-
-#ifdef MUL_MAT_ID
-    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
-#endif
-
-    const uint a_offset =
-#ifdef MUL_MAT_ID
-            expert_id * p.expert_stride_a +
-#endif
-            batch_idx_a * p.batch_stride_a;
-    const uint b_offset =
-#ifdef MUL_MAT_ID
-            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_b;
-    const uint d_offset =
-#ifdef MUL_MAT_ID
-            expert_idx0 * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_d;
-
-    const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
-
-    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
-
-    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
-
-    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
-
-    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
-    const uint q_offset = 32*v_im + l0;
-    const uint s_offset = 8*v_im;
-    const uint y_offset = 128*v_im + l0;
-
-    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
-
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-        const uint y_idx = i * QUANT_K + y_offset;
-
-        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
-
-        FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
-        FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            sum1 += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3);
-            sum2 += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF)
-                  + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF);
-        }
-        tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
-    }
-}
-"""
-mul_mat_vec_q3_K_body = """
-layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
-
-shared FLOAT_TYPE tmp[32];
-
-void main() {
-    const uint row = gl_WorkGroupID.x;
-    const uint batch_idx = gl_GlobalInvocationID.y;
-#ifdef MUL_MAT_ID
-    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
-    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
-#endif
-
-    const uint i13 = batch_idx / p.ne12;
-    const uint i12 = batch_idx % p.ne12;
-
-    const uint i03 = i13 / p.broadcast3;
-    const uint i02 = i12 / p.broadcast2;
-
-    const uint batch_idx_a = i03 * p.ne02 + i02;
-
-#ifdef MUL_MAT_ID
-    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
-#endif
-
-    const uint a_offset =
-#ifdef MUL_MAT_ID
-            expert_id * p.expert_stride_a +
-#endif
-            batch_idx_a * p.batch_stride_a;
-    const uint b_offset =
-#ifdef MUL_MAT_ID
-            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_b;
-    const uint d_offset =
-#ifdef MUL_MAT_ID
-            expert_idx0 * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_d;
-
-    const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
-
-    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
-
-    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
-
-    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
-
-    const uint8_t m = uint8_t(1 << (4 * v_im));
-
-    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
-    const uint q_offset = 32*v_im + l0;
-    const uint y_offset = 128*v_im + l0;
-
-    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
-
-    const uint s_shift = 4 * v_im;
-
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-        const uint y_idx = i * QUANT_K + y_offset;
-
-        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
-
-        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            sum += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4))
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4));
-        }
-        tmp[16 * ix + tid] += d * sum;
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
-    }
-}
-"""
-mul_mat_vec_q4_K_body = """
-layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
-
-shared FLOAT_TYPE tmp[32];
-
-void main() {
-    const uint row = gl_WorkGroupID.x;
-    const uint batch_idx = gl_GlobalInvocationID.y;
-#ifdef MUL_MAT_ID
-    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
-    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
-#endif
-
-    const uint i13 = batch_idx / p.ne12;
-    const uint i12 = batch_idx % p.ne12;
-
-    const uint i03 = i13 / p.broadcast3;
-    const uint i02 = i12 / p.broadcast2;
-
-    const uint batch_idx_a = i03 * p.ne02 + i02;
-
-#ifdef MUL_MAT_ID
-    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
-#endif
-
-    const uint a_offset =
-#ifdef MUL_MAT_ID
-            expert_id * p.expert_stride_a +
-#endif
-            batch_idx_a * p.batch_stride_a;
-    const uint b_offset =
-#ifdef MUL_MAT_ID
-            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_b;
-    const uint d_offset =
-#ifdef MUL_MAT_ID
-            expert_idx0 * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_d;
-
-    const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
-
-    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
-
-    const uint step = 8/K_QUANTS_PER_ITERATION;             // 8 or 4
-
-    const uint il = tid/step;                               // 0...3
-    const uint ir = tid - step*il;                          // 0...7 or 0...3
-    const uint n =  2 * K_QUANTS_PER_ITERATION;             // 2 or 4
-
-    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const uint v_in = il % 2;
-
-    const uint l0 = n * (2 * ir + v_in);            // 0...15
-    const uint q_offset = 32*v_im + l0;
-    const uint y_offset = 64*v_im + l0;
-
-    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
-
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-        const uint y1_idx = i * QUANT_K + y_offset;
-        const uint y2_idx = y1_idx + 128;
-
-        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
-
-        const uint8_t sc0 = uint8_t(  data_a[ib0 + i].scales[v_im * 2    ]       & 0x3f);
-        const uint8_t sc1 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 1]       & 0x3f);
-        const uint8_t sc2 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 4]       & 0x3f);
-        const uint8_t sc3 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 5]       & 0x3f);
-        const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2    ] & 0xc0) >> 2));
-        const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
-        const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
-        const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
-
-#if K_QUANTS_PER_ITERATION == 2
-        const uint8_t q4_0  = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
-        const uint8_t q4_1  = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
-        const uint8_t q4_2  = uint8_t(data_a[ib0 + i].qs[q_offset +  2] & 0xf);
-        const uint8_t q4_3  = uint8_t(data_a[ib0 + i].qs[q_offset +  3] & 0xf);
-        const uint8_t q4_4  = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
-        const uint8_t q4_5  = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
-        const uint8_t q4_6  = uint8_t(data_a[ib0 + i].qs[q_offset +  2]  >> 4);
-        const uint8_t q4_7  = uint8_t(data_a[ib0 + i].qs[q_offset +  3]  >> 4);
-        const uint8_t q4_8  = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
-        const uint8_t q4_9  = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
-        const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf);
-        const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf);
-        const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
-        const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
-        const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66]  >> 4);
-        const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67]  >> 4);
-
-        const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1 + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3);
-        const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_5 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7);
-        const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx]) * q4_8 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_9 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * q4_10 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11);
-        const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_12 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_13 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * q4_14 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15);
-        const FLOAT_TYPE smin = FLOAT_TYPE(
-            FLOAT_TYPE(data_b[b_offset + y1_idx    ]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx    ]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * sc7
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7
-        );
-        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin);
-#else
-        const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
-        const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
-        const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
-        const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
-        const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
-        const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
-        const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
-        const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
-
-        const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx     ]) * q4_0  + FLOAT_TYPE(data_b[b_offset + y1_idx +  1]) * q4_1);
-        const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_2  + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3);
-        const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx     ]) * q4_4  + FLOAT_TYPE(data_b[b_offset + y2_idx +  1]) * q4_5);
-        const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7);
-        const FLOAT_TYPE smin = FLOAT_TYPE(
-            FLOAT_TYPE(data_b[b_offset + y1_idx]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7
-        );
-
-        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin);
-#endif
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
-    }
-}
-"""
-mul_mat_vec_q5_K_body = """
-layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
-
-shared FLOAT_TYPE tmp[32];
-
-void main() {
-    const uint row = gl_WorkGroupID.x;
-    const uint batch_idx = gl_GlobalInvocationID.y;
-#ifdef MUL_MAT_ID
-    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
-    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
-#endif
-
-    const uint i13 = batch_idx / p.ne12;
-    const uint i12 = batch_idx % p.ne12;
-
-    const uint i03 = i13 / p.broadcast3;
-    const uint i02 = i12 / p.broadcast2;
-
-    const uint batch_idx_a = i03 * p.ne02 + i02;
-
-#ifdef MUL_MAT_ID
-    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
-#endif
-
-    const uint a_offset =
-#ifdef MUL_MAT_ID
-            expert_id * p.expert_stride_a +
-#endif
-            batch_idx_a * p.batch_stride_a;
-    const uint b_offset =
-#ifdef MUL_MAT_ID
-            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_b;
-    const uint d_offset =
-#ifdef MUL_MAT_ID
-            expert_idx0 * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_d;
-
-    const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
-
-    const uint tid = gl_LocalInvocationID.x/2;  // 0...31 or 0...16
-    const uint ix  = gl_LocalInvocationID.x%2;  // 0 or 0, 1
-
-    const uint il = tid/4;                           // 0...3
-    const uint ir = tid - 4*il;                      // 0...7 or 0...3
-
-    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const uint v_in = il % 2;
-
-    const uint l0 = 4*ir + 2*v_in;                   // 0...15
-    const uint q_offset = 32*v_im + l0;
-    const uint y_offset = 64*v_im + l0;
-
-    const uint8_t hm1 = uint8_t(1 << (2*v_im));
-    const uint8_t hm2 = uint8_t(hm1 << 4);
-
-    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
-
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) {
-        const uint y1_idx = i * QUANT_K + y_offset;
-        const uint y2_idx = y1_idx + 128;
-
-        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
-        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
-
-        const uint8_t sc0 = uint8_t(  data_a[ib0 + i].scales[v_im * 2    ]       & 0x3f);
-        const uint8_t sc1 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 1]       & 0x3f);
-        const uint8_t sc2 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 4]       & 0x3f);
-        const uint8_t sc3 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 5]       & 0x3f);
-        const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2    ] & 0xc0) >> 2));
-        const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
-        const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
-        const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
-
-        const uint8_t q4_0  = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
-        const uint8_t q4_1  = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
-        const uint8_t q4_2  = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf);
-        const uint8_t q4_3  = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf);
-        const uint8_t q4_4  = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
-        const uint8_t q4_5  = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
-        const uint8_t q4_6  = uint8_t(data_a[ib0 + i].qs[q_offset + 16]  >> 4);
-        const uint8_t q4_7  = uint8_t(data_a[ib0 + i].qs[q_offset + 17]  >> 4);
-        const uint8_t q4_8  = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
-        const uint8_t q4_9  = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
-        const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf);
-        const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf);
-        const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
-        const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
-        const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80]  >> 4);
-        const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81]  >> 4);
-
-        const FLOAT_TYPE sx = FLOAT_TYPE(
-            FLOAT_TYPE(data_b[b_offset + y1_idx     ]) * (q4_0 + (((data_a[ib0 + i].qh[l0     ] & hm1) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y1_idx +  1]) * (q4_1 + (((data_a[ib0 + i].qh[l0 +  1] & hm1) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))
-        );
-        const FLOAT_TYPE sy = FLOAT_TYPE(
-            FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * (q4_4 + (((data_a[ib0 + i].qh[l0     ] & (hm1 << 1)) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * (q4_5 + (((data_a[ib0 + i].qh[l0 +  1] & (hm1 << 1)) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))
-        );
-        const FLOAT_TYPE sz = FLOAT_TYPE(
-            FLOAT_TYPE(data_b[b_offset + y2_idx     ]) * (q4_8  + (((data_a[ib0 + i].qh[l0     ] & hm2) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y2_idx +  1]) * (q4_9  + (((data_a[ib0 + i].qh[l0 +  1] & hm2) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))
-        );
-        const FLOAT_TYPE sw = FLOAT_TYPE(
-            FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * (q4_12 + (((data_a[ib0 + i].qh[l0     ] & (hm2 << 1)) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * (q4_13 + (((data_a[ib0 + i].qh[l0 +  1] & (hm2 << 1)) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0))
-          + FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))
-        );
-        const FLOAT_TYPE smin = FLOAT_TYPE(
-            (FLOAT_TYPE(data_b[b_offset + y1_idx]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17])) * sc2 + (FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49])) * sc3
-          + (FLOAT_TYPE(data_b[b_offset + y2_idx]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17])) * sc6 + (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7
-        );
-        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin);
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier();
-    }
-    if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
-    }
-}
-"""
-mul_mat_vec_q6_K_body = """
-layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
-
-shared FLOAT_TYPE tmp[32];
-
-void main() {
-    const uint row = gl_WorkGroupID.x;
-    const uint batch_idx = gl_GlobalInvocationID.y;
-#ifdef MUL_MAT_ID
-    const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0;
-    const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0;
-#endif
-
-    const uint i13 = batch_idx / p.ne12;
-    const uint i12 = batch_idx % p.ne12;
-
-    const uint i03 = i13 / p.broadcast3;
-    const uint i02 = i12 / p.broadcast2;
-
-    const uint batch_idx_a = i03 * p.ne02 + i02;
-
-#ifdef MUL_MAT_ID
-    const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0];
-#endif
-
-    const uint a_offset =
-#ifdef MUL_MAT_ID
-            expert_id * p.expert_stride_a +
-#endif
-            batch_idx_a * p.batch_stride_a;
-    const uint b_offset =
-#ifdef MUL_MAT_ID
-            (expert_idx0 % p.ne11) * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_b;
-    const uint d_offset =
-#ifdef MUL_MAT_ID
-            expert_idx0 * p.expert_stride_b0 +
-            expert_idx1 * p.expert_stride_b1 +
-#endif
-            batch_idx * p.batch_stride_d;
-
-    const uint num_blocks_per_row = p.ncols / QUANT_K;
-    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
-
-    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
-
-    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
-
-    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
-
-#if K_QUANTS_PER_ITERATION == 1
-    const uint l0 = v_in;                                   // 0...15
-    const uint is = 0;
-#else
-    const uint l0 = 4 * v_in;                               // 0, 4, 8, ..., 28
-    const uint is = v_in / 4;
-#endif
-
-    const uint ql_offset = 64*v_im + l0;
-    const uint qh_offset = 32*v_im + l0;
-    const uint s_offset  =  8*v_im + is;
-    const uint y_offset = 128*v_im + l0;
-
-    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
-
-    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-        const uint y_idx   = i * QUANT_K + y_offset;
-
-        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
-
-#if K_QUANTS_PER_ITERATION == 1
-        FLOAT_TYPE sum = FLOAT_TYPE(data_b[b_offset + y_idx +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x03) << 4)) - 32)
-                       + FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32)
-                       + FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x0c) << 2)) - 32)
-                       + FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32)
-                       + FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x30) >> 0)) - 32)
-                       + FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32)
-                       + FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0xc0) >> 2)) - 32)
-                       + FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32);
-        tmp[16 * ix + tid] += sum;
-#else
-        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-        [[unroll]] for (int l = 0; l < 4; ++l) {
-            sum += FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32)
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32)
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32)
-                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32);
-        }
-        tmp[16 * ix + tid] += sum;
-#endif
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-       }
-        barrier();
-    }
-    if (tid == 0) {
-        data_d[d_offset + row] = D_TYPE(tmp[0]);
-    }
-}
-"""
-
-mul_mat_p021_src = """#version 450
-
-#extension GL_EXT_control_flow_attributes : enable
-#extension GL_EXT_shader_16bit_storage : require
-
-#define BLOCK_SIZE 32
-#define FLOAT_TYPE float
-
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
-
-layout (push_constant) uniform parameter
-{
-    uint ncols_x;
-    uint nrows_x;
-    uint nchannels_x;
-    uint nchannels_y;
-    uint b_offset;
-    uint d_offset;
-} p;
-
-shared FLOAT_TYPE tmp[BLOCK_SIZE];
-
-void main() {
-    const uint tid = gl_LocalInvocationID.x;
-    const uint row_x = gl_GlobalInvocationID.y;
-    const uint channel = gl_GlobalInvocationID.z;
-    const uint channel_x = channel / (p.nchannels_y / p.nchannels_x);
-
-    const uint nrows_y = p.ncols_x;
-    const uint nrows_dst = p.nrows_x;
-    const uint row_dst = row_x;
-
-    tmp[tid] = FLOAT_TYPE(0.0f);
-
-    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
-        const uint col_x = col_x0 + tid;
-
-        if (col_x >= p.ncols_x) {
-            break;
-        }
-
-        // x is transposed and permuted
-        const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x;
-        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
-
-        const uint row_y = col_x;
-
-        // y is not transposed but permuted
-        const uint iy = channel*nrows_y + row_y;
-
-        tmp[tid] += xi * FLOAT_TYPE(data_b[iy]);
-    }
-
-    // dst is not transposed and not permuted
-    const uint idst = channel*nrows_dst + row_dst;
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier();
-    }
-
-    if (tid == 0) {
-        dst[idst] = tmp[0];
-    }
-}
-"""
-
-
-mul_mat_nc_src = """#version 450
-
-#extension GL_EXT_control_flow_attributes : enable
-#extension GL_EXT_shader_16bit_storage : require
-
-#define BLOCK_SIZE 32
-#define FLOAT_TYPE float
-
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
-
-layout (push_constant) uniform parameter
-{
-    uint ncols_x;
-    uint nrows_x;
-    uint row_stride_x;
-    uint channel_stride_x;
-    uint channel_x_divisor;
-    uint b_offset;
-    uint d_offset;
-} p;
-
-shared FLOAT_TYPE tmp[BLOCK_SIZE];
-
-void main() {
-    const uint tid       = gl_LocalInvocationID.x;
-    const uint row_x     = gl_GlobalInvocationID.y;
-    const uint channel   = gl_GlobalInvocationID.z;
-    const uint channel_x = channel / p.channel_x_divisor;
-
-    const uint nrows_y   = p.ncols_x;
-    const uint nrows_dst = p.nrows_x;
-    const uint row_dst   = row_x;
-
-    const uint idst = channel*nrows_dst + row_dst;
-
-    tmp[tid] = 0.0f;
-
-    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
-        const uint col_x = col_x0 + tid;
-
-        if (col_x >= p.ncols_x) {
-            break;
-        }
-
-        const uint row_y = col_x;
-
-        const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
-        const uint iy = channel*nrows_y + row_y;
-
-        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
-
-        tmp[tid] += xi * FLOAT_TYPE(data_b[iy]);
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
-        if (tid < s) {
-            tmp[tid] += tmp[tid + s];
-        }
-        barrier();
-    }
-
-    if (tid == 0) {
-        dst[idst] = tmp[0];
-    }
-}
-"""
-
-generic_head = """
-#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-layout (push_constant) uniform parameter
-{
-    uint KX;
-    uint KY;
-    float param1;
-    float param2;
-} p;
-"""
-
-generic_unary_op_head = """#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-layout (push_constant) uniform parameter
-{
-    uint ne;
-    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
-    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
-    uint d_offset;
-    float param1; float param2;
-} p;"""
-
-generic_unary_op_layout = """
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};"""
-
-generic_unary_op_funcs = """
-uint src0_idx(uint idx) {
-    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
-    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
-    const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
-    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
-    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
-}
-
-uint dst_idx(uint idx) {
-    const uint i13 = idx / (p.ne12*p.ne11*p.ne10);
-    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
-    const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10);
-    const uint i12_offset = i12*p.ne11*p.ne10;
-    const uint i11 = (idx - i13_offset - i12_offset) / p.ne10;
-    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
-    return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
-}"""
-
-generic_unary_op_main = """
-void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
-        return;
-    }
-"""
-
-generic_unary_op_combined = f"{generic_unary_op_head}\n{generic_unary_op_layout}\n{generic_unary_op_funcs}\n{generic_unary_op_main}"
-
-generic_binary_op_head = """#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-layout (push_constant) uniform parameter
-{
-    uint ne;
-    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
-    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
-    uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
-    uint d_offset;
-    float param1; float param2;
-} p;"""
-
-generic_binary_op_layout = """
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};"""
-
-generic_binary_op_funcs = """
-uint src0_idx(uint idx) {
-    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
-    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
-    const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
-    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
-    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
-}
-
-uint src1_idx(uint idx) {
-    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
-    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
-    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
-    const uint i02_offset = i02*p.ne01*p.ne00;
-    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
-    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
-
-    return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10;
-}
-
-uint dst_idx(uint idx) {
-    const uint i23 = idx / (p.ne22*p.ne21*p.ne20);
-    const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20;
-    const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20);
-    const uint i22_offset = i22*p.ne21*p.ne20;
-    const uint i21 = (idx - i23_offset - i22_offset) / p.ne20;
-    const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20;
-    return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20;
-}"""
-
-generic_binary_op_main = """
-void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
-        return;
-    }
-"""
-
-generic_binary_op_combined = f"{generic_binary_op_head}\n{generic_binary_op_layout}\n{generic_binary_op_funcs}\n{generic_binary_op_main}"
-
-# MUL F32
-mul_body = """
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
-}
-"""
-
-# ADD
-add_body = """
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
-}
-"""
-
-# SCALE
-scale_body = """
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(p.param1));
-}
-"""
-
-# SQR
-sqr_body = """
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val * val);
-}
-"""
-
-# CLAMP
-clamp_body = """
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
-}
-"""
-
-# CPY
-cpy_end = """
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
-}
-"""
-# Causes an optimization error otherwise
-cpy_f16_f16_end = """
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = data_a[src0_idx(gl_GlobalInvocationID.x)];
-}
-"""
-
-# GET_ROWS
-get_rows_float_body = """
-void main() {
-    const uint i00 = gl_GlobalInvocationID.x;
-    const uint i10 = gl_GlobalInvocationID.y;
-    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
-    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
-
-    if (i00 >= p.ne00) {
-        return;
-    }
-
-    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
-
-    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
-    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
-
-#ifndef OPTIMIZATION_ERROR_WORKAROUND
-    data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]);
-#else
-    data_d[d_offset + i00] = data_a[a_offset + i00];
-#endif
-}
-"""
-
-get_rows_body = """
-void main() {
-    const uint i00 = (gl_GlobalInvocationID.x)*2;
-    const uint i10 = gl_GlobalInvocationID.y;
-    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
-    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
-
-    if (i00 >= p.ne00) {
-        return;
-    }
-
-    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
-
-    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
-    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
-
-    const uint ib = a_offset + i00/QUANT_K; // block index
-    const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index
-    const uint iybs = i00 - i00%QUANT_K; // dst block start index
-    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
-
-    vec2 v = dequantize(ib, iqs, 0);
-
-    data_d[d_offset + iybs + iqs           ] = D_TYPE(v.x);
-    data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y);
-}
-"""
-
-# UNARY
-gelu_body = """
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const float GELU_COEF_A    = 0.044715f;
-    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
-    const uint i = gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float xi = float(data_a[i]);
-    const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi);
-    data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1)));
-}
-"""
-
-silu_body = """
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float xi = float(data_a[i]);
-    data_d[i] = D_TYPE(xi / (1.0f + exp(-xi)));
-}
-"""
-
-relu_body = """
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    data_d[i] = max(float(data_a[i]), 0);
-}
-"""
-
-# DIAG_MASK_INF
-diag_mask_inf_head = """#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-layout (push_constant) uniform parameter
-{
-    uint ncols;
-    uint rows_per_channel;
-    uint n_past;
-} p;
-"""
-diag_mask_inf_body = """
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint col = gl_GlobalInvocationID.y;
-    const uint row = gl_GlobalInvocationID.x;
-
-    if (col >= p.ncols) {
-        return;
-    }
-
-    const uint i = row*p.ncols + col;
-    if (col > p.n_past + row % p.rows_per_channel) {
-        data_d[i] = D_TYPE(uintBitsToFloat(0xFF800000));
-    } else {
-        data_d[i] = D_TYPE(data_a[i]);
-    }
-}
-"""
-
-# NORMS
-norm_body = """
-#extension GL_EXT_control_flow_attributes : enable
-#define BLOCK_SIZE 512
-
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-shared vec2 sum[BLOCK_SIZE];
-
-void main() {
-    const uint row = gl_WorkGroupID.x;
-    const uint tid = gl_LocalInvocationID.x;
-
-    sum[tid] = vec2(0.0f, 0.0f);
-
-    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
-        const float xi = float(data_a[row*p.KX + col]);
-        sum[tid].x += xi;
-        sum[tid].y += xi * xi;
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
-        if (tid < s) {
-            sum[tid] += sum[tid + s];
-        }
-        barrier();
-    }
-
-    const float mean = sum[0].x / p.KX;
-    const float var = sum[0].y / p.KX - mean * mean;
-    const float inv_std = inversesqrt(var + p.param1);
-
-    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
-        data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std);
-    }
-}
-"""
-
-rms_norm_body = """
-#extension GL_EXT_control_flow_attributes : enable
-#define BLOCK_SIZE 512
-
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-shared FLOAT_TYPE sum[BLOCK_SIZE];
-
-void main() {
-    const uint row = gl_WorkGroupID.x;
-    const uint tid = gl_LocalInvocationID.x;
-
-    sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp
-
-    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
-        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[row*p.KX + col]);
-        sum[tid] += xi * xi;
-    }
-
-    // sum up partial sums and write back result
-    barrier();
-    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
-        if (tid < s) {
-            sum[tid] += sum[tid + s];
-        }
-        barrier();
-    }
-
-    const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(p.KX);
-    const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1));
-
-    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
-        data_d[row*p.KX + col] = D_TYPE(scale * FLOAT_TYPE(data_a[row*p.KX + col]));
-    }
-}
-"""
-
-# SOFT_MAX
-soft_max_head = """
-#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-layout (push_constant) uniform parameter
-{
-    uint KX;
-    uint KY;
-    float scale;
-    float max_bias;
-    float m0;
-    float m1;
-    uint n_head_log2;
-} p;
-"""
-
-soft_max_body = """
-#extension GL_EXT_control_flow_attributes : enable
-#define BLOCK_SIZE 512
-
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
-layout (binding = 2) buffer D {D_TYPE data_d[];};
-
-shared FLOAT_TYPE vals[BLOCK_SIZE];
-
-void main() {
-    const uint tid = gl_LocalInvocationID.x;
-    const uint rowx = gl_WorkGroupID.x;
-    const uint rowy = rowx % p.KY;
-
-    float slope = 1.0f;
-
-    // ALiBi
-    if (p.max_bias > 0.0f) {
-        const uint h = rowx/p.KY; // head index
-
-        const float base = h < p.n_head_log2 ? p.m0 : p.m1;
-        const uint   exp  = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
-
-        slope = pow(base, exp);
-    }
-
-    // Find max
-    FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000);
-
-    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
-        const uint col = col0 + tid;
-
-        if (col >= p.KX) {
-            break;
-        }
-
-        max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)));
-    }
-    vals[tid] = max_val;
-
-    barrier();
-    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
-        if (tid < s) {
-            vals[tid] = max(vals[tid], vals[tid + s]);
-        }
-        barrier();
-    }
-
-    max_val = vals[0];
-    barrier();
-
-    // Sum up values
-    vals[tid] = FLOAT_TYPE(0.0f);
-
-    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
-        const uint col = col0 + tid;
-
-        if (col >= p.KX) {
-            break;
-        }
-
-        const uint i = rowx * p.KX + col;
-        const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
-        vals[tid] += val;
-        data_d[i] = D_TYPE(val);
-    }
-
-    barrier();
-    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
-        if (tid < s) {
-            vals[tid] += vals[tid + s];
-        }
-        barrier();
-    }
-
-    const D_TYPE divisor = D_TYPE(vals[0]);
-
-    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
-        const uint col = col0 + tid;
-
-        if (col >= p.KX) {
-            break;
-        }
-
-        data_d[rowx*p.KX + col] /= divisor;
-    }
-}
-"""
-
-# ROPE
-rope_src = """
-#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {int data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
-
-layout (push_constant) uniform parameter {
-    uint ncols;
-    float freq_scale;
-    uint p_delta_rows;
-    float freq_base;
-    float ext_factor;
-    float attn_factor;
-    float corr_dims[4];
-} p;
-
-float rope_yarn_ramp(const float low, const float high, const uint i0) {
-    const float y = (i0 / 2 - low) / max(0.001f, high - low);
-    return 1.0f - min(1.0f, max(0.0f, y));
-}
-
-void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) {
-    float mscale = p.attn_factor;
-    // Get n-d rotational scaling corrected for extrapolation
-    float theta_interp = p.freq_scale * theta_extrap;
-    float theta = theta_interp;
-    if (p.ext_factor != 0.0f) {
-        float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
-        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-
-        // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
-    }
-    cos_theta = cos(theta) * mscale;
-    sin_theta = sin(theta) * mscale;
-}
-
-void main() {
-    const uint col = gl_GlobalInvocationID.y * 2;
-    const uint row = gl_GlobalInvocationID.x;
-
-    if (col >= p.ncols) {
-        return;
-    }
-
-    const uint i = row*p.ncols + col;
-    const uint i2 = row/p.p_delta_rows;
-
-    const int pos = data_b[i2];
-    const float theta_base = pos * pow(p.freq_base, -float(col)/p.ncols);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, col, cos_theta, sin_theta);
-
-    const float x0 = float(data_a[i + 0]);
-    const float x1 = float(data_a[i + 1]);
-
-    data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
-    data_d[i + 1] = D_TYPE(x0*sin_theta + x1*cos_theta);
-}
-"""
-
-rope_neox_src = """
-#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {int data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
-
-layout (push_constant) uniform parameter {
-    uint ncols;
-    uint ndims;
-    float freq_scale;
-    uint p_delta_rows;
-    float freq_base;
-    float ext_factor;
-    float attn_factor;
-    float corr_dims[4];
-    float theta_scale;
-    float inv_ndims;
-} p;
-
-float rope_yarn_ramp(const float low, const float high, const uint i0) {
-    const float y = (i0 / 2 - low) / max(0.001f, high - low);
-    return 1.0f - min(1.0f, max(0.0f, y));
-}
-
-void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) {
-    float mscale = p.attn_factor;
-    // Get n-d rotational scaling corrected for extrapolation
-    float theta_interp = p.freq_scale * theta_extrap;
-    float theta = theta_interp;
-    if (p.ext_factor != 0.0f) {
-        float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
-        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-
-        // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
-    }
-    cos_theta = cos(theta) * mscale;
-    sin_theta = sin(theta) * mscale;
-}
-
-void main() {
-    const uint col = gl_GlobalInvocationID.y * 2;
-    const uint row = gl_GlobalInvocationID.x;
-
-    if (col >= p.ncols) {
-        return;
-    }
-
-    const uint ib = col / p.ndims;
-    const uint ic = col % p.ndims;
-
-    if (ib > 0) {
-        const uint i = row*p.ncols + ib*p.ndims + ic;
-
-        data_d[i + 0] = data_a[i + 0];
-        data_d[i + 1] = data_a[i + 1];
-
-        return;
-    }
-
-    const uint i  = row*p.ncols + ib*p.ndims + ic/2;
-    const uint i2 = row/p.p_delta_rows;
-
-    const float cur_rot = p.inv_ndims * ic - ib;
-
-    const int pos = data_b[i2];
-    const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, uint(cur_rot), cos_theta, sin_theta);
-
-    const float x0 = float(data_a[i + 0]);
-    const float x1 = float(data_a[i + p.ndims/2]);
-
-    data_d[i + 0]        = D_TYPE(x0*cos_theta - x1*sin_theta);
-    data_d[i + p.ndims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
-}
-"""
-
-argsort_src = """
-#version 450
-
-#extension GL_EXT_shader_16bit_storage : require
-
-#define BLOCK_SIZE 1024
-#define ASC 0
-
-layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
-layout (binding = 1)          buffer D {int data_d[];};
-
-layout (push_constant) uniform parameter {
-    uint ncols;
-    uint ncols_pad;
-    uint order;
-} p;
-
-shared int dst_row[BLOCK_SIZE];
-
-void swap(uint idx0, uint idx1) {
-    int tmp = dst_row[idx0];
-    dst_row[idx0] = dst_row[idx1];
-    dst_row[idx1] = tmp;
-}
-
-void main() {
-    // bitonic sort
-    const int col = int(gl_LocalInvocationID.x);
-    const uint row = gl_WorkGroupID.y;
-
-    if (col >= p.ncols_pad) {
-        return;
-    }
-
-    const uint row_offset = row * p.ncols;
-
-    // initialize indices
-    dst_row[col] = col;
-    barrier();
-
-    for (uint k = 2; k <= p.ncols_pad; k *= 2) {
-        for (uint j = k / 2; j > 0; j /= 2) {
-            const uint ixj = col ^ j;
-            if (ixj > col) {
-                if ((col & k) == 0) {
-                    if (dst_row[col] >= p.ncols ||
-                        (dst_row[ixj] < p.ncols && (p.order == ASC ?
-                            data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] :
-                            data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]]))
-                    ) {
-                        swap(col, ixj);
-                    }
-                } else {
-                    if (dst_row[ixj] >= p.ncols ||
-                        (dst_row[col] < p.ncols && (p.order == ASC ?
-                            data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] :
-                            data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]]))
-                    ) {
-                        swap(col, ixj);
-                    }
-                }
-            }
-            barrier();
-        }
-    }
-
-    if (col < p.ncols) {
-        data_d[row_offset + col] = dst_row[col];
-    }
-}
-"""
-
-GLSLC = "glslc"
-
-VK_NUM_TYPES = 16
-
-GGML_TYPE_F32  = 0
-GGML_TYPE_F16  = 1
-GGML_TYPE_Q4_0 = 2
-GGML_TYPE_Q4_1 = 3
-GGML_TYPE_Q5_0 = 6
-GGML_TYPE_Q5_1 = 7
-GGML_TYPE_Q8_0 = 8
-GGML_TYPE_Q8_1 = 9
-GGML_TYPE_Q2_K = 10
-GGML_TYPE_Q3_K = 11
-GGML_TYPE_Q4_K = 12
-GGML_TYPE_Q5_K = 13
-GGML_TYPE_Q6_K = 14
-GGML_TYPE_Q8_K = 15
-
-
-type_names = {
-    GGML_TYPE_F32: "f32",
-    GGML_TYPE_F16: "f16",
-    GGML_TYPE_Q4_0: "q4_0",
-    GGML_TYPE_Q4_1: "q4_1",
-    GGML_TYPE_Q5_0: "q5_0",
-    GGML_TYPE_Q5_1: "q5_1",
-    GGML_TYPE_Q8_0: "q8_0",
-    GGML_TYPE_Q8_1: "q8_1",
-    GGML_TYPE_Q2_K: "q2_K",
-    GGML_TYPE_Q3_K: "q3_K",
-    GGML_TYPE_Q4_K: "q4_K",
-    GGML_TYPE_Q5_K: "q5_K",
-    GGML_TYPE_Q6_K: "q6_K",
-    GGML_TYPE_Q8_K: "q8_K",
-}
-
-K_QUANTS_PER_ITERATION = 2
-
-ASYNCIO_CONCURRENCY = 64
-
-output_dir = gettempdir()
-
-lock = asyncio.Lock()
-shader_fnames = []
-
-
-async def string_to_spv(name, code, defines, fp16=True):
-    f = NamedTemporaryFile(mode="w", delete=False)
-    f.write(code)
-    f.flush()
-
-    name = f"{name}{'_fp32' if not fp16 else ''}"
-    fname = os.path.join(output_dir, f"{name}.comp")
-
-    cmd = [GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", f.name, "-o", fname]
-
-    cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
-
-    proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE)
-
-    stdout, stderr = await proc.communicate()
-
-    stdout = stdout.decode()
-    error = stderr.decode()
-
-    if proc.returncode:
-        # Generate preprocessed code
-        cmd = [GLSLC, "-E", f.name]
-        cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
-
-        proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE)
-
-        stdout, stderr = await proc.communicate()
-
-        logger.info(" ".join(cmd))
-
-        if proc.returncode:
-            raise RuntimeError(f"{name=} {f.name=} {stdout=} {stderr=}")
-
-        preprocessed_code = stdout.decode()
-
-        cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
-        code_with_lines = "\n".join([f"{i + 1}: {line}" for i, line in enumerate(preprocessed_code.splitlines())])
-        logger.error(f"cannot compile {name}\n\n{code_with_lines}\n\n{error}")
-        f.close()
-        os.remove(f.name)
-        sys.exit(proc.returncode)
-
-    f.close()
-    os.remove(f.name)
-
-    async with lock:
-        shader_fnames.append((name, fname))
-
-
-async def main():
-    logger.info("ggml_vulkan: Generating and compiling shaders to SPIR-V")
-
-    tasks = []
-
-    stream = []
-
-    for fp16 in (False, True):
-        # mulmat
-        if fp16:
-            shader_float_type = shader_f16
-            load_vec = "8"
-            vec_type_f16 = "f16mat2x4"
-            vec_type = "mat2x4"
-        else:
-            shader_float_type = shader_f32
-            load_vec = "4"
-            vec_type_f16 = "f16vec4"
-            vec_type = "vec4"
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2))
-        tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        tasks.append(string_to_spv("matmul_f32_f16", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f32_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
-
-        tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
-
-        tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q4_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q4_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q4_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q4_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q5_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q5_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q5_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q5_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q8_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q8_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q2_K_defines, mulmat_body1, mulmat_load_q2_K, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q2_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q2_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q2_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q2_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q3_K_defines, mulmat_body1, mulmat_load_q3_K, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q3_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q3_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q3_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q3_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_K_defines, mulmat_body1, mulmat_load_q4_K, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q4_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q4_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_K_defines, mulmat_body1, mulmat_load_q5_K, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q5_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q5_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        stream.clear()
-        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q6_K_defines, mulmat_body1, mulmat_load_q6_K, mulmat_body2))
-        tasks.append(string_to_spv("matmul_q6_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv("matmul_q6_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # MUL_MAT_ID
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_f32", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # tasks.append(string_to_spv("matmul_id_f16", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_f16_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
-
-        # tasks.append(string_to_spv("matmul_id_f16_f32", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_f16_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q4_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q4_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q4_1_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q4_1_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q5_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q5_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q5_1_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q5_1_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q8_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q8_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q2_K_defines, mulmat_body1, mulmat_load_q2_K, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q2_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q2_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q2_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q2_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q3_K_defines, mulmat_body1, mulmat_load_q3_K, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q3_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q3_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q3_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q3_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_K_defines, mulmat_body1, mulmat_load_q4_K, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q4_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q4_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_K_defines, mulmat_body1, mulmat_load_q5_K, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q5_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q5_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-        # stream.clear()
-        # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q6_K_defines, mulmat_body1, mulmat_load_q6_K, mulmat_body2))
-        # tasks.append(string_to_spv("matmul_id_q6_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        # tasks.append(string_to_spv("matmul_id_q6_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
-
-    # Shaders where precision is needed, so no fp16 version
-
-    # mul mat vec
-    for i in range(0, VK_NUM_TYPES):
-        stream.clear()
-        stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32))
-
-        if i == GGML_TYPE_F16:
-            stream.extend((shader_f16_defines, mul_mat_vec_layout, shader_float_dequant_func, mul_mat_vec_body))
-        elif i == GGML_TYPE_Q4_0:
-            stream.extend((shader_q4_0_defines, mul_mat_vec_layout, shader_q4_0_dequant_func, mul_mat_vec_body))
-        elif i == GGML_TYPE_Q4_1:
-            stream.extend((shader_q4_1_defines, mul_mat_vec_layout, shader_q4_1_dequant_func, mul_mat_vec_body))
-        elif i == GGML_TYPE_Q5_0:
-            stream.extend((shader_q5_0_defines, mul_mat_vec_layout, shader_q5_0_dequant_func, mul_mat_vec_body))
-        elif i == GGML_TYPE_Q5_1:
-            stream.extend((shader_q5_1_defines, mul_mat_vec_layout, shader_q5_1_dequant_func, mul_mat_vec_body))
-        elif i == GGML_TYPE_Q8_0:
-            stream.extend((shader_q8_0_defines, mul_mat_vec_layout, shader_q8_0_dequant_func, mul_mat_vec_body))
-        elif i == GGML_TYPE_Q2_K:
-            stream.extend((shader_q2_K_defines, mul_mat_vec_layout, mul_mat_vec_q2_K_body))
-        elif i == GGML_TYPE_Q3_K:
-            stream.extend((shader_q3_K_defines, mul_mat_vec_layout, mul_mat_vec_q3_K_body))
-        elif i == GGML_TYPE_Q4_K:
-            stream.extend((shader_q4_K_defines, mul_mat_vec_layout, mul_mat_vec_q4_K_body))
-        elif i == GGML_TYPE_Q5_K:
-            stream.extend((shader_q5_K_defines, mul_mat_vec_layout, mul_mat_vec_q5_K_body))
-        elif i == GGML_TYPE_Q6_K:
-            stream.extend((shader_q6_K_defines, mul_mat_vec_layout, mul_mat_vec_q6_K_body))
-        else:
-            continue
-
-        tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
-        tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f16_f32", "".join(stream), {"B_TYPE": "float16_t", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
-
-        # tasks.append(string_to_spv(f"mul_mat_vec_id_{type_names[i]}_f32", "".join(stream), {"MUL_MAT_ID": "1", "B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
-
-    # Dequant shaders
-    for i in range(0, VK_NUM_TYPES):
-        stream.clear()
-
-        stream.extend((dequant_head, shader_int8_ext, shader_f32))
-
-        if i == GGML_TYPE_F32:
-            stream.append(dequant_f32_body)
-        elif i == GGML_TYPE_Q4_0:
-            stream.extend((shader_q4_0_defines, dequant_q4_0_body))
-        elif i == GGML_TYPE_Q4_1:
-            stream.extend((shader_q4_1_defines, dequant_q4_1_body))
-        elif i == GGML_TYPE_Q5_0:
-            stream.extend((shader_q5_0_defines, dequant_q5_0_body))
-        elif i == GGML_TYPE_Q5_1:
-            stream.extend((shader_q5_1_defines, dequant_q5_1_body))
-        elif i == GGML_TYPE_Q8_0:
-            stream.extend((shader_q8_0_defines, dequant_q8_0_body))
-        elif i == GGML_TYPE_Q2_K:
-            stream.extend((shader_q2_K_defines, dequant_q2_K_body))
-        elif i == GGML_TYPE_Q3_K:
-            stream.extend((shader_q3_K_defines, dequant_q3_K_body))
-        elif i == GGML_TYPE_Q4_K:
-            stream.extend((shader_q4_K_defines, dequant_q4_K_body))
-        elif i == GGML_TYPE_Q5_K:
-            stream.extend((shader_q5_K_defines, dequant_q5_K_body))
-        elif i == GGML_TYPE_Q6_K:
-            stream.extend((shader_q6_K_defines, dequant_q6_K_body))
-        else:
-            continue
-
-        tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}))
-
-    # get_rows
-    for i in range(0, VK_NUM_TYPES):
-        stream.clear()
-        stream.extend((generic_binary_op_head, shader_int8_ext, shader_f32))
-        optimization_workaround = False
-
-        if i == GGML_TYPE_F32:
-            stream.extend((shader_f32_defines, generic_binary_op_layout, generic_binary_op_funcs, get_rows_float_body))
-        elif i == GGML_TYPE_F16:
-            stream.extend((shader_f16_defines, generic_binary_op_layout, generic_binary_op_funcs, get_rows_float_body))
-            optimization_workaround = True
-        elif i == GGML_TYPE_Q4_0:
-            stream.extend((shader_q4_0_defines, generic_binary_op_layout, shader_q4_0_dequant_func, generic_binary_op_funcs, get_rows_body))
-        elif i == GGML_TYPE_Q4_1:
-            stream.extend((shader_q4_1_defines, generic_binary_op_layout, shader_q4_1_dequant_func, generic_binary_op_funcs, get_rows_body))
-        elif i == GGML_TYPE_Q5_0:
-            stream.extend((shader_q5_0_defines, generic_binary_op_layout, shader_q5_0_dequant_func, generic_binary_op_funcs, get_rows_body))
-        elif i == GGML_TYPE_Q5_1:
-            stream.extend((shader_q5_1_defines, generic_binary_op_layout, shader_q5_1_dequant_func, generic_binary_op_funcs, get_rows_body))
-        elif i == GGML_TYPE_Q8_0:
-            stream.extend((shader_q8_0_defines, generic_binary_op_layout, shader_q8_0_dequant_func, generic_binary_op_funcs, get_rows_body))
-        else:
-            continue
-
-        if optimization_workaround:
-            tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"}))
-        else:
-            tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float16_t"}))
-        tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
-
-    # Norms
-    tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("cpy_f32_f32", f"{generic_unary_op_combined}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("cpy_f32_f16", f"{generic_unary_op_combined}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}))
-    tasks.append(string_to_spv("cpy_f16_f16", f"{generic_unary_op_combined}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
-
-    tasks.append(string_to_spv("add_f32", f"{generic_binary_op_combined}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}))
-    tasks.append(string_to_spv("mul_f32", f"{generic_binary_op_combined}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("scale_f32", f"{generic_unary_op_combined}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("sqr_f32", f"{generic_unary_op_combined}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("clamp_f32", f"{generic_unary_op_combined}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("soft_max_f32_f16", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float16_t", "C_TYPE": "float16_t", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
-
-    tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
-
-    tasks.append(string_to_spv("argsort_f32", argsort_src, {"A_TYPE": "float"}))
-
-    # Helper to decorate tasks with semaphore acquisition.
-    async def withSemaphore(sem, task):
-        async with sem:
-            return await task
-
-    # Run tasks concurrently guarded by a concurrency limit.
-    sem = asyncio.Semaphore(ASYNCIO_CONCURRENCY)
-    await asyncio.gather(*(withSemaphore(sem, task) for task in tasks))
-
-    with open("ggml-vulkan-shaders.hpp", "w") as f:
-        f.write("#include <cstdint>\n\n")
-        for name, path in sorted(shader_fnames):
-
-            with open(path, "rb") as spv:
-                counter = 0
-                newline_counter = 0
-                f.write(f"unsigned char {name}_data[] = {{\n")
-                for val in spv.read():
-                    f.write(f"0x{val:02x},")
-                    newline_counter += 1
-                    counter += 1
-                    if newline_counter >= 12:
-                        newline_counter = 0
-                        f.write("\n")
-            f.write("\n};\n")
-            f.write(f"const uint64_t {name}_len = {counter};\n\n")
-            os.remove(path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="GGML Vulkan Shader Generator")
-
-    parser.add_argument("--glslc", help="Path to glslc")
-    parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
-
-    args = parser.parse_args()
-
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
-
-    if args.glslc:
-        GLSLC = args.glslc
-
-    asyncio.run(main())
diff --git a/gguf-py/README.md b/gguf-py/README.md
index a04c22759793d..24af96a17a5bb 100644
--- a/gguf-py/README.md
+++ b/gguf-py/README.md
@@ -3,7 +3,7 @@
 This is a Python package for writing binary files in the [GGUF](https://github.com/ggerganov/ggml/pull/302)
 (GGML Universal File) format.
 
-See [convert-llama-hf-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert-hf-to-gguf.py)
+See [convert_hf_to_gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py)
 as an example for its usage.
 
 ## Installation
@@ -15,13 +15,13 @@ pip install gguf
 
 [examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model.
 
-[scripts/gguf-dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-dump.py) — Dumps a GGUF file's metadata to the console.
+[scripts/gguf_dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_dump.py) — Dumps a GGUF file's metadata to the console.
 
-[scripts/gguf-set-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-set-metadata.py) — Allows changing simple metadata values in a GGUF file by key.
+[scripts/gguf_set_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_set_metadata.py) — Allows changing simple metadata values in a GGUF file by key.
 
-[scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files.
+[scripts/gguf_convert_endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_convert_endian.py) — Allows converting the endianness of GGUF files.
 
-[scripts/gguf-new-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-new-metadata.py) — Copies a GGUF file with added/modified/removed metadata values.
+[scripts/gguf_new_metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf_new_metadata.py) — Copies a GGUF file with added/modified/removed metadata values.
 
 ## Development
 Maintainers who participate in development of this package are advised to install it in editable mode:
@@ -78,6 +78,13 @@ python -m build
 python -m twine upload dist/*
 ```
 
+## Run Unit Tests
+
+From root of this repository you can run this command to run all the unit tests
+
+```bash
+python -m unittest discover ./gguf-py -v
+```
+
 ## TODO
-- [ ] Add tests
 - [ ] Include conversion scripts as command line entry points in this package.
diff --git a/gguf-py/examples/writer.py b/gguf-py/examples/writer.py
index f39eed1afe763..731873a7d666c 100755
--- a/gguf-py/examples/writer.py
+++ b/gguf-py/examples/writer.py
@@ -15,7 +15,6 @@ def writer_example() -> None:
     # Example usage with a file
     gguf_writer = GGUFWriter("example.gguf", "llama")
 
-    gguf_writer.add_architecture()
     gguf_writer.add_block_count(12)
     gguf_writer.add_uint32("answer", 42)  # Write a 32-bit integer
     gguf_writer.add_float32("answer_in_float", 42.0)  # Write a 32-bit float
diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py
index ea5146b161bc8..243defc4c1ca4 100644
--- a/gguf-py/gguf/__init__.py
+++ b/gguf-py/gguf/__init__.py
@@ -5,3 +5,5 @@
 from .quants import *
 from .tensor_mapping import *
 from .vocab import *
+from .utility import *
+from .metadata import *
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 978fcada3b42c..f63ec450a4e09 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -19,31 +19,81 @@
 
 class Keys:
     class General:
-        ARCHITECTURE         = "general.architecture"
-        QUANTIZATION_VERSION = "general.quantization_version"
-        ALIGNMENT            = "general.alignment"
-        NAME                 = "general.name"
-        AUTHOR               = "general.author"
-        VERSION              = "general.version"
-        URL                  = "general.url"
-        DESCRIPTION          = "general.description"
-        LICENSE              = "general.license"
-        SOURCE_URL           = "general.source.url"
-        SOURCE_HF_REPO       = "general.source.huggingface.repository"
-        FILE_TYPE            = "general.file_type"
+        TYPE                       = "general.type"
+        ARCHITECTURE               = "general.architecture"
+        QUANTIZATION_VERSION       = "general.quantization_version"
+        ALIGNMENT                  = "general.alignment"
+        FILE_TYPE                  = "general.file_type"
+
+        # Authorship Metadata
+        NAME                       = "general.name"
+        AUTHOR                     = "general.author"
+        VERSION                    = "general.version"
+        ORGANIZATION               = "general.organization"
+
+        FINETUNE                   = "general.finetune"
+        BASENAME                   = "general.basename"
+
+        DESCRIPTION                = "general.description"
+        QUANTIZED_BY               = "general.quantized_by"
+
+        SIZE_LABEL                 = "general.size_label"
+
+        # Licensing details
+        LICENSE                    = "general.license"
+        LICENSE_NAME               = "general.license.name"
+        LICENSE_LINK               = "general.license.link"
+
+        # Typically represents the converted GGUF repo (Unless native)
+        URL                        = "general.url" # Model Website/Paper
+        DOI                        = "general.doi"
+        UUID                       = "general.uuid"
+        REPO_URL                   = "general.repo_url" # Model Source Repository (git/svn/etc...)
+
+        # Model Source during conversion
+        SOURCE_URL                 = "general.source.url" # Model Website/Paper
+        SOURCE_DOI                 = "general.source.doi"
+        SOURCE_UUID                = "general.source.uuid"
+        SOURCE_REPO_URL            = "general.source.repo_url" # Model Source Repository (git/svn/etc...)
+
+        # Base Model Source. There can be more than one source if it's a merged
+        # model like with 'Mistral-7B-Merge-14-v0.1'. This will assist in
+        # tracing linage of models as it is finetuned or merged over time.
+        BASE_MODEL_COUNT           = "general.base_model.count"
+        BASE_MODEL_NAME            = "general.base_model.{id}.name"
+        BASE_MODEL_AUTHOR          = "general.base_model.{id}.author"
+        BASE_MODEL_VERSION         = "general.base_model.{id}.version"
+        BASE_MODEL_ORGANIZATION    = "general.base_model.{id}.organization"
+        BASE_MODEL_URL             = "general.base_model.{id}.url" # Model Website/Paper
+        BASE_MODEL_DOI             = "general.base_model.{id}.doi"
+        BASE_MODEL_UUID            = "general.base_model.{id}.uuid"
+        BASE_MODEL_REPO_URL        = "general.base_model.{id}.repo_url" # Model Source Repository (git/svn/etc...)
+
+        # Array based KV stores
+        TAGS                       = "general.tags"
+        LANGUAGES                  = "general.languages"
+        DATASETS                   = "general.datasets"
 
     class LLM:
-        VOCAB_SIZE            = "{arch}.vocab_size"
-        CONTEXT_LENGTH        = "{arch}.context_length"
-        EMBEDDING_LENGTH      = "{arch}.embedding_length"
-        BLOCK_COUNT           = "{arch}.block_count"
-        FEED_FORWARD_LENGTH   = "{arch}.feed_forward_length"
-        USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
-        TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"
-        EXPERT_COUNT          = "{arch}.expert_count"
-        EXPERT_USED_COUNT     = "{arch}.expert_used_count"
-        POOLING_TYPE          = "{arch}.pooling_type"
-        LOGIT_SCALE           = "{arch}.logit_scale"
+        VOCAB_SIZE                        = "{arch}.vocab_size"
+        CONTEXT_LENGTH                    = "{arch}.context_length"
+        EMBEDDING_LENGTH                  = "{arch}.embedding_length"
+        BLOCK_COUNT                       = "{arch}.block_count"
+        LEADING_DENSE_BLOCK_COUNT         = "{arch}.leading_dense_block_count"
+        FEED_FORWARD_LENGTH               = "{arch}.feed_forward_length"
+        EXPERT_FEED_FORWARD_LENGTH        = "{arch}.expert_feed_forward_length"
+        EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length"
+        USE_PARALLEL_RESIDUAL             = "{arch}.use_parallel_residual"
+        TENSOR_DATA_LAYOUT                = "{arch}.tensor_data_layout"
+        EXPERT_COUNT                      = "{arch}.expert_count"
+        EXPERT_USED_COUNT                 = "{arch}.expert_used_count"
+        EXPERT_SHARED_COUNT               = "{arch}.expert_shared_count"
+        EXPERT_WEIGHTS_SCALE              = "{arch}.expert_weights_scale"
+        POOLING_TYPE                      = "{arch}.pooling_type"
+        LOGIT_SCALE                       = "{arch}.logit_scale"
+        DECODER_START_TOKEN_ID            = "{arch}.decoder_start_token_id"
+        ATTN_LOGIT_SOFTCAPPING            = "{arch}.attn_logit_softcapping"
+        FINAL_LOGIT_SOFTCAPPING           = "{arch}.final_logit_softcapping"
 
     class Attention:
         HEAD_COUNT        = "{arch}.attention.head_count"
@@ -55,14 +105,25 @@ class Attention:
         LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
         LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
         CAUSAL            = "{arch}.attention.causal"
+        Q_LORA_RANK       = "{arch}.attention.q_lora_rank"
+        KV_LORA_RANK      = "{arch}.attention.kv_lora_rank"
+        REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
+        SLIDING_WINDOW    = "{arch}.attention.sliding_window"
 
     class Rope:
-        DIMENSION_COUNT      = "{arch}.rope.dimension_count"
-        FREQ_BASE            = "{arch}.rope.freq_base"
-        SCALING_TYPE         = "{arch}.rope.scaling.type"
-        SCALING_FACTOR       = "{arch}.rope.scaling.factor"
-        SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
-        SCALING_FINETUNED    = "{arch}.rope.scaling.finetuned"
+        DIMENSION_COUNT         = "{arch}.rope.dimension_count"
+        FREQ_BASE               = "{arch}.rope.freq_base"
+        SCALING_TYPE            = "{arch}.rope.scaling.type"
+        SCALING_FACTOR          = "{arch}.rope.scaling.factor"
+        SCALING_ATTN_FACTOR     = "{arch}.rope.scaling.attn_factor"
+        SCALING_ORIG_CTX_LEN    = "{arch}.rope.scaling.original_context_length"
+        SCALING_FINETUNED       = "{arch}.rope.scaling.finetuned"
+        SCALING_YARN_LOG_MUL    = "{arch}.rope.scaling.yarn_log_multiplier"
+
+    class Split:
+        LLM_KV_SPLIT_NO            = "split.no"
+        LLM_KV_SPLIT_COUNT         = "split.count"
+        LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"
 
     class SSM:
         CONV_KERNEL    = "{arch}.ssm.conv_kernel"
@@ -71,116 +132,177 @@ class SSM:
         TIME_STEP_RANK = "{arch}.ssm.time_step_rank"
 
     class Tokenizer:
-        MODEL            = "tokenizer.ggml.model"
-        PRE              = "tokenizer.ggml.pre"
-        LIST             = "tokenizer.ggml.tokens"
-        TOKEN_TYPE       = "tokenizer.ggml.token_type"
-        TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count"  # for BERT-style token types
-        SCORES           = "tokenizer.ggml.scores"
-        MERGES           = "tokenizer.ggml.merges"
-        BOS_ID           = "tokenizer.ggml.bos_token_id"
-        EOS_ID           = "tokenizer.ggml.eos_token_id"
-        UNK_ID           = "tokenizer.ggml.unknown_token_id"
-        SEP_ID           = "tokenizer.ggml.seperator_token_id"
-        PAD_ID           = "tokenizer.ggml.padding_token_id"
-        CLS_ID           = "tokenizer.ggml.cls_token_id"
-        MASK_ID          = "tokenizer.ggml.mask_token_id"
-        ADD_BOS          = "tokenizer.ggml.add_bos_token"
-        ADD_EOS          = "tokenizer.ggml.add_eos_token"
-        ADD_PREFIX       = "tokenizer.ggml.add_space_prefix"
-        HF_JSON          = "tokenizer.huggingface.json"
-        RWKV             = "tokenizer.rwkv.world"
-        CHAT_TEMPLATE    = "tokenizer.chat_template"
-        CHAT_TEMPLATE_N  = "tokenizer.chat_template.{name}"
-        CHAT_TEMPLATES   = "tokenizer.chat_templates"
+        MODEL                = "tokenizer.ggml.model"
+        PRE                  = "tokenizer.ggml.pre"
+        LIST                 = "tokenizer.ggml.tokens"
+        TOKEN_TYPE           = "tokenizer.ggml.token_type"
+        TOKEN_TYPE_COUNT     = "tokenizer.ggml.token_type_count"  # for BERT-style token types
+        SCORES               = "tokenizer.ggml.scores"
+        MERGES               = "tokenizer.ggml.merges"
+        BOS_ID               = "tokenizer.ggml.bos_token_id"
+        EOS_ID               = "tokenizer.ggml.eos_token_id"
+        UNK_ID               = "tokenizer.ggml.unknown_token_id"
+        SEP_ID               = "tokenizer.ggml.seperator_token_id"
+        PAD_ID               = "tokenizer.ggml.padding_token_id"
+        CLS_ID               = "tokenizer.ggml.cls_token_id"
+        MASK_ID              = "tokenizer.ggml.mask_token_id"
+        ADD_BOS              = "tokenizer.ggml.add_bos_token"
+        ADD_EOS              = "tokenizer.ggml.add_eos_token"
+        ADD_PREFIX           = "tokenizer.ggml.add_space_prefix"
+        REMOVE_EXTRA_WS      = "tokenizer.ggml.remove_extra_whitespaces"
+        PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap"
+        HF_JSON              = "tokenizer.huggingface.json"
+        RWKV                 = "tokenizer.rwkv.world"
+        CHAT_TEMPLATE        = "tokenizer.chat_template"
+        CHAT_TEMPLATE_N      = "tokenizer.chat_template.{name}"
+        CHAT_TEMPLATES       = "tokenizer.chat_templates"
         # FIM/Infill special tokens constants
-        PREFIX_ID        = "tokenizer.ggml.prefix_token_id"
-        SUFFIX_ID        = "tokenizer.ggml.suffix_token_id"
-        MIDDLE_ID        = "tokenizer.ggml.middle_token_id"
-        EOT_ID           = "tokenizer.ggml.eot_token_id"
+        PREFIX_ID            = "tokenizer.ggml.prefix_token_id"
+        SUFFIX_ID            = "tokenizer.ggml.suffix_token_id"
+        MIDDLE_ID            = "tokenizer.ggml.middle_token_id"
+        EOT_ID               = "tokenizer.ggml.eot_token_id"
+        EOM_ID               = "tokenizer.ggml.eom_token_id"
 
+    class Adapter:
+        TYPE       = "adapter.type"
+        LORA_ALPHA = "adapter.lora.alpha"
 
 #
 # recommended mapping of model tensor names for storage in gguf
 #
 
 
+class GGUFType:
+    MODEL   = "model"
+    ADAPTER = "adapter"
+
+
 class MODEL_ARCH(IntEnum):
-    LLAMA      = auto()
-    FALCON     = auto()
-    BAICHUAN   = auto()
-    GROK       = auto()
-    GPT2       = auto()
-    GPTJ       = auto()
-    GPTNEOX    = auto()
-    MPT        = auto()
-    STARCODER  = auto()
-    PERSIMMON  = auto()
-    REFACT     = auto()
-    BERT       = auto()
-    NOMIC_BERT = auto()
+    LLAMA        = auto()
+    FALCON       = auto()
+    BAICHUAN     = auto()
+    GROK         = auto()
+    GPT2         = auto()
+    GPTJ         = auto()
+    GPTNEOX      = auto()
+    MPT          = auto()
+    STARCODER    = auto()
+    REFACT       = auto()
+    BERT         = auto()
+    NOMIC_BERT   = auto()
     JINA_BERT_V2 = auto()
-    BLOOM      = auto()
-    STABLELM   = auto()
-    QWEN       = auto()
-    QWEN2      = auto()
-    QWEN2MOE   = auto()
-    PHI2       = auto()
-    PHI3       = auto()
-    PLAMO      = auto()
-    CODESHELL  = auto()
-    ORION      = auto()
-    INTERNLM2  = auto()
-    MINICPM    = auto()
-    GEMMA      = auto()
-    STARCODER2 = auto()
-    MAMBA      = auto()
-    XVERSE     = auto()
-    COMMAND_R  = auto()
-    DBRX       = auto()
-    OLMO       = auto()
+    BLOOM        = auto()
+    STABLELM     = auto()
+    QWEN         = auto()
+    QWEN2        = auto()
+    QWEN2MOE     = auto()
+    PHI2         = auto()
+    PHI3         = auto()
+    PLAMO        = auto()
+    CODESHELL    = auto()
+    ORION        = auto()
+    INTERNLM2    = auto()
+    MINICPM      = auto()
+    GEMMA        = auto()
+    GEMMA2       = auto()
+    STARCODER2   = auto()
+    MAMBA        = auto()
+    XVERSE       = auto()
+    COMMAND_R    = auto()
+    DBRX         = auto()
+    OLMO         = auto()
+    OPENELM      = auto()
+    ARCTIC       = auto()
+    DEEPSEEK2    = auto()
+    CHATGLM      = auto()
+    BITNET       = auto()
+    T5           = auto()
+    T5ENCODER    = auto()
+    JAIS         = auto()
 
 
 class MODEL_TENSOR(IntEnum):
-    TOKEN_EMBD         = auto()
-    TOKEN_EMBD_NORM    = auto()
-    TOKEN_TYPES        = auto()
-    POS_EMBD           = auto()
-    OUTPUT             = auto()
-    OUTPUT_NORM        = auto()
-    ROPE_FREQS         = auto()
-    ATTN_Q             = auto()
-    ATTN_K             = auto()
-    ATTN_V             = auto()
-    ATTN_QKV           = auto()
-    ATTN_OUT           = auto()
-    ATTN_NORM          = auto()
-    ATTN_NORM_2        = auto()
-    ATTN_OUT_NORM      = auto()
-    ATTN_ROT_EMBD      = auto()
-    FFN_GATE_INP       = auto()
-    FFN_GATE_INP_SHEXP = auto()
-    FFN_NORM           = auto()
-    FFN_GATE           = auto()
-    FFN_DOWN           = auto()
-    FFN_UP             = auto()
-    FFN_ACT            = auto()
-    FFN_GATE_EXP       = auto()
-    FFN_DOWN_EXP       = auto()
-    FFN_UP_EXP         = auto()
-    FFN_GATE_SHEXP     = auto()
-    FFN_DOWN_SHEXP     = auto()
-    FFN_UP_SHEXP       = auto()
-    ATTN_Q_NORM        = auto()
-    ATTN_K_NORM        = auto()
-    LAYER_OUT_NORM     = auto()
-    SSM_IN             = auto()
-    SSM_CONV1D         = auto()
-    SSM_X              = auto()
-    SSM_DT             = auto()
-    SSM_A              = auto()
-    SSM_D              = auto()
-    SSM_OUT            = auto()
+    TOKEN_EMBD           = auto()
+    TOKEN_EMBD_NORM      = auto()
+    TOKEN_TYPES          = auto()
+    POS_EMBD             = auto()
+    OUTPUT               = auto()
+    OUTPUT_NORM          = auto()
+    ROPE_FREQS           = auto()
+    ROPE_FACTORS_LONG    = auto()
+    ROPE_FACTORS_SHORT   = auto()
+    ATTN_Q               = auto()
+    ATTN_K               = auto()
+    ATTN_V               = auto()
+    ATTN_QKV             = auto()
+    ATTN_OUT             = auto()
+    ATTN_NORM            = auto()
+    ATTN_NORM_2          = auto()
+    ATTN_OUT_NORM        = auto()
+    ATTN_POST_NORM       = auto()
+    ATTN_ROT_EMBD        = auto()
+    FFN_GATE_INP         = auto()
+    FFN_GATE_INP_SHEXP   = auto()
+    FFN_NORM             = auto()
+    FFN_PRE_NORM         = auto()
+    FFN_POST_NORM        = auto()
+    FFN_GATE             = auto()
+    FFN_DOWN             = auto()
+    FFN_UP               = auto()
+    FFN_ACT              = auto()
+    FFN_NORM_EXP         = auto()
+    FFN_GATE_EXP         = auto()
+    FFN_DOWN_EXP         = auto()
+    FFN_UP_EXP           = auto()
+    FFN_GATE_SHEXP       = auto()
+    FFN_DOWN_SHEXP       = auto()
+    FFN_UP_SHEXP         = auto()
+    ATTN_Q_NORM          = auto()
+    ATTN_K_NORM          = auto()
+    LAYER_OUT_NORM       = auto()
+    SSM_IN               = auto()
+    SSM_CONV1D           = auto()
+    SSM_X                = auto()
+    SSM_DT               = auto()
+    SSM_A                = auto()
+    SSM_D                = auto()
+    SSM_OUT              = auto()
+    ATTN_Q_A             = auto()
+    ATTN_Q_B             = auto()
+    ATTN_KV_A_MQA        = auto()
+    ATTN_KV_B            = auto()
+    ATTN_Q_A_NORM        = auto()
+    ATTN_KV_A_NORM       = auto()
+    FFN_SUB_NORM         = auto()
+    ATTN_SUB_NORM        = auto()
+    DEC_ATTN_NORM        = auto()
+    DEC_ATTN_Q           = auto()
+    DEC_ATTN_K           = auto()
+    DEC_ATTN_V           = auto()
+    DEC_ATTN_OUT         = auto()
+    DEC_ATTN_REL_B       = auto()
+    DEC_CROSS_ATTN_NORM  = auto()
+    DEC_CROSS_ATTN_Q     = auto()
+    DEC_CROSS_ATTN_K     = auto()
+    DEC_CROSS_ATTN_V     = auto()
+    DEC_CROSS_ATTN_OUT   = auto()
+    DEC_CROSS_ATTN_REL_B = auto()
+    DEC_FFN_NORM         = auto()
+    DEC_FFN_GATE         = auto()
+    DEC_FFN_DOWN         = auto()
+    DEC_FFN_UP           = auto()
+    DEC_OUTPUT_NORM      = auto()
+    ENC_ATTN_NORM        = auto()
+    ENC_ATTN_Q           = auto()
+    ENC_ATTN_K           = auto()
+    ENC_ATTN_V           = auto()
+    ENC_ATTN_OUT         = auto()
+    ENC_ATTN_REL_B       = auto()
+    ENC_FFN_NORM         = auto()
+    ENC_FFN_GATE         = auto()
+    ENC_FFN_DOWN         = auto()
+    ENC_FFN_UP           = auto()
+    ENC_OUTPUT_NORM      = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -193,7 +315,6 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.GPTNEOX:        "gptneox",
     MODEL_ARCH.MPT:            "mpt",
     MODEL_ARCH.STARCODER:      "starcoder",
-    MODEL_ARCH.PERSIMMON:      "persimmon",
     MODEL_ARCH.REFACT:         "refact",
     MODEL_ARCH.BERT:           "bert",
     MODEL_ARCH.NOMIC_BERT:     "nomic-bert",
@@ -211,54 +332,105 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.INTERNLM2:      "internlm2",
     MODEL_ARCH.MINICPM:        "minicpm",
     MODEL_ARCH.GEMMA:          "gemma",
+    MODEL_ARCH.GEMMA2:         "gemma2",
     MODEL_ARCH.STARCODER2:     "starcoder2",
     MODEL_ARCH.MAMBA:          "mamba",
     MODEL_ARCH.XVERSE:         "xverse",
     MODEL_ARCH.COMMAND_R:      "command-r",
     MODEL_ARCH.DBRX:           "dbrx",
     MODEL_ARCH.OLMO:           "olmo",
+    MODEL_ARCH.OPENELM:        "openelm",
+    MODEL_ARCH.ARCTIC:         "arctic",
+    MODEL_ARCH.DEEPSEEK2:      "deepseek2",
+    MODEL_ARCH.CHATGLM:        "chatglm",
+    MODEL_ARCH.BITNET:         "bitnet",
+    MODEL_ARCH.T5:             "t5",
+    MODEL_ARCH.T5ENCODER:      "t5encoder",
+    MODEL_ARCH.JAIS:           "jais",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
-    MODEL_TENSOR.TOKEN_EMBD:         "token_embd",
-    MODEL_TENSOR.TOKEN_EMBD_NORM:    "token_embd_norm",
-    MODEL_TENSOR.TOKEN_TYPES:        "token_types",
-    MODEL_TENSOR.POS_EMBD:           "position_embd",
-    MODEL_TENSOR.OUTPUT_NORM:        "output_norm",
-    MODEL_TENSOR.OUTPUT:             "output",
-    MODEL_TENSOR.ROPE_FREQS:         "rope_freqs",
-    MODEL_TENSOR.ATTN_NORM:          "blk.{bid}.attn_norm",
-    MODEL_TENSOR.ATTN_NORM_2:        "blk.{bid}.attn_norm_2",
-    MODEL_TENSOR.ATTN_QKV:           "blk.{bid}.attn_qkv",
-    MODEL_TENSOR.ATTN_Q:             "blk.{bid}.attn_q",
-    MODEL_TENSOR.ATTN_K:             "blk.{bid}.attn_k",
-    MODEL_TENSOR.ATTN_V:             "blk.{bid}.attn_v",
-    MODEL_TENSOR.ATTN_OUT:           "blk.{bid}.attn_output",
-    MODEL_TENSOR.ATTN_ROT_EMBD:      "blk.{bid}.attn_rot_embd",
-    MODEL_TENSOR.ATTN_Q_NORM:        "blk.{bid}.attn_q_norm",
-    MODEL_TENSOR.ATTN_K_NORM:        "blk.{bid}.attn_k_norm",
-    MODEL_TENSOR.ATTN_OUT_NORM:      "blk.{bid}.attn_output_norm",
-    MODEL_TENSOR.FFN_GATE_INP:       "blk.{bid}.ffn_gate_inp",
-    MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp",
-    MODEL_TENSOR.FFN_NORM:           "blk.{bid}.ffn_norm",
-    MODEL_TENSOR.FFN_GATE:           "blk.{bid}.ffn_gate",
-    MODEL_TENSOR.FFN_DOWN:           "blk.{bid}.ffn_down",
-    MODEL_TENSOR.FFN_UP:             "blk.{bid}.ffn_up",
-    MODEL_TENSOR.FFN_GATE_SHEXP:     "blk.{bid}.ffn_gate_shexp",
-    MODEL_TENSOR.FFN_DOWN_SHEXP:     "blk.{bid}.ffn_down_shexp",
-    MODEL_TENSOR.FFN_UP_SHEXP:       "blk.{bid}.ffn_up_shexp",
-    MODEL_TENSOR.FFN_ACT:            "blk.{bid}.ffn",
-    MODEL_TENSOR.FFN_GATE_EXP:       "blk.{bid}.ffn_gate_exps",
-    MODEL_TENSOR.FFN_DOWN_EXP:       "blk.{bid}.ffn_down_exps",
-    MODEL_TENSOR.FFN_UP_EXP:         "blk.{bid}.ffn_up_exps",
-    MODEL_TENSOR.LAYER_OUT_NORM:     "blk.{bid}.layer_output_norm",
-    MODEL_TENSOR.SSM_IN:             "blk.{bid}.ssm_in",
-    MODEL_TENSOR.SSM_CONV1D:         "blk.{bid}.ssm_conv1d",
-    MODEL_TENSOR.SSM_X:              "blk.{bid}.ssm_x",
-    MODEL_TENSOR.SSM_DT:             "blk.{bid}.ssm_dt",
-    MODEL_TENSOR.SSM_A:              "blk.{bid}.ssm_a",
-    MODEL_TENSOR.SSM_D:              "blk.{bid}.ssm_d",
-    MODEL_TENSOR.SSM_OUT:            "blk.{bid}.ssm_out",
+    MODEL_TENSOR.TOKEN_EMBD:           "token_embd",
+    MODEL_TENSOR.TOKEN_EMBD_NORM:      "token_embd_norm",
+    MODEL_TENSOR.TOKEN_TYPES:          "token_types",
+    MODEL_TENSOR.POS_EMBD:             "position_embd",
+    MODEL_TENSOR.OUTPUT_NORM:          "output_norm",
+    MODEL_TENSOR.OUTPUT:               "output",
+    MODEL_TENSOR.ROPE_FREQS:           "rope_freqs",
+    MODEL_TENSOR.ROPE_FACTORS_LONG:    "rope_factors_long",
+    MODEL_TENSOR.ROPE_FACTORS_SHORT:   "rope_factors_short",
+    MODEL_TENSOR.ATTN_NORM:            "blk.{bid}.attn_norm",
+    MODEL_TENSOR.ATTN_NORM_2:          "blk.{bid}.attn_norm_2",
+    MODEL_TENSOR.ATTN_QKV:             "blk.{bid}.attn_qkv",
+    MODEL_TENSOR.ATTN_Q:               "blk.{bid}.attn_q",
+    MODEL_TENSOR.ATTN_K:               "blk.{bid}.attn_k",
+    MODEL_TENSOR.ATTN_V:               "blk.{bid}.attn_v",
+    MODEL_TENSOR.ATTN_OUT:             "blk.{bid}.attn_output",
+    MODEL_TENSOR.ATTN_ROT_EMBD:        "blk.{bid}.attn_rot_embd",
+    MODEL_TENSOR.ATTN_Q_NORM:          "blk.{bid}.attn_q_norm",
+    MODEL_TENSOR.ATTN_K_NORM:          "blk.{bid}.attn_k_norm",
+    MODEL_TENSOR.ATTN_OUT_NORM:        "blk.{bid}.attn_output_norm",
+    MODEL_TENSOR.ATTN_POST_NORM:       "blk.{bid}.post_attention_norm",
+    MODEL_TENSOR.FFN_GATE_INP:         "blk.{bid}.ffn_gate_inp",
+    MODEL_TENSOR.FFN_GATE_INP_SHEXP:   "blk.{bid}.ffn_gate_inp_shexp",
+    MODEL_TENSOR.FFN_NORM:             "blk.{bid}.ffn_norm",
+    MODEL_TENSOR.FFN_PRE_NORM:         "blk.{bid}.ffn_norm",
+    MODEL_TENSOR.FFN_POST_NORM:        "blk.{bid}.post_ffw_norm",
+    MODEL_TENSOR.FFN_GATE:             "blk.{bid}.ffn_gate",
+    MODEL_TENSOR.FFN_DOWN:             "blk.{bid}.ffn_down",
+    MODEL_TENSOR.FFN_UP:               "blk.{bid}.ffn_up",
+    MODEL_TENSOR.FFN_GATE_SHEXP:       "blk.{bid}.ffn_gate_shexp",
+    MODEL_TENSOR.FFN_DOWN_SHEXP:       "blk.{bid}.ffn_down_shexp",
+    MODEL_TENSOR.FFN_UP_SHEXP:         "blk.{bid}.ffn_up_shexp",
+    MODEL_TENSOR.FFN_ACT:              "blk.{bid}.ffn",
+    MODEL_TENSOR.FFN_NORM_EXP:         "blk.{bid}.ffn_norm_exps",
+    MODEL_TENSOR.FFN_GATE_EXP:         "blk.{bid}.ffn_gate_exps",
+    MODEL_TENSOR.FFN_DOWN_EXP:         "blk.{bid}.ffn_down_exps",
+    MODEL_TENSOR.FFN_UP_EXP:           "blk.{bid}.ffn_up_exps",
+    MODEL_TENSOR.LAYER_OUT_NORM:       "blk.{bid}.layer_output_norm",
+    MODEL_TENSOR.SSM_IN:               "blk.{bid}.ssm_in",
+    MODEL_TENSOR.SSM_CONV1D:           "blk.{bid}.ssm_conv1d",
+    MODEL_TENSOR.SSM_X:                "blk.{bid}.ssm_x",
+    MODEL_TENSOR.SSM_DT:               "blk.{bid}.ssm_dt",
+    MODEL_TENSOR.SSM_A:                "blk.{bid}.ssm_a",
+    MODEL_TENSOR.SSM_D:                "blk.{bid}.ssm_d",
+    MODEL_TENSOR.SSM_OUT:              "blk.{bid}.ssm_out",
+    MODEL_TENSOR.ATTN_Q_A:             "blk.{bid}.attn_q_a",
+    MODEL_TENSOR.ATTN_Q_B:             "blk.{bid}.attn_q_b",
+    MODEL_TENSOR.ATTN_KV_A_MQA:        "blk.{bid}.attn_kv_a_mqa",
+    MODEL_TENSOR.ATTN_KV_B:            "blk.{bid}.attn_kv_b",
+    MODEL_TENSOR.ATTN_Q_A_NORM:        "blk.{bid}.attn_q_a_norm",
+    MODEL_TENSOR.ATTN_KV_A_NORM:       "blk.{bid}.attn_kv_a_norm",
+    MODEL_TENSOR.ATTN_SUB_NORM:        "blk.{bid}.attn_sub_norm",
+    MODEL_TENSOR.FFN_SUB_NORM:         "blk.{bid}.ffn_sub_norm",
+    MODEL_TENSOR.DEC_ATTN_NORM:        "dec.blk.{bid}.attn_norm",
+    MODEL_TENSOR.DEC_ATTN_Q:           "dec.blk.{bid}.attn_q",
+    MODEL_TENSOR.DEC_ATTN_K:           "dec.blk.{bid}.attn_k",
+    MODEL_TENSOR.DEC_ATTN_V:           "dec.blk.{bid}.attn_v",
+    MODEL_TENSOR.DEC_ATTN_OUT:         "dec.blk.{bid}.attn_o",
+    MODEL_TENSOR.DEC_ATTN_REL_B:       "dec.blk.{bid}.attn_rel_b",
+    MODEL_TENSOR.DEC_CROSS_ATTN_NORM:  "dec.blk.{bid}.cross_attn_norm",
+    MODEL_TENSOR.DEC_CROSS_ATTN_Q:     "dec.blk.{bid}.cross_attn_q",
+    MODEL_TENSOR.DEC_CROSS_ATTN_K:     "dec.blk.{bid}.cross_attn_k",
+    MODEL_TENSOR.DEC_CROSS_ATTN_V:     "dec.blk.{bid}.cross_attn_v",
+    MODEL_TENSOR.DEC_CROSS_ATTN_OUT:   "dec.blk.{bid}.cross_attn_o",
+    MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: "dec.blk.{bid}.cross_attn_rel_b",
+    MODEL_TENSOR.DEC_FFN_NORM:         "dec.blk.{bid}.ffn_norm",
+    MODEL_TENSOR.DEC_FFN_GATE:         "dec.blk.{bid}.ffn_gate",
+    MODEL_TENSOR.DEC_FFN_DOWN:         "dec.blk.{bid}.ffn_down",
+    MODEL_TENSOR.DEC_FFN_UP:           "dec.blk.{bid}.ffn_up",
+    MODEL_TENSOR.DEC_OUTPUT_NORM:      "dec.output_norm",
+    MODEL_TENSOR.ENC_ATTN_NORM:        "enc.blk.{bid}.attn_norm",
+    MODEL_TENSOR.ENC_ATTN_Q:           "enc.blk.{bid}.attn_q",
+    MODEL_TENSOR.ENC_ATTN_K:           "enc.blk.{bid}.attn_k",
+    MODEL_TENSOR.ENC_ATTN_V:           "enc.blk.{bid}.attn_v",
+    MODEL_TENSOR.ENC_ATTN_OUT:         "enc.blk.{bid}.attn_o",
+    MODEL_TENSOR.ENC_ATTN_REL_B:       "enc.blk.{bid}.attn_rel_b",
+    MODEL_TENSOR.ENC_FFN_NORM:         "enc.blk.{bid}.ffn_norm",
+    MODEL_TENSOR.ENC_FFN_GATE:         "enc.blk.{bid}.ffn_gate",
+    MODEL_TENSOR.ENC_FFN_DOWN:         "enc.blk.{bid}.ffn_down",
+    MODEL_TENSOR.ENC_FFN_UP:           "enc.blk.{bid}.ffn_up",
+    MODEL_TENSOR.ENC_OUTPUT_NORM:      "enc.output_norm",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -387,6 +559,7 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.TOKEN_EMBD_NORM,
         MODEL_TENSOR.TOKEN_TYPES,
+        MODEL_TENSOR.ATTN_NORM_2,
         MODEL_TENSOR.ATTN_OUT_NORM,
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_Q_NORM,
@@ -426,20 +599,6 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
-    MODEL_ARCH.PERSIMMON: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-        MODEL_TENSOR.ATTN_Q_NORM,
-        MODEL_TENSOR.ATTN_K_NORM,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-    ],
     MODEL_ARCH.REFACT: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -631,6 +790,7 @@ class MODEL_TENSOR(IntEnum):
     ],
     MODEL_ARCH.MINICPM: [
         MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
         MODEL_TENSOR.OUTPUT_NORM,
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_NORM,
@@ -661,6 +821,21 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.FFN_NORM,
     ],
+    MODEL_ARCH.GEMMA2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_PRE_NORM,
+        MODEL_TENSOR.FFN_POST_NORM,
+    ],
     MODEL_ARCH.STARCODER2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -743,6 +918,153 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.OPENELM: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.ARCTIC: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_NORM_EXP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
+    MODEL_ARCH.DEEPSEEK2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_A,
+        MODEL_TENSOR.ATTN_Q_B,
+        MODEL_TENSOR.ATTN_KV_A_MQA,
+        MODEL_TENSOR.ATTN_KV_B,
+        MODEL_TENSOR.ATTN_Q_A_NORM,
+        MODEL_TENSOR.ATTN_KV_A_NORM,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+    ],
+    MODEL_ARCH.CHATGLM : [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.BITNET: [
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_SUB_NORM,
+        MODEL_TENSOR.FFN_SUB_NORM,
+    ],
+    MODEL_ARCH.T5: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.DEC_ATTN_NORM,
+        MODEL_TENSOR.DEC_ATTN_Q,
+        MODEL_TENSOR.DEC_ATTN_K,
+        MODEL_TENSOR.DEC_ATTN_V,
+        MODEL_TENSOR.DEC_ATTN_OUT,
+        MODEL_TENSOR.DEC_ATTN_REL_B,
+        MODEL_TENSOR.DEC_CROSS_ATTN_NORM,
+        MODEL_TENSOR.DEC_CROSS_ATTN_Q,
+        MODEL_TENSOR.DEC_CROSS_ATTN_K,
+        MODEL_TENSOR.DEC_CROSS_ATTN_V,
+        MODEL_TENSOR.DEC_CROSS_ATTN_OUT,
+        MODEL_TENSOR.DEC_CROSS_ATTN_REL_B,
+        MODEL_TENSOR.DEC_FFN_NORM,
+        MODEL_TENSOR.DEC_FFN_GATE,
+        MODEL_TENSOR.DEC_FFN_DOWN,
+        MODEL_TENSOR.DEC_FFN_UP,
+        MODEL_TENSOR.DEC_OUTPUT_NORM,
+        MODEL_TENSOR.ENC_ATTN_NORM,
+        MODEL_TENSOR.ENC_ATTN_Q,
+        MODEL_TENSOR.ENC_ATTN_K,
+        MODEL_TENSOR.ENC_ATTN_V,
+        MODEL_TENSOR.ENC_ATTN_OUT,
+        MODEL_TENSOR.ENC_ATTN_REL_B,
+        MODEL_TENSOR.ENC_FFN_NORM,
+        MODEL_TENSOR.ENC_FFN_GATE,
+        MODEL_TENSOR.ENC_FFN_DOWN,
+        MODEL_TENSOR.ENC_FFN_UP,
+        MODEL_TENSOR.ENC_OUTPUT_NORM,
+    ],
+    MODEL_ARCH.T5ENCODER: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ENC_ATTN_NORM,
+        MODEL_TENSOR.ENC_ATTN_Q,
+        MODEL_TENSOR.ENC_ATTN_K,
+        MODEL_TENSOR.ENC_ATTN_V,
+        MODEL_TENSOR.ENC_ATTN_OUT,
+        MODEL_TENSOR.ENC_ATTN_REL_B,
+        MODEL_TENSOR.ENC_FFN_NORM,
+        MODEL_TENSOR.ENC_FFN_GATE,
+        MODEL_TENSOR.ENC_FFN_DOWN,
+        MODEL_TENSOR.ENC_FFN_UP,
+        MODEL_TENSOR.ENC_OUTPUT_NORM,
+    ],
+    MODEL_ARCH.JAIS: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_UP,
+    ],
     # TODO
 }
 
@@ -756,9 +1078,6 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
-    MODEL_ARCH.PERSIMMON: [
-        MODEL_TENSOR.ROPE_FREQS,
-    ],
     MODEL_ARCH.QWEN: [
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
@@ -779,6 +1098,13 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
+    MODEL_ARCH.DEEPSEEK2: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
+    MODEL_ARCH.CHATGLM: [
+        MODEL_TENSOR.ROPE_FREQS,
+    ],
 }
 
 #
@@ -837,6 +1163,9 @@ class GGMLQuantizationType(IntEnum):
     F64     = 28
     IQ1_M   = 29
     BF16    = 30
+    Q4_0_4_4 = 31
+    Q4_0_4_8 = 32
+    Q4_0_8_8 = 33
 
 
 # TODO: add GGMLFileType from ggml_ftype in ggml.h
@@ -849,7 +1178,7 @@ class LlamaFileType(IntEnum):
     MOSTLY_F16           = 1   # except 1d tensors
     MOSTLY_Q4_0          = 2   # except 1d tensors
     MOSTLY_Q4_1          = 3   # except 1d tensors
-    MOSTLY_Q4_1_SOME_F16 = 4   # tok_embeddings.weight and output.weight are F16
+    # MOSTLY_Q4_1_SOME_F16 = 4   # tok_embeddings.weight and output.weight are F16
     # MOSTLY_Q4_2        = 5   # support has been removed
     # MOSTLY_Q4_3        = 6   # support has been removed
     MOSTLY_Q8_0          = 7   # except 1d tensors
@@ -878,6 +1207,9 @@ class LlamaFileType(IntEnum):
     MOSTLY_IQ4_XS        = 30  # except 1d tensors
     MOSTLY_IQ1_M         = 31  # except 1d tensors
     MOSTLY_BF16          = 32  # except 1d tensors
+    MOSTLY_Q4_0_4_4      = 33  # except 1d tensors
+    MOSTLY_Q4_0_4_8      = 34  # except 1d tensors
+    MOSTLY_Q4_0_8_8      = 35  # except 1d tensors
 
     GUESSED              = 1024  # not specified in the model file
 
@@ -919,9 +1251,8 @@ def get_type(val: Any) -> GGUFValueType:
             raise ValueError(f"Unknown type: {type(val)}")
 
 
-# Note: Does not support GGML_QKK_64
-QK_K = 256
 # Items here are (block size, type size)
+QK_K = 256
 GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
     GGMLQuantizationType.F32:     (1, 4),
     GGMLQuantizationType.F16:     (1, 2),
@@ -952,6 +1283,9 @@ def get_type(val: Any) -> GGUFValueType:
     GGMLQuantizationType.F64:     (1, 8),
     GGMLQuantizationType.IQ1_M:   (256, QK_K // 8 + QK_K // 16  + QK_K // 32),
     GGMLQuantizationType.BF16:    (1, 2),
+    GGMLQuantizationType.Q4_0_4_4:(32, 2 + 16),
+    GGMLQuantizationType.Q4_0_4_8:(32, 2 + 16),
+    GGMLQuantizationType.Q4_0_8_8:(32, 2 + 16),
 }
 
 
@@ -967,7 +1301,6 @@ def get_type(val: Any) -> GGUFValueType:
 KEY_GENERAL_DESCRIPTION          = Keys.General.DESCRIPTION
 KEY_GENERAL_LICENSE              = Keys.General.LICENSE
 KEY_GENERAL_SOURCE_URL           = Keys.General.SOURCE_URL
-KEY_GENERAL_SOURCE_HF_REPO       = Keys.General.SOURCE_HF_REPO
 KEY_GENERAL_FILE_TYPE            = Keys.General.FILE_TYPE
 
 # LLM
@@ -1021,3 +1354,4 @@ def get_type(val: Any) -> GGUFValueType:
 KEY_TOKENIZER_SUFFIX_ID  = Keys.Tokenizer.SUFFIX_ID
 KEY_TOKENIZER_MIDDLE_ID  = Keys.Tokenizer.MIDDLE_ID
 KEY_TOKENIZER_EOT_ID     = Keys.Tokenizer.EOT_ID
+KEY_TOKENIZER_EOM_ID     = Keys.Tokenizer.EOM_ID
diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py
index 21b089f8a2937..e8e61abf86ae4 100644
--- a/gguf-py/gguf/gguf_reader.py
+++ b/gguf-py/gguf/gguf_reader.py
@@ -12,6 +12,8 @@
 import numpy as np
 import numpy.typing as npt
 
+from .quants import quant_shape_to_byte_shape
+
 if __name__ == "__main__":
     import sys
     from pathlib import Path
@@ -65,8 +67,9 @@ class ReaderTensor(NamedTuple):
 
 class GGUFReader:
     # I - same as host, S - swapped
-    byte_order: Literal['I'] | Literal['S'] = 'I'
+    byte_order: Literal['I', 'S'] = 'I'
     alignment: int = GGUF_DEFAULT_ALIGNMENT
+    data_offset: int
 
     # Note: Internal helper, API may change.
     gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = {
@@ -83,12 +86,16 @@ class GGUFReader:
         GGUFValueType.BOOL:    np.bool_,
     }
 
-    def __init__(self, path: os.PathLike[str] | str, mode: Literal['r'] | Literal['r+'] | Literal['c'] = 'r'):
+    def __init__(self, path: os.PathLike[str] | str, mode: Literal['r', 'r+', 'c'] = 'r'):
         self.data = np.memmap(path, mode = mode)
         offs = 0
+
+        # Check for GGUF magic
         if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC:
             raise ValueError('GGUF magic invalid')
         offs += 4
+
+        # Check GGUF version
         temp_version = self._get(offs, np.uint32)
         if temp_version[0] & 65535 == 0:
             # If we get 0 here that means it's (probably) a GGUF file created for
@@ -101,12 +108,16 @@ def __init__(self, path: os.PathLike[str] | str, mode: Literal['r'] | Literal['r
         self.fields: OrderedDict[str, ReaderField] = OrderedDict()
         self.tensors: list[ReaderTensor] = []
         offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32]))
+
+        # Check tensor count and kv count
         temp_counts = self._get(offs, np.uint64, 2)
         offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64]))
         offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64]))
         tensor_count, kv_count = temp_counts
         offs = self._build_fields(offs, kv_count)
-        offs, tensors_fields = self._build_tensors_fields(offs, tensor_count)
+
+        # Build Tensor Info Fields
+        offs, tensors_fields = self._build_tensor_info(offs, tensor_count)
         new_align = self.fields.get('general.alignment')
         if new_align is not None:
             if new_align.types != [GGUFValueType.UINT32]:
@@ -115,6 +126,7 @@ def __init__(self, path: os.PathLike[str] | str, mode: Literal['r'] | Literal['r
         padding = offs % self.alignment
         if padding != 0:
             offs += self.alignment - padding
+        self.data_offset = offs
         self._build_tensors(offs, tensors_fields)
 
     _DT = TypeVar('_DT', bound = npt.DTypeLike)
@@ -128,7 +140,7 @@ def get_tensor(self, idx: int) -> ReaderTensor:
         return self.tensors[idx]
 
     def _get(
-        self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I'] | Literal['S'] | Literal['<'] = None,
+        self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I', 'S', '<'] = None,
     ) -> npt.NDArray[Any]:
         count = int(count)
         itemsize = int(np.empty([], dtype = dtype).itemsize)
@@ -191,18 +203,29 @@ def _get_field_parts(
         # We can't deal with this one.
         raise ValueError('Unknown/unhandled field type {gtype}')
 
-    def _get_tensor(self, orig_offs: int) -> ReaderField:
+    def _get_tensor_info_field(self, orig_offs: int) -> ReaderField:
         offs = orig_offs
+
+        # Get Tensor Name
         name_len, name_data = self._get_str(offs)
         offs += int(name_len.nbytes + name_data.nbytes)
+
+        # Get Tensor Dimensions Count
         n_dims = self._get(offs, np.uint32)
         offs += int(n_dims.nbytes)
+
+        # Get Tensor Dimension Array
         dims = self._get(offs, np.uint64, n_dims[0])
         offs += int(dims.nbytes)
+
+        # Get Tensor Encoding Scheme Type
         raw_dtype = self._get(offs, np.uint32)
         offs += int(raw_dtype.nbytes)
+
+        # Get Tensor Offset
         offset_tensor = self._get(offs, np.uint64)
         offs += int(offset_tensor.nbytes)
+
         return ReaderField(
             orig_offs,
             str(bytes(name_data), encoding = 'utf-8'),
@@ -231,10 +254,10 @@ def _build_fields(self, offs: int, count: int) -> int:
             offs += field_size
         return offs
 
-    def _build_tensors_fields(self, offs: int, count: int) -> tuple[int, list[ReaderField]]:
+    def _build_tensor_info(self, offs: int, count: int) -> tuple[int, list[ReaderField]]:
         tensor_fields = []
         for _ in range(count):
-            field = self._get_tensor(offs)
+            field = self._get_tensor_info_field(offs)
             offs += sum(int(part.nbytes) for part in field.parts)
             tensor_fields.append(field)
         return offs, tensor_fields
@@ -251,6 +274,7 @@ def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
             tensor_names.add(tensor_name)
             ggml_type = GGMLQuantizationType(raw_dtype[0])
             n_elems = int(np.prod(dims))
+            np_dims = tuple(reversed(dims.tolist()))
             block_size, type_size = GGML_QUANT_SIZES[ggml_type]
             n_bytes = n_elems * type_size // block_size
             data_offs = int(start_offs + offset_tensor[0])
@@ -279,6 +303,7 @@ def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
             else:
                 item_count = n_bytes
                 item_type = np.uint8
+                np_dims = quant_shape_to_byte_shape(np_dims, ggml_type)
             tensors.append(ReaderTensor(
                 name = tensor_name,
                 tensor_type = ggml_type,
@@ -286,7 +311,7 @@ def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
                 n_elements = n_elems,
                 n_bytes = n_bytes,
                 data_offset = data_offs,
-                data = self._get(data_offs, item_type, item_count),
+                data = self._get(data_offs, item_type, item_count).reshape(np_dims),
                 field = field,
             ))
         self.tensors = tensors
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index d5e323a52ef14..76385a82872c9 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -5,7 +5,10 @@
 import shutil
 import struct
 import tempfile
+from dataclasses import dataclass
 from enum import Enum, auto
+from math import prod
+from pathlib import Path
 from io import BufferedWriter
 from typing import IO, Any, Sequence, Mapping
 from string import ascii_letters, digits
@@ -13,7 +16,6 @@
 import numpy as np
 
 from .constants import (
-    GGML_QUANT_SIZES,
     GGUF_DEFAULT_ALIGNMENT,
     GGUF_MAGIC,
     GGUF_VERSION,
@@ -26,20 +28,44 @@
     TokenType,
 )
 
+from .quants import quant_shape_from_byte_shape
+
 logger = logging.getLogger(__name__)
 
 
+SHARD_NAME_FORMAT = "{:s}-{:05d}-of-{:05d}.gguf"
+
+
+@dataclass
+class TensorInfo:
+    shape: Sequence[int]
+    dtype: GGMLQuantizationType
+    nbytes: int
+    tensor: np.ndarray[Any, Any] | None = None
+
+
+@dataclass
+class GGUFValue:
+    value: Any
+    type: GGUFValueType
+
+
 class WriterState(Enum):
+    NO_FILE = auto()
     EMPTY   = auto()
     HEADER  = auto()
     KV_DATA = auto()
     TI_DATA = auto()
+    WEIGHTS = auto()
 
 
 class GGUFWriter:
-    fout: BufferedWriter
+    fout: list[BufferedWriter] | None
+    path: Path | None
     temp_file: tempfile.SpooledTemporaryFile[bytes] | None
-    tensors: list[np.ndarray[Any, Any]]
+    tensors: list[dict[str, TensorInfo]]
+    kv_data: list[dict[str, GGUFValue]]
+    state: WriterState
     _simple_value_packing = {
         GGUFValueType.UINT8:   "B",
         GGUFValueType.INT8:    "b",
@@ -55,141 +81,240 @@ class GGUFWriter:
     }
 
     def __init__(
-        self, path: os.PathLike[str] | str, arch: str, use_temp_file: bool = True,
-        endianess: GGUFEndian = GGUFEndian.LITTLE,
+        self, path: os.PathLike[str] | str | None, arch: str, use_temp_file: bool = False, endianess: GGUFEndian = GGUFEndian.LITTLE,
+        split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False
     ):
-        self.fout = open(path, "wb")
+        self.fout = None
+        self.path = Path(path) if path else None
         self.arch = arch
         self.endianess = endianess
-        self.offset_tensor = 0
         self.data_alignment = GGUF_DEFAULT_ALIGNMENT
-        self.kv_data = bytearray()
-        self.kv_data_count = 0
-        self.ti_data = bytearray()
-        self.ti_data_count = 0
-        self.ti_names = set()
         self.use_temp_file = use_temp_file
         self.temp_file = None
-        self.tensors = []
+        self.tensors = [{}]
+        self.kv_data = [{}]
+        self.split_max_tensors = split_max_tensors
+        self.split_max_size = split_max_size
+        self.dry_run = dry_run
+        self.small_first_shard = small_first_shard
         logger.info("gguf: This GGUF file is for {0} Endian only".format(
             "Big" if self.endianess == GGUFEndian.BIG else "Little",
         ))
-        self.state = WriterState.EMPTY
+        self.state = WriterState.NO_FILE
+
+        if self.small_first_shard:
+            self.tensors.append({})
 
         self.add_architecture()
 
-    def write_header_to_file(self) -> None:
+    def get_total_parameter_count(self) -> tuple[int, int, int, int]:
+        total_params = 0
+        shared_params = 0
+        expert_params = 0
+
+        expert_sum = 0
+        n_expert_tensors = 0
+
+        last_lora_a: tuple[str, TensorInfo] | None = None
+
+        for tensors in self.tensors:
+            for name, info in tensors.items():
+
+                shape = info.shape
+
+                if name.endswith(".lora_a"):
+                    last_lora_a = (name, info)
+                    continue
+                elif name.endswith(".lora_b"):
+                    if last_lora_a is None or last_lora_a[0] != name[:-1] + "a":
+                        # Bail when the LoRA pair can't be found trivially
+                        logger.warning("can't measure LoRA size correctly, tensor order is unusual")
+                        return 0, 0, 0, 0
+                    else:
+                        shape = (*shape[:-1], last_lora_a[1].shape[-1])
+
+                size = prod(shape)
+
+                if "_exps." in name:
+                    expert_params += (size // shape[-3])
+                    expert_sum += shape[-3]
+                    n_expert_tensors += 1
+                else:
+                    shared_params += size
+
+                total_params += size
+
+        # Hopefully this should work even for variable-expert-count models
+        expert_count = (expert_sum // n_expert_tensors) if n_expert_tensors > 0 else 0
+
+        # Negate the total to signal it's likely not exact
+        if last_lora_a is not None:
+            total_params = -total_params
+
+        # NOTE: keep the output in the same order as accepted by 'size_label' in gguf-py/gguf/utility.py
+        return total_params, shared_params, expert_params, expert_count
+
+    def format_shard_names(self, path: Path) -> list[Path]:
+        if len(self.tensors) == 1:
+            return [path]
+        return [path.with_name(SHARD_NAME_FORMAT.format(path.stem, i + 1, len(self.tensors))) for i in range(len(self.tensors))]
+
+    def open_output_file(self, path: Path | None = None) -> None:
+        if self.state is WriterState.EMPTY and self.fout is not None and (path is None or path == self.path):
+            # allow calling this multiple times as long as the path is the same
+            return
+
+        if self.state is not WriterState.NO_FILE:
+            raise ValueError(f'Expected output file to be not yet opened, got {self.state}')
+
+        if path is not None:
+            self.path = path
+
+        if self.path is not None:
+            filenames = self.print_plan()
+            self.fout = [open(filename, "wb") for filename in filenames]
+            self.state = WriterState.EMPTY
+
+    def print_plan(self) -> list[Path]:
+        logger.info("Writing the following files:")
+        assert self.path is not None
+        filenames = self.format_shard_names(self.path)
+        assert len(filenames) == len(self.tensors)
+        for name, tensors in zip(filenames, self.tensors):
+            logger.info(f"{name}: n_tensors = {len(tensors)}, total_size = {GGUFWriter.format_n_bytes_to_str(sum(ti.nbytes for ti in tensors.values()))}")
+
+        if self.dry_run:
+            logger.info("Dry run, not writing files")
+            for name in filenames:
+                print(name)  # noqa: NP100
+            exit()
+
+        return filenames
+
+    def add_shard_kv_data(self) -> None:
+        if len(self.tensors) == 1:
+            return
+
+        total_tensors = sum(len(t) for t in self.tensors)
+        assert self.fout is not None
+        total_splits = len(self.fout)
+        self.kv_data.extend({} for _ in range(len(self.kv_data), total_splits))
+        for i, kv_data in enumerate(self.kv_data):
+            kv_data[Keys.Split.LLM_KV_SPLIT_NO] = GGUFValue(i, GGUFValueType.UINT16)
+            kv_data[Keys.Split.LLM_KV_SPLIT_COUNT] = GGUFValue(total_splits, GGUFValueType.UINT16)
+            kv_data[Keys.Split.LLM_KV_SPLIT_TENSORS_COUNT] = GGUFValue(total_tensors, GGUFValueType.INT32)
+
+    def write_header_to_file(self, path: Path | None = None) -> None:
+        if len(self.tensors) == 1 and (self.split_max_tensors != 0 or self.split_max_size != 0):
+            logger.warning("Model fails split requirements, not splitting")
+
+        self.open_output_file(path)
+
         if self.state is not WriterState.EMPTY:
             raise ValueError(f'Expected output file to be empty, got {self.state}')
 
-        self._write_packed("<I", GGUF_MAGIC, skip_pack_prefix = True)
-        self._write_packed("I", GGUF_VERSION)
-        self._write_packed("Q", self.ti_data_count)
-        self._write_packed("Q", self.kv_data_count)
-        self.flush()
+        assert self.fout is not None
+        assert len(self.fout) == len(self.tensors)
+        assert len(self.kv_data) == 1
+
+        self.add_shard_kv_data()
+
+        for fout, tensors, kv_data in zip(self.fout, self.tensors, self.kv_data):
+            fout.write(self._pack("<I", GGUF_MAGIC, skip_pack_prefix = True))
+            fout.write(self._pack("I", GGUF_VERSION))
+            fout.write(self._pack("Q", len(tensors)))
+            fout.write(self._pack("Q", len(kv_data)))
+            fout.flush()
         self.state = WriterState.HEADER
 
     def write_kv_data_to_file(self) -> None:
         if self.state is not WriterState.HEADER:
             raise ValueError(f'Expected output file to contain the header, got {self.state}')
+        assert self.fout is not None
+
+        for fout, kv_data in zip(self.fout, self.kv_data):
+            kv_bytes = bytearray()
+
+            for key, val in kv_data.items():
+                kv_bytes += self._pack_val(key, GGUFValueType.STRING, add_vtype=False)
+                kv_bytes += self._pack_val(val.value, val.type, add_vtype=True)
+
+            fout.write(kv_bytes)
 
-        self.fout.write(self.kv_data)
         self.flush()
         self.state = WriterState.KV_DATA
 
     def write_ti_data_to_file(self) -> None:
         if self.state is not WriterState.KV_DATA:
             raise ValueError(f'Expected output file to contain KV data, got {self.state}')
-
-        self.fout.write(self.ti_data)
-        self.flush()
+        assert self.fout is not None
+
+        for fout, tensors in zip(self.fout, self.tensors):
+            ti_data = bytearray()
+            offset_tensor = 0
+
+            for name, ti in tensors.items():
+                ti_data += self._pack_val(name, GGUFValueType.STRING, add_vtype=False)
+                n_dims = len(ti.shape)
+                ti_data += self._pack("I", n_dims)
+                for j in range(n_dims):
+                    ti_data += self._pack("Q", ti.shape[n_dims - 1 - j])
+                ti_data += self._pack("I", ti.dtype)
+                ti_data += self._pack("Q", offset_tensor)
+                offset_tensor += GGUFWriter.ggml_pad(ti.nbytes, self.data_alignment)
+
+            fout.write(ti_data)
+            fout.flush()
         self.state = WriterState.TI_DATA
 
-    def add_key(self, key: str) -> None:
-        self.add_val(key, GGUFValueType.STRING, add_vtype=False)
+    def add_key_value(self, key: str, val: Any, vtype: GGUFValueType) -> None:
+        if any(key in kv_data for kv_data in self.kv_data):
+            raise ValueError(f'Duplicated key name {key!r}')
+
+        self.kv_data[0][key] = GGUFValue(value=val, type=vtype)
 
     def add_uint8(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT8)
+        self.add_key_value(key,val, GGUFValueType.UINT8)
 
     def add_int8(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT8)
+        self.add_key_value(key, val, GGUFValueType.INT8)
 
     def add_uint16(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT16)
+        self.add_key_value(key, val, GGUFValueType.UINT16)
 
     def add_int16(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT16)
+        self.add_key_value(key, val, GGUFValueType.INT16)
 
     def add_uint32(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT32)
+        self.add_key_value(key, val, GGUFValueType.UINT32)
 
     def add_int32(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT32)
+        self.add_key_value(key, val, GGUFValueType.INT32)
 
     def add_float32(self, key: str, val: float) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.FLOAT32)
+        self.add_key_value(key, val, GGUFValueType.FLOAT32)
 
     def add_uint64(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT64)
+        self.add_key_value(key, val, GGUFValueType.UINT64)
 
     def add_int64(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT64)
+        self.add_key_value(key, val, GGUFValueType.INT64)
 
     def add_float64(self, key: str, val: float) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.FLOAT64)
+        self.add_key_value(key, val, GGUFValueType.FLOAT64)
 
     def add_bool(self, key: str, val: bool) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.BOOL)
+        self.add_key_value(key, val, GGUFValueType.BOOL)
 
     def add_string(self, key: str, val: str) -> None:
         if not val:
             return
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.STRING)
+        self.add_key_value(key, val, GGUFValueType.STRING)
 
     def add_array(self, key: str, val: Sequence[Any]) -> None:
-        if not isinstance(val, Sequence):
-            raise ValueError("Value must be a sequence for array type")
-
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.ARRAY)
-
-    def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None:
-        if vtype is None:
-            vtype = GGUFValueType.get_type(val)
-
-        if add_vtype:
-            self.kv_data += self._pack("I", vtype)
-            self.kv_data_count += 1
-
-        pack_fmt = self._simple_value_packing.get(vtype)
-        if pack_fmt is not None:
-            self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL)
-        elif vtype == GGUFValueType.STRING:
-            encoded_val = val.encode("utf-8") if isinstance(val, str) else val
-            self.kv_data += self._pack("Q", len(encoded_val))
-            self.kv_data += encoded_val
-        elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val:
-            ltype = GGUFValueType.get_type(val[0])
-            if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
-                raise ValueError("All items in a GGUF array should be of the same type")
-            self.kv_data += self._pack("I", ltype)
-            self.kv_data += self._pack("Q", len(val))
-            for item in val:
-                self.add_val(item, add_vtype=False)
-        else:
-            raise ValueError("Invalid GGUF metadata value type or value")
+        if len(val) == 0:
+            return
+        self.add_key_value(key, val, GGUFValueType.ARRAY)
 
     @staticmethod
     def ggml_pad(x: int, n: int) -> int:
@@ -199,16 +324,12 @@ def add_tensor_info(
         self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype,
         tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None,
     ) -> None:
-        if self.state is not WriterState.EMPTY:
-            raise ValueError(f'Expected output file to be empty, got {self.state}')
+        if self.state is not WriterState.NO_FILE:
+            raise ValueError(f'Expected output file to be not yet opened, got {self.state}')
 
-        if name in self.ti_names:
-            raise ValueError(f'Duplicated tensor name {name}')
-        self.ti_names.add(name)
+        if any(name in tensors for tensors in self.tensors):
+            raise ValueError(f'Duplicated tensor name {name!r}')
 
-        encoded_name = name.encode("utf-8")
-        self.ti_data += self._pack("Q", len(encoded_name))
-        self.ti_data += encoded_name
         if raw_dtype is None:
             if tensor_dtype == np.float16:
                 dtype = GGMLQuantizationType.F16
@@ -229,18 +350,20 @@ def add_tensor_info(
         else:
             dtype = raw_dtype
             if tensor_dtype == np.uint8:
-                block_size, type_size = GGML_QUANT_SIZES[raw_dtype]
-                if tensor_shape[-1] % type_size != 0:
-                    raise ValueError(f"Quantized tensor row size ({tensor_shape[-1]}) is not a multiple of {dtype.name} type size ({type_size})")
-                tensor_shape = tuple(tensor_shape[:-1]) + (tensor_shape[-1] // type_size * block_size,)
-        n_dims = len(tensor_shape)
-        self.ti_data += self._pack("I", n_dims)
-        for i in range(n_dims):
-            self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
-        self.ti_data += self._pack("I", dtype)
-        self.ti_data += self._pack("Q", self.offset_tensor)
-        self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
-        self.ti_data_count += 1
+                tensor_shape = quant_shape_from_byte_shape(tensor_shape, raw_dtype)
+
+        # make sure there is at least one tensor before splitting
+        if len(self.tensors[-1]) > 0:
+            if (  # split when over tensor limit
+                self.split_max_tensors != 0
+                and len(self.tensors[-1]) >= self.split_max_tensors
+            ) or (   # split when over size limit
+                self.split_max_size != 0
+                and sum(ti.nbytes for ti in self.tensors[-1].values()) + tensor_nbytes > self.split_max_size
+            ):
+                self.tensors.append({})
+
+        self.tensors[-1][name] = TensorInfo(shape=tensor_shape, dtype=dtype, nbytes=tensor_nbytes)
 
     def add_tensor(
         self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None,
@@ -254,10 +377,10 @@ def add_tensor(
             self.temp_file = fp
 
         shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape
-        self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
+        self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype=raw_dtype)
 
         if self.temp_file is None:
-            self.tensors.append(tensor)
+            self.tensors[-1][name].tensor = tensor
             return
 
         tensor.tofile(self.temp_file)
@@ -269,100 +392,205 @@ def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None
             fp.write(bytes([0] * pad))
 
     def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None:
-        if self.state is not WriterState.TI_DATA:
-            raise ValueError(f'Expected output file to contain tensor info, got {self.state}')
+        if self.state is not WriterState.TI_DATA and self.state is not WriterState.WEIGHTS:
+            raise ValueError(f'Expected output file to contain tensor info or weights, got {self.state}')
+        assert self.fout is not None
 
         if self.endianess == GGUFEndian.BIG:
             tensor.byteswap(inplace=True)
-        self.write_padding(self.fout, self.fout.tell())
-        tensor.tofile(self.fout)
-        self.write_padding(self.fout, tensor.nbytes)
+
+        file_id = -1
+        for i, tensors in enumerate(self.tensors):
+            if len(tensors) > 0:
+                file_id = i
+                break
+
+        fout = self.fout[file_id]
+
+        # pop the first tensor info
+        # TODO: cleaner way to get the first key
+        first_tensor_name = [name for name, _ in zip(self.tensors[file_id].keys(), range(1))][0]
+        ti = self.tensors[file_id].pop(first_tensor_name)
+        assert ti.nbytes == tensor.nbytes
+
+        self.write_padding(fout, fout.tell())
+        tensor.tofile(fout)
+        self.write_padding(fout, tensor.nbytes)
+
+        self.state = WriterState.WEIGHTS
 
     def write_tensors_to_file(self, *, progress: bool = False) -> None:
         self.write_ti_data_to_file()
 
-        self.write_padding(self.fout, self.fout.tell())
+        assert self.fout is not None
+
+        for fout in self.fout:
+            self.write_padding(fout, fout.tell())
 
         if self.temp_file is None:
-            self.tensors.reverse()  # to pop from the "beginning" in constant time
+            shard_bar = None
+            bar = None
 
             if progress:
                 from tqdm import tqdm
 
-                total_bytes = sum(t.nbytes for t in self.tensors)
+                total_bytes = sum(ti.nbytes for t in self.tensors for ti in t.values())
 
+                if len(self.fout) > 1:
+                    shard_bar = tqdm(desc=f"Shard (0/{len(self.fout)})", total=None, unit="byte", unit_scale=True)
                 bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True)
 
-                while True:
-                    try:
-                        tensor = self.tensors.pop()
-                    except IndexError:
-                        break
-                    tensor.tofile(self.fout)
-                    bar.update(tensor.nbytes)
-                    self.write_padding(self.fout, tensor.nbytes)
-                return
-            while True:
-                try:
-                    tensor = self.tensors.pop()
-                except IndexError:
-                    break
-                tensor.tofile(self.fout)
-                self.write_padding(self.fout, tensor.nbytes)
-            return
+            for i, (fout, tensors) in enumerate(zip(self.fout, self.tensors)):
+                if shard_bar is not None:
+                    shard_bar.set_description(f"Shard ({i + 1}/{len(self.fout)})")
+                    total = sum(ti.nbytes for ti in tensors.values())
+                    shard_bar.reset(total=(total if total > 0 else None))
+
+                # relying on the fact that Python dicts preserve insertion order (since 3.7)
+                for ti in tensors.values():
+                    assert ti.tensor is not None  # can only iterate once over the tensors
+                    assert ti.tensor.nbytes == ti.nbytes
+                    ti.tensor.tofile(fout)
+                    if shard_bar is not None:
+                        shard_bar.update(ti.nbytes)
+                    if bar is not None:
+                        bar.update(ti.nbytes)
+                    self.write_padding(fout, ti.nbytes)
+                    ti.tensor = None
+        else:
+            self.temp_file.seek(0)
 
-        self.temp_file.seek(0)
+            shutil.copyfileobj(self.temp_file, self.fout[0 if not self.small_first_shard else 1])
+            self.flush()
+            self.temp_file.close()
 
-        shutil.copyfileobj(self.temp_file, self.fout)
-        self.flush()
-        self.temp_file.close()
+        self.state = WriterState.WEIGHTS
 
     def flush(self) -> None:
-        self.fout.flush()
+        assert self.fout is not None
+        for fout in self.fout:
+            fout.flush()
 
     def close(self) -> None:
-        self.fout.close()
+        if self.fout is not None:
+            for fout in self.fout:
+                fout.close()
+            self.fout = None
+
+    def add_type(self, type_name: str) -> None:
+        self.add_string(Keys.General.TYPE, type_name)
 
     def add_architecture(self) -> None:
         self.add_string(Keys.General.ARCHITECTURE, self.arch)
 
+    def add_quantization_version(self, quantization_version: int) -> None:
+        self.add_uint32(Keys.General.QUANTIZATION_VERSION, quantization_version)
+
+    def add_custom_alignment(self, alignment: int) -> None:
+        self.data_alignment = alignment
+        self.add_uint32(Keys.General.ALIGNMENT, alignment)
+
+    def add_file_type(self, ftype: int) -> None:
+        self.add_uint32(Keys.General.FILE_TYPE, ftype)
+
+    def add_name(self, name: str) -> None:
+        self.add_string(Keys.General.NAME, name)
+
     def add_author(self, author: str) -> None:
         self.add_string(Keys.General.AUTHOR, author)
 
     def add_version(self, version: str) -> None:
         self.add_string(Keys.General.VERSION, version)
 
-    def add_tensor_data_layout(self, layout: str) -> None:
-        self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
+    def add_organization(self, organization: str) -> None:
+        self.add_string(Keys.General.ORGANIZATION, organization)
 
-    def add_url(self, url: str) -> None:
-        self.add_string(Keys.General.URL, url)
+    def add_finetune(self, finetune: str) -> None:
+        self.add_string(Keys.General.FINETUNE, finetune)
+
+    def add_basename(self, basename: str) -> None:
+        self.add_string(Keys.General.BASENAME, basename)
 
     def add_description(self, description: str) -> None:
         self.add_string(Keys.General.DESCRIPTION, description)
 
-    def add_licence(self, licence: str) -> None:
-        self.add_string(Keys.General.LICENSE, licence)
+    def add_quantized_by(self, quantized: str) -> None:
+        self.add_string(Keys.General.QUANTIZED_BY, quantized)
+
+    def add_size_label(self, size_label: str) -> None:
+        self.add_string(Keys.General.SIZE_LABEL, size_label)
+
+    def add_license(self, license: str) -> None:
+        self.add_string(Keys.General.LICENSE, license)
+
+    def add_license_name(self, license: str) -> None:
+        self.add_string(Keys.General.LICENSE_NAME, license)
+
+    def add_license_link(self, license: str) -> None:
+        self.add_string(Keys.General.LICENSE_LINK, license)
+
+    def add_url(self, url: str) -> None:
+        self.add_string(Keys.General.URL, url)
+
+    def add_doi(self, doi: str) -> None:
+        self.add_string(Keys.General.DOI, doi)
+
+    def add_uuid(self, uuid: str) -> None:
+        self.add_string(Keys.General.UUID, uuid)
+
+    def add_repo_url(self, repo_url: str) -> None:
+        self.add_string(Keys.General.REPO_URL, repo_url)
 
     def add_source_url(self, url: str) -> None:
         self.add_string(Keys.General.SOURCE_URL, url)
 
-    def add_source_hf_repo(self, repo: str) -> None:
-        self.add_string(Keys.General.SOURCE_HF_REPO, repo)
+    def add_source_doi(self, doi: str) -> None:
+        self.add_string(Keys.General.SOURCE_DOI, doi)
 
-    def add_file_type(self, ftype: int) -> None:
-        self.add_uint32(Keys.General.FILE_TYPE, ftype)
+    def add_source_uuid(self, uuid: str) -> None:
+        self.add_string(Keys.General.SOURCE_UUID, uuid)
 
-    def add_name(self, name: str) -> None:
-        self.add_string(Keys.General.NAME, name)
+    def add_source_repo_url(self, repo_url: str) -> None:
+        self.add_string(Keys.General.SOURCE_REPO_URL, repo_url)
 
-    def add_quantization_version(self, quantization_version: int) -> None:
-        self.add_uint32(
-            Keys.General.QUANTIZATION_VERSION, quantization_version)
+    def add_base_model_count(self, source_count: int) -> None:
+        self.add_uint32(Keys.General.BASE_MODEL_COUNT, source_count)
 
-    def add_custom_alignment(self, alignment: int) -> None:
-        self.data_alignment = alignment
-        self.add_uint32(Keys.General.ALIGNMENT, alignment)
+    def add_base_model_name(self, source_id: int, name: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_NAME.format(id=source_id), name)
+
+    def add_base_model_author(self, source_id: int, author: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_AUTHOR.format(id=source_id), author)
+
+    def add_base_model_version(self, source_id: int, version: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_VERSION.format(id=source_id), version)
+
+    def add_base_model_organization(self, source_id: int, organization: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_ORGANIZATION.format(id=source_id), organization)
+
+    def add_base_model_url(self, source_id: int, url: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_URL.format(id=source_id), url)
+
+    def add_base_model_doi(self, source_id: int, doi: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_DOI.format(id=source_id), doi)
+
+    def add_base_model_uuid(self, source_id: int, uuid: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_UUID.format(id=source_id), uuid)
+
+    def add_base_model_repo_url(self, source_id: int, repo_url: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_REPO_URL.format(id=source_id), repo_url)
+
+    def add_tags(self, tags: Sequence[str]) -> None:
+        self.add_array(Keys.General.TAGS, tags)
+
+    def add_languages(self, languages: Sequence[str]) -> None:
+        self.add_array(Keys.General.LANGUAGES, languages)
+
+    def add_datasets(self, datasets: Sequence[str]) -> None:
+        self.add_array(Keys.General.DATASETS, datasets)
+
+    def add_tensor_data_layout(self, layout: str) -> None:
+        self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
 
     def add_vocab_size(self, size: int) -> None:
         self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
@@ -376,17 +604,38 @@ def add_embedding_length(self, length: int) -> None:
     def add_block_count(self, length: int) -> None:
         self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
 
-    def add_feed_forward_length(self, length: int) -> None:
-        self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+    def add_leading_dense_block_count(self, length: int) -> None:
+        self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length)
+
+    def add_feed_forward_length(self, length: int | Sequence[int]) -> None:
+        if isinstance(length, int):
+            self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+        else:
+            self.add_array(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+
+    def add_expert_feed_forward_length(self, length: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+
+    def add_expert_shared_feed_forward_length(self, length: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_SHARED_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
 
     def add_parallel_residual(self, use: bool) -> None:
         self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
 
-    def add_head_count(self, count: int) -> None:
-        self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
+    def add_decoder_start_token_id(self, id: int) -> None:
+        self.add_uint32(Keys.LLM.DECODER_START_TOKEN_ID.format(arch=self.arch), id)
 
-    def add_head_count_kv(self, count: int) -> None:
-        self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
+    def add_head_count(self, count: int | Sequence[int]) -> None:
+        if isinstance(count, int):
+            self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
+        else:
+            self.add_array(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
+
+    def add_head_count_kv(self, count: int | Sequence[int]) -> None:
+        if isinstance(count, int):
+            self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
+        else:
+            self.add_array(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
 
     def add_key_length(self, length: int) -> None:
         self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length)
@@ -403,12 +652,24 @@ def add_clamp_kqv(self, value: float) -> None:
     def add_logit_scale(self, value: float) -> None:
         self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value)
 
+    def add_attn_logit_softcapping(self, value: float) -> None:
+        self.add_float32(Keys.LLM.ATTN_LOGIT_SOFTCAPPING.format(arch=self.arch), value)
+
+    def add_final_logit_softcapping(self, value: float) -> None:
+        self.add_float32(Keys.LLM.FINAL_LOGIT_SOFTCAPPING.format(arch=self.arch), value)
+
     def add_expert_count(self, count: int) -> None:
         self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
 
     def add_expert_used_count(self, count: int) -> None:
         self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
 
+    def add_expert_shared_count(self, count: int) -> None:
+        self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count)
+
+    def add_expert_weights_scale(self, value: float) -> None:
+        self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value)
+
     def add_layer_norm_eps(self, value: float) -> None:
         self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
 
@@ -418,6 +679,18 @@ def add_layer_norm_rms_eps(self, value: float) -> None:
     def add_causal_attention(self, value: bool) -> None:
         self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
 
+    def add_q_lora_rank(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length)
+
+    def add_kv_lora_rank(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length)
+
+    def add_relative_attn_buckets_count(self, value: int) -> None:
+        self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value)
+
+    def add_sliding_window(self, value: int) -> None:
+        self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value)
+
     def add_pooling_type(self, value: PoolingType) -> None:
         self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
 
@@ -433,12 +706,18 @@ def add_rope_scaling_type(self, value: RopeScalingType) -> None:
     def add_rope_scaling_factor(self, value: float) -> None:
         self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value)
 
+    def add_rope_scaling_attn_factors(self, value: float) -> None:
+        self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value)
+
     def add_rope_scaling_orig_ctx_len(self, value: int) -> None:
         self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value)
 
     def add_rope_scaling_finetuned(self, value: bool) -> None:
         self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
 
+    def add_rope_scaling_yarn_log_mul(self, value: float) -> None:
+        self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value)
+
     def add_ssm_conv_kernel(self, value: int) -> None:
         self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)
 
@@ -502,6 +781,12 @@ def add_add_eos_token(self, value: bool) -> None:
     def add_add_space_prefix(self, value: bool) -> None:
         self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
 
+    def add_remove_extra_whitespaces(self, value: bool) -> None:
+        self.add_bool(Keys.Tokenizer.REMOVE_EXTRA_WS, value)
+
+    def add_precompiled_charsmap(self, charsmap: Sequence[bytes]) -> None:
+        self.add_array(Keys.Tokenizer.PRECOMPILED_CHARSMAP, charsmap)
+
     def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
         if not isinstance(value, str):
             template_default = None
@@ -543,11 +828,58 @@ def add_middle_token_id(self, id: int) -> None:
     def add_eot_token_id(self, id: int) -> None:
         self.add_uint32(Keys.Tokenizer.EOT_ID, id)
 
+    def add_eom_token_id(self, id: int) -> None:
+        self.add_uint32(Keys.Tokenizer.EOM_ID, id)
+
     def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes:
         pack_prefix = ''
         if not skip_pack_prefix:
             pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>'
         return struct.pack(f'{pack_prefix}{fmt}', value)
 
-    def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None:
-        self.fout.write(self._pack(fmt, value, skip_pack_prefix))
+    def _pack_val(self, val: Any, vtype: GGUFValueType, add_vtype: bool) -> bytes:
+        kv_data = bytearray()
+
+        if add_vtype:
+            kv_data += self._pack("I", vtype)
+
+        pack_fmt = self._simple_value_packing.get(vtype)
+        if pack_fmt is not None:
+            kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL)
+        elif vtype == GGUFValueType.STRING:
+            encoded_val = val.encode("utf-8") if isinstance(val, str) else val
+            kv_data += self._pack("Q", len(encoded_val))
+            kv_data += encoded_val
+        elif vtype == GGUFValueType.ARRAY:
+
+            if not isinstance(val, Sequence):
+                raise ValueError("Invalid GGUF metadata array, expecting sequence")
+
+            if len(val) == 0:
+                raise ValueError("Invalid GGUF metadata array. Empty array")
+
+            if isinstance(val, bytes):
+                ltype = GGUFValueType.UINT8
+            else:
+                ltype = GGUFValueType.get_type(val[0])
+                if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
+                    raise ValueError("All items in a GGUF array should be of the same type")
+            kv_data += self._pack("I", ltype)
+            kv_data += self._pack("Q", len(val))
+            for item in val:
+                kv_data += self._pack_val(item, ltype, add_vtype=False)
+        else:
+            raise ValueError("Invalid GGUF metadata value type or value")
+
+        return kv_data
+
+    @staticmethod
+    def format_n_bytes_to_str(num: int) -> str:
+        if num == 0:
+            return "negligible - metadata only"
+        fnum = float(num)
+        for unit in ("", "K", "M", "G"):
+            if abs(fnum) < 1000.0:
+                return f"{fnum:3.1f}{unit}"
+            fnum /= 1000.0
+        return f"{fnum:.1f}T - over 1TB, split recommended"
diff --git a/gguf-py/gguf/lazy.py b/gguf-py/gguf/lazy.py
index 1167335b83ab3..8d4fece2dca86 100644
--- a/gguf-py/gguf/lazy.py
+++ b/gguf-py/gguf/lazy.py
@@ -3,10 +3,8 @@
 
 import logging
 from typing import Any, Callable
-from collections import deque
 
 import numpy as np
-from numpy._typing import _Shape
 from numpy.typing import DTypeLike
 
 
@@ -16,16 +14,16 @@
 class LazyMeta(ABCMeta):
 
     def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs):
-        def __getattr__(self, __name: str) -> Any:
-            meta_attr = getattr(self._meta, __name)
+        def __getattr__(self, name: str) -> Any:
+            meta_attr = getattr(self._meta, name)
             if callable(meta_attr):
                 return type(self)._wrap_fn(
-                    (lambda s, *args, **kwargs: getattr(s, __name)(*args, **kwargs)),
+                    (lambda s, *args, **kwargs: getattr(s, name)(*args, **kwargs)),
                     use_self=self,
                 )
             elif isinstance(meta_attr, self._tensor_type):
                 # e.g. self.T with torch.Tensor should still be wrapped
-                return type(self)._wrap_fn(lambda s: getattr(s, __name))(self)
+                return type(self)._wrap_fn(lambda s: getattr(s, name))(self)
             else:
                 # no need to wrap non-tensor properties,
                 # and they likely don't depend on the actual contents of the tensor
@@ -75,20 +73,18 @@ class LazyBase(ABC, metaclass=LazyMeta):
     _tensor_type: type
     _meta: Any
     _data: Any | None
-    _lazy: deque[LazyBase]  # shared within a graph, to avoid deep recursion when making eager
     _args: tuple
-    _func: Callable[[tuple], Any] | None
+    _kwargs: dict[str, Any]
+    _func: Callable[[Any], Any] | None
 
-    def __init__(self, *, meta: Any, data: Any | None = None, lazy: deque[LazyBase] | None = None, args: tuple = (), func: Callable[[tuple], Any] | None = None):
+    def __init__(self, *, meta: Any, data: Any | None = None, args: tuple = (), kwargs: dict[str, Any] | None = None, func: Callable[[Any], Any] | None = None):
         super().__init__()
         self._meta = meta
         self._data = data
-        self._lazy = lazy if lazy is not None else deque()
         self._args = args
+        self._kwargs = kwargs if kwargs is not None else {}
         self._func = func
         assert self._func is not None or self._data is not None
-        if self._data is None:
-            self._lazy.append(self)
 
     def __init_subclass__(cls) -> None:
         if "_tensor_type" not in cls.__dict__:
@@ -118,6 +114,7 @@ def wrapped_fn(*args, **kwargs):
             args = ((use_self,) if use_self is not None else ()) + args
 
             meta_args = LazyBase._recurse_apply(args, lambda t: t._meta)
+            # TODO: maybe handle tensors in kwargs too
 
             if isinstance(meta_noop, bool) and not meta_noop:
                 try:
@@ -141,21 +138,7 @@ def wrapped_fn(*args, **kwargs):
                         res = cls.meta_with_dtype_and_shape(meta_noop, res.shape)
 
             if isinstance(res, cls._tensor_type):
-                def collect_replace(t: LazyBase):
-                    if collect_replace.shared_lazy is None:
-                        collect_replace.shared_lazy = t._lazy
-                    else:
-                        collect_replace.shared_lazy.extend(t._lazy)
-                        t._lazy = collect_replace.shared_lazy
-
-                # emulating a static variable
-                collect_replace.shared_lazy = None
-
-                LazyBase._recurse_apply(args, collect_replace)
-
-                shared_lazy = collect_replace.shared_lazy
-
-                return cls(meta=cls.eager_to_meta(res), lazy=shared_lazy, args=args, func=lambda a: fn(*a, **kwargs))
+                return cls(meta=cls.eager_to_meta(res), args=args, kwargs=kwargs, func=fn)
             else:
                 del res  # not needed
                 # non-tensor return likely relies on the contents of the args
@@ -167,25 +150,18 @@ def collect_replace(t: LazyBase):
     @classmethod
     def to_eager(cls, t: Any) -> Any:
         def simple_to_eager(_t: LazyBase) -> Any:
-            def already_eager_to_eager(_t: LazyBase) -> Any:
-                assert _t._data is not None
+            if _t._data is not None:
                 return _t._data
 
-            while _t._data is None:
-                lt = _t._lazy.popleft()
-                if lt._data is not None:
-                    # Lazy tensor did not belong in the lazy queue.
-                    # Weirdly only happens with Bloom models...
-                    # likely because tensors aren't unique in the queue.
-                    # The final output is still the same as in eager mode,
-                    # so it's safe to ignore this.
-                    continue
-                assert lt._func is not None
-                lt._args = cls._recurse_apply(lt._args, already_eager_to_eager)
-                lt._data = lt._func(lt._args)
-                # sanity check
-                assert lt._data.dtype == lt._meta.dtype
-                assert lt._data.shape == lt._meta.shape
+            # NOTE: there's a recursion limit in Python (usually 1000)
+
+            assert _t._func is not None
+            _t._args = cls._recurse_apply(_t._args, simple_to_eager)
+            _t._data = _t._func(*_t._args, **_t._kwargs)
+            # sanity check
+            assert _t._data is not None
+            assert _t._data.dtype == _t._meta.dtype
+            assert _t._data.shape == _t._meta.shape
 
             return _t._data
 
@@ -204,7 +180,7 @@ def meta_with_dtype_and_shape(cls, dtype: Any, shape: Any) -> Any: pass
     @classmethod
     def from_eager(cls, t: Any) -> Any:
         if type(t) is cls:
-            # already eager
+            # already lazy
             return t
         elif isinstance(t, cls._tensor_type):
             return cls(meta=cls.eager_to_meta(t), data=t)
@@ -215,8 +191,10 @@ def from_eager(cls, t: Any) -> Any:
 class LazyNumpyTensor(LazyBase):
     _tensor_type = np.ndarray
 
+    shape: tuple[int, ...]  # Makes the type checker happy in quants.py
+
     @classmethod
-    def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: _Shape) -> np.ndarray[Any, Any]:
+    def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: tuple[int, ...]) -> np.ndarray[Any, Any]:
         # The initial idea was to use np.nan as the fill value,
         # but non-float types like np.int16 can't use that.
         # So zero it is.
@@ -226,8 +204,7 @@ def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: _Shape) -> np.ndarra
     def astype(self, dtype, *args, **kwargs):
         meta = type(self).meta_with_dtype_and_shape(dtype, self._meta.shape)
         full_args = (self, dtype,) + args
-        # very important to pass the shared _lazy deque, or else there's an infinite loop somewhere.
-        return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs)))
+        return type(self)(meta=meta, args=full_args, kwargs=kwargs, func=(lambda a, *args, **kwargs: a.astype(*args, **kwargs)))
 
     def tofile(self, *args, **kwargs):
         eager = LazyNumpyTensor.to_eager(self)
diff --git a/gguf-py/gguf/metadata.py b/gguf-py/gguf/metadata.py
new file mode 100644
index 0000000000000..db318542a279b
--- /dev/null
+++ b/gguf-py/gguf/metadata.py
@@ -0,0 +1,510 @@
+from __future__ import annotations
+
+import re
+import json
+import yaml
+import logging
+from pathlib import Path
+from typing import Any, Literal, Optional
+from dataclasses import dataclass
+
+from .constants import Keys
+
+import gguf
+
+logger = logging.getLogger("metadata")
+
+
+@dataclass
+class Metadata:
+    # Authorship Metadata to be written to GGUF KV Store
+    name: Optional[str] = None
+    author: Optional[str] = None
+    version: Optional[str] = None
+    organization: Optional[str] = None
+    finetune: Optional[str] = None
+    basename: Optional[str] = None
+    description: Optional[str] = None
+    quantized_by: Optional[str] = None
+    size_label: Optional[str] = None
+    url: Optional[str] = None
+    doi: Optional[str] = None
+    uuid: Optional[str] = None
+    repo_url: Optional[str] = None
+    source_url: Optional[str] = None
+    source_doi: Optional[str] = None
+    source_uuid: Optional[str] = None
+    source_repo_url: Optional[str] = None
+    license: Optional[str] = None
+    license_name: Optional[str] = None
+    license_link: Optional[str] = None
+    base_models: Optional[list[dict]] = None
+    tags: Optional[list[str]] = None
+    languages: Optional[list[str]] = None
+    datasets: Optional[list[str]] = None
+
+    @staticmethod
+    def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, total_params: int = 0) -> Metadata:
+        # This grabs as many contextual authorship metadata as possible from the model repository
+        # making any conversion as required to match the gguf kv store metadata format
+        # as well as giving users the ability to override any authorship metadata that may be incorrect
+
+        # Create a new Metadata instance
+        metadata = Metadata()
+
+        model_card = Metadata.load_model_card(model_path)
+        hf_params = Metadata.load_hf_parameters(model_path)
+        # TODO: load adapter_config.json when possible, it usually contains the base model of the LoRA adapter
+
+        # heuristics
+        metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_path, total_params)
+
+        # Metadata Override File Provided
+        # This is based on LLM_KV_NAMES mapping in llama.cpp
+        metadata_override = Metadata.load_metadata_override(metadata_override_path)
+
+        metadata.name            = metadata_override.get(Keys.General.NAME,            metadata.name)
+        metadata.author          = metadata_override.get(Keys.General.AUTHOR,          metadata.author)
+        metadata.version         = metadata_override.get(Keys.General.VERSION,         metadata.version)
+        metadata.organization    = metadata_override.get(Keys.General.ORGANIZATION,    metadata.organization)
+
+        metadata.finetune        = metadata_override.get(Keys.General.FINETUNE,        metadata.finetune)
+        metadata.basename        = metadata_override.get(Keys.General.BASENAME,        metadata.basename)
+
+        metadata.description     = metadata_override.get(Keys.General.DESCRIPTION,     metadata.description)
+        metadata.quantized_by    = metadata_override.get(Keys.General.QUANTIZED_BY,    metadata.quantized_by)
+
+        metadata.size_label      = metadata_override.get(Keys.General.SIZE_LABEL,      metadata.size_label)
+        metadata.license_name    = metadata_override.get(Keys.General.LICENSE_NAME,    metadata.license_name)
+        metadata.license_link    = metadata_override.get(Keys.General.LICENSE_LINK,    metadata.license_link)
+
+        metadata.url             = metadata_override.get(Keys.General.URL,             metadata.url)
+        metadata.doi             = metadata_override.get(Keys.General.DOI,             metadata.doi)
+        metadata.uuid            = metadata_override.get(Keys.General.UUID,            metadata.uuid)
+        metadata.repo_url        = metadata_override.get(Keys.General.REPO_URL,        metadata.repo_url)
+
+        metadata.source_url      = metadata_override.get(Keys.General.SOURCE_URL,      metadata.source_url)
+        metadata.source_doi      = metadata_override.get(Keys.General.SOURCE_DOI,      metadata.source_doi)
+        metadata.source_uuid     = metadata_override.get(Keys.General.SOURCE_UUID,     metadata.source_uuid)
+        metadata.source_repo_url = metadata_override.get(Keys.General.SOURCE_REPO_URL, metadata.source_repo_url)
+
+        # Base Models is received here as an array of models
+        metadata.base_models     = metadata_override.get("general.base_models",        metadata.base_models)
+
+        metadata.tags            = metadata_override.get(Keys.General.TAGS,            metadata.tags)
+        metadata.languages       = metadata_override.get(Keys.General.LANGUAGES,       metadata.languages)
+        metadata.datasets        = metadata_override.get(Keys.General.DATASETS,        metadata.datasets)
+
+        # Direct Metadata Override (via direct cli argument)
+        if model_name is not None:
+            metadata.name = model_name
+
+        return metadata
+
+    @staticmethod
+    def load_metadata_override(metadata_override_path: Optional[Path] = None) -> dict[str, Any]:
+        if metadata_override_path is None or not metadata_override_path.is_file():
+            return {}
+
+        with open(metadata_override_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    @staticmethod
+    def load_model_card(model_path: Optional[Path] = None) -> dict[str, Any]:
+        if model_path is None or not model_path.is_dir():
+            return {}
+
+        model_card_path = model_path / "README.md"
+
+        if not model_card_path.is_file():
+            return {}
+
+        # The model card metadata is assumed to always be in YAML
+        # ref: https://github.com/huggingface/transformers/blob/a5c642fe7a1f25d3bdcd76991443ba6ff7ee34b2/src/transformers/modelcard.py#L468-L473
+        with open(model_card_path, "r", encoding="utf-8") as f:
+            if f.readline() == "---\n":
+                raw = f.read().partition("---\n")[0]
+                data = yaml.safe_load(raw)
+                if isinstance(data, dict):
+                    return data
+                else:
+                    logger.error(f"while reading YAML model card frontmatter, data is {type(data)} instead of dict")
+                    return {}
+            else:
+                return {}
+
+    @staticmethod
+    def load_hf_parameters(model_path: Optional[Path] = None) -> dict[str, Any]:
+        if model_path is None or not model_path.is_dir():
+            return {}
+
+        config_path = model_path / "config.json"
+
+        if not config_path.is_file():
+            return {}
+
+        with open(config_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    @staticmethod
+    def id_to_title(string):
+        # Convert capitalization into title form unless acronym or version number
+        return ' '.join([w.title() if w.islower() and not re.match(r'^(v\d+(?:\.\d+)*|\d.*)$', w) else w for w in string.strip().replace('-', ' ').split()])
+
+    @staticmethod
+    def get_model_id_components(model_id: Optional[str] = None, total_params: int = 0) -> tuple[str | None, str | None, str | None, str | None, str | None, str | None]:
+        # Huggingface often store model id as '<org>/<model name>'
+        # so let's parse it and apply some heuristics if possible for model name components
+
+        if model_id is None:
+            # model ID missing
+            return None, None, None, None, None, None
+
+        if ' ' in model_id:
+            # model ID is actually a normal human sentence
+            # which means its most likely a normal model name only
+            # not part of the hugging face naming standard, but whatever
+            return model_id, None, None, None, None, None
+
+        if '/' in model_id:
+            # model ID (huggingface style)
+            org_component, model_full_name_component = model_id.split('/', 1)
+        else:
+            # model ID but missing org components
+            org_component, model_full_name_component = None, model_id
+
+        # Check if we erroneously matched against './' or '../' etc...
+        if org_component is not None and len(org_component) > 0 and org_component[0] == '.':
+            org_component = None
+
+        name_parts: list[str] = model_full_name_component.split('-')
+
+        # Remove empty parts
+        for i in reversed(range(len(name_parts))):
+            if len(name_parts[i]) == 0:
+                del name_parts[i]
+
+        name_types: list[
+            set[Literal["basename", "size_label", "finetune", "version", "type"]]
+        ] = [set() for _ in name_parts]
+
+        # Annotate the name
+        for i, part in enumerate(name_parts):
+            # Version
+            if re.fullmatch(r'(v|iter)?\d+([.]\d+)*', part, re.IGNORECASE):
+                name_types[i].add("version")
+            # Quant type (should not be there for base models, but still annotated)
+            elif re.fullmatch(r'i?q\d(_\w)*|b?fp?(16|32)', part, re.IGNORECASE):
+                name_types[i].add("type")
+                name_parts[i] = part.upper()
+            # Model size
+            elif i > 0 and re.fullmatch(r'(([A]|\d+[x])?\d+([._]\d+)?[KMBT][\d]?|small|mini|medium|large|x?xl)', part, re.IGNORECASE):
+                part = part.replace("_", ".")
+                # Handle weird bloom-7b1 notation
+                if part[-1].isdecimal():
+                    part = part[:-2] + "." + part[-1] + part[-2]
+                # Normalize the size suffixes
+                if len(part) > 1 and part[-2].isdecimal():
+                    if part[-1] in "kmbt":
+                        part = part[:-1] + part[-1].upper()
+                if total_params != 0:
+                    try:
+                        label_params = float(part[:-1]) * pow(1000, " KMBT".find(part[-1]))
+                        # Only use it as a size label if it's close or bigger than the model size
+                        # Note that LoRA adapters don't necessarily include all layers,
+                        # so this is why bigger label sizes are accepted.
+                        # Do not use the size label when it's smaller than 1/8 of the model size
+                        if (total_params < 0 and label_params < abs(total_params) // 8) or (
+                            # Check both directions when the current model isn't a LoRA adapter
+                            total_params > 0 and abs(label_params - total_params) > 7 * total_params // 8
+                        ):
+                            # Likely a context length
+                            name_types[i].add("finetune")
+                            # Lowercase the size when it's a context length
+                            part = part[:-1] + part[-1].lower()
+                    except ValueError:
+                        # Failed to convert the size label to float, use it anyway
+                        pass
+                if len(name_types[i]) == 0:
+                    name_types[i].add("size_label")
+                name_parts[i] = part
+            # Some easy to recognize finetune names
+            elif i > 0 and re.fullmatch(r'chat|instruct|vision|lora', part, re.IGNORECASE):
+                if total_params < 0 and part.lower() == "lora":
+                    # ignore redundant "lora" in the finetune part when the output is a lora adapter
+                    name_types[i].add("type")
+                else:
+                    name_types[i].add("finetune")
+
+        # Ignore word-based size labels when there is at least a number-based one present
+        # TODO: should word-based size labels always be removed instead?
+        if any(c.isdecimal() for n, t in zip(name_parts, name_types) if "size_label" in t for c in n):
+            for n, t in zip(name_parts, name_types):
+                if "size_label" in t:
+                    if all(c.isalpha() for c in n):
+                        t.remove("size_label")
+
+        at_start = True
+        # Find the basename through the annotated name
+        for part, t in zip(name_parts, name_types):
+            if at_start and ((len(t) == 0 and part[0].isalpha()) or "version" in t):
+                t.add("basename")
+            else:
+                if at_start:
+                    at_start = False
+                if len(t) == 0:
+                    t.add("finetune")
+
+        # Remove the basename annotation from trailing version
+        for part, t in zip(reversed(name_parts), reversed(name_types)):
+            if "basename" in t and len(t) > 1:
+                t.remove("basename")
+            else:
+                break
+
+        basename = "-".join(n for n, t in zip(name_parts, name_types) if "basename" in t) or None
+        # Deduplicate size labels using order-preserving 'dict' ('set' seems to sort the keys)
+        size_label = "-".join(dict.fromkeys(s for s, t in zip(name_parts, name_types) if "size_label" in t).keys()) or None
+        finetune = "-".join(f for f, t in zip(name_parts, name_types) if "finetune" in t) or None
+        # TODO: should the basename version always be excluded?
+        # NOTE: multiple finetune versions are joined together
+        version = "-".join(v for v, t, in zip(name_parts, name_types) if "version" in t and "basename" not in t) or None
+
+        if size_label is None and finetune is None and version is None:
+            # Too ambiguous, output nothing
+            basename = None
+
+        return model_full_name_component, org_component, basename, finetune, version, size_label
+
+    @staticmethod
+    def apply_metadata_heuristic(metadata: Metadata, model_card: Optional[dict] = None, hf_params: Optional[dict] = None, model_path: Optional[Path] = None, total_params: int = 0) -> Metadata:
+        # Reference Model Card Metadata: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+
+        # Model Card Heuristics
+        ########################
+        if model_card is not None:
+
+            def use_model_card_metadata(metadata_key: str, model_card_key: str):
+                if model_card_key in model_card and getattr(metadata, metadata_key, None) is None:
+                    setattr(metadata, metadata_key, model_card.get(model_card_key))
+
+            def use_array_model_card_metadata(metadata_key: str, model_card_key: str):
+                # Note: Will append rather than replace if already exist
+                tags_value = model_card.get(model_card_key, None)
+                if tags_value is None:
+                    return
+
+                current_value = getattr(metadata, metadata_key, None)
+                if current_value is None:
+                    current_value = []
+
+                if isinstance(tags_value, str):
+                    current_value.append(tags_value)
+                elif isinstance(tags_value, list):
+                    current_value.extend(tags_value)
+
+                setattr(metadata, metadata_key, current_value)
+
+            # LLAMA.cpp's direct internal convention
+            # (Definitely not part of hugging face formal/informal standard)
+            #########################################
+            use_model_card_metadata("name", "name")
+            use_model_card_metadata("author", "author")
+            use_model_card_metadata("version", "version")
+            use_model_card_metadata("organization", "organization")
+            use_model_card_metadata("description", "description")
+            use_model_card_metadata("finetune", "finetune")
+            use_model_card_metadata("basename", "basename")
+            use_model_card_metadata("size_label", "size_label")
+            use_model_card_metadata("source_url", "url")
+            use_model_card_metadata("source_doi", "doi")
+            use_model_card_metadata("source_uuid", "uuid")
+            use_model_card_metadata("source_repo_url", "repo_url")
+
+            # LLAMA.cpp's huggingface style convention
+            # (Definitely not part of hugging face formal/informal standard... but with model_ appended to match their style)
+            ###########################################
+            use_model_card_metadata("name", "model_name")
+            use_model_card_metadata("author", "model_author")
+            use_model_card_metadata("version", "model_version")
+            use_model_card_metadata("organization", "model_organization")
+            use_model_card_metadata("description", "model_description")
+            use_model_card_metadata("finetune", "model_finetune")
+            use_model_card_metadata("basename", "model_basename")
+            use_model_card_metadata("size_label", "model_size_label")
+            use_model_card_metadata("source_url", "model_url")
+            use_model_card_metadata("source_doi", "model_doi")
+            use_model_card_metadata("source_uuid", "model_uuid")
+            use_model_card_metadata("source_repo_url", "model_repo_url")
+
+            # Hugging Face Direct Convention
+            #################################
+
+            # Not part of huggingface model card standard but notice some model creator using it
+            # such as TheBloke in 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF'
+            use_model_card_metadata("name", "model_name")
+            use_model_card_metadata("author", "model_creator")
+            use_model_card_metadata("basename", "model_type")
+
+            if "base_model" in model_card:
+                # This represents the parent models that this is based on
+                # Example: stabilityai/stable-diffusion-xl-base-1.0. Can also be a list (for merges)
+                # Example of merges: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1/blob/main/README.md
+                metadata_base_models = []
+                base_model_value = model_card.get("base_model", None)
+
+                if base_model_value is not None:
+                    if isinstance(base_model_value, str):
+                        metadata_base_models.append(base_model_value)
+                    elif isinstance(base_model_value, list):
+                        metadata_base_models.extend(base_model_value)
+
+                if metadata.base_models is None:
+                    metadata.base_models = []
+
+                for model_id in metadata_base_models:
+                    # NOTE: model size of base model is assumed to be similar to the size of the current model
+                    model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params)
+                    base_model = {}
+                    if model_full_name_component is not None:
+                        base_model["name"] = Metadata.id_to_title(model_full_name_component)
+                    if org_component is not None:
+                        base_model["organization"] = Metadata.id_to_title(org_component)
+                    if version is not None:
+                        base_model["version"] = version
+                    if org_component is not None and model_full_name_component is not None:
+                        base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}"
+                    metadata.base_models.append(base_model)
+
+            use_model_card_metadata("license", "license")
+            use_model_card_metadata("license_name", "license_name")
+            use_model_card_metadata("license_link", "license_link")
+
+            use_array_model_card_metadata("tags", "tags")
+            use_array_model_card_metadata("tags", "pipeline_tag")
+
+            use_array_model_card_metadata("languages", "languages")
+            use_array_model_card_metadata("languages", "language")
+
+            use_array_model_card_metadata("datasets", "datasets")
+            use_array_model_card_metadata("datasets", "dataset")
+
+        # Hugging Face Parameter Heuristics
+        ####################################
+
+        if hf_params is not None:
+
+            hf_name_or_path = hf_params.get("_name_or_path")
+            if hf_name_or_path is not None and hf_name_or_path.count('/') <= 1:
+                # Use _name_or_path only if its actually a model name and not some computer path
+                # e.g. 'meta-llama/Llama-2-7b-hf'
+                model_id = hf_name_or_path
+                model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params)
+                if metadata.name is None and model_full_name_component is not None:
+                    metadata.name = Metadata.id_to_title(model_full_name_component)
+                if metadata.organization is None and org_component is not None:
+                    metadata.organization = Metadata.id_to_title(org_component)
+                if metadata.basename is None and basename is not None:
+                    metadata.basename = basename
+                if metadata.finetune is None and finetune is not None:
+                    metadata.finetune = finetune
+                if metadata.version is None and version is not None:
+                    metadata.version = version
+                if metadata.size_label is None and size_label is not None:
+                    metadata.size_label = size_label
+
+        # Directory Folder Name Fallback Heuristics
+        ############################################
+        if model_path is not None:
+            model_id = model_path.name
+            model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params)
+            if metadata.name is None and model_full_name_component is not None:
+                metadata.name = Metadata.id_to_title(model_full_name_component)
+            if metadata.organization is None and org_component is not None:
+                metadata.organization = Metadata.id_to_title(org_component)
+            if metadata.basename is None and basename is not None:
+                metadata.basename = basename
+            if metadata.finetune is None and finetune is not None:
+                metadata.finetune = finetune
+            if metadata.version is None and version is not None:
+                metadata.version = version
+            if metadata.size_label is None and size_label is not None:
+                metadata.size_label = size_label
+
+        return metadata
+
+    def set_gguf_meta_model(self, gguf_writer: gguf.GGUFWriter):
+        assert self.name is not None
+        gguf_writer.add_name(self.name)
+
+        if self.author is not None:
+            gguf_writer.add_author(self.author)
+        if self.version is not None:
+            gguf_writer.add_version(self.version)
+        if self.organization is not None:
+            gguf_writer.add_organization(self.organization)
+
+        if self.finetune is not None:
+            gguf_writer.add_finetune(self.finetune)
+        if self.basename is not None:
+            gguf_writer.add_basename(self.basename)
+
+        if self.description is not None:
+            gguf_writer.add_description(self.description)
+        if self.quantized_by is not None:
+            gguf_writer.add_quantized_by(self.quantized_by)
+
+        if self.size_label is not None:
+            gguf_writer.add_size_label(self.size_label)
+
+        if self.license is not None:
+            gguf_writer.add_license(self.license)
+        if self.license_name is not None:
+            gguf_writer.add_license_name(self.license_name)
+        if self.license_link is not None:
+            gguf_writer.add_license_link(self.license_link)
+
+        if self.url is not None:
+            gguf_writer.add_url(self.url)
+        if self.doi is not None:
+            gguf_writer.add_doi(self.doi)
+        if self.uuid is not None:
+            gguf_writer.add_uuid(self.uuid)
+        if self.repo_url is not None:
+            gguf_writer.add_repo_url(self.repo_url)
+
+        if self.source_url is not None:
+            gguf_writer.add_source_url(self.source_url)
+        if self.source_doi is not None:
+            gguf_writer.add_source_doi(self.source_doi)
+        if self.source_uuid is not None:
+            gguf_writer.add_source_uuid(self.source_uuid)
+        if self.source_repo_url is not None:
+            gguf_writer.add_source_repo_url(self.source_repo_url)
+
+        if self.base_models is not None:
+            gguf_writer.add_base_model_count(len(self.base_models))
+            for key, base_model_entry in enumerate(self.base_models):
+                if "name" in base_model_entry:
+                    gguf_writer.add_base_model_name(key, base_model_entry["name"])
+                if "author" in base_model_entry:
+                    gguf_writer.add_base_model_author(key, base_model_entry["author"])
+                if "version" in base_model_entry:
+                    gguf_writer.add_base_model_version(key, base_model_entry["version"])
+                if "organization" in base_model_entry:
+                    gguf_writer.add_base_model_organization(key, base_model_entry["organization"])
+                if "url" in base_model_entry:
+                    gguf_writer.add_base_model_url(key, base_model_entry["url"])
+                if "doi" in base_model_entry:
+                    gguf_writer.add_base_model_doi(key, base_model_entry["doi"])
+                if "uuid" in base_model_entry:
+                    gguf_writer.add_base_model_uuid(key, base_model_entry["uuid"])
+                if "repo_url" in base_model_entry:
+                    gguf_writer.add_base_model_repo_url(key, base_model_entry["repo_url"])
+
+        if self.tags is not None:
+            gguf_writer.add_tags(self.tags)
+        if self.languages is not None:
+            gguf_writer.add_languages(self.languages)
+        if self.datasets is not None:
+            gguf_writer.add_datasets(self.datasets)
diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py
index e7fc0eae3f64b..ff589b85245e5 100644
--- a/gguf-py/gguf/quants.py
+++ b/gguf-py/gguf/quants.py
@@ -1,109 +1,1188 @@
 from __future__ import annotations
-from typing import Callable
+from abc import ABC, abstractmethod
+from typing import Any, Callable, Sequence
+from math import log2, ceil
 
 from numpy.typing import DTypeLike
 
-from .constants import GGML_QUANT_SIZES, GGMLQuantizationType
+from .constants import GGML_QUANT_SIZES, GGMLQuantizationType, QK_K
 from .lazy import LazyNumpyTensor
 
 import numpy as np
 
 
-# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
-def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray:
-    n = n.astype(np.float32, copy=False).view(np.int32)
-    # force nan to quiet
-    n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n)
-    # flush subnormals to zero
-    n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n)
-    # round to nearest even
-    n = (n + (0x7fff + ((n >> 16) & 1))) >> 16
-    return n.astype(np.int16)
+def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]:
+    block_size, type_size = GGML_QUANT_SIZES[quant_type]
+    if shape[-1] % block_size != 0:
+        raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})")
+    return (*shape[:-1], shape[-1] // block_size * type_size)
+
+
+def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]:
+    block_size, type_size = GGML_QUANT_SIZES[quant_type]
+    if shape[-1] % type_size != 0:
+        raise ValueError(f"Quantized tensor bytes per row ({shape[-1]}) is not a multiple of {quant_type.name} type size ({type_size})")
+    return (*shape[:-1], shape[-1] // type_size * block_size)
 
 
 # This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
-def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
+def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
     rows = arr.reshape((-1, arr.shape[-1]))
     osize = 1
     for dim in oshape:
         osize *= dim
     out = np.empty(shape=osize, dtype=otype)
     # compute over groups of 16 rows (arbitrary, but seems good for performance)
-    n_groups = rows.shape[0] // 16
+    n_groups = (rows.shape[0] // 16) or 1
     np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out)
     return out.reshape(oshape)
 
 
-def __quantize_bf16_array(n: np.ndarray) -> np.ndarray:
-    return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.int16, oshape=n.shape)
+# round away from zero
+# ref: https://stackoverflow.com/a/59143326/22827863
+def np_roundf(n: np.ndarray) -> np.ndarray:
+    a = abs(n)
+    floored = np.floor(a)
+    b = floored + np.floor(2 * (a - floored))
+    return np.sign(n) * b
+
+
+class QuantError(Exception): ...
 
 
-__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.int16)
+_type_traits: dict[GGMLQuantizationType, type[__Quant]] = {}
 
 
-def quantize_bf16(n: np.ndarray):
-    if type(n) is LazyNumpyTensor:
-        return __quantize_bf16_lazy(n)
+def quantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
+    if qtype == GGMLQuantizationType.F32:
+        return data.astype(np.float32, copy=False)
+    elif qtype == GGMLQuantizationType.F16:
+        return data.astype(np.float16, copy=False)
+    elif (q := _type_traits.get(qtype)) is not None:
+        return q.quantize(data)
     else:
-        return __quantize_bf16_array(n)
+        raise NotImplementedError(f"Quantization for {qtype.name} is not yet implemented")
 
 
-__q8_block_size, __q8_type_size = GGML_QUANT_SIZES[GGMLQuantizationType.Q8_0]
+def dequantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
+    if qtype == GGMLQuantizationType.F32:
+        return data.view(np.float32)
+    elif qtype == GGMLQuantizationType.F16:
+        return data.view(np.float16).astype(np.float32)
+    elif (q := _type_traits.get(qtype)) is not None:
+        return q.dequantize(data)
+    else:
+        raise NotImplementedError(f"Dequantization for {qtype.name} is not yet implemented")
 
 
-def can_quantize_to_q8_0(n: np.ndarray) -> bool:
-    return n.shape[-1] % __q8_block_size == 0
+class __Quant(ABC):
+    qtype: GGMLQuantizationType
+    block_size: int
+    type_size: int
 
+    grid: np.ndarray[Any, np.dtype[np.float32]] | None = None
+    grid_shape: tuple[int, int] = (0, 0)
+    grid_map: tuple[int | float, ...] = ()
+    grid_hex: bytes | None = None
 
-# round away from zero
-# ref: https://stackoverflow.com/a/59143326/22827863
-def np_roundf(n: np.ndarray) -> np.ndarray:
-    a = abs(n)
-    floored = np.floor(a)
-    b = floored + np.floor(2 * (a - floored))
-    return np.sign(n) * b
+    def __init__(self):
+        return TypeError("Quant conversion classes can't have instances")
 
+    def __init_subclass__(cls, qtype: GGMLQuantizationType) -> None:
+        cls.qtype = qtype
+        cls.block_size, cls.type_size = GGML_QUANT_SIZES[qtype]
+        cls.__quantize_lazy = LazyNumpyTensor._wrap_fn(
+            cls.__quantize_array,
+            meta_noop=(np.uint8, cls.__shape_to_bytes)
+        )
+        cls.__dequantize_lazy = LazyNumpyTensor._wrap_fn(
+            cls.__dequantize_array,
+            meta_noop=(np.float32, cls.__shape_from_bytes)
+        )
+        assert qtype not in _type_traits
+        _type_traits[qtype] = cls
 
-def __quantize_q8_0_shape_change(s: tuple[int, ...]) -> tuple[int, ...]:
-    return (*s[:-1], s[-1] // __q8_block_size * __q8_type_size)
+    @classmethod
+    def init_grid(cls):
+        if cls.grid is not None or cls.grid_hex is None:
+            return
 
+        bits_per_elem = ceil(log2(len(cls.grid_map)))
+        assert bits_per_elem != 0, cls.qtype.name
+        elems_per_byte = 8 // bits_per_elem
 
-# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
-def __quantize_q8_0_rows(n: np.ndarray) -> np.ndarray:
-    shape = n.shape
-    assert shape[-1] % __q8_block_size == 0
+        grid = np.frombuffer(cls.grid_hex, dtype=np.uint8)
+        # decode hexadecimal chars from grid
+        grid = grid.reshape((-1, 2))
+        grid = (np.where(grid > 0x40, grid + 9, grid) & 0x0F) << np.array([4, 0], dtype=np.uint8).reshape((1, 2))
+        grid = grid[..., 0] | grid[..., 1]
+        # unpack the grid values
+        grid = grid.reshape((-1, 1)) >> np.array([i for i in range(0, 8, 8 // elems_per_byte)], dtype=np.uint8).reshape((1, elems_per_byte))
+        grid = (grid & ((1 << bits_per_elem) - 1)).reshape((-1, 1))
+        grid_map = np.array(cls.grid_map, dtype=np.float32).reshape((1, -1))
+        grid = np.take_along_axis(grid_map, grid, axis=-1)
+        cls.grid = grid.reshape((1, 1, *cls.grid_shape))
 
-    n_blocks = n.size // __q8_block_size
+    @classmethod
+    @abstractmethod
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        raise NotImplementedError
 
-    blocks = n.reshape((n_blocks, __q8_block_size)).astype(np.float32, copy=False)
+    @classmethod
+    @abstractmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        raise NotImplementedError
 
-    d = abs(blocks).max(axis=1, keepdims=True) / 127
-    with np.errstate(divide="ignore"):
-        id = np.where(d == 0, 0, 1 / d)
-    qs = np_roundf(blocks * id)
+    @classmethod
+    def quantize_rows(cls, rows: np.ndarray) -> np.ndarray:
+        rows = rows.astype(np.float32, copy=False)
+        shape = rows.shape
+        n_blocks = rows.size // cls.block_size
+        blocks = rows.reshape((n_blocks, cls.block_size))
+        blocks = cls.quantize_blocks(blocks)
+        assert blocks.dtype == np.uint8
+        assert blocks.shape[-1] == cls.type_size
+        return blocks.reshape(cls.__shape_to_bytes(shape))
 
-    # (n_blocks, 2)
-    d = d.astype(np.float16).view(np.uint8)
-    # (n_blocks, block_size)
-    qs = qs.astype(np.int8).view(np.uint8)
+    @classmethod
+    def dequantize_rows(cls, rows: np.ndarray) -> np.ndarray:
+        rows = rows.view(np.uint8)
+        shape = rows.shape
+        n_blocks = rows.size // cls.type_size
+        blocks = rows.reshape((n_blocks, cls.type_size))
+        blocks = cls.dequantize_blocks(blocks)
+        assert blocks.dtype == np.float32
+        assert blocks.shape[-1] == cls.block_size
+        return blocks.reshape(cls.__shape_from_bytes(shape))
 
-    assert d.shape[1] + qs.shape[1] == __q8_type_size
+    @classmethod
+    def __shape_to_bytes(cls, shape: Sequence[int]):
+        return quant_shape_to_byte_shape(shape, cls.qtype)
 
-    return np.concatenate([d, qs], axis=1).reshape(__quantize_q8_0_shape_change(shape))
+    @classmethod
+    def __shape_from_bytes(cls, shape: Sequence[int]):
+        return quant_shape_from_byte_shape(shape, cls.qtype)
 
+    @classmethod
+    def __quantize_array(cls, array: np.ndarray) -> np.ndarray:
+        return _apply_over_grouped_rows(cls.quantize_rows, arr=array, otype=np.uint8, oshape=cls.__shape_to_bytes(array.shape))
 
-def __quantize_q8_0_array(n: np.ndarray) -> np.ndarray:
-    return __apply_over_grouped_rows(__quantize_q8_0_rows, arr=n, otype=np.uint8, oshape=__quantize_q8_0_shape_change(n.shape))
+    @classmethod
+    def __dequantize_array(cls, array: np.ndarray) -> np.ndarray:
+        cls.init_grid()
+        return _apply_over_grouped_rows(cls.dequantize_rows, arr=array, otype=np.float32, oshape=cls.__shape_from_bytes(array.shape))
 
+    @classmethod
+    def __quantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any:
+        pass
 
-__quantize_q8_0_lazy = LazyNumpyTensor._wrap_fn(
-    __quantize_q8_0_array,
-    meta_noop=(np.uint8, __quantize_q8_0_shape_change),
-)
+    @classmethod
+    def __dequantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any:
+        pass
 
+    @classmethod
+    def can_quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> bool:
+        return tensor.shape[-1] % cls.block_size == 0
 
-def quantize_q8_0(data: np.ndarray):
-    if type(data) is LazyNumpyTensor:
-        return __quantize_q8_0_lazy(data)
-    else:
-        return __quantize_q8_0_array(data)
+    @classmethod
+    def quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray:
+        if not cls.can_quantize(tensor):
+            raise QuantError(f"Can't quantize tensor with shape {tensor.shape} to {cls.qtype.name}")
+        if isinstance(tensor, LazyNumpyTensor):
+            return cls.__quantize_lazy(tensor)
+        else:
+            return cls.__quantize_array(tensor)
+
+    @classmethod
+    def dequantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray:
+        if isinstance(tensor, LazyNumpyTensor):
+            return cls.__dequantize_lazy(tensor)
+        else:
+            return cls.__dequantize_array(tensor)
+
+
+class BF16(__Quant, qtype=GGMLQuantizationType.BF16):
+    @classmethod
+    # same as ggml_compute_fp32_to_bf16 in ggml-impl.h
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n = blocks.view(np.uint32)
+        # force nan to quiet
+        n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n)
+        # round to nearest even
+        n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16
+        return n.astype(np.uint16).view(np.uint8)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        return (blocks.view(np.int16).astype(np.int32) << 16).view(np.float32)
+
+
+class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0):
+    @classmethod
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        imax = abs(blocks).argmax(axis=-1, keepdims=True)
+        max = np.take_along_axis(blocks, imax, axis=-1)
+
+        d = max / -8
+        with np.errstate(divide="ignore"):
+            id = np.where(d == 0, 0, 1 / d)
+        # FIXME: Q4_0's reference rounding is cursed and depends on FMA
+        qs = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(8.5), dtype=np.float32).astype(np.uint8).clip(0, 15)
+
+        qs = qs.reshape((n_blocks, 2, cls.block_size // 2))
+        qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4))
+
+        d = d.astype(np.float16).view(np.uint8)
+
+        return np.concatenate([d, qs], axis=-1)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, qs = np.hsplit(blocks, [2])
+
+        d = d.view(np.float16).astype(np.float32)
+
+        qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+        qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.int8) - np.int8(8)
+
+        return (d * qs.astype(np.float32))
+
+
+class Q4_1(__Quant, qtype=GGMLQuantizationType.Q4_1):
+    @classmethod
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        max = blocks.max(axis=-1, keepdims=True)
+        min = blocks.min(axis=-1, keepdims=True)
+
+        d = (max - min) / 15
+        with np.errstate(divide="ignore"):
+            id = np.where(d == 0, 0, 1 / d)
+        qs = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 15)
+
+        qs = qs.reshape((n_blocks, 2, cls.block_size // 2))
+        qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4))
+
+        d = d.astype(np.float16).view(np.uint8)
+        m = min.astype(np.float16).view(np.uint8)
+
+        return np.concatenate([d, m, qs], axis=-1)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        m, qs = np.hsplit(rest, [2])
+
+        d = d.view(np.float16).astype(np.float32)
+        m = m.view(np.float16).astype(np.float32)
+
+        qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+        qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.float32)
+
+        return (d * qs) + m
+
+
+class Q5_0(__Quant, qtype=GGMLQuantizationType.Q5_0):
+    @classmethod
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        imax = abs(blocks).argmax(axis=-1, keepdims=True)
+        max = np.take_along_axis(blocks, imax, axis=-1)
+
+        d = max / -16
+        with np.errstate(divide="ignore"):
+            id = np.where(d == 0, 0, 1 / d)
+        # FIXME: Q5_0's reference rounding is cursed and depends on FMA
+        q = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(16.5), dtype=np.float32).astype(np.uint8).clip(0, 31)
+
+        qs = q.reshape((n_blocks, 2, cls.block_size // 2))
+        qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4))
+
+        qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4)
+
+        d = d.astype(np.float16).view(np.uint8)
+
+        return np.concatenate([d, qh, qs], axis=-1)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        qh, qs = np.hsplit(rest, [4])
+
+        d = d.view(np.float16).astype(np.float32)
+        qh = qh.view(np.uint32)
+
+        qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32))
+        ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+        qh = (qh & np.uint32(0x01)).astype(np.uint8)
+        ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1))
+
+        qs = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(16)
+
+        return (d * qs.astype(np.float32))
+
+
+class Q5_1(__Quant, qtype=GGMLQuantizationType.Q5_1):
+    @classmethod
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        max = blocks.max(axis=-1, keepdims=True)
+        min = blocks.min(axis=-1, keepdims=True)
+
+        d = (max - min) / 31
+        with np.errstate(divide="ignore"):
+            id = np.where(d == 0, 0, 1 / d)
+        q = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 31)
+
+        qs = q.reshape((n_blocks, 2, cls.block_size // 2))
+        qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4))
+
+        qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4)
+
+        d = d.astype(np.float16).view(np.uint8)
+        m = min.astype(np.float16).view(np.uint8)
+
+        return np.concatenate([d, m, qh, qs], axis=-1)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        m, rest = np.hsplit(rest, [2])
+        qh, qs = np.hsplit(rest, [4])
+
+        d = d.view(np.float16).astype(np.float32)
+        m = m.view(np.float16).astype(np.float32)
+        qh = qh.view(np.uint32)
+
+        qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32))
+        ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+        qh = (qh & np.uint32(0x01)).astype(np.uint8)
+        ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1))
+
+        qs = (ql | (qh << np.uint8(4))).astype(np.float32)
+
+        return (d * qs) + m
+
+
+class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0):
+    @classmethod
+    # Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+
+        d = abs(blocks).max(axis=1, keepdims=True) / 127
+        with np.errstate(divide="ignore"):
+            id = np.where(d == 0, 0, 1 / d)
+        qs = np_roundf(blocks * id)
+
+        # (n_blocks, 2)
+        d = d.astype(np.float16).view(np.uint8)
+        # (n_blocks, block_size)
+        qs = qs.astype(np.int8).view(np.uint8)
+
+        return np.concatenate([d, qs], axis=1)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        d, x = np.split(blocks, [2], axis=1)
+        d = d.view(np.float16).astype(np.float32)
+        x = x.view(np.int8).astype(np.float32)
+
+        return (x * d)
+
+
+class Q2_K(__Quant, qtype=GGMLQuantizationType.Q2_K):
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        scales, rest = np.hsplit(blocks, [QK_K // 16])
+        qs, rest = np.hsplit(rest, [QK_K // 4])
+        d, dmin = np.hsplit(rest, [2])
+
+        d = d.view(np.float16).astype(np.float32)
+        dmin = dmin.view(np.float16).astype(np.float32)
+
+        # (n_blocks, 16, 1)
+        dl = (d * (scales & 0xF).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1))
+        ml = (dmin * (scales >> 4).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1))
+
+        shift = np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
+
+        qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & np.uint8(3)
+
+        qs = qs.reshape((n_blocks, QK_K // 16, 16)).astype(np.float32)
+
+        qs = dl * qs - ml
+
+        return qs.reshape((n_blocks, -1))
+
+
+class Q3_K(__Quant, qtype=GGMLQuantizationType.Q3_K):
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        hmask, rest = np.hsplit(blocks, [QK_K // 8])
+        qs, rest = np.hsplit(rest, [QK_K // 4])
+        scales, d = np.hsplit(rest, [12])
+
+        d = d.view(np.float16).astype(np.float32)
+
+        # The scales are packed at 6-bit each in this pattern:
+        #  0: IIIIAAAA
+        #  1: JJJJBBBB
+        #  2: KKKKCCCC
+        #  3: LLLLDDDD
+        #  4: MMMMEEEE
+        #  5: NNNNFFFF
+        #  6: OOOOGGGG
+        #  7: PPPPHHHH
+        #  8: MMIIEEAA
+        #  9: NNJJFFBB
+        # 10: OOKKGGCC
+        # 11: PPLLHHDD
+        lscales, hscales = np.hsplit(scales, [8])
+        lscales = lscales.reshape((n_blocks, 1, 8)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 2, 1))
+        lscales = lscales.reshape((n_blocks, 16))
+        hscales = hscales.reshape((n_blocks, 1, 4)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 4, 1))
+        hscales = hscales.reshape((n_blocks, 16))
+        scales = (lscales & np.uint8(0x0F)) | ((hscales & np.uint8(0x03)) << np.uint8(4))
+        scales = (scales.astype(np.int8) - np.int8(32)).astype(np.float32)
+
+        dl = (d * scales).reshape((n_blocks, 16, 1))
+
+        ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
+        qh = hmask.reshape(n_blocks, -1, 1, 32) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1))
+        ql = ql.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(3)
+        qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(1))
+        qh = qh ^ np.uint8(1)  # strangely, the offset is zero when the bitmask is 1
+        q = (ql.astype(np.int8) - (qh << np.uint8(2)).astype(np.int8)).astype(np.float32)
+
+        return (dl * q).reshape((n_blocks, QK_K))
+
+
+class Q4_K(__Quant, qtype=GGMLQuantizationType.Q4_K):
+    K_SCALE_SIZE = 12
+
+    @staticmethod
+    def get_scale_min(scales: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        n_blocks = scales.shape[0]
+        scales = scales.view(np.uint8)
+        ### Unpacking the following: ###
+        #  0 EEAAAAAA
+        #  1 FFBBBBBB
+        #  2 GGCCCCCC
+        #  3 HHDDDDDD
+        #  4 eeaaaaaa
+        #  5 ffbbbbbb
+        #  6 ggcccccc
+        #  7 hhdddddd
+        #  8 eeeeEEEE
+        #  9 ffffFFFF
+        # 10 ggggGGGG
+        # 11 hhhhHHHH
+        scales = scales.reshape((n_blocks, 3, 4))
+        d, m, m_d = np.split(scales, 3, axis=-2)
+
+        sc = np.concatenate([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], axis=-1)
+        min = np.concatenate([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], axis=-1)
+
+        return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8)))
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        dmin, rest = np.hsplit(rest, [2])
+        scales, qs = np.hsplit(rest, [cls.K_SCALE_SIZE])
+
+        d = d.view(np.float16).astype(np.float32)
+        dmin = dmin.view(np.float16).astype(np.float32)
+
+        sc, m = Q4_K.get_scale_min(scales)
+
+        d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1))
+        dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1))
+
+        qs = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+        qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 32)).astype(np.float32)
+
+        return (d * qs - dm).reshape((n_blocks, QK_K))
+
+
+class Q5_K(__Quant, qtype=GGMLQuantizationType.Q5_K):
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        dmin, rest = np.hsplit(rest, [2])
+        scales, rest = np.hsplit(rest, [Q4_K.K_SCALE_SIZE])
+        qh, qs = np.hsplit(rest, [QK_K // 8])
+
+        d = d.view(np.float16).astype(np.float32)
+        dmin = dmin.view(np.float16).astype(np.float32)
+
+        sc, m = Q4_K.get_scale_min(scales)
+
+        d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1))
+        dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1))
+
+        ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+        qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1))
+        ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32))
+        qh = (qh & np.uint8(0x01)).reshape((n_blocks, -1, 32))
+        q = (ql | (qh << np.uint8(4))).astype(np.float32)
+
+        return (d * q - dm).reshape((n_blocks, QK_K))
+
+
+class Q6_K(__Quant, qtype=GGMLQuantizationType.Q6_K):
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        ql, rest = np.hsplit(blocks, [QK_K // 2])
+        qh, rest = np.hsplit(rest, [QK_K // 4])
+        scales, d = np.hsplit(rest, [QK_K // 16])
+
+        scales = scales.view(np.int8).astype(np.float32)
+        d = d.view(np.float16).astype(np.float32)
+        d = (d * scales).reshape((n_blocks, QK_K // 16, 1))
+
+        ql = ql.reshape((n_blocks, -1, 1, 64)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+        ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32))
+        qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
+        qh = (qh & np.uint8(0x03)).reshape((n_blocks, -1, 32))
+        q = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(32)
+        q = q.reshape((n_blocks, QK_K // 16, -1)).astype(np.float32)
+
+        return (d * q).reshape((n_blocks, QK_K))
+
+
+class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS):
+    ksigns: bytes = (
+        b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f"
+        b"\x90\x11\x12\x93\x14\x95\x96\x17\x18\x99\x9a\x1b\x9c\x1d\x1e\x9f"
+        b"\xa0\x21\x22\xa3\x24\xa5\xa6\x27\x28\xa9\xaa\x2b\xac\x2d\x2e\xaf"
+        b"\x30\xb1\xb2\x33\xb4\x35\x36\xb7\xb8\x39\x3a\xbb\x3c\xbd\xbe\x3f"
+        b"\xc0\x41\x42\xc3\x44\xc5\xc6\x47\x48\xc9\xca\x4b\xcc\x4d\x4e\xcf"
+        b"\x50\xd1\xd2\x53\xd4\x55\x56\xd7\xd8\x59\x5a\xdb\x5c\xdd\xde\x5f"
+        b"\x60\xe1\xe2\x63\xe4\x65\x66\xe7\xe8\x69\x6a\xeb\x6c\xed\xee\x6f"
+        b"\xf0\x71\x72\xf3\x74\xf5\xf6\x77\x78\xf9\xfa\x7b\xfc\x7d\x7e\xff"
+    )
+
+    # iq2xxs_grid, but with each byte of the original packed in 2 bits,
+    # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
+    grid_shape = (256, 8)
+    grid_map = (0x08, 0x19, 0x2b)
+    grid_hex = (
+        b"00000200050008000a00110014002000220028002a0041004400500058006100"
+        b"6400800082008a00a20001010401100115014001840198010002020222028202"
+        b"010404041004210424044004420448046004810484049004a404000502050805"
+        b"200546056905800591050906100640068406a406000805080808140828084108"
+        b"440850085208880804094009020a140a01100410101021104010601084109010"
+        b"951000110811201150115a118011241245120014081420142514491480141815"
+        b"6215001616160118041810184018811800190519a019511a002002200a204420"
+        b"6120802082202921482100220222012404241024402456240025412564259026"
+        b"082820289428442a014004401040184021402440404048405640604081408440"
+        b"9040004120416141804185410142104248425642684200440844204480449944"
+        b"124524450046014804481048404845480049584961498249454a904a00500850"
+        b"1150195020508050885004514251a4519152905492540a550156545600581158"
+        b"195864584059085a046010604060686000615561186260620064056410651265"
+        b"84654268008002800a8041808280048118814081118201840484108415844084"
+        b"608400854685948509864086608602880489118a0490109024904090a1901691"
+        b"8091459200942294449451958198209902a050a085a009a100a218a450a804a9"
+    )
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, qs = np.hsplit(blocks, [2])
+
+        d = d.view(np.float16).astype(np.float32)
+
+        qs = qs.view(np.uint32).reshape(n_blocks, -1, 2)
+
+        db = d * (np.float32(0.5) + (qs[..., 1] >> 28).astype(np.float32)) * np.float32(0.25)
+        db = db.reshape((n_blocks, -1, 1, 1))
+
+        # get the sign indices and unpack the bits
+        signs = qs[..., 1].reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4))
+        ksigns = np.frombuffer(cls.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128))
+        signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1))
+        signs = np.take_along_axis(ksigns, signs, axis=-1)
+        signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8))
+        signs = signs & np.uint8(0x01)
+        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
+        signs = signs.reshape((n_blocks, -1, 4, 8))
+
+        assert cls.grid is not None
+        grid = np.take_along_axis(cls.grid, qs[..., 0].copy().view(np.uint8).reshape((n_blocks, -1, 1, 1)), axis=-2)
+        grid = grid.reshape((n_blocks, -1, 4, 8))
+
+        return (db * grid * signs).reshape((n_blocks, -1))
+
+
+class IQ2_XS(__Quant, qtype=GGMLQuantizationType.IQ2_XS):
+    # iq2xs_grid, but with each byte of the original packed in 2 bits,
+    # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
+    grid_shape = (512, 8)
+    grid_map = (0x08, 0x19, 0x2b)
+    grid_hex = (
+        b"00000200050008000a0011001400160019002000220025002800410044004600"
+        b"49005000520055005800610064008000820085008800910094009900a0000101"
+        b"04010601090110011201150118011a0121012401400142014501480151015401"
+        b"6001680181018401900100020202050208021102140220024102440250025502"
+        b"80028a0201040404060409041004120415041804210424044004420445044804"
+        b"5104540456046004810484049004000502050505080511051405200541054405"
+        b"500561058005010604061006260640064206840600080208050808080a081108"
+        b"14082008250841084408500858088008a008aa08010904091009400981098909"
+        b"000a200a280a960aa00a01100410061009101010121015101810211024104010"
+        b"4210451048105110541060106a10811084109010001102110511081111111411"
+        b"2011411144115011801194119611011204120612101240126012001402140514"
+        b"0814111414142014411444144914501464148014011504151015401500161416"
+        b"49160118041810181218401854188618001905196619511aa91a002002200520"
+        b"08200a201120142020204120442050208020a020012104211021402148216521"
+        b"002222228022a82201240424102429244024002541255225992501261a26a626"
+        b"002808280a28202855288828a22868299029082a202a822a882a8a2a01400440"
+        b"0640094010401240154018402140244040404240454048404a40514054406040"
+        b"6540814084409040004102410541084111411441204141414441504180418541"
+        b"a241014204421042124229424042004402440544084411441444194420444144"
+        b"4444504480449444014504451045244540459a4500460a464446504601480448"
+        b"1048404845485448624800491149444950496949044a00500250055008501150"
+        b"145020502850415044505050805001510451105115514051425100524452aa52"
+        b"0154045410542154405460548154a154005508558055885521566856a1560058"
+        b"14584158505899581a5940594259855a0160046010604060546062608660a960"
+        b"006124624a62926200641664106540654565a46501686a682569066a546a626a"
+        b"00800280058008801180148020802a8041804480508080808280a880aa800181"
+        b"0481068110814081518159810082208280828282a082a8820184048410841284"
+        b"158440846084898400854485a58518866a860088088825885a8880888288a888"
+        b"0689228a808a888a968aa88a0190049010904090569084900091229164915692"
+        b"89920094059444945094589429959095929541965198a6984999159a609a00a0"
+        b"02a008a00aa020a02aa0a0a051a159a1a6a100a202a208a22aa280a2a0a240a4"
+        b"95a465a698a60aa820a822a828a8a0a8a8a804a984a986a928aa2aaa91aaaaaa"
+    )
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        qs, scales = np.hsplit(rest, [2 * QK_K // 8])
+
+        d = d.view(np.float16).astype(np.float32)
+        qs = qs.view(np.uint16)
+
+        scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
+        scales = (scales & 0x0F).reshape((n_blocks, -1))
+        db = d * (np.float32(0.5) + scales) * np.float32(0.25)
+        db = db.reshape((n_blocks, -1, 1, 1))
+
+        # get the sign indices and unpack the bits
+        signs = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape(1, 1, 128)
+        signs = np.take_along_axis(signs, (qs >> 9).reshape((n_blocks, -1, 1)), axis=-1)
+        signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
+        signs = signs & np.uint8(0x01)
+        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
+        signs = signs.reshape((n_blocks, -1, 2, 8))
+
+        assert cls.grid is not None
+        grid = np.take_along_axis(cls.grid, (qs & np.uint16(511)).reshape((n_blocks, -1, 1, 1)), axis=-2)
+        grid = grid.reshape((n_blocks, -1, 2, 8))
+
+        return (db * grid * signs).reshape((n_blocks, -1))
+
+
+class IQ2_S(__Quant, qtype=GGMLQuantizationType.IQ2_S):
+    # iq2s_grid, but with each byte of the original packed in 2 bits,
+    # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
+    grid_shape = (1024, 8)
+    grid_map = (0x08, 0x19, 0x2b)
+    grid_hex = (
+        b"00000200050008000a0011001400160019002000220025002800410044004600"
+        b"490050005200550058006100640066006900800082008500880091009400a000"
+        b"a500aa0001010401060109011001120115011801210124014001420145014801"
+        b"510154015601590160016501680181018401900192019501a101a40100020202"
+        b"050208021102140220022a02410244024602490250025502800285028a029402"
+        b"a202010404040604090410041204150418042104240426042904400442044504"
+        b"48044a0451045404560459046004620465048104840486048904900495049804"
+        b"a104a40400050205050508050a05110514051605190520052505280541054405"
+        b"46054905500552055505580561056405800582058505880591059405a0050106"
+        b"0406060609061006150640064506480651065406600681068406900600080208"
+        b"050808081108140816081908200825082a084108440846084908500852085508"
+        b"580861086408800885089408aa08010904091009120915091809210940094509"
+        b"480951095409600981099009000a110a140a220a280a2a0a500a990a01100410"
+        b"0610091010101210151018102110241026104010421045104810511054105610"
+        b"59106010621065106810811084108610901095109810a110a410001102110511"
+        b"08110a1111111411161119112011221125112811411144114611491150115211"
+        b"5511581161116411801182118511881191119411011204120912101215122112"
+        b"2412401245125112541281128412901200140214051408141114141416141914"
+        b"2014251428144114441446144914501452145514581461146414801482148514"
+        b"881491149414a014011504150615091510151215151518152115241540154215"
+        b"4515481551155415601581158415901500160516081611161416201641164416"
+        b"50168016aa160118041806180918101815181818211840184218451848185118"
+        b"541860188118841800190219051908191119141920194119441950196919a219"
+        b"041a101a401a561a00200220052008201120142016201920202025202a204120"
+        b"4420502052205520642080208a209420aa200121042110211221152121214021"
+        b"4221452151215421602181218421902100220a22222228222a22442250228822"
+        b"8a22a82201240424062409241024152418242124242440244224452448245124"
+        b"5424602481248424902400250525082511251425202541254425502566258025"
+        b"0126042610264026592600280528112814284128442850288a28aa2801290429"
+        b"102995290a2a222a642a882a8a2a014004400640094010401240154018401a40"
+        b"21402440264040404240454048404a4051405440564059406040624065408140"
+        b"8440904095409840a140a4400041024105410841114114411641194120412241"
+        b"2541414144414641494150415241554158416141644180418241854188419141"
+        b"9441a04101420442104212421542184224424042454248425142544260428142"
+        b"844200440244054408440a441144144416441944204422442544284441444444"
+        b"46444944504452445544584461446444804482448544884491449444a0440145"
+        b"0445064509451045124515451845214524454045424545454845514554456045"
+        b"6a4581458445904500460246054608461146144620464146444650468046a546"
+        b"0148044809481048124815481848214824484048424845484848514854486048"
+        b"84489048004902490549084911491449204941494449504980499649014a044a"
+        b"104a404a00500250055008501150145016501950205022502550285041504450"
+        b"4650495050505250555058506150645080508250855088509150945001510451"
+        b"0651095110511251155118512151245140514251455148515151545160518151"
+        b"8451905100520552085211521452205241524452505269528052015404540654"
+        b"0954105412541554185421542454405442544554485451545454605481548454"
+        b"9054005502550555085511551455205541554455505580550156045610562656"
+        b"405600580258055808581158145820584158445850585a588058015904591059"
+        b"4059005a195a855aa85a01600460066010601260156018602160246040604560"
+        b"4860516054606060846090600061026105610861116114612061416144615061"
+        b"806199610462106240625662a162006405640864116414642064416444645064"
+        b"806401650465106540654a656865926500669466016804681068656898680069"
+        b"2a69426aa16a0080028005800880118014801980208025804180448050805280"
+        b"5580588061808080858091809480018104810981108112811581188121812481"
+        b"408142814581488151815481818184819081a981008205820a82118214824182"
+        b"4482508201840484068409841084128415841884218440844284458448845184"
+        b"5484608481848484908400850285058508851185148520854185448550858085"
+        b"8a85018604861086298640860088058811881488418844885088a28801890489"
+        b"40896589228a588a5a8a828aa28a019004900990109012901590189024904090"
+        b"4290459048905190549060908190849090900091059111911491419144915091"
+        b"5a910192049210924092a6920094029405940894119414942094419444945094"
+        b"8094969401950495109540959895a19500964696649601980498109826984098"
+        b"a998009949995299909a00a005a00aa014a022a02aa041a044a050a0a2a0aaa0"
+        b"40a165a102a20aa222a228a22aa282a288a28aa2a8a201a404a410a440a489a4"
+        b"a4a400a519a551a60aa828a8a2a854a986a908aa0aaa20aa22aa28aa88aaaaaa"
+    )
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        qs, rest = np.hsplit(rest, [QK_K // 8])
+        signs, rest = np.hsplit(rest, [QK_K // 8])
+        qh, scales = np.hsplit(rest, [QK_K // 32])
+
+        d = d.view(np.float16).astype(np.float32)
+
+        scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
+        scales = (scales & 0x0F).reshape((n_blocks, -1))
+        db = d * (np.float32(0.5) + scales) * np.float32(0.25)
+        db = db.reshape((n_blocks, -1, 1, 1))
+
+        # unpack the sign bits
+        signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
+        signs = signs & np.uint8(0x01)
+        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
+        signs = signs.reshape((n_blocks, -1, 2, 8))
+
+        qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4))
+        qs = qs.astype(np.uint16) | ((qh & 0x03).astype(np.uint16) << 8).reshape((n_blocks, -1))
+
+        assert cls.grid is not None
+        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
+        grid = grid.reshape((n_blocks, -1, 2, 8))
+
+        return (db * grid * signs).reshape((n_blocks, -1))
+
+
+class IQ3_XXS(__Quant, qtype=GGMLQuantizationType.IQ3_XXS):
+    grid_shape = (256, 4)
+    grid_map = (0x04, 0x0c, 0x14, 0x1c, 0x24, 0x2c, 0x34, 0x3e)
+    grid_hex = (
+        b"0000020004001100130017002000220031004200730075000101030110011201"
+        b"2101250130013201410154017001000202020402110220022202310233023702"
+        b"5102570275020103070310031203250370031304370444045704730475040105"
+        b"0705320552053506640610071407160743076107011003101010121021102310"
+        b"3010321034104710501000110211111120112211011203121012121221123012"
+        b"7212001302132013311346136613011405145014201524154615711505162217"
+        b"4017002002201120132020202220262031204220012103210521102112212121"
+        b"3021632167217021002202221122172220222222372240225522012310231423"
+        b"7023742335245324032527254125742501270327162745270130103012302130"
+        b"2330503065307230003102312031313144314631013203321032253252327232"
+        b"1133333330344734723400350635223555351436363663363337603704401740"
+        b"3540374053405740744120423742404260426642074345430444514464442545"
+        b"4345704505471047124730471250415070500051065126515551145232527252"
+        b"0253535310542354275472540255315550562457425724604460466064602161"
+        b"6161176264623063366344640565526533660367216703700570077010703270"
+        b"5270267140711272457252720073157333736073217441740075027524753076"
+    )
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        qs, scales = np.hsplit(rest, [QK_K // 4])
+
+        d = d.view(np.float16).astype(np.float32)
+        scales = scales.view(np.uint32)
+
+        db = d * (np.float32(0.5) + (scales >> 28).astype(np.float32)) * np.float32(0.5)
+        db = db.reshape((n_blocks, -1, 1, 1))
+
+        # get the sign indices and unpack the bits
+        signs = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4))
+        ksigns = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128))
+        signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1))
+        signs = np.take_along_axis(ksigns, signs, axis=-1)
+        signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8))
+        signs = signs & np.uint8(0x01)
+        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
+        signs = signs.reshape((n_blocks, -1, 4, 8))
+
+        assert cls.grid is not None
+        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
+        grid = grid.reshape((n_blocks, -1, 4, 8))
+
+        return (db * grid * signs).reshape((n_blocks, -1))
+
+
+class IQ3_S(__Quant, qtype=GGMLQuantizationType.IQ3_S):
+    grid_shape = (512, 4)
+    grid_map = (0x01, 0x03, 0x05, 0x07, 0x09, 0x0b, 0x0d, 0x0f)
+    grid_hex = (
+        b"0000010002000500070010001100120014001600200021002500330040004200"
+        b"4500470051005300600062007100740077000001010102010401100111011501"
+        b"2001230127013101350144016101650172010002010205020702100213021602"
+        b"2102250230023402420245024702510253027002730203031103150320032203"
+        b"3103330336034403500352036703710375030004130417042104240432044004"
+        b"4304510470040205040520052205260533054105450547056605730506061106"
+        b"1306310652067106000702070407200722072607330750075407001001100210"
+        b"0410101011101310151017102010221031103410361054105610611072100011"
+        b"0111031106111011141121113011331141115011521170117611001212121512"
+        b"1712201224123212401243125512601272120113041307131013131321132713"
+        b"3013341341136213701303140514121414143114331442144614501454140115"
+        b"1015131521153015321551152016241627164416461601170317101712172117"
+        b"3517411762177017002001200320052007201020122014201620212023202720"
+        b"3020322041204320452050205220672070207320752000210221102113211721"
+        b"2221252131213421422151210122042207222122232230223722412253225722"
+        b"7122742200230223052311232223242331233323422350236623012407242024"
+        b"2324322435244124722475240425112522253725402553257025002602260726"
+        b"2126552661260527112726273027432750270230113013301530173022303130"
+        b"3330353042304430473051306330713001310331053114312131233140316031"
+        b"7231763100321232203232323432503201331033143321332333273330334133"
+        b"4333473355337333033411341634223431345234603464340135103512352535"
+        b"3235443556357335163641360137033720372237353700400440124020402440"
+        b"2740324041405040704002410741114113412241304135414341514155410142"
+        b"0342104215422142334240425742624270420443114313432043224331433543"
+        b"0044024424443744404471440545074521456245134634466046104715473047"
+        b"4347514702501050145022504050445047505250665074500151035105511251"
+        b"2151325172510052115223523052365253520253075310532753445351536553"
+        b"7353015404542054325446541255265551555355425602570457225711601360"
+        b"1560316033606060006120612761646112623462426255626262706200631463"
+        b"2163406325644364626400650365346560650566406611671367007004700770"
+        b"2070227036704070547062700271117124714371457101720472107216722172"
+        b"3072517202733273357353730174057413742074507422754275027631760077"
+    )
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        qs, rest = np.hsplit(rest, [QK_K // 4])
+        qh, rest = np.hsplit(rest, [QK_K // 32])
+        signs, scales = np.hsplit(rest, [QK_K // 8])
+
+        d = d.view(np.float16).astype(np.float32)
+
+        scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
+        scales = (scales & 0x0F).reshape((n_blocks, -1))
+        db = d * (1 + 2 * scales)
+        db = db.reshape((n_blocks, -1, 1, 1))
+
+        # unpack the sign bits
+        signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
+        signs = signs & np.uint8(0x01)
+        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
+        signs = signs.reshape((n_blocks, -1, 4, 8))
+
+        qh = qh.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8)
+        qh = (qh & 0x01).astype(np.uint16).reshape((n_blocks, -1))
+        qs = qs.astype(np.uint16) | (qh << 8)
+
+        assert cls.grid is not None
+        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
+        grid = grid.reshape((n_blocks, -1, 4, 8))
+
+        return (db * grid * signs).reshape((n_blocks, -1))
+
+
+class IQ1_S(__Quant, qtype=GGMLQuantizationType.IQ1_S):
+    # iq1s_grid, with each byte packed into 2 bits
+    # -1, 0, 1 <=> 0, 1, 2
+    grid_shape = (2048, 8)
+    grid_map = (-1, 0, 1)
+    grid_hex = (
+        b"00000200050008000a00110015002000220028002a0045005100540056006500"
+        b"8000820088008a009500a000a200a800aa000401050111011401160119011a01"
+        b"2501410146014901520155015a0161016401660168018501910194019601a501"
+        b"0002020208020a0215022002220228022a024502510259026402690280028202"
+        b"88028a02910295029902a002a202a802aa021104140416042504410449045504"
+        b"5a046404650491049904a5040105040505050605150518051a05290540054505"
+        b"4a0550055105540555055605590560056205650568056a058105910595059805"
+        b"9a05a105a405a505a605a9051406190641064406500652065506580660066106"
+        b"6606690685069106940699060008020808080a0815082008220828082a084508"
+        b"5108560865088008820888088a089508a008a208a808aa080509110914091909"
+        b"2409250941095009510955096109640969099109940996099909a509000a020a"
+        b"080a0a0a150a200a220a280a2a0a450a510a590a610a650a800a820a850a880a"
+        b"8a0a950aa00aa20aa80aaa0a1010111014101910241025104110441050105510"
+        b"58106110641065106910911094109610a110a510011104110611091110111211"
+        b"1511181121112411291145114a11501151115211541155115611591160116511"
+        b"841192119511a111a41111121412161225124012461249125212551258125a12"
+        b"641266128512911294129612a512011406140914141415141814191421142614"
+        b"41144514461448144a1451145414551456145914621465146814841489149014"
+        b"94149514981499149a14a114a414a514a914021505150a151115141515151615"
+        b"191520152215251528152a154115441545154615511552155415551556155915"
+        b"5a1561156415651566156915801582158415851588158a159015911594159515"
+        b"961599159a15a015a215a51501160416051606161516161618161a1621162616"
+        b"401642164416451648164a165116551656165816591661166416651668166916"
+        b"6a1686168a1692169516a416a916111816182518411844184618491850185518"
+        b"58185a1860186118641866186918851891189418a5181019121915191a192119"
+        b"25194219441945194819511954195519561959195a19601965196a1989199119"
+        b"921995199819a119a619a919091a161a241a261a441a461a491a501a521a551a"
+        b"581a611a661a691a851a911a961a9a1a0020022008200a201520202022202520"
+        b"28202a20452051205920612065208020822088208a209520a020a220a520a820"
+        b"aa2005211121142119212521422144214921552158215a216121642165216621"
+        b"8521902196219921a521012208220a22112215222022222228222a2245225122"
+        b"562259226522812288228a2291229522a022a222a822aa220524142416241924"
+        b"252444244524462449245224552458245a2466248524912494249924a124a524"
+        b"0925152521252925402545254825512554255525592562256525682589259025"
+        b"9425952598259a25a125a425a625a92505261026122619262526412649265526"
+        b"6026612669268426862690269a260028022808280a2815282028222828282a28"
+        b"45285128542865288028822888288a28a028a228a828aa280929112914291929"
+        b"2529462949295229552961296429662969298529902996299929a429a529002a"
+        b"022a082a0a2a202a222a282a2a2a452a512a562a592a652a802a822a882a8a2a"
+        b"952aa02aa22aa82aaa2a054011401640254049405240554058405a4061406440"
+        b"664094409940a140a6400041014104410641094112411541164118411a412141"
+        b"26412941454148414a41514154415541564159415a41654168416a4181418441"
+        b"8641904192419541a041a141a241054211421442164225424142524255425a42"
+        b"6442694289429442a5420144154419442944454448444a445144544455445644"
+        b"61446244654468446a44814486448944904492449544a044a144a94401450245"
+        b"05450a4511451445154516451945204525452a45414544454545464549455045"
+        b"5145544555455645584559456145644565456645694582458445854588459145"
+        b"94459545964599459a45a545a845aa450146054609461446154618461a462146"
+        b"2446294640464246454648465046514652465546564659466246654668468146"
+        b"85468a4694469546a146a446a6460548114815481a4825484248494850485548"
+        b"5848614864486648694885489148944896489948a5480149054906490a491049"
+        b"144915491849214924492649404945494a495149524954495549564959496049"
+        b"6249654966496a49864989499249954996499849a149a449a649a949164a444a"
+        b"464a494a554a584a5a4a644a694a944aa54a0150045005500650095012501550"
+        b"1a50215024502950405045504850515054505550565059506550685086508950"
+        b"95509850a050a150a650a9500551085109510a51115114511551165118511951"
+        b"20512551265128512a5141514451455146514951505151515251545155515651"
+        b"585159515a51615164516551665169518251855191519451955196519951a051"
+        b"a551aa5101520652125215521a5221522452425245524a525152545255525652"
+        b"595262526552855290529252955299529a52a452045405541154145415541654"
+        b"185419542154255428542a54415444544554465449544a545054515454545554"
+        b"5654585459545a54615462546454655466546954805488548a54915494549554"
+        b"96549954a154a454a554aa540155025504550555065509551055115512551455"
+        b"1555165519551a55215524552555265529554055415542554455455546554855"
+        b"4955505551555255545555555655585559555a55605561556455655566556855"
+        b"69556a5581558455855589558a559055915594559555965598559955a155a455"
+        b"a555a655a9550056015602560456065608560956115614561556185619562056"
+        b"2156225624562556265628562956415645564656485649564a56505651565256"
+        b"545655565656585659565a566156645665566956825685568656885689568a56"
+        b"915695569a56a256a556a656a856a95604580558065809581058155818582158"
+        b"2a58455848584a58515854585558565858585958605862586458655882588958"
+        b"9058925895589858a158a9580159025905590a59115914591559165919592559"
+        b"41594459455946594959505951595259545955595659585959595a5961596459"
+        b"655966596959815985598959915994599559965998599959a559045a085a155a"
+        b"1a5a205a255a265a295a455a485a495a515a555a565a585a595a625a655a685a"
+        b"6a5a815a8a5a925a955a965a985a9a5aa15a0560146016601960256044605060"
+        b"5560566058605a60616064606660696081609660a56001610461066109611261"
+        b"15612161226126612961456149615161556156615961656166616a6184618a61"
+        b"92619561a161a661a96111621662196240624162466255625662586260628562"
+        b"91629662a56211641264156416641a6421642664296440644264456448644a64"
+        b"516454645564566459645a646064626465648464856489649064926494649564"
+        b"966498649a64a164a464a964056508650a651165156516651965446545654665"
+        b"496550655165546555655665596561656465656566656965866589658a659165"
+        b"9565966599659a65a265a565a665a86502660966156620662666286629664066"
+        b"456648664a66516654665566566658665a666066656668668066826685668a66"
+        b"9466966698669966a066a466a666aa661668196825684168526855685a686168"
+        b"6968856891689868a66801690469106915692169246926692969406941694569"
+        b"4669486951695469556956695969606965696a69826984698a699569a169a469"
+        b"a569a969116a166a186a416a446a496a506a556a586a5a6a646a656a696a866a"
+        b"946a986a9a6aa66a0080028008800a802080228028802a804580508051805480"
+        b"5680598065808080828088808a809580a080a280a880aa800581118114811681"
+        b"1981258141814481498150815281558156815881598164816681698185818981"
+        b"948196819981a5810082028208820a8215822082228228822a82518254825982"
+        b"65828082828288828a829582a082a282a882aa82148419844184448451845584"
+        b"5a846184648469849484998401850985128515851a8526852985408541854585"
+        b"4885518554855585568559855a856585668568856a8581858485868589859085"
+        b"928595859885a68511861686198625864186448649864a865086558659865a86"
+        b"618666866a86858691869a86a4860088028808880a8815882088228828882a88"
+        b"41884588518854885988658869888088828888888a889588a088a288a888aa88"
+        b"05890689118914891689258941894489468949895089528955895a8961896489"
+        b"858996899989a589008a028a088a0a8a158a208a228a288a2a8a458a518a548a"
+        b"568a808a828a888a8a8a958aa08aa28aa88aaa8a059011901690189019902590"
+        b"419046904990559058905a9069906a9085909190949096909990a59001910491"
+        b"069109911091159118911a912191249126912991409145915091519154915591"
+        b"569159916291659184918691929195919891a191a491a691a991059211921492"
+        b"19922592449246924992509252925592589266926992859294929692a9920194"
+        b"04940694109415941894269440944a9451945494559456945894599460946194"
+        b"62946594849486949294949495949894a194a9940095059508950a9510951195"
+        b"14951595169519952195259529952a9541954495459546954995509551955295"
+        b"549555955695589559955a956195649565956695699581958595889591959295"
+        b"94959595969599959a95a095a295a595a895aa95019604961096159619962096"
+        b"2696299645964896499651965296559656965996659668968296849689968a96"
+        b"929694969596a496a696a9960598169819982598419846985098529855985698"
+        b"5a98649865988598919896989998a59804990699099910991299159918991a99"
+        b"209921992499269940994299459948994a995199549955995699599962996599"
+        b"66996a99819984999099929995999a99a199a699059a159a259a449a469a499a"
+        b"509a559a589a619a859a919a949a959a969a00a002a008a00aa015a020a022a0"
+        b"28a02aa045a051a054a056a059a080a082a088a08aa095a0a0a0a2a0a8a0aaa0"
+        b"05a109a111a114a116a119a11aa146a149a151a155a158a15aa161a164a185a1"
+        b"90a192a196a199a102a208a20aa210a219a222a228a22aa245a251a256a259a2"
+        b"65a280a282a288a28aa295a2a0a2a2a2a8a2aaa219a425a441a444a450a454a4"
+        b"55a458a45aa461a465a466a468a469a485a406a509a510a512a515a518a526a5"
+        b"29a542a545a551a554a555a556a559a565a56aa581a584a585a586a589a592a5"
+        b"95a598a505a611a616a61aa621a625a644a646a64aa652a655a656a658a660a6"
+        b"62a686a690a695a696a699a6a1a6a4a6a6a600a802a808a80aa820a822a828a8"
+        b"2aa851a854a856a859a880a882a888a88aa895a8a0a8a2a8a8a8aaa805a914a9"
+        b"19a921a925a941a950a955a95aa961a966a969a990a996a900aa02aa08aa0aaa"
+        b"20aa22aa28aa2aaa51aa54aa56aa80aa82aa88aa8aaa95aaa0aaa2aaa8aaaaaa"
+    )
+
+    delta = np.float32(0.125)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        qs, qh = np.hsplit(rest, [QK_K // 8])
+
+        d = d.view(np.float16).astype(np.float32)
+        qh = qh.view(np.uint16)
+
+        dl = d * (2 * ((qh >> 12) & 7) + 1)
+        dl = dl.reshape((n_blocks, -1, 1, 1))
+        delta = np.where((qh & np.uint16(0x8000)) == 0, cls.delta, -cls.delta)
+        delta = delta.reshape((n_blocks, -1, 1, 1))
+
+        qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4))
+        qs = qs.astype(np.uint16) | ((qh & 7) << 8).reshape((n_blocks, -1))
+
+        assert cls.grid is not None
+        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
+        grid = grid.reshape((n_blocks, -1, 4, 8))
+
+        return (dl * (grid + delta)).reshape((n_blocks, -1))
+
+
+class IQ1_M(__Quant, qtype=GGMLQuantizationType.IQ1_M):
+    grid_shape = IQ1_S.grid_shape
+    grid_map = IQ1_S.grid_map
+    grid_hex = IQ1_S.grid_hex
+
+    delta = IQ1_S.delta
+
+    # Okay *this* type is weird. It's the only one which stores the f16 scales in multiple parts.
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        qs, rest = np.hsplit(blocks, [QK_K // 8])
+        qh, scales = np.hsplit(rest, [QK_K // 16])
+
+        # The f16 scale is packed across multiple bytes
+        scales = scales.view(np.uint16)
+        d = (scales.reshape((n_blocks, 4)) & np.uint16(0xF000)) >> np.array([12, 8, 4, 0], dtype=np.uint16).reshape((1, 4))
+        d = d[..., 0] | d[..., 1] | d[..., 2] | d[..., 3]
+        d = d.view(np.float16).astype(np.float32).reshape((n_blocks, 1))
+
+        scales = scales.reshape(n_blocks, -1, 1) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4))
+        scales = (scales & 0x07).reshape((n_blocks, -1))
+        dl = d * (2 * scales + 1)
+        dl = dl.reshape((n_blocks, -1, 2, 1, 1))
+
+        qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
+        qs = qs.astype(np.uint16) | ((qh & 0x07).astype(np.uint16) << 8).reshape((n_blocks, -1))
+
+        delta = np.where(qh & 0x08 == 0, cls.delta, -cls.delta)
+        delta = delta.reshape((n_blocks, -1, 2, 2, 1))
+
+        assert cls.grid is not None
+        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
+        grid = grid.reshape((n_blocks, -1, 2, 2, 8))
+
+        return (dl * (grid + delta)).reshape((n_blocks, -1))
+
+
+class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL):
+    kvalues = (-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, qs = np.hsplit(blocks, [2])
+
+        d = d.view(np.float16).astype(np.float32)
+
+        qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+
+        qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 1))
+
+        kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16)
+        qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1))
+
+        return (d * qs)
+
+
+class IQ4_XS(__Quant, qtype=GGMLQuantizationType.IQ4_XS):
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, rest = np.hsplit(blocks, [2])
+        scales_h, rest = np.hsplit(rest, [2])
+        scales_l, qs = np.hsplit(rest, [QK_K // 64])
+
+        d = d.view(np.float16).astype(np.float32)
+        scales_h = scales_h.view(np.uint16)
+
+        scales_l = scales_l.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
+        scales_h = scales_h.reshape((n_blocks, 1, -1)) >> np.array([2 * i for i in range(QK_K // 32)], dtype=np.uint16).reshape((1, -1, 1))
+        scales_l = scales_l.reshape((n_blocks, -1)) & np.uint8(0x0F)
+        scales_h = scales_h.reshape((n_blocks, -1)).astype(np.uint8) & np.uint8(0x03)
+
+        scales = (scales_l | (scales_h << np.uint8(4))).astype(np.int8) - np.int8(32)
+        dl = (d * scales.astype(np.float32)).reshape((n_blocks, -1, 1))
+
+        qs = qs.reshape((n_blocks, -1, 1, 16)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
+        qs = qs.reshape((n_blocks, -1, 32, 1)) & np.uint8(0x0F)
+
+        kvalues = np.array(IQ4_NL.kvalues, dtype=np.int8).reshape((1, 1, 1, -1))
+        qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1, 32))
+
+        return (dl * qs).reshape((n_blocks, -1))
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 8e1cac9152f55..9aa2209e2f69f 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -10,7 +10,7 @@ class TensorNameMap:
         # Token embeddings
         MODEL_TENSOR.TOKEN_EMBD: (
             "gpt_neox.embed_in",                         # gptneox
-            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen dbrx
+            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen dbrx jais
             "transformer.word_embeddings",               # falcon
             "word_embeddings",                           # bloom
             "model.embed_tokens",                        # llama-hf
@@ -24,6 +24,9 @@ class TensorNameMap:
             "backbone.embedding",                        # mamba
             "backbone.embeddings",                       # mamba-hf
             "transformer.in_out_embed",                  # Grok
+            "embedding.word_embeddings",                 # chatglm
+            "transformer.token_embeddings",              # openelm
+            "shared",                                    # t5
         ),
 
         # Token type embeddings
@@ -36,6 +39,7 @@ class TensorNameMap:
             "word_embeddings_layernorm",  # bloom
             "embeddings.LayerNorm",       # bert
             "emb_ln",                     # nomic-bert
+            "transformer.norm",           # openelm
         ),
 
         # Position embeddings
@@ -48,16 +52,17 @@ class TensorNameMap:
         # Output
         MODEL_TENSOR.OUTPUT: (
             "embed_out",                 # gptneox
-            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
+            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais
             "output",                    # llama-pth bloom internlm2
             "word_embeddings_for_head",  # persimmon
             "lm_head.linear",            # phi2
+            "output_layer",              # chatglm
         ),
 
         # Output norm
         MODEL_TENSOR.OUTPUT_NORM: (
             "gpt_neox.final_layer_norm",               # gptneox
-            "transformer.ln_f",                        # gpt2 gpt-j falcon
+            "transformer.ln_f",                        # gpt2 gpt-j falcon jais
             "model.norm",                              # llama-hf baichuan internlm2
             "norm",                                    # llama-pth
             "transformer.norm_f",                      # mpt dbrx
@@ -68,11 +73,14 @@ class TensorNameMap:
             "model.norm_f",                            # mamba-qbert
             "backbone.norm_f",                         # mamba
             "transformer.rms_norm",                    # Grok
+            "encoder.final_layernorm",                 # chatglm
+            "transformer.norm",                        # openelm
         ),
 
         # Rope frequencies
         MODEL_TENSOR.ROPE_FREQS: (
             "rope.freqs",  # llama-pth
+            "rotary_pos_emb.inv_freq",  # chatglm
         ),
     }
 
@@ -80,7 +88,7 @@ class TensorNameMap:
         # Attention norm
         MODEL_TENSOR.ATTN_NORM: (
             "gpt_neox.layers.{bid}.input_layernorm",                # gptneox
-            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact qwen
+            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact qwen jais
             "transformer.blocks.{bid}.norm_1",                      # mpt
             "transformer.h.{bid}.input_layernorm",                  # falcon7b
             "h.{bid}.input_layernorm",                              # bloom
@@ -97,17 +105,20 @@ class TensorNameMap:
             "backbone.layers.{bid}.norm",                           # mamba
             "transformer.decoder_layer.{bid}.rms_norm",             # Grok
             "transformer.blocks.{bid}.norm_attn_norm.norm_1",       # dbrx
+            "encoder.layers.{bid}.input_layernorm",                 # chatglm
+            "transformer.layers.{bid}.attn_norm",                   # openelm
         ),
 
         # Attention norm 2
         MODEL_TENSOR.ATTN_NORM_2: (
             "transformer.h.{bid}.ln_attn",  # falcon40b
+            "encoder.layer.{bid}.layer_norm_1",             # jina-v2-code
         ),
 
         # Attention query-key-value
         MODEL_TENSOR.ATTN_QKV: (
             "gpt_neox.layers.{bid}.attention.query_key_value",                     # gptneox
-            "transformer.h.{bid}.attn.c_attn",                                     # gpt2 qwen
+            "transformer.h.{bid}.attn.c_attn",                                     # gpt2 qwen jais
             "transformer.blocks.{bid}.attn.Wqkv",                                  # mpt
             "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv",                   # dbrx
             "transformer.h.{bid}.self_attention.query_key_value",                  # falcon
@@ -117,7 +128,9 @@ class TensorNameMap:
             "h.{bid}.attn.c_attn",                                                 # gpt2
             "transformer.h.{bid}.mixer.Wqkv",                                      # phi2
             "encoder.layers.{bid}.attn.Wqkv",                                      # nomic-bert
-            "model.layers.{bid}.self_attn.qkv_proj"                                # phi3
+            "model.layers.{bid}.self_attn.qkv_proj",                               # phi3
+            "encoder.layers.{bid}.self_attention.query_key_value",                 # chatglm
+            "transformer.layers.{bid}.attn.qkv_proj",                              # openelm
         ),
 
         # Attention query
@@ -128,7 +141,7 @@ class TensorNameMap:
             "transformer.h.{bid}.attn.q_proj",                           # gpt-j
             "model.layers.layers.{bid}.self_attn.q_proj",                # plamo
             "model.layers.{bid}.attention.wq",                           # internlm2
-            "transformer.decoder_layer.{bid}.multi_head_attention.query" # Grok
+            "transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok
         ),
 
         # Attention key
@@ -140,7 +153,7 @@ class TensorNameMap:
             "transformer.h.{bid}.attn.k",                              # refact
             "model.layers.layers.{bid}.self_attn.k_proj",              # plamo
             "model.layers.{bid}.attention.wk",                         # internlm2
-            "transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok
+            "transformer.decoder_layer.{bid}.multi_head_attention.key",# Grok
         ),
 
         # Attention value
@@ -158,7 +171,7 @@ class TensorNameMap:
         # Attention output
         MODEL_TENSOR.ATTN_OUT: (
             "gpt_neox.layers.{bid}.attention.dense",                        # gptneox
-            "transformer.h.{bid}.attn.c_proj",                              # gpt2 refact qwen
+            "transformer.h.{bid}.attn.c_proj",                              # gpt2 refact qwen jais
             "transformer.blocks.{bid}.attn.out_proj",                       # mpt
             "transformer.h.{bid}.self_attention.dense",                     # falcon
             "h.{bid}.self_attention.dense",                                 # bloom
@@ -175,6 +188,8 @@ class TensorNameMap:
             "encoder.layers.{bid}.attn.out_proj",                           # nomic-bert
             "transformer.decoder_layer.{bid}.multi_head_attention.linear",  # Grok
             "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj",        # dbrx
+            "encoder.layers.{bid}.self_attention.dense",                    # chatglm
+            "transformer.layers.{bid}.attn.out_proj",                       # openelm
         ),
 
         # Attention output norm
@@ -185,6 +200,10 @@ class TensorNameMap:
             "transformer.blocks.{bid}.norm_attn_norm.norm_2",  # dbrx
         ),
 
+        MODEL_TENSOR.ATTN_POST_NORM: (
+            "model.layers.{bid}.post_attention_layernorm",     # gemma2
+        ),
+
         # Rotary embeddings
         MODEL_TENSOR.ATTN_ROT_EMBD: (
             "model.layers.{bid}.self_attn.rotary_emb.inv_freq",        # llama-hf
@@ -196,7 +215,7 @@ class TensorNameMap:
         # Feed-forward norm
         MODEL_TENSOR.FFN_NORM: (
             "gpt_neox.layers.{bid}.post_attention_layernorm",                # gptneox
-            "transformer.h.{bid}.ln_2",                                      # gpt2 refact qwen
+            "transformer.h.{bid}.ln_2",                                      # gpt2 refact qwen jais
             "h.{bid}.post_attention_layernorm",                              # bloom
             "transformer.blocks.{bid}.norm_2",                               # mpt
             "model.layers.{bid}.post_attention_layernorm",                   # llama-hf
@@ -206,6 +225,18 @@ class TensorNameMap:
             "h.{bid}.ln_2",                                                  # gpt2
             "model.layers.{bid}.ffn_norm",                                   # internlm2
             "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
+            "encoder.layers.{bid}.post_attention_layernorm",                 # chatglm
+            "transformer.layers.{bid}.ffn_norm",                             # openelm
+        ),
+
+        # Post feed-forward norm
+        MODEL_TENSOR.FFN_PRE_NORM: (
+            "model.layers.{bid}.pre_feedforward_layernorm", # gemma2
+        ),
+
+        # Post feed-forward norm
+        MODEL_TENSOR.FFN_POST_NORM: (
+            "model.layers.{bid}.post_feedforward_layernorm", # gemma2
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (
@@ -223,7 +254,7 @@ class TensorNameMap:
         # Feed-forward up
         MODEL_TENSOR.FFN_UP: (
             "gpt_neox.layers.{bid}.mlp.dense_h_to_4h",                # gptneox
-            "transformer.h.{bid}.mlp.c_fc",                           # gpt2
+            "transformer.h.{bid}.mlp.c_fc",                           # gpt2 jais
             "transformer.blocks.{bid}.ffn.up_proj",                   # mpt
             "transformer.h.{bid}.mlp.dense_h_to_4h",                  # falcon
             "h.{bid}.mlp.dense_h_to_4h",                              # bloom
@@ -244,6 +275,8 @@ class TensorNameMap:
             "encoder.layers.{bid}.mlp.fc11",                          # nomic-bert
             "model.layers.{bid}.mlp.c_fc",                            # starcoder2
             "encoder.layer.{bid}.mlp.gated_layers_v",                 # jina-bert-v2
+            "model.layers.{bid}.residual_mlp.w3",                     # arctic
+            "encoder.layers.{bid}.mlp.dense_h_to_4h",                 # chatglm
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
@@ -255,6 +288,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.up_proj",  # qwen2moe
+            "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek2
         ),
 
         # AWQ-activation gate
@@ -267,11 +301,13 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.gate_proj",           # llama-hf refact
             "layers.{bid}.feed_forward.w1",               # llama-pth
             "transformer.h.{bid}.mlp.w2",                 # qwen
+            "transformer.h.{bid}.mlp.c_fc2",              # jais
             "model.layers.layers.{bid}.mlp.gate_proj",    # plamo
             "model.layers.{bid}.feed_forward.w1",         # internlm2
             "encoder.layers.{bid}.mlp.fc12",              # nomic-bert
             "encoder.layer.{bid}.mlp.gated_layers_w",     # jina-bert-v2
             "transformer.h.{bid}.mlp.linear_1",           # refact
+            "model.layers.{bid}.residual_mlp.w1",         # arctic
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
@@ -283,12 +319,13 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.gate_proj",  # qwen2moe
+            "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek2
         ),
 
         # Feed-forward down
         MODEL_TENSOR.FFN_DOWN: (
             "gpt_neox.layers.{bid}.mlp.dense_4h_to_h",                # gptneox
-            "transformer.h.{bid}.mlp.c_proj",                         # gpt2 refact qwen
+            "transformer.h.{bid}.mlp.c_proj",                         # gpt2 refact qwen jais
             "transformer.blocks.{bid}.ffn.down_proj",                 # mpt
             "transformer.h.{bid}.mlp.dense_4h_to_h",                  # falcon
             "h.{bid}.mlp.dense_4h_to_h",                              # bloom
@@ -306,6 +343,10 @@ class TensorNameMap:
             "encoder.layers.{bid}.mlp.fc2",                           # nomic-bert
             "model.layers.{bid}.mlp.c_proj",                          # starcoder2
             "encoder.layer.{bid}.mlp.wo",                             # jina-bert-v2
+            "transformer.layers.{bid}.ffn.proj_2",                    # openelm
+            "model.layers.{bid}.residual_mlp.w2",                     # arctic
+            "encoder.layer.{bid}.mlp.down_layer",                     # jina-bert-v2
+            "encoder.layers.{bid}.mlp.dense_4h_to_h",                 # chatglm
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (
@@ -317,6 +358,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.down_proj",  # qwen2moe
+            "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek2
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
@@ -324,7 +366,8 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon
             "model.layers.{bid}.self_attn.q_norm",                            # cohere
             "transformer.blocks.{bid}.attn.q_ln",                             # sea-lion
-            "encoder.layer.{bid}.attention.self.layer_norm_q"                 # jina-bert-v2
+            "encoder.layer.{bid}.attention.self.layer_norm_q",                # jina-bert-v2
+            "transformer.layers.{bid}.attn.q_norm",                           # openelm
         ),
 
         MODEL_TENSOR.ATTN_K_NORM: (
@@ -332,7 +375,8 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.k_layernorm",                       # persimmon
             "model.layers.{bid}.self_attn.k_norm",                            # cohere
             "transformer.blocks.{bid}.attn.k_ln",                             # sea-lion
-            "encoder.layer.{bid}.attention.self.layer_norm_k"                 # jina-bert-v2
+            "encoder.layer.{bid}.attention.self.layer_norm_k",                # jina-bert-v2
+            "transformer.layers.{bid}.attn.k_norm",                           # openelm
         ),
 
         MODEL_TENSOR.ROPE_FREQS: (
@@ -344,6 +388,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.norm2",                   # nomic-bert
             "transformer.decoder_layer.{bid}.rms_norm_3",   # Grok
             "encoder.layer.{bid}.mlp.layernorm",            # jina-bert-v2
+            "encoder.layer.{bid}.layer_norm_2"              # jina-v2-code
         ),
 
         MODEL_TENSOR.SSM_IN: (
@@ -380,6 +425,164 @@ class TensorNameMap:
             "model.layers.{bid}.out_proj",
             "backbone.layers.{bid}.mixer.out_proj",
         ),
+
+        MODEL_TENSOR.ATTN_Q_A: (
+            "model.layers.{bid}.self_attn.q_a_proj", # deepseek2
+        ),
+
+        MODEL_TENSOR.ATTN_Q_B: (
+            "model.layers.{bid}.self_attn.q_b_proj", # deepseek2
+        ),
+
+        MODEL_TENSOR.ATTN_KV_A_MQA: (
+            "model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2
+        ),
+
+        MODEL_TENSOR.ATTN_KV_B: (
+            "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2
+        ),
+
+        MODEL_TENSOR.ATTN_Q_A_NORM: (
+            "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2
+        ),
+
+        MODEL_TENSOR.ATTN_KV_A_NORM: (
+            "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2
+        ),
+
+        MODEL_TENSOR.ATTN_SUB_NORM: (
+            "model.layers.{bid}.self_attn.inner_attn_ln",  # bitnet
+        ),
+
+        MODEL_TENSOR.FFN_SUB_NORM: (
+            "model.layers.{bid}.mlp.ffn_layernorm",  # bitnet
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_NORM: (
+            "decoder.block.{bid}.layer.0.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_Q: (
+            "decoder.block.{bid}.layer.0.SelfAttention.q", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_K: (
+            "decoder.block.{bid}.layer.0.SelfAttention.k", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_V: (
+            "decoder.block.{bid}.layer.0.SelfAttention.v", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_OUT: (
+            "decoder.block.{bid}.layer.0.SelfAttention.o", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_REL_B: (
+            "decoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_NORM: (
+            "decoder.block.{bid}.layer.1.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_Q: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.q", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_K: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.k", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_V: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.v", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_OUT: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.o", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.relative_attention_bias", # t5
+        ),
+
+        MODEL_TENSOR.DEC_FFN_NORM: (
+            "decoder.block.{bid}.layer.2.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.DEC_FFN_GATE: (
+            "decoder.block.{bid}.layer.2.DenseReluDense.wi_0", # flan-t5
+        ),
+
+        MODEL_TENSOR.DEC_FFN_UP: (
+            "decoder.block.{bid}.layer.2.DenseReluDense.wi",   # t5
+            "decoder.block.{bid}.layer.2.DenseReluDense.wi_1", # flan-t5
+        ),
+
+        MODEL_TENSOR.DEC_FFN_DOWN: (
+            "decoder.block.{bid}.layer.2.DenseReluDense.wo", # t5
+        ),
+
+        MODEL_TENSOR.DEC_OUTPUT_NORM: (
+            "decoder.final_layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_NORM: (
+            "encoder.block.{bid}.layer.0.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_Q: (
+            "encoder.block.{bid}.layer.0.SelfAttention.q", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_K: (
+            "encoder.block.{bid}.layer.0.SelfAttention.k", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_V: (
+            "encoder.block.{bid}.layer.0.SelfAttention.v", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_OUT: (
+            "encoder.block.{bid}.layer.0.SelfAttention.o", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_REL_B: (
+            "encoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias", # t5
+        ),
+
+        MODEL_TENSOR.ENC_FFN_NORM: (
+            "encoder.block.{bid}.layer.1.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.ENC_FFN_GATE: (
+            "encoder.block.{bid}.layer.1.DenseReluDense.wi_0", # flan-t5
+        ),
+
+        MODEL_TENSOR.ENC_FFN_UP: (
+            "encoder.block.{bid}.layer.1.DenseReluDense.wi",   # t5
+            "encoder.block.{bid}.layer.1.DenseReluDense.wi_1", # flan-t5
+        ),
+
+        MODEL_TENSOR.ENC_FFN_DOWN: (
+            "encoder.block.{bid}.layer.1.DenseReluDense.wo", # t5
+        ),
+
+        MODEL_TENSOR.ENC_OUTPUT_NORM: (
+            "encoder.final_layer_norm", # t5
+        ),
+    }
+
+    # architecture-specific block mappings
+    arch_block_mappings_cfg: dict[MODEL_ARCH, dict[MODEL_TENSOR, tuple[str, ...]]] = {
+        MODEL_ARCH.ARCTIC: {
+            MODEL_TENSOR.FFN_NORM: (
+                "model.layers.{bid}.residual_layernorm",
+            ),
+            MODEL_TENSOR.FFN_NORM_EXP: (
+                "model.layers.{bid}.post_attention_layernorm",
+            ),
+        },
     }
 
     mapping: dict[str, tuple[MODEL_TENSOR, str]]
@@ -393,18 +596,18 @@ def __init__(self, arch: MODEL_ARCH, n_blocks: int):
             self.mapping[tensor_name] = (tensor, tensor_name)
             for key in keys:
                 self.mapping[key] = (tensor, tensor_name)
+        if arch in self.arch_block_mappings_cfg:
+            self.block_mappings_cfg.update(self.arch_block_mappings_cfg[arch])
         for bid in range(n_blocks):
             for tensor, keys in self.block_mappings_cfg.items():
                 if tensor not in MODEL_TENSORS[arch]:
                     continue
-                # TODO: make this configurable
-                n_experts = 60
-                for xid in range(n_experts):
-                    tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
-                    self.mapping[tensor_name] = (tensor, tensor_name)
-                    for key in keys:
-                        key = key.format(bid = bid, xid = xid)
-                        self.mapping[key] = (tensor, tensor_name)
+
+                tensor_name = TENSOR_NAMES[tensor].format(bid = bid)
+                self.mapping[tensor_name] = (tensor, tensor_name)
+                for key in keys:
+                    key = key.format(bid = bid)
+                    self.mapping[key] = (tensor, tensor_name)
 
     def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
         result = self.mapping.get(key)
diff --git a/gguf-py/gguf/utility.py b/gguf-py/gguf/utility.py
new file mode 100644
index 0000000000000..40d59b75ee04e
--- /dev/null
+++ b/gguf-py/gguf/utility.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from typing import Literal
+
+
+def fill_templated_filename(filename: str, output_type: str | None) -> str:
+    # Given a file name fill in any type templates e.g. 'some-model-name.{ftype}.gguf'
+    ftype_lowercase: str = output_type.lower() if output_type is not None else ""
+    ftype_uppercase: str = output_type.upper() if output_type is not None else ""
+    return filename.format(ftype_lowercase,
+                           outtype=ftype_lowercase, ftype=ftype_lowercase,
+                           OUTTYPE=ftype_uppercase, FTYPE=ftype_uppercase)
+
+
+def model_weight_count_rounded_notation(model_params_count: int, min_digits: int = 2) -> str:
+    if model_params_count > 1e12 :
+        # Trillions Of Parameters
+        scaled_model_params = model_params_count * 1e-12
+        scale_suffix = "T"
+    elif model_params_count > 1e9 :
+        # Billions Of Parameters
+        scaled_model_params = model_params_count * 1e-9
+        scale_suffix = "B"
+    elif model_params_count > 1e6 :
+        # Millions Of Parameters
+        scaled_model_params = model_params_count * 1e-6
+        scale_suffix = "M"
+    else:
+        # Thousands Of Parameters
+        scaled_model_params = model_params_count * 1e-3
+        scale_suffix = "K"
+
+    fix = max(min_digits - len(str(round(scaled_model_params)).lstrip('0')), 0)
+
+    return f"{scaled_model_params:.{fix}f}{scale_suffix}"
+
+
+def size_label(total_params: int, shared_params: int, expert_params: int, expert_count: int) -> str:
+
+    if expert_count > 0:
+        pretty_size = model_weight_count_rounded_notation(abs(shared_params) + abs(expert_params), min_digits=2)
+        size_class = f"{expert_count}x{pretty_size}"
+    else:
+        size_class = model_weight_count_rounded_notation(abs(total_params), min_digits=2)
+
+    return size_class
+
+
+def naming_convention(model_name: str | None, base_name: str | None, finetune_string: str | None, version_string: str | None, size_label: str | None, output_type: str | None, model_type: Literal['vocab', 'LoRA'] | None = None) -> str:
+    # Reference: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#gguf-naming-convention
+
+    if base_name is not None:
+        name = base_name.strip().replace(' ', '-').replace('/', '-')
+    elif model_name is not None:
+        name = model_name.strip().replace(' ', '-').replace('/', '-')
+    else:
+        name = "ggml-model"
+
+    parameters = f"-{size_label}" if size_label is not None else ""
+
+    finetune = f"-{finetune_string.strip().replace(' ', '-')}" if finetune_string is not None else ""
+
+    version = f"-{version_string.strip().replace(' ', '-')}" if version_string is not None else ""
+
+    encoding = f"-{output_type.strip().replace(' ', '-').upper()}" if output_type is not None else ""
+
+    kind = f"-{model_type.strip().replace(' ', '-')}" if model_type is not None else ""
+
+    return f"{name}{parameters}{finetune}{version}{encoding}{kind}"
diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py
index 3ba99be4f4489..dc574991381a8 100644
--- a/gguf-py/gguf/vocab.py
+++ b/gguf-py/gguf/vocab.py
@@ -1,10 +1,15 @@
 from __future__ import annotations
 
+import re
 import logging
 import json
 import os
 from pathlib import Path
-from typing import Any, Callable, Sequence, Mapping, Iterable
+from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable
+
+from sentencepiece import SentencePieceProcessor
+
+import gguf
 
 from .gguf_writer import GGUFWriter
 
@@ -163,3 +168,298 @@ def _try_load_from_config_json(self, path: Path) -> bool:
         for typ in self.special_token_types:
             self._set_special_token(typ, config.get(f'{typ}_token_id'))
         return True
+
+
+@runtime_checkable
+class BaseVocab(Protocol):
+    tokenizer_model: ClassVar[str]
+    name: ClassVar[str]
+
+
+@runtime_checkable
+class Vocab(BaseVocab, Protocol):
+    vocab_size: int
+    added_tokens_dict: dict[str, int]
+    added_tokens_list: list[str]
+    fname_tokenizer: Path
+
+    def __init__(self, base_path: Path): ...
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
+
+
+class NoVocab(BaseVocab):
+    tokenizer_model = "no_vocab"
+    name = "no_vocab"
+
+    def __repr__(self) -> str:
+        return "<NoVocab for a model without integrated vocabulary>"
+
+
+class BpeVocab(Vocab):
+    tokenizer_model = "gpt2"
+    name = "bpe"
+
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+
+        if (fname_tokenizer := base_path / 'vocab.json').exists():
+            # "slow" tokenizer
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                self.vocab = json.load(f)
+
+            try:
+                # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
+                with open(base_path / 'added_tokens.json', encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        else:
+            # "fast" tokenizer
+            fname_tokenizer = base_path / 'tokenizer.json'
+
+            # if this fails, FileNotFoundError propagates to caller
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+
+            tokenizer_model: dict[str, Any] = tokenizer_json['model']
+            if (
+                tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
+                or tokenizer_json['decoder']['type'] != 'ByteLevel'
+            ):
+                raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
+
+            self.vocab = tokenizer_model["vocab"]
+
+            if (added := tokenizer_json.get('added_tokens')) is not None:
+                # Added tokens here can be duplicates of the main vocabulary.
+                added_tokens = {item['content']: item['id']
+                                for item in added
+                                if item['content'] not in self.vocab}
+
+        vocab_size   = len(self.vocab)
+        expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
+        actual_ids   = sorted(added_tokens.values())
+        if expected_ids != actual_ids:
+            expected_end_id = vocab_size + len(actual_ids) - 1
+            raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
+                             f"{vocab_size} - {expected_end_id}; got {actual_ids}")
+
+        items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
+        self.added_tokens_dict    = added_tokens
+        self.added_tokens_list    = [text for (text, idx) in items]
+        self.vocab_size_base      = vocab_size
+        self.vocab_size           = self.vocab_size_base + len(self.added_tokens_list)
+        self.fname_tokenizer      = fname_tokenizer
+
+    def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
+
+        for i, _ in enumerate(self.vocab):
+            yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
+
+    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        for text in self.added_tokens_list:
+            score = -1000.0
+            yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
+
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        yield from self.bpe_tokens()
+        yield from self.added_tokens()
+
+    def __repr__(self) -> str:
+        return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
+
+
+class SentencePieceVocab(Vocab):
+    tokenizer_model = "llama"
+    name = "spm"
+
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+        if (fname_tokenizer := base_path / 'tokenizer.model').exists():
+            # normal location
+            try:
+                with open(base_path / 'added_tokens.json', encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
+            # not found in alternate location either
+            raise FileNotFoundError('Cannot find tokenizer.model')
+
+        self.sentencepiece_tokenizer = SentencePieceProcessor()
+        self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
+        vocab_size = self.sentencepiece_tokenizer.vocab_size()
+
+        new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
+        expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
+        actual_new_ids   = sorted(new_tokens.keys())
+
+        if expected_new_ids != actual_new_ids:
+            raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
+
+        # Token pieces that were added to the base vocabulary.
+        self.added_tokens_dict  = added_tokens
+        self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
+        self.vocab_size_base    = vocab_size
+        self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
+        self.fname_tokenizer    = fname_tokenizer
+
+    def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        tokenizer = self.sentencepiece_tokenizer
+        for i in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(i)
+            text         = piece.encode("utf-8")
+            score: float = tokenizer.GetScore(i)
+
+            toktype = gguf.TokenType.NORMAL
+            if tokenizer.IsUnknown(i):
+                toktype = gguf.TokenType.UNKNOWN
+            if tokenizer.IsControl(i):
+                toktype = gguf.TokenType.CONTROL
+
+            # NOTE: I think added_tokens are user defined.
+            # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
+            # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
+
+            if tokenizer.IsUnused(i):
+                toktype = gguf.TokenType.UNUSED
+            if tokenizer.IsByte(i):
+                toktype = gguf.TokenType.BYTE
+
+            yield text, score, toktype
+
+    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        for text in self.added_tokens_list:
+            score = -1000.0
+            yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
+
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        yield from self.sentencepiece_tokens()
+        yield from self.added_tokens()
+
+    def __repr__(self) -> str:
+        return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
+
+
+class LlamaHfVocab(Vocab):
+    tokenizer_model = "llama"
+    name = "hfft"
+
+    def __init__(self, base_path: Path):
+        fname_tokenizer = base_path / 'tokenizer.json'
+        # if this fails, FileNotFoundError propagates to caller
+        with open(fname_tokenizer, encoding='utf-8') as f:
+            tokenizer_json = json.load(f)
+
+        # pre-check so we know if we need transformers
+        tokenizer_model: dict[str, Any] = tokenizer_json['model']
+        is_llama3 = (
+            tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
+            and not tokenizer_model.get('byte_fallback', True)
+        )
+        if is_llama3:
+            raise TypeError('Llama 3 must be converted with BpeVocab')
+
+        if not is_llama3 and (
+            tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
+            or tokenizer_json['decoder']['type'] != 'Sequence'
+        ):
+            raise FileNotFoundError('Cannot find Llama BPE tokenizer')
+
+        try:
+            from transformers import AutoTokenizer
+        except ImportError as e:
+            raise ImportError(
+                "To use LlamaHfVocab, please install the `transformers` package. "
+                "You can install it with `pip install transformers`."
+            ) from e
+
+        # Allow the tokenizer to default to slow or fast versions.
+        # Explicitly set tokenizer to use local paths.
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            base_path,
+            cache_dir=base_path,
+            local_files_only=True,
+        )
+        assert self.tokenizer.is_fast  # assume tokenizer.json is used
+
+        # Initialize lists and dictionaries for added tokens
+        self.added_tokens_list = []
+        self.added_tokens_dict = dict()
+        self.added_tokens_ids  = set()
+
+        # Process added tokens
+        for tok, tokidx in sorted(
+            self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
+        ):
+            # Only consider added tokens that are not in the base vocabulary
+            if tokidx >= self.tokenizer.vocab_size:
+                self.added_tokens_list.append(tok)
+                self.added_tokens_dict[tok] = tokidx
+                self.added_tokens_ids.add(tokidx)
+
+        # Store special tokens and their IDs
+        self.specials = {
+            tok: self.tokenizer.get_vocab()[tok]
+            for tok in self.tokenizer.all_special_tokens
+        }
+        self.special_ids = set(self.tokenizer.all_special_ids)
+
+        # Set vocabulary sizes
+        self.vocab_size_base = self.tokenizer.vocab_size
+        self.vocab_size      = self.vocab_size_base + len(self.added_tokens_list)
+
+        self.fname_tokenizer = fname_tokenizer
+
+    def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        reverse_vocab = {
+            id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
+        }
+
+        for token_id in range(self.vocab_size_base):
+            # Skip processing added tokens here
+            if token_id in self.added_tokens_ids:
+                continue
+
+            # Convert token text to bytes
+            token_text = reverse_vocab[token_id].encode("utf-8")
+
+            # Yield token text, score, and type
+            yield token_text, self.get_token_score(token_id), self.get_token_type(
+                token_id, token_text, self.special_ids  # Reuse already stored special IDs
+            )
+
+    def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
+        # Special case for byte tokens
+        if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
+            return gguf.TokenType.BYTE
+
+        # Determine token type based on whether it's a special token
+        return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
+
+    def get_token_score(self, token_id: int) -> float:
+        # Placeholder for actual logic to determine the token's score
+        # This needs to be implemented based on specific requirements
+        return -1000.0  # Default score
+
+    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        for text in self.added_tokens_list:
+            if text in self.specials:
+                toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
+                score = self.get_token_score(self.specials[text])
+            else:
+                toktype = gguf.TokenType.USER_DEFINED
+                score = -1000.0
+
+            yield text.encode("utf-8"), score, toktype
+
+    def has_newline_token(self):
+        return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab
+
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        yield from self.hf_tokens()
+        yield from self.added_tokens()
+
+    def __repr__(self) -> str:
+        return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 36e63ee3b7cd6..19f6761e2f912 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.9.0"
+version = "0.9.1"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [
@@ -22,6 +22,7 @@ classifiers = [
 python = ">=3.8"
 numpy = ">=1.17"
 tqdm = ">=4.27"
+pyyaml = ">=5.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^5.2"
diff --git a/gguf-py/scripts/__init__.py b/gguf-py/scripts/__init__.py
index 1ad45639a62ea..e77f2e9c97c31 100644
--- a/gguf-py/scripts/__init__.py
+++ b/gguf-py/scripts/__init__.py
@@ -1,13 +1,6 @@
-import os
+# pyright: reportUnusedImport=false
 
-from importlib import import_module
-
-
-os.environ["NO_LOCAL_GGUF"] = "TRUE"
-
-gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main
-gguf_dump_entrypoint           = import_module("scripts.gguf-dump").main
-gguf_set_metadata_entrypoint   = import_module("scripts.gguf-set-metadata").main
-gguf_new_metadata_entrypoint   = import_module("scripts.gguf-new-metadata").main
-
-del import_module, os
+from .gguf_convert_endian import main as gguf_convert_endian_entrypoint
+from .gguf_dump import main as gguf_dump_entrypoint
+from .gguf_set_metadata import main as gguf_set_metadata_entrypoint
+from .gguf_new_metadata import main as gguf_new_metadata_entrypoint
diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py
deleted file mode 100755
index 1a37a7b91409d..0000000000000
--- a/gguf-py/scripts/gguf-dump.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import logging
-import argparse
-import os
-import sys
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-# Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from gguf import GGUFReader, GGUFValueType  # noqa: E402
-
-logger = logging.getLogger("gguf-dump")
-
-
-def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]:
-    host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG'
-    if reader.byte_order == 'S':
-        file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE'
-    else:
-        file_endian = host_endian
-    return (host_endian, file_endian)
-
-
-# For more information about what field.parts and field.data represent,
-# please see the comments in the modify_gguf.py example.
-def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
-    host_endian, file_endian = get_file_host_endian(reader)
-    print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.')  # noqa: NP100
-    print(f'* Dumping {len(reader.fields)} key/value pair(s)')  # noqa: NP100
-    for n, field in enumerate(reader.fields.values(), 1):
-        if not field.types:
-            pretty_type = 'N/A'
-        elif field.types[0] == GGUFValueType.ARRAY:
-            nest_count = len(field.types) - 1
-            pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
-        else:
-            pretty_type = str(field.types[-1].name)
-
-        log_message = f'  {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}'
-        if len(field.types) == 1:
-            curr_type = field.types[0]
-            if curr_type == GGUFValueType.STRING:
-                log_message += ' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60]))
-            elif field.types[0] in reader.gguf_scalar_to_np:
-                log_message += ' = {0}'.format(field.parts[-1][0])
-        print(log_message)  # noqa: NP100
-    if args.no_tensors:
-        return
-    print(f'* Dumping {len(reader.tensors)} tensor(s)')  # noqa: NP100
-    for n, tensor in enumerate(reader.tensors, 1):
-        prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
-        print(f'  {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}')  # noqa: NP100
-
-
-def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None:
-    import json
-    host_endian, file_endian = get_file_host_endian(reader)
-    metadata: dict[str, Any] = {}
-    tensors: dict[str, Any] = {}
-    result = {
-        "filename": args.model,
-        "endian": file_endian,
-        "metadata": metadata,
-        "tensors": tensors,
-    }
-    for idx, field in enumerate(reader.fields.values()):
-        curr: dict[str, Any] = {
-            "index": idx,
-            "type": field.types[0].name if field.types else 'UNKNOWN',
-            "offset": field.offset,
-        }
-        metadata[field.name] = curr
-        if field.types[:1] == [GGUFValueType.ARRAY]:
-            curr["array_types"] = [t.name for t in field.types][1:]
-            if not args.json_array:
-                continue
-            itype = field.types[-1]
-            if itype == GGUFValueType.STRING:
-                curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data]
-            else:
-                curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()]
-        elif field.types[0] == GGUFValueType.STRING:
-            curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8")
-        else:
-            curr["value"] = field.parts[-1].tolist()[0]
-    if not args.no_tensors:
-        for idx, tensor in enumerate(reader.tensors):
-            tensors[tensor.name] = {
-                "index": idx,
-                "shape": tensor.shape.tolist(),
-                "type": tensor.tensor_type.name,
-                "offset": tensor.field.offset,
-            }
-    json.dump(result, sys.stdout)
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
-    parser.add_argument("model",           type=str,            help="GGUF format model filename")
-    parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
-    parser.add_argument("--json",       action="store_true", help="Produce JSON output")
-    parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
-    parser.add_argument("--verbose",    action="store_true", help="increase output verbosity")
-
-    args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
-
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
-
-    if not args.json:
-        logger.info(f'* Loading: {args.model}')
-
-    reader = GGUFReader(args.model, 'r')
-
-    if args.json:
-        dump_metadata_json(reader, args)
-    else:
-        dump_metadata(reader, args)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf_convert_endian.py
similarity index 100%
rename from gguf-py/scripts/gguf-convert-endian.py
rename to gguf-py/scripts/gguf_convert_endian.py
diff --git a/gguf-py/scripts/gguf_dump.py b/gguf-py/scripts/gguf_dump.py
new file mode 100755
index 0000000000000..1b65465419ddb
--- /dev/null
+++ b/gguf-py/scripts/gguf_dump.py
@@ -0,0 +1,454 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import logging
+import argparse
+import os
+import re
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+# Necessary to load the local gguf package
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from gguf import GGUFReader, GGUFValueType, ReaderTensor  # noqa: E402
+
+logger = logging.getLogger("gguf-dump")
+
+
+def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]:
+    host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG'
+    if reader.byte_order == 'S':
+        file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE'
+    else:
+        file_endian = host_endian
+    return (host_endian, file_endian)
+
+
+# For more information about what field.parts and field.data represent,
+# please see the comments in the modify_gguf.py example.
+def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
+    host_endian, file_endian = get_file_host_endian(reader)
+    print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.')  # noqa: NP100
+    print(f'* Dumping {len(reader.fields)} key/value pair(s)')  # noqa: NP100
+    for n, field in enumerate(reader.fields.values(), 1):
+        if not field.types:
+            pretty_type = 'N/A'
+        elif field.types[0] == GGUFValueType.ARRAY:
+            nest_count = len(field.types) - 1
+            pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
+        else:
+            pretty_type = str(field.types[-1].name)
+
+        log_message = f'  {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}'
+        if len(field.types) == 1:
+            curr_type = field.types[0]
+            if curr_type == GGUFValueType.STRING:
+                log_message += ' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60]))
+            elif field.types[0] in reader.gguf_scalar_to_np:
+                log_message += ' = {0}'.format(field.parts[-1][0])
+        print(log_message)  # noqa: NP100
+    if args.no_tensors:
+        return
+    print(f'* Dumping {len(reader.tensors)} tensor(s)')  # noqa: NP100
+    for n, tensor in enumerate(reader.tensors, 1):
+        prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
+        print(f'  {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}')  # noqa: NP100
+
+
+def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None:
+    import json
+    host_endian, file_endian = get_file_host_endian(reader)
+    metadata: dict[str, Any] = {}
+    tensors: dict[str, Any] = {}
+    result = {
+        "filename": args.model,
+        "endian": file_endian,
+        "metadata": metadata,
+        "tensors": tensors,
+    }
+    for idx, field in enumerate(reader.fields.values()):
+        curr: dict[str, Any] = {
+            "index": idx,
+            "type": field.types[0].name if field.types else 'UNKNOWN',
+            "offset": field.offset,
+        }
+        metadata[field.name] = curr
+        if field.types[:1] == [GGUFValueType.ARRAY]:
+            curr["array_types"] = [t.name for t in field.types][1:]
+            if not args.json_array:
+                continue
+            itype = field.types[-1]
+            if itype == GGUFValueType.STRING:
+                curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data]
+            else:
+                curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()]
+        elif field.types[0] == GGUFValueType.STRING:
+            curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8")
+        else:
+            curr["value"] = field.parts[-1].tolist()[0]
+    if not args.no_tensors:
+        for idx, tensor in enumerate(reader.tensors):
+            tensors[tensor.name] = {
+                "index": idx,
+                "shape": tensor.shape.tolist(),
+                "type": tensor.tensor_type.name,
+                "offset": tensor.field.offset,
+            }
+    json.dump(result, sys.stdout)
+
+
+def markdown_table_with_alignment_support(header_map: list[dict[str, str]], data: list[dict[str, Any]]):
+    # JSON to Markdown table formatting: https://stackoverflow.com/a/72983854/2850957
+
+    # Alignment Utility Function
+    def strAlign(padding: int, alignMode: str | None, strVal: str):
+        if alignMode == 'center':
+            return strVal.center(padding)
+        elif alignMode == 'right':
+            return strVal.rjust(padding - 1) + ' '
+        elif alignMode == 'left':
+            return ' ' + strVal.ljust(padding - 1)
+        else: # default left
+            return ' ' + strVal.ljust(padding - 1)
+
+    def dashAlign(padding: int, alignMode: str | None):
+        if alignMode == 'center':
+            return ':' + '-' * (padding - 2) + ':'
+        elif alignMode == 'right':
+            return '-' * (padding - 1) + ':'
+        elif alignMode == 'left':
+            return ':' + '-' * (padding - 1)
+        else: # default left
+            return '-' * (padding)
+
+    # Calculate Padding For Each Column Based On Header and Data Length
+    rowsPadding = {}
+    for index, columnEntry in enumerate(header_map):
+        padCount = max([len(str(v)) for d in data for k, v in d.items() if k == columnEntry['key_name']], default=0) + 2
+        headerPadCount = len(columnEntry['header_name']) + 2
+        rowsPadding[index] = headerPadCount if padCount <= headerPadCount else padCount
+
+    # Render Markdown Header
+    rows = []
+    rows.append('|'.join(strAlign(rowsPadding[index], columnEntry.get('align'), str(columnEntry['header_name'])) for index, columnEntry in enumerate(header_map)))
+    rows.append('|'.join(dashAlign(rowsPadding[index], columnEntry.get('align')) for index, columnEntry in enumerate(header_map)))
+
+    # Render Tabular Data
+    for item in data:
+        rows.append('|'.join(strAlign(rowsPadding[index], columnEntry.get('align'), str(item[columnEntry['key_name']])) for index, columnEntry in enumerate(header_map)))
+
+    # Convert Tabular String Rows Into String
+    tableString = ""
+    for row in rows:
+        tableString += f'|{row}|\n'
+
+    return tableString
+
+
+def element_count_rounded_notation(count: int) -> str:
+    if count > 1e15 :
+        # Quadrillion
+        scaled_amount = count * 1e-15
+        scale_suffix = "Q"
+    elif count > 1e12 :
+        # Trillions
+        scaled_amount = count * 1e-12
+        scale_suffix = "T"
+    elif count > 1e9 :
+        # Billions
+        scaled_amount = count * 1e-9
+        scale_suffix = "B"
+    elif count > 1e6 :
+        # Millions
+        scaled_amount = count * 1e-6
+        scale_suffix = "M"
+    elif count > 1e3 :
+        # Thousands
+        scaled_amount = count * 1e-3
+        scale_suffix = "K"
+    else:
+        # Under Thousands
+        scaled_amount = count
+        scale_suffix = ""
+    return f"{'~' if count > 1e3 else ''}{round(scaled_amount)}{scale_suffix}"
+
+
+def translate_tensor_name(name):
+    words = name.split(".")
+
+    # Source: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#standardized-tensor-names
+    abbreviation_dictionary = {
+        'token_embd': 'Token embedding',
+        'pos_embd': 'Position embedding',
+        'output_norm': 'Output normalization',
+        'output': 'Output',
+        'attn_norm': 'Attention normalization',
+        'attn_norm_2': 'Attention normalization',
+        'attn_qkv': 'Attention query-key-value',
+        'attn_q': 'Attention query',
+        'attn_k': 'Attention key',
+        'attn_v': 'Attention value',
+        'attn_output': 'Attention output',
+        'ffn_norm': 'Feed-forward network normalization',
+        'ffn_up': 'Feed-forward network "up"',
+        'ffn_gate': 'Feed-forward network "gate"',
+        'ffn_down': 'Feed-forward network "down"',
+        'ffn_gate_inp': 'Expert-routing layer for the Feed-forward network in Mixture of Expert models',
+        'ffn_gate_exp': 'Feed-forward network "gate" layer per expert in Mixture of Expert models',
+        'ffn_down_exp': 'Feed-forward network "down" layer per expert in Mixture of Expert models',
+        'ffn_up_exp': 'Feed-forward network "up" layer per expert in Mixture of Expert models',
+        'ssm_in': 'State space model input projections',
+        'ssm_conv1d': 'State space model rolling/shift',
+        'ssm_x': 'State space model selective parametrization',
+        'ssm_a': 'State space model state compression',
+        'ssm_d': 'State space model skip connection',
+        'ssm_dt': 'State space model time step',
+        'ssm_out': 'State space model output projection',
+        'blk': 'Block',
+        'enc': 'Encoder',
+        'dec': 'Decoder',
+    }
+
+    expanded_words = []
+    for word in words:
+        word_norm = word.strip().lower()
+        if word_norm in abbreviation_dictionary:
+            expanded_words.append(abbreviation_dictionary[word_norm].title())
+        else:
+            expanded_words.append(word.title())
+
+    return ' '.join(expanded_words)
+
+
+def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
+    host_endian, file_endian = get_file_host_endian(reader)
+    markdown_content = ""
+    markdown_content += f'# {args.model} - GGUF Internal File Dump\n\n'
+    markdown_content += f'- Endian: {file_endian} endian\n'
+    markdown_content += '\n'
+    markdown_content += '## Key Value Metadata Store\n\n'
+    markdown_content += f'There are {len(reader.fields)} key-value pairs in this file\n'
+    markdown_content += '\n'
+
+    kv_dump_table: list[dict[str, str | int]] = []
+    for n, field in enumerate(reader.fields.values(), 1):
+        if not field.types:
+            pretty_type = 'N/A'
+        elif field.types[0] == GGUFValueType.ARRAY:
+            nest_count = len(field.types) - 1
+            pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
+        else:
+            pretty_type = str(field.types[-1].name)
+
+        def escape_markdown_inline_code(value_string):
+            # Find the longest contiguous sequence of backticks in the string then
+            # wrap string with appropriate number of backticks required to escape it
+            max_backticks = max((len(match.group(0)) for match in re.finditer(r'`+', value_string)), default=0)
+            inline_code_marker = '`' * (max_backticks + 1)
+
+            # If the string starts or ends with a backtick, add a space at the beginning and end
+            if value_string.startswith('`') or value_string.endswith('`'):
+                value_string = f" {value_string} "
+
+            return f"{inline_code_marker}{value_string}{inline_code_marker}"
+
+        total_elements = len(field.data)
+        value = ""
+        if len(field.types) == 1:
+            curr_type = field.types[0]
+            if curr_type == GGUFValueType.STRING:
+                truncate_length = 60
+                value_string = str(bytes(field.parts[-1]), encoding='utf-8')
+                if len(value_string) > truncate_length:
+                    head = escape_markdown_inline_code(value_string[:truncate_length // 2])
+                    tail = escape_markdown_inline_code(value_string[-truncate_length // 2:])
+                    value = "{head}...{tail}".format(head=head, tail=tail)
+                else:
+                    value = escape_markdown_inline_code(value_string)
+            elif curr_type in reader.gguf_scalar_to_np:
+                value = str(field.parts[-1][0])
+        else:
+            if field.types[0] == GGUFValueType.ARRAY:
+                curr_type = field.types[1]
+                array_elements = []
+
+                if curr_type == GGUFValueType.STRING:
+                    render_element = min(5, total_elements)
+                    for element_pos in range(render_element):
+                        truncate_length = 30
+                        value_string = str(bytes(field.parts[-1 - (total_elements - element_pos - 1) * 2]), encoding='utf-8')
+                        if len(value_string) > truncate_length:
+                            head = escape_markdown_inline_code(value_string[:truncate_length // 2])
+                            tail = escape_markdown_inline_code(value_string[-truncate_length // 2:])
+                            value = "{head}...{tail}".format(head=head, tail=tail)
+                        else:
+                            value = escape_markdown_inline_code(value_string)
+                        array_elements.append(value)
+
+                elif curr_type in reader.gguf_scalar_to_np:
+                    render_element = min(7, total_elements)
+                    for element_pos in range(render_element):
+                        array_elements.append(str(field.parts[-1 - (total_elements - element_pos - 1)][0]))
+
+                value = f'[ {", ".join(array_elements).strip()}{", ..." if total_elements > len(array_elements) else ""} ]'
+
+        kv_dump_table.append({"n":n, "pretty_type":pretty_type, "total_elements":total_elements, "field_name":field.name, "value":value})
+
+    kv_dump_table_header_map = [
+        {'key_name':'n',                'header_name':'POS',      'align':'right'},
+        {'key_name':'pretty_type',      'header_name':'TYPE',     'align':'left'},
+        {'key_name':'total_elements',   'header_name':'Count',    'align':'right'},
+        {'key_name':'field_name',       'header_name':'Key',      'align':'left'},
+        {'key_name':'value',            'header_name':'Value',    'align':'left'},
+    ]
+
+    markdown_content += markdown_table_with_alignment_support(kv_dump_table_header_map, kv_dump_table)
+
+    markdown_content += "\n"
+
+    if not args.no_tensors:
+        # Group tensors by their prefix and maintain order
+        tensor_prefix_order: list[str] = []
+        tensor_name_to_key: dict[str, int] = {}
+        tensor_groups: dict[str, list[ReaderTensor]] = {}
+        total_elements = sum(tensor.n_elements for tensor in reader.tensors)
+
+        # Parsing Tensors Record
+        for key, tensor in enumerate(reader.tensors):
+            tensor_components = tensor.name.split('.')
+
+            # Classify Tensor Group
+            tensor_group_name = "base"
+            if tensor_components[0] == 'blk':
+                tensor_group_name = f"{tensor_components[0]}.{tensor_components[1]}"
+            elif tensor_components[0] in ['enc', 'dec'] and tensor_components[1] == 'blk':
+                tensor_group_name = f"{tensor_components[0]}.{tensor_components[1]}.{tensor_components[2]}"
+            elif tensor_components[0] in ['enc', 'dec']:
+                tensor_group_name = f"{tensor_components[0]}"
+
+            # Check if new Tensor Group
+            if tensor_group_name not in tensor_groups:
+                tensor_groups[tensor_group_name] = []
+                tensor_prefix_order.append(tensor_group_name)
+
+            # Record Tensor and Tensor Position
+            tensor_groups[tensor_group_name].append(tensor)
+            tensor_name_to_key[tensor.name] = key
+
+        # Tensors Mapping Dump
+        markdown_content += f'## Tensors Overview {element_count_rounded_notation(total_elements)} Elements\n\n'
+        markdown_content += f'Total number of elements in all tensors: {total_elements} Elements\n'
+        markdown_content += '\n'
+
+        for group in tensor_prefix_order:
+            tensors = tensor_groups[group]
+            group_elements = sum(tensor.n_elements for tensor in tensors)
+            markdown_content += f"- [{translate_tensor_name(group)} Tensor Group - {element_count_rounded_notation(group_elements)} Elements](#{group.replace('.', '_')})\n"
+
+        markdown_content += "\n"
+
+        markdown_content += "### Tensor Data Offset\n"
+        markdown_content += '\n'
+        markdown_content += 'This table contains the offset and data segment relative to start of file\n'
+        markdown_content += '\n'
+
+        tensor_mapping_table: list[dict[str, str | int]] = []
+        for key, tensor in enumerate(reader.tensors):
+            data_offset_pretty = '{0:#16x}'.format(tensor.data_offset)
+            data_size_pretty = '{0:#16x}'.format(tensor.n_bytes)
+            tensor_mapping_table.append({"t_id":key, "layer_name":tensor.name, "data_offset":data_offset_pretty, "data_size":data_size_pretty})
+
+        tensors_mapping_table_header_map = [
+            {'key_name':'t_id',         'header_name':'T_ID',               'align':'right'},
+            {'key_name':'layer_name',   'header_name':'Tensor Layer Name',  'align':'left'},
+            {'key_name':'data_offset',  'header_name':'Data Offset (B)',    'align':'right'},
+            {'key_name':'data_size',    'header_name':'Data Size (B)',      'align':'right'},
+        ]
+
+        markdown_content += markdown_table_with_alignment_support(tensors_mapping_table_header_map, tensor_mapping_table)
+        markdown_content += "\n"
+
+        for group in tensor_prefix_order:
+            tensors = tensor_groups[group]
+            group_elements = sum(tensor.n_elements for tensor in tensors)
+            group_percentage = group_elements / total_elements * 100
+            markdown_content += f"### <a name=\"{group.replace('.', '_')}\">{translate_tensor_name(group)} Tensor Group : {element_count_rounded_notation(group_elements)} Elements</a>\n\n"
+
+            # Precalculate column sizing for visual consistency
+            prettify_element_est_count_size: int = 1
+            prettify_element_count_size: int = 1
+            prettify_dimension_max_widths: dict[int, int] = {}
+            for tensor in tensors:
+                prettify_element_est_count_size = max(prettify_element_est_count_size, len(str(element_count_rounded_notation(tensor.n_elements))))
+                prettify_element_count_size = max(prettify_element_count_size, len(str(tensor.n_elements)))
+                for i, dimension_size in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape))):
+                    prettify_dimension_max_widths[i] = max(prettify_dimension_max_widths.get(i,1), len(str(dimension_size)))
+
+            # Generate Tensor Layer Table Content
+            tensor_dump_table: list[dict[str, str | int]] = []
+            for tensor in tensors:
+                human_friendly_name = translate_tensor_name(tensor.name.replace(".weight", ".(W)").replace(".bias", ".(B)"))
+                pretty_dimension = ' x '.join(f'{str(d):>{prettify_dimension_max_widths[i]}}' for i, d in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape))))
+                element_count_est = f"({element_count_rounded_notation(tensor.n_elements):>{prettify_element_est_count_size}})"
+                element_count_string = f"{element_count_est} {tensor.n_elements:>{prettify_element_count_size}}"
+                type_name_string = f"{tensor.tensor_type.name}"
+                tensor_dump_table.append({"t_id":tensor_name_to_key[tensor.name], "layer_name":tensor.name, "human_layer_name":human_friendly_name, "element_count":element_count_string, "pretty_dimension":pretty_dimension, "tensor_type":type_name_string})
+
+            tensor_dump_table_header_map = [
+                {'key_name':'t_id',             'header_name':'T_ID',                             'align':'right'},
+                {'key_name':'layer_name',       'header_name':'Tensor Layer Name',                'align':'left'},
+                {'key_name':'human_layer_name', 'header_name':'Human Friendly Tensor Layer Name', 'align':'left'},
+                {'key_name':'element_count',    'header_name':'Elements',                         'align':'left'},
+                {'key_name':'pretty_dimension', 'header_name':'Shape',                            'align':'left'},
+                {'key_name':'tensor_type',      'header_name':'Type',                             'align':'left'},
+            ]
+
+            markdown_content += markdown_table_with_alignment_support(tensor_dump_table_header_map, tensor_dump_table)
+
+            markdown_content += "\n"
+            markdown_content += f"- Total elements in {group}: ({element_count_rounded_notation(group_elements):>4}) {group_elements}\n"
+            markdown_content += f"- Percentage of total elements: {group_percentage:.2f}%\n"
+            markdown_content += "\n\n"
+
+    print(markdown_content)  # noqa: NP100
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
+    parser.add_argument("model",           type=str,            help="GGUF format model filename")
+    parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
+    parser.add_argument("--json",       action="store_true", help="Produce JSON output")
+    parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
+    parser.add_argument("--data-offset",    action="store_true", help="Start of data offset")
+    parser.add_argument("--data-alignment", action="store_true", help="Data alignment applied globally to data field")
+    parser.add_argument("--markdown",   action="store_true", help="Produce markdown output")
+    parser.add_argument("--verbose",    action="store_true", help="increase output verbosity")
+
+    args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
+
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    if not args.json and not args.markdown and not args.data_offset and not args.data_alignment:
+        logger.info(f'* Loading: {args.model}')
+
+    reader = GGUFReader(args.model, 'r')
+
+    if args.json:
+        dump_metadata_json(reader, args)
+    elif args.markdown:
+        dump_markdown_metadata(reader, args)
+    elif args.data_offset:
+        print(reader.data_offset)  # noqa: NP100
+    elif args.data_alignment:
+        print(reader.alignment)  # noqa: NP100
+    else:
+        dump_metadata(reader, args)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/gguf-py/scripts/gguf_hash.py b/gguf-py/scripts/gguf_hash.py
new file mode 100755
index 0000000000000..ee34d09bfe7ef
--- /dev/null
+++ b/gguf-py/scripts/gguf_hash.py
@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import uuid
+import hashlib
+
+import logging
+import argparse
+import os
+import sys
+from pathlib import Path
+
+from tqdm import tqdm
+
+# Necessary to load the local gguf package
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from gguf import GGUFReader  # noqa: E402
+
+
+logger = logging.getLogger("gguf-hash")
+
+# UUID_NAMESPACE_LLAMA_CPP = uuid.uuid5(uuid.NAMESPACE_URL, 'en.wikipedia.org/wiki/Llama.cpp')
+UUID_NAMESPACE_LLAMA_CPP = uuid.UUID('ef001206-dadc-5f6d-a15f-3359e577d4e5')
+
+
+# For more information about what field.parts and field.data represent,
+# please see the comments in the modify_gguf.py example.
+def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar: bool, no_layer: bool) -> None:
+    sha1 = hashlib.sha1()
+    sha256 = hashlib.sha256()
+    uuidv5_sha1 = hashlib.sha1()
+    uuidv5_sha1.update(UUID_NAMESPACE_LLAMA_CPP.bytes)
+
+    # Total Weight Calculation For Progress Bar
+    total_weights = 0
+    for n, tensor in enumerate(reader.tensors, 1):
+
+        # We don't need these
+        if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
+            continue
+
+        # Calculate Tensor Volume
+        sum_weights_in_tensor = 1
+        for dim in tensor.shape:
+            sum_weights_in_tensor *= dim
+        total_weights += sum_weights_in_tensor
+
+    # Hash Progress Bar
+    bar = tqdm(desc="Hashing", total=total_weights, unit="weights", unit_scale=True, disable=disable_progress_bar)
+
+    # Hashing Process
+    for tensor in reader.tensors:
+
+        # We don't need these
+        if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
+            continue
+
+        # Progressbar
+        sum_weights_in_tensor = 1
+        for dim in tensor.shape:
+            sum_weights_in_tensor *= dim
+        bar.update(sum_weights_in_tensor)
+
+        if not no_layer:
+
+            sha1_layer = hashlib.sha1()
+            sha1_layer.update(tensor.data.data)
+            print("sha1      {0}  {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
+
+            sha256_layer = hashlib.sha256()
+            sha256_layer.update(tensor.data.data)
+            print("sha256    {0}  {1}:{2}".format(sha256_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
+
+        sha1.update(tensor.data.data)
+        sha256.update(tensor.data.data)
+        uuidv5_sha1.update(tensor.data.data)
+
+    # Flush Hash Progress Bar
+    bar.close()
+
+    # Display Hash Output
+    print("sha1      {0}  {1}".format(sha1.hexdigest(), filename)) # noqa: NP100
+    print("sha256    {0}  {1}".format(sha256.hexdigest(), filename)) # noqa: NP100
+    print("uuid      {0}  {1}".format(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5), filename)) # noqa: NP100
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
+    parser.add_argument("model",         type=str,            help="GGUF format model filename")
+    parser.add_argument("--no-layer",    action="store_true", help="exclude per layer hash")
+    parser.add_argument("--verbose",     action="store_true", help="increase output verbosity")
+    parser.add_argument("--progressbar", action="store_true", help="enable progressbar")
+    args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+    reader = GGUFReader(args.model, 'r')
+    gguf_hash(reader, args.model, not args.progressbar, args.no_layer)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf_new_metadata.py
similarity index 94%
rename from gguf-py/scripts/gguf-new-metadata.py
rename to gguf-py/scripts/gguf_new_metadata.py
index 63d3c5d8fdcf4..fce52a8c1164e 100755
--- a/gguf-py/scripts/gguf-new-metadata.py
+++ b/gguf-py/scripts/gguf_new_metadata.py
@@ -1,4 +1,6 @@
 #!/usr/bin/env python3
+from __future__ import annotations
+
 import logging
 import argparse
 import os
@@ -101,8 +103,7 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new
             logger.debug(f'Copying {field.name}')
 
         if val.value is not None:
-            writer.add_key(field.name)
-            writer.add_val(val.value, val.type)
+            writer.add_key_value(field.name, val.value, val.type)
 
     if gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata:
         logger.debug('Adding chat template(s)')
@@ -111,16 +112,13 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new
 
     for key, val in new_metadata.items():
         logger.debug(f'Adding {key}: "{val.value}" {val.description}')
-        writer.add_key(key)
-        writer.add_val(val.value, val.type)
+        writer.add_key_value(key, val.value, val.type)
 
     total_bytes = 0
 
     for tensor in reader.tensors:
         total_bytes += tensor.n_bytes
-        # Dimensions are written in reverse order, so flip them first
-        shape = np.flipud(tensor.shape).tolist()
-        writer.add_tensor_info(tensor.name, shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type)
+        writer.add_tensor_info(tensor.name, tensor.data.shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type)
 
     bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True)
 
@@ -146,6 +144,7 @@ def main() -> None:
     parser.add_argument("--general-description",                       type=str,  help="The models general.description", metavar='"Description ..."')
     parser.add_argument("--chat-template",                             type=str,  help="Chat template string (or JSON string containing templates)", metavar='"{% ... %} ..."')
     parser.add_argument("--chat-template-config",                      type=Path, help="Config file containing chat template(s)", metavar='tokenizer_config.json')
+    parser.add_argument("--pre-tokenizer",                             type=str,  help="The models tokenizer.ggml.pre", metavar='"pre tokenizer"')
     parser.add_argument("--remove-metadata",      action="append",     type=str,  help="Remove metadata (by key name) from output model", metavar='general.url')
     parser.add_argument("--special-token",        action="append",     type=str,  help="Special token by value", nargs=2, metavar=(' | '.join(token_names.keys()), '"<token>"'))
     parser.add_argument("--special-token-by-id",  action="append",     type=str,  help="Special token by id", nargs=2, metavar=(' | '.join(token_names.keys()), '0'))
@@ -174,6 +173,9 @@ def main() -> None:
             if template:
                 new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template)
 
+    if args.pre_tokenizer:
+        new_metadata[gguf.Keys.Tokenizer.PRE] = MetadataDetails(gguf.GGUFValueType.STRING, args.pre_tokenizer)
+
     if remove_metadata:
         logger.warning('*** Warning *** Warning *** Warning **')
         logger.warning('* Most metadata is required for a fully functional GGUF file,')
diff --git a/gguf-py/scripts/gguf-set-metadata.py b/gguf-py/scripts/gguf_set_metadata.py
similarity index 100%
rename from gguf-py/scripts/gguf-set-metadata.py
rename to gguf-py/scripts/gguf_set_metadata.py
diff --git a/gguf-py/tests/__init__.py b/gguf-py/tests/__init__.py
new file mode 100644
index 0000000000000..d23ff9cb7380c
--- /dev/null
+++ b/gguf-py/tests/__init__.py
@@ -0,0 +1 @@
+from .test_metadata import *
diff --git a/gguf-py/tests/test_gguf.py b/gguf-py/tests/test_gguf.py
deleted file mode 100644
index 0adeb7d55731a..0000000000000
--- a/gguf-py/tests/test_gguf.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gguf  # noqa: F401
-
-# TODO: add tests
-
-
-def test_write_gguf() -> None:
-    pass
diff --git a/gguf-py/tests/test_metadata.py b/gguf-py/tests/test_metadata.py
new file mode 100755
index 0000000000000..81a2a30ae60f4
--- /dev/null
+++ b/gguf-py/tests/test_metadata.py
@@ -0,0 +1,203 @@
+#!/usr/bin/env python3
+
+import unittest
+from pathlib import Path
+import os
+import sys
+
+# Necessary to load the local gguf package
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import gguf
+
+
+class TestMetadataMethod(unittest.TestCase):
+
+    def test_id_to_title(self):
+        self.assertEqual(gguf.Metadata.id_to_title("Mixtral-8x7B-Instruct-v0.1"), "Mixtral 8x7B Instruct v0.1")
+        self.assertEqual(gguf.Metadata.id_to_title("Meta-Llama-3-8B"), "Meta Llama 3 8B")
+        self.assertEqual(gguf.Metadata.id_to_title("hermes-2-pro-llama-3-8b-DPO"), "Hermes 2 Pro Llama 3 8b DPO")
+
+    def test_get_model_id_components(self):
+        # This is the basic standard form with organization marker
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mistral/Mixtral-8x7B-Instruct-v0.1"),
+                         ('Mixtral-8x7B-Instruct-v0.1', "Mistral", 'Mixtral', 'Instruct', 'v0.1', '8x7B'))
+
+        # Similar to basic standard form but without organization marker
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct-v0.1"),
+                         ('Mixtral-8x7B-Instruct-v0.1', None, 'Mixtral', 'Instruct', 'v0.1', '8x7B'))
+
+        # Missing version
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct"),
+                         ('Mixtral-8x7B-Instruct', None, 'Mixtral', 'Instruct', None, '8x7B'))
+
+        # Missing finetune
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-v0.1"),
+                         ('Mixtral-8x7B-v0.1', None, 'Mixtral', None, 'v0.1', '8x7B'))
+
+        # Base name and size label only
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B"),
+                         ('Mixtral-8x7B', None, 'Mixtral', None, None, '8x7B'))
+
+        # Base name and version only
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-v0.1"),
+                         ('Mixtral-v0.1', None, 'Mixtral', None, 'v0.1', None))
+
+        ## Edge Cases ##
+
+        # This is too ambiguous... best to err on caution and output nothing
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral"),
+                         ('Mixtral', None, None, None, None, None))
+
+        # Basename has numbers mixed in and also size label provided. Must avoid capturing number in basename
+        self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Meta-Llama-3-8B"),
+                         ('Meta-Llama-3-8B', "NousResearch", 'Meta-Llama-3', None, None, '8B'))
+
+        # Non standard naming
+        self.assertEqual(gguf.Metadata.get_model_id_components("Qwen1.5-MoE-A2.7B-Chat"),
+                         ('Qwen1.5-MoE-A2.7B-Chat', None, 'Qwen1.5-MoE', 'Chat', None, 'A2.7B'))
+
+        # Capture 'sub size labels' e.g. A14B in '57B-A14B' usually refers to activated params/weight count
+        self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-57B-A14B-Instruct"),
+                         ('Qwen2-57B-A14B-Instruct', None, 'Qwen2', 'Instruct', None, '57B-A14B'))
+
+        # Check that it can handle a real model id with no version code
+        # Note that 4k in this string is non standard and microsoft were referring to context length rather than weight count
+        self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Phi-3-mini-4k-instruct", 4 * 10**9),
+                         ('Phi-3-mini-4k-instruct', 'microsoft', 'Phi-3', '4k-instruct', None, 'mini'))
+
+        # There is some legitimate models with only thousands of parameters
+        self.assertEqual(gguf.Metadata.get_model_id_components("delphi-suite/stories-llama2-50k", 50 * 10**3),
+                         ('stories-llama2-50k', 'delphi-suite', 'stories-llama2', None, None, '50K'))
+
+        # Non standard and not easy to disambiguate
+        self.assertEqual(gguf.Metadata.get_model_id_components("DeepSeek-Coder-V2-Lite-Instruct"),
+                         ('DeepSeek-Coder-V2-Lite-Instruct', None, 'DeepSeek-Coder-V2-Lite', 'Instruct', None, None))
+
+        # This is a real model_id where they append 2DPO to refer to Direct Preference Optimization
+        self.assertEqual(gguf.Metadata.get_model_id_components("crestf411/daybreak-kunoichi-2dpo-7b"),
+                         ('daybreak-kunoichi-2dpo-7b', 'crestf411', 'daybreak-kunoichi', '2dpo', None, '7B'))
+
+        # This is a real model id where the weight size has a decimal point
+        self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-0.5B-Instruct"),
+                         ('Qwen2-0.5B-Instruct', None, 'Qwen2', 'Instruct', None, '0.5B'))
+
+        # Uses an underscore in the size label
+        self.assertEqual(gguf.Metadata.get_model_id_components("smallcloudai/Refact-1_6B-fim"),
+                         ('Refact-1_6B-fim', 'smallcloudai', 'Refact', 'fim', None, '1.6B'))
+
+        # Uses Iter3 for the version
+        self.assertEqual(gguf.Metadata.get_model_id_components("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3"),
+                         ('Gemma-2-9B-It-SPPO-Iter3', 'UCLA-AGI', 'Gemma-2', 'It-SPPO', 'Iter3', '9B'))
+
+        # Has two potential versions in the basename
+        self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Hermes-2-Theta-Llama-3-8B"),
+                         ('Hermes-2-Theta-Llama-3-8B', 'NousResearch', 'Hermes-2-Theta-Llama-3', None, None, '8B'))
+
+        # Potential version in the basename
+        self.assertEqual(gguf.Metadata.get_model_id_components("SeaLLMs/SeaLLMs-v3-7B-Chat"),
+                         ('SeaLLMs-v3-7B-Chat', 'SeaLLMs', 'SeaLLMs-v3', 'Chat', None, '7B'))
+
+        # Underscore in the basename, and 1m for the context size
+        self.assertEqual(gguf.Metadata.get_model_id_components("internlm/internlm2_5-7b-chat-1m", 7 * 10**9),
+                         ('internlm2_5-7b-chat-1m', 'internlm', 'internlm2_5', 'chat-1m', None, '7B'))
+
+        # Version before the finetune name
+        self.assertEqual(gguf.Metadata.get_model_id_components("pszemraj/jamba-900M-v0.13-KIx2"),
+                         ('jamba-900M-v0.13-KIx2', 'pszemraj', 'jamba', 'KIx2', 'v0.13', '900M'))
+
+        # TODO: hf suffix which could be ignored but isn't
+        self.assertEqual(gguf.Metadata.get_model_id_components("state-spaces/mamba-2.8b-hf"),
+                         ('mamba-2.8b-hf', 'state-spaces', 'mamba', 'hf', None, '2.8B'))
+
+        # Two sizes, don't merge them, the other is the number of tokens on which it was trained
+        self.assertEqual(gguf.Metadata.get_model_id_components("abacaj/llama-161M-100B", 161 * 10**6),
+                         ('llama-161M-100B', 'abacaj', 'llama', '100b', None, '161M'))
+
+        # It's a trap, there is no size label
+        self.assertEqual(gguf.Metadata.get_model_id_components("SparseLLM/relu-100B", 1340 * 10**6),
+                         ('relu-100B', 'SparseLLM', 'relu', '100b', None, None))
+
+        # Weird size notation
+        self.assertEqual(gguf.Metadata.get_model_id_components("bigscience/bloom-7b1-petals"),
+                         ('bloom-7b1-petals', 'bigscience', 'bloom', 'petals', None, '7.1B'))
+
+        # Ignore full-text size labels when there are number-based ones, and deduplicate size labels
+        self.assertEqual(gguf.Metadata.get_model_id_components("MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1"),
+                         ('GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1', 'MaziyarPanahi', 'GreenNode-mini', 'multilingual-v1olet-Mistral-Instruct', 'v0.1', '7B'))
+
+        # Instruct in a name without a size label
+        self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/Mistral-Nemo-Instruct-2407"),
+                         ('Mistral-Nemo-Instruct-2407', 'mistralai', 'Mistral-Nemo', 'Instruct', '2407', None))
+
+        # Non-obvious splitting relying on 'chat' keyword
+        self.assertEqual(gguf.Metadata.get_model_id_components("deepseek-ai/DeepSeek-V2-Chat-0628"),
+                         ('DeepSeek-V2-Chat-0628', 'deepseek-ai', 'DeepSeek-V2', 'Chat', '0628', None))
+
+        # Multiple versions
+        self.assertEqual(gguf.Metadata.get_model_id_components("OpenGVLab/Mini-InternVL-Chat-2B-V1-5"),
+                         ('Mini-InternVL-Chat-2B-V1-5', 'OpenGVLab', 'Mini-InternVL', 'Chat', 'V1-5', '2B'))
+
+        # TODO: DPO in the name
+        self.assertEqual(gguf.Metadata.get_model_id_components("jondurbin/bagel-dpo-2.8b-v0.2"),
+                         ('bagel-dpo-2.8b-v0.2', 'jondurbin', 'bagel-dpo', None, 'v0.2', '2.8B'))
+
+        # DPO in name, but can't be used for the finetune to keep 'LLaMA-3' in the basename
+        self.assertEqual(gguf.Metadata.get_model_id_components("voxmenthe/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized"),
+                         ('SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized', 'voxmenthe', 'SFR-Iterative-DPO-LLaMA-3', 'R-unquantized', None, '8B'))
+
+        # Too ambiguous
+        # TODO: should "base" be a 'finetune' or 'size_label'?
+        # (in this case it should be a size label, but other models use it to signal that they are not finetuned)
+        self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Florence-2-base"),
+                         ('Florence-2-base', 'microsoft', None, None, None, None))
+
+        ## Invalid cases ##
+
+        # Start with a dash and has dashes in rows
+        self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/-Mistral--Nemo-Base-2407-"),
+                         ('-Mistral--Nemo-Base-2407-', 'mistralai', 'Mistral-Nemo-Base', None, '2407', None))
+
+        ## LoRA ##
+
+        self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B"),
+                         ('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration-LoRA', None, '8B'))
+
+        # Negative size --> output is a LoRA adaper --> prune "LoRA" out of the name to avoid redundancy with the suffix
+        self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B", -1234),
+                         ('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration', None, '8B'))
+
+    def test_apply_metadata_heuristic_from_model_card(self):
+        model_card = {
+            'tags': ['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'],
+            'model-index': [{'name': 'Mixtral-8x7B-Instruct-v0.1', 'results': []}],
+            'language': ['en'],
+            'datasets': ['teknium/OpenHermes-2.5'],
+            'widget': [{'example_title': 'Hermes 2 Pro', 'messages': [{'role': 'system', 'content': 'You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.'}, {'role': 'user', 'content': 'Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.'}]}],
+            'base_model': ["EmbeddedLLM/Mistral-7B-Merge-14-v0", "janai-hq/trinity-v1"]
+        }
+        got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
+        expect = gguf.Metadata()
+        expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': '14-v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}]
+        expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl']
+        expect.languages=['en']
+        expect.datasets=['teknium/OpenHermes-2.5']
+
+        self.assertEqual(got, expect)
+
+    def test_apply_metadata_heuristic_from_hf_parameters(self):
+        hf_params = {"_name_or_path": "./hermes-2-pro-llama-3-8b-DPO"}
+        got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=hf_params, model_path=None)
+        expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B')
+        self.assertEqual(got, expect)
+
+    def test_apply_metadata_heuristic_from_model_dir(self):
+        model_dir_path = Path("./hermes-2-pro-llama-3-8b-DPO")
+        got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=None, model_path=model_dir_path)
+        expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B')
+        self.assertEqual(got, expect)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/gguf-py/tests/test_quants.py b/gguf-py/tests/test_quants.py
new file mode 100755
index 0000000000000..8b7a85c2c36d7
--- /dev/null
+++ b/gguf-py/tests/test_quants.py
@@ -0,0 +1,237 @@
+#!/usr/bin/env python3
+
+# Test gguf.quants so that it exactly matches the C implementation of the (de)quantization
+
+# NOTE: this is kind of a mess, but at least it worked for initially testing the Python implementations.
+
+from __future__ import annotations
+
+import argparse
+from math import prod
+import os
+import sys
+from pathlib import Path
+import ctypes
+import logging
+import numpy as np
+
+# Necessary to load the local gguf package
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import gguf
+from gguf.constants import GGMLQuantizationType
+
+
+logger = logging.getLogger("test-quants")
+
+
+c_float_p = ctypes.POINTER(ctypes.c_float)
+
+
+class ggml_init_params(ctypes.Structure):
+    _fields_ = [
+        ("mem_size", ctypes.c_size_t),
+        ("mem_buffer", ctypes.c_void_p),
+        ("no_alloc", ctypes.c_bool),
+    ]
+
+
+class GGMLQuants:
+    libggml: ctypes.CDLL
+
+    def __init__(self, libggml: Path):
+        self.libggml = ctypes.CDLL(str(libggml))
+        self.libggml.ggml_quantize_chunk.restype = ctypes.c_size_t
+        # enum ggml_type   type,
+        #    const float * src,
+        #           void * dst,
+        #        int64_t   start,
+        #        int64_t   nrows,
+        #        int64_t   n_per_row,
+        #    const float * imatrix) {
+        self.libggml.ggml_quantize_chunk.argtypes = (
+            ctypes.c_int,
+            ctypes.POINTER(ctypes.c_float),
+            ctypes.c_void_p,
+            ctypes.c_int64,
+            ctypes.c_int64,
+            ctypes.c_int64,
+            ctypes.POINTER(ctypes.c_float),
+        )
+
+        self.libggml.ggml_quantize_requires_imatrix.restype = ctypes.c_bool
+        self.libggml.ggml_quantize_requires_imatrix.argtypes = (ctypes.c_int,)
+
+        for t in (
+            "q4_0", "q4_1", "q5_0", "q5_1", "q8_0",
+            "q2_K", "q3_K", "q4_K", "q5_K", "q6_K",
+            "iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m",
+            "iq4_nl", "iq4_xs",
+        ):
+            dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + t)
+            dequant_func.restype = None
+            dequant_func.argtypes = (ctypes.c_void_p, ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
+
+        self.libggml.ggml_fp16_to_fp32_row.restype = None
+        self.libggml.ggml_fp16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
+        self.libggml.ggml_bf16_to_fp32_row.restype = None
+        self.libggml.ggml_bf16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64)
+
+        self.libggml.ggml_init.argtypes = (ggml_init_params,)
+
+        self.libggml.ggml_init(ggml_init_params(1 * 1024 * 1024, 0, False))
+
+    def dequantize(self, tensor: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
+        result = np.zeros(gguf.quant_shape_from_byte_shape(tensor.shape, qtype), dtype=np.float32, order="C")
+        if qtype == GGMLQuantizationType.F32:
+            # no-op
+            result = tensor.view(np.float32)
+        elif qtype == GGMLQuantizationType.F16:
+            self.libggml.ggml_fp16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size)
+        elif qtype == GGMLQuantizationType.BF16:
+            self.libggml.ggml_bf16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size)
+        else:
+            lw_qname = qtype.name.lower()
+            if lw_qname[-1] == "k":
+                lw_qname = lw_qname[:-1] + "K"
+            dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + lw_qname)
+            dequant_func(tensor.ctypes.data_as(ctypes.c_void_p), result.ctypes.data_as(c_float_p), result.size)
+        return result
+
+    def quantize(self, data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
+        result = np.zeros(gguf.quant_shape_to_byte_shape(data.shape, qtype), dtype=np.uint8, order="C")
+        if self.libggml.ggml_quantize_requires_imatrix(qtype.value):
+            # TODO: is a column-wise sum of squares appropriate?
+            qw = np.sum((data * data).reshape((-1, data.shape[-1])), axis=0).ctypes.data_as(c_float_p)
+        else:
+            qw = ctypes.cast(0, c_float_p)
+        result_size = self.libggml.ggml_quantize_chunk(qtype.value, data.ctypes.data_as(c_float_p), result.ctypes.data_as(ctypes.c_void_p), 0, prod(data.shape[:-1]), data.shape[-1], qw)
+        assert result.size == result_size
+        return result
+
+
+def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType) -> bool:
+    same = np.array_equal(t1, t2)
+    if same:
+        return True
+    else:
+        block_size, type_size = gguf.GGML_QUANT_SIZES[qtype]
+        if t1.dtype == np.float32:
+            t1 = t1.reshape((-1, block_size))
+            t2 = t2.reshape((-1, block_size))
+        else:
+            t1 = t1.reshape((-1, type_size))
+            t2 = t2.reshape((-1, type_size))
+        x = t1.view(np.uint8) ^ t2.view(np.uint8)
+        diff_bits = np.count_nonzero(np.unpackbits(x, axis=-1), axis=-1)
+        num_bad_blocks = np.count_nonzero(diff_bits, axis=0)
+        if num_bad_blocks == 0 and t1.shape == t2.shape:
+            logger.debug("Bits are equal, but arrays don't match, likely contains NANs")
+            return True
+        logger.debug(f"{num_bad_blocks} bad blocks ({100 * num_bad_blocks / x.shape[0]:.6f}%)")
+        bad_block_id = np.argmax(diff_bits, axis=0)
+        logger.debug(f"Worst block id: {bad_block_id}")
+        logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}")
+
+        sum_diff_bits = np.sum(diff_bits)
+        logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits/(x.size * 8):.6f}%)")
+        return False
+
+
+def do_test(libggml_path: Path, quick: bool = False):
+    ggml_quants = GGMLQuants(libggml_path)
+
+    np.set_printoptions(precision=None, threshold=(4 * 256) + 1, formatter={"int": lambda n: "0x%02X" % n})
+
+    r = np.random.randn(8, 1024, 1024).astype(np.float32, copy=False)
+
+    for qtype in (GGMLQuantizationType.F16, *gguf.quants._type_traits.keys()):
+        has_dequantize = False
+        has_quantize = False
+
+        try:
+            gguf.dequantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][1]), dtype=np.uint8), qtype)
+            has_dequantize = True
+        except (NotImplementedError, AssertionError) as e:
+            if isinstance(e, AssertionError):
+                logger.error(f"Error with {qtype.name}: {e}")
+                raise e
+        try:
+            gguf.quantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][0]), dtype=np.float32), qtype)
+            has_quantize = True
+        except (NotImplementedError, AssertionError) as e:
+            if isinstance(e, AssertionError):
+                logger.error(f"Error with {qtype.name}: {e}")
+                raise e
+
+        if not has_dequantize and not has_quantize:
+            continue
+
+        logger.info(f"Testing {qtype.name}")
+
+        rc = r.copy(order="C")
+
+        pyq = None
+        ggq = None
+
+        if has_quantize:
+            logger.debug(f"Quantizing to {qtype.name} with Python")
+            pyq = gguf.quants.quantize(rc, qtype)
+
+            logger.debug(f"Quantizing to {qtype.name} with C")
+            ggq = ggml_quants.quantize(rc, qtype)
+
+            if qtype == GGMLQuantizationType.F16:
+                pyq = pyq.view(np.uint8)
+            quant_equal = compare_tensors(pyq, ggq, qtype)
+
+            if not quant_equal:
+                logger.error(f"Quantization to {qtype.name} does not match ❌")
+            else:
+                logger.info(f"Quantization to {qtype.name} matches exactly ✅")
+
+        if has_dequantize:
+            if ggq is None and not quick:
+                logger.debug(f"Quantizing to {qtype.name} with C")
+                ggq = ggml_quants.quantize(rc, qtype)
+
+            if ggq is not None:
+                logger.debug(f"Dequantizing from {qtype.name} with Python")
+                pydq = gguf.quants.dequantize(ggq, qtype)
+                logger.debug(f"Dequantizing from {qtype.name} with C")
+                ggdq = ggml_quants.dequantize(ggq, qtype)
+
+                dequant_equal = compare_tensors(pydq, ggdq, qtype)
+
+                if not dequant_equal:
+                    logger.error(f"Dequantization from {qtype.name} does not match ❌")
+                else:
+                    logger.info(f"Dequantization from {qtype.name} matches exactly ✅")
+
+            rq_shape = gguf.quants.quant_shape_to_byte_shape((8, 1024, 1024 // 2), qtype)
+            rq = np.random.random(rq_shape).astype(np.float16).view(np.uint8)
+
+            logger.debug(f"Dequantizing random f16 data as {qtype.name} with Python")
+            pydq = gguf.quants.dequantize(rq, qtype)
+            logger.debug(f"Dequantizing random f16 data as {qtype.name} with C")
+            ggdq = ggml_quants.dequantize(rq, qtype)
+
+            dequant_equal = compare_tensors(pydq, ggdq, qtype)
+
+            if not dequant_equal:
+                logger.error(f"Dequantization from random f16 data as {qtype.name} does not match ❌")
+            else:
+                logger.info(f"Dequantization from random f16 data as {qtype.name} matches exactly ✅")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Test Python (de)quantization against the reference C implementation")
+    parser.add_argument("--libggml", type=Path, default=Path(__file__).parent.parent.parent / "build" / "ggml" / "src" / "libggml.so", help="The path to libggml.so")
+    parser.add_argument("--quick", action="store_true", help="Don't quantize with C when it's not strictly necessary")
+
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.DEBUG)
+
+    do_test(args.libggml, args.quick)
diff --git a/grammars/README.md b/grammars/README.md
index 2b8384d9d330e..01b02abb4de9c 100644
--- a/grammars/README.md
+++ b/grammars/README.md
@@ -4,7 +4,7 @@ GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.
 
 ## Background
 
-[Bakus-Naur Form (BNF)](https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_form) is a notation for describing the syntax of formal languages like programming languages, file formats, and protocols. GBNF is an extension of BNF that primarily adds a few modern regex-like features.
+[Backus-Naur Form (BNF)](https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_form) is a notation for describing the syntax of formal languages like programming languages, file formats, and protocols. GBNF is an extension of BNF that primarily adds a few modern regex-like features.
 
 ## Basics
 
@@ -59,9 +59,13 @@ Parentheses `()` can be used to group sequences, which allows for embedding alte
 
 ## Repetition and Optional Symbols
 
-- `*` after a symbol or sequence means that it can be repeated zero or more times.
-- `+` denotes that the symbol or sequence should appear one or more times.
-- `?` makes the preceding symbol or sequence optional.
+- `*` after a symbol or sequence means that it can be repeated zero or more times (equivalent to `{0,}`).
+- `+` denotes that the symbol or sequence should appear one or more times (equivalent to `{1,}`).
+- `?` makes the preceding symbol or sequence optional (equivalent to `{0,1}`).
+- `{m}` repeats the precedent symbol or sequence exactly `m` times
+- `{m,}` repeats the precedent symbol or sequence at least `m` times
+- `{m,n}` repeats the precedent symbol or sequence at between `m` and `n` times (included)
+- `{0,n}` repeats the precedent symbol or sequence at most `n` times (included)
 
 ## Comments and newlines
 
@@ -87,9 +91,11 @@ item ::= [^\n]+ "\n"
 
 This guide provides a brief overview. Check out the GBNF files in this directory (`grammars/`) for examples of full grammars. You can try them out with:
 ```
-./main -m <model> --grammar-file grammars/some-grammar.gbnf -p 'Some prompt'
+./llama-cli -m <model> --grammar-file grammars/some-grammar.gbnf -p 'Some prompt'
 ```
 
+`llama.cpp` can also convert JSON schemas to grammars either ahead of time or at each request, see below.
+
 ## Troubleshooting
 
 Grammars currently have performance gotchas (see https://github.com/ggerganov/llama.cpp/issues/4218).
@@ -98,4 +104,279 @@ Grammars currently have performance gotchas (see https://github.com/ggerganov/ll
 
 A common pattern is to allow repetitions of a pattern `x` up to N times.
 
-While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) will result in extremely slow inference. Instead, you can write `(x (x (x ... (x)?...)?)?)?` (w/ N-deep nesting)
+While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) may result in extremely slow sampling. Instead, you can write `x{0,N}` (or `(x (x (x ... (x)?...)?)?)?` w/ N-deep nesting in earlier llama.cpp versions).
+
+## Using GBNF grammars
+
+You can use GBNF grammars:
+
+- In [llama-server](../examples/server)'s completion endpoints, passed as the `grammar` body field
+- In [llama-cli](../examples/main), passed as the `--grammar` & `--grammar-file` flags
+- With [llama-gbnf-validator](../examples/gbnf-validator) tool, to test them against strings.
+
+## JSON Schemas → GBNF
+
+`llama.cpp` supports converting a subset of https://json-schema.org/ to GBNF grammars:
+
+- In [llama-server](../examples/server):
+    - For any completion endpoints, passed as the `json_schema` body field
+    - For the `/chat/completions` endpoint, passed inside the `result_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}`)
+- In [llama-cli](../examples/main), passed as the `--json` / `-j` flag
+- To convert to a grammar ahead of time:
+    - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py)
+    - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI)
+
+Take a look at [tests](../tests/test-json-schema-to-grammar.cpp) to see which features are likely supported (you'll also find usage examples in https://github.com/ggerganov/llama.cpp/pull/5978, https://github.com/ggerganov/llama.cpp/pull/6659 & https://github.com/ggerganov/llama.cpp/pull/6555).
+
+```bash
+llama-cli \
+  -hfr bartowski/Phi-3-medium-128k-instruct-GGUF \
+  -hff Phi-3-medium-128k-instruct-Q8_0.gguf \
+  -j '{
+    "type": "array",
+    "items": {
+        "type": "object",
+        "properties": {
+            "name": {
+                "type": "string",
+                "minLength": 1,
+                "maxLength": 100
+            },
+            "age": {
+                "type": "integer",
+                "minimum": 0,
+                "maximum": 150
+            }
+        },
+        "required": ["name", "age"],
+        "additionalProperties": false
+    },
+    "minItems": 10,
+    "maxItems": 100
+  }' \
+  -p 'Generate a {name, age}[] JSON array with famous actors of all ages.'
+```
+
+<details>
+
+<summary>Show grammar</summary>
+
+You can convert any schema in command-line with:
+
+```bash
+examples/json_schema_to_grammar.py name-age-schema.json
+```
+
+```
+char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+item ::= "{" space item-name-kv "," space item-age-kv "}" space
+item-age ::= ([0-9] | ([1-8] [0-9] | [9] [0-9]) | "1" ([0-4] [0-9] | [5] "0")) space
+item-age-kv ::= "\"age\"" space ":" space item-age
+item-name ::= "\"" char{1,100} "\"" space
+item-name-kv ::= "\"name\"" space ":" space item-name
+root ::= "[" space item ("," space item){9,99} "]" space
+space ::= | " " | "\n" [ \t]{0,20}
+```
+
+</details>
+
+Here is also a list of known limitations (contributions welcome):
+
+- `additionalProperties` defaults to `false` (produces faster grammars + reduces hallucinations).
+- `"additionalProperties": true` may produce keys that contain unescaped newlines.
+- Unsupported features are skipped silently. It is currently advised to use the command-line Python converter (see above) to see any warnings, and to inspect the resulting grammar / test it w/ [llama-gbnf-validator](../examples/gbnf-validator/gbnf-validator.cpp).
+- Can't mix `properties` w/ `anyOf` / `oneOf` in the same type (https://github.com/ggerganov/llama.cpp/issues/7703)
+- [prefixItems](https://json-schema.org/draft/2020-12/json-schema-core#name-prefixitems) is broken (but [items](https://json-schema.org/draft/2020-12/json-schema-core#name-items) works)
+- `minimum`, `exclusiveMinimum`, `maximum`, `exclusiveMaximum`: only supported for `"type": "integer"` for now, not `number`
+- Nested `$ref`s are broken (https://github.com/ggerganov/llama.cpp/issues/8073)
+- [pattern](https://json-schema.org/draft/2020-12/json-schema-validation#name-pattern)s must start with `^` and end with `$`
+- Remote `$ref`s not supported in the C++ version (Python & JavaScript versions fetch https refs)
+- `string` [formats](https://json-schema.org/draft/2020-12/json-schema-validation#name-defined-formats) lack `uri`, `email`
+- No [`patternProperties`](https://json-schema.org/draft/2020-12/json-schema-core#name-patternproperties)
+
+And a non-exhaustive list of other unsupported features that are unlikely to be implemented (hard and/or too slow to support w/ stateless grammars):
+
+- [`uniqueItems`](https://json-schema.org/draft/2020-12/json-schema-validation#name-uniqueitems)
+- [`contains`](https://json-schema.org/draft/2020-12/json-schema-core#name-contains) / `minContains`
+- `$anchor` (cf. [dereferencing](https://json-schema.org/draft/2020-12/json-schema-core#name-dereferencing))
+- [`not`](https://json-schema.org/draft/2020-12/json-schema-core#name-not)
+- [Conditionals](https://json-schema.org/draft/2020-12/json-schema-core#name-keywords-for-applying-subsche) `if` / `then` / `else` / `dependentSchemas`
+
+### A word about additionalProperties
+
+> [!WARNING]
+> The JSON schemas spec states `object`s accept [additional properties](https://json-schema.org/understanding-json-schema/reference/object#additionalproperties) by default.
+> Since this is slow and seems prone to hallucinations, we default to no additional properties.
+> You can set `"additionalProperties": true` in the the schema of any object to explicitly allow additional properties.
+
+If you're using [Pydantic](https://pydantic.dev/) to generate schemas, you can enable additional properties with the `extra` config on each model class:
+
+```python
+# pip install pydantic
+import json
+from typing import Annotated, List
+from pydantic import BaseModel, Extra, Field
+class QAPair(BaseModel):
+    class Config:
+        extra = 'allow'  # triggers additionalProperties: true in the JSON schema
+    question: str
+    concise_answer: str
+    justification: str
+
+class Summary(BaseModel):
+    class Config:
+        extra = 'allow'
+    key_facts: List[Annotated[str, Field(pattern='- .{5,}')]]
+    question_answers: List[Annotated[List[QAPair], Field(min_items=5)]]
+
+print(json.dumps(Summary.model_json_schema(), indent=2))
+```
+
+<details>
+<summary>Show JSON schema & grammar</summary>
+
+```json
+{
+  "$defs": {
+    "QAPair": {
+      "additionalProperties": true,
+      "properties": {
+        "question": {
+          "title": "Question",
+          "type": "string"
+        },
+        "concise_answer": {
+          "title": "Concise Answer",
+          "type": "string"
+        },
+        "justification": {
+          "title": "Justification",
+          "type": "string"
+        }
+      },
+      "required": [
+        "question",
+        "concise_answer",
+        "justification"
+      ],
+      "title": "QAPair",
+      "type": "object"
+    }
+  },
+  "additionalProperties": true,
+  "properties": {
+    "key_facts": {
+      "items": {
+        "pattern": "^- .{5,}$",
+        "type": "string"
+      },
+      "title": "Key Facts",
+      "type": "array"
+    },
+    "question_answers": {
+      "items": {
+        "items": {
+          "$ref": "#/$defs/QAPair"
+        },
+        "minItems": 5,
+        "type": "array"
+      },
+      "title": "Question Answers",
+      "type": "array"
+    }
+  },
+  "required": [
+    "key_facts",
+    "question_answers"
+  ],
+  "title": "Summary",
+  "type": "object"
+}
+```
+
+```
+QAPair ::= "{" space QAPair-question-kv "," space QAPair-concise-answer-kv "," space QAPair-justification-kv ( "," space ( QAPair-additional-kv ( "," space QAPair-additional-kv )* ) )? "}" space
+QAPair-additional-k ::= ["] ( [c] ([o] ([n] ([c] ([i] ([s] ([e] ([_] ([a] ([n] ([s] ([w] ([e] ([r] char+ | [^"r] char*) | [^"e] char*) | [^"w] char*) | [^"s] char*) | [^"n] char*) | [^"a] char*) | [^"_] char*) | [^"e] char*) | [^"s] char*) | [^"i] char*) | [^"c] char*) | [^"n] char*) | [^"o] char*) | [j] ([u] ([s] ([t] ([i] ([f] ([i] ([c] ([a] ([t] ([i] ([o] ([n] char+ | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"a] char*) | [^"c] char*) | [^"i] char*) | [^"f] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"u] char*) | [q] ([u] ([e] ([s] ([t] ([i] ([o] ([n] char+ | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"e] char*) | [^"u] char*) | [^"cjq] char* )? ["] space
+QAPair-additional-kv ::= QAPair-additional-k ":" space value
+QAPair-concise-answer-kv ::= "\"concise_answer\"" space ":" space string
+QAPair-justification-kv ::= "\"justification\"" space ":" space string
+QAPair-question-kv ::= "\"question\"" space ":" space string
+additional-k ::= ["] ( [k] ([e] ([y] ([_] ([f] ([a] ([c] ([t] ([s] char+ | [^"s] char*) | [^"t] char*) | [^"c] char*) | [^"a] char*) | [^"f] char*) | [^"_] char*) | [^"y] char*) | [^"e] char*) | [q] ([u] ([e] ([s] ([t] ([i] ([o] ([n] ([_] ([a] ([n] ([s] ([w] ([e] ([r] ([s] char+ | [^"s] char*) | [^"r] char*) | [^"e] char*) | [^"w] char*) | [^"s] char*) | [^"n] char*) | [^"a] char*) | [^"_] char*) | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"e] char*) | [^"u] char*) | [^"kq] char* )? ["] space
+additional-kv ::= additional-k ":" space value
+array ::= "[" space ( value ("," space value)* )? "]" space
+boolean ::= ("true" | "false") space
+char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+decimal-part ::= [0-9]{1,16}
+dot ::= [^\x0A\x0D]
+integral-part ::= [0] | [1-9] [0-9]{0,15}
+key-facts ::= "[" space (key-facts-item ("," space key-facts-item)*)? "]" space
+key-facts-item ::= "\"" "- " key-facts-item-1{5,} "\"" space
+key-facts-item-1 ::= dot
+key-facts-kv ::= "\"key_facts\"" space ":" space key-facts
+null ::= "null" space
+number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+question-answers ::= "[" space (question-answers-item ("," space question-answers-item)*)? "]" space
+question-answers-item ::= "[" space question-answers-item-item ("," space question-answers-item-item){4,} "]" space
+question-answers-item-item ::= QAPair
+question-answers-kv ::= "\"question_answers\"" space ":" space question-answers
+root ::= "{" space key-facts-kv "," space question-answers-kv ( "," space ( additional-kv ( "," space additional-kv )* ) )? "}" space
+space ::= | " " | "\n" [ \t]{0,20}
+string ::= "\"" char* "\"" space
+value ::= object | array | string | number | boolean | null
+```
+
+</details>
+
+If you're using [Zod](https://zod.dev/), you can make your objects to explicitly allow extra properties w/ `nonstrict()` / `passthrough()` (or explicitly no extra props w/ `z.object(...).strict()` or `z.strictObject(...)`) but note that [zod-to-json-schema](https://github.com/StefanTerdell/zod-to-json-schema) currently always sets `"additionalProperties": false` anyway.
+
+```js
+import { z } from 'zod';
+import { zodToJsonSchema } from 'zod-to-json-schema';
+
+const Foo = z.object({
+  age: z.number().positive(),
+  email: z.string().email(),
+}).strict();
+
+console.log(zodToJsonSchema(Foo));
+```
+
+<details>
+<summary>Show JSON schema & grammar</summary>
+
+```json
+{
+  "type": "object",
+  "properties": {
+    "age": {
+      "type": "number",
+      "exclusiveMinimum": 0
+    },
+    "email": {
+      "type": "string",
+      "format": "email"
+    }
+  },
+  "required": [
+    "age",
+    "email"
+  ],
+  "additionalProperties": false,
+  "$schema": "http://json-schema.org/draft-07/schema#"
+}
+```
+
+```
+age-kv ::= "\"age\"" space ":" space number
+char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+decimal-part ::= [0-9]{1,16}
+email-kv ::= "\"email\"" space ":" space string
+integral-part ::= [0] | [1-9] [0-9]{0,15}
+number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+root ::= "{" space age-kv "," space email-kv "}" space
+space ::= | " " | "\n" [ \t]{0,20}
+string ::= "\"" char* "\"" space
+```
+
+</details>
diff --git a/grammars/json.gbnf b/grammars/json.gbnf
index a8a80752e6562..b6448c87b72ec 100644
--- a/grammars/json.gbnf
+++ b/grammars/json.gbnf
@@ -16,10 +16,10 @@ array  ::=
 string ::=
   "\"" (
     [^"\\\x7F\x00-\x1F] |
-    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
+    "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes
   )* "\"" ws
 
-number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [0-9] [1-9]{0,15})? ws
 
 # Optional space: by convention, applied in this grammar after literal chars when allowed
-ws ::= ([ \t\n] ws)?
+ws ::= | " " | "\n" [ \t]{0,20}
diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf
index 31a3202f863c1..b3dc6f9b16277 100644
--- a/grammars/json_arr.gbnf
+++ b/grammars/json_arr.gbnf
@@ -25,10 +25,10 @@ array  ::=
 string ::=
   "\"" (
     [^"\\\x7F\x00-\x1F] |
-    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
+    "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes
   )* "\"" ws
 
-number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [1-9] [0-9]{0,15})? ws
 
 # Optional space: by convention, applied in this grammar after literal chars when allowed
-ws ::= ([ \t\n] ws)?
+ws ::= | " " | "\n" [ \t]{0,20}
diff --git a/llama.h b/include/llama.h
similarity index 83%
rename from llama.h
rename to include/llama.h
index b7bf2afcb403e..ed81aa4697d17 100644
--- a/llama.h
+++ b/include/llama.h
@@ -33,17 +33,15 @@
 
 #define LLAMA_DEFAULT_SEED 0xFFFFFFFF
 
-#define LLAMA_MAX_RNG_STATE (64*1024)
-
 #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
 #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
 #define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
 
 #define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
-#define LLAMA_SESSION_VERSION 6
+#define LLAMA_SESSION_VERSION 8
 
 #define LLAMA_STATE_SEQ_MAGIC   LLAMA_FILE_MAGIC_GGSQ
-#define LLAMA_STATE_SEQ_VERSION 1
+#define LLAMA_STATE_SEQ_VERSION 2
 
 #ifdef __cplusplus
 extern "C" {
@@ -67,6 +65,7 @@ extern "C" {
         LLAMA_VOCAB_TYPE_SPM  = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
         LLAMA_VOCAB_TYPE_BPE  = 2, // GPT-2 tokenizer based on byte-level BPE
         LLAMA_VOCAB_TYPE_WPM  = 3, // BERT tokenizer based on WordPiece
+        LLAMA_VOCAB_TYPE_UGM  = 4, // T5 tokenizer based on Unigram
     };
 
     // pre-tokenization types
@@ -85,18 +84,26 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_QWEN2          = 11,
         LLAMA_VOCAB_PRE_TYPE_OLMO           = 12,
         LLAMA_VOCAB_PRE_TYPE_DBRX           = 13,
+        LLAMA_VOCAB_PRE_TYPE_SMAUG          = 14,
+        LLAMA_VOCAB_PRE_TYPE_PORO           = 15,
+        LLAMA_VOCAB_PRE_TYPE_CHATGLM3       = 16,
+        LLAMA_VOCAB_PRE_TYPE_CHATGLM4       = 17,
+        LLAMA_VOCAB_PRE_TYPE_VIKING         = 18,
+        LLAMA_VOCAB_PRE_TYPE_JAIS           = 19,
+        LLAMA_VOCAB_PRE_TYPE_TEKKEN         = 20,
+        LLAMA_VOCAB_PRE_TYPE_SMOLLM         = 21,
+        LLAMA_VOCAB_PRE_TYPE_CODESHELL      = 22,
+        LLAMA_VOCAB_PRE_TYPE_BLOOM          = 23,
+        LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH   = 24,
     };
 
-    // note: these values should be synchronized with ggml_rope
-    // TODO: maybe move this enum to ggml.h (ggml_rope_type)
     enum llama_rope_type {
         LLAMA_ROPE_TYPE_NONE = -1,
-        LLAMA_ROPE_TYPE_NORM =  0,
-        LLAMA_ROPE_TYPE_NEOX =  2,
-        LLAMA_ROPE_TYPE_GLM  =  4,
+        LLAMA_ROPE_TYPE_NORM = 0,
+        LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX,
     };
 
-    enum llama_token_type {
+    enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
         LLAMA_TOKEN_TYPE_UNDEFINED    = 0,
         LLAMA_TOKEN_TYPE_NORMAL       = 1,
         LLAMA_TOKEN_TYPE_UNKNOWN      = 2,
@@ -106,13 +113,27 @@ extern "C" {
         LLAMA_TOKEN_TYPE_BYTE         = 6,
     };
 
+    enum llama_token_attr {
+        LLAMA_TOKEN_ATTR_UNDEFINED    = 0,
+        LLAMA_TOKEN_ATTR_UNKNOWN      = 1 << 0,
+        LLAMA_TOKEN_ATTR_UNUSED       = 1 << 1,
+        LLAMA_TOKEN_ATTR_NORMAL       = 1 << 2,
+        LLAMA_TOKEN_ATTR_CONTROL      = 1 << 3,  // SPECIAL?
+        LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4,
+        LLAMA_TOKEN_ATTR_BYTE         = 1 << 5,
+        LLAMA_TOKEN_ATTR_NORMALIZED   = 1 << 6,
+        LLAMA_TOKEN_ATTR_LSTRIP       = 1 << 7,
+        LLAMA_TOKEN_ATTR_RSTRIP       = 1 << 8,
+        LLAMA_TOKEN_ATTR_SINGLE_WORD  = 1 << 9,
+    };
+
     // model file types
     enum llama_ftype {
         LLAMA_FTYPE_ALL_F32              = 0,
         LLAMA_FTYPE_MOSTLY_F16           = 1,  // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q4_0          = 2,  // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q4_1          = 3,  // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4,  // tok_embeddings.weight and output.weight are F16
+        // LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4,  // tok_embeddings.weight and output.weight are F16
         // LLAMA_FTYPE_MOSTLY_Q4_2       = 5,  // support has been removed
         // LLAMA_FTYPE_MOSTLY_Q4_3       = 6,  // support has been removed
         LLAMA_FTYPE_MOSTLY_Q8_0          = 7,  // except 1d tensors
@@ -141,6 +162,9 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_IQ4_XS        = 30, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ1_M         = 31, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_BF16          = 32, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0_4_4      = 33, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0_4_8      = 34, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0_8_8      = 35, // except 1d tensors
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
@@ -158,6 +182,13 @@ extern "C" {
         LLAMA_POOLING_TYPE_NONE = 0,
         LLAMA_POOLING_TYPE_MEAN = 1,
         LLAMA_POOLING_TYPE_CLS  = 2,
+        LLAMA_POOLING_TYPE_LAST = 3,
+    };
+
+    enum llama_attention_type {
+        LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1,
+        LLAMA_ATTENTION_TYPE_CAUSAL      = 0,
+        LLAMA_ATTENTION_TYPE_NON_CAUSAL  = 1,
     };
 
     enum llama_split_mode {
@@ -264,6 +295,8 @@ extern "C" {
         bool check_tensors; // validate model tensor data
     };
 
+    // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
+    //       https://github.com/ggerganov/llama.cpp/pull/7544
     struct llama_context_params {
         uint32_t seed;              // RNG seed, -1 for random
         uint32_t n_ctx;             // text context, 0 = from model
@@ -275,7 +308,7 @@ extern "C" {
 
         enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
         enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
-                                                        // (ignored if no pooling layer)
+        enum llama_attention_type    attention_type;    // attention type to use for embeddings
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
         float    rope_freq_base;   // RoPE base frequency, 0 = from model
@@ -290,14 +323,14 @@ extern "C" {
         ggml_backend_sched_eval_callback cb_eval;
         void * cb_eval_user_data;
 
-        enum ggml_type type_k; // data type for K cache
-        enum ggml_type type_v; // data type for V cache
+        enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
+        enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
         bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
         bool embeddings;  // if true, extract embeddings (together with logits)
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
-        bool flash_attn;  // whether to use flash attention
+        bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
 
         // Abort callback
         // if it returns true, execution of llama_decode() will be aborted
@@ -311,7 +344,7 @@ extern "C" {
         int32_t nthread;                     // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
         enum llama_ftype ftype;              // quantize to this llama_ftype
         enum ggml_type output_tensor_type;   // output tensor type
-        enum ggml_type token_embedding_type; // itoken embeddings tensor type
+        enum ggml_type token_embedding_type; // token embeddings tensor type
         bool allow_requantize;               // allow quantizing non-f32/f16 tensors
         bool quantize_output_tensor;         // quantize output.weight
         bool only_copy;                      // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
@@ -348,6 +381,9 @@ extern "C" {
         // modifies a preceding LLAMA_GRETYPE_CHAR or
         // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
         LLAMA_GRETYPE_CHAR_ALT       = 6,
+
+        // any character (.)
+        LLAMA_GRETYPE_CHAR_ANY       = 7,
     };
 
     typedef struct llama_grammar_element {
@@ -375,6 +411,9 @@ extern "C" {
         const char * content;
     } llama_chat_message;
 
+    // lora adapter
+    struct llama_lora_adapter;
+
     // Helpers for getting default parameters
     LLAMA_API struct llama_model_params llama_model_default_params(void);
     LLAMA_API struct llama_context_params llama_context_default_params(void);
@@ -421,8 +460,8 @@ extern "C" {
 
     LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
 
-    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model   * model);
-    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model   * model);
+    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model * model);
+    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model * model);
 
     LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
     LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
@@ -461,24 +500,48 @@ extern "C" {
     // Get a llama model tensor
     LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
 
+    // Returns true if the model contains an encoder that requires llama_encode() call
+    LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
+
+    // Returns true if the model contains a decoder that requires llama_decode() call
+    LLAMA_API bool llama_model_has_decoder(const struct llama_model * model);
+
+    // For encoder-decoder models, this function returns id of the token that must be provided
+    // to the decoder to start generating output sequence. For other models, it returns -1.
+    LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);
+
     // Returns 0 on success
     LLAMA_API uint32_t llama_model_quantize(
             const char * fname_inp,
             const char * fname_out,
             const llama_model_quantize_params * params);
 
-    // Apply a LoRA adapter to a loaded model
-    // path_base_model is the path to a higher quality model to use as a base for
-    // the layers modified by the adapter. Can be NULL to use the current loaded model.
-    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
-    // will be applied on top of the previous one
-    // Returns 0 on success
-    LLAMA_API int32_t llama_model_apply_lora_from_file(
-            const struct llama_model * model,
-                          const char * path_lora,
-                               float   scale,
-                          const char * path_base_model,
-                             int32_t   n_threads);
+    // Load a LoRA adapter from file
+    // The loaded adapter will be associated to the given model, and will be free when the model is deleted
+    LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
+            struct llama_model * model,
+            const char * path_lora);
+
+    // Add a loaded LoRA adapter to given context
+    // This will not modify model's weight
+    LLAMA_API int32_t llama_lora_adapter_set(
+            struct llama_context * ctx,
+            struct llama_lora_adapter * adapter,
+            float scale);
+
+    // Remove a specific LoRA adapter from given context
+    // Return -1 if the adapter is not present in the context
+    LLAMA_API int32_t llama_lora_adapter_remove(
+            struct llama_context * ctx,
+            struct llama_lora_adapter * adapter);
+
+    // Remove all LoRA adapters from given context
+    LLAMA_API void llama_lora_adapter_clear(
+            struct llama_context * ctx);
+
+    // Manually free a LoRA adapter
+    // Note: loaded adapters will be free when the associated model is deleted
+    LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
 
     // Apply a loaded control vector to a llama_context, or if data is NULL, clear
     // the currently loaded vector.
@@ -628,10 +691,11 @@ extern "C" {
     // State / sessions
     //
 
-    // Returns the maximum size in bytes of the state (rng, logits, embedding
-    // and kv_cache) - will often be smaller after compacting tokens
-    LLAMA_API size_t llama_state_get_size(const struct llama_context * ctx);
-    LLAMA_API DEPRECATED(size_t llama_get_state_size(const struct llama_context * ctx),
+    // Returns the *actual* size in bytes of the state
+    // (rng, logits, embedding and kv_cache)
+    // Only use when saving the state, not when restoring it, otherwise the size may be too small.
+    LLAMA_API size_t llama_state_get_size(struct llama_context * ctx);
+    LLAMA_API DEPRECATED(size_t llama_get_state_size(struct llama_context * ctx),
         "use llama_state_get_size instead");
 
     // Copies the state to the specified destination address.
@@ -639,7 +703,8 @@ extern "C" {
     // Returns the number of bytes copied
     LLAMA_API size_t llama_state_get_data(
             struct llama_context * ctx,
-                         uint8_t * dst);
+                         uint8_t * dst,
+                          size_t   size);
     LLAMA_API DEPRECATED(size_t llama_copy_state_data(
             struct llama_context * ctx,
                          uint8_t * dst),
@@ -649,7 +714,8 @@ extern "C" {
     // Returns the number of bytes read
     LLAMA_API size_t llama_state_set_data(
             struct llama_context * ctx,
-                   const uint8_t * src);
+                   const uint8_t * src,
+                          size_t   size);
     LLAMA_API DEPRECATED(size_t llama_set_state_data(
             struct llama_context * ctx,
                    const uint8_t * src),
@@ -691,6 +757,7 @@ extern "C" {
     LLAMA_API size_t llama_state_seq_get_data(
             struct llama_context * ctx,
                          uint8_t * dst,
+                          size_t   size,
                     llama_seq_id   seq_id);
 
     // Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence
@@ -700,6 +767,7 @@ extern "C" {
     LLAMA_API size_t llama_state_seq_set_data(
             struct llama_context * ctx,
                    const uint8_t * src,
+                          size_t   size,
                     llama_seq_id   dest_seq_id);
 
     LLAMA_API size_t llama_state_seq_save_file(
@@ -746,6 +814,14 @@ extern "C" {
     // Frees a batch of tokens allocated with llama_batch_init()
     LLAMA_API void llama_batch_free(struct llama_batch batch);
 
+    // Processes a batch of tokens with the ecoder part of the encoder-decoder model.
+    // Stores the encoder output internally for later use by the decoder cross-attention layers.
+    //   0 - success
+    // < 0 - error
+    LLAMA_API int32_t llama_encode(
+            struct llama_context * ctx,
+              struct llama_batch   batch);
+
     // Positive return values does not mean a fatal error, but rather a warning.
     //   0 - success
     //   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
@@ -759,6 +835,16 @@ extern "C" {
     // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
     LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
 
+    // Get the number of threads used for generation of a single token.
+    LLAMA_API uint32_t llama_n_threads(struct llama_context * ctx);
+
+    // Get the number of threads used for prompt and batch processing (multiple token).
+    LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx);
+
+    // Set whether the model is in embeddings mode or not
+    // If true, embeddings will be returned but logits will not
+    LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
+
     // Set whether to use causal attention or not
     // If set to true, the model will only attend to the past tokens
     LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
@@ -812,23 +898,24 @@ extern "C" {
 
     LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
 
-    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
+    LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token);
 
     // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
     LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
 
+    // Identify if Token Id is a control token or a render-able token
+    LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token);
+
     // Special tokens
     LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
     LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
     LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
     LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
     LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
+    LLAMA_API llama_token llama_token_pad(const struct llama_model * model); // padding
 
-    // Returns -1 if unknown, 1 for true or 0 for false.
-    LLAMA_API int32_t         llama_add_bos_token(const struct llama_model * model);
-
-    // Returns -1 if unknown, 1 for true or 0 for false.
-    LLAMA_API int32_t         llama_add_eos_token(const struct llama_model * model);
+    LLAMA_API bool llama_add_bos_token(const struct llama_model * model);
+    LLAMA_API bool llama_add_eos_token(const struct llama_model * model);
 
     // Codellama infill tokens
     LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
@@ -844,6 +931,7 @@ extern "C" {
     /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
     /// @return Returns the number of tokens on success, no more than n_tokens_max
     /// @return Returns a negative number on failure - the number of tokens that would have been returned
+    /// @param add_special Allow to add BOS and EOS tokens if model is configured to do so.
     /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
     ///                      as plaintext. Does not insert a leading space.
     LLAMA_API int32_t llama_tokenize(
@@ -858,15 +946,35 @@ extern "C" {
     // Token Id -> Piece.
     // Uses the vocabulary in the provided context.
     // Does not write null terminator to the buffer.
-    // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix')
     // @param special If true, special tokens are rendered in the output.
     LLAMA_API int32_t llama_token_to_piece(
               const struct llama_model * model,
                            llama_token   token,
                                   char * buf,
                                int32_t   length,
+                               int32_t   lstrip,
                                   bool   special);
 
+    /// @details Convert the provided tokens into text (inverse of llama_tokenize()).
+    /// @param text The char pointer must be large enough to hold the resulting text.
+    /// @return Returns the number of chars/bytes on success, no more than text_len_max.
+    /// @return Returns a negative number on failure - the number of chars/bytes that would have been returned.
+    /// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so.
+    /// @param unparse_special If true, special tokens are rendered in the output.
+    LLAMA_API int32_t llama_detokenize(
+        const struct llama_model * model,
+               const llama_token * tokens,
+                         int32_t   n_tokens,
+                            char * text,
+                         int32_t   text_len_max,
+                            bool   remove_special,
+                            bool   unparse_special);
+
+    //
+    // Chat templates
+    //
+
     /// Apply chat template. Inspired by hf apply_chat_template() on python.
     /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
     /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
@@ -890,6 +998,12 @@ extern "C" {
     // Grammar
     //
 
+    /// Initialize a llama_grammar.
+    ///
+    /// @param rules The rule elements of the grammar to initialize.
+    /// @param n_rules The number of rules.
+    /// @param start_rule_index The index of the root rule (the starting point of the grammar).
+    /// @return The initialized llama_grammar or nullptr if initialization failed.
     LLAMA_API struct llama_grammar * llama_grammar_init(
             const llama_grammar_element ** rules,
                                  size_t    n_rules,
@@ -899,6 +1013,23 @@ extern "C" {
 
     LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
 
+    /// @details Apply constraints from grammar
+    LLAMA_API void llama_grammar_sample(
+            const struct llama_grammar * grammar,
+            const struct llama_context * ctx,
+                llama_token_data_array * candidates);
+    LLAMA_API DEPRECATED(void llama_sample_grammar(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+      const struct llama_grammar * grammar),
+        "use llama_grammar_sample instead");
+
+    /// @details Accepts the sampled token into the grammar
+    LLAMA_API void llama_grammar_accept_token(
+            struct llama_grammar * grammar,
+            struct llama_context * ctx,
+                     llama_token   token);
+
     //
     // Sampling functions
     //
@@ -980,12 +1111,6 @@ extern "C" {
           llama_token_data_array * candidates,
                            float   temp);
 
-    /// @details Apply constraints from grammar
-    LLAMA_API void llama_sample_grammar(
-            struct llama_context * ctx,
-          llama_token_data_array * candidates,
-      const struct llama_grammar * grammar);
-
     /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
     /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
@@ -1023,56 +1148,10 @@ extern "C" {
             struct llama_context * ctx,
           llama_token_data_array * candidates);
 
-    /// @details Accepts the sampled token into the grammar
-    LLAMA_API void llama_grammar_accept_token(
-            struct llama_context * ctx,
-            struct llama_grammar * grammar,
-                     llama_token   token);
-
     //
-    // Beam search
+    // Model split
     //
 
-    struct llama_beam_view {
-        const llama_token * tokens;
-
-        size_t n_tokens;
-        float  p;        // Cumulative beam probability (renormalized relative to all beams)
-        bool   eob;      // Callback should set this to true when a beam is at end-of-beam.
-    };
-
-    // Passed to beam_search_callback function.
-    // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
-    // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
-    // These pointers are valid only during the synchronous callback, so should not be saved.
-    struct llama_beams_state {
-        struct llama_beam_view * beam_views;
-
-        size_t n_beams;               // Number of elements in beam_views[].
-        size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
-        bool   last_call;             // True iff this is the last callback invocation.
-    };
-
-    // Type of pointer to the beam_search_callback function.
-    // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
-    // passed back to beam_search_callback. This avoids having to use global variables in the callback.
-    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
-
-    /// @details Deterministically returns entire sentence constructed by a beam search.
-    /// @param ctx Pointer to the llama_context.
-    /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
-    /// @param callback_data A pointer that is simply passed back to callback.
-    /// @param n_beams Number of beams to use.
-    /// @param n_past Number of tokens already evaluated.
-    /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
-    LLAMA_API void llama_beam_search(
-                   struct llama_context * ctx,
-        llama_beam_search_callback_fn_t   callback,
-                                   void * callback_data,
-                                 size_t   n_beams,
-                                int32_t   n_past,
-                                int32_t   n_predict);
-
     /// @details Build a split GGUF final path for this chunk.
     ///          llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
     //  Returns the split_path length.
@@ -1111,38 +1190,45 @@ extern "C" {
 
 struct ggml_tensor;
 
+const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
+    struct llama_context * ctx
+);
+
 struct llama_partial_utf8 {
     uint32_t value;    // bit value so far (unshifted)
     int      n_remain; // num bytes remaining; -1 indicates invalid sequence
 };
 
-struct llama_grammar {
-    const std::vector<std::vector<llama_grammar_element>>   rules;
-    std::vector<std::vector<const llama_grammar_element *>> stacks;
-
-    // buffer for partially generated UTF-8 sequence from accepted tokens
-    llama_partial_utf8                                      partial_utf8;
-};
-
 struct llama_grammar_candidate {
     size_t               index;
     const uint32_t     * code_points;
     llama_partial_utf8   partial_utf8;
 };
 
-const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
-    struct llama_context * ctx
-);
+using llama_grammar_rule  = std::vector<      llama_grammar_element>;
+using llama_grammar_stack = std::vector<const llama_grammar_element *>;
+
+using llama_grammar_rules      = std::vector<llama_grammar_rule>;
+using llama_grammar_stacks     = std::vector<llama_grammar_stack>;
+using llama_grammar_candidates = std::vector<llama_grammar_candidate>;
+
+const llama_grammar_rules  & llama_grammar_get_rules (const struct llama_grammar * grammar);
+      llama_grammar_stacks & llama_grammar_get_stacks(      struct llama_grammar * grammar);
 
 void llama_grammar_accept(
-        const std::vector<std::vector<llama_grammar_element>>         & rules,
-        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const uint32_t                                                  chr,
-        std::vector<std::vector<const llama_grammar_element *>>       & new_stacks);
+        const llama_grammar_rules  & rules,
+        const llama_grammar_stacks & stacks,
+        const uint32_t chr,
+              llama_grammar_stacks & new_stacks);
+
+std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
+        const llama_grammar_rules & rules,
+        const llama_grammar_stack & stack,
+        const llama_grammar_candidates & candidates);
 
 std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
         const std::string & src,
-        llama_partial_utf8   partial_start);
+        llama_partial_utf8 partial_start);
 
 // Randomly selects a token from the candidates based on their probabilities using given std::mt19937.
 // This is a temporary workaround in order to fix race conditions when sampling with multiple sequences.
diff --git a/models/ggml-vocab-bert-bge.gguf.inp b/models/ggml-vocab-bert-bge.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-bert-bge.gguf.inp
+++ b/models/ggml-vocab-bert-bge.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-bert-bge.gguf.out b/models/ggml-vocab-bert-bge.gguf.out
index e4a76cdb07d3f..a62566ce75676 100644
--- a/models/ggml-vocab-bert-bge.gguf.out
+++ b/models/ggml-vocab-bert-bge.gguf.out
@@ -31,6 +31,7 @@
  1027
  1005 3690
  7592 1010 1061 1005 2035 999 2129 2024 2017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995
+ 999 999 999 999 999 999
  1017
  3943
  21211
@@ -40,4 +41,6 @@
  21211 22394 22394
  21211 22394 22394 2509
  21211 22394 22394 22394
+ 12731 2050 19710
+ 5860 18117
  100 1006 3671 1007 100 1006 3674 7861 29147 2483 9530 16280 23854 1007 100 100 1017 3943 21211 21211 2509 21211 22394 21211 22394 2509 21211 22394 22394 21211 22394 22394 2509 1017 1012 1017 1017 1012 1012 1017 1017 1012 1012 1012 1017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 1011 1011 1011 1011 1011 1011 1027 1027 1027 1027 1027 1027 1027 1192 15290 29754 14150 1192 10260 1181 29755 29436 29741 10260 16856 29747 23925 10325 1005 1005 1005 1005 1005 1005 1036 1036 1036 1036 1036 1036 1036 1000 1000 1000 1000 1012 1012 1012 1012 1012 1012 999 999 999 999 999 999 1029 1029 1029 1029 1029 1029 1045 1005 2310 2042 1005 2409 2002 1005 1055 2045 1010 1005 2128 2017 2469 1029 1005 1049 2025 2469 1045 1005 2222 2191 2009 1010 1005 1040 2017 2066 2070 5572 1029 2057 1005 2310 1037 1005 2222
diff --git a/models/ggml-vocab-command-r.gguf.inp b/models/ggml-vocab-command-r.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-command-r.gguf.inp
+++ b/models/ggml-vocab-command-r.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-command-r.gguf.out b/models/ggml-vocab-command-r.gguf.out
index cc4277daa1d25..3f6b418888740 100644
--- a/models/ggml-vocab-command-r.gguf.out
+++ b/models/ggml-vocab-command-r.gguf.out
@@ -31,6 +31,7 @@
  206 1857
  14 4515
  28339 19 1770 14 1954 8 4070 1955 1933 80503 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372
+ 57178 10251
  26
  26 26
  26 26 26
@@ -40,4 +41,6 @@
  26 26 26 26 26 26 26
  26 26 26 26 26 26 26 26
  26 26 26 26 26 26 26 26 26
+ 42 30719 12584
+ 3642 4388
  127731 51628 205 57788 18494 97469 126134 206 2226 256 230 1737 18258 16 80503 122 35927 2226 242 112 57462 1737 54457 223165 106230 2096 16 48389 11254 107 255 2226 107 255 228 26 228 26 26 228 26 26 26 228 26 26 26 26 228 26 26 26 26 26 228 26 26 26 26 26 26 228 26 26 26 26 26 26 26 228 26 26 26 26 26 26 26 26 228 26 21 26 228 26 2271 26 228 26 3834 26 182018 230 174833 38111 249 86325 241 38111 245 86325 232 38111 252 38111 123 38111 261 165 24629 38111 261 38111 103 174833 38111 235 188568 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 8391 158343 3512 40071 2196 3236 8750 1764 37097 41168 29721 32797 25646 3802 4975 4975 116167 57178 10251 154048 27292 1767 5125 2632 2155 91 2378 1919 1914 2782 19 2155 3354 1933 5470 38 2155 52 2068 5470 1767 4961 3059 1894 19 2155 43 1933 3026 2725 23186 38 2930 14 20676 1671 14 83 51
diff --git a/models/ggml-vocab-deepseek-coder.gguf.inp b/models/ggml-vocab-deepseek-coder.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-deepseek-coder.gguf.inp
+++ b/models/ggml-vocab-deepseek-coder.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-deepseek-coder.gguf.out b/models/ggml-vocab-deepseek-coder.gguf.out
index 9ccc560d694ca..52c4111a18d73 100644
--- a/models/ggml-vocab-deepseek-coder.gguf.out
+++ b/models/ggml-vocab-deepseek-coder.gguf.out
@@ -31,6 +31,7 @@
  185 405
  6 2895
  17535 11 320 6 435 0 1717 417 340 12394 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239
+ 15330 3023
  18
  18 18
  18 18 18
@@ -40,4 +41,6 @@
  18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18 18
+ 34 155 119 242 64 24297 155 119 216 83
+ 1607 2539
  185 207 185 185 207 185 185 185 207 12405 459 22758 185 243 185 315 185 251 185 730 185 10047 235 209 334 8760 8 12394 233 114 350 222 10047 221 104 169 116 224 334 4684 3909 992 24330 262 29651 612 8 207 156 237 214 12394 99 234 10047 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 524 18 207 18 1202 18 207 155 239 209 155 239 114 155 239 228 155 240 220 155 239 224 155 240 211 155 239 231 155 239 115 155 239 240 155 240 210 155 239 240 155 239 95 155 239 114 155 239 214 10047 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239 18155 374 17194 28 2861 6478 616 2251 14994 31269 4191 6 4686 4686 10252 3358 3358 3409 524 15330 3023 15031 5668 303 6 312 798 651 83 839 362 6 82 741 11 651 1369 340 2037 30 651 44 441 2037 303 6 642 1098 359 11 651 35 340 833 738 10860 30 998 6 10709 245 6 75 43
diff --git a/models/ggml-vocab-deepseek-llm.gguf.inp b/models/ggml-vocab-deepseek-llm.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-deepseek-llm.gguf.inp
+++ b/models/ggml-vocab-deepseek-llm.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-deepseek-llm.gguf.out b/models/ggml-vocab-deepseek-llm.gguf.out
index fd94b896d24e7..0191b7a115582 100644
--- a/models/ggml-vocab-deepseek-llm.gguf.out
+++ b/models/ggml-vocab-deepseek-llm.gguf.out
@@ -31,6 +31,7 @@
  185 403
  6 2906
  17464 11 320 6 436 0 1724 418 340 33701 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239
+ 15278 3033
  18
  18 18
  18 18 18
@@ -40,4 +41,6 @@
  18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18 18
+ 34 32555 242 64 23708 32555 216 83
+ 1763 2550
  185 207 185 185 207 185 185 185 207 11969 486 22504 185 243 185 300 185 251 185 663 185 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 12356 99 234 10044 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 526 18 207 18 1204 18 207 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71899 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 78827 55170 76659 620 91754 31116 36804 4885 4885 10897 4390 4390 41047 15278 3033 14986 5675 304 6 313 803 655 33326 362 6 82 745 11 655 1374 340 2049 30 655 44 441 2049 304 6 647 1099 359 11 655 35 340 837 742 10842 30 1003 6 10699 245 6 75 43
diff --git a/models/ggml-vocab-falcon.gguf.inp b/models/ggml-vocab-falcon.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-falcon.gguf.inp
+++ b/models/ggml-vocab-falcon.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-falcon.gguf.out b/models/ggml-vocab-falcon.gguf.out
index 209b04cdaf330..64a48d97f71f4 100644
--- a/models/ggml-vocab-falcon.gguf.out
+++ b/models/ggml-vocab-falcon.gguf.out
@@ -31,6 +31,7 @@
  1212 40
  18 4932
  9856 23 291 18 436 12 1265 362 299 8196 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236
+ 51520
  30
  3138
  22287
@@ -40,4 +41,6 @@
  22287 22287 30
  22287 22287 3138
  22287 22287 22287
+ 46 19768 239 76 9634 19768 213 95
+ 1080 1502
  1212 4824 1001 1212 192 204 663 49453 2069 742 561 1501 193 2571 232 206 204 19 11003 20 8196 126 283 219 48778 116 13392 204 19 51831 732 63209 1741 7955 522 20 22438 211 3346 111 231 2571 111 231 204 30 204 3138 204 22287 204 22287 30 204 22287 3138 204 22287 22287 204 22287 22287 30 204 22287 22287 3138 204 30 25 30 204 30 513 30 204 30 951 30 27171 236 206 38154 126 38154 225 167 237 217 38154 221 167 237 208 38154 228 38154 127 38154 237 167 237 207 38154 237 38154 107 38154 126 38154 211 20589 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 204 37057 2228 10666 5052 133 6207 151 215 150 134 5052 133 6279 5052 223 151 216 49679 123 53110 47043 7795 204 7544 7544 7544 8543 8543 17593 3513 3513 12844 51520 17664 4247 295 18 298 650 204 18 95 693 332 18 94 629 23 204 18 1553 299 1310 42 204 18 56 416 1310 295 18 567 717 334 23 204 18 47 299 606 596 6696 42 703 18 16139 241 18 87 55
diff --git a/models/ggml-vocab-gpt-2.gguf.inp b/models/ggml-vocab-gpt-2.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-gpt-2.gguf.inp
+++ b/models/ggml-vocab-gpt-2.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-gpt-2.gguf.out b/models/ggml-vocab-gpt-2.gguf.out
index 78430f0d31fdc..17a13bdfc3d93 100644
--- a/models/ggml-vocab-gpt-2.gguf.out
+++ b/models/ggml-vocab-gpt-2.gguf.out
@@ -31,6 +31,7 @@
  198 796
  6 6980
  15496 11 331 6 439 0 1374 389 345 30325 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252
+ 13896 3228
  18
  2091
  20370
@@ -40,4 +41,6 @@
  24840 20370
  24840 24840
  24840 2091 20370
+ 34 157 119 255 64 16049 157 119 229 83
+ 1221 1371
  198 220 628 220 628 198 220 197 220 197 197 220 197 198 220 220 198 220 220 220 198 220 220 220 220 198 220 220 220 220 220 198 8582 248 222 357 11265 8 30325 114 447 235 8582 234 104 37929 357 48101 795 13210 271 1673 36686 515 8 14519 227 12520 99 247 8582 99 247 513 4747 23460 513 20370 23460 2091 23460 20370 23460 24840 23460 2091 20370 513 13 18 513 492 18 513 986 18 28053 252 222 157 252 114 157 252 241 157 253 233 157 252 237 157 253 224 157 252 244 157 252 115 157 252 253 157 253 223 157 252 253 157 252 95 157 252 114 157 252 227 47249 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 40103 1421 18604 12466 121 16843 141 231 15166 12466 121 16142 12466 239 141 232 30143 140 111 16142 21169 21727 31583 18849 705 39115 6 33153 15506 63 15931 15931 16317 13896 3228 9805 3548 314 1053 587 705 44040 339 338 612 11 705 2200 345 1654 30 705 44 407 1654 314 1183 787 340 11 705 35 345 588 617 8887 30 775 6 26979 257 6 75 43
diff --git a/models/ggml-vocab-gpt2.gguf b/models/ggml-vocab-gpt2.gguf
deleted file mode 100644
index 1fbc72c1e4d9e..0000000000000
Binary files a/models/ggml-vocab-gpt2.gguf and /dev/null differ
diff --git a/models/ggml-vocab-llama-bpe.gguf.inp b/models/ggml-vocab-llama-bpe.gguf.inp
index 9380bf355202a..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-llama-bpe.gguf.inp
+++ b/models/ggml-vocab-llama-bpe.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
@@ -104,5 +110,3 @@ __ggml_vocab_test__
      
 🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
 __ggml_vocab_test__
- Việt
-__ggml_vocab_test__
diff --git a/models/ggml-vocab-llama-bpe.gguf.out b/models/ggml-vocab-llama-bpe.gguf.out
index 1f3607fb6a378..4b35cf93f7133 100644
--- a/models/ggml-vocab-llama-bpe.gguf.out
+++ b/models/ggml-vocab-llama-bpe.gguf.out
@@ -31,6 +31,7 @@
  198 284
  6 11639
  9906 11 379 65948 0 2650 527 499 27623 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909
+ 17523 3001
  18
  1644
  8765
@@ -40,5 +41,6 @@
  8765 8765 18
  8765 8765 1644
  8765 8765 8765
+ 34 91163 101798
+ 2624 2402
  198 4815 15073 66597 8004 1602 2355 79772 11187 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 11410 99 247 9468 99 247 220 18 220 1644 220 8765 220 8765 18 220 8765 1644 220 8765 8765 220 8765 8765 18 220 8765 8765 1644 220 18 13 18 220 18 497 18 220 18 1131 18 220 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 76460 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 56560 54337 19175 102118 13373 64571 34694 3114 112203 80112 3436 106451 14196 14196 74694 3089 3089 29249 17523 3001 27708 7801 358 3077 1027 364 83 820 568 596 1070 11 364 793 499 2771 30 364 44 539 2771 358 3358 1304 433 11 364 35 499 1093 1063 15600 30 1226 6 43712 264 64966 43
- 101798
diff --git a/models/ggml-vocab-llama-spm.gguf.inp b/models/ggml-vocab-llama-spm.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-llama-spm.gguf.inp
+++ b/models/ggml-vocab-llama-spm.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-llama-spm.gguf.out b/models/ggml-vocab-llama-spm.gguf.out
index 9c3327cb54380..93aacf8bae4bf 100644
--- a/models/ggml-vocab-llama-spm.gguf.out
+++ b/models/ggml-vocab-llama-spm.gguf.out
@@ -31,6 +31,7 @@
  29871 13 353
  525 3152
  15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739
+ 1738 6824 21004
  29871 29941
  29871 29941 29941
  29871 29941 29941 29941
@@ -40,4 +41,6 @@
  29871 29941 29941 29941 29941 29941 29941 29941
  29871 29941 29941 29941 29941 29941 29941 29941 29941
  29871 29941 29941 29941 29941 29941 29941 29941 29941 29941
+ 315 228 190 176 29874 10630 30529 29873
+ 29871 2313 3163
  29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931
diff --git a/models/ggml-vocab-mpt.gguf.inp b/models/ggml-vocab-mpt.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-mpt.gguf.inp
+++ b/models/ggml-vocab-mpt.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-mpt.gguf.out b/models/ggml-vocab-mpt.gguf.out
index d8d0fe90900bb..372c751bf77da 100644
--- a/models/ggml-vocab-mpt.gguf.out
+++ b/models/ggml-vocab-mpt.gguf.out
@@ -31,6 +31,7 @@
  187 426
  8 8685
  12092 13 340 8 455 2 1359 403 368 49042 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241
+ 18963 4672
  20
  1610
  20084
@@ -40,4 +41,6 @@
  26409 20084
  26409 26409
  26409 1610 20084
+ 36 6829 244 66 17721 35177 85
+ 1262 2196
  586 1744 33525 186 209 623 28910 187 50276 187 50275 187 50274 187 50273 187 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 22692 101 236 14931 101 236 495 5922 30057 495 20084 495 26409 30057 20084 495 26409 1610 495 26409 20084 495 15 20 495 537 20 495 1051 20 209 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 14931 235 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 16081 6877 12880 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 42011 35033 34842 11202 9739 9739 33021 18963 4672 25561 8220 309 1849 644 686 42618 344 434 627 13 686 1848 368 2119 32 686 46 417 2119 309 1833 1056 352 13 686 37 368 751 690 10331 32 844 8 31516 247 8 77 45
diff --git a/models/ggml-vocab-phi-3.gguf b/models/ggml-vocab-phi-3.gguf
index f8022a385e4aa..745be416a798a 100644
Binary files a/models/ggml-vocab-phi-3.gguf and b/models/ggml-vocab-phi-3.gguf differ
diff --git a/models/ggml-vocab-phi-3.gguf.inp b/models/ggml-vocab-phi-3.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-phi-3.gguf.inp
+++ b/models/ggml-vocab-phi-3.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-phi-3.gguf.out b/models/ggml-vocab-phi-3.gguf.out
index 9c3327cb54380..93aacf8bae4bf 100644
--- a/models/ggml-vocab-phi-3.gguf.out
+++ b/models/ggml-vocab-phi-3.gguf.out
@@ -31,6 +31,7 @@
  29871 13 353
  525 3152
  15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739
+ 1738 6824 21004
  29871 29941
  29871 29941 29941
  29871 29941 29941 29941
@@ -40,4 +41,6 @@
  29871 29941 29941 29941 29941 29941 29941 29941
  29871 29941 29941 29941 29941 29941 29941 29941 29941
  29871 29941 29941 29941 29941 29941 29941 29941 29941 29941
+ 315 228 190 176 29874 10630 30529 29873
+ 29871 2313 3163
  29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931
diff --git a/models/ggml-vocab-qwen2.gguf.inp b/models/ggml-vocab-qwen2.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-qwen2.gguf.inp
+++ b/models/ggml-vocab-qwen2.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-qwen2.gguf.out b/models/ggml-vocab-qwen2.gguf.out
index 401a510e86f3a..18b4b45cd152f 100644
--- a/models/ggml-vocab-qwen2.gguf.out
+++ b/models/ggml-vocab-qwen2.gguf.out
@@ -31,6 +31,7 @@
  198 284
  6 11385
  9707 11 379 64848 0 2585 525 498 26525 223 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216
+ 17085 2928
  18
  18 18
  18 18 18
@@ -40,4 +41,6 @@
  18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18 18
+ 34 90063 128324
+ 2560 2347
  198 4710 14731 65497 7847 1572 2303 78672 10947 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 11162 99 247 149955 220 18 220 18 18 220 18 18 18 220 18 18 18 18 220 18 18 18 18 18 220 18 18 18 18 18 18 220 18 18 18 18 18 18 18 220 18 18 18 18 18 18 18 18 220 18 13 18 220 18 496 18 220 18 1112 18 220 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 144534 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 55460 53237 18658 14144 1456 13073 63471 33594 3038 133178 79012 3355 4605 4605 13874 13874 73594 3014 3014 28149 17085 2928 26610 7646 358 3003 1012 364 83 813 566 594 1052 11 364 787 498 2704 30 364 44 537 2704 358 3278 1281 432 11 364 35 498 1075 1045 15243 30 1205 6 42612 264 63866 43
diff --git a/models/ggml-vocab-refact.gguf.inp b/models/ggml-vocab-refact.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-refact.gguf.inp
+++ b/models/ggml-vocab-refact.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-refact.gguf.out b/models/ggml-vocab-refact.gguf.out
index 06b15c090c0f8..63d8305c3f1f3 100644
--- a/models/ggml-vocab-refact.gguf.out
+++ b/models/ggml-vocab-refact.gguf.out
@@ -31,6 +31,7 @@
  203 280
  25 34666
  8279 30 533 25 464 19 4971 884 844 18458 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838
+ 9163 3202
  37
  37 37
  37 37 37
@@ -40,4 +41,6 @@
  37 37 37 37 37 37 37
  37 37 37 37 37 37 37 37
  37 37 37 37 37 37 37 37 37
+ 53 33934 83 33217 17102 102
+ 1214 12258
  334 719 8878 202 10885 4222 16104 28570 203 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 5945 118 252 3807 118 252 225 37 225 37 37 225 37 37 37 225 37 37 37 37 225 37 37 37 37 37 225 37 37 37 37 37 37 225 37 37 37 37 37 37 37 225 37 37 37 37 37 37 37 37 225 37 32 37 225 37 497 37 225 37 1179 37 225 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 36628 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 20921 16623 13028 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 5881 9592 13299 914 31753 31359 9163 3202 35472 10397 439 4763 2583 330 102 1455 938 1182 2017 30 330 613 844 3654 49 330 63 646 3654 439 4621 1930 561 30 330 54 844 2124 1629 35993 49 2688 25 7709 312 25 94 62
diff --git a/models/ggml-vocab-stablelm.gguf b/models/ggml-vocab-stablelm.gguf
deleted file mode 100644
index ebb0cdb7d6a4a..0000000000000
Binary files a/models/ggml-vocab-stablelm.gguf and /dev/null differ
diff --git a/models/ggml-vocab-starcoder.gguf.inp b/models/ggml-vocab-starcoder.gguf.inp
index 0a89107c60d7f..9baf7d77ae6b5 100644
--- a/models/ggml-vocab-starcoder.gguf.inp
+++ b/models/ggml-vocab-starcoder.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-starcoder.gguf.out b/models/ggml-vocab-starcoder.gguf.out
index ccb55c7feeef8..87e2465d363e4 100644
--- a/models/ggml-vocab-starcoder.gguf.out
+++ b/models/ggml-vocab-starcoder.gguf.out
@@ -31,6 +31,7 @@
  222 299
  44 34719
  8302 49 553 44 483 38 4998 904 863 18445 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892
+ 9221 3226
  56
  56 56
  56 56 56
@@ -40,4 +41,6 @@
  56 56 56 56 56 56 56
  56 56 56 56 56 56 56 56
  56 56 56 56 56 56 56 56 56
+ 72 34269 102 33245 17234 121
+ 1236 12266
  353 736 8886 221 10883 4238 16101 28540 222 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 5954 137 271 3822 137 271 244 56 244 56 56 244 56 56 56 244 56 56 56 56 244 56 56 56 56 56 244 56 56 56 56 56 56 244 56 56 56 56 56 56 56 244 56 56 56 56 56 56 56 56 244 56 51 56 244 56 516 56 244 56 1198 56 244 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 36570 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892 20895 16625 13047 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721 5918 9643 13298 932 31723 31330 9221 3226 35426 10400 457 4783 2602 349 121 1477 957 1200 2038 49 349 632 863 3673 68 349 82 666 3673 457 4650 1949 580 49 349 73 863 2144 1649 35941 68 2726 44 7728 331 44 113 81
diff --git a/pocs/vdot/CMakeLists.txt b/pocs/vdot/CMakeLists.txt
index fb89a1cd4e833..d5405ad2991d1 100644
--- a/pocs/vdot/CMakeLists.txt
+++ b/pocs/vdot/CMakeLists.txt
@@ -1,9 +1,9 @@
-set(TARGET vdot)
+set(TARGET llama-vdot)
 add_executable(${TARGET} vdot.cpp)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 
-set(TARGET q8dot)
+set(TARGET llama-q8dot)
 add_executable(${TARGET} q8dot.cpp)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000000000..eb6baa6c749c0
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1197 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
+name = "atomicwrites"
+version = "1.4.1"
+description = "Atomic file writes."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "atomicwrites-1.4.1.tar.gz", hash = "sha256:81b2c9071a49367a7f770170e5eec8cb66567cfbbc8c73d20ce5ca4a8d71cf11"},
+]
+
+[[package]]
+name = "attrs"
+version = "23.2.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+]
+
+[package.extras]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
+[[package]]
+name = "certifi"
+version = "2024.2.2"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.3.2"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
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+    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
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+    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
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+    {file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"},
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000..25e2e20b24896
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,44 @@
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+[tool.poetry.dependencies]
+python = ">=3.9"
+numpy = "^1.25.0"
+sentencepiece = ">=0.1.98,<0.2.0"
+transformers = ">=4.35.2,<5.0.0"
+protobuf = ">=4.21.0,<5.0.0"
+gguf = { path = "./gguf-py" }
+torch = { version = "^2.2.0", source = "pytorch" }
+
+[tool.poetry.dev-dependencies]
+pytest = "^5.2"
+
+
+# Force wheel + cpu
+# For discussion and context see https://github.com/python-poetry/poetry#6409
+[[tool.poetry.source]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl/cpu"
+priority = "explicit"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.scripts]
+llama-convert-hf-to-gguf = "convert_hf_to_gguf:main"
+llama-convert-llama-ggml-to-gguf = "convert_llama_ggml_to_gguf:main"
+llama-ggml-vk-generate-shaders = "ggml_vk_generate_shaders:main"
diff --git a/pyrightconfig.json b/pyrightconfig.json
index 020a71a4ec214..6016f4b6d0120 100644
--- a/pyrightconfig.json
+++ b/pyrightconfig.json
@@ -1,3 +1,21 @@
 {
   "extraPaths": ["gguf-py"],
-}
+  "pythonVersion": "3.9",
+  "pythonPlatform": "All",
+  "reportUnusedImport": "warning",
+  "reportDuplicateImport": "error",
+  "reportDeprecated": "warning",
+  "reportUnnecessaryTypeIgnoreComment": "warning",
+  "executionEnvironments": [
+    {
+      // TODO: make this version override work correctly
+      "root": "gguf-py",
+      "pythonVersion": "3.8",
+    },
+    {
+      // uses match expressions in steps.py
+      "root": "examples/server/tests",
+      "pythonVersion": "3.10",
+    },
+  ],
+ }
diff --git a/requirements.txt b/requirements.txt
index e7d14e16ac73c..9e190ae27de38 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,9 +4,9 @@
 # Package versions must stay compatible across all top-level python scripts.
 #
 
--r ./requirements/requirements-convert.txt
+-r ./requirements/requirements-convert_legacy_llama.txt
 
--r ./requirements/requirements-convert-hf-to-gguf.txt
--r ./requirements/requirements-convert-hf-to-gguf-update.txt
--r ./requirements/requirements-convert-llama-ggml-to-gguf.txt
--r ./requirements/requirements-convert-persimmon-to-gguf.txt
+-r ./requirements/requirements-convert_hf_to_gguf.txt
+-r ./requirements/requirements-convert_hf_to_gguf_update.txt
+-r ./requirements/requirements-convert_llama_ggml_to_gguf.txt
+-r ./requirements/requirements-convert_lora_to_gguf.txt
diff --git a/requirements/requirements-all.txt b/requirements/requirements-all.txt
new file mode 100644
index 0000000000000..94de59d7e1860
--- /dev/null
+++ b/requirements/requirements-all.txt
@@ -0,0 +1,12 @@
+-r ../examples/llava/requirements.txt
+-r ../examples/server/bench/requirements.txt
+-r ../examples/server/tests/requirements.txt
+
+-r ./requirements-compare-llama-bench.txt
+-r ./requirements-pydantic.txt
+-r ./requirements-test-tokenizer-random.txt
+
+-r ./requirements-convert_hf_to_gguf.txt
+-r ./requirements-convert_hf_to_gguf_update.txt
+-r ./requirements-convert_legacy_llama.txt
+-r ./requirements-convert_llama_ggml_to_gguf.txt
diff --git a/requirements/requirements-compare-llama-bench.txt b/requirements/requirements-compare-llama-bench.txt
new file mode 100644
index 0000000000000..e0aaa32043ce2
--- /dev/null
+++ b/requirements/requirements-compare-llama-bench.txt
@@ -0,0 +1,2 @@
+tabulate~=0.9.0
+GitPython~=3.1.43
diff --git a/requirements/requirements-convert-hf-to-gguf-update.txt b/requirements/requirements-convert-hf-to-gguf-update.txt
deleted file mode 100644
index 6ac4026107fbe..0000000000000
--- a/requirements/requirements-convert-hf-to-gguf-update.txt
+++ /dev/null
@@ -1,2 +0,0 @@
--r ./requirements-convert.txt
-torch~=2.1.1
diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt
deleted file mode 100644
index 6ac4026107fbe..0000000000000
--- a/requirements/requirements-convert-hf-to-gguf.txt
+++ /dev/null
@@ -1,2 +0,0 @@
--r ./requirements-convert.txt
-torch~=2.1.1
diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert-llama-ggml-to-gguf.txt
deleted file mode 100644
index a0f37cd1c71e4..0000000000000
--- a/requirements/requirements-convert-llama-ggml-to-gguf.txt
+++ /dev/null
@@ -1 +0,0 @@
--r ./requirements-convert.txt
diff --git a/requirements/requirements-convert-persimmon-to-gguf.txt b/requirements/requirements-convert-persimmon-to-gguf.txt
deleted file mode 100644
index 6ac4026107fbe..0000000000000
--- a/requirements/requirements-convert-persimmon-to-gguf.txt
+++ /dev/null
@@ -1,2 +0,0 @@
--r ./requirements-convert.txt
-torch~=2.1.1
diff --git a/requirements/requirements-convert_hf_to_gguf.txt b/requirements/requirements-convert_hf_to_gguf.txt
new file mode 100644
index 0000000000000..8cb9c354f0152
--- /dev/null
+++ b/requirements/requirements-convert_hf_to_gguf.txt
@@ -0,0 +1,3 @@
+-r ./requirements-convert_legacy_llama.txt
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch~=2.2.1
diff --git a/requirements/requirements-convert_hf_to_gguf_update.txt b/requirements/requirements-convert_hf_to_gguf_update.txt
new file mode 100644
index 0000000000000..8cb9c354f0152
--- /dev/null
+++ b/requirements/requirements-convert_hf_to_gguf_update.txt
@@ -0,0 +1,3 @@
+-r ./requirements-convert_legacy_llama.txt
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch~=2.2.1
diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert_legacy_llama.txt
similarity index 85%
rename from requirements/requirements-convert.txt
rename to requirements/requirements-convert_legacy_llama.txt
index 7ab1228cb33ff..1d07b09522f61 100644
--- a/requirements/requirements-convert.txt
+++ b/requirements/requirements-convert_legacy_llama.txt
@@ -1,4 +1,4 @@
-numpy~=1.24.4
+numpy~=1.26.4
 sentencepiece~=0.2.0
 transformers>=4.40.1,<5.0.0
 gguf>=0.1.0
diff --git a/requirements/requirements-convert_llama_ggml_to_gguf.txt b/requirements/requirements-convert_llama_ggml_to_gguf.txt
new file mode 100644
index 0000000000000..afe2747d448d4
--- /dev/null
+++ b/requirements/requirements-convert_llama_ggml_to_gguf.txt
@@ -0,0 +1 @@
+-r ./requirements-convert_legacy_llama.txt
diff --git a/requirements/requirements-convert_lora_to_gguf.txt b/requirements/requirements-convert_lora_to_gguf.txt
new file mode 100644
index 0000000000000..5758076c41dc1
--- /dev/null
+++ b/requirements/requirements-convert_lora_to_gguf.txt
@@ -0,0 +1,2 @@
+-r ./requirements-convert_hf_to_gguf.txt
+--extra-index-url https://download.pytorch.org/whl/cpu
diff --git a/requirements/requirements-pydantic.txt b/requirements/requirements-pydantic.txt
new file mode 100644
index 0000000000000..bdd423e07ea36
--- /dev/null
+++ b/requirements/requirements-pydantic.txt
@@ -0,0 +1,3 @@
+docstring_parser~=0.15
+pydantic~=2.6.3
+requests
diff --git a/requirements/requirements-test-tokenizer-random.txt b/requirements/requirements-test-tokenizer-random.txt
new file mode 100644
index 0000000000000..2785e71a25718
--- /dev/null
+++ b/requirements/requirements-test-tokenizer-random.txt
@@ -0,0 +1 @@
+cffi~=1.16.0
diff --git a/scripts/build-info.sh b/scripts/build-info.sh
index 32682afbdbd52..fa9e7bacdb8cf 100755
--- a/scripts/build-info.sh
+++ b/scripts/build-info.sh
@@ -8,20 +8,20 @@ build_compiler="unknown"
 build_target="unknown"
 
 if out=$(git rev-list --count HEAD); then
-  # git is broken on WSL so we need to strip extra newlines
-  build_number=$(printf '%s' "$out" | tr -d '\n')
+    # git is broken on WSL so we need to strip extra newlines
+    build_number=$(printf '%s' "$out" | tr -d '\n')
 fi
 
 if out=$(git rev-parse --short HEAD); then
-  build_commit=$(printf '%s' "$out" | tr -d '\n')
+    build_commit=$(printf '%s' "$out" | tr -d '\n')
 fi
 
 if out=$($CC --version | head -1); then
-  build_compiler=$out
+    build_compiler=$out
 fi
 
 if out=$($CC -dumpmachine); then
-  build_target=$out
+    build_target=$out
 fi
 
 echo "int LLAMA_BUILD_NUMBER = ${build_number};"
diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh
index 6a7400d3c3a0b..d3bbded130daf 100755
--- a/scripts/check-requirements.sh
+++ b/scripts/check-requirements.sh
@@ -97,9 +97,9 @@ check_requirements() {
 }
 
 check_convert_script() {
-    local py=$1             # e.g. ./convert-hf-to-gguf.py
-    local pyname=${py##*/}  # e.g. convert-hf-to-gguf.py
-    pyname=${pyname%.py}    # e.g. convert-hf-to-gguf
+    local py=$1             # e.g. ./convert_hf_to_gguf.py
+    local pyname=${py##*/}  # e.g. convert_hf_to_gguf.py
+    pyname=${pyname%.py}    # e.g. convert_hf_to_gguf
 
     info "$py: beginning check"
 
@@ -108,6 +108,11 @@ check_convert_script() {
         fatal "$py missing requirements. Expected: $reqs"
     fi
 
+    # Check that all sub-requirements are added to top-level requirements.txt
+    if ! grep -qF "$reqs" requirements.txt; then
+        fatal "$reqs needs to be added to requirements.txt"
+    fi
+
     local venv="$workdir/$pyname-venv"
     python3 -m venv "$venv"
 
@@ -134,12 +139,7 @@ EOF
 
 readonly ignore_eq_eq='check_requirements: ignore "=="'
 
-for req in "$reqs_dir"/*; do
-    # Check that all sub-requirements are added to top-level requirements.txt
-    if ! grep -qF "$req" requirements.txt; then
-        fatal "$req needs to be added to requirements.txt"
-    fi
-
+for req in */**/requirements*.txt; do
     # Make sure exact release versions aren't being pinned in the requirements
     # Filters out the ignore string
     if grep -vF "$ignore_eq_eq" "$req" | grep -q '=='; then
@@ -166,12 +166,12 @@ if (( do_cleanup )); then
     rm -rf -- "$all_venv"
 fi
 
-check_convert_script convert.py
-for py in convert-*.py; do
-    # skip convert-hf-to-gguf-update.py
+check_convert_script examples/convert_legacy_llama.py
+for py in convert_*.py; do
+    # skip convert_hf_to_gguf_update.py
     # TODO: the check is failing for some reason:
     #       https://github.com/ggerganov/llama.cpp/actions/runs/8875330981/job/24364557177?pr=6920
-    [[ $py == convert-hf-to-gguf-update.py ]] && continue
+    [[ $py == convert_hf_to_gguf_update.py ]] && continue
 
     check_convert_script "$py"
 done
diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh
index fd0ee88b23748..70679f4e56470 100755
--- a/scripts/compare-commits.sh
+++ b/scripts/compare-commits.sh
@@ -10,16 +10,18 @@ set -x
 
 bench_args="${@:3}"
 
-rm -f llama-bench.sqlite
+rm -f llama-bench.sqlite > /dev/null
 
-# to test a backend, call the script with the corresponding environment variable (e.g. LLAMA_CUDA=1 ./scripts/compare-commits.sh ...)
+# to test a backend, call the script with the corresponding environment variable (e.g. GGML_CUDA=1 ./scripts/compare-commits.sh ...)
 
-git checkout $1
-make clean && make -j32 $make_opts llama-bench
-./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite
+git checkout $1 > /dev/null
+make clean > /dev/null
+make -j$(nproc) $make_opts llama-bench > /dev/null
+./llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
 
-git checkout $2
-make clean && make -j32 $make_opts llama-bench
-./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite
+git checkout $2 > /dev/null
+make clean > /dev/null
+make -j$(nproc) $make_opts llama-bench > /dev/null
+./llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
 
 ./scripts/compare-llama-bench.py -b $1 -c $2
diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index 0ede9e67c2f15..92b9e682a9f20 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -19,22 +19,22 @@
 
 # Properties by which to differentiate results per commit:
 KEY_PROPERTIES = [
-    "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas",
-    "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads",
-    "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen"
+    "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas",
+    "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads",
+    "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen"
 ]
 
 # Properties that are boolean and are converted to Yes/No for the table:
-BOOL_PROPERTIES = ["cuda", "opencl", "metal", "gpu_blas", "blas"]
+BOOL_PROPERTIES = ["cuda", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"]
 
 # Header names for the table:
 PRETTY_NAMES = {
-    "cuda": "CUDA", "opencl": "OpenCL", "metal": "Metal", "gpu_blas": "GPU BLAS", "blas": "BLAS",
-    "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model",
-    "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters",
-    "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
-    "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO",
-    "tensor_split": "Tensor split"
+    "cuda": "CUDA", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC",
+    "gpu_blas": "GPU BLAS", "blas": "BLAS", "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model",
+    "model_size": "Model Size [GiB]", "model_n_params": "Num. of Par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size",
+    "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "split_mode": "Split mode",
+    "main_gpu": "Main GPU", "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split",
+    "use_mmap": "Use mmap", "embeddings": "Embeddings",
 }
 
 DEFAULT_SHOW = ["model_type"]  # Always show these properties by default.
@@ -123,13 +123,13 @@
 
 try:
     repo = git.Repo(".", search_parent_directories=True)
-except git.exc.InvalidGitRepositoryError:
+except git.InvalidGitRepositoryError:
     repo = None
 
 
-def find_parent_in_data(commit):
+def find_parent_in_data(commit: git.Commit):
     """Helper function to find the most recent parent measured in number of commits for which there is data."""
-    heap = [(0, commit)]
+    heap: list[tuple[int, git.Commit]] = [(0, commit)]
     seen_hexsha8 = set()
     while heap:
         depth, current_commit = heapq.heappop(heap)
@@ -144,7 +144,7 @@ def find_parent_in_data(commit):
     return None
 
 
-def get_all_parent_hexsha8s(commit):
+def get_all_parent_hexsha8s(commit: git.Commit):
     """Helper function to recursively get hexsha8 values for all parents of a commit."""
     unvisited = [commit]
     visited   = []
diff --git a/scripts/convert-gg.sh b/scripts/convert-gg.sh
deleted file mode 100755
index 01fda16fd7efc..0000000000000
--- a/scripts/convert-gg.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-set -e
-
-# LLaMA v1
-python3 convert.py ../llama1/7B  --outfile models/llama-7b/ggml-model-f16.gguf  --outtype f16
-python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16
-python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16
-python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16
-
-# LLaMA v2
-python3 convert.py ../llama2/llama-2-7b  --outfile models/llama-7b-v2/ggml-model-f16.gguf  --outtype f16
-python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16
-python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16
-
-# Code Llama
-python3 convert.py ../codellama/CodeLlama-7b/  --outfile models/codellama-7b/ggml-model-f16.gguf  --outtype f16
-python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16
-python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16
-
-# Falcon
-python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b  1
-mv -v ../falcon/falcon-7b/ggml-model-f16.gguf models/falcon-7b/ggml-model-f16.gguf
-
-python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-40b 1
-mv -v ../falcon/falcon-40b/ggml-model-f16.gguf models/falcon-40b/ggml-model-f16.gguf
diff --git a/scripts/debug-test.sh b/scripts/debug-test.sh
index 7b2b601a96477..91946c514e6b2 100755
--- a/scripts/debug-test.sh
+++ b/scripts/debug-test.sh
@@ -110,7 +110,7 @@ rm -rf "$build_dir" && mkdir "$build_dir" || abort "Failed to make $build_dir"
 ###########################################################
 
 # Note: test-eval-callback requires -DLLAMA_CURL
-cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_CURL=1 || abort "Failed to build enviroment"
+cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DGGML_CUDA=1 -DLLAMA_CURL=1 || abort "Failed to build enviroment"
 pushd "$build_dir"
 make -j || abort "Failed to compile"
 popd > /dev/null || exit 1
diff --git a/scripts/gen-unicode-data.py b/scripts/gen-unicode-data.py
index 744873c2a4986..2d9bde01c3ca7 100644
--- a/scripts/gen-unicode-data.py
+++ b/scripts/gen-unicode-data.py
@@ -1,97 +1,159 @@
-import regex
-import ctypes
-import unicodedata
-
-
-class CoodepointFlags (ctypes.Structure):
-    _fields_ = [  # see definition in unicode.h
-        ("is_undefined",   ctypes.c_uint16, 1),
-        ("is_number",      ctypes.c_uint16, 1),  # regex: \p{N}
-        ("is_letter",      ctypes.c_uint16, 1),  # regex: \p{L}
-        ("is_separator",   ctypes.c_uint16, 1),  # regex: \p{Z}
-        ("is_accent_mark", ctypes.c_uint16, 1),  # regex: \p{M}
-        ("is_punctuation", ctypes.c_uint16, 1),  # regex: \p{P}
-        ("is_symbol",      ctypes.c_uint16, 1),  # regex: \p{S}
-        ("is_control",     ctypes.c_uint16, 1),  # regex: \p{C}
-    ]
+from __future__ import annotations
 
-
-assert (ctypes.sizeof(CoodepointFlags) == 2)
+import array
+import unicodedata
+import requests
 
 
 MAX_CODEPOINTS = 0x110000
 
-regex_number      = regex.compile(r'\p{N}')
-regex_letter      = regex.compile(r'\p{L}')
-regex_separator   = regex.compile(r'\p{Z}')
-regex_accent_mark = regex.compile(r'\p{M}')
-regex_punctuation = regex.compile(r'\p{P}')
-regex_symbol      = regex.compile(r'\p{S}')
-regex_control     = regex.compile(r'\p{C}')
-regex_whitespace  = regex.compile(r'\s')
-
-codepoint_flags = (CoodepointFlags * MAX_CODEPOINTS)()
+UNICODE_DATA_URL = "https://www.unicode.org/Public/UCD/latest/ucd/UnicodeData.txt"
+
+
+# see https://www.unicode.org/L2/L1999/UnicodeData.html
+def unicode_data_iter():
+    res = requests.get(UNICODE_DATA_URL)
+    res.raise_for_status()
+    data = res.content.decode()
+
+    prev = []
+
+    for line in data.splitlines():
+        # ej: 0000;<control>;Cc;0;BN;;;;;N;NULL;;;;
+        line = line.split(";")
+
+        cpt = int(line[0], base=16)
+        assert cpt < MAX_CODEPOINTS
+
+        cpt_lower = int(line[-2] or "0", base=16)
+        assert cpt_lower < MAX_CODEPOINTS
+
+        cpt_upper = int(line[-3] or "0", base=16)
+        assert cpt_upper < MAX_CODEPOINTS
+
+        categ = line[2].strip()
+        assert len(categ) == 2
+
+        bidir = line[4].strip()
+        assert len(categ) == 2
+
+        name = line[1]
+        if name.endswith(", First>"):
+            prev = (cpt, cpt_lower, cpt_upper, categ, bidir)
+            continue
+        if name.endswith(", Last>"):
+            assert prev[1:] == (0, 0, categ, bidir)
+            for c in range(prev[0], cpt):
+                yield (c, cpt_lower, cpt_upper, categ, bidir)
+
+        yield (cpt, cpt_lower, cpt_upper, categ, bidir)
+
+
+# see definition in unicode.h
+CODEPOINT_FLAG_UNDEFINED   = 0x0001  #
+CODEPOINT_FLAG_NUMBER      = 0x0002  # \p{N}
+CODEPOINT_FLAG_LETTER      = 0x0004  # \p{L}
+CODEPOINT_FLAG_SEPARATOR   = 0x0008  # \p{Z}
+CODEPOINT_FLAG_MARK        = 0x0010  # \p{M}
+CODEPOINT_FLAG_PUNCTUATION = 0x0020  # \p{P}
+CODEPOINT_FLAG_SYMBOL      = 0x0040  # \p{S}
+CODEPOINT_FLAG_CONTROL     = 0x0080  # \p{C}
+
+UNICODE_CATEGORY_TO_FLAG = {
+    "Cn": CODEPOINT_FLAG_UNDEFINED,    # Undefined
+    "Cc": CODEPOINT_FLAG_CONTROL,      # Control
+    "Cf": CODEPOINT_FLAG_CONTROL,      # Format
+    "Co": CODEPOINT_FLAG_CONTROL,      # Private Use
+    "Cs": CODEPOINT_FLAG_CONTROL,      # Surrrogate
+    "Ll": CODEPOINT_FLAG_LETTER,       # Lowercase Letter
+    "Lm": CODEPOINT_FLAG_LETTER,       # Modifier Letter
+    "Lo": CODEPOINT_FLAG_LETTER,       # Other Letter
+    "Lt": CODEPOINT_FLAG_LETTER,       # Titlecase Letter
+    "Lu": CODEPOINT_FLAG_LETTER,       # Uppercase Letter
+    "L&": CODEPOINT_FLAG_LETTER,       # Cased Letter
+    "Mc": CODEPOINT_FLAG_MARK,         # Spacing Mark
+    "Me": CODEPOINT_FLAG_MARK,         # Enclosing Mark
+    "Mn": CODEPOINT_FLAG_MARK,         # Nonspacing Mark
+    "Nd": CODEPOINT_FLAG_NUMBER,       # Decimal Number
+    "Nl": CODEPOINT_FLAG_NUMBER,       # Letter Number
+    "No": CODEPOINT_FLAG_NUMBER,       # Other Number
+    "Pc": CODEPOINT_FLAG_PUNCTUATION,  # Connector Punctuation
+    "Pd": CODEPOINT_FLAG_PUNCTUATION,  # Dash Punctuation
+    "Pe": CODEPOINT_FLAG_PUNCTUATION,  # Close Punctuation
+    "Pf": CODEPOINT_FLAG_PUNCTUATION,  # Final Punctuation
+    "Pi": CODEPOINT_FLAG_PUNCTUATION,  # Initial Punctuation
+    "Po": CODEPOINT_FLAG_PUNCTUATION,  # Other Punctuation
+    "Ps": CODEPOINT_FLAG_PUNCTUATION,  # Open Punctuation
+    "Sc": CODEPOINT_FLAG_SYMBOL,       # Currency Symbol
+    "Sk": CODEPOINT_FLAG_SYMBOL,       # Modifier Symbol
+    "Sm": CODEPOINT_FLAG_SYMBOL,       # Math Symbol
+    "So": CODEPOINT_FLAG_SYMBOL,       # Other Symbol
+    "Zl": CODEPOINT_FLAG_SEPARATOR,    # Line Separator
+    "Zp": CODEPOINT_FLAG_SEPARATOR,    # Paragraph Separator
+    "Zs": CODEPOINT_FLAG_SEPARATOR,    # Space Separator
+}
+
+
+codepoint_flags = array.array('H', [CODEPOINT_FLAG_UNDEFINED]) * MAX_CODEPOINTS
 table_whitespace = []
 table_lowercase = []
 table_uppercase = []
 table_nfd = []
 
-for codepoint in range(MAX_CODEPOINTS):
+for (cpt, cpt_lower, cpt_upper, categ, bidir) in unicode_data_iter():
     # convert codepoint to unicode character
-    char = chr(codepoint)
-
-    # regex categories
-    flags = codepoint_flags[codepoint]
-    flags.is_number      = bool(regex_number.match(char))
-    flags.is_letter      = bool(regex_letter.match(char))
-    flags.is_separator   = bool(regex_separator.match(char))
-    flags.is_accent_mark = bool(regex_accent_mark.match(char))
-    flags.is_punctuation = bool(regex_punctuation.match(char))
-    flags.is_symbol      = bool(regex_symbol.match(char))
-    flags.is_control     = bool(regex_control.match(char))
-    flags.is_undefined   = bytes(flags)[0] == 0
-    assert (not flags.is_undefined)
-
-    # whitespaces
-    if bool(regex_whitespace.match(char)):
-        table_whitespace.append(codepoint)
+    char = chr(cpt)
+
+    # codepoint category flags
+    codepoint_flags[cpt] = UNICODE_CATEGORY_TO_FLAG[categ]
 
     # lowercase conversion
-    lower = ord(char.lower()[0])
-    if codepoint != lower:
-        table_lowercase.append((codepoint, lower))
+    if cpt_lower:
+        table_lowercase.append((cpt, cpt_lower))
 
     # uppercase conversion
-    upper = ord(char.upper()[0])
-    if codepoint != upper:
-        table_uppercase.append((codepoint, upper))
+    if cpt_upper:
+        table_uppercase.append((cpt, cpt_upper))
 
     # NFD normalization
     norm = ord(unicodedata.normalize('NFD', char)[0])
-    if codepoint != norm:
-        table_nfd.append((codepoint, norm))
+    if cpt != norm:
+        table_nfd.append((cpt, norm))
+
+
+# whitespaces, see "<White_Space>" https://www.unicode.org/Public/UCD/latest/ucd/PropList.txt
+table_whitespace.extend(range(0x0009, 0x000D + 1))
+table_whitespace.extend(range(0x2000, 0x200A + 1))
+table_whitespace.extend([0x0020, 0x0085, 0x00A0, 0x1680, 0x2028, 0x2029, 0x202F, 0x205F, 0x3000])
+
+
+# sort by codepoint
+table_whitespace.sort()
+table_lowercase.sort()
+table_uppercase.sort()
+table_nfd.sort()
 
 
 # group ranges with same flags
-ranges_flags = [(0, codepoint_flags[0])]  # start, flags
+ranges_flags: list[tuple[int, int]] = [(0, codepoint_flags[0])]  # start, flags
 for codepoint, flags in enumerate(codepoint_flags):
-    if bytes(flags) != bytes(ranges_flags[-1][1]):
+    if flags != ranges_flags[-1][1]:
         ranges_flags.append((codepoint, flags))
-ranges_flags.append((MAX_CODEPOINTS, CoodepointFlags()))
+ranges_flags.append((MAX_CODEPOINTS, 0x0000))
 
 
 # group ranges with same nfd
-ranges_nfd = [(0, 0, 0)]  # start, last, nfd
+ranges_nfd: list[tuple[int, int, int]] = [(0, 0, 0)]  # start, last, nfd
 for codepoint, norm in table_nfd:
     start = ranges_nfd[-1][0]
     if ranges_nfd[-1] != (start, codepoint - 1, norm):
-        ranges_nfd.append(None)
+        ranges_nfd.append(None)  # type: ignore[arg-type]  # dummy, will be replaced below
         start = codepoint
     ranges_nfd[-1] = (start, codepoint, norm)
 
 
-# Generate 'unicode-data.cpp'
-
+# Generate 'unicode-data.cpp':
+#   python ./scripts//gen-unicode-data.py > unicode-data.cpp
 
 def out(line=""):
     print(line, end='\n')  # noqa
@@ -110,22 +172,22 @@ def out(line=""):
 
 out("const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // start, flags // last=next_start-1")
 for codepoint, flags in ranges_flags:
-    flags = int.from_bytes(bytes(flags), "little")
     out("{0x%06X, 0x%04X}," % (codepoint, flags))
 out("};\n")
 
 out("const std::unordered_set<uint32_t> unicode_set_whitespace = {")
-out(", ".join("0x%06X" % cpt for cpt in table_whitespace))
+for codepoint in table_whitespace:
+    out("0x%06X," % codepoint)
 out("};\n")
 
 out("const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {")
-for tuple in table_lowercase:
-    out("{0x%06X, 0x%06X}," % tuple)
+for tuple_lw in table_lowercase:
+    out("{0x%06X, 0x%06X}," % tuple_lw)
 out("};\n")
 
 out("const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {")
-for tuple in table_uppercase:
-    out("{0x%06X, 0x%06X}," % tuple)
+for tuple_up in table_uppercase:
+    out("{0x%06X, 0x%06X}," % tuple_up)
 out("};\n")
 
 out("const std::vector<range_nfd> unicode_ranges_nfd = {  // start, last, nfd")
diff --git a/scripts/get-hellaswag.sh b/scripts/get-hellaswag.sh
index 121979fe2b069..4e1b1cc15f01a 100755
--- a/scripts/get-hellaswag.sh
+++ b/scripts/get-hellaswag.sh
@@ -4,7 +4,7 @@ wget https://raw.githubusercontent.com/klosax/hellaswag_text_data/main/hellaswag
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f hellaswag_val_full.txt --hellaswag [--hellaswag-tasks N] [other params]"
+echo "  ./llama-perplexity -m model.gguf -f hellaswag_val_full.txt --hellaswag [--hellaswag-tasks N] [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/get-wikitext-103.sh b/scripts/get-wikitext-103.sh
index 880dd5cbe2230..9c65fafbcc50b 100755
--- a/scripts/get-wikitext-103.sh
+++ b/scripts/get-wikitext-103.sh
@@ -4,7 +4,7 @@ wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f wiki.test.raw [other params]"
+echo "  ./llama-perplexity -m model.gguf -f wiki.test.raw [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/get-wikitext-2.sh b/scripts/get-wikitext-2.sh
index b01476a46015a..5f3845ef59a9e 100755
--- a/scripts/get-wikitext-2.sh
+++ b/scripts/get-wikitext-2.sh
@@ -5,7 +5,7 @@ unzip wikitext-2-raw-v1.zip
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f wikitext-2-raw/wiki.test.raw [other params]"
+echo "  ./llama-perplexity -m model.gguf -f wikitext-2-raw/wiki.test.raw [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/get-winogrande.sh b/scripts/get-winogrande.sh
index 5f234468e1643..f1fc0e2d47adb 100755
--- a/scripts/get-winogrande.sh
+++ b/scripts/get-winogrande.sh
@@ -4,7 +4,7 @@ wget https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp/raw
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f winogrande-debiased-eval.csv --winogrande [--winogrande-tasks N] [other params]"
+echo "  ./llama-perplexity -m model.gguf -f winogrande-debiased-eval.csv --winogrande [--winogrande-tasks N] [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/hf.sh b/scripts/hf.sh
index 58f83d6fe92a5..85c2c4d9a952e 100755
--- a/scripts/hf.sh
+++ b/scripts/hf.sh
@@ -3,9 +3,9 @@
 # Shortcut for downloading HF models
 #
 # Usage:
-#   ./main -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#   ./main -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#   ./main -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./llama-cli -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./llama-cli -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./llama-cli -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
 #
 
 # all logs go to stderr
diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh
index 2058ceabf9730..6e56e1ed0908c 100644
--- a/scripts/pod-llama.sh
+++ b/scripts/pod-llama.sh
@@ -42,7 +42,7 @@ git clone https://github.com/ggerganov/llama.cpp
 
 cd llama.cpp
 
-LLAMA_CUDA=1 make -j
+GGML_CUDA=1 make -j
 
 ln -sfn /workspace/TinyLlama-1.1B-Chat-v0.3  ./models/tinyllama-1b
 ln -sfn /workspace/CodeLlama-7b-hf           ./models/codellama-7b
@@ -60,7 +60,7 @@ cd /workspace/llama.cpp
 mkdir build-cublas
 cd build-cublas
 
-cmake -DLLAMA_CUDA=1 ../
+cmake -DGGML_CUDA=1 ../
 make -j
 
 if [ "$1" -eq "0" ]; then
@@ -75,11 +75,11 @@ if [ "$1" -eq "1" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/tinyllama-1b  --outfile ./models/tinyllama-1b/ggml-model-f16.gguf  --outtype f16
+    python3 examples/convert_legacy_llama.py ./models/tinyllama-1b  --outfile ./models/tinyllama-1b/ggml-model-f16.gguf  --outtype f16
 
-    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "2" ]; then
@@ -90,11 +90,11 @@ if [ "$1" -eq "2" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-7b  --outfile ./models/codellama-7b/ggml-model-f16.gguf  --outtype f16
+    python3 examples/convert_legacy_llama.py ./models/codellama-7b  --outfile ./models/codellama-7b/ggml-model-f16.gguf  --outtype f16
 
-    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "3" ]; then
@@ -105,11 +105,11 @@ if [ "$1" -eq "3" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
+    python3 examples/convert_legacy_llama.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
 
-    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "4" ]; then
@@ -120,11 +120,11 @@ if [ "$1" -eq "4" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
+    python3 examples/convert_legacy_llama.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
 
-    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "5" ]; then
@@ -135,11 +135,11 @@ if [ "$1" -eq "5" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-7b-instruct  --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf  --outtype f16
+    python3 examples/convert_legacy_llama.py ./models/codellama-7b-instruct  --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf  --outtype f16
 
-    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "6" ]; then
@@ -150,11 +150,11 @@ if [ "$1" -eq "6" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
+    python3 examples/convert_legacy_llama.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
 
-    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "7" ]; then
@@ -165,11 +165,11 @@ if [ "$1" -eq "7" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
+    python3 examples/convert_legacy_llama.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
 
-    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "1" ]; then
@@ -181,22 +181,22 @@ if [ "$1" -eq "1" ]; then
     ../scripts/get-wikitext-2.sh
     unzip wikitext-2-raw-v1.zip
 
-    make -j && ./bin/perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32
+    make -j && ./bin/llama-perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32
 
     # batched
     cd /workspace/llama.cpp
 
-    LLAMA_CUDA=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999
+    GGML_CUDA=1 make -j && ./llama-batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999
 
     # batched-bench
     cd /workspace/llama.cpp
 
-    LLAMA_CUDA=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32
+    GGML_CUDA=1 make -j && ./llama-batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32
 
     # parallel
     cd /workspace/llama.cpp
 
-    LLAMA_CUDA=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb
+    GGML_CUDA=1 make -j && ./llama-parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb
 
 fi
 
@@ -204,10 +204,9 @@ fi
 #if [ "$1" -eq "7" ]; then
 #    cd /workspace/llama.cpp
 #
-#    LLAMA_CUDA=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0
+#    GGML_CUDA=1 make -j && ./llama-speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0
 #fi
 
 # more benches
-#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf  4096 1 99 1 512,3200 128,128,800 1
-#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
-
+#GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf  4096 1 99 1 512,3200 128,128,800 1
+#GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
diff --git a/scripts/qnt-all.sh b/scripts/qnt-all.sh
index b4c2a159e2bf5..bc43738a2f498 100755
--- a/scripts/qnt-all.sh
+++ b/scripts/qnt-all.sh
@@ -26,5 +26,5 @@ set -e
 mkdir -p ${out}
 
 for q in ${qnt[@]}; do
-    time ./bin/quantize ../models/${model}/ggml-model-f16.gguf ../models/${model}/ggml-model-${q}.gguf ${q} 2>&1 ${args} | tee ${out}/qnt-${q}.txt
+    time ./bin/llama-quantize ../models/${model}/ggml-model-f16.gguf ../models/${model}/ggml-model-${q}.gguf ${q} 2>&1 ${args} | tee ${out}/qnt-${q}.txt
 done
diff --git a/scripts/run-all-ppl.sh b/scripts/run-all-ppl.sh
index e04d61d7fe091..e15f74f1b666d 100755
--- a/scripts/run-all-ppl.sh
+++ b/scripts/run-all-ppl.sh
@@ -26,5 +26,5 @@ out="../tmp/results-${model}"
 mkdir -p ${out}
 
 for q in ${qnt[@]}; do
-    time ./bin/perplexity -m ../models/${model}/ggml-model-f16.gguf -f ./wiki.test.raw ${args} 2>&1 | tee ${out}/ppl-${q}.txt
+    time ./bin/llama-perplexity -m ../models/${model}/ggml-model-f16.gguf -f ./wiki.test.raw ${args} 2>&1 | tee ${out}/ppl-${q}.txt
 done
diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py
index e986a36045549..ee21eab371418 100755
--- a/scripts/run-with-preset.py
+++ b/scripts/run-with-preset.py
@@ -10,15 +10,15 @@
 
 logger = logging.getLogger("run-with-preset")
 
-CLI_ARGS_MAIN_PERPLEXITY = [
+CLI_ARGS_LLAMA_CLI_PERPLEXITY = [
     "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape",
     "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag",
-    "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "instruct",
+    "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix",
     "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base",
     "low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock",
     "model", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q",
     "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt",
-    "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "random-prompt", "repeat-last-n",
+    "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n",
     "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed",
     "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical",
     "verbose-prompt"
@@ -29,7 +29,7 @@
     "n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose"
 ]
 
-CLI_ARGS_SERVER = [
+CLI_ARGS_LLAMA_SERVER = [
     "alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base",
     "low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q",
     "numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split",
@@ -37,7 +37,7 @@
 ]
 
 description = """Run llama.cpp binaries with presets from YAML file(s).
-To specify which binary should be run, specify the "binary" property (main, perplexity, llama-bench, and server are supported).
+To specify which binary should be run, specify the "binary" property (llama-cli, llama-perplexity, llama-bench, and llama-server are supported).
 To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument.
 
 Formatting considerations:
@@ -77,19 +77,19 @@
 
 props = {prop.replace("_", "-"): val for prop, val in props.items()}
 
-binary = props.pop("binary", "main")
+binary = props.pop("binary", "llama-cli")
 if known_args.binary:
     binary = known_args.binary
 
 if os.path.exists(f"./{binary}"):
     binary = f"./{binary}"
 
-if binary.lower().endswith("main") or binary.lower().endswith("perplexity"):
-    cli_args = CLI_ARGS_MAIN_PERPLEXITY
+if binary.lower().endswith("llama-cli") or binary.lower().endswith("llama-perplexity"):
+    cli_args = CLI_ARGS_LLAMA_CLI_PERPLEXITY
 elif binary.lower().endswith("llama-bench"):
     cli_args = CLI_ARGS_LLAMA_BENCH
-elif binary.lower().endswith("server"):
-    cli_args = CLI_ARGS_SERVER
+elif binary.lower().endswith("llama-server"):
+    cli_args = CLI_ARGS_LLAMA_SERVER
 else:
     logger.error(f"Unknown binary: {binary}")
     sys.exit(1)
diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh
index eb6ce458e5399..802592a3e0d3b 100644
--- a/scripts/server-llm.sh
+++ b/scripts/server-llm.sh
@@ -3,7 +3,7 @@
 # Helper script for deploying llama.cpp server with a single Bash command
 #
 # - Works on Linux and macOS
-# - Supports: CPU, CUDA, Metal, OpenCL
+# - Supports: CPU, CUDA, Metal
 # - Can run all GGUF models from HuggingFace
 # - Can serve requests in parallel
 # - Always builds latest llama.cpp from GitHub
@@ -19,7 +19,7 @@
 #   --port:            port number, default is 8888
 #   --repo:            path to a repo containing GGUF model files
 #   --wtype:           weights type (f16, q8_0, q4_0, q4_1), default is user-input
-#   --backend:         cpu, cuda, metal, opencl, depends on the OS
+#   --backend:         cpu, cuda, metal, depends on the OS
 #   --gpu-id:          gpu id, default is 0
 #   --n-parallel:      number of parallel requests, default is 8
 #   --n-kv:            KV cache size, default is 4096
@@ -72,7 +72,7 @@ function print_usage {
     printf "  --port:             port number, default is 8888\n"
     printf "  --repo:             path to a repo containing GGUF model files\n"
     printf "  --wtype:            weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
-    printf "  --backend:          cpu, cuda, metal, opencl, depends on the OS\n"
+    printf "  --backend:          cpu, cuda, metal, depends on the OS\n"
     printf "  --gpu-id:           gpu id, default is 0\n"
     printf "  --n-parallel:       number of parallel requests, default is 8\n"
     printf "  --n-kv:             KV cache size, default is 4096\n"
@@ -380,16 +380,13 @@ fi
 
 if [[ "$backend" == "cuda" ]]; then
     printf "[+] Building with CUDA backend\n"
-    LLAMA_CUDA=1 make -j server $log
+    GGML_CUDA=1 make -j llama-server $log
 elif [[ "$backend" == "cpu" ]]; then
     printf "[+] Building with CPU backend\n"
-    make -j server $log
+    make -j llama-server $log
 elif [[ "$backend" == "metal" ]]; then
     printf "[+] Building with Metal backend\n"
-    make -j server $log
-elif [[ "$backend" == "opencl" ]]; then
-    printf "[+] Building with OpenCL backend\n"
-    LLAMA_CLBLAST=1 make -j server $log
+    make -j llama-server $log
 else
     printf "[-] Unknown backend: %s\n" "$backend"
     exit 1
@@ -407,8 +404,6 @@ elif [[ "$backend" == "cpu" ]]; then
     args="-ngl 0"
 elif [[ "$backend" == "metal" ]]; then
     args="-ngl 999"
-elif [[ "$backend" == "opencl" ]]; then
-    args="-ngl 999"
 else
     printf "[-] Unknown backend: %s\n" "$backend"
     exit 1
@@ -418,6 +413,6 @@ if [[ $verbose -eq 1 ]]; then
     args="$args --verbose"
 fi
 
-./server -m "../$wfile" --host 0.0.0.0 --port "$port" -c $n_kv -np "$n_parallel" $args
+./llama-server -m "../$wfile" --host 0.0.0.0 --port "$port" -c $n_kv -np "$n_parallel" $args
 
 exit 0
diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index cf22afc41ce65..b29892565209f 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -53,14 +53,20 @@ while read c; do
     fi
 
     git format-patch -k $c~1..$c --stdout -- \
-        include/ggml/ggml*.h \
+        CMakeLists.txt \
+        src/CMakeLists.txt \
+        cmake/FindSIMD.cmake \
         src/ggml*.h \
         src/ggml*.c \
         src/ggml*.cpp \
         src/ggml*.m \
         src/ggml*.metal \
         src/ggml*.cu \
+        src/ggml-cann/* \
         src/ggml-cuda/* \
+        src/ggml-sycl/* \
+        src/vulkan-shaders/* \
+        include/ggml*.h \
         tests/test-opt.cpp \
         tests/test-grad0.cpp \
         tests/test-quantize-fns.cpp \
@@ -93,34 +99,43 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
 
     # replace filenames:
     #
-    # src/ggml.c                  -> ggml.c
-    # src/ggml-alloc.c            -> ggml-alloc.c
-    # src/ggml-backend-impl.h     -> ggml-backend-impl.h
-    # src/ggml-backend.c          -> ggml-backend.c
-    # src/ggml-common.h           -> ggml-common.h
-    # src/ggml-cuda/*             -> ggml-cuda/
-    # src/ggml-cuda.cu            -> ggml-cuda.cu
-    # src/ggml-cuda.h             -> ggml-cuda.h
-    # src/ggml-impl.h             -> ggml-impl.h
-    # src/ggml-kompute.cpp        -> ggml-kompute.cpp
-    # src/ggml-kompute.h          -> ggml-kompute.h
-    # src/ggml-metal.h            -> ggml-metal.h
-    # src/ggml-metal.m            -> ggml-metal.m
-    # src/ggml-mpi.h              -> ggml-mpi.h
-    # src/ggml-mpi.c              -> ggml-mpi.c
-    # src/ggml-opencl.cpp         -> ggml-opencl.cpp
-    # src/ggml-opencl.h           -> ggml-opencl.h
-    # src/ggml-quants.c           -> ggml-quants.c
-    # src/ggml-quants.h           -> ggml-quants.h
-    # src/ggml-rpc.cpp            -> ggml-rpc.cpp
-    # src/ggml-rpc.h              -> ggml-rpc.h
-    # src/ggml-sycl.cpp           -> ggml-sycl.cpp
-    # src/ggml-sycl.h             -> ggml-sycl.h
-    # src/ggml-vulkan.cpp         -> ggml-vulkan.cpp
-    # src/ggml-vulkan.h           -> ggml-vulkan.h
-    # include/ggml/ggml.h         -> ggml.h
-    # include/ggml/ggml-alloc.h   -> ggml-alloc.h
-    # include/ggml/ggml-backend.h -> ggml-backend.h
+    # CMakelists.txt          -> ggml/CMakeLists.txt
+    # src/CMakeLists.txt      -> ggml/src/CMakeLists.txt
+    # cmake/FindSIMD.cmake    -> ggml/cmake/FindSIMD.cmake
+    #
+    # src/ggml.c              -> ggml/src/ggml.c
+    # src/ggml-aarch64.c      -> ggml/src/ggml-aarch64.c
+    # src/ggml-aarch64.h      -> ggml/src/ggml-aarch64.h
+    # src/ggml-alloc.c        -> ggml/src/ggml-alloc.c
+    # src/ggml-backend-impl.h -> ggml/src/ggml-backend-impl.h
+    # src/ggml-backend.c      -> ggml/src/ggml-backend.c
+    # src/ggml-cann/*         -> ggml/src/ggml-cann/
+    # src/ggml-cann.cpp       -> ggml/src/ggml-cann.cpp
+    # src/ggml-common.h       -> ggml/src/ggml-common.h
+    # src/ggml-cuda/*         -> ggml/src/ggml-cuda/
+    # src/ggml-cuda.cu        -> ggml/src/ggml-cuda.cu
+    # src/ggml-impl.h         -> ggml/src/ggml-impl.h
+    # src/ggml-kompute.cpp    -> ggml/src/ggml-kompute.cpp
+    # src/ggml-metal.m        -> ggml/src/ggml-metal.m
+    # src/ggml-quants.c       -> ggml/src/ggml-quants.c
+    # src/ggml-quants.h       -> ggml/src/ggml-quants.h
+    # src/ggml-rpc.cpp        -> ggml/src/ggml-rpc.cpp
+    # src/ggml-sycl/*         -> ggml/src/ggml-sycl/
+    # src/ggml-sycl.cpp       -> ggml/src/ggml-sycl.cpp
+    # src/ggml-vulkan.cpp     -> ggml/src/ggml-vulkan.cpp
+    # src/vulkan-shaders/*    -> ggml/src/vulkan-shaders/
+    #
+    # include/ggml.h         -> ggml/include/ggml.h
+    # include/ggml-alloc.h   -> ggml/include/ggml-alloc.h
+    # include/ggml-backend.h -> ggml/include/ggml-backend.h
+    # include/ggml-blas.h    -> ggml/include/ggml-blas.h
+    # include/ggml-cann.h    -> ggml/include/ggml-cann.h
+    # include/ggml-cuda.h    -> ggml/include/ggml-cuda.h
+    # include/ggml-kompute.h -> ggml/include/ggml-kompute.h
+    # include/ggml-metal.h   -> ggml/include/ggml-metal.h
+    # include/ggml-rpc.h     -> ggml/include/ggml-rpc.h
+    # include/ggml-sycl.h    -> ggml/include/ggml-sycl.h
+    # include/ggml-vulkan.h  -> ggml/include/ggml-vulkan.h
     #
     # tests/test-opt.cpp           -> tests/test-opt.cpp
     # tests/test-grad0.cpp         -> tests/test-grad0.cpp
@@ -128,45 +143,51 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # tests/test-quantize-perf.cpp -> tests/test-quantize-perf.cpp
     # tests/test-backend-ops.cpp   -> tests/test-backend-ops.cpp
     #
-    # LICENSE                      -> LICENSE
-    # scripts/gen-authors.sh       -> scripts/gen-authors.sh
-
-    cat ggml-src.patch | sed \
-        -e 's/src\/ggml\.c/ggml.c/g' \
-        -e 's/src\/ggml-alloc\.c/ggml-alloc.c/g' \
-        -e 's/src\/ggml-backend-impl\.h/ggml-backend-impl.h/g' \
-        -e 's/src\/ggml-backend\.c/ggml-backend.c/g' \
-        -e 's/src\/ggml-common\.h/ggml-common.h/g' \
-        -e 's/src\/ggml-cuda\//ggml-cuda\//g' \
-        -e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \
-        -e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \
-        -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \
-        -e 's/src\/ggml-kompute\.cpp/ggml-kompute.cpp/g' \
-        -e 's/src\/ggml-kompute\.h/ggml-kompute.h/g' \
-        -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \
-        -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \
-        -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \
-        -e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \
-        -e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \
-        -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \
-        -e 's/src\/ggml-quants\.c/ggml-quants.c/g' \
-        -e 's/src\/ggml-quants\.h/ggml-quants.h/g' \
-        -e 's/src\/ggml-rpc\.cpp/ggml-rpc.cpp/g' \
-        -e 's/src\/ggml-rpc\.h/ggml-rpc.h/g' \
-        -e 's/src\/ggml-sycl\.cpp/ggml-sycl.cpp/g' \
-        -e 's/src\/ggml-sycl\.h/ggml-sycl.h/g' \
-        -e 's/src\/ggml-vulkan\.cpp/ggml-vulkan.cpp/g' \
-        -e 's/src\/ggml-vulkan\.h/ggml-vulkan.h/g' \
-        -e 's/include\/ggml\/ggml\.h/ggml.h/g' \
-        -e 's/include\/ggml\/ggml-alloc\.h/ggml-alloc.h/g' \
-        -e 's/include\/ggml\/ggml-backend\.h/ggml-backend.h/g' \
-        -e 's/tests\/test-opt\.cpp/tests\/test-opt.cpp/g' \
-        -e 's/tests\/test-grad0\.cpp/tests\/test-grad0.cpp/g' \
-        -e 's/tests\/test-quantize-fns\.cpp/tests\/test-quantize-fns.cpp/g' \
-        -e 's/tests\/test-quantize-perf\.cpp/tests\/test-quantize-perf.cpp/g' \
-        -e 's/tests\/test-backend-ops\.cpp/tests\/test-backend-ops.cpp/g' \
-        -e 's/LICENSE/LICENSE/g' \
-        -e 's/scripts\/gen-authors\.sh/scripts\/gen-authors.sh/g' \
+    # LICENSE                -> LICENSE
+    # scripts/gen-authors.sh -> scripts/gen-authors.sh
+
+    cat ggml-src.patch | sed -E \
+        -e 's/([[:space:]]|[ab]\/)CMakeLists.txt/\1ggml\/CMakeLists.txt/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/CMakeLists.txt/\1ggml\/src\/CMakeLists.txt/g' \
+        -e 's/([[:space:]]|[ab]\/)cmake\/FindSIMD.cmake/\1ggml\/cmake\/FindSIMD.cmake/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml\.c/\1ggml\/src\/ggml.c/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.c/\1ggml\/src\/ggml-aarch64.c/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.h/\1ggml\/src\/ggml-aarch64.h/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-alloc\.c/\1ggml\/src\/ggml-alloc.c/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-backend-impl\.h/\1ggml\/src\/ggml-backend-impl.h/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-backend\.c/\1ggml\/src\/ggml-backend.c/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-cann\//\1ggml\/src\/ggml-cann\//g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-cann\.cpp/\1ggml\/src\/ggml-cann.cpp/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-common\.h/\1ggml\/src\/ggml-common.h/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-cuda\//\1ggml\/src\/ggml-cuda\//g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-cuda\.cu/\1ggml\/src\/ggml-cuda.cu/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-impl\.h/\1ggml\/src\/ggml-impl.h/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-kompute\.cpp/\1ggml\/src\/ggml-kompute.cpp/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-metal\.m/\1ggml\/src\/ggml-metal.m/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-quants\.c/\1ggml\/src\/ggml-quants.c/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-quants\.h/\1ggml\/src\/ggml-quants.h/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-rpc\.cpp/\1ggml\/src\/ggml-rpc.cpp/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\//\1ggml\/src\/ggml-sycl\//g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\.cpp/\1ggml\/src\/ggml-sycl.cpp/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-vulkan\.cpp/\1ggml\/src\/ggml-vulkan.cpp/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/vulkan-shaders\//\1ggml\/src\/vulkan-shaders\//g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml\.h/\1ggml\/include\/ggml.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-alloc\.h/\1ggml\/include\/ggml-alloc.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-backend\.h/\1ggml\/include\/ggml-backend.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-blas\.h/\1ggml\/include\/ggml-blas.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-cann\.h/\1ggml\/include\/ggml-cann.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-cuda\.h/\1ggml\/include\/ggml-cuda.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-kompute\.h/\1ggml\/include\/ggml-kompute.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-metal\.h/\1ggml\/include\/ggml-metal.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-rpc\.h/\1ggml\/include\/ggml-rpc.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-sycl\.h/\1ggml\/include\/ggml-sycl.h/g' \
+        -e 's/([[:space:]]|[ab]\/)include\/ggml-vulkan\.h/\1ggml\/include\/ggml-vulkan.h/g' \
+        -e 's/([[:space:]]|[ab]\/)examples\/common\.h/\1examples\/common.h/g' \
+        -e 's/([[:space:]]|[ab]\/)examples\/common\.cpp/\1examples\/common.cpp/g' \
+        -e 's/([[:space:]]|[ab]\/)examples\/common-ggml\.h/\1examples\/common-ggml.h/g' \
+        -e 's/([[:space:]]|[ab]\/)examples\/common-ggml\.cpp/\1examples\/common-ggml.cpp/g' \
+        -e 's/([[:space:]]|[ab]\/)LICENSE/\1LICENSE/g' \
+        -e 's/([[:space:]]|[ab]\/)scripts\/gen-authors\.sh/\1scripts\/gen-authors.sh/g' \
         > ggml-src.patch.tmp
     mv ggml-src.patch.tmp ggml-src.patch
 
diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 57bede67b4f19..eef6768b149db 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-126d34985705a5a2222723c145cb4e125ac689f3
+797faa25af14126eb30134d4033139ae3c5428ed
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index ec47fb27c1202..30a62e0888953 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -1,38 +1,49 @@
 #!/bin/bash
 
-cp -rpv ../ggml/src/ggml.c                  ./ggml.c
-cp -rpv ../ggml/src/ggml-alloc.c            ./ggml-alloc.c
-cp -rpv ../ggml/src/ggml-backend-impl.h     ./ggml-backend-impl.h
-cp -rpv ../ggml/src/ggml-backend.c          ./ggml-backend.c
-cp -rpv ../ggml/src/ggml-common.h           ./ggml-common.h
-cp -rpv ../ggml/src/ggml-cuda/*             ./ggml-cuda/
-cp -rpv ../ggml/src/ggml-cuda.cu            ./ggml-cuda.cu
-cp -rpv ../ggml/src/ggml-cuda.h             ./ggml-cuda.h
-cp -rpv ../ggml/src/ggml-impl.h             ./ggml-impl.h
-cp -rpv ../ggml/src/ggml-kompute.cpp        ./ggml-kompute.cpp
-cp -rpv ../ggml/src/ggml-kompute.h          ./ggml-kompute.h
-cp -rpv ../ggml/src/ggml-metal.h            ./ggml-metal.h
-cp -rpv ../ggml/src/ggml-metal.m            ./ggml-metal.m
-cp -rpv ../ggml/src/ggml-metal.metal        ./ggml-metal.metal
-cp -rpv ../ggml/src/ggml-mpi.h              ./ggml-mpi.h
-cp -rpv ../ggml/src/ggml-mpi.c              ./ggml-mpi.c
-cp -rpv ../ggml/src/ggml-opencl.cpp         ./ggml-opencl.cpp
-cp -rpv ../ggml/src/ggml-opencl.h           ./ggml-opencl.h
-cp -rpv ../ggml/src/ggml-quants.c           ./ggml-quants.c
-cp -rpv ../ggml/src/ggml-quants.h           ./ggml-quants.h
-cp -rpv ../ggml/src/ggml-rpc.cpp            ./ggml-rpc.cpp
-cp -rpv ../ggml/src/ggml-rpc.h              ./ggml-rpc.h
-cp -rpv ../ggml/src/ggml-sycl.cpp           ./ggml-sycl.cpp
-cp -rpv ../ggml/src/ggml-sycl.h             ./ggml-sycl.h
-cp -rpv ../ggml/src/ggml-vulkan.cpp         ./ggml-vulkan.cpp
-cp -rpv ../ggml/src/ggml-vulkan.h           ./ggml-vulkan.h
-cp -rpv ../ggml/include/ggml/ggml.h         ./ggml.h
-cp -rpv ../ggml/include/ggml/ggml-alloc.h   ./ggml-alloc.h
-cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h
+cp -rpv ../ggml/CMakeLists.txt       ./ggml/CMakeLists.txt
+cp -rpv ../ggml/src/CMakeLists.txt   ./ggml/src/CMakeLists.txt
+cp -rpv ../ggml/cmake/FindSIMD.cmake ./ggml/cmake/FindSIMD.cmake
 
-cp -rpv ../ggml/tests/test-opt.cpp         ./tests/test-opt.cpp
-cp -rpv ../ggml/tests/test-grad0.cpp       ./tests/test-grad0.cpp
-cp -rpv ../ggml/tests/test-backend-ops.cpp ./tests/test-backend-ops.cpp
+cp -rpv ../ggml/src/ggml.c              ./ggml/src/ggml.c
+cp -rpv ../ggml/src/ggml-aarch64.c      ./ggml/src/ggml-aarch64.c
+cp -rpv ../ggml/src/ggml-aarch64.h      ./ggml/src/ggml-aarch64.h
+cp -rpv ../ggml/src/ggml-alloc.c        ./ggml/src/ggml-alloc.c
+cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml/src/ggml-backend-impl.h
+cp -rpv ../ggml/src/ggml-backend.c      ./ggml/src/ggml-backend.c
+cp -rpv ../ggml/src/ggml-cann/*         ./ggml/src/ggml-cann/
+cp -rpv ../ggml/src/ggml-cann.cpp       ./ggml/src/ggml-cann.cpp
+cp -rpv ../ggml/src/ggml-common.h       ./ggml/src/ggml-common.h
+cp -rpv ../ggml/src/ggml-cuda/*         ./ggml/src/ggml-cuda/
+cp -rpv ../ggml/src/ggml-cuda.cu        ./ggml/src/ggml-cuda.cu
+cp -rpv ../ggml/src/ggml-impl.h         ./ggml/src/ggml-impl.h
+cp -rpv ../ggml/src/ggml-kompute.cpp    ./ggml/src/ggml-kompute.cpp
+cp -rpv ../ggml/src/ggml-metal.m        ./ggml/src/ggml-metal.m
+cp -rpv ../ggml/src/ggml-metal.metal    ./ggml/src/ggml-metal.metal
+cp -rpv ../ggml/src/ggml-quants.c       ./ggml/src/ggml-quants.c
+cp -rpv ../ggml/src/ggml-quants.h       ./ggml/src/ggml-quants.h
+cp -rpv ../ggml/src/ggml-rpc.cpp        ./ggml/src/ggml-rpc.cpp
+cp -rpv ../ggml/src/ggml-sycl/*         ./ggml/src/ggml-sycl/
+cp -rpv ../ggml/src/ggml-sycl.cpp       ./ggml/src/ggml-sycl.cpp
+cp -rpv ../ggml/src/ggml-vulkan.cpp     ./ggml/src/ggml-vulkan.cpp
+cp -rpv ../ggml/src/vulkan-shaders/*    ./ggml/src/vulkan-shaders/
 
-cp -rpv ../LICENSE                         ./LICENSE
-cp -rpv ../ggml/scripts/gen-authors.sh     ./scripts/gen-authors.sh
+cp -rpv ../ggml/include/ggml.h         ./ggml/include/ggml.h
+cp -rpv ../ggml/include/ggml-alloc.h   ./ggml/include/ggml-alloc.h
+cp -rpv ../ggml/include/ggml-backend.h ./ggml/include/ggml-backend.h
+cp -rpv ../ggml/include/ggml-blas.h    ./ggml/include/ggml-blas.h
+cp -rpv ../ggml/include/ggml-cann.h    ./ggml/include/ggml-cann.h
+cp -rpv ../ggml/include/ggml-cuda.h    ./ggml/include/ggml-cuda.h
+cp -rpv ../ggml/include/ggml-kompute.h ./ggml/include/ggml-kompute.h
+cp -rpv ../ggml/include/ggml-metal.h   ./ggml/include/ggml-metal.h
+cp -rpv ../ggml/include/ggml-rpc.h     ./ggml/include/ggml-rpc.h
+cp -rpv ../ggml/include/ggml-sycl.h    ./ggml/include/ggml-sycl.h
+cp -rpv ../ggml/include/ggml-vulkan.h  ./ggml/include/ggml-vulkan.h
+
+cp -rpv ../ggml/tests/test-opt.cpp           ./tests/test-opt.cpp
+cp -rpv ../ggml/tests/test-grad0.cpp         ./tests/test-grad0.cpp
+cp -rpv ../ggml/tests/test-quantize-fns.cpp  ./tests/test-quantize-fns.cpp
+cp -rpv ../ggml/tests/test-quantize-perf.cpp ./tests/test-quantize-perf.cpp
+cp -rpv ../ggml/tests/test-backend-ops.cpp   ./tests/test-backend-ops.cpp
+
+cp -rpv ../LICENSE                     ./LICENSE
+cp -rpv ../ggml/scripts/gen-authors.sh ./scripts/gen-authors.sh
diff --git a/spm-headers/ggml-alloc.h b/spm-headers/ggml-alloc.h
index a49d385a1b864..0361ffc386a1f 120000
--- a/spm-headers/ggml-alloc.h
+++ b/spm-headers/ggml-alloc.h
@@ -1 +1 @@
-../ggml-alloc.h
\ No newline at end of file
+../ggml/include/ggml-alloc.h
\ No newline at end of file
diff --git a/spm-headers/ggml-backend.h b/spm-headers/ggml-backend.h
index 17c2cf14fe02b..7295f0f0da742 120000
--- a/spm-headers/ggml-backend.h
+++ b/spm-headers/ggml-backend.h
@@ -1 +1 @@
-../ggml-backend.h
\ No newline at end of file
+../ggml/include/ggml-backend.h
\ No newline at end of file
diff --git a/spm-headers/ggml-metal.h b/spm-headers/ggml-metal.h
new file mode 120000
index 0000000000000..aefad5fa04ced
--- /dev/null
+++ b/spm-headers/ggml-metal.h
@@ -0,0 +1 @@
+../ggml/include/ggml-metal.h
\ No newline at end of file
diff --git a/spm-headers/ggml.h b/spm-headers/ggml.h
index 39215298f981b..0bdfeacbdbead 120000
--- a/spm-headers/ggml.h
+++ b/spm-headers/ggml.h
@@ -1 +1 @@
-../ggml.h
\ No newline at end of file
+../ggml/include/ggml.h
\ No newline at end of file
diff --git a/spm-headers/llama.h b/spm-headers/llama.h
index 9acceb980c264..b31388f0dd652 120000
--- a/spm-headers/llama.h
+++ b/spm-headers/llama.h
@@ -1 +1 @@
-../llama.h
\ No newline at end of file
+../include/llama.h
\ No newline at end of file
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
new file mode 100644
index 0000000000000..46a6ad56202f7
--- /dev/null
+++ b/src/CMakeLists.txt
@@ -0,0 +1,33 @@
+# TODO: should not use this
+if (WIN32)
+    if (BUILD_SHARED_LIBS)
+        set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
+    endif()
+endif()
+
+#
+# libraries
+#
+
+# llama
+
+add_library(llama
+            ../include/llama.h
+            llama.cpp
+            llama-vocab.cpp
+            llama-grammar.cpp
+            llama-sampling.cpp
+            unicode.h
+            unicode.cpp
+            unicode-data.cpp
+            )
+
+target_include_directories(llama PUBLIC . ../include)
+target_compile_features   (llama PUBLIC cxx_std_11) # don't bump
+
+target_link_libraries(llama PUBLIC ggml)
+
+if (BUILD_SHARED_LIBS)
+    set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD)
+endif()
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
new file mode 100644
index 0000000000000..b123d733100ce
--- /dev/null
+++ b/src/llama-grammar.cpp
@@ -0,0 +1,539 @@
+#include "llama-grammar.h"
+
+#include "llama-vocab.h"
+#include "llama-sampling.h"
+
+#include <algorithm>
+
+// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
+// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
+std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
+        const std::string & src,
+        llama_partial_utf8 partial_start) {
+    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
+    const char          * pos      = src.c_str();
+    std::vector<uint32_t> code_points;
+
+    // common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
+    code_points.reserve(src.size() + 1);
+    uint32_t value    = partial_start.value;
+    int      n_remain = partial_start.n_remain;
+
+    // continue previous decode, if applicable
+    while (*pos != 0 && n_remain > 0) {
+        uint8_t next_byte = static_cast<uint8_t>(*pos);
+        if ((next_byte >> 6) != 2) {
+            // invalid sequence, abort
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
+        }
+        value = (value << 6) + (next_byte & 0x3F);
+        ++pos;
+        --n_remain;
+    }
+
+    if (partial_start.n_remain > 0 && n_remain == 0) {
+        code_points.push_back(value);
+    }
+
+    // decode any subsequent utf-8 sequences, which may end in an incomplete one
+    while (*pos != 0) {
+        uint8_t first_byte = static_cast<uint8_t>(*pos);
+        uint8_t highbits   = first_byte >> 4;
+                n_remain   = lookup[highbits] - 1;
+
+        if (n_remain < 0) {
+            // invalid sequence, abort
+            code_points.clear();
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
+        }
+
+        uint8_t mask  = (1 << (7 - n_remain)) - 1;
+                value = first_byte & mask;
+
+        ++pos;
+        while (*pos != 0 && n_remain > 0) {
+            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
+            ++pos;
+            --n_remain;
+        }
+        if (n_remain == 0) {
+            code_points.push_back(value);
+        }
+    }
+    code_points.push_back(0);
+
+    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
+}
+
+const llama_grammar_rules & llama_grammar_get_rules(const struct llama_grammar * grammar) {
+    return grammar->rules;
+}
+
+llama_grammar_stacks & llama_grammar_get_stacks(struct llama_grammar * grammar) {
+    return grammar->stacks;
+}
+
+// returns true iff pos points to the end of one of the definitions of a rule
+static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
+    switch (pos->type) {
+        case LLAMA_GRETYPE_END: return true;  // NOLINT
+        case LLAMA_GRETYPE_ALT: return true;  // NOLINT
+        default:                return false;
+    }
+}
+
+// returns true iff chr satisfies the char range at pos (regular or inverse range)
+// asserts that pos is pointing to a char range element
+static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
+        const llama_grammar_element * pos,
+        const uint32_t                chr) {
+
+    bool found            = false;
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
+
+    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
+
+    do {
+        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+            // inclusive range, e.g. [a-z]
+            found = found || (pos->value <= chr && chr <= pos[1].value);
+            pos += 2;
+        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
+            // Any character matches "."
+            found = true;
+            pos += 1;
+        } else {
+            // exact char match, e.g. [a] or "a"
+            found = found || pos->value == chr;
+            pos += 1;
+        }
+    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
+
+    return std::make_pair(found == is_positive_char, pos);
+}
+
+// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
+// range at pos (regular or inverse range)
+// asserts that pos is pointing to a char range element
+static bool llama_grammar_match_partial_char(
+        const llama_grammar_element * pos,
+        const llama_partial_utf8      partial_utf8) {
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
+    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
+
+    uint32_t partial_value = partial_utf8.value;
+    int      n_remain      = partial_utf8.n_remain;
+
+    // invalid sequence or 7-bit char split across 2 bytes (overlong)
+    if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
+        return false;
+    }
+
+    // range of possible code points this partial UTF-8 sequence could complete to
+    uint32_t low  = partial_value << (n_remain * 6);
+    uint32_t high = low | ((1 << (n_remain * 6)) - 1);
+
+    if (low == 0) {
+        if (n_remain == 2) {
+            low = 1 << 11;
+        } else if (n_remain == 3) {
+            low = 1 << 16;
+        }
+    }
+
+    do {
+        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+            // inclusive range, e.g. [a-z]
+            if (pos->value <= high && low <= pos[1].value) {
+                return is_positive_char;
+            }
+            pos += 2;
+        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
+            // Any character matches "."
+            return true;
+        } else {
+            // exact char match, e.g. [a] or "a"
+            if (low <= pos->value && pos->value <= high) {
+                return is_positive_char;
+            }
+            pos += 1;
+        }
+    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
+
+    return !is_positive_char;
+}
+
+// transforms a grammar pushdown stack into N possible stacks, all ending
+// at a character range (terminal element)
+static void llama_grammar_advance_stack(
+        const llama_grammar_rules  & rules,
+        const llama_grammar_stack  & stack,
+              llama_grammar_stacks & new_stacks) {
+    if (stack.empty()) {
+        if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
+            new_stacks.emplace_back(stack);
+        }
+        return;
+    }
+
+    const llama_grammar_element * pos = stack.back();
+
+    switch (pos->type) {
+        case LLAMA_GRETYPE_RULE_REF: {
+            const size_t                  rule_id = static_cast<size_t>(pos->value);
+            const llama_grammar_element * subpos  = rules[rule_id].data();
+            do {
+                // init new stack without the top (pos)
+                llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
+                if (!llama_grammar_is_end_of_sequence(pos + 1)) {
+                    // if this rule ref is followed by another element, add that to stack
+                    new_stack.push_back(pos + 1);
+                }
+                if (!llama_grammar_is_end_of_sequence(subpos)) {
+                    // if alternate is nonempty, add to stack
+                    new_stack.push_back(subpos);
+                }
+                llama_grammar_advance_stack(rules, new_stack, new_stacks);
+                while (!llama_grammar_is_end_of_sequence(subpos)) {
+                    // scan to end of alternate def
+                    subpos++;
+                }
+                if (subpos->type == LLAMA_GRETYPE_ALT) {
+                    // there's another alternate def of this rule to process
+                    subpos++;
+                } else {
+                    break;
+                }
+            } while (true);
+            break;
+        }
+        case LLAMA_GRETYPE_CHAR:
+        case LLAMA_GRETYPE_CHAR_NOT:
+        case LLAMA_GRETYPE_CHAR_ANY:
+            if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
+                // only add the stack if it's not a duplicate of one we already have
+                new_stacks.emplace_back(stack);
+            }
+            break;
+        default:
+            // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
+            // (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
+            // those
+            GGML_ABORT("fatal error");
+    }
+}
+
+// takes a set of possible pushdown stacks on a grammar, which are required to
+// be positioned at a character range (see `llama_grammar_advance_stack`), and
+// produces the N possible stacks if the given char is accepted at those
+// positions
+void llama_grammar_accept(
+        const llama_grammar_rules  & rules,
+        const llama_grammar_stacks & stacks,
+        const uint32_t               chr,
+              llama_grammar_stacks & new_stacks) {
+    new_stacks.clear();
+
+    for (const auto & stack : stacks) {
+        if (stack.empty()) {
+            continue;
+        }
+
+        auto match = llama_grammar_match_char(stack.back(), chr);
+        if (match.first) {
+            const llama_grammar_element * pos = match.second;
+
+            // update top of stack to next element, if any
+            llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
+            if (!llama_grammar_is_end_of_sequence(pos)) {
+                new_stack.push_back(pos);
+            }
+            llama_grammar_advance_stack(rules, new_stack, new_stacks);
+        }
+    }
+}
+
+static llama_grammar_candidates llama_grammar_reject_candidates(
+        const llama_grammar_rules  & rules,
+        const llama_grammar_stacks & stacks,
+        const llama_grammar_candidates & candidates) {
+    GGML_ASSERT(!stacks.empty()); // REVIEW
+
+    if (candidates.empty()) {
+        return {};
+    }
+
+    auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
+
+    for (size_t i = 1, size = stacks.size(); i < size; ++i) {
+        rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
+    }
+    return rejects;
+}
+
+llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
+        const llama_grammar_rules      & rules,
+        const llama_grammar_stack      & stack,
+        const llama_grammar_candidates & candidates) {
+
+    llama_grammar_candidates rejects;
+    rejects.reserve(candidates.size());
+
+    if (stack.empty()) {
+        for (const auto & tok : candidates) {
+            if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
+                rejects.push_back(tok);
+            }
+        }
+        return rejects;
+    }
+
+    const llama_grammar_element * stack_pos = stack.back();
+
+    llama_grammar_candidates next_candidates;
+    next_candidates.reserve(candidates.size());
+
+    for (const auto & tok : candidates) {
+        if (*tok.code_points == 0) {
+            // reached end of full codepoints in token, reject iff it ended in a partial sequence
+            // that cannot satisfy this position in grammar
+            if (tok.partial_utf8.n_remain != 0 &&
+                    !llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
+                rejects.push_back(tok);
+            }
+        } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
+            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
+        } else {
+            rejects.push_back(tok);
+        }
+    }
+
+    const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
+
+    // update top of stack to next element, if any
+    llama_grammar_stack stack_after(stack.begin(), stack.end() - 1);
+    if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
+        stack_after.push_back(stack_pos_after);
+    }
+    llama_grammar_stacks next_stacks;
+    llama_grammar_advance_stack(rules, stack_after, next_stacks);
+
+    auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
+    for (const auto & tok : next_rejects) {
+        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
+    }
+
+    return rejects;
+}
+
+static bool llama_grammar_detect_left_recursion(
+        const llama_grammar_rules & rules,
+        size_t rule_index,
+        std::vector<bool> * rules_visited,
+        std::vector<bool> * rules_in_progress,
+        std::vector<bool> * rules_may_be_empty) {
+    if ((*rules_in_progress)[rule_index]) {
+        return true;
+    }
+
+    (*rules_in_progress)[rule_index] = true;
+
+    const llama_grammar_rule & rule = rules[rule_index];
+
+    // First check if the rule might produce the empty string. This could be done combined with the second
+    // step but it's more readable as two steps.
+    bool at_rule_start = true;
+    for (size_t i = 0; i < rule.size(); i++) {
+        if (llama_grammar_is_end_of_sequence(&rule[i])) {
+            if (at_rule_start) {
+                (*rules_may_be_empty)[rule_index] = true;
+                break;
+            }
+            at_rule_start = true;
+        } else {
+            at_rule_start = false;
+        }
+    }
+
+    // Second, recurse into leftmost nonterminals (or next-leftmost as long as the previous nonterminal may
+    // be empty)
+    bool recurse_into_nonterminal = true;
+    for (size_t i = 0; i < rule.size(); i++) {
+        if (rule[i].type == LLAMA_GRETYPE_RULE_REF && recurse_into_nonterminal) {
+            if (llama_grammar_detect_left_recursion(rules, (size_t)rule[i].value, rules_visited, rules_in_progress, rules_may_be_empty)) {
+                return true;
+            }
+            if (!((*rules_may_be_empty)[(size_t)rule[i].value])) {
+                recurse_into_nonterminal = false;
+            }
+        } else if (llama_grammar_is_end_of_sequence(&rule[i])) {
+            recurse_into_nonterminal = true;
+        } else {
+            recurse_into_nonterminal = false;
+        }
+    }
+
+    (*rules_in_progress)[rule_index] = false;
+    (*rules_visited)[rule_index] = true;
+    return false;
+}
+
+//
+// grammar - external
+//
+
+struct llama_grammar * llama_grammar_init_impl(
+            const llama_grammar_element ** rules,
+                                 size_t    n_rules,
+                                 size_t    start_rule_index) {
+    const llama_grammar_element * pos;
+
+    // copy rule definitions into vectors
+    llama_grammar_rules vec_rules(n_rules);
+    for (size_t i = 0; i < n_rules; i++) {
+        for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
+            vec_rules[i].push_back(*pos);
+        }
+        vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
+    }
+
+    // Check for left recursion
+    std::vector<bool> rules_visited(n_rules);
+    std::vector<bool> rules_in_progress(n_rules);
+    std::vector<bool> rules_may_be_empty(n_rules);
+    for (size_t i = 0; i < n_rules; i++) {
+        if (rules_visited[i]) {
+            continue;
+        }
+        if (llama_grammar_detect_left_recursion(vec_rules, i, &rules_visited, &rules_in_progress, &rules_may_be_empty)) {
+            LLAMA_LOG_ERROR("unsupported grammar, left recursion detected for nonterminal at index %zu", i);
+            return nullptr;
+        }
+    }
+
+    // loop over alternates of start rule to build initial stacks
+    llama_grammar_stacks stacks;
+    pos = vec_rules[start_rule_index].data();
+    do {
+        llama_grammar_stack stack;
+        if (!llama_grammar_is_end_of_sequence(pos)) {
+            // if alternate is nonempty, add to stack
+            stack.push_back(pos);
+        }
+        llama_grammar_advance_stack(vec_rules, stack, stacks);
+        while (!llama_grammar_is_end_of_sequence(pos)) {
+            // scan to end of alternate def
+            pos++;
+        }
+        if (pos->type == LLAMA_GRETYPE_ALT) {
+            // there's another alternate def of this rule to process
+            pos++;
+        } else {
+            break;
+        }
+    } while (true);
+
+    // Important: vec_rules has to be moved here, not copied, because stacks contains
+    // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar
+    // then the pointers would be invalidated when the local vec_rules goes out of scope.
+    return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
+}
+
+void llama_grammar_free_impl(struct llama_grammar * grammar) {
+    delete grammar;
+}
+
+struct llama_grammar * llama_grammar_copy_impl(const struct llama_grammar * grammar) {
+    llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
+
+    // redirect elements in stacks to point to new rules
+    for (size_t is = 0; is < result->stacks.size(); is++) {
+        for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
+            for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
+                for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
+                    if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
+                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
+                    }
+                }
+            }
+        }
+    }
+
+    return result;
+}
+
+void llama_grammar_sample_impl(const struct llama_grammar * grammar, const struct llama_vocab * vocab, const struct llama_sampling * smpl, llama_token_data_array * candidates) {
+    GGML_ASSERT(grammar);
+    GGML_ASSERT(vocab);
+
+    int64_t t_start_sample_us = ggml_time_us();
+
+    bool allow_eog = false;
+    for (const auto & stack : grammar->stacks) {
+        if (stack.empty()) {
+            allow_eog = true;
+            break;
+        }
+    }
+
+    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
+    candidates_decoded.reserve(candidates->size);
+
+    llama_grammar_candidates candidates_grammar;
+    candidates_grammar.reserve(candidates->size);
+
+    for (size_t i = 0; i < candidates->size; ++i) {
+        const llama_token id      = candidates->data[i].id;
+        const std::string & piece = vocab->cache_token_to_piece.at(id);
+
+        if (llama_token_is_eog_impl(*vocab, id)) {
+            if (!allow_eog) {
+                candidates->data[i].logit = -INFINITY;
+            }
+        } else if (piece.empty() || piece[0] == 0) {
+            candidates->data[i].logit = -INFINITY;
+        } else {
+            candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
+            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
+        }
+    }
+
+    const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
+    for (const auto & reject : rejects) {
+        candidates->data[reject.index].logit = -INFINITY;
+    }
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
+
+void llama_grammar_accept_token_impl(struct llama_grammar * grammar, const struct llama_vocab * vocab, const struct llama_sampling * smpl, llama_token token) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    if (llama_token_is_eog_impl(*vocab, token)) {
+        for (const auto & stack : grammar->stacks) {
+            if (stack.empty()) {
+                return;
+            }
+        }
+        GGML_ABORT("fatal error");
+    }
+
+    const std::string & piece = vocab->cache_token_to_piece.at(token);
+
+    // Note terminating 0 in decoded string
+    const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
+    const auto & code_points = decoded.first;
+
+    llama_grammar_stacks tmp_new_stacks;
+    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
+        llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks);
+        grammar->stacks = tmp_new_stacks;
+    }
+
+    grammar->partial_utf8 = decoded.second;
+    GGML_ASSERT(!grammar->stacks.empty());
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
diff --git a/src/llama-grammar.h b/src/llama-grammar.h
new file mode 100644
index 0000000000000..695ea0632bb84
--- /dev/null
+++ b/src/llama-grammar.h
@@ -0,0 +1,39 @@
+#pragma once
+
+#include "llama-impl.h"
+
+struct llama_vocab;
+struct llama_sampling;
+
+struct llama_grammar {
+    const llama_grammar_rules  rules;
+          llama_grammar_stacks stacks;
+
+    // buffer for partially generated UTF-8 sequence from accepted tokens
+    llama_partial_utf8 partial_utf8;
+};
+
+//
+// internal API
+//
+
+struct llama_grammar * llama_grammar_init_impl(
+            const llama_grammar_element ** rules,
+                                 size_t    n_rules,
+                                 size_t    start_rule_index);
+
+void llama_grammar_free_impl(struct llama_grammar * grammar);
+
+struct llama_grammar * llama_grammar_copy_impl(const struct llama_grammar * grammar);
+
+void llama_grammar_sample_impl(
+        const struct llama_grammar * grammar,
+          const struct llama_vocab * vocab,
+       const struct llama_sampling * smpl,
+            llama_token_data_array * candidates);
+
+void llama_grammar_accept_token_impl(
+              struct llama_grammar * grammar,
+          const struct llama_vocab * vocab,
+       const struct llama_sampling * smpl,
+                       llama_token   token);
diff --git a/src/llama-impl.h b/src/llama-impl.h
new file mode 100644
index 0000000000000..399b134a7f9bc
--- /dev/null
+++ b/src/llama-impl.h
@@ -0,0 +1,41 @@
+#pragma once
+
+#define LLAMA_API_INTERNAL
+#include "llama.h"
+
+#ifdef __GNUC__
+#ifdef __MINGW32__
+#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+#else
+#define LLAMA_ATTRIBUTE_FORMAT(...)
+#endif
+
+//
+// logging
+//
+
+LLAMA_ATTRIBUTE_FORMAT(2, 3)
+void llama_log_internal        (ggml_log_level level, const char * format, ...);
+void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
+
+#define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
+#define LLAMA_LOG_WARN(...)  llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
+#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+
+//
+// helpers
+//
+
+static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    if (search.empty()) {
+        return; // Avoid infinite loop if 'search' is an empty string
+    }
+    size_t pos = 0;
+    while ((pos = s.find(search, pos)) != std::string::npos) {
+        s.replace(pos, search.length(), replace);
+        pos += replace.length();
+    }
+}
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
new file mode 100644
index 0000000000000..8f4841d9daf7b
--- /dev/null
+++ b/src/llama-sampling.cpp
@@ -0,0 +1,635 @@
+#include "llama-sampling.h"
+
+#include <algorithm>
+#include <cstring>
+#include <ctime>
+#include <cfloat>
+#include <numeric>
+#include <unordered_map>
+
+static void llama_log_softmax(float * array, size_t size) {
+    float max_l = *std::max_element(array, array + size);
+    float sum = 0.f;
+    for (size_t i = 0; i < size; ++i) {
+        float p = expf(array[i] - max_l);
+        sum += p;
+        array[i] = p;
+    }
+
+    for (size_t i = 0; i < size; ++i) {
+        array[i] = logf(array[i] / sum);
+    }
+}
+
+void llama_set_rng_seed_impl(struct llama_sampling * smpl, uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
+        seed = time(NULL);
+    }
+
+    smpl->rng.seed(seed);
+}
+
+void llama_sample_softmax_impl(struct llama_sampling * smpl, llama_token_data_array * candidates) {
+    GGML_ASSERT(candidates->size > 0);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Sort the logits in descending order
+    if (!candidates->sorted) {
+        std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+            return a.logit > b.logit;
+        });
+        candidates->sorted = true;
+    }
+
+    float max_l = candidates->data[0].logit;
+    float cum_sum = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        float p = expf(candidates->data[i].logit - max_l);
+        candidates->data[i].p = p;
+        cum_sum += p;
+    }
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].p /= cum_sum;
+    }
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_top_k_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
+    // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
+    // if (k >= (int32_t)candidates->size) {
+    //     return;
+    // }
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    if (k <= 0) {
+        k = candidates->size;
+    }
+
+    k = std::max(k, (int) min_keep);
+    k = std::min(k, (int) candidates->size);
+
+    // Sort scores in descending order
+    if (!candidates->sorted) {
+        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
+            return a.logit > b.logit;
+        };
+        if (k <= 128) {
+            std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
+        } else {
+            constexpr int   nbuckets     = 128;
+            constexpr float bucket_low   = -10.0f;
+            constexpr float bucket_high  =  10.0f;
+            constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
+            constexpr float bucket_inter = -bucket_low * bucket_scale;
+
+            std::vector<int> bucket_idx(candidates->size);
+            std::vector<int> histo(nbuckets, 0);
+
+            for (int i = 0; i < (int)candidates->size; ++i) {
+                const float val = candidates->data[i].logit;
+                int ib = int(bucket_scale * val + bucket_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
+                ib = std::max(0, std::min(nbuckets-1, ib));
+                bucket_idx[i] = ib;
+                ++histo[ib];
+            }
+            int nhave = 0;
+            int ib = nbuckets - 1;
+            for ( ; ib >= 0; --ib) {
+                nhave += histo[ib];
+                if (nhave >= k) break;
+            }
+            std::vector<llama_token_data> tmp_tokens(nhave);
+            auto ptr = tmp_tokens.data();
+            std::vector<llama_token_data*> bucket_ptrs;
+            bucket_ptrs.reserve(nbuckets - ib);
+            for (int j = nbuckets - 1; j >= ib; --j) {
+                bucket_ptrs.push_back(ptr);
+                ptr += histo[j];
+            }
+            for (int i = 0; i < (int)candidates->size; ++i) {
+                int j = bucket_idx[i];
+                if (j >= ib) {
+                    *bucket_ptrs[nbuckets-1-j]++ = candidates->data[i];
+                }
+            }
+
+            ptr = tmp_tokens.data();
+            int ndone = 0;
+            for (int j = nbuckets-1; j > ib; --j) {
+                std::sort(ptr, ptr + histo[j], comp);
+                ptr += histo[j];
+                ndone += histo[j];
+            }
+            std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);
+
+            std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data));
+
+        }
+        candidates->sorted = true;
+    }
+    candidates->size = k;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_top_p_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep) {
+    if (p >= 1.0f) {
+        return;
+    }
+
+    llama_sample_softmax_impl(smpl, candidates);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Compute the cumulative probabilities
+    float cum_sum = 0.0f;
+    size_t last_idx = candidates->size;
+
+    for (size_t i = 0; i < candidates->size; ++i) {
+        cum_sum += candidates->data[i].p;
+
+        // Check if the running sum is at least p or if we have kept at least min_keep tokens
+        // we set the last index to i+1 to indicate that the current iterate should be included in the set
+        if (cum_sum >= p && i + 1 >= min_keep) {
+            last_idx = i + 1;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the top-p tokens
+    candidates->size = last_idx;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_min_p_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep) {
+    if (p <= 0.0f || !candidates->size) {
+        return;
+    }
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    bool min_p_applied = false;
+
+    // if the candidates aren't sorted, try the unsorted implementation first
+    if (!candidates->sorted) {
+        std::vector<llama_token_data> filtered_tokens;
+
+        float max_logit = -FLT_MAX;
+        for (size_t i = 0; i < candidates->size; ++i) {
+            max_logit = std::max(max_logit, candidates->data[i].logit);
+        }
+        const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max
+
+        for (size_t i = 0; i < candidates->size; ++i) {
+            if (candidates->data[i].logit >= min_logit) {
+                filtered_tokens.push_back(candidates->data[i]);
+            }
+        }
+
+        // if we have enough values the operation was a success
+        if (filtered_tokens.size() >= min_keep) {
+            memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
+            candidates->size = filtered_tokens.size();
+            min_p_applied = true;
+        }
+    }
+
+    // if the candidates are sorted or the unsorted implementation failed, use this implementation
+    if (!min_p_applied) {
+        // Sort the logits in descending order
+        if (!candidates->sorted) {
+            std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+                return a.logit > b.logit;
+            });
+            candidates->sorted = true;
+        }
+
+        const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
+        size_t i = 1; // first token always matches
+
+        for (; i < candidates->size; ++i) {
+            if (candidates->data[i].logit < min_logit && i >= min_keep) {
+                break; // prob too small
+            }
+        }
+
+        // Resize the output vector to keep only the matching tokens
+        candidates->size = i;
+    }
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_tail_free_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float z, size_t min_keep) {
+    if (z >= 1.0f || candidates->size <= 2) {
+        return;
+    }
+
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Compute the first and second derivatives
+    std::vector<float> first_derivatives(candidates->size - 1);
+    std::vector<float> second_derivatives(candidates->size - 2);
+
+    for (size_t i = 0; i < first_derivatives.size(); ++i) {
+        first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
+    }
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
+    }
+
+    // Calculate absolute value of second derivatives
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        second_derivatives[i] = std::abs(second_derivatives[i]);
+    }
+
+    // Normalize the second derivatives
+    {
+        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
+
+        if (second_derivatives_sum > 1e-6f) {
+            for (float & value : second_derivatives) {
+                value /= second_derivatives_sum;
+            }
+        } else {
+            for (float & value : second_derivatives) {
+                value = 1.0f / second_derivatives.size();
+            }
+        }
+    }
+
+    float cum_sum = 0.0f;
+    size_t last_idx = candidates->size;
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        cum_sum += second_derivatives[i];
+
+        // Check if the running sum is greater than z or if we have kept at least min_keep tokens
+        if (cum_sum > z && i >= min_keep) {
+            last_idx = i;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the tokens above the tail location
+    candidates->size = last_idx;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_typical_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep) {
+    // Reference implementation:
+    // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
+    if (p >= 1.0f) {
+        return;
+    }
+
+    // Compute the softmax of logits and calculate entropy
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    float entropy = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        entropy += -candidates->data[i].p * logf(candidates->data[i].p);
+    }
+
+    // Compute the absolute difference between negative log probability and entropy for each candidate
+    std::vector<float> shifted_scores;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
+        shifted_scores.push_back(shifted_score);
+    }
+
+    // Sort tokens based on the shifted_scores and their corresponding indices
+    std::vector<size_t> indices(candidates->size);
+    std::iota(indices.begin(), indices.end(), 0);
+
+    std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
+        return shifted_scores[a] < shifted_scores[b];
+    });
+
+    // Compute the cumulative probabilities
+    float cum_sum = 0.0f;
+    size_t last_idx = indices.size();
+
+    for (size_t i = 0; i < indices.size(); ++i) {
+        size_t idx = indices[i];
+        cum_sum += candidates->data[idx].p;
+
+        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
+        if (cum_sum > p && i >= min_keep - 1) {
+            last_idx = i + 1;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the locally typical tokens
+    std::vector<llama_token_data> new_candidates;
+    for (size_t i = 0; i < last_idx; ++i) {
+        size_t idx = indices[i];
+        new_candidates.push_back(candidates->data[idx]);
+    }
+
+    // Replace the data in candidates with the new_candidates data
+    std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
+    candidates->size = new_candidates.size();
+    candidates->sorted = false;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_entropy_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float min_temp, float max_temp, float exponent_val) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // no need to do anything if there is only one (or zero) candidates
+    if(candidates->size <= 1) {
+        return;
+    }
+
+    // Calculate maximum possible entropy
+    float max_entropy = -logf(1.0f / candidates->size);
+
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+
+    // Calculate entropy of the softmax probabilities
+    float entropy = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        float prob = candidates->data[i].p;
+        if (prob > 0.0f) { // Ensure no log(0)
+            entropy -= prob * logf(prob);
+        }
+    }
+
+    // Normalize the entropy (max_entropy cannot be 0 here because we checked candidates->size != 1 above)
+    float normalized_entropy = entropy / max_entropy;
+
+    // Map the normalized entropy to the desired temperature range using the power function
+    float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);
+
+#ifdef DEBUG
+    LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp);
+    LLAMA_LOG_INFO("Entropy: %f\n", entropy);
+    LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy);
+    LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy);
+    LLAMA_LOG_INFO("Exponent: %f\n", exponent_val);
+    LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp);
+#endif
+
+    // Apply the dynamically calculated temperature scaling
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].logit /= dyn_temp;
+    }
+
+    // Re-compute softmax probabilities after scaling logits with dynamic temperature
+    double max_l_double = candidates->data[0].logit;
+    double cum_sum_double = 0.0;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        double p = exp(candidates->data[i].logit - max_l_double);
+        candidates->data[i].p = p; // Store the scaled probability
+        cum_sum_double += p;
+    }
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].p /= cum_sum_double; // Re-normalize the probabilities
+    }
+
+#ifdef DEBUG
+    // Print the updated top 25 probabilities after temperature scaling
+    LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n");
+    for (size_t i = 0; i < 25 && i < candidates->size; ++i) {
+        LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates->data[i].p * 100.0f);
+    }
+#endif
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_temp_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float temp) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].logit /= temp;
+    }
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_repetition_penalties_impl(
+        struct llama_sampling * smpl,
+       llama_token_data_array * candidates,
+            const llama_token * last_tokens,
+                       size_t   penalty_last_n,
+                       float   penalty_repeat,
+                       float   penalty_freq,
+                       float   penalty_present) {
+    if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
+        return;
+    }
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Create a frequency map to count occurrences of each token in last_tokens
+    std::unordered_map<llama_token, int> token_count;
+    for (size_t i = 0; i < penalty_last_n; ++i) {
+        token_count[last_tokens[i]]++;
+    }
+
+    // Apply frequency and presence penalties to the candidates
+    for (size_t i = 0; i < candidates->size; ++i) {
+        const auto token_iter = token_count.find(candidates->data[i].id);
+        if (token_iter == token_count.end()) {
+            continue;
+        }
+
+        const int count = token_iter->second;
+
+        // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
+        // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
+        if (candidates->data[i].logit <= 0) {
+            candidates->data[i].logit *= penalty_repeat;
+        } else {
+            candidates->data[i].logit /= penalty_repeat;
+        }
+
+        candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
+    }
+
+    candidates->sorted = false;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_apply_guidance_impl(
+        struct llama_sampling * smpl,
+                        float * logits,
+                        float * logits_guidance,
+                        float   scale) {
+    GGML_ASSERT(smpl);
+
+    const auto t_start_sample_us = ggml_time_us();
+    const auto n_vocab = smpl->n_vocab;
+
+    llama_log_softmax(logits, n_vocab);
+    llama_log_softmax(logits_guidance, n_vocab);
+
+    for (int i = 0; i < n_vocab; ++i) {
+              auto & l = logits[i];
+        const auto & g = logits_guidance[i];
+
+        l = scale * (l - g) + g;
+    }
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
+
+llama_token llama_sample_token_mirostat_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
+    GGML_ASSERT(smpl);
+
+    const int32_t n_vocab = float(smpl->n_vocab);
+
+    int64_t t_start_sample_us = ggml_time_us();
+
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+
+    // Estimate s_hat using the most probable m tokens
+    float s_hat = 0.0;
+    float sum_ti_bi = 0.0;
+    float sum_ti_sq = 0.0;
+    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
+        float t_i = logf(float(i + 2) / float(i + 1));
+        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
+        sum_ti_bi += t_i * b_i;
+        sum_ti_sq += t_i * t_i;
+    }
+    s_hat = sum_ti_bi / sum_ti_sq;
+
+    // Compute k from the estimated s_hat and target surprise value
+    float epsilon_hat = s_hat - 1;
+    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(n_vocab, -epsilon_hat)), 1 / s_hat);
+
+    // Sample the next word X using top-k sampling
+    llama_sample_top_k_impl((struct llama_sampling *) nullptr, candidates, int(k), 1);
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    llama_token X = llama_sample_token_impl(smpl, candidates);
+    t_start_sample_us = ggml_time_us();
+
+    // Compute error as the difference between observed surprise and target surprise value
+    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return candidate.id == X;
+    }));
+    float observed_surprise = -log2f(candidates->data[X_idx].p);
+    float e = observed_surprise - tau;
+
+    // Update mu using the learning rate and error
+    *mu = *mu - eta * e;
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    return X;
+}
+
+llama_token llama_sample_token_mirostat_v2_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, float * mu) {
+    int64_t t_start_sample_us;
+    t_start_sample_us = ggml_time_us();
+
+    llama_sample_softmax_impl(smpl, candidates);
+
+    // Truncate the words with surprise values greater than mu
+    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return -log2f(candidate.p) > *mu;
+    }));
+
+    if (candidates->size == 0) {
+        candidates->size = 1;
+    }
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+
+    // Normalize the probabilities of the remaining words
+    llama_sample_softmax_impl(smpl, candidates);
+
+    // Sample the next word X from the remaining words
+    llama_token X = llama_sample_token_impl(smpl, candidates);
+    t_start_sample_us = ggml_time_us();
+
+    // Compute error as the difference between observed surprise and target surprise value
+    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return candidate.id == X;
+    }));
+    float observed_surprise = -log2f(candidates->data[X_idx].p);
+    float e = observed_surprise - tau;
+
+    // Update mu using the learning rate and error
+    *mu = *mu - eta * e;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+    return X;
+}
+
+llama_token llama_sample_token_greedy_impl(struct llama_sampling * smpl, llama_token_data_array * candidates) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Find max element
+    auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+        return a.logit < b.logit;
+    });
+
+    llama_token result = max_iter->id;
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+        smpl->n_sample++;
+    }
+    return result;
+}
+
+llama_token llama_sample_token_with_rng_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, std::mt19937 & rng) {
+    GGML_ASSERT(smpl);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+
+    std::vector<float> probs;
+    probs.reserve(candidates->size);
+    for (size_t i = 0; i < candidates->size; ++i) {
+        probs.push_back(candidates->data[i].p);
+    }
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    llama_token result = candidates->data[idx].id;
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    smpl->n_sample++;
+
+    return result;
+}
+
+llama_token llama_sample_token_impl(struct llama_sampling * smpl, llama_token_data_array * candidates) {
+    return llama_sample_token_with_rng_impl(smpl, candidates, smpl->rng);
+}
diff --git a/src/llama-sampling.h b/src/llama-sampling.h
new file mode 100644
index 0000000000000..f7f8e3ef706bc
--- /dev/null
+++ b/src/llama-sampling.h
@@ -0,0 +1,56 @@
+#pragma once
+
+#include "llama-impl.h"
+
+struct llama_sampling {
+    llama_sampling(int32_t n_vocab) : n_vocab(n_vocab) {}
+
+    std::mt19937 rng;
+
+    int32_t n_vocab = 0;
+
+    mutable int64_t t_sample_us = 0;
+    mutable int32_t n_sample = 0;
+
+    void reset_timings() const {
+        t_sample_us = 0;
+        n_sample = 0;
+    }
+};
+
+//
+// internal API
+//
+
+void llama_set_rng_seed_impl(struct llama_sampling * smpl, uint32_t seed);
+
+void llama_sample_softmax_impl  (struct llama_sampling * smpl, llama_token_data_array * candidates);
+void llama_sample_top_k_impl    (struct llama_sampling * smpl, llama_token_data_array * candidates, int32_t k, size_t min_keep);
+void llama_sample_top_p_impl    (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
+void llama_sample_min_p_impl    (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
+void llama_sample_tail_free_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float z, size_t min_keep);
+void llama_sample_typical_impl  (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
+void llama_sample_entropy_impl  (struct llama_sampling * smpl, llama_token_data_array * candidates, float min_temp, float max_temp, float exponent_val);
+void llama_sample_temp_impl     (struct llama_sampling * smpl, llama_token_data_array * candidates, float temp);
+
+void llama_sample_repetition_penalties_impl(
+        struct llama_sampling * smpl,
+       llama_token_data_array * candidates,
+            const llama_token * last_tokens,
+                       size_t   penalty_last_n,
+                        float   penalty_repeat,
+                        float   penalty_freq,
+                        float   penalty_present);
+
+void llama_sample_apply_guidance_impl(
+        struct llama_sampling * smpl,
+                        float * logits,
+                        float * logits_guidance,
+                        float   scale);
+
+llama_token llama_sample_token_mirostat_impl   (struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu);
+llama_token llama_sample_token_mirostat_v2_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, float * mu);
+llama_token llama_sample_token_greedy_impl     (struct llama_sampling * smpl, llama_token_data_array * candidates);
+llama_token llama_sample_token_with_rng_impl   (struct llama_sampling * smpl, llama_token_data_array * candidates, std::mt19937 & rng);
+llama_token llama_sample_token_impl            (struct llama_sampling * smpl, llama_token_data_array * candidates);
+
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
new file mode 100644
index 0000000000000..11fffce9386d7
--- /dev/null
+++ b/src/llama-vocab.cpp
@@ -0,0 +1,1717 @@
+#include "llama-vocab.h"
+
+#include "unicode.h"
+
+#include <algorithm>
+#include <cassert>
+#include <cfloat>
+#include <climits>
+#include <cstdarg>
+#include <cstring>
+#include <forward_list>
+#include <queue>
+#include <sstream>
+
+//
+// helpers
+//
+
+LLAMA_ATTRIBUTE_FORMAT(1, 2)
+static std::string format(const char * fmt, ...) {
+    va_list ap;
+    va_list ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    GGML_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+
+struct naive_trie {
+    naive_trie() : has_value(false), value(0) {
+    }
+    void insert(const char * key, size_t len, int32_t value = 0) {
+        if (len == 0) {
+            this->has_value = true;
+            this->value = value;
+            return;
+        }
+        char c = key[0];
+        auto res = children.find(c);
+        if (res != children.end()) {
+            res->second.insert(key + 1, len - 1, value);
+        } else {
+            auto res = children.insert(std::make_pair(c, naive_trie()));
+            res.first->second.insert(key + 1, len - 1, value);
+        }
+    }
+    std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) {
+        if (len == 0 || offset == len) {
+            return std::make_pair(key, offset);
+        }
+        char c = key[offset];
+        auto res = children.find(c);
+        if (res != children.end()) {
+            return res->second.get_longest_prefix(key, len, offset + 1);
+        } else {
+            return std::make_pair(key, offset);
+        }
+    }
+    struct naive_trie * traverse(const char c) {
+        auto res = children.find(c);
+        if (res != children.end()) {
+            return &res->second;
+        } else {
+            return NULL;
+        }
+    }
+    std::map<char, struct naive_trie> children;
+    bool has_value;
+    llama_token value;
+};
+
+//
+// impl
+//
+
+int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
+    GGML_ASSERT(token_left.find(' ')   == std::string::npos);
+    GGML_ASSERT(token_left.find('\n')  == std::string::npos);
+    GGML_ASSERT(token_right.find(' ')  == std::string::npos);
+    GGML_ASSERT(token_right.find('\n') == std::string::npos);
+
+    auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
+    if (it == bpe_ranks.end()) {
+        return -1;
+    }
+
+    return it->second;
+}
+
+static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
+    return vocab.type;
+}
+
+static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
+}
+
+static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
+}
+
+static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
+}
+
+static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
+}
+
+static bool llama_is_user_defined_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
+}
+
+static bool llama_is_unused_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNUSED;
+}
+
+static uint8_t llama_token_to_byte(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
+    GGML_ASSERT(llama_is_byte_token(vocab, id));
+    const auto & token_data = vocab.id_to_token.at(id);
+    switch (llama_vocab_get_type(vocab)) {
+        case LLAMA_VOCAB_TYPE_SPM:
+        case LLAMA_VOCAB_TYPE_UGM: {
+            auto buf = token_data.text.substr(3, 2);
+            return strtol(buf.c_str(), NULL, 16);
+        }
+        case LLAMA_VOCAB_TYPE_BPE: {
+            GGML_ABORT("fatal error");
+            //return unicode_utf8_to_byte(token_data.text); // TODO: why is this here after GGML_ASSERT?
+        }
+        case LLAMA_VOCAB_TYPE_WPM: {
+            GGML_ABORT("fatal error");
+        }
+        default:
+            GGML_ABORT("fatal error");
+    }
+}
+
+static void llama_escape_whitespace(std::string & text) {
+    replace_all(text, " ", "\xe2\x96\x81");
+}
+
+static void llama_unescape_whitespace(std::string & word) {
+    replace_all(word, "\xe2\x96\x81", " ");
+}
+
+struct llm_symbol {
+    using index = int;
+    index prev;
+    index next;
+    const char * text;
+    size_t n;
+};
+
+static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
+
+//
+// SPM tokenizer
+// original implementation:
+// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
+//
+
+struct llm_bigram_spm {
+    struct comparator {
+        bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
+            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
+        }
+    };
+    using queue_storage = std::vector<llm_bigram_spm>;
+    using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
+    llm_symbol::index left;
+    llm_symbol::index right;
+    float score;
+    size_t size;
+};
+
+struct llm_tokenizer_spm {
+    llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        // split string into utf8 chars
+        int index = 0;
+        size_t offs = 0;
+        while (offs < text.size()) {
+            llm_symbol sym;
+            size_t len = unicode_len_utf8(text[offs]);
+            sym.text = text.c_str() + offs;
+            sym.n = std::min(len, text.size() - offs);
+            offs += sym.n;
+            sym.prev = index - 1;
+            sym.next = offs == text.size() ? -1 : index + 1;
+            index++;
+            symbols.emplace_back(sym);
+        }
+
+        // seed the work queue with all possible 2-character tokens.
+        for (size_t i = 1; i < symbols.size(); ++i) {
+            try_add_bigram(i - 1, i);
+        }
+
+        // keep substituting the highest frequency pairs for as long as we can.
+        while (!work_queue.empty()) {
+            auto bigram = work_queue.top();
+            work_queue.pop();
+
+            auto & left_sym = symbols[bigram.left];
+            auto & right_sym = symbols[bigram.right];
+
+            // if one of the symbols already got merged, skip it.
+            if (left_sym.n == 0 || right_sym.n == 0 ||
+                left_sym.n + right_sym.n != bigram.size) {
+                continue;
+            }
+
+            // merge the right sym into the left one
+            left_sym.n += right_sym.n;
+            right_sym.n = 0;
+
+            //LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
+
+            // remove the right sym from the chain
+            left_sym.next = right_sym.next;
+            if (right_sym.next >= 0) {
+                symbols[right_sym.next].prev = bigram.left;
+            }
+
+            // find more substitutions
+            try_add_bigram(left_sym.prev, bigram.left);
+            try_add_bigram(bigram.left, left_sym.next);
+        }
+
+        for (int i = 0; i != -1; i = symbols[i].next) {
+            auto & symbol = symbols[i];
+            resegment(symbol, output);
+        }
+    }
+
+private:
+    void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
+        auto text = std::string(symbol.text, symbol.n);
+        auto token = vocab.token_to_id.find(text);
+
+        // Do we need to support is_unused?
+        if (token != vocab.token_to_id.end()) {
+            output.push_back((*token).second);
+            return;
+        }
+
+        const auto p = rev_merge.find(text);
+
+        if (p == rev_merge.end()) {
+            // output any symbols that did not form tokens as bytes.
+            output.reserve(output.size() + symbol.n);
+            for (int j = 0; j < (int)symbol.n; ++j) {
+                llama_vocab::id token_id = llama_byte_to_token_impl(vocab, symbol.text[j]);
+                output.push_back(token_id);
+            }
+            return;
+        }
+
+        resegment(symbols[p->second.first],  output);
+        resegment(symbols[p->second.second], output);
+    }
+
+    void try_add_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
+        }
+
+        const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
+        auto token = vocab.token_to_id.find(text);
+
+        if (token == vocab.token_to_id.end()) {
+            return;
+        }
+
+        if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
+            return;
+        }
+
+        const auto & tok_data = vocab.id_to_token[(*token).second];
+
+        llm_bigram_spm bigram;
+        bigram.left  = left;
+        bigram.right = right;
+        bigram.score = tok_data.score;
+        bigram.size  = text.size();
+
+        work_queue.push(bigram);
+
+        // Do we need to support is_unused?
+        rev_merge[text] = std::make_pair(left, right);
+    }
+
+    const llama_vocab & vocab;
+
+    std::vector<llm_symbol> symbols;
+    llm_bigram_spm::queue work_queue;
+
+    std::map<std::string, std::pair<int, int>> rev_merge;
+};
+
+//
+// BPE tokenizer
+// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
+// tried to simplify unicode stuff, so most likely does not work 100% correctly!
+//
+
+// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
+
+struct llm_bigram_bpe {
+    struct comparator {
+        bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
+            return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
+        }
+    };
+
+    using queue_storage = std::vector<llm_bigram_bpe>;
+    using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
+    llm_symbol::index left;
+    llm_symbol::index right;
+    std::string text;
+    int rank;
+    size_t size;
+};
+
+struct llm_tokenizer_bpe {
+    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {
+        GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE);
+        switch (vocab.type_pre) {
+            case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+
+                    // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DBRX:
+            case LLAMA_VOCAB_PRE_TYPE_SMAUG:
+                regex_exprs = {
+                    // same as llama3
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
+                regex_exprs = {
+                    "[\r\n]",
+                    "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿDŽ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿff-stﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
+                    "\\s?[!-/:-~！-／：-～‘-‟　-。]+",
+                    "\\s+$",
+                    "[一-龥ࠀ-一가-퟿]+",
+                    "\\p{N}+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
+                regex_exprs = {
+                    "[\r\n]",
+                    "\\s?\\p{L}+",
+                    "\\s?\\p{P}+",
+                    "[一-龥ࠀ-一가-퟿]+",
+                    "\\p{N}",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_FALCON:
+                regex_exprs = {
+                    "[\\p{P}\\$\\+<=>\\^~\\|`]+",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                    "[0-9][0-9][0-9]",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_STARCODER:
+            case LLAMA_VOCAB_PRE_TYPE_REFACT:
+            case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
+            case LLAMA_VOCAB_PRE_TYPE_SMOLLM:
+            case LLAMA_VOCAB_PRE_TYPE_CODESHELL:
+                regex_exprs = {
+                    "\\p{N}",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_GPT2:
+            case LLAMA_VOCAB_PRE_TYPE_MPT:
+            case LLAMA_VOCAB_PRE_TYPE_OLMO:
+            case LLAMA_VOCAB_PRE_TYPE_JAIS:
+                regex_exprs = {
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
+            case LLAMA_VOCAB_PRE_TYPE_QWEN2:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_PORO:
+            case LLAMA_VOCAB_PRE_TYPE_BLOOM:
+            case LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH:
+                regex_exprs = {
+                    " ?[^(\\s|.,!?…。，、।۔،)]+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_CHATGLM4:
+                regex_exprs = {
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_VIKING:
+                regex_exprs = {
+                    " ?[^(\\s|.,!?…。，、।۔،)]+",
+                    "\\p{N}",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_TEKKEN:
+                // original regex from tokenizer.json
+                // "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                regex_exprs = {
+                    "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            default:
+                // default regex for BPE tokenization pre-processing
+                regex_exprs = {
+                    "[\\p{P}\\$\\+<=>\\^~\\|]+",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                    "\\p{N}+",
+                    "[0-9][0-9][0-9]",
+                };
+                break;
+        }
+    }
+
+    void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) const {
+        output.push_back(token_id);
+    }
+
+    bool append_bos(std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_bos) {
+            GGML_ASSERT(vocab.special_bos_id != -1);
+            output.push_back(vocab.special_bos_id);
+            return true;
+        }
+        return false;
+    }
+
+    bool append_eos(std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_eos) {
+            GGML_ASSERT(vocab.special_eos_id != -1);
+            output.push_back(vocab.special_eos_id);
+            return true;
+        }
+        return false;
+    }
+
+    void check_double_bos_eos(const std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+            LLAMA_LOG_WARN(
+                "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                "Are you sure this is what you want?\n", __FUNCTION__);
+        }
+        if (vocab.tokenizer_add_eos && output.size() >= 2 && *(output.end()-2) == vocab.special_eos_id) {
+            LLAMA_LOG_WARN(
+                "%s: Added a EOS token to the prompt as specified by the model but the prompt "
+                "also ends with a EOS token. So now the final prompt ends with 2 EOS tokens. "
+                "Are you sure this is what you want?\n", __FUNCTION__);
+        }
+    }
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        int final_prev_index = -1;
+
+        const auto word_collection = unicode_regex_split(text, regex_exprs);
+
+        symbols_final.clear();
+
+        for (auto & word : word_collection) {
+            work_queue = llm_bigram_bpe::queue();
+            symbols.clear();
+
+            int index = 0;
+            size_t offset = 0;
+
+            if (vocab.tokenizer_ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
+                symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
+                offset = word.size();
+            }
+
+            while (offset < word.size()) {
+                llm_symbol sym;
+                size_t char_len = std::min(word.size() - offset, (size_t) unicode_len_utf8(word[offset]));
+                sym.text = word.c_str() + offset;
+                sym.n = char_len;
+                offset += sym.n;
+                sym.prev = index - 1;
+                sym.next = offset == word.size() ? -1 : index + 1;
+                index++;
+                symbols.emplace_back(sym);
+            }
+            for (size_t i = 1; i < symbols.size(); ++i) {
+                add_new_bigram(i - 1, i);
+            }
+
+            // build token(s)
+            while (!work_queue.empty()) {
+                auto bigram = work_queue.top();
+                work_queue.pop();
+
+                auto & left_symbol = symbols[bigram.left];
+                auto & right_symbol = symbols[bigram.right];
+
+                if (left_symbol.n == 0 || right_symbol.n == 0) {
+                    continue;
+                }
+                std::string left_token = std::string(left_symbol.text, left_symbol.n);
+                std::string right_token = std::string(right_symbol.text, right_symbol.n);
+                if (left_token + right_token != bigram.text) {
+                    continue;  // Skip this bigram if it's outdated
+                }
+
+                // merge the right sym into the left one
+                left_symbol.n += right_symbol.n;
+                right_symbol.n = 0;
+
+                // remove the right sym from the chain
+                left_symbol.next = right_symbol.next;
+                if (right_symbol.next >= 0) {
+                    symbols[right_symbol.next].prev = bigram.left;
+                }
+
+                add_new_bigram(left_symbol.prev, bigram.left);  // left side of current symbol
+                add_new_bigram(bigram.left, left_symbol.next);  // right side of current symbol
+            }
+
+            // add the finished tokens to the final list keeping correct order for next and prev
+            for (auto & sym : symbols) {
+                if (sym.n > 0) {
+                    sym.prev = final_prev_index;
+                    sym.next = -1;
+                    if (final_prev_index != -1) {
+                        symbols_final[final_prev_index].next = symbols_final.size();
+                    }
+                    symbols_final.emplace_back(sym);
+                    final_prev_index = symbols_final.size() - 1;
+                }
+            }
+        }
+
+        symbols = symbols_final;
+
+        if (!symbols.empty()) {
+            for (int i = 0; i != -1; i = symbols[i].next) {
+                auto & symbol = symbols[i];
+                if (symbol.n == 0) {
+                    continue;
+                }
+
+                const std::string str = std::string(symbol.text, symbol.n);
+                const auto token = vocab.token_to_id.find(str);
+
+                if (token == vocab.token_to_id.end()) {
+                    for (auto j = str.begin(); j != str.end(); ++j) {
+                        std::string byte_str(1, *j);
+                        auto token_multibyte = vocab.token_to_id.find(byte_str);
+                        if (token_multibyte != vocab.token_to_id.end()) {
+                            output.push_back(token_multibyte->second);
+                        }
+                    }
+                } else {
+                    output.push_back((*token).second);
+                }
+            }
+        }
+    }
+
+private:
+    void add_new_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
+        }
+
+        std::string left_token  = std::string(symbols[left].text,  symbols[left].n);
+        std::string right_token = std::string(symbols[right].text, symbols[right].n);
+
+        int rank_found = -1;
+
+        rank_found = vocab.find_bpe_rank(left_token, right_token);
+
+        if (rank_found < 0) {
+            return;
+        }
+
+        llm_bigram_bpe bigram;
+
+        bigram.left  = left;
+        bigram.right = right;
+        bigram.text  = left_token + right_token;
+        bigram.size  = left_token.size() + right_token.size();
+        bigram.rank  = rank_found;
+
+        work_queue.push(bigram);
+    }
+
+    const llama_vocab & vocab;
+
+    std::vector<std::string> regex_exprs;
+
+    std::vector<llm_symbol> symbols;
+    std::vector<llm_symbol> symbols_final;
+
+    llm_bigram_bpe::queue work_queue;
+};
+
+//
+// WPM tokenizer
+//
+
+struct llm_tokenizer_wpm {
+    llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) const {
+        const auto & token_map = vocab.token_to_id;
+
+        // normalize and split by whitespace
+        std::vector<std::string> words = preprocess(text);
+
+        // bos token prepended already
+
+        // find the longest tokens that form the words
+        for (const std::string & word : words) {
+            // skip empty words
+            if (word.size() == 0) {
+                continue;
+            }
+
+            // prepend phantom space
+            const std::string word1 = "\xe2\x96\x81" + word;
+            const int n = word1.size();
+
+            const size_t current_tokens = output.size();
+
+            // we're at the start of a new word
+            // move through character position in word
+            for (int i = 0; i < n; ++i) {
+                // loop through possible match length
+                bool match = false;
+                for (int j = std::min(n, i + vocab.max_token_len + 1); j > i; j--) {
+                    auto it = token_map.find(word1.substr(i, j - i));
+                    if (it != token_map.end()) {
+                        output.push_back(it->second);
+                        match = true;
+                        i = j - 1;
+                        break;
+                    }
+                }
+
+                if (!match) { // discard all
+                    output.resize(current_tokens);
+                    break;  // and discard next tokens
+                }
+            }
+
+            // we didn't find any matches for this word
+            if (current_tokens == output.size()) {
+                output.push_back(vocab.special_unk_id);
+            }
+        }
+    }
+
+    // TODO: reduce string copies by using cpts_offs array
+    std::vector<std::string> preprocess(const std::string & text) const {
+        const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+        std::vector<std::string> words(1, "");
+
+        for (const uint32_t cpt : cpts_nfd) {
+            const auto flags = unicode_cpt_flags(cpt);
+
+            if (flags.is_whitespace) {
+                if (words.back().size()) {  // finish previous word if any
+                    words.emplace_back();
+                }
+                continue;
+            }
+
+            assert (!flags.is_separator);
+            if (cpt == 0 || cpt == 0xFFFD || flags.is_control) {
+                continue;
+            }
+
+            const std::string s = unicode_cpt_to_utf8(unicode_tolower(cpt));
+            if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) {
+                if (words.back().size()) {  // finish previous word if any
+                    words.emplace_back();
+                }
+                words.back() = s;       // single char word
+                words.emplace_back();   // start a new word
+            } else {
+                words.back() += s;  // append char to word
+            }
+        }
+
+        if (!words.back().size()) {
+            words.pop_back();
+        }
+
+        return words;
+    }
+
+    static bool is_chinese_char(uint32_t cpt) {
+        return
+            (cpt >= 0x04E00 && cpt <= 0x09FFF) ||
+            (cpt >= 0x03400 && cpt <= 0x04DBF) ||
+            (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
+            (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
+            (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
+            (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
+            (cpt >= 0x0F900 && cpt <= 0x0FAFF) ||
+            (cpt >= 0x2F800 && cpt <= 0x2FA1F);
+            //(cpt >= 0x3000  && cpt <= 0x303F)  ||
+            //(cpt >= 0xFF00  && cpt <= 0xFFEF);
+    }
+
+    const llama_vocab & vocab;
+};
+
+//
+// UGM tokenizer
+//
+
+struct llm_tokenizer_ugm {
+    llm_tokenizer_ugm(const llama_vocab & vocab) : vocab(vocab) {
+        if (vocab.precompiled_charsmap.size() > 0) {
+            size_t charsmap_offset = 0;
+
+            // First four bytes of precompiled_charsmap contains length of binary
+            // blob containing XOR-compressed compact double array (XCDA) entries
+            uint32_t xcda_blob_size = *(const uint32_t *) &vocab.precompiled_charsmap[0];
+            charsmap_offset += sizeof(xcda_blob_size);
+            if (xcda_blob_size + charsmap_offset >= vocab.precompiled_charsmap.size()) {
+                throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
+            }
+
+            // Next xcda_blob_size bytes contain entries of XOR-compressed compact
+            // double array (XCDA). Each entry is bit-packed into a 32-bit integer.
+            xcda_array = (const uint32_t *) &vocab.precompiled_charsmap[charsmap_offset];
+            xcda_array_size = xcda_blob_size / sizeof(uint32_t);
+            charsmap_offset += xcda_blob_size;
+
+            // Remaining bytes of precompiled charsmap contain null-terminated
+            // replacement strings for prefixes matched by the XCDA.
+            prefix_replacements = &vocab.precompiled_charsmap[charsmap_offset];
+            prefix_replacements_size = vocab.precompiled_charsmap.size() - charsmap_offset;
+        }
+
+        for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
+            const auto &token_data = vocab.id_to_token[id];
+
+            if (llama_is_normal_token(vocab, id)) {
+                min_score = std::min<float>(min_score, token_data.score);
+                max_score = std::max<float>(max_score, token_data.score);
+            }
+
+            if (llama_is_normal_token(vocab, id) ||
+                llama_is_user_defined_token(vocab, id) ||
+                llama_is_unused_token(vocab, id)) {
+                token_matcher.insert(token_data.text.data(), token_data.text.size(), id);
+            }
+
+            if (llama_is_user_defined_token(vocab, id)) {
+                user_defined_token_matcher.insert(token_data.text.data(), token_data.text.size());
+            }
+        }
+
+        unknown_token_score = min_score - unknown_token_score_penalty;
+    }
+
+    /* This implementation is based on SentencePiece optimized Viterbi algorithm for
+     * unigram language models. The general idea is to:
+     * - move along the input sequence in steps of one UTF code point,
+     * - at each step find all possible tokenizations of the prefix by
+     *   traversing the tokens trie,
+     * - for each tokenization store the best one so far (by higher score)
+     * - use the position in sequence after given token as an index to store
+     *   results
+     * - if there was no valid tokenization of the current UTF code point
+     *   then use unknown token with additional score penalty
+     * After processing the whole sequence we backtrack from the end to get
+     * the best tokenization.
+    */
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        // get current size of output (for reversal later)
+        size_t output_size = output.size();
+
+        // normalize the input first
+        std::string normalized;
+        normalize(text, &normalized);
+        size_t input_len = normalized.size();
+        if (input_len == 0) {
+            return;
+        }
+
+        // initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores
+        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.special_unk_id, 0, -FLT_MAX});
+        // at the beginning tokenization score is zero
+        tokenization_results[0] = { vocab.special_unk_id, 0, 0 };
+
+        for (size_t input_offset = 0; input_offset < input_len;) {
+            size_t prefix_offset = input_offset;
+            // calculate how many code units are in the currently processed UTF code point
+            size_t n_utf8_code_units = std::min<size_t>(unicode_len_utf8(normalized[input_offset]), input_len - input_offset);
+
+            // traverse the token matcher trie to find a matching token
+            bool single_codepoint_token_found = false;
+            const struct best_tokenization & current_best = tokenization_results[input_offset];
+            struct naive_trie * node  = token_matcher.traverse(normalized[prefix_offset++]);
+
+            while (prefix_offset <= input_len && node != NULL) {
+                // check if we found valid token in prefix
+                if (node->has_value) {
+                    // check if it corresponds to the whole UTF code point
+                    if (prefix_offset - input_offset == n_utf8_code_units) {
+                        single_codepoint_token_found = true;
+                    }
+                    llama_token token_id = node->value;
+                    const auto & token_data = vocab.id_to_token[token_id];
+
+                    // we set the user-defined token scores to 0 to make them more likely to be selected
+                    // (normal token scores are log probabilities, so they are negative)
+                    // score type is double here to make tokenization results exactly
+                    // the same as in the HF tokenizer using SentencePiece
+                    const double token_score = llama_is_user_defined_token(vocab, token_id) ? 0.0 : token_data.score;
+                    const double challenger_score = current_best.score_sum + token_score;
+                    struct best_tokenization & current_champ = tokenization_results[prefix_offset];
+                    if (challenger_score > current_champ.score_sum) {
+                        struct best_tokenization challenger = { token_id, input_offset, (float) challenger_score };
+                        current_champ = challenger;
+                    }
+                }
+                node = node->traverse(normalized[prefix_offset++]);
+            }
+
+            // if we didn't find a valid token corresponding to the whole UTF code point
+            // then use unknown token as the tokenization of this UTF code point
+            if (!single_codepoint_token_found) {
+                const double challenger_score = current_best.score_sum + unknown_token_score;
+                prefix_offset = input_offset + n_utf8_code_units;
+                struct best_tokenization & current_champ = tokenization_results[prefix_offset];
+                if (challenger_score > current_champ.score_sum) {
+                    struct best_tokenization challenger = { vocab.special_unk_id, input_offset, (float) challenger_score };
+                    current_champ = challenger;
+                }
+            }
+
+            // move to the next UTF code point
+            input_offset += n_utf8_code_units;
+        }
+
+        // now backtrack from the end to gather token ids of the best tokenization
+        // merge sequences of consecutive unknown tokens into single unknown tokens
+        bool is_prev_unknown = false;
+        for (struct best_tokenization & tokenization = tokenization_results[input_len]; ; tokenization = tokenization_results[tokenization.input_offset]) {
+            bool is_unknown = tokenization.token_id == vocab.special_unk_id;
+            if (!(is_prev_unknown && is_unknown)) {
+                output.push_back(tokenization.token_id);
+            }
+            if (tokenization.input_offset == 0) {
+                break;
+            }
+            is_prev_unknown = is_unknown;
+        }
+
+        // reverse the output since we added tokens starting from the end of the input
+        std::reverse(output.begin() + output_size, output.end());
+    }
+
+private:
+    const llama_vocab & vocab;
+
+    // helper structure for returning normalization results
+    struct normalization_result {
+        const char * normalized;
+        size_t normalized_len;
+        size_t consumed_input;
+    };
+
+    void normalize(const std::string& input, std::string * normalized) {
+        normalized->clear();
+        normalized->reserve(input.size() * 3);
+
+        const std::string space = vocab.tokenizer_escape_whitespaces ? escaped_space : " ";
+
+        bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
+        bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
+        bool shall_merge_spaces = vocab.tokenizer_remove_extra_whitespaces;
+
+        bool is_space_prepended = false;
+        bool processing_non_ws = false;
+
+        size_t input_len = input.size();
+
+        for (size_t input_offset = 0; input_offset < input_len; ) {
+            auto norm_res = normalize_prefix(input, input_offset);
+            for (size_t i = 0; i < norm_res.normalized_len; i++) {
+                char c = norm_res.normalized[i];
+                if (c != ' ') {
+                    if (!processing_non_ws) {
+                        processing_non_ws = true;
+                        if ((shall_prepend_space && !is_space_prepended) || shall_merge_spaces) {
+                            normalized->append(space);
+                            is_space_prepended = true;
+                        }
+                    }
+                    normalized->push_back(c);
+                } else {
+                    if (processing_non_ws) {
+                        processing_non_ws = false;
+                    }
+                    if (!shall_merge_spaces) {
+                        normalized->append(space);
+                    }
+                }
+            }
+
+            input_offset += norm_res.consumed_input;
+        }
+
+        if (shall_append_space) {
+            normalized->append(space);
+        }
+    }
+
+    /*
+     * This structure is a view wrapper for XOR-compressed double array (XCDA)
+     * See Shunsuke Kanda (2018). Space- and Time-Efficient String Dictionaries.
+     * Eeach bit-packed entry contains:
+     * - BASE array value in bits 10-30
+     * - LCHECK array value in bits 0-7
+     * - LEAF array value in bit 9
+     * Entries containing indexes of replacement sequences have set bit 31
+     */
+    struct xcda_array_view {
+    public:
+        xcda_array_view(const uint32_t * xcda_array, size_t xcda_array_size) : xcda_array(xcda_array), xcda_array_size(xcda_array_size) {
+        }
+        uint32_t get_base(size_t index) {
+            uint32_t packed_node = get_node(index);
+            return (packed_node >> 10) << ((packed_node & (1U << 9)) >> 6);
+        }
+        uint32_t get_lcheck(size_t index) {
+            uint32_t packed_node = get_node(index);
+            return packed_node & ((1U << 31) | 0xff);
+        }
+        bool get_leaf(size_t index) {
+            uint32_t packed_node = get_node(index);
+            return (packed_node >> 8) & 1;
+        }
+        uint32_t get_value(size_t index) {
+            uint32_t packed_node = get_node(index);
+            return packed_node & ((1U << 31) - 1);
+        }
+    private:
+        uint32_t get_node(size_t index) {
+            if (index > xcda_array_size) {
+                throw std::runtime_error("Index out of array bounds in XCDA array!");
+            }
+            return xcda_array[index];
+        }
+        const uint32_t * xcda_array;
+        size_t xcda_array_size;
+    };
+
+    struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) {
+        if (input_offset == input.size()) {
+            return { &input[input_offset], 0, 0 };
+        }
+
+        // if input prefix matches some user-defined token return this token as normalization result
+        auto user_defined_token_match = user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
+        if (user_defined_token_match.second > 0) {
+            return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second };
+        }
+
+        size_t longest_prefix_length = 0;
+        size_t longest_prefix_offset = 0;
+
+        if (xcda_array_size > 0) {
+            struct xcda_array_view xcda_view(xcda_array, xcda_array_size);
+
+            // Find the longest normalized sequence matching the input prefix by walking
+            // the XOR-compressed compact double array (XCDA) starting from the root node
+            // We find the index of the next node by calculating BASE[s] ^ c where s is
+            // the index of the previous node and c is a numerical character value
+            uint32_t node_index = 0;
+            // get BASE of the root node
+            node_index = xcda_view.get_base(node_index);
+            for (size_t prefix_offset = input_offset; prefix_offset < input.size(); prefix_offset++) {
+                unsigned char c = input[prefix_offset];
+                if (c == 0) {
+                    break;
+                }
+                node_index ^= c;
+                // if value of LCHECK is not c it means that this is not a child of
+                // the previous node, so we stop matching
+                if (xcda_view.get_lcheck(node_index) != c) {
+                    break;
+                }
+                bool is_leaf = xcda_view.get_leaf(node_index);
+                // get BASE of the current node
+                node_index ^= xcda_view.get_base(node_index);
+                // if LEAF of the current node is true, it means that its BASE points to the node
+                // containing index of replacement sequence for currently matched input prefix
+                if (is_leaf)
+                {
+                    longest_prefix_length = prefix_offset - input_offset + 1;
+                    // get index of replacement sequence for currently matched input prefix
+                    longest_prefix_offset = xcda_view.get_value(node_index);
+                }
+            }
+        }
+
+        if (longest_prefix_length > 0) {
+            // we have a match, so return the replacement sequence
+            if (longest_prefix_offset >= prefix_replacements_size) {
+                throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
+            }
+            const char * prefix_replacement = &prefix_replacements[longest_prefix_offset];
+            return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
+        } else {
+            // check if the input prefix contains a valid sequence of UTF-8 code units
+            try {
+                // if yes, return this sequence unmodified
+                size_t prefix_offset = input_offset;
+                unicode_cpt_from_utf8(input, prefix_offset);
+                return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset };
+            } catch (std::invalid_argument & /*ex*/) {
+                // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER
+                return { "\xEF\xBF\xBD", 3, 1 };
+            }
+        }
+    }
+
+    // escaped space symbol - U+2581 (Lower One Eighth Block)
+    const std::string escaped_space = "\xE2\x96\x81";
+
+    const char * prefix_replacements = NULL;
+    size_t prefix_replacements_size = 0;
+
+    const uint32_t * xcda_array = NULL;
+    size_t xcda_array_size = 0;
+
+    struct naive_trie user_defined_token_matcher;
+
+    // this structure stores the best tokenization so far at input_offset
+    struct best_tokenization {
+        llama_token token_id;
+        size_t input_offset;
+        float score_sum;
+    };
+
+    float min_score = FLT_MAX;
+    float max_score = -FLT_MAX;
+
+    float unknown_token_score_penalty = 10.0;
+    float unknown_token_score;
+
+    struct naive_trie token_matcher;
+};
+
+//
+// (de-) tokenize
+//
+
+typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
+    FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
+    FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
+} FRAGMENT_BUFFER_VARIANT_TYPE;
+
+struct fragment_buffer_variant {
+    fragment_buffer_variant(llama_vocab::id _token)
+    :
+        type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
+        token(_token),
+        raw_text(_dummy),
+        offset(0),
+        length(0) {}
+
+    fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
+    :
+        type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
+        token((llama_vocab::id) - 1),
+        raw_text(_raw_text),
+        offset(_offset),
+        length(_length){
+            GGML_ASSERT(_offset >= 0);
+            GGML_ASSERT(_length >= 1);
+            GGML_ASSERT(offset + length <= raw_text.length());
+        }
+
+    const FRAGMENT_BUFFER_VARIANT_TYPE type;
+    const llama_vocab::id token;
+    const std::string _dummy;
+    const std::string & raw_text;
+    const uint64_t offset;
+    const uint64_t length;
+};
+
+// #define PRETOKENIZERDEBUG
+
+static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) {
+    // for each special token
+    for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
+        const auto & data = vocab.id_to_token[special_id];
+        const auto & special_token = data.text;
+
+        if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
+            // Ignore control and unknown tokens when parse_special == false
+            continue;
+            // User-defined tokens are still pre-tokenized before everything else
+            // ref: https://github.com/huggingface/tokenizers/blob/fdd26ba9a3f0c133427aab0423888cbde91362d7/tokenizers/src/tokenizer/mod.rs#L726
+            // This is mostly relevant for neox-style tokenizers (mpt, olmo, stablelm, etc.)
+        }
+
+        // for each text fragment
+        std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
+        while (it != buffer.end()) {
+            auto & fragment = (*it);
+
+            // if a fragment is text ( not yet processed )
+            if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                auto & raw_text = fragment.raw_text;
+
+                auto raw_text_base_offset = fragment.offset;
+                auto raw_text_base_length = fragment.length;
+
+                // loop over the text
+                while (true) {
+                    // find the first occurrence of a given special token in this fragment
+                    //  passing offset argument only limit the "search area" but match coordinates
+                    //  are still relative to the source full raw_text
+                    auto match = raw_text.find(special_token, raw_text_base_offset);
+
+                    // no occurrences found, stop processing this fragment for a given special token
+                    if (match == std::string::npos) break;
+
+                    // check if match is within bounds of offset <-> length
+                    if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
+
+#ifdef PRETOKENIZERDEBUG
+                    LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
+#endif
+                    auto source = std::distance(buffer.begin(), it);
+
+                    // if match is further than base offset
+                    //  then we have some text to the left of it
+                    if (match > raw_text_base_offset) {
+                        // left
+                        const int64_t left_reminder_offset = raw_text_base_offset + 0;
+                        int64_t left_reminder_length = match - raw_text_base_offset;
+
+                        if (data.attr & LLAMA_TOKEN_ATTR_LSTRIP) {
+                            while (left_reminder_length > 0 && isspace(raw_text[left_reminder_offset + left_reminder_length - 1])) {
+                                left_reminder_length--;
+                            }
+                        }
+
+                        if (left_reminder_length > 0) {
+                            buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length);
+                            it++;
+                        }
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
+#endif
+                    }
+
+                    // special token
+                    buffer.emplace_after(it, special_id);
+                    it++;
+
+                    // right
+                    if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
+                        int64_t right_reminder_offset = match + special_token.length();
+                        int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
+
+                        if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) {
+                            while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) {
+                                right_reminder_offset++;
+                                right_reminder_length--;
+                            }
+                        }
+
+                        if (right_reminder_length > 0) {
+                            buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length);
+                            it++;
+                        }
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
+#endif
+
+                        if (source == 0) {
+                            buffer.erase_after(buffer.before_begin());
+                        } else {
+                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+                        }
+
+                        // repeat for the right side
+                        raw_text_base_offset = right_reminder_offset;
+                        raw_text_base_length = right_reminder_length;
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
+#endif
+                    } else {
+                        if (source == 0) {
+                            buffer.erase_after(buffer.before_begin());
+                        } else {
+                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+                        }
+                        break;
+                    }
+                }
+            }
+            it++;
+        }
+    }
+}
+
+std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
+    std::vector<llama_vocab::id> output;
+    std::forward_list<fragment_buffer_variant> fragment_buffer;
+
+    if (!raw_text.empty()) {
+        fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
+        tokenizer_st_partition(vocab, fragment_buffer, parse_special);
+    }
+
+    switch (vocab.type) {
+        case LLAMA_VOCAB_TYPE_SPM:
+            {
+                // OG tokenizer behavior:
+                //
+                // tokenizer.encode('', add_special_tokens=True)  returns [1]
+                // tokenizer.encode('', add_special_tokens=False) returns []
+
+                bool is_prev_special = true;  // prefix with space if first token
+
+                if (add_special && vocab.tokenizer_add_bos) {
+                    GGML_ASSERT(vocab.special_bos_id != -1);
+                    output.push_back(vocab.special_bos_id);
+                    is_prev_special = true;
+                }
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+                        // prefix with space if previous is special
+                        if (vocab.tokenizer_add_space_prefix && is_prev_special) {
+                            raw_text = " " + raw_text;
+                        }
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        llm_tokenizer_spm tokenizer(vocab);
+                        llama_escape_whitespace(raw_text);
+                        tokenizer.tokenize(raw_text, output);
+                        is_prev_special = false;
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                        is_prev_special = true;
+                    }
+                }
+
+                if (add_special && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+                    LLAMA_LOG_WARN(
+                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                        "Are you sure this is what you want?\n", __FUNCTION__);
+                }
+
+                if (add_special && vocab.tokenizer_add_eos) {
+                    GGML_ASSERT(vocab.special_eos_id != -1);
+                    output.push_back(vocab.special_eos_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_BPE:
+            {
+                llm_tokenizer_bpe tokenizer(vocab);
+
+                if (add_special) {
+                    tokenizer.append_bos(output);
+                }
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        tokenizer.tokenize(raw_text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        tokenizer.append(fragment.token, output);
+                    }
+                }
+
+                if (add_special) {
+                    tokenizer.append_eos(output);
+                    tokenizer.check_double_bos_eos(output);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_WPM:
+            {
+                if (add_special) {
+                    GGML_ASSERT(vocab.special_cls_id != -1);
+                    output.push_back(vocab.special_cls_id);
+                }
+
+                llm_tokenizer_wpm tokenizer(vocab);
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        tokenizer.tokenize(raw_text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                    }
+                }
+
+                if (add_special) {
+                    GGML_ASSERT(vocab.special_sep_id != -1);
+                    output.push_back(vocab.special_sep_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_UGM:
+            {
+                llm_tokenizer_ugm tokenizer(vocab);
+
+                if (add_special && vocab.tokenizer_add_bos != 0) {
+                    GGML_ASSERT(vocab.special_bos_id != -1);
+                    output.push_back(vocab.special_bos_id);
+                }
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        tokenizer.tokenize(raw_text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                    }
+                }
+
+                if (add_special && vocab.tokenizer_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+                    LLAMA_LOG_WARN(
+                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                        "Are you sure this is what you want?\n", __FUNCTION__);
+                }
+
+                if (add_special && vocab.tokenizer_add_eos == 1) {
+                    GGML_ASSERT(vocab.special_eos_id != -1);
+                    output.push_back(vocab.special_eos_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_NONE:
+            GGML_ABORT("fatal error");
+    }
+
+    return output;
+}
+
+llama_token llama_byte_to_token_impl(const llama_vocab & vocab, uint8_t ch) {
+    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
+    static const char * hex = "0123456789ABCDEF";
+    switch (llama_vocab_get_type(vocab)) {
+        case LLAMA_VOCAB_TYPE_SPM:
+        case LLAMA_VOCAB_TYPE_UGM: {
+            const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
+            auto token = vocab.token_to_id.find(buf);
+            if (token != vocab.token_to_id.end()) {
+                return (*token).second;
+            }
+            // Try to fall back to just the byte as a string
+            const char buf2[2] = { (char)ch, 0 };
+            return vocab.token_to_id.at(buf2);
+        }
+        case LLAMA_VOCAB_TYPE_WPM:
+        case LLAMA_VOCAB_TYPE_BPE: {
+            return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
+        }
+        default:
+            GGML_ABORT("fatal error");
+    }
+}
+
+const char * llama_token_get_text_impl(const struct llama_vocab & vocab, llama_token token) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[token].text.c_str();
+}
+
+float llama_token_get_score_impl(const struct llama_vocab & vocab, llama_token token) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[token].score;
+}
+
+llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, llama_token token) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[token].attr;
+}
+
+bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token) {
+    return token != -1 && (
+        token == llama_token_eos_impl(vocab) ||
+        token == llama_token_eot_impl(vocab) ||
+        token == llama_token_eom_impl(vocab)
+    );
+}
+
+bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token) {
+    return llama_is_control_token(vocab, token);
+}
+
+llama_token llama_token_bos_impl(const struct llama_vocab & vocab) {
+    return vocab.special_bos_id;
+}
+
+llama_token llama_token_eos_impl(const struct llama_vocab & vocab) {
+    return vocab.special_eos_id;
+}
+
+llama_token llama_token_cls_impl(const struct llama_vocab & vocab) {
+    return vocab.special_cls_id;
+}
+
+llama_token llama_token_sep_impl(const struct llama_vocab & vocab) {
+    return vocab.special_sep_id;
+}
+
+llama_token llama_token_nl_impl(const struct llama_vocab & vocab) {
+    return vocab.linefeed_id;
+}
+
+llama_token llama_token_pad_impl(const struct llama_vocab & vocab) {
+    return vocab.special_pad_id;
+}
+
+bool llama_add_bos_token_impl(const struct llama_vocab & vocab) {
+    return vocab.tokenizer_add_bos;
+}
+
+bool llama_add_eos_token_impl(const struct llama_vocab & vocab) {
+    return vocab.tokenizer_add_eos;
+}
+
+llama_token llama_token_prefix_impl(const struct llama_vocab & vocab) {
+    return vocab.special_prefix_id;
+}
+
+llama_token llama_token_middle_impl(const struct llama_vocab & vocab) {
+    return vocab.special_middle_id;
+}
+
+llama_token llama_token_suffix_impl(const struct llama_vocab & vocab) {
+    return vocab.special_suffix_id;
+}
+
+llama_token llama_token_eot_impl(const struct llama_vocab & vocab) {
+    return vocab.special_eot_id;
+}
+
+llama_token llama_token_eom_impl(const struct llama_vocab & vocab) {
+    return vocab.special_eom_id;
+}
+
+int32_t llama_tokenize_impl(
+    const struct llama_vocab & vocab,
+                  const char * text,
+                     int32_t   text_len,
+                 llama_token * tokens,
+                     int32_t   n_tokens_max,
+                        bool   add_special,
+                        bool   parse_special) {
+    auto res = llama_tokenize_internal(vocab, std::string(text, text_len), add_special, parse_special);
+    if (n_tokens_max < (int) res.size()) {
+        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
+        return -((int) res.size());
+    }
+
+    for (size_t i = 0; i < res.size(); i++) {
+        tokens[i] = res[i];
+    }
+
+    return res.size();
+}
+
+static std::string llama_decode_text(const std::string & text) {
+    std::string decoded_text;
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+    for (const auto cpt : cpts) {
+        const auto utf8 = unicode_cpt_to_utf8(cpt);
+        try {
+            decoded_text += unicode_utf8_to_byte(utf8);
+        } catch (const std::out_of_range & /*e*/) {
+            decoded_text += "[UNK_BYTE_0x";
+            for (const auto c : utf8) {
+                decoded_text += format("%02x", (uint8_t) c);
+            }
+            decoded_text += text + "]";
+        }
+    }
+
+    return decoded_text;
+}
+
+// does not write null-terminator to buf
+int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) {
+    // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843
+    static const int attr_special = LLAMA_TOKEN_ATTR_UNKNOWN | LLAMA_TOKEN_ATTR_CONTROL;
+    const llama_token_attr attr = llama_token_get_attr_impl(vocab, token);
+    if (!special && (attr & attr_special)) {
+        return 0;
+    }
+
+    // copy piece chars to output text buffer
+    // skip up to 'lstrip' leading spaces before copying
+    auto _try_copy = [=] (const char * token, size_t size) -> int32_t {
+        for (int32_t i = 0; i < lstrip && size && *token == ' '; ++i) {
+            token++;
+            size--;
+        }
+        if (length < (int32_t)size) {
+            return -(int32_t) size;
+        }
+        memcpy(buf, token, size);
+        return (int32_t) size;
+    };
+
+    // if we have a cache - use it
+    {
+        const auto & cache = vocab.cache_token_to_piece;
+
+        if (!cache.empty()) {
+            const auto & result = cache.at(token);
+            return _try_copy(result.data(), result.size());
+        }
+    }
+
+    if (0 <= token && token < (int32_t) vocab.id_to_token.size()) {
+        const std::string & token_text = vocab.id_to_token[token].text;
+        switch (llama_vocab_get_type(vocab)) {
+            case LLAMA_VOCAB_TYPE_WPM:
+            case LLAMA_VOCAB_TYPE_SPM:
+            case LLAMA_VOCAB_TYPE_UGM: {
+                // NOTE: we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
+                    return _try_copy(token_text.data(), token_text.size());
+                } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
+                    std::string result = token_text;
+                    llama_unescape_whitespace(result);
+                    return _try_copy(result.data(), result.size());
+                } else if (attr & LLAMA_TOKEN_ATTR_BYTE) {
+                    char byte = (char) llama_token_to_byte(vocab, token);
+                    return _try_copy((char*) &byte, 1);
+                }
+                break;
+            }
+            case LLAMA_VOCAB_TYPE_BPE: {
+                // NOTE: we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
+                    return _try_copy(token_text.data(), token_text.size());
+                } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
+                    std::string result = llama_decode_text(token_text);
+                    return _try_copy(result.data(), result.size());
+                }
+                break;
+            }
+            default:
+                GGML_ABORT("fatal error");
+        }
+    }
+
+    return 0;
+}
+
+int32_t llama_detokenize_impl(
+        const struct llama_vocab & vocab,
+               const llama_token * tokens,
+                         int32_t   n_tokens,
+                            char * text,
+                         int32_t   text_len_max,
+                            bool   remove_special,
+                            bool   unparse_special) {
+    int32_t avail = text_len_max;
+    int32_t total = 0;
+
+    // remove the leading space
+    bool remove_space = vocab.tokenizer_add_space_prefix;
+
+    if (remove_special && vocab.tokenizer_add_bos) {
+        if (n_tokens > 0 && tokens[0] == vocab.special_bos_id) {
+            remove_space = false;
+            n_tokens--;
+            tokens++;
+        }
+    }
+
+    if (remove_special && vocab.tokenizer_add_eos) {
+        if (n_tokens > 0 && tokens[n_tokens-1] == vocab.special_eos_id) {
+            n_tokens--;
+        }
+    }
+
+    for (int32_t i = 0; i < n_tokens; ++i) {
+        GGML_ASSERT(avail >= 0);
+        int32_t n_chars = llama_token_to_piece_impl(vocab, tokens[i], text, avail, remove_space, unparse_special);
+        remove_space = false;
+        if (n_chars < 0) {
+            avail = 0;
+            total -= n_chars;
+        } else if (n_chars > 0) {
+            avail -= n_chars;
+            text  += n_chars;
+            total += n_chars;
+        }
+    }
+
+    if (total > text_len_max) {
+        return -total;
+    }
+
+    if (vocab.tokenizer_clean_spaces) {
+        text -= total;  // restart text
+
+        // first pass: characters ?!.,  //TODO: where do these characters come from?
+        const int32_t total1 = total;
+        total = total ? 1 : 0;
+        for (int32_t i = 1; i < total1; ++i) {
+            const char x = text[i];
+            if (text[i - 1] == ' ') {
+                if (x == '?' || x == '!' || x == '.' || x == ',') {  // " ?", " !", " .", " ,"
+                    total--;  // remove space
+                }
+            }
+            text[total++] = x;
+        }
+
+        // second pass: strip single apostrophe between spaces
+        const int32_t total2 = total;
+        total = total ? 1 : 0;
+        for (int32_t i = 1; i < total2; ++i) {
+            const char x = text[i];
+            if (x == '\'' && i + 1 < total2 && text[i - 1] == ' ' && text[i + 1] == ' ') {  // " ' "
+                total--;           // remove prev space
+                text[++i] = '\0';  // remove next space
+            }
+            text[total++] = x;
+        }
+
+        // third pass: apostrophe contractions  //NOTE: this makes sense?
+        const int32_t total3 = total;
+        total = total ? 1 : 0;
+        for (int32_t i = 1; i < total3; ++i) {
+            const char x = text[i];
+            if (text[i - 1] == ' ') {
+                if (x == '\'' && i + 1 < total3) {
+                    const char x1 = text[i + 1];
+                    if (x1 == 't' || x1 == 'd') {  // " 't", " 'd"
+                        //total--;  // remove space
+                    } else if (x1 == 's' || x1 == 'm') {  // " 's", " 'm"
+                        total--;  // remove space
+                    } else if (i + 2 < total3) {
+                        const char x2 = text[i + 2];
+                        if ((x1 == 'l' && x2 == 'l')) {  // " 'll"
+                            //total--;  // remove space
+                        } else if ((x1 == 'r' && x2 == 'e') || (x1 == 'v' && x2 == 'e')) {  // " 're", " 've"
+                            total--;  // remove space
+                        } else {
+                            //total--;  // remove space
+                        }
+                    } else {
+                        //total--;  // remove space
+                    }
+                }
+            }
+            text[total++] = x;
+        }
+    }
+
+    return total <= text_len_max ? total : -total;
+}
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
new file mode 100644
index 0000000000000..6e8f30be43ba1
--- /dev/null
+++ b/src/llama-vocab.h
@@ -0,0 +1,132 @@
+#pragma once
+
+#include "llama-impl.h"
+
+#include <string>
+#include <vector>
+#include <unordered_map>
+#include <map>
+
+struct llama_vocab {
+    using id    = llama_token;
+    using token = std::string;
+    using tattr = llama_token_attr;
+
+    struct token_data {
+        token text;
+        float score;
+        tattr attr;
+    };
+
+    enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
+    enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+
+    int max_token_len = 0; // used for optimizing longest token search
+
+    std::unordered_map<token, id> token_to_id;
+    std::vector<token_data>       id_to_token;
+
+    std::vector<id>    cache_special_tokens;
+    std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = true);
+
+    std::map<std::pair<std::string, std::string>, int> bpe_ranks;
+
+    // default LLaMA special tokens
+    id special_bos_id  = 1;
+    id special_eos_id  = 2;
+    id special_unk_id  = 0;
+    id special_sep_id  = -1;
+    id special_pad_id  = -1;
+    id special_cls_id  = -1;
+    id special_mask_id = -1;
+
+    id linefeed_id       = 13;
+    id special_prefix_id = -1;
+    id special_suffix_id = -1;
+    id special_middle_id = -1;
+    id special_eot_id    = -1; // TODO: move above after "eos_id", and here add "file separator" token
+    id special_eom_id    = -1;
+
+    // tokenizer flags
+    bool tokenizer_add_space_prefix = false;
+    bool tokenizer_add_bos          = false;
+    bool tokenizer_add_eos          = false;
+    bool tokenizer_ignore_merges    = false;
+    bool tokenizer_clean_spaces     = false;  // clean_up_tokenization_spaces
+    bool tokenizer_remove_extra_whitespaces   = false;
+    bool tokenizer_escape_whitespaces         = true;
+    bool tokenizer_treat_whitespace_as_suffix = false;
+
+    std::vector<char> precompiled_charsmap;
+
+    int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
+};
+
+const struct llama_vocab * llama_get_vocab(const struct llama_context * ctx);
+
+//
+// internal API
+//
+
+// TODO: rename to llama_tokenize_impl
+// TODO: This should probably be in llama.h
+std::vector<llama_vocab::id> llama_tokenize_internal(
+        const llama_vocab & vocab,
+        std::string raw_text,
+        bool add_special,
+        bool parse_special = false);
+
+llama_token llama_byte_to_token_impl(const llama_vocab & vocab, uint8_t ch);
+
+const char * llama_token_get_text_impl(const struct llama_vocab & vocab, llama_token token);
+
+float llama_token_get_score_impl(const struct llama_vocab & vocab, llama_token token);
+
+llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, llama_token token);
+
+bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token);
+
+bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token);
+
+llama_token llama_token_bos_impl(const struct llama_vocab & vocab);
+llama_token llama_token_eos_impl(const struct llama_vocab & vocab);
+llama_token llama_token_cls_impl(const struct llama_vocab & vocab);
+llama_token llama_token_sep_impl(const struct llama_vocab & vocab);
+llama_token llama_token_nl_impl (const struct llama_vocab & vocab);
+llama_token llama_token_pad_impl(const struct llama_vocab & vocab);
+
+bool llama_add_bos_token_impl(const struct llama_vocab & vocab);
+bool llama_add_eos_token_impl(const struct llama_vocab & vocab);
+
+llama_token llama_token_prefix_impl(const struct llama_vocab & vocab);
+llama_token llama_token_middle_impl(const struct llama_vocab & vocab);
+llama_token llama_token_suffix_impl(const struct llama_vocab & vocab);
+llama_token llama_token_eot_impl   (const struct llama_vocab & vocab);
+llama_token llama_token_eom_impl   (const struct llama_vocab & vocab);
+
+int32_t llama_tokenize_impl(
+        const struct llama_vocab & vocab,
+                      const char * text,
+                         int32_t   text_len,
+                     llama_token * tokens,
+                         int32_t   n_tokens_max,
+                            bool   add_special,
+                            bool   parse_special);
+
+// does not write null-terminator to buf
+int32_t llama_token_to_piece_impl(
+        const struct llama_vocab & vocab,
+                     llama_token   token,
+                            char * buf,
+                         int32_t   length,
+                         int32_t   lstrip,
+                            bool   special);
+
+int32_t llama_detokenize_impl(
+        const struct llama_vocab & vocab,
+               const llama_token * tokens,
+                         int32_t   n_tokens,
+                            char * text,
+                         int32_t   text_len_max,
+                            bool   remove_special,
+                            bool   unparse_special);
diff --git a/llama.cpp b/src/llama.cpp
similarity index 66%
rename from llama.cpp
rename to src/llama.cpp
index ca3e9fcc0c249..ee36de977cdc2 100644
--- a/llama.cpp
+++ b/src/llama.cpp
@@ -1,5 +1,7 @@
-#define LLAMA_API_INTERNAL
-#include "llama.h"
+#include "llama-impl.h"
+#include "llama-vocab.h"
+#include "llama-grammar.h"
+#include "llama-sampling.h"
 
 #include "unicode.h"
 
@@ -13,26 +15,26 @@
 
 #ifdef GGML_USE_CUDA
 #  include "ggml-cuda.h"
-#elif defined(GGML_USE_CLBLAST)
-#  include "ggml-opencl.h"
 #elif defined(GGML_USE_VULKAN)
 #  include "ggml-vulkan.h"
 #elif defined(GGML_USE_SYCL)
 #  include "ggml-sycl.h"
 #elif defined(GGML_USE_KOMPUTE)
 #   include "ggml-kompute.h"
+#elif defined(GGML_USE_CANN)
+#   include "ggml-cann.h"
+#endif
+
+#ifdef GGML_USE_BLAS
+#  include "ggml-blas.h"
 #endif
 
 #ifdef GGML_USE_METAL
 #  include "ggml-metal.h"
 #endif
-#ifndef QK_K
-#  ifdef GGML_QKK_64
-#    define QK_K 64
-#  else
-#    define QK_K 256
-#  endif
-#endif
+
+// TODO: replace with ggml API call
+#define QK_K 256
 
 #ifdef __has_include
     #if __has_include(<unistd.h>)
@@ -59,6 +61,12 @@
     #include <io.h>
 #endif
 
+#if __cplusplus >= 202000L
+    #define LU8(x) (const char*)(u8##x)
+#else
+    #define LU8(x) u8##x
+#endif
+
 #include <algorithm>
 #include <array>
 #include <cassert>
@@ -73,7 +81,6 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
-#include <forward_list>
 #include <fstream>
 #include <functional>
 #include <future>
@@ -83,9 +90,6 @@
 #include <memory>
 #include <mutex>
 #include <numeric>
-#include <queue>
-#include <random>
-#include <regex>
 #include <set>
 #include <sstream>
 #include <thread>
@@ -96,53 +100,25 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-#ifdef __GNUC__
-#ifdef __MINGW32__
-#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
-#else
-#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
-#endif
-#else
-#define LLAMA_ATTRIBUTE_FORMAT(...)
-#endif
-
-#define LLAMA_MAX_NODES   8192
-#define LLAMA_MAX_EXPERTS 60
-
-//
-// logging
-//
-
-LLAMA_ATTRIBUTE_FORMAT(2, 3)
-static void llama_log_internal        (ggml_log_level level, const char* format, ...);
-static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
-
-#define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
-#define LLAMA_LOG_WARN(...)  llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
-#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+// bump if necessary
+#define LLAMA_MAX_LAYERS  512
+#define LLAMA_MAX_EXPERTS 160  // DeepSeekV2
 
 //
 // helpers
 //
 
-static size_t utf8_len(char src) {
-    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
-    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
-    return lookup[highbits];
-}
-
-static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
-    std::string result;
-    for (size_t pos = 0; ; pos += search.length()) {
-        auto new_pos = s.find(search, pos);
-        if (new_pos == std::string::npos) {
-            result += s.substr(pos, s.size() - pos);
-            break;
-        }
-        result += s.substr(pos, new_pos - pos) + replace;
-        pos = new_pos;
+// trim whitespace from the beginning and end of a string
+static std::string trim(const std::string & str) {
+    size_t start = 0;
+    size_t end = str.size();
+    while (start < end && isspace(str[start])) {
+        start += 1;
+    }
+    while (end > start && isspace(str[end - 1])) {
+        end -= 1;
     }
-    s = std::move(result);
+    return str.substr(start, end - start);
 }
 
 static bool is_float_close(float a, float b, float abs_tol) {
@@ -202,7 +178,6 @@ enum llm_arch {
     LLM_ARCH_GPTNEOX,
     LLM_ARCH_MPT,
     LLM_ARCH_STARCODER,
-    LLM_ARCH_PERSIMMON,
     LLM_ARCH_REFACT,
     LLM_ARCH_BERT,
     LLM_ARCH_NOMIC_BERT,
@@ -220,12 +195,21 @@ enum llm_arch {
     LLM_ARCH_INTERNLM2,
     LLM_ARCH_MINICPM,
     LLM_ARCH_GEMMA,
+    LLM_ARCH_GEMMA2,
     LLM_ARCH_STARCODER2,
     LLM_ARCH_MAMBA,
     LLM_ARCH_XVERSE,
     LLM_ARCH_COMMAND_R,
     LLM_ARCH_DBRX,
     LLM_ARCH_OLMO,
+    LLM_ARCH_OPENELM,
+    LLM_ARCH_ARCTIC,
+    LLM_ARCH_DEEPSEEK2,
+    LLM_ARCH_CHATGLM,
+    LLM_ARCH_BITNET,
+    LLM_ARCH_T5,
+    LLM_ARCH_T5ENCODER,
+    LLM_ARCH_JAIS,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -239,7 +223,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_MPT,             "mpt"          },
     { LLM_ARCH_BAICHUAN,        "baichuan"     },
     { LLM_ARCH_STARCODER,       "starcoder"    },
-    { LLM_ARCH_PERSIMMON,       "persimmon"    },
     { LLM_ARCH_REFACT,          "refact"       },
     { LLM_ARCH_BERT,            "bert"         },
     { LLM_ARCH_NOMIC_BERT,      "nomic-bert"   },
@@ -257,16 +240,26 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_INTERNLM2,       "internlm2"    },
     { LLM_ARCH_MINICPM,         "minicpm"      },
     { LLM_ARCH_GEMMA,           "gemma"        },
+    { LLM_ARCH_GEMMA2,          "gemma2"       },
     { LLM_ARCH_STARCODER2,      "starcoder2"   },
     { LLM_ARCH_MAMBA,           "mamba"        },
     { LLM_ARCH_XVERSE,          "xverse"       },
     { LLM_ARCH_COMMAND_R,       "command-r"    },
     { LLM_ARCH_DBRX,            "dbrx"         },
     { LLM_ARCH_OLMO,            "olmo"         },
+    { LLM_ARCH_OPENELM,         "openelm"      },
+    { LLM_ARCH_ARCTIC,          "arctic"       },
+    { LLM_ARCH_DEEPSEEK2,       "deepseek2"    },
+    { LLM_ARCH_CHATGLM,         "chatglm"      },
+    { LLM_ARCH_BITNET,          "bitnet"       },
+    { LLM_ARCH_T5,              "t5"           },
+    { LLM_ARCH_T5ENCODER,       "t5encoder"    },
+    { LLM_ARCH_JAIS,            "jais"         },
     { LLM_ARCH_UNKNOWN,         "(unknown)"    },
 };
 
 enum llm_kv {
+    LLM_KV_GENERAL_TYPE,
     LLM_KV_GENERAL_ARCHITECTURE,
     LLM_KV_GENERAL_QUANTIZATION_VERSION,
     LLM_KV_GENERAL_ALIGNMENT,
@@ -283,13 +276,21 @@ enum llm_kv {
     LLM_KV_CONTEXT_LENGTH,
     LLM_KV_EMBEDDING_LENGTH,
     LLM_KV_BLOCK_COUNT,
+    LLM_KV_LEADING_DENSE_BLOCK_COUNT,
     LLM_KV_FEED_FORWARD_LENGTH,
+    LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
+    LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
     LLM_KV_USE_PARALLEL_RESIDUAL,
     LLM_KV_TENSOR_DATA_LAYOUT,
     LLM_KV_EXPERT_COUNT,
     LLM_KV_EXPERT_USED_COUNT,
+    LLM_KV_EXPERT_SHARED_COUNT,
+    LLM_KV_EXPERT_WEIGHTS_SCALE,
     LLM_KV_POOLING_TYPE,
     LLM_KV_LOGIT_SCALE,
+    LLM_KV_DECODER_START_TOKEN_ID,
+    LLM_KV_ATTN_LOGIT_SOFTCAPPING,
+    LLM_KV_FINAL_LOGIT_SOFTCAPPING,
 
     LLM_KV_ATTENTION_HEAD_COUNT,
     LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -300,14 +301,20 @@ enum llm_kv {
     LLM_KV_ATTENTION_LAYERNORM_EPS,
     LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
     LLM_KV_ATTENTION_CAUSAL,
+    LLM_KV_ATTENTION_Q_LORA_RANK,
+    LLM_KV_ATTENTION_KV_LORA_RANK,
+    LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
+    LLM_KV_ATTENTION_SLIDING_WINDOW,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_FREQ_BASE,
     LLM_KV_ROPE_SCALE_LINEAR,
     LLM_KV_ROPE_SCALING_TYPE,
     LLM_KV_ROPE_SCALING_FACTOR,
+    LLM_KV_ROPE_SCALING_ATTN_FACTOR,
     LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
     LLM_KV_ROPE_SCALING_FINETUNED,
+    LLM_KV_ROPE_SCALING_YARN_LOG_MUL,
 
     LLM_KV_SPLIT_NO,
     LLM_KV_SPLIT_COUNT,
@@ -335,15 +342,22 @@ enum llm_kv {
     LLM_KV_TOKENIZER_ADD_BOS,
     LLM_KV_TOKENIZER_ADD_EOS,
     LLM_KV_TOKENIZER_ADD_PREFIX,
+    LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,
+    LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
     LLM_KV_TOKENIZER_HF_JSON,
     LLM_KV_TOKENIZER_RWKV,
     LLM_KV_TOKENIZER_PREFIX_ID,
     LLM_KV_TOKENIZER_SUFFIX_ID,
     LLM_KV_TOKENIZER_MIDDLE_ID,
     LLM_KV_TOKENIZER_EOT_ID,
+    LLM_KV_TOKENIZER_EOM_ID,
+
+    LLM_KV_ADAPTER_TYPE,
+    LLM_KV_ADAPTER_LORA_ALPHA,
 };
 
 static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
+    { LLM_KV_GENERAL_TYPE,                  "general.type"                          },
     { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
     { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
     { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
@@ -356,35 +370,49 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
     { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },
 
-    { LLM_KV_VOCAB_SIZE,                    "%s.vocab_size"            },
-    { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"        },
-    { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"      },
-    { LLM_KV_BLOCK_COUNT,                   "%s.block_count"           },
-    { LLM_KV_FEED_FORWARD_LENGTH,           "%s.feed_forward_length"   },
-    { LLM_KV_USE_PARALLEL_RESIDUAL,         "%s.use_parallel_residual" },
-    { LLM_KV_TENSOR_DATA_LAYOUT,            "%s.tensor_data_layout"    },
-    { LLM_KV_EXPERT_COUNT,                  "%s.expert_count"          },
-    { LLM_KV_EXPERT_USED_COUNT,             "%s.expert_used_count"     },
-    { LLM_KV_POOLING_TYPE ,                 "%s.pooling_type"          },
-    { LLM_KV_LOGIT_SCALE,                   "%s.logit_scale"           },
-
-    { LLM_KV_ATTENTION_HEAD_COUNT,          "%s.attention.head_count"             },
-    { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
-    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,      "%s.attention.max_alibi_bias"         },
-    { LLM_KV_ATTENTION_CLAMP_KQV,           "%s.attention.clamp_kqv"              },
-    { LLM_KV_ATTENTION_KEY_LENGTH,          "%s.attention.key_length"             },
-    { LLM_KV_ATTENTION_VALUE_LENGTH,        "%s.attention.value_length"           },
-    { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
-    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
-    { LLM_KV_ATTENTION_CAUSAL,              "%s.attention.causal"                 },
+    { LLM_KV_VOCAB_SIZE,                        "%s.vocab_size"                        },
+    { LLM_KV_CONTEXT_LENGTH,                    "%s.context_length"                    },
+    { LLM_KV_EMBEDDING_LENGTH,                  "%s.embedding_length"                  },
+    { LLM_KV_BLOCK_COUNT,                       "%s.block_count"                       },
+    { LLM_KV_LEADING_DENSE_BLOCK_COUNT,         "%s.leading_dense_block_count"         },
+    { LLM_KV_FEED_FORWARD_LENGTH,               "%s.feed_forward_length"               },
+    { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
+    { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
+    { LLM_KV_USE_PARALLEL_RESIDUAL,             "%s.use_parallel_residual"             },
+    { LLM_KV_TENSOR_DATA_LAYOUT,                "%s.tensor_data_layout"                },
+    { LLM_KV_EXPERT_COUNT,                      "%s.expert_count"                      },
+    { LLM_KV_EXPERT_USED_COUNT,                 "%s.expert_used_count"                 },
+    { LLM_KV_EXPERT_SHARED_COUNT,               "%s.expert_shared_count"               },
+    { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
+    { LLM_KV_POOLING_TYPE ,                     "%s.pooling_type"                      },
+    { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
+    { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
+    { LLM_KV_ATTN_LOGIT_SOFTCAPPING,            "%s.attn_logit_softcapping"            },
+    { LLM_KV_FINAL_LOGIT_SOFTCAPPING,           "%s.final_logit_softcapping"           },
+
+    { LLM_KV_ATTENTION_HEAD_COUNT,             "%s.attention.head_count"             },
+    { LLM_KV_ATTENTION_HEAD_COUNT_KV,          "%s.attention.head_count_kv"          },
+    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,         "%s.attention.max_alibi_bias"         },
+    { LLM_KV_ATTENTION_CLAMP_KQV,              "%s.attention.clamp_kqv"              },
+    { LLM_KV_ATTENTION_KEY_LENGTH,             "%s.attention.key_length"             },
+    { LLM_KV_ATTENTION_VALUE_LENGTH,           "%s.attention.value_length"           },
+    { LLM_KV_ATTENTION_LAYERNORM_EPS,          "%s.attention.layer_norm_epsilon"     },
+    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      "%s.attention.layer_norm_rms_epsilon" },
+    { LLM_KV_ATTENTION_CAUSAL,                 "%s.attention.causal"                 },
+    { LLM_KV_ATTENTION_Q_LORA_RANK,            "%s.attention.q_lora_rank"            },
+    { LLM_KV_ATTENTION_KV_LORA_RANK,           "%s.attention.kv_lora_rank"           },
+    { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
+    { LLM_KV_ATTENTION_SLIDING_WINDOW,         "%s.attention.sliding_window"         },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
     { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
     { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
     { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,      "%s.rope.scaling.attn_factor"             },
     { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
     { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
+    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     "%s.rope.scaling.yarn_log_multiplier"     },
 
     { LLM_KV_SPLIT_NO,                      "split.no"            },
     { LLM_KV_SPLIT_COUNT,                   "split.count"         },
@@ -395,29 +423,35 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_SSM_STATE_SIZE,                "%s.ssm.state_size"     },
     { LLM_KV_SSM_TIME_STEP_RANK,            "%s.ssm.time_step_rank" },
 
-    { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
-    { LLM_KV_TOKENIZER_PRE,                 "tokenizer.ggml.pre"                },
-    { LLM_KV_TOKENIZER_LIST,                "tokenizer.ggml.tokens"             },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE,          "tokenizer.ggml.token_type"         },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,    "tokenizer.ggml.token_type_count"   },
-    { LLM_KV_TOKENIZER_SCORES,              "tokenizer.ggml.scores"             },
-    { LLM_KV_TOKENIZER_MERGES,              "tokenizer.ggml.merges"             },
-    { LLM_KV_TOKENIZER_BOS_ID,              "tokenizer.ggml.bos_token_id"       },
-    { LLM_KV_TOKENIZER_EOS_ID,              "tokenizer.ggml.eos_token_id"       },
-    { LLM_KV_TOKENIZER_UNK_ID,              "tokenizer.ggml.unknown_token_id"   },
-    { LLM_KV_TOKENIZER_SEP_ID,              "tokenizer.ggml.seperator_token_id" },
-    { LLM_KV_TOKENIZER_PAD_ID,              "tokenizer.ggml.padding_token_id"   },
-    { LLM_KV_TOKENIZER_CLS_ID,              "tokenizer.ggml.cls_token_id"       },
-    { LLM_KV_TOKENIZER_MASK_ID,             "tokenizer.ggml.mask_token_id"      },
-    { LLM_KV_TOKENIZER_ADD_BOS,             "tokenizer.ggml.add_bos_token"      },
-    { LLM_KV_TOKENIZER_ADD_EOS,             "tokenizer.ggml.add_eos_token"      },
-    { LLM_KV_TOKENIZER_ADD_PREFIX,          "tokenizer.ggml.add_space_prefix"   },
-    { LLM_KV_TOKENIZER_HF_JSON,             "tokenizer.huggingface.json"        },
-    { LLM_KV_TOKENIZER_RWKV,                "tokenizer.rwkv.world"              },
-    { LLM_KV_TOKENIZER_PREFIX_ID,           "tokenizer.ggml.prefix_token_id"    },
-    { LLM_KV_TOKENIZER_SUFFIX_ID,           "tokenizer.ggml.suffix_token_id"    },
-    { LLM_KV_TOKENIZER_MIDDLE_ID,           "tokenizer.ggml.middle_token_id"    },
-    { LLM_KV_TOKENIZER_EOT_ID,              "tokenizer.ggml.eot_token_id"       },
+    { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
+    { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
+    { LLM_KV_TOKENIZER_LIST,                 "tokenizer.ggml.tokens"                   },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE,           "tokenizer.ggml.token_type"               },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,     "tokenizer.ggml.token_type_count"         },
+    { LLM_KV_TOKENIZER_SCORES,               "tokenizer.ggml.scores"                   },
+    { LLM_KV_TOKENIZER_MERGES,               "tokenizer.ggml.merges"                   },
+    { LLM_KV_TOKENIZER_BOS_ID,               "tokenizer.ggml.bos_token_id"             },
+    { LLM_KV_TOKENIZER_EOS_ID,               "tokenizer.ggml.eos_token_id"             },
+    { LLM_KV_TOKENIZER_UNK_ID,               "tokenizer.ggml.unknown_token_id"         },
+    { LLM_KV_TOKENIZER_SEP_ID,               "tokenizer.ggml.seperator_token_id"       },
+    { LLM_KV_TOKENIZER_PAD_ID,               "tokenizer.ggml.padding_token_id"         },
+    { LLM_KV_TOKENIZER_CLS_ID,               "tokenizer.ggml.cls_token_id"             },
+    { LLM_KV_TOKENIZER_MASK_ID,              "tokenizer.ggml.mask_token_id"            },
+    { LLM_KV_TOKENIZER_ADD_BOS,              "tokenizer.ggml.add_bos_token"            },
+    { LLM_KV_TOKENIZER_ADD_EOS,              "tokenizer.ggml.add_eos_token"            },
+    { LLM_KV_TOKENIZER_ADD_PREFIX,           "tokenizer.ggml.add_space_prefix"         },
+    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,      "tokenizer.ggml.remove_extra_whitespaces" },
+    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap"     },
+    { LLM_KV_TOKENIZER_HF_JSON,              "tokenizer.huggingface.json"              },
+    { LLM_KV_TOKENIZER_RWKV,                 "tokenizer.rwkv.world"                    },
+    { LLM_KV_TOKENIZER_PREFIX_ID,            "tokenizer.ggml.prefix_token_id"          },
+    { LLM_KV_TOKENIZER_SUFFIX_ID,            "tokenizer.ggml.suffix_token_id"          },
+    { LLM_KV_TOKENIZER_MIDDLE_ID,            "tokenizer.ggml.middle_token_id"          },
+    { LLM_KV_TOKENIZER_EOT_ID,               "tokenizer.ggml.eot_token_id"             },
+    { LLM_KV_TOKENIZER_EOM_ID,               "tokenizer.ggml.eom_token_id"             },
+
+    { LLM_KV_ADAPTER_TYPE,                  "adapter.type"       },
+    { LLM_KV_ADAPTER_LORA_ALPHA,            "adapter.lora.alpha" },
 };
 
 struct LLM_KV {
@@ -438,6 +472,8 @@ enum llm_tensor {
     LLM_TENSOR_OUTPUT,
     LLM_TENSOR_OUTPUT_NORM,
     LLM_TENSOR_ROPE_FREQS,
+    LLM_TENSOR_ROPE_FACTORS_LONG,
+    LLM_TENSOR_ROPE_FACTORS_SHORT,
     LLM_TENSOR_ATTN_Q,
     LLM_TENSOR_ATTN_K,
     LLM_TENSOR_ATTN_V,
@@ -446,10 +482,12 @@ enum llm_tensor {
     LLM_TENSOR_ATTN_NORM,
     LLM_TENSOR_ATTN_NORM_2,
     LLM_TENSOR_ATTN_OUT_NORM,
+    LLM_TENSOR_ATTN_POST_NORM,
     LLM_TENSOR_ATTN_ROT_EMBD,
     LLM_TENSOR_FFN_GATE_INP,
     LLM_TENSOR_FFN_GATE_INP_SHEXP,
     LLM_TENSOR_FFN_NORM,
+    LLM_TENSOR_FFN_POST_NORM,
     LLM_TENSOR_FFN_GATE,
     LLM_TENSOR_FFN_DOWN,
     LLM_TENSOR_FFN_UP,
@@ -457,6 +495,7 @@ enum llm_tensor {
     LLM_TENSOR_FFN_DOWN_EXP,  // split experts for backward compatibility
     LLM_TENSOR_FFN_GATE_EXP,
     LLM_TENSOR_FFN_UP_EXP,
+    LLM_TENSOR_FFN_NORM_EXPS,
     LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
     LLM_TENSOR_FFN_GATE_EXPS,
     LLM_TENSOR_FFN_UP_EXPS,
@@ -473,6 +512,42 @@ enum llm_tensor {
     LLM_TENSOR_SSM_A,
     LLM_TENSOR_SSM_D,
     LLM_TENSOR_SSM_OUT,
+    LLM_TENSOR_ATTN_Q_A,
+    LLM_TENSOR_ATTN_Q_B,
+    LLM_TENSOR_ATTN_KV_A_MQA,
+    LLM_TENSOR_ATTN_KV_B,
+    LLM_TENSOR_ATTN_Q_A_NORM,
+    LLM_TENSOR_ATTN_KV_A_NORM,
+    LLM_TENSOR_ATTN_SUB_NORM,
+    LLM_TENSOR_FFN_SUB_NORM,
+    LLM_TENSOR_DEC_ATTN_NORM,
+    LLM_TENSOR_DEC_ATTN_Q,
+    LLM_TENSOR_DEC_ATTN_K,
+    LLM_TENSOR_DEC_ATTN_V,
+    LLM_TENSOR_DEC_ATTN_OUT,
+    LLM_TENSOR_DEC_ATTN_REL_B,
+    LLM_TENSOR_DEC_CROSS_ATTN_NORM,
+    LLM_TENSOR_DEC_CROSS_ATTN_Q,
+    LLM_TENSOR_DEC_CROSS_ATTN_K,
+    LLM_TENSOR_DEC_CROSS_ATTN_V,
+    LLM_TENSOR_DEC_CROSS_ATTN_OUT,
+    LLM_TENSOR_DEC_CROSS_ATTN_REL_B,
+    LLM_TENSOR_DEC_FFN_NORM,
+    LLM_TENSOR_DEC_FFN_GATE,
+    LLM_TENSOR_DEC_FFN_DOWN,
+    LLM_TENSOR_DEC_FFN_UP,
+    LLM_TENSOR_DEC_OUTPUT_NORM,
+    LLM_TENSOR_ENC_ATTN_NORM,
+    LLM_TENSOR_ENC_ATTN_Q,
+    LLM_TENSOR_ENC_ATTN_K,
+    LLM_TENSOR_ENC_ATTN_V,
+    LLM_TENSOR_ENC_ATTN_OUT,
+    LLM_TENSOR_ENC_ATTN_REL_B,
+    LLM_TENSOR_ENC_FFN_NORM,
+    LLM_TENSOR_ENC_FFN_GATE,
+    LLM_TENSOR_ENC_FFN_DOWN,
+    LLM_TENSOR_ENC_FFN_UP,
+    LLM_TENSOR_ENC_OUTPUT_NORM,
 };
 
 static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -595,23 +670,6 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
-    {
-        LLM_ARCH_PERSIMMON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd"},
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm"},
-            { LLM_TENSOR_OUTPUT,          "output"},
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm"},
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv"},
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output"},
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm"},
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down"},
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up"},
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd"},
-        },
-    },
     {
         LLM_ARCH_MPT,
         {
@@ -700,6 +758,7 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
             { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
             { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
             { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
             { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
             { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
@@ -822,18 +881,20 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
     {
         LLM_ARCH_PHI3,
         {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
+            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,           "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
         },
     },
     {
@@ -949,6 +1010,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_GEMMA2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+        },
+    },
     {
         LLM_ARCH_STARCODER2,
         {
@@ -1049,6 +1128,174 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_OPENELM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_ARCTIC,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_NORM_EXPS,   "blk.%d.ffn_norm_exps" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_DEEPSEEK2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q_A_NORM,      "blk.%d.attn_q_a_norm" },
+            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_A,           "blk.%d.attn_q_a" },
+            { LLM_TENSOR_ATTN_Q_B,           "blk.%d.attn_q_b" },
+            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
+            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+        },
+    },
+    {
+        LLM_ARCH_CHATGLM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
+    {
+        LLM_ARCH_BITNET,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_SUB_NORM,      "blk.%d.attn_sub_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_SUB_NORM,       "blk.%d.ffn_sub_norm" },
+        },
+    },
+    {
+        LLM_ARCH_T5,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
+            { LLM_TENSOR_OUTPUT,               "output" },
+            { LLM_TENSOR_DEC_OUTPUT_NORM,      "dec.output_norm" },
+            { LLM_TENSOR_DEC_ATTN_NORM,        "dec.blk.%d.attn_norm" },
+            { LLM_TENSOR_DEC_ATTN_Q,           "dec.blk.%d.attn_q" },
+            { LLM_TENSOR_DEC_ATTN_K,           "dec.blk.%d.attn_k" },
+            { LLM_TENSOR_DEC_ATTN_V,           "dec.blk.%d.attn_v" },
+            { LLM_TENSOR_DEC_ATTN_OUT,         "dec.blk.%d.attn_o" },
+            { LLM_TENSOR_DEC_ATTN_REL_B,       "dec.blk.%d.attn_rel_b" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "dec.blk.%d.cross_attn_norm" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_Q,     "dec.blk.%d.cross_attn_q" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_K,     "dec.blk.%d.cross_attn_k" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_V,     "dec.blk.%d.cross_attn_v" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_OUT,   "dec.blk.%d.cross_attn_o" },
+            { LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "dec.blk.%d.cross_attn_rel_b" },
+            { LLM_TENSOR_DEC_FFN_NORM,         "dec.blk.%d.ffn_norm" },
+            { LLM_TENSOR_DEC_FFN_GATE,         "dec.blk.%d.ffn_gate" },
+            { LLM_TENSOR_DEC_FFN_DOWN,         "dec.blk.%d.ffn_down" },
+            { LLM_TENSOR_DEC_FFN_UP,           "dec.blk.%d.ffn_up" },
+            { LLM_TENSOR_ENC_OUTPUT_NORM,      "enc.output_norm" },
+            { LLM_TENSOR_ENC_ATTN_NORM,        "enc.blk.%d.attn_norm" },
+            { LLM_TENSOR_ENC_ATTN_Q,           "enc.blk.%d.attn_q" },
+            { LLM_TENSOR_ENC_ATTN_K,           "enc.blk.%d.attn_k" },
+            { LLM_TENSOR_ENC_ATTN_V,           "enc.blk.%d.attn_v" },
+            { LLM_TENSOR_ENC_ATTN_OUT,         "enc.blk.%d.attn_o" },
+            { LLM_TENSOR_ENC_ATTN_REL_B,       "enc.blk.%d.attn_rel_b" },
+            { LLM_TENSOR_ENC_FFN_NORM,         "enc.blk.%d.ffn_norm" },
+            { LLM_TENSOR_ENC_FFN_GATE,         "enc.blk.%d.ffn_gate" },
+            { LLM_TENSOR_ENC_FFN_DOWN,         "enc.blk.%d.ffn_down" },
+            { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_T5ENCODER,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
+            { LLM_TENSOR_OUTPUT,               "output" },
+            { LLM_TENSOR_ENC_OUTPUT_NORM,      "enc.output_norm" },
+            { LLM_TENSOR_ENC_ATTN_NORM,        "enc.blk.%d.attn_norm" },
+            { LLM_TENSOR_ENC_ATTN_Q,           "enc.blk.%d.attn_q" },
+            { LLM_TENSOR_ENC_ATTN_K,           "enc.blk.%d.attn_k" },
+            { LLM_TENSOR_ENC_ATTN_V,           "enc.blk.%d.attn_v" },
+            { LLM_TENSOR_ENC_ATTN_OUT,         "enc.blk.%d.attn_o" },
+            { LLM_TENSOR_ENC_ATTN_REL_B,       "enc.blk.%d.attn_rel_b" },
+            { LLM_TENSOR_ENC_FFN_NORM,         "enc.blk.%d.ffn_norm" },
+            { LLM_TENSOR_ENC_FFN_GATE,         "enc.blk.%d.ffn_gate" },
+            { LLM_TENSOR_ENC_FFN_DOWN,         "enc.blk.%d.ffn_down" },
+            { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_JAIS,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -1216,6 +1463,126 @@ struct no_init {
 };
 
 struct llama_file {
+
+#if defined(_WIN32)
+    // use FILE * so we don't have to re-open the file to mmap
+    FILE * fp;
+    HANDLE fp_win32;
+    size_t size;
+
+private:
+    std::string GetErrorMessageWin32(DWORD error_code) const {
+        std::string ret;
+        LPSTR lpMsgBuf = NULL;
+        DWORD bufLen = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+                                    NULL, error_code, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&lpMsgBuf, 0, NULL);
+        if (!bufLen) {
+            ret = format("Win32 error code: %s", error_code);
+        } else {
+            ret = lpMsgBuf;
+            LocalFree(lpMsgBuf);
+        }
+
+        return ret;
+    }
+
+public:
+
+    llama_file(const char * fname, const char * mode) {
+        fp = ggml_fopen(fname, mode);
+        if (fp == NULL) {
+            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+        }
+        fp_win32 = (HANDLE) _get_osfhandle(_fileno(fp));
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
+    size_t tell() const {
+        // SetFilePointerEx returns the current position when seeking relative 0 bytes
+        LARGE_INTEGER li;
+        li.QuadPart = 0;
+        BOOL ret = SetFilePointerEx(fp_win32, li, &li, FILE_CURRENT);
+        if (!ret) {
+            throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+        }
+
+        return li.QuadPart;
+    }
+
+    void seek(size_t offset, int whence) const {
+        // no need to convert SEEK_* to FILE_*. The enums are the same.
+        // Still, keep static asserts to avoid failures in the future.
+        static_assert(SEEK_SET == FILE_BEGIN, "SEEK_SET != FILE_BEGIN");
+        static_assert(SEEK_CUR == FILE_CURRENT, "SEEK_CUR != FILE_CURRENT");
+        static_assert(SEEK_END == FILE_END, "SEEK_END != FILE_END");
+
+        LARGE_INTEGER li;
+        li.QuadPart = offset;
+        BOOL ret = SetFilePointerEx(fp_win32, li, NULL, whence);
+        if (!ret) {
+            throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+        }
+    }
+
+    void read_raw(void * ptr, size_t len) const {
+        // On Win32 ReadFile is significant faster than fread which is again significant faster than std::fstream. Thus
+        // use the Win32 API to do file io instead of the C/C++ library functions.
+
+        // There are conditions under which ReadFile cannot read chunks >64MB.
+        // Thus split the operation into smaller chunks if len exceeds this limit.
+        size_t bytes_read = 0;
+        while (bytes_read < len) {
+            size_t chunk_size = std::min<size_t>(len - bytes_read, 64*1024*1024);
+            DWORD chunk_read = 0;
+            BOOL result = ReadFile(fp_win32, reinterpret_cast<char*>(ptr) + bytes_read, chunk_size, &chunk_read, NULL);
+            if (!result) {
+                throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+            }
+            if (chunk_read < chunk_size || chunk_read == 0) {
+                throw std::runtime_error("unexpectedly reached end of file");
+            }
+
+            bytes_read += chunk_read;
+        } ;
+    }
+
+    uint32_t read_u32() const {
+        uint32_t val;
+        read_raw(&val, sizeof(val));
+        return val;
+    }
+
+    void write_raw(const void * ptr, size_t len) const {
+        // There are conditions under which WriteFile cannot write chunks >64MB.
+        // Thus split the operation into smaller chunks if len exceeds this limit.
+        size_t bytes_written = 0;
+        while (bytes_written < len) {
+            size_t chunk_size = std::min<size_t>(len - bytes_written, 64*1024*1024);
+            DWORD chunk_written = 0;
+            BOOL result = WriteFile(fp_win32, reinterpret_cast<char const*>(ptr) + bytes_written, chunk_size, &chunk_written, NULL);
+            if (!result) {
+                throw std::runtime_error(format("write error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+            }
+            if (chunk_written < chunk_size || chunk_written == 0) {
+                throw std::runtime_error("unexpectedly failed to write bytes");
+            }
+
+            bytes_written += chunk_written;
+        }
+    }
+
+    void write_u32(std::uint32_t val) const {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~llama_file() {
+        if (fp) {
+            std::fclose(fp);
+        }
+    }
+#else
     // use FILE * so we don't have to re-open the file to mmap
     FILE * fp;
     size_t size;
@@ -1236,7 +1603,10 @@ struct llama_file {
 #else
         long ret = std::ftell(fp);
 #endif
-        GGML_ASSERT(ret != -1); // this really shouldn't fail
+        if (ret == -1) {
+            throw std::runtime_error(format("ftell error: %s", strerror(errno)));
+        }
+
         return (size_t) ret;
     }
 
@@ -1246,7 +1616,9 @@ struct llama_file {
 #else
         int ret = std::fseek(fp, (long) offset, whence);
 #endif
-        GGML_ASSERT(ret == 0); // same
+        if (ret != 0) {
+            throw std::runtime_error(format("seek error: %s", strerror(errno)));
+        }
     }
 
     void read_raw(void * ptr, size_t len) const {
@@ -1289,6 +1661,7 @@ struct llama_file {
             std::fclose(fp);
         }
     }
+#endif
 };
 using llama_files = std::vector<std::unique_ptr<llama_file>>;
 
@@ -1643,19 +2016,21 @@ struct llama_mlock {
 };
 using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
 
-static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
-    std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
-    if (n_tokens < 0) {
-        result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
-        GGML_ASSERT(check == -n_tokens);
+// NOTE: avoid ever using this except for building the token_to_piece caches
+static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
+    std::string piece;
+    piece.resize(piece.capacity());  // using string internal cache
+    const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
+    if (n_chars < 0) {
+        piece.resize(-n_chars);
+        int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
+        GGML_ASSERT(check == -n_chars);
     }
     else {
-        result.resize(n_tokens);
+        piece.resize(n_chars);
     }
 
-    return std::string(result.data(), result.size());
+    return piece;
 }
 
 static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) {
@@ -1696,6 +2071,8 @@ struct llama_state {
         ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
 #elif defined(GGML_USE_CUDA)
         ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data);
+#elif defined(GGML_USE_CANN)
+        ggml_backend_cann_log_set_callback(log_callback, log_callback_user_data);
 #endif
     }
 
@@ -1709,23 +2086,43 @@ static llama_state g_state;
 // available llama models
 enum e_model {
     MODEL_UNKNOWN,
+    MODEL_14M,
     MODEL_17M,
     MODEL_22M,
     MODEL_33M,
+    MODEL_60M,
+    MODEL_70M,
+    MODEL_80M,
     MODEL_109M,
     MODEL_137M,
+    MODEL_160M,
+    MODEL_220M,
+    MODEL_250M,
+    MODEL_270M,
     MODEL_335M,
+    MODEL_410M,
+    MODEL_450M,
+    MODEL_770M,
+    MODEL_780M,
     MODEL_0_5B,
     MODEL_1B,
+    MODEL_1_3B,
+    MODEL_1_4B,
     MODEL_2B,
+    MODEL_2_8B,
     MODEL_3B,
     MODEL_4B,
+    MODEL_6B,
+    MODEL_6_9B,
     MODEL_7B,
     MODEL_8B,
+    MODEL_9B,
+    MODEL_11B,
     MODEL_12B,
     MODEL_13B,
     MODEL_14B,
     MODEL_15B,
+    MODEL_16B,
     MODEL_20B,
     MODEL_30B,
     MODEL_34B,
@@ -1733,6 +2130,7 @@ enum e_model {
     MODEL_40B,
     MODEL_65B,
     MODEL_70B,
+    MODEL_236B,
     MODEL_314B,
     MODEL_SMALL,
     MODEL_MEDIUM,
@@ -1742,6 +2140,9 @@ enum e_model {
     MODEL_8x7B,
     MODEL_8x22B,
     MODEL_16x12B,
+    MODEL_10B_128x3_66B,
+    MODEL_57B_A14B,
+    MODEL_27B,
 };
 
 static const size_t kiB = 1024;
@@ -1751,27 +2152,44 @@ static const size_t GiB = 1024*MiB;
 struct llama_hparams {
     bool vocab_only;
     bool rope_finetuned;
+    bool use_par_res;
 
     uint32_t n_vocab;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
-    uint32_t n_head;
-    uint32_t n_head_kv;
     uint32_t n_layer;
     uint32_t n_rot;
+    uint32_t n_swa = 0; // sliding window attention (SWA)
     uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
     uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
-    uint32_t n_ff;
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
     uint32_t n_vocab_type = 0; // for BERT-style token types
+    uint32_t n_rel_attn_bkts = 0;
+
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
+
+    uint32_t n_layer_dense_lead = 0;
+    uint32_t n_lora_q = 0;
+    uint32_t n_lora_kv = 0;
+    uint32_t n_ff_exp = 0;
+    uint32_t n_ff_shexp = 0;
+    uint32_t n_expert_shared = 0;
+    float    expert_weights_scale = 0.0;
 
     float f_norm_eps;
     float f_norm_rms_eps;
 
+    float f_attn_logit_softcapping = 50.0f;
+    float f_final_logit_softcapping = 30.0f;
+
+    float    rope_attn_factor = 1.0f;
     float    rope_freq_base_train;
     float    rope_freq_scale_train;
-    uint32_t n_yarn_orig_ctx;
+    uint32_t n_ctx_orig_yarn;
+    float    rope_yarn_log_mul;
 
     // for State Space Models
     uint32_t ssm_d_conv  = 0;
@@ -1783,8 +2201,13 @@ struct llama_hparams {
     float f_max_alibi_bias = 0.0f;
     float f_logit_scale    = 0.0f;
 
-    bool causal_attn = true;
-    bool use_alibi   = false;
+    bool causal_attn   = true;
+    bool use_alibi     = false;
+    bool attn_soft_cap = false;
+
+    // needed by encoder-decoder models (e.g. T5, FLAN-T5)
+    // ref: https://github.com/ggerganov/llama.cpp/pull/8141
+    llama_token dec_start_token_id = -1;
 
     enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
     enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
@@ -1795,47 +2218,94 @@ struct llama_hparams {
         if (this->n_vocab       != other.n_vocab)       return true;
         if (this->n_ctx_train   != other.n_ctx_train)   return true;
         if (this->n_embd        != other.n_embd)        return true;
-        if (this->n_head        != other.n_head)        return true;
-        if (this->n_head_kv     != other.n_head_kv)     return true;
         if (this->n_layer       != other.n_layer)       return true;
         if (this->n_rot         != other.n_rot)         return true;
+        if (this->n_swa         != other.n_swa)         return true;
         if (this->n_embd_head_k != other.n_embd_head_k) return true;
         if (this->n_embd_head_v != other.n_embd_head_v) return true;
-        if (this->n_ff          != other.n_ff)          return true;
         if (this->n_expert      != other.n_expert)      return true;
         if (this->n_expert_used != other.n_expert_used) return true;
 
+        if (this->n_head_arr    != other.n_head_arr)    return true;
+        if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
+        if (this->n_ff_arr      != other.n_ff_arr)      return true;
+
+        if (this->n_rel_attn_bkts    != other.n_rel_attn_bkts)    return true;
+        if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
+        if (this->n_lora_q           != other.n_lora_q)           return true;
+        if (this->n_lora_kv          != other.n_lora_kv)          return true;
+        if (this->n_ff_exp           != other.n_ff_exp)           return true;
+        if (this->n_ff_shexp         != other.n_ff_shexp)         return true;
+        if (this->n_expert_shared    != other.n_expert_shared)    return true;
+
         if (this->rope_finetuned  != other.rope_finetuned)  return true;
-        if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
+        if (this->n_ctx_orig_yarn != other.n_ctx_orig_yarn) return true;
 
         if (this->ssm_d_conv  != other.ssm_d_conv)  return true;
         if (this->ssm_d_inner != other.ssm_d_inner) return true;
         if (this->ssm_d_state != other.ssm_d_state) return true;
         if (this->ssm_dt_rank != other.ssm_dt_rank) return true;
 
+        if (this->dec_start_token_id != other.dec_start_token_id) return true;
+
         const float EPSILON = 1e-9f;
 
         if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
         if (!is_float_close(this->f_norm_rms_eps,        other.f_norm_rms_eps,        EPSILON)) return true;
+        if (!is_float_close(this->rope_attn_factor,      other.rope_attn_factor,      EPSILON)) return true;
         if (!is_float_close(this->rope_freq_base_train,  other.rope_freq_base_train,  EPSILON)) return true;
         if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
+        if (!is_float_close(this->expert_weights_scale,  other.expert_weights_scale,  EPSILON)) return true;
+        if (!is_float_close(this->rope_yarn_log_mul,     other.rope_yarn_log_mul,     EPSILON)) return true;
 
         return false;
     }
 
-    uint32_t n_gqa() const {
-        if (n_head_kv == 0) {
-            return 0;
+    uint32_t n_head(uint32_t il = 0) const {
+        if (il < n_layer) {
+            return n_head_arr[il];
         }
-        return n_head/n_head_kv;
-    }
 
-    uint32_t n_embd_k_gqa() const { // dimension of key embeddings across all k-v heads
-        return n_embd_head_k * n_head_kv;
+        GGML_ABORT("fatal error");
     }
 
-    uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads
-        return n_embd_head_v * n_head_kv;
+    uint32_t n_head_kv(uint32_t il = 0) const {
+        if (il < n_layer) {
+            return n_head_kv_arr[il];
+        }
+
+        GGML_ABORT("fatal error");
+    }
+
+    uint32_t n_ff(uint32_t il = 0) const {
+        if (il < n_layer) {
+            return n_ff_arr[il];
+        }
+
+        GGML_ABORT("fatal error");
+    }
+
+    uint32_t n_gqa(uint32_t il = 0) const {
+        const uint32_t n_head    = this->n_head(il);
+        const uint32_t n_head_kv = this->n_head_kv(il);
+
+        if (n_head_kv == 0) {
+            return 0;
+        }
+
+        return n_head/n_head_kv;
+    }
+
+    uint32_t n_embd_k_gqa(uint32_t il = 0) const { // dimension of key embeddings across all k-v heads
+        const uint32_t n_head_kv = this->n_head_kv(il);
+
+        return n_embd_head_k * n_head_kv;
+    }
+
+    uint32_t n_embd_v_gqa(uint32_t il = 0) const { // dimension of value embeddings across all k-v heads
+        const uint32_t n_head_kv = this->n_head_kv(il);
+
+        return n_embd_head_v * n_head_kv;
     }
 
     uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
@@ -1851,6 +2321,8 @@ struct llama_hparams {
     }
 };
 
+static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
+
 struct llama_cparams {
     uint32_t n_ctx;           // context size used during inference
     uint32_t n_batch;
@@ -1862,7 +2334,7 @@ struct llama_cparams {
     float rope_freq_base;
     float rope_freq_scale;
 
-    uint32_t n_yarn_orig_ctx;
+    uint32_t n_ctx_orig_yarn;
     // These hyperparameters are not exposed in GGUF, because all
     // existing YaRN models use the same values for them.
     float yarn_ext_factor;
@@ -1882,6 +2354,7 @@ struct llama_cparams {
     void * cb_eval_user_data;
 };
 
+// TODO: separate into "llama_layer_enc" and "llama_layer_dec"
 struct llama_layer {
     // normalization
     struct ggml_tensor * attn_norm;
@@ -1894,6 +2367,13 @@ struct llama_layer {
     struct ggml_tensor * attn_k_norm_b;
     struct ggml_tensor * attn_out_norm;
     struct ggml_tensor * attn_out_norm_b;
+    struct ggml_tensor * attn_q_a_norm;
+    struct ggml_tensor * attn_kv_a_norm;
+    struct ggml_tensor * attn_sub_norm;
+    struct ggml_tensor * attn_post_norm;
+    struct ggml_tensor * ffn_sub_norm;
+    struct ggml_tensor * attn_norm_cross;
+    struct ggml_tensor * attn_norm_enc;
 
     // attention
     struct ggml_tensor * wq;
@@ -1901,6 +2381,18 @@ struct llama_layer {
     struct ggml_tensor * wv;
     struct ggml_tensor * wo;
     struct ggml_tensor * wqkv;
+    struct ggml_tensor * wq_a;
+    struct ggml_tensor * wq_b;
+    struct ggml_tensor * wkv_a_mqa;
+    struct ggml_tensor * wkv_b;
+    struct ggml_tensor * wq_cross;
+    struct ggml_tensor * wk_cross;
+    struct ggml_tensor * wv_cross;
+    struct ggml_tensor * wo_cross;
+    struct ggml_tensor * wq_enc;
+    struct ggml_tensor * wk_enc;
+    struct ggml_tensor * wv_enc;
+    struct ggml_tensor * wo_enc;
 
     // attention bias
     struct ggml_tensor * bq;
@@ -1909,16 +2401,27 @@ struct llama_layer {
     struct ggml_tensor * bo;
     struct ggml_tensor * bqkv;
 
+    // relative position bias
+    struct ggml_tensor * attn_rel_b;
+    struct ggml_tensor * attn_rel_b_enc;
+    struct ggml_tensor * attn_rel_b_cross;
+
     // normalization
     struct ggml_tensor * ffn_norm;
     struct ggml_tensor * ffn_norm_b;
+    struct ggml_tensor * ffn_post_norm;
     struct ggml_tensor * layer_out_norm;
     struct ggml_tensor * layer_out_norm_b;
+    struct ggml_tensor * ffn_norm_exps;
+    struct ggml_tensor * ffn_norm_enc;
 
     // ff
     struct ggml_tensor * ffn_gate; // w1
     struct ggml_tensor * ffn_down; // w2
     struct ggml_tensor * ffn_up;   // w3
+    struct ggml_tensor * ffn_gate_enc;
+    struct ggml_tensor * ffn_down_enc;
+    struct ggml_tensor * ffn_up_enc;
 
     // ff MoE
     struct ggml_tensor * ffn_gate_inp;
@@ -1933,8 +2436,9 @@ struct llama_layer {
     struct ggml_tensor * ffn_up_shexp;
 
     // ff bias
-    struct ggml_tensor * ffn_down_b; // b2
-    struct ggml_tensor * ffn_up_b;   // b3
+    struct ggml_tensor * ffn_gate_b = nullptr;
+    struct ggml_tensor * ffn_down_b = nullptr; // b2
+    struct ggml_tensor * ffn_up_b   = nullptr; // b3
     struct ggml_tensor * ffn_act;
 
     // mamba proj
@@ -1951,6 +2455,20 @@ struct llama_layer {
     // mamba bias
     struct ggml_tensor * ssm_conv1d_b;
     struct ggml_tensor * ssm_dt_b;
+
+    // long rope factors
+    struct ggml_tensor * rope_long  = nullptr;
+    struct ggml_tensor * rope_short = nullptr;
+    struct ggml_tensor * rope_freqs = nullptr;
+
+    // bitnet scale
+    struct ggml_tensor * wq_scale;
+    struct ggml_tensor * wk_scale;
+    struct ggml_tensor * wv_scale;
+    struct ggml_tensor * wo_scale;
+    struct ggml_tensor * ffn_gate_scale;
+    struct ggml_tensor * ffn_up_scale;
+    struct ggml_tensor * ffn_down_scale;
 };
 
 struct llama_kv_cell {
@@ -2028,13 +2546,21 @@ struct llama_control_vector {
     int32_t layer_start = -1;
     int32_t layer_end   = -1;
 
-    ggml_tensor * tensor_for(int il) const {
+    struct ggml_tensor * tensor_for(int il) const {
         if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
             return nullptr;
         }
         return tensors[il];
     }
 
+    struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const {
+        ggml_tensor * layer_dir = tensor_for(il);
+        if (layer_dir != nullptr) {
+            cur = ggml_add(ctx, cur, layer_dir);
+        }
+        return cur;
+    }
+
     ~llama_control_vector() {
         for (struct ggml_context * ctx : ctxs) {
             ggml_free(ctx);
@@ -2045,62 +2571,6 @@ struct llama_control_vector {
     }
 };
 
-struct llama_vocab {
-    using id    = int32_t;
-    using token = std::string;
-    using ttype = llama_token_type;
-
-    struct token_data {
-        token text;
-        float score;
-        ttype type;
-    };
-
-    enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
-    enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-
-    std::unordered_map<token, id> token_to_id;
-    std::vector<token_data>       id_to_token;
-
-    std::unordered_map<token, id> special_tokens_cache;
-
-    std::map<std::pair<std::string, std::string>, int> bpe_ranks;
-
-    // default LLaMA special tokens
-    id special_bos_id  = 1;
-    id special_eos_id  = 2;
-    id special_unk_id  = 0;
-    id special_sep_id  = -1;
-    id special_pad_id  = -1;
-    id special_cls_id  = -1;
-    id special_mask_id = -1;
-
-    int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add.
-    int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add.
-
-    id linefeed_id       = 13;
-    id special_prefix_id = -1;
-    id special_suffix_id = -1;
-    id special_middle_id = -1;
-    id special_eot_id    = -1; // TODO: move above after "eos_id", and here add "file separator" token
-
-    bool add_space_prefix = true;
-
-    int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
-        GGML_ASSERT(token_left.find(' ') == std::string::npos);
-        GGML_ASSERT(token_left.find('\n') == std::string::npos);
-        GGML_ASSERT(token_right.find(' ') == std::string::npos);
-        GGML_ASSERT(token_right.find('\n') == std::string::npos);
-
-        auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
-        if (it == bpe_ranks.end()) {
-            return -1;
-        }
-
-        return it->second;
-    }
-};
-
 struct llama_model {
     e_model     type  = MODEL_UNKNOWN;
     llm_arch    arch  = LLM_ARCH_UNKNOWN;
@@ -2121,6 +2591,7 @@ struct llama_model {
     struct ggml_tensor * output_norm_b;
     struct ggml_tensor * output;
     struct ggml_tensor * output_b;
+    struct ggml_tensor * output_norm_enc;
 
     std::vector<llama_layer> layers;
 
@@ -2166,6 +2637,9 @@ struct llama_model {
     int64_t t_load_us = 0;
     int64_t t_start_us = 0;
 
+    // keep track of loaded lora adapters
+    std::set<struct llama_lora_adapter *> lora_adapters;
+
     ~llama_model() {
         for (struct ggml_context * ctx : ctxs) {
             ggml_free(ctx);
@@ -2178,11 +2652,19 @@ struct llama_model {
 #endif
             ggml_backend_buffer_free(buf);
         }
+        while (!lora_adapters.empty()) {
+            llama_lora_adapter_free(*lora_adapters.begin());
+        }
     }
 };
 
 struct llama_context {
-    llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
+    llama_context(const llama_model & model)
+        : model(model)
+        , sampling(llama_n_vocab(&model))
+        , t_start_us(model.t_start_us)
+        , t_load_us(model.t_load_us) {}
+
     ~llama_context() {
         ggml_backend_sched_free(sched);
 
@@ -2193,33 +2675,34 @@ struct llama_context {
         ggml_backend_buffer_free(buf_output);
     }
 
-    llama_cparams cparams;
+    const struct llama_model & model;
+
+    struct llama_cparams        cparams;
+    struct llama_sampling       sampling;
+    struct llama_kv_cache       kv_self;
+    struct llama_control_vector cvec;
+
+    std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
 
     std::vector<ggml_backend_t> backends;
 #ifdef GGML_USE_METAL
     ggml_backend_t backend_metal = nullptr;
+#endif
+#ifdef GGML_USE_BLAS
+    ggml_backend_t backend_blas = nullptr;
 #endif
     ggml_backend_t backend_cpu = nullptr;
 
-    const llama_model & model;
-
-    // key + value cache for the self attention
-    struct llama_kv_cache kv_self;
-
-    std::mt19937 rng;
-
     bool has_evaluated_once = false;
 
     int64_t t_start_us;
     int64_t t_load_us;
-    int64_t t_sample_us = 0;
     int64_t t_p_eval_us = 0;
     int64_t t_eval_us   = 0;
 
     int64_t t_compute_start_us = 0;
     int64_t n_queued_tokens = 0;
 
-    int32_t n_sample = 0; // number of tokens sampled
     int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
     int32_t n_eval   = 0; // number of eval calls
 
@@ -2245,6 +2728,13 @@ struct llama_context {
     // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
     std::map<llama_seq_id, std::vector<float>> embd_seq;
 
+    // whether we are computing encoder output or decoder output
+    bool is_encoding = false;
+
+    // output of the encoder part of the encoder-decoder models
+    std::vector<float> embd_enc;
+    std::vector<std::set<llama_seq_id>> seq_ids_enc;
+
     // memory buffers used to evaluate the model
     std::vector<uint8_t> buf_compute_meta;
     ggml_backend_sched_t sched = nullptr;
@@ -2253,29 +2743,96 @@ struct llama_context {
     void *              abort_callback_data = nullptr;
 
     // input tensors
-    struct ggml_tensor * inp_tokens;    // I32 [n_batch]
-    struct ggml_tensor * inp_embd;      // F32 [n_embd, n_batch]
-    struct ggml_tensor * inp_pos;       // I32 [n_batch]
-    struct ggml_tensor * inp_out_ids;   // I32 [n_outputs]
-    struct ggml_tensor * inp_KQ_mask;   // F32 [kv_size, n_batch]
-    struct ggml_tensor * inp_K_shift;   // I32 [kv_size]
-    struct ggml_tensor * inp_mean;      // F32 [n_batch, n_batch]
-    struct ggml_tensor * inp_cls;       // I32 [n_batch]
-    struct ggml_tensor * inp_s_copy;    // I32 [kv_size]
-    struct ggml_tensor * inp_s_mask;    // F32 [1, n_kv]
-    struct ggml_tensor * inp_s_seq;     // I32 [n_kv, n_batch]
-
-    // control vectors
-    struct llama_control_vector cvec;
+    struct ggml_tensor * inp_tokens;      // I32 [n_batch]
+    struct ggml_tensor * inp_embd;        // F32 [n_embd, n_batch]
+    struct ggml_tensor * inp_pos;         // I32 [n_batch]
+    struct ggml_tensor * inp_out_ids;     // I32 [n_outputs]
+    struct ggml_tensor * inp_KQ_mask;     // F32 [kv_size, n_batch]
+    struct ggml_tensor * inp_KQ_mask_swa; // F32 [kv_size, n_batch]
+    struct ggml_tensor * inp_K_shift;     // I32 [kv_size]
+    struct ggml_tensor * inp_mean;        // F32 [n_batch, n_batch]
+    struct ggml_tensor * inp_cls;         // I32 [n_batch]
+    struct ggml_tensor * inp_s_copy;      // I32 [kv_size]
+    struct ggml_tensor * inp_s_mask;      // F32 [1, n_kv]
+    struct ggml_tensor * inp_s_seq;       // I32 [n_kv, n_batch]
+    struct ggml_tensor * inp_pos_bucket;    // I32 [n_batch|n_kv, n_batch]
+    struct ggml_tensor * inp_embd_enc;      // F32 [n_embd, n_outputs_enc]
+    struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
+};
+
+struct llama_lora_weight {
+    struct ggml_tensor * a = nullptr;
+    struct ggml_tensor * b = nullptr;
+    llama_lora_weight() = default;
+    llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b): a(a), b(b) {}
+};
+
+struct llama_lora_adapter {
+    struct llama_model * base_model;
+    // map tensor name to lora_a_b
+    std::unordered_map<std::string, struct llama_lora_weight> ab_map;
+    std::vector<struct ggml_context *> ctxs;
+    std::vector<ggml_backend_buffer_t> bufs;
+
+    float alpha;
+
+    llama_lora_adapter(struct llama_model * base_model): base_model(base_model) {
+        base_model->lora_adapters.insert(this);
+    }
+
+    llama_lora_weight * get_weight(struct ggml_tensor * w) {
+        std::string name(w->name);
+        auto pos = ab_map.find(name);
+        if (ab_map.find(name) != ab_map.end()) {
+            return &pos->second;
+        }
+        return nullptr;
+    }
+
+    ~llama_lora_adapter() {
+        for (struct ggml_context * ctx : ctxs) {
+            ggml_free(ctx);
+        }
+        for (ggml_backend_buffer_t buf : bufs) {
+            ggml_backend_buffer_free(buf);
+        }
+        auto pos = base_model->lora_adapters.find(this);
+        if (pos != base_model->lora_adapters.end()) {
+            base_model->lora_adapters.erase(pos);
+        }
+    }
 };
 
+static size_t llama_get_device_count(const llama_model & model) {
+    size_t count = 1;
+#if defined(GGML_USE_CUDA)
+    count = ggml_backend_cuda_get_device_count();
+#elif defined(GGML_USE_SYCL)
+    count = ggml_backend_sycl_get_device_count();
+#elif defined(GGML_USE_VULKAN)
+    count = ggml_backend_vk_get_device_count();
+#elif defined(GGML_USE_CANN)
+    return ggml_backend_cann_get_device_count();
+#endif
+#if defined(GGML_USE_RPC)
+    count += model.rpc_servers.size();
+#endif
+    return count;
+    GGML_UNUSED(model);
+}
+
 static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) {
     ggml_backend_buffer_type_t buft = nullptr;
 
-#ifdef GGML_USE_RPC
-    std::string endpoint = model.rpc_servers[gpu];
-    buft = ggml_backend_rpc_buffer_type(endpoint.c_str());
-#elif defined(GGML_USE_METAL)
+#if defined(GGML_USE_RPC)
+    int dev_count = (int)llama_get_device_count(model);
+    int rpc_count = (int)model.rpc_servers.size();
+    if (gpu >= dev_count - rpc_count) {
+        const char * endpoint = model.rpc_servers[gpu - dev_count + rpc_count].c_str();
+        return ggml_backend_rpc_buffer_type(endpoint);
+    }
+#endif
+#if defined(GGML_USE_METAL)
     buft = ggml_backend_metal_buffer_type();
 #elif defined(GGML_USE_CUDA)
     buft = ggml_backend_cuda_buffer_type(gpu);
@@ -2283,13 +2840,13 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_
     buft = ggml_backend_vk_buffer_type(gpu);
 #elif defined(GGML_USE_SYCL)
     buft = ggml_backend_sycl_buffer_type(gpu);
-#elif defined(GGML_USE_CLBLAST)
-    buft = ggml_backend_opencl_buffer_type();
 #elif defined(GGML_USE_KOMPUTE)
     buft = ggml_backend_kompute_buffer_type(gpu);
     if (buft == nullptr) {
         LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
     }
+#elif defined(GGML_USE_CANN)
+    buft = ggml_backend_cann_buffer_type(gpu);
 #endif
 
     if (buft == nullptr) {
@@ -2323,29 +2880,19 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_mo
     GGML_UNUSED(tensor_split);
 }
 
-static size_t llama_get_device_count(const llama_model & model) {
-#if defined(GGML_USE_RPC)
-    return model.rpc_servers.size();
-#elif defined(GGML_USE_CUDA)
-    return ggml_backend_cuda_get_device_count();
-#elif defined(GGML_USE_SYCL)
-    return ggml_backend_sycl_get_device_count();
-#elif defined(GGML_USE_VULKAN)
-    return ggml_backend_vk_get_device_count();
-#else
-    return 1;
-#endif
-    GGML_UNUSED(model);
-}
-
 static size_t llama_get_device_memory(const llama_model & model, int device) {
 #if defined(GGML_USE_RPC)
-    size_t total;
-    size_t free;
-    std::string endpoint = model.rpc_servers[device];
-    ggml_backend_rpc_get_device_memory(endpoint.c_str(), &free, &total);
-    return free;
-#elif defined(GGML_USE_CUDA)
+    int dev_count = (int)llama_get_device_count(model);
+    int rpc_count = (int)model.rpc_servers.size();
+    if (device >= dev_count - rpc_count) {
+        size_t total;
+        size_t free;
+        const char * endpoint = model.rpc_servers[device - dev_count + rpc_count].c_str();
+        ggml_backend_rpc_get_device_memory(endpoint, &free, &total);
+        return free;
+    }
+#endif
+#if defined(GGML_USE_CUDA)
     size_t total;
     size_t free;
     ggml_backend_cuda_get_device_memory(device, &free, &total);
@@ -2360,6 +2907,11 @@ static size_t llama_get_device_memory(const llama_model & model, int device) {
     size_t free;
     ggml_backend_vk_get_device_memory(device, &free, &total);
     return free;
+#elif defined(GGML_USE_CANN)
+    size_t total;
+    size_t free;
+    ggml_backend_cann_get_device_memory(device, &free, &total);
+    return free;
 #else
     return 1;
 #endif
@@ -2383,22 +2935,13 @@ static bool llama_kv_cache_init(
 
     const struct llama_hparams & hparams = model.hparams;
 
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
-    const int64_t  n_layer      = hparams.n_layer;
+    const int64_t  n_layer = hparams.n_layer;
 
     cache.has_shift = false;
 
     // TODO: find a nicer way to add other recurrent model architectures
     cache.recurrent = model.arch == LLM_ARCH_MAMBA;
-    cache.v_trans   = !cparams.flash_attn;
-
-    // TODO: support mixed recurrent Transformer architectures
-    // NOTE: (!a || b) is a logical implication (a -> b)
-    GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s());
-    GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s());
-    GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa());
-    GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa());
+    cache.v_trans   = !cache.recurrent && !cparams.flash_attn;
 
     cache.head = 0;
     cache.size = kv_size;
@@ -2417,10 +2960,6 @@ static bool llama_kv_cache_init(
         }
     }
 
-#ifdef GGML_USE_CLBLAST
-    offload = false;
-#endif
-
     // count used buffer types
     std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
     if (offload) {
@@ -2453,6 +2992,9 @@ static bool llama_kv_cache_init(
     cache.v_l.reserve(n_layer);
 
     for (int i = 0; i < (int) n_layer; i++) {
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
+
         struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
         ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
         ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
@@ -2486,7 +3028,6 @@ static bool llama_kv_cache_init(
 static bool llama_kv_cache_find_slot(
            struct llama_kv_cache & cache,
         const struct llama_batch & batch) {
-    const uint32_t n_ctx    = cache.size;
     const uint32_t n_tokens = batch.n_tokens;
 
     if (cache.recurrent) {
@@ -2537,16 +3078,16 @@ static bool llama_kv_cache_find_slot(
     }
     // otherwise, one cell per token.
 
-    if (n_tokens > n_ctx) {
-        LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
+    if (n_tokens > cache.size) {
+        LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size);
         return false;
     }
 
     uint32_t n_tested = 0;
 
     while (true) {
-        if (cache.head + n_tokens > n_ctx) {
-            n_tested += n_ctx - cache.head;
+        if (cache.head + n_tokens > cache.size) {
+            n_tested += cache.size - cache.head;
             cache.head = 0;
             continue;
         }
@@ -2565,7 +3106,7 @@ static bool llama_kv_cache_find_slot(
             break;
         }
 
-        if (n_tested >= n_ctx) {
+        if (n_tested >= cache.size) {
             //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
             return false;
         }
@@ -2734,6 +3275,8 @@ static void llama_kv_cache_seq_add(
 
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+    // If there is no range then return early to avoid looping over the cache.
+    if (p0 == p1) return;
 
     if (cache.recurrent) {
         // for Mamba-like models, only the pos needs to be shifted
@@ -2778,6 +3321,8 @@ static void llama_kv_cache_seq_div(
                           int   d) {
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+    // If there is no range then return early to avoid looping over the cache.
+    if (p0 == p1) return;
 
     if (cache.recurrent) {
         // for Mamba-like models, only the pos needs to be changed
@@ -3030,6 +3575,10 @@ namespace GGUFMeta {
 
 using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
 
+static size_t llama_model_max_nodes(const llama_model & model) {
+    return std::max<size_t>(8192, model.tensors_by_name.size()*5);
+}
+
 struct llama_model_loader {
     int n_kv      = 0;
     int n_tensors = 0;
@@ -3080,7 +3629,7 @@ struct llama_model_loader {
         }
 
         if (param_overrides_p != nullptr) {
-            for (const struct llama_model_kv_override *p = param_overrides_p; p->key[0] != 0; p++) {
+            for (const struct llama_model_kv_override * p = param_overrides_p; p->key[0] != 0; p++) {
                 kv_overrides.insert({std::string(p->key), *p});
             }
         }
@@ -3234,6 +3783,9 @@ struct llama_model_loader {
                 case GGML_TYPE_IQ4_NL:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL;  break;
                 case GGML_TYPE_IQ4_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS;  break;
                 case GGML_TYPE_IQ3_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_S;   break;
+                case GGML_TYPE_Q4_0_4_4: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_4; break;
+                case GGML_TYPE_Q4_0_4_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_8; break;
+                case GGML_TYPE_Q4_0_8_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_8_8; break;
                 default:
                     {
                         LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
@@ -3245,7 +3797,7 @@ struct llama_model_loader {
             ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
 
             {
-                const int kid = gguf_find_key(meta, "general.file_type");
+                const int kid = gguf_find_key(meta, "general.file_type"); // TODO: use LLM_KV
                 if (kid >= 0) {
                     ftype = (llama_ftype) gguf_get_val_u32(meta, kid);
                 }
@@ -3325,6 +3877,72 @@ struct llama_model_loader {
         return get_arr_n(llm_kv(kid), result, required);
     }
 
+    template<typename T>
+    bool get_arr(const std::string & key, std::vector<T> & result, const bool required = true) {
+        const int kid = gguf_find_key(meta, key.c_str());
+
+        if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) {
+            if (required) {
+                throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        struct GGUFMeta::ArrayInfo arr_info =
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+        switch (arr_info.gt) {
+            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
+            case GGUF_TYPE_INT32:   GGML_ASSERT(
+                                            (std::is_same<T,  int32_t>::value) ||
+                                            (std::is_same<T, uint32_t>::value));  break;
+            default:
+                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+        }
+
+        result.resize(arr_info.length);
+        result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length);
+
+        return true;
+    }
+
+    template<typename T, size_t N_MAX>
+    bool get_arr(const std::string & key, std::array<T, N_MAX> & result, const bool required = true) {
+        const int kid = gguf_find_key(meta, key.c_str());
+
+        if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) {
+            if (required) {
+                throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        struct GGUFMeta::ArrayInfo arr_info =
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+        switch (arr_info.gt) {
+            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
+            case GGUF_TYPE_INT32:   GGML_ASSERT(
+                                            (std::is_same<T,  int32_t>::value) ||
+                                            (std::is_same<T, uint32_t>::value));  break;
+            default:
+                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+        }
+
+        if (arr_info.length > N_MAX) {
+            throw std::runtime_error(format("array length %u for key %s exceeds max %u", (uint32_t) arr_info.length, key.c_str(), (uint32_t) N_MAX));
+        }
+
+        std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
+
+        return true;
+    }
+
+    template<typename T>
+    bool get_arr(const enum llm_kv kid, T & result, const bool required = true) {
+        return get_arr(llm_kv(kid), result, required);
+    }
+
     template<typename T>
     bool get_key(const std::string & key, T & result, const bool required = true) {
         auto it = kv_overrides.find(key);
@@ -3346,6 +3964,52 @@ struct llama_model_loader {
         return get_key(llm_kv(kid), result, required);
     }
 
+    // get array of n <= N_MAX elements, or a single element repeated n times
+    template<typename T, size_t N_MAX>
+    bool get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, const bool required = true) {
+        const int kid = gguf_find_key(meta, key.c_str());
+
+        if (kid < 0) {
+            if (required) {
+                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        if (n > N_MAX) {
+            throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str()));
+        }
+
+        if (gguf_get_kv_type(meta, kid) == GGUF_TYPE_ARRAY) {
+            struct GGUFMeta::ArrayInfo arr_info =
+                GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+            if (n != arr_info.length) {
+                throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length));
+            }
+
+            return get_arr(key, result, required);
+        } else {
+            T value;
+
+            bool ok = get_key(key, value, required);
+            if (!ok) {
+                return false;
+            }
+
+            for (uint32_t i = 0; i < n; i++) {
+                result[i] = value;
+            }
+
+            return true;
+        }
+    }
+
+    template<typename T>
+    bool get_key_or_arr(const enum llm_kv kid, T & result, uint32_t n, const bool required = true) {
+        return get_key_or_arr(llm_kv(kid), result, n, required);
+    }
+
     std::string get_arch_name() const {
         return arch_name;
     }
@@ -3399,11 +4063,15 @@ struct llama_model_loader {
         return get_tensor_meta(get_tensor_name(i));
     }
 
-    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) {
+    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) {
         struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
         ggml_set_name(tensor, ggml_get_name(cur));
 
-        n_created++;
+        if (duplicated) {
+            size_data += ggml_nbytes(cur);
+        } else {
+            n_created++;
+        }
 
         return tensor;
     }
@@ -3438,14 +4106,17 @@ struct llama_model_loader {
         return cur;
     }
 
-    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
-        const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+    static const int TENSOR_NOT_REQUIRED = 1;
+    static const int TENSOR_DUPLICATED   = 2;
+
+    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, int flags = 0) {
+        const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
 
         if (cur == NULL) {
             return NULL;
         }
 
-        return create_tensor_for(ctx, cur);
+        return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED);
     }
 
     struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector<int64_t> & ne, size_t offset, bool required = true) {
@@ -3568,6 +4239,44 @@ struct llama_model_loader {
         std::vector<no_init<uint8_t>> read_buf;
         std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;
 
+#if defined(GGML_USE_CUDA)
+        // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
+        // NVMe raid configurations might require more / larger buffers.
+        constexpr size_t n_buffers = 4;
+        constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
+
+        std::vector<ggml_backend_buffer_t> host_buffers;
+        std::vector<void*> host_ptrs;
+        std::vector<ggml_backend_event_t> events;
+        size_t buffer_idx = 0; // buffer to use for async loads
+
+        ggml_backend_t cuda_backend = nullptr;
+        if (!use_mmap && !check_tensors) {
+            // When not using mmaped io use async uploads from pinned memory to GPU memory.
+            // First determine if the CUDA backend is active, and if so, determine the device ID.
+            ggml_backend_buffer_t buf = bufs_mmap.count(0) ? bufs_mmap.at(0) : nullptr;
+            if (buf) {
+                ggml_backend_buffer_type_t buffer_type = ggml_backend_buffer_get_type(buf);
+                for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
+                    auto * cuda_buffer_type = ggml_backend_cuda_buffer_type(i);
+                    if (buffer_type == cuda_buffer_type) {
+                        cuda_backend = ggml_backend_cuda_init(i);
+                        break;
+                    }
+                }
+            }
+
+            // If the cuda backend is active create pinned memory buffers and events for synchronisation.
+            if (cuda_backend) {
+                for (size_t idx = 0; idx < n_buffers; ++idx) {
+                    host_buffers.emplace_back(ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buffer_size));
+                    host_ptrs.emplace_back(ggml_backend_buffer_get_base(host_buffers[idx]));
+                    events.emplace_back(ggml_backend_event_new(cuda_backend));
+                }
+            }
+        }
+#endif
+
         for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
             const auto * weight = get_weight(ggml_get_name(cur));
             if (weight == nullptr) {
@@ -3623,12 +4332,36 @@ struct llama_model_loader {
                         }));
                     }
                 } else {
-                    read_buf.resize(n_size);
-                    file->seek(weight->offs, SEEK_SET);
-                    file->read_raw(read_buf.data(), n_size);
-                    ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
-                    if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
-                        throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+#if defined(GGML_USE_CUDA)
+                    // If cuda_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
+                    if (cuda_backend) {
+                        file->seek(weight->offs, SEEK_SET);
+
+                        size_t bytes_read = 0;
+
+                        while (bytes_read < n_size) {
+                            size_t read_iteration = std::min<size_t>(buffer_size, n_size - bytes_read);
+
+                            ggml_backend_event_synchronize(events[buffer_idx]);
+                            file->read_raw(host_ptrs[buffer_idx], read_iteration);
+                            ggml_backend_tensor_set_async(cuda_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
+                            ggml_backend_event_record(events[buffer_idx]);
+
+                            bytes_read += read_iteration;
+                            ++buffer_idx;
+                            buffer_idx %= n_buffers;
+                        }
+                    }
+                    else
+#endif
+                    {
+                        read_buf.resize(n_size);
+                        file->seek(weight->offs, SEEK_SET);
+                        file->read_raw(read_buf.data(), n_size);
+                        ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
+                        if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
+                            throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+                        }
                     }
                 }
             }
@@ -3636,6 +4369,18 @@ struct llama_model_loader {
             size_done += n_size;
         }
 
+#if defined(GGML_USE_CUDA)
+        // free temporary resources used for async cuda uploads
+        if (cuda_backend) {
+            for (size_t idx = 0; idx < n_buffers;++idx) {
+                ggml_backend_event_synchronize(events[idx]);
+                ggml_backend_event_free(events[idx]);
+                ggml_backend_buffer_free(host_buffers[idx]);
+            }
+            ggml_backend_free(cuda_backend);
+        }
+#endif
+
         // check validation results
         bool validation_failed = false;
         for (auto & future : validation_result) {
@@ -3704,40 +4449,39 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
     }
 
     switch (ftype) {
-        case LLAMA_FTYPE_ALL_F32:     return "all F32";
-        case LLAMA_FTYPE_MOSTLY_F16:  return "F16";
-        case LLAMA_FTYPE_MOSTLY_BF16: return "BF16";
-        case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0";
-        case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
-        case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
-                                      return "Q4_1, some F16";
-        case LLAMA_FTYPE_MOSTLY_Q5_0: return "Q5_0";
-        case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1";
-        case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
-
-        // K-quants
-        case LLAMA_FTYPE_MOSTLY_Q2_K:   return "Q2_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q6_K:   return "Q6_K";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_S:  return "IQ2_S - 2.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_M:  return "IQ2_M - 2.7 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_S  :return "IQ1_S - 1.5625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_M  :return "IQ1_M - 1.75 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_S:  return "IQ3_S - 3.4375 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_M:  return "IQ3_S mix - 3.66 bpw";
+        case LLAMA_FTYPE_ALL_F32:         return "all F32";
+        case LLAMA_FTYPE_MOSTLY_F16:      return "F16";
+        case LLAMA_FTYPE_MOSTLY_BF16:     return "BF16";
+        case LLAMA_FTYPE_MOSTLY_Q4_0:     return "Q4_0";
+        case LLAMA_FTYPE_MOSTLY_Q4_1:     return "Q4_1";
+        case LLAMA_FTYPE_MOSTLY_Q5_0:     return "Q5_0";
+        case LLAMA_FTYPE_MOSTLY_Q5_1:     return "Q5_1";
+        case LLAMA_FTYPE_MOSTLY_Q8_0:     return "Q8_0";
+        case LLAMA_FTYPE_MOSTLY_Q2_K:     return "Q2_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q2_K_S:   return "Q2_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_S:   return "Q5_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:   return "Q5_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q6_K:     return "Q6_K";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:  return "IQ2_XXS - 2.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:   return "IQ2_XS - 2.3125 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:    return "IQ2_S - 2.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:    return "IQ2_M - 2.7 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS:   return "IQ3_XS - 3.3 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:  return "IQ3_XXS - 3.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:    return "IQ3_S - 3.4375 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:    return "IQ3_S mix - 3.66 bpw";
+        case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: return "Q4_0_4_4";
+        case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: return "Q4_0_4_8";
+        case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: return "Q4_0_8_8";
 
         default: return "unknown, may not work";
     }
@@ -3745,37 +4489,64 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
 
 static const char * llama_model_type_name(e_model type) {
     switch (type) {
-        case MODEL_22M:    return "22M";
-        case MODEL_33M:    return "33M";
-        case MODEL_109M:   return "109M";
-        case MODEL_137M:   return "137M";
-        case MODEL_0_5B:   return "0.5B";
-        case MODEL_1B:     return "1B";
-        case MODEL_2B:     return "2B";
-        case MODEL_3B:     return "3B";
-        case MODEL_7B:     return "7B";
-        case MODEL_8B:     return "8B";
-        case MODEL_12B:    return "12B";
-        case MODEL_13B:    return "13B";
-        case MODEL_14B:    return "14B";
-        case MODEL_15B:    return "15B";
-        case MODEL_20B:    return "20B";
-        case MODEL_30B:    return "30B";
-        case MODEL_34B:    return "34B";
-        case MODEL_35B:    return "35B";
-        case MODEL_40B:    return "40B";
-        case MODEL_65B:    return "65B";
-        case MODEL_70B:    return "70B";
-        case MODEL_314B:   return "314B";
-        case MODEL_SMALL:  return "0.1B";
-        case MODEL_MEDIUM: return "0.4B";
-        case MODEL_LARGE:  return "0.8B";
-        case MODEL_XL:     return "1.5B";
-        case MODEL_A2_7B:  return "A2.7B";
-        case MODEL_8x7B:   return "8x7B";
-        case MODEL_8x22B:  return "8x22B";
-        case MODEL_16x12B: return "16x12B";
-        default:           return "?B";
+        case MODEL_14M:           return "14M";
+        case MODEL_17M:           return "17M";
+        case MODEL_22M:           return "22M";
+        case MODEL_33M:           return "33M";
+        case MODEL_60M:           return "60M";
+        case MODEL_70M:           return "70M";
+        case MODEL_80M:           return "80M";
+        case MODEL_109M:          return "109M";
+        case MODEL_137M:          return "137M";
+        case MODEL_160M:          return "160M";
+        case MODEL_220M:          return "220M";
+        case MODEL_250M:          return "250M";
+        case MODEL_270M:          return "270M";
+        case MODEL_335M:          return "335M";
+        case MODEL_410M:          return "410M";
+        case MODEL_450M:          return "450M";
+        case MODEL_770M:          return "770M";
+        case MODEL_780M:          return "780M";
+        case MODEL_0_5B:          return "0.5B";
+        case MODEL_1B:            return "1B";
+        case MODEL_1_3B:          return "1.3B";
+        case MODEL_1_4B:          return "1.4B";
+        case MODEL_2B:            return "2B";
+        case MODEL_2_8B:          return "2.8B";
+        case MODEL_3B:            return "3B";
+        case MODEL_4B:            return "4B";
+        case MODEL_6B:            return "6B";
+        case MODEL_6_9B:          return "6.9B";
+        case MODEL_7B:            return "7B";
+        case MODEL_8B:            return "8B";
+        case MODEL_9B:            return "9B";
+        case MODEL_11B:           return "11B";
+        case MODEL_12B:           return "12B";
+        case MODEL_13B:           return "13B";
+        case MODEL_14B:           return "14B";
+        case MODEL_15B:           return "15B";
+        case MODEL_16B:           return "16B";
+        case MODEL_20B:           return "20B";
+        case MODEL_30B:           return "30B";
+        case MODEL_34B:           return "34B";
+        case MODEL_35B:           return "35B";
+        case MODEL_40B:           return "40B";
+        case MODEL_65B:           return "65B";
+        case MODEL_70B:           return "70B";
+        case MODEL_236B:          return "236B";
+        case MODEL_314B:          return "314B";
+        case MODEL_SMALL:         return "0.1B";
+        case MODEL_MEDIUM:        return "0.4B";
+        case MODEL_LARGE:         return "0.8B";
+        case MODEL_XL:            return "1.5B";
+        case MODEL_A2_7B:         return "A2.7B";
+        case MODEL_8x7B:          return "8x7B";
+        case MODEL_8x22B:         return "8x22B";
+        case MODEL_16x12B:        return "16x12B";
+        case MODEL_10B_128x3_66B: return "10B+128x3.66B";
+        case MODEL_57B_A14B:      return "57B.A14B";
+        case MODEL_27B:           return "27B";
+        default:                  return "?B";
     }
 }
 
@@ -3785,6 +4556,7 @@ static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
         case LLAMA_VOCAB_TYPE_SPM:  return "SPM";
         case LLAMA_VOCAB_TYPE_BPE:  return "BPE";
         case LLAMA_VOCAB_TYPE_WPM:  return "WPM";
+        case LLAMA_VOCAB_TYPE_UGM:  return "UGM";
         default:                    return "unknown";
     }
 }
@@ -3817,20 +4589,18 @@ static void llm_load_hparams(
     ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
 
     // get hparams kv
-    ml.get_key(LLM_KV_VOCAB_SIZE,           hparams.n_vocab,       false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
+    ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
 
     // everything past this point is not vocab-related
     if (hparams.vocab_only) {
         return;
     }
 
-    ml.get_key(LLM_KV_CONTEXT_LENGTH,       hparams.n_ctx_train);
-    ml.get_key(LLM_KV_EMBEDDING_LENGTH,     hparams.n_embd);
-    ml.get_key(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff);
-    ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
-    ml.get_key(LLM_KV_BLOCK_COUNT,          hparams.n_layer);
-    ml.get_key(LLM_KV_EXPERT_COUNT,         hparams.n_expert,      false);
-    ml.get_key(LLM_KV_EXPERT_USED_COUNT,    hparams.n_expert_used, false);
+    ml.get_key(LLM_KV_CONTEXT_LENGTH,    hparams.n_ctx_train);
+    ml.get_key(LLM_KV_EMBEDDING_LENGTH,  hparams.n_embd);
+    ml.get_key(LLM_KV_BLOCK_COUNT,       hparams.n_layer);
+    ml.get_key(LLM_KV_EXPERT_COUNT,      hparams.n_expert,      false);
+    ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
 
     GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
     GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
@@ -3840,16 +4610,25 @@ static void llm_load_hparams(
         GGML_ASSERT(hparams.n_expert_used == 0);
     }
 
+    // zero-out the per-layer hparams
+    std::fill(hparams.n_head_arr.begin(),    hparams.n_head_arr.end(),    0);
+    std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
+    std::fill(hparams.n_ff_arr.begin(),      hparams.n_ff_arr.end(),      0);
+
+    ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer);
+    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer);
+
     // n_head_kv is optional, default to n_head
-    hparams.n_head_kv = hparams.n_head;
-    ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv, false);
+    hparams.n_head_kv_arr = hparams.n_head_arr;
+
+    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false);
 
     bool rope_finetuned = false;
     ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
     hparams.rope_finetuned = rope_finetuned;
 
-    hparams.n_yarn_orig_ctx = hparams.n_ctx_train;
-    ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_yarn_orig_ctx, false);
+    hparams.n_ctx_orig_yarn = hparams.n_ctx_train;
+    ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false);
 
     // rope_freq_base (optional)
     hparams.rope_freq_base_train = 10000.0f;
@@ -3868,27 +4647,35 @@ static void llm_load_hparams(
     }
     hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
 
-    // sanity check for n_rot (optional)
-    {
-        hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
+    ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
+
+    // non-transformer models do not have attention heads
+    if (hparams.n_head() > 0) {
+        // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
+        // gpt-j n_rot = rotary_dim
+
+        hparams.n_embd_head_k = hparams.n_embd / hparams.n_head();
+        ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
+
+        hparams.n_embd_head_v = hparams.n_embd / hparams.n_head();
+        ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
+
+        // sanity check for n_rot (optional)
+        hparams.n_rot = hparams.n_embd_head_k;
 
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
 
         if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
-            if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
-                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
+            if (hparams.n_rot != hparams.n_embd_head_k) {
+                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
             }
         }
-        // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
-        // gpt-j n_rot = rotary_dim
+    } else {
+        hparams.n_rot = 0;
+        hparams.n_embd_head_k = 0;
+        hparams.n_embd_head_v = 0;
     }
 
-    hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
-    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
-
-    hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
-    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
-
     // arch-specific KVs
     switch (model.arch) {
         case LLM_ARCH_LLAMA:
@@ -3905,11 +4692,13 @@ static void llm_load_hparams(
                     switch (hparams.n_layer) {
                         case 22: model.type = e_model::MODEL_1B; break;
                         case 26: model.type = e_model::MODEL_3B; break;
-                        case 32: model.type = hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break;
+                        // granite uses a vocab with len 49152
+                        case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
+                        case 36: model.type = e_model::MODEL_8B; break; // granite
                         case 40: model.type = e_model::MODEL_13B; break;
                         case 48: model.type = e_model::MODEL_34B; break;
                         case 60: model.type = e_model::MODEL_30B; break;
-                        case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
+                        case 80: model.type = hparams.n_head() == hparams.n_head_kv() ? e_model::MODEL_65B : e_model::MODEL_70B; break;
                         default: model.type = e_model::MODEL_UNKNOWN;
                     }
                 }
@@ -3967,14 +4756,6 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
-        case LLM_ARCH_PERSIMMON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 36: model.type = e_model::MODEL_8B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
         case LLM_ARCH_REFACT:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -4086,16 +4867,20 @@ static void llm_load_hparams(
                 switch (hparams.n_layer) {
                     case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = hparams.n_head == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
+                    case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
                     case 80: model.type = e_model::MODEL_70B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
         case LLM_ARCH_QWEN2MOE:
             {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_A2_7B; break;
+                    case 28: model.type = e_model::MODEL_57B_A14B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -4116,8 +4901,25 @@ static void llm_load_hparams(
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_3B; break;
+                    case 40: model.type = e_model::MODEL_14B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
+
+                // for backward compatibility ; see: https://github.com/ggerganov/llama.cpp/pull/8931
+                if ((hparams.n_layer == 32 || hparams.n_layer == 40) && hparams.n_ctx_train == 4096) {
+                    // default value for Phi-3-mini-4k-instruct and Phi-3-medium-4k-instruct
+                    hparams.n_swa = 2047;
+                } else if (hparams.n_layer == 32 && hparams.n_head_kv(0) == 32 && hparams.n_ctx_train == 131072) {
+                    // default value for Phi-3-mini-128k-instruct
+                    hparams.n_swa = 262144;
+                } else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) {
+                    // default value for Phi-3-medium-128k-instruct
+                    hparams.n_swa = 131072;
+                }
+                bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+                if (!found_swa && hparams.n_swa == 0) {
+                    throw std::runtime_error("invalid value for sliding_window");
+                }
             } break;
         case LLM_ARCH_PLAMO:
             {
@@ -4143,7 +4945,7 @@ static void llm_load_hparams(
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 42: model.type = e_model::MODEL_SMALL; break;
+                    case 42: model.type = e_model::MODEL_7B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -4175,13 +4977,31 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                }
             } break;
-        case LLM_ARCH_STARCODER2:
+        case LLM_ARCH_GEMMA2:
             {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                hparams.n_swa = 4096; // default value of gemma 2
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
+                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
+                hparams.attn_soft_cap = true;
+
+                switch (hparams.n_layer) {
+                    case 26: model.type = e_model::MODEL_2B; break;
+                    case 42: model.type = e_model::MODEL_9B; break;
+                    case 46: model.type = e_model::MODEL_27B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_STARCODER2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
                     case 30: model.type = e_model::MODEL_3B; break;
                     case 32: model.type = e_model::MODEL_7B; break;
                     case 40: model.type = e_model::MODEL_15B; break;
+                    case 52: model.type = e_model::MODEL_20B; break; // granite
+                    case 88: model.type = e_model::MODEL_34B; break; // granite
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -4256,6 +5076,165 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_OPENELM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                case 16: model.type = e_model::MODEL_270M; break;
+                case 20: model.type = e_model::MODEL_450M; break;
+                case 28: model.type = e_model::MODEL_1B; break;
+                case 36: model.type = e_model::MODEL_3B; break;
+                default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_GPTNEOX:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
+                switch (hparams.n_layer) {
+                    case 6:
+                        switch (hparams.n_ff()) {
+                            case 512: model.type = e_model::MODEL_14M; break;
+                            case 2048: model.type = e_model::MODEL_70M; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 12:
+                        switch (hparams.n_ff()) {
+                            case 3072: model.type = e_model::MODEL_160M; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 16:
+                        switch (hparams.n_ff()) {
+                            case 8192: model.type = e_model::MODEL_1B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 24:
+                        switch (hparams.n_ff()) {
+                            case 4096: model.type = e_model::MODEL_410M; break;
+                            case 8192: model.type = e_model::MODEL_1_4B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 32:
+                        switch (hparams.n_ff()) {
+                            case 10240: model.type = e_model::MODEL_2_8B; break;
+                            case 16384: model.type = e_model::MODEL_6_9B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 36:
+                        switch (hparams.n_ff()) {
+                            case 20480: model.type = e_model::MODEL_12B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 44:
+                        switch (hparams.n_ff()) {
+                            case 24576: model.type = e_model::MODEL_20B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_ARCTIC:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                if (hparams.n_expert == 128) {
+                    switch (hparams.n_layer) {
+                        case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
+                        default: model.type = e_model::MODEL_UNKNOWN;
+                    }
+                } else {
+                    model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_DEEPSEEK2:
+            {
+                bool is_lite = (hparams.n_layer == 27);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+                if (!is_lite) {
+                    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
+                }
+                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
+
+                switch (hparams.n_layer) {
+                    case 27: model.type = e_model::MODEL_16B; break;
+                    case 60: model.type = e_model::MODEL_236B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_CHATGLM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 28: model.type = e_model::MODEL_6B; break;
+                    case 40: model.type = e_model::MODEL_9B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_BITNET:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 26: model.type = e_model::MODEL_3B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_T5:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+
+                uint32_t dec_start_token_id;
+                if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
+                    hparams.dec_start_token_id = dec_start_token_id;
+                }
+
+                switch (hparams.n_layer) {
+                    case 6:  model.type = e_model::MODEL_60M;  break; // t5-small
+                    case 8:  model.type = e_model::MODEL_80M;  break; // flan-t5-small
+                    case 12:
+                        switch (hparams.n_ff()) {
+                            case 3072: model.type = e_model::MODEL_220M; break; // t5-base
+                            case 2048: model.type = e_model::MODEL_250M; break; // flan-t5-base
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 24:
+                        switch (hparams.n_ff()) {
+                            case 4096:  model.type = e_model::MODEL_770M; break; // t5-large
+                            case 2816:  model.type = e_model::MODEL_780M; break; // flan-t5-large
+                            case 16384: model.type = e_model::MODEL_3B;   break; // t5-3b
+                            case 5120:  model.type = e_model::MODEL_3B;   break; // flan-t5-xl
+                            case 65536: model.type = e_model::MODEL_11B;  break; // t5-11b
+                            case 10240: model.type = e_model::MODEL_11B;  break; // flan-t5-xxl
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_T5ENCODER:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+                model.type = e_model::MODEL_UNKNOWN;
+            } break;
+        case LLM_ARCH_JAIS:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1_3B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    /* TODO: add variants */
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         default: (void)0;
     }
 
@@ -4268,12 +5247,6 @@ static void llm_load_hparams(
     hparams.rope_type = llama_rope_type(&model);
 }
 
-// TODO: This should probably be in llama.h
-static std::vector<llama_vocab::id> llama_tokenize_internal(
-    const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special = false
-);
-static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
-
 static void llm_load_vocab(
         llama_model_loader & ml,
         llama_model & model) {
@@ -4316,40 +5289,6 @@ static void llm_load_vocab(
             vocab.special_pad_id  = -1;
             vocab.special_cls_id  = -1;
             vocab.special_mask_id = -1;
-
-            // For Fill-In-the-Middle (FIM)/infill models which where converted
-            // prior to support of FIM special tokens in GGUF, the following
-            // will allow those models to continue to work. The general names
-            // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
-            // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
-            // new versions of these models have been published.
-            std::string gen_name;
-            ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
-
-            std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
-                [](unsigned char c){ return std::tolower(c); });
-
-            if (gen_name.find("code") != std::string::npos) {
-                if (model.arch == LLM_ARCH_LLAMA) {
-                    vocab.special_prefix_id = 32007;
-                    vocab.special_suffix_id = 32008;
-                    vocab.special_middle_id = 32009;
-                    vocab.special_eot_id    = 32010;
-                } else if (model.arch == LLM_ARCH_GEMMA) {
-                    vocab.special_prefix_id = 67;
-                    vocab.special_suffix_id = 69;
-                    vocab.special_middle_id = 68;
-                    // TODO: this is not EOT, it is "file separator" token, needs fix
-                    //       https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
-                    //vocab.special_eot_id    = 70;
-                    vocab.special_eot_id    = 107;
-                }
-            }
-
-            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
-            if (add_space_prefix_keyidx != -1) {
-                vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
-            } // The default value of add_space_prefix is true.
         } else if (tokenizer_model == "bert") {
             vocab.type = LLAMA_VOCAB_TYPE_WPM;
 
@@ -4361,16 +5300,9 @@ static void llm_load_vocab(
             vocab.special_pad_id  = 0;
             vocab.special_cls_id  = 101;
             vocab.special_mask_id = 103;
-            vocab.add_space_prefix = false;
-        } else {
-            if (tokenizer_model == "gpt2") {
-                vocab.type = LLAMA_VOCAB_TYPE_BPE;
-            } else {
-                LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str());
-                LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
-                vocab.type = LLAMA_VOCAB_TYPE_SPM;
-                return;
-            }
+        } else if (tokenizer_model == "gpt2") {
+            vocab.type = LLAMA_VOCAB_TYPE_BPE;
+
             // read bpe merges and populate bpe ranks
             const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
             if (merges_keyidx == -1) {
@@ -4378,7 +5310,6 @@ static void llm_load_vocab(
             }
 
             const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
-
             for (int i = 0; i < n_merges; i++) {
                 const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
                 GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
@@ -4404,10 +5335,43 @@ static void llm_load_vocab(
             vocab.special_pad_id  = -1;
             vocab.special_cls_id  = -1;
             vocab.special_mask_id = -1;
+        } else if (tokenizer_model == "t5") {
+            vocab.type = LLAMA_VOCAB_TYPE_UGM;
+
+            // default special tokens
+            vocab.special_bos_id  = -1;
+            vocab.special_eos_id  = 1;
+            vocab.special_unk_id  = 2;
+            vocab.special_sep_id  = -1;
+            vocab.special_pad_id  = 0;
+            vocab.special_cls_id  = -1;
+            vocab.special_mask_id = -1;
+
+            const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
+            if (precompiled_charsmap_keyidx != -1) {
+                size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
+                const char * precompiled_charsmap = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
+                vocab.precompiled_charsmap.assign(precompiled_charsmap, precompiled_charsmap + n_precompiled_charsmap);
+#ifdef IS_BIG_ENDIAN
+                // correct endiannes of data in precompiled_charsmap binary blob
+                uint32_t * xcda_blob_size = (uint32_t *) &vocab.precompiled_charsmap[0];
+                *xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
+                assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
+                size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
+                uint32_t * xcda_array = (uint32_t *) &vocab.precompiled_charsmap[sizeof(uint32_t)];
+                for (size_t i = 0; i < xcda_array_size; ++i) {
+                    xcda_array[i] = __builtin_bswap32(xcda_array[i]);
+                }
+#endif
+            }
+        } else {
+            throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
         }
 
         // for now, only BPE models have pre-tokenizers
         if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
+            vocab.tokenizer_add_space_prefix = false;
+            vocab.tokenizer_clean_spaces = true;
             if (tokenizer_pre.empty()) {
                 LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
                 LLAMA_LOG_WARN("%s:                                             \n", __func__);
@@ -4417,20 +5381,23 @@ static void llm_load_vocab(
                 LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
                 LLAMA_LOG_WARN("%s:                                             \n", __func__);
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            } else if (
-                    tokenizer_pre == "default") {
+            } else if (tokenizer_pre == "default") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
             } else if (
                     tokenizer_pre == "llama3"   ||
                     tokenizer_pre == "llama-v3" ||
                     tokenizer_pre == "llama-bpe") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
+                vocab.tokenizer_ignore_merges = true;
+                vocab.tokenizer_add_bos = true;
             } else if (
                     tokenizer_pre == "deepseek-llm") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
+                vocab.tokenizer_clean_spaces = false;
             } else if (
                     tokenizer_pre == "deepseek-coder") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
+                vocab.tokenizer_clean_spaces = false;
             } else if (
                     tokenizer_pre == "falcon") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
@@ -4442,10 +5409,12 @@ static void llm_load_vocab(
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER;
             } else if (
                     tokenizer_pre == "gpt-2"   ||
+                    tokenizer_pre == "phi-2"   ||
                     tokenizer_pre == "jina-es" ||
                     tokenizer_pre == "jina-de" ||
                     tokenizer_pre == "jina-v2-es" ||
-                    tokenizer_pre == "jina-v2-de") {
+                    tokenizer_pre == "jina-v2-de" ||
+                    tokenizer_pre == "jina-v2-code") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
             } else if (
                     tokenizer_pre == "refact") {
@@ -4453,9 +5422,11 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "command-r") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
+                vocab.tokenizer_clean_spaces = false;
             } else if (
                 tokenizer_pre == "qwen2") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+                vocab.tokenizer_clean_spaces = false;
             } else if (
                 tokenizer_pre == "stablelm2") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
@@ -4465,12 +5436,68 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "dbrx") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX;
+            } else if (
+                tokenizer_pre == "smaug-bpe") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
+            } else if (
+                tokenizer_pre == "poro-chat") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "chatglm-bpe") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
+                vocab.special_bos_id  = -1;
+            } else if (
+                tokenizer_pre == "viking") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "jais") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
+            } else if (
+                tokenizer_pre == "tekken") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_TEKKEN;
+                vocab.tokenizer_clean_spaces = false;
+                vocab.tokenizer_ignore_merges = true;
+                vocab.tokenizer_add_bos = true;
+            } else if (
+                tokenizer_pre == "smollm") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
+                vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "codeshell") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
+            } else if (
+                tokenizer_pre == "bloom") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_BLOOM;
+            } else if (
+                tokenizer_pre == "gpt3-finnish") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_space_prefix = true;
+            vocab.tokenizer_clean_spaces = false;
+            vocab.tokenizer_add_bos = true;
+            vocab.tokenizer_add_eos = false;
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_space_prefix = false;
+            vocab.tokenizer_clean_spaces = true;
+            vocab.tokenizer_add_bos = true;
+            vocab.tokenizer_add_eos = false;
+        } else if (vocab.type == LLAMA_VOCAB_TYPE_UGM) {
+            vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+            vocab.tokenizer_add_bos = false;
+            vocab.tokenizer_add_eos = true;
         } else {
             vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
         }
+
+        ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX,      vocab.tokenizer_add_space_prefix,         false);
+        ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.tokenizer_remove_extra_whitespaces, false);
     }
 
     const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
@@ -4499,18 +5526,70 @@ static void llm_load_vocab(
         GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
 
         vocab.token_to_id[word] = i;
+        vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size());
 
         auto & token_data = vocab.id_to_token[i];
         token_data.text  = std::move(word);
         token_data.score = scores ? scores[i] : 0.0f;
-        token_data.type  = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL;
+        token_data.attr  = LLAMA_TOKEN_ATTR_NORMAL;
+
+        if (toktypes) {  //TODO: remove, required until per token attributes are available from GGUF file
+            switch(toktypes[i]) {
+                case LLAMA_TOKEN_TYPE_UNKNOWN:      token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN;      break;
+                case LLAMA_TOKEN_TYPE_UNUSED:       token_data.attr = LLAMA_TOKEN_ATTR_UNUSED;       break;
+                case LLAMA_TOKEN_TYPE_NORMAL:       token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;       break;
+                case LLAMA_TOKEN_TYPE_CONTROL:      token_data.attr = LLAMA_TOKEN_ATTR_CONTROL;      break;
+                case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
+                case LLAMA_TOKEN_TYPE_BYTE:         token_data.attr = LLAMA_TOKEN_ATTR_BYTE;         break;
+                case LLAMA_TOKEN_TYPE_UNDEFINED:    token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
+                default:                            token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
+            }
+        }
     }
     GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
 
     // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
     if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+        // For Fill-In-the-Middle (FIM)/infill models which where converted
+        // prior to support of FIM special tokens in GGUF, the following
+        // will allow those models to continue to work. The general names
+        // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
+        // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
+        // new versions of these models have been published.
+        std::string gen_name;
+        ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
+
+        std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
+            [](unsigned char c){ return std::tolower(c); });
+
+        if (gen_name.find("code") != std::string::npos) {
+            if (model.arch == LLM_ARCH_LLAMA
+              && 32010 < vocab.id_to_token.size()
+              && vocab.id_to_token[32007].text.find("<PRE>") != std::string::npos
+              && vocab.id_to_token[32008].text.find("<SUF>") != std::string::npos
+              && vocab.id_to_token[32009].text.find("<MID>") != std::string::npos
+              && vocab.id_to_token[32010].text.find("<EOT>") != std::string::npos) {
+                vocab.special_prefix_id = 32007;
+                vocab.special_suffix_id = 32008;
+                vocab.special_middle_id = 32009;
+                vocab.special_eot_id    = 32010;
+            } else if (model.arch == LLM_ARCH_GEMMA
+              && 107 < vocab.id_to_token.size()
+              && vocab.id_to_token[67].text == "<|fim_prefix|>"
+              && vocab.id_to_token[69].text == "<|fim_suffix|>"
+              && vocab.id_to_token[68].text == "<|fim_middle|>"
+              && vocab.id_to_token[107].text == "<end_of_turn>") {
+                vocab.special_prefix_id = 67;
+                vocab.special_suffix_id = 69;
+                vocab.special_middle_id = 68;
+                // TODO: this is not EOT, it is "file separator" token, needs fix
+                //       https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
+                //vocab.special_eot_id    = 70;
+                vocab.special_eot_id    = 107;
+            }
+        }
         try {
-            vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
+            vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n');
         } catch (const std::exception & e) {
             LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
             vocab.linefeed_id = vocab.special_pad_id;
@@ -4537,6 +5616,7 @@ static void llm_load_vocab(
             { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_suffix_id },
             { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id },
             { LLM_KV_TOKENIZER_EOT_ID,    vocab.special_eot_id    },
+            { LLM_KV_TOKENIZER_EOM_ID,    vocab.special_eom_id    },
         };
 
         for (const auto & it : special_token_types) {
@@ -4560,10 +5640,10 @@ static void llm_load_vocab(
             bool temp = true;
 
             if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
-                vocab.special_add_bos = int(temp);
+                vocab.tokenizer_add_bos = temp;
             }
             if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
-                vocab.special_add_eos = int(temp);
+                vocab.tokenizer_add_eos = temp;
             }
         }
 
@@ -4580,7 +5660,8 @@ static void llm_load_vocab(
                         (t.first == "<|eot_id|>" ||
                          t.first == "<|im_end|>" ||
                          t.first == "<|end|>" ||
-                         t.first == "<end_of_turn>"
+                         t.first == "<end_of_turn>" ||
+                         t.first == "<|endoftext|>"
                         )
                    ) {
                     vocab.special_eot_id = t.second;
@@ -4588,100 +5669,103 @@ static void llm_load_vocab(
                 }
             }
         }
+
+        // find EOM token: "<|eom_id|>"
+        //
+        // TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOM_ID
+        //       for now, we apply this workaround to find the EOM token based on its text
+        if (vocab.special_eom_id == -1) {
+            const auto & t = vocab.token_to_id.find("<|eom_id|>");
+            if (t != vocab.token_to_id.end()) {
+                vocab.special_eom_id = t->second;
+            }
+        }
     }
 
     // build special tokens cache
     {
-        // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type,
-        //  and will always be correctly labeled in 'added_tokens.json' etc.
-        // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed
-        //  to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer
-        //  are special tokens.
-        // From testing, this appears to correlate 1:1 with special tokens.
-        //
+        for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
+            if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
+                vocab.cache_special_tokens.push_back(id);
+            }
+        }
 
-        // Counting special tokens and verifying in only one direction
-        //  is sufficient to detect difference in those two sets.
-        //
-        uint32_t special_tokens_count_by_type = 0;
-        uint32_t special_tokens_count_from_verification = 0;
+        std::sort(vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
+            [&] (const llama_vocab::id a, const llama_vocab::id b) {
+                return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
+            }
+        );
 
-        bool special_tokens_definition_mismatch = false;
+        LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
+    }
 
-        for (const auto & t : vocab.token_to_id) {
-            const auto & token = t.first;
-            const auto & id    = t.second;
+    // build token to piece cache
+    {
+        size_t size_cache = 0;
 
-            // Count all non-normal tokens in the vocab while iterating
-            if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
-                special_tokens_count_by_type++;
-            }
+        std::vector<llama_vocab::token> cache_token_to_piece(n_vocab);
 
-            // Skip single character tokens
-            if (token.length() > 1) {
-                bool is_tokenizable = false;
+        for (uint32_t id = 0; id < n_vocab; ++id) {
+            cache_token_to_piece[id] = llama_token_to_piece(&model, id, true);
 
-                // Split token string representation in two, in all possible ways
-                //  and check if both halves can be matched to a valid token
-                for (unsigned i = 1; i < token.length();) {
-                    const auto left  = token.substr(0, i);
-                    const auto right = token.substr(i);
+            size_cache += cache_token_to_piece[id].size();
+        }
 
-                    // check if we didnt partition in the middle of a utf sequence
-                    auto utf = utf8_len(left.at(left.length() - 1));
+        std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
 
-                    if (utf == 1) {
-                        if (vocab.token_to_id.find(left)  != vocab.token_to_id.end() &&
-                            vocab.token_to_id.find(right) != vocab.token_to_id.end() ) {
-                            is_tokenizable = true;
-                            break;
-                        }
-                        i++;
-                    } else {
-                        // skip over the rest of multibyte utf sequence
-                        i += utf - 1;
-                    }
+        LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
+    }
+
+    // Handle per token attributes
+    //NOTE: Each model customizes per token attributes.
+    //NOTE: Per token attributes are missing from the GGUF file.
+    //TODO: Extract attributes from GGUF file.
+    {
+        auto _contains_any = [] (const std::string &str, const std::vector<std::string> &substrs) -> bool {
+            for (auto substr : substrs) {
+                if (str.find(substr) < std::string::npos) {
+                    return true;
                 }
+            }
+            return false;
+        };
 
-                if (!is_tokenizable) {
-                    // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1
-                    //  it's faster to re-filter them here, since there are way less candidates now
+        auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) {
+            uint32_t current = vocab.id_to_token.at(id).attr;
+            current = value ? (current | attr) : (current & ~attr);
+            vocab.id_to_token[id].attr = (llama_token_attr) current;
+        };
 
-                    // Calculate a total "utf" length of a token string representation
-                    size_t utf8_str_len = 0;
-                    for (unsigned i = 0; i < token.length();) {
-                        utf8_str_len++;
-                        i += utf8_len(token.at(i));
-                    }
+        auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
+            _set_tokenid_attr(vocab.token_to_id.at(token), attr, value);
+        };
 
-                    // And skip the ones which are one character
-                    if (utf8_str_len > 1) {
-                        // At this point what we have left are special tokens only
-                        vocab.special_tokens_cache[token] = id;
+        std::string model_name;
+        std::string tokenizer_pre;
 
-                        // Count manually found special tokens
-                        special_tokens_count_from_verification++;
+        ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
+        ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
 
-                        // If this manually found special token is not marked as such, flag a mismatch
-                        if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) {
-                            special_tokens_definition_mismatch = true;
-                        }
-                    }
-                }
+        // model name to lowercase
+        std::transform(model_name.begin(), model_name.end(), model_name.begin(),
+            [] (const std::string::value_type x) {
+                return std::tolower(x);
             }
-        }
+        );
 
-        if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) {
-            LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n",
-                __func__,
-                special_tokens_count_from_verification, vocab.id_to_token.size(),
-                special_tokens_count_by_type, vocab.id_to_token.size()
-            );
-        } else {
-            LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n",
-                __func__,
-                special_tokens_count_from_verification, vocab.id_to_token.size()
-            );
+        // set attributes by model/tokenizer name
+        if (_contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})) {
+            _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
+        } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
+            for (auto id : vocab.cache_special_tokens) {
+                _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (auto token : {"</s>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (auto token : {"<unk>", "<s>", "<|endoftext|>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
+            }
         }
     }
 }
@@ -4692,43 +5776,78 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
 
     const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
 
+    auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
+        bool is_var = false;
+
+        std::vector<uint32_t> v;
+        for (uint32_t i = 0; i < n; ++i) {
+            v.push_back(f(i));
+            if (v[i] != v[0]) {
+                is_var = true;
+            }
+        }
+
+        std::stringstream ss;
+
+        if (is_var) {
+            ss << "[";
+            for (uint32_t i = 0; i < n; ++i) {
+                ss << v[i];
+                if (i < n - 1) {
+                    ss << ", ";
+                }
+            }
+            ss << "]";
+        } else {
+            ss << v[0];
+        }
+
+        return ss.str();
+    };
+
     // hparams
     LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
     LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch));
     LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
     LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
     LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
-    LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
-    LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
-    LLAMA_LOG_INFO("%s: n_head           = %u\n",     __func__, hparams.n_head);
-    LLAMA_LOG_INFO("%s: n_head_kv        = %u\n",     __func__, hparams.n_head_kv);
-    LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
-    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
-    LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
-    LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
-    LLAMA_LOG_INFO("%s: n_gqa            = %u\n",     __func__, hparams.n_gqa());
-    LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %u\n",     __func__, hparams.n_embd_k_gqa());
-    LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %u\n",     __func__, hparams.n_embd_v_gqa());
-    LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
-    LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
-    LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
-    LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
-    LLAMA_LOG_INFO("%s: f_logit_scale    = %.1e\n",   __func__, hparams.f_logit_scale);
-    LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
-    LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
-    LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
-    LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
-    LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
-    LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
-    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
-    LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
-    LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
-    LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
-    LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
-    LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
-    LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
-    LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
-    LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
+    LLAMA_LOG_INFO("%s: vocab_only       = %d\n",     __func__, hparams.vocab_only);
+
+    if (!hparams.vocab_only) {
+        LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
+        LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
+        LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
+        LLAMA_LOG_INFO("%s: n_head           = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head(il);    }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_head_kv        = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
+        LLAMA_LOG_INFO("%s: n_swa            = %u\n",     __func__, hparams.n_swa);
+        LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
+        LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
+        LLAMA_LOG_INFO("%s: n_gqa            = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il);        }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
+        LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
+        LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
+        LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
+        LLAMA_LOG_INFO("%s: f_logit_scale    = %.1e\n",   __func__, hparams.f_logit_scale);
+        LLAMA_LOG_INFO("%s: n_ff             = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
+        LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
+        LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
+        LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
+        LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
+        LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
+        LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
+        LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
+        LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
+        LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
+        LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
+        LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
+        LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
+        LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
+    }
+
     LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
     LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
     if (ml.n_elements >= 1e12) {
@@ -4763,6 +5882,23 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token        = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
     if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token        = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
     if (vocab.special_eot_id    != -1) { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,    vocab.id_to_token[vocab.special_eot_id].text.c_str() );    }
+
+    LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
+
+    if (model.arch == LLM_ARCH_DEEPSEEK2) {
+        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
+        LLAMA_LOG_INFO("%s: n_lora_q             = %d\n",     __func__, hparams.n_lora_q);
+        LLAMA_LOG_INFO("%s: n_lora_kv            = %d\n",     __func__, hparams.n_lora_kv);
+        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
+        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
+        LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
+    }
+
+    if (model.arch == LLM_ARCH_QWEN2MOE) {
+        LLAMA_LOG_INFO("%s: n_ff_exp         = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_ff_shexp       = %d\n",     __func__, hparams.n_ff_shexp);
+    }
 }
 
 // Returns false if cancelled by progress_callback
@@ -4780,19 +5916,12 @@ static bool llm_load_tensors(
 
     auto & hparams = model.hparams;
 
-#ifdef GGML_USE_SYCL
-    // disable MoE with SYCL until mul_mat_id is updated
-    if (hparams.n_expert > 0) {
-        n_gpu_layers = 0;
-    }
-#endif
-
     model.split_mode   = split_mode;
     model.main_gpu     = main_gpu;
     model.n_gpu_layers = n_gpu_layers;
 
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0);
+    const int n_layer     = hparams.n_layer;
+    const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0);
     bool use_mmap_buffer = true;
 
     // there is very little benefit to offloading the input layer, so always keep it on the CPU
@@ -4802,7 +5931,7 @@ static bool llm_load_tensors(
     model.buft_layer.resize(n_layer);
 
     // assign cpu layers
-    for (int64_t i = 0; i < i_gpu_start; ++i) {
+    for (int i = 0; i < i_gpu_start; ++i) {
         model.buft_layer[i] = llama_default_buffer_type_cpu(true);
     }
 
@@ -4832,7 +5961,7 @@ static bool llm_load_tensors(
 
         // assign the repeating layers to the devices according to the splits
         int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
-        for (int64_t i = i_gpu_start; i < n_layer; ++i) {
+        for (int i = i_gpu_start; i < n_layer; ++i) {
             int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
             model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu);
         }
@@ -4852,7 +5981,7 @@ static bool llm_load_tensors(
             split_buft = llama_default_buffer_type_offload(model, main_gpu);
         }
         // assign the repeating layers
-        for (int64_t i = i_gpu_start; i < n_layer; ++i) {
+        for (int i = i_gpu_start; i < n_layer; ++i) {
             model.buft_layer[i] = {
                 split_buft,
                 llama_default_buffer_type_offload(model, main_gpu)
@@ -4875,7 +6004,7 @@ static bool llm_load_tensors(
     buft_layer_count[model.buft_input.buft_matrix]++;
     buft_layer_count[model.buft_output.buft]++;
     buft_layer_count[model.buft_output.buft_matrix]++;
-    for (int64_t i = 0; i < n_layer; ++i) {
+    for (int i = 0; i < n_layer; ++i) {
         buft_layer_count[model.buft_layer[i].buft]++;
         buft_layer_count[model.buft_layer[i].buft_matrix]++;
     }
@@ -4905,26 +6034,32 @@ static bool llm_load_tensors(
 
     // create tensors for the weights
     {
-        const int64_t n_embd       = hparams.n_embd;
-        const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
-        const int64_t n_embd_gqa   = n_embd_v_gqa;
-        const int64_t n_vocab      = hparams.n_vocab;
-        const int64_t n_vocab_type = hparams.n_vocab_type;
-        const int64_t n_ff         = hparams.n_ff;
-        const int64_t n_expert     = hparams.n_expert;
+        // note: cast to int64_t since we will use these for the tensor dimensions
+        const int64_t n_head        = hparams.n_head();
+        const int64_t n_head_kv     = hparams.n_head_kv();
+        const int64_t n_embd        = hparams.n_embd;
+        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
+        const int64_t n_embd_head_k = hparams.n_embd_head_k;
+        const int64_t n_embd_head_v = hparams.n_embd_head_v;
+        const int64_t n_ff          = hparams.n_ff();
+        const int64_t n_embd_gqa    = n_embd_v_gqa;
+        const int64_t n_vocab       = hparams.n_vocab;
+        const int64_t n_vocab_type  = hparams.n_vocab_type;
+        const int64_t n_expert      = hparams.n_expert;
+        const int64_t n_expert_used = hparams.n_expert_used;
+        const int64_t n_ctx_train   = hparams.n_ctx_train;
 
         if (n_expert > 0 && hparams.n_expert_used == 0) {
             throw std::runtime_error("model has expert layers but no expert layers are used");
         }
 
-        GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
         ggml_context * ctx_input        = ctx_map.at(model.buft_input.buft);
         ggml_context * ctx_output       = ctx_map.at(model.buft_output.buft);
         ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
-        auto ctx_for_layer              = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); };
-        auto ctx_for_layer_split        = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); };
+
+        auto ctx_for_layer       = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); };
+        auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); };
 
         model.layers.resize(n_layer);
 
@@ -4939,14 +6074,11 @@ static bool llm_load_tensors(
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        if (model.arch != LLM_ARCH_MINICPM){
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
-                            // if output is NULL, init from the input tok embed
-                            if (model.output == NULL) {
-                                model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                                ml.n_created--; // artificial tensor
-                                ml.size_data += ggml_nbytes(model.output);
-                            }
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -4958,27 +6090,34 @@ static bool llm_load_tensors(
 
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
-                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
-                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
 
                         // optional bias tensors
-                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     false);
-                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
-                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);
-                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     false);
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
+                        layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_embd/n_head/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+
                         if (n_expert == 0) {
                             layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                             layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                             layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+
+                            // optional MLP bias
+                            layer.ffn_gate_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                            layer.ffn_down_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                            layer.ffn_up_b   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         } else {
                             layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
 
-                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
+                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
                             if (layer.ffn_gate_exps) {
                                 layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
                                 layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
@@ -5020,12 +6159,11 @@ static bool llm_load_tensors(
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
                         // if output is NULL, init from the input tok embed
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -5046,9 +6184,9 @@ static bool llm_load_tensors(
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
-                        layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+                        layer.ffn_gate_inp  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert});
+                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
                         if (layer.ffn_gate_exps) {
                             layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
                             layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
@@ -5100,12 +6238,12 @@ static bool llm_load_tensors(
 
                     auto & layer = model.layers[i];
 
-                    layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,  "weight", i), {n_embd});
+                    layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
                     layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                     layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-                    layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+                    layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
 
                     layer.ffn_gate_inp  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert});
                     layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert});
@@ -5147,14 +6285,12 @@ static bool llm_load_tensors(
 
                     // output
                     {
-                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
 
-                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         if (!model.output) {
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
                         }
                     }
 
@@ -5167,8 +6303,8 @@ static bool llm_load_tensors(
                         layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                         layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false);
-                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, false);
+                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                         layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
@@ -5180,18 +6316,16 @@ static bool llm_load_tensors(
             case LLM_ARCH_STARCODER:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, hparams.n_ctx_train});
+                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train});
 
                     // output
                     {
                         model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
-                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         if (!model.output) {
                             // needs to be on GPU
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
 
                     }
@@ -5208,8 +6342,8 @@ static bool llm_load_tensors(
                         layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                         layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
 
-                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
-                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
 
                         layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                         layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
@@ -5217,49 +6351,8 @@ static bool llm_load_tensors(
                         layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
                         layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
 
-                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff});
-                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff});
-                    }
-                } break;
-            case LLM_ARCH_PERSIMMON:
-                {
-                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab});
-
-                    {
-                        model.output_norm    = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output_norm_b  = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
-                        model.output         = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        ggml_context * ctx_layer = ctx_for_layer(i);
-                        ggml_context * ctx_split = ctx_for_layer_split(i);
-
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm     = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd});
-                        layer.attn_norm_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd});
-
-                        layer.wqkv          = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV,    "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
-                        layer.bqkv          = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa});
-
-                        layer.wo            = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,    "weight", i), {n_embd, n_embd});
-                        layer.bo            = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd});
-
-                        layer.ffn_down      = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,    "weight", i), {n_ff, n_embd});
-                        layer.ffn_down_b    = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd});
-
-                        layer.ffn_up        = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,      "weight", i), {n_embd, n_ff});
-                        layer.ffn_up_b      = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff});
-
-                        layer.ffn_norm      = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd});
-                        layer.ffn_norm_b    = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,    "bias",   i), {n_embd});
-
-                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64});
-                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {64});
-
-                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64});
-                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {64});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff});
+                        layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff});
                     }
                 } break;
             case LLM_ARCH_BERT:
@@ -5267,8 +6360,9 @@ static bool llm_load_tensors(
                 {
                     model.tok_embd     = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab});
                     model.type_embd    = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
+
                     if (model.arch == LLM_ARCH_BERT) {
-                        model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, hparams.n_ctx_train});
+                        model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train});
                     }
 
                     model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
@@ -5281,33 +6375,32 @@ static bool llm_load_tensors(
                         auto & layer = model.layers[i];
 
                         if (model.arch == LLM_ARCH_BERT) {
-                            layer.wq   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
-                            layer.bq   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd});
+                            layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                            layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd});
 
-                            layer.wk   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
-                            layer.bk   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa});
+                            layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                            layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa});
 
-                            layer.wv   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
-                            layer.bv   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa});
+                            layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                            layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa});
                         } else {
                             layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                         }
 
-                        layer.wo              = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd});
 
                         layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
                         layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd});
 
-                        layer.ffn_up          = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
-                        layer.ffn_down        = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd});
 
                         if (model.arch == LLM_ARCH_BERT) {
-                            layer.bo         = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
-                            layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
-
-                            layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
+                            layer.bo         = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
+                            layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff});
+                            layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
                         } else {
-                            layer.ffn_gate   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                            layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
                         }
 
                         layer.layer_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
@@ -5316,8 +6409,9 @@ static bool llm_load_tensors(
                 } break;
             case LLM_ARCH_JINA_BERT_V2:
                 {
-                    model.tok_embd     = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // word_embeddings
-                    model.type_embd    = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); //token_type_embeddings
+                    model.tok_embd  = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // word_embeddings
+                    model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); // token_type_embeddings
+
                     model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
                     model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}); //LayerNorm bias
 
@@ -5327,35 +6421,38 @@ static bool llm_load_tensors(
 
                         auto & layer = model.layers[i]; // JinaBertLayer
 
-                        layer.wq   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
-                        layer.bq   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd});
 
-                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
-                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false);
+                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.wk   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
-                        layer.bk   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias",   i), {n_embd_gqa});
 
-                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
-                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false);
+                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.wv   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
-                        layer.bv   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias",   i), {n_embd_gqa});
 
-                        layer.wo              = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd}); //output_dens
-                        layer.bo              = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "bias", i), {n_embd}); //output_dens
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens
+                        layer.bo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}); //output_dens
 
                         layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
-                        layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd});
+                        layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias",   i), {n_embd});
+
+                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
-                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE,    "weight", i), {n_embd, n_ff});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
 
-                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,        "weight", i), {n_ff, n_embd});
-                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN,      "bias", i), {n_embd});
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd});
 
-                        layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM,        "weight", i), {n_embd});
-                        layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM,        "bias", i), {n_embd});
+                        layer.layer_out_norm   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+                        layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias",   i), {n_embd});
                     }
                 } break;
             case LLM_ARCH_BLOOM:
@@ -5378,39 +6475,37 @@ static bool llm_load_tensors(
                         auto & layer = model.layers[i];
 
                         layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
-                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias",   i), {n_embd});
 
                         layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
-                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias",   i), {n_embd + 2*n_embd_gqa});
 
                         layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
-                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd});
 
                         layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
-                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias",   i), {n_embd});
 
                         layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
-                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd});
 
-                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
-                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias",   i), {n_ff});
                     }
                 } break;
             case LLM_ARCH_MPT:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, hparams.n_ctx_train}, false);
+                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                     // output
                     {
-                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, false);
+                        model.output_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         if (!model.output) {
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
                         }
                     }
 
@@ -5421,31 +6516,31 @@ static bool llm_load_tensors(
                         auto & layer = model.layers[i];
 
                         layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
-                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, false);
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
-                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
-                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, false);
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
-                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, false);
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
-                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, false);
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
-                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, false);
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
-                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, false);
+                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
-                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, false);
+                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         // AWQ ScaleActivation layer
-                        layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
+                        layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                     }
                 } break;
             case LLM_ARCH_STABLELM:
@@ -5474,17 +6569,17 @@ static bool llm_load_tensors(
                         layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
                         // optional bias tensors, present in Stable LM 2 1.6B
-                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     false);
-                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
-                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         // optional q and k layernorms, present in StableLM 2 12B
-                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false);
-                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false);
+                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head},    llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
-                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false);
-                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, false);
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
@@ -5527,12 +6622,10 @@ static bool llm_load_tensors(
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -5593,20 +6686,23 @@ static bool llm_load_tensors(
 
                         layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
 
-                        GGML_ASSERT(hparams.n_expert      > 0);
-                        GGML_ASSERT(hparams.n_expert_used > 0);
+                        GGML_ASSERT(n_expert      > 0);
+                        GGML_ASSERT(n_expert_used > 0);
 
                         // MoE branch
-                        auto n_ff_exp = n_ff / hparams.n_expert_used;
+                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
                         layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert});
                         layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert});
                         layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert});
 
                         // Shared expert branch
+                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
                         layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
-                        layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd,   n_ff});
-                        layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {  n_ff, n_embd});
-                        layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp});
+                        layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp,     n_embd});
+                        layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp});
                     }
                 } break;
             case LLM_ARCH_PHI2:
@@ -5630,8 +6726,8 @@ static bool llm_load_tensors(
                         layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                         layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false);
-                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         if (layer.wqkv == nullptr) {
                             layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
@@ -5656,6 +6752,8 @@ static bool llm_load_tensors(
                 } break;
             case LLM_ARCH_PHI3:
                 {
+                    const int64_t n_embd_head = n_embd / n_head;
+
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab });
 
                     // output
@@ -5665,20 +6763,23 @@ static bool llm_load_tensors(
                     }
 
                     for (int i = 0; i < n_layer; ++i) {
-                        ggml_context* ctx_layer = ctx_for_layer(i);
-                        ggml_context* ctx_split = ctx_for_layer_split(i);
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-                        auto& layer = model.layers[i];
+                        auto & layer = model.layers[i];
 
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd });
 
-                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, false);
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd });
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd });
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd });
 
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd });
                         layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff });
+
+                        layer.rope_long  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+                        layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                     }
                 } break;
             case LLM_ARCH_PLAMO:
@@ -5712,7 +6813,7 @@ static bool llm_load_tensors(
             case LLM_ARCH_GPT2:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"),   {n_embd, hparams.n_ctx_train});
+                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train});
 
                     // output
                     {
@@ -5847,16 +6948,36 @@ static bool llm_load_tensors(
 
                     // output
                     model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
-                    ml.n_created--; // artificial tensor
-                    ml.size_data += ggml_nbytes(model.output);
+                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                    }
+                } break;
+            case LLM_ARCH_GEMMA2:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-                    const int64_t n_ff          = hparams.n_ff;
-                    const int64_t n_embd_head_k = hparams.n_embd_head_k;
-                    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
-                    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
+                    // output
+                    model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
 
-                    for (uint32_t i = 0; i < n_layer; ++i) {
+                    for (int i = 0; i < n_layer; ++i) {
                         ggml_context * ctx_layer = ctx_for_layer(i);
                         ggml_context * ctx_split = ctx_for_layer_split(i);
 
@@ -5864,15 +6985,17 @@ static bool llm_load_tensors(
 
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head});
                         layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
                         layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+                        layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                         layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd});
                     }
                 } break;
             case LLM_ARCH_STARCODER2:
@@ -5884,12 +7007,10 @@ static bool llm_load_tensors(
                         model.output_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
 
-                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
 
                     }
@@ -5931,6 +7052,7 @@ static bool llm_load_tensors(
                     const int64_t d_inner = hparams.ssm_d_inner;
                     const int64_t d_state = hparams.ssm_d_state;
                     const int64_t dt_rank = hparams.ssm_dt_rank;
+
                     // only an expansion factor of 2 is supported for now
                     GGML_ASSERT(2 * n_embd == d_inner);
 
@@ -5940,12 +7062,10 @@ static bool llm_load_tensors(
                     {
                         model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
 
-                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed, duplicated to allow offloading
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -5983,15 +7103,20 @@ static bool llm_load_tensors(
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                     }
+
                     for (int i = 0; i < n_layer; ++i) {
                         ggml_context * ctx_layer = ctx_for_layer(i);
                         ggml_context * ctx_split = ctx_for_layer_split(i);
+
                         auto & layer = model.layers[i];
+
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
                         layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                         layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                         layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                         layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
@@ -6006,9 +7131,7 @@ static bool llm_load_tensors(
                     {
                         model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         // init output from the input tok embed
-                        model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                        ml.n_created--; // artificial tensor
-                        ml.size_data += ggml_nbytes(model.output);
+                        model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                     }
 
                     for (int i = 0; i < n_layer; ++i) {
@@ -6020,8 +7143,8 @@ static bool llm_load_tensors(
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
                         if (n_layer >= 64){
-                            layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head});
-                            layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv});
+                            layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head});
+                            layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv});
                         }
 
                         layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
@@ -6040,12 +7163,10 @@ static bool llm_load_tensors(
 
                     // output
                     {
-                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -6059,1262 +7180,1615 @@ static bool llm_load_tensors(
                         layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                         layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                         layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
-            default:
-                throw std::runtime_error("unknown architecture");
-        }
-    }
+            case LLM_ARCH_OPENELM:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-    ml.done_getting_tensors();
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        // init output from the input tok embed
+                        model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                    }
 
-    ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
-    model.mappings.reserve(ml.mappings.size());
+                    for (int i = 0; i < n_layer; ++i) {
+                        const int64_t n_head      =   hparams.n_head(i);
+                        const int64_t n_head_qkv  = 2*hparams.n_head_kv(i) + n_head;
+                        const int64_t n_ff        =   hparams.n_ff(i);
 
-    // create the backend buffers
-    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
-    ctx_bufs.reserve(ctx_map.size());
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
-    size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
-    model.bufs.reserve(n_max_backend_buffer);
+                        auto & layer = model.layers[i];
 
-    for (auto & it : ctx_map) {
-        ggml_backend_buffer_type_t buft = it.first;
-        ggml_context * ctx              = it.second;
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-        llama_buf_map bufs;
-        bufs.reserve(n_max_backend_buffer);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k});
+                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k});
+                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd});
 
-        // only the mmap region containing the tensors in the model is mapped to the backend buffer
-        // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
-        // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
-        if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(true)) {
-            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
-                void * addr = nullptr;
-                size_t first, last;
-                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
-                if (first >= last) {
-                    continue;
-                }
-                ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
-                if (buf == nullptr) {
-                    throw std::runtime_error("unable to allocate backend CPU buffer");
-                }
-                model.bufs.push_back(buf);
-                bufs.emplace(idx, buf);
-#ifdef GGML_USE_CUDA
-                if (n_layer >= n_gpu_layers) {
-                    ggml_backend_cuda_register_host_buffer(
-                        ggml_backend_buffer_get_base(buf),
-                        ggml_backend_buffer_get_size(buf));
-                }
-#endif
-            }
-        }
-#ifdef GGML_USE_METAL
-        else if (ml.use_mmap && use_mmap_buffer && buft == ggml_backend_metal_buffer_type()) {
-            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
-                const size_t max_size = ggml_get_max_tensor_size(ctx);
-                void * addr = nullptr;
-                size_t first, last;
-                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
-                if (first >= last) {
-                    continue;
-                }
-                ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
-                if (buf == nullptr) {
-                    throw std::runtime_error("unable to allocate backend metal buffer");
-                }
-                model.bufs.push_back(buf);
-                bufs.emplace(idx, buf);
-            }
-        }
-#endif
-        else {
-            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
-            if (buf == nullptr) {
-                throw std::runtime_error("unable to allocate backend buffer");
-            }
-            model.bufs.push_back(buf);
-            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
-                model.mlock_bufs.emplace_back(new llama_mlock);
-                auto & mlock_buf = model.mlock_bufs.back();
-                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
-                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
-            }
-            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
-                bufs.emplace(idx, buf);
-            }
-        }
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                    }
+                } break;
+            case LLM_ARCH_GPTNEOX:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-        if (bufs.empty()) {
-            throw std::runtime_error("failed to allocate buffer");
-        }
+                    // output
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
 
-        for (auto & buf : bufs) {
-            // indicate that this buffer contains weights
-            // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
-            ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-        }
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-        ctx_bufs.emplace_back(ctx, bufs);
-    }
+                        auto & layer = model.layers[i];
 
-    if (llama_supports_gpu_offload()) {
-        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
-        if (n_gpu_layers > (int) hparams.n_layer) {
-            LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
-        }
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
 
-        const int max_backend_supported_layers = hparams.n_layer + 1;
-        const int max_offloadable_layers       = hparams.n_layer + 1;
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
 
-        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
-    }
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
 
-    // print memory requirements
-    for (ggml_backend_buffer_t buf : model.bufs) {
-        LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
-    }
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
 
-    // populate tensors_by_name
-    for (ggml_context * ctx : model.ctxs) {
-        for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
-            model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
-        }
-    }
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
+                    }
+                } break;
+            case LLM_ARCH_ARCTIC:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-    // load tensor data
-    for (auto & it : ctx_bufs) {
-        ggml_context * ctx = it.first;
-        auto & bufs = it.second;
-        if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
-            return false;
-        }
-    }
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-    if (use_mmap_buffer) {
-        for (auto & mapping : ml.mappings) {
-            model.mappings.emplace_back(std::move(mapping));
-        }
-    }
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                        }
+                    }
 
-    // loading time will be recalculate after the first eval, so
-    // we take page faults deferred by mmap() into consideration
-    model.t_load_us = ggml_time_us() - model.t_start_us;
-    return true;
-}
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
-static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
-    try {
-        llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
+                        auto & layer = model.layers[i];
 
-        model.hparams.vocab_only = params.vocab_only;
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-        try {
-            llm_load_arch(ml, model);
-        } catch(const std::exception & e) {
-            throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
-        }
-        try {
-            llm_load_hparams(ml, model);
-        } catch(const std::exception & e) {
-            throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
-        }
-        try {
-            llm_load_vocab(ml, model);
-        } catch(const std::exception & e) {
-            throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
-        }
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-        llm_load_print_meta(ml, model);
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
-        if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
-            model.hparams.n_vocab != model.vocab.id_to_token.size()) {
-            throw std::runtime_error("vocab size mismatch");
-        }
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_embd});
 
-        if (params.vocab_only) {
-            LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
-            return 0;
-        }
+                        layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+                        layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd});
+                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
+                        layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
+                        layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
+                    }
+                } break;
+            case LLM_ARCH_DEEPSEEK2:
+                {
+                    const bool is_lite = (hparams.n_layer == 27);
 
-#ifdef GGML_USE_KOMPUTE
-        if (params.n_gpu_layers > 0 && (
-            !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON)
-            || !(
-                model.ftype == LLAMA_FTYPE_ALL_F32 ||
-                model.ftype == LLAMA_FTYPE_MOSTLY_F16 ||
-                model.ftype == LLAMA_FTYPE_MOSTLY_BF16 ||
-                model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
-                model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1
-            )
-        )) {
-            // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file
-            LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__);
-            params.n_gpu_layers = 0;
-        }
-#endif
+                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
+                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
 
-#ifdef GGML_USE_SYCL
-        if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
-            ggml_backend_sycl_set_single_device_mode(params.main_gpu);
-            //SYCL use device index (0, 1, 2) directly, uer input device id, then convert to device index.
-            params.main_gpu = ggml_backend_sycl_get_device_index(params.main_gpu);
-        } else {
-            ggml_backend_sycl_set_mul_device_mode();
-        }
-#endif
+                    const int64_t q_lora_rank  = hparams.n_lora_q;
+                    const int64_t kv_lora_rank = hparams.n_lora_kv;
 
-        if (!llm_load_tensors(
-            ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
-            params.progress_callback, params.progress_callback_user_data
-        )) {
-            return -2;
-        }
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
-        return -1;
-    }
+                    const int64_t n_ff_exp        = hparams.n_ff_exp;
+                    const int64_t n_expert_shared = hparams.n_expert_shared;
 
-    return 0;
-}
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-//
-// llm_build
-//
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
 
-using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-enum llm_ffn_op_type {
-    LLM_FFN_SILU,
-    LLM_FFN_GELU,
-    LLM_FFN_RELU,
-    LLM_FFN_RELU_SQR,
-};
+                        auto & layer = model.layers[i];
 
-enum llm_ffn_gate_type {
-    LLM_FFN_SEQ,
-    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
-};
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        if (!is_lite) {
+                            layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank});
+                        }
 
-enum llm_norm_type {
-    LLM_NORM,
-    LLM_NORM_RMS,
-};
+                        layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank});
 
-static struct ggml_tensor * llm_build_inp_embd(
-        struct ggml_context * ctx,
-       struct llama_context & lctx,
-        const llama_hparams & hparams,
-          const llama_batch & batch,
-         struct ggml_tensor * tok_embd,
-         const llm_build_cb & cb) {
-    const int64_t n_embd = hparams.n_embd;
+                        if (!is_lite) {
+                            layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank});
+                            layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k});
+                        } else {
+                            layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
+                        }
 
-    struct ggml_tensor * inpL;
+                        layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)});
+                        layer.wkv_b     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)});
+                        layer.wo        = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd});
 
-    if (batch.token) {
-        lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
-        cb(lctx.inp_tokens, "inp_tokens", -1);
-        ggml_set_input(lctx.inp_tokens);
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
-        inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
-    } else {
-       lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
-        inpL = lctx.inp_embd;
-        ggml_set_input(lctx.inp_embd);
-    }
+                        if (i < (int) hparams.n_layer_dense_lead) {
+                            layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                            layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                            layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        } else {
+                            layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
 
-    cb(inpL, "inp_embd", -1);
+                            GGML_ASSERT(n_expert      > 0);
+                            GGML_ASSERT(n_expert_used > 0);
 
-    return inpL;
-}
+                            // MoE branch
+                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert});
+                            layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert});
+                            layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert});
 
-static void llm_build_kv_store(
-        struct ggml_context * ctx,
-        const llama_hparams & hparams,
-        const llama_cparams & cparams,
-       const llama_kv_cache & kv,
-         struct ggml_cgraph * graph,
-         struct ggml_tensor * k_cur,
-         struct ggml_tensor * v_cur,
-                    int32_t   n_tokens,
-                    int32_t   kv_head,
-         const llm_build_cb & cb,
-                    int64_t   il) {
-    const int64_t n_ctx = cparams.n_ctx;
+                            // Shared expert branch
+                            layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared});
+                            layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd});
+                            layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared});
+                        }
+                    }
+                } break;
+            case LLM_ARCH_BITNET:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-    const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
-    const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                    }
 
-    GGML_ASSERT(kv.size == n_ctx);
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
-            (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
-    cb(k_cache_view, "k_cache_view", il);
+                        auto & layer = model.layers[i];
 
-    // note: storing RoPE-ed version of K in the KV cache
-    ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));
+                        layer.attn_norm     = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd});
+                        layer.attn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd});
+
+                        layer.wq       = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wq_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "scale",  i), {1});
+                        layer.wk       = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wk_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "scale",  i), {1});
+                        layer.wv       = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "scale",  i), {1});
+                        layer.wo       = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.wo_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "scale",  i), {1});
+
+                        layer.ffn_norm     = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,     "weight", i), {n_embd});
+                        layer.ffn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff});
+
+                        layer.ffn_gate       = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+                        layer.ffn_gate_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "scale",  i), {1});
+                        layer.ffn_down       = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "scale",  i), {1});
+                        layer.ffn_up         = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_scale   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "scale",  i), {1});
+                    }
+                } break;
+            case LLM_ARCH_T5:
+                {
+                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
 
-    assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-    struct ggml_tensor * v_cache_view = nullptr;
+                    // output
+                    {
+                        model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm     = ml.create_tensor(ctx_output, tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd});
 
-    if (cparams.flash_attn) {
-        v_cache_view = ggml_view_1d(ctx, kv.v_l[il], n_tokens*n_embd_v_gqa,
-                (kv_head)*ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa));
-    } else {
-        // note: the V cache is transposed when not using flash attention
-        v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
-                (  n_ctx)*ggml_element_size(kv.v_l[il]),
-                (kv_head)*ggml_element_size(kv.v_l[il]));
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                        }
+                    }
 
-        v_cur = ggml_transpose(ctx, v_cur);
-    }
-    cb(v_cache_view, "v_cache_view", il);
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-    ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur, v_cache_view));
-}
+                        auto & layer = model.layers[i];
 
-static struct ggml_tensor * llm_build_norm(
-        struct ggml_context * ctx,
-         struct ggml_tensor * cur,
-        const llama_hparams & hparams,
-         struct ggml_tensor * mw,
-         struct ggml_tensor * mb,
-              llm_norm_type   type,
-         const llm_build_cb & cb,
-                        int   il) {
-    switch (type) {
-        case LLM_NORM:     cur = ggml_norm    (ctx, cur, hparams.f_norm_eps);     break;
-        case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break;
-    }
+                        layer.attn_norm_enc  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd});
+                        layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-    if (mw || mb) {
-        cb(cur, "norm", il);
-    }
+                        layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
 
-    if (mw) {
-        cur = ggml_mul(ctx, cur, mw);
-        if (mb) {
-            cb(cur, "norm_w", il);
-        }
-    }
+                        layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up_enc   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff});
 
-    if (mb) {
-        cur = ggml_add(ctx, cur, mb);
-    }
+                        layer.attn_norm  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd});
+                        layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-    return cur;
-}
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
 
-static struct ggml_tensor * llm_build_ffn(
-        struct ggml_context * ctx,
-         struct ggml_tensor * cur,
-         struct ggml_tensor * up,
-         struct ggml_tensor * up_b,
-         struct ggml_tensor * gate,
-         struct ggml_tensor * gate_b,
-         struct ggml_tensor * down,
-         struct ggml_tensor * down_b,
-         struct ggml_tensor * act_scales,
-            llm_ffn_op_type   type_op,
-          llm_ffn_gate_type   type_gate,
-         const llm_build_cb & cb,
-                        int   il) {
-    struct ggml_tensor * tmp = up ? ggml_mul_mat(ctx, up, cur) : cur;
-    cb(tmp, "ffn_up", il);
+                        layer.attn_norm_cross  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "weight", i), {n_embd});
+                        // this tensor seems to be unused in HF transformers implementation
+                        layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-    if (up_b) {
-        tmp = ggml_add(ctx, tmp, up_b);
-        cb(tmp, "ffn_up_b", il);
-    }
+                        layer.wq_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wk_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
 
-    if (gate) {
-        switch (type_gate) {
-            case LLM_FFN_SEQ:
-                {
-                    cur = ggml_mul_mat(ctx, gate, tmp);
-                    cb(cur, "ffn_gate", il);
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
                 } break;
-            case LLM_FFN_PAR:
+            case LLM_ARCH_T5ENCODER:
                 {
-                    cur = ggml_mul_mat(ctx, gate, cur);
-                    cb(cur, "ffn_gate", il);
-                } break;
-        }
+                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
 
-        if (gate_b) {
-            cur = ggml_add(ctx, cur, gate_b);
-            cb(cur, "ffn_gate_b", il);
-        }
-    } else {
-        cur = tmp;
-    }
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-    switch (type_op) {
-        case LLM_FFN_SILU:
-            {
-                cur = ggml_silu(ctx, cur);
-                cb(cur, "ffn_silu", il);
-            } break;
-        case LLM_FFN_GELU:
-            {
-                cur = ggml_gelu(ctx, cur);
-                cb(cur, "ffn_gelu", il);
-                if (act_scales != NULL) {
-                    cur = ggml_div(ctx, cur, act_scales);
-                    cb(cur, "ffn_act", il);
-                }
-            } break;
-        case LLM_FFN_RELU:
-            {
-                cur = ggml_relu(ctx, cur);
-                cb(cur, "ffn_relu", il);
-            } break;
-        case LLM_FFN_RELU_SQR:
-            {
-                cur = ggml_relu(ctx, cur);
-                cb(cur, "ffn_relu", il);
+                    // output
+                    {
+                        model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                        }
+                    }
 
-                cur = ggml_sqr(ctx, cur);
-                cb(cur, "ffn_sqr(relu)", il);
-            } break;
-    }
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-    if (type_gate == LLM_FFN_PAR) {
-        cur = ggml_mul(ctx, cur, tmp);
-        cb(cur, "ffn_gate_par", il);
-    }
+                        auto & layer = model.layers[i];
 
-    cur = ggml_mul_mat(ctx, down, cur);
-    if (down_b) {
-        cb(cur, "ffn_down", il);
-    }
+                        layer.attn_norm_enc  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd});
+                        layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-    if (down_b) {
-        cur = ggml_add(ctx, cur, down_b);
-    }
+                        layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
 
-    return cur;
-}
+                        layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up_enc   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
+            case LLM_ARCH_JAIS:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-static struct ggml_tensor * llm_build_moe_ffn(
-        struct ggml_context * ctx,
-         struct ggml_tensor * cur,
-         struct ggml_tensor * gate_inp,
-         struct ggml_tensor * up_exps,
-         struct ggml_tensor * gate_exps,
-         struct ggml_tensor * down_exps,
-                    int64_t   n_expert,
-                    int64_t   n_expert_used,
-            llm_ffn_op_type   type_op,
-                       bool   norm_w,
-         const llm_build_cb & cb,
-                        int   il) {
-    int64_t n_embd = cur->ne[0];
-    int64_t n_tokens = cur->ne[1];
+                    // Output
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
 
-    ggml_tensor * logits = ggml_mul_mat(ctx, gate_inp, cur); // [n_expert, n_tokens]
-    cb(logits, "ffn_moe_logits", il);
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-    ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
-    cb(probs, "ffn_moe_probs", il);
+                        auto & layer = model.layers[i];
 
-    // select experts
-    ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens]
-    cb(selected_experts->src[0], "ffn_moe_argsort", il);
-    cb(selected_experts, "ffn_moe_topk", il);
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd});
 
-    ggml_tensor * weights = ggml_get_rows(ctx,
-            ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
-    cb(weights, "ffn_moe_weights", il);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
 
-    if (norm_w) {
-        weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
 
-        ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
-        cb(weights_sum, "ffn_moe_weights_sum", il);
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
 
-        weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
-        cb(weights, "ffn_moe_weights_norm", il);
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
 
-        weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
-    }
+                        layer.ffn_gate   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_gate_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE,   "bias", i),   {n_ff});
 
-    cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
-    ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
-    cb(up, "ffn_moe_up", il);
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
+                    }
+                } break;
+            case LLM_ARCH_CHATGLM:
+                {
+                    model.tok_embd   = ml.create_tensor(ctx_input,  tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab});
 
-    ggml_tensor * gate = ggml_mul_mat_id(ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
-    cb(gate, "ffn_moe_gate", il);
+                    // output
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
 
-    switch (type_op) {
-        case LLM_FFN_SILU:
-            {
-                gate = ggml_silu(ctx, gate);
-                cb(gate, "ffn_moe_silu", il);
-            } break;
-        case LLM_FFN_GELU:
-            {
-                gate = ggml_gelu(ctx, gate);
-                cb(gate, "ffn_moe_gelu", il);
-            } break;
-        default:
-            GGML_ASSERT(false);
-    }
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-    ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens]
-    cb(par, "ffn_moe_gate_par", il);
+                        auto & layer = model.layers[i];
 
-    ggml_tensor * experts = ggml_mul_mat_id(ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
-    cb(experts, "ffn_moe_down", il);
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-    experts = ggml_mul(ctx, experts, weights);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + (hparams.n_embd_head_k << 2)});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + (hparams.n_embd_head_k << 2)});
 
-    // aggregate experts
-    ggml_tensor * moe_out = nullptr;
-    for (int i = 0; i < n_expert_used; ++i) {
-        ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
-                experts->nb[2], i*experts->nb[1]);
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-        if (i == 0) {
-            moe_out = cur_expert;
-        } else {
-            moe_out = ggml_add(ctx, moe_out, cur_expert);
-        }
-    }
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
-    if (n_expert_used == 1) {
-        // avoid returning a non-contiguous tensor
-        moe_out = ggml_cont(ctx, moe_out);
-    }
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2});
 
-    return moe_out;
-}
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                    }
+                } break;
+            default:
+                throw std::runtime_error("unknown architecture");
+        }
+    }
 
-static struct ggml_tensor * llm_build_kqv(
-        struct ggml_context * ctx,
-          const llama_model & model,
-        const llama_hparams & hparams,
-        const llama_cparams & cparams,
-       const llama_kv_cache & kv,
-         struct ggml_cgraph * graph,
-         struct ggml_tensor * wo,
-         struct ggml_tensor * wo_b,
-         struct ggml_tensor * q_cur,
-         struct ggml_tensor * kq_mask,
-                    int32_t   n_tokens,
-                    int32_t   n_kv,
-                    float     kq_scale,
-         const llm_build_cb & cb,
-                    int       il) {
-    const int64_t n_ctx         = cparams.n_ctx;
-    const int64_t n_head        = hparams.n_head;
-    const int64_t n_head_kv     = hparams.n_head_kv;
-    const int64_t n_embd_head_k = hparams.n_embd_head_k;
-    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
-    const int64_t n_embd_head_v = hparams.n_embd_head_v;
-    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
+    ml.done_getting_tensors();
 
-    struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
-    cb(q, "q", il);
+    ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
+    model.mappings.reserve(ml.mappings.size());
 
-    struct ggml_tensor * k =
-        ggml_view_3d(ctx, kv.k_l[il],
-                n_embd_head_k, n_kv, n_head_kv,
-                ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
-                ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
-                0);
-    cb(k, "k", il);
+    // create the backend buffers
+    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
+    ctx_bufs.reserve(ctx_map.size());
 
-    struct ggml_tensor * cur;
+    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
+    size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
+    model.bufs.reserve(n_max_backend_buffer);
 
-    if (cparams.flash_attn) {
-        GGML_UNUSED(model);
-        GGML_UNUSED(n_ctx);
+    for (auto & it : ctx_map) {
+        ggml_backend_buffer_type_t buft = it.first;
+        ggml_context * ctx              = it.second;
 
-        // split cached v into n_head heads (not transposed)
-        struct ggml_tensor * v =
-            ggml_view_3d(ctx, kv.v_l[il],
-                    n_embd_head_v, n_kv, n_head_kv,
-                    ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa),
-                    ggml_row_size(kv.v_l[il]->type, n_embd_head_v),
-                    0);
-        cb(v, "v", il);
+        llama_buf_map bufs;
+        bufs.reserve(n_max_backend_buffer);
 
-        cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias);
+        // only the mmap region containing the tensors in the model is mapped to the backend buffer
+        // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
+        // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
+        if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(true)) {
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                void * addr = nullptr;
+                size_t first, last;
+                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+                if (first >= last) {
+                    continue;
+                }
+                ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
+                if (buf == nullptr) {
+                    throw std::runtime_error("unable to allocate backend CPU buffer");
+                }
+                model.bufs.push_back(buf);
+                bufs.emplace(idx, buf);
+#ifdef GGML_USE_CUDA
+                if (n_layer >= n_gpu_layers) {
+                    ggml_backend_cuda_register_host_buffer(
+                        ggml_backend_buffer_get_base(buf),
+                        ggml_backend_buffer_get_size(buf));
+                }
+#endif
+            }
+        }
+#ifdef GGML_USE_METAL
+        else if (ml.use_mmap && use_mmap_buffer && buft == ggml_backend_metal_buffer_type()) {
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                const size_t max_size = ggml_get_max_tensor_size(ctx);
+                void * addr = nullptr;
+                size_t first, last;
+                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+                if (first >= last) {
+                    continue;
+                }
+                ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
+                if (buf == nullptr) {
+                    throw std::runtime_error("unable to allocate backend metal buffer");
+                }
+                model.bufs.push_back(buf);
+                bufs.emplace(idx, buf);
+            }
+        }
+#endif
+        else {
+            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+            if (buf == nullptr) {
+                throw std::runtime_error("unable to allocate backend buffer");
+            }
+            model.bufs.push_back(buf);
+            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
+                model.mlock_bufs.emplace_back(new llama_mlock);
+                auto & mlock_buf = model.mlock_bufs.back();
+                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
+                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
+            }
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                bufs.emplace(idx, buf);
+            }
+        }
 
-        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) {
-            ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
+        if (bufs.empty()) {
+            throw std::runtime_error("failed to allocate buffer");
         }
 
-        cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
-    } else {
-        struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
-        cb(kq, "kq", il);
-
-        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) {
-            // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
-            // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
-            ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+        for (auto & buf : bufs) {
+            // indicate that this buffer contains weights
+            // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
+            ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
         }
 
-        if (model.arch == LLM_ARCH_GROK) {
-            // need to do the following:
-            // multiply by attn_output_multiplyer of 0.08838834764831845
-            // and then :
-            // kq = 30 * tanh(kq / 30)
-            // before the softmax below
+        ctx_bufs.emplace_back(ctx, bufs);
+    }
 
-            //try from phi2
-            //ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+    if (llama_supports_gpu_offload()) {
+        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
 
-            kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
-            kq = ggml_scale(ctx, kq, 30);
+        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
+        if (n_gpu_layers > (int) hparams.n_layer) {
+            LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
         }
 
-        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
-        cb(kq, "kq_soft_max_ext", il);
+        const int max_backend_supported_layers = hparams.n_layer + 1;
+        const int max_offloadable_layers       = hparams.n_layer + 1;
 
-        GGML_ASSERT(kv.size == n_ctx);
+        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
+    }
 
-        // split cached v into n_head heads
-        struct ggml_tensor * v =
-            ggml_view_3d(ctx, kv.v_l[il],
-                    n_kv, n_embd_head_v, n_head_kv,
-                    ggml_element_size(kv.v_l[il])*n_ctx,
-                    ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
-                    0);
-        cb(v, "v", il);
+    // print memory requirements
+    for (ggml_backend_buffer_t buf : model.bufs) {
+        LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
+    }
 
-        struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
-        cb(kqv, "kqv", il);
+    // populate tensors_by_name
+    for (ggml_context * ctx : model.ctxs) {
+        for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
+            model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
+        }
+    }
 
-        struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
-        cb(kqv_merged, "kqv_merged", il);
+    // load tensor data
+    for (auto & it : ctx_bufs) {
+        ggml_context * ctx = it.first;
+        auto & bufs = it.second;
+        if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
+            return false;
+        }
+    }
 
-        cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_v*n_head, n_tokens);
-        cb(cur, "kqv_merged_cont", il);
+    if (use_mmap_buffer) {
+        for (auto & mapping : ml.mappings) {
+            model.mappings.emplace_back(std::move(mapping));
+        }
     }
 
-    ggml_build_forward_expand(graph, cur);
+    // loading time will be recalculate after the first eval, so
+    // we take page faults deferred by mmap() into consideration
+    model.t_load_us = ggml_time_us() - model.t_start_us;
+    return true;
+}
 
-    cur = ggml_mul_mat(ctx, wo, cur);
-    if (wo_b) {
-        cb(cur, "kqv_wo", il);
+// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
+static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
+    try {
+        llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
+
+        model.hparams.vocab_only = params.vocab_only;
+
+        try {
+            llm_load_arch(ml, model);
+        } catch(const std::exception & e) {
+            throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
+        }
+        try {
+            llm_load_hparams(ml, model);
+        } catch(const std::exception & e) {
+            throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
+        }
+        try {
+            llm_load_vocab(ml, model);
+        } catch(const std::exception & e) {
+            throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
+        }
+
+        llm_load_print_meta(ml, model);
+
+        if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
+            model.hparams.n_vocab != model.vocab.id_to_token.size()) {
+            throw std::runtime_error("vocab size mismatch");
+        }
+
+        if (params.vocab_only) {
+            LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
+            return 0;
+        }
+
+#ifdef GGML_USE_KOMPUTE
+        if (params.n_gpu_layers > 0 && (
+            !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON)
+            || !(
+                model.ftype == LLAMA_FTYPE_ALL_F32 ||
+                model.ftype == LLAMA_FTYPE_MOSTLY_F16 ||
+                model.ftype == LLAMA_FTYPE_MOSTLY_BF16 ||
+                model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
+                model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1
+            )
+        )) {
+            // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file
+            LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__);
+            params.n_gpu_layers = 0;
+        }
+#endif
+
+        if (!llm_load_tensors(
+            ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
+            params.progress_callback, params.progress_callback_user_data
+        )) {
+            return -2;
+        }
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
+        return -1;
     }
 
-    if (wo_b) {
-        cur = ggml_add(ctx, cur, wo_b);
+    return 0;
+}
+
+//
+// llm_build
+//
+
+using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
+
+enum llm_ffn_op_type {
+    LLM_FFN_SILU,
+    LLM_FFN_GELU,
+    LLM_FFN_RELU,
+    LLM_FFN_RELU_SQR,
+    LLM_FFN_SWIGLU,
+};
+
+enum llm_ffn_gate_type {
+    LLM_FFN_SEQ,
+    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
+};
+
+enum llm_norm_type {
+    LLM_NORM,
+    LLM_NORM_RMS,
+};
+
+static struct ggml_tensor * llm_build_inp_embd(
+        struct ggml_context * ctx,
+       struct llama_context & lctx,
+        const llama_hparams & hparams,
+          const llama_batch & batch,
+         struct ggml_tensor * tok_embd,
+         const llm_build_cb & cb) {
+    const int64_t n_embd = hparams.n_embd;
+
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
+        cb(lctx.inp_tokens, "inp_tokens", -1);
+        ggml_set_input(lctx.inp_tokens);
+
+        inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
+    } else {
+       lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+        inpL = lctx.inp_embd;
+        ggml_set_input(lctx.inp_embd);
     }
 
-    return cur;
+    cb(inpL, "inp_embd", -1);
+
+    return inpL;
 }
 
-static struct ggml_tensor * llm_build_kv(
+static void llm_build_kv_store(
         struct ggml_context * ctx,
-          const llama_model & model,
         const llama_hparams & hparams,
         const llama_cparams & cparams,
        const llama_kv_cache & kv,
          struct ggml_cgraph * graph,
-         struct ggml_tensor * wo,
-         struct ggml_tensor * wo_b,
          struct ggml_tensor * k_cur,
          struct ggml_tensor * v_cur,
-         struct ggml_tensor * q_cur,
-         struct ggml_tensor * kq_mask,
                     int32_t   n_tokens,
                     int32_t   kv_head,
-                    int32_t   n_kv,
-                    float     kq_scale,
          const llm_build_cb & cb,
-                    int       il) {
+                    int64_t   il) {
+    const int64_t n_ctx = cparams.n_ctx;
 
-    // these nodes are added to the graph together so that they are not reordered
-    // by doing so, the number of splits in the graph is reduced
-    ggml_build_forward_expand(graph, q_cur);
-    ggml_build_forward_expand(graph, k_cur);
-    ggml_build_forward_expand(graph, v_cur);
+    const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+    const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
 
-    llm_build_kv_store(ctx, hparams, cparams, kv, graph, k_cur, v_cur, n_tokens, kv_head, cb, il);
+    GGML_ASSERT(kv.size == n_ctx);
 
-    struct ggml_tensor * cur;
+    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
+            (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
+    cb(k_cache_view, "k_cache_view", il);
 
-    cur  = llm_build_kqv(ctx, model, hparams, cparams, kv, graph, wo, wo_b,
-            q_cur, kq_mask, n_tokens, n_kv, kq_scale, cb, il);
-    cb(cur, "kqv_out", il);
+    // note: storing RoPE-ed version of K in the KV cache
+    ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));
 
-    return cur;
-}
+    assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens);
 
-struct llm_build_context {
-    const llama_model    & model;
-          llama_context  & lctx;
-    const llama_hparams  & hparams;
-    const llama_cparams  & cparams;
-    const llama_batch    & batch;
-    const llama_kv_cache & kv_self;
+    struct ggml_tensor * v_cache_view = nullptr;
 
-    const int64_t n_embd;
-    const int64_t n_layer;
-    const int64_t n_rot;
-    const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
-    const int64_t n_head;
-    const int64_t n_head_kv;
-    const int64_t n_embd_head_k;
-    const int64_t n_embd_k_gqa;
-    const int64_t n_embd_head_v;
-    const int64_t n_embd_v_gqa;
-    const int64_t n_expert;
-    const int64_t n_expert_used;
+    if (cparams.flash_attn) {
+        v_cache_view = ggml_view_1d(ctx, kv.v_l[il], n_tokens*n_embd_v_gqa,
+                (kv_head)*ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa));
+    } else {
+        // note: the V cache is transposed when not using flash attention
+        v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
+                (  n_ctx)*ggml_element_size(kv.v_l[il]),
+                (kv_head)*ggml_element_size(kv.v_l[il]));
 
-    const float freq_base;
-    const float freq_scale;
-    const float ext_factor;
-    const float attn_factor;
-    const float beta_fast;
-    const float beta_slow;
-    const float norm_eps;
-    const float norm_rms_eps;
-
-    const int32_t n_tokens;
-    const int32_t n_kv;     // size of KV cache to consider (n_kv <= kv_self.size)
-    const int32_t n_outputs;
-    const int32_t kv_head;  // index of where we store new KV data in the cache
-    const int32_t n_orig_ctx;
+        v_cur = ggml_transpose(ctx, v_cur);
+    }
+    cb(v_cache_view, "v_cache_view", il);
 
-    const bool flash_attn;
+    ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur, v_cache_view));
+}
 
-    const enum llama_pooling_type pooling_type;
-    const enum llama_rope_type    rope_type;
+// do mat_mul, while optionally apply lora
+static struct ggml_tensor * llm_build_lora_mm(
+        struct llama_context & lctx,
+         struct ggml_context * ctx0,
+          struct ggml_tensor * w,
+          struct ggml_tensor * cur) {
+    struct ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
+    for (auto & it : lctx.lora_adapters) {
+        struct llama_lora_weight * lora = it.first->get_weight(w);
+        if (lora == nullptr) {
+            continue;
+        }
+        const float alpha = it.first->alpha;
+        const float rank  = (float) lora->b->ne[0];
+        const float scale = alpha ? it.second * alpha / rank : it.second;
+        struct ggml_tensor * ab_cur = ggml_mul_mat(
+            ctx0, lora->b,
+            ggml_mul_mat(ctx0, lora->a, cur)
+        );
+        ab_cur = ggml_scale(ctx0, ab_cur, scale);
+        res = ggml_add(ctx0, res, ab_cur);
+    }
+    return res;
+}
 
-    const llm_build_cb & cb;
+// do mat_mul_id, while optionally apply lora
+static struct ggml_tensor * llm_build_lora_mm_id(
+        struct llama_context & lctx,
+         struct ggml_context * ctx0,
+          struct ggml_tensor * w,   // struct ggml_tensor * as
+          struct ggml_tensor * cur, // struct ggml_tensor * b
+          struct ggml_tensor * ids) {
+    struct ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
+    for (auto & it : lctx.lora_adapters) {
+        struct llama_lora_weight * lora = it.first->get_weight(w);
+        if (lora == nullptr) {
+            continue;
+        }
+        const float alpha = it.first->alpha;
+        const float rank  = (float) lora->b->ne[0];
+        const float scale = alpha ? it.second * alpha / rank : it.second;
+        struct ggml_tensor * ab_cur = ggml_mul_mat_id(
+            ctx0, lora->b,
+            ggml_mul_mat_id(ctx0, lora->a, cur, ids),
+            ids
+        );
+        ab_cur = ggml_scale(ctx0, ab_cur, scale);
+        res = ggml_add(ctx0, res, ab_cur);
+    }
+    return res;
+}
 
-    std::vector<uint8_t> & buf_compute_meta;
+static struct ggml_tensor * llm_build_norm(
+        struct ggml_context * ctx,
+         struct ggml_tensor * cur,
+        const llama_hparams & hparams,
+         struct ggml_tensor * mw,
+         struct ggml_tensor * mb,
+              llm_norm_type   type,
+         const llm_build_cb & cb,
+                        int   il) {
+    switch (type) {
+        case LLM_NORM:     cur = ggml_norm    (ctx, cur, hparams.f_norm_eps);     break;
+        case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break;
+    }
 
-    struct ggml_context * ctx0 = nullptr;
+    if (mw || mb) {
+        cb(cur, "norm", il);
+    }
 
-    // TODO: consider making the entire interface noexcept
-    llm_build_context(
-        llama_context  & lctx,
-    const llama_batch  & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) :
-        model            (lctx.model),
-        lctx             (lctx),
-        hparams          (model.hparams),
-        cparams          (lctx.cparams),
-        batch            (batch),
-        kv_self          (lctx.kv_self),
-        n_embd           (hparams.n_embd),
-        n_layer          (hparams.n_layer),
-        n_rot            (hparams.n_rot),
-        n_ctx            (cparams.n_ctx),
-        n_head           (hparams.n_head),
-        n_head_kv        (hparams.n_head_kv),
-        n_embd_head_k    (hparams.n_embd_head_k),
-        n_embd_k_gqa     (hparams.n_embd_k_gqa()),
-        n_embd_head_v    (hparams.n_embd_head_v),
-        n_embd_v_gqa     (hparams.n_embd_v_gqa()),
-        n_expert         (hparams.n_expert),
-        n_expert_used    (hparams.n_expert_used),
-        freq_base        (cparams.rope_freq_base),
-        freq_scale       (cparams.rope_freq_scale),
-        ext_factor       (cparams.yarn_ext_factor),
-        attn_factor      (cparams.yarn_attn_factor),
-        beta_fast        (cparams.yarn_beta_fast),
-        beta_slow        (cparams.yarn_beta_slow),
-        norm_eps         (hparams.f_norm_eps),
-        norm_rms_eps     (hparams.f_norm_rms_eps),
-        n_tokens         (batch.n_tokens),
-        n_kv             (worst_case ? kv_self.size : kv_self.n),
-        n_outputs        (worst_case ? n_tokens : lctx.n_outputs),
-        kv_head          (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
-        n_orig_ctx       (cparams.n_yarn_orig_ctx),
-        flash_attn       (cparams.flash_attn),
-        pooling_type     (cparams.pooling_type),
-        rope_type        (hparams.rope_type),
-        cb               (cb),
-        buf_compute_meta (lctx.buf_compute_meta) {
-            // all initializations should be done in init()
+    if (mw) {
+        cur = ggml_mul(ctx, cur, mw);
+        if (mb) {
+            cb(cur, "norm_w", il);
         }
+    }
 
-    void init() {
-        struct ggml_init_params params = {
-            /*.mem_size   =*/ buf_compute_meta.size(),
-            /*.mem_buffer =*/ buf_compute_meta.data(),
-            /*.no_alloc   =*/ true,
-        };
+    if (mb) {
+        cur = ggml_add(ctx, cur, mb);
+    }
 
-        ctx0 = ggml_init(params);
+    return cur;
+}
 
-        lctx.inp_tokens  = nullptr;
-        lctx.inp_embd    = nullptr;
-        lctx.inp_pos     = nullptr;
-        lctx.inp_out_ids = nullptr;
-        lctx.inp_KQ_mask = nullptr;
-        lctx.inp_K_shift = nullptr;
-        lctx.inp_mean    = nullptr;
-        lctx.inp_cls     = nullptr;
-        lctx.inp_s_copy  = nullptr;
-        lctx.inp_s_mask  = nullptr;
-        lctx.inp_s_seq   = nullptr;
-    }
+static struct ggml_tensor * llm_build_ffn(
+        struct ggml_context * ctx,
+       struct llama_context & lctx,
+         struct ggml_tensor * cur,
+         struct ggml_tensor * up,
+         struct ggml_tensor * up_b,
+         struct ggml_tensor * up_s,
+         struct ggml_tensor * gate,
+         struct ggml_tensor * gate_b,
+         struct ggml_tensor * gate_s,
+         struct ggml_tensor * down,
+         struct ggml_tensor * down_b,
+         struct ggml_tensor * down_s,
+         struct ggml_tensor * act_scales,
+            llm_ffn_op_type   type_op,
+          llm_ffn_gate_type   type_gate,
+         const llm_build_cb & cb,
+                        int   il) {
+    struct ggml_tensor * tmp = up ? llm_build_lora_mm(lctx, ctx, up, cur) : cur;
+    cb(tmp, "ffn_up", il);
 
-    void free() {
-        if (ctx0) {
-            ggml_free(ctx0);
-            ctx0 = nullptr;
-        }
+    if (up_b) {
+        tmp = ggml_add(ctx, tmp, up_b);
+        cb(tmp, "ffn_up_b", il);
     }
 
-    struct ggml_cgraph * build_k_shift() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+    if (up_s) {
+        tmp = ggml_mul(ctx, tmp, up_s);
+        cb(tmp, "ffn_up_s", il);
+    }
 
-        GGML_ASSERT(kv_self.size == n_ctx);
+    if (gate) {
+        switch (type_gate) {
+            case LLM_FFN_SEQ:
+                {
+                    cur = llm_build_lora_mm(lctx, ctx, gate, tmp);
+                    cb(cur, "ffn_gate", il);
+                } break;
+            case LLM_FFN_PAR:
+                {
+                    cur = llm_build_lora_mm(lctx, ctx, gate, cur);
+                    cb(cur, "ffn_gate", il);
+                } break;
+        }
 
-        lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
-        cb(lctx.inp_K_shift, "K_shift", -1);
-        ggml_set_input(lctx.inp_K_shift);
+        if (gate_b) {
+            cur = ggml_add(ctx, cur, gate_b);
+            cb(cur, "ffn_gate_b", il);
+        }
 
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * tmp =
-                // we rotate only the first n_rot dimensions
-                ggml_rope_custom_inplace(ctx0,
-                        ggml_view_3d(ctx0, kv_self.k_l[il],
-                            n_embd_head_k, n_head_kv, n_ctx,
-                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
-                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
-                            0),
-                        lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow);
-            cb(tmp, "K_shifted", il);
-            ggml_build_forward_expand(gf, tmp);
+        if (gate_s) {
+            cur = ggml_mul(ctx, cur, gate_s);
+            cb(cur, "ffn_gate_s", il);
         }
 
-        return gf;
+    } else {
+        cur = tmp;
     }
 
-    struct ggml_cgraph * build_s_copy() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+    switch (type_op) {
+        case LLM_FFN_SILU:
+            {
+                cur = ggml_silu(ctx, cur);
+                cb(cur, "ffn_silu", il);
+            } break;
+        case LLM_FFN_GELU:
+            {
+                cur = ggml_gelu(ctx, cur);
+                cb(cur, "ffn_gelu", il);
+                if (act_scales != NULL) {
+                    cur = ggml_div(ctx, cur, act_scales);
+                    cb(cur, "ffn_act", il);
+                }
+            } break;
+        case LLM_FFN_RELU:
+            {
+                cur = ggml_relu(ctx, cur);
+                cb(cur, "ffn_relu", il);
+            } break;
+        case LLM_FFN_RELU_SQR:
+            {
+                cur = ggml_relu(ctx, cur);
+                cb(cur, "ffn_relu", il);
 
-        GGML_ASSERT(kv_self.recurrent);
+                cur = ggml_sqr(ctx, cur);
+                cb(cur, "ffn_sqr(relu)", il);
+            } break;
+        case LLM_FFN_SWIGLU:
+            {
+                // Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
+                int64_t split_point = cur->ne[0] / 2;
+                struct ggml_tensor * x0 = ggml_cont(ctx, ggml_view_2d(ctx, cur, split_point, cur->ne[1], cur->nb[1], 0));
+                struct ggml_tensor * x1 = ggml_cont(ctx, ggml_view_2d(ctx, cur, split_point, cur->ne[1], cur->nb[1], split_point * ggml_element_size(cur)));
 
-        struct ggml_tensor * state_copy = build_inp_s_copy();
+                x0 = ggml_silu(ctx, x0);
+                cb(cur, "ffn_silu", il);
 
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
-            struct ggml_tensor * ssm_states  = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
+                cur = ggml_mul(ctx, x0, x1);
+                cb(cur, "ffn_mul", il);
+            } break;
+    }
 
-            conv_states = ggml_get_rows(ctx0, conv_states, state_copy);
-            ssm_states  = ggml_get_rows(ctx0,  ssm_states, state_copy);
+    if (type_gate == LLM_FFN_PAR) {
+        cur = ggml_mul(ctx, cur, tmp);
+        cb(cur, "ffn_gate_par", il);
+    }
 
-            // TODO: name the intermediate tensors with cb()
+    if (down) {
+        cur = llm_build_lora_mm(lctx, ctx, down, cur);
+    }
 
-            ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il]));
-            ggml_build_forward_expand(gf, ggml_cpy(ctx0,  ssm_states, kv_self.v_l[il]));
-        }
+    if (down_b) {
+        cb(cur, "ffn_down", il);
+    }
 
-        return gf;
+    if (down_b) {
+        cur = ggml_add(ctx, cur, down_b);
     }
 
-    struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+    if (down_s) {
+        cur = ggml_mul(ctx, cur, down_s);
+        cb(cur, "ffn_down_s", il);
+    }
 
-        for (uint32_t i = 0; i < ids.size(); ++i) {
-            const uint32_t id = ids[i];
+    return cur;
+}
 
-            if (i == id || id == ids.size()) {
-                continue;
-            }
+static struct ggml_tensor * llm_build_moe_ffn(
+        struct ggml_context * ctx,
+       struct llama_context & lctx,
+         struct ggml_tensor * cur,
+         struct ggml_tensor * gate_inp,
+         struct ggml_tensor * up_exps,
+         struct ggml_tensor * gate_exps,
+         struct ggml_tensor * down_exps,
+                    int64_t   n_expert,
+                    int64_t   n_expert_used,
+            llm_ffn_op_type   type_op,
+                       bool   norm_w,
+                       bool   scale_w,
+                      float   w_scale,
+         const llm_build_cb & cb,
+                        int   il) {
+    int64_t n_embd = cur->ne[0];
+    int64_t n_tokens = cur->ne[1];
 
-            uint32_t nm = 1;
+    ggml_tensor * logits = llm_build_lora_mm(lctx, ctx, gate_inp, cur); // [n_expert, n_tokens]
+    cb(logits, "ffn_moe_logits", il);
 
-            while (i + nm < ids.size() && ids[i + nm] == id + nm) {
-                nm++;
-            }
+    ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
+    cb(probs, "ffn_moe_probs", il);
 
-            for (int il = 0; il < n_layer; ++il) {
-                ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
-                        n_embd_k_gqa, nm,
-                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
-                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));
+    // select experts
+    ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens]
+    cb(selected_experts->src[0], "ffn_moe_argsort", il);
+    cb(selected_experts, "ffn_moe_topk", il);
 
-                ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
-                        n_embd_k_gqa, nm,
-                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
-                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));
-
-                ggml_tensor * view_v_src;
-                ggml_tensor * view_v_dst;
+    ggml_tensor * weights = ggml_get_rows(ctx,
+            ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
+    cb(weights, "ffn_moe_weights", il);
 
-                if (flash_attn) {
-                    // NOTE: the V cache is not transposed when using flash attention
-                    view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
-                            n_embd_v_gqa, nm,
-                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
-                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*i));
+    if (norm_w) {
+        weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);
 
-                    view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
-                            n_embd_v_gqa, nm,
-                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
-                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*id));
-                } else {
-                    view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
-                            nm, n_embd_v_gqa,
-                            ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
-                            ggml_row_size(kv_self.v_l[il]->type, i));
+        ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
+        cb(weights_sum, "ffn_moe_weights_sum", il);
 
-                    view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
-                            nm, n_embd_v_gqa,
-                            ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
-                            ggml_row_size(kv_self.v_l[il]->type, id));
-                }
+        weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
+        cb(weights, "ffn_moe_weights_norm", il);
 
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
-            }
+        weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
+    }
+    if (scale_w) {
+        weights = ggml_scale(ctx, weights, w_scale);
+        cb(weights, "ffn_moe_weights_scaled", il);
+    }
 
-            i += nm - 1;
-        }
+    cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
+    ggml_tensor * up = llm_build_lora_mm_id(lctx, ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+    cb(up, "ffn_moe_up", il);
 
-        //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
+    ggml_tensor * gate = llm_build_lora_mm_id(lctx, ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+    cb(gate, "ffn_moe_gate", il);
 
-        return gf;
+    switch (type_op) {
+        case LLM_FFN_SILU:
+            {
+                gate = ggml_silu(ctx, gate);
+                cb(gate, "ffn_moe_silu", il);
+            } break;
+        case LLM_FFN_GELU:
+            {
+                gate = ggml_gelu(ctx, gate);
+                cb(gate, "ffn_moe_gelu", il);
+            } break;
+        default:
+            GGML_ABORT("fatal error");
     }
 
-    struct ggml_tensor * build_inp_pos() {
-        lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(lctx.inp_pos, "inp_pos", -1);
-        ggml_set_input(lctx.inp_pos);
-        return lctx.inp_pos;
-    }
+    ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens]
+    cb(par, "ffn_moe_gate_par", il);
 
-    struct ggml_tensor * build_inp_out_ids() {
-        lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
-        cb(lctx.inp_out_ids, "inp_out_ids", -1);
-        ggml_set_input(lctx.inp_out_ids);
-        return lctx.inp_out_ids;
-    }
+    ggml_tensor * experts = llm_build_lora_mm_id(lctx, ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
+    cb(experts, "ffn_moe_down", il);
 
-    struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
-        if (causal) {
-            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+    experts = ggml_mul(ctx, experts, weights);
+
+    // aggregate experts
+    ggml_tensor * moe_out = nullptr;
+    for (int i = 0; i < n_expert_used; ++i) {
+        ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
+                experts->nb[2], i*experts->nb[1]);
+
+        if (i == 0) {
+            moe_out = cur_expert;
         } else {
-            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+            moe_out = ggml_add(ctx, moe_out, cur_expert);
         }
-        cb(lctx.inp_KQ_mask, "KQ_mask", -1);
-        ggml_set_input(lctx.inp_KQ_mask);
-        return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask;
     }
 
-    struct ggml_tensor * build_inp_mean() {
-        lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
-        cb(lctx.inp_mean, "inp_mean", -1);
-        ggml_set_input(lctx.inp_mean);
-        return lctx.inp_mean;
+    if (n_expert_used == 1) {
+        // avoid returning a non-contiguous tensor
+        moe_out = ggml_cont(ctx, moe_out);
     }
 
-    struct ggml_tensor * build_inp_cls() {
-        lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(lctx.inp_cls, "inp_cls", -1);
-        ggml_set_input(lctx.inp_cls);
-        return lctx.inp_cls;
-    }
+    return moe_out;
+}
 
-    struct ggml_tensor * build_inp_s_copy() {
-        lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size);
-        cb(lctx.inp_s_copy, "inp_s_copy", -1);
-        ggml_set_input(lctx.inp_s_copy);
-        return lctx.inp_s_copy;
-    }
+static struct ggml_tensor * llm_build_kqv(
+        struct ggml_context * ctx,
+       struct llama_context & lctx,
+       const llama_kv_cache & kv,
+         struct ggml_cgraph * graph,
+         struct ggml_tensor * wo,
+         struct ggml_tensor * wo_b,
+         struct ggml_tensor * q_cur,
+         struct ggml_tensor * kq_mask,
+                    int32_t   n_tokens,
+                    int32_t   n_kv,
+                    float     kq_scale,
+         const llm_build_cb & cb,
+                    int       il) {
+    const llama_model   & model   = lctx.model;
+    const llama_hparams & hparams = lctx.model.hparams;
+    const llama_cparams & cparams = lctx.cparams;
 
-    struct ggml_tensor * build_inp_s_mask() {
-        lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
-        cb(lctx.inp_s_mask, "inp_s_mask", -1);
-        ggml_set_input(lctx.inp_s_mask);
-        return lctx.inp_s_mask;
-    }
+    const int64_t n_ctx         = cparams.n_ctx;
+    const int64_t n_head        = hparams.n_head(il);
+    const int64_t n_head_kv     = hparams.n_head_kv(il);
+    const int64_t n_embd_head_k = hparams.n_embd_head_k;
+    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(il);
+    const int64_t n_embd_head_v = hparams.n_embd_head_v;
+    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(il);
 
-    struct ggml_tensor * build_inp_s_seq() {
-        lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
-        cb(lctx.inp_s_seq, "inp_s_seq", -1);
-        ggml_set_input(lctx.inp_s_seq);
-        return lctx.inp_s_seq;
-    }
+    struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
+    cb(q, "q", il);
 
-    struct ggml_cgraph * build_llama() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+    struct ggml_tensor * k =
+        ggml_view_3d(ctx, kv.k_l[il],
+                n_embd_head_k, n_kv, n_head_kv,
+                ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
+                ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
+                0);
+    cb(k, "k", il);
 
-        // mutable variable, needed during the last layer of the computation to skip unused tokens
-        int32_t n_tokens = this->n_tokens;
+    struct ggml_tensor * cur;
 
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
+    if (cparams.flash_attn) {
+        GGML_UNUSED(model);
+        GGML_UNUSED(n_ctx);
 
-        struct ggml_tensor * cur;
-        struct ggml_tensor * inpL;
+        // split cached v into n_head heads (not transposed)
+        struct ggml_tensor * v =
+            ggml_view_3d(ctx, kv.v_l[il],
+                    n_embd_head_v, n_kv, n_head_kv,
+                    ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa),
+                    ggml_row_size(kv.v_l[il]->type, n_embd_head_v),
+                    0);
+        cb(v, "v", il);
 
-        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+        cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias);
 
-        // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = build_inp_pos();
+        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) {
+            ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
+        }
 
-        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+        cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
+    } else {
+        struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
+        cb(kq, "kq", il);
 
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
+        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2) {
+            // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
+            // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
+            ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+        }
 
-            // norm
-            cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, cb, il);
-            cb(cur, "attn_norm", il);
+        if (model.arch == LLM_ARCH_GROK) {
+            // need to do the following:
+            // multiply by attn_output_multiplyer of 0.08838834764831845
+            // and then :
+            // kq = 30 * tanh(kq / 30)
+            // before the softmax below
 
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-                if (model.layers[il].bq) {
-                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
-                    cb(Qcur, "Qcur", il);
-                }
+            //try from phi2
+            //ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-                if (model.layers[il].bk) {
-                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
-                    cb(Kcur, "Kcur", il);
-                }
+            kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
+            kq = ggml_scale(ctx, kq, 30);
+        }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-                if (model.layers[il].bv) {
-                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
-                    cb(Vcur, "Vcur", il);
-                }
+        if (hparams.attn_soft_cap) {
+            kq = ggml_scale(ctx, kq, 1.0f / hparams.f_attn_logit_softcapping);
+            kq = ggml_tanh(ctx, kq);
+            kq = ggml_scale(ctx, kq, hparams.f_attn_logit_softcapping);
+        }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(Qcur, "Qcur", il);
+        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
+        cb(kq, "kq_soft_max_ext", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(Kcur, "Kcur", il);
+        GGML_ASSERT(kv.size == n_ctx);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-            }
+        // split cached v into n_head heads
+        struct ggml_tensor * v =
+            ggml_view_3d(ctx, kv.v_l[il],
+                    n_kv, n_embd_head_v, n_head_kv,
+                    ggml_element_size(kv.v_l[il])*n_ctx,
+                    ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
+                    0);
+        cb(v, "v", il);
 
-            if (il == n_layer - 1) {
-                // skip computing output for unused tokens
-                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                n_tokens = n_outputs;
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
+        struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
+        cb(kqv, "kqv", il);
 
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            if (model.layers[il].ffn_gate_inp == nullptr) {
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // MoE branch
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = llm_build_moe_ffn(ctx0, cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, true,
-                        cb, il);
-                cb(cur, "ffn_moe_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
+        struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
+        cb(kqv_merged, "kqv_merged", il);
 
-            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
-            if (layer_dir != nullptr) {
-                cur = ggml_add(ctx0, cur, layer_dir);
-            }
-            cb(cur, "l_out", il);
+        cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_v*n_head, n_tokens);
+        cb(cur, "kqv_merged_cont", il);
+    }
 
-            // input for next layer
-            inpL = cur;
-        }
+    ggml_build_forward_expand(graph, cur);
 
-        cur = inpL;
+    if (wo) {
+        cur = llm_build_lora_mm(lctx, ctx, wo, cur);
+    }
 
-        cur = llm_build_norm(ctx0, cur, hparams,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, cb, -1);
-        cb(cur, "result_norm", -1);
+    if (wo_b) {
+        cb(cur, "kqv_wo", il);
+    }
 
-        // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
-        cb(cur, "result_output", -1);
+    if (wo_b) {
+        cur = ggml_add(ctx, cur, wo_b);
+    }
 
-        ggml_build_forward_expand(gf, cur);
+    return cur;
+}
 
-        return gf;
-    }
+static struct ggml_tensor * llm_build_kv(
+        struct ggml_context * ctx,
+       struct llama_context & lctx,
+       const llama_kv_cache & kv,
+         struct ggml_cgraph * graph,
+         struct ggml_tensor * wo,
+         struct ggml_tensor * wo_b,
+         struct ggml_tensor * k_cur,
+         struct ggml_tensor * v_cur,
+         struct ggml_tensor * q_cur,
+         struct ggml_tensor * kq_mask,
+                    int32_t   n_tokens,
+                    int32_t   kv_head,
+                    int32_t   n_kv,
+                    float     kq_scale,
+         const llm_build_cb & cb,
+                    int       il) {
+    const llama_hparams & hparams = lctx.model.hparams;
+    const llama_cparams & cparams = lctx.cparams;
 
-    struct ggml_cgraph * build_baichuan() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+    // these nodes are added to the graph together so that they are not reordered
+    // by doing so, the number of splits in the graph is reduced
+    ggml_build_forward_expand(graph, q_cur);
+    ggml_build_forward_expand(graph, k_cur);
+    ggml_build_forward_expand(graph, v_cur);
 
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
+    llm_build_kv_store(ctx, hparams, cparams, kv, graph, k_cur, v_cur, n_tokens, kv_head, cb, il);
 
-        struct ggml_tensor * cur;
-        struct ggml_tensor * inpL;
+    struct ggml_tensor * cur;
 
-        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+    cur  = llm_build_kqv(ctx, lctx, kv, graph, wo, wo_b,
+            q_cur, kq_mask, n_tokens, n_kv, kq_scale, cb, il);
+    cb(cur, "kqv_out", il);
 
-        // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
+    return cur;
+}
 
-        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+struct llm_build_context {
+    const llama_model    & model;
+          llama_context  & lctx;
+    const llama_hparams  & hparams;
+    const llama_cparams  & cparams;
+    const llama_batch    & batch;
+    const llama_kv_cache & kv_self;
 
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
+    const int64_t n_embd;
+    const int64_t n_layer;
+    const int64_t n_rot;
+    const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
+    const int64_t n_head;
+    const int64_t n_head_kv;
+    const int64_t n_embd_head_k;
+    const int64_t n_embd_k_gqa;
+    const int64_t n_embd_head_v;
+    const int64_t n_embd_v_gqa;
+    const int64_t n_expert;
+    const int64_t n_expert_used;
 
-            cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, cb, il);
-            cb(cur, "attn_norm", il);
+    const float freq_base;
+    const float freq_scale;
+    const float ext_factor;
+    const float attn_factor;
+    const float beta_fast;
+    const float beta_slow;
+    const float norm_eps;
+    const float norm_rms_eps;
 
-            // self-attention
-            {
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
+    const int32_t n_tokens;
+    const int32_t n_kv;     // size of KV cache to consider (n_kv <= kv_self.size)
+    const int32_t n_outputs;
+    const int32_t n_outputs_enc;
+    const int32_t kv_head;  // index of where we store new KV data in the cache
+    const int32_t n_ctx_orig;
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
+    const bool flash_attn;
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
+    const enum llama_pooling_type pooling_type;
+    const enum llama_rope_type    rope_type;
 
-                switch (model.type) {
-                    case MODEL_7B:
-                        Qcur = ggml_rope_custom(
-                            ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                            ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-                        Kcur = ggml_rope_custom(
-                            ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                            ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-                        break;
-                    case MODEL_13B:
-                        Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
-                        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
-                        break;
-                    default:
-                        GGML_ASSERT(false);
-                }
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
+    const llm_build_cb & cb;
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
-                        model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-            }
+    std::vector<uint8_t> & buf_compute_meta;
 
-            if (il == n_layer - 1) {
-                // skip computing output for unused tokens
-                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
+    struct ggml_context * ctx0 = nullptr;
 
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
+    // TODO: consider making the entire interface noexcept
+    llm_build_context(
+        llama_context  & lctx,
+    const llama_batch  & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) :
+        model            (lctx.model),
+        lctx             (lctx),
+        hparams          (model.hparams),
+        cparams          (lctx.cparams),
+        batch            (batch),
+        kv_self          (lctx.kv_self),
+        n_embd           (hparams.n_embd),
+        n_layer          (hparams.n_layer),
+        n_rot            (hparams.n_rot),
+        n_ctx            (cparams.n_ctx),
+        n_head           (hparams.n_head()),
+        n_head_kv        (hparams.n_head_kv()),
+        n_embd_head_k    (hparams.n_embd_head_k),
+        n_embd_k_gqa     (hparams.n_embd_k_gqa()),
+        n_embd_head_v    (hparams.n_embd_head_v),
+        n_embd_v_gqa     (hparams.n_embd_v_gqa()),
+        n_expert         (hparams.n_expert),
+        n_expert_used    (hparams.n_expert_used),
+        freq_base        (cparams.rope_freq_base),
+        freq_scale       (cparams.rope_freq_scale),
+        ext_factor       (cparams.yarn_ext_factor),
+        attn_factor      (cparams.yarn_attn_factor),
+        beta_fast        (cparams.yarn_beta_fast),
+        beta_slow        (cparams.yarn_beta_slow),
+        norm_eps         (hparams.f_norm_eps),
+        norm_rms_eps     (hparams.f_norm_rms_eps),
+        n_tokens         (batch.n_tokens),
+        n_kv             (worst_case ? kv_self.size : kv_self.n),
+        n_outputs        (worst_case ? n_tokens : lctx.n_outputs),
+        n_outputs_enc    (worst_case ? n_tokens : lctx.embd_enc.size() / hparams.n_embd),
+        kv_head          (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
+        n_ctx_orig       (cparams.n_ctx_orig_yarn),
+        flash_attn       (cparams.flash_attn),
+        pooling_type     (cparams.pooling_type),
+        rope_type        (hparams.rope_type),
+        cb               (cb),
+        buf_compute_meta (lctx.buf_compute_meta) {
+            // all initializations should be done in init()
+        }
 
-            // feed-forward network
-            {
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                cb(cur, "ffn_norm", il);
+    void init() {
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ buf_compute_meta.size(),
+            /*.mem_buffer =*/ buf_compute_meta.data(),
+            /*.no_alloc   =*/ true,
+        };
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-                cb(cur, "ffn_out", il);
-            }
+        ctx0 = ggml_init(params);
 
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "l_out", il);
+        lctx.inp_tokens      = nullptr;
+        lctx.inp_embd        = nullptr;
+        lctx.inp_pos         = nullptr;
+        lctx.inp_out_ids     = nullptr;
+        lctx.inp_KQ_mask     = nullptr;
+        lctx.inp_KQ_mask_swa = nullptr;
+        lctx.inp_K_shift     = nullptr;
+        lctx.inp_mean        = nullptr;
+        lctx.inp_cls         = nullptr;
+        lctx.inp_s_copy      = nullptr;
+        lctx.inp_s_mask      = nullptr;
+        lctx.inp_s_seq       = nullptr;
+        lctx.inp_pos_bucket    = nullptr;
+        lctx.inp_embd_enc      = nullptr;
+        lctx.inp_KQ_mask_cross = nullptr;
+    }
 
-            // input for next layer
-            inpL = cur;
+    void free() {
+        if (ctx0) {
+            ggml_free(ctx0);
+            ctx0 = nullptr;
         }
+    }
 
-        cur = inpL;
-
-        cur = llm_build_norm(ctx0, cur, hparams,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, cb, -1);
-        cb(cur, "result_norm", -1);
+    struct ggml_cgraph * build_k_shift() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-        // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
-        cb(cur, "result_output", -1);
+        GGML_ASSERT(kv_self.size == n_ctx);
+
+        lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        cb(lctx.inp_K_shift, "K_shift", -1);
+        ggml_set_input(lctx.inp_K_shift);
+
+        for (int il = 0; il < n_layer; ++il) {
+            const int64_t n_head_kv = hparams.n_head_kv(il);
+            const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+            struct ggml_tensor * rope_factors = build_rope_factors(il);
+            struct ggml_tensor * tmp =
+                // we rotate only the first n_rot dimensions
+                ggml_rope_ext_inplace(ctx0,
+                        ggml_view_3d(ctx0, kv_self.k_l[il],
+                            n_embd_head_k, n_head_kv, n_ctx,
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                            0),
+                        lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+
+            cb(tmp, "K_shifted", il);
+            ggml_build_forward_expand(gf, tmp);
+        }
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_s_copy() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        GGML_ASSERT(kv_self.recurrent);
+
+        struct ggml_tensor * state_copy = build_inp_s_copy();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
+            struct ggml_tensor * ssm_states  = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
+
+            conv_states = ggml_get_rows(ctx0, conv_states, state_copy);
+            ssm_states  = ggml_get_rows(ctx0,  ssm_states, state_copy);
+
+            // TODO: name the intermediate tensors with cb()
+
+            ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il]));
+            ggml_build_forward_expand(gf, ggml_cpy(ctx0,  ssm_states, kv_self.v_l[il]));
+        }
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        for (uint32_t i = 0; i < ids.size(); ++i) {
+            const uint32_t id = ids[i];
+
+            if (i == id || id == ids.size()) {
+                continue;
+            }
+
+            uint32_t nm = 1;
+
+            while (i + nm < ids.size() && ids[i + nm] == id + nm) {
+                nm++;
+            }
+
+            for (int il = 0; il < n_layer; ++il) {
+                const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+                const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
+                ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
+                        n_embd_k_gqa, nm,
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));
+
+                ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
+                        n_embd_k_gqa, nm,
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));
+
+                ggml_tensor * view_v_src;
+                ggml_tensor * view_v_dst;
+
+                if (flash_attn) {
+                    // NOTE: the V cache is not transposed when using flash attention
+                    view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
+                            n_embd_v_gqa, nm,
+                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
+                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*i));
+
+                    view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
+                            n_embd_v_gqa, nm,
+                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
+                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*id));
+                } else {
+                    view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
+                            nm, n_embd_v_gqa,
+                            ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
+                            ggml_row_size(kv_self.v_l[il]->type, i));
+
+                    view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
+                            nm, n_embd_v_gqa,
+                            ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
+                            ggml_row_size(kv_self.v_l[il]->type, id));
+                }
+
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
+            }
+
+            i += nm - 1;
+        }
+
+        //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
+
+        return gf;
+    }
+
+    struct ggml_tensor * build_inp_pos() {
+        lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(lctx.inp_pos, "inp_pos", -1);
+        ggml_set_input(lctx.inp_pos);
+        return lctx.inp_pos;
+    }
+
+    struct ggml_tensor * build_rope_factors(int il) {
+        // choose long/short freq factors based on the context size
+        const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max;
+
+        if (model.layers[il].rope_freqs != nullptr) {
+            return model.layers[il].rope_freqs;
+        }
+
+        if (n_ctx_pre_seq > hparams.n_ctx_orig_yarn) {
+            return model.layers[il].rope_long;
+        }
+
+        return model.layers[il].rope_short;
+    }
+
+    struct ggml_tensor * build_inp_out_ids() {
+        lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
+        cb(lctx.inp_out_ids, "inp_out_ids", -1);
+        ggml_set_input(lctx.inp_out_ids);
+        return lctx.inp_out_ids;
+    }
+
+    struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
+        lctx.inp_KQ_mask = causal
+            ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
+            : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        cb(lctx.inp_KQ_mask, "KQ_mask", -1);
+        ggml_set_input(lctx.inp_KQ_mask);
+
+        return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask;
+    }
+
+    struct ggml_tensor * build_inp_KQ_mask_swa(bool causal = true) {
+        GGML_ASSERT(hparams.n_swa > 0);
+
+        lctx.inp_KQ_mask_swa = causal
+            ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
+            : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        cb(lctx.inp_KQ_mask_swa, "KQ_mask_swa", -1);
+        ggml_set_input(lctx.inp_KQ_mask_swa);
+
+        return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask_swa, GGML_TYPE_F16) : lctx.inp_KQ_mask_swa;
+    }
+
+    struct ggml_tensor * build_inp_mean() {
+        lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
+        cb(lctx.inp_mean, "inp_mean", -1);
+        ggml_set_input(lctx.inp_mean);
+        return lctx.inp_mean;
+    }
+
+    struct ggml_tensor * build_inp_cls() {
+        lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(lctx.inp_cls, "inp_cls", -1);
+        ggml_set_input(lctx.inp_cls);
+        return lctx.inp_cls;
+    }
+
+    struct ggml_tensor * build_inp_s_copy() {
+        lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size);
+        cb(lctx.inp_s_copy, "inp_s_copy", -1);
+        ggml_set_input(lctx.inp_s_copy);
+        return lctx.inp_s_copy;
+    }
+
+    struct ggml_tensor * build_inp_s_mask() {
+        lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
+        cb(lctx.inp_s_mask, "inp_s_mask", -1);
+        ggml_set_input(lctx.inp_s_mask);
+        return lctx.inp_s_mask;
+    }
+
+    struct ggml_tensor * build_inp_s_seq() {
+        lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
+        cb(lctx.inp_s_seq, "inp_s_seq", -1);
+        ggml_set_input(lctx.inp_s_seq);
+        return lctx.inp_s_seq;
+    }
+
+    struct ggml_cgraph * append_pooling(struct ggml_cgraph * gf) {
+        // find result_norm tensor for input
+        struct ggml_tensor * inp = nullptr;
+        for (int i = gf->n_nodes - 1; i >= 0; --i) {
+            inp = gf->nodes[i];
+            if (strcmp(inp->name, "result_norm") == 0 || strcmp(inp->name, "result_embd") == 0) {
+                break;
+            } else {
+                inp = nullptr;
+            }
+        }
+        GGML_ASSERT(inp != nullptr && "missing result_norm/result_embd tensor");
+
+        struct ggml_tensor * cur;
+
+        switch (pooling_type) {
+            case LLAMA_POOLING_TYPE_MEAN:
+                {
+                    struct ggml_tensor * inp_mean = build_inp_mean();
+                    cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
+                } break;
+            case LLAMA_POOLING_TYPE_CLS:
+            case LLAMA_POOLING_TYPE_LAST:
+                {
+                    struct ggml_tensor * inp_cls = build_inp_cls();
+                    cur = ggml_get_rows(ctx0, inp, inp_cls);
+                } break;
+            case LLAMA_POOLING_TYPE_NONE:
+                {
+                    cur = inp;
+                } break;
+            default:
+                {
+                    GGML_ABORT("unknown pooling type");
+                }
+        }
+
+        cb(cur, "result_embd_pooled", -1);
 
         ggml_build_forward_expand(gf, cur);
 
         return gf;
     }
 
-    struct ggml_cgraph * build_xverse() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+    struct ggml_tensor * llm_build_pos_bucket(bool causal) {
+        if (causal) {
+            lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv,     n_tokens);
+        } else {
+            lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_tokens);
+        }
+
+        ggml_set_input(lctx.inp_pos_bucket);
+        cb(lctx.inp_pos_bucket, "pos_bucket", -1);
+
+        return lctx.inp_pos_bucket;
+    }
+
+    struct ggml_tensor * llm_build_pos_bias(struct ggml_tensor * pos_bucket, struct ggml_tensor * attn_rel_b) {
+        struct ggml_tensor * pos_bucket_1d = ggml_view_1d(ctx0, pos_bucket, pos_bucket->ne[0] * pos_bucket->ne[1], 0);
+        cb(pos_bucket_1d, "pos_bucket_1d", -1);
+
+        struct ggml_tensor * pos_bias = ggml_get_rows(ctx0, attn_rel_b, pos_bucket_1d);
+        cb(pos_bias, "pos_bias", -1);
+
+        pos_bias = ggml_view_3d(ctx0, pos_bias, pos_bias->ne[0], lctx.inp_pos_bucket->ne[0], lctx.inp_pos_bucket->ne[1], ggml_element_size(pos_bias) * pos_bias->ne[0], ggml_element_size(pos_bias) * pos_bias->ne[0] * lctx.inp_pos_bucket->ne[0],  0);
+        cb(pos_bias, "pos_bias", -1);
+
+        pos_bias = ggml_permute(ctx0, pos_bias, 2, 0, 1, 3);
+        cb(pos_bias, "pos_bias", -1);
+
+        pos_bias = ggml_cont(ctx0, pos_bias);
+        cb(pos_bias, "pos_bias", -1);
+
+        return pos_bias;
+    }
+
+    struct ggml_tensor * llm_build_inp_embd_enc() {
+        const int64_t n_embd = hparams.n_embd;
+        lctx.inp_embd_enc = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_outputs_enc);
+        ggml_set_input(lctx.inp_embd_enc);
+        cb(lctx.inp_embd_enc, "embd_enc", -1);
+        return lctx.inp_embd_enc;
+    }
+
+    struct ggml_tensor * llm_build_inp_KQ_mask_cross() {
+        lctx.inp_KQ_mask_cross = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_outputs_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        ggml_set_input(lctx.inp_KQ_mask_cross);
+        cb(lctx.inp_KQ_mask_cross, "KQ_mask_cross", -1);
+        return lctx.inp_KQ_mask_cross;
+    }
+
+    struct ggml_cgraph * build_llama() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -7334,6 +8808,7 @@ struct llm_build_context {
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
 
+            // norm
             cur = llm_build_norm(ctx0, inpL, hparams,
                     model.layers[il].attn_norm, NULL,
                     LLM_NORM_RMS, cb, il);
@@ -7341,37 +8816,55 @@ struct llm_build_context {
 
             // self-attention
             {
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
-                        model.layers[il].wo, NULL,
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
 
             if (il == n_layer - 1) {
                 // skip computing output for unused tokens
                 struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                cur   = ggml_get_rows(ctx0,      cur, inp_out_ids);
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                 inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
@@ -7379,25 +8872,265 @@ struct llm_build_context {
             cb(ffn_inp, "ffn_inp", il);
 
             // feed-forward network
-            {
+            if (model.layers[il].ffn_gate_inp == nullptr) {
                 cur = llm_build_norm(ctx0, ffn_inp, hparams,
                         model.layers[il].ffn_norm, NULL,
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "l_out", il);
+            } else {
+                // MoE branch
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
 
-            // input for next layer
+                cur = llm_build_moe_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        cb, il);
+                cb(cur, "ffn_moe_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_baichuan() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                switch (model.type) {
+                    case MODEL_7B:
+                        Qcur = ggml_rope_ext(
+                            ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+                        Kcur = ggml_rope_ext(
+                            ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+                        break;
+                    case MODEL_13B:
+                        Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
+                        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
+                        break;
+                    default:
+                        GGML_ABORT("fatal error");
+                }
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_xverse() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,      cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
             inpL = cur;
         }
 
@@ -7407,7 +9140,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -7416,7 +9149,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_falcon() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -7456,7 +9189,7 @@ struct llm_build_context {
                     cur = attn_norm;
                 }
 
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
@@ -7471,19 +9204,19 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 // using mode = 2 for neox mode
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -7500,19 +9233,18 @@ struct llm_build_context {
 
             // feed forward
             {
-                cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
-                        model.layers[il].ffn_up,   NULL,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, attn_norm, // !! use the attn norm, not the result
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        NULL,                      NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "l_out", il);
-
             cur = ggml_add(ctx0, cur, inpL);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -7528,7 +9260,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -7537,7 +9269,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_grok() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -7573,42 +9305,42 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
@@ -7640,13 +9372,14 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_moe_ffn(ctx0, cur,
+            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_gate_inp,
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
                     model.layers[il].ffn_down_exps,
                     n_expert, n_expert_used,
                     LLM_FFN_GELU, true,
+                    false, 0.0,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -7663,10 +9396,7 @@ struct llm_build_context {
             cur = ggml_add(ctx0, cur, ffn_inp);
             cb(cur, "ffn_out", il);
 
-            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
-            if (layer_dir != nullptr) {
-                cur = ggml_add(ctx0, cur, layer_dir);
-            }
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -7681,7 +9411,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
         // Grok
         // multiply logits by output_multiplier_scale of 0.5773502691896257
@@ -7696,7 +9426,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_dbrx() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -7732,7 +9462,7 @@ struct llm_build_context {
                 struct ggml_tensor * Kcur = nullptr;
                 struct ggml_tensor * Vcur = nullptr;
 
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
@@ -7746,21 +9476,21 @@ struct llm_build_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -7783,23 +9513,21 @@ struct llm_build_context {
                                  LLM_NORM, cb, il);
             cb(cur, "attn_out_norm", il);
 
-            cur = llm_build_moe_ffn(ctx0, cur,
+            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_gate_inp,
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
                     model.layers[il].ffn_down_exps,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, true,
+                    false, 0.0,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
             cur = ggml_add(ctx0, cur, ffn_inp);
             cb(cur, "ffn_out", il);
 
-            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
-            if (layer_dir != nullptr) {
-                cur = ggml_add(ctx0, cur, layer_dir);
-            }
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -7814,7 +9542,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
         cb(cur, "result_output", -1);
 
@@ -7824,7 +9552,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_starcoder() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -7856,7 +9584,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -7872,7 +9600,7 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -7896,17 +9624,21 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
-            inpL = ggml_add(ctx0, cur, ffn_inp);
-            cb(inpL, "l_out", il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
         }
 
         cur = llm_build_norm(ctx0, inpL, hparams,
@@ -7915,7 +9647,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -7923,256 +9655,49 @@ struct llm_build_context {
         return gf;
     }
 
-    struct ggml_cgraph * build_persimmon() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+    struct ggml_cgraph * build_refact() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head   == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head/2 == hparams.n_rot);
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
         inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-        // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = build_inp_pos();
-
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
         struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * residual = inpL;
+            struct ggml_tensor * inpSA = inpL;
 
             cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, cb, il);
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
             cb(cur, "attn_norm", il);
 
-            // self attention
+            // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
 
-                // split qkv
-                GGML_ASSERT(n_head_kv == n_head);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
-                cb(tmpqkv, "tmpqkv", il);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
 
-                struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
-                cb(tmpqkv_perm, "tmpqkv", il);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * tmpq = ggml_view_3d(
-                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                        0
-                        );
-                cb(tmpq, "tmpq", il);
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * tmpk = ggml_view_3d(
-                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
-                        );
-                cb(tmpk, "tmpk", il);
-
-                // Q/K Layernorm
-                tmpq = llm_build_norm(ctx0, tmpq, hparams,
-                        model.layers[il].attn_q_norm,
-                        model.layers[il].attn_q_norm_b,
-                        LLM_NORM, cb, il);
-                cb(tmpq, "tmpq", il);
-
-                tmpk = llm_build_norm(ctx0, tmpk, hparams,
-                        model.layers[il].attn_k_norm,
-                        model.layers[il].attn_k_norm_b,
-                        LLM_NORM, cb, il);
-                cb(tmpk, "tmpk", il);
-
-                // RoPE the first n_rot of q/k, pass the other half, and concat.
-                struct ggml_tensor * qrot = ggml_view_3d(
-                        ctx0, tmpq, n_rot, n_head, n_tokens,
-                        ggml_element_size(tmpq) * n_embd_head,
-                        ggml_element_size(tmpq) * n_embd_head * n_head,
-                        0
-                        );
-                cb(qrot, "qrot", il);
-
-                struct ggml_tensor * krot = ggml_view_3d(
-                        ctx0, tmpk, n_rot, n_head, n_tokens,
-                        ggml_element_size(tmpk) * n_embd_head,
-                        ggml_element_size(tmpk) * n_embd_head * n_head,
-                        0
-                        );
-                cb(krot, "krot", il);
-
-                // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
-                struct ggml_tensor * qpass = ggml_view_3d(
-                        ctx0, tmpq, n_rot, n_head, n_tokens,
-                        ggml_element_size(tmpq) * n_embd_head,
-                        ggml_element_size(tmpq) * n_embd_head * n_head,
-                        ggml_element_size(tmpq) * n_rot
-                        );
-                cb(qpass, "qpass", il);
-
-                struct ggml_tensor * kpass = ggml_view_3d(
-                        ctx0, tmpk, n_rot, n_head, n_tokens,
-                        ggml_element_size(tmpk) * n_embd_head,
-                        ggml_element_size(tmpk) * n_embd_head * n_head,
-                        ggml_element_size(tmpk) * n_rot
-                        );
-                cb(kpass, "kpass", il);
-
-                struct ggml_tensor * qrotated = ggml_rope_custom(
-                    ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
-                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(qrotated, "qrotated", il);
-
-                struct ggml_tensor * krotated = ggml_rope_custom(
-                    ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
-                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(krotated, "krotated", il);
-
-                // ggml currently only supports concatenation on dim=2
-                // so we need to permute qrot, qpass, concat, then permute back.
-                qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
-                cb(qrotated, "qrotated", il);
-
-                krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
-                cb(krotated, "krotated", il);
-
-                qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
-                cb(qpass, "qpass", il);
-
-                kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
-                cb(kpass, "kpass", il);
-
-                struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
-                cb(Qcur, "Qcur", il);
-
-                struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
-                cb(Kcur, "Kcur", il);
-
-                struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
-                cb(Q, "Q", il);
-
-                Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
-                cb(Kcur, "Kcur", il);
-
-                struct ggml_tensor * Vcur = ggml_view_3d(
-                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
-                        );
-                cb(Vcur, "Vcur", il);
-
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Q, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-            }
-
-            if (il == n_layer - 1) {
-                // skip computing output for unused tokens
-                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                cur      = ggml_get_rows(ctx0,      cur, inp_out_ids);
-                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
-            }
-
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, cb, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
-                        NULL,
-                        LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "l_out", il);
-
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = llm_build_norm(ctx0, cur, hparams,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, cb, -1);
-        cb(cur, "result_norm", -1);
-
-        cur = ggml_mul_mat(ctx0, model.output, cur);
-        cb(cur, "result_output", -1);
-
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
-
-    struct ggml_cgraph * build_refact() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
-
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        struct ggml_tensor * cur;
-        struct ggml_tensor * inpL;
-
-        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
-
-        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
-
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
-
-            cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, cb, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                cb(Kcur, "Kcur", il);
-
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                cb(Qcur, "Qcur", il);
-
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
-                        model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-            }
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
 
             if (il == n_layer - 1) {
                 // skip computing output for unused tokens
@@ -8191,16 +9716,17 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -8215,7 +9741,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -8224,7 +9750,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_bert() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -8238,8 +9764,6 @@ struct llm_build_context {
         if (model.arch != LLM_ARCH_JINA_BERT_V2) {
             inp_pos = build_inp_pos();
         }
-        struct ggml_tensor * inp_mean = build_inp_mean();
-        struct ggml_tensor * inp_cls  = build_inp_cls();
 
         // construct input embeddings (token, type, position)
         inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
@@ -8269,7 +9793,7 @@ struct llm_build_context {
 
             // self-attention
             if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) {
-                Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
+                Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur), model.layers[il].bq);
                 cb(Qcur, "Qcur", il);
 
                 if (model.layers[il].attn_q_norm) {
@@ -8279,7 +9803,7 @@ struct llm_build_context {
                             LLM_NORM, cb, il);
                 }
 
-                Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
+                Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur), model.layers[il].bk);
                 cb(Kcur, "Kcur", il);
 
                 if (model.layers[il].attn_k_norm) {
@@ -8288,14 +9812,14 @@ struct llm_build_context {
                             model.layers[il].attn_k_norm_b,
                             LLM_NORM, cb, il);
                 }
-                Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
+                Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur), model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
             } else {
                 // compute Q and K and RoPE them
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
@@ -8306,16 +9830,16 @@ struct llm_build_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -8344,7 +9868,7 @@ struct llm_build_context {
 
             ggml_build_forward_expand(gf, cur);
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+            cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
             if (model.layers[il].bo) {
                 cb(cur, "kqv_wo", il);
             }
@@ -8367,29 +9891,34 @@ struct llm_build_context {
             // attention layer norm
             cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
 
+            if (model.layers[il].attn_norm_2 != nullptr) {
+                cur = ggml_add(ctx0, cur, inpL); // re-add the layer input
+                cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, cb, il);
+            }
+
             struct ggml_tensor * ffn_inp = cur;
             cb(ffn_inp, "ffn_inp", il);
 
             // feed-forward network
             if (model.arch == LLM_ARCH_BERT) {
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
             } else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL,                        NULL,
+                        model.layers[il].ffn_gate, NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
             } else {
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
             }
@@ -8409,35 +9938,13 @@ struct llm_build_context {
         cur = inpL;
         cb(cur, "result_embd", -1);
 
-        // pooling layer
-        switch (pooling_type) {
-            case LLAMA_POOLING_TYPE_NONE:
-                {
-                    // nop
-                } break;
-            case LLAMA_POOLING_TYPE_MEAN:
-                {
-                    cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
-                    cb(cur, "result_embd_pooled", -1);
-                } break;
-            case LLAMA_POOLING_TYPE_CLS:
-                {
-                    cur = ggml_get_rows(ctx0, cur, inp_cls);
-                    cb(cur, "result_embd_pooled", -1);
-                } break;
-            case LLAMA_POOLING_TYPE_UNSPECIFIED:
-                {
-                    GGML_ASSERT(false && "Invalid pooling type");
-                } break;
-        }
-
         ggml_build_forward_expand(gf, cur);
 
         return gf;
     }
 
     struct ggml_cgraph * build_bloom() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -8466,7 +9973,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -8482,7 +9989,7 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -8506,17 +10013,21 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
-            inpL = ggml_add(ctx0, cur, ffn_inp);
-            cb(inpL, "l_out", il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
         }
 
         cur = llm_build_norm(ctx0, inpL, hparams,
@@ -8525,7 +10036,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -8534,7 +10045,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_mpt() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -8572,7 +10083,7 @@ struct llm_build_context {
             {
                 cur = attn_norm;
 
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 if (model.layers[il].bqkv){
@@ -8610,13 +10121,13 @@ struct llm_build_context {
                     Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                     Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-                    cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                    cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                             model.layers[il].wo, model.layers[il].bo,
                             Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
                 } else {
                     Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                    cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                    cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                             model.layers[il].wo, model.layers[il].bo,
                             Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
                 }
@@ -8640,16 +10151,17 @@ struct llm_build_context {
                         model.layers[il].ffn_norm_b,
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         model.layers[il].ffn_act,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -8664,7 +10176,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -8704,21 +10216,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -8746,21 +10258,21 @@ struct llm_build_context {
                 }
 
 
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -8788,16 +10300,17 @@ struct llm_build_context {
                     // parallel residual
                     cur = inpSA;
                 }
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -8813,7 +10326,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -8822,7 +10335,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_qwen() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -8848,7 +10361,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -8866,19 +10379,19 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 // using mode = 2 for neox mode
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -8900,16 +10413,17 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -8924,7 +10438,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -8933,7 +10447,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_qwen2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -8962,36 +10476,36 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9012,15 +10526,16 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    model.layers[il].ffn_gate, NULL,
-                    model.layers[il].ffn_down, NULL,
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
                     NULL,
                     LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
             cb(cur, "ffn_out", il);
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -9035,7 +10550,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9044,7 +10559,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_qwen2moe() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -9076,36 +10591,36 @@ struct llm_build_context {
             // self_attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9128,29 +10643,30 @@ struct llm_build_context {
             cb(cur, "ffn_norm", il);
 
             ggml_tensor * moe_out =
-                    llm_build_moe_ffn(ctx0, cur,
+                    llm_build_moe_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_gate_inp,
                         model.layers[il].ffn_up_exps,
                         model.layers[il].ffn_gate_exps,
                         model.layers[il].ffn_down_exps,
                         n_expert, n_expert_used,
                         LLM_FFN_SILU, false,
+                        false, 0.0,
                         cb, il);
             cb(cur, "ffn_moe_out", il);
 
             // FFN shared expert
             {
-                ggml_tensor * cur_gate_inp = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
+                ggml_tensor * cur_gate_inp = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
                 cb(cur_gate_inp, "ffn_shexp_gate_inp", il);
 
                 // sigmoid
                 ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
                 cb(cur_gate, "ffn_shexp_gate", il);
 
-                ggml_tensor * cur_ffn = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up_shexp,   NULL,
-                        model.layers[il].ffn_gate_shexp, NULL,
-                        model.layers[il].ffn_down_shexp, NULL,
+                ggml_tensor * cur_ffn = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up_shexp,   NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur_ffn, "ffn_shexp", il);
@@ -9165,6 +10681,7 @@ struct llm_build_context {
             }
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -9179,7 +10696,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9188,7 +10705,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_phi2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -9221,7 +10738,7 @@ struct llm_build_context {
                 struct ggml_tensor * Vcur = nullptr;
 
                 if (model.layers[il].wqkv) {
-                    cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
+                    cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
                     cb(cur, "wqkv", il);
 
                     cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -9231,9 +10748,9 @@ struct llm_build_context {
                     Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                     Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
                 } else {
-                    Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
-                    Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
-                    Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+                    Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                    Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                    Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                 }
 
                 cb(Qcur, "Qcur", il);
@@ -9243,8 +10760,8 @@ struct llm_build_context {
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
@@ -9254,13 +10771,13 @@ struct llm_build_context {
                 Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
@@ -9275,21 +10792,21 @@ struct llm_build_context {
 
             // FF
             {
-                ffn_output = llm_build_ffn(ctx0, attn_norm_output,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                ffn_output = llm_build_ffn(ctx0, lctx, attn_norm_output,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(ffn_output, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, cur, ffn_output);
-            cb(cur, "l_out", il);
-
             cur = ggml_add(ctx0, cur, inpL);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
+            // input for next layer
             inpL = cur;
         }
 
@@ -9299,7 +10816,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output_no_bias", -1);
 
         cur = ggml_add(ctx0, cur, model.output_b);
@@ -9309,7 +10826,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_phi3() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
@@ -9324,13 +10841,16 @@ struct llm_build_context {
         struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+        struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();
 
         for (int il = 0; il < n_layer; ++il) {
             auto residual = inpL;
 
             // self-attention
             {
+                // rope freq factors for 128k context
+                struct ggml_tensor * rope_factors = build_rope_factors(il);
+
                 struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
                     model.layers[il].attn_norm,
                     NULL,
@@ -9342,7 +10862,7 @@ struct llm_build_context {
                 struct ggml_tensor * Vcur = nullptr;
 
                 if (model.layers[il].wqkv) {
-                    cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
+                    cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
                     cb(cur, "wqkv", il);
 
                     Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
@@ -9350,9 +10870,9 @@ struct llm_build_context {
                     Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
                 }
                 else {
-                    Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
-                    Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
-                    Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+                    Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                    Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                    Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                 }
 
                 cb(Qcur, "Qcur", il);
@@ -9362,8 +10882,8 @@ struct llm_build_context {
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
@@ -9371,15 +10891,15 @@ struct llm_build_context {
                 Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+                        Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
 
             if (il == n_layer - 1) {
@@ -9401,25 +10921,20 @@ struct llm_build_context {
             // special-case: the up and gate tensors are merged into a single tensor
             // TOOD: support into llm_build_ffn
             {
-                struct ggml_tensor* up = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
-                cb(up, "ffn_up", il);
-
-                auto g = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), 0));
-                auto y = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), up->nb[1] / 2));
-
-                y = ggml_mul(ctx0, y, ggml_silu(ctx0, g));
-                cb(y, "ffn_gate", il);
-
-                auto down = ggml_mul_mat(ctx0, model.layers[il].ffn_down, y);
-                cb(down, "ffn_down", il);
-
-                cur = down;
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        NULL,                      NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, residual, cur);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
+            // input for next layer
             inpL = cur;
         }
 
@@ -9429,7 +10944,7 @@ struct llm_build_context {
             LLM_NORM_RMS, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9469,28 +10984,28 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos,
-                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos, nullptr,
+                        n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos,
-                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr,
+                        n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9508,19 +11023,18 @@ struct llm_build_context {
 
             // feed-forward network
             {
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up, NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, cur, sa_out);
-            cb(cur, "l_out", il);
-
             cur = ggml_add(ctx0, cur, inpL);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -9535,7 +11049,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9544,7 +11058,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_gpt2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -9577,7 +11091,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -9593,7 +11107,7 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9617,17 +11131,21 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
-            inpL = ggml_add(ctx0, cur, ffn_inp);
-            cb(inpL, "l_out", il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
         }
 
         cur = llm_build_norm(ctx0, inpL, hparams,
@@ -9636,7 +11154,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9645,7 +11163,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_codeshell() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
@@ -9672,7 +11190,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -9686,21 +11204,21 @@ struct llm_build_context {
                 cb(tmpk, "tmpk", il);
                 cb(Vcur, "Vcur", il);
 
-                struct ggml_tensor * Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                struct ggml_tensor * Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                struct ggml_tensor * Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9724,17 +11242,21 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
-            inpL = ggml_add(ctx0, cur, ffn_inp);
-            cb(inpL, "l_out", il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
         }
 
         cur = llm_build_norm(ctx0, inpL, hparams,
@@ -9743,7 +11265,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9752,7 +11274,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_orion() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -9781,42 +11303,42 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 // if (model.layers[il].bq) {
                 //     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                 //     cb(Qcur, "Qcur", il);
                 // }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 // if (model.layers[il].bk) {
                 //     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                 //     cb(Kcur, "Kcur", il);
                 // }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 // if (model.layers[il].bv) {
                 //     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                 //     cb(Vcur, "Vcur", il);
                 // }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9837,15 +11359,16 @@ struct llm_build_context {
                     LLM_NORM, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    model.layers[il].ffn_gate, NULL,
-                    model.layers[il].ffn_down, NULL,
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
                     NULL,
                     LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
             cb(cur, "ffn_out", il);
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -9860,7 +11383,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9869,7 +11392,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_internlm2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -9898,42 +11421,42 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9954,15 +11477,16 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    model.layers[il].ffn_gate, NULL,
-                    model.layers[il].ffn_down, NULL,
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
                     NULL,
                     LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
             cb(cur, "ffn_out", il);
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -9977,7 +11501,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9989,7 +11513,7 @@ struct llm_build_context {
     //      https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
     // based on the original build_llama() function
     struct ggml_cgraph * build_minicpm() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -10028,42 +11552,42 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10090,10 +11614,10 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
@@ -10104,6 +11628,7 @@ struct llm_build_context {
             cb(cur, "hidden_scaled_ffn", -1);
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -10123,7 +11648,7 @@ struct llm_build_context {
         cb(cur, "lmhead_scaling", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10132,7 +11657,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_gemma() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head_k = hparams.n_embd_head_k;
 
@@ -10160,31 +11685,31 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos,
-                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
                 Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
                 cb(Qcur, "Qcur_scaled", il);
 
-                Kcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos,
-                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
@@ -10206,16 +11731,17 @@ struct llm_build_context {
 
             // feed-forward network
             {
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up, NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
             }
 
             cur = ggml_add(ctx0, cur, sa_out);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -10230,7 +11756,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10238,101 +11764,113 @@ struct llm_build_context {
         return gf;
     }
 
-    struct ggml_cgraph * build_starcoder2() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+    struct ggml_cgraph * build_gemma2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
+        const int64_t n_embd_head_k = hparams.n_embd_head_k;
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
         inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
+        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+        cb(inpL, "inp_scaled", -1);
+
         // inp_pos - contains the positions
         struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+        // gemma 2 requires different mask for layers using sliding window (SWA)
+        struct ggml_tensor * KQ_mask     = build_inp_KQ_mask(true);
+        struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(true);
 
         for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
+            // (il % 2) layers use SWA
+            struct ggml_tensor * KQ_mask_l = (il % 2 == 0) ? KQ_mask_swa : KQ_mask;
 
             // norm
             cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
-                    LLM_NORM, cb, il);
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
             cb(cur, "attn_norm", il);
 
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
-                if (model.layers[il].bq) {
-                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
-                    cb(Qcur, "Qcur", il);
-                }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
-                if (model.layers[il].bk) {
-                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
-                    cb(Kcur, "Kcur", il);
-                }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
-                if (model.layers[il].bv) {
-                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
-                    cb(Vcur, "Vcur", il);
-                }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                );
+                Qcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                );
+                // ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
+                switch (model.type) {
+                    case e_model::MODEL_2B:
+                    case e_model::MODEL_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
+                    case e_model::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
+                    default: GGML_ABORT("fatal error");
+                };
+                cb(Qcur, "Qcur_scaled", il);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
 
+            cur = llm_build_norm(ctx0, cur, hparams,
+                    model.layers[il].attn_post_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_post_norm", il);
+
             if (il == n_layer - 1) {
                 // skip computing output for unused tokens
                 struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
+            struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+            cb(sa_out, "sa_out", il);
 
-            cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
-                    LLM_NORM, cb, il);
+            cur = llm_build_norm(ctx0, sa_out, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+            // feed-forward network
+            {
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
-                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
-            cb(cur, "ffn_out", il);
-            cur = ggml_add(ctx0, cur, ffn_inp);
+                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = llm_build_norm(ctx0, cur, hparams,
+                model.layers[il].ffn_post_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+            cb(cur, "ffn_post_norm", -1);
+
+            cur = ggml_add(ctx0, cur, sa_out);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -10342,12 +11880,138 @@ struct llm_build_context {
         cur = inpL;
 
         cur = llm_build_norm(ctx0, cur, hparams,
-                model.output_norm, model.output_norm_b,
-                LLM_NORM, cb, -1);
-        cb(cur, "result_norm", -1);
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+        // final logit soft-capping
+        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
+        cur = ggml_tanh(ctx0, cur);
+        cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+
+    struct ggml_cgraph * build_starcoder2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10356,7 +12020,7 @@ struct llm_build_context {
     }
 
     struct ggml_cgraph * build_mamba() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t d_model = n_embd;
         const int64_t d_conv  = hparams.ssm_d_conv;
@@ -10399,7 +12063,7 @@ struct llm_build_context {
             cb(cur, "attn_norm", il);
 
             // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens}
-            struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur);
+            struct ggml_tensor * xz = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_in, cur);
             // split the above in two
             // => {d_inner, n_tokens}
             struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0);
@@ -10438,14 +12102,14 @@ struct llm_build_context {
             // ssm
             {
                 // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens}
-                struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x);
+                struct ggml_tensor * x_db = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_x, x);
                 // split
                 struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0);
                 struct ggml_tensor * B  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
                 struct ggml_tensor * C  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));
 
                 // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
-                dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt);
+                dt = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_dt, dt);
                 dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
 
                 // Custom operator to optimize the parallel associative scan
@@ -10476,11 +12140,12 @@ struct llm_build_context {
                 y = ggml_mul(ctx0, y, ggml_silu(ctx0, z));
 
                 // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens}
-                cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_out, y);
             }
 
             // residual
             cur = ggml_add(ctx0, cur, inpL);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -10494,7 +12159,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10504,7 +12169,7 @@ struct llm_build_context {
 
     struct ggml_cgraph * build_command_r() {
 
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -10533,21 +12198,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -10579,21 +12244,21 @@ struct llm_build_context {
                     cb(Kcur, "Kcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10610,10 +12275,10 @@ struct llm_build_context {
 
             // feed-forward network
             {
-                cur = llm_build_ffn(ctx0, ffn_inp,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                cur = llm_build_ffn(ctx0, lctx, ffn_inp,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
@@ -10622,6 +12287,7 @@ struct llm_build_context {
             // add together residual + FFN + self-attention
             cur = ggml_add(ctx0, cur, inpL);
             cur = ggml_add(ctx0, cur, attn_out);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -10636,7 +12302,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
         if (f_logit_scale) {
             cur = ggml_scale(ctx0, cur, f_logit_scale);
@@ -10657,7 +12323,7 @@ struct llm_build_context {
     //   * removed bias
     //   * removed MoE
     struct ggml_cgraph * build_olmo() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         // mutable variable, needed during the last layer of the computation to skip unused tokens
         int32_t n_tokens = this->n_tokens;
@@ -10689,42 +12355,42 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (hparams.f_clamp_kqv > 0.0f) {
                     Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (hparams.f_clamp_kqv > 0.0f) {
                     Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (hparams.f_clamp_kqv > 0.0f) {
                     Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, nullptr,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10746,10 +12412,10 @@ struct llm_build_context {
                     LLM_NORM, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    model.layers[il].ffn_gate, NULL,
-                    model.layers[il].ffn_down, NULL,
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
                     NULL,
                     LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
             cb(cur, "ffn_out", il);
@@ -10757,10 +12423,7 @@ struct llm_build_context {
             cur = ggml_add(ctx0, cur, ffn_inp);
             cb(cur, "ffn_out", il);
 
-            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
-            if (layer_dir != nullptr) {
-                cur = ggml_add(ctx0, cur, layer_dir);
-            }
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -10775,3520 +12438,2900 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
 
         return gf;
     }
-};
-
-static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
-    llama_batch dummy;
-    dummy.n_tokens = 0;
 
-    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+    struct ggml_cgraph * build_openelm() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-    struct llm_build_context llm(lctx, dummy, cb, false);
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-    llm.init();
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-    struct ggml_cgraph * result = llm.build_defrag(ids);
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
-    llm.free();
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-    return result;
-}
+        for (int il = 0; il < n_layer; ++il) {
+            const int64_t n_head    = hparams.n_head(il);
+            const int64_t n_head_kv = hparams.n_head_kv(il);
+            const int64_t n_head_qkv = 2*n_head_kv + n_head;
 
-static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
-    llama_batch dummy;
-    dummy.n_tokens = 0;
+            cur = inpL;
+            struct ggml_tensor * residual = cur;
 
-    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-    struct llm_build_context llm(lctx, dummy, cb, false);
+            // self-attention
+            {
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
 
-    llm.init();
+                cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);
 
-    struct ggml_cgraph * result = llm.build_k_shift();
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, cur->nb[1], cur->nb[2], 0));
+                cb(Qcur, "Qcur", il);
 
-    llm.free();
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head));
+                cb(Kcur, "Kcur", il);
 
-    return result;
-}
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv)));
+                cb(Vcur, "Vcur", il);
 
-static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
-    llama_batch dummy;
-    dummy.n_tokens = 0;
+                Qcur = llm_build_norm(ctx0, Qcur, hparams,
+                        model.layers[il].attn_q_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(Qcur, "Qcur", il);
 
-    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+                Kcur = llm_build_norm(ctx0, Kcur, hparams,
+                        model.layers[il].attn_k_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(Kcur, "Kcur", il);
 
-    struct llm_build_context llm(lctx, dummy, cb, false);
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
 
-    llm.init();
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
 
-    struct ggml_cgraph * result = llm.build_s_copy();
+                Vcur = ggml_reshape_2d(ctx0, Vcur, n_embd_head * n_head_kv, n_tokens);
+                cb(Qcur, "Vcur", il);
 
-    llm.free();
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
 
-    return result;
-}
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+            }
 
-static struct ggml_cgraph * llama_build_graph(
-         llama_context & lctx,
-     const llama_batch & batch,
-                  bool   worst_case) {
-    const auto & model = lctx.model;
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
+            cb(ffn_inp, "ffn_inp", il);
 
-    // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
-    llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
-        if (il >= 0) {
-            ggml_format_name(cur, "%s-%d", name, il);
-        } else {
-            ggml_set_name(cur, name);
-        }
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
 
-        if (!lctx.cparams.offload_kqv) {
-            if (strcmp(name, "kqv_merged_cont") == 0) {
-                // all nodes between the KV store and the attention output are run on the CPU
-                ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu);
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
             }
-        }
 
-        // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
-        // FIXME: fix in ggml_backend_sched
-        const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
-        if (batch.n_tokens < 32 || full_offload) {
-            if (il != -1 && strcmp(name, "norm") == 0) {
-                for (auto * backend : lctx.backends) {
-                    if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
-                        ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
-                        break;
-                    }
-                }
-            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            inpL = cur;
         }
-    };
 
-    struct ggml_cgraph * result = NULL;
+        cur = inpL;
 
-    struct llm_build_context llm(lctx, batch, cb, worst_case);
+        // norm
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
-    llm.init();
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-    switch (model.arch) {
-        case LLM_ARCH_LLAMA:
-            {
-                result = llm.build_llama();
-            } break;
-        case LLM_ARCH_BAICHUAN:
-            {
-                result = llm.build_baichuan();
-            } break;
-        case LLM_ARCH_FALCON:
-            {
-                result = llm.build_falcon();
-            } break;
-        case LLM_ARCH_GROK:
-            {
-                result = llm.build_grok();
-            } break;
-        case LLM_ARCH_STARCODER:
-            {
-                result = llm.build_starcoder();
-            } break;
-        case LLM_ARCH_PERSIMMON:
-            {
-                result = llm.build_persimmon();
-            } break;
-        case LLM_ARCH_REFACT:
-            {
-                result = llm.build_refact();
-            } break;
-        case LLM_ARCH_BERT:
-        case LLM_ARCH_JINA_BERT_V2:
-        case LLM_ARCH_NOMIC_BERT:
-            {
-                result = llm.build_bert();
-            } break;
-        case LLM_ARCH_BLOOM:
-            {
-                result = llm.build_bloom();
-            } break;
-        case LLM_ARCH_MPT:
-            {
-                result = llm.build_mpt();
-            } break;
-         case LLM_ARCH_STABLELM:
-            {
-                result = llm.build_stablelm();
-            } break;
-        case LLM_ARCH_QWEN:
-            {
-                result = llm.build_qwen();
-            } break;
-        case LLM_ARCH_QWEN2:
-            {
-                result = llm.build_qwen2();
-            } break;
-        case LLM_ARCH_QWEN2MOE:
-            {
-                result = llm.build_qwen2moe();
-            } break;
-        case LLM_ARCH_PHI2:
-            {
-                result = llm.build_phi2();
-            } break;
-        case LLM_ARCH_PHI3:
-            {
-                result = llm.build_phi3();
-            } break;
-        case LLM_ARCH_PLAMO:
-            {
-                result = llm.build_plamo();
-            } break;
-        case LLM_ARCH_GPT2:
-            {
-                result = llm.build_gpt2();
-            } break;
-        case LLM_ARCH_CODESHELL:
-            {
-                result = llm.build_codeshell();
-            } break;
-        case LLM_ARCH_ORION:
-            {
-                result = llm.build_orion();
-            } break;
-        case LLM_ARCH_INTERNLM2:
-            {
-                result = llm.build_internlm2();
-            } break;
-        case LLM_ARCH_MINICPM:
-            {
-                result = llm.build_minicpm();
-            } break;
-        case LLM_ARCH_GEMMA:
-            {
-                result = llm.build_gemma();
-            } break;
-        case LLM_ARCH_STARCODER2:
-            {
-                result = llm.build_starcoder2();
-            } break;
-        case LLM_ARCH_MAMBA:
-            {
-                result = llm.build_mamba();
-            } break;
-        case LLM_ARCH_XVERSE:
-            {
-                result = llm.build_xverse();
-            } break;
-        case LLM_ARCH_COMMAND_R:
-            {
-                result = llm.build_command_r();
-            } break;
-        case LLM_ARCH_DBRX:
-            {
-                result = llm.build_dbrx();
-            } break;
-        case LLM_ARCH_OLMO:
-            {
-                result = llm.build_olmo();
-            } break;
-        default:
-            GGML_ASSERT(false);
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
     }
 
-    llm.free();
+    struct ggml_cgraph * build_gptneox() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-    return result;
-}
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-static void llama_set_k_shift(llama_context & lctx) {
-    const int64_t kv_size = lctx.kv_self.size;
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-    int32_t * data = (int32_t *) lctx.inp_K_shift->data;
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
-    for (int i = 0; i < kv_size; ++i) {
-        data[i] = lctx.kv_self.cells[i].delta;
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            // ffn
+            if (hparams.use_par_res) {
+                // attention and ffn are computed in parallel
+                // x = x + attn(ln1(x)) + ffn(ln2(x))
+
+                struct ggml_tensor * attn_out = cur;
+
+                cur = llm_build_norm(ctx0, inpL, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+
+                cur = ggml_add(ctx0, cur, inpL);
+                cb(cur, "ffn_out", il);
+
+                cur = ggml_add(ctx0, cur, attn_out);
+                cur = lctx.cvec.apply_to(ctx0, cur, il);
+                cb(cur, "l_out", il);
+
+                // input for next layer
+                inpL = cur;
+            } else {
+                // attention and ffn are computed sequentially
+                // x = x + attn(ln1(x))
+                // x = x + ffn(ln2(x))
+
+                struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+                cb(ffn_inp, "ffn_inp", il);
+
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        NULL,                      NULL,                        NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+
+                cur = ggml_add(ctx0, cur, ffn_inp);
+                cur = lctx.cvec.apply_to(ctx0, cur, il);
+                cb(cur, "l_out", il);
+
+                // input for next layer
+                inpL = cur;
+            }
+        }
+
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
     }
-}
 
-static void llama_set_s_copy(llama_context & lctx) {
-    const int64_t kv_size = lctx.kv_self.size;
+    struct ggml_cgraph * build_arctic() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-    assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
+            cb(ffn_out, "ffn_out", il);
+
+            // MoE
+            cur = llm_build_norm(ctx0, inpSA, hparams,
+                    model.layers[il].ffn_norm_exps, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm_exps", il);
+
+            cur = llm_build_moe_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, true,
+                    false, 0.0,
+                    cb, il);
+            cb(cur, "ffn_moe_out", il);
 
-    int32_t * data = (int32_t *) lctx.inp_s_copy->data;
+            cur = ggml_add(ctx0, cur, ffn_out);
+            cb(cur, "ffn_out", il);
 
-    for (int i = 0; i < kv_size; ++i) {
-        data[i] = lctx.kv_self.cells[i].src;
-    }
-}
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
 
-static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
-    //
-    // set input data
-    //
+            // input for next layer
+            inpL = cur;
+        }
 
-    const auto & hparams = lctx.model.hparams;
-    const auto & cparams = lctx.cparams;
-    const auto & kv_self = lctx.kv_self;
+        cur = inpL;
 
-    if (batch.token) {
-        const int64_t n_tokens = batch.n_tokens;
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
-        ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
-    }
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-    if (batch.embd) {
-        const int64_t n_embd   = hparams.n_embd;
-        const int64_t n_tokens = batch.n_tokens;
+        ggml_build_forward_expand(gf, cur);
 
-        ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+        return gf;
     }
 
-    if (batch.pos && lctx.inp_pos) {
-        const int64_t n_tokens = batch.n_tokens;
+    struct ggml_cgraph * build_deepseek2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-        ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
-    }
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
 
-    if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
-        GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
-        const int64_t n_tokens = batch.n_tokens;
+        bool is_lite = (hparams.n_layer == 27);
 
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
-        int32_t * data = (int32_t *) lctx.inp_out_ids->data;
+        // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
+        // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
+        const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
+        const float kq_scale = 1.0f*mscale*mscale/sqrtf(float(hparams.n_embd_head_k));
+        const float attn_factor_scaled = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
 
-        if (lctx.n_outputs == n_tokens) {
-            for (int i = 0; i < n_tokens; ++i) {
-                data[i] = i;
-            }
-        } else if (batch.logits) {
-            int32_t n_outputs = 0;
-            for (int i = 0; i < n_tokens; ++i) {
-                if (batch.logits[i]) {
-                    data[n_outputs++] = i;
-                }
-            }
-            // the graph needs to have been passed the correct number of outputs
-            GGML_ASSERT(lctx.n_outputs == n_outputs);
-        } else if (lctx.n_outputs == 1) {
-            // only keep last output
-            data[0] = n_tokens - 1;
-        } else {
-            GGML_ASSERT(lctx.n_outputs == 0);
-        }
-    }
+        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
+        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+        const uint32_t kv_lora_rank = hparams.n_lora_kv;
 
-    GGML_ASSERT(
-        // (!a || b) is a logical implication (a -> b)
-        // !hparams.causal_attn -> !cparams.causal_attn
-        (hparams.causal_attn || !cparams.causal_attn) &&
-        "causal attention with embedding models is not supported"
-    );
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    if (lctx.inp_KQ_mask) {
-        // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
-        if (cparams.causal_attn) {
-            const int64_t n_kv     = kv_self.n;
-            const int64_t n_tokens = batch.n_tokens;
+        // {n_embd, n_tokens}
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
-            float * data = (float *) lctx.inp_KQ_mask->data;
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-            // For causal attention, use only the previous KV cells
-            // of the correct sequence for each token of the batch.
-            // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
-            for (int h = 0; h < 1; ++h) {
-                for (int j = 0; j < n_tokens; ++j) {
-                    const llama_pos    pos    = batch.pos[j];
-                    const llama_seq_id seq_id = batch.seq_id[j][0];
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-                    for (int i = 0; i < n_kv; ++i) {
-                        float f;
-                        if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
-                            f = -INFINITY;
-                        } else {
-                            if (hparams.use_alibi) {
-                                f = -fabs(lctx.kv_self.cells[i].pos - pos);
-                            } else {
-                                f = 0.0f;
-                            }
-                        }
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
-                    }
-                }
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-                for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
-                    for (int j = 0; j < n_kv; ++j) {
-                        data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
-                    }
+            // self_attention
+            {
+                struct ggml_tensor * q = NULL;
+                if (!is_lite) {
+                    // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
+                    cb(q, "q", il);
+
+                    q = llm_build_norm(ctx0, q, hparams,
+                            model.layers[il].attn_q_a_norm, NULL,
+                            LLM_NORM_RMS, cb, il);
+                    cb(q, "q", il);
+
+                    // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
+                    cb(q, "q", il);
+                } else {
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                    cb(q, "q", il);
                 }
-            }
-        } else {
-            // when using kv cache, the mask needs to match the kv cache size
-            const int64_t n_tokens = batch.n_tokens;
-            const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens;
 
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        0);
+                cb(q_nope, "q_nope", il);
+
+                // and {n_head * n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        ggml_row_size(q->type, n_embd_head_qk_nope));
+                cb(q_pe, "q_pe", il);
+
+                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
+                cb(kv_pe_compresseed, "kv_pe_compresseed", il);
+
+                // split into {kv_lora_rank, n_tokens}
+                struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        0);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // and {n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        kv_pe_compresseed->nb[1],
+                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
+                cb(k_pe, "k_pe", il);
+
+                kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
+                kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
+                        model.layers[il].attn_kv_a_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
+                struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
+                cb(kv, "kv", il);
+
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
+                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        0);
+                cb(k_nope, "k_nope", il);
+
+                // and {n_head * n_embd_head_v, n_tokens}
+                struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_cont(ctx0, v_states);
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens,
+                    ggml_row_size(kv->type, hparams.n_embd_head_v * n_head),
+                    0);
+                cb(v_states, "v_states", il);
 
-            float * data = (float *) lctx.inp_KQ_mask->data;
+                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                q_pe = ggml_rope_ext(
+                    ctx0, q_pe, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor_scaled, beta_fast, beta_slow
+                );
+                cb(q_pe, "q_pe", il);
+
+                // shared RoPE key
+                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                k_pe = ggml_rope_ext(
+                    ctx0, k_pe, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor_scaled, beta_fast, beta_slow
+                );
+                cb(k_pe, "k_pe", il);
 
-            for (int h = 0; h < 1; ++h) {
-                for (int j = 0; j < n_tokens; ++j) {
-                    const llama_seq_id seq_id = batch.seq_id[j][0];
+                struct ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
+                cb(q_states, "q_states", il);
 
-                    for (int i = 0; i < n_tokens; ++i) {
-                        float f = -INFINITY;
-                        for (int s = 0; s < batch.n_seq_id[i]; ++s) {
-                            if (batch.seq_id[i][s] == seq_id) {
-                                if (hparams.use_alibi) {
-                                    f = -fabs(batch.pos[i] - batch.pos[j]);
-                                } else {
-                                    f = 0.0f;
-                                }
-                                break;
-                            }
-                        }
+                struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+                cb(k_states, "k_states", il);
 
-                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = f;
-                    }
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
+            }
 
-                    for (int i = n_tokens; i < n_stride; ++i) {
-                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY;
-                    }
-                }
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
-        }
-    }
 
-    if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
-        const int64_t n_tokens = batch.n_tokens;
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
 
-        GGML_ASSERT(lctx.inp_mean);
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
 
-        float * data = (float *) lctx.inp_mean->data;
-        memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
+            if ((uint32_t) il < hparams.n_layer_dense_lead) {
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                ggml_tensor * moe_out =
+                        llm_build_moe_ffn(ctx0, lctx, cur,
+                            model.layers[il].ffn_gate_inp,
+                            model.layers[il].ffn_up_exps,
+                            model.layers[il].ffn_gate_exps,
+                            model.layers[il].ffn_down_exps,
+                            n_expert, n_expert_used,
+                            LLM_FFN_SILU, false,
+                            true, hparams.expert_weights_scale,
+                            cb, il);
+                cb(moe_out, "ffn_moe_out", il);
+
+                // FFN shared expert
+                {
+                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, cur,
+                            model.layers[il].ffn_up_shexp,   NULL, NULL,
+                            model.layers[il].ffn_gate_shexp, NULL, NULL,
+                            model.layers[il].ffn_down_shexp, NULL, NULL,
+                            NULL,
+                            LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                    cb(ffn_shexp, "ffn_shexp", il);
 
-        std::vector<uint64_t> sum(n_tokens, 0);
-        for (int i = 0; i < n_tokens; ++i) {
-            const llama_seq_id seq_id = batch.seq_id[i][0];
+                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                    cb(cur, "ffn_out", il);
+                }
+            }
 
-            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
 
-            sum[seq_id] += 1;
+            // input for next layer
+            inpL = cur;
         }
 
-        std::vector<float> div(n_tokens, 0.0f);
-        for (int i = 0; i < n_tokens; ++i) {
-            const uint64_t s = sum[i];
-            if (s > 0) {
-                div[i] = 1.0f/float(s);
-            }
-        }
+        cur = inpL;
 
-        for (int i = 0; i < n_tokens; ++i) {
-            const llama_seq_id seq_id = batch.seq_id[i][0];
-            data[seq_id*n_tokens + i] = div[seq_id];
-        }
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
     }
 
-    if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
-        const int64_t n_tokens = batch.n_tokens;
+    struct ggml_cgraph * build_bitnet() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-        GGML_ASSERT(lctx.inp_cls);
-        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
-        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-        for (int i = 0; i < n_tokens; ++i) {
-            const llama_seq_id seq_id = batch.seq_id[i][0];
-            const llama_pos    pos    = batch.pos[i];
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
-            if (pos == 0) {
-                data[seq_id] = i;
-            }
-        }
-    }
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-    if (kv_self.recurrent) {
-        const int64_t n_kv = kv_self.n;
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-        if (lctx.inp_s_mask) {
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
-            float * data = (float *) lctx.inp_s_mask->data;
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-            // states which are not affected by the current batch are left untouched
-            for (int i = 0; i < n_kv; ++i) {
-                llama_seq_id    seq_id       = i + lctx.kv_self.head;
-                llama_kv_cell & kv_cell      = lctx.kv_self.cells[seq_id];
-                bool            has_self_seq = kv_cell.has_seq_id(seq_id);
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                Qcur = ggml_mul(ctx0, Qcur, model.layers[il].wq_scale);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
 
-                data[i] = (float) has_self_seq;
+                // B1.K
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                Kcur = ggml_mul(ctx0, Kcur, model.layers[il].wk_scale);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
 
-                // ensure current sequences will be kept
-                if (!has_self_seq && kv_cell.pos >= 0) {
-                    kv_cell.seq_id.insert(seq_id);
+                // B1.V
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                Vcur = ggml_mul(ctx0, Vcur, model.layers[il].wv_scale);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
                 }
-            }
-        }
-        // For Mamba (and other recurrent architectures),
-        // update the correct state(s)/sequence(s) for each token of the batch.
-        // Like with the KQ_mask, if a token in the batch has multiple sequences,
-        // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv).
-        if (lctx.inp_s_seq) {
-            const int64_t n_tokens = batch.n_tokens;
 
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer));
-            int32_t * data = (int32_t *) lctx.inp_s_seq->data;
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
 
-            for (int j = 0; j < n_tokens; ++j) {
-                const int32_t n_seq = batch.n_seq_id[j];
-                GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
 
-                for (int i = 0; i < n_kv; ++i) {
-                    if (i < n_seq) {
-                        // for this type of model, the head is the minimum seq_id of the batch
-                        data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head;
-                    } else {
-                        data[j*n_kv + i] = -1;
-                    }
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        NULL, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+
+                cur = llm_build_norm(ctx0, cur, hparams,
+                        model.layers[il].attn_sub_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "attn_sub_norm", il);
+
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
+                cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale);
+                if (model.layers[il].bo) {
+                    cur = ggml_add(ctx0, cur, model.layers[il].bo);
                 }
+                cb(cur, "attn_o_out", il);
             }
-        }
-    }
-}
-
-// Make sure enough space is available for outputs.
-// Returns max number of outputs for which space was reserved.
-static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
-    const auto & cparams = lctx.cparams;
-    const auto & hparams = lctx.model.hparams;
 
-    const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
 
-    const auto n_batch = cparams.n_batch;
-    const auto n_vocab = hparams.n_vocab;
-    const auto n_embd  = hparams.n_embd;
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
 
-    // TODO: use a per-batch flag for logits presence instead
-    const bool has_logits = cparams.causal_attn;
-    const bool has_embd   = cparams.embeddings && (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
+            // feed-forward forward
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
 
-    const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
-    const size_t embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
+            cur = llm_build_ffn(ctx0, lctx, cur,
+                    model.layers[il].ffn_up,   NULL, model.layers[il].ffn_up_scale,
+                    model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_scale,
+                    NULL,                      NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_sub_out", il);
 
-    if (lctx.output_ids.empty()) {
-        // init, never resized afterwards
-        lctx.output_ids.resize(n_batch);
-    }
+            cur = llm_build_norm(ctx0, cur, hparams,
+                            model.layers[il].ffn_sub_norm, NULL,
+                            LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_sub_norm", il);
 
-    const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0;
-    const size_t new_size  = (logits_size + embd_size) * sizeof(float);
+            cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_down, cur);
+            cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale);
+            cb(cur, "ffn_down", il);
 
-    // alloc only when more than the current capacity is required
-    // TODO: also consider shrinking the buffer
-    if (!lctx.buf_output || prev_size < new_size) {
-        if (lctx.buf_output) {
-#ifndef NDEBUG
-            // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
-            LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
-#endif
-            ggml_backend_buffer_free(lctx.buf_output);
-            lctx.buf_output = nullptr;
-            lctx.logits = nullptr;
-            lctx.embd = nullptr;
-        }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
 
-        lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), new_size);
-        if (lctx.buf_output == nullptr) {
-            LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
-            return 0;
+            // input for next layer
+            inpL = cur;
         }
-    }
-
-    float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output);
 
-    lctx.logits = has_logits ? output_base               : nullptr;
-    lctx.embd   = has_embd   ? output_base + logits_size : nullptr;
+        cur = inpL;
 
-    lctx.output_size = n_outputs_max;
-    lctx.logits_size = logits_size;
-    lctx.embd_size   = embd_size;
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
-    // set all ids as invalid (negative)
-    std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.tok_embd, cur);
+        cb(cur, "result_output", -1);
 
-    ggml_backend_buffer_clear(lctx.buf_output, 0);
+        ggml_build_forward_expand(gf, cur);
+        return gf;
+    }
 
-    lctx.n_outputs = 0;
+    struct ggml_cgraph * build_t5_encoder() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-    return n_outputs_max;
-}
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-static void llama_graph_compute(
-        llama_context & lctx,
-          ggml_cgraph * gf,
-                  int   n_threads) {
-#ifdef GGML_USE_METAL
-    if (ggml_backend_is_metal(lctx.backend_metal)) {
-        ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
-    }
-#endif
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    if (lctx.backend_cpu != nullptr) {
-        ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
-        ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
-    }
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-    ggml_backend_sched_graph_compute_async(lctx.sched, gf);
+        GGML_ASSERT(lctx.is_encoding);
+        struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false);
 
-    // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
-}
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask_enc = build_inp_KQ_mask(false);
 
-// decode a batch of tokens by evaluating the transformer
-//
-//   - lctx:      llama context
-//   - batch:     batch to evaluate
-//
-// return 0 on success
-// return positive int on warning
-// return negative int on error
-//
-static int llama_decode_internal(
-         llama_context & lctx,
-           llama_batch   batch_all) { // TODO: rename back to batch
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-    const uint32_t n_tokens_all = batch_all.n_tokens;
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm_enc, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-    if (n_tokens_all == 0) {
-        LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
-        return -1;
-    }
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_enc, cur);
+                cb(Qcur, "Qcur", il);
 
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_enc, cur);
+                cb(Kcur, "Kcur", il);
 
-    GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_enc, cur);
+                cb(Vcur, "Vcur", il);
 
-    GGML_ASSERT(n_tokens_all <= cparams.n_batch);
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
+                struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
 
-    if (lctx.t_compute_start_us == 0) {
-        lctx.t_compute_start_us = ggml_time_us();
-    }
-    lctx.n_queued_tokens += n_tokens_all;
+                struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+                cb(kq, "kq", il);
 
-    auto & kv_self = lctx.kv_self;
+                struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
+                struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_enc, attn_rel_b);
+                struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
+                cb(kq_b, "kq_b", il);
 
-    const int64_t n_embd  = hparams.n_embd;
-    const int64_t n_vocab = hparams.n_vocab;
+                kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_enc, 1.0f, hparams.f_max_alibi_bias);
+                cb(kq, "kq_soft_max_ext", il);
 
-    uint32_t n_outputs = 0;
-    uint32_t n_outputs_prev = 0;
+                struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
+                cb(v, "v", il);
 
-    const auto n_ubatch = cparams.n_ubatch;
+                struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
+                cb(kqv, "kqv", il);
 
-    std::vector<llama_pos> pos;
-    std::vector<int32_t>                   n_seq_id;
-    std::vector<llama_seq_id *>            seq_id_arr;
-    std::vector<std::vector<llama_seq_id>> seq_id;
+                struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                cb(kqv_merged, "kqv_merged", il);
 
-    // count outputs
-    if (batch_all.logits) {
-        for (uint32_t i = 0; i < n_tokens_all; ++i) {
-            n_outputs += batch_all.logits[i] != 0;
-        }
-    } else if (lctx.logits_all || (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
-        n_outputs = n_tokens_all;
-    } else {
-        // keep last output only
-        n_outputs = 1;
-    }
+                cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+                cb(cur, "kqv_merged_cont", il);
 
-    // reserve output buffer
-    if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
-        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
-        return -2;
-    };
+                ggml_build_forward_expand(gf, cur);
 
-    // set output mappings
-    if (batch_all.logits) {
-        int32_t i_logits = 0;
-        for (uint32_t i = 0; i < n_tokens_all; ++i) {
-            if (batch_all.logits[i]) {
-                lctx.output_ids[i] = i_logits++;
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_enc, cur);
+                cb(cur, "kqv_out", il);
             }
-        }
-    } else {
-        for (uint32_t i = 0; i < n_outputs; ++i) {
-            lctx.output_ids[i] = i;
-        }
-    }
-
-    for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
-        const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);
-        llama_batch u_batch = {
-            /* .n_tokens   = */ (int32_t) n_tokens,
-            /* .token      = */ batch_all.token     ? batch_all.token    + cur_token        : nullptr,
-            /* .embd       = */ batch_all.embd      ? batch_all.embd     + cur_token*n_embd : nullptr,
-            /* .pos        = */ batch_all.pos       ? batch_all.pos      + cur_token        : nullptr,
-            /* .n_seq_id   = */ batch_all.n_seq_id  ? batch_all.n_seq_id + cur_token        : nullptr,
-            /* .seq_id     = */ batch_all.seq_id    ? batch_all.seq_id   + cur_token        : nullptr,
-            /* .logits     = */ batch_all.logits    ? batch_all.logits   + cur_token        : nullptr,
-            /* .all_pos_0  = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1,
-            /* .all_pos_1  = */ batch_all.all_pos_1,
-            /* .all_seq_id = */ batch_all.all_seq_id,
-        };
-
-        // count the outputs in this u_batch
-        {
-            int32_t n_outputs_new = 0;
 
-            if (u_batch.logits) {
-                for (uint32_t i = 0; i < n_tokens; i++) {
-                    n_outputs_new += u_batch.logits[i] != 0;
-                }
-            } else if (n_outputs == n_tokens_all) {
-                n_outputs_new = n_tokens;
-            } else {
-                // keep last output only
-                if (cur_token + n_tokens >= n_tokens_all) {
-                    n_outputs_new = 1;
-                }
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
-            // needs to happen before the graph is built
-            lctx.n_outputs = n_outputs_new;
-        }
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
 
-        int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
-        GGML_ASSERT(n_threads > 0);
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm_enc, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
 
-        // helpers for smoother batch API transition
-        // after deprecating the llama_eval calls, these will be removed
-        if (u_batch.pos == nullptr) {
-            pos.resize(n_tokens);
-            for (uint32_t i = 0; i < n_tokens; i++) {
-                pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1;
+                // T5 uses relu, flan-T5 uses gelu-gated
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up_enc,   NULL, NULL,
+                        model.layers[il].ffn_gate_enc, NULL, NULL,
+                        model.layers[il].ffn_down_enc, NULL, NULL,
+                        NULL,
+                        model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
+                        model.layers[il].ffn_gate_enc ? LLM_FFN_PAR  : LLM_FFN_SEQ,
+                        cb, il);
+                cb(cur, "ffn_out", il);
             }
 
-            u_batch.pos = pos.data();
-        }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
 
-        if (u_batch.seq_id == nullptr) {
-            n_seq_id.resize(n_tokens);
-            seq_id.resize(n_tokens);
-            seq_id_arr.resize(n_tokens);
-            for (uint32_t i = 0; i < n_tokens; i++) {
-                n_seq_id[i] = 1;
-                seq_id[i].resize(1);
-                seq_id[i][0] = u_batch.all_seq_id;
-                seq_id_arr[i] = seq_id[i].data();
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
             }
+            cb(cur, "l_out", il);
 
-            u_batch.n_seq_id = n_seq_id.data();
-            u_batch.seq_id = seq_id_arr.data();
+            // input for next layer
+            inpL = cur;
         }
 
-        // non-causal masks do not use the KV cache
-        if (hparams.causal_attn) {
-            llama_kv_cache_update(&lctx);
+        cur = inpL;
+        cb(cur, "result_embd", -1);
 
-            // if we have enough unused cells before the current head ->
-            //   better to start searching from the beginning of the cache, hoping to fill it
-            if (kv_self.head > kv_self.used + 2*n_tokens) {
-                kv_self.head = 0;
-            }
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm_enc, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
-            if (!llama_kv_cache_find_slot(kv_self, u_batch)) {
-                return 1;
-            }
+        ggml_build_forward_expand(gf, cur);
 
-            if (!kv_self.recurrent) {
-                // a heuristic, to avoid attending the full cache if it is not yet utilized
-                // after enough generations, the benefit from this heuristic disappears
-                // if we start defragmenting the cache, the benefit from this will be more important
-                const uint32_t pad = llama_kv_cache_get_padding(cparams);
-                kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad)));
-                //kv_self.n = llama_kv_cache_cell_max(kv_self);
-            }
-        }
+        return gf;
+    }
 
-        //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
+    struct ggml_cgraph * build_t5_decoder() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-        ggml_backend_sched_reset(lctx.sched);
-        ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
 
-        ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-        // the output is always the last tensor in the graph
-        struct ggml_tensor * res  = gf->nodes[gf->n_nodes - 1];
-        struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-        if (lctx.n_outputs == 0) {
-            // no output
-            res  = nullptr;
-            embd = nullptr;
-        } else if (!hparams.causal_attn) {
-            res = nullptr; // do not extract logits for embedding models such as BERT
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-            // token or sequence embeddings
-            embd = gf->nodes[gf->n_nodes - 1];
+        GGML_ASSERT(!lctx.is_encoding);
+        GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first");
 
-            GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
-        } else if (cparams.embeddings) {
-            // the embeddings could be in the second to last tensor, or any of the previous tensors
-            int i_embd = gf->n_nodes - 2;
-            for (int i = 3; strcmp(embd->name, "result_norm") != 0; ++i) {
-                i_embd = gf->n_nodes - i;
-                if (i_embd < 0) { break; }
-                embd = gf->nodes[i_embd];
-            }
-            GGML_ASSERT(i_embd >= 0 && "missing result_norm tensor");
-
-            // TODO: use a per-batch flag to know when to skip logits while keeping embeddings
-            if (!cparams.causal_attn) {
-                res = nullptr; // do not extract logits when not needed
-                // skip computing logits
-                // TODO: is this safe?
-                gf->n_nodes = i_embd + 1;
-            }
-        } else {
-            embd = nullptr; // do not extract embeddings when not needed
-            GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
-        }
-        // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+        struct ggml_tensor * embd_enc       = llm_build_inp_embd_enc();
+        struct ggml_tensor * pos_bucket_dec = llm_build_pos_bucket(true);
 
-        // for big prompts, if BLAS is enabled, it is better to use only one thread
-        // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
-        // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
-        //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
-        //       with the BLAS calls. need a better solution
-        // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
-        //                   being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
-        if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
-            n_threads = std::min(4, n_threads);
-        }
+        struct ggml_tensor * KQ_mask_dec   = build_inp_KQ_mask();
+        struct ggml_tensor * KQ_mask_cross = llm_build_inp_KQ_mask_cross();
 
-        ggml_backend_sched_alloc_graph(lctx.sched, gf);
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-        llama_set_inputs(lctx, u_batch);
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-        llama_graph_compute(lctx, gf, n_threads);
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
 
-        // update the kv ring buffer
-        {
-            kv_self.head += n_tokens;
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
 
-            // Ensure kv cache head points to a valid index.
-            if (kv_self.head >= kv_self.size) {
-                kv_self.head = 0;
-            }
-        }
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
 
-#ifdef GGML_PERF
-        // print timing information per ggml operation (for debugging purposes)
-        // requires GGML_PERF to be defined
-        ggml_graph_print(gf);
-#endif
+                llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
 
-        // plot the computation graph in dot format (for debugging purposes)
-        //if (n_past%100 == 0) {
-        //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
-        //}
+                struct ggml_tensor * k =
+                    ggml_view_3d(ctx0, kv_self.k_l[il],
+                            n_embd_head_k, n_kv, n_head_kv,
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+                            0);
+                cb(k, "k", il);
 
-        // extract logits
-        if (res) {
-            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
-            GGML_ASSERT(backend_res != nullptr);
-            GGML_ASSERT(lctx.logits != nullptr);
+                struct ggml_tensor * v =
+                    ggml_view_3d(ctx0, kv_self.v_l[il],
+                            n_kv, n_embd_head_v, n_head_kv,
+                            ggml_element_size(kv_self.v_l[il])*n_ctx,
+                            ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v,
+                            0);
+                cb(v, "v", il);
 
-            float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
-            const int32_t n_outputs_new = lctx.n_outputs;
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-            if (n_outputs_new) {
-                GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
-                GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size);
-                ggml_backend_tensor_get_async(backend_res, res, logits_out, 0, n_outputs_new*n_vocab*sizeof(float));
-            }
-        }
+                struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
 
-        // extract embeddings
-        if (embd) {
-            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
-            GGML_ASSERT(backend_embd != nullptr);
+                struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+                cb(kq, "kq", il);
 
-            switch (cparams.pooling_type) {
-                case LLAMA_POOLING_TYPE_NONE:
-                    {
-                        // extract token embeddings
-                        GGML_ASSERT(lctx.embd != nullptr);
-                        float * embd_out = lctx.embd + n_outputs_prev*n_embd;
-                        const int32_t n_outputs_new = lctx.n_outputs;
+                struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
+                struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_dec, attn_rel_b);
+                struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
+                cb(kq_b, "kq_b", il);
 
-                        if (n_outputs_new) {
-                            GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
-                            GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
-                            ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
-                        }
-                    } break;
-                case LLAMA_POOLING_TYPE_CLS:
-                case LLAMA_POOLING_TYPE_MEAN:
-                    {
-                        GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);
+                kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_dec, 1.0f, hparams.f_max_alibi_bias);
+                cb(kq, "kq_soft_max_ext", il);
 
-                        // extract sequence embeddings
-                        auto & embd_seq_out = lctx.embd_seq;
-                        embd_seq_out.clear();
+                struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
+                cb(kqv, "kqv", il);
 
-                        for (uint32_t i = 0; i < n_tokens; i++) {
-                            const llama_seq_id seq_id = u_batch.seq_id[i][0];
-                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
-                                continue;
-                            }
-                            embd_seq_out[seq_id].resize(n_embd);
-                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
-                        }
-                    } break;
-                case LLAMA_POOLING_TYPE_UNSPECIFIED:
-                    {
-                        GGML_ASSERT(false && "unknown pooling type");
-                    } break;
+                struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                cb(kqv_merged, "kqv_merged", il);
+
+                cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+                cb(cur, "kqv_merged_cont", il);
+
+                ggml_build_forward_expand(gf, cur);
+
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
+                cb(cur, "kqv_out", il);
             }
-        }
-        n_outputs_prev += lctx.n_outputs;
-    }
 
-    // set to total number of outputs in the batch, for use in llama_get_logits_ith
-    lctx.n_outputs = n_outputs;
+            cur = ggml_add(ctx0, cur, inpSA);
+            cb(cur, "cross_inp", il);
 
-    // wait for the computation to finish (automatically done when obtaining the model output)
-    //llama_synchronize(&lctx);
+            struct ggml_tensor * inpCA = cur;
 
-    // decide if we need to defrag the kv cache
-    if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
-        const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;
+            // norm
+            cur = llm_build_norm(ctx0, cur, hparams,
+                    model.layers[il].attn_norm_cross, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm_cross", il);
 
-        // queue defragmentation for next llama_kv_cache_update
-        if (fragmentation > cparams.defrag_thold) {
-            //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
+            // cross-attention
+            {
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_cross, cur);
+                cb(Qcur, "Qcur", il);
 
-            llama_kv_cache_defrag(kv_self);
-        }
-    }
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_cross, embd_enc);
+                cb(Kcur, "Kcur", il);
 
-    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
-    // overlap with device computation.
-    ggml_backend_sched_reset(lctx.sched);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_cross, embd_enc);
+                cb(Vcur, "Vcur", il);
 
-    return 0;
-}
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc);
 
+                struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
 
-// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
-static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
-    auto & kv_self = lctx.kv_self;
+                struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+                cb(kq, "kq", il);
 
-    const auto & hparams = lctx.model.hparams;
+                kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
+                cb(kq, "kq_soft_max_ext", il);
 
-    const uint32_t n_layer = hparams.n_layer;
+                struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
+                cb(v, "v", il);
 
-    const uint32_t n_kv   = llama_kv_cache_cell_max(kv_self);
-    const uint32_t n_used = kv_self.used;
+                struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);
+                cb(kqv, "kqv", il);
 
-    assert(n_used <= n_kv);
+                struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                cb(kqv_merged, "kqv_merged", il);
 
-    //const int64_t t_start = ggml_time_us();
+                cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+                cb(cur, "kqv_merged_cont", il);
 
-    // number of cells moved
-    uint32_t n_moves = 0;
+                ggml_build_forward_expand(gf, cur);
 
-    // each move requires 6*n_layer tensors (see build_defrag)
-    //   - source view, destination view, copy operation
-    //   - x2 for keys and values
-    //const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer);
-    // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
-    const uint32_t max_moves = (LLAMA_MAX_NODES - 2*n_layer)/(6*n_layer);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_cross, cur);
+                cb(cur, "kqv_out", il);
+            }
 
-    // determine which KV cells to move where
-    //
-    //  cell i moves to ids[i]
-    //
-    //  if ids[i] == i || ids[i] == n_kv, then cell i is not moved
-    //
-    std::vector<uint32_t> ids(n_kv, n_kv);
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+                inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                // T5 uses relu, flan-T5 uses gelu-gated
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
+                        model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
+                        cb, il);
+                cb(cur, "ffn_out", il);
+            }
 
-    for (uint32_t i0 = 0; i0 < n_used; ++i0) {
-        const auto & cell0 = kv_self.cells[i0];
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
 
-        if (!cell0.is_empty()) {
-            ids[i0] = i0;
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
+            cb(cur, "l_out", il);
 
-            continue;
+            // input for next layer
+            inpL = cur;
         }
 
-        // found a hole - fill it with data from the end of the cache
+        cur = inpL;
+        cb(cur, "result_embd", -1);
 
-        uint32_t nh = 1;
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
-        // determine the size of the hole
-        while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
-            nh++;
-        }
+        // lm_head
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-        uint32_t nf = 0;
-        uint32_t is = n_kv - 1;
+        ggml_build_forward_expand(gf, cur);
 
-        // starting from the end, find nh non-empty cells
-        for (; is > i0; --is) {
-            const auto & cell1 = kv_self.cells[is];
+        return gf;
+    }
 
-            if (cell1.is_empty() || ids[is] != n_kv) {
-                continue;
-            }
+    struct ggml_cgraph * build_jais() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-            // non-empty cell which is not yet moved
-            nf++;
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-            if (nf == nh) {
-                break;
-            }
-        }
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-        // this can only happen if `n_used` is not accurate, which would be a bug
-        GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-        nf = 0;
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-        uint32_t i1 = is;
+        for (int il = 0; il < n_layer; ++il) {
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
 
-        // are we moving a continuous block of memory?
-        bool cont = false;
+            // self-attention
+            {
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
 
-        // should we stop searching for the next move?
-        bool stop = false;
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
 
-        // go back and move the nf cells to the hole
-        for (; i1 < n_kv; ++i1) {
-            auto & cell1 = kv_self.cells[i1];
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*cur->nb[0]*(n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa)));
 
-            if (cell1.is_empty() || ids[i1] != n_kv) {
-                if (n_moves == max_moves) {
-                    stop = true;
-                    break;
-                }
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
 
-                cont = false;
-                continue;
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/float(n_embd_head), cb, il);
             }
 
-            // this cell goes to (i0 + nf)
-            ids[i1] = i0 + nf;
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
 
-            // move the cell meta data
-            kv_self.cells[i0 + nf] = cell1;
+            // add the input
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
 
-            // clear the old cell and move the head there
-            cell1 = llama_kv_cell();
-            kv_self.head = n_used;
+            // FF
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
 
-            if (!cont) {
-                n_moves++;
-                cont = true;
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
             }
 
-            nf++;
-
-            if (nf == nh) {
-                break;
-            }
+            inpL = ggml_add(ctx0, cur, ffn_inp);
+            cb(inpL, "l_out", il);
         }
 
-        if (stop || n_moves == max_moves) {
-            break;
-        }
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
 
-        //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
-        i0 += nh - 1;
-    }
+        cb(cur, "result_output", -1);
 
-    if (n_moves == 0) {
-        return;
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
     }
 
-    //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
+    struct ggml_cgraph * build_chatglm() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
-    //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
-#if 0
-    // CPU defrag
-    //
-    // TODO: optimizations are possible:
-    //       - multiple threads
-    //       - avoid copying to the host memory when already there
-    //
-    // likely not worth the effort, as we have ggml_graph based defrag
-    //
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-    const uint32_t kv_size = kv_self.size;
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
-    std::vector<uint8_t> buf_k;
-    std::vector<uint8_t> buf_v;
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-    for (uint32_t il = 0; il < n_layer; ++il) {
-        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
-        const size_t k_size     = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
-        const size_t v_size    = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-        buf_k.resize(k_size);
-        buf_v.resize(v_size);
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = nullptr;
+                struct ggml_tensor * Kcur = nullptr;
+                struct ggml_tensor * Vcur = nullptr;
 
-        ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
-        ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
 
-        // batch move [i, i+nm) to [id, id+nm)
-        // note: cells can move only to a lower index
-        for (uint32_t i = 0; i < n_kv; ++i) {
-            const uint32_t id = ids[i];
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
 
-            if (i == id || id == n_kv) {
-                continue;
-            }
+                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
-            uint32_t nm = 1;
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+                //printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor);
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur_rope", il);
 
-            while (i + nm < n_kv && ids[i + nm] == id + nm) {
-                nm++;
-            }
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur_rope", il);
 
-            // move keys
-            {
-                const int64_t os =  i*k_size_row;
-                const int64_t od = id*k_size_row;
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
 
-                memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
             }
 
-            // move values (note: they are transposed)
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            // Add the input
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // FF
             {
-                const int64_t os =  i;
-                const int64_t od = id;
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, lctx, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        NULL,                      NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
 
-                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                    memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
-                }
             }
 
-            i += nm - 1;
-        }
+            inpL = ggml_add(ctx0, cur, ffn_inp);
+            cb(inpL, "l_out", il);
+        }
+
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm,
+                NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-        ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
-        ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
     }
-#else
-    // ggml_graph defrag
+};
 
-    ggml_backend_sched_reset(lctx.sched);
+static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
 
-    ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
 
-    llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
-#endif
+    struct llm_build_context llm(lctx, dummy, cb, false);
 
-    //const int64_t t_end = ggml_time_us();
+    llm.init();
 
-    //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
+    struct ggml_cgraph * result = llm.build_defrag(ids);
+
+    llm.free();
+
+    return result;
 }
 
-static void llama_kv_cache_update_internal(struct llama_context & lctx) {
-    bool need_reserve = false;
+static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
 
-    // apply K-shift if needed
-    if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
-        {
-            ggml_backend_sched_reset(lctx.sched);
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
 
-            ggml_cgraph * gf = llama_build_graph_k_shift(lctx);
+    struct llm_build_context llm(lctx, dummy, cb, false);
 
-            ggml_backend_sched_alloc_graph(lctx.sched, gf);
+    llm.init();
 
-            llama_set_k_shift(lctx);
+    struct ggml_cgraph * result = llm.build_k_shift();
 
-            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+    llm.free();
 
-            need_reserve = true;
-        }
+    return result;
+}
 
-        {
-            auto & kv_self = lctx.kv_self;
+static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
 
-            kv_self.has_shift = false;
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
 
-            for (uint32_t i = 0; i < kv_self.size; ++i) {
-                kv_self.cells[i].delta = 0;
-            }
-        }
-    }
+    struct llm_build_context llm(lctx, dummy, cb, false);
 
-    if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) {
-        {
-            ggml_backend_sched_reset(lctx.sched);
+    llm.init();
 
-            ggml_cgraph * gf = llama_build_graph_s_copy(lctx);
+    struct ggml_cgraph * result = llm.build_s_copy();
 
-            ggml_backend_sched_alloc_graph(lctx.sched, gf);
+    llm.free();
 
-            llama_set_s_copy(lctx);
+    return result;
+}
 
-            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+static struct ggml_cgraph * llama_build_graph(
+         llama_context & lctx,
+     const llama_batch & batch,
+                  bool   worst_case) {
+    const auto & model = lctx.model;
 
-            need_reserve = true;
+    // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
+    llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
+        if (il >= 0) {
+            ggml_format_name(cur, "%s-%d", name, il);
+        } else {
+            ggml_set_name(cur, name);
         }
 
-        {
-            auto & kv_self = lctx.kv_self;
-
-            kv_self.do_copy = false;
+        if (!lctx.cparams.offload_kqv) {
+            if (strcmp(name, "kqv_merged_cont") == 0) {
+                // all nodes between the KV store and the attention output are run on the CPU
+                ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu);
+            }
+        }
 
-            for (uint32_t i = 0; i < kv_self.size; ++i) {
-                kv_self.cells[i].src = i;
+        // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
+        // FIXME: fix in ggml_backend_sched
+        const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
+        if (batch.n_tokens < 32 || full_offload) {
+            if (il != -1 && strcmp(name, "norm") == 0) {
+                for (auto * backend : lctx.backends) {
+                    if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) &&
+                        (ggml_backend_supports_op(backend, cur) || ggml_backend_offload_op(backend, cur))) {
+                        ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
+                        break;
+                    }
+                }
             }
         }
-    }
+    };
 
-    // defragment the KV cache if needed
-    if (lctx.kv_self.do_defrag) {
-        llama_kv_cache_defrag_internal(lctx);
+    struct ggml_cgraph * result = NULL;
 
-        need_reserve = true;
+    struct llm_build_context llm(lctx, batch, cb, worst_case);
+
+    llm.init();
+
+    switch (model.arch) {
+        case LLM_ARCH_LLAMA:
+            {
+                result = llm.build_llama();
+            } break;
+        case LLM_ARCH_BAICHUAN:
+            {
+                result = llm.build_baichuan();
+            } break;
+        case LLM_ARCH_FALCON:
+            {
+                result = llm.build_falcon();
+            } break;
+        case LLM_ARCH_GROK:
+            {
+                result = llm.build_grok();
+            } break;
+        case LLM_ARCH_STARCODER:
+            {
+                result = llm.build_starcoder();
+            } break;
+        case LLM_ARCH_REFACT:
+            {
+                result = llm.build_refact();
+            } break;
+        case LLM_ARCH_BERT:
+        case LLM_ARCH_JINA_BERT_V2:
+        case LLM_ARCH_NOMIC_BERT:
+            {
+                result = llm.build_bert();
+            } break;
+        case LLM_ARCH_BLOOM:
+            {
+                result = llm.build_bloom();
+            } break;
+        case LLM_ARCH_MPT:
+            {
+                result = llm.build_mpt();
+            } break;
+         case LLM_ARCH_STABLELM:
+            {
+                result = llm.build_stablelm();
+            } break;
+        case LLM_ARCH_QWEN:
+            {
+                result = llm.build_qwen();
+            } break;
+        case LLM_ARCH_QWEN2:
+            {
+                result = llm.build_qwen2();
+            } break;
+        case LLM_ARCH_QWEN2MOE:
+            {
+                result = llm.build_qwen2moe();
+            } break;
+        case LLM_ARCH_PHI2:
+            {
+                result = llm.build_phi2();
+            } break;
+        case LLM_ARCH_PHI3:
+            {
+                result = llm.build_phi3();
+            } break;
+        case LLM_ARCH_PLAMO:
+            {
+                result = llm.build_plamo();
+            } break;
+        case LLM_ARCH_GPT2:
+            {
+                result = llm.build_gpt2();
+            } break;
+        case LLM_ARCH_CODESHELL:
+            {
+                result = llm.build_codeshell();
+            } break;
+        case LLM_ARCH_ORION:
+            {
+                result = llm.build_orion();
+            } break;
+        case LLM_ARCH_INTERNLM2:
+            {
+                result = llm.build_internlm2();
+            } break;
+        case LLM_ARCH_MINICPM:
+            {
+                result = llm.build_minicpm();
+            } break;
+        case LLM_ARCH_GEMMA:
+            {
+                result = llm.build_gemma();
+            } break;
+        case LLM_ARCH_GEMMA2:
+            {
+                result = llm.build_gemma2();
+            } break;
+        case LLM_ARCH_STARCODER2:
+            {
+                result = llm.build_starcoder2();
+            } break;
+        case LLM_ARCH_MAMBA:
+            {
+                result = llm.build_mamba();
+            } break;
+        case LLM_ARCH_XVERSE:
+            {
+                result = llm.build_xverse();
+            } break;
+        case LLM_ARCH_COMMAND_R:
+            {
+                result = llm.build_command_r();
+            } break;
+        case LLM_ARCH_DBRX:
+            {
+                result = llm.build_dbrx();
+            } break;
+        case LLM_ARCH_OLMO:
+            {
+                result = llm.build_olmo();
+            } break;
+        case LLM_ARCH_OPENELM:
+            {
+                result = llm.build_openelm();
+            } break;
+        case LLM_ARCH_GPTNEOX:
+            {
+                result = llm.build_gptneox();
+            } break;
+        case LLM_ARCH_ARCTIC:
+            {
+                result = llm.build_arctic();
+            } break;
+        case LLM_ARCH_DEEPSEEK2:
+            {
+                result = llm.build_deepseek2();
+            } break;
+        case LLM_ARCH_CHATGLM:
+            {
+                result = llm.build_chatglm();
+            } break;
+        case LLM_ARCH_BITNET:
+            {
+                result = llm.build_bitnet();
+            } break;
+        case LLM_ARCH_T5:
+            {
+                if (lctx.is_encoding) {
+                    result = llm.build_t5_encoder();
+                } else {
+                    result = llm.build_t5_decoder();
+                }
+            } break;
+        case LLM_ARCH_T5ENCODER:
+            {
+                result = llm.build_t5_encoder();
+            } break;
+        case LLM_ARCH_JAIS:
+            {
+                result = llm.build_jais();
+            } break;
+        default:
+            GGML_ABORT("fatal error");
+    }
 
-        lctx.kv_self.do_defrag = false;
+    // add on pooling layer
+    if (lctx.cparams.embeddings) {
+        result = llm.append_pooling(result);
     }
 
-    // reserve a worst case graph again
-    if (need_reserve) {
-        // TODO: extract to a function
-        // build worst-case graph
-        int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
-        int n_past = lctx.cparams.n_ctx - n_tokens;
-        llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
-        ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
+    llm.free();
 
-        // initialize scheduler with the worst-case graph
-        ggml_backend_sched_reset(lctx.sched);
-        if (!ggml_backend_sched_reserve(lctx.sched, gf)) {
-            LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
-        }
-    }
+    return result;
 }
 
-//
-// tokenizer
-//
+static void llama_set_k_shift(llama_context & lctx) {
+    const int64_t kv_size = lctx.kv_self.size;
 
-static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
-    return vocab.type;
-}
+    assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
 
-static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
-}
+    int32_t * data = (int32_t *) lctx.inp_K_shift->data;
 
-static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
+    for (int i = 0; i < kv_size; ++i) {
+        data[i] = lctx.kv_self.cells[i].delta;
+    }
 }
 
-static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
-}
+static void llama_set_s_copy(llama_context & lctx) {
+    const int64_t kv_size = lctx.kv_self.size;
 
-static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
-}
+    assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
 
-static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
-}
+    int32_t * data = (int32_t *) lctx.inp_s_copy->data;
 
-static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
-    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
-    GGML_ASSERT(llama_is_byte_token(vocab, id));
-    const auto & token_data = vocab.id_to_token.at(id);
-    switch (llama_vocab_get_type(vocab)) {
-        case LLAMA_VOCAB_TYPE_SPM: {
-            auto buf = token_data.text.substr(3, 2);
-            return strtol(buf.c_str(), NULL, 16);
-        }
-        case LLAMA_VOCAB_TYPE_BPE: {
-            GGML_ASSERT(false);
-            return unicode_utf8_to_byte(token_data.text); // TODO: why is this here after GGML_ASSERT?
-        }
-        case LLAMA_VOCAB_TYPE_WPM: {
-            GGML_ASSERT(false);
-        }
-        default:
-            GGML_ASSERT(false);
+    for (int i = 0; i < kv_size; ++i) {
+        data[i] = lctx.kv_self.cells[i].src;
     }
 }
 
-static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
-    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
-    static const char * hex = "0123456789ABCDEF";
-    switch (llama_vocab_get_type(vocab)) {
-        case LLAMA_VOCAB_TYPE_SPM: {
-            const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
-            auto token = vocab.token_to_id.find(buf);
-            if (token != vocab.token_to_id.end()) {
-                return (*token).second;
-            }
-            // Try to fall back to just the byte as a string
-            const char buf2[2] = { (char)ch, 0 };
-            return vocab.token_to_id.at(buf2);
-        }
-        case LLAMA_VOCAB_TYPE_WPM:
-        case LLAMA_VOCAB_TYPE_BPE: {
-            return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
-        }
-        default:
-            GGML_ASSERT(false);
+static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
+    // TODO move to hparams if a T5 variant appears that uses a different value
+    const int64_t max_distance = 128;
+
+    if (bidirectional) {
+        n_buckets >>= 1;
     }
-}
 
-static void llama_escape_whitespace(std::string & text) {
-    replace_all(text, " ", "\xe2\x96\x81");
-}
+    const int64_t max_exact = n_buckets >> 1;
 
-static void llama_unescape_whitespace(std::string & word) {
-    replace_all(word, "\xe2\x96\x81", " ");
+    int32_t relative_position = x - y;
+    int32_t relative_bucket = 0;
+    if (bidirectional) {
+        relative_bucket += (relative_position > 0) * n_buckets;
+        relative_position = abs(relative_position);
+    } else {
+        relative_position = -std::min<int32_t>(relative_position, 0);
+    }
+    int32_t relative_position_if_large = floorf(max_exact + logf(1.0 * relative_position / max_exact) * (n_buckets - max_exact) / log(1.0 * max_distance / max_exact));
+    relative_position_if_large = std::min<int32_t>(relative_position_if_large, n_buckets - 1);
+    relative_bucket += (relative_position < max_exact ? relative_position : relative_position_if_large);
+    return relative_bucket;
 }
 
-struct llm_symbol {
-    using index = int;
-    index prev;
-    index next;
-    const char * text;
-    size_t n;
-};
-
-static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
-
-// SPM tokenizer
-// original implementation:
-// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
-
-struct llm_bigram_spm {
-    struct comparator {
-        bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
-            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
-        }
-    };
-    using queue_storage = std::vector<llm_bigram_spm>;
-    using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
-    llm_symbol::index left;
-    llm_symbol::index right;
-    float score;
-    size_t size;
-};
-
-struct llm_tokenizer_spm {
-    llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}
-
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-        // split string into utf8 chars
-        int index = 0;
-        size_t offs = 0;
-        while (offs < text.size()) {
-            llm_symbol sym;
-            size_t len = utf8_len(text[offs]);
-            sym.text = text.c_str() + offs;
-            sym.n = std::min(len, text.size() - offs);
-            offs += sym.n;
-            sym.prev = index - 1;
-            sym.next = offs == text.size() ? -1 : index + 1;
-            index++;
-            symbols.emplace_back(sym);
-        }
-
-        // seed the work queue with all possible 2-character tokens.
-        for (size_t i = 1; i < symbols.size(); ++i) {
-            try_add_bigram(i - 1, i);
-        }
-
-        // keep substituting the highest frequency pairs for as long as we can.
-        while (!work_queue.empty()) {
-            auto bigram = work_queue.top();
-            work_queue.pop();
-
-            auto & left_sym = symbols[bigram.left];
-            auto & right_sym = symbols[bigram.right];
-
-            // if one of the symbols already got merged, skip it.
-            if (left_sym.n == 0 || right_sym.n == 0 ||
-                left_sym.n + right_sym.n != bigram.size) {
-                continue;
-            }
-
-            // merge the right sym into the left one
-            left_sym.n += right_sym.n;
-            right_sym.n = 0;
-
-            //LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
+static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
+    //
+    // set input data
+    //
 
-            // remove the right sym from the chain
-            left_sym.next = right_sym.next;
-            if (right_sym.next >= 0) {
-                symbols[right_sym.next].prev = bigram.left;
-            }
+    const auto & hparams = lctx.model.hparams;
+    const auto & cparams = lctx.cparams;
+    const auto & kv_self = lctx.kv_self;
 
-            // find more substitutions
-            try_add_bigram(left_sym.prev, bigram.left);
-            try_add_bigram(bigram.left, left_sym.next);
-        }
+    if (batch.token) {
+        const int64_t n_tokens = batch.n_tokens;
 
-        for (int i = 0; i != -1; i = symbols[i].next) {
-            auto & symbol = symbols[i];
-            resegment(symbol, output);
-        }
+        ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
     }
 
-private:
-    void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
-        auto text = std::string(symbol.text, symbol.n);
-        auto token = vocab.token_to_id.find(text);
-
-        // Do we need to support is_unused?
-        if (token != vocab.token_to_id.end()) {
-            output.push_back((*token).second);
-            return;
-        }
-
-        const auto p = rev_merge.find(text);
-
-        if (p == rev_merge.end()) {
-            // output any symbols that did not form tokens as bytes.
-            output.reserve(output.size() + symbol.n);
-            for (int j = 0; j < (int)symbol.n; ++j) {
-                llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
-                output.push_back(token_id);
-            }
-            return;
-        }
+    if (batch.embd) {
+        const int64_t n_embd   = hparams.n_embd;
+        const int64_t n_tokens = batch.n_tokens;
 
-        resegment(symbols[p->second.first],  output);
-        resegment(symbols[p->second.second], output);
+        ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
     }
 
-    void try_add_bigram(int left, int right) {
-        if (left == -1 || right == -1) {
-            return;
-        }
-
-        const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
-        auto token = vocab.token_to_id.find(text);
-
-        if (token == vocab.token_to_id.end()) {
-            return;
-        }
-
-        if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
-            return;
-        }
-
-        const auto & tok_data = vocab.id_to_token[(*token).second];
-
-        llm_bigram_spm bigram;
-        bigram.left  = left;
-        bigram.right = right;
-        bigram.score = tok_data.score;
-        bigram.size  = text.size();
-
-        work_queue.push(bigram);
+    if (batch.pos && lctx.inp_pos) {
+        const int64_t n_tokens = batch.n_tokens;
 
-        // Do we need to support is_unused?
-        rev_merge[text] = std::make_pair(left, right);
+        ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
     }
 
-    const llama_vocab & vocab;
-
-    std::vector<llm_symbol> symbols;
-    llm_bigram_spm::queue work_queue;
-
-    std::map<std::string, std::pair<int, int>> rev_merge;
-};
-
-// BPE tokenizer
-// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
-// tried to simplify unicode stuff, so most likely does not work 100% correctly!
-
-// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
-
-struct llm_bigram_bpe {
-    struct comparator {
-        bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
-            return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
-        }
-    };
+    if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
+        const int64_t n_tokens = batch.n_tokens;
 
-    using queue_storage = std::vector<llm_bigram_bpe>;
-    using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
-    llm_symbol::index left;
-    llm_symbol::index right;
-    std::string text;
-    int rank;
-    size_t size;
-};
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
+        int32_t * data = (int32_t *) lctx.inp_out_ids->data;
 
-struct llm_tokenizer_bpe {
-    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}
-
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-        int final_prev_index = -1;
-        bool ignore_merges = false;
-
-        std::vector<std::string> word_collection;
-        switch (vocab.type) {
-            case LLAMA_VOCAB_TYPE_BPE:
-                switch (vocab.type_pre) {
-                    case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
-                        ignore_merges = true;
-                        word_collection = unicode_regex_split(text, {
-                            // original regex from tokenizer.json
-                            //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-
-                            // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
-                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_DBRX:
-                        word_collection = unicode_regex_split(text, {
-                            // same as llama3
-                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
-                        word_collection = unicode_regex_split(text, {
-                            "[\r\n]",
-                            "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿDŽ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿff-stﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
-                            "\\s?[!-/:-~！-／：-～‘-‟　-。]+",
-                            "\\s+$",
-                            "[一-龥ࠀ-一가-퟿]+",
-                            "\\p{N}+",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
-                        word_collection = unicode_regex_split(text, {
-                            "[\r\n]",
-                            "\\s?\\p{L}+",
-                            "\\s?\\p{P}+",
-                            "[一-龥ࠀ-一가-퟿]+",
-                            "\\p{N}",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_FALCON:
-                        word_collection = unicode_regex_split(text, {
-                            "[\\p{P}\\$\\+<=>\\^~\\|]+",
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                            "[0-9][0-9][0-9]",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_MPT:
-                        // TODO: MPT pre-tokenization regexes are unknown
-                        //       the following are close, but not exact. run the following:
-                        //       ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf
-                        GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed");
-                        word_collection = unicode_regex_split(text, {
-                            "\\s?\\p{L}+",
-                            "\\s?\\p{P}+",
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_STARCODER:
-                    case LLAMA_VOCAB_PRE_TYPE_REFACT:
-                    case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
-                        word_collection = unicode_regex_split(text, {
-                            "\\p{N}",
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_GPT2:
-                    case LLAMA_VOCAB_PRE_TYPE_OLMO:
-                        word_collection = unicode_regex_split(text, {
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                        });
-                        break;
-                    case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
-                    case LLAMA_VOCAB_PRE_TYPE_QWEN2:
-                        word_collection = unicode_regex_split(text, {
-                            // original regex from tokenizer.json
-                            // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
-                            "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                        });
-                        break;
-                    default:
-                        // default regex for BPE tokenization pre-processing
-                        word_collection = unicode_regex_split(text, {
-                            "[\\p{P}\\$\\+<=>\\^~\\|]+",
-                            "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                            "\\p{N}+",
-                            "[0-9][0-9][0-9]",
-                        });
-                        break;
+        if (lctx.n_outputs == n_tokens) {
+            for (int i = 0; i < n_tokens; ++i) {
+                data[i] = i;
+            }
+        } else if (batch.logits) {
+            int32_t n_outputs = 0;
+            for (int i = 0; i < n_tokens; ++i) {
+                if (batch.logits[i]) {
+                    data[n_outputs++] = i;
                 }
-                break;
-            default:
-                GGML_ASSERT(false);
-                break;
+            }
+            // the graph needs to have been passed the correct number of outputs
+            GGML_ASSERT(lctx.n_outputs == n_outputs);
+        } else if (lctx.n_outputs == 1) {
+            // only keep last output
+            data[0] = n_tokens - 1;
+        } else {
+            GGML_ASSERT(lctx.n_outputs == 0);
         }
+    }
+
+    GGML_ASSERT(
+        // (!a || b) is a logical implication (a -> b)
+        // !hparams.causal_attn -> !cparams.causal_attn
+        (hparams.causal_attn || !cparams.causal_attn) &&
+        "causal attention is not supported by this model"
+    );
 
-        symbols_final.clear();
+    if (lctx.inp_KQ_mask || lctx.inp_KQ_mask_swa) {
+        // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
+        if (cparams.causal_attn && !lctx.is_encoding) {
+            const int64_t n_kv     = kv_self.n;
+            const int64_t n_tokens = batch.n_tokens;
 
-        for (auto & word : word_collection) {
-            work_queue = llm_bigram_bpe::queue();
-            symbols.clear();
 
-            int index = 0;
-            size_t offset = 0;
+            float * data     = nullptr;
+            float * data_swa = nullptr;
 
-            if (ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
-                symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
-                offset = word.size();
+            if (lctx.inp_KQ_mask) {
+                GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+                data = (float *) lctx.inp_KQ_mask->data;
             }
 
-            while (offset < word.size()) {
-                llm_symbol sym;
-                size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset]));
-                sym.text = word.c_str() + offset;
-                sym.n = char_len;
-                offset += sym.n;
-                sym.prev = index - 1;
-                sym.next = offset == word.size() ? -1 : index + 1;
-                index++;
-                symbols.emplace_back(sym);
-            }
-            for (size_t i = 1; i < symbols.size(); ++i) {
-                add_new_bigram(i - 1, i);
+            if (lctx.inp_KQ_mask_swa) {
+                GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_swa->buffer));
+                data_swa = (float *) lctx.inp_KQ_mask_swa->data;
             }
 
-            // build token(s)
-            while (!work_queue.empty()) {
-                auto bigram = work_queue.top();
-                work_queue.pop();
-
-                auto & left_symbol = symbols[bigram.left];
-                auto & right_symbol = symbols[bigram.right];
+            // For causal attention, use only the previous KV cells
+            // of the correct sequence for each token of the batch.
+            // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    const llama_pos    pos    = batch.pos[j];
+                    const llama_seq_id seq_id = batch.seq_id[j][0];
 
-                if (left_symbol.n == 0 || right_symbol.n == 0) {
-                    continue;
-                }
-                std::string left_token = std::string(left_symbol.text, left_symbol.n);
-                std::string right_token = std::string(right_symbol.text, right_symbol.n);
-                if (left_token + right_token != bigram.text) {
-                    continue;  // Skip this bigram if it's outdated
-                }
+                    for (int i = 0; i < n_kv; ++i) {
+                        float f;
+                        if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
+                            f = -INFINITY;
+                        } else {
+                            if (hparams.use_alibi) {
+                                f = -std::abs(lctx.kv_self.cells[i].pos - pos);
+                            } else {
+                                f = 0.0f;
+                            }
+                        }
 
-                // merge the right sym into the left one
-                left_symbol.n += right_symbol.n;
-                right_symbol.n = 0;
+                        if (data) {
+                            data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
+                        }
 
-                // remove the right sym from the chain
-                left_symbol.next = right_symbol.next;
-                if (right_symbol.next >= 0) {
-                    symbols[right_symbol.next].prev = bigram.left;
+                        // may need to cut off old tokens for sliding window
+                        if (data_swa) {
+                            if (pos - lctx.kv_self.cells[i].pos >= (int32_t)hparams.n_swa) {
+                                f = -INFINITY;
+                            }
+                            data_swa[h*(n_kv*n_tokens) + j*n_kv + i] = f;
+                        }
+                    }
                 }
 
-                add_new_bigram(left_symbol.prev, bigram.left);  // left side of current symbol
-                add_new_bigram(bigram.left, left_symbol.next);  // right side of current symbol
-            }
+                if (data) {
+                    for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                        for (int j = 0; j < n_kv; ++j) {
+                            data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                        }
+                    }
+                }
 
-            // add the finished tokens to the final list keeping correct order for next and prev
-            for (auto & sym : symbols) {
-                if (sym.n > 0) {
-                    sym.prev = final_prev_index;
-                    sym.next = -1;
-                    if (final_prev_index != -1) {
-                        symbols_final[final_prev_index].next = symbols_final.size();
+                if (data_swa) {
+                    for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                        for (int j = 0; j < n_kv; ++j) {
+                            data_swa[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                        }
                     }
-                    symbols_final.emplace_back(sym);
-                    final_prev_index = symbols_final.size() - 1;
                 }
             }
-        }
+        } else {
+            // when using kv cache, the mask needs to match the kv cache size
+            const int64_t n_tokens = batch.n_tokens;
+            const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens;
 
-        symbols = symbols_final;
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
 
-        if (!symbols.empty()) {
-            for (int i = 0; i != -1; i = symbols[i].next) {
-                auto & symbol = symbols[i];
-                if (symbol.n == 0) {
-                    continue;
-                }
+            float * data = (float *) lctx.inp_KQ_mask->data;
 
-                const std::string str = std::string(symbol.text, symbol.n);
-                const auto token = vocab.token_to_id.find(str);
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    const llama_seq_id seq_id = batch.seq_id[j][0];
 
-                if (token == vocab.token_to_id.end()) {
-                    for (auto j = str.begin(); j != str.end(); ++j) {
-                        std::string byte_str(1, *j);
-                        auto token_multibyte = vocab.token_to_id.find(byte_str);
-                        if (token_multibyte == vocab.token_to_id.end()) {
-                            throw std::runtime_error("ERROR: byte not found in vocab");
+                    for (int i = 0; i < n_tokens; ++i) {
+                        float f = -INFINITY;
+                        for (int s = 0; s < batch.n_seq_id[i]; ++s) {
+                            if (batch.seq_id[i][s] == seq_id) {
+                                if (hparams.use_alibi) {
+                                    f = -std::abs(batch.pos[i] - batch.pos[j]);
+                                } else {
+                                    f = 0.0f;
+                                }
+                                break;
+                            }
                         }
-                        output.push_back((*token_multibyte).second);
+
+                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = f;
+                    }
+
+                    for (int i = n_tokens; i < n_stride; ++i) {
+                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY;
                     }
-                } else {
-                    output.push_back((*token).second);
                 }
             }
         }
     }
 
-private:
-    void add_new_bigram(int left, int right) {
-        if (left == -1 || right == -1) {
-            return;
-        }
+    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
+        const int64_t n_tokens = batch.n_tokens;
 
-        std::string left_token  = std::string(symbols[left].text,  symbols[left].n);
-        std::string right_token = std::string(symbols[right].text, symbols[right].n);
+        GGML_ASSERT(lctx.inp_mean);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
+
+        float * data = (float *) lctx.inp_mean->data;
+        memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
 
-        int rank_found = -1;
+        std::vector<uint64_t> sum(n_tokens, 0);
+        for (int i = 0; i < n_tokens; ++i) {
+            const llama_seq_id seq_id = batch.seq_id[i][0];
 
-        rank_found = vocab.find_bpe_rank(left_token, right_token);
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
 
-        if (rank_found < 0) {
-            return;
+            sum[seq_id] += 1;
         }
 
-        llm_bigram_bpe bigram;
-
-        bigram.left  = left;
-        bigram.right = right;
-        bigram.text  = left_token + right_token;
-        bigram.size  = left_token.size() + right_token.size();
-        bigram.rank  = rank_found;
+        std::vector<float> div(n_tokens, 0.0f);
+        for (int i = 0; i < n_tokens; ++i) {
+            const uint64_t s = sum[i];
+            if (s > 0) {
+                div[i] = 1.0f/float(s);
+            }
+        }
 
-        work_queue.push(bigram);
+        for (int i = 0; i < n_tokens; ++i) {
+            const llama_seq_id seq_id = batch.seq_id[i][0];
+            data[seq_id*n_tokens + i] = div[seq_id];
+        }
     }
 
-    const llama_vocab & vocab;
+    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
+        const int64_t n_tokens = batch.n_tokens;
 
-    std::vector<llm_symbol> symbols;
-    std::vector<llm_symbol> symbols_final;
+        GGML_ASSERT(lctx.inp_cls);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
 
-    llm_bigram_bpe::queue work_queue;
-};
+        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
 
-struct llm_tokenizer_wpm {
-    llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
+        for (int i = 0; i < n_tokens; ++i) {
+            const llama_seq_id seq_id = batch.seq_id[i][0];
+            const llama_pos    pos    = batch.pos[i];
 
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-        auto * token_map = &vocab.token_to_id;
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
 
-        // normalize and split by whitespace
-        std::vector<std::string> words = preprocess(text);
+            if (pos == 0) {
+                data[seq_id] = i;
+            }
+        }
+    }
 
-        // bos token prepended already
+    if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
+        const int64_t n_tokens = batch.n_tokens;
 
-        // find the longest tokens that form the words
-        for (const std::string &word : words) {
-            // skip empty words
-            if (word.size() == 0) {
-                continue;
-            }
+        GGML_ASSERT(lctx.inp_cls);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
 
-            // prepend phantom space
-            std::string word1 = "\xe2\x96\x81" + word;
-            int n = word1.size();
-
-            // we're at the start of a new word
-            int i = 0;
-            bool match_any = false;
-
-            // move through character position in word
-            while (i < n) {
-                // loop through possible match length
-                bool match = false;
-                for (int j = n; j > i; j--) {
-                    auto it = token_map->find(word1.substr(i, j - i));
-                    if (it != token_map->end()) {
-                        output.push_back(it->second);
-                        match = true;
-                        match_any = true;
-                        i = j;
-                        break;
-                    }
-                }
+        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
 
-                // must be an unknown character
-                if (!match) {
-                    i++;
-                }
-            }
+        std::vector<int> last_pos(n_tokens, -1);
+        std::vector<int> last_row(n_tokens, -1);
 
-            // we didn't find any matches for this word
-            if (!match_any) {
-                output.push_back(vocab.special_unk_id);
-            }
-        }
-    }
+        for (int i = 0; i < n_tokens; ++i) {
+            const llama_seq_id seq_id = batch.seq_id[i][0];
+            const llama_pos    pos    = batch.pos[i];
 
-    std::vector<std::string> preprocess(const std::string & text) {
-        std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST");
 
-        // strip accents, strip control, uniformize whitespace,
-        // to lowercase, pad chinese characters, pad punctuation
-        std::string new_str = "";
-        for (uint32_t code : cpts_nfd) {
-            const codepoint_flags flags = unicode_cpt_flags(code);
-            if (flags.is_accent_mark || flags.is_control) {
-                continue;
-            }
-            code = unicode_tolower(code);
-            if (flags.is_separator || flags.is_whitespace) {  //####FIXME: is_separator ?
-                code = ' ';
-            }
-            std::string s = unicode_cpt_to_utf8(code);
-            if (flags.is_punctuation || is_ascii_punct(code) || is_chinese_char(code)) {
-                new_str += " ";
-                new_str += s;
-                new_str += " ";
-            } else {
-                new_str += s;
+            if (pos >= last_pos[seq_id]) {
+                last_pos[seq_id] = pos;
+                last_row[seq_id] = i;
             }
         }
 
-        // split by whitespace
-        uint64_t l = 0;
-        uint64_t r = 0;
-        std::vector<std::string> words;
-        while (r < new_str.size()) {
-            // if is whitespace
-            if (isspace(new_str[r], std::locale::classic())) {
-                if (r > l) words.push_back(new_str.substr(l, (r - l)));
-                l = r + 1;
-                r = l;
-            } else {
-                r += 1;
+        for (int i = 0; i < n_tokens; ++i) {
+            if (last_row[i] >= 0) {
+                data[i] = last_row[i];
             }
         }
-        if (r > l) {
-            words.push_back(new_str.substr(l, (r - l)));
-        }
-        return words;
-    }
-
-    bool is_ascii_punct(uint32_t code) {
-        if (code > 0xFF) {
-            return false;
-        }
-        auto c = char(static_cast<unsigned char>(code));
-        return ispunct(c, std::locale::classic());
     }
 
-    bool is_chinese_char(uint32_t cpt) {
-        if ((cpt >= 0x4E00  && cpt <= 0x9FFF)  ||
-            (cpt >= 0x3400  && cpt <= 0x4DBF)  ||
-            (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
-            (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
-            (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
-            (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
-            (cpt >= 0xF900  && cpt <= 0xFAFF)  ||
-            (cpt >= 0x2F800 && cpt <= 0x2FA1F) ||
-            (cpt >= 0x3000  && cpt <= 0x303F)  ||
-            (cpt >= 0xFF00  && cpt <= 0xFFEF)) {
-            return true; // NOLINT
-        }
-        return false;
-    }
+    if (kv_self.recurrent) {
+        const int64_t n_kv = kv_self.n;
 
-    const llama_vocab & vocab;
-};
+        if (lctx.inp_s_mask) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
+            float * data = (float *) lctx.inp_s_mask->data;
 
-typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
-    FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
-    FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
-} FRAGMENT_BUFFER_VARIANT_TYPE;
-
-struct fragment_buffer_variant {
-    fragment_buffer_variant(llama_vocab::id _token)
-    :
-        type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
-        token(_token),
-        raw_text(_dummy),
-        offset(0),
-        length(0) {}
-
-    fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
-    :
-        type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
-        token((llama_vocab::id) - 1),
-        raw_text(_raw_text),
-        offset(_offset),
-        length(_length){
-            GGML_ASSERT(_offset >= 0);
-            GGML_ASSERT(_length >= 1);
-            GGML_ASSERT(offset + length <= raw_text.length());
-        }
-
-    const FRAGMENT_BUFFER_VARIANT_TYPE type;
-    const llama_vocab::id token;
-    const std::string _dummy;
-    const std::string & raw_text;
-    const uint64_t offset;
-    const uint64_t length;
-};
+            // states which are not affected by the current batch are left untouched
+            for (int i = 0; i < n_kv; ++i) {
+                llama_seq_id    seq_id       = i + lctx.kv_self.head;
+                llama_kv_cell & kv_cell      = lctx.kv_self.cells[seq_id];
+                bool            has_self_seq = kv_cell.has_seq_id(seq_id);
 
-// #define PRETOKENIZERDEBUG
+                data[i] = (float) has_self_seq;
 
-static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
-    // for each special token
-    for (const auto & st: vocab.special_tokens_cache) {
-        const auto & special_token = st.first;
-        const auto & special_id    = st.second;
+                // ensure current sequences will be kept
+                if (!has_self_seq && kv_cell.pos >= 0) {
+                    kv_cell.seq_id.insert(seq_id);
+                }
+            }
+        }
+        // For Mamba (and other recurrent architectures),
+        // update the correct state(s)/sequence(s) for each token of the batch.
+        // Like with the KQ_mask, if a token in the batch has multiple sequences,
+        // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv).
+        if (lctx.inp_s_seq) {
+            const int64_t n_tokens = batch.n_tokens;
 
-        // for each text fragment
-        std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
-        while (it != buffer.end()) {
-            auto & fragment = (*it);
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer));
+            int32_t * data = (int32_t *) lctx.inp_s_seq->data;
 
-            // if a fragment is text ( not yet processed )
-            if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                auto * raw_text = &(fragment.raw_text);
+            for (int j = 0; j < n_tokens; ++j) {
+                const int32_t n_seq = batch.n_seq_id[j];
+                GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence
 
-                auto raw_text_base_offset = fragment.offset;
-                auto raw_text_base_length = fragment.length;
+                for (int i = 0; i < n_kv; ++i) {
+                    if (i < n_seq) {
+                        // for this type of model, the head is the minimum seq_id of the batch
+                        data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head;
+                    } else {
+                        data[j*n_kv + i] = -1;
+                    }
+                }
+            }
+        }
+    }
 
-                // loop over the text
-                while (true) {
-                    // find the first occurrence of a given special token in this fragment
-                    //  passing offset argument only limit the "search area" but match coordinates
-                    //  are still relative to the source full raw_text
-                    auto match = raw_text->find(special_token, raw_text_base_offset);
+    if (lctx.inp_pos_bucket) {
+        const int64_t n_tokens = batch.n_tokens;
 
-                    // no occurrences found, stop processing this fragment for a given special token
-                    if (match == std::string::npos) break;
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer));
 
-                    // check if match is within bounds of offset <-> length
-                    if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
+        int32_t * data = (int32_t *) lctx.inp_pos_bucket->data;
 
-#ifdef PRETOKENIZERDEBUG
-                    LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
-#endif
-                    auto source = std::distance(buffer.begin(), it);
-
-                    // if match is further than base offset
-                    //  then we have some text to the left of it
-                    if (match > raw_text_base_offset) {
-                        // left
-                        const int64_t left_reminder_offset = raw_text_base_offset + 0;
-                        const int64_t left_reminder_length = match - raw_text_base_offset;
-                        buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
-#endif
-                        it++;
+        if (!lctx.is_encoding) {
+            const int64_t n_kv = kv_self.n;
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    for (int i = 0; i < n_kv; ++i) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
                     }
+                }
+            }
+        } else {
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    for (int i = 0; i < n_tokens; ++i) {
+                        data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(batch.pos[i], batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
+                    }
+                }
+            }
+        }
+    }
 
-                    // special token
-                    buffer.emplace_after(it, special_id);
-                    it++;
-
-                    // right
-                    if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
-                        const int64_t right_reminder_offset = match + special_token.length();
-                        const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
-                        buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);
+    if (!lctx.is_encoding && lctx.inp_embd_enc) {
+        assert(lctx.inp_embd_enc->type == GGML_TYPE_F32);
+        assert((size_t) ggml_nelements(lctx.inp_embd_enc) == lctx.embd_enc.size());
 
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
-#endif
+        ggml_backend_tensor_set(lctx.inp_embd_enc, lctx.embd_enc.data(), 0, ggml_nbytes(lctx.inp_embd_enc));
+    }
 
-                        it++;
+    if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) {
+        const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd;
+        const int64_t n_tokens = batch.n_tokens;
 
-                        if (source == 0) {
-                            buffer.erase_after(buffer.before_begin());
-                        } else {
-                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
-                        }
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer));
 
-                        // repeat for the right side
-                        raw_text_base_offset = right_reminder_offset;
-                        raw_text_base_length = right_reminder_length;
+        float * data = (float *) lctx.inp_KQ_mask_cross->data;
 
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
-#endif
-                    } else {
-                        if (source == 0) {
-                            buffer.erase_after(buffer.before_begin());
-                        } else {
-                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                for (int i = 0; i < n_output_enc; ++i) {
+                    float f = -INFINITY;
+                    for (int s = 0; s < batch.n_seq_id[j]; ++s) {
+                        const llama_seq_id seq_id = batch.seq_id[j][s];
+                        if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) {
+                            f = 0.0f;
                         }
-                        break;
                     }
+                    data[h*(n_output_enc*n_tokens) + j*n_output_enc + i] = f;
+                }
+            }
+
+            for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                for (int j = 0; j < n_output_enc; ++j) {
+                    data[h*(n_output_enc*n_tokens) + i*n_output_enc + j] = -INFINITY;
                 }
             }
-            it++;
         }
     }
 }
 
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
-    std::vector<llama_vocab::id> output;
-    std::forward_list<fragment_buffer_variant> fragment_buffer;
-
-    if (!raw_text.empty()) {
-        fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
-        if (parse_special) tokenizer_st_partition(vocab, fragment_buffer);
-    }
+// Make sure enough space is available for outputs.
+// Returns max number of outputs for which space was reserved.
+static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
+    const auto & cparams = lctx.cparams;
+    const auto & hparams = lctx.model.hparams;
 
-    switch (vocab.type) {
-        case LLAMA_VOCAB_TYPE_SPM:
-            {
-                // OG tokenizer behavior:
-                //
-                // tokenizer.encode('', add_special_tokens=True)  returns [1]
-                // tokenizer.encode('', add_special_tokens=False) returns []
-
-                if (add_special && vocab.special_add_bos != 0) {
-                    GGML_ASSERT(vocab.special_bos_id != -1);
-                    output.push_back(vocab.special_bos_id);
-                }
+    const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
 
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        // without adding this leading whitespace, we do not get the same results as the original tokenizer
-
-                        // TODO: It's likely possible to get rid of this string copy entirely
-                        //  by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
-                        //  and passing 'add space prefix' as bool argument
-                        //
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-                        if (&fragment == &fragment_buffer.front()) {
-                            if (vocab.add_space_prefix) {
-                                raw_text = " " + raw_text; // prefix with space if the first token is not special
-                            }
-                        }
+    const auto n_batch = cparams.n_batch;
+    const auto n_vocab = hparams.n_vocab;
+    const auto n_embd  = hparams.n_embd;
 
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        llm_tokenizer_spm tokenizer(vocab);
-                        llama_escape_whitespace(raw_text);
-                        tokenizer.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                    }
-                }
+    // TODO: use a per-batch flag for logits presence instead
+    const bool has_logits = !cparams.embeddings;
+    const bool has_embd   =  cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
 
-                if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
-                    LLAMA_LOG_WARN(
-                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
-                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
-                        "Are you sure this is what you want?\n", __FUNCTION__);
-                }
+    const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
+    const size_t embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
 
-                if (add_special && vocab.special_add_eos == 1) {
-                    GGML_ASSERT(vocab.special_eos_id != -1);
-                    output.push_back(vocab.special_eos_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_BPE:
-            {
-                if (add_special && vocab.special_add_bos != 0) {
-                    GGML_ASSERT(vocab.special_bos_id != -1);
-                    output.push_back(vocab.special_bos_id);
-                }
+    if (lctx.output_ids.empty()) {
+        // init, never resized afterwards
+        lctx.output_ids.resize(n_batch);
+    }
 
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+    const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0;
+    const size_t new_size  = (logits_size + embd_size) * sizeof(float);
 
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+    // alloc only when more than the current capacity is required
+    // TODO: also consider shrinking the buffer
+    if (!lctx.buf_output || prev_size < new_size) {
+        if (lctx.buf_output) {
+#ifndef NDEBUG
+            // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
+            LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
-                        llm_tokenizer_bpe tokenizer(vocab);
-                        tokenizer.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                    }
-                }
+            ggml_backend_buffer_free(lctx.buf_output);
+            lctx.buf_output = nullptr;
+            lctx.logits = nullptr;
+            lctx.embd = nullptr;
+        }
 
-                if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
-                    LLAMA_LOG_WARN(
-                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
-                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
-                        "Are you sure this is what you want?\n", __FUNCTION__);
-                }
+        lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), new_size);
+        if (lctx.buf_output == nullptr) {
+            LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
+            return 0;
+        }
+    }
 
-                if (add_special && vocab.special_add_eos == 1) {
-                    GGML_ASSERT(vocab.special_add_eos != -1);
-                    output.push_back(vocab.special_eos_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_WPM:
-            {
-                if (add_special) {
-                    GGML_ASSERT(vocab.special_cls_id != -1);
-                    output.push_back(vocab.special_cls_id);
-                }
+    float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output);
 
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+    lctx.logits = has_logits ? output_base               : nullptr;
+    lctx.embd   = has_embd   ? output_base + logits_size : nullptr;
 
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        llm_tokenizer_wpm tokenizer(vocab);
-                        tokenizer.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                    }
-                }
+    lctx.output_size = n_outputs_max;
+    lctx.logits_size = logits_size;
+    lctx.embd_size   = embd_size;
 
-                if (add_special) {
-                    GGML_ASSERT(vocab.special_sep_id != -1);
-                    output.push_back(vocab.special_sep_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_NONE:
-            GGML_ASSERT(false);
-    }
+    // set all ids as invalid (negative)
+    std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
 
-    return output;
-}
+    ggml_backend_buffer_clear(lctx.buf_output, 0);
 
-//
-// grammar - internal
-//
+    lctx.n_outputs = 0;
 
+    return n_outputs_max;
+}
 
-// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
-// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
-std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
-        const std::string & src,
-        llama_partial_utf8   partial_start) {
-    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
-    const char          * pos      = src.c_str();
-    std::vector<uint32_t> code_points;
-    // common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
-    code_points.reserve(src.size() + 1);
-    uint32_t              value    = partial_start.value;
-    int                   n_remain = partial_start.n_remain;
 
-    // continue previous decode, if applicable
-    while (*pos != 0 && n_remain > 0) {
-        uint8_t next_byte = static_cast<uint8_t>(*pos);
-        if ((next_byte >> 6) != 2) {
-            // invalid sequence, abort
-            code_points.push_back(0);
-            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
-        }
-        value = (value << 6) + (next_byte & 0x3F);
-        ++pos;
-        --n_remain;
+static void llama_graph_compute(
+        llama_context & lctx,
+          ggml_cgraph * gf,
+                  int   n_threads) {
+#ifdef GGML_USE_METAL
+    if (ggml_backend_is_metal(lctx.backend_metal)) {
+        ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
     }
+#endif
 
-    if (partial_start.n_remain > 0 && n_remain == 0) {
-        code_points.push_back(value);
+    if (lctx.backend_cpu != nullptr) {
+        ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
+        ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
     }
+#ifdef GGML_USE_BLAS
+    if (lctx.backend_blas != nullptr) {
+        ggml_backend_blas_set_n_threads(lctx.backend_blas, n_threads);
+    }
+#endif
 
-    // decode any subsequent utf-8 sequences, which may end in an incomplete one
-    while (*pos != 0) {
-        uint8_t  first_byte = static_cast<uint8_t>(*pos);
-        uint8_t  highbits   = first_byte >> 4;
-                 n_remain   = lookup[highbits] - 1;
+    ggml_backend_sched_graph_compute_async(lctx.sched, gf);
 
-        if (n_remain < 0) {
-            // invalid sequence, abort
-            code_points.clear();
-            code_points.push_back(0);
-            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
-        }
+    // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
+}
 
-        uint8_t  mask       = (1 << (7 - n_remain)) - 1;
-                 value      = first_byte & mask;
-        ++pos;
-        while (*pos != 0 && n_remain > 0) {
-            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
-            ++pos;
-            --n_remain;
-        }
-        if (n_remain == 0) {
-            code_points.push_back(value);
-        }
-    }
-    code_points.push_back(0);
+// decode a batch of tokens by evaluating the transformer
+//
+//   - lctx:      llama context
+//   - batch:     batch to evaluate
+//
+// return 0 on success
+// return positive int on warning
+// return negative int on error
+//
+static int llama_decode_internal(
+         llama_context & lctx,
+           llama_batch   batch_all) { // TODO: rename back to batch
 
-    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
-}
+    lctx.is_encoding = false;
+    const uint32_t n_tokens_all = batch_all.n_tokens;
 
-// returns true iff pos points to the end of one of the definitions of a rule
-static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
-    switch (pos->type) {
-        case LLAMA_GRETYPE_END: return true;  // NOLINT
-        case LLAMA_GRETYPE_ALT: return true;  // NOLINT
-        default:                return false;
+    if (n_tokens_all == 0) {
+        LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
+        return -1;
     }
-}
 
-// returns true iff chr satisfies the char range at pos (regular or inverse range)
-// asserts that pos is pointing to a char range element
-static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
-        const llama_grammar_element * pos,
-        const uint32_t                chr) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
 
-    bool found            = false;
-    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
+    GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT
 
-    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
+    GGML_ASSERT(n_tokens_all <= cparams.n_batch);
 
-    do {
-        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
-            // inclusive range, e.g. [a-z]
-            found = found || (pos->value <= chr && chr <= pos[1].value);
-            pos += 2;
-        } else {
-            // exact char match, e.g. [a] or "a"
-            found = found || pos->value == chr;
-            pos += 1;
-        }
-    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
+    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
 
-    return std::make_pair(found == is_positive_char, pos);
-}
+    if (lctx.t_compute_start_us == 0) {
+        lctx.t_compute_start_us = ggml_time_us();
+    }
+    lctx.n_queued_tokens += n_tokens_all;
 
-// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
-// range at pos (regular or inverse range)
-// asserts that pos is pointing to a char range element
-static bool llama_grammar_match_partial_char(
-        const llama_grammar_element * pos,
-        const llama_partial_utf8      partial_utf8) {
+    auto & kv_self = lctx.kv_self;
+
+    const int64_t n_embd  = hparams.n_embd;
+    const int64_t n_vocab = hparams.n_vocab;
 
-    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
-    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
+    uint32_t n_outputs = 0;
+    uint32_t n_outputs_prev = 0;
 
-    uint32_t partial_value = partial_utf8.value;
-    int      n_remain      = partial_utf8.n_remain;
+    const auto n_ubatch = cparams.n_ubatch;
 
-    // invalid sequence or 7-bit char split across 2 bytes (overlong)
-    if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
-        return false;
-    }
+    // TODO: simplify or deprecate
+    std::vector<llama_pos> pos;
+    std::vector<int32_t>                   n_seq_id;
+    std::vector<llama_seq_id *>            seq_id_arr;
+    std::vector<std::vector<llama_seq_id>> seq_id;
 
-    // range of possible code points this partial UTF-8 sequence could complete to
-    uint32_t low  = partial_value << (n_remain * 6);
-    uint32_t high = low | ((1 << (n_remain * 6)) - 1);
+    // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
+    const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
 
-    if (low == 0) {
-        if (n_remain == 2) {
-            low = 1 << 11;
-        } else if (n_remain == 3) {
-            low = 1 << 16;
+    // count outputs
+    if (batch_all.logits && !embd_pooled) {
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            n_outputs += batch_all.logits[i] != 0;
         }
+    } else if (lctx.logits_all || embd_pooled) {
+        n_outputs = n_tokens_all;
+    } else {
+        // keep last output only
+        n_outputs = 1;
     }
 
-    do {
-        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
-            // inclusive range, e.g. [a-z]
-            if (pos->value <= high && low <= pos[1].value) {
-                return is_positive_char;
-            }
-            pos += 2;
-        } else {
-            // exact char match, e.g. [a] or "a"
-            if (low <= pos->value && pos->value <= high) {
-                return is_positive_char;
+    // reserve output buffer
+    if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
+        return -2;
+    };
+
+    // set output mappings
+    if (batch_all.logits) {
+        int32_t i_logits = 0;
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            if (batch_all.logits[i]) {
+                lctx.output_ids[i] = i_logits++;
             }
-            pos += 1;
         }
-    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
-
-    return !is_positive_char;
-}
-
-
-// transforms a grammar pushdown stack into N possible stacks, all ending
-// at a character range (terminal element)
-static void llama_grammar_advance_stack(
-        const std::vector<std::vector<llama_grammar_element>>   & rules,
-        const std::vector<const llama_grammar_element *>        & stack,
-        std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
-
-    if (stack.empty()) {
-        if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
-            new_stacks.emplace_back(stack);
+    } else {
+        for (uint32_t i = 0; i < n_outputs; ++i) {
+            lctx.output_ids[i] = i;
         }
-        return;
     }
 
-    const llama_grammar_element * pos = stack.back();
+    for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
+        const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);
+        llama_batch u_batch = {
+            /* .n_tokens   = */ (int32_t) n_tokens,
+            /* .token      = */ batch_all.token     ? batch_all.token    + cur_token        : nullptr,
+            /* .embd       = */ batch_all.embd      ? batch_all.embd     + cur_token*n_embd : nullptr,
+            /* .pos        = */ batch_all.pos       ? batch_all.pos      + cur_token        : nullptr,
+            /* .n_seq_id   = */ batch_all.n_seq_id  ? batch_all.n_seq_id + cur_token        : nullptr,
+            /* .seq_id     = */ batch_all.seq_id    ? batch_all.seq_id   + cur_token        : nullptr,
+            /* .logits     = */ batch_all.logits    ? batch_all.logits   + cur_token        : nullptr,
+            /* .all_pos_0  = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1,
+            /* .all_pos_1  = */ batch_all.all_pos_1,
+            /* .all_seq_id = */ batch_all.all_seq_id,
+        };
+
+        // count the outputs in this u_batch
+        {
+            int32_t n_outputs_new = 0;
 
-    switch (pos->type) {
-        case LLAMA_GRETYPE_RULE_REF: {
-            const size_t                  rule_id = static_cast<size_t>(pos->value);
-            const llama_grammar_element * subpos  = rules[rule_id].data();
-            do {
-                // init new stack without the top (pos)
-                std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
-                if (!llama_grammar_is_end_of_sequence(pos + 1)) {
-                    // if this rule ref is followed by another element, add that to stack
-                    new_stack.push_back(pos + 1);
-                }
-                if (!llama_grammar_is_end_of_sequence(subpos)) {
-                    // if alternate is nonempty, add to stack
-                    new_stack.push_back(subpos);
-                }
-                llama_grammar_advance_stack(rules, new_stack, new_stacks);
-                while (!llama_grammar_is_end_of_sequence(subpos)) {
-                    // scan to end of alternate def
-                    subpos++;
+            if (u_batch.logits && !embd_pooled) {
+                for (uint32_t i = 0; i < n_tokens; i++) {
+                    n_outputs_new += u_batch.logits[i] != 0;
                 }
-                if (subpos->type == LLAMA_GRETYPE_ALT) {
-                    // there's another alternate def of this rule to process
-                    subpos++;
-                } else {
-                    break;
+            } else if (n_outputs == n_tokens_all) {
+                n_outputs_new = n_tokens;
+            } else {
+                // keep last output only
+                if (cur_token + n_tokens >= n_tokens_all) {
+                    n_outputs_new = 1;
                 }
-            } while (true);
-            break;
-        }
-        case LLAMA_GRETYPE_CHAR:
-        case LLAMA_GRETYPE_CHAR_NOT:
-            if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
-                // only add the stack if it's not a duplicate of one we already have
-                new_stacks.emplace_back(stack);
             }
-            break;
-        default:
-            // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
-            // (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
-            // those
-            GGML_ASSERT(false);
-    }
-}
-
-// takes a set of possible pushdown stacks on a grammar, which are required to
-// be positioned at a character range (see `llama_grammar_advance_stack`), and
-// produces the N possible stacks if the given char is accepted at those
-// positions
-void llama_grammar_accept(
-        const std::vector<std::vector<llama_grammar_element>>         & rules,
-        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const uint32_t                                                  chr,
-        std::vector<std::vector<const llama_grammar_element *>>       & new_stacks) {
 
-    new_stacks.clear();
-
-    for (const auto & stack : stacks) {
-        if (stack.empty()) {
-            continue;
+            // needs to happen before the graph is built
+            lctx.n_outputs = n_outputs_new;
         }
 
-        auto match = llama_grammar_match_char(stack.back(), chr);
-        if (match.first) {
-            const llama_grammar_element * pos = match.second;
+        int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+        GGML_ASSERT(n_threads > 0);
 
-            // update top of stack to next element, if any
-            std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
-            if (!llama_grammar_is_end_of_sequence(pos)) {
-                new_stack.push_back(pos);
+        // helpers for smoother batch API transition
+        // after deprecating the llama_eval calls, these will be removed
+        if (u_batch.pos == nullptr) {
+            pos.resize(n_tokens);
+            for (uint32_t i = 0; i < n_tokens; i++) {
+                pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1;
             }
-            llama_grammar_advance_stack(rules, new_stack, new_stacks);
+
+            u_batch.pos = pos.data();
         }
-    }
-}
 
-static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
-        const std::vector<std::vector<llama_grammar_element>>         & rules,
-        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const std::vector<llama_grammar_candidate>                    & candidates);
+        if (u_batch.seq_id == nullptr) {
+            n_seq_id.resize(n_tokens);
+            seq_id.resize(n_tokens);
+            seq_id_arr.resize(n_tokens);
+            for (uint32_t i = 0; i < n_tokens; i++) {
+                n_seq_id[i] = 1;
+                seq_id[i].resize(1);
+                seq_id[i][0] = u_batch.all_seq_id;
+                seq_id_arr[i] = seq_id[i].data();
+            }
 
-static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
-        const std::vector<std::vector<llama_grammar_element>> & rules,
-        const std::vector<const llama_grammar_element *>      & stack,
-        const std::vector<llama_grammar_candidate>            & candidates) {
+            u_batch.n_seq_id = n_seq_id.data();
+            u_batch.seq_id = seq_id_arr.data();
+        }
 
-    std::vector<llama_grammar_candidate> rejects;
-    rejects.reserve(candidates.size());
+        // non-causal masks do not use the KV cache
+        if (hparams.causal_attn) {
+            llama_kv_cache_update(&lctx);
 
-    if (stack.empty()) {
-        for (const auto & tok : candidates) {
-            if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
-                rejects.push_back(tok);
+            // if we have enough unused cells before the current head ->
+            //   better to start searching from the beginning of the cache, hoping to fill it
+            if (kv_self.head > kv_self.used + 2*n_tokens) {
+                kv_self.head = 0;
             }
-        }
-        return rejects;
-    }
 
-    const llama_grammar_element * stack_pos = stack.back();
-
-    std::vector<llama_grammar_candidate> next_candidates;
-    next_candidates.reserve(candidates.size());
+            if (!llama_kv_cache_find_slot(kv_self, u_batch)) {
+                return 1;
+            }
 
-    for (const auto & tok : candidates) {
-        if (*tok.code_points == 0) {
-            // reached end of full codepoints in token, reject iff it ended in a partial sequence
-            // that cannot satisfy this position in grammar
-            if (tok.partial_utf8.n_remain != 0 &&
-                    !llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
-                rejects.push_back(tok);
+            if (!kv_self.recurrent) {
+                // a heuristic, to avoid attending the full cache if it is not yet utilized
+                // after enough generations, the benefit from this heuristic disappears
+                // if we start defragmenting the cache, the benefit from this will be more important
+                const uint32_t pad = llama_kv_cache_get_padding(cparams);
+                kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad)));
+                //kv_self.n = llama_kv_cache_cell_max(kv_self);
             }
-        } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
-            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
-        } else {
-            rejects.push_back(tok);
         }
-    }
-
-    const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
 
-    // update top of stack to next element, if any
-    std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
-    if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
-        stack_after.push_back(stack_pos_after);
-    }
-    std::vector<std::vector<const llama_grammar_element *>> next_stacks;
-    llama_grammar_advance_stack(rules, stack_after, next_stacks);
-
-    auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
-    for (const auto & tok : next_rejects) {
-        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
-    }
+        //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
 
-    return rejects;
-}
+        ggml_backend_sched_reset(lctx.sched);
+        ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
 
-static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
-        const std::vector<std::vector<llama_grammar_element>>         & rules,
-        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const std::vector<llama_grammar_candidate>                    & candidates) {
-    GGML_ASSERT(!stacks.empty()); // REVIEW
+        ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);
 
-    if (candidates.empty()) {
-        return std::vector<llama_grammar_candidate>();
-    }
+        // the output is always the last tensor in the graph
+        struct ggml_tensor * res  = gf->nodes[gf->n_nodes - 1];
+        struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
 
-    auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
+        if (lctx.n_outputs == 0) {
+            // no output
+            res  = nullptr;
+            embd = nullptr;
+        } else if (cparams.embeddings) {
+            res  = nullptr; // do not extract logits for embedding case
+            embd = nullptr;
+            for (int i = gf->n_nodes - 1; i >= 0; --i) {
+                if (strcmp(gf->nodes[i]->name, "result_embd_pooled") == 0) {
+                    embd = gf->nodes[i];
+                    break;
+                }
+            }
+            GGML_ASSERT(embd != nullptr && "missing embeddings tensor");
+        } else {
+            embd = nullptr; // do not extract embeddings when not needed
+            GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
+        }
+        // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
 
-    for (size_t i = 1, size = stacks.size(); i < size; ++i) {
-        rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
-    }
-    return rejects;
-}
+        ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
-static bool llama_grammar_detect_left_recursion(
-        const std::vector<std::vector<llama_grammar_element>> & rules,
-        size_t                                                  rule_index,
-        std::vector<bool>                                     * rules_visited,
-        std::vector<bool>                                     * rules_in_progress,
-        std::vector<bool>                                     * rules_may_be_empty) {
-    if ((*rules_in_progress)[rule_index]) {
-        return true;
-    }
+        llama_set_inputs(lctx, u_batch);
 
-    (*rules_in_progress)[rule_index] = true;
+        llama_graph_compute(lctx, gf, n_threads);
 
-    const std::vector<llama_grammar_element> & rule = rules[rule_index];
+        // update the kv ring buffer
+        {
+            kv_self.head += n_tokens;
 
-    // First check if the rule might produce the empty string. This could be done combined with the second
-    // step but it's more readable as two steps.
-    bool at_rule_start = true;
-    for (size_t i = 0; i < rule.size(); i++) {
-        if (llama_grammar_is_end_of_sequence(&rule[i])) {
-            if (at_rule_start) {
-                (*rules_may_be_empty)[rule_index] = true;
-                break;
+            // Ensure kv cache head points to a valid index.
+            if (kv_self.head >= kv_self.size) {
+                kv_self.head = 0;
             }
-            at_rule_start = true;
-        } else {
-            at_rule_start = false;
         }
-    }
 
-    // Second, recurse into leftmost nonterminals (or next-leftmost as long as the previous nonterminal may
-    // be empty)
-    bool recurse_into_nonterminal = true;
-    for (size_t i = 0; i < rule.size(); i++) {
-        if (rule[i].type == LLAMA_GRETYPE_RULE_REF && recurse_into_nonterminal) {
-            if (llama_grammar_detect_left_recursion(rules, (size_t)rule[i].value, rules_visited, rules_in_progress, rules_may_be_empty)) {
-                return true;
-            }
-            if (!((*rules_may_be_empty)[(size_t)rule[i].value])) {
-                recurse_into_nonterminal = false;
-            }
-        } else if (llama_grammar_is_end_of_sequence(&rule[i])) {
-            recurse_into_nonterminal = true;
-        } else {
-            recurse_into_nonterminal = false;
-        }
-    }
+        // plot the computation graph in dot format (for debugging purposes)
+        //if (n_past%100 == 0) {
+        //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
+        //}
 
-    (*rules_in_progress)[rule_index] = false;
-    (*rules_visited)[rule_index] = true;
-    return false;
-}
+        // extract logits
+        if (res) {
+            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
+            GGML_ASSERT(backend_res != nullptr);
+            GGML_ASSERT(lctx.logits != nullptr);
 
-//
-// grammar - external
-//
+            float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
+            const int32_t n_outputs_new = lctx.n_outputs;
 
-struct llama_grammar * llama_grammar_init(
-            const llama_grammar_element ** rules,
-                                 size_t    n_rules,
-                                 size_t    start_rule_index) {
-    const llama_grammar_element * pos;
-
-    // copy rule definitions into vectors
-    std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
-    for (size_t i = 0; i < n_rules; i++) {
-        for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
-            vec_rules[i].push_back(*pos);
-        }
-        vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
-    }
-
-    // Check for left recursion
-    std::vector<bool> rules_visited(n_rules);
-    std::vector<bool> rules_in_progress(n_rules);
-    std::vector<bool> rules_may_be_empty(n_rules);
-    for (size_t i = 0; i < n_rules; i++) {
-        if (rules_visited[i]) {
-            continue;
-        }
-        if (llama_grammar_detect_left_recursion(vec_rules, i, &rules_visited, &rules_in_progress, &rules_may_be_empty)) {
-            throw std::runtime_error(format("unsupported grammar, left recursion detected for nonterminal at index %zu", i));
+            if (n_outputs_new) {
+                GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
+                GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size);
+                ggml_backend_tensor_get_async(backend_res, res, logits_out, 0, n_outputs_new*n_vocab*sizeof(float));
+            }
         }
-    }
 
-    // loop over alternates of start rule to build initial stacks
-    std::vector<std::vector<const llama_grammar_element *>> stacks;
-    pos = vec_rules[start_rule_index].data();
-    do {
-        std::vector<const llama_grammar_element *> stack;
-        if (!llama_grammar_is_end_of_sequence(pos)) {
-            // if alternate is nonempty, add to stack
-            stack.push_back(pos);
-        }
-        llama_grammar_advance_stack(vec_rules, stack, stacks);
-        while (!llama_grammar_is_end_of_sequence(pos)) {
-            // scan to end of alternate def
-            pos++;
-        }
-        if (pos->type == LLAMA_GRETYPE_ALT) {
-            // there's another alternate def of this rule to process
-            pos++;
-        } else {
-            break;
-        }
-    } while (true);
+        // extract embeddings
+        if (embd) {
+            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
+            GGML_ASSERT(backend_embd != nullptr);
 
-    // Important: vec_rules has to be moved here, not copied, because stacks contains
-    // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar
-    // then the pointers would be invalidated when the local vec_rules goes out of scope.
-    return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
-}
+            switch (cparams.pooling_type) {
+                case LLAMA_POOLING_TYPE_NONE:
+                    {
+                        // extract token embeddings
+                        GGML_ASSERT(lctx.embd != nullptr);
+                        float * embd_out = lctx.embd + n_outputs_prev*n_embd;
+                        const int32_t n_outputs_new = lctx.n_outputs;
 
-void llama_grammar_free(struct llama_grammar * grammar) {
-    delete grammar;
-}
+                        if (n_outputs_new) {
+                            GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
+                            GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
+                            ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_MEAN:
+                case LLAMA_POOLING_TYPE_CLS:
+                case LLAMA_POOLING_TYPE_LAST:
+                    {
+                        // extract sequence embeddings
+                        auto & embd_seq_out = lctx.embd_seq;
+                        embd_seq_out.clear();
 
-struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
-    llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
-
-    // redirect elements in stacks to point to new rules
-    for (size_t is = 0; is < result->stacks.size(); is++) {
-        for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
-            for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
-                for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
-                    if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
-                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
+                        for (uint32_t i = 0; i < n_tokens; i++) {
+                            const llama_seq_id seq_id = u_batch.seq_id[i][0];
+                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
+                                continue;
+                            }
+                            embd_seq_out[seq_id].resize(n_embd);
+                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                    {
+                        GGML_ABORT("unknown pooling type");
                     }
-                }
             }
         }
+        n_outputs_prev += lctx.n_outputs;
     }
 
-    return result;
-}
-
-//
-// sampling
-//
-
-void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
-    if (seed == LLAMA_DEFAULT_SEED) {
-        seed = time(NULL);
-    }
-    ctx->rng.seed(seed);
-}
-
-void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
-    GGML_ASSERT(candidates->size > 0);
-
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    // Sort the logits in descending order
-    if (!candidates->sorted) {
-        std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
-            return a.logit > b.logit;
-        });
-        candidates->sorted = true;
-    }
-
-    float max_l = candidates->data[0].logit;
-    float cum_sum = 0.0f;
-    for (size_t i = 0; i < candidates->size; ++i) {
-        float p = expf(candidates->data[i].logit - max_l);
-        candidates->data[i].p = p;
-        cum_sum += p;
-    }
-    for (size_t i = 0; i < candidates->size; ++i) {
-        candidates->data[i].p /= cum_sum;
-    }
+    // set to total number of outputs in the batch, for use in llama_get_logits_ith
+    lctx.n_outputs = n_outputs;
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
+    // wait for the computation to finish (automatically done when obtaining the model output)
+    //llama_synchronize(&lctx);
 
-void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
-    // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
-    // if (k >= (int32_t)candidates->size) {
-    //     return;
-    // }
+    // decide if we need to defrag the kv cache
+    if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
+        const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;
 
-    const int64_t t_start_sample_us = ggml_time_us();
+        // queue defragmentation for next llama_kv_cache_update
+        if (fragmentation > cparams.defrag_thold) {
+            //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
 
-    if (k <= 0) {
-        k = candidates->size;
+            llama_kv_cache_defrag(kv_self);
+        }
     }
 
-    k = std::max(k, (int) min_keep);
-    k = std::min(k, (int) candidates->size);
+    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
+    // overlap with device computation.
+    ggml_backend_sched_reset(lctx.sched);
 
-    // Sort scores in descending order
-    if (!candidates->sorted) {
-        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
-            return a.logit > b.logit;
-        };
-        if (k <= 128) {
-            std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
-        } else {
-            constexpr int   nbuckets     = 128;
-            constexpr float bucket_low   = -10.0f;
-            constexpr float bucket_high  =  10.0f;
-            constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
-            constexpr float bucker_inter = -bucket_low * bucket_scale;
-
-            std::vector<int> bucket_idx(candidates->size);
-            std::vector<int> histo(nbuckets, 0);
-
-            for (int i = 0; i < (int)candidates->size; ++i) {
-                const float val = candidates->data[i].logit;
-                int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
-                ib = std::max(0, std::min(nbuckets-1, ib));
-                bucket_idx[i] = ib;
-                ++histo[ib];
-            }
-            int nhave = 0;
-            int ib = nbuckets - 1;
-            for ( ; ib >= 0; --ib) {
-                nhave += histo[ib];
-                if (nhave >= k) break;
-            }
-            std::vector<llama_token_data> tmp_tokens(nhave);
-            auto ptr = tmp_tokens.data();
-            std::vector<llama_token_data*> bucket_ptrs;
-            bucket_ptrs.reserve(nbuckets - ib);
-            for (int j = nbuckets - 1; j >= ib; --j) {
-                bucket_ptrs.push_back(ptr);
-                ptr += histo[j];
-            }
-            for (int i = 0; i < (int)candidates->size; ++i) {
-                int j = bucket_idx[i];
-                if (j >= ib) {
-                    *bucket_ptrs[nbuckets-1-j]++ = candidates->data[i];
-                }
-            }
+    return 0;
+}
 
-            ptr = tmp_tokens.data();
-            int ndone = 0;
-            for (int j = nbuckets-1; j > ib; --j) {
-                std::sort(ptr, ptr + histo[j], comp);
-                ptr += histo[j];
-                ndone += histo[j];
-            }
-            std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);
+// encode a batch of tokens by evaluating the encoder part of the transformer
+//
+//   - lctx:      llama context
+//   - batch:     batch to evaluate
+//
+// return 0 on success
+// return positive int on warning
+// return negative int on error
+//
+static int llama_encode_internal(
+         llama_context & lctx,
+           llama_batch   batch) {
 
-            std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data));
+    lctx.is_encoding = true;
 
-        }
-        candidates->sorted = true;
-    }
-    candidates->size = k;
+    const uint32_t n_tokens = batch.n_tokens;
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    if (n_tokens == 0) {
+        LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
+        return -1;
     }
-}
 
-void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
-    if (p >= 1.0f) {
-        return;
-    }
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
 
-    llama_sample_softmax(ctx, candidates);
+    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
 
-    const int64_t t_start_sample_us = ggml_time_us();
+    // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
+    GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
 
-    // Compute the cumulative probabilities
-    float cum_sum = 0.0f;
-    size_t last_idx = candidates->size;
+    if (lctx.t_compute_start_us == 0) {
+        lctx.t_compute_start_us = ggml_time_us();
+    }
 
-    for (size_t i = 0; i < candidates->size; ++i) {
-        cum_sum += candidates->data[i].p;
+    lctx.n_queued_tokens += n_tokens;
 
-        // Check if the running sum is at least p or if we have kept at least min_keep tokens
-        // we set the last index to i+1 to indicate that the current iterate should be included in the set
-        if (cum_sum >= p && i + 1 >= min_keep) {
-            last_idx = i + 1;
-            break;
-        }
-    }
+    const int64_t n_embd = hparams.n_embd;
 
-    // Resize the output vector to keep only the top-p tokens
-    candidates->size = last_idx;
+    // TODO: simplify or deprecate
+    std::vector<llama_pos> pos;
+    std::vector<int32_t>                   n_seq_id;
+    std::vector<llama_seq_id *>            seq_id_arr;
+    std::vector<std::vector<llama_seq_id>> seq_id;
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
+    // reserve output buffer
+    if (llama_output_reserve(lctx, n_tokens) < n_tokens) {
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
+        return -2;
+    };
 
-void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
-    if (p <= 0.0f || !candidates->size) {
-        return;
+    for (uint32_t i = 0; i < n_tokens; ++i) {
+        lctx.output_ids[i] = i;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    bool min_p_applied = false;
+    lctx.inp_embd_enc = NULL;
+    lctx.n_outputs = n_tokens;
 
-    // if the candidates aren't sorted, try the unsorted implementation first
-    if (!candidates->sorted) {
-        std::vector<llama_token_data> filtered_tokens;
+    const int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+    GGML_ASSERT(n_threads > 0);
 
-        float max_logit = -FLT_MAX;
-        for (size_t i = 0; i < candidates->size; ++i) {
-            max_logit = std::max(max_logit, candidates->data[i].logit);
+    // helpers for smoother batch API transition
+    // after deprecating the llama_eval calls, these will be removed
+    if (batch.pos == nullptr) {
+        pos.resize(n_tokens);
+        for (uint32_t i = 0; i < n_tokens; i++) {
+            pos[i] = batch.all_pos_0 + i*batch.all_pos_1;
         }
-        const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max
 
-        for (size_t i = 0; i < candidates->size; ++i) {
-            if (candidates->data[i].logit >= min_logit) {
-                filtered_tokens.push_back(candidates->data[i]);
-            }
-        }
+        batch.pos = pos.data();
+    }
 
-        // if we have enough values the operation was a success
-        if (filtered_tokens.size() >= min_keep) {
-            memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
-            candidates->size = filtered_tokens.size();
-            min_p_applied = true;
+    if (batch.seq_id == nullptr) {
+        n_seq_id.resize(n_tokens);
+        seq_id.resize(n_tokens);
+        seq_id_arr.resize(n_tokens);
+        for (uint32_t i = 0; i < n_tokens; i++) {
+            n_seq_id[i] = 1;
+            seq_id[i].resize(1);
+            seq_id[i][0] = batch.all_seq_id;
+            seq_id_arr[i] = seq_id[i].data();
         }
+
+        batch.n_seq_id = n_seq_id.data();
+        batch.seq_id = seq_id_arr.data();
     }
 
-    // if the candidates are sorted or the unsorted implementation failed, use this implementation
-    if (!min_p_applied) {
-        // Sort the logits in descending order
-        if (!candidates->sorted) {
-            std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
-                return a.logit > b.logit;
-            });
-            candidates->sorted = true;
-        }
+    ggml_backend_sched_reset(lctx.sched);
+    ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
+
+    ggml_cgraph * gf = llama_build_graph(lctx, batch, false);
 
-        const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
-        size_t i = 1; // first token always matches
+    // the output embeddings after the final encoder normalization
+    struct ggml_tensor * embd = nullptr;
 
-        for (; i < candidates->size; ++i) {
-            if (candidates->data[i].logit < min_logit && i >= min_keep) {
-                break; // prob too small
+    // there are two cases here
+    if (llama_model_has_decoder(&lctx.model)) {
+        // first case is an encoder-decoder T5 model where embeddings are passed to decoder
+        embd = gf->nodes[gf->n_nodes - 1];
+        GGML_ASSERT(strcmp(embd->name, "result_norm") == 0 && "missing result_output tensor");
+    } else {
+        // second case is an encoder-only T5 model
+        if (cparams.embeddings) {
+            // only output embeddings if required
+            embd = gf->nodes[gf->n_nodes - 1];
+            if (strcmp(embd->name, "result_embd_pooled") != 0) {
+                embd = gf->nodes[gf->n_nodes - 2];
             }
+            GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
         }
-
-        // Resize the output vector to keep only the matching tokens
-        candidates->size = i;
-    }
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
     }
-}
 
-void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
-    if (z >= 1.0f || candidates->size <= 2) {
-        return;
-    }
+    ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
-    llama_sample_softmax(nullptr, candidates);
-    const int64_t t_start_sample_us = ggml_time_us();
+    llama_set_inputs(lctx, batch);
 
-    // Compute the first and second derivatives
-    std::vector<float> first_derivatives(candidates->size - 1);
-    std::vector<float> second_derivatives(candidates->size - 2);
+    llama_graph_compute(lctx, gf, n_threads);
 
-    for (size_t i = 0; i < first_derivatives.size(); ++i) {
-        first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
-    }
-    for (size_t i = 0; i < second_derivatives.size(); ++i) {
-        second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
-    }
+    // extract embeddings
+    if (embd) {
+        ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
+        GGML_ASSERT(backend_embd != nullptr);
 
-    // Calculate absolute value of second derivatives
-    for (size_t i = 0; i < second_derivatives.size(); ++i) {
-        second_derivatives[i] = std::abs(second_derivatives[i]);
-    }
+        if (llama_model_has_decoder(&lctx.model)) {
+            lctx.embd_enc.resize(n_tokens*n_embd);
+            float * embd_out = lctx.embd_enc.data();
 
-    // Normalize the second derivatives
-    {
-        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
+            ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float));
 
-        if (second_derivatives_sum > 1e-6f) {
-            for (float & value : second_derivatives) {
-                value /= second_derivatives_sum;
+            // remember the sequence ids used during the encoding - needed for cross attention later
+            lctx.seq_ids_enc.resize(n_tokens);
+            for (uint32_t i = 0; i < n_tokens; i++) {
+                for (int s = 0; s < batch.n_seq_id[i]; s++) {
+                    llama_seq_id seq_id = batch.seq_id[i][s];
+                    lctx.seq_ids_enc[i].insert(seq_id);
+                }
             }
         } else {
-            for (float & value : second_derivatives) {
-                value = 1.0f / second_derivatives.size();
-            }
-        }
-    }
+            GGML_ASSERT(lctx.embd != nullptr);
+
+            switch (cparams.pooling_type) {
+                case LLAMA_POOLING_TYPE_NONE:
+                    {
+                        // extract token embeddings
+                        GGML_ASSERT(lctx.embd != nullptr);
+                        float * embd_out = lctx.embd;
 
-    float cum_sum = 0.0f;
-    size_t last_idx = candidates->size;
-    for (size_t i = 0; i < second_derivatives.size(); ++i) {
-        cum_sum += second_derivatives[i];
+                        GGML_ASSERT(n_tokens*n_embd <= (int64_t) lctx.embd_size);
+                        ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float));
+                    } break;
+                case LLAMA_POOLING_TYPE_MEAN:
+                case LLAMA_POOLING_TYPE_CLS:
+                case LLAMA_POOLING_TYPE_LAST:
+                    {
+                        // extract sequence embeddings
+                        auto & embd_seq_out = lctx.embd_seq;
+                        embd_seq_out.clear();
 
-        // Check if the running sum is greater than z or if we have kept at least min_keep tokens
-        if (cum_sum > z && i >= min_keep) {
-            last_idx = i;
-            break;
+                        for (uint32_t i = 0; i < n_tokens; i++) {
+                            const llama_seq_id seq_id = batch.seq_id[i][0];
+                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
+                                continue;
+                            }
+                            embd_seq_out[seq_id].resize(n_embd);
+                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                    {
+                        GGML_ABORT("unknown pooling type");
+                    }
+            }
         }
     }
 
-    // Resize the output vector to keep only the tokens above the tail location
-    candidates->size = last_idx;
+    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
+    // overlap with device computation.
+    ggml_backend_sched_reset(lctx.sched);
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
+    return 0;
 }
 
-void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
-    // Reference implementation:
-    // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
-    if (p >= 1.0f) {
-        return;
-    }
+// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
+static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
+    auto & kv_self = lctx.kv_self;
 
-    // Compute the softmax of logits and calculate entropy
-    llama_sample_softmax(nullptr, candidates);
+    const auto & hparams = lctx.model.hparams;
 
-    const int64_t t_start_sample_us = ggml_time_us();
+    const uint32_t n_layer = hparams.n_layer;
 
-    float entropy = 0.0f;
-    for (size_t i = 0; i < candidates->size; ++i) {
-        entropy += -candidates->data[i].p * logf(candidates->data[i].p);
-    }
+    const uint32_t n_kv   = llama_kv_cache_cell_max(kv_self);
+    const uint32_t n_used = kv_self.used;
 
-    // Compute the absolute difference between negative log probability and entropy for each candidate
-    std::vector<float> shifted_scores;
-    for (size_t i = 0; i < candidates->size; ++i) {
-        float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
-        shifted_scores.push_back(shifted_score);
-    }
+    assert(n_used <= n_kv);
+
+    //const int64_t t_start = ggml_time_us();
+
+    // number of cells moved
+    uint32_t n_moves = 0;
 
-    // Sort tokens based on the shifted_scores and their corresponding indices
-    std::vector<size_t> indices(candidates->size);
-    std::iota(indices.begin(), indices.end(), 0);
+    // each move requires 6*n_layer tensors (see build_defrag)
+    //   - source view, destination view, copy operation
+    //   - x2 for keys and values
+    //const uint32_t max_moves = llama_model_max_nodes(model)/(6*n_layer);
+    // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
+    const uint32_t max_moves = (llama_model_max_nodes(lctx.model) - 2*n_layer)/(6*n_layer);
 
-    std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
-        return shifted_scores[a] < shifted_scores[b];
-    });
+    // determine which KV cells to move where
+    //
+    //  cell i moves to ids[i]
+    //
+    //  if ids[i] == i || ids[i] == n_kv, then cell i is not moved
+    //
+    std::vector<uint32_t> ids(n_kv, n_kv);
 
-    // Compute the cumulative probabilities
-    float cum_sum = 0.0f;
-    size_t last_idx = indices.size();
+    for (uint32_t i0 = 0; i0 < n_used; ++i0) {
+        const auto & cell0 = kv_self.cells[i0];
 
-    for (size_t i = 0; i < indices.size(); ++i) {
-        size_t idx = indices[i];
-        cum_sum += candidates->data[idx].p;
+        if (!cell0.is_empty()) {
+            ids[i0] = i0;
 
-        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
-        if (cum_sum > p && i >= min_keep - 1) {
-            last_idx = i + 1;
-            break;
+            continue;
         }
-    }
 
-    // Resize the output vector to keep only the locally typical tokens
-    std::vector<llama_token_data> new_candidates;
-    for (size_t i = 0; i < last_idx; ++i) {
-        size_t idx = indices[i];
-        new_candidates.push_back(candidates->data[idx]);
-    }
+        // found a hole - fill it with data from the end of the cache
 
-    // Replace the data in candidates with the new_candidates data
-    std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
-    candidates->size = new_candidates.size();
-    candidates->sorted = false;
+        uint32_t nh = 1;
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
+        // determine the size of the hole
+        while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
+            nh++;
+        }
 
-void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
-    const int64_t t_start_sample_us = ggml_time_us();
+        uint32_t nf = 0;
+        uint32_t is = n_kv - 1;
 
-    // no need to do anything if there is only one (or zero) candidates
-    if(candidates_p->size <= 1) {
-        return;
-    }
+        // starting from the end, find nh non-empty cells
+        for (; is > i0; --is) {
+            const auto & cell1 = kv_self.cells[is];
 
-    // Calculate maximum possible entropy
-    float max_entropy = -logf(1.0f / candidates_p->size);
+            if (cell1.is_empty() || ids[is] != n_kv) {
+                continue;
+            }
 
-    llama_sample_softmax(nullptr, candidates_p);
+            // non-empty cell which is not yet moved
+            nf++;
 
-    // Calculate entropy of the softmax probabilities
-    float entropy = 0.0f;
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        float prob = candidates_p->data[i].p;
-        if (prob > 0.0f) { // Ensure no log(0)
-            entropy -= prob * logf(prob);
+            if (nf == nh) {
+                break;
+            }
         }
-    }
 
-    // Normalize the entropy (max_entropy cannot be 0 here because we checked candidates_p->size != 1 above)
-    float normalized_entropy = entropy / max_entropy;
+        // this can only happen if `n_used` is not accurate, which would be a bug
+        GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
 
-    // Map the normalized entropy to the desired temperature range using the power function
-    float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);
+        nf = 0;
 
-#ifdef DEBUG
-    LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp);
-    LLAMA_LOG_INFO("Entropy: %f\n", entropy);
-    LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy);
-    LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy);
-    LLAMA_LOG_INFO("Exponent: %f\n", exponent_val);
-    LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp);
-#endif
+        uint32_t i1 = is;
 
-    // Apply the dynamically calculated temperature scaling
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        candidates_p->data[i].logit /= dyn_temp;
-    }
+        // are we moving a continuous block of memory?
+        bool cont = false;
 
-    // Re-compute softmax probabilities after scaling logits with dynamic temperature
-    double max_l_double = candidates_p->data[0].logit;
-    double cum_sum_double = 0.0;
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        double p = exp(candidates_p->data[i].logit - max_l_double);
-        candidates_p->data[i].p = p; // Store the scaled probability
-        cum_sum_double += p;
-    }
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        candidates_p->data[i].p /= cum_sum_double; // Re-normalize the probabilities
-    }
+        // should we stop searching for the next move?
+        bool stop = false;
 
-#ifdef DEBUG
-    // Print the updated top 25 probabilities after temperature scaling
-    LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n");
-    for (size_t i = 0; i < 25 && i < candidates_p->size; ++i) {
-        LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates_p->data[i].p * 100.0f);
-    }
-#endif
+        // go back and move the nf cells to the hole
+        for (; i1 < n_kv; ++i1) {
+            auto & cell1 = kv_self.cells[i1];
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
+            if (cell1.is_empty() || ids[i1] != n_kv) {
+                if (n_moves == max_moves) {
+                    stop = true;
+                    break;
+                }
 
-void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
-    const int64_t t_start_sample_us = ggml_time_us();
+                cont = false;
+                continue;
+            }
 
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        candidates_p->data[i].logit /= temp;
-    }
+            // this cell goes to (i0 + nf)
+            ids[i1] = i0 + nf;
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
+            // move the cell meta data
+            kv_self.cells[i0 + nf] = cell1;
 
-void llama_sample_repetition_penalties(
-            struct llama_context * ctx,
-          llama_token_data_array * candidates,
-               const llama_token * last_tokens,
-                          size_t   penalty_last_n,
-                           float   penalty_repeat,
-                           float   penalty_freq,
-                           float   penalty_present) {
-    if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
-        return;
-    }
+            // clear the old cell and move the head there
+            cell1 = llama_kv_cell();
+            kv_self.head = n_used;
 
-    const int64_t t_start_sample_us = ggml_time_us();
+            if (!cont) {
+                n_moves++;
+                cont = true;
+            }
 
-    // Create a frequency map to count occurrences of each token in last_tokens
-    std::unordered_map<llama_token, int> token_count;
-    for (size_t i = 0; i < penalty_last_n; ++i) {
-        token_count[last_tokens[i]]++;
-    }
+            nf++;
 
-    // Apply frequency and presence penalties to the candidates
-    for (size_t i = 0; i < candidates->size; ++i) {
-        const auto token_iter = token_count.find(candidates->data[i].id);
-        if (token_iter == token_count.end()) {
-            continue;
+            if (nf == nh) {
+                break;
+            }
         }
 
-        const int count = token_iter->second;
-
-        // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
-        // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
-        if (candidates->data[i].logit <= 0) {
-            candidates->data[i].logit *= penalty_repeat;
-        } else {
-            candidates->data[i].logit /= penalty_repeat;
+        if (stop || n_moves == max_moves) {
+            break;
         }
 
-        candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
-    }
-
-    candidates->sorted = false;
+        //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+        i0 += nh - 1;
     }
-}
-
-void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
-    GGML_ASSERT(ctx);
-    const int64_t t_start_sample_us = ggml_time_us();
 
-    bool allow_eog = false;
-    for (const auto & stack : grammar->stacks) {
-        if (stack.empty()) {
-            allow_eog = true;
-            break;
-        }
+    if (n_moves == 0) {
+        return;
     }
 
-    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
-    candidates_decoded.reserve(candidates->size);
-    std::vector<llama_grammar_candidate>                              candidates_grammar;
-    candidates_grammar.reserve(candidates->size);
+    //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
 
-    for (size_t i = 0; i < candidates->size; ++i) {
-        const llama_token id    = candidates->data[i].id;
-        const std::string piece = llama_token_to_piece(ctx, id, false);
+    //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
 
-        if (llama_token_is_eog(&ctx->model, id)) {
-            if (!allow_eog) {
-                candidates->data[i].logit = -INFINITY;
-            }
-        } else if (piece.empty() || piece[0] == 0) {
-            candidates->data[i].logit = -INFINITY;
-        } else {
-            candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
-            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
-        }
-    }
+#if 0
+    // CPU defrag
+    //
+    // TODO: optimizations are possible:
+    //       - multiple threads
+    //       - avoid copying to the host memory when already there
+    //
+    // likely not worth the effort, as we have ggml_graph based defrag
+    //
 
-    const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
-    for (const auto & reject : rejects) {
-        candidates->data[reject.index].logit = -INFINITY;
-    }
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-}
+    const uint32_t kv_size = kv_self.size;
 
-static void llama_log_softmax(float * array, size_t size) {
-    float max_l = *std::max_element(array, array + size);
-    float sum = 0.f;
-    for (size_t i = 0; i < size; ++i) {
-        float p = expf(array[i] - max_l);
-        sum += p;
-        array[i] = p;
-    }
+    std::vector<uint8_t> buf_k;
+    std::vector<uint8_t> buf_v;
 
-    for (size_t i = 0; i < size; ++i) {
-        array[i] = logf(array[i] / sum);
-    }
-}
+    for (uint32_t il = 0; il < n_layer; ++il) {
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        const size_t k_size     = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);
 
-void llama_sample_apply_guidance(
-          struct llama_context * ctx,
-                         float * logits,
-                         float * logits_guidance,
-                         float   scale) {
-    GGML_ASSERT(ctx);
+        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+        const size_t v_size    = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);
 
-    const auto t_start_sample_us = ggml_time_us();
-    const auto n_vocab = llama_n_vocab(llama_get_model(ctx));
+        buf_k.resize(k_size);
+        buf_v.resize(v_size);
 
-    llama_log_softmax(logits, n_vocab);
-    llama_log_softmax(logits_guidance, n_vocab);
+        ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
+        ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
 
-    for (int i = 0; i < n_vocab; ++i) {
-              auto & l = logits[i];
-        const auto & g = logits_guidance[i];
+        // batch move [i, i+nm) to [id, id+nm)
+        // note: cells can move only to a lower index
+        for (uint32_t i = 0; i < n_kv; ++i) {
+            const uint32_t id = ids[i];
 
-        l = scale * (l - g) + g;
-    }
+            if (i == id || id == n_kv) {
+                continue;
+            }
 
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-}
+            uint32_t nm = 1;
 
-llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
-    GGML_ASSERT(ctx);
-
-    auto N = float(llama_n_vocab(llama_get_model(ctx)));
-    int64_t t_start_sample_us;
-    t_start_sample_us = ggml_time_us();
-
-    llama_sample_softmax(nullptr, candidates);
-
-    // Estimate s_hat using the most probable m tokens
-    float s_hat = 0.0;
-    float sum_ti_bi = 0.0;
-    float sum_ti_sq = 0.0;
-    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
-        float t_i = logf(float(i + 2) / float(i + 1));
-        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
-        sum_ti_bi += t_i * b_i;
-        sum_ti_sq += t_i * t_i;
-    }
-    s_hat = sum_ti_bi / sum_ti_sq;
-
-    // Compute k from the estimated s_hat and target surprise value
-    float epsilon_hat = s_hat - 1;
-    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
-
-    // Sample the next word X using top-k sampling
-    llama_sample_top_k(nullptr, candidates, int(k), 1);
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    llama_token X = llama_sample_token(ctx, candidates);
-    t_start_sample_us = ggml_time_us();
-
-    // Compute error as the difference between observed surprise and target surprise value
-    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
-        return candidate.id == X;
-    }));
-    float observed_surprise = -log2f(candidates->data[X_idx].p);
-    float e = observed_surprise - tau;
-
-    // Update mu using the learning rate and error
-    *mu = *mu - eta * e;
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    return X;
-}
+            while (i + nm < n_kv && ids[i + nm] == id + nm) {
+                nm++;
+            }
 
-llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
-    int64_t t_start_sample_us;
-    t_start_sample_us = ggml_time_us();
+            // move keys
+            {
+                const int64_t os =  i*k_size_row;
+                const int64_t od = id*k_size_row;
+
+                memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
+            }
 
-    llama_sample_softmax(ctx, candidates);
+            // move values (note: they are transposed)
+            {
+                const int64_t os =  i;
+                const int64_t od = id;
 
-    // Truncate the words with surprise values greater than mu
-    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
-        return -log2f(candidate.p) > *mu;
-    }));
+                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                    memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
+                }
+            }
 
-    if (candidates->size == 0) {
-        candidates->size = 1;
-    }
+            i += nm - 1;
+        }
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+        ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
+        ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
     }
+#else
+    // ggml_graph defrag
 
-    // Normalize the probabilities of the remaining words
-    llama_sample_softmax(ctx, candidates);
+    ggml_backend_sched_reset(lctx.sched);
 
-    // Sample the next word X from the remaining words
-    llama_token X = llama_sample_token(ctx, candidates);
-    t_start_sample_us = ggml_time_us();
+    ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
 
-    // Compute error as the difference between observed surprise and target surprise value
-    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
-        return candidate.id == X;
-    }));
-    float observed_surprise = -log2f(candidates->data[X_idx].p);
-    float e = observed_surprise - tau;
+    llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+#endif
 
-    // Update mu using the learning rate and error
-    *mu = *mu - eta * e;
+    //const int64_t t_end = ggml_time_us();
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-    return X;
+    //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
 }
 
-llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    // Find max element
-    auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
-        return a.logit < b.logit;
-    });
+static void llama_kv_cache_update_internal(struct llama_context & lctx) {
+    bool need_reserve = false;
 
-    llama_token result = max_iter->id;
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-        ctx->n_sample++;
-    }
-    return result;
-}
+    // apply K-shift if needed
+    if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
+        if (lctx.model.arch == LLM_ARCH_DEEPSEEK2) { // not supported due to MLA
+            GGML_ABORT("Deepseek2 does not support K-shift");
+        }
 
-llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) {
-    GGML_ASSERT(ctx);
+        {
+            ggml_backend_sched_reset(lctx.sched);
 
-    const int64_t t_start_sample_us = ggml_time_us();
-    llama_sample_softmax(nullptr, candidates);
+            ggml_cgraph * gf = llama_build_graph_k_shift(lctx);
 
-    std::vector<float> probs;
-    probs.reserve(candidates->size);
-    for (size_t i = 0; i < candidates->size; ++i) {
-        probs.push_back(candidates->data[i].p);
-    }
+            ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
-    std::discrete_distribution<> dist(probs.begin(), probs.end());
-    int idx = dist(rng);
+            llama_set_k_shift(lctx);
 
-    llama_token result = candidates->data[idx].id;
+            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
 
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    ctx->n_sample++;
-    return result;
-}
+            need_reserve = true;
+        }
 
-llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
-    return llama_sample_token_with_rng(ctx, candidates, ctx->rng);
-}
+        {
+            auto & kv_self = lctx.kv_self;
 
-void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
-    const int64_t t_start_sample_us = ggml_time_us();
+            kv_self.has_shift = false;
 
-    if (llama_token_is_eog(&ctx->model, token)) {
-        for (const auto & stack : grammar->stacks) {
-            if (stack.empty()) {
-                return;
+            for (uint32_t i = 0; i < kv_self.size; ++i) {
+                kv_self.cells[i].delta = 0;
             }
         }
-        GGML_ASSERT(false);
     }
 
-    const std::string piece = llama_token_to_piece(ctx, token, false);
+    if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) {
+        {
+            ggml_backend_sched_reset(lctx.sched);
 
-    // Note terminating 0 in decoded string
-    const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
-    const auto & code_points = decoded.first;
-    std::vector<std::vector<const llama_grammar_element *>> tmp_new_stacks;
-    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
-        llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks);
-        grammar->stacks = tmp_new_stacks;
-    }
-    grammar->partial_utf8 = decoded.second;
-    GGML_ASSERT(!grammar->stacks.empty());
+            ggml_cgraph * gf = llama_build_graph_s_copy(lctx);
 
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-}
+            ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
-//
-// Beam search
-//
+            llama_set_s_copy(lctx);
 
-struct llama_beam {
-    std::vector<llama_token> tokens;
-    float p;  // Cumulative beam probability (renormalized relative to all beams)
-    bool eob; // Initialize end-of-beam to false. Callback sets this to true.
-    // Sort beams by probability. In case of ties, prefer beams at eob.
-    bool operator<(const llama_beam & rhs) const {
-        return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
-    }
-    // Shift off first n tokens and discard them.
-    void shift_tokens(const size_t n) {
-        if (n) {
-            std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
-            tokens.resize(tokens.size() - n);
+            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+
+            need_reserve = true;
         }
-    }
-    llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
-};
 
-// A struct for calculating logit-related info.
-struct llama_logit_info {
-    const float * const logits;
-    const int n_vocab;
-    const float max_l;
-    const float normalizer;
-    struct sum_exp {
-        float max_l;
-        float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
-    };
-    llama_logit_info(llama_context * ctx)
-      : logits(llama_get_logits(ctx))
-      , n_vocab(llama_n_vocab(llama_get_model(ctx)))
-      , max_l(*std::max_element(logits, logits + n_vocab))
-      , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
-      { }
-    llama_token_data get_token_data(const llama_token token_id) const {
-        constexpr auto p = std::numeric_limits<float>::quiet_NaN();  // never used
-        return {token_id, logits[token_id], p};
-    }
-    // Return top k token_data by logit.
-    std::vector<llama_token_data> top_k(size_t k) {
-        std::vector<llama_token_data> min_heap;  // min-heap by logit
-        const llama_token k_min = std::min(static_cast<llama_token>(k), n_vocab);
-        min_heap.reserve(k_min);
-        for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
-            min_heap.push_back(get_token_data(token_id));
-        }
-        auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
-        std::make_heap(min_heap.begin(), min_heap.end(), comp);
-        for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
-            if (min_heap.front().logit < logits[token_id]) {
-                std::pop_heap(min_heap.begin(), min_heap.end(), comp);
-                min_heap.back().id = token_id;
-                min_heap.back().logit = logits[token_id];
-                std::push_heap(min_heap.begin(), min_heap.end(), comp);
-            }
-        }
-        return min_heap;
-    }
-    float probability_from_logit(float logit) const {
-        return normalizer * std::exp(logit - max_l);
-    }
-};
+        {
+            auto & kv_self = lctx.kv_self;
 
-struct llama_beam_search_data {
-    llama_context * ctx;
-    size_t n_beams;
-    int n_past;
-    int n_predict;
-    std::vector<llama_beam> beams;
-    std::vector<llama_beam> next_beams;
-
-    // Re-calculated on each loop iteration
-    size_t common_prefix_length;
-
-    // Used to communicate to/from callback on beams state.
-    std::vector<llama_beam_view> beam_views;
-
-    llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict)
-      : ctx(ctx)
-      , n_beams(n_beams)
-      , n_past(n_past)
-      , n_predict(n_predict)
-      , beam_views(n_beams) {
-        beams.reserve(n_beams);
-        next_beams.reserve(n_beams);
-    }
-
-    // Collapse beams to a single beam given by index.
-    void collapse_beams(const size_t beam_idx) {
-        if (0u < beam_idx) {
-            std::swap(beams[0], beams[beam_idx]);
-        }
-        beams.resize(1);
-    }
-
-    // Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
-    // The repetitive patterns below reflect the 2 stages of heaps:
-    //  * Gather elements until the vector is full, then call std::make_heap() on it.
-    //  * If the heap is full and a new element is found that should be included, pop the
-    //    least element to the back(), replace it with the new, then push it into the heap.
-    void fill_next_beams_by_top_probabilities(llama_beam & beam) {
-        // Min-heaps use a greater-than comparator.
-        const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
-        if (beam.eob) {
-            // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
-            if (next_beams.size() < n_beams) {
-                next_beams.push_back(std::move(beam));
-                if (next_beams.size() == n_beams) {
-                    std::make_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            } else if (next_beams.front().p < beam.p) {
-                std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                next_beams.back() = std::move(beam);
-                std::push_heap(next_beams.begin(), next_beams.end(), comp);
-            }
-        } else {
-            // beam is not at end-of-sentence, so branch with next top_k tokens.
-            if (!beam.tokens.empty()) {
-                llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0));
-            }
-            llama_logit_info logit_info(ctx);
-            std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
-
-            // Clear the kv slot so that other beams may try different tokens at this position. The llama_decode()
-            // call in loop() will conclusively fill in the kv slot once the beams converge at this position.
-            llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
-
-            size_t i=0;
-            if (next_beams.size() < n_beams) {
-                for (; next_beams.size() < n_beams ; ++i) {
-                    llama_beam next_beam = beam;
-                    next_beam.tokens.push_back(next_tokens[i].id);
-                    next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
-                    next_beams.push_back(std::move(next_beam));
-                }
-                std::make_heap(next_beams.begin(), next_beams.end(), comp);
-            } else {
-                for (; next_beams.front().p == 0.0f ; ++i) {
-                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                    next_beams.back() = beam;
-                    next_beams.back().tokens.push_back(next_tokens[i].id);
-                    next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
-                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            }
-            for (; i < n_beams ; ++i) {
-                const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
-                if (next_beams.front().p < next_p) {
-                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                    next_beams.back() = beam;
-                    next_beams.back().tokens.push_back(next_tokens[i].id);
-                    next_beams.back().p = next_p;
-                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            }
-        }
-    }
+            kv_self.do_copy = false;
 
-    // Find common_prefix_length based on beams.
-    // Requires beams is not empty.
-    size_t find_common_prefix_length() {
-        size_t common_prefix_length = beams[0].tokens.size();
-        for (size_t i = 1 ; i < beams.size() ; ++i) {
-            common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
-            for (size_t j = 0 ; j < common_prefix_length ; ++j) {
-                if (beams[0].tokens[j] != beams[i].tokens[j]) {
-                    common_prefix_length = j;
-                    break;
-                }
+            for (uint32_t i = 0; i < kv_self.size; ++i) {
+                kv_self.cells[i].src = i;
             }
         }
-        return common_prefix_length;
     }
 
-    // Construct beams_state to send back to caller via the callback function.
-    // Side effect: set common_prefix_length = find_common_prefix_length();
-    llama_beams_state get_beams_state(const bool last_call) {
-        for (size_t i = 0 ; i < beams.size() ; ++i) {
-            beam_views[i] = beams[i].view();
-        }
-        common_prefix_length = find_common_prefix_length();
-        return {beam_views.data(), beams.size(), common_prefix_length, last_call};
-    }
+    // defragment the KV cache if needed
+    if (lctx.kv_self.do_defrag) {
+        llama_kv_cache_defrag_internal(lctx);
 
-    // Loop:
-    //  * while i < n_predict, AND
-    //  * any of the beams have not yet reached end-of-beam (eob), AND
-    //  * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
-    //    (since all other beam probabilities can only decrease)
-    void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
-        beams.push_back({{}, 1.0f, false});  // Start with one empty beam w/ probability = 1.0 and !eob.
-        const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
-        for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
-                       !beams[top_beam_index()].eob ; ++i) {
-            callback(callback_data, get_beams_state(false));  // Sets common_prefix_length
-            update_beams_from_beam_views();   // Update values (p,eob) that callback may have changed.
-            if (common_prefix_length) {
-                llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0));
-                n_past += common_prefix_length;
-            }
-            // Zero-out next_beam probabilities to place them last in following min-heap.
-            std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
-            for (llama_beam & beam : beams) {
-                beam.shift_tokens(common_prefix_length);
-                fill_next_beams_by_top_probabilities(beam);
-            }
-            // next_beams become the beams of next/final iteration. Swap them to re-use memory.
-            beams.swap(next_beams);
-            renormalize_beam_probabilities(beams);
-        }
-        collapse_beams(top_beam_index());
-        callback(callback_data, get_beams_state(true));
-    }
+        need_reserve = true;
 
-    // As beams grow, the cumulative probabilities decrease.
-    // Renormalize them to avoid floating point underflow.
-    static void renormalize_beam_probabilities(std::vector<llama_beam> & beams) {
-        const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
-        const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
-        std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
+        lctx.kv_self.do_defrag = false;
     }
 
-    // Assumes beams is non-empty.  Uses llama_beam::operator<() for ordering.
-    size_t top_beam_index() {
-        return std::max_element(beams.begin(), beams.end()) - beams.begin();
-    }
+    // reserve a worst case graph again
+    if (need_reserve) {
+        // TODO: extract to a function
+        // build worst-case graph
+        int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
+        int n_past = lctx.cparams.n_ctx - n_tokens;
+        llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+        ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
 
-    // Copy (p,eob) for each beam which may have been changed by the callback.
-    void update_beams_from_beam_views() {
-        for (size_t i = 0 ; i < beams.size() ; ++i) {
-            beams[i].p = beam_views[i].p;
-            beams[i].eob = beam_views[i].eob;
+        // initialize scheduler with the worst-case graph
+        ggml_backend_sched_reset(lctx.sched);
+        if (!ggml_backend_sched_reserve(lctx.sched, gf)) {
+            LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
         }
     }
-};
-
-void llama_beam_search(llama_context * ctx,
-                       llama_beam_search_callback_fn_t callback, void * callback_data,
-                       size_t n_beams, int n_past, int n_predict) {
-    assert(ctx);
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict);
-
-    beam_search_data.loop(callback, callback_data);
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    ctx->n_sample++;
 }
 
 //
@@ -14350,7 +15393,7 @@ static void llama_tensor_dequantize_internal(
         } else if (ggml_is_quantized(tensor->type)) {
             qtype.to_float(tensor->data, f32_output, nelements);
         } else {
-            GGML_ASSERT(false); // unreachable
+            GGML_ABORT("fatal error"); // unreachable
         }
         return;
     }
@@ -14402,13 +15445,13 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
     const llm_arch arch = qs.model.arch;
     const auto       tn = LLM_TN(arch);
 
-    auto use_more_bits = [](int i_layer, int num_layers) -> bool {
-        return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
+    auto use_more_bits = [](int i_layer, int n_layers) -> bool {
+        return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
     };
     const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
     auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
         if (n_expert > 1) {
-            // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
+            // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but occasionally randomly
             // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
             // for getting the current layer as I initially thought, and we need to resort to parsing the
             // tensor name.
@@ -14455,6 +15498,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
                 new_type = GGML_TYPE_IQ3_S;
             }
+            else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 ||
+                     new_type == GGML_TYPE_Q4_0_8_8) {
+                new_type = GGML_TYPE_Q4_0;
+            }
         }
     } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
                ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
@@ -14506,8 +15553,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
                 use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
-        else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
-                (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
         if (qs.model.type == MODEL_70B) {
             // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
             // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
@@ -14640,10 +15685,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
     //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
     //}
     bool convert_incompatible_tensor = false;
-    if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
-        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
-        new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
-        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
+    if (new_type == GGML_TYPE_Q2_K    || new_type == GGML_TYPE_Q3_K    || new_type == GGML_TYPE_Q4_K   ||
+        new_type == GGML_TYPE_Q5_K    || new_type == GGML_TYPE_Q6_K    || new_type == GGML_TYPE_IQ4_XS ||
+        new_type == GGML_TYPE_IQ2_XS  || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S  ||
+        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S   || new_type == GGML_TYPE_IQ3_S  ||
         new_type == GGML_TYPE_IQ1_M) {
         int nx = tensor->ne[0];
         int ny = tensor->ne[1];
@@ -14769,6 +15814,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         case LLAMA_FTYPE_MOSTLY_IQ4_XS:  default_type = GGML_TYPE_IQ4_XS;  break;
         case LLAMA_FTYPE_MOSTLY_IQ3_S:   default_type = GGML_TYPE_IQ3_S;   break;
         case LLAMA_FTYPE_MOSTLY_IQ3_M:   default_type = GGML_TYPE_IQ3_S;   break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: default_type = GGML_TYPE_Q4_0_4_4; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: default_type = GGML_TYPE_Q4_0_4_8; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: default_type = GGML_TYPE_Q4_0_8_8; break;
 
         default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
     }
@@ -14810,6 +15858,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         if (imatrix_data) {
             LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
             qs.has_imatrix = true;
+            // check imatrix for nans or infs
+            for (const auto & kv : *imatrix_data) {
+                for (float f : kv.second) {
+                    if (!std::isfinite(f)) {
+                        throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
+                    }
+                }
+            }
         }
     }
 
@@ -14818,8 +15874,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
     // copy the KV pairs from the input file
     gguf_set_kv     (ctx_out, ml.meta);
-    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
-    gguf_set_val_u32(ctx_out, "general.file_type", ftype);
+    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
+    gguf_set_val_u32(ctx_out, "general.file_type", ftype); // TODO: use LLM_KV
+
     // Remove split metadata
     gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
     gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
@@ -14861,10 +15918,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
     // sanity checks
     //
-    //  - qs.n_attention_wv == 0                     for Mamba       models
-    //  - qs.n_attention_wv == model.hparams.n_layer for Transformer models
+    //  - qs.n_attention_wv == 0                         for Mamba           models
+    //  - qs.n_attention_wv == model.hparams.n_layer     for Transformer     models
+    //  - qs.n_attention_wv == 3 * model.hparams.n_layer for Encoder-Decoder models
     //
-    GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer) && "n_attention_wv is unexpected");
+    GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer || qs.n_attention_wv == 3 * (int)model.hparams.n_layer) && "n_attention_wv is unexpected");
 
     size_t total_size_org = 0;
     size_t total_size_new = 0;
@@ -14989,6 +16047,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         quantize &= name.find("ssm_x.weight")      == std::string::npos;
         quantize &= name.find("ssm_dt.weight")     == std::string::npos;
 
+        // do not quantize relative position bias (T5)
+        quantize &= name.find("attn_rel_b.weight") == std::string::npos;
+
         enum ggml_type new_type;
         void * new_data;
         size_t new_size;
@@ -15067,6 +16128,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 f32_data = (float *) f32_conv_buf.data();
             }
 
+            int chunk_size_multiplier = 1;
+            if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 || new_type == GGML_TYPE_Q4_0_8_8) {
+                if ((new_type == GGML_TYPE_Q4_0_8_8) && (tensor->ne[1] % 8 != 0)) new_type = GGML_TYPE_Q4_0;
+                else if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
+                if (new_type == GGML_TYPE_Q4_0_8_8) chunk_size_multiplier = 8;
+                else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8) chunk_size_multiplier = 4;
+            }
+
             LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
             fflush(stdout);
 
@@ -15079,7 +16148,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             const int64_t nrows = tensor->ne[1];
 
             static const int64_t min_chunk_size = 32 * 512;
-            const int64_t chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);
+            const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row)) *
+                                       chunk_size_multiplier;
 
             const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
             const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
@@ -15121,282 +16191,214 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     }
 }
 
-static int llama_apply_lora_from_file_internal(
-    const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
-) {
-    LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
-
-    const int64_t t_start_lora_us = ggml_time_us();
-
-    llama_file fin(path_lora, "rb");
-
-    // verify magic and version
-    {
-        uint32_t magic = fin.read_u32();
-        if (magic != LLAMA_FILE_MAGIC_GGLA) {
-            LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
-            return 1;
-        }
-
-        uint32_t format_version = fin.read_u32();
-        if (format_version != 1) {
-            LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
-            return 1;
-        }
-    }
-
-    int32_t lora_r = fin.read_u32();
-    int32_t lora_alpha = fin.read_u32();
-    float scaling = scale * (float)lora_alpha / (float)lora_r;
-
-    LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
-
-    // load base model
-    std::unique_ptr<llama_model_loader> ml;
-    if (path_base_model) {
-        LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
-        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr));
-        ml->init_mappings(/*prefetch*/ false); // no prefetching
-    }
-
-    struct tensor_meta {
-        std::string name;
-        ggml_type type;
-        int32_t ne[2];
-        size_t offset;
-    };
-    std::map<std::string, tensor_meta> tensor_meta_map;
-
-    // load all tensor meta
-    while (true) {
-        if (fin.tell() == fin.size) {
-            // eof
-            break;
-        }
-
-        int32_t n_dims;
-        int32_t name_len;
-        int32_t ftype;
-
-        fin.read_raw(&n_dims, sizeof(n_dims));
-        fin.read_raw(&name_len, sizeof(name_len));
-        fin.read_raw(&ftype, sizeof(ftype));
-
-        if (n_dims != 1 && n_dims != 2) {
-            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
-            return 1;
-        }
-
-        int32_t ne[2] = { 1, 1 };
-        for (int i = 0; i < n_dims; ++i) {
-            fin.read_raw(&ne[i], sizeof(ne[i]));
-        }
-
-        std::string name;
-        {
-            GGML_ASSERT(name_len < GGML_MAX_NAME);
-            char buf[GGML_MAX_NAME];
-            fin.read_raw(buf, name_len);
-            name = std::string(buf, name_len);
-        }
-
-        // check for lora suffix
-        std::string lora_suffix;
-        if (name.length() > 6) {
-            lora_suffix = name.substr(name.length() - 6);
-        }
-        if (lora_suffix != ".loraA" && lora_suffix != ".loraB") {
-            LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
-            return 1;
-        }
-
-        // tensor type
-        ggml_type wtype;
-        switch (ftype) {
-            case 0: wtype = GGML_TYPE_F32;  break;
-            case 1: wtype = GGML_TYPE_F16;  break;
-            default:
-                    {
-                        LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
-                                __func__, ftype);
-                        return 1;
-                    }
-        }
-
-        // data offset
-        size_t offset = fin.tell();
-        offset = (offset + 31) & -32;
-
-        // skip tensor data
-        fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET);
-
-        tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset });
-    }
-
-    bool warned = false;
-    int n_tensors = 0;
-
-    // apply
-    ggml_backend_t backend_cpu = ggml_backend_cpu_init();
-    if (backend_cpu == nullptr) {
-        LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__);
-        return 1;
-    }
-    ggml_backend_cpu_set_n_threads(backend_cpu, n_threads);
-
-    std::vector<no_init<uint8_t>> read_buf;
-    for (const auto & it : model.tensors_by_name) {
-        const std::string & base_name = it.first;
-        ggml_tensor * model_t = it.second;
-
-        if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() ||
-            tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) {
-            continue;
-        }
-
-        tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA");
-        tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB");
-
-        ggml_init_params lora_init_params = {
-            /* .mem_size   */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
-            /* .mem_buffer */ nullptr,
-            /* .no_alloc   */ true,
-        };
-        ggml_context * lora_ctx = ggml_init(lora_init_params);
-        if (lora_ctx == nullptr) {
-            LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__);
-            ggml_backend_free(backend_cpu);
-            return 1;
-        }
-
-        // create tensors
-        ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]);
-        ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]);
-        ggml_set_name(loraA, metaA.name.c_str());
-        ggml_set_name(loraB, metaB.name.c_str());
-
-        ggml_tensor * base_t;
-        if (ml) {
-            if (!ml->get_tensor_meta(base_name.c_str())) {
-                LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
-                return 1;
-            }
-            base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str()));
-        } else {
-            base_t = ggml_dup_tensor(lora_ctx, model_t);
-        }
-        ggml_set_name(base_t, base_name.c_str());
-
-        // allocate in backend buffer
-        ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
-        if (lora_buf == nullptr) {
-            LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__);
-            return 1;
-        }
+static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) {
+    LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
 
-        // load tensor data
-        auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
-            read_buf.resize(ggml_nbytes(tensor));
-            fin.seek(tensor_meta.offset, SEEK_SET);
-            fin.read_raw(read_buf.data(), ggml_nbytes(tensor));
-            ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size());
+    ggml_context * ctx = nullptr;
+    struct gguf_init_params meta_gguf_params = {
+        /* .no_alloc = */ true,
+        /* .ctx      = */ &ctx,
+    };
+    struct gguf_context * ctx_gguf = gguf_init_from_file(path_lora, meta_gguf_params);
+    if (!ctx_gguf) {
+        throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
+    }
+
+    // check metadata
+    {
+        auto get_kv_str = [&](const std::string & key) -> std::string {
+            int id = gguf_find_key(ctx_gguf, key.c_str());
+            return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id));
         };
-        load_tensor(metaA, loraA);
-        load_tensor(metaB, loraB);
+        auto get_kv_f32 = [&](const std::string & key) -> float {
+            int id = gguf_find_key(ctx_gguf, key.c_str());
+            return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id);
+        };
+        LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
 
-        // load base model tensor data
-        if (ml) {
-            ml->load_data_for(base_t);
-        } else {
-            ggml_backend_tensor_copy(model_t, base_t);
+        auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
+        if (general_type != "adapter") {
+            gguf_free(ctx_gguf);
+            throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
         }
 
-        if (ggml_is_quantized(base_t->type) && !warned) {
-            LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
-                            "use a f16 or f32 base model with --lora-base\n", __func__);
-            warned = true;
+        auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
+        auto general_arch = llm_arch_from_string(general_arch_str);
+        if (general_arch != model->arch) {
+            gguf_free(ctx_gguf);
+            throw std::runtime_error("model arch and LoRA arch mismatch");
         }
 
-        if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
-            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
-                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
-            ggml_free(lora_ctx);
-            ggml_backend_buffer_free(lora_buf);
-            ggml_backend_free(backend_cpu);
-            return 1;
+        auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
+        if (adapter_type != "lora") {
+            gguf_free(ctx_gguf);
+            throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
         }
 
-        auto build_lora_graph = [&]() {
-            // w = w + BA*s
-            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
-            ggml_set_name(BA, "BA");
+        adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
+    }
 
-            if (scaling != 1.0f) {
-                BA = ggml_scale(lora_ctx, BA, scaling);
-                ggml_set_name(BA, "BA_scaled");
-            }
+    int n_tensors = gguf_get_n_tensors(ctx_gguf);
 
-            ggml_tensor * r;
-            r = ggml_add_inplace(lora_ctx, base_t, BA);
-            ggml_set_name(r, "r_add");
+    // contexts for each buffer type
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    auto get_ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+        auto it = ctx_map.find(buft);
+        if (it == ctx_map.end()) {
+            // add a new context
+            struct ggml_init_params params = {
+                /*.mem_size   =*/ n_tensors*ggml_tensor_overhead(),
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+            ggml_context * buft_ctx = ggml_init(params);
+            ctx_map[buft] = buft_ctx;
+            return buft_ctx;
+        };
+        return it->second;
+    };
 
-            if (base_t->type != model_t->type) {
-                // convert the result to the model type
-                r = ggml_cast(lora_ctx, r, model_t->type);
-                ggml_set_name(r, "r_cast");
+    // bundle lora_a and lora_b into pairs
+    std::map<std::string, llama_lora_weight> ab_map;
+    auto str_endswith = [](const std::string & str, const std::string & suffix) {
+        return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
+    };
+    for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+        std::string name(cur->name);
+        if (str_endswith(name, ".lora_a")) {
+            replace_all(name, ".lora_a", "");
+            if (ab_map.find(name) == ab_map.end()) {
+                ab_map[name] = llama_lora_weight(cur, nullptr);
+            } else {
+                ab_map[name].a = cur;
             }
+        } else if (str_endswith(name, ".lora_b")) {
+            replace_all(name, ".lora_b", "");
+            if (ab_map.find(name) == ab_map.end()) {
+                ab_map[name] = llama_lora_weight(nullptr, cur);
+            } else {
+                ab_map[name].b = cur;
+            }
+        } else {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
+        }
+    }
 
-            return r;
-        };
-
-        ggml_cgraph * gf = ggml_new_graph(lora_ctx);
-        ggml_tensor * r = build_lora_graph();
-        ggml_build_forward_expand(gf, r);
+    // add tensors
+    for (auto & it : ab_map) {
+        const std::string & name = it.first;
+        llama_lora_weight & w = it.second;
 
-        ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
-        if (graph_buf == nullptr) {
-            LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__);
-            ggml_free(lora_ctx);
-            ggml_backend_buffer_free(lora_buf);
-            ggml_backend_free(backend_cpu);
-            return 1;
+        if (!w.a || !w.b) {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
         }
 
-        ggml_backend_graph_compute(backend_cpu, gf);
-
-        ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r));
+        // device buft and device ctx
+        auto * model_tensor = llama_get_model_tensor(model, name.c_str());
+        if (!model_tensor) {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
+        }
+        struct ggml_context * dev_ctx = get_ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
+        // validate tensor shape
+        if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("tensor '" + name + "' has incorrect shape");
+        }
+        if (w.a->ne[1] != w.b->ne[0]) {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
+        }
+        // save tensor to adapter
+        struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
+        struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
+        ggml_set_name(tensor_a, w.a->name);
+        ggml_set_name(tensor_b, w.b->name);
+        adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
+    }
 
-#if 0
-        // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU
-        //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE);
+    // allocate tensors / buffers and zero
+    {
+        adapter.ctxs.reserve(ctx_map.size());
+        adapter.bufs.reserve(ctx_map.size());
+        for (auto it : ctx_map) {
+            ggml_backend_buffer_type_t buft = it.first;
+            ggml_context * ctx_dev = it.second;
+            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft);
+            if (!buf) {
+                gguf_free(ctx_gguf);
+                ggml_free(ctx);
+                throw std::runtime_error("failed to allocate buffer for lora adapter\n");
+            }
+            LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+            adapter.ctxs.push_back(ctx_dev);
+            adapter.bufs.push_back(buf);
+        }
+    }
 
-        // sched compute
-        ggml_build_forward_expand(gf, build_graph());
-        ggml_backend_sched_init_measure(sched, gf);
+    // set tensor data
+    {
+        llama_file gguf_file(path_lora, "rb");
+        std::vector<uint8_t> read_buf;
+        auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
+            size_t offs = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, gguf_find_tensor(ctx_gguf, orig->name));
+            size_t size = ggml_nbytes(orig);
+            read_buf.resize(size);
+            gguf_file.seek(offs, SEEK_SET);
+            gguf_file.read_raw(read_buf.data(), size);
+            ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
+        };
+        for (auto & it : adapter.ab_map) {
+            auto orig = ab_map[it.first];
+            auto dev  = it.second;
+            set_tensor(orig.a, dev.a);
+            set_tensor(orig.b, dev.b);
+        }
+    }
 
-        // create the graph again, since the previous one was destroyed by the measure
-        ggml_graph_clear(gf);
-        ggml_build_forward_expand(gf, build_graph());
-        ggml_backend_sched_graph_compute(sched, gf);
-        ggml_backend_sched_free(sched);
-#endif
+    LLAMA_LOG_INFO("%s: loaded %ld tensors from lora file\n", __func__, adapter.ab_map.size()*2);
 
-        ggml_backend_buffer_free(lora_buf);
-        ggml_backend_buffer_free(graph_buf);
-        ggml_free(lora_ctx);
+    // free ctx for reading gguf
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
+}
 
-        n_tensors++;
-        if (n_tensors % 4 == 0) {
-            LLAMA_LOG_INFO(".");
-        }
+int32_t llama_lora_adapter_set(
+            struct llama_context * ctx,
+            struct llama_lora_adapter * adapter,
+            float scale) {
+    if (ctx->cparams.flash_attn) {
+        LLAMA_LOG_ERROR("%s: flash_attn is not compatible with LoRA\n", __func__);
+        return -1;
     }
+    ctx->lora_adapters[adapter] = scale;
+    return 0;
+}
 
-    ggml_backend_free(backend_cpu);
+int32_t llama_lora_adapter_remove(
+            struct llama_context * ctx,
+            struct llama_lora_adapter * adapter) {
+    auto pos = ctx->lora_adapters.find(adapter);
+    if (pos != ctx->lora_adapters.end()) {
+        ctx->lora_adapters.erase(pos);
+        return 0;
+    }
+    return -1;
+}
 
-    const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
-    LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
+void llama_lora_adapter_clear(struct llama_context * ctx) {
+    ctx->lora_adapters.clear();
+}
 
-    return 0;
+void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
+    delete adapter;
 }
 
 //
@@ -15437,6 +16439,7 @@ struct llama_context_params llama_context_default_params() {
         /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
         /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
         /*.pooling_type                =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
+        /*.attention_type              =*/ LLAMA_ATTENTION_TYPE_UNSPECIFIED,
         /*.rope_freq_base              =*/ 0.0f,
         /*.rope_freq_scale             =*/ 0.0f,
         /*.yarn_ext_factor             =*/ -1.0f,
@@ -15489,6 +16492,8 @@ size_t llama_max_devices(void) {
     return GGML_SYCL_MAX_DEVICES;
 #elif defined(GGML_USE_VULKAN)
     return GGML_VK_MAX_DEVICES;
+#elif defined(GGML_USE_CANN)
+    return GGML_CANN_MAX_DEVICES;
 #else
     return 1;
 #endif
@@ -15503,7 +16508,7 @@ bool llama_supports_mlock(void) {
 }
 
 bool llama_supports_gpu_offload(void) {
-#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL)   || defined(GGML_USE_VULKAN) || \
     defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC)
     // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
     return true;
@@ -15560,7 +16565,7 @@ struct llama_model * llama_load_model_from_file(
             return true;
         };
     }
-    if (params.rpc_servers != nullptr) {
+    if (params.rpc_servers != nullptr && params.rpc_servers[0] != '\0') {
         // split the servers set them into model->rpc_servers
         std::string servers(params.rpc_servers);
         size_t pos = 0;
@@ -15614,6 +16619,22 @@ struct llama_context * llama_new_context_with_model(
         params.flash_attn = false;
     }
 
+    if (params.flash_attn && model->hparams.attn_soft_cap) {
+        LLAMA_LOG_WARN("%s: flash_attn is not compatible with attn_soft_cap - forcing off\n", __func__);
+        params.flash_attn = false;
+    }
+
+
+    if (params.flash_attn && model->hparams.n_embd_head_k != model->hparams.n_embd_head_v) {
+        LLAMA_LOG_WARN("%s: flash_attn requires n_embd_head_k == n_embd_head_v - forcing off\n", __func__);
+        params.flash_attn = false;
+    }
+
+    if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) {
+        LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
+        return nullptr;
+    }
+
     llama_context * ctx = new llama_context(*model);
 
     const auto & hparams = model->hparams;
@@ -15652,8 +16673,8 @@ struct llama_context * llama_new_context_with_model(
 
     cparams.n_ubatch         = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
 
-    cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
-                               hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
+    cparams.n_ctx_orig_yarn  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
+                               hparams.n_ctx_orig_yarn != 0 ? hparams.n_ctx_orig_yarn :
                                                               hparams.n_ctx_train;
 
     cparams.cb_eval           = params.cb_eval;
@@ -15672,7 +16693,7 @@ struct llama_context * llama_new_context_with_model(
         cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
     }
 
-    cparams.causal_attn = hparams.causal_attn;
+    cparams.yarn_attn_factor *= hparams.rope_attn_factor;
 
     if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
         if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
@@ -15682,6 +16703,12 @@ struct llama_context * llama_new_context_with_model(
         }
     }
 
+    if (params.attention_type == LLAMA_ATTENTION_TYPE_UNSPECIFIED) {
+        cparams.causal_attn = hparams.causal_attn;
+    } else {
+        cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL;
+    }
+
     if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
@@ -15696,8 +16723,10 @@ struct llama_context * llama_new_context_with_model(
     ctx->abort_callback      = params.abort_callback;
     ctx->abort_callback_data = params.abort_callback_data;
 
-    ctx->rng                 = std::mt19937(params.seed);
-    ctx->logits_all          = params.logits_all;
+    ctx->sampling.rng = std::mt19937(params.seed);
+    ctx->logits_all   = params.logits_all;
+    // build worst-case graph for encoder if a model contains encoder
+    ctx->is_encoding  = llama_model_has_encoder(model);
 
     uint32_t kv_size = cparams.n_ctx;
     ggml_type type_k = params.type_k;
@@ -15717,17 +16746,7 @@ struct llama_context * llama_new_context_with_model(
 
     if (!hparams.vocab_only) {
         // initialize backends
-#if defined(GGML_USE_RPC)
-        for (auto & server : model->rpc_servers) {
-            ggml_backend_t backend = ggml_backend_rpc_init(server.c_str());
-            if (backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to connect RPC backend to %s\n", __func__, server.c_str());
-                llama_free(ctx);
-                return nullptr;
-            }
-            ctx->backends.push_back(backend);
-        }
-#elif defined(GGML_USE_METAL)
+#if defined(GGML_USE_METAL)
         if (model->n_gpu_layers > 0) {
             ctx->backend_metal = ggml_backend_metal_init();
             if (ctx->backend_metal == nullptr) {
@@ -15766,7 +16785,7 @@ struct llama_context * llama_new_context_with_model(
             return nullptr;
         }
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
-            ggml_backend_t backend = ggml_backend_vk_init(0);
+            ggml_backend_t backend = ggml_backend_vk_init(model->main_gpu);
             if (backend == nullptr) {
                 LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
                 llama_free(ctx);
@@ -15789,8 +16808,7 @@ struct llama_context * llama_new_context_with_model(
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
             ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
             if (backend == nullptr) {
-                int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu);
-                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu);
+                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu);
                 llama_free(ctx);
                 return nullptr;
             }
@@ -15800,9 +16818,7 @@ struct llama_context * llama_new_context_with_model(
             for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) {
                 ggml_backend_t backend = ggml_backend_sycl_init(i);
                 if (backend == nullptr) {
-                    int id_list[GGML_SYCL_MAX_DEVICES];
-                    ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES);
-                    LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i);
+                    LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d for No.%d backend\n", __func__, i, i);
                     llama_free(ctx);
                     return nullptr;
                 }
@@ -15819,6 +16835,53 @@ struct llama_context * llama_new_context_with_model(
             }
             ctx->backends.push_back(backend);
         }
+#elif defined(GGML_USE_CANN)
+    // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+    // TODO: ggml_backend_cann is not support split tensor now, just leave code here.
+    if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+        ggml_backend_t backend = ggml_backend_cann_init(model->main_gpu);
+        if (backend == nullptr) {
+            LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, model->main_gpu);
+            llama_free(ctx);
+            return nullptr;
+        }
+        ctx->backends.push_back(backend);
+    } else {
+        // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
+        // TODO: currently, CANN can't use multi-gpus, just leave code here for further cann version.
+        for (int32_t device = 0; device < ggml_backend_cann_get_device_count(); ++device) {
+            ggml_backend_t backend = ggml_backend_cann_init(device);
+            if (backend == nullptr) {
+                LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, device);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(backend);
+        }
+    }
+#endif
+
+#ifdef GGML_USE_BLAS
+        ctx->backend_blas = ggml_backend_blas_init();
+        if (ctx->backend_blas == nullptr) {
+            LLAMA_LOG_WARN("%s: failed to initialize BLAS backend\n", __func__);
+        } else {
+            ctx->backends.push_back(ctx->backend_blas);
+        }
+#endif
+
+#if defined(GGML_USE_RPC)
+        if (model->n_gpu_layers > 0) {
+            for (const auto & endpoint : model->rpc_servers) {
+                ggml_backend_t backend = ggml_backend_rpc_init(endpoint.c_str());
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize RPC to '%s'\n", __func__, endpoint.c_str());
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
+        }
 #endif
         ctx->backend_cpu = ggml_backend_cpu_init();
         if (ctx->backend_cpu == nullptr) {
@@ -15879,8 +16942,10 @@ struct llama_context * llama_new_context_with_model(
                 }
             }
 
+            const size_t max_nodes = llama_model_max_nodes(*model);
+
             // buffer used to store the computation graph and the tensor meta data
-            ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false));
+            ctx->buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));
 
             // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
             bool pipeline_parallel =
@@ -15893,7 +16958,7 @@ struct llama_context * llama_new_context_with_model(
             // currently this is only implemented in the CUDA backend
             pipeline_parallel = false;
 #endif
-            ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES, pipeline_parallel);
+            ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), max_nodes, pipeline_parallel);
 
             if (pipeline_parallel) {
                 LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched));
@@ -15937,10 +17002,14 @@ void llama_free(struct llama_context * ctx) {
     delete ctx;
 }
 
-const llama_model * llama_get_model(const struct llama_context * ctx) {
+const struct llama_model * llama_get_model(const struct llama_context * ctx) {
     return &ctx->model;
 }
 
+const struct llama_vocab * llama_get_vocab(const struct llama_context * ctx) {
+    return &ctx->model.vocab;
+}
+
 uint32_t llama_n_ctx(const struct llama_context * ctx) {
     return ctx->cparams.n_ctx;
 }
@@ -15966,12 +17035,14 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         // these models do not use RoPE
         case LLM_ARCH_GPT2:
         case LLM_ARCH_GPTJ:
-        case LLM_ARCH_GPTNEOX:
         case LLM_ARCH_MPT:
         case LLM_ARCH_REFACT:
         case LLM_ARCH_BLOOM:
         case LLM_ARCH_MAMBA:
         case LLM_ARCH_JINA_BERT_V2:
+        case LLM_ARCH_T5:
+        case LLM_ARCH_T5ENCODER:
+        case LLM_ARCH_JAIS:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
@@ -15979,36 +17050,41 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_BAICHUAN:
         case LLM_ARCH_STARCODER:
         case LLM_ARCH_PLAMO:
-        case LLM_ARCH_CODESHELL:
         case LLM_ARCH_ORION:
         case LLM_ARCH_INTERNLM2:
         case LLM_ARCH_MINICPM:
         case LLM_ARCH_XVERSE:
         case LLM_ARCH_COMMAND_R:
         case LLM_ARCH_OLMO:
+        case LLM_ARCH_ARCTIC:
+        case LLM_ARCH_DEEPSEEK2:
+        case LLM_ARCH_CHATGLM:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2
         case LLM_ARCH_FALCON:
         case LLM_ARCH_GROK:
         case LLM_ARCH_DBRX:
-        case LLM_ARCH_PERSIMMON:
         case LLM_ARCH_BERT:
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_STABLELM:
+        case LLM_ARCH_BITNET:
         case LLM_ARCH_QWEN:
         case LLM_ARCH_QWEN2:
         case LLM_ARCH_QWEN2MOE:
         case LLM_ARCH_PHI2:
         case LLM_ARCH_PHI3:
         case LLM_ARCH_GEMMA:
+        case LLM_ARCH_GEMMA2:
         case LLM_ARCH_STARCODER2:
+        case LLM_ARCH_OPENELM:
+        case LLM_ARCH_GPTNEOX:
+        case LLM_ARCH_CODESHELL:
             return LLAMA_ROPE_TYPE_NEOX;
 
         // all model arches should be listed explicitly here
         case LLM_ARCH_UNKNOWN:
-            GGML_ASSERT(false && "unknown architecture");
-            break;
+            GGML_ABORT("unknown architecture");
     }
 
     return LLAMA_ROPE_TYPE_NONE;
@@ -16111,6 +17187,25 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch
     return it->second;
 }
 
+bool llama_model_has_encoder(const struct llama_model * model) {
+    switch (model->arch) {
+        case LLM_ARCH_T5:        return true;
+        case LLM_ARCH_T5ENCODER: return true;
+        default:                 return false;
+    }
+}
+
+bool llama_model_has_decoder(const struct llama_model * model) {
+    switch (model->arch) {
+        case LLM_ARCH_T5ENCODER: return false;
+        default:                 return true;
+    }
+}
+
+llama_token llama_model_decoder_start_token(const struct llama_model * model) {
+    return model->hparams.dec_start_token_id;
+}
+
 uint32_t llama_model_quantize(
         const char * fname_inp,
         const char * fname_out,
@@ -16124,12 +17219,14 @@ uint32_t llama_model_quantize(
     }
 }
 
-int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
+struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
     try {
-        return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
+        struct llama_lora_adapter * adapter = new llama_lora_adapter(model);
+        llama_lora_adapter_init_internal(model, path_lora, *adapter);
+        return adapter;
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
-        return 1;
+        return nullptr;
     }
 }
 
@@ -16162,6 +17259,7 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
     }
 
     // make tensors
+    cvec.tensors.reserve(model.hparams.n_layer);
     cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
     for (size_t il = 1; il < model.hparams.n_layer; il++) {
         struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft);
@@ -16170,6 +17268,8 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
     }
 
     // allocate tensors / buffers and zero
+    cvec.ctxs.reserve(ctx_map.size());
+    cvec.bufs.reserve(ctx_map.size());
     for (auto it : ctx_map) {
         ggml_backend_buffer_type_t buft = it.first;
         ggml_context * ctx = it.second;
@@ -16374,18 +17474,18 @@ void llama_kv_cache_update(struct llama_context * ctx) {
 }
 
 // deprecated
-size_t llama_get_state_size(const struct llama_context * ctx) {
+size_t llama_get_state_size(struct llama_context * ctx) {
     return llama_state_get_size(ctx);
 }
 
 // deprecated
 size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
-    return llama_state_get_data(ctx, dst);
+    return llama_state_get_data(ctx, dst, -1);
 }
 
 // deprecated
 size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
-    return llama_state_set_data(ctx, src);
+    return llama_state_set_data(ctx, src, -1);
 }
 
 // deprecated
@@ -16398,301 +17498,282 @@ bool llama_save_session_file(struct llama_context * ctx, const char * path_sessi
     return llama_state_save_file(ctx, path_session, tokens, n_token_count);
 }
 
-// Returns the *maximum* size of the state
-size_t llama_state_get_size(const struct llama_context * ctx) {
-    const auto & cparams = ctx->cparams;
-    const auto & hparams = ctx->model.hparams;
-
-    // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
-    // for reference, std::mt19937(1337) serializes to 6701 bytes.
-    const size_t s_rng_size        = sizeof(size_t);
-    const size_t s_rng             = LLAMA_MAX_RNG_STATE;
-    const size_t s_n_outputs       = sizeof(size_t);
-    // assume worst case for outputs although only currently set ones are serialized
-    const size_t s_output_pos      = ctx->cparams.n_batch * sizeof(int32_t);
-    const size_t s_logits_size     = sizeof(size_t);
-    const size_t s_logits          = ctx->logits_size ? cparams.n_batch * hparams.n_vocab * sizeof(float) : 0;
-    const size_t s_embedding_size  = sizeof(size_t);
-    const size_t s_embedding       = ctx->embd_size   ? cparams.n_batch * hparams.n_embd  * sizeof(float) : 0;
-    const size_t s_kv_buf_size     = sizeof(size_t);
-    const size_t s_kv_head         = sizeof(uint32_t);
-    const size_t s_kv_size         = sizeof(uint32_t);
-    const size_t s_kv_used         = sizeof(uint32_t);
-    const size_t s_v_trans         = sizeof(uint32_t);
-    const size_t s_kv              = ctx->kv_self.total_size();
-    const size_t s_kv_cell         = sizeof(llama_pos) + sizeof(size_t) + cparams.n_seq_max*sizeof(llama_seq_id);
-    const size_t s_kv_cells        = ctx->kv_self.size * s_kv_cell;
-
-    const size_t s_total = (
-        + s_rng_size
-        + s_rng
-        + s_n_outputs
-        + s_output_pos
-        + s_logits_size
-        + s_logits
-        + s_embedding_size
-        + s_embedding
-        + s_kv_buf_size
-        + s_kv_head
-        + s_kv_size
-        + s_kv_used
-        + s_v_trans
-        + s_kv
-        + s_kv_cells
-    );
-
-    // on session change it is very likely that the state size has changed - so we need to update this function
-    static_assert(LLAMA_SESSION_VERSION == 6, "So you just bumped the session version - good. But did you remember to update llama_state_get_size?");
-
-    return s_total;
-}
-
-// llama_context_data
-struct llama_data_context {
+// TODO: replace all non-fatal assertions with returned errors or exceptions
+struct llama_data_write {
     virtual void write(const void * src, size_t size) = 0;
+    virtual void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) = 0;
     virtual size_t get_size_written() = 0;
-    virtual ~llama_data_context() = default;
-};
+    virtual ~llama_data_write() = default;
 
-struct llama_data_buffer_context : llama_data_context {
-    uint8_t * ptr;
-    size_t size_written = 0;
+    void write_string(const std::string & str) {
+        uint32_t str_size = str.size();
 
-    llama_data_buffer_context(uint8_t * p) : ptr(p) {}
-
-    void write(const void * src, size_t size) override {
-        memcpy(ptr, src, size);
-        ptr += size;
-        size_written += size;
+        write(&str_size,  sizeof(str_size));
+        write(str.data(), str_size);
     }
 
-    size_t get_size_written() override {
-        return size_written;
+    void write_model_info(const struct llama_context * ctx) {
+        std::string arch_str = LLM_ARCH_NAMES.at(ctx->model.arch);
+        write_string(arch_str);
+        // TODO: add more model-specific info which should prevent loading the session file if not identical
     }
-};
 
-struct llama_data_file_context : llama_data_context {
-    llama_file * file;
-    size_t size_written = 0;
+    void write_rng(const std::mt19937 & rng) {
+        std::ostringstream rng_ss;
+        rng_ss << rng;
 
-    llama_data_file_context(llama_file * f) : file(f) {}
+        const std::string & rng_str = rng_ss.str();
 
-    void write(const void * src, size_t size) override {
-        file->write_raw(src, size);
-        size_written += size;
+        write_string(rng_str);
     }
 
-    size_t get_size_written() override {
-        return size_written;
-    }
-};
+    void write_output_ids(const struct llama_context * ctx) {
+        const uint32_t n_outputs = ctx->n_outputs;
 
-/** copy state data into either a buffer or file depending on the passed in context
- *
- * file context:
- * llama_file file("/path", "wb");
- * llama_data_file_context data_ctx(&file);
- * llama_state_get_data(ctx, &data_ctx);
- *
- * buffer context:
- * std::vector<uint8_t> buf(max_size, 0);
- * llama_data_buffer_context data_ctx(&buf.data());
- * llama_state_get_data(ctx, &data_ctx);
- *
-*/
-static void llama_state_get_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
-    llama_synchronize(ctx);
+        std::vector<int32_t> output_pos;
 
-    // copy rng
-    {
-        std::ostringstream rng_ss;
-        rng_ss << ctx->rng;
+        const size_t    n_batch = ctx->cparams.n_batch;
+        const auto & output_ids = ctx->output_ids;
+
+        GGML_ASSERT(n_outputs <= ctx->output_size);
 
-        const std::string & rng_str  = rng_ss.str();
-        const size_t        rng_size = rng_str.size();
+        output_pos.resize(n_outputs);
+
+        // build a more compact representation of the output ids
+        for (size_t i = 0; i < n_batch; ++i) {
+            // map an output id to a position in the batch
+            int32_t pos = output_ids[i];
+            if (pos >= 0) {
+                GGML_ASSERT((uint32_t) pos < n_outputs);
+                output_pos[pos] = i;
+            }
+        }
 
-        GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
+        write(&n_outputs, sizeof(n_outputs));
 
-        data_ctx->write(&rng_size,      sizeof(rng_size));
-        data_ctx->write(rng_str.data(), rng_size);
+        if (n_outputs) {
+            write(output_pos.data(), n_outputs * sizeof(int32_t));
+        }
     }
 
-    // copy outputs
-    {
-        // Can't use ctx->n_outputs because it's not for the
-        // entire last batch when n_ubatch is smaller than n_batch
-        size_t n_outputs = 0;
+    void write_logits(const struct llama_context * ctx) {
+        const uint64_t logits_size = std::min((uint64_t) ctx->logits_size, (uint64_t) ctx->n_outputs * ctx->model.hparams.n_vocab);
 
-        // copy output ids
-        {
-            std::vector<int32_t> output_pos;
+        write(&logits_size, sizeof(logits_size));
+
+        if (logits_size) {
+            write(ctx->logits, logits_size * sizeof(float));
+        }
+    }
+
+    void write_embeddings(const struct llama_context * ctx) {
+        const uint64_t embeddings_size = std::min((uint64_t) ctx->embd_size, (uint64_t) ctx->n_outputs * ctx->model.hparams.n_embd);
+
+        write(&embeddings_size, sizeof(embeddings_size));
+
+        if (embeddings_size) {
+            write(ctx->embd, embeddings_size * sizeof(float));
+        }
+    }
 
-            const size_t    n_batch = ctx->cparams.n_batch;
-            const auto & output_ids = ctx->output_ids;
+    void write_kv_cache_meta(const llama_kv_cache & kv_self, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) {
 
-            output_pos.resize(ctx->output_size);
+        for (const auto & range : cell_ranges) {
+            for (uint32_t i = range.first; i < range.second; ++i) {
+                const auto & cell = kv_self.cells[i];
+                const llama_pos pos      = cell.pos;
+                const uint32_t  n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0;
+
+                write(&pos,      sizeof(pos));
+                write(&n_seq_id, sizeof(n_seq_id));
 
-            // build a more compact representation of the output ids
-            for (size_t i = 0; i < n_batch; ++i) {
-                // map an output id to a position in the batch
-                int32_t pos = output_ids[i];
-                if (pos >= 0) {
-                    if ((size_t) pos >= n_outputs) {
-                        n_outputs = pos + 1;
+                if (n_seq_id) {
+                    for (auto seq_id : cell.seq_id) {
+                        write(&seq_id, sizeof(seq_id));
                     }
-                    GGML_ASSERT((size_t) pos < ctx->output_size);
-                    output_pos[pos] = i;
                 }
             }
+        }
+    }
 
-            data_ctx->write(&n_outputs, sizeof(n_outputs));
+    void write_kv_cache_data(const struct llama_context * ctx, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) {
+        const struct llama_kv_cache & kv_self = ctx->kv_self;
+        const struct llama_hparams & hparams = ctx->model.hparams;
 
-            if (n_outputs) {
-                data_ctx->write(output_pos.data(), n_outputs * sizeof(int32_t));
-            }
-        }
+        const uint32_t v_trans = kv_self.v_trans ? 1 : 0;
+        const uint32_t n_layer = hparams.n_layer;
 
-        // copy logits
-        {
-            const size_t logits_size = std::min(ctx->logits_size, n_outputs * ctx->model.hparams.n_vocab);
+        write(&v_trans, sizeof(v_trans));
+        write(&n_layer, sizeof(n_layer));
 
-            data_ctx->write(&logits_size, sizeof(logits_size));
+        std::vector<uint8_t> tmp_buf;
 
-            if (logits_size) {
-                data_ctx->write(ctx->logits, logits_size * sizeof(float));
-            }
-        }
+        // Iterate and write all the keys first, each row is a cell
+        // Get whole range at a time
+        for (uint32_t il = 0; il < n_layer; ++il) {
+            const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
 
-        // copy embeddings
-        {
-            const size_t embeddings_size = std::min(ctx->embd_size, n_outputs * ctx->model.hparams.n_embd);
+            // Write key type
+            const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+            write(&k_type_i, sizeof(k_type_i));
 
-            data_ctx->write(&embeddings_size, sizeof(embeddings_size));
+            // Write row size of key
+            const uint64_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+            write(&k_size_row, sizeof(k_size_row));
 
-            if (embeddings_size) {
-                data_ctx->write(ctx->embd, embeddings_size * sizeof(float));
+            // Read each range of cells of k_size length each into tmp_buf and write out
+            for (const auto & range : cell_ranges) {
+                const size_t range_size = range.second - range.first;
+                const size_t buf_size = range_size * k_size_row;
+                write_tensor_data(kv_self.k_l[il], range.first * k_size_row, buf_size);
             }
         }
-    }
 
-    // copy kv cache
-    {
-        const auto & kv_self = ctx->kv_self;
-        const auto & hparams = ctx->model.hparams;
-
-        const uint32_t n_layer      = hparams.n_layer;
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
-        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
-
-        // NOTE: kv_size and kv_buf_size are mostly used for sanity checks
-        const uint32_t kv_head     = llama_kv_cache_cell_max(kv_self);
-        const uint32_t kv_size     = kv_self.size;
-        const size_t   kv_buf_size = kv_self.total_size() / (kv_size ? kv_size : 1) * kv_head;
-        const uint32_t kv_used     = kv_self.used;
-        const uint32_t v_trans     = kv_self.v_trans ? 1 : 0;
-
-        data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
-        data_ctx->write(&kv_head,     sizeof(kv_head));
-        data_ctx->write(&kv_size,     sizeof(kv_size));
-        data_ctx->write(&kv_used,     sizeof(kv_used));
-        data_ctx->write(&v_trans,     sizeof(v_trans));
-
-        if (kv_buf_size) {
-            const size_t pre_kv_buf_size = data_ctx->get_size_written();
-
-            std::vector<uint8_t> tmp_buf;
-            for (int il = 0; il < (int) n_layer; ++il) {
-                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
-
-                tmp_buf.resize(k_size);
-                ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size());
-                data_ctx->write(tmp_buf.data(), tmp_buf.size());
-
-                if (kv_self.recurrent || !kv_self.v_trans) {
-                    // v is contiguous for recurrent models
-                    // TODO: use other tensors for state models than k and v
-                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
-
-                    tmp_buf.resize(v_size);
-                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size());
-                    data_ctx->write(tmp_buf.data(), tmp_buf.size());
-                    continue;
-                }
+        if (!kv_self.v_trans) {
+            for (uint32_t il = 0; il < n_layer; ++il) {
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
 
-                // v is not contiguous, copy row by row
-                const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
-                const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);
+                // Write value type
+                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+                write(&v_type_i, sizeof(v_type_i));
 
-                tmp_buf.resize(v_row_size);
-                for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
-                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size());
-                    data_ctx->write(tmp_buf.data(), tmp_buf.size());
+                // Write row size of value
+                const uint64_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
+                write(&v_size_row, sizeof(v_size_row));
+
+                // Read each range of cells of v_size length each into tmp_buf and write out
+                for (const auto & range : cell_ranges) {
+                    const size_t range_size = range.second - range.first;
+                    const size_t buf_size = range_size * v_size_row;
+                    write_tensor_data(kv_self.v_l[il], range.first * v_size_row, buf_size);
                 }
             }
-            GGML_ASSERT(kv_buf_size == data_ctx->get_size_written() - pre_kv_buf_size);
-        }
+        } else {
+            // When v is transposed, we also need the element size and get the element ranges from each row
+            const uint32_t kv_size = kv_self.size;
+            for (uint32_t il = 0; il < n_layer; ++il) {
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
 
-        for (uint32_t i = 0; i < kv_head; ++i) {
-            const auto & cell = kv_self.cells[i];
+                // Write value type
+                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+                write(&v_type_i, sizeof(v_type_i));
 
-            const llama_pos pos         = cell.pos;
-            const size_t    seq_id_size = cell.seq_id.size();
+                // Write element size
+                const uint32_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+                write(&v_size_el, sizeof(v_size_el));
 
-            data_ctx->write(&pos,         sizeof(pos));
-            data_ctx->write(&seq_id_size, sizeof(seq_id_size));
+                // Write GQA embedding size
+                write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
 
-            for (auto seq_id : cell.seq_id) {
-                data_ctx->write(&seq_id, sizeof(seq_id));
+                // For each row, we get the element values of each cell
+                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                    // Read each range of cells of v_size_el length each into tmp_buf and write out
+                    for (const auto & range : cell_ranges) {
+                        const size_t range_size = range.second - range.first;
+                        const size_t src_offset = (range.first + j * kv_size) * v_size_el;
+                        const size_t buf_size = range_size * v_size_el;
+                        write_tensor_data(kv_self.v_l[il], src_offset, buf_size);
+                    }
+                }
             }
         }
     }
-}
 
-size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst) {
-    llama_data_buffer_context data_ctx(dst);
-    llama_state_get_data_internal(ctx, &data_ctx);
+    void write_kv_cache(const struct llama_context * ctx, llama_seq_id seq_id = -1) {
+        const struct llama_kv_cache & kv_self = ctx->kv_self;
+        std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
+        uint32_t cell_count = 0;
 
-    return data_ctx.get_size_written();
-}
+        // Count the number of cells with the specified seq_id
+        // Find all the ranges of cells with this seq id (or all, when -1)
+        uint32_t cell_range_begin = kv_self.size;
+        for (uint32_t i = 0; i < kv_self.size; ++i) {
+            const auto & cell = kv_self.cells[i];
+            if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
+                ++cell_count;
+                if (cell_range_begin == kv_self.size) {
+                    cell_range_begin = i;
+                }
+            } else {
+                if (cell_range_begin != kv_self.size) {
+                    cell_ranges.emplace_back(cell_range_begin, i);
+                    cell_range_begin = kv_self.size;
+                }
+            }
+        }
+        if (cell_range_begin != kv_self.size) {
+            cell_ranges.emplace_back(cell_range_begin, kv_self.size);
+        }
 
-// Sets the state reading from the specified source address
-size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
-    llama_synchronize(ctx);
+        // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
+        uint32_t cell_count_check = 0;
+        for (const auto & range : cell_ranges) {
+            cell_count_check += range.second - range.first;
+        }
+        GGML_ASSERT(cell_count == cell_count_check);
 
-    const uint8_t * inp = src;
+        write(&cell_count, sizeof(cell_count));
 
-    // set rng
-    {
-        size_t rng_size;
-        memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
+        write_kv_cache_meta(kv_self, cell_ranges, seq_id);
+        write_kv_cache_data(ctx, cell_ranges);
+    }
+};
+
+struct llama_data_read {
+    virtual const uint8_t * read(size_t size) = 0;
+    virtual void read_to(void * dst, size_t size) = 0;
+    virtual size_t get_size_read() = 0;
+    virtual ~llama_data_read() = default;
+
+    void read_string(std::string & str) {
+        uint32_t str_size;
+        read_to(&str_size, sizeof(str_size));
+
+        str.assign((const char *) read(str_size), str_size);
+    }
 
-        GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
+    // validate model information
+    void read_model_info(const struct llama_context * ctx) {
+        std::string cur_arch_str = LLM_ARCH_NAMES.at(ctx->model.arch);
+        std::string arch_str;
+        read_string(arch_str);
+        if (cur_arch_str != arch_str) {
+            throw std::runtime_error(format("wrong model arch: '%s' instead of '%s'", arch_str.c_str(), cur_arch_str.c_str()));
+        }
+        // TODO: add more info which needs to be identical but which is not verified otherwise
+    }
 
-        std::string rng_str((const char *)inp, rng_size); inp += rng_size;
+    void read_rng(std::mt19937 & rng) {
+        std::string rng_str;
+        read_string(rng_str);
 
         std::istringstream rng_ss(rng_str);
-        rng_ss >> ctx->rng;
+        rng_ss >> rng;
 
-        GGML_ASSERT(!rng_ss.fail());
+        if (rng_ss.fail()) {
+            throw std::runtime_error("failed to load RNG state");
+        }
     }
 
-    // set output ids
-    {
-        size_t n_outputs;
+    void read_output_ids(struct llama_context * ctx) {
         std::vector<int32_t> output_pos;
 
-        memcpy(&n_outputs, inp, sizeof(n_outputs)); inp += sizeof(n_outputs);
+        uint32_t n_outputs;
+        read_to(&n_outputs, sizeof(n_outputs));
 
-        GGML_ASSERT(n_outputs <= llama_output_reserve(*ctx, n_outputs));
+        if (n_outputs > llama_output_reserve(*ctx, n_outputs)) {
+            throw std::runtime_error("could not reserve outputs");
+        }
 
         if (n_outputs) {
             output_pos.resize(n_outputs);
-            memcpy(output_pos.data(), inp, n_outputs * sizeof(int32_t));
-            inp += n_outputs * sizeof(int32_t);
+            read_to(output_pos.data(), n_outputs * sizeof(int32_t));
 
             for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
                 int32_t id = output_pos[i];
-                GGML_ASSERT((uint32_t) id < ctx->cparams.n_batch);
+                if ((uint32_t) id >= ctx->cparams.n_batch) {
+                    throw std::runtime_error(format("invalid output id, %d does not fit in batch size of %u", id, ctx->cparams.n_batch));
+                }
                 ctx->output_ids[id] = i;
             }
 
@@ -16700,596 +17781,571 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
         }
     }
 
-    // set logits
-    {
-        size_t logits_size;
-
-        memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
+    void read_logits(struct llama_context * ctx) {
+        uint64_t logits_size;
+        read_to(&logits_size, sizeof(logits_size));
 
-        GGML_ASSERT(ctx->logits_size >= logits_size);
+        if (ctx->logits_size < logits_size) {
+            throw std::runtime_error("logits buffer too small");
+        }
 
         if (logits_size) {
-            memcpy(ctx->logits, inp, logits_size * sizeof(float));
-            inp += logits_size * sizeof(float);
+            read_to(ctx->logits, logits_size * sizeof(float));
         }
     }
 
-    // set embeddings
-    {
-        size_t embeddings_size;
-
-        memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size);
+    void read_embeddings(struct llama_context * ctx) {
+        uint64_t embeddings_size;
+        read_to(&embeddings_size, sizeof(embeddings_size));
 
-        GGML_ASSERT(ctx->embd_size >= embeddings_size);
+        if (ctx->embd_size < embeddings_size) {
+            throw std::runtime_error("embeddings buffer too small");
+        }
 
         if (embeddings_size) {
-            memcpy(ctx->embd, inp, embeddings_size * sizeof(float));
-            inp += embeddings_size * sizeof(float);
+            read_to(ctx->embd, embeddings_size * sizeof(float));
         }
     }
 
-    // set kv cache
-    {
-        const auto & kv_self = ctx->kv_self;
-        const auto & hparams = ctx->model.hparams;
+    bool read_kv_cache_meta(struct llama_context * ctx, uint32_t cell_count, llama_seq_id dest_seq_id = -1) {
+        struct llama_kv_cache & kv_self = ctx->kv_self;
 
-        const uint32_t n_layer      = hparams.n_layer;
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
-        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+        if (dest_seq_id != -1) {
+            // single sequence
 
-        size_t   kv_buf_size;
-        uint32_t kv_head;
-        uint32_t kv_size;
-        uint32_t kv_used;
-        uint32_t v_trans;
+            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
 
-        memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
-        memcpy(&kv_head,     inp, sizeof(kv_head));     inp += sizeof(kv_head);
-        memcpy(&kv_size,     inp, sizeof(kv_size));     inp += sizeof(kv_size);
-        memcpy(&kv_used,     inp, sizeof(kv_used));     inp += sizeof(kv_used);
-        memcpy(&v_trans,     inp, sizeof(v_trans));     inp += sizeof(v_trans);
+            llama_batch batch = llama_batch_init(cell_count, 0, 1);
+            batch.n_tokens = cell_count;
+            for (uint32_t i = 0; i < cell_count; ++i) {
+                llama_pos pos;
+                uint32_t n_seq_id;
 
-        GGML_ASSERT(kv_self.v_trans == (bool) v_trans); // incompatible V transposition
+                read_to(&pos, sizeof(pos));
+                read_to(&n_seq_id, sizeof(n_seq_id));
 
-        if (kv_self.size != kv_size) {
-            // the KV cache needs to be big enough to load all the KV cells from the saved state
-            GGML_ASSERT(kv_self.size >= kv_head);
+                if (n_seq_id != 0) {
+                    LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
+                    return false;
+                }
 
-            LLAMA_LOG_INFO("%s: state contains %d KV cells, was saved with kv_size=%d, but is loaded with kv_size=%d (fine, but different)\n",
-                __func__, kv_head, kv_size, kv_self.size);
-        }
+                batch.pos[i] = pos;
+                batch.n_seq_id[i] = 1;
+                batch.seq_id[i][0] = dest_seq_id;
+            }
+            if (!llama_kv_cache_find_slot(kv_self, batch)) {
+                llama_batch_free(batch);
+                LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
+                return false;
+            }
 
-        llama_kv_cache_clear(ctx);
+            // DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
+            // Assume that this is one contiguous block of cells
+            GGML_ASSERT(kv_self.head + cell_count <= kv_self.size);
+            GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]);
+            GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]);
+            GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id));
+            GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id));
 
-        if (kv_buf_size) {
-            const size_t pre_kv_buf_size = inp - src;
+            // Cleanup
+            llama_batch_free(batch);
+        } else {
+            // whole KV cache restore
 
-            GGML_ASSERT(kv_self.total_size() >= kv_buf_size);
+            if (cell_count > kv_self.size) {
+                LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__);
+                return false;
+            }
 
-            for (int il = 0; il < (int) n_layer; ++il) {
-                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+            llama_kv_cache_clear(kv_self);
 
-                ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
-                inp += k_size;
+            for (uint32_t i = 0; i < cell_count; ++i) {
+                llama_kv_cell & cell = kv_self.cells[i];
 
-                if (kv_self.recurrent || !kv_self.v_trans) {
-                    // v is contiguous for recurrent models
-                    // TODO: use other tensors for state models than k and v
-                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+                llama_pos pos;
+                uint32_t  n_seq_id;
 
-                    ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size);
-                    inp += v_size;
-                    continue;
-                }
+                read_to(&pos,      sizeof(pos));
+                read_to(&n_seq_id, sizeof(n_seq_id));
 
-                // v is not contiguous, copy row by row
-                const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
-                const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_self.size);
+                cell.pos = pos;
 
-                for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
-                    ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size);
-                    inp += v_row_size;
+                for (uint32_t j = 0; j < n_seq_id; ++j) {
+                    llama_seq_id seq_id;
+                    read_to(&seq_id, sizeof(seq_id));
+
+                    if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) {
+                        LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx));
+                        return false;
+                    }
+
+                    cell.seq_id.insert(seq_id);
                 }
             }
-            GGML_ASSERT(kv_buf_size == inp - src - pre_kv_buf_size);
+
+            kv_self.head = 0;
+            kv_self.used = cell_count;
         }
 
-        ctx->kv_self.head = kv_head;
-        ctx->kv_self.used = kv_used;
+        return true;
+    }
+
+    bool read_kv_cache_data(struct llama_context * ctx, uint32_t cell_count) {
+        const struct llama_hparams & hparams = ctx->model.hparams;
+        struct llama_kv_cache & kv_self = ctx->kv_self;
+        uint32_t v_trans;
+        uint32_t n_layer;
+        read_to(&v_trans, sizeof(v_trans));
+        read_to(&n_layer, sizeof(n_layer));
 
-        for (uint32_t i = 0; i < kv_head; ++i) {
-            llama_pos pos;
-            size_t    seq_id_size;
+        if (n_layer != hparams.n_layer) {
+            LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer);
+            return false;
+        }
+        if (cell_count > kv_self.size) {
+            LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, kv_self.size);
+            return false;
+        }
+        if (kv_self.v_trans != (bool) v_trans) {
+            LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__);
+            return false;
+        }
 
-            memcpy(&pos,         inp, sizeof(pos));         inp += sizeof(pos);
-            memcpy(&seq_id_size, inp, sizeof(seq_id_size)); inp += sizeof(seq_id_size);
+        // For each layer, read the keys for each cell, one row is one cell, read as one contiguous block
+        for (uint32_t il = 0; il < n_layer; ++il) {
+            const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
 
-            ctx->kv_self.cells[i].pos = pos;
+            // Read type of key
+            int32_t k_type_i_ref;
+            read_to(&k_type_i_ref, sizeof(k_type_i_ref));
+            const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+            if (k_type_i != k_type_i_ref) {
+                LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
+                return false;
+            }
 
-            llama_seq_id seq_id;
+            // Read row size of key
+            uint64_t k_size_row_ref;
+            read_to(&k_size_row_ref, sizeof(k_size_row_ref));
+            const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+            if (k_size_row != k_size_row_ref) {
+                LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, (size_t) k_size_row_ref, il);
+                return false;
+            }
 
-            for (size_t j = 0; j < seq_id_size; ++j) {
-                memcpy(&seq_id, inp, sizeof(seq_id)); inp += sizeof(seq_id);
-                ctx->kv_self.cells[i].seq_id.insert(seq_id);
+            if (cell_count) {
+                // Read and set the keys for the whole cell range
+                ggml_backend_tensor_set(kv_self.k_l[il], read(cell_count * k_size_row), kv_self.head * k_size_row, cell_count * k_size_row);
             }
         }
-    }
 
-    const size_t nread    = inp - src;
-    const size_t max_size = llama_state_get_size(ctx);
+        if (!kv_self.v_trans) {
+            for (uint32_t il = 0; il < n_layer; ++il) {
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
 
-    GGML_ASSERT(nread <= max_size);
-
-    return nread;
-}
+                // Read type of value
+                int32_t v_type_i_ref;
+                read_to(&v_type_i_ref, sizeof(v_type_i_ref));
+                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+                if (v_type_i != v_type_i_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+                    return false;
+                }
 
-static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
-    llama_file file(path_session, "rb");
+                // Read row size of value
+                uint64_t v_size_row_ref;
+                read_to(&v_size_row_ref, sizeof(v_size_row_ref));
+                const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
+                if (v_size_row != v_size_row_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, (size_t) v_size_row_ref, il);
+                    return false;
+                }
 
-    // sanity checks
-    {
-        const uint32_t magic   = file.read_u32();
-        const uint32_t version = file.read_u32();
+                if (cell_count) {
+                    // Read and set the values for the whole cell range
+                    ggml_backend_tensor_set(kv_self.v_l[il], read(cell_count * v_size_row), kv_self.head * v_size_row, cell_count * v_size_row);
+                }
+            }
+        } else {
+            // For each layer, read the values for each cell (transposed)
+            for (uint32_t il = 0; il < n_layer; ++il) {
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+                // Read type of value
+                int32_t v_type_i_ref;
+                read_to(&v_type_i_ref, sizeof(v_type_i_ref));
+                const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+                if (v_type_i != v_type_i_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+                    return false;
+                }
 
-        if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
-            LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
-            return false;
-        }
+                // Read element size of value
+                uint32_t v_size_el_ref;
+                read_to(&v_size_el_ref, sizeof(v_size_el_ref));
+                const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+                if (v_size_el != v_size_el_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, (size_t) v_size_el_ref, il);
+                    return false;
+                }
 
-        llama_hparams session_hparams;
-        file.read_raw(&session_hparams, sizeof(llama_hparams));
+                // Read GQA embedding size
+                uint32_t n_embd_v_gqa_ref;
+                read_to(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
+                if (n_embd_v_gqa != n_embd_v_gqa_ref) {
+                    LLAMA_LOG_ERROR("%s: mismatched GQA embedding size (%u != %u, layer %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref, il);
+                    return false;
+                }
 
-        if (session_hparams != ctx->model.hparams) {
-            LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
-            return false;
+                if (cell_count) {
+                    // For each row in the transposed matrix, read the values for the whole cell range
+                    for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                        const size_t dst_offset = (kv_self.head + j * kv_self.size) * v_size_el;
+                        ggml_backend_tensor_set(kv_self.v_l[il], read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+                    }
+                }
+            }
         }
+        return true;
     }
 
-    // load the prompt
-    {
-        const uint32_t n_token_count = file.read_u32();
+    void read_kv_cache(struct llama_context * ctx, llama_seq_id seq_id = -1) {
+        uint32_t cell_count;
+        read_to(&cell_count, sizeof(cell_count));
 
-        if (n_token_count > n_token_capacity) {
-            LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
-            return false;
-        }
+        bool res = read_kv_cache_meta(ctx, cell_count, seq_id) && read_kv_cache_data(ctx, cell_count);
 
-        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
-        *n_token_count_out = n_token_count;
+        if (!res) {
+            if (seq_id == -1) {
+                llama_kv_cache_clear(ctx);
+            } else {
+                llama_kv_cache_seq_rm(ctx, seq_id, -1, -1);
+            }
+            throw std::runtime_error("failed to restore kv cache");
+        }
     }
+};
 
-    // restore the context state
-    {
-        const size_t n_state_size_cur = file.size - file.tell();
-        const size_t n_state_size_max = llama_state_get_size(ctx);
+struct llama_data_write_dummy : llama_data_write {
+    size_t size_written = 0;
 
-        if (n_state_size_cur > n_state_size_max) {
-            LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
-            return false;
-        }
+    llama_data_write_dummy() {}
 
-        std::vector<uint8_t> state_data(n_state_size_max);
-        file.read_raw(state_data.data(), n_state_size_cur);
+    void write(const void * /* src */, size_t size) override {
+        size_written += size;
+    }
 
-        llama_state_set_data(ctx, state_data.data());
+    void write_tensor_data(const struct ggml_tensor * /* tensor */, size_t /* offset */, size_t size) override {
+        size_written += size;
     }
 
-    return true;
-}
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
 
-bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
-    try {
-        return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
-        return false;
+struct llama_data_write_buffer : llama_data_write {
+    uint8_t * ptr;
+    size_t buf_size = 0;
+    size_t size_written = 0;
+
+    llama_data_write_buffer(uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
+
+    void write(const void * src, size_t size) override {
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+        memcpy(ptr, src, size);
+        ptr += size;
+        size_written += size;
+        buf_size -= size;
     }
-}
 
-static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
-    llama_file file(path_session, "wb");
+    void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) override {
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+        ggml_backend_tensor_get(tensor, ptr, offset, size);
+        ptr += size;
+        size_written += size;
+        buf_size -= size;
+    }
 
-    file.write_u32(LLAMA_SESSION_MAGIC);
-    file.write_u32(LLAMA_SESSION_VERSION);
+    size_t get_size_written() override {
+        return size_written;
+    }
+};
 
-    file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
+struct llama_data_read_buffer : llama_data_read {
+    const uint8_t * ptr;
+    size_t buf_size = 0;
+    size_t size_read = 0;
 
-    // save the prompt
-    file.write_u32((uint32_t) n_token_count);
-    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
+    llama_data_read_buffer(const uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
 
-    // save the context state using stream saving
-    llama_data_file_context data_ctx(&file);
-    llama_state_get_data_internal(ctx, &data_ctx);
+    const uint8_t * read(size_t size) override {
+        const uint8_t * base_ptr = ptr;
+        if (size > buf_size) {
+            throw std::runtime_error("unexpectedly reached end of buffer");
+        }
+        ptr += size;
+        size_read += size;
+        buf_size -= size;
+        return base_ptr;
+    }
 
-    return true;
-}
+    void read_to(void * dst, size_t size) override {
+        memcpy(dst, read(size), size);
+    }
 
-bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
-    try {
-        return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("error saving session file: %s\n", err.what());
-        return false;
+    size_t get_size_read() override {
+        return size_read;
     }
-}
+};
 
-size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) {
-    // save the size of size_t as a uint32_t for safety check
-    const size_t size_t_size_size = sizeof(uint32_t);
+struct llama_data_write_file : llama_data_write {
+    llama_file * file;
+    size_t size_written = 0;
+    std::vector<uint8_t> temp_buffer;
 
-    // other values
-    const size_t s_cell_count_size = sizeof(uint32_t);
-    const size_t s_layer_count_size = sizeof(uint32_t);
-    const size_t n_embd_v_gqa_size = sizeof(uint32_t);
+    llama_data_write_file(llama_file * f) : file(f) {}
 
-    size_t s_cell_count = 0;
-    size_t s_cell_data_size = 0;
-    const auto & kv_self = ctx->kv_self;
-    const auto & hparams = ctx->model.hparams;
+    void write(const void * src, size_t size) override {
+        file->write_raw(src, size);
+        size_written += size;
+    }
 
-    const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+    void write_tensor_data(const struct ggml_tensor * tensor, size_t offset, size_t size) override {
+        temp_buffer.resize(size);
+        ggml_backend_tensor_get(tensor, temp_buffer.data(), offset, size);
+        write(temp_buffer.data(), temp_buffer.size());
+    }
 
-    for (uint32_t i = 0; i < kv_self.size; ++i) {
-        const auto & cell = kv_self.cells[i];
-        if (cell.seq_id.count(seq_id) > 0) {
-            ++s_cell_count;
-            s_cell_data_size += sizeof(llama_pos);
-        }
+    size_t get_size_written() override {
+        return size_written;
     }
+};
 
-    for (int il = 0; il < (int)n_layer; ++il) {
-        // types of keys and values
-        s_cell_data_size += sizeof(int32_t) * 2;
-        // k_size_row and v_size_el values of layer
-        s_cell_data_size += sizeof(size_t) * 2;
+struct llama_data_read_file : llama_data_read {
+    llama_file * file;
+    size_t size_read = 0;
+    std::vector<uint8_t> temp_buffer;
 
-        // keys
-        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
-        s_cell_data_size += k_size_row * s_cell_count;
+    llama_data_read_file(llama_file * f) : file(f) {}
 
-        // values (transposed)
-        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
-        s_cell_data_size += v_size_el * s_cell_count * n_embd_v_gqa;
+    void read_to(void * dst, size_t size) override {
+        file->read_raw(dst, size);
+        size_read += size;
     }
 
-    const size_t s_total = (
-        size_t_size_size +
-        s_cell_count_size +
-        s_layer_count_size +
-        n_embd_v_gqa_size +
-        s_cell_data_size
-        );
+    const uint8_t * read(size_t size) override {
+        temp_buffer.resize(size);
+        read_to(temp_buffer.data(), size);
+        return temp_buffer.data();
+    }
 
-    return s_total;
-}
+    size_t get_size_read() override {
+        return size_read;
+    }
+};
 
-static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_context & data_ctx, llama_seq_id seq_id) {
+/** copy state data into either a buffer or file depending on the passed in context
+ *
+ * file context:
+ * llama_file file("/path", "wb");
+ * llama_data_write_file data_ctx(&file);
+ * llama_state_get_data_internal(ctx, data_ctx);
+ *
+ * buffer context:
+ * std::vector<uint8_t> buf(max_size, 0);
+ * llama_data_write_buffer data_ctx(buf.data(), max_size);
+ * llama_state_get_data_internal(ctx, data_ctx);
+ *
+*/
+static size_t llama_state_get_data_internal(struct llama_context * ctx, llama_data_write & data_ctx) {
     llama_synchronize(ctx);
 
-    const auto & kv_self = ctx->kv_self;
-    GGML_ASSERT(!kv_self.recurrent); // not implemented
+    data_ctx.write_model_info(ctx);
 
-    // Save the size of size_t as a uint32_t for safety check
-    const uint32_t size_t_size = sizeof(size_t);
-    data_ctx.write(&size_t_size, sizeof(size_t_size));
+    data_ctx.write_rng(ctx->sampling.rng);
 
-    std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
-    uint32_t cell_count = 0;
+    // copy outputs
+    data_ctx.write_output_ids(ctx);
+    data_ctx.write_logits(ctx);
+    data_ctx.write_embeddings(ctx);
 
-    // Count the number of cells with the specified seq_id
-    // Find all the ranges of cells with this seq id
-    {
-        uint32_t cell_range_begin = kv_self.size;
-        for (uint32_t i = 0; i < kv_self.size; ++i) {
-            const auto & cell = kv_self.cells[i];
-            if (cell.has_seq_id(seq_id)) {
-                ++cell_count;
-                if (cell_range_begin == kv_self.size) {
-                    cell_range_begin = i;
-                }
-            }
-            else {
-                if (cell_range_begin != kv_self.size) {
-                    cell_ranges.emplace_back(cell_range_begin, i);
-                    cell_range_begin = kv_self.size;
-                }
-            }
-        }
-        if (cell_range_begin != kv_self.size) {
-            cell_ranges.emplace_back(cell_range_begin, kv_self.size);
-        }
+    data_ctx.write_kv_cache(ctx);
 
-        // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
-        uint32_t cell_count_check = 0;
-        for (const auto & range : cell_ranges) {
-            cell_count_check += range.second - range.first;
-        }
-        GGML_ASSERT(cell_count == cell_count_check);
+    return data_ctx.get_size_written();
+}
+
+size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst, size_t size) {
+    llama_data_write_buffer data_ctx(dst, size);
+    try {
+        return llama_state_get_data_internal(ctx, data_ctx);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error saving state: %s\n", __func__, err.what());
+        return 0;
     }
+}
 
-    // Write the cell count
-    data_ctx.write(&cell_count, sizeof(cell_count));
+// Returns the *actual* size of the state.
+// Intended to be used when saving to state to a buffer.
+size_t llama_state_get_size(struct llama_context * ctx) {
+    llama_data_write_dummy data_ctx;
+    try {
+        return llama_state_get_data_internal(ctx, data_ctx);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error getting state size: %s\n", __func__, err.what());
+        return 0;
+    }
+}
 
-    const auto & hparams = ctx->model.hparams;
-    const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+static size_t llama_state_set_data_internal(struct llama_context * ctx, llama_data_read & data_ctx) {
+    llama_synchronize(ctx);
 
-    // Write the layer count
-    data_ctx.write(&n_layer, sizeof(n_layer));
+    data_ctx.read_model_info(ctx);
 
-    // Write n_embd_v_gqa
-    data_ctx.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
+    // set rng
+    data_ctx.read_rng(ctx->sampling.rng);
 
-    // Iterate the ranges and write all the pos (this is the token position in the prompt)
-    for (const auto & range : cell_ranges) {
-        for (uint32_t i = range.first; i < range.second; ++i) {
-            const auto & cell = kv_self.cells[i];
-            data_ctx.write(&cell.pos, sizeof(cell.pos));
-        }
-    }
+    // set outputs
+    data_ctx.read_output_ids(ctx);
+    data_ctx.read_logits(ctx);
+    data_ctx.read_embeddings(ctx);
 
-    // Iterate and write all the keys first, each row is a cell
-    // Get whole range at a time
-    std::vector<uint8_t> tmp_buf;
-    for (int il = 0; il < (int)n_layer; ++il) {
-        // Write key type
-        const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
-        data_ctx.write(&k_type_i, sizeof(k_type_i));
+    data_ctx.read_kv_cache(ctx);
 
-        // Write row size of key
-        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
-        data_ctx.write(&k_size_row, sizeof(k_size_row));
+    return data_ctx.get_size_read();
+}
 
-        // Read each range of cells of k_size length each into tmp_buf and write out
-        for (const auto & range : cell_ranges) {
-            const size_t range_size = range.second - range.first;
-            tmp_buf.resize(range_size * k_size_row);
-            ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), range.first * k_size_row, range_size * k_size_row);
-            data_ctx.write(tmp_buf.data(), tmp_buf.size());
-        }
+// Sets the state reading from the specified source address
+size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src, size_t size) {
+    llama_data_read_buffer data_ctx(src, size);
+    try {
+        return llama_state_set_data_internal(ctx, data_ctx);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading state: %s\n", __func__, err.what());
+        return 0;
     }
+}
 
-    // TODO: simplify, reduce copy-paste
-    if (!kv_self.v_trans) {
-        for (int il = 0; il < (int)n_layer; ++il) {
-            // Write value type
-            const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
-            data_ctx.write(&v_type_i, sizeof(v_type_i));
+static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    llama_file file(path_session, "rb");
 
-            // Write row size of value
-            const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
-            data_ctx.write(&v_size_row, sizeof(v_size_row));
+    // sanity checks
+    {
+        const uint32_t magic   = file.read_u32();
+        const uint32_t version = file.read_u32();
 
-            // Read each range of cells of v_size length each into tmp_buf and write out
-            for (const auto & range : cell_ranges) {
-                const size_t range_size = range.second - range.first;
-                tmp_buf.resize(range_size * v_size_row);
-                ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), range.first * v_size_row, range_size * v_size_row);
-                data_ctx.write(tmp_buf.data(), tmp_buf.size());
-            }
-        }
-    } else {
-        // For the values, they are transposed, so we also need the element size and get the element ranges from each row
-        const uint32_t kv_size = kv_self.size;
-        for (int il = 0; il < (int)n_layer; ++il) {
-            // Write value type
-            const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
-            data_ctx.write(&v_type_i, sizeof(v_type_i));
-
-            // Write element size
-            const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
-            data_ctx.write(&v_size_el, sizeof(v_size_el));
-
-            // For each row, we get the element values of each cell
-            for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                // Read each range of cells of v_size_el length each into tmp_buf and write out
-                for (const auto & range : cell_ranges) {
-                    const size_t range_size = range.second - range.first;
-                    const size_t src_offset = (range.first + j * kv_size) * v_size_el;
-                    tmp_buf.resize(range_size * v_size_el);
-                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), src_offset, tmp_buf.size());
-                    data_ctx.write(tmp_buf.data(), tmp_buf.size());
-                }
-            }
+        if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
+            LLAMA_LOG_ERROR("%s: unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
+            return false;
         }
     }
 
-    return data_ctx.get_size_written();
-}
+    // load the prompt
+    {
+        const uint32_t n_token_count = file.read_u32();
 
-size_t llama_state_seq_get_data(struct llama_context* ctx, uint8_t* dst, llama_seq_id seq_id) {
-    llama_data_buffer_context data_ctx(dst);
-    return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
-}
+        if (n_token_count > n_token_capacity) {
+            LLAMA_LOG_ERROR("%s: token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
+            return false;
+        }
 
-size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, llama_seq_id dest_seq_id) {
-    llama_synchronize(ctx);
+        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
+        *n_token_count_out = n_token_count;
+    }
 
-    auto & kv_self = ctx->kv_self;
-    GGML_ASSERT(!kv_self.recurrent); // not implemented
+    // restore the context state
+    {
+        const size_t n_state_size_cur = file.size - file.tell();
 
-    // Wipe the slot
-    llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+        llama_data_read_file data_ctx(&file);
+        const size_t n_read = llama_state_set_data_internal(ctx, data_ctx);
 
-    const uint8_t * inp = src;
+        if (n_read != n_state_size_cur) {
+            LLAMA_LOG_ERROR("%s: did not read all of the session file data! size %zu, got %zu\n", __func__, n_state_size_cur, n_read);
+            return false;
+        }
+    }
+    return true;
+}
 
-    // Read size of size_t
-    uint32_t size_t_size;
-    memcpy(&size_t_size, inp, sizeof(size_t_size));
-    inp += sizeof(size_t_size);
-    if (size_t_size != sizeof(size_t)) {
-        LLAMA_LOG_ERROR("%s: size_t size mismatch\n", __func__);
-        return 0;
+bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    try {
+        return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading session file: %s\n", __func__, err.what());
+        return false;
     }
+}
 
-    // Read the cell count
-    uint32_t cell_count;
-    memcpy(&cell_count, inp, sizeof(cell_count));
-    inp += sizeof(cell_count);
+static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    llama_file file(path_session, "wb");
 
-    // Read the layer count
-    uint32_t n_layer_ref;
-    memcpy(&n_layer_ref, inp, sizeof(n_layer_ref));
-    inp += sizeof(n_layer_ref);
+    file.write_u32(LLAMA_SESSION_MAGIC);
+    file.write_u32(LLAMA_SESSION_VERSION);
 
-    // Read n_embd_v_gqa
-    uint32_t n_embd_v_gqa_ref;
-    memcpy(&n_embd_v_gqa_ref, inp, sizeof(n_embd_v_gqa_ref));
-    inp += sizeof(n_embd_v_gqa_ref);
+    // save the prompt
+    file.write_u32((uint32_t) n_token_count);
+    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
 
-    // Sanity check model compatibility
-    const auto & hparams = ctx->model.hparams;
-    const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
-    if (n_layer != n_layer_ref) {
-        LLAMA_LOG_ERROR("%s: mismatched n_layer (%d != %d)\n", __func__, n_layer, n_layer_ref);
-        return 0;
-    }
-    if (n_embd_v_gqa != n_embd_v_gqa_ref) {
-        LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref);
-        return 0;
-    }
+    // save the context state using stream saving
+    llama_data_write_file data_ctx(&file);
+    llama_state_get_data_internal(ctx, data_ctx);
 
-    // Allocate the new cells for the slot
-    if (cell_count) {
-        llama_batch batch = llama_batch_init(cell_count, 0, 1);
-        batch.n_tokens = cell_count;
-        for (uint32_t i = 0; i < cell_count; ++i) {
-            llama_pos pos;
-            memcpy(&pos, inp, sizeof(pos));
-            inp += sizeof(pos);
+    return true;
+}
 
-            batch.pos[i] = pos;
-            batch.n_seq_id[i] = 1;
-            batch.seq_id[i][0] = dest_seq_id;
-        }
-        if (!llama_kv_cache_find_slot(kv_self, batch)) {
-            llama_batch_free(batch);
-            LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
-            return 0;
-        }
+bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    try {
+        return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error saving session file: %s\n", __func__, err.what());
+        return false;
+    }
+}
 
-        // DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
-        // Assume that this is one contiguous block of cells
-        GGML_ASSERT(kv_self.head + cell_count <= kv_self.size);
-        GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]);
-        GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]);
-        GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id));
-        GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id));
+static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_write & data_ctx, llama_seq_id seq_id) {
+    llama_synchronize(ctx);
 
-        // Cleanup
-        llama_batch_free(batch);
-    }
+    data_ctx.write_kv_cache(ctx, seq_id);
 
-    const uint32_t kv_size = kv_self.size;
-    const uint32_t kv_head = kv_self.head;
-
-    // For each layer, read the keys for each cell, one row is one cell, read as one contiguous blo
-    for (int il = 0; il < (int)n_layer; ++il) {
-        // Read type of key
-        int32_t k_type_i_ref;
-        memcpy(&k_type_i_ref, inp, sizeof(k_type_i_ref));
-        inp += sizeof(k_type_i_ref);
-        const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
-        if (k_type_i != k_type_i_ref) {
-            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
-            LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
-            return 0;
-        }
+    return data_ctx.get_size_written();
+}
 
-        // Read row size of key
-        size_t k_size_row_ref;
-        memcpy(&k_size_row_ref, inp, sizeof(k_size_row_ref));
-        inp += sizeof(k_size_row_ref);
-        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
-        if (k_size_row != k_size_row_ref) {
-            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
-            LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, k_size_row_ref, il);
-            return 0;
-        }
+size_t llama_state_seq_get_size(struct llama_context * ctx, llama_seq_id seq_id) {
+    llama_data_write_dummy data_ctx;
+    return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+}
 
-        if (cell_count) {
-            // Read and set the keys for the whole cell range
-            ggml_backend_tensor_set(kv_self.k_l[il], inp, kv_head * k_size_row, cell_count * k_size_row);
-            inp += cell_count * k_size_row;
-        }
+size_t llama_state_seq_get_data(struct llama_context * ctx, uint8_t * dst, size_t size, llama_seq_id seq_id) {
+    llama_data_write_buffer data_ctx(dst, size);
+    try {
+        return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error saving sequence state: %s\n", __func__, err.what());
+        return 0;
     }
+}
 
-    // TODO: simplify, reduce copy-paste
-    if (!kv_self.v_trans) {
-        for (int il = 0; il < (int)n_layer; ++il) {
-            // Read type of value
-            int32_t v_type_i_ref;
-            memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
-            inp += sizeof(v_type_i_ref);
-            const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
-            if (v_type_i != v_type_i_ref) {
-                llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
-                LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
-                return 0;
-            }
+static size_t llama_state_seq_set_data_internal(struct llama_context * ctx, llama_data_read & data_ctx, llama_seq_id dest_seq_id) {
+    llama_synchronize(ctx);
 
-            // Read row size of value
-            size_t v_size_row_ref;
-            memcpy(&v_size_row_ref, inp, sizeof(v_size_row_ref));
-            inp += sizeof(v_size_row_ref);
-            const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
-            if (v_size_row != v_size_row_ref) {
-                llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
-                LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, v_size_row_ref, il);
-                return 0;
-            }
+    data_ctx.read_kv_cache(ctx, dest_seq_id);
 
-            if (cell_count) {
-                // Read and set the values for the whole cell range
-                ggml_backend_tensor_set(kv_self.v_l[il], inp, kv_head * v_size_row, cell_count * v_size_row);
-                inp += cell_count * v_size_row;
-            }
-        }
-    } else {
-        // For each layer, read the values for each cell (transposed)
-        for (int il = 0; il < (int)n_layer; ++il) {
-            // Read type of value
-            int32_t v_type_i_ref;
-            memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
-            inp += sizeof(v_type_i_ref);
-            const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
-            if (v_type_i != v_type_i_ref) {
-                llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
-                LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
-                return 0;
-            }
-
-            // Read element size of value
-            size_t v_size_el_ref;
-            memcpy(&v_size_el_ref, inp, sizeof(v_size_el_ref));
-            inp += sizeof(v_size_el_ref);
-            const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
-            if (v_size_el != v_size_el_ref) {
-                llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
-                LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, v_size_el_ref, il);
-                return 0;
-            }
+    return data_ctx.get_size_read();
+}
 
-            if (cell_count) {
-                // For each row in the transposed matrix, read the values for the whole cell range
-                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                    const size_t dst_offset = (kv_head + j * kv_size) * v_size_el;
-                    ggml_backend_tensor_set(kv_self.v_l[il], inp, dst_offset, cell_count * v_size_el);
-                    inp += cell_count * v_size_el;
-                }
-            }
-        }
+size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, size_t size, llama_seq_id dest_seq_id) {
+    llama_data_read_buffer data_ctx(src, size);
+    try {
+        return llama_state_seq_set_data_internal(ctx, data_ctx, dest_seq_id);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: error loading sequence state: %s\n", __func__, err.what());
+        return 0;
     }
-
-    const size_t nread = inp - src;
-
-    return nread;
 }
 
 static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
@@ -17299,11 +18355,11 @@ static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, con
     file.write_u32(LLAMA_STATE_SEQ_VERSION);
 
     // save the prompt
-    file.write_u32((uint32_t)n_token_count);
+    file.write_u32((uint32_t) n_token_count);
     file.write_raw(tokens, sizeof(llama_token) * n_token_count);
 
     // save the context state using stream saving
-    llama_data_file_context data_ctx(&file);
+    llama_data_write_file data_ctx(&file);
     llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
 
     const size_t res = file.tell();
@@ -17341,9 +18397,8 @@ static size_t llama_state_seq_load_file_internal(struct llama_context * ctx, con
     // restore the context state
     {
         const size_t state_size = file.size - file.tell();
-        std::vector<uint8_t> state_data(state_size);
-        file.read_raw(state_data.data(), state_size);
-        const size_t nread = llama_state_seq_set_data(ctx, state_data.data(), dest_seq_id);
+        llama_data_read_file data_ctx(&file);
+        const size_t nread = llama_state_seq_set_data_internal(ctx, data_ctx, dest_seq_id);
         if (!nread) {
             LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
             return 0;
@@ -17359,7 +18414,7 @@ size_t llama_state_seq_save_file(struct llama_context * ctx, const char * filepa
     try {
         return llama_state_seq_save_file_internal(ctx, filepath, seq_id, tokens, n_token_count);
     } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("error saving sequence state file: %s\n", err.what());
+        LLAMA_LOG_ERROR("%s: error saving sequence state file: %s\n", __func__, err.what());
         return 0;
     }
 }
@@ -17368,7 +18423,7 @@ size_t llama_state_seq_load_file(struct llama_context * ctx, const char * filepa
     try {
         return llama_state_seq_load_file_internal(ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out);
     } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("error loading sequence state file: %s\n", err.what());
+        LLAMA_LOG_ERROR("%s: error loading sequence state file: %s\n", __func__, err.what());
         return 0;
     }
 }
@@ -17378,11 +18433,23 @@ void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_
     ctx->cparams.n_threads_batch = n_threads_batch;
 }
 
+uint32_t llama_n_threads(struct llama_context * ctx) {
+    return ctx->cparams.n_threads;
+}
+
+uint32_t llama_n_threads_batch(struct llama_context * ctx) {
+    return ctx->cparams.n_threads_batch;
+}
+
 void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
     ctx->abort_callback      = abort_callback;
     ctx->abort_callback_data = abort_callback_data;
 }
 
+void llama_set_embeddings(struct llama_context * ctx, bool embeddings) {
+    ctx->cparams.embeddings = embeddings;
+}
+
 void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
     ctx->cparams.causal_attn = causal_attn;
 }
@@ -17442,6 +18509,17 @@ void llama_batch_free(struct llama_batch batch) {
     if (batch.logits)   free(batch.logits);
 }
 
+int32_t llama_encode(
+        struct llama_context * ctx,
+          struct llama_batch   batch) {
+    const int ret = llama_encode_internal(*ctx, batch);
+    if (ret < 0) {
+        LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret);
+    }
+
+    return ret;
+}
+
 int32_t llama_decode(
         struct llama_context * ctx,
           struct llama_batch   batch) {
@@ -17517,7 +18595,7 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
 #ifndef NDEBUG
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
 #endif
         return nullptr;
     }
@@ -17562,7 +18640,7 @@ float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
 #ifndef NDEBUG
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
 #endif
         return nullptr;
     }
@@ -17579,72 +18657,82 @@ float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id
     return it->second.data();
 }
 
+//
+// vocab
+//
+
 const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
-    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return model->vocab.id_to_token[token].text.c_str();
+    return llama_token_get_text_impl(model->vocab, token);
 }
 
 float llama_token_get_score(const struct llama_model * model, llama_token token) {
-    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return model->vocab.id_to_token[token].score;
+    return llama_token_get_score_impl(model->vocab, token);
 }
 
-llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
-    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return model->vocab.id_to_token[token].type;
+enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
+    return llama_token_get_attr_impl(model->vocab, token);
 }
 
 bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
-    return token != -1 && (
-        token == llama_token_eos(model) ||
-        token == llama_token_eot(model)
-    );
+    return llama_token_is_eog_impl(model->vocab, token);
+}
+
+bool llama_token_is_control(const struct llama_model * model, llama_token token) {
+    return llama_token_is_control_impl(model->vocab, token);
 }
 
 llama_token llama_token_bos(const struct llama_model * model) {
-    return model->vocab.special_bos_id;
+    return llama_token_bos_impl(model->vocab);
 }
 
 llama_token llama_token_eos(const struct llama_model * model) {
-    return model->vocab.special_eos_id;
+    return llama_token_eos_impl(model->vocab);
 }
 
 llama_token llama_token_cls(const struct llama_model * model) {
-    return model->vocab.special_cls_id;
+    return llama_token_cls_impl(model->vocab);
 }
 
 llama_token llama_token_sep(const struct llama_model * model) {
-    return model->vocab.special_sep_id;
+    return llama_token_sep_impl(model->vocab);
 }
 
-llama_token llama_token_nl(const struct llama_model * model) {
-    return model->vocab.linefeed_id;
+llama_token llama_token_nl (const struct llama_model * model) {
+    return llama_token_nl_impl(model->vocab);
 }
 
-int32_t llama_add_bos_token(const struct llama_model * model) {
-    return model->vocab.special_add_bos;
+llama_token llama_token_pad(const struct llama_model * model) {
+    return llama_token_pad_impl(model->vocab);
 }
 
-int32_t llama_add_eos_token(const struct llama_model * model) {
-    return model->vocab.special_add_eos;
+bool llama_add_bos_token(const struct llama_model * model) {
+    return llama_add_bos_token_impl(model->vocab);
+}
+
+bool llama_add_eos_token(const struct llama_model * model) {
+    return llama_add_eos_token_impl(model->vocab);
 }
 
 llama_token llama_token_prefix(const struct llama_model * model) {
-    return model->vocab.special_prefix_id;
+    return llama_token_prefix_impl(model->vocab);
 }
 
 llama_token llama_token_middle(const struct llama_model * model) {
-    return model->vocab.special_middle_id;
+    return llama_token_middle_impl(model->vocab);
 }
 
 llama_token llama_token_suffix(const struct llama_model * model) {
-    return model->vocab.special_suffix_id;
+    return llama_token_suffix_impl(model->vocab);
 }
 
 llama_token llama_token_eot(const struct llama_model * model) {
-    return model->vocab.special_eot_id;
+    return llama_token_eot_impl(model->vocab);
 }
 
+//
+// tokenization
+//
+
 int32_t llama_tokenize(
     const struct llama_model * model,
                   const char * text,
@@ -17653,113 +18741,33 @@ int32_t llama_tokenize(
                      int32_t   n_tokens_max,
                         bool   add_special,
                         bool   parse_special) {
-    auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_special, parse_special);
-
-    if (n_tokens_max < (int) res.size()) {
-        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
-        return -((int) res.size());
-    }
-
-    for (size_t i = 0; i < res.size(); i++) {
-        tokens[i] = res[i];
-    }
-
-    return res.size();
+    return llama_tokenize_impl(model->vocab, text, text_len, tokens, n_tokens_max, add_special, parse_special);
 }
 
-static std::string llama_decode_text(const std::string & text) {
-    std::string decoded_text;
-
-    const auto cpts = unicode_cpts_from_utf8(text);
-    for (const auto cpt : cpts) {
-        decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(cpt));
-    }
-
-    return decoded_text;
+int32_t llama_token_to_piece(
+    const struct llama_model * model,
+                 llama_token   token,
+                        char * buf,
+                     int32_t   length,
+                     int32_t   lstrip,
+                        bool   special) {
+    return llama_token_to_piece_impl(model->vocab, token, buf, length, lstrip, special);
 }
 
-// does not write null-terminator to buf
-int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
-    if (0 <= token && token < llama_n_vocab(model)) {
-        switch (llama_vocab_get_type(model->vocab)) {
-        case LLAMA_VOCAB_TYPE_WPM:
-        case LLAMA_VOCAB_TYPE_SPM: {
-            // NOTE: we accept all unsupported token types,
-            // suppressing them like CONTROL tokens.
-            if (llama_is_normal_token(model->vocab, token)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                llama_unescape_whitespace(result);
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (
-                    (llama_is_user_defined_token(model->vocab, token)) ||
-                    (llama_is_control_token     (model->vocab, token) && special)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
-                if (length < 3) {
-                    return -3;
-                }
-                memcpy(buf, "\xe2\x96\x85", 3);
-                return 3;
-            } else if (llama_is_byte_token(model->vocab, token)) {
-                if (length < 1) {
-                    return -1;
-                }
-                buf[0] = llama_token_to_byte(model->vocab, token);
-                return 1;
-            }
-            break;
-        }
-        case LLAMA_VOCAB_TYPE_BPE: {
-            // NOTE: we accept all unsupported token types,
-            // suppressing them like CONTROL tokens.
-            if (llama_is_normal_token(model->vocab, token)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                result = llama_decode_text(result);
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (
-                    (llama_is_user_defined_token(model->vocab, token)) ||
-                    (llama_is_control_token     (model->vocab, token) && special)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            }
-            break;
-        }
-        default:
-            GGML_ASSERT(false);
-        }
-    }
-    return 0;
+int32_t llama_detokenize(
+    const struct llama_model * model,
+           const llama_token * tokens,
+                     int32_t   n_tokens,
+                        char * text,
+                     int32_t   text_len_max,
+                        bool   remove_special,
+                        bool   unparse_special) {
+    return llama_detokenize_impl(model->vocab, tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
 }
 
-// trim whitespace from the beginning and end of a string
-static std::string trim(const std::string & str) {
-    size_t start = 0;
-    size_t end = str.size();
-    while (start < end && isspace(str[start])) {
-        start += 1;
-    }
-    while (end > start && isspace(str[end - 1])) {
-        end -= 1;
-    }
-    return str.substr(start, end - start);
-}
+//
+// chat templates
+//
 
 // Simple version of "llama_apply_chat_template" that only works with strings
 // This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
@@ -17769,7 +18777,10 @@ static int32_t llama_chat_apply_template_internal(
     std::string & dest, bool add_ass) {
     // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
     std::stringstream ss;
-    if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) {
+    auto tmpl_contains = [&tmpl](std::string haystack) -> bool {
+        return tmpl.find(haystack) != std::string::npos;
+    };
+    if (tmpl == "chatml" || tmpl_contains("<|im_start|>")) {
         // chatml template
         for (auto message : chat) {
             ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
@@ -17777,16 +18788,16 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|im_start|>assistant\n";
         }
-    } else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) {
+    } else if (tmpl == "llama2" || tmpl == "mistral" || tmpl_contains("[INST]")) {
         // llama2 template and its variants
         // [variant] support system message
-        bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos;
+        bool support_system_message = tmpl_contains("<<SYS>>") || tmpl == "mistral";
         // [variant] space before + after response
-        bool space_around_response = tmpl.find("' ' + eos_token") != std::string::npos;
+        bool space_around_response = tmpl_contains("' ' + eos_token");
         // [variant] add BOS inside history
-        bool add_bos_inside_history = tmpl.find("bos_token + '[INST]") != std::string::npos;
+        bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]");
         // [variant] trim spaces from the input message
-        bool strip_message = tmpl.find("content.strip()") != std::string::npos;
+        bool strip_message = tmpl_contains("content.strip()");
         // construct the prompt
         bool is_inside_turn = true; // skip BOS at the beginning
         ss << "[INST] ";
@@ -17812,7 +18823,16 @@ static int32_t llama_chat_apply_template_internal(
             }
         }
         // llama2 templates seem to not care about "add_generation_prompt"
-    } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
+    } else if (tmpl == "phi3" || (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>"))) {
+        // Phi 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>\n" << message->content << "<|end|>\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
+    } else if (tmpl == "zephyr" || tmpl_contains("<|user|>")) {
         // zephyr template
         for (auto message : chat) {
             ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
@@ -17820,7 +18840,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
-    } else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) {
+    } else if (tmpl == "monarch" || tmpl_contains("bos_token + message['role']")) {
         // mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
         for (auto message : chat) {
             std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
@@ -17829,7 +18849,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<s>assistant\n";
         }
-    } else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
+    } else if (tmpl == "gemma" || tmpl == "gemma2" || tmpl_contains("<start_of_turn>")) {
         // google/gemma-7b-it
         std::string system_prompt = "";
         for (auto message : chat) {
@@ -17851,7 +18871,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<start_of_turn>model\n";
         }
-    } else if (tmpl == "orion" || tmpl.find("'\\n\\nAssistant: ' + eos_token") != std::string::npos) {
+    } else if (tmpl == "orion" || tmpl_contains("'\\n\\nAssistant: ' + eos_token")) {
         // OrionStarAI/Orion-14B-Chat
         std::string system_prompt = "";
         for (auto message : chat) {
@@ -17871,7 +18891,7 @@ static int32_t llama_chat_apply_template_internal(
                 ss << message->content << "</s>";
             }
         }
-    } else if (tmpl == "openchat" || tmpl.find("GPT4 Correct ") != std::string::npos) {
+    } else if (tmpl == "openchat" || tmpl_contains("GPT4 Correct ")) {
         // openchat/openchat-3.5-0106,
         for (auto message : chat) {
             std::string role(message->role);
@@ -17885,13 +18905,13 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "GPT4 Correct Assistant:";
         }
-    } else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl.find("USER: ") != std::string::npos && tmpl.find("ASSISTANT: ") != std::string::npos)) {
+    } else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: "))) {
         // eachadea/vicuna-13b-1.1 (and Orca variant)
         for (auto message : chat) {
             std::string role(message->role);
             if (role == "system") {
                 // Orca-Vicuna variant uses a system prefix
-                if (tmpl == "vicuna-orca" || tmpl.find("SYSTEM: ") != std::string::npos) {
+                if (tmpl == "vicuna-orca" || tmpl_contains("SYSTEM: ")) {
                     ss << "SYSTEM: " << message->content << "\n";
                 } else {
                     ss << message->content << "\n\n";
@@ -17905,7 +18925,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "ASSISTANT:";
         }
-    } else if (tmpl == "deepseek" || (tmpl.find("### Instruction:") != std::string::npos && tmpl.find("<|EOT|>") != std::string::npos)) {
+    } else if (tmpl == "deepseek" || (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>"))) {
         // deepseek-ai/deepseek-coder-33b-instruct
         for (auto message : chat) {
             std::string role(message->role);
@@ -17920,7 +18940,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "### Response:\n";
         }
-    } else if (tmpl == "command-r" || (tmpl.find("<|START_OF_TURN_TOKEN|>") != std::string::npos && tmpl.find("<|USER_TOKEN|>") != std::string::npos)) {
+    } else if (tmpl == "command-r" || (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>"))) {
         // CohereForAI/c4ai-command-r-plus
         for (auto message : chat) {
             std::string role(message->role);
@@ -17935,7 +18955,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
         }
-    } else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) {
+    } else if (tmpl == "llama3" || (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>"))) {
         // Llama 3
         for (auto message : chat) {
             std::string role(message->role);
@@ -17944,14 +18964,51 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
         }
-    } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) {
-        // Phi 3
+    } else if (tmpl == "chatglm3" || tmpl_contains("[gMASK]sop")) {
+        // chatglm3-6b
+        ss << "[gMASK]" << "sop";
         for (auto message : chat) {
             std::string role(message->role);
-            ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n";
+            ss << "<|" << role << "|>" << "\n " << message->content;
         }
         if (add_ass) {
-            ss << "<|assistant|>\n";
+            ss << "<|assistant|>";
+        }
+    } else if (tmpl == "chatglm4" || tmpl_contains("[gMASK]<sop>")) {
+        ss << "[gMASK]" << "<sop>";
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>" << "\n" << message->content;
+        }
+        if (add_ass) {
+            ss << "<|assistant|>";
+        }
+    } else if (tmpl == "minicpm" || tmpl_contains(LU8("<用户>"))) {
+        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "user") {
+                ss << LU8("<用户>");
+                ss << trim(message->content);
+                ss << "<AI>";
+            } else {
+                ss << trim(message->content);
+            }
+        }
+    } else if (tmpl == "deepseek2" || tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
+        // DeepSeek-V2
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content << "\n\n";
+            } else if (role == "user") {
+                ss << "User: " << message->content << "\n\n";
+            } else if (role == "assistant") {
+                ss << "Assistant: " << message->content << LU8("<｜end▁of▁sentence｜>");
+            }
+        }
+        if (add_ass) {
+            ss << "Assistant:";
         }
     } else {
         // template not supported
@@ -17961,7 +19018,7 @@ static int32_t llama_chat_apply_template_internal(
     return dest.size();
 }
 
-LLAMA_API int32_t llama_chat_apply_template(
+int32_t llama_chat_apply_template(
                 const struct llama_model * model,
                               const char * tmpl,
          const struct llama_chat_message * chat,
@@ -18002,7 +19059,126 @@ LLAMA_API int32_t llama_chat_apply_template(
     return res;
 }
 
-LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
+//
+// grammar
+//
+
+struct llama_grammar * llama_grammar_init(
+        const llama_grammar_element ** rules,
+        size_t    n_rules,
+        size_t    start_rule_index) {
+    return llama_grammar_init_impl(rules, n_rules, start_rule_index);
+}
+
+void llama_grammar_free(struct llama_grammar * grammar) {
+    llama_grammar_free_impl(grammar);
+}
+
+struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
+    return llama_grammar_copy_impl(grammar);
+}
+
+void llama_grammar_sample(
+      const struct llama_grammar * grammar,
+      const struct llama_context * ctx,
+          llama_token_data_array * candidates) {
+    llama_grammar_sample_impl(grammar, &ctx->model.vocab, &ctx->sampling, candidates);
+}
+
+void llama_sample_grammar(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+      const struct llama_grammar * grammar) {
+    llama_grammar_sample(grammar, ctx, candidates);
+}
+
+void llama_grammar_accept_token(
+            struct llama_grammar * grammar,
+            struct llama_context * ctx,
+                     llama_token   token) {
+    llama_grammar_accept_token_impl(grammar, &ctx->model.vocab, &ctx->sampling, token);
+}
+
+//
+// sampling
+//
+
+void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
+    llama_set_rng_seed_impl(&ctx->sampling, seed);
+}
+
+void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
+    llama_sample_softmax_impl(ctx ? &ctx->sampling : nullptr, candidates);
+}
+
+void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
+    llama_sample_top_k_impl(ctx ? &ctx->sampling : nullptr, candidates, k, min_keep);
+}
+
+void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    llama_sample_top_p_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    llama_sample_min_p_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
+    llama_sample_tail_free_impl(ctx ? &ctx->sampling : nullptr, candidates, z, min_keep);
+}
+
+void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    llama_sample_typical_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
+    llama_sample_entropy_impl(ctx ? &ctx->sampling : nullptr, candidates_p, min_temp, max_temp, exponent_val);
+}
+
+void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
+    llama_sample_temp_impl(ctx ? &ctx->sampling : nullptr, candidates_p, temp);
+}
+
+void llama_sample_repetition_penalties(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+               const llama_token * last_tokens,
+                          size_t   penalty_last_n,
+                           float   penalty_repeat,
+                           float   penalty_freq,
+                           float   penalty_present) {
+    llama_sample_repetition_penalties_impl(ctx ? &ctx->sampling : nullptr, candidates, last_tokens, penalty_last_n, penalty_repeat, penalty_freq, penalty_present);
+}
+
+void llama_sample_apply_guidance(
+          struct llama_context * ctx,
+                         float * logits,
+                         float * logits_guidance,
+                         float   scale) {
+    llama_sample_apply_guidance_impl(&ctx->sampling, logits, logits_guidance, scale);
+}
+
+llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
+    return llama_sample_token_mirostat_impl(&ctx->sampling, candidates, tau, eta, m, mu);
+}
+
+llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
+    return llama_sample_token_mirostat_v2_impl(ctx ? &ctx->sampling : nullptr, candidates, tau, eta, mu);
+}
+
+llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
+    return llama_sample_token_greedy_impl(ctx ? &ctx->sampling : nullptr, candidates);
+}
+
+llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) {
+    return llama_sample_token_with_rng_impl(&ctx->sampling, candidates, rng);
+}
+
+llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
+    return llama_sample_token_with_rng_impl(&ctx->sampling, candidates, ctx->sampling.rng);
+}
+
+int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
     static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
     if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
         return strlen(split_path);
@@ -18031,11 +19207,11 @@ struct llama_timings llama_get_timings(struct llama_context * ctx) {
         /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
         /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
         /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
-        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
+        /*.t_sample_ms =*/ 1e-3 * ctx->sampling.t_sample_us,
         /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
         /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,
 
-        /*.n_sample =*/ std::max(1, ctx->n_sample),
+        /*.n_sample =*/ std::max(1, ctx->sampling.n_sample),
         /*.n_p_eval =*/ std::max(0, ctx->n_p_eval),
         /*.n_eval   =*/ std::max(1, ctx->n_eval),
     };
@@ -18058,10 +19234,11 @@ void llama_print_timings(struct llama_context * ctx) {
 }
 
 void llama_reset_timings(struct llama_context * ctx) {
-    ctx->t_start_us = ggml_time_us();
-    ctx->t_sample_us = ctx->n_sample = 0;
+    ctx->t_start_us  = ggml_time_us();
     ctx->t_eval_us   = ctx->n_eval   = 0;
     ctx->t_p_eval_us = ctx->n_p_eval = 0;
+
+    ctx->sampling.reset_timings();
 }
 
 const char * llama_print_system_info(void) {
@@ -18077,6 +19254,7 @@ const char * llama_print_system_info(void) {
     s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | ";
     s += "FMA = "         + std::to_string(ggml_cpu_has_fma())         + " | ";
     s += "NEON = "        + std::to_string(ggml_cpu_has_neon())        + " | ";
+    s += "SVE = "         + std::to_string(ggml_cpu_has_sve())         + " | ";
     s += "ARM_FMA = "     + std::to_string(ggml_cpu_has_arm_fma())     + " | ";
     s += "F16C = "        + std::to_string(ggml_cpu_has_f16c())        + " | ";
     s += "FP16_VA = "     + std::to_string(ggml_cpu_has_fp16_va())     + " | ";
@@ -18086,11 +19264,7 @@ const char * llama_print_system_info(void) {
     s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
     s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
     s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
-#ifdef GGML_USE_LLAMAFILE
-    s += "LLAMAFILE = 1 | ";
-#else
-    s += "LLAMAFILE = 0 | ";
-#endif
+    s += "LLAMAFILE = "   + std::to_string(ggml_cpu_has_llamafile())   + " | ";
 
     return s.c_str();
 }
@@ -18107,20 +19281,20 @@ void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
     fprintf(stream, "mst_p_eval: %.2f  # ms / token during prompt processing\n",
             1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
     fprintf(stream, "mst_sample: %.2f  # ms / token during sampling\n",
-            1.0e-3 * ctx->t_sample_us / ctx->n_sample);
+            1.0e-3 * ctx->sampling.t_sample_us / ctx->sampling.n_sample);
     fprintf(stream, "n_eval: %d  # number of tokens generated (excluding the first one)\n", ctx->n_eval);
     fprintf(stream, "n_p_eval: %d  # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
-    fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->n_sample);
+    fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->sampling.n_sample);
     fprintf(stream, "t_eval_us: %" PRId64 "  # total microseconds spent generating tokens\n", ctx->t_eval_us);
     fprintf(stream, "t_load_us: %" PRId64 "  # total microseconds spent loading the model\n", ctx->t_load_us);
     fprintf(stream, "t_p_eval_us: %" PRId64 "  # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
-    fprintf(stream, "t_sample_us: %" PRId64 "  # total microseconds spent sampling\n", ctx->t_sample_us);
+    fprintf(stream, "t_sample_us: %" PRId64 "  # total microseconds spent sampling\n", ctx->sampling.t_sample_us);
     fprintf(stream, "ts_eval: %.2f  # tokens / second during generation\n",
             1.0e6 * ctx->n_eval / ctx->t_eval_us);
     fprintf(stream, "ts_p_eval: %.2f  # tokens / second during prompt processing\n",
             1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
     fprintf(stream, "ts_sample: %.2f  # tokens / second during sampling\n",
-            1.0e6 * ctx->n_sample / ctx->t_sample_us);
+            1.0e6 * ctx->sampling.n_sample / ctx->sampling.t_sample_us);
 }
 
 // For internal test use
@@ -18137,6 +19311,8 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) {
     ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
 #elif defined(GGML_USE_CUDA)
     ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
+#elif defined(GGML_USE_CANN)
+    ggml_backend_cann_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
 #endif
 }
 
@@ -18157,14 +19333,14 @@ static void llama_log_internal_v(ggml_log_level level, const char * format, va_l
     va_end(args_copy);
 }
 
-static void llama_log_internal(ggml_log_level level, const char * format, ...) {
+void llama_log_internal(ggml_log_level level, const char * format, ...) {
     va_list args;
     va_start(args, format);
     llama_log_internal_v(level, format, args);
     va_end(args);
 }
 
-static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
+void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
     (void) level;
     (void) user_data;
     fputs(text, stderr);
diff --git a/unicode-data.cpp b/src/unicode-data.cpp
similarity index 89%
rename from unicode-data.cpp
rename to src/unicode-data.cpp
index d7c1c898d8828..02bdf782380fe 100644
--- a/unicode-data.cpp
+++ b/src/unicode-data.cpp
@@ -68,36 +68,36 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x000370, 0x0004},
 {0x000375, 0x0040},
 {0x000376, 0x0004},
-{0x000378, 0x0080},
+{0x000378, 0x0001},
 {0x00037A, 0x0004},
 {0x00037E, 0x0020},
 {0x00037F, 0x0004},
-{0x000380, 0x0080},
+{0x000380, 0x0001},
 {0x000384, 0x0040},
 {0x000386, 0x0004},
 {0x000387, 0x0020},
 {0x000388, 0x0004},
-{0x00038B, 0x0080},
+{0x00038B, 0x0001},
 {0x00038C, 0x0004},
-{0x00038D, 0x0080},
+{0x00038D, 0x0001},
 {0x00038E, 0x0004},
-{0x0003A2, 0x0080},
+{0x0003A2, 0x0001},
 {0x0003A3, 0x0004},
 {0x0003F6, 0x0040},
 {0x0003F7, 0x0004},
 {0x000482, 0x0040},
 {0x000483, 0x0010},
 {0x00048A, 0x0004},
-{0x000530, 0x0080},
+{0x000530, 0x0001},
 {0x000531, 0x0004},
-{0x000557, 0x0080},
+{0x000557, 0x0001},
 {0x000559, 0x0004},
 {0x00055A, 0x0020},
 {0x000560, 0x0004},
 {0x000589, 0x0020},
-{0x00058B, 0x0080},
+{0x00058B, 0x0001},
 {0x00058D, 0x0040},
-{0x000590, 0x0080},
+{0x000590, 0x0001},
 {0x000591, 0x0010},
 {0x0005BE, 0x0020},
 {0x0005BF, 0x0010},
@@ -107,12 +107,13 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0005C4, 0x0010},
 {0x0005C6, 0x0020},
 {0x0005C7, 0x0010},
-{0x0005C8, 0x0080},
+{0x0005C8, 0x0001},
 {0x0005D0, 0x0004},
-{0x0005EB, 0x0080},
+{0x0005EB, 0x0001},
 {0x0005EF, 0x0004},
 {0x0005F3, 0x0020},
-{0x0005F5, 0x0080},
+{0x0005F5, 0x0001},
+{0x000600, 0x0080},
 {0x000606, 0x0040},
 {0x000609, 0x0020},
 {0x00060B, 0x0040},
@@ -145,16 +146,17 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0006FD, 0x0040},
 {0x0006FF, 0x0004},
 {0x000700, 0x0020},
-{0x00070E, 0x0080},
+{0x00070E, 0x0001},
+{0x00070F, 0x0080},
 {0x000710, 0x0004},
 {0x000711, 0x0010},
 {0x000712, 0x0004},
 {0x000730, 0x0010},
-{0x00074B, 0x0080},
+{0x00074B, 0x0001},
 {0x00074D, 0x0004},
 {0x0007A6, 0x0010},
 {0x0007B1, 0x0004},
-{0x0007B2, 0x0080},
+{0x0007B2, 0x0001},
 {0x0007C0, 0x0002},
 {0x0007CA, 0x0004},
 {0x0007EB, 0x0010},
@@ -162,7 +164,7 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0007F6, 0x0040},
 {0x0007F7, 0x0020},
 {0x0007FA, 0x0004},
-{0x0007FB, 0x0080},
+{0x0007FB, 0x0001},
 {0x0007FD, 0x0010},
 {0x0007FE, 0x0040},
 {0x000800, 0x0004},
@@ -173,20 +175,22 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x000825, 0x0010},
 {0x000828, 0x0004},
 {0x000829, 0x0010},
-{0x00082E, 0x0080},
+{0x00082E, 0x0001},
 {0x000830, 0x0020},
-{0x00083F, 0x0080},
+{0x00083F, 0x0001},
 {0x000840, 0x0004},
 {0x000859, 0x0010},
-{0x00085C, 0x0080},
+{0x00085C, 0x0001},
 {0x00085E, 0x0020},
-{0x00085F, 0x0080},
+{0x00085F, 0x0001},
 {0x000860, 0x0004},
-{0x00086B, 0x0080},
+{0x00086B, 0x0001},
 {0x000870, 0x0004},
 {0x000888, 0x0040},
 {0x000889, 0x0004},
-{0x00088F, 0x0080},
+{0x00088F, 0x0001},
+{0x000890, 0x0080},
+{0x000892, 0x0001},
 {0x000898, 0x0010},
 {0x0008A0, 0x0004},
 {0x0008CA, 0x0010},
@@ -205,35 +209,35 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x000970, 0x0020},
 {0x000971, 0x0004},
 {0x000981, 0x0010},
-{0x000984, 0x0080},
+{0x000984, 0x0001},
 {0x000985, 0x0004},
-{0x00098D, 0x0080},
+{0x00098D, 0x0001},
 {0x00098F, 0x0004},
-{0x000991, 0x0080},
+{0x000991, 0x0001},
 {0x000993, 0x0004},
-{0x0009A9, 0x0080},
+{0x0009A9, 0x0001},
 {0x0009AA, 0x0004},
-{0x0009B1, 0x0080},
+{0x0009B1, 0x0001},
 {0x0009B2, 0x0004},
-{0x0009B3, 0x0080},
+{0x0009B3, 0x0001},
 {0x0009B6, 0x0004},
-{0x0009BA, 0x0080},
+{0x0009BA, 0x0001},
 {0x0009BC, 0x0010},
 {0x0009BD, 0x0004},
 {0x0009BE, 0x0010},
-{0x0009C5, 0x0080},
+{0x0009C5, 0x0001},
 {0x0009C7, 0x0010},
-{0x0009C9, 0x0080},
+{0x0009C9, 0x0001},
 {0x0009CB, 0x0010},
 {0x0009CE, 0x0004},
-{0x0009CF, 0x0080},
+{0x0009CF, 0x0001},
 {0x0009D7, 0x0010},
-{0x0009D8, 0x0080},
+{0x0009D8, 0x0001},
 {0x0009DC, 0x0004},
-{0x0009DE, 0x0080},
+{0x0009DE, 0x0001},
 {0x0009DF, 0x0004},
 {0x0009E2, 0x0010},
-{0x0009E4, 0x0080},
+{0x0009E4, 0x0001},
 {0x0009E6, 0x0002},
 {0x0009F0, 0x0004},
 {0x0009F2, 0x0040},
@@ -242,173 +246,173 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0009FC, 0x0004},
 {0x0009FD, 0x0020},
 {0x0009FE, 0x0010},
-{0x0009FF, 0x0080},
+{0x0009FF, 0x0001},
 {0x000A01, 0x0010},
-{0x000A04, 0x0080},
+{0x000A04, 0x0001},
 {0x000A05, 0x0004},
-{0x000A0B, 0x0080},
+{0x000A0B, 0x0001},
 {0x000A0F, 0x0004},
-{0x000A11, 0x0080},
+{0x000A11, 0x0001},
 {0x000A13, 0x0004},
-{0x000A29, 0x0080},
+{0x000A29, 0x0001},
 {0x000A2A, 0x0004},
-{0x000A31, 0x0080},
+{0x000A31, 0x0001},
 {0x000A32, 0x0004},
-{0x000A34, 0x0080},
+{0x000A34, 0x0001},
 {0x000A35, 0x0004},
-{0x000A37, 0x0080},
+{0x000A37, 0x0001},
 {0x000A38, 0x0004},
-{0x000A3A, 0x0080},
+{0x000A3A, 0x0001},
 {0x000A3C, 0x0010},
-{0x000A3D, 0x0080},
+{0x000A3D, 0x0001},
 {0x000A3E, 0x0010},
-{0x000A43, 0x0080},
+{0x000A43, 0x0001},
 {0x000A47, 0x0010},
-{0x000A49, 0x0080},
+{0x000A49, 0x0001},
 {0x000A4B, 0x0010},
-{0x000A4E, 0x0080},
+{0x000A4E, 0x0001},
 {0x000A51, 0x0010},
-{0x000A52, 0x0080},
+{0x000A52, 0x0001},
 {0x000A59, 0x0004},
-{0x000A5D, 0x0080},
+{0x000A5D, 0x0001},
 {0x000A5E, 0x0004},
-{0x000A5F, 0x0080},
+{0x000A5F, 0x0001},
 {0x000A66, 0x0002},
 {0x000A70, 0x0010},
 {0x000A72, 0x0004},
 {0x000A75, 0x0010},
 {0x000A76, 0x0020},
-{0x000A77, 0x0080},
+{0x000A77, 0x0001},
 {0x000A81, 0x0010},
-{0x000A84, 0x0080},
+{0x000A84, 0x0001},
 {0x000A85, 0x0004},
-{0x000A8E, 0x0080},
+{0x000A8E, 0x0001},
 {0x000A8F, 0x0004},
-{0x000A92, 0x0080},
+{0x000A92, 0x0001},
 {0x000A93, 0x0004},
-{0x000AA9, 0x0080},
+{0x000AA9, 0x0001},
 {0x000AAA, 0x0004},
-{0x000AB1, 0x0080},
+{0x000AB1, 0x0001},
 {0x000AB2, 0x0004},
-{0x000AB4, 0x0080},
+{0x000AB4, 0x0001},
 {0x000AB5, 0x0004},
-{0x000ABA, 0x0080},
+{0x000ABA, 0x0001},
 {0x000ABC, 0x0010},
 {0x000ABD, 0x0004},
 {0x000ABE, 0x0010},
-{0x000AC6, 0x0080},
+{0x000AC6, 0x0001},
 {0x000AC7, 0x0010},
-{0x000ACA, 0x0080},
+{0x000ACA, 0x0001},
 {0x000ACB, 0x0010},
-{0x000ACE, 0x0080},
+{0x000ACE, 0x0001},
 {0x000AD0, 0x0004},
-{0x000AD1, 0x0080},
+{0x000AD1, 0x0001},
 {0x000AE0, 0x0004},
 {0x000AE2, 0x0010},
-{0x000AE4, 0x0080},
+{0x000AE4, 0x0001},
 {0x000AE6, 0x0002},
 {0x000AF0, 0x0020},
 {0x000AF1, 0x0040},
-{0x000AF2, 0x0080},
+{0x000AF2, 0x0001},
 {0x000AF9, 0x0004},
 {0x000AFA, 0x0010},
-{0x000B00, 0x0080},
+{0x000B00, 0x0001},
 {0x000B01, 0x0010},
-{0x000B04, 0x0080},
+{0x000B04, 0x0001},
 {0x000B05, 0x0004},
-{0x000B0D, 0x0080},
+{0x000B0D, 0x0001},
 {0x000B0F, 0x0004},
-{0x000B11, 0x0080},
+{0x000B11, 0x0001},
 {0x000B13, 0x0004},
-{0x000B29, 0x0080},
+{0x000B29, 0x0001},
 {0x000B2A, 0x0004},
-{0x000B31, 0x0080},
+{0x000B31, 0x0001},
 {0x000B32, 0x0004},
-{0x000B34, 0x0080},
+{0x000B34, 0x0001},
 {0x000B35, 0x0004},
-{0x000B3A, 0x0080},
+{0x000B3A, 0x0001},
 {0x000B3C, 0x0010},
 {0x000B3D, 0x0004},
 {0x000B3E, 0x0010},
-{0x000B45, 0x0080},
+{0x000B45, 0x0001},
 {0x000B47, 0x0010},
-{0x000B49, 0x0080},
+{0x000B49, 0x0001},
 {0x000B4B, 0x0010},
-{0x000B4E, 0x0080},
+{0x000B4E, 0x0001},
 {0x000B55, 0x0010},
-{0x000B58, 0x0080},
+{0x000B58, 0x0001},
 {0x000B5C, 0x0004},
-{0x000B5E, 0x0080},
+{0x000B5E, 0x0001},
 {0x000B5F, 0x0004},
 {0x000B62, 0x0010},
-{0x000B64, 0x0080},
+{0x000B64, 0x0001},
 {0x000B66, 0x0002},
 {0x000B70, 0x0040},
 {0x000B71, 0x0004},
 {0x000B72, 0x0002},
-{0x000B78, 0x0080},
+{0x000B78, 0x0001},
 {0x000B82, 0x0010},
 {0x000B83, 0x0004},
-{0x000B84, 0x0080},
+{0x000B84, 0x0001},
 {0x000B85, 0x0004},
-{0x000B8B, 0x0080},
+{0x000B8B, 0x0001},
 {0x000B8E, 0x0004},
-{0x000B91, 0x0080},
+{0x000B91, 0x0001},
 {0x000B92, 0x0004},
-{0x000B96, 0x0080},
+{0x000B96, 0x0001},
 {0x000B99, 0x0004},
-{0x000B9B, 0x0080},
+{0x000B9B, 0x0001},
 {0x000B9C, 0x0004},
-{0x000B9D, 0x0080},
+{0x000B9D, 0x0001},
 {0x000B9E, 0x0004},
-{0x000BA0, 0x0080},
+{0x000BA0, 0x0001},
 {0x000BA3, 0x0004},
-{0x000BA5, 0x0080},
+{0x000BA5, 0x0001},
 {0x000BA8, 0x0004},
-{0x000BAB, 0x0080},
+{0x000BAB, 0x0001},
 {0x000BAE, 0x0004},
-{0x000BBA, 0x0080},
+{0x000BBA, 0x0001},
 {0x000BBE, 0x0010},
-{0x000BC3, 0x0080},
+{0x000BC3, 0x0001},
 {0x000BC6, 0x0010},
-{0x000BC9, 0x0080},
+{0x000BC9, 0x0001},
 {0x000BCA, 0x0010},
-{0x000BCE, 0x0080},
+{0x000BCE, 0x0001},
 {0x000BD0, 0x0004},
-{0x000BD1, 0x0080},
+{0x000BD1, 0x0001},
 {0x000BD7, 0x0010},
-{0x000BD8, 0x0080},
+{0x000BD8, 0x0001},
 {0x000BE6, 0x0002},
 {0x000BF3, 0x0040},
-{0x000BFB, 0x0080},
+{0x000BFB, 0x0001},
 {0x000C00, 0x0010},
 {0x000C05, 0x0004},
-{0x000C0D, 0x0080},
+{0x000C0D, 0x0001},
 {0x000C0E, 0x0004},
-{0x000C11, 0x0080},
+{0x000C11, 0x0001},
 {0x000C12, 0x0004},
-{0x000C29, 0x0080},
+{0x000C29, 0x0001},
 {0x000C2A, 0x0004},
-{0x000C3A, 0x0080},
+{0x000C3A, 0x0001},
 {0x000C3C, 0x0010},
 {0x000C3D, 0x0004},
 {0x000C3E, 0x0010},
-{0x000C45, 0x0080},
+{0x000C45, 0x0001},
 {0x000C46, 0x0010},
-{0x000C49, 0x0080},
+{0x000C49, 0x0001},
 {0x000C4A, 0x0010},
-{0x000C4E, 0x0080},
+{0x000C4E, 0x0001},
 {0x000C55, 0x0010},
-{0x000C57, 0x0080},
+{0x000C57, 0x0001},
 {0x000C58, 0x0004},
-{0x000C5B, 0x0080},
+{0x000C5B, 0x0001},
 {0x000C5D, 0x0004},
-{0x000C5E, 0x0080},
+{0x000C5E, 0x0001},
 {0x000C60, 0x0004},
 {0x000C62, 0x0010},
-{0x000C64, 0x0080},
+{0x000C64, 0x0001},
 {0x000C66, 0x0002},
-{0x000C70, 0x0080},
+{0x000C70, 0x0001},
 {0x000C77, 0x0020},
 {0x000C78, 0x0002},
 {0x000C7F, 0x0040},
@@ -416,124 +420,124 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x000C81, 0x0010},
 {0x000C84, 0x0020},
 {0x000C85, 0x0004},
-{0x000C8D, 0x0080},
+{0x000C8D, 0x0001},
 {0x000C8E, 0x0004},
-{0x000C91, 0x0080},
+{0x000C91, 0x0001},
 {0x000C92, 0x0004},
-{0x000CA9, 0x0080},
+{0x000CA9, 0x0001},
 {0x000CAA, 0x0004},
-{0x000CB4, 0x0080},
+{0x000CB4, 0x0001},
 {0x000CB5, 0x0004},
-{0x000CBA, 0x0080},
+{0x000CBA, 0x0001},
 {0x000CBC, 0x0010},
 {0x000CBD, 0x0004},
 {0x000CBE, 0x0010},
-{0x000CC5, 0x0080},
+{0x000CC5, 0x0001},
 {0x000CC6, 0x0010},
-{0x000CC9, 0x0080},
+{0x000CC9, 0x0001},
 {0x000CCA, 0x0010},
-{0x000CCE, 0x0080},
+{0x000CCE, 0x0001},
 {0x000CD5, 0x0010},
-{0x000CD7, 0x0080},
+{0x000CD7, 0x0001},
 {0x000CDD, 0x0004},
-{0x000CDF, 0x0080},
+{0x000CDF, 0x0001},
 {0x000CE0, 0x0004},
 {0x000CE2, 0x0010},
-{0x000CE4, 0x0080},
+{0x000CE4, 0x0001},
 {0x000CE6, 0x0002},
-{0x000CF0, 0x0080},
+{0x000CF0, 0x0001},
 {0x000CF1, 0x0004},
 {0x000CF3, 0x0010},
-{0x000CF4, 0x0080},
+{0x000CF4, 0x0001},
 {0x000D00, 0x0010},
 {0x000D04, 0x0004},
-{0x000D0D, 0x0080},
+{0x000D0D, 0x0001},
 {0x000D0E, 0x0004},
-{0x000D11, 0x0080},
+{0x000D11, 0x0001},
 {0x000D12, 0x0004},
 {0x000D3B, 0x0010},
 {0x000D3D, 0x0004},
 {0x000D3E, 0x0010},
-{0x000D45, 0x0080},
+{0x000D45, 0x0001},
 {0x000D46, 0x0010},
-{0x000D49, 0x0080},
+{0x000D49, 0x0001},
 {0x000D4A, 0x0010},
 {0x000D4E, 0x0004},
 {0x000D4F, 0x0040},
-{0x000D50, 0x0080},
+{0x000D50, 0x0001},
 {0x000D54, 0x0004},
 {0x000D57, 0x0010},
 {0x000D58, 0x0002},
 {0x000D5F, 0x0004},
 {0x000D62, 0x0010},
-{0x000D64, 0x0080},
+{0x000D64, 0x0001},
 {0x000D66, 0x0002},
 {0x000D79, 0x0040},
 {0x000D7A, 0x0004},
-{0x000D80, 0x0080},
+{0x000D80, 0x0001},
 {0x000D81, 0x0010},
-{0x000D84, 0x0080},
+{0x000D84, 0x0001},
 {0x000D85, 0x0004},
-{0x000D97, 0x0080},
+{0x000D97, 0x0001},
 {0x000D9A, 0x0004},
-{0x000DB2, 0x0080},
+{0x000DB2, 0x0001},
 {0x000DB3, 0x0004},
-{0x000DBC, 0x0080},
+{0x000DBC, 0x0001},
 {0x000DBD, 0x0004},
-{0x000DBE, 0x0080},
+{0x000DBE, 0x0001},
 {0x000DC0, 0x0004},
-{0x000DC7, 0x0080},
+{0x000DC7, 0x0001},
 {0x000DCA, 0x0010},
-{0x000DCB, 0x0080},
+{0x000DCB, 0x0001},
 {0x000DCF, 0x0010},
-{0x000DD5, 0x0080},
+{0x000DD5, 0x0001},
 {0x000DD6, 0x0010},
-{0x000DD7, 0x0080},
+{0x000DD7, 0x0001},
 {0x000DD8, 0x0010},
-{0x000DE0, 0x0080},
+{0x000DE0, 0x0001},
 {0x000DE6, 0x0002},
-{0x000DF0, 0x0080},
+{0x000DF0, 0x0001},
 {0x000DF2, 0x0010},
 {0x000DF4, 0x0020},
-{0x000DF5, 0x0080},
+{0x000DF5, 0x0001},
 {0x000E01, 0x0004},
 {0x000E31, 0x0010},
 {0x000E32, 0x0004},
 {0x000E34, 0x0010},
-{0x000E3B, 0x0080},
+{0x000E3B, 0x0001},
 {0x000E3F, 0x0040},
 {0x000E40, 0x0004},
 {0x000E47, 0x0010},
 {0x000E4F, 0x0020},
 {0x000E50, 0x0002},
 {0x000E5A, 0x0020},
-{0x000E5C, 0x0080},
+{0x000E5C, 0x0001},
 {0x000E81, 0x0004},
-{0x000E83, 0x0080},
+{0x000E83, 0x0001},
 {0x000E84, 0x0004},
-{0x000E85, 0x0080},
+{0x000E85, 0x0001},
 {0x000E86, 0x0004},
-{0x000E8B, 0x0080},
+{0x000E8B, 0x0001},
 {0x000E8C, 0x0004},
-{0x000EA4, 0x0080},
+{0x000EA4, 0x0001},
 {0x000EA5, 0x0004},
-{0x000EA6, 0x0080},
+{0x000EA6, 0x0001},
 {0x000EA7, 0x0004},
 {0x000EB1, 0x0010},
 {0x000EB2, 0x0004},
 {0x000EB4, 0x0010},
 {0x000EBD, 0x0004},
-{0x000EBE, 0x0080},
+{0x000EBE, 0x0001},
 {0x000EC0, 0x0004},
-{0x000EC5, 0x0080},
+{0x000EC5, 0x0001},
 {0x000EC6, 0x0004},
-{0x000EC7, 0x0080},
+{0x000EC7, 0x0001},
 {0x000EC8, 0x0010},
-{0x000ECF, 0x0080},
+{0x000ECF, 0x0001},
 {0x000ED0, 0x0002},
-{0x000EDA, 0x0080},
+{0x000EDA, 0x0001},
 {0x000EDC, 0x0004},
-{0x000EE0, 0x0080},
+{0x000EE0, 0x0001},
 {0x000F00, 0x0004},
 {0x000F01, 0x0040},
 {0x000F04, 0x0020},
@@ -552,26 +556,26 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x000F3A, 0x0020},
 {0x000F3E, 0x0010},
 {0x000F40, 0x0004},
-{0x000F48, 0x0080},
+{0x000F48, 0x0001},
 {0x000F49, 0x0004},
-{0x000F6D, 0x0080},
+{0x000F6D, 0x0001},
 {0x000F71, 0x0010},
 {0x000F85, 0x0020},
 {0x000F86, 0x0010},
 {0x000F88, 0x0004},
 {0x000F8D, 0x0010},
-{0x000F98, 0x0080},
+{0x000F98, 0x0001},
 {0x000F99, 0x0010},
-{0x000FBD, 0x0080},
+{0x000FBD, 0x0001},
 {0x000FBE, 0x0040},
 {0x000FC6, 0x0010},
 {0x000FC7, 0x0040},
-{0x000FCD, 0x0080},
+{0x000FCD, 0x0001},
 {0x000FCE, 0x0040},
 {0x000FD0, 0x0020},
 {0x000FD5, 0x0040},
 {0x000FD9, 0x0020},
-{0x000FDB, 0x0080},
+{0x000FDB, 0x0001},
 {0x001000, 0x0004},
 {0x00102B, 0x0010},
 {0x00103F, 0x0004},
@@ -595,56 +599,56 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00109A, 0x0010},
 {0x00109E, 0x0040},
 {0x0010A0, 0x0004},
-{0x0010C6, 0x0080},
+{0x0010C6, 0x0001},
 {0x0010C7, 0x0004},
-{0x0010C8, 0x0080},
+{0x0010C8, 0x0001},
 {0x0010CD, 0x0004},
-{0x0010CE, 0x0080},
+{0x0010CE, 0x0001},
 {0x0010D0, 0x0004},
 {0x0010FB, 0x0020},
 {0x0010FC, 0x0004},
-{0x001249, 0x0080},
+{0x001249, 0x0001},
 {0x00124A, 0x0004},
-{0x00124E, 0x0080},
+{0x00124E, 0x0001},
 {0x001250, 0x0004},
-{0x001257, 0x0080},
+{0x001257, 0x0001},
 {0x001258, 0x0004},
-{0x001259, 0x0080},
+{0x001259, 0x0001},
 {0x00125A, 0x0004},
-{0x00125E, 0x0080},
+{0x00125E, 0x0001},
 {0x001260, 0x0004},
-{0x001289, 0x0080},
+{0x001289, 0x0001},
 {0x00128A, 0x0004},
-{0x00128E, 0x0080},
+{0x00128E, 0x0001},
 {0x001290, 0x0004},
-{0x0012B1, 0x0080},
+{0x0012B1, 0x0001},
 {0x0012B2, 0x0004},
-{0x0012B6, 0x0080},
+{0x0012B6, 0x0001},
 {0x0012B8, 0x0004},
-{0x0012BF, 0x0080},
+{0x0012BF, 0x0001},
 {0x0012C0, 0x0004},
-{0x0012C1, 0x0080},
+{0x0012C1, 0x0001},
 {0x0012C2, 0x0004},
-{0x0012C6, 0x0080},
+{0x0012C6, 0x0001},
 {0x0012C8, 0x0004},
-{0x0012D7, 0x0080},
+{0x0012D7, 0x0001},
 {0x0012D8, 0x0004},
-{0x001311, 0x0080},
+{0x001311, 0x0001},
 {0x001312, 0x0004},
-{0x001316, 0x0080},
+{0x001316, 0x0001},
 {0x001318, 0x0004},
-{0x00135B, 0x0080},
+{0x00135B, 0x0001},
 {0x00135D, 0x0010},
 {0x001360, 0x0020},
 {0x001369, 0x0002},
-{0x00137D, 0x0080},
+{0x00137D, 0x0001},
 {0x001380, 0x0004},
 {0x001390, 0x0040},
-{0x00139A, 0x0080},
+{0x00139A, 0x0001},
 {0x0013A0, 0x0004},
-{0x0013F6, 0x0080},
+{0x0013F6, 0x0001},
 {0x0013F8, 0x0004},
-{0x0013FE, 0x0080},
+{0x0013FE, 0x0001},
 {0x001400, 0x0020},
 {0x001401, 0x0004},
 {0x00166D, 0x0040},
@@ -653,28 +657,28 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x001680, 0x0008},
 {0x001681, 0x0004},
 {0x00169B, 0x0020},
-{0x00169D, 0x0080},
+{0x00169D, 0x0001},
 {0x0016A0, 0x0004},
 {0x0016EB, 0x0020},
 {0x0016EE, 0x0002},
 {0x0016F1, 0x0004},
-{0x0016F9, 0x0080},
+{0x0016F9, 0x0001},
 {0x001700, 0x0004},
 {0x001712, 0x0010},
-{0x001716, 0x0080},
+{0x001716, 0x0001},
 {0x00171F, 0x0004},
 {0x001732, 0x0010},
 {0x001735, 0x0020},
-{0x001737, 0x0080},
+{0x001737, 0x0001},
 {0x001740, 0x0004},
 {0x001752, 0x0010},
-{0x001754, 0x0080},
+{0x001754, 0x0001},
 {0x001760, 0x0004},
-{0x00176D, 0x0080},
+{0x00176D, 0x0001},
 {0x00176E, 0x0004},
-{0x001771, 0x0080},
+{0x001771, 0x0001},
 {0x001772, 0x0010},
-{0x001774, 0x0080},
+{0x001774, 0x0001},
 {0x001780, 0x0004},
 {0x0017B4, 0x0010},
 {0x0017D4, 0x0020},
@@ -683,80 +687,80 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0017DB, 0x0040},
 {0x0017DC, 0x0004},
 {0x0017DD, 0x0010},
-{0x0017DE, 0x0080},
+{0x0017DE, 0x0001},
 {0x0017E0, 0x0002},
-{0x0017EA, 0x0080},
+{0x0017EA, 0x0001},
 {0x0017F0, 0x0002},
-{0x0017FA, 0x0080},
+{0x0017FA, 0x0001},
 {0x001800, 0x0020},
 {0x00180B, 0x0010},
 {0x00180E, 0x0080},
 {0x00180F, 0x0010},
 {0x001810, 0x0002},
-{0x00181A, 0x0080},
+{0x00181A, 0x0001},
 {0x001820, 0x0004},
-{0x001879, 0x0080},
+{0x001879, 0x0001},
 {0x001880, 0x0004},
 {0x001885, 0x0010},
 {0x001887, 0x0004},
 {0x0018A9, 0x0010},
 {0x0018AA, 0x0004},
-{0x0018AB, 0x0080},
+{0x0018AB, 0x0001},
 {0x0018B0, 0x0004},
-{0x0018F6, 0x0080},
+{0x0018F6, 0x0001},
 {0x001900, 0x0004},
-{0x00191F, 0x0080},
+{0x00191F, 0x0001},
 {0x001920, 0x0010},
-{0x00192C, 0x0080},
+{0x00192C, 0x0001},
 {0x001930, 0x0010},
-{0x00193C, 0x0080},
+{0x00193C, 0x0001},
 {0x001940, 0x0040},
-{0x001941, 0x0080},
+{0x001941, 0x0001},
 {0x001944, 0x0020},
 {0x001946, 0x0002},
 {0x001950, 0x0004},
-{0x00196E, 0x0080},
+{0x00196E, 0x0001},
 {0x001970, 0x0004},
-{0x001975, 0x0080},
+{0x001975, 0x0001},
 {0x001980, 0x0004},
-{0x0019AC, 0x0080},
+{0x0019AC, 0x0001},
 {0x0019B0, 0x0004},
-{0x0019CA, 0x0080},
+{0x0019CA, 0x0001},
 {0x0019D0, 0x0002},
-{0x0019DB, 0x0080},
+{0x0019DB, 0x0001},
 {0x0019DE, 0x0040},
 {0x001A00, 0x0004},
 {0x001A17, 0x0010},
-{0x001A1C, 0x0080},
+{0x001A1C, 0x0001},
 {0x001A1E, 0x0020},
 {0x001A20, 0x0004},
 {0x001A55, 0x0010},
-{0x001A5F, 0x0080},
+{0x001A5F, 0x0001},
 {0x001A60, 0x0010},
-{0x001A7D, 0x0080},
+{0x001A7D, 0x0001},
 {0x001A7F, 0x0010},
 {0x001A80, 0x0002},
-{0x001A8A, 0x0080},
+{0x001A8A, 0x0001},
 {0x001A90, 0x0002},
-{0x001A9A, 0x0080},
+{0x001A9A, 0x0001},
 {0x001AA0, 0x0020},
 {0x001AA7, 0x0004},
 {0x001AA8, 0x0020},
-{0x001AAE, 0x0080},
+{0x001AAE, 0x0001},
 {0x001AB0, 0x0010},
-{0x001ACF, 0x0080},
+{0x001ACF, 0x0001},
 {0x001B00, 0x0010},
 {0x001B05, 0x0004},
 {0x001B34, 0x0010},
 {0x001B45, 0x0004},
-{0x001B4D, 0x0080},
+{0x001B4D, 0x0001},
 {0x001B50, 0x0002},
 {0x001B5A, 0x0020},
 {0x001B61, 0x0040},
 {0x001B6B, 0x0010},
 {0x001B74, 0x0040},
 {0x001B7D, 0x0020},
-{0x001B7F, 0x0080},
+{0x001B7F, 0x0001},
 {0x001B80, 0x0010},
 {0x001B83, 0x0004},
 {0x001BA1, 0x0010},
@@ -764,25 +768,25 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x001BB0, 0x0002},
 {0x001BBA, 0x0004},
 {0x001BE6, 0x0010},
-{0x001BF4, 0x0080},
+{0x001BF4, 0x0001},
 {0x001BFC, 0x0020},
 {0x001C00, 0x0004},
 {0x001C24, 0x0010},
-{0x001C38, 0x0080},
+{0x001C38, 0x0001},
 {0x001C3B, 0x0020},
 {0x001C40, 0x0002},
-{0x001C4A, 0x0080},
+{0x001C4A, 0x0001},
 {0x001C4D, 0x0004},
 {0x001C50, 0x0002},
 {0x001C5A, 0x0004},
 {0x001C7E, 0x0020},
 {0x001C80, 0x0004},
-{0x001C89, 0x0080},
+{0x001C89, 0x0001},
 {0x001C90, 0x0004},
-{0x001CBB, 0x0080},
+{0x001CBB, 0x0001},
 {0x001CBD, 0x0004},
 {0x001CC0, 0x0020},
-{0x001CC8, 0x0080},
+{0x001CC8, 0x0001},
 {0x001CD0, 0x0010},
 {0x001CD3, 0x0020},
 {0x001CD4, 0x0010},
@@ -793,50 +797,50 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x001CF5, 0x0004},
 {0x001CF7, 0x0010},
 {0x001CFA, 0x0004},
-{0x001CFB, 0x0080},
+{0x001CFB, 0x0001},
 {0x001D00, 0x0004},
 {0x001DC0, 0x0010},
 {0x001E00, 0x0004},
-{0x001F16, 0x0080},
+{0x001F16, 0x0001},
 {0x001F18, 0x0004},
-{0x001F1E, 0x0080},
+{0x001F1E, 0x0001},
 {0x001F20, 0x0004},
-{0x001F46, 0x0080},
+{0x001F46, 0x0001},
 {0x001F48, 0x0004},
-{0x001F4E, 0x0080},
+{0x001F4E, 0x0001},
 {0x001F50, 0x0004},
-{0x001F58, 0x0080},
+{0x001F58, 0x0001},
 {0x001F59, 0x0004},
-{0x001F5A, 0x0080},
+{0x001F5A, 0x0001},
 {0x001F5B, 0x0004},
-{0x001F5C, 0x0080},
+{0x001F5C, 0x0001},
 {0x001F5D, 0x0004},
-{0x001F5E, 0x0080},
+{0x001F5E, 0x0001},
 {0x001F5F, 0x0004},
-{0x001F7E, 0x0080},
+{0x001F7E, 0x0001},
 {0x001F80, 0x0004},
-{0x001FB5, 0x0080},
+{0x001FB5, 0x0001},
 {0x001FB6, 0x0004},
 {0x001FBD, 0x0040},
 {0x001FBE, 0x0004},
 {0x001FBF, 0x0040},
 {0x001FC2, 0x0004},
-{0x001FC5, 0x0080},
+{0x001FC5, 0x0001},
 {0x001FC6, 0x0004},
 {0x001FCD, 0x0040},
 {0x001FD0, 0x0004},
-{0x001FD4, 0x0080},
+{0x001FD4, 0x0001},
 {0x001FD6, 0x0004},
-{0x001FDC, 0x0080},
+{0x001FDC, 0x0001},
 {0x001FDD, 0x0040},
 {0x001FE0, 0x0004},
 {0x001FED, 0x0040},
-{0x001FF0, 0x0080},
+{0x001FF0, 0x0001},
 {0x001FF2, 0x0004},
-{0x001FF5, 0x0080},
+{0x001FF5, 0x0001},
 {0x001FF6, 0x0004},
 {0x001FFD, 0x0040},
-{0x001FFF, 0x0080},
+{0x001FFF, 0x0001},
 {0x002000, 0x0008},
 {0x00200B, 0x0080},
 {0x002010, 0x0020},
@@ -850,9 +854,11 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x002053, 0x0020},
 {0x00205F, 0x0008},
 {0x002060, 0x0080},
+{0x002065, 0x0001},
+{0x002066, 0x0080},
 {0x002070, 0x0002},
 {0x002071, 0x0004},
-{0x002072, 0x0080},
+{0x002072, 0x0001},
 {0x002074, 0x0002},
 {0x00207A, 0x0040},
 {0x00207D, 0x0020},
@@ -860,13 +866,13 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x002080, 0x0002},
 {0x00208A, 0x0040},
 {0x00208D, 0x0020},
-{0x00208F, 0x0080},
+{0x00208F, 0x0001},
 {0x002090, 0x0004},
-{0x00209D, 0x0080},
+{0x00209D, 0x0001},
 {0x0020A0, 0x0040},
-{0x0020C1, 0x0080},
+{0x0020C1, 0x0001},
 {0x0020D0, 0x0010},
-{0x0020F1, 0x0080},
+{0x0020F1, 0x0001},
 {0x002100, 0x0040},
 {0x002102, 0x0004},
 {0x002103, 0x0040},
@@ -898,15 +904,15 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x002183, 0x0004},
 {0x002185, 0x0002},
 {0x00218A, 0x0040},
-{0x00218C, 0x0080},
+{0x00218C, 0x0001},
 {0x002190, 0x0040},
 {0x002308, 0x0020},
 {0x00230C, 0x0040},
 {0x002329, 0x0020},
 {0x00232B, 0x0040},
-{0x002427, 0x0080},
+{0x002427, 0x0001},
 {0x002440, 0x0040},
-{0x00244B, 0x0080},
+{0x00244B, 0x0001},
 {0x002460, 0x0002},
 {0x00249C, 0x0040},
 {0x0024EA, 0x0002},
@@ -924,62 +930,62 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0029DC, 0x0040},
 {0x0029FC, 0x0020},
 {0x0029FE, 0x0040},
-{0x002B74, 0x0080},
+{0x002B74, 0x0001},
 {0x002B76, 0x0040},
-{0x002B96, 0x0080},
+{0x002B96, 0x0001},
 {0x002B97, 0x0040},
 {0x002C00, 0x0004},
 {0x002CE5, 0x0040},
 {0x002CEB, 0x0004},
 {0x002CEF, 0x0010},
 {0x002CF2, 0x0004},
-{0x002CF4, 0x0080},
+{0x002CF4, 0x0001},
 {0x002CF9, 0x0020},
 {0x002CFD, 0x0002},
 {0x002CFE, 0x0020},
 {0x002D00, 0x0004},
-{0x002D26, 0x0080},
+{0x002D26, 0x0001},
 {0x002D27, 0x0004},
-{0x002D28, 0x0080},
+{0x002D28, 0x0001},
 {0x002D2D, 0x0004},
-{0x002D2E, 0x0080},
+{0x002D2E, 0x0001},
 {0x002D30, 0x0004},
-{0x002D68, 0x0080},
+{0x002D68, 0x0001},
 {0x002D6F, 0x0004},
 {0x002D70, 0x0020},
-{0x002D71, 0x0080},
+{0x002D71, 0x0001},
 {0x002D7F, 0x0010},
 {0x002D80, 0x0004},
-{0x002D97, 0x0080},
+{0x002D97, 0x0001},
 {0x002DA0, 0x0004},
-{0x002DA7, 0x0080},
+{0x002DA7, 0x0001},
 {0x002DA8, 0x0004},
-{0x002DAF, 0x0080},
+{0x002DAF, 0x0001},
 {0x002DB0, 0x0004},
-{0x002DB7, 0x0080},
+{0x002DB7, 0x0001},
 {0x002DB8, 0x0004},
-{0x002DBF, 0x0080},
+{0x002DBF, 0x0001},
 {0x002DC0, 0x0004},
-{0x002DC7, 0x0080},
+{0x002DC7, 0x0001},
 {0x002DC8, 0x0004},
-{0x002DCF, 0x0080},
+{0x002DCF, 0x0001},
 {0x002DD0, 0x0004},
-{0x002DD7, 0x0080},
+{0x002DD7, 0x0001},
 {0x002DD8, 0x0004},
-{0x002DDF, 0x0080},
+{0x002DDF, 0x0001},
 {0x002DE0, 0x0010},
 {0x002E00, 0x0020},
 {0x002E2F, 0x0004},
 {0x002E30, 0x0020},
 {0x002E50, 0x0040},
 {0x002E52, 0x0020},
-{0x002E5E, 0x0080},
+{0x002E5E, 0x0001},
 {0x002E80, 0x0040},
-{0x002E9A, 0x0080},
+{0x002E9A, 0x0001},
 {0x002E9B, 0x0040},
-{0x002EF4, 0x0080},
+{0x002EF4, 0x0001},
 {0x002F00, 0x0040},
-{0x002FD6, 0x0080},
+{0x002FD6, 0x0001},
 {0x002FF0, 0x0040},
 {0x003000, 0x0008},
 {0x003001, 0x0020},
@@ -999,9 +1005,9 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00303B, 0x0004},
 {0x00303D, 0x0020},
 {0x00303E, 0x0040},
-{0x003040, 0x0080},
+{0x003040, 0x0001},
 {0x003041, 0x0004},
-{0x003097, 0x0080},
+{0x003097, 0x0001},
 {0x003099, 0x0010},
 {0x00309B, 0x0040},
 {0x00309D, 0x0004},
@@ -1009,21 +1015,21 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0030A1, 0x0004},
 {0x0030FB, 0x0020},
 {0x0030FC, 0x0004},
-{0x003100, 0x0080},
+{0x003100, 0x0001},
 {0x003105, 0x0004},
-{0x003130, 0x0080},
+{0x003130, 0x0001},
 {0x003131, 0x0004},
-{0x00318F, 0x0080},
+{0x00318F, 0x0001},
 {0x003190, 0x0040},
 {0x003192, 0x0002},
 {0x003196, 0x0040},
 {0x0031A0, 0x0004},
 {0x0031C0, 0x0040},
-{0x0031E4, 0x0080},
+{0x0031E4, 0x0001},
 {0x0031EF, 0x0040},
 {0x0031F0, 0x0004},
 {0x003200, 0x0040},
-{0x00321F, 0x0080},
+{0x00321F, 0x0001},
 {0x003220, 0x0002},
 {0x00322A, 0x0040},
 {0x003248, 0x0002},
@@ -1037,9 +1043,9 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x003400, 0x0004},
 {0x004DC0, 0x0040},
 {0x004E00, 0x0004},
-{0x00A48D, 0x0080},
+{0x00A48D, 0x0001},
 {0x00A490, 0x0040},
-{0x00A4C7, 0x0080},
+{0x00A4C7, 0x0001},
 {0x00A4D0, 0x0004},
 {0x00A4FE, 0x0020},
 {0x00A500, 0x0004},
@@ -1047,7 +1053,7 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00A610, 0x0004},
 {0x00A620, 0x0002},
 {0x00A62A, 0x0004},
-{0x00A62C, 0x0080},
+{0x00A62C, 0x0001},
 {0x00A640, 0x0004},
 {0x00A66F, 0x0010},
 {0x00A673, 0x0020},
@@ -1059,20 +1065,20 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00A6E6, 0x0002},
 {0x00A6F0, 0x0010},
 {0x00A6F2, 0x0020},
-{0x00A6F8, 0x0080},
+{0x00A6F8, 0x0001},
 {0x00A700, 0x0040},
 {0x00A717, 0x0004},
 {0x00A720, 0x0040},
 {0x00A722, 0x0004},
 {0x00A789, 0x0040},
 {0x00A78B, 0x0004},
-{0x00A7CB, 0x0080},
+{0x00A7CB, 0x0001},
 {0x00A7D0, 0x0004},
-{0x00A7D2, 0x0080},
+{0x00A7D2, 0x0001},
 {0x00A7D3, 0x0004},
-{0x00A7D4, 0x0080},
+{0x00A7D4, 0x0001},
 {0x00A7D5, 0x0004},
-{0x00A7DA, 0x0080},
+{0x00A7DA, 0x0001},
 {0x00A7F2, 0x0004},
 {0x00A802, 0x0010},
 {0x00A803, 0x0004},
@@ -1083,20 +1089,20 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00A823, 0x0010},
 {0x00A828, 0x0040},
 {0x00A82C, 0x0010},
-{0x00A82D, 0x0080},
+{0x00A82D, 0x0001},
 {0x00A830, 0x0002},
 {0x00A836, 0x0040},
-{0x00A83A, 0x0080},
+{0x00A83A, 0x0001},
 {0x00A840, 0x0004},
 {0x00A874, 0x0020},
-{0x00A878, 0x0080},
+{0x00A878, 0x0001},
 {0x00A880, 0x0010},
 {0x00A882, 0x0004},
 {0x00A8B4, 0x0010},
-{0x00A8C6, 0x0080},
+{0x00A8C6, 0x0001},
 {0x00A8CE, 0x0020},
 {0x00A8D0, 0x0002},
-{0x00A8DA, 0x0080},
+{0x00A8DA, 0x0001},
 {0x00A8E0, 0x0010},
 {0x00A8F2, 0x0004},
 {0x00A8F8, 0x0020},
@@ -1110,35 +1116,35 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00A92E, 0x0020},
 {0x00A930, 0x0004},
 {0x00A947, 0x0010},
-{0x00A954, 0x0080},
+{0x00A954, 0x0001},
 {0x00A95F, 0x0020},
 {0x00A960, 0x0004},
-{0x00A97D, 0x0080},
+{0x00A97D, 0x0001},
 {0x00A980, 0x0010},
 {0x00A984, 0x0004},
 {0x00A9B3, 0x0010},
 {0x00A9C1, 0x0020},
-{0x00A9CE, 0x0080},
+{0x00A9CE, 0x0001},
 {0x00A9CF, 0x0004},
 {0x00A9D0, 0x0002},
-{0x00A9DA, 0x0080},
+{0x00A9DA, 0x0001},
 {0x00A9DE, 0x0020},
 {0x00A9E0, 0x0004},
 {0x00A9E5, 0x0010},
 {0x00A9E6, 0x0004},
 {0x00A9F0, 0x0002},
 {0x00A9FA, 0x0004},
-{0x00A9FF, 0x0080},
+{0x00A9FF, 0x0001},
 {0x00AA00, 0x0004},
 {0x00AA29, 0x0010},
-{0x00AA37, 0x0080},
+{0x00AA37, 0x0001},
 {0x00AA40, 0x0004},
 {0x00AA43, 0x0010},
 {0x00AA44, 0x0004},
 {0x00AA4C, 0x0010},
-{0x00AA4E, 0x0080},
+{0x00AA4E, 0x0001},
 {0x00AA50, 0x0002},
-{0x00AA5A, 0x0080},
+{0x00AA5A, 0x0001},
 {0x00AA5C, 0x0020},
 {0x00AA60, 0x0004},
 {0x00AA77, 0x0040},
@@ -1155,7 +1161,7 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00AAC0, 0x0004},
 {0x00AAC1, 0x0010},
 {0x00AAC2, 0x0004},
-{0x00AAC3, 0x0080},
+{0x00AAC3, 0x0001},
 {0x00AADB, 0x0004},
 {0x00AADE, 0x0020},
 {0x00AAE0, 0x0004},
@@ -1163,90 +1169,93 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00AAF0, 0x0020},
 {0x00AAF2, 0x0004},
 {0x00AAF5, 0x0010},
-{0x00AAF7, 0x0080},
+{0x00AAF7, 0x0001},
 {0x00AB01, 0x0004},
-{0x00AB07, 0x0080},
+{0x00AB07, 0x0001},
 {0x00AB09, 0x0004},
-{0x00AB0F, 0x0080},
+{0x00AB0F, 0x0001},
 {0x00AB11, 0x0004},
-{0x00AB17, 0x0080},
+{0x00AB17, 0x0001},
 {0x00AB20, 0x0004},
-{0x00AB27, 0x0080},
+{0x00AB27, 0x0001},
 {0x00AB28, 0x0004},
-{0x00AB2F, 0x0080},
+{0x00AB2F, 0x0001},
 {0x00AB30, 0x0004},
 {0x00AB5B, 0x0040},
 {0x00AB5C, 0x0004},
 {0x00AB6A, 0x0040},
-{0x00AB6C, 0x0080},
+{0x00AB6C, 0x0001},
 {0x00AB70, 0x0004},
 {0x00ABE3, 0x0010},
 {0x00ABEB, 0x0020},
 {0x00ABEC, 0x0010},
-{0x00ABEE, 0x0080},
+{0x00ABEE, 0x0001},
 {0x00ABF0, 0x0002},
-{0x00ABFA, 0x0080},
+{0x00ABFA, 0x0001},
 {0x00AC00, 0x0004},
-{0x00D7A4, 0x0080},
+{0x00D7A4, 0x0001},
 {0x00D7B0, 0x0004},
-{0x00D7C7, 0x0080},
+{0x00D7C7, 0x0001},
 {0x00D7CB, 0x0004},
-{0x00D7FC, 0x0080},
+{0x00D7FC, 0x0001},
+{0x00D800, 0x0080},
 {0x00F900, 0x0004},
-{0x00FA6E, 0x0080},
+{0x00FA6E, 0x0001},
 {0x00FA70, 0x0004},
-{0x00FADA, 0x0080},
+{0x00FADA, 0x0001},
 {0x00FB00, 0x0004},
-{0x00FB07, 0x0080},
+{0x00FB07, 0x0001},
 {0x00FB13, 0x0004},
-{0x00FB18, 0x0080},
+{0x00FB18, 0x0001},
 {0x00FB1D, 0x0004},
 {0x00FB1E, 0x0010},
 {0x00FB1F, 0x0004},
 {0x00FB29, 0x0040},
 {0x00FB2A, 0x0004},
-{0x00FB37, 0x0080},
+{0x00FB37, 0x0001},
 {0x00FB38, 0x0004},
-{0x00FB3D, 0x0080},
+{0x00FB3D, 0x0001},
 {0x00FB3E, 0x0004},
-{0x00FB3F, 0x0080},
+{0x00FB3F, 0x0001},
 {0x00FB40, 0x0004},
-{0x00FB42, 0x0080},
+{0x00FB42, 0x0001},
 {0x00FB43, 0x0004},
-{0x00FB45, 0x0080},
+{0x00FB45, 0x0001},
 {0x00FB46, 0x0004},
 {0x00FBB2, 0x0040},
-{0x00FBC3, 0x0080},
+{0x00FBC3, 0x0001},
 {0x00FBD3, 0x0004},
 {0x00FD3E, 0x0020},
 {0x00FD40, 0x0040},
 {0x00FD50, 0x0004},
-{0x00FD90, 0x0080},
+{0x00FD90, 0x0001},
 {0x00FD92, 0x0004},
-{0x00FDC8, 0x0080},
+{0x00FDC8, 0x0001},
 {0x00FDCF, 0x0040},
-{0x00FDD0, 0x0080},
+{0x00FDD0, 0x0001},
 {0x00FDF0, 0x0004},
 {0x00FDFC, 0x0040},
 {0x00FE00, 0x0010},
 {0x00FE10, 0x0020},
-{0x00FE1A, 0x0080},
+{0x00FE1A, 0x0001},
 {0x00FE20, 0x0010},
 {0x00FE30, 0x0020},
-{0x00FE53, 0x0080},
+{0x00FE53, 0x0001},
 {0x00FE54, 0x0020},
 {0x00FE62, 0x0040},
 {0x00FE63, 0x0020},
 {0x00FE64, 0x0040},
-{0x00FE67, 0x0080},
+{0x00FE67, 0x0001},
 {0x00FE68, 0x0020},
 {0x00FE69, 0x0040},
 {0x00FE6A, 0x0020},
-{0x00FE6C, 0x0080},
+{0x00FE6C, 0x0001},
 {0x00FE70, 0x0004},
-{0x00FE75, 0x0080},
+{0x00FE75, 0x0001},
 {0x00FE76, 0x0004},
-{0x00FEFD, 0x0080},
+{0x00FEFD, 0x0001},
+{0x00FEFF, 0x0080},
+{0x00FF00, 0x0001},
 {0x00FF01, 0x0020},
 {0x00FF04, 0x0040},
 {0x00FF05, 0x0020},
@@ -1268,260 +1277,261 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x00FF5E, 0x0040},
 {0x00FF5F, 0x0020},
 {0x00FF66, 0x0004},
-{0x00FFBF, 0x0080},
+{0x00FFBF, 0x0001},
 {0x00FFC2, 0x0004},
-{0x00FFC8, 0x0080},
+{0x00FFC8, 0x0001},
 {0x00FFCA, 0x0004},
-{0x00FFD0, 0x0080},
+{0x00FFD0, 0x0001},
 {0x00FFD2, 0x0004},
-{0x00FFD8, 0x0080},
+{0x00FFD8, 0x0001},
 {0x00FFDA, 0x0004},
-{0x00FFDD, 0x0080},
+{0x00FFDD, 0x0001},
 {0x00FFE0, 0x0040},
-{0x00FFE7, 0x0080},
+{0x00FFE7, 0x0001},
 {0x00FFE8, 0x0040},
-{0x00FFEF, 0x0080},
+{0x00FFEF, 0x0001},
+{0x00FFF9, 0x0080},
 {0x00FFFC, 0x0040},
-{0x00FFFE, 0x0080},
+{0x00FFFE, 0x0001},
 {0x010000, 0x0004},
-{0x01000C, 0x0080},
+{0x01000C, 0x0001},
 {0x01000D, 0x0004},
-{0x010027, 0x0080},
+{0x010027, 0x0001},
 {0x010028, 0x0004},
-{0x01003B, 0x0080},
+{0x01003B, 0x0001},
 {0x01003C, 0x0004},
-{0x01003E, 0x0080},
+{0x01003E, 0x0001},
 {0x01003F, 0x0004},
-{0x01004E, 0x0080},
+{0x01004E, 0x0001},
 {0x010050, 0x0004},
-{0x01005E, 0x0080},
+{0x01005E, 0x0001},
 {0x010080, 0x0004},
-{0x0100FB, 0x0080},
+{0x0100FB, 0x0001},
 {0x010100, 0x0020},
-{0x010103, 0x0080},
+{0x010103, 0x0001},
 {0x010107, 0x0002},
-{0x010134, 0x0080},
+{0x010134, 0x0001},
 {0x010137, 0x0040},
 {0x010140, 0x0002},
 {0x010179, 0x0040},
 {0x01018A, 0x0002},
 {0x01018C, 0x0040},
-{0x01018F, 0x0080},
+{0x01018F, 0x0001},
 {0x010190, 0x0040},
-{0x01019D, 0x0080},
+{0x01019D, 0x0001},
 {0x0101A0, 0x0040},
-{0x0101A1, 0x0080},
+{0x0101A1, 0x0001},
 {0x0101D0, 0x0040},
 {0x0101FD, 0x0010},
-{0x0101FE, 0x0080},
+{0x0101FE, 0x0001},
 {0x010280, 0x0004},
-{0x01029D, 0x0080},
+{0x01029D, 0x0001},
 {0x0102A0, 0x0004},
-{0x0102D1, 0x0080},
+{0x0102D1, 0x0001},
 {0x0102E0, 0x0010},
 {0x0102E1, 0x0002},
-{0x0102FC, 0x0080},
+{0x0102FC, 0x0001},
 {0x010300, 0x0004},
 {0x010320, 0x0002},
-{0x010324, 0x0080},
+{0x010324, 0x0001},
 {0x01032D, 0x0004},
 {0x010341, 0x0002},
 {0x010342, 0x0004},
 {0x01034A, 0x0002},
-{0x01034B, 0x0080},
+{0x01034B, 0x0001},
 {0x010350, 0x0004},
 {0x010376, 0x0010},
-{0x01037B, 0x0080},
+{0x01037B, 0x0001},
 {0x010380, 0x0004},
-{0x01039E, 0x0080},
+{0x01039E, 0x0001},
 {0x01039F, 0x0020},
 {0x0103A0, 0x0004},
-{0x0103C4, 0x0080},
+{0x0103C4, 0x0001},
 {0x0103C8, 0x0004},
 {0x0103D0, 0x0020},
 {0x0103D1, 0x0002},
-{0x0103D6, 0x0080},
+{0x0103D6, 0x0001},
 {0x010400, 0x0004},
-{0x01049E, 0x0080},
+{0x01049E, 0x0001},
 {0x0104A0, 0x0002},
-{0x0104AA, 0x0080},
+{0x0104AA, 0x0001},
 {0x0104B0, 0x0004},
-{0x0104D4, 0x0080},
+{0x0104D4, 0x0001},
 {0x0104D8, 0x0004},
-{0x0104FC, 0x0080},
+{0x0104FC, 0x0001},
 {0x010500, 0x0004},
-{0x010528, 0x0080},
+{0x010528, 0x0001},
 {0x010530, 0x0004},
-{0x010564, 0x0080},
+{0x010564, 0x0001},
 {0x01056F, 0x0020},
 {0x010570, 0x0004},
-{0x01057B, 0x0080},
+{0x01057B, 0x0001},
 {0x01057C, 0x0004},
-{0x01058B, 0x0080},
+{0x01058B, 0x0001},
 {0x01058C, 0x0004},
-{0x010593, 0x0080},
+{0x010593, 0x0001},
 {0x010594, 0x0004},
-{0x010596, 0x0080},
+{0x010596, 0x0001},
 {0x010597, 0x0004},
-{0x0105A2, 0x0080},
+{0x0105A2, 0x0001},
 {0x0105A3, 0x0004},
-{0x0105B2, 0x0080},
+{0x0105B2, 0x0001},
 {0x0105B3, 0x0004},
-{0x0105BA, 0x0080},
+{0x0105BA, 0x0001},
 {0x0105BB, 0x0004},
-{0x0105BD, 0x0080},
+{0x0105BD, 0x0001},
 {0x010600, 0x0004},
-{0x010737, 0x0080},
+{0x010737, 0x0001},
 {0x010740, 0x0004},
-{0x010756, 0x0080},
+{0x010756, 0x0001},
 {0x010760, 0x0004},
-{0x010768, 0x0080},
+{0x010768, 0x0001},
 {0x010780, 0x0004},
-{0x010786, 0x0080},
+{0x010786, 0x0001},
 {0x010787, 0x0004},
-{0x0107B1, 0x0080},
+{0x0107B1, 0x0001},
 {0x0107B2, 0x0004},
-{0x0107BB, 0x0080},
+{0x0107BB, 0x0001},
 {0x010800, 0x0004},
-{0x010806, 0x0080},
+{0x010806, 0x0001},
 {0x010808, 0x0004},
-{0x010809, 0x0080},
+{0x010809, 0x0001},
 {0x01080A, 0x0004},
-{0x010836, 0x0080},
+{0x010836, 0x0001},
 {0x010837, 0x0004},
-{0x010839, 0x0080},
+{0x010839, 0x0001},
 {0x01083C, 0x0004},
-{0x01083D, 0x0080},
+{0x01083D, 0x0001},
 {0x01083F, 0x0004},
-{0x010856, 0x0080},
+{0x010856, 0x0001},
 {0x010857, 0x0020},
 {0x010858, 0x0002},
 {0x010860, 0x0004},
 {0x010877, 0x0040},
 {0x010879, 0x0002},
 {0x010880, 0x0004},
-{0x01089F, 0x0080},
+{0x01089F, 0x0001},
 {0x0108A7, 0x0002},
-{0x0108B0, 0x0080},
+{0x0108B0, 0x0001},
 {0x0108E0, 0x0004},
-{0x0108F3, 0x0080},
+{0x0108F3, 0x0001},
 {0x0108F4, 0x0004},
-{0x0108F6, 0x0080},
+{0x0108F6, 0x0001},
 {0x0108FB, 0x0002},
 {0x010900, 0x0004},
 {0x010916, 0x0002},
-{0x01091C, 0x0080},
+{0x01091C, 0x0001},
 {0x01091F, 0x0020},
 {0x010920, 0x0004},
-{0x01093A, 0x0080},
+{0x01093A, 0x0001},
 {0x01093F, 0x0020},
-{0x010940, 0x0080},
+{0x010940, 0x0001},
 {0x010980, 0x0004},
-{0x0109B8, 0x0080},
+{0x0109B8, 0x0001},
 {0x0109BC, 0x0002},
 {0x0109BE, 0x0004},
 {0x0109C0, 0x0002},
-{0x0109D0, 0x0080},
+{0x0109D0, 0x0001},
 {0x0109D2, 0x0002},
 {0x010A00, 0x0004},
 {0x010A01, 0x0010},
-{0x010A04, 0x0080},
+{0x010A04, 0x0001},
 {0x010A05, 0x0010},
-{0x010A07, 0x0080},
+{0x010A07, 0x0001},
 {0x010A0C, 0x0010},
 {0x010A10, 0x0004},
-{0x010A14, 0x0080},
+{0x010A14, 0x0001},
 {0x010A15, 0x0004},
-{0x010A18, 0x0080},
+{0x010A18, 0x0001},
 {0x010A19, 0x0004},
-{0x010A36, 0x0080},
+{0x010A36, 0x0001},
 {0x010A38, 0x0010},
-{0x010A3B, 0x0080},
+{0x010A3B, 0x0001},
 {0x010A3F, 0x0010},
 {0x010A40, 0x0002},
-{0x010A49, 0x0080},
+{0x010A49, 0x0001},
 {0x010A50, 0x0020},
-{0x010A59, 0x0080},
+{0x010A59, 0x0001},
 {0x010A60, 0x0004},
 {0x010A7D, 0x0002},
 {0x010A7F, 0x0020},
 {0x010A80, 0x0004},
 {0x010A9D, 0x0002},
-{0x010AA0, 0x0080},
+{0x010AA0, 0x0001},
 {0x010AC0, 0x0004},
 {0x010AC8, 0x0040},
 {0x010AC9, 0x0004},
 {0x010AE5, 0x0010},
-{0x010AE7, 0x0080},
+{0x010AE7, 0x0001},
 {0x010AEB, 0x0002},
 {0x010AF0, 0x0020},
-{0x010AF7, 0x0080},
+{0x010AF7, 0x0001},
 {0x010B00, 0x0004},
-{0x010B36, 0x0080},
+{0x010B36, 0x0001},
 {0x010B39, 0x0020},
 {0x010B40, 0x0004},
-{0x010B56, 0x0080},
+{0x010B56, 0x0001},
 {0x010B58, 0x0002},
 {0x010B60, 0x0004},
-{0x010B73, 0x0080},
+{0x010B73, 0x0001},
 {0x010B78, 0x0002},
 {0x010B80, 0x0004},
-{0x010B92, 0x0080},
+{0x010B92, 0x0001},
 {0x010B99, 0x0020},
-{0x010B9D, 0x0080},
+{0x010B9D, 0x0001},
 {0x010BA9, 0x0002},
-{0x010BB0, 0x0080},
+{0x010BB0, 0x0001},
 {0x010C00, 0x0004},
-{0x010C49, 0x0080},
+{0x010C49, 0x0001},
 {0x010C80, 0x0004},
-{0x010CB3, 0x0080},
+{0x010CB3, 0x0001},
 {0x010CC0, 0x0004},
-{0x010CF3, 0x0080},
+{0x010CF3, 0x0001},
 {0x010CFA, 0x0002},
 {0x010D00, 0x0004},
 {0x010D24, 0x0010},
-{0x010D28, 0x0080},
+{0x010D28, 0x0001},
 {0x010D30, 0x0002},
-{0x010D3A, 0x0080},
+{0x010D3A, 0x0001},
 {0x010E60, 0x0002},
-{0x010E7F, 0x0080},
+{0x010E7F, 0x0001},
 {0x010E80, 0x0004},
-{0x010EAA, 0x0080},
+{0x010EAA, 0x0001},
 {0x010EAB, 0x0010},
 {0x010EAD, 0x0020},
-{0x010EAE, 0x0080},
+{0x010EAE, 0x0001},
 {0x010EB0, 0x0004},
-{0x010EB2, 0x0080},
+{0x010EB2, 0x0001},
 {0x010EFD, 0x0010},
 {0x010F00, 0x0004},
 {0x010F1D, 0x0002},
 {0x010F27, 0x0004},
-{0x010F28, 0x0080},
+{0x010F28, 0x0001},
 {0x010F30, 0x0004},
 {0x010F46, 0x0010},
 {0x010F51, 0x0002},
 {0x010F55, 0x0020},
-{0x010F5A, 0x0080},
+{0x010F5A, 0x0001},
 {0x010F70, 0x0004},
 {0x010F82, 0x0010},
 {0x010F86, 0x0020},
-{0x010F8A, 0x0080},
+{0x010F8A, 0x0001},
 {0x010FB0, 0x0004},
 {0x010FC5, 0x0002},
-{0x010FCC, 0x0080},
+{0x010FCC, 0x0001},
 {0x010FE0, 0x0004},
-{0x010FF7, 0x0080},
+{0x010FF7, 0x0001},
 {0x011000, 0x0010},
 {0x011003, 0x0004},
 {0x011038, 0x0010},
 {0x011047, 0x0020},
-{0x01104E, 0x0080},
+{0x01104E, 0x0001},
 {0x011052, 0x0002},
 {0x011070, 0x0010},
 {0x011071, 0x0004},
 {0x011073, 0x0010},
 {0x011075, 0x0004},
-{0x011076, 0x0080},
+{0x011076, 0x0001},
 {0x01107F, 0x0010},
 {0x011083, 0x0004},
 {0x0110B0, 0x0010},
@@ -1529,26 +1539,28 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0110BD, 0x0080},
 {0x0110BE, 0x0020},
 {0x0110C2, 0x0010},
-{0x0110C3, 0x0080},
+{0x0110C3, 0x0001},
+{0x0110CD, 0x0080},
+{0x0110CE, 0x0001},
 {0x0110D0, 0x0004},
-{0x0110E9, 0x0080},
+{0x0110E9, 0x0001},
 {0x0110F0, 0x0002},
-{0x0110FA, 0x0080},
+{0x0110FA, 0x0001},
 {0x011100, 0x0010},
 {0x011103, 0x0004},
 {0x011127, 0x0010},
-{0x011135, 0x0080},
+{0x011135, 0x0001},
 {0x011136, 0x0002},
 {0x011140, 0x0020},
 {0x011144, 0x0004},
 {0x011145, 0x0010},
 {0x011147, 0x0004},
-{0x011148, 0x0080},
+{0x011148, 0x0001},
 {0x011150, 0x0004},
 {0x011173, 0x0010},
 {0x011174, 0x0020},
 {0x011176, 0x0004},
-{0x011177, 0x0080},
+{0x011177, 0x0001},
 {0x011180, 0x0010},
 {0x011183, 0x0004},
 {0x0111B3, 0x0010},
@@ -1562,159 +1574,159 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x0111DB, 0x0020},
 {0x0111DC, 0x0004},
 {0x0111DD, 0x0020},
-{0x0111E0, 0x0080},
+{0x0111E0, 0x0001},
 {0x0111E1, 0x0002},
-{0x0111F5, 0x0080},
+{0x0111F5, 0x0001},
 {0x011200, 0x0004},
-{0x011212, 0x0080},
+{0x011212, 0x0001},
 {0x011213, 0x0004},
 {0x01122C, 0x0010},
 {0x011238, 0x0020},
 {0x01123E, 0x0010},
 {0x01123F, 0x0004},
 {0x011241, 0x0010},
-{0x011242, 0x0080},
+{0x011242, 0x0001},
 {0x011280, 0x0004},
-{0x011287, 0x0080},
+{0x011287, 0x0001},
 {0x011288, 0x0004},
-{0x011289, 0x0080},
+{0x011289, 0x0001},
 {0x01128A, 0x0004},
-{0x01128E, 0x0080},
+{0x01128E, 0x0001},
 {0x01128F, 0x0004},
-{0x01129E, 0x0080},
+{0x01129E, 0x0001},
 {0x01129F, 0x0004},
 {0x0112A9, 0x0020},
-{0x0112AA, 0x0080},
+{0x0112AA, 0x0001},
 {0x0112B0, 0x0004},
 {0x0112DF, 0x0010},
-{0x0112EB, 0x0080},
+{0x0112EB, 0x0001},
 {0x0112F0, 0x0002},
-{0x0112FA, 0x0080},
+{0x0112FA, 0x0001},
 {0x011300, 0x0010},
-{0x011304, 0x0080},
+{0x011304, 0x0001},
 {0x011305, 0x0004},
-{0x01130D, 0x0080},
+{0x01130D, 0x0001},
 {0x01130F, 0x0004},
-{0x011311, 0x0080},
+{0x011311, 0x0001},
 {0x011313, 0x0004},
-{0x011329, 0x0080},
+{0x011329, 0x0001},
 {0x01132A, 0x0004},
-{0x011331, 0x0080},
+{0x011331, 0x0001},
 {0x011332, 0x0004},
-{0x011334, 0x0080},
+{0x011334, 0x0001},
 {0x011335, 0x0004},
-{0x01133A, 0x0080},
+{0x01133A, 0x0001},
 {0x01133B, 0x0010},
 {0x01133D, 0x0004},
 {0x01133E, 0x0010},
-{0x011345, 0x0080},
+{0x011345, 0x0001},
 {0x011347, 0x0010},
-{0x011349, 0x0080},
+{0x011349, 0x0001},
 {0x01134B, 0x0010},
-{0x01134E, 0x0080},
+{0x01134E, 0x0001},
 {0x011350, 0x0004},
-{0x011351, 0x0080},
+{0x011351, 0x0001},
 {0x011357, 0x0010},
-{0x011358, 0x0080},
+{0x011358, 0x0001},
 {0x01135D, 0x0004},
 {0x011362, 0x0010},
-{0x011364, 0x0080},
+{0x011364, 0x0001},
 {0x011366, 0x0010},
-{0x01136D, 0x0080},
+{0x01136D, 0x0001},
 {0x011370, 0x0010},
-{0x011375, 0x0080},
+{0x011375, 0x0001},
 {0x011400, 0x0004},
 {0x011435, 0x0010},
 {0x011447, 0x0004},
 {0x01144B, 0x0020},
 {0x011450, 0x0002},
 {0x01145A, 0x0020},
-{0x01145C, 0x0080},
+{0x01145C, 0x0001},
 {0x01145D, 0x0020},
 {0x01145E, 0x0010},
 {0x01145F, 0x0004},
-{0x011462, 0x0080},
+{0x011462, 0x0001},
 {0x011480, 0x0004},
 {0x0114B0, 0x0010},
 {0x0114C4, 0x0004},
 {0x0114C6, 0x0020},
 {0x0114C7, 0x0004},
-{0x0114C8, 0x0080},
+{0x0114C8, 0x0001},
 {0x0114D0, 0x0002},
-{0x0114DA, 0x0080},
+{0x0114DA, 0x0001},
 {0x011580, 0x0004},
 {0x0115AF, 0x0010},
-{0x0115B6, 0x0080},
+{0x0115B6, 0x0001},
 {0x0115B8, 0x0010},
 {0x0115C1, 0x0020},
 {0x0115D8, 0x0004},
 {0x0115DC, 0x0010},
-{0x0115DE, 0x0080},
+{0x0115DE, 0x0001},
 {0x011600, 0x0004},
 {0x011630, 0x0010},
 {0x011641, 0x0020},
 {0x011644, 0x0004},
-{0x011645, 0x0080},
+{0x011645, 0x0001},
 {0x011650, 0x0002},
-{0x01165A, 0x0080},
+{0x01165A, 0x0001},
 {0x011660, 0x0020},
-{0x01166D, 0x0080},
+{0x01166D, 0x0001},
 {0x011680, 0x0004},
 {0x0116AB, 0x0010},
 {0x0116B8, 0x0004},
 {0x0116B9, 0x0020},
-{0x0116BA, 0x0080},
+{0x0116BA, 0x0001},
 {0x0116C0, 0x0002},
-{0x0116CA, 0x0080},
+{0x0116CA, 0x0001},
 {0x011700, 0x0004},
-{0x01171B, 0x0080},
+{0x01171B, 0x0001},
 {0x01171D, 0x0010},
-{0x01172C, 0x0080},
+{0x01172C, 0x0001},
 {0x011730, 0x0002},
 {0x01173C, 0x0020},
 {0x01173F, 0x0040},
 {0x011740, 0x0004},
-{0x011747, 0x0080},
+{0x011747, 0x0001},
 {0x011800, 0x0004},
 {0x01182C, 0x0010},
 {0x01183B, 0x0020},
-{0x01183C, 0x0080},
+{0x01183C, 0x0001},
 {0x0118A0, 0x0004},
 {0x0118E0, 0x0002},
-{0x0118F3, 0x0080},
+{0x0118F3, 0x0001},
 {0x0118FF, 0x0004},
-{0x011907, 0x0080},
+{0x011907, 0x0001},
 {0x011909, 0x0004},
-{0x01190A, 0x0080},
+{0x01190A, 0x0001},
 {0x01190C, 0x0004},
-{0x011914, 0x0080},
+{0x011914, 0x0001},
 {0x011915, 0x0004},
-{0x011917, 0x0080},
+{0x011917, 0x0001},
 {0x011918, 0x0004},
 {0x011930, 0x0010},
-{0x011936, 0x0080},
+{0x011936, 0x0001},
 {0x011937, 0x0010},
-{0x011939, 0x0080},
+{0x011939, 0x0001},
 {0x01193B, 0x0010},
 {0x01193F, 0x0004},
 {0x011940, 0x0010},
 {0x011941, 0x0004},
 {0x011942, 0x0010},
 {0x011944, 0x0020},
-{0x011947, 0x0080},
+{0x011947, 0x0001},
 {0x011950, 0x0002},
-{0x01195A, 0x0080},
+{0x01195A, 0x0001},
 {0x0119A0, 0x0004},
-{0x0119A8, 0x0080},
+{0x0119A8, 0x0001},
 {0x0119AA, 0x0004},
 {0x0119D1, 0x0010},
-{0x0119D8, 0x0080},
+{0x0119D8, 0x0001},
 {0x0119DA, 0x0010},
 {0x0119E1, 0x0004},
 {0x0119E2, 0x0020},
 {0x0119E3, 0x0004},
 {0x0119E4, 0x0010},
-{0x0119E5, 0x0080},
+{0x0119E5, 0x0001},
 {0x011A00, 0x0004},
 {0x011A01, 0x0010},
 {0x011A0B, 0x0004},
@@ -1723,7 +1735,7 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x011A3B, 0x0010},
 {0x011A3F, 0x0020},
 {0x011A47, 0x0010},
-{0x011A48, 0x0080},
+{0x011A48, 0x0001},
 {0x011A50, 0x0004},
 {0x011A51, 0x0010},
 {0x011A5C, 0x0004},
@@ -1731,117 +1743,117 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x011A9A, 0x0020},
 {0x011A9D, 0x0004},
 {0x011A9E, 0x0020},
-{0x011AA3, 0x0080},
+{0x011AA3, 0x0001},
 {0x011AB0, 0x0004},
-{0x011AF9, 0x0080},
+{0x011AF9, 0x0001},
 {0x011B00, 0x0020},
-{0x011B0A, 0x0080},
+{0x011B0A, 0x0001},
 {0x011C00, 0x0004},
-{0x011C09, 0x0080},
+{0x011C09, 0x0001},
 {0x011C0A, 0x0004},
 {0x011C2F, 0x0010},
-{0x011C37, 0x0080},
+{0x011C37, 0x0001},
 {0x011C38, 0x0010},
 {0x011C40, 0x0004},
 {0x011C41, 0x0020},
-{0x011C46, 0x0080},
+{0x011C46, 0x0001},
 {0x011C50, 0x0002},
-{0x011C6D, 0x0080},
+{0x011C6D, 0x0001},
 {0x011C70, 0x0020},
 {0x011C72, 0x0004},
-{0x011C90, 0x0080},
+{0x011C90, 0x0001},
 {0x011C92, 0x0010},
-{0x011CA8, 0x0080},
+{0x011CA8, 0x0001},
 {0x011CA9, 0x0010},
-{0x011CB7, 0x0080},
+{0x011CB7, 0x0001},
 {0x011D00, 0x0004},
-{0x011D07, 0x0080},
+{0x011D07, 0x0001},
 {0x011D08, 0x0004},
-{0x011D0A, 0x0080},
+{0x011D0A, 0x0001},
 {0x011D0B, 0x0004},
 {0x011D31, 0x0010},
-{0x011D37, 0x0080},
+{0x011D37, 0x0001},
 {0x011D3A, 0x0010},
-{0x011D3B, 0x0080},
+{0x011D3B, 0x0001},
 {0x011D3C, 0x0010},
-{0x011D3E, 0x0080},
+{0x011D3E, 0x0001},
 {0x011D3F, 0x0010},
 {0x011D46, 0x0004},
 {0x011D47, 0x0010},
-{0x011D48, 0x0080},
+{0x011D48, 0x0001},
 {0x011D50, 0x0002},
-{0x011D5A, 0x0080},
+{0x011D5A, 0x0001},
 {0x011D60, 0x0004},
-{0x011D66, 0x0080},
+{0x011D66, 0x0001},
 {0x011D67, 0x0004},
-{0x011D69, 0x0080},
+{0x011D69, 0x0001},
 {0x011D6A, 0x0004},
 {0x011D8A, 0x0010},
-{0x011D8F, 0x0080},
+{0x011D8F, 0x0001},
 {0x011D90, 0x0010},
-{0x011D92, 0x0080},
+{0x011D92, 0x0001},
 {0x011D93, 0x0010},
 {0x011D98, 0x0004},
-{0x011D99, 0x0080},
+{0x011D99, 0x0001},
 {0x011DA0, 0x0002},
-{0x011DAA, 0x0080},
+{0x011DAA, 0x0001},
 {0x011EE0, 0x0004},
 {0x011EF3, 0x0010},
 {0x011EF7, 0x0020},
-{0x011EF9, 0x0080},
+{0x011EF9, 0x0001},
 {0x011F00, 0x0010},
 {0x011F02, 0x0004},
 {0x011F03, 0x0010},
 {0x011F04, 0x0004},
-{0x011F11, 0x0080},
+{0x011F11, 0x0001},
 {0x011F12, 0x0004},
 {0x011F34, 0x0010},
-{0x011F3B, 0x0080},
+{0x011F3B, 0x0001},
 {0x011F3E, 0x0010},
 {0x011F43, 0x0020},
 {0x011F50, 0x0002},
-{0x011F5A, 0x0080},
+{0x011F5A, 0x0001},
 {0x011FB0, 0x0004},
-{0x011FB1, 0x0080},
+{0x011FB1, 0x0001},
 {0x011FC0, 0x0002},
 {0x011FD5, 0x0040},
-{0x011FF2, 0x0080},
+{0x011FF2, 0x0001},
 {0x011FFF, 0x0020},
 {0x012000, 0x0004},
-{0x01239A, 0x0080},
+{0x01239A, 0x0001},
 {0x012400, 0x0002},
-{0x01246F, 0x0080},
+{0x01246F, 0x0001},
 {0x012470, 0x0020},
-{0x012475, 0x0080},
+{0x012475, 0x0001},
 {0x012480, 0x0004},
-{0x012544, 0x0080},
+{0x012544, 0x0001},
 {0x012F90, 0x0004},
 {0x012FF1, 0x0020},
-{0x012FF3, 0x0080},
+{0x012FF3, 0x0001},
 {0x013000, 0x0004},
 {0x013430, 0x0080},
 {0x013440, 0x0010},
 {0x013441, 0x0004},
 {0x013447, 0x0010},
-{0x013456, 0x0080},
+{0x013456, 0x0001},
 {0x014400, 0x0004},
-{0x014647, 0x0080},
+{0x014647, 0x0001},
 {0x016800, 0x0004},
-{0x016A39, 0x0080},
+{0x016A39, 0x0001},
 {0x016A40, 0x0004},
-{0x016A5F, 0x0080},
+{0x016A5F, 0x0001},
 {0x016A60, 0x0002},
-{0x016A6A, 0x0080},
+{0x016A6A, 0x0001},
 {0x016A6E, 0x0020},
 {0x016A70, 0x0004},
-{0x016ABF, 0x0080},
+{0x016ABF, 0x0001},
 {0x016AC0, 0x0002},
-{0x016ACA, 0x0080},
+{0x016ACA, 0x0001},
 {0x016AD0, 0x0004},
-{0x016AEE, 0x0080},
+{0x016AEE, 0x0001},
 {0x016AF0, 0x0010},
 {0x016AF5, 0x0020},
-{0x016AF6, 0x0080},
+{0x016AF6, 0x0001},
 {0x016B00, 0x0004},
 {0x016B30, 0x0010},
 {0x016B37, 0x0020},
@@ -1849,81 +1861,82 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x016B40, 0x0004},
 {0x016B44, 0x0020},
 {0x016B45, 0x0040},
-{0x016B46, 0x0080},
+{0x016B46, 0x0001},
 {0x016B50, 0x0002},
-{0x016B5A, 0x0080},
+{0x016B5A, 0x0001},
 {0x016B5B, 0x0002},
-{0x016B62, 0x0080},
+{0x016B62, 0x0001},
 {0x016B63, 0x0004},
-{0x016B78, 0x0080},
+{0x016B78, 0x0001},
 {0x016B7D, 0x0004},
-{0x016B90, 0x0080},
+{0x016B90, 0x0001},
 {0x016E40, 0x0004},
 {0x016E80, 0x0002},
 {0x016E97, 0x0020},
-{0x016E9B, 0x0080},
+{0x016E9B, 0x0001},
 {0x016F00, 0x0004},
-{0x016F4B, 0x0080},
+{0x016F4B, 0x0001},
 {0x016F4F, 0x0010},
 {0x016F50, 0x0004},
 {0x016F51, 0x0010},
-{0x016F88, 0x0080},
+{0x016F88, 0x0001},
 {0x016F8F, 0x0010},
 {0x016F93, 0x0004},
-{0x016FA0, 0x0080},
+{0x016FA0, 0x0001},
 {0x016FE0, 0x0004},
 {0x016FE2, 0x0020},
 {0x016FE3, 0x0004},
 {0x016FE4, 0x0010},
-{0x016FE5, 0x0080},
+{0x016FE5, 0x0001},
 {0x016FF0, 0x0010},
-{0x016FF2, 0x0080},
+{0x016FF2, 0x0001},
 {0x017000, 0x0004},
-{0x0187F8, 0x0080},
+{0x0187F8, 0x0001},
 {0x018800, 0x0004},
-{0x018CD6, 0x0080},
+{0x018CD6, 0x0001},
 {0x018D00, 0x0004},
-{0x018D09, 0x0080},
+{0x018D09, 0x0001},
 {0x01AFF0, 0x0004},
-{0x01AFF4, 0x0080},
+{0x01AFF4, 0x0001},
 {0x01AFF5, 0x0004},
-{0x01AFFC, 0x0080},
+{0x01AFFC, 0x0001},
 {0x01AFFD, 0x0004},
-{0x01AFFF, 0x0080},
+{0x01AFFF, 0x0001},
 {0x01B000, 0x0004},
-{0x01B123, 0x0080},
+{0x01B123, 0x0001},
 {0x01B132, 0x0004},
-{0x01B133, 0x0080},
+{0x01B133, 0x0001},
 {0x01B150, 0x0004},
-{0x01B153, 0x0080},
+{0x01B153, 0x0001},
 {0x01B155, 0x0004},
-{0x01B156, 0x0080},
+{0x01B156, 0x0001},
 {0x01B164, 0x0004},
-{0x01B168, 0x0080},
+{0x01B168, 0x0001},
 {0x01B170, 0x0004},
-{0x01B2FC, 0x0080},
+{0x01B2FC, 0x0001},
 {0x01BC00, 0x0004},
-{0x01BC6B, 0x0080},
+{0x01BC6B, 0x0001},
 {0x01BC70, 0x0004},
-{0x01BC7D, 0x0080},
+{0x01BC7D, 0x0001},
 {0x01BC80, 0x0004},
-{0x01BC89, 0x0080},
+{0x01BC89, 0x0001},
 {0x01BC90, 0x0004},
-{0x01BC9A, 0x0080},
+{0x01BC9A, 0x0001},
 {0x01BC9C, 0x0040},
 {0x01BC9D, 0x0010},
 {0x01BC9F, 0x0020},
 {0x01BCA0, 0x0080},
+{0x01BCA4, 0x0001},
 {0x01CF00, 0x0010},
-{0x01CF2E, 0x0080},
+{0x01CF2E, 0x0001},
 {0x01CF30, 0x0010},
-{0x01CF47, 0x0080},
+{0x01CF47, 0x0001},
 {0x01CF50, 0x0040},
-{0x01CFC4, 0x0080},
+{0x01CFC4, 0x0001},
 {0x01D000, 0x0040},
-{0x01D0F6, 0x0080},
+{0x01D0F6, 0x0001},
 {0x01D100, 0x0040},
-{0x01D127, 0x0080},
+{0x01D127, 0x0001},
 {0x01D129, 0x0040},
 {0x01D165, 0x0010},
 {0x01D16A, 0x0040},
@@ -1935,57 +1948,57 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x01D18C, 0x0040},
 {0x01D1AA, 0x0010},
 {0x01D1AE, 0x0040},
-{0x01D1EB, 0x0080},
+{0x01D1EB, 0x0001},
 {0x01D200, 0x0040},
 {0x01D242, 0x0010},
 {0x01D245, 0x0040},
-{0x01D246, 0x0080},
+{0x01D246, 0x0001},
 {0x01D2C0, 0x0002},
-{0x01D2D4, 0x0080},
+{0x01D2D4, 0x0001},
 {0x01D2E0, 0x0002},
-{0x01D2F4, 0x0080},
+{0x01D2F4, 0x0001},
 {0x01D300, 0x0040},
-{0x01D357, 0x0080},
+{0x01D357, 0x0001},
 {0x01D360, 0x0002},
-{0x01D379, 0x0080},
+{0x01D379, 0x0001},
 {0x01D400, 0x0004},
-{0x01D455, 0x0080},
+{0x01D455, 0x0001},
 {0x01D456, 0x0004},
-{0x01D49D, 0x0080},
+{0x01D49D, 0x0001},
 {0x01D49E, 0x0004},
-{0x01D4A0, 0x0080},
+{0x01D4A0, 0x0001},
 {0x01D4A2, 0x0004},
-{0x01D4A3, 0x0080},
+{0x01D4A3, 0x0001},
 {0x01D4A5, 0x0004},
-{0x01D4A7, 0x0080},
+{0x01D4A7, 0x0001},
 {0x01D4A9, 0x0004},
-{0x01D4AD, 0x0080},
+{0x01D4AD, 0x0001},
 {0x01D4AE, 0x0004},
-{0x01D4BA, 0x0080},
+{0x01D4BA, 0x0001},
 {0x01D4BB, 0x0004},
-{0x01D4BC, 0x0080},
+{0x01D4BC, 0x0001},
 {0x01D4BD, 0x0004},
-{0x01D4C4, 0x0080},
+{0x01D4C4, 0x0001},
 {0x01D4C5, 0x0004},
-{0x01D506, 0x0080},
+{0x01D506, 0x0001},
 {0x01D507, 0x0004},
-{0x01D50B, 0x0080},
+{0x01D50B, 0x0001},
 {0x01D50D, 0x0004},
-{0x01D515, 0x0080},
+{0x01D515, 0x0001},
 {0x01D516, 0x0004},
-{0x01D51D, 0x0080},
+{0x01D51D, 0x0001},
 {0x01D51E, 0x0004},
-{0x01D53A, 0x0080},
+{0x01D53A, 0x0001},
 {0x01D53B, 0x0004},
-{0x01D53F, 0x0080},
+{0x01D53F, 0x0001},
 {0x01D540, 0x0004},
-{0x01D545, 0x0080},
+{0x01D545, 0x0001},
 {0x01D546, 0x0004},
-{0x01D547, 0x0080},
+{0x01D547, 0x0001},
 {0x01D54A, 0x0004},
-{0x01D551, 0x0080},
+{0x01D551, 0x0001},
 {0x01D552, 0x0004},
-{0x01D6A6, 0x0080},
+{0x01D6A6, 0x0001},
 {0x01D6A8, 0x0004},
 {0x01D6C1, 0x0040},
 {0x01D6C2, 0x0004},
@@ -2007,7 +2020,7 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x01D7AA, 0x0004},
 {0x01D7C3, 0x0040},
 {0x01D7C4, 0x0004},
-{0x01D7CC, 0x0080},
+{0x01D7CC, 0x0001},
 {0x01D7CE, 0x0002},
 {0x01D800, 0x0040},
 {0x01DA00, 0x0010},
@@ -2019,251 +2032,283 @@ const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // st
 {0x01DA84, 0x0010},
 {0x01DA85, 0x0040},
 {0x01DA87, 0x0020},
-{0x01DA8C, 0x0080},
+{0x01DA8C, 0x0001},
 {0x01DA9B, 0x0010},
-{0x01DAA0, 0x0080},
+{0x01DAA0, 0x0001},
 {0x01DAA1, 0x0010},
-{0x01DAB0, 0x0080},
+{0x01DAB0, 0x0001},
 {0x01DF00, 0x0004},
-{0x01DF1F, 0x0080},
+{0x01DF1F, 0x0001},
 {0x01DF25, 0x0004},
-{0x01DF2B, 0x0080},
+{0x01DF2B, 0x0001},
 {0x01E000, 0x0010},
-{0x01E007, 0x0080},
+{0x01E007, 0x0001},
 {0x01E008, 0x0010},
-{0x01E019, 0x0080},
+{0x01E019, 0x0001},
 {0x01E01B, 0x0010},
-{0x01E022, 0x0080},
+{0x01E022, 0x0001},
 {0x01E023, 0x0010},
-{0x01E025, 0x0080},
+{0x01E025, 0x0001},
 {0x01E026, 0x0010},
-{0x01E02B, 0x0080},
+{0x01E02B, 0x0001},
 {0x01E030, 0x0004},
-{0x01E06E, 0x0080},
+{0x01E06E, 0x0001},
 {0x01E08F, 0x0010},
-{0x01E090, 0x0080},
+{0x01E090, 0x0001},
 {0x01E100, 0x0004},
-{0x01E12D, 0x0080},
+{0x01E12D, 0x0001},
 {0x01E130, 0x0010},
 {0x01E137, 0x0004},
-{0x01E13E, 0x0080},
+{0x01E13E, 0x0001},
 {0x01E140, 0x0002},
-{0x01E14A, 0x0080},
+{0x01E14A, 0x0001},
 {0x01E14E, 0x0004},
 {0x01E14F, 0x0040},
-{0x01E150, 0x0080},
+{0x01E150, 0x0001},
 {0x01E290, 0x0004},
 {0x01E2AE, 0x0010},
-{0x01E2AF, 0x0080},
+{0x01E2AF, 0x0001},
 {0x01E2C0, 0x0004},
 {0x01E2EC, 0x0010},
 {0x01E2F0, 0x0002},
-{0x01E2FA, 0x0080},
+{0x01E2FA, 0x0001},
 {0x01E2FF, 0x0040},
-{0x01E300, 0x0080},
+{0x01E300, 0x0001},
 {0x01E4D0, 0x0004},
 {0x01E4EC, 0x0010},
 {0x01E4F0, 0x0002},
-{0x01E4FA, 0x0080},
+{0x01E4FA, 0x0001},
 {0x01E7E0, 0x0004},
-{0x01E7E7, 0x0080},
+{0x01E7E7, 0x0001},
 {0x01E7E8, 0x0004},
-{0x01E7EC, 0x0080},
+{0x01E7EC, 0x0001},
 {0x01E7ED, 0x0004},
-{0x01E7EF, 0x0080},
+{0x01E7EF, 0x0001},
 {0x01E7F0, 0x0004},
-{0x01E7FF, 0x0080},
+{0x01E7FF, 0x0001},
 {0x01E800, 0x0004},
-{0x01E8C5, 0x0080},
+{0x01E8C5, 0x0001},
 {0x01E8C7, 0x0002},
 {0x01E8D0, 0x0010},
-{0x01E8D7, 0x0080},
+{0x01E8D7, 0x0001},
 {0x01E900, 0x0004},
 {0x01E944, 0x0010},
 {0x01E94B, 0x0004},
-{0x01E94C, 0x0080},
+{0x01E94C, 0x0001},
 {0x01E950, 0x0002},
-{0x01E95A, 0x0080},
+{0x01E95A, 0x0001},
 {0x01E95E, 0x0020},
-{0x01E960, 0x0080},
+{0x01E960, 0x0001},
 {0x01EC71, 0x0002},
 {0x01ECAC, 0x0040},
 {0x01ECAD, 0x0002},
 {0x01ECB0, 0x0040},
 {0x01ECB1, 0x0002},
-{0x01ECB5, 0x0080},
+{0x01ECB5, 0x0001},
 {0x01ED01, 0x0002},
 {0x01ED2E, 0x0040},
 {0x01ED2F, 0x0002},
-{0x01ED3E, 0x0080},
+{0x01ED3E, 0x0001},
 {0x01EE00, 0x0004},
-{0x01EE04, 0x0080},
+{0x01EE04, 0x0001},
 {0x01EE05, 0x0004},
-{0x01EE20, 0x0080},
+{0x01EE20, 0x0001},
 {0x01EE21, 0x0004},
-{0x01EE23, 0x0080},
+{0x01EE23, 0x0001},
 {0x01EE24, 0x0004},
-{0x01EE25, 0x0080},
+{0x01EE25, 0x0001},
 {0x01EE27, 0x0004},
-{0x01EE28, 0x0080},
+{0x01EE28, 0x0001},
 {0x01EE29, 0x0004},
-{0x01EE33, 0x0080},
+{0x01EE33, 0x0001},
 {0x01EE34, 0x0004},
-{0x01EE38, 0x0080},
+{0x01EE38, 0x0001},
 {0x01EE39, 0x0004},
-{0x01EE3A, 0x0080},
+{0x01EE3A, 0x0001},
 {0x01EE3B, 0x0004},
-{0x01EE3C, 0x0080},
+{0x01EE3C, 0x0001},
 {0x01EE42, 0x0004},
-{0x01EE43, 0x0080},
+{0x01EE43, 0x0001},
 {0x01EE47, 0x0004},
-{0x01EE48, 0x0080},
+{0x01EE48, 0x0001},
 {0x01EE49, 0x0004},
-{0x01EE4A, 0x0080},
+{0x01EE4A, 0x0001},
 {0x01EE4B, 0x0004},
-{0x01EE4C, 0x0080},
+{0x01EE4C, 0x0001},
 {0x01EE4D, 0x0004},
-{0x01EE50, 0x0080},
+{0x01EE50, 0x0001},
 {0x01EE51, 0x0004},
-{0x01EE53, 0x0080},
+{0x01EE53, 0x0001},
 {0x01EE54, 0x0004},
-{0x01EE55, 0x0080},
+{0x01EE55, 0x0001},
 {0x01EE57, 0x0004},
-{0x01EE58, 0x0080},
+{0x01EE58, 0x0001},
 {0x01EE59, 0x0004},
-{0x01EE5A, 0x0080},
+{0x01EE5A, 0x0001},
 {0x01EE5B, 0x0004},
-{0x01EE5C, 0x0080},
+{0x01EE5C, 0x0001},
 {0x01EE5D, 0x0004},
-{0x01EE5E, 0x0080},
+{0x01EE5E, 0x0001},
 {0x01EE5F, 0x0004},
-{0x01EE60, 0x0080},
+{0x01EE60, 0x0001},
 {0x01EE61, 0x0004},
-{0x01EE63, 0x0080},
+{0x01EE63, 0x0001},
 {0x01EE64, 0x0004},
-{0x01EE65, 0x0080},
+{0x01EE65, 0x0001},
 {0x01EE67, 0x0004},
-{0x01EE6B, 0x0080},
+{0x01EE6B, 0x0001},
 {0x01EE6C, 0x0004},
-{0x01EE73, 0x0080},
+{0x01EE73, 0x0001},
 {0x01EE74, 0x0004},
-{0x01EE78, 0x0080},
+{0x01EE78, 0x0001},
 {0x01EE79, 0x0004},
-{0x01EE7D, 0x0080},
+{0x01EE7D, 0x0001},
 {0x01EE7E, 0x0004},
-{0x01EE7F, 0x0080},
+{0x01EE7F, 0x0001},
 {0x01EE80, 0x0004},
-{0x01EE8A, 0x0080},
+{0x01EE8A, 0x0001},
 {0x01EE8B, 0x0004},
-{0x01EE9C, 0x0080},
+{0x01EE9C, 0x0001},
 {0x01EEA1, 0x0004},
-{0x01EEA4, 0x0080},
+{0x01EEA4, 0x0001},
 {0x01EEA5, 0x0004},
-{0x01EEAA, 0x0080},
+{0x01EEAA, 0x0001},
 {0x01EEAB, 0x0004},
-{0x01EEBC, 0x0080},
+{0x01EEBC, 0x0001},
 {0x01EEF0, 0x0040},
-{0x01EEF2, 0x0080},
+{0x01EEF2, 0x0001},
 {0x01F000, 0x0040},
-{0x01F02C, 0x0080},
+{0x01F02C, 0x0001},
 {0x01F030, 0x0040},
-{0x01F094, 0x0080},
+{0x01F094, 0x0001},
 {0x01F0A0, 0x0040},
-{0x01F0AF, 0x0080},
+{0x01F0AF, 0x0001},
 {0x01F0B1, 0x0040},
-{0x01F0C0, 0x0080},
+{0x01F0C0, 0x0001},
 {0x01F0C1, 0x0040},
-{0x01F0D0, 0x0080},
+{0x01F0D0, 0x0001},
 {0x01F0D1, 0x0040},
-{0x01F0F6, 0x0080},
+{0x01F0F6, 0x0001},
 {0x01F100, 0x0002},
 {0x01F10D, 0x0040},
-{0x01F1AE, 0x0080},
+{0x01F1AE, 0x0001},
 {0x01F1E6, 0x0040},
-{0x01F203, 0x0080},
+{0x01F203, 0x0001},
 {0x01F210, 0x0040},
-{0x01F23C, 0x0080},
+{0x01F23C, 0x0001},
 {0x01F240, 0x0040},
-{0x01F249, 0x0080},
+{0x01F249, 0x0001},
 {0x01F250, 0x0040},
-{0x01F252, 0x0080},
+{0x01F252, 0x0001},
 {0x01F260, 0x0040},
-{0x01F266, 0x0080},
+{0x01F266, 0x0001},
 {0x01F300, 0x0040},
-{0x01F6D8, 0x0080},
+{0x01F6D8, 0x0001},
 {0x01F6DC, 0x0040},
-{0x01F6ED, 0x0080},
+{0x01F6ED, 0x0001},
 {0x01F6F0, 0x0040},
-{0x01F6FD, 0x0080},
+{0x01F6FD, 0x0001},
 {0x01F700, 0x0040},
-{0x01F777, 0x0080},
+{0x01F777, 0x0001},
 {0x01F77B, 0x0040},
-{0x01F7DA, 0x0080},
+{0x01F7DA, 0x0001},
 {0x01F7E0, 0x0040},
-{0x01F7EC, 0x0080},
+{0x01F7EC, 0x0001},
 {0x01F7F0, 0x0040},
-{0x01F7F1, 0x0080},
+{0x01F7F1, 0x0001},
 {0x01F800, 0x0040},
-{0x01F80C, 0x0080},
+{0x01F80C, 0x0001},
 {0x01F810, 0x0040},
-{0x01F848, 0x0080},
+{0x01F848, 0x0001},
 {0x01F850, 0x0040},
-{0x01F85A, 0x0080},
+{0x01F85A, 0x0001},
 {0x01F860, 0x0040},
-{0x01F888, 0x0080},
+{0x01F888, 0x0001},
 {0x01F890, 0x0040},
-{0x01F8AE, 0x0080},
+{0x01F8AE, 0x0001},
 {0x01F8B0, 0x0040},
-{0x01F8B2, 0x0080},
+{0x01F8B2, 0x0001},
 {0x01F900, 0x0040},
-{0x01FA54, 0x0080},
+{0x01FA54, 0x0001},
 {0x01FA60, 0x0040},
-{0x01FA6E, 0x0080},
+{0x01FA6E, 0x0001},
 {0x01FA70, 0x0040},
-{0x01FA7D, 0x0080},
+{0x01FA7D, 0x0001},
 {0x01FA80, 0x0040},
-{0x01FA89, 0x0080},
+{0x01FA89, 0x0001},
 {0x01FA90, 0x0040},
-{0x01FABE, 0x0080},
+{0x01FABE, 0x0001},
 {0x01FABF, 0x0040},
-{0x01FAC6, 0x0080},
+{0x01FAC6, 0x0001},
 {0x01FACE, 0x0040},
-{0x01FADC, 0x0080},
+{0x01FADC, 0x0001},
 {0x01FAE0, 0x0040},
-{0x01FAE9, 0x0080},
+{0x01FAE9, 0x0001},
 {0x01FAF0, 0x0040},
-{0x01FAF9, 0x0080},
+{0x01FAF9, 0x0001},
 {0x01FB00, 0x0040},
-{0x01FB93, 0x0080},
+{0x01FB93, 0x0001},
 {0x01FB94, 0x0040},
-{0x01FBCB, 0x0080},
+{0x01FBCB, 0x0001},
 {0x01FBF0, 0x0002},
-{0x01FBFA, 0x0080},
+{0x01FBFA, 0x0001},
 {0x020000, 0x0004},
-{0x02A6E0, 0x0080},
+{0x02A6E0, 0x0001},
 {0x02A700, 0x0004},
-{0x02B73A, 0x0080},
+{0x02B73A, 0x0001},
 {0x02B740, 0x0004},
-{0x02B81E, 0x0080},
+{0x02B81E, 0x0001},
 {0x02B820, 0x0004},
-{0x02CEA2, 0x0080},
+{0x02CEA2, 0x0001},
 {0x02CEB0, 0x0004},
-{0x02EBE1, 0x0080},
+{0x02EBE1, 0x0001},
 {0x02EBF0, 0x0004},
-{0x02EE5E, 0x0080},
+{0x02EE5E, 0x0001},
 {0x02F800, 0x0004},
-{0x02FA1E, 0x0080},
+{0x02FA1E, 0x0001},
 {0x030000, 0x0004},
-{0x03134B, 0x0080},
+{0x03134B, 0x0001},
 {0x031350, 0x0004},
-{0x0323B0, 0x0080},
+{0x0323B0, 0x0001},
+{0x0E0001, 0x0080},
+{0x0E0002, 0x0001},
+{0x0E0020, 0x0080},
+{0x0E0080, 0x0001},
 {0x0E0100, 0x0010},
-{0x0E01F0, 0x0080},
+{0x0E01F0, 0x0001},
+{0x0F0000, 0x0080},
+{0x0FFFFE, 0x0001},
+{0x100000, 0x0080},
+{0x10FFFE, 0x0001},
 {0x110000, 0x0000},
 };
 
 const std::unordered_set<uint32_t> unicode_set_whitespace = {
-0x000009, 0x00000A, 0x00000B, 0x00000C, 0x00000D, 0x000020, 0x000085, 0x0000A0, 0x001680, 0x002000, 0x002001, 0x002002, 0x002003, 0x002004, 0x002005, 0x002006, 0x002007, 0x002008, 0x002009, 0x00200A, 0x002028, 0x002029, 0x00202F, 0x00205F, 0x003000
+0x000009,
+0x00000A,
+0x00000B,
+0x00000C,
+0x00000D,
+0x000020,
+0x000085,
+0x0000A0,
+0x001680,
+0x002000,
+0x002001,
+0x002002,
+0x002003,
+0x002004,
+0x002005,
+0x002006,
+0x002007,
+0x002008,
+0x002009,
+0x00200A,
+0x002028,
+0x002029,
+0x00202F,
+0x00205F,
+0x003000,
 };
 
 const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
@@ -3222,6 +3267,7 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
 {0x002C2C, 0x002C5C},
 {0x002C2D, 0x002C5D},
 {0x002C2E, 0x002C5E},
+{0x002C2F, 0x002C5F},
 {0x002C60, 0x002C61},
 {0x002C62, 0x00026B},
 {0x002C63, 0x001D7D},
@@ -3402,12 +3448,16 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
 {0x00A7BA, 0x00A7BB},
 {0x00A7BC, 0x00A7BD},
 {0x00A7BE, 0x00A7BF},
+{0x00A7C0, 0x00A7C1},
 {0x00A7C2, 0x00A7C3},
 {0x00A7C4, 0x00A794},
 {0x00A7C5, 0x000282},
 {0x00A7C6, 0x001D8E},
 {0x00A7C7, 0x00A7C8},
 {0x00A7C9, 0x00A7CA},
+{0x00A7D0, 0x00A7D1},
+{0x00A7D6, 0x00A7D7},
+{0x00A7D8, 0x00A7D9},
 {0x00A7F5, 0x00A7F6},
 {0x00FF21, 0x00FF41},
 {0x00FF22, 0x00FF42},
@@ -3511,6 +3561,41 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
 {0x0104D1, 0x0104F9},
 {0x0104D2, 0x0104FA},
 {0x0104D3, 0x0104FB},
+{0x010570, 0x010597},
+{0x010571, 0x010598},
+{0x010572, 0x010599},
+{0x010573, 0x01059A},
+{0x010574, 0x01059B},
+{0x010575, 0x01059C},
+{0x010576, 0x01059D},
+{0x010577, 0x01059E},
+{0x010578, 0x01059F},
+{0x010579, 0x0105A0},
+{0x01057A, 0x0105A1},
+{0x01057C, 0x0105A3},
+{0x01057D, 0x0105A4},
+{0x01057E, 0x0105A5},
+{0x01057F, 0x0105A6},
+{0x010580, 0x0105A7},
+{0x010581, 0x0105A8},
+{0x010582, 0x0105A9},
+{0x010583, 0x0105AA},
+{0x010584, 0x0105AB},
+{0x010585, 0x0105AC},
+{0x010586, 0x0105AD},
+{0x010587, 0x0105AE},
+{0x010588, 0x0105AF},
+{0x010589, 0x0105B0},
+{0x01058A, 0x0105B1},
+{0x01058C, 0x0105B3},
+{0x01058D, 0x0105B4},
+{0x01058E, 0x0105B5},
+{0x01058F, 0x0105B6},
+{0x010590, 0x0105B7},
+{0x010591, 0x0105B8},
+{0x010592, 0x0105B9},
+{0x010594, 0x0105BB},
+{0x010595, 0x0105BC},
 {0x010C80, 0x010CC0},
 {0x010C81, 0x010CC1},
 {0x010C82, 0x010CC2},
@@ -3690,7 +3775,6 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x000079, 0x000059},
 {0x00007A, 0x00005A},
 {0x0000B5, 0x00039C},
-{0x0000DF, 0x000053},
 {0x0000E0, 0x0000C0},
 {0x0000E1, 0x0000C1},
 {0x0000E2, 0x0000C2},
@@ -3758,7 +3842,6 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x000144, 0x000143},
 {0x000146, 0x000145},
 {0x000148, 0x000147},
-{0x000149, 0x0002BC},
 {0x00014B, 0x00014A},
 {0x00014D, 0x00014C},
 {0x00014F, 0x00014E},
@@ -3831,7 +3914,6 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x0001EB, 0x0001EA},
 {0x0001ED, 0x0001EC},
 {0x0001EF, 0x0001EE},
-{0x0001F0, 0x00004A},
 {0x0001F2, 0x0001F1},
 {0x0001F3, 0x0001F1},
 {0x0001F5, 0x0001F4},
@@ -3917,12 +3999,10 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x00037B, 0x0003FD},
 {0x00037C, 0x0003FE},
 {0x00037D, 0x0003FF},
-{0x000390, 0x000399},
 {0x0003AC, 0x000386},
 {0x0003AD, 0x000388},
 {0x0003AE, 0x000389},
 {0x0003AF, 0x00038A},
-{0x0003B0, 0x0003A5},
 {0x0003B1, 0x000391},
 {0x0003B2, 0x000392},
 {0x0003B3, 0x000393},
@@ -4163,7 +4243,6 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x000584, 0x000554},
 {0x000585, 0x000555},
 {0x000586, 0x000556},
-{0x000587, 0x000535},
 {0x0010D0, 0x001C90},
 {0x0010D1, 0x001C91},
 {0x0010D2, 0x001C92},
@@ -4303,11 +4382,6 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x001E91, 0x001E90},
 {0x001E93, 0x001E92},
 {0x001E95, 0x001E94},
-{0x001E96, 0x000048},
-{0x001E97, 0x000054},
-{0x001E98, 0x000057},
-{0x001E99, 0x000059},
-{0x001E9A, 0x000041},
 {0x001E9B, 0x001E60},
 {0x001EA1, 0x001EA0},
 {0x001EA3, 0x001EA2},
@@ -4393,13 +4467,9 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x001F43, 0x001F4B},
 {0x001F44, 0x001F4C},
 {0x001F45, 0x001F4D},
-{0x001F50, 0x0003A5},
 {0x001F51, 0x001F59},
-{0x001F52, 0x0003A5},
 {0x001F53, 0x001F5B},
-{0x001F54, 0x0003A5},
 {0x001F55, 0x001F5D},
-{0x001F56, 0x0003A5},
 {0x001F57, 0x001F5F},
 {0x001F60, 0x001F68},
 {0x001F61, 0x001F69},
@@ -4423,89 +4493,41 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x001F7B, 0x001FEB},
 {0x001F7C, 0x001FFA},
 {0x001F7D, 0x001FFB},
-{0x001F80, 0x001F08},
-{0x001F81, 0x001F09},
-{0x001F82, 0x001F0A},
-{0x001F83, 0x001F0B},
-{0x001F84, 0x001F0C},
-{0x001F85, 0x001F0D},
-{0x001F86, 0x001F0E},
-{0x001F87, 0x001F0F},
-{0x001F88, 0x001F08},
-{0x001F89, 0x001F09},
-{0x001F8A, 0x001F0A},
-{0x001F8B, 0x001F0B},
-{0x001F8C, 0x001F0C},
-{0x001F8D, 0x001F0D},
-{0x001F8E, 0x001F0E},
-{0x001F8F, 0x001F0F},
-{0x001F90, 0x001F28},
-{0x001F91, 0x001F29},
-{0x001F92, 0x001F2A},
-{0x001F93, 0x001F2B},
-{0x001F94, 0x001F2C},
-{0x001F95, 0x001F2D},
-{0x001F96, 0x001F2E},
-{0x001F97, 0x001F2F},
-{0x001F98, 0x001F28},
-{0x001F99, 0x001F29},
-{0x001F9A, 0x001F2A},
-{0x001F9B, 0x001F2B},
-{0x001F9C, 0x001F2C},
-{0x001F9D, 0x001F2D},
-{0x001F9E, 0x001F2E},
-{0x001F9F, 0x001F2F},
-{0x001FA0, 0x001F68},
-{0x001FA1, 0x001F69},
-{0x001FA2, 0x001F6A},
-{0x001FA3, 0x001F6B},
-{0x001FA4, 0x001F6C},
-{0x001FA5, 0x001F6D},
-{0x001FA6, 0x001F6E},
-{0x001FA7, 0x001F6F},
-{0x001FA8, 0x001F68},
-{0x001FA9, 0x001F69},
-{0x001FAA, 0x001F6A},
-{0x001FAB, 0x001F6B},
-{0x001FAC, 0x001F6C},
-{0x001FAD, 0x001F6D},
-{0x001FAE, 0x001F6E},
-{0x001FAF, 0x001F6F},
+{0x001F80, 0x001F88},
+{0x001F81, 0x001F89},
+{0x001F82, 0x001F8A},
+{0x001F83, 0x001F8B},
+{0x001F84, 0x001F8C},
+{0x001F85, 0x001F8D},
+{0x001F86, 0x001F8E},
+{0x001F87, 0x001F8F},
+{0x001F90, 0x001F98},
+{0x001F91, 0x001F99},
+{0x001F92, 0x001F9A},
+{0x001F93, 0x001F9B},
+{0x001F94, 0x001F9C},
+{0x001F95, 0x001F9D},
+{0x001F96, 0x001F9E},
+{0x001F97, 0x001F9F},
+{0x001FA0, 0x001FA8},
+{0x001FA1, 0x001FA9},
+{0x001FA2, 0x001FAA},
+{0x001FA3, 0x001FAB},
+{0x001FA4, 0x001FAC},
+{0x001FA5, 0x001FAD},
+{0x001FA6, 0x001FAE},
+{0x001FA7, 0x001FAF},
 {0x001FB0, 0x001FB8},
 {0x001FB1, 0x001FB9},
-{0x001FB2, 0x001FBA},
-{0x001FB3, 0x000391},
-{0x001FB4, 0x000386},
-{0x001FB6, 0x000391},
-{0x001FB7, 0x000391},
-{0x001FBC, 0x000391},
+{0x001FB3, 0x001FBC},
 {0x001FBE, 0x000399},
-{0x001FC2, 0x001FCA},
-{0x001FC3, 0x000397},
-{0x001FC4, 0x000389},
-{0x001FC6, 0x000397},
-{0x001FC7, 0x000397},
-{0x001FCC, 0x000397},
+{0x001FC3, 0x001FCC},
 {0x001FD0, 0x001FD8},
 {0x001FD1, 0x001FD9},
-{0x001FD2, 0x000399},
-{0x001FD3, 0x000399},
-{0x001FD6, 0x000399},
-{0x001FD7, 0x000399},
 {0x001FE0, 0x001FE8},
 {0x001FE1, 0x001FE9},
-{0x001FE2, 0x0003A5},
-{0x001FE3, 0x0003A5},
-{0x001FE4, 0x0003A1},
 {0x001FE5, 0x001FEC},
-{0x001FE6, 0x0003A5},
-{0x001FE7, 0x0003A5},
-{0x001FF2, 0x001FFA},
-{0x001FF3, 0x0003A9},
-{0x001FF4, 0x00038F},
-{0x001FF6, 0x0003A9},
-{0x001FF7, 0x0003A9},
-{0x001FFC, 0x0003A9},
+{0x001FF3, 0x001FFC},
 {0x00214E, 0x002132},
 {0x002170, 0x002160},
 {0x002171, 0x002161},
@@ -4597,6 +4619,7 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x002C5C, 0x002C2C},
 {0x002C5D, 0x002C2D},
 {0x002C5E, 0x002C2E},
+{0x002C5F, 0x002C2F},
 {0x002C61, 0x002C60},
 {0x002C65, 0x00023A},
 {0x002C66, 0x00023E},
@@ -4800,9 +4823,13 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x00A7BB, 0x00A7BA},
 {0x00A7BD, 0x00A7BC},
 {0x00A7BF, 0x00A7BE},
+{0x00A7C1, 0x00A7C0},
 {0x00A7C3, 0x00A7C2},
 {0x00A7C8, 0x00A7C7},
 {0x00A7CA, 0x00A7C9},
+{0x00A7D1, 0x00A7D0},
+{0x00A7D7, 0x00A7D6},
+{0x00A7D9, 0x00A7D8},
 {0x00A7F6, 0x00A7F5},
 {0x00AB53, 0x00A7B3},
 {0x00AB70, 0x0013A0},
@@ -4885,18 +4912,6 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x00ABBD, 0x0013ED},
 {0x00ABBE, 0x0013EE},
 {0x00ABBF, 0x0013EF},
-{0x00FB00, 0x000046},
-{0x00FB01, 0x000046},
-{0x00FB02, 0x000046},
-{0x00FB03, 0x000046},
-{0x00FB04, 0x000046},
-{0x00FB05, 0x000053},
-{0x00FB06, 0x000053},
-{0x00FB13, 0x000544},
-{0x00FB14, 0x000544},
-{0x00FB15, 0x000544},
-{0x00FB16, 0x00054E},
-{0x00FB17, 0x000544},
 {0x00FF41, 0x00FF21},
 {0x00FF42, 0x00FF22},
 {0x00FF43, 0x00FF23},
@@ -4999,6 +5014,41 @@ const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
 {0x0104F9, 0x0104D1},
 {0x0104FA, 0x0104D2},
 {0x0104FB, 0x0104D3},
+{0x010597, 0x010570},
+{0x010598, 0x010571},
+{0x010599, 0x010572},
+{0x01059A, 0x010573},
+{0x01059B, 0x010574},
+{0x01059C, 0x010575},
+{0x01059D, 0x010576},
+{0x01059E, 0x010577},
+{0x01059F, 0x010578},
+{0x0105A0, 0x010579},
+{0x0105A1, 0x01057A},
+{0x0105A3, 0x01057C},
+{0x0105A4, 0x01057D},
+{0x0105A5, 0x01057E},
+{0x0105A6, 0x01057F},
+{0x0105A7, 0x010580},
+{0x0105A8, 0x010581},
+{0x0105A9, 0x010582},
+{0x0105AA, 0x010583},
+{0x0105AB, 0x010584},
+{0x0105AC, 0x010585},
+{0x0105AD, 0x010586},
+{0x0105AE, 0x010587},
+{0x0105AF, 0x010588},
+{0x0105B0, 0x010589},
+{0x0105B1, 0x01058A},
+{0x0105B3, 0x01058C},
+{0x0105B4, 0x01058D},
+{0x0105B5, 0x01058E},
+{0x0105B6, 0x01058F},
+{0x0105B7, 0x010590},
+{0x0105B8, 0x010591},
+{0x0105B9, 0x010592},
+{0x0105BB, 0x010594},
+{0x0105BC, 0x010595},
 {0x010CC0, 0x010C80},
 {0x010CC1, 0x010C81},
 {0x010CC2, 0x010C82},
@@ -6980,4 +7030,3 @@ const std::vector<range_nfd> unicode_ranges_nfd = {  // start, last, nfd
 {0x02FA1C, 0x02FA1C, 0x009F3B},
 {0x02FA1D, 0x02FA1D, 0x02A600},
 };
-
diff --git a/unicode-data.h b/src/unicode-data.h
similarity index 100%
rename from unicode-data.h
rename to src/unicode-data.h
diff --git a/unicode.cpp b/src/unicode.cpp
similarity index 90%
rename from unicode.cpp
rename to src/unicode.cpp
index 056a4c74172c7..46650bff06d15 100644
--- a/unicode.cpp
+++ b/src/unicode.cpp
@@ -1,3 +1,7 @@
+#if defined(_MSC_VER)
+#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
+#endif
+
 #include "unicode.h"
 #include "unicode-data.h"
 
@@ -15,6 +19,12 @@
 #include <locale>
 #include <codecvt>
 
+size_t unicode_len_utf8(char src) {
+    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
+    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
+    return lookup[highbits];
+}
+
 static std::string unicode_cpts_to_utf8(const std::vector<uint32_t> & cps) {
     std::string result;
     for (size_t i = 0; i < cps.size(); ++i) {
@@ -23,7 +33,7 @@ static std::string unicode_cpts_to_utf8(const std::vector<uint32_t> & cps) {
     return result;
 }
 
-static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
+uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
     assert(offset < utf8.size());
     if (!(utf8[offset + 0] & 0x80)) {
         auto result = utf8[offset + 0];
@@ -226,13 +236,13 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
         assert(offset_end <= cpts.size());
         start = offset_end;
 
-        auto _get_cpt = [&] (const size_t pos) -> char32_t {
-            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
+        static const uint32_t OUT_OF_RANGE = 0xFFFFFFFF;
+        auto _get_cpt = [&] (const size_t pos) -> uint32_t {
+            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
         };
 
         auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
-            static const codepoint_flags undef(codepoint_flags::UNDEFINED);
-            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef;
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : codepoint_flags{};
         };
 
         size_t _prev_end = offset_ini;
@@ -253,18 +263,18 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
         };
 
         for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
-            const char32_t cpt = _get_cpt(pos);
+            const uint32_t cpt = _get_cpt(pos);
             const auto flags = _get_flags(pos);
 
             // regex: 's|'t|'re|'ve|'m|'ll|'d
             if (cpt == '\'' && pos+1 < offset_end) {
-                char32_t cpt_next = _get_cpt(pos+1);
+                uint32_t cpt_next = _get_cpt(pos+1);
                 if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
                     pos += _add_token(pos+2);
                     continue;
                 }
                 if (pos+2 < offset_end) {
-                    char32_t cpt_next_next = _get_cpt(pos+2);
+                    uint32_t cpt_next_next = _get_cpt(pos+2);
                     if ((cpt_next == 'r' && cpt_next_next == 'e') ||
                         (cpt_next == 'v' && cpt_next_next == 'e') ||
                         (cpt_next == 'l' && cpt_next_next == 'l')) {
@@ -294,9 +304,9 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
                 continue;
             }
             // regex: <space>?[^\s\p{L}\p{N}]+
-            if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
+            if (!(flags2.is_whitespace | flags2.is_letter | flags2.is_number) && flags2.as_uint()) {
                 pos += (cpt == ' ');
-                while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
+                while (!(flags2.is_whitespace | flags2.is_letter | flags2.is_number) && flags2.as_uint()) {
                     flags2 = _get_flags(++pos);
                 }
                 _add_token(pos);
@@ -309,7 +319,7 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
             }
 
             // regex: \s+(?!\S)
-            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) {
+            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != OUT_OF_RANGE) {
                 pos += num_whitespaces - 1;
                 _add_token(pos);
                 continue;
@@ -344,13 +354,13 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
         assert(offset_end <= cpts.size());
         start = offset_end;
 
-        auto _get_cpt = [&] (const size_t pos) -> char32_t {
-            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
+        static const uint32_t OUT_OF_RANGE = 0xFFFFFFFF;
+        auto _get_cpt = [&] (const size_t pos) -> uint32_t {
+            return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
         };
 
         auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
-            static const codepoint_flags undef(codepoint_flags::UNDEFINED);
-            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef;
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : codepoint_flags{};
         };
 
         size_t _prev_end = offset_ini;
@@ -371,18 +381,18 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
         };
 
         for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
-            const char32_t cpt = _get_cpt(pos);
+            const uint32_t cpt = _get_cpt(pos);
             const auto flags = _get_flags(pos);
 
             // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive
             if (cpt == '\'' && pos+1 < offset_end) {
-                char32_t cpt_next = unicode_tolower(_get_cpt(pos+1));
+                uint32_t cpt_next = unicode_tolower(_get_cpt(pos+1));
                 if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
                     pos += _add_token(pos+2);
                     continue;
                 }
                 if (pos+2 < offset_end) {
-                    char32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2));
+                    uint32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2));
                     if ((cpt_next == 'r' && cpt_next_next == 'e') ||
                         (cpt_next == 'v' && cpt_next_next == 'e') ||
                         (cpt_next == 'l' && cpt_next_next == 'l')) {
@@ -392,8 +402,8 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
                 }
             }
 
-            // regex: [^\r\n\p{L}\p{N}]?\p{L}+  //####FIXME: the first \p{L} is correct?
-            if (!(cpt == '\r' || cpt == '\n' || /*flags.is_letter |*/ flags.is_number)) {
+            // regex: [^\r\n\p{L}\p{N}]?\p{L}+
+            if (!(cpt == '\r' || cpt == '\n' || flags.is_number)) {
                 if (flags.is_letter || _get_flags(pos+1).is_letter) {  // one or more letters
                     pos++;
                     while (_get_flags(pos).is_letter) {
@@ -419,12 +429,12 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
 
             // regex: <space>?[^\s\p{L}\p{N}]+[\r\n]*
             auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags);
-            if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
+            if (!(flags2.is_whitespace | flags2.is_letter | flags2.is_number) && flags.as_uint()) {
                 pos += (cpt == ' ');
-                while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
+                while (!(flags2.is_whitespace | flags2.is_letter | flags2.is_number) && flags2.as_uint()) {
                     flags2 = _get_flags(++pos);
                 }
-                char32_t cpt2 = _get_cpt(pos);
+                uint32_t cpt2 = _get_cpt(pos);
                 while (cpt2 == '\r' || cpt2 == '\n') {
                     cpt2 = _get_cpt(++pos);
                 }
@@ -435,7 +445,7 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
             size_t num_whitespaces = 0;
             size_t last_end_r_or_n = 0;
             while (_get_flags(pos+num_whitespaces).is_whitespace) {
-                char32_t cpt2 = _get_cpt(pos+num_whitespaces);
+                uint32_t cpt2 = _get_cpt(pos+num_whitespaces);
                 if (cpt2 == '\r' || cpt2 == '\n') {
                     last_end_r_or_n = pos + num_whitespaces + 1;
                 }
@@ -450,7 +460,7 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
             }
 
             // regex: \s+(?!\S)
-            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) {
+            if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != OUT_OF_RANGE) {
                 pos += num_whitespaces - 1;
                 _add_token(pos);
                 continue;
@@ -594,6 +604,7 @@ std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & c
 
 std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
     std::vector<uint32_t> result;
+    result.reserve(utf8.size());
     size_t offset = 0;
     while (offset < utf8.size()) {
         result.push_back(unicode_cpt_from_utf8(utf8, offset));
@@ -626,7 +637,7 @@ uint8_t unicode_utf8_to_byte(const std::string & utf8) {
     return map.at(utf8);
 }
 
-char32_t unicode_tolower(char32_t cp) {
+uint32_t unicode_tolower(uint32_t cp) {
     auto it = unicode_map_lowercase.find(cp);
     return it == unicode_map_lowercase.end() ? cp : it->second;
 }
@@ -679,10 +690,14 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
                 continue;
             }
 
-            const int cpt_flag = unicode_cpt_flags(cpts[i]).category_flag();
+            const auto flags = unicode_cpt_flags(cpts[i]);
 
-            if (k_ucat_cpt.find(cpt_flag) != k_ucat_cpt.end()) {
-                text_collapsed[i] = k_ucat_cpt.at(cpt_flag);
+            if (flags.is_whitespace) {
+                //NOTE: C++ std::regex \s does not mach 0x85, Rust and Python regex does.
+                //text_collapsed[i] = (char) 0x85;  // <Next Line> as whitespace fallback
+                text_collapsed[i] = (char) 0x0B;    // <vertical tab> as whitespace fallback
+            } else if (k_ucat_cpt.find(flags.category_flag()) != k_ucat_cpt.end()) {
+                text_collapsed[i] = k_ucat_cpt.at(flags.category_flag());
             } else {
                 text_collapsed[i] = (char) 0xD0; // fallback
             }
@@ -766,9 +781,16 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
                 bpe_offsets = unicode_regex_split_stl(text_collapsed, regex_expr_collapsed, bpe_offsets);
             } else {
                 // no unicode category used, we can use std::wregex directly
-                const std::wstring wtext       = unicode_wstring_from_utf8(text);
                 const std::wstring wregex_expr = unicode_wstring_from_utf8(regex_expr);
 
+                // std::wregex \s does not mach non-ASCII whitespaces, using 0x0B as fallback
+                std::wstring wtext(cpts.begin(), cpts.end());
+                for (size_t i = 0; i < wtext.size(); ++i) {
+                    if (wtext[i] > 0x7F && unicode_cpt_flags(wtext[i]).is_whitespace) {
+                        wtext[i] = 0x0B;
+                    }
+                }
+
                 //printf("text: %s\n", text.c_str());
                 //printf("regex_expr: %s\n", regex_expr.c_str());
                 bpe_offsets = unicode_regex_split_stl(wtext, wregex_expr, bpe_offsets);
diff --git a/unicode.h b/src/unicode.h
similarity index 91%
rename from unicode.h
rename to src/unicode.h
index 7513be4ad0d4f..008532a242ab8 100644
--- a/unicode.h
+++ b/src/unicode.h
@@ -4,6 +4,8 @@
 #include <string>
 #include <vector>
 
+// TODO: prefix all symbols with "llama_"
+
 struct codepoint_flags {
     enum {
         UNDEFINED       = 0x0001,
@@ -46,8 +48,10 @@ struct codepoint_flags {
     }
 };
 
+size_t unicode_len_utf8(char src);
 
 std::string unicode_cpt_to_utf8(uint32_t cp);
+uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset);
 std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
 
 std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
@@ -58,6 +62,6 @@ codepoint_flags unicode_cpt_flags(const std::string & utf8);
 std::string unicode_byte_to_utf8(uint8_t byte);
 uint8_t unicode_utf8_to_byte(const std::string & utf8);
 
-char32_t unicode_tolower(char32_t cp);
+uint32_t unicode_tolower(uint32_t cp);
 
 std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs);
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 766a017524237..0207e3a5943c9 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -70,21 +70,19 @@ add_executable(test-tokenizer-0 test-tokenizer-0.cpp)
 target_link_libraries(test-tokenizer-0 PRIVATE common)
 install(TARGETS test-tokenizer-0 RUNTIME)
 
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-bert-bge          ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bert-bge.gguf)
-# TODO: enable when fixed
-#       https://github.com/ggerganov/llama.cpp/pull/7036
-#llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-#llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf)
-#llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-command-r         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-command-r.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-qwen2.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
 
 # build test-tokenizer-1-bpe target once and add many tests
 add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp)
@@ -92,16 +90,14 @@ target_link_libraries(test-tokenizer-1-bpe PRIVATE common)
 install(TARGETS test-tokenizer-1-bpe RUNTIME)
 
 # TODO: disabled due to slowness
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-stablelm  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-2     ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
 #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-neox  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
 #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-refact    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
 #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt2      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-bloom     ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf)
 
 # build test-tokenizer-1-spm target once and add many tests
 add_executable(test-tokenizer-1-spm test-tokenizer-1-spm.cpp)
@@ -129,8 +125,11 @@ llama_target_and_test(test-rope.cpp)
 llama_target_and_test(test-model-load-cancel.cpp  LABEL "model")
 llama_target_and_test(test-autorelease.cpp        LABEL "model")
 
-llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
-target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
+# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
+if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
+    llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
+    target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
+endif()
 
 # dummy executable - not installed
 get_filename_component(TEST_TARGET test-c.c NAME_WE)
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index c74e253db4b3b..2f4117a627de0 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1,7 +1,6 @@
 #include <ggml.h>
 #include <ggml-alloc.h>
 #include <ggml-backend.h>
-#include <ggml-backend-impl.h>
 
 #include <algorithm>
 #include <array>
@@ -80,14 +79,22 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
                 im = nullptr;
             }
         }
+
         ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
         GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size()));
+        // TODO: other cases
+        //#pragma omp parallel for
+        //for (int i = 0; i < tensor->ne[1]; i++) {
+        //    ggml_quantize_chunk(tensor->type, data.data(), dataq.data(),
+        //        i * tensor->ne[0], 1, tensor->ne[0], im);
+        //}
+
         ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
     } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
         // This is going to create some weird integers though.
         ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor));
     } else {
-        GGML_ASSERT(false);
+        GGML_ABORT("fatal error");
     }
 }
 
@@ -125,7 +132,7 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
                         tt.to_float(&buf[i], vq.data(), bs);
                         tv.insert(tv.end(), vq.begin(), vq.end());
                     } else {
-                        GGML_ASSERT(false);
+                        GGML_ABORT("fatal error");
                     }
                 }
             }
@@ -642,20 +649,29 @@ struct test_case {
 struct test_unary : public test_case {
     const ggml_unary_op op;
     const ggml_type type;
-    const std::array<int64_t, 4> ne;
+    const std::array<int64_t, 4> ne_a;
+    int v; // view (1 : non-contiguous a)
 
     std::string vars() override {
-        return VARS_TO_STR2(type, ne);
+        return VARS_TO_STR3(type, ne_a, v);
     }
 
     test_unary(ggml_unary_op op,
             ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {128, 10, 10, 10})
-        : op(op), type(type), ne(ne) {}
+            std::array<int64_t, 4> ne_a = {128, 10, 10, 10},
+            int v = 0)
+        : op(op), type(type), ne_a(ne_a), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * in = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * out = ggml_unary(ctx, in, op);
+        ggml_tensor * a;
+        if (v & 1) {
+            auto ne = ne_a; ne[0] *= 3;
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
+        } else {
+            a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        }
+        ggml_tensor * out = ggml_unary(ctx, a, op);
         return out;
     }
 
@@ -751,7 +767,7 @@ struct test_dup : public test_case {
     }
 
     test_dup(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 1},
+            std::array<int64_t, 4> ne = {10, 10, 20, 1},
             std::array<int64_t, 4> permute = {0, 0, 0, 0})
         : type(type), ne(ne), permute(permute),
             _use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
@@ -771,9 +787,15 @@ struct test_cpy : public test_case {
     const ggml_type type_src;
     const ggml_type type_dst;
     const std::array<int64_t, 4> ne;
+    const std::array<int64_t, 4> permute;
+    bool _src_use_permute;
 
     std::string vars() override {
-        return VARS_TO_STR3(type_src, type_dst, ne);
+        return VARS_TO_STR4(type_src, type_dst, ne, permute);
+    }
+
+    double max_nmse_err() override {
+        return 1e-6;
     }
 
     size_t op_size(ggml_tensor * t) override {
@@ -781,12 +803,17 @@ struct test_cpy : public test_case {
     }
 
     test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 1})
-        : type_src(type_src), type_dst(type_dst), ne(ne) {}
+            std::array<int64_t, 4> ne = {10, 10, 10, 1},
+            std::array<int64_t, 4> permute = {0, 0, 0, 0})
+        : type_src(type_src), type_dst(type_dst), ne(ne), permute(permute),
+          _src_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
-        ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, ne.data());
+        if (_src_use_permute) {
+            src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
+        }
+        ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
         ggml_tensor * out = ggml_cpy(ctx, src, dst);
         return out;
     }
@@ -1054,6 +1081,33 @@ struct test_sqr : public test_case {
     }
 };
 
+// GGML_OP_SQRT
+struct test_sqrt : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne;
+
+    std::string vars() override {
+        return VARS_TO_STR2(type, ne);
+    }
+
+    test_sqrt(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = {10, 10, 10, 10})
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_tensor * out = ggml_sqrt(ctx, a);
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        // fill with positive values
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            init_tensor_uniform(t, 0.0f, 100.0f);
+        }
+    }
+};
+
 // GGML_OP_CLAMP
 struct test_clamp : public test_case {
     const ggml_type type;
@@ -1135,27 +1189,41 @@ struct test_soft_max : public test_case {
     }
 };
 
+
 // GGML_OP_ROPE
 struct test_rope : public test_case {
     const ggml_type type;
-    const std::array<int64_t, 4> ne;
+    const std::array<int64_t, 4> ne_a;
     int n_dims;
     int mode;
-    int n_ctx;
+    int n_ctx; // used to generate positions
+    float fs; // freq_scale
+    float ef; // ext_factor
+    float af; // attn_factor
+    bool ff;
+    int v; // view (1 : non-contiguous a)
 
     std::string vars() override {
-        return VARS_TO_STR5(type, ne, n_dims, mode, n_ctx);
+        return VARS_TO_STR10(type, ne_a, n_dims, mode, n_ctx, fs, ef, af, ff, v);
     }
 
     test_rope(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 1},
-            int n_dims = 10, int mode = 0, int n_ctx = 512)
-        : type(type), ne(ne), n_dims(n_dims), mode(mode), n_ctx(n_ctx) {}
+            std::array<int64_t, 4> ne_a = {10, 10, 10, 1},
+            int n_dims = 10, int mode = 0, int n_ctx = 512, float fs = 1.0f, float ef = 0.0f, float af = 0.0f, bool ff = false, int v = 0)
+        : type(type), ne_a(ne_a), n_dims(n_dims), mode(mode), n_ctx(n_ctx), fs(fs), ef(ef), af(af), ff(ff), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]);
-        ggml_tensor * out = ggml_rope(ctx, a, pos, n_dims, mode, n_ctx);
+        ggml_tensor * a;
+        if (v & 1) {
+            auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
+        } else {
+            a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        }
+        ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2]);
+        ggml_tensor * freq = ff ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_dims/2) : nullptr;
+        ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
         return out;
     }
 
@@ -1163,13 +1231,18 @@ struct test_rope : public test_case {
         for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
             if (t->type == GGML_TYPE_I32) {
                 // pos
-                std::vector<int> data(ne[2]);
-                for (int i = 0; i < ne[2]; i++) {
+                std::vector<int> data(ne_a[2]);
+                for (int i = 0; i < ne_a[2]; i++) {
                     data[i] = rand() % n_ctx;
                 }
-                ggml_backend_tensor_set(t, data.data(), 0, ne[2] * sizeof(int));
+                ggml_backend_tensor_set(t, data.data(), 0, ne_a[2] * sizeof(int));
             } else {
-                init_tensor_uniform(t);
+                if (t->ne[0] == n_dims/2) {
+                    // frequency factors in the range [0.9f, 1.1f]
+                    init_tensor_uniform(t, 0.9f, 1.1f);
+                } else {
+                    init_tensor_uniform(t);
+                }
             }
         }
     }
@@ -1209,6 +1282,32 @@ struct test_pool2d : public test_case {
     }
 };
 
+// GGML_OP_CONV_TRANSPOSE_1D
+struct test_conv_transpose_1d : public test_case {
+    const std::array<int64_t, 4> ne_input;
+    const std::array<int64_t, 4> ne_kernel;
+
+    const int s0; // stride
+    const int p0; // padding
+    const int d0; // dilation
+
+    std::string vars() override {
+        return VARS_TO_STR5(ne_input, ne_kernel, s0, p0, d0);
+    }
+
+    test_conv_transpose_1d(std::array<int64_t, 4> ne_input = {197, 32, 1, 1}, // [input_width, input_height, input_channels, 1]
+                           std::array<int64_t, 4> ne_kernel = {16, 32, 32, 1}, // [kernel_width, kernel_height, input_channels, 1]
+                           int s0 = 1, int p0 = 0, int d0 = 1)
+        : ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), p0(p0), d0(d0) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data());
+        ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_kernel.data());
+        ggml_tensor * out = ggml_conv_transpose_1d(ctx, kernel, input, s0, p0, d0);
+        return out;
+    }
+};
+
 // GGML_OP_IM2COL
 struct test_im2col : public test_case {
     const ggml_type type_input;
@@ -1222,7 +1321,7 @@ struct test_im2col : public test_case {
     // padding
     const int p0;
     const int p1;
-    // dilatation
+    // dilation
     const int d0;
     const int d1;
     // mode
@@ -1252,22 +1351,41 @@ struct test_im2col : public test_case {
 // GGML_OP_CONCAT
 struct test_concat : public test_case {
     const ggml_type type;
-    const std::array<int64_t, 4> ne;
-    const int64_t b_ne2;
+    const std::array<int64_t, 4> ne_a;
+    const int64_t ne_b_d;
+    const int dim;
+    const int v; // view (1 << 0: non-cont a, 1 << 1: non-cont b)
 
     std::string vars() override {
-        return VARS_TO_STR3(type, ne, b_ne2);
+        return VARS_TO_STR5(type, ne_a, ne_b_d, dim, v);
     }
 
     test_concat(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 10},
-            int64_t b_ne2 = 10)
-        : type(type), ne(ne), b_ne2(b_ne2) {}
+            std::array<int64_t, 4> ne_a = {10, 10, 10, 10},
+            int64_t ne_b_d = 10,
+            int dim = 2, int v = 0)
+        : type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * b = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], b_ne2, ne[3]);
-        ggml_tensor * out = ggml_concat(ctx, a, b);
+        auto ne_b = ne_a;
+        ne_b[dim] = ne_b_d;
+        ggml_tensor * a;
+        if (v & 1) {
+            auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
+        } else {
+            a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        }
+        ggml_tensor * b;
+        if (v & 2) {
+            auto ne = ne_b; ne[0] *= 3; ne[1] *= 2; ne[2] *= 4;
+            b = ggml_new_tensor(ctx, type, 4, ne.data());
+            b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0);
+        } else {
+            b = ggml_new_tensor(ctx, type, 4, ne_b.data());
+        }
+        ggml_tensor * out = ggml_concat(ctx, a, b, dim);
         return out;
     }
 };
@@ -1316,7 +1434,7 @@ struct test_argsort : public test_case {
                     ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float));
                 }
             } else {
-                GGML_ASSERT(false);
+                GGML_ABORT("fatal error");
             }
         }
     }
@@ -1393,6 +1511,7 @@ struct test_group_norm : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
     const int32_t num_groups;
+    const float eps;
 
     std::string vars() override {
         return VARS_TO_STR3(type, ne, num_groups);
@@ -1400,12 +1519,13 @@ struct test_group_norm : public test_case {
 
     test_group_norm(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {64, 64, 320, 1},
-            int32_t num_groups = 32)
-        : type(type), ne(ne), num_groups(num_groups) {}
+            int32_t num_groups = 32,
+            float eps = 1e-6f)
+        : type(type), ne(ne), num_groups(num_groups), eps(eps) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * out = ggml_group_norm(ctx, a, num_groups);
+        ggml_tensor * out = ggml_group_norm(ctx, a, num_groups, eps);
         return out;
     }
 };
@@ -1533,21 +1653,25 @@ struct test_flash_attn_ext : public test_case {
 
     const float max_bias; // ALiBi
 
+    const ggml_type type_KV;
+
     std::string vars() override {
-        return VARS_TO_STR6(hs, nh, kv, nb, mask, max_bias);
+        return VARS_TO_STR7(hs, nh, kv, nb, mask, max_bias, type_KV);
     }
 
     double max_nmse_err() override {
         return 5e-4;
     }
 
-    test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f)
-        : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias) {}
+    test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, ggml_type type_KV = GGML_TYPE_F16)
+        : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), type_KV(type_KV) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1);
-        ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1);
-        ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1);
+        const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV));
+
+        ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs_padded, nb, nh, 1);
+        ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV,       hs_padded, kv, nh, 1);
+        ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV,       hs_padded, kv, nh, 1);
         ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr;
         ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias);
         return out;
@@ -1574,7 +1698,7 @@ struct llama_hparams {
 
     // cparams
     static constexpr uint32_t n_ctx = 512; // user-specified context size
-    static constexpr uint32_t n_orig_ctx = n_ctx;
+    static constexpr uint32_t n_ctx_orig = n_ctx;
 
     // batch
     int32_t n_tokens;
@@ -1763,15 +1887,15 @@ struct test_llama : public test_llm {
                 struct ggml_tensor * Kcur = ggml_mul_mat(ctx, wk, cur);
                 struct ggml_tensor * Vcur = ggml_mul_mat(ctx, wv, cur);
 
-                Qcur = ggml_rope_custom(
-                    ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head,    hp.n_tokens), inp_pos,
-                    hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head,    hp.n_tokens), inp_pos, nullptr,
+                    hp.n_rot, 0, hp.n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
 
-                Kcur = ggml_rope_custom(
-                    ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos,
-                    hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos, nullptr,
+                    hp.n_rot, 0, hp.n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
 
@@ -1889,13 +2013,13 @@ struct test_falcon : public test_llm {
                 Kcur = ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens);
 
                 // using mode = 2 for neox mode
-                Qcur = ggml_rope_custom(
-                    ctx, Qcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx, Qcur, inp_pos, nullptr, hp.n_rot, 2, hp.n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
 
-                Kcur = ggml_rope_custom(
-                    ctx, Kcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx, Kcur, inp_pos, nullptr, hp.n_rot, 2, hp.n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
 
@@ -1972,12 +2096,15 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
         GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
         GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
+        GGML_TYPE_BF16,
     };
 
     // unary ops
-    for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
-        test_cases.emplace_back(new test_unary((ggml_unary_op) op));
-        test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }));
+    for (int v : {0, 1}) {
+        for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
+            test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 128, 10, 10, 10 }, v));
+            test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }, v));
+        }
     }
 
     test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));
@@ -2014,6 +2141,19 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
 
     test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
     test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
+    // test cases for 1D im2col
+    test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));
+    test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));
+
+    test_cases.emplace_back(new test_conv_transpose_1d());
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 3, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 2, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 1, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,2,2,1}, 2, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,2,2,1}, 1, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
+
 
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
@@ -2027,12 +2167,22 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
     test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
     test_cases.emplace_back(new test_dup(GGML_TYPE_I16));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F32, {10, 10, 5, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F16, {10, 10, 5, 1}, {0, 2, 1, 3})); // dup by rows
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F32, {10, 10, 5, 1}, {1, 0, 2, 3}));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F16, {10, 10, 5, 1}, {1, 0, 2, 3})); // dup dst not-contiguous
     test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
 
     for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
         for (ggml_type type_dst : all_types) {
            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
+           test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
+        }
+    }
+    for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+        for (ggml_type type_dst : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {1, 0, 2, 3})); // cpy not-contiguous
         }
     }
 
@@ -2082,6 +2232,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
     }
 
+#if 1
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1,  1}, {1, 1}));
@@ -2101,10 +2252,31 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2}));
         }
     }
+#else
+    // m = a rows
+    // n = b rows
+    // k = cols
+    std::uniform_int_distribution<> dist_m(1, 128);
+    std::uniform_int_distribution<> dist_n(16, 128);
+    std::uniform_int_distribution<> dist_k(1, 16);
+    for (int i = 0; i < 1000; i++) {
+        for (ggml_type type_a : all_types) {
+            for (ggml_type type_b : {GGML_TYPE_F32}) {
+                int m = dist_m(rng);
+                int n = dist_n(rng);
+                int k = dist_k(rng) * ggml_blck_size(type_a);
+                test_cases.emplace_back(new test_mul_mat(type_a, type_b, m, n, k, { 1,  1}, {1, 1}));
+            }
+        }
+    }
+#endif
 
     for (ggml_type type_a : other_types) {
         for (ggml_type type_b : {GGML_TYPE_F32}) {
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1,  1}, {1, 1}));
+            if (ggml_blck_size(type_a) != 256) {
+                test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, ggml_blck_size(type_a), {1,  1}, {1, 1}));
+            }
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {1,  1}, {1, 1}));
         }
     }
 
@@ -2148,6 +2320,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     }
 
     test_cases.emplace_back(new test_sqr());
+    test_cases.emplace_back(new test_sqrt());
     test_cases.emplace_back(new test_clamp());
 
     test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10,  1,  1}, 5));
@@ -2163,7 +2336,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         for (int n = 0; n < 10; ++n) {
             int64_t ne0 = dist_ne0(rng);
             int64_t ne1 = dist_ne1(rng);
-            test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, n/2 == 0, 0.1f, ne0 < 1000 ? 4.0f : 0.0f));
+            test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, GGML_TYPE_F32, {ne0, ne1, 1, 1}, n/2 == 0, 0.1f, ne0 < 1000 ? 4.0f : 0.0f));
         }
 
         exponent <<= 1;
@@ -2182,26 +2355,53 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
             }
         }
     }
-
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, true, 0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 8.0f));
 
-    for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
-        test_cases.emplace_back(new test_rope(type, {128,  32, 10, 1}, 128, 0, 512)); // llama 7B
-        test_cases.emplace_back(new test_rope(type, {128,  40, 10, 1}, 128, 0, 512)); // llama 13B
-        test_cases.emplace_back(new test_rope(type, {128,  52, 10, 1}, 128, 0, 512)); // llama 30B
-        test_cases.emplace_back(new test_rope(type, {128,  64, 10, 1}, 128, 0, 512)); // llama 65B
-        test_cases.emplace_back(new test_rope(type, { 64,   1, 10, 1},  64, 2, 512)); // neox (falcon 7B)
-        test_cases.emplace_back(new test_rope(type, { 64,  71, 10, 1},  64, 2, 512)); // neox (falcon 7B)
-        test_cases.emplace_back(new test_rope(type, { 64,   8, 10, 1},  64, 2, 512)); // neox (falcon 40B)
-        test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1},  64, 2, 512)); // neox (falcon 40B)
-        test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  20, 2, 512)); // neox (stablelm)
-        test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  32, 2, 512)); // neox (phi-2)
+    {
+        bool all = true;
+
+        for (float v : { 0, 1 }) {
+            for (float fs : { 1.0f, 1.4245f }) {
+                for (float ef : { 0.0f, 0.7465f }) {
+                    for (float af : { 1.0f, 1.4245f }) {
+                        for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+                            for (bool ff : {false, true}) { // freq_factors
+                                test_cases.emplace_back(new test_rope(type, {128,  32, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 7B
+
+                                if (all) {
+                                    test_cases.emplace_back(new test_rope(type, {128,  40, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 13B
+                                    test_cases.emplace_back(new test_rope(type, {128,  52, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 30B
+                                    test_cases.emplace_back(new test_rope(type, {128,  64, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 65B
+                                }
+
+                                if (all) {
+                                    test_cases.emplace_back(new test_rope(type, { 64,   1, 10, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B)
+                                    test_cases.emplace_back(new test_rope(type, { 64,  71, 10, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B)
+                                    test_cases.emplace_back(new test_rope(type, { 64,   8, 10, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
+                                    test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  20, 2, 512, fs, ef, af, ff, v)); // neox (stablelm)
+                                    test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  32, 2, 512, fs, ef, af, ff, v)); // neox (phi-2)
+                                }
+
+                                test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
+                            }
+                        }
+
+                        all = false;
+                    }
+                }
+            }
+        }
     }
 
-    test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
-    test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
+    for (int v : { 0, 1, 2, 3 }) {
+        for (int dim : { 0, 1, 2, 3, }) {
+            test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim, v));
+            test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim, v));
+        }
+    }
 
     for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
@@ -2227,7 +2427,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
                 for (int nh : { 32, }) {
                     for (int kv : { 512, 1024, }) {
                         for (int nb : { 1, 2, 4, 8, }) {
-                            test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias));
+                            for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
+                                test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, type_KV));
+                            }
                         }
                     }
                 }
@@ -2267,7 +2469,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         return true;
     }
 
-    GGML_ASSERT(false);
+    GGML_ABORT("fatal error");
     return false;
 }
 
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 4fe9183b92cfd..a8222caeefb88 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -1,4 +1,3 @@
-#include <iostream>
 #include <string>
 #include <vector>
 #include <sstream>
@@ -7,6 +6,7 @@
 #include <cassert>
 
 #include "llama.h"
+#include "common.h"
 
 int main(void) {
     llama_chat_message conversation[] = {
@@ -49,8 +49,22 @@ int main(void) {
         "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'  + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
         // Llama-3
         "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
-        // Phi-3
-        "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + ' ' + message['content'] + '<|end|> ' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|> ' }}{% else %}{{ eos_token }}{% endif %}"
+        //Phi-3-mini
+        "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
+        //Phi-3-small
+        "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
+        //Phi-3-medium
+        "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
+        //Phi-3-vision
+        "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
+        // ChatGLM3
+        "{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
+        // ChatGLM4
+        u8"[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
+        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+        u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
+        // DeepSeek-V2
+        "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -79,8 +93,22 @@ int main(void) {
         "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
         // Llama 3
         "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-        // Phi 3
-        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\nI am an assistant<|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        //Phi-3-mini
+        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        //Phi-3-small
+        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        //Phi-3-medium
+        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        //Phi-3-vision
+        "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        // ChatGLM3
+        "[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n    I am an assistant   <|user|>\n Another question<|assistant|>",
+        // ChatGLM4
+        "[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n   I am an assistant   <|user|>\nAnother question<|assistant|>",
+        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+        u8"You are a helpful assistant<用户>Hello<AI>Hi there<用户>Who are you<AI>I am an assistant<用户>Another question<AI>",
+        // DeepSeek-V2
+        u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant:    I am an assistant   <｜end▁of▁sentence｜>User: Another question\n\nAssistant:",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;
@@ -104,8 +132,46 @@ int main(void) {
         );
         formatted_chat.resize(res);
         std::string output(formatted_chat.data(), formatted_chat.size());
-        std::cout << output << "\n-------------------------\n";
+        printf("%s\n", output.c_str());
+        printf("-------------------------\n");
         assert(output == expected);
     }
+
+
+    // test llama_chat_format_single for system message
+    printf("\n\n=== llama_chat_format_single (system message) ===\n\n");
+    std::vector<llama_chat_msg> chat2;
+    llama_chat_msg sys_msg{"system", "You are a helpful assistant"};
+
+    auto fmt_sys = [&](std::string tmpl) {
+        auto output = llama_chat_format_single(nullptr, tmpl, chat2, sys_msg, false);
+        printf("fmt_sys(%s) : %s\n", tmpl.c_str(), output.c_str());
+        printf("-------------------------\n");
+        return output;
+    };
+    assert(fmt_sys("chatml") == "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n");
+    assert(fmt_sys("llama2") == "[INST] You are a helpful assistant\n");
+    assert(fmt_sys("gemma")  == ""); // for gemma, system message is merged with user message
+    assert(fmt_sys("llama3") == "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|>");
+
+
+    // test llama_chat_format_single for user message
+    printf("\n\n=== llama_chat_format_single (user message) ===\n\n");
+    chat2.push_back({"system", "You are a helpful assistant"});
+    chat2.push_back({"user", "Hello"});
+    chat2.push_back({"assistant", "I am assistant"});
+    llama_chat_msg new_msg{"user", "How are you"};
+
+    auto fmt_single = [&](std::string tmpl) {
+        auto output = llama_chat_format_single(nullptr, tmpl, chat2, new_msg, true);
+        printf("fmt_single(%s) : %s\n", tmpl.c_str(), output.c_str());
+        printf("-------------------------\n");
+        return output;
+    };
+    assert(fmt_single("chatml") == "\n<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n");
+    assert(fmt_single("llama2") == "[INST] How are you [/INST]");
+    assert(fmt_single("gemma")  == "\n<start_of_turn>user\nHow are you<end_of_turn>\n<start_of_turn>model\n");
+    assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n");
+
     return 0;
 }
diff --git a/tests/test-double-float.cpp b/tests/test-double-float.cpp
index 753dae911b0cb..6aac4737a7df0 100644
--- a/tests/test-double-float.cpp
+++ b/tests/test-double-float.cpp
@@ -14,7 +14,7 @@
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdouble-promotion"
 
-// ggml.c::quantize_row_q4_0_reference
+// ggml.c::quantize_row_q4_0_ref
 inline static uint8_t round_orig(float v0) { return ((int8_t) (round(v0))) + 8; }
 
 // ggml.c::ggml_silu_f32
@@ -24,7 +24,7 @@ inline static float silu_orig(float x) {
 
 #pragma GCC diagnostic pop
 
-// ggml.c::quantize_row_q4_0_reference
+// ggml.c::quantize_row_q4_0_ref
 inline static uint8_t round_float(float v0) { return (int8_t)roundf(v0) + 8; }
 
 // ggml.c::ggml_silu_f32
diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp
index 8ff76c8910c49..a353276459b2d 100644
--- a/tests/test-grad0.cpp
+++ b/tests/test-grad0.cpp
@@ -1465,7 +1465,7 @@ int main(int argc, const char ** argv) {
                             continue;
                         }
 
-                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode, 0));
+                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode));
 
                         GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
                         check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
@@ -1505,7 +1505,7 @@ int main(int argc, const char ** argv) {
                             continue;
                         }
 
-                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode, 0));
+                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode));
 
                         GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
                         check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);
@@ -1515,90 +1515,50 @@ int main(int argc, const char ** argv) {
         }
 
         // flash_attn f32
-        {
-            srand(seed);
-            const int nargs = 3;
-
-            int64_t ne2[4];
-
-            get_random_dims(ne2, 4);
-            int64_t D = ne2[0];
-            int64_t N = ne2[1];
-            int64_t M = ne2[2] + N;
-            int64_t B = ne2[3];
-
-            for (int masked = 0; masked <= 1; ++masked) {
-                for (int ndims = 2; ndims <= 4; ++ndims) {
-                    int max_nrep = (ndims >= 3) ? 2 : 1;
-                    for (int nrep = 1; nrep < max_nrep; ++nrep) {
-                        int64_t neq[4] = { D, N, B*nrep, ne[3] };
-                        int64_t nek[4] = { D, M, B, ne[3] };
-                        int64_t nev[4] = { M, D, B, ne[3] };
-                        if (ndims == 2) {
-                            neq[2] = 1; neq[3] = 1;
-                            nek[2] = 1; nek[3] = 1;
-                            nev[2] = 1; nev[3] = 1;
-                        } else if (ndims == 3) {
-                            neq[3] = 1;
-                            nek[3] = 1;
-                            nev[3] = 1;
-                        }
-                        x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f);
-                        x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f);
-                        x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f);
-                        ggml_set_param(ctx0, x[0]);
-                        ggml_set_param(ctx0, x[1]);
-                        ggml_set_param(ctx0, x[2]);
-
-                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
-
-                        check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
-                    }
-                }
-            }
-        }
-
-        // flash_attn f16, not yet fully implemented
-        if(0)
-        {
-            srand(seed);
-            const int nargs = 3;
-
-            int64_t ne2[4];
-
-            get_random_dims(ne2, 4);
-            int64_t D = ne2[0];
-            int64_t N = ne2[1];
-            int64_t M = ne2[2] + N;
-            int64_t B = ne2[3];
-
-            for (int masked = 0; masked <= 1; ++masked) {
-                for (int ndims = 2; ndims <= 4; ++ndims) {
-                    int64_t neq[4] = { D, N, B, ne[3] };
-                    int64_t nek[4] = { D, M, B, ne[3] };
-                    int64_t nev[4] = { M, D, B, ne[3] };
-                    if (ndims == 2) {
-                        neq[2] = 1; neq[3] = 1;
-                        nek[2] = 1; nek[3] = 1;
-                        nev[2] = 1; nev[3] = 1;
-                    } else if (ndims == 3) {
-                        neq[3] = 1;
-                        nek[3] = 1;
-                        nev[3] = 1;
-                    }
-                    x[0] = get_random_tensor_f16(ctx0, ndims, neq, -0.1250f, 0.1250f);
-                    x[1] = get_random_tensor_f16(ctx0, ndims, nek, -0.1250f, 0.1250f);
-                    x[2] = get_random_tensor_f16(ctx0, ndims, nev, -0.1250f, 0.1250f);
-                    ggml_set_param(ctx0, x[0]);
-                    ggml_set_param(ctx0, x[1]);
-                    ggml_set_param(ctx0, x[2]);
-
-                    struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
+        // TODO: adapt to ggml_flash_attn_ext() changes
+        //{
+        //    srand(seed);
+        //    const int nargs = 3;
+
+        //    int64_t ne2[4];
+
+        //    get_random_dims(ne2, 4);
+        //    int64_t D = ne2[0];
+        //    int64_t N = ne2[1];
+        //    int64_t M = ne2[2] + N;
+        //    int64_t B = ne2[3];
+
+        //    for (int masked = 0; masked <= 1; ++masked) {
+        //        for (int ndims = 2; ndims <= 4; ++ndims) {
+        //            int max_nrep = (ndims >= 3) ? 2 : 1;
+        //            for (int nrep = 1; nrep < max_nrep; ++nrep) {
+        //                int64_t neq[4] = { D, N, B*nrep, ne[3] };
+        //                int64_t nek[4] = { D, M, B, ne[3] };
+        //                int64_t nev[4] = { M, D, B, ne[3] };
+        //                if (ndims == 2) {
+        //                    neq[2] = 1; neq[3] = 1;
+        //                    nek[2] = 1; nek[3] = 1;
+        //                    nev[2] = 1; nev[3] = 1;
+        //                } else if (ndims == 3) {
+        //                    neq[3] = 1;
+        //                    nek[3] = 1;
+        //                    nev[3] = 1;
+        //                }
+        //                x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f);
+        //                x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f);
+        //                x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f);
+        //                ggml_set_param(ctx0, x[0]);
+        //                ggml_set_param(ctx0, x[1]);
+        //                ggml_set_param(ctx0, x[2]);
+
+        //                struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
+
+        //                check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
+        //            }
+        //        }
+        //    }
+        //}
 
-                    check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
-                }
-            }
-        }
         ggml_free(ctx0);
     }
 
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
index 01c5bb27aabb9..68f971bfe2cf3 100644
--- a/tests/test-grammar-integration.cpp
+++ b/tests/test-grammar-integration.cpp
@@ -7,11 +7,14 @@
 #include "ggml.h"
 #include "llama.h"
 #include "grammar-parser.h"
+#include "json-schema-to-grammar.h"
 #include "unicode.h"
 #include <cassert>
 #include <string>
 #include <vector>
 
+using json = nlohmann::ordered_json;
+
 static llama_grammar* build_grammar(const std::string & grammar_str) {
     auto parsed_grammar = grammar_parser::parse(grammar_str.c_str());
 
@@ -31,31 +34,36 @@ static llama_grammar* build_grammar(const std::string & grammar_str) {
 static bool test_build_grammar_fails(const std::string & grammar_str) {
     fprintf(stderr, "⚫ Testing failure for grammar: %s\n", grammar_str.c_str());
     bool grammar_fails = false;
-    try {
-        build_grammar(grammar_str);
+    llama_grammar * grammar = build_grammar(grammar_str);
+    if (grammar != nullptr) {
         fprintf(stderr, "  ❌ Expected build failure, but succeeded\n");
-    } catch (const std::exception & err) {
+    } else {
         grammar_fails = true;
         fprintf(stdout, "  ✅︎\n");
     }
     return grammar_fails;
 }
 
-static bool match_string(const std::string & input, llama_grammar* grammar) {
+static bool match_string(const std::string & input, llama_grammar * grammar) {
     auto decoded = decode_utf8(input, {});
 
     const auto & code_points = decoded.first;
 
+    const llama_grammar_rules  & rules      = llama_grammar_get_rules (grammar);
+          llama_grammar_stacks & cur_stacks = llama_grammar_get_stacks(grammar);
+
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
-        auto prev_stacks = grammar->stacks;
-        llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
-        if (grammar->stacks.empty()) {
+        const llama_grammar_stacks prev_stacks = llama_grammar_get_stacks(grammar); // copy
+
+        llama_grammar_accept(rules, prev_stacks, *it, cur_stacks);
+
+        if (cur_stacks.empty()) {
             // no stacks means that the grammar failed to match at this point
             return false;
         }
     }
 
-    for (const auto & stack : grammar->stacks) {
+    for (const auto & stack : cur_stacks) {
         if (stack.empty()) {
             // An empty stack means that the grammar has been completed
             return true;
@@ -65,14 +73,16 @@ static bool match_string(const std::string & input, llama_grammar* grammar) {
     return false;
 }
 
-static void test_grammar(const std::string & test_desc, const std::string & grammar_str, const std::vector<std::string> & passing_strings, const std::vector<std::string> & failing_strings) {
-    fprintf(stderr, "⚫ Testing %s. Grammar: %s\n", test_desc.c_str(), grammar_str.c_str());
+static void test(const std::string & test_desc, const std::string & grammar_str, const std::vector<std::string> & passing_strings, const std::vector<std::string> & failing_strings) {
+    fprintf(stderr, "⚫ Testing %s\n%s\n", test_desc.c_str(), grammar_str.c_str());
     fflush(stderr);
 
     auto grammar = build_grammar(grammar_str);
 
     // Save the original grammar stacks so that we can reset after every new string we want to test
-    auto original_stacks = grammar->stacks;
+    const llama_grammar_stacks original_stacks = llama_grammar_get_stacks(grammar);
+
+    llama_grammar_stacks & cur_stacks = llama_grammar_get_stacks(grammar);
 
     fprintf(stderr, "  🔵 Valid strings:\n");
 
@@ -85,6 +95,23 @@ static void test_grammar(const std::string & test_desc, const std::string & gram
 
         if (!matched) {
             fprintf(stderr, "❌ (failed to match)\n");
+
+            // DEBUG: Write strings to files so that we can analyze more easily with gbnf-validator program to see exactly where things failed.
+            // DEBUG: Write the grammar_str to test-grammar-integration.grammar.gbnf
+            FILE* grammar_file = fopen("test-grammar-integration.grammar.gbnf", "w");
+            if (grammar_file) {
+                fprintf(grammar_file, "%s", grammar_str.c_str());
+                fclose(grammar_file);
+            }
+
+            // DEBUG: Write the test string to test-grammar-integration.string.txt
+            FILE* string_file = fopen("test-grammar-integration.string.txt", "w");
+            if (string_file) {
+                fprintf(string_file, "%s", test_string.c_str());
+                fclose(string_file);
+            }
+
+            fprintf(stderr, "\n NOTE: Debug grammar file generated. To analyze this failure in detail, run the following command:     ./llama-gbnf-validator test-grammar-integration.grammar.gbnf test-grammar-integration.string.txt\n\n");
         } else {
             fprintf(stdout, "✅︎\n");
         }
@@ -92,7 +119,7 @@ static void test_grammar(const std::string & test_desc, const std::string & gram
         assert(matched);
 
         // Reset the grammar stacks
-        grammar->stacks = original_stacks;
+        cur_stacks = original_stacks;
     }
 
     fprintf(stderr, "  🟠 Invalid strings:\n");
@@ -112,14 +139,264 @@ static void test_grammar(const std::string & test_desc, const std::string & gram
         assert(!matched);
 
         // Reset the grammar stacks
-        grammar->stacks = original_stacks;
+        cur_stacks = original_stacks;
     }
 
     // Clean up allocated memory
     llama_grammar_free(grammar);
 }
+static void test_grammar(const std::string & test_desc, const std::string & grammar_str, const std::vector<std::string> & passing_strings, const std::vector<std::string> & failing_strings) {
+    test(test_desc + ". Grammar: " + grammar_str, grammar_str, passing_strings, failing_strings);
+}
+static void test_schema(const std::string & test_desc, const std::string & schema_str, const std::vector<std::string> & passing_strings, const std::vector<std::string> & failing_strings) {
+    test(test_desc + ". Schema: " + schema_str, json_schema_to_grammar(json::parse(schema_str)), passing_strings, failing_strings);
+}
 
 static void test_simple_grammar() {
+    test_schema(
+        "min 0",
+        R"""({
+            "type": "integer",
+            "minimum": 0
+        })""",
+        // Passing strings
+        {
+            "0",
+            "10",
+            "12",
+            "10000",
+        },
+        // Failing strings
+        {
+            "-1",
+            "-10",
+            "-10000",
+            "-100000000000000000000000000000000",
+            "100000000000000000000000000000000",
+            "00",
+            "01",
+            "-0",
+        }
+    );
+    test_schema(
+        "min 2",
+        // Schema
+        R"""({
+            "type": "integer",
+            "minimum": 2
+        })""",
+        // Passing strings
+        {
+            "2",
+            "3",
+            "4",
+            "10",
+            "20",
+            "1234567890000000",
+        },
+        // Failing strings
+        {
+            "0",
+            "1",
+            "-1",
+            "-100",
+            "0",
+            "1",
+            "01",
+            "02",
+            "12345678900000000",
+        }
+    );
+    test_schema(
+        "min 456",
+        R"""({
+            "type": "integer",
+            "minimum": 456
+        })""",
+        // Passing strings
+        {
+            "456",
+            "4560",
+            "457",
+            "460",
+            "500",
+        },
+        // Failing strings
+        {
+            "455",
+            "356",
+            "50",
+            "050",
+            "-1",
+            "-456",
+        }
+    );
+    test_schema(
+        "min -123",
+        R"""({
+            "type": "integer",
+            "minimum": -123
+        })""",
+        // Passing strings
+        {
+            "-123",
+            "-122",
+            "-11",
+            "-1",
+            "0",
+            "1",
+            "123",
+            "1234",
+            "2345",
+        },
+        // Failing strings
+        {
+            "-1234",
+            "-124",
+        }
+    );
+
+    test_schema(
+        "max 9999",
+        // Schema
+        R"""({
+            "type": "integer",
+            "maximum": 9999
+        })""",
+        // Passing strings
+        {
+            "-99999",
+            "0",
+            "9999",
+        },
+        // Failing strings
+        {
+            "10000",
+            "99991",
+        }
+    );
+    test_schema(
+        "max -9999",
+        // Schema
+        R"""({
+            "type": "integer",
+            "maximum": -9999
+        })""",
+        // Passing strings
+        {
+            "-10000",
+            "-9999",
+        },
+        // Failing strings
+        {
+            "-9998",
+            "0",
+            "9999",
+        }
+    );
+    test_schema(
+        "min 5 max 30",
+        // Schema
+        R"""({
+            "type": "integer",
+            "minimum": 5,
+            "maximum": 30
+        })""",
+        // Passing strings
+        {
+            "5",
+            "10",
+            "30",
+        },
+        // Failing strings
+        {
+            "05",
+            "4",
+            "-1",
+            "31",
+            "123",
+            "0123",
+        }
+    );
+    test_schema(
+        "min -1 max 1",
+        R"""({
+            "type": "integer",
+            "minimum": -1,
+            "maximum": 1
+        })""",
+        // Passing strings
+        {
+            "-1",
+            "0",
+            "1",
+        },
+        // Failing strings
+        {
+            "-11",
+            "-10",
+            "-2",
+            "2",
+            "10",
+            "11",
+        }
+    );
+    test_schema(
+        "min -123 max 42",
+        R"""({
+            "type": "integer",
+            "minimum": -123,
+            "maximum": 42
+        })""",
+        // Passing strings
+        {
+            "-123",
+            "-122",
+            "-13",
+            "-11",
+            "-2",
+            "-1",
+            "0",
+            "1",
+            "5",
+            "10",
+            "39",
+            "40",
+            "42",
+        },
+        // Failing strings
+        {
+            "-0123",
+            "-124",
+            "-1123",
+            "-200",
+            "43",
+            "123",
+            "0123",
+        }
+    );
+    test_schema(
+        "exclusive min / max",
+        // Schema
+        R"""({
+            "type": "integer",
+            "exclusiveMinimum": 0,
+            "exclusiveMaximum": 10000
+        })""",
+        // Passing strings
+        {
+            "1",
+            "9999",
+        },
+        // Failing strings
+        {
+            "0",
+            "01",
+            "10000",
+            "99999",
+        }
+    );
+
     // Test case for a simple grammar
     test_grammar(
         "simple grammar",
@@ -205,6 +482,33 @@ static void test_complex_grammar() {
     );
 }
 
+static void test_special_chars() {
+    // A collection of tests to exercise special characters such as "."
+    test_grammar(
+        "special characters",
+        // Grammar
+        R"""(
+            root ::= ... "abc" ...
+            )""",
+        // Passing strings
+        {
+            "abcabcabc",
+            "aaaabcccc",
+            // NOTE: Also ensures that multi-byte characters still count as a single character
+            "🔵🟠✅abc❌🟠🔵"
+        },
+        // Failing strings
+        {
+            "aaabcccc",
+            "aaaaabcccc",
+            "aaaabccc",
+            "aaaabccccc",
+            "🔵🟠✅❌abc❌✅🟠🔵"
+            "🔵🟠abc🟠🔵"
+        }
+    );
+}
+
 static void test_quantifiers() {
     // A collection of tests to exercise * + and ? quantifiers
 
@@ -292,15 +596,92 @@ static void test_quantifiers() {
             "catyyy",
         }
     );
+    test_grammar(
+        "simple exact repetition",
+        // Grammar
+        R"""(
+            root ::= [ab]{4}
+        )""",
+        // Passing strings
+        {
+            "aaaa",
+            "bbbb",
+            "abab",
+        },
+        // Failing strings
+        {
+            "a",
+            "b",
+            "aaaaa",
+        }
+    );
+    test_grammar(
+        "simple min repetition",
+        // Grammar
+        R"""(
+            root ::= [ab]{4,}
+        )""",
+        // Passing strings
+        {
+            "aaaa",
+            "aaaaab",
+            "bbbb",
+            "ababab",
+        },
+        // Failing strings
+        {
+            "",
+            "aba",
+        }
+    );
+    test_grammar(
+        "simple max repetition",
+        // Grammar
+        R"""(
+            root ::= [ab]{0,4}
+        )""",
+        // Passing strings
+        {
+            "",
+            "a",
+            "aa",
+            "aaa",
+            "aaab",
+        },
+        // Failing strings
+        {
+            "aaaaa",
+        }
+    );
+    test_grammar(
+        "min / max repetition",
+        // Grammar
+        R"""(
+            root ::= ("0x" [A-F0-9]{2} " "?){3,5}
+        )""",
+        // Passing strings
+        {
+            "0xFF 0x12 0xAB",
+            "0xFF 0x12 0xAB 0x00 0x00",
+        },
+        // Failing strings
+        {
+            "",
+            "0xFF",
+            "0xFF 0x12",
+            "0xFF 0x12 0xAB 0x00 0x00 0x00",
+        }
+    );
 }
 
 static void test_failure_missing_root() {
     fprintf(stderr, "⚫ Testing missing root node:\n");
     // Test case for a grammar that is missing a root rule
-    const std::string grammar_str = R"""(rot ::= expr
-expr ::= term ("+" term)*
-term ::= number
-number ::= [0-9]+)""";
+    const std::string grammar_str = R"""(
+        rot ::= expr
+        expr ::= term ("+" term)*
+        term ::= number
+        number ::= [0-9]+)""";
 
     grammar_parser::parse_state parsed_grammar = grammar_parser::parse(grammar_str.c_str());
 
@@ -317,10 +698,10 @@ static void test_failure_missing_reference() {
 
     // Test case for a grammar that is missing a referenced rule
     const std::string grammar_str =
-R"""(root ::= expr
-expr ::= term ("+" term)*
-term ::= numero
-number ::= [0-9]+)""";
+        R"""(root ::= expr
+        expr ::= term ("+" term)*
+        term ::= numero
+        number ::= [0-9]+)""";
 
     fprintf(stderr, "    Expected error:  ");
 
@@ -342,37 +723,603 @@ static void test_failure_left_recursion() {
 
     // Test more complicated left recursion detection
     const std::string medium_str = R"""(
-root ::= asdf
-asdf ::= "a" | asdf "a"
-)""";
+        root ::= asdf
+        asdf ::= "a" | asdf "a"
+        )""";
     assert(test_build_grammar_fails(medium_str));
 
     // Test even more complicated left recursion detection
     const std::string hard_str = R"""(
-root ::= asdf
-asdf ::= "a" | foo "b"
-foo ::= "c" | asdf "d" | "e")""";
+        root ::= asdf
+        asdf ::= "a" | foo "b"
+        foo ::= "c" | asdf "d" | "e")""";
     assert(test_build_grammar_fails(hard_str));
 
     // Test yet even more complicated left recursion detection
     const std::string hardest_str = R"""(
-root ::= asdf
-asdf ::= "a" | foo "b"
-foo ::= "c" | empty asdf "d" | "e"
-empty ::= "blah" | )""";
+        root ::= asdf
+        asdf ::= "a" | foo "b"
+        foo ::= "c" | empty asdf "d" | "e"
+        empty ::= "blah" | )""";
     assert(test_build_grammar_fails(hardest_str));
 
     fprintf(stderr, "  ✅︎ Passed\n");
 }
 
+static void test_json_schema() {
+    // Note that this is similar to the regular grammar tests,
+    //  but we convert each json schema to a grammar before parsing.
+    // Otherwise, this test structure is the same.
+
+    test_schema(
+        "empty schema (object)",
+        // Schema
+        R"""(
+            {}
+        )""",
+        // Passing strings
+        {
+            R"""({})""",
+            R"""({"foo": "bar"})""",
+        },
+        // Failing strings
+        {
+            "",
+            "[]",
+            "null",
+            R"""("")""",
+            "true",
+        }
+    );
+
+    test_schema(
+        "exotic formats (list)",
+        // Schema
+        R"""({
+            "items": [
+                { "format": "date" },
+                { "format": "uuid" },
+                { "format": "time" },
+                { "format": "date-time" }
+            ]
+        })""",
+        // Passing strings
+        {
+            // "{}", // NOTE: This string passes for this schema on https://www.jsonschemavalidator.net/ -- should it?
+            // "[]", // NOTE: This string passes for this schema on https://www.jsonschemavalidator.net/ -- should it?
+            R"""(["2012-04-23", "12345678-1234-1234-1234-1234567890ab", "18:25:43.511Z", "2012-04-23T18:25:43.511Z"])""",
+            //R"""(["2012-04-23","12345678-1234-1234-1234-1234567890ab"])""", // NOTE: This string passes for this schema on https://www.jsonschemavalidator.net/ -- should it?
+            //R"""({"foo": "bar"})""", // NOTE: This string passes for this schema on https://www.jsonschemavalidator.net/ -- should it?
+        },
+        // Failing strings
+        {
+            R"""(["foo", "bar"])""",
+            R"""(["12345678-1234-1234-1234-1234567890ab"])""",
+        }
+    );
+
+    test_schema(
+        "string",
+        // Schema
+        R"""({
+            "type": "string"
+        })""",
+        // Passing strings
+        {
+            R"""("foo")""",
+            R"""("bar")""",
+            R"""("")""",
+        },
+        // Failing strings
+        {
+            R"""({})""",
+            R"""("foo": "bar")""",
+        }
+    );
+
+    test_schema(
+        "string w/ min length 1",
+        // Schema
+        R"""({
+            "type": "string",
+            "minLength": 1
+        })""",
+        // Passing strings
+        {
+            R"""("foo")""",
+            R"""("bar")""",
+        },
+        // Failing strings
+        {
+            R"""("")""",
+            R"""({})""",
+            R"""("foo": "bar")""",
+        }
+    );
+
+    test_schema(
+        "string w/ min length 3",
+        // Schema
+        R"""({
+                "type": "string",
+                "minLength": 3
+        })""",
+        // Passing strings
+        {
+            R"""("foo")""",
+            R"""("bar")""",
+            R"""("foobar")""",
+        },
+        // Failing strings
+        {
+            R"""("")""",
+            R"""("f")""",
+            R"""("fo")""",
+        }
+    );
+
+    test_schema(
+        "string w/ max length",
+        // Schema
+        R"""({
+            "type": "string",
+            "maxLength": 3
+        })""",
+        // Passing strings
+        {
+            R"""("foo")""",
+            R"""("bar")""",
+            R"""("")""",
+            R"""("f")""",
+            R"""("fo")""",
+        },
+        // Failing strings
+        {
+            R"""("foobar")""",
+        }
+    );
+
+    test_schema(
+        "string w/ min & max length",
+        // Schema
+        R"""({
+            "type": "string",
+            "minLength": 1,
+            "maxLength": 4
+        })""",
+        // Passing strings
+        {
+            R"""("foo")""",
+            R"""("bar")""",
+            R"""("f")""",
+            R"""("barf")""",
+        },
+        // Failing strings
+        {
+            R"""("")""",
+            R"""("barfo")""",
+            R"""("foobar")""",
+        }
+    );
+
+    test_schema(
+        "boolean",
+        // Schema
+        R"""({
+            "type": "boolean"
+        })""",
+        // Passing strings
+        {
+            "true",
+            "false",
+        },
+        // Failing strings
+        {
+            R"""("")""",
+            R"""("true")""",
+            R"""(True)""",
+            R"""(FALSE)""",
+        }
+    );
+
+    test_schema(
+        "integer",
+        // Schema
+        R"""({
+            "type": "integer"
+        })""",
+        // Passing strings
+        {
+            R"""(0)""",
+            R"""(12345)""",
+            R"""(1234567890123456)""",
+        },
+        // Failing strings
+        {
+            R"""()""",
+            R"""(01)""",
+            R"""(007)""",
+            R"""(12345678901234567  )""",
+        }
+    );
+
+    test_schema(
+        "string const",
+        // Schema
+        R"""({
+            "const": "foo"
+        })""",
+        // Passing strings
+        {
+            R"""("foo")""",
+        },
+        // Failing strings
+        {
+            R"""(foo)""",
+            R"""("bar")""",
+        }
+    );
+
+    test_schema(
+        "non-string const",
+        // Schema
+        R"""({
+            "const": true
+        })""",
+        // Passing strings
+        {
+            R"""(true)""",
+        },
+        // Failing strings
+        {
+            R"""()""",
+            R"""(foo)""",
+            R"""("true")""",
+        }
+    );
+
+    test_schema(
+        "non-string const",
+        // Schema
+        R"""({
+            "enum": ["red", "amber", "green", null, 42, ["foo"]]
+        })""",
+        // Passing strings
+        {
+            R"""("red")""",
+            R"""(null)""",
+            R"""(42)""",
+            R"""(["foo"])""",
+        },
+        // Failing strings
+        {
+            R"""()""",
+            R"""(420)""",
+            R"""(true)""",
+            R"""(foo)""",
+        }
+    );
+
+    test_schema(
+        "simple pattern",
+        // Schema
+        R"""({
+            "pattern": "^[a-zA-Z0-9_-]*$"
+        })""",
+        // Passing strings
+        {
+            R"""("")""",
+            R"""("He_llo-12")""",
+        },
+        // Failing strings
+        {
+            R"""("!")""",
+            R"""("Hello World")""",
+        }
+    );
+
+    test_schema(
+        "pattern with escapes",
+        // Schema
+        R"""({
+            "pattern": "^a\\^\\$\\.\\[\\]\\(\\)\\|\\{\\}\\*\\+\\?b$"
+        })""",
+        // Passing strings
+        {
+            R"""("a^$.[]()|{}*+?b")""",
+        },
+        // Failing strings
+        {
+            R"""("ab")""",
+        }
+    );
+
+    test_schema(
+        "",
+        // Schema
+        R"""(
+            {
+                "type": ["array", "null"],
+                "items": { "type": "string" }
+            }
+        )""",
+        // Passing strings
+        {
+            "null",
+            "[]",
+            "[\"123\"]",
+            "[\"foo\", \"bar\"]",
+        },
+        // Failing strings
+        {
+            "",
+            "[123]",
+            "\"foo\"",
+            "[\"foo\", 42]",
+        }
+    );
+
+    test_schema(
+        "min+max items",
+        // Schema
+        R"""({
+            "items": {
+                "type": ["number", "integer"]
+            },
+            "minItems": 3,
+            "maxItems": 5
+        })""",
+        // Passing strings
+        {
+            R"""([1, 2, 3])""",
+            R"""([1, 2, 3, 4])""",
+            R"""([1, 2, 3, 4, 5])""",
+        },
+        // Failing strings
+        {
+            R"""([1, 2])""",
+            R"""([1, 2, 3, 4, 5, 6])""",
+            R"""(1)""",
+        }
+    );
+
+    // Properties (from: https://json-schema.org/understanding-json-schema/reference/object#properties)
+    test_schema(
+        "object properties",
+        // Schema
+        R"""({
+            "type": "object",
+            "properties": {
+                "number": { "type": "number" },
+                "street_name": { "type": "string" },
+                "street_type": { "enum": ["Street", "Avenue", "Boulevard"] }
+            }
+        })""",
+        // Passing strings
+        {
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+            // "By default, leaving out properties is valid"
+            R"""({ "street_name": "Pennsylvania" })""",
+            R"""({ "number": 1600, "street_name": "Pennsylvania" })""",
+            // "By extension, even an empty object is valid"
+            R"""({})""",
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue" })""",
+        },
+        // Failing strings
+        {
+            // Change datatype from number to string
+            R"""({ "number": "1600", "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+            // Reorder properties
+            R"""({ "street_name": "Pennsylvania", "number": 1600 })""",
+            // Reorder properties
+            R"""({ "number": "1600", "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+            // "Additional properties default to false for generation, even though the spec says true.
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue", "direction":"NW"})""",
+
+        }
+    );
+
+    test_schema(
+        "additional properties can't override other properties",
+        R"""({
+            "properties": {
+                "a": {"type": "integer"},
+                "b": {"type": "integer"}
+            },
+            "additionalProperties": true
+        })""",
+        // Passing strings
+        {
+            R"""({"a": 42})""",
+            R"""({"c": ""})""",
+            R"""({"a": 42, "c": ""})""",
+            R"""({"a_": ""})""",
+        },
+        // Failing strings
+        {
+            R"""()""",
+            R"""({"a": ""})""",
+            R"""({"a": "", "b": ""})""",
+        }
+    );
+
+    // Properties (from: https://json-schema.org/understanding-json-schema/reference/object#properties)
+    test_schema(
+        "object properties, additionalProperties: true",
+        // Schema
+        R"""({
+            "type": "object",
+            "properties": {
+                "number": { "type": "number" },
+                "street_name": { "type": "string" },
+                "street_type": { "enum": ["Street", "Avenue", "Boulevard"] }
+            },
+            "additionalProperties": true
+        })""",
+        // Passing strings
+        {
+            // "By extension, even an empty object is valid"
+            R"""({})""",
+            R"""({"number":1600,"street_name":"Pennsylvania","street_type":"Avenue"})""",
+            // "By default, leaving out properties is valid"
+            R"""({ "street_name": "Pennsylvania" })""",
+            R"""({ "number": 1600, "street_name": "Pennsylvania" })""",
+            // "By default, providing additional properties is valid"
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue", "direction":"NW"})""",
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue" })""",
+        },
+        // Failing strings
+        {
+            // Change datatype from number to string
+            R"""({ "number": "1600", "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+            // Reorder properties
+            R"""({ "street_name": "Pennsylvania", "number": 1600, "street_type":"Avenue"})""",
+        }
+    );
+
+    // Additional properties: false
+    test_schema(
+        "required + optional props each in original order",
+        // Schema
+        R"""({
+            "type": "object",
+            "properties": {
+                "number": { "type": "number" },
+                "street_name": { "type": "string" },
+                "street_type": { "enum": ["Street", "Avenue", "Boulevard"] }
+            },
+            "additionalProperties": false
+        })""",
+        // Passing strings
+        {
+            R"""({ "street_name": "Pennsylvania" })""",
+            R"""({ "number": 1600, "street_type":"Avenue"})""",
+            R"""({ "number": 1600, "street_name": "Pennsylvania" })""",
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+            // Spaces are permitted around enum values
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue" })""",
+        },
+        // Failing strings
+        {
+            // Reorder properties
+            R"""({ "street_type": "Avenue", "number": 1600 })""",
+            // Add "direction"
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue", "direction": "NW" })""",
+        }
+    );
+
+    test_schema(
+        "required + optional props each in original order",
+        // Schema
+        R"""({
+            "properties": {
+                "b": {"type": "string"},
+                "a": {"type": "string"},
+                "d": {"type": "string"},
+                "c": {"type": "string"}
+            },
+            "required": ["a", "b"],
+            "additionalProperties": false
+        })""",
+        // Passing strings
+        {
+            R"""({"b": "foo", "a": "bar"})""",
+            R"""({"b":"foo","a":"bar","d":"qux"})""",
+            R"""({"b":"foo", "a":"bar", "d":"qux", "c":"baz"})""",
+        },
+        // Failing strings
+        {
+            R"""({"a": "foo", "b": "bar"})""",
+            R"""({"b": "bar"})""",
+            R"""({"a": "foo", "c": "baz"})""",
+            R"""({"a":"foo", "b":"bar", "c":"baz", "d":"qux"})""",
+        }
+    );
+
+    // NOTE: Example from https://json-schema.org/learn/getting-started-step-by-step#define-required-properties
+    test_schema(
+        "required props",
+        // Schema
+        R"""({
+            "$schema": "https://json-schema.org/draft/2020-12/schema",
+            "$id": "https://example.com/product.schema.json",
+            "title": "Product",
+            "description": "A product from Acme's catalog",
+            "type": "object",
+            "properties": {
+                "productId": {
+                "description": "The unique identifier for a product",
+                "type": "integer"
+                },
+                "productName": {
+                "description": "Name of the product",
+                "type": "string"
+                },
+                "price": {
+                "description": "The price of the product",
+                "type": "number",
+                "exclusiveMinimum": 0
+                },
+                "tags": {
+                "description": "Tags for the product",
+                "type": "array",
+                "items": {
+                    "type": "string"
+                },
+                "minItems": 1,
+                "uniqueItems": true
+                },
+                "dimensions": {
+                "type": "object",
+                "properties": {
+                    "length": {
+                    "type": "number"
+                    },
+                    "width": {
+                    "type": "number"
+                    },
+                    "height": {
+                    "type": "number"
+                    }
+                },
+                "required": [ "length", "width", "height" ]
+                }
+            },
+            "required": [ "productId", "productName", "price" ]
+        })""",
+        // Passing strings
+        {
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50})""",
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50, "tags": ["home", "green"]})""",
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50, "tags": ["home", "green"], "dimensions": {"length": 785, "width": 250.5, "height": -0.359}})""",
+        },
+        // Failing strings
+        {
+            R"""({})""", // Missing all required properties
+            R"""({"productName": "A green door", "price": 12.50, "productId": 1})""", // Out of order properties
+            // TODO: The following line should fail, but currently it passes. `exclusiveMinimum` is not supported, as it would likely be too difficult to implement.
+            //  Perhaps special checks for minimum and maximum values of 0 could be added (since that's relatively easy to do with grammars), but anything else would likely be too complex.
+            // R"""({"productId": 1, "productName": "A green door", "price": -12.50})""",
+            R"""({"productId": 1, "productName": "A green door"})""", // Missing required property (price)
+            R"""({"productName": "A green door", "price": 12.50})""", // Missing required property (productId)
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50, "tags": []})""", // tags is empty, but minItems is 1
+            R"""({"productId": 1, "productName": "A green door", "price": 12.50, "dimensions": {"length": 785, "width": 250.5, "height": -0.359}, "tags": ["home", "green"]})""", // Tags and dimensions are out of order
+            // TODO: The following line should fail, but currently it passes. `uniqueItems` is not supported, as it would likely be too difficult to implement.
+            // R"""({"productId": 1, "productName": "A green door", "price": 12.50, "tags": ["home", "green", "home"]})""",
+        }
+    );
+}
+
 int main() {
     fprintf(stdout, "Running grammar integration tests...\n");
     test_simple_grammar();
     test_complex_grammar();
+    test_special_chars();
     test_quantifiers();
     test_failure_missing_root();
     test_failure_missing_reference();
     test_failure_left_recursion();
+    test_json_schema();
     fprintf(stdout, "All tests passed.\n");
     return 0;
 }
diff --git a/tests/test-grammar-parser.cpp b/tests/test-grammar-parser.cpp
index 91939e276df07..5df5abb25394c 100644
--- a/tests/test-grammar-parser.cpp
+++ b/tests/test-grammar-parser.cpp
@@ -7,28 +7,79 @@
 
 #include <cassert>
 
-int main()
-{
-    grammar_parser::parse_state parsed_grammar;
+static const char * type_str(llama_gretype type) {
+    switch (type) {
+        case LLAMA_GRETYPE_CHAR: return "LLAMA_GRETYPE_CHAR";
+        case LLAMA_GRETYPE_CHAR_NOT: return "LLAMA_GRETYPE_CHAR_NOT";
+        case LLAMA_GRETYPE_CHAR_ALT: return "LLAMA_GRETYPE_CHAR_ALT";
+        case LLAMA_GRETYPE_CHAR_RNG_UPPER: return "LLAMA_GRETYPE_CHAR_RNG_UPPER";
+        case LLAMA_GRETYPE_RULE_REF: return "LLAMA_GRETYPE_RULE_REF";
+        case LLAMA_GRETYPE_ALT: return "LLAMA_GRETYPE_ALT";
+        case LLAMA_GRETYPE_END: return "LLAMA_GRETYPE_END";
+        default: return "?";
+    }
+}
 
-    const char *grammar_bytes = R"""(root  ::= (expr "=" term "\n")+
-expr  ::= term ([-+*/] term)*
-term  ::= [0-9]+)""";
+static void verify_parsing(const char *grammar_bytes, const std::vector<std::pair<std::string, uint32_t>> expected, const std::vector<llama_grammar_element> &expected_rules) {
+    uint32_t index = 0;
+    grammar_parser::parse_state parsed_grammar = grammar_parser::parse(grammar_bytes);
 
-    parsed_grammar = grammar_parser::parse(grammar_bytes);
+    std::map<uint32_t, std::string> symbol_names;
+    for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it) {
+        symbol_names[it->second] = it->first;
+    }
 
-    std::vector<std::pair<std::string, uint32_t>> expected = {
-        {"expr", 2},
-        {"expr_5", 5},
-        {"expr_6", 6},
-        {"root", 0},
-        {"root_1", 1},
-        {"root_4", 4},
-        {"term", 3},
-        {"term_7", 7},
+    auto print_all = [&]() {
+        fprintf(stderr, "    verify_parsing(R\"\"\"(%s)\"\"\", {\n", grammar_bytes);
+        for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it) {
+            fprintf(stderr, "        {\"%s\", %u},\n", it->first.c_str(), it->second);
+        }
+        fprintf(stderr, "    }, {\n");
+        for (size_t i_rule = 0; i_rule < parsed_grammar.rules.size(); i_rule++) {
+            fprintf(stderr, "        // %s (index %zu)\n", symbol_names[i_rule].c_str(), i_rule);
+            auto & rule = parsed_grammar.rules[i_rule];
+            for (uint32_t i = 0; i < rule.size(); i++) {
+                std::string rule_str;
+                fprintf(stderr, "        {%s, ", type_str(rule[i].type));
+                if (rule[i].type == LLAMA_GRETYPE_CHAR || rule[i].type == LLAMA_GRETYPE_CHAR_ALT ||
+                    rule[i].type == LLAMA_GRETYPE_CHAR_NOT || rule[i].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+                    char c = rule[i].value;
+                    if (c == '\n') {
+                        fprintf(stderr, "'\\n'");
+                    } else if (c == '\t') {
+                        fprintf(stderr, "'\\t'");
+                    } else if (c == '\r') {
+                        fprintf(stderr, "'\\r'");
+                    } else if (c == '\0') {
+                        fprintf(stderr, "'\\0'");
+                    } else {
+                        fprintf(stderr, "'%c'", c);
+                    }
+                } else if (rule[i].type == LLAMA_GRETYPE_RULE_REF) {
+                    fprintf(stderr, "/* %s */ %u", symbol_names[rule[i].value].c_str(), rule[i].value);
+                } else {
+                    fprintf(stderr, "%u", rule[i].value);
+                }
+                fprintf(stderr, "},\n");
+            }
+        }
+        fprintf(stderr, "    });\n");
     };
 
-    uint32_t index = 0;
+    if (getenv("TEST_GRAMMAR_PARSER_PRINT_ALL")) {
+        print_all();
+        fprintf(stderr, "\n");
+        return;
+    }
+
+    fprintf(stderr, "Testing grammar:%s\n", grammar_bytes);
+
+    if (parsed_grammar.symbol_ids.size() != expected.size()) {
+        fprintf(stderr, "Code to update expectation (set TEST_GRAMMAR_PARSER_PRINT_ALL=1 to print all):\n");
+        print_all();
+        assert(parsed_grammar.symbol_ids.size() == expected.size());
+    }
+
     for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it)
     {
         std::string key = it->first;
@@ -38,51 +89,18 @@ term  ::= [0-9]+)""";
         // pretty print error message before asserting
         if (expected_pair.first != key || expected_pair.second != value)
         {
+            fprintf(stderr, "index: %u\n", index);
             fprintf(stderr, "expected_pair: %s, %u\n", expected_pair.first.c_str(), expected_pair.second);
             fprintf(stderr, "actual_pair: %s, %u\n", key.c_str(), value);
             fprintf(stderr, "expected_pair != actual_pair\n");
+            fprintf(stderr, "Code to update expectation (set TEST_GRAMMAR_PARSER_PRINT_ALL=1 to print all):\n");
+            print_all();
         }
 
         assert(expected_pair.first == key && expected_pair.second == value);
 
         index++;
     }
-    std::vector<llama_grammar_element> expected_rules = {
-        {LLAMA_GRETYPE_RULE_REF, 4},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 2},
-        {LLAMA_GRETYPE_CHAR, 61},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_CHAR, 10},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_RULE_REF, 6},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 7},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 1},
-        {LLAMA_GRETYPE_RULE_REF, 4},
-        {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_RULE_REF, 1},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 45},
-        {LLAMA_GRETYPE_CHAR_ALT, 43},
-        {LLAMA_GRETYPE_CHAR_ALT, 42},
-        {LLAMA_GRETYPE_CHAR_ALT, 47},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 5},
-        {LLAMA_GRETYPE_RULE_REF, 6},
-        {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
-        {LLAMA_GRETYPE_RULE_REF, 7},
-        {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_CHAR, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
-        {LLAMA_GRETYPE_END, 0},
-    };
 
     index = 0;
     for (auto rule : parsed_grammar.rules)
@@ -97,28 +115,306 @@ term  ::= [0-9]+)""";
             if (expected_element.type != element.type || expected_element.value != element.value)
             {
                 fprintf(stderr, "index: %u\n", index);
-                fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value);
-                fprintf(stderr, "actual_element: %d, %u\n", element.type, element.value);
+                fprintf(stderr, "expected_element: %s, %u\n", type_str(expected_element.type), expected_element.value);
+                fprintf(stderr, "actual_element: %s, %u\n", type_str(element.type), element.value);
                 fprintf(stderr, "expected_element != actual_element\n");
+                fprintf(stderr, "all elements:\n");
+                fprintf(stderr, "Code to update expectation (set TEST_GRAMMAR_PARSER_PRINT_ALL=1 to print all):\n");
+                print_all();
             }
 
             assert(expected_element.type == element.type && expected_element.value == element.value);
             index++;
         }
     }
+}
+
+static void verify_failure(const char *grammar_bytes) {
+    fprintf(stderr, "Testing expected failure:%s\n", grammar_bytes);
+    auto result = grammar_parser::parse(grammar_bytes);
+    assert(result.rules.empty() && "should have failed");
+}
+
+int main()
+{
+    verify_failure(R"""(
+        root ::= "a"{,}"
+    )""");
+
+    verify_failure(R"""(
+        root ::= "a"{,10}"
+    )""");
 
-    const char *longer_grammar_bytes = R"""(
-    root  ::= (expr "=" ws term "\n")+
-    expr  ::= term ([-+*/] term)*
-    term  ::= ident | num | "(" ws expr ")" ws
-    ident ::= [a-z] [a-z0-9_]* ws
-    num   ::= [0-9]+ ws
-    ws    ::= [ \t\n]*
-    )""";
+    verify_parsing(R"""(
+        root  ::= "a"
+    )""", {
+        {"root", 0},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a" | [bdx-z] | [^1-3]
+    )""", {
+        {"root", 0},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_CHAR, 'b'},
+        {LLAMA_GRETYPE_CHAR_ALT, 'd'},
+        {LLAMA_GRETYPE_CHAR_ALT, 'x'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 'z'},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_CHAR_NOT, '1'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '3'},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= a+
+        a     ::= "a"
+    )""", {
+        {"a", 1},
+        {"root", 0},
+        {"root_2", 2},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* a */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_END, 0},
+        // a (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+        // root_2 (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* a */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"+
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= a?
+        a     ::= "a"
+    )""", {
+        {"a", 1},
+        {"root", 0},
+        {"root_2", 2},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_END, 0},
+        // a (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+        // root_2 (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* a */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
 
-    parsed_grammar = grammar_parser::parse(longer_grammar_bytes);
+    verify_parsing(R"""(
+        root  ::= "a"?
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= a*
+        a     ::= "a"
+    )""", {
+        {"a", 1},
+        {"root", 0},
+        {"root_2", 2},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_END, 0},
+        // a (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+        // root_2 (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* a */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"*
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
 
-    expected = {
+    verify_parsing(R"""(
+        root  ::= "a"{2}
+    )""", {
+        {"root", 0},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"{2,}
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"{ 4}
+    )""", {
+        {"root", 0},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"{2,4}
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+        {"root_2", 2},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+        // root_2 (index 2)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= (expr "=" term "\n")+
+        expr  ::= term ([-+*/] term)*
+        term  ::= [0-9]+
+    )""", {
+        {"expr", 2},
+        {"expr_5", 5},
+        {"expr_6", 6},
+        {"root", 0},
+        {"root_1", 1},
+        {"root_4", 4},
+        {"term", 3},
+        {"term_7", 7},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_4 */ 4},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_RULE_REF, /* expr */ 2},
+        {LLAMA_GRETYPE_CHAR, '='},
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 3},
+        {LLAMA_GRETYPE_CHAR, '\n'},
+        {LLAMA_GRETYPE_END, 0},
+        // expr (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 3},
+        {LLAMA_GRETYPE_RULE_REF, /* expr_6 */ 6},
+        {LLAMA_GRETYPE_END, 0},
+        // term (index 3)
+        {LLAMA_GRETYPE_CHAR, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_RULE_REF, /* term_7 */ 7},
+        {LLAMA_GRETYPE_END, 0},
+        // root_4 (index 4)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_4 */ 4},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+        // expr_5 (index 5)
+        {LLAMA_GRETYPE_CHAR, '-'},
+        {LLAMA_GRETYPE_CHAR_ALT, '+'},
+        {LLAMA_GRETYPE_CHAR_ALT, '*'},
+        {LLAMA_GRETYPE_CHAR_ALT, '/'},
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 3},
+        {LLAMA_GRETYPE_END, 0},
+        // expr_6 (index 6)
+        {LLAMA_GRETYPE_RULE_REF, /* expr_5 */ 5},
+        {LLAMA_GRETYPE_RULE_REF, /* expr_6 */ 6},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+        // term_7 (index 7)
+        {LLAMA_GRETYPE_CHAR, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_RULE_REF, /* term_7 */ 7},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= (expr "=" ws term "\n")+
+        expr  ::= term ([-+*/] term)*
+        term  ::= ident | num | "(" ws expr ")" ws
+        ident ::= [a-z] [a-z0-9_]* ws
+        num   ::= [0-9]+ ws
+        ws    ::= [ \t\n]*
+    )""", {
         {"expr", 2},
         {"expr_6", 6},
         {"expr_7", 7},
@@ -132,119 +428,88 @@ term  ::= [0-9]+)""";
         {"term", 4},
         {"ws", 3},
         {"ws_12", 12},
-    };
-
-    index = 0;
-    for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it)
-    {
-        std::string key = it->first;
-        uint32_t value = it->second;
-        std::pair<std::string, uint32_t> expected_pair = expected[index];
-
-        // pretty print error message before asserting
-        if (expected_pair.first != key || expected_pair.second != value)
-        {
-            fprintf(stderr, "expected_pair: %s, %u\n", expected_pair.first.c_str(), expected_pair.second);
-            fprintf(stderr, "actual_pair: %s, %u\n", key.c_str(), value);
-            fprintf(stderr, "expected_pair != actual_pair\n");
-        }
-
-        assert(expected_pair.first == key && expected_pair.second == value);
-
-        index++;
-    }
-    expected_rules = {
-        {LLAMA_GRETYPE_RULE_REF, 5},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_5 */ 5},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 2},
-        {LLAMA_GRETYPE_CHAR, 61},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_RULE_REF, 4},
-        {LLAMA_GRETYPE_CHAR, 10},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_RULE_REF, /* expr */ 2},
+        {LLAMA_GRETYPE_CHAR, '='},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 4},
+        {LLAMA_GRETYPE_CHAR, '\n'},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 4},
-        {LLAMA_GRETYPE_RULE_REF, 7},
+        // expr (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 4},
+        {LLAMA_GRETYPE_RULE_REF, /* expr_7 */ 7},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 12},
+        // ws (index 3)
+        {LLAMA_GRETYPE_RULE_REF, /* ws_12 */ 12},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 8},
+        // term (index 4)
+        {LLAMA_GRETYPE_RULE_REF, /* ident */ 8},
         {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_RULE_REF, 9},
+        {LLAMA_GRETYPE_RULE_REF, /* num */ 9},
         {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_CHAR, 40},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_RULE_REF, 2},
-        {LLAMA_GRETYPE_CHAR, 41},
-        {LLAMA_GRETYPE_RULE_REF, 3},
+        {LLAMA_GRETYPE_CHAR, '('},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
+        {LLAMA_GRETYPE_RULE_REF, /* expr */ 2},
+        {LLAMA_GRETYPE_CHAR, ')'},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 1},
-        {LLAMA_GRETYPE_RULE_REF, 5},
+        // root_5 (index 5)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_5 */ 5},
         {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_RULE_REF, 1},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 45},
-        {LLAMA_GRETYPE_CHAR_ALT, 43},
-        {LLAMA_GRETYPE_CHAR_ALT, 42},
-        {LLAMA_GRETYPE_CHAR_ALT, 47},
-        {LLAMA_GRETYPE_RULE_REF, 4},
+        // expr_6 (index 6)
+        {LLAMA_GRETYPE_CHAR, '-'},
+        {LLAMA_GRETYPE_CHAR_ALT, '+'},
+        {LLAMA_GRETYPE_CHAR_ALT, '*'},
+        {LLAMA_GRETYPE_CHAR_ALT, '/'},
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 4},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 6},
-        {LLAMA_GRETYPE_RULE_REF, 7},
+        // expr_7 (index 7)
+        {LLAMA_GRETYPE_RULE_REF, /* expr_6 */ 6},
+        {LLAMA_GRETYPE_RULE_REF, /* expr_7 */ 7},
         {LLAMA_GRETYPE_ALT, 0},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 97},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
-        {LLAMA_GRETYPE_RULE_REF, 10},
-        {LLAMA_GRETYPE_RULE_REF, 3},
+        // ident (index 8)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 'z'},
+        {LLAMA_GRETYPE_RULE_REF, /* ident_10 */ 10},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 11},
-        {LLAMA_GRETYPE_RULE_REF, 3},
+        // num (index 9)
+        {LLAMA_GRETYPE_CHAR, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_RULE_REF, /* num_11 */ 11},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 97},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
-        {LLAMA_GRETYPE_CHAR_ALT, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
-        {LLAMA_GRETYPE_CHAR_ALT, 95},
-        {LLAMA_GRETYPE_RULE_REF, 10},
+        // ident_10 (index 10)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 'z'},
+        {LLAMA_GRETYPE_CHAR_ALT, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_CHAR_ALT, '_'},
+        {LLAMA_GRETYPE_RULE_REF, /* ident_10 */ 10},
         {LLAMA_GRETYPE_ALT, 0},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
-        {LLAMA_GRETYPE_RULE_REF, 11},
+        // num_11 (index 11)
+        {LLAMA_GRETYPE_CHAR, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_RULE_REF, /* num_11 */ 11},
         {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_CHAR, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 32},
-        {LLAMA_GRETYPE_CHAR_ALT, 9},
-        {LLAMA_GRETYPE_CHAR_ALT, 10},
-        {LLAMA_GRETYPE_RULE_REF, 12},
+        // ws_12 (index 12)
+        {LLAMA_GRETYPE_CHAR, ' '},
+        {LLAMA_GRETYPE_CHAR_ALT, '\t'},
+        {LLAMA_GRETYPE_CHAR_ALT, '\n'},
+        {LLAMA_GRETYPE_RULE_REF, /* ws_12 */ 12},
         {LLAMA_GRETYPE_ALT, 0},
         {LLAMA_GRETYPE_END, 0},
-    };
-
-    index = 0;
-    for (auto rule : parsed_grammar.rules)
-    {
-        // compare rule to expected rule
-        for (uint32_t i = 0; i < rule.size(); i++)
-        {
-            llama_grammar_element element = rule[i];
-            llama_grammar_element expected_element = expected_rules[index];
-
-            // pretty print error message before asserting
-            if (expected_element.type != element.type || expected_element.value != element.value)
-            {
-                fprintf(stderr, "index: %u\n", index);
-                fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value);
-                fprintf(stderr, "actual_element: %d, %u\n", element.type, element.value);
-                fprintf(stderr, "expected_element != actual_element\n");
-            }
-
-            assert(expected_element.type == element.type && expected_element.value == element.value);
-            index++;
-        }
-    }
+    });
 
     return 0;
 }
diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp
index c5361b5b8912c..65486ac5c2d1b 100755
--- a/tests/test-json-schema-to-grammar.cpp
+++ b/tests/test-json-schema-to-grammar.cpp
@@ -80,6 +80,232 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         runner(tc);
     };
 
+    test({
+        SUCCESS,
+        "min 0",
+        R"""({
+            "type": "integer",
+            "minimum": 0
+        })""",
+        R"""(
+            root ::= ([0] | [1-9] [0-9]{0,15}) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min 1",
+        R"""({
+            "type": "integer",
+            "minimum": 1
+        })""",
+        R"""(
+            root ::= ([1-9] [0-9]{0,15}) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min 3",
+        R"""({
+            "type": "integer",
+            "minimum": 3
+        })""",
+        R"""(
+            root ::= ([1-2] [0-9]{1,15} | [3-9] [0-9]{0,15}) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min 9",
+        R"""({
+            "type": "integer",
+            "minimum": 9
+        })""",
+        R"""(
+            root ::= ([1-8] [0-9]{1,15} | [9] [0-9]{0,15}) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min 10",
+        R"""({
+            "type": "integer",
+            "minimum": 10
+        })""",
+        R"""(
+            root ::= ([1] ([0-9]{1,15}) | [2-9] [0-9]{1,15}) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min 25",
+        R"""({
+            "type": "integer",
+            "minimum": 25
+        })""",
+        R"""(
+            root ::= ([1] [0-9]{2,15} | [2] ([0-4] [0-9]{1,14} | [5-9] [0-9]{0,14}) | [3-9] [0-9]{1,15}) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "max 30",
+        R"""({
+            "type": "integer",
+            "maximum": 30
+        })""",
+        R"""(
+            root ::= ("-" [1-9] [0-9]{0,15} | [0-9] | ([1-2] [0-9] | [3] "0")) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min -5",
+        R"""({
+            "type": "integer",
+            "minimum": -5
+        })""",
+        R"""(
+            root ::= ("-" ([0-5]) | [0] | [1-9] [0-9]{0,15}) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min -123",
+        R"""({
+            "type": "integer",
+            "minimum": -123
+        })""",
+        R"""(
+            root ::= ("-" ([0-9] | ([1-8] [0-9] | [9] [0-9]) | "1" ([0-1] [0-9] | [2] [0-3])) | [0] | [1-9] [0-9]{0,15}) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "max -5",
+        R"""({
+            "type": "integer",
+            "maximum": -5
+        })""",
+        R"""(
+            root ::= ("-" ([0-4] [0-9]{1,15} | [5-9] [0-9]{0,15})) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "max 1",
+        R"""({
+            "type": "integer",
+            "maximum": 1
+        })""",
+        R"""(
+            root ::= ("-" [1-9] [0-9]{0,15} | [0-1]) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "max 100",
+        R"""({
+            "type": "integer",
+            "maximum": 100
+        })""",
+        R"""(
+            root ::= ("-" [1-9] [0-9]{0,15} | [0-9] | ([1-8] [0-9] | [9] [0-9]) | "100") space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min 0 max 23",
+        R"""({
+            "type": "integer",
+            "minimum": 0,
+            "maximum": 23
+        })""",
+        R"""(
+            root ::= ([0-9] | ([1] [0-9] | [2] [0-3])) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min 15 max 300",
+        R"""({
+            "type": "integer",
+            "minimum": 15,
+            "maximum": 300
+        })""",
+        R"""(
+            root ::= (([1] ([5-9]) | [2-9] [0-9]) | ([1-2] [0-9]{2} | [3] "00")) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min 5 max 30",
+        R"""({
+            "type": "integer",
+            "minimum": 5,
+            "maximum": 30
+        })""",
+        R"""(
+            root ::= ([5-9] | ([1-2] [0-9] | [3] "0")) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min -123 max 42",
+        R"""({
+            "type": "integer",
+            "minimum": -123,
+            "maximum": 42
+        })""",
+        R"""(
+            root ::= ("-" ([0-9] | ([1-8] [0-9] | [9] [0-9]) | "1" ([0-1] [0-9] | [2] [0-3])) | [0-9] | ([1-3] [0-9] | [4] [0-2])) space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min -10 max 10",
+        R"""({
+            "type": "integer",
+            "minimum": -10,
+            "maximum": 10
+        })""",
+        R"""(
+            root ::= ("-" ([0-9] | "10") | [0-9] | "10") space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
     test({
         FAILURE,
         "unknown type",
@@ -105,14 +331,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             array ::= "[" space ( value ("," space value)* )? "]" space
             boolean ::= ("true" | "false") space
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             null ::= "null" space
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
             root ::= object
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
             value ::= object | array | string | number | boolean | null
         )"""
@@ -130,18 +356,18 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             ]
         })""",
         R"""(
-            date ::= [0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( "0" [1-9] | [1-2] [0-9] | "3" [0-1] )
+            date ::= [0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( "0" [1-9] | [1-2] [0-9] | "3" [0-1] )
             date-string ::= "\"" date "\"" space
             date-time ::= date "T" time
             date-time-string ::= "\"" date-time "\"" space
             root ::= "[" space tuple-0 "," space uuid "," space tuple-2 "," space tuple-3 "]" space
-            space ::= " "?
-            time ::= ([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )
+            space ::= | " " | "\n" [ \t]{0,20}
+            time ::= ([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )
             time-string ::= "\"" time "\"" space
             tuple-0 ::= date-string
             tuple-2 ::= time-string
             tuple-3 ::= date-time-string
-            uuid ::= "\"" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "\"" space
+            uuid ::= "\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space
         )"""
     });
 
@@ -152,9 +378,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "type": "string"
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "\"" char* "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -166,9 +392,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minLength": 1
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "\"" char+ "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -180,9 +406,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minLength": 3
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            root ::= "\"" char char char (char)* "\"" space
-            space ::= " "?
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            root ::= "\"" char{3,} "\"" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -194,9 +420,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxLength": 3
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            root ::= "\"" (char (char (char)?)?)? "\"" space
-            space ::= " "?
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            root ::= "\"" char{0,3} "\"" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -209,9 +435,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxLength": 4
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            root ::= "\"" char (char (char (char)?)?)? "\"" space
-            space ::= " "?
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            root ::= "\"" char{1,4} "\"" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -223,7 +449,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= ("true" | "false") space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -234,9 +460,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "type": "integer"
         })""",
         R"""(
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             root ::= ("-"? integral-part) space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -247,8 +473,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "const": "foo"
         })""",
         R"""(
-            root ::= "\"foo\""
-            space ::= " "?
+            root ::= "\"foo\"" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -259,8 +485,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "const": 123
         })""",
         R"""(
-            root ::= "123"
-            space ::= " "?
+            root ::= "123" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -271,8 +497,40 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "enum": ["red", "amber", "green", null, 42, ["foo"]]
         })""",
         R"""(
-            root ::= "\"red\"" | "\"amber\"" | "\"green\"" | "null" | "42" | "[\"foo\"]"
-            space ::= " "?
+            root ::= ("\"red\"" | "\"amber\"" | "\"green\"" | "null" | "42" | "[\"foo\"]") space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string array",
+        R"""({
+            "type": "array",
+            "prefixItems": { "type": "string" }
+        })""",
+        R"""(
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            root ::= "[" space (string ("," space string)*)? "]" space
+            space ::= | " " | "\n" [ \t]{0,20}
+            string ::= "\"" char* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "nullable string array",
+        R"""({
+            "type": ["array", "null"],
+            "prefixItems": { "type": "string" }
+        })""",
+        R"""(
+            alternative-0 ::= "[" space (string ("," space string)*)? "]" space
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            null ::= "null" space
+            root ::= alternative-0 | null
+            space ::= | " " | "\n" [ \t]{0,20}
+            string ::= "\"" char* "\"" space
         )"""
     });
 
@@ -283,9 +541,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "prefixItems": [{ "type": "string" }]
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "[" space string "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -297,12 +555,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "prefixItems": [{ "type": "string" }, { "type": "number" }]
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "[" space string "," space number "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -314,10 +572,10 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "type": "number"
         })""",
         R"""(
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             root ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -332,8 +590,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             boolean ::= ("true" | "false") space
-            root ::= "[" space boolean "," space boolean ("," space boolean)* "]" space
-            space ::= " "?
+            root ::= "[" space boolean ("," space boolean)+ "]" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -348,8 +606,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             boolean ::= ("true" | "false") space
-            root ::= "[" space (boolean)? "]" space
-            space ::= " "?
+            root ::= "[" space boolean? "]" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -365,7 +623,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             boolean ::= ("true" | "false") space
             root ::= "[" space (boolean ("," space boolean)?)? "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -380,13 +638,51 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxItems": 5
         })""",
         R"""(
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            decimal-part ::= [0-9]{1,16}
             integer ::= ("-"? integral-part) space
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             item ::= number | integer
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "[" space item "," space item "," space item ("," space item ("," space item)?)? "]" space
-            space ::= " "?
+            root ::= "[" space item ("," space item){2,4} "]" space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min + max items with min + max values across zero",
+        R"""({
+            "items": {
+                "type": "integer",
+                "minimum": -12,
+                "maximum": 207
+            },
+            "minItems": 3,
+            "maxItems": 5
+        })""",
+        R"""(
+            item ::= ("-" ([0-9] | "1" [0-2]) | [0-9] | ([1-8] [0-9] | [9] [0-9]) | ([1] [0-9]{2} | [2] "0" [0-7])) space
+            root ::= "[" space item ("," space item){2,4} "]" space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min + max items with min + max values",
+        R"""({
+            "items": {
+                "type": "integer",
+                "minimum": 12,
+                "maximum": 207
+            },
+            "minItems": 3,
+            "maxItems": 5
+        })""",
+        R"""(
+            item ::= (([1] ([2-9]) | [2-9] [0-9]) | ([1] [0-9]{2} | [2] "0" [0-7])) space
+            root ::= "[" space item ("," space item){2,4} "]" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -399,7 +695,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "\"" "ab" "c"? "d"* "ef" "g"+ ("hij")? "kl" "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -412,7 +708,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "\"" "[]{}()|+*?" "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -425,7 +721,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "\"" "\"" "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -438,9 +734,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             dot ::= [^\x0A\x0D]
-            root ::= "\"" ("(" root-1 (root-1 (root-1)?)? ")")? root-1 root-1 root-1 "-" root-1 root-1 root-1 root-1 " " "aaa" ("a" ("a")?)? "nd" dot dot dot "\"" space
+            root ::= "\"" ("(" root-1{1,3} ")")? root-1{3,3} "-" root-1{4,4} " " "a"{3,5} "nd" dot dot dot "\"" space
             root-1 ::= [0-9]
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -466,9 +762,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             a-kv ::= "\"a\"" space ":" space string
             b-kv ::= "\"b\"" space ":" space string
             c-kv ::= "\"c\"" space ":" space string
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "{" space b-kv "," space c-kv "," space a-kv "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -486,9 +782,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             a-kv ::= "\"a\"" space ":" space string
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "{" space  (a-kv )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -510,9 +806,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             b-kv ::= "\"b\"" space ":" space string
             b-rest ::= ( "," space c-kv )?
             c-kv ::= "\"c\"" space ":" space string
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "{" space  (a-kv a-rest | b-kv b-rest | c-kv )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -534,11 +830,11 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             a-kv ::= "\"a\"" space ":" space string
             b-kv ::= "\"b\"" space ":" space string
             c-kv ::= "\"c\"" space ":" space string
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             d-kv ::= "\"d\"" space ":" space string
             d-rest ::= ( "," space c-kv )?
             root ::= "{" space b-kv "," space a-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -552,14 +848,13 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             additional-kv ::= string ":" space additional-value
-            additional-kvs ::= additional-kv ( "," space additional-kv )*
             additional-value ::= "[" space (number ("," space number)*)? "]" space
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space  (additional-kvs )? "}" space
-            space ::= " "?
+            root ::= "{" space  (additional-kv ( "," space additional-kv )* )? "}" space
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -574,14 +869,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             array ::= "[" space ( value ("," space value)* )? "]" space
             boolean ::= ("true" | "false") space
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             null ::= "null" space
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
             root ::= object
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
             value ::= object | array | string | number | boolean | null
         )"""
@@ -596,14 +891,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             array ::= "[" space ( value ("," space value)* )? "]" space
             boolean ::= ("true" | "false") space
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             null ::= "null" space
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
             root ::= object
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
             value ::= object | array | string | number | boolean | null
         )"""
@@ -618,7 +913,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "{" space  "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -635,14 +930,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             a-kv ::= "\"a\"" space ":" space number
-            additional-kv ::= string ":" space string
-            additional-kvs ::= additional-kv ( "," space additional-kv )*
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            additional-k ::= ["] ( [a] char+ | [^"a] char* )? ["] space
+            additional-kv ::= additional-k ":" space string
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space a-kv ( "," space ( additional-kvs ) )? "}" space
-            space ::= " "?
+            root ::= "{" space a-kv ( "," space ( additional-kv ( "," space additional-kv )* ) )? "}" space
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -659,16 +954,15 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             a-kv ::= "\"a\"" space ":" space number
-            a-rest ::= additional-kvs
-            additional-kv ::= string ":" space number
-            additional-kvs ::= additional-kv ( "," space additional-kv )*
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            a-rest ::= ( "," space additional-kv )*
+            additional-k ::= ["] ( [a] char+ | [^"a] char* )? ["] space
+            additional-kv ::= additional-k ":" space number
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space  (a-kv a-rest | additional-kvs )? "}" space
-            space ::= " "?
-            string ::= "\"" char* "\"" space
+            root ::= "{" space  (a-kv a-rest | additional-kv ( "," space additional-kv )* )? "}" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -678,25 +972,100 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""({
             "type": "object",
             "properties": {
-                "a": {"type": "number"},
-                "b": {"type": "number"}
+                "and": {"type": "number"},
+                "also": {"type": "number"}
             },
-            "required": ["a"],
+            "required": ["and"],
             "additionalProperties": {"type": "number"}
         })""",
         R"""(
-            a-kv ::= "\"a\"" space ":" space number
-            additional-kv ::= string ":" space number
-            additional-kvs ::= additional-kv ( "," space additional-kv )*
-            b-kv ::= "\"b\"" space ":" space number
-            b-rest ::= additional-kvs
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            additional-k ::= ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["] space
+            additional-kv ::= additional-k ":" space number
+            also-kv ::= "\"also\"" space ":" space number
+            also-rest ::= ( "," space additional-kv )*
+            and-kv ::= "\"and\"" space ":" space number
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space a-kv ( "," space ( b-kv b-rest | additional-kvs ) )? "}" space
-            space ::= " "?
-            string ::= "\"" char* "\"" space
+            root ::= "{" space and-kv ( "," space ( also-kv also-rest | additional-kv ( "," space additional-kv )* ) )? "}" space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "optional props with empty name",
+        R"""({
+            "properties": {
+                "": {"type": "integer"},
+                "a": {"type": "integer"}
+            },
+            "additionalProperties": {"type": "integer"}
+        })""",
+        R"""(
+            -kv ::= "\"\"" space ":" space root
+            -rest ::= ( "," space a-kv )? a-rest
+            a-kv ::= "\"a\"" space ":" space integer
+            a-rest ::= ( "," space additional-kv )*
+            additional-k ::= ["] ( [a] char+ | [^"a] char* ) ["] space
+            additional-kv ::= additional-k ":" space integer
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            integer ::= ("-"? integral-part) space
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
+            root ::= ("-"? integral-part) space
+            root0 ::= "{" space  (-kv -rest | a-kv a-rest | additional-kv ( "," space additional-kv )* )? "}" space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "optional props with nested names",
+        R"""({
+            "properties": {
+                "a": {"type": "integer"},
+                "aa": {"type": "integer"}
+            },
+            "additionalProperties": {"type": "integer"}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space integer
+            a-rest ::= ( "," space aa-kv )? aa-rest
+            aa-kv ::= "\"aa\"" space ":" space integer
+            aa-rest ::= ( "," space additional-kv )*
+            additional-k ::= ["] ( [a] ([a] char+ | [^"a] char*) | [^"a] char* )? ["] space
+            additional-kv ::= additional-k ":" space integer
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            integer ::= ("-"? integral-part) space
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
+            root ::= "{" space  (a-kv a-rest | aa-kv aa-rest | additional-kv ( "," space additional-kv )* )? "}" space
+            space ::= | " " | "\n" [ \t]{0,20}
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "optional props with common prefix",
+        R"""({
+            "properties": {
+                "ab": {"type": "integer"},
+                "ac": {"type": "integer"}
+            },
+            "additionalProperties": {"type": "integer"}
+        })""",
+        R"""(
+            ab-kv ::= "\"ab\"" space ":" space integer
+            ab-rest ::= ( "," space ac-kv )? ac-rest
+            ac-kv ::= "\"ac\"" space ":" space integer
+            ac-rest ::= ( "," space additional-kv )*
+            additional-k ::= ["] ( [a] ([b] char+ | [c] char+ | [^"bc] char*) | [^"a] char* )? ["] space
+            additional-kv ::= additional-k ":" space integer
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+            integer ::= ("-"? integral-part) space
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
+            root ::= "{" space  (ab-kv ab-rest | ac-kv ac-rest | additional-kv ( "," space additional-kv )* )? "}" space
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -721,11 +1090,11 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             }
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             foo ::= "{" space foo-a-kv "}" space
             foo-a-kv ::= "\"a\"" space ":" space string
             root ::= foo
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -753,13 +1122,13 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             alternative-1 ::= bar
             bar ::= "{" space  (bar-b-kv )? "}" space
             bar-b-kv ::= "\"b\"" space ":" space number
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            decimal-part ::= [0-9]{1,16}
             foo ::= "{" space  (foo-a-kv )? "}" space
             foo-a-kv ::= "\"a\"" space ":" space number
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= alternative-0 | alternative-1
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -799,11 +1168,11 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             c-kv ::= "\"c\"" space ":" space number
             d-kv ::= "\"d\"" space ":" space number
             d-rest ::= ( "," space c-kv )?
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "{" space a-kv "," space b-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -842,8 +1211,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "definitions": {}
         })""",
         R"""(
-            decimal-part ::= [0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
-            integral-part ::= [0-9] | [1-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9] ([0-9])?)?)?)?)?)?)?)?)?)?)?)?)?)?)?
+            decimal-part ::= [0-9]{1,16}
+            integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             number- ::= "{" space number-number-kv "}" space
             number-kv ::= "\"number\"" space ":" space number-
@@ -851,7 +1220,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             number-number-kv ::= "\"number\"" space ":" space number-number
             number-number-root-kv ::= "\"root\"" space ":" space number
             root ::= "{" space number-kv "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 }
@@ -870,26 +1239,30 @@ int main() {
         }
     });
 
-    if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python --version") == 0)) {
-        test_all("Python", [](const TestCase & tc) {
-            write("test-json-schema-input.tmp", tc.schema);
-            tc.verify_status(std::system(
-                "python ./examples/json_schema_to_grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
-            tc.verify(read("test-grammar-output.tmp"));
-        });
+    if (getenv("LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR")) {
+        fprintf(stderr, "\033[33mWARNING: Skipping slow tests on emulator.\n\033[0m");
     } else {
-        fprintf(stderr, "\033[33mWARNING: Python not found, skipping Python JSON schema -> grammar tests.\n\033[0m");
-    }
+        if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python -c \"import sys; exit(1) if sys.version_info < (3, 8) else print('Python version is sufficient')\"") == 0)) {
+            test_all("Python", [](const TestCase & tc) {
+                write("test-json-schema-input.tmp", tc.schema);
+                tc.verify_status(std::system(
+                    "python ./examples/json_schema_to_grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
+                tc.verify(read("test-grammar-output.tmp"));
+            });
+        } else {
+            fprintf(stderr, "\033[33mWARNING: Python not found (min version required is 3.8), skipping Python JSON schema -> grammar tests.\n\033[0m");
+        }
 
-    if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {
-        test_all("JavaScript", [](const TestCase & tc) {
-            write("test-json-schema-input.tmp", tc.schema);
-            tc.verify_status(std::system(
-                "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
-            tc.verify(read("test-grammar-output.tmp"));
-        });
-    } else {
-        fprintf(stderr, "\033[33mWARNING: Node not found, skipping JavaScript JSON schema -> grammar tests.\n\033[0m");
+        if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {
+            test_all("JavaScript", [](const TestCase & tc) {
+                write("test-json-schema-input.tmp", tc.schema);
+                tc.verify_status(std::system(
+                    "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
+                tc.verify(read("test-grammar-output.tmp"));
+            });
+        } else {
+            fprintf(stderr, "\033[33mWARNING: Node not found, skipping JavaScript JSON schema -> grammar tests.\n\033[0m");
+        }
     }
 
     test_all("Check Expectations Validity", [](const TestCase & tc) {
diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp
index 27ca4d2656c5d..1f3a267b39f9b 100644
--- a/tests/test-llama-grammar.cpp
+++ b/tests/test-llama-grammar.cpp
@@ -2,10 +2,12 @@
 #undef NDEBUG
 #endif
 
-#include "llama.cpp" // TODO: not great
+#define LLAMA_API_INTERNAL
+#include "llama.h"
 #include "grammar-parser.h"
 
 #include <cassert>
+#include <stdexcept>
 
 int main()
 {
@@ -112,10 +114,14 @@ int main()
         }
     }
 
-    llama_grammar *grammar = NULL;
+    llama_grammar * grammar = NULL;
     std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
-    grammar = llama_grammar_init(
-        grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+
+    grammar = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+    if (grammar == nullptr)
+    {
+        throw std::runtime_error("Failed to initialize llama_grammar");
+    }
 
     std::vector<std::vector<llama_grammar_element>> expected_stacks = {
         {
@@ -168,7 +174,7 @@ int main()
         }};
 
     auto index = 0;
-    for (auto stack : grammar->stacks)
+    for (auto stack : llama_grammar_get_stacks(grammar))
     {
         // compare stack to expected_stack
         for (uint32_t i = 0; i < stack.size(); i++)
@@ -370,13 +376,13 @@ int main()
         },
     };
 
-    std::vector<llama_grammar_candidate> rejects = llama_grammar_reject_candidates_for_stack(grammar->rules, grammar->stacks[0], next_candidates);
+    std::vector<llama_grammar_candidate> rejects = llama_grammar_reject_candidates_for_stack(llama_grammar_get_rules(grammar), llama_grammar_get_stacks(grammar)[0], next_candidates);
 
     std::vector<std::vector<llama_grammar_candidate>> all_rejects;
 
-    for (std::size_t count = 0; count < grammar->stacks.size(); ++count)
+    for (std::size_t count = 0; count < llama_grammar_get_stacks(grammar).size(); ++count)
     {
-        rejects = llama_grammar_reject_candidates_for_stack(grammar->rules, grammar->stacks[count], next_candidates);
+        rejects = llama_grammar_reject_candidates_for_stack(llama_grammar_get_rules(grammar), llama_grammar_get_stacks(grammar)[count], next_candidates);
         all_rejects.push_back(rejects);
     }
 
@@ -397,6 +403,6 @@ int main()
         delete[] candidate.code_points;
         candidate.code_points = nullptr;
     }
-    delete grammar;
+    llama_grammar_free(grammar);
     return 0;
 }
diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp
index e690ac6c85a71..c97458d1df2b8 100644
--- a/tests/test-quantize-fns.cpp
+++ b/tests/test-quantize-fns.cpp
@@ -60,7 +60,7 @@ static float reference_quantization_error(ggml_type_traits_t & qfns, size_t test
     qfns.from_float(test_data, tmp_q.data(), test_size);
     qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
 
-    qfns.from_float_reference(test_data, tmp_q.data(), test_size);
+    qfns.from_float_ref(test_data, tmp_q.data(), test_size);
     qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
 
     return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp
index 48d9fae3dc06e..24e066053219a 100644
--- a/tests/test-quantize-perf.cpp
+++ b/tests/test-quantize-perf.cpp
@@ -285,7 +285,7 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void) -> float {
-                        qfns.from_float_reference(test_data1, test_q1, size);
+                        qfns.from_float_ref(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = ggml_row_size(type, size);
diff --git a/tests/test-rope.cpp b/tests/test-rope.cpp
index 26c1f42dc0e95..8159e276af617 100644
--- a/tests/test-rope.cpp
+++ b/tests/test-rope.cpp
@@ -162,12 +162,12 @@ int main(int /*argc*/, const char ** /*argv*/) {
         x = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
 
         // 100, 101, 102, ..., 172
-        struct ggml_tensor * r0 = ggml_rope(ctx0, x,  p0, n_rot, mode, 1024);
+        struct ggml_tensor * r0 = ggml_rope(ctx0, x,  p0, n_rot, mode);
         // -67, -67, -67, ..., -67
-        struct ggml_tensor * r1 = ggml_rope(ctx0, r0, p1, n_rot, mode, 1024); // "context swap", i.e. forget n_past_0 - n_past_2 tokens
+        struct ggml_tensor * r1 = ggml_rope(ctx0, r0, p1, n_rot, mode); // "context swap", i.e. forget n_past_0 - n_past_2 tokens
 
         //  33,  34,  35, ..., 105
-        struct ggml_tensor * r2 = ggml_rope(ctx0, x,  p2, n_rot, mode, 1024);
+        struct ggml_tensor * r2 = ggml_rope(ctx0, x,  p2, n_rot, mode);
 
         ggml_cgraph * gf = ggml_new_graph(ctx0);
 
@@ -218,4 +218,3 @@ int main(int /*argc*/, const char ** /*argv*/) {
 
     return 0;
 }
-
diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index 6374958fee8e6..de858bd3b87e3 100644
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -166,12 +166,12 @@ static void test_sampler_queue(
     for (auto s : samplers_sequence) {
         switch (s){
             case 'k': llama_sample_top_k    (nullptr, &candidates_p, top_k, 1); break;
-            case 'f': GGML_ASSERT(false && "tail_free test not implemented");   break;
-            case 'y': GGML_ASSERT(false && "typical test not implemented");     break;
+            case 'f': GGML_ABORT("tail_free test not implemented");   break;
+            case 'y': GGML_ABORT("typical test not implemented");     break;
             case 'p': llama_sample_top_p    (nullptr, &candidates_p, top_p, 1); break;
             case 'm': llama_sample_min_p    (nullptr, &candidates_p, min_p, 1); break;
-            case 't': GGML_ASSERT(false && "temperature test not implemented"); break;
-            default : GGML_ASSERT(false && "Unknown sampler");                  break;
+            case 't': GGML_ABORT("temperature test not implemented"); break;
+            default : GGML_ABORT("Unknown sampler");                  break;
         }
 
         llama_sample_softmax(nullptr, &candidates_p); // make sure tokens are sorted for tests
@@ -222,7 +222,7 @@ static void test_sampler_queue(
             GGML_ASSERT(candidates_p.data[0].id == max_token_id);
             GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
         } else {
-            GGML_ASSERT(false);
+            GGML_ABORT("fatal error");
         }
     }
 
diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp
index d478f104148a6..d3d21331bfd3d 100644
--- a/tests/test-tokenizer-0.cpp
+++ b/tests/test-tokenizer-0.cpp
@@ -195,11 +195,11 @@ int main(int argc, char **argv) {
     const bool add_special = false;
 
     for (const auto & test_kv : k_tests) {
-        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special);
+        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);
 
         printf("\n");
         printf("src: '%s'\n", test_kv.first.c_str());
-        printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str());
+        printf("res: '%s'\n", llama_detokenize(ctx, res).c_str());
         printf("tok: ");
         for (const auto & tok : res) {
             printf("%d ", tok);
@@ -216,8 +216,8 @@ int main(int argc, char **argv) {
         if (!correct) {
             fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
             fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
-                llama_detokenize_bpe(ctx, res).c_str(),
-                llama_detokenize_bpe(ctx, test_kv.second).c_str());
+                llama_detokenize(ctx, res).c_str(),
+                llama_detokenize(ctx, test_kv.second).c_str());
             fprintf(stderr, "%s : expected tokens: ", __func__);
             for (const auto & t : test_kv.second) {
                 fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str());
@@ -253,7 +253,7 @@ int main(int argc, char **argv) {
         {
             const auto t_start = ggml_time_us();
 
-            res = llama_tokenize(ctx, text, add_special);
+            res = llama_tokenize(ctx, text, add_special, false);
 
             const auto t_end = ggml_time_us();
 
@@ -272,7 +272,7 @@ int main(int argc, char **argv) {
             }
 
             for (const auto & tok : res) {
-                //ofs << tok << " '" << string_strip(llama_detokenize_bpe(ctx, std::vector<int>{tok})) << "'" << std::endl;
+                //ofs << tok << " '" << string_strip(llama_detokenize(ctx, std::vector<int>{tok})) << "'" << std::endl;
                 ofs << tok << "\n";
             }
         }
diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh
index 2fb8632d810c4..4d2b8365547df 100755
--- a/tests/test-tokenizer-0.sh
+++ b/tests/test-tokenizer-0.sh
@@ -17,12 +17,19 @@ make -j tests/test-tokenizer-0
 
 printf "Testing %s on %s ...\n" $name $input
 
+set -e
+
+printf "Tokenizing using (py)  Python AutoTokenizer ...\n"
 python3 ./tests/test-tokenizer-0.py ./models/tokenizers/$name --fname-tok $input > /tmp/test-tokenizer-0-$name-py.log 2>&1
-cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in"
 
+printf "Tokenizing using (cpp) llama.cpp ...\n"
 ./tests/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1
+
+cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in"
 cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in"
 
+set +e
+
 diff $input.tok $input.tokcpp > /dev/null 2>&1
 
 if [ $? -eq 0 ]; then
diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp
index 209a04ad6f77a..9498387e0f212 100644
--- a/tests/test-tokenizer-1-bpe.cpp
+++ b/tests/test-tokenizer-1-bpe.cpp
@@ -11,6 +11,7 @@
 #include <string>
 #include <thread>
 #include <vector>
+#include <atomic>
 
 int main(int argc, char **argv) {
     if (argc < 2 || argc > 3) {
@@ -63,7 +64,10 @@ int main(int argc, char **argv) {
         }
     }
 
-    GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_BPE);
+    //GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_BPE);
+    if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_BPE) {
+        return 99;
+    }
 
 #ifdef _WIN32
     // We need this for unicode console support
@@ -74,7 +78,7 @@ int main(int argc, char **argv) {
     const int n_vocab = llama_n_vocab(model);
 
     for (int i = 0; i < n_vocab; ++i) {
-        std::string str = llama_detokenize_bpe(ctx, std::vector<int>(1, i));
+        std::string str = llama_detokenize(ctx, std::vector<int>(1, i));
         try {
             auto cps = unicode_cpts_from_utf8(str);
             std::vector<llama_token> tokens = llama_tokenize(ctx, str, false, true);
@@ -90,7 +94,7 @@ int main(int argc, char **argv) {
                 fprintf(stderr, "]\n");
                 return 2;
             }
-            std::string check = llama_detokenize_bpe(ctx, tokens);
+            std::string check = llama_detokenize(ctx, tokens);
             if (check != str) {
                 fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n",
                     __func__, i, str.c_str(), str.length(), check.c_str(), check.length());
@@ -108,26 +112,23 @@ int main(int argc, char **argv) {
 
         std::vector<std::thread> threads(nthread);
 
+        std::atomic_int errcode = {};
+
         for (int i = 0; i < nthread; ++i) {
-            threads[i] = std::thread([i, nthread, ctx]() {
-                for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) {
-                    if (!( // NOLINT
-                                (cp < 0x03       || cp >  0x05)   && cp != 0x0b && cp != 0x11 &&
-                                (cp < 0x13       || cp >  0x17)   && cp != 0x19 &&
-                                (cp < 0x1c       || cp >  0x1e)   &&
-                                (cp < 0xd800     || cp >  0xdfff) &&
-                                (cp < 0x00040000 || cp >= 0x000e0000)
-                        )) {
+            threads[i] = std::thread([i, nthread, ctx, &errcode]() {
+                for (uint32_t cp = i; !errcode && cp < 0x00110000; cp += nthread) {
+                    if ((0x0000D800 <= cp && cp <= 0x0000DFFF) ||  // surrogates \p{Cs}
+                        (0x00040000 <= cp && cp <= 0x000E0000)) {  // undefined  \p{Cn}
                         continue;
                     }
 
                     std::string str = unicode_cpt_to_utf8(cp);
                     std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
-                    std::string check = llama_detokenize_bpe(ctx, tokens);
+                    std::string check = llama_detokenize(ctx, tokens);
                     if (cp != 9601 && str != check) {
-                        fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
+                        fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
                                 cp, check.c_str(), check.length(), str.c_str(), str.length());
-                        std::exit(3);
+                        errcode = 3;
                     }
                 }
             });
@@ -136,6 +137,10 @@ int main(int argc, char **argv) {
         for (auto & t : threads) {
             t.join();
         }
+
+        if (errcode) {
+            return errcode;
+        }
     }
 
     llama_free_model(model);
diff --git a/tests/test-tokenizer-1-spm.cpp b/tests/test-tokenizer-1-spm.cpp
index ac2333ddaf886..7ca9e2ca6a671 100644
--- a/tests/test-tokenizer-1-spm.cpp
+++ b/tests/test-tokenizer-1-spm.cpp
@@ -11,6 +11,7 @@
 #include <string>
 #include <thread>
 #include <vector>
+#include <atomic>
 
 int main(int argc, char ** argv) {
     if (argc < 2) {
@@ -51,7 +52,10 @@ int main(int argc, char ** argv) {
         }
     }
 
-    GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
+    //GGML_ASSERT(llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
+    if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_SPM) {
+        return 99;
+    }
 
 #ifdef _WIN32
     // We need this for unicode console support
@@ -62,9 +66,9 @@ int main(int argc, char ** argv) {
     const int n_vocab = llama_n_vocab(model);
 
     for (int i = 0; i < n_vocab; ++i) {
-        std::string str = llama_detokenize_spm(ctx, std::vector<int>(1, i));
-        std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
-        std::string check = llama_detokenize_spm(ctx, tokens);
+        std::string str = llama_detokenize(ctx, std::vector<int>(1, i), true);
+        std::vector<llama_token> tokens = llama_tokenize(ctx, str, false, true);
+        std::string check = llama_detokenize(ctx, tokens);
         if (check != str) {
             fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n",
                 __func__, i, str.c_str(), str.length(), check.c_str(), check.length());
@@ -78,20 +82,23 @@ int main(int argc, char ** argv) {
 
         std::vector<std::thread> threads(nthread);
 
+        std::atomic_int errcode = {};
+
         for (int i = 0; i < nthread; ++i) {
-            threads[i] = std::thread([i, nthread, ctx]() {
-                for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) {
-                    if (cp >= 0xd800 && cp <= 0xdfff) {
+            threads[i] = std::thread([i, nthread, ctx, &errcode]() {
+                for (uint32_t cp = i; !errcode && cp < 0x00110000; cp += nthread) {
+                    if ((0x0000D800 <= cp && cp <= 0x0000DFFF) ||  // surrogates \p{Cs}
+                        (0x00040000 <= cp && cp <= 0x000E0000)) {  // undefined \p{Cn}
                         continue;
                     }
 
                     std::string str = unicode_cpt_to_utf8(cp);
-                    std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
-                    std::string check = llama_detokenize_spm(ctx, tokens);
+                    std::vector<llama_token> tokens = llama_tokenize(ctx, str, false, true);
+                    std::string check = llama_detokenize(ctx, tokens);
                     if (cp != 9601 && str != check) {
-                        fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
+                        fprintf(stderr, "error: codepoint 0x%x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
                                 cp, check.c_str(), check.length(), str.c_str(), str.length());
-                        std::exit(3);
+                        errcode = 3;
                     }
                 }
             });
@@ -100,6 +107,10 @@ int main(int argc, char ** argv) {
         for (auto & t : threads) {
             t.join();
         }
+
+        if(errcode) {
+            return errcode;
+        }
     }
 
     llama_free_model(model);
diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py
index d5a6f185fbcd5..9ebe6c89185a3 100644
--- a/tests/test-tokenizer-random.py
+++ b/tests/test-tokenizer-random.py
@@ -6,33 +6,42 @@
 #   python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe
 #
 
+from __future__ import annotations
+
 import time
 import logging
 import argparse
 import subprocess
 import random
+import unicodedata
 
-from typing import Callable, Iterator
+from pathlib import Path
+from typing import Any, Iterator, cast
+from typing_extensions import Buffer
 
 import cffi
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, PreTrainedTokenizer
+
 
-logger = logging.getLogger("test-tokenizer-random-bpe")
+logger = logging.getLogger("test-tokenizer-random")
 
 
 class LibLlama:
 
-    DEFAULT_PATH_LLAMA_H = "./llama.h"
-    DEFAULT_PATH_LIBLLAMA = "./build/libllama.so"  # CMakeLists.txt: BUILD_SHARED_LIBS ON
+    DEFAULT_PATH_LLAMA_H = "./include/llama.h"
+    DEFAULT_PATH_INCLUDES = ["./ggml/include/", "./include/"]
+    DEFAULT_PATH_LIBLLAMA = "./build/src/libllama.so"  # CMakeLists.txt: BUILD_SHARED_LIBS ON
 
-    def __init__(self, path_llama_h: str = None, path_libllama: str = None):
+    def __init__(self, path_llama_h: str | None = None, path_includes: list[str] = [], path_libllama: str | None = None):
         path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H
+        path_includes = path_includes or self.DEFAULT_PATH_INCLUDES
         path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA
-        (self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_libllama)
+        (self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_includes, path_libllama)
         self.lib.llama_backend_init()
 
-    def _load_libllama_cffi(self, path_llama_h: str, path_libllama: str):
-        cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)=", path_llama_h]
+    def _load_libllama_cffi(self, path_llama_h: str, path_includes: list[str], path_libllama: str) -> tuple[cffi.FFI, Any]:
+        cmd = ["gcc", "-O0", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)="]
+        cmd += ["-I" + path for path in path_includes] + [path_llama_h]
         res = subprocess.run(cmd, stdout=subprocess.PIPE)
         assert (res.returncode == 0)
         source = res.stdout.decode()
@@ -63,7 +72,7 @@ def context_default_params(self, **kwargs):
 class LibLlamaModel:
 
     def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
-        self.lib = libllama.lib
+        self.lib: Any = libllama.lib
         self.ffi = libllama.ffi
         if isinstance(mparams, dict):
             mparams = libllama.model_default_params(**mparams)
@@ -77,6 +86,7 @@ def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
             raise RuntimeError("error: failed to create context for model '%s'" % path_model)
         n_tokens_max = self.lib.llama_n_ctx(self.ctx)
         self.token_ids = self.ffi.new("llama_token[]", n_tokens_max)
+        self.text_buff = self.ffi.new("uint8_t[]", 1024)
 
     def free(self):
         if self.ctx:
@@ -87,14 +97,78 @@ def free(self):
         self.model = None
         self.lib = None
 
-    def tokenize(self, text: str, n_tokens_max: int = 0, add_special: bool = False, parse_special: bool = False) -> list[int]:
-        n_tokens_max = n_tokens_max if n_tokens_max > 0 else len(self.token_ids)
-        text = text.encode("utf-8")
-        num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, n_tokens_max, add_special, parse_special)
-        if num < 0:
-            return []
+    def tokenize(self, text: str, add_special: bool = False, parse_special: bool = False) -> list[int]:
+        encoded_text: bytes = text.encode("utf-8")
+        num = self.lib.llama_tokenize(self.model, encoded_text, len(encoded_text), self.token_ids, len(self.token_ids), add_special, parse_special)
+        while num < 0 and len(self.token_ids) < (16 << 20):
+            self.token_ids = self.ffi.new("llama_token[]", -2 * num)
+            num = self.lib.llama_tokenize(self.model, encoded_text, len(encoded_text), self.token_ids, len(self.token_ids), add_special, parse_special)
         return list(self.token_ids[0:num])
 
+    def detokenize(self, ids: list[int], remove_special: bool = False, unparse_special: bool = False) -> str:
+        if len(self.token_ids) < len(ids):
+            self.token_ids = self.ffi.new("llama_token[]", 2 * len(ids))
+        for i, id in enumerate(ids):
+            self.token_ids[i] = id
+        num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
+        while num < 0 and len(self.text_buff) < (16 << 20):
+            self.text_buff = self.ffi.new("uint8_t[]", -2 * num)
+            num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
+        return str(cast(Buffer, self.ffi.buffer(self.text_buff, num)), encoding="utf-8", errors="replace")  # replace errors with '\uFFFD'
+
+
+class Tokenizer:
+
+    def encode(self, text: str) -> list[int]:
+        raise NotImplementedError
+
+    def decode(self, ids: list[int]) -> str:
+        raise NotImplementedError
+
+
+class TokenizerGroundtruth (Tokenizer):
+
+    def __init__(self, dir_tokenizer: str):
+        self.model: PreTrainedTokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
+        # guess BOS and EOS
+        ids = self.encode("a")
+        assert 1 <= len(ids) <= 3
+        add_bos_token = len(ids) > 1 and self.model.bos_token_id == ids[0]
+        add_eos_token = len(ids) > 1 and self.model.eos_token_id == ids[-1]
+        self.add_bos_token = getattr(self.model, "add_bos_token", add_bos_token)
+        self.add_eos_token = getattr(self.model, "add_eos_token", add_eos_token)
+        # build vocab
+        tokens = list(self.model.get_vocab().values())
+        self.vocab = self.model.batch_decode(tokens, skip_special_tokens=True)
+        self.vocab = list(sorted(self.vocab))
+        # tokens and lists
+        self.special_tokens = list(self.model.all_special_tokens)
+        self.added_tokens   = self.model.batch_decode(self.model.added_tokens_encoder.values(), skip_special_tokens=False)
+        self.bos_token = self.model.bos_token
+        self.eos_token = self.model.eos_token
+
+    def encode(self, text: str) -> list[int]:
+        return self.model.encode(text, add_special_tokens=True)
+
+    def decode(self, ids: list[int]) -> str:
+        return self.model.decode(ids, skip_special_tokens=False)
+
+
+class TokenizerLlamaCpp (Tokenizer):
+
+    libllama: LibLlama | None = None
+
+    def __init__(self, vocab_file: str):
+        if not self.libllama:
+            self.libllama = LibLlama()
+        self.model = LibLlamaModel(self.libllama, vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
+
+    def encode(self, text: str) -> list[int]:
+        return self.model.tokenize(text, add_special=True, parse_special=True)
+
+    def decode(self, ids: list[int]) -> str:
+        return self.model.detokenize(ids, remove_special=False, unparse_special=True)
+
 
 def generator_custom_text() -> Iterator[str]:
     """General tests"""
@@ -153,19 +227,92 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
         'Ⅵ-a',       # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms
         '\uFEFF//',   # unicode_ranges_control, 0xFEFF (BOM)
         'Cửa Việt',   # llama-3, ignore_merges = true
-        '<s>a',       # TODO: Phi-3 fail
-        'a\na',       # TODO: Bert fail
+        '<s>a',       # Phi-3 fail
+        '<unk><|endoftext|><s>',  # Phi-3 fail
+        'a\na',            # bert fail
+        '"`',              # falcon
+        ' \u2e4e',         # falcon
+        '\n\x0b  ',        # falcon
+        'a\xa0\xa0\x00b',  # jina-v2-es
+        'one <mask>',      # jina-v2-es  <mask> lstrip=true
+        'a </s> b',        # rstrip phi-3
+        'a <mask> b',      # lstrip jina-v2
+        '\xa0aC',          # deepseek
+        '\u2029 \uA3E4',   # deepseek-llm
+        "a ?",
+        'å',               # mpt
+        '\U000ac517',      # utf-8 encode error, falcon
+        '\U000522f4',      # utf-8 encode error, starcoder
+        "<s><s><unk><s>a<s>b<s>c<unk>d<unk></s>",
+        "<s> <s> <unk><s>a<s>b<s>c<unk>d<unk></s>",
     ]
 
 
-def generator_vocab_words(vocab: list[str]) -> Iterator[str]:
+def generator_vocab_words(tokenizer: TokenizerGroundtruth) -> Iterator[str]:
     """Brute force check all vocab words"""
-    yield from vocab
+    yield from tokenizer.vocab
+
+
+def generator_ascii_lr_strip() -> Iterator[str]:
+    WHITESPACES = ["", " ", "  "]
+    CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""]
+    for char1 in CHARACTERS:
+        for char2 in CHARACTERS:
+            for lstrip in WHITESPACES:
+                for rstrip in WHITESPACES:
+                    yield lstrip + char1 + char2 + rstrip
+                    yield lstrip + char1 + rstrip + char2
+                    yield char1 + lstrip + char2 + rstrip
+
+
+def generator_apostrophe() -> Iterator[str]:
+    WHITESPACES = ["", " ", "  "]
+    CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""]
+    for char1 in CHARACTERS:
+        for char2 in CHARACTERS:
+            for lstrip in WHITESPACES:
+                for rstrip in WHITESPACES:
+                    yield char1 + lstrip + "'" + rstrip + char2
+                    yield char1 + char2 + lstrip + "'" + rstrip + "z"
+                    yield "a" + lstrip + "'" + rstrip + char1 + char2
+
+
+def generator_added_lr_strip(tokenizer: TokenizerGroundtruth) -> Iterator[str]:
+    WHITESPACES = ["", " ", "  ", "\n", "\r\n", "\n\n", "\t", "\t\t"]
+    all_tokens = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens)))
+    for token in all_tokens:
+        for lstrip in WHITESPACES:
+            for rstrip in WHITESPACES:
+                yield lstrip + token + rstrip
+                yield "a" + lstrip + token + rstrip
+                yield lstrip + token + rstrip + "z"
+                yield "a" + lstrip + token + rstrip + "z"
+
+
+def generator_random_added_tokens(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
+    separations = [" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"]
+    all_tokens  = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens + separations)))
+    rand = random.Random()
+    for m in range(iterations):
+        rand.seed(m)
+        words = rand.choices(all_tokens, k=500)
+        if words and words[0] == tokenizer.bos_token:  # skip spam warning of double BOS
+            while len(words) > 1 and words[1] == tokenizer.bos_token:  # leave one starting BOS
+                words.pop(0)
+            if tokenizer.add_bos_token:  # drop all starting BOS
+                words.pop(0)
+        if words and words[-1] == tokenizer.eos_token:  # skip spam warning of double EOS
+            while len(words) > 1 and words[-2] == tokenizer.eos_token:  # leave one trailing EOS
+                words.pop(-1)
+            if tokenizer.add_bos_token:  # drop all trailing EOS
+                words.pop(-1)
+        yield "".join(words)
 
 
 def generator_random_chars(iterations=100) -> Iterator[str]:
     """Brute force random text with simple characters"""
 
+    NUM_WORDS = 400
     WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
     CHARS = list(sorted(set("""
         ABCDEFGHIJKLMNOPQRSTUVWXYZ
@@ -179,20 +326,58 @@ def generator_random_chars(iterations=100) -> Iterator[str]:
     for m in range(iterations):
         rand.seed(m)
         text = []
-        num_words = rand.randint(300, 400)
-        for i in range(num_words):
+        for _ in range(NUM_WORDS):
             k = rand.randint(1, 7)
             word = rand.choices(CHARS, k=k)
-            space = rand.choice(WHITESPACES)
-            text.append("".join(word) + space)
+            word.append(rand.choice(WHITESPACES))
+            text.append("".join(word))
         yield "".join(text)
 
 
-def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[str]:
+def generator_unicodes() -> Iterator[str]:
+    """Iterate unicode characters"""
+
+    MAX_CODEPOINTS = 0x30000  # 0x110000
+
+    def _valid(cpt):
+        if cpt >= 0x30000:  # unassigned and supplement­ary
+            return False
+        # if cpt == 0x2029:  # deepseek-llm
+        #    return False
+        if unicodedata.category(chr(cpt)) in ("Cn", "Cs", "Co"):  # undefined, surrogates, private
+            return False
+        return True
+
+    characters = [chr(cpt) for cpt in range(0, MAX_CODEPOINTS) if _valid(cpt)]
+
+    yield from characters
+
+
+def generator_random_unicodes(iterations=100) -> Iterator[str]:
+    """Brute force random text with unicode characters"""
+
+    NUM_WORDS = 200
+    WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
+
+    characters = list(generator_unicodes())
+
+    rand = random.Random()
+    for m in range(iterations):
+        rand.seed(m)
+        text = []
+        for _ in range(NUM_WORDS):
+            k = rand.randint(1, 7)
+            word = rand.choices(characters, k=k)
+            word.append(rand.choice(WHITESPACES))
+            text.append("".join(word))
+        yield "".join(text)
+
+
+def generator_random_vocab_chars(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
     """Brute force random text with vocab characters"""
 
     vocab_chars = set()
-    for word in vocab:
+    for word in tokenizer.vocab:
         vocab_chars.update(word)
     vocab_chars = list(sorted(vocab_chars))
 
@@ -203,10 +388,10 @@ def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[s
         yield "".join(text)
 
 
-def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[str]:
+def generator_random_vocab_words(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
     """Brute force random text from vocab words"""
 
-    vocab = [w.strip() for w in vocab]
+    vocab = [w.strip() for w in tokenizer.vocab]
     yield from vocab
 
     rand = random.Random()
@@ -222,27 +407,9 @@ def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[s
         yield "".join(text)
 
 
-def generator_random_bytes(iterations=100) -> Iterator[str]:
-    """Brute force random bytes"""
-
-    WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
+def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLlamaCpp, generator: Iterator[str]):
 
-    rand = random.Random()
-    for m in range(iterations):
-        rand.seed(m)
-        text = []
-        num_words = rand.randint(300, 400)
-        for i in range(num_words):
-            k = rand.randint(1, 8)
-            word = [chr(r) for r in rand.randbytes(k) if r]
-            word.append(rand.choice(WHITESPACES))
-            text.append("".join(word))
-        yield "".join(text)
-
-
-def test_compare_tokenizer(func_tokenize1: Callable, func_tokenize2: Callable, generator: Iterator[str]):
-
-    def find_first_mismatch(ids1: list[int], ids2: list[int]):
+    def find_first_mismatch(ids1: list[int] | str, ids2: list[int] | str):
         for i, (a, b) in enumerate(zip(ids1, ids2)):
             if a != b:
                 return i
@@ -250,53 +417,150 @@ def find_first_mismatch(ids1: list[int], ids2: list[int]):
             return -1
         return min(len(ids1), len(ids2))
 
-    t0 = time.perf_counter()
-    logger.info("%s: %s" % (generator.__name__, "ini"))
+    def check_detokenizer(text: str, text1: str, text2: str) -> bool:
+        if text1 == text2:  # equal to TokenizerGroundtruth?
+            return True
+        # equal to source text?
+        if tokenizer1.add_bos_token:  # remove BOS
+            if text2.startswith(tokenizer1.bos_token):
+                text2 = text2[len(tokenizer1.bos_token):]
+        if tokenizer1.add_eos_token:  # remove EOS
+            if text2.endswith(tokenizer1.eos_token):
+                text2 = text2[:-len(tokenizer1.eos_token)]
+        return text == text2
+
+    t_encode1 = 0
+    t_encode2 = 0
+    t_decode1 = 0
+    t_decode2 = 0
+    t_start = time.perf_counter()
+    encode_errors = 0
+    decode_errors = 0
+    MAX_ERRORS = 10
+
+    logger.info("%s: %s" % (generator.__qualname__, "ini"))
     for text in generator:
-        ids1 = func_tokenize1(text)
-        ids2 = func_tokenize2(text)
-        if ids1 != ids2:
+        # print(repr(text), text.encode())
+        # print(repr(text), hex(ord(text[0])), text.encode())
+        t0 = time.perf_counter()
+        ids1 = tokenizer1.encode(text)
+        t1 = time.perf_counter()
+        ids2 = tokenizer2.encode(text)
+        t2 = time.perf_counter()
+        text1 = tokenizer1.decode(ids1)
+        t3 = time.perf_counter()
+        text2 = tokenizer2.decode(ids1)
+        t4 = time.perf_counter()
+        t_encode1 += t1 - t0
+        t_encode2 += t2 - t1
+        t_decode1 += t3 - t2
+        t_decode2 += t4 - t3
+        if encode_errors < MAX_ERRORS and ids1 != ids2:
             i = find_first_mismatch(ids1, ids2)
-            ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1]
-            ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1]
-            logger.info(" TokenIDs: " + str(ids1))
-            logger.info(" Expected: " + str(ids2))
-            raise Exception()
-    t1 = time.perf_counter()
-    logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0))
-
-
-def main(argv: list[str] = None):
+            ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
+            ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
+            logger.error(" Expected: " + str(ids1))
+            logger.error("   Result: " + str(ids2))
+            encode_errors += 1
+            logger.error(f" {encode_errors=}")
+        if decode_errors < MAX_ERRORS and not check_detokenizer(text, text1, text2):
+            i = find_first_mismatch(text1, text2)
+            text1 = list(text1[max(0, i - 2) : i + 5 + 1])
+            text2 = list(text2[max(0, i - 2) : i + 5 + 1])
+            logger.error(" Expected: " + " ".join(hex(ord(x)) for x in text1))
+            logger.error("   Result: " + " ".join(hex(ord(x)) for x in text2))
+            decode_errors += 1
+            logger.error(f" {decode_errors=}")
+        if encode_errors >= MAX_ERRORS and decode_errors >= MAX_ERRORS:
+            logger.error(f" EXIT: {encode_errors=} {decode_errors=}")
+            # raise Exception()
+            break
+
+    t_total = time.perf_counter() - t_start
+    logger.info(f"{generator.__qualname__}: end,  {t_encode1=:.3f} {t_encode2=:.3f}  {t_decode1=:.3f} {t_decode2=:.3f}  {t_total=:.3f}")
+
+
+def main(argv: list[str] | None = None):
     parser = argparse.ArgumentParser()
-    parser.add_argument("vocab_file", help="path to vocab 'gguf' file")
-    parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
+    parser.add_argument("vocab_file", type=str, help="path to vocab 'gguf' file")
+    parser.add_argument("dir_tokenizer", type=str, help="directory containing 'tokenizer.model' file")
     parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
     args = parser.parse_args(argv)
 
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
-
-    model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
-    tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
+    logging.basicConfig(level = logging.DEBUG if args.verbose else logging.INFO)
+    logger.info(f"VOCABFILE: '{args.vocab_file}'")
 
-    def func_tokenize2(text: str):
-        return tokenizer.encode(text, add_special_tokens=False)
+    tokenizer1 = TokenizerGroundtruth(args.dir_tokenizer)
+    tokenizer2 = TokenizerLlamaCpp(args.vocab_file)
 
-    parse_special = all(len(func_tokenize2(t)) == 1 for t in tokenizer.all_special_tokens)
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text())
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text_edge_cases())
+    compare_tokenizers(tokenizer1, tokenizer2, generator_ascii_lr_strip())
+    compare_tokenizers(tokenizer1, tokenizer2, generator_apostrophe())
+    compare_tokenizers(tokenizer1, tokenizer2, generator_unicodes())
+    compare_tokenizers(tokenizer1, tokenizer2, generator_vocab_words(tokenizer1))
+    compare_tokenizers(tokenizer1, tokenizer2, generator_added_lr_strip(tokenizer1))
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_added_tokens(tokenizer1, 10_000))
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_chars(10_000))
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_unicodes(10_000))
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_chars(tokenizer1, 10_000))
+    # compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_words(tokenizer1, 5_000))
 
-    def func_tokenize1(text: str):
-        return model.tokenize(text, add_special=False, parse_special=parse_special)
-
-    vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text())
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 10_000))
-    # test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_bytes(10_000)) # FAIL
-
-    model.free()
+    tokenizer2.model.free()
 
 
 if __name__ == "__main__":
-    main()
+    # main()
+
+    if True:
+        logging.basicConfig(
+            level    = logging.DEBUG,
+            format   = "%(asctime)s.%(msecs)03d %(name)s %(levelname)s %(message)s",
+            datefmt  = "%Y-%m-%d %H:%M:%S",
+            filename = logger.name + ".log",
+            filemode = "a"
+        )
+    logging.basicConfig(
+        level    = logging.DEBUG,
+        format   = "%(levelname)s %(message)s",
+    )
+
+    path_tokenizers   = Path("./models/tokenizers/")
+    path_vocab_format = "./models/ggml-vocab-%s.gguf"
+
+    tokenizers = [
+        "llama-spm",      # SPM
+        "phi-3",          # SPM
+        "gemma",          # SPM
+        "gemma-2",        # SPM
+        "baichuan",       # SPM
+        "bert-bge",       # WPM
+        "jina-v2-en",     # WPM
+        "llama-bpe",      # BPE
+        "phi-2",          # BPE
+        "deepseek-llm",   # BPE
+        "deepseek-coder", # BPE
+        "falcon",         # BPE
+        "mpt",            # BPE
+        "starcoder",      # BPE
+        "gpt-2",          # BPE
+        "stablelm2",      # BPE
+        "refact",         # BPE
+        "qwen2",          # BPE
+        "olmo",           # BPE
+        "jina-v2-es",     # BPE
+        "jina-v2-de",     # BPE
+        "smaug-bpe",      # BPE
+        "poro-chat",      # BPE
+        "jina-v2-code",   # BPE
+        "viking",         # BPE
+        "jais",           # BPE
+    ]
+
+    logger.info("=" * 50)
+    for tokenizer in tokenizers:
+        logger.info("-" * 50)
+        logger.info(f"TOKENIZER: '{tokenizer}'")
+        vocab_file = Path(path_vocab_format % tokenizer)
+        dir_tokenizer = path_tokenizers / tokenizer
+        main([str(vocab_file), str(dir_tokenizer), "--verbose"])