diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2/README.md
index bcb96b93446..bc9a5bf6d9d 100644
--- a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2/README.md
+++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2/README.md
@@ -5,9 +5,7 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
 ## 0. Requirements
 To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
-### 1. Install
+## 1. Install
 We suggest using conda to manage environment:
 
 On Linux:
@@ -29,7 +27,11 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[all]
 ```
 
-### 2. Run
+## 2. Run
+
+### Example 1: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
+
 ```
 python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
 ```
@@ -63,7 +65,7 @@ numactl -C 0-47 -m 0 python ./generate.py
 ```
 
 #### 2.3 Sample Output
-#### [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)
+##### [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)
 ```log
 Inference time: xxxx s
 -------------------- Prompt --------------------
@@ -88,31 +90,9 @@ Inference time: xxxx s
 答： Artificial Intelligence (AI) refers to the ability of a computer or machine to perform tasks that typically require human-like intelligence, such as understanding language, recognizing patterns
 ```
 
-## Example 2: Stream Chat using `stream_chat()` API
+### Example 2: Stream Chat using `stream_chat()` API
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a ChatGLM2 model to stream chat, with IPEX-LLM INT4 optimizations.
-### 1. Install
-We suggest using conda to manage environment:
-
-On Linux:
-
-```bash
-conda create -n llm python=3.11 # recommend to use Python 3.11
-conda activate llm
-
-# install the latest ipex-llm nightly build with 'all' option
-pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
-```
-
-On Windows:
-
-```cmd
-conda create -n llm python=3.11
-conda activate llm
-
-pip install --pre --upgrade ipex-llm[all]
-```
 
-### 2. Run
 **Stream Chat using `stream_chat()` API**:
 ```
 python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION
diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3/README.md
index 059fad28dd5..5a14aec47d7 100644
--- a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3/README.md
+++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3/README.md
@@ -5,9 +5,7 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
 ## 0. Requirements
 To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
-### 1. Install
+## 1. Install
 We suggest using conda to manage environment:
 
 On Linux:
@@ -29,7 +27,11 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[all]
 ```
 
-### 2. Run
+## 2. Run
+
+### Example 1: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
+
 ```
 python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
 ```
@@ -63,7 +65,7 @@ numactl -C 0-47 -m 0 python ./generate.py
 ```
 
 #### 2.3 Sample Output
-#### [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b)
+##### [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b)
 ```log
 Inference time: xxxx s
 -------------------- Prompt --------------------
@@ -89,31 +91,9 @@ What is AI?
 AI stands for Artificial Intelligence. It refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as recognizing speech or making
 ```
 
-## Example 2: Stream Chat using `stream_chat()` API
+### Example 2: Stream Chat using `stream_chat()` API
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a ChatGLM3 model to stream chat, with IPEX-LLM INT4 optimizations.
-### 1. Install
-We suggest using conda to manage environment:
-
-On Linux:
-
-```bash
-conda create -n llm python=3.11 # recommend to use Python 3.11
-conda activate llm
-
-# install the latest ipex-llm nightly build with 'all' option
-pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
-```
-
-On Windows:
-
-```cmd
-conda create -n llm python=3.11
-conda activate llm
-
-pip install --pre --upgrade ipex-llm[all]
-```
 
-### 2. Run
 **Stream Chat using `stream_chat()` API**:
 ```
 python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION
diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4/README.md
index d3d2966159c..cf4c99d1201 100644
--- a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4/README.md
+++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4/README.md
@@ -5,9 +5,7 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
 ## 0. Requirements
 To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a GLM-4 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
-### 1. Install
+## 1. Install
 We suggest using conda to manage environment:
 
 On Linux:
@@ -20,7 +18,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
 
 # install tiktoken required for GLM-4
-pip install tiktoken
+pip install "tiktoken>=0.7.0"
 ```
 
 On Windows:
@@ -31,10 +29,14 @@ conda activate llm
 
 pip install --pre --upgrade ipex-llm[all]
 
-pip install tiktoken
+pip install "tiktoken>=0.7.0"
 ```
 
-### 2. Run
+## 2. Run
+
+### Example 1: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a GLM-4 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
+
 ```
 python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
 ```
@@ -95,36 +97,9 @@ What is AI?
 Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term "art
 ```
 
-## Example 2: Stream Chat using `stream_chat()` API
+### Example 2: Stream Chat using `stream_chat()` API
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a GLM-4 model to stream chat, with IPEX-LLM INT4 optimizations.
-### 1. Install
-We suggest using conda to manage environment:
-
-On Linux:
-
-```bash
-conda create -n llm python=3.11 # recommend to use Python 3.11
-conda activate llm
-
-# install the latest ipex-llm nightly build with 'all' option
-pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
-
-# install tiktoken required for GLM-4
-pip install tiktoken
-```
-
-On Windows:
-
-```cmd
-conda create -n llm python=3.11
-conda activate llm
-
-pip install --pre --upgrade ipex-llm[all]
-
-pip install tiktoken
-```
 
-### 2. Run
 **Stream Chat using `stream_chat()` API**:
 ```
 python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION
diff --git a/python/llm/example/CPU/PyTorch-Models/Model/glm4/README.md b/python/llm/example/CPU/PyTorch-Models/Model/glm4/README.md
index 9a1cf56069d..2af60c6f49a 100644
--- a/python/llm/example/CPU/PyTorch-Models/Model/glm4/README.md
+++ b/python/llm/example/CPU/PyTorch-Models/Model/glm4/README.md
@@ -21,7 +21,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
 
 # install tiktoken required for GLM-4
-pip install tiktoken
+pip install "tiktoken>=0.7.0"
 ```
 
 On Windows:
@@ -32,7 +32,7 @@ conda activate llm
 
 pip install --pre --upgrade ipex-llm[all]
 
-pip install tiktoken
+pip install "tiktoken>=0.7.0"
 ```
 
 ### 2. Run
diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2/README.md
index dab814eea0c..407534acbc5 100644
--- a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2/README.md
+++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2/README.md
@@ -5,11 +5,8 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
 ## 0. Requirements
 To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
-
-### 1. Install
-#### 1.1 Installation on Linux
+## 1. Install
+### 1.1 Installation on Linux
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11
@@ -18,7 +15,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 ```
 
-#### 1.2 Installation on Windows
+### 1.2 Installation on Windows
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11 libuv
@@ -28,7 +25,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 ```
 
-### 2. Configures OneAPI environment variables for Linux
+## 2. Configures OneAPI environment variables for Linux
 
 > [!NOTE]
 > Skip this step if you are running on Windows.
@@ -39,9 +36,9 @@ This is a required step on Linux for APT or offline installed oneAPI. Skip this
 source /opt/intel/oneapi/setvars.sh
 ```
 
-### 3. Runtime Configurations
+## 3. Runtime Configurations
 For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
+### 3.1 Configurations for Linux
 <details>
 
 <summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
@@ -78,7 +75,7 @@ export BIGDL_LLM_XMX_DISABLED=1
 
 </details>
 
-#### 3.2 Configurations for Windows
+### 3.2 Configurations for Windows
 <details>
 
 <summary>For Intel iGPU</summary>
@@ -103,7 +100,11 @@ set SYCL_CACHE_PERSISTENT=1
 > [!NOTE]
 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
 
-### 4. Running examples
+## 4. Running examples
+
+### Example 1: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
+
 ```
 python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
 ```
@@ -139,103 +140,9 @@ Inference time: xxxx s
 答： Artificial Intelligence (AI) refers to the ability of a computer or machine to perform tasks that typically require human-like intelligence, such as understanding language, recognizing patterns
 ```
 
-## Example 2: Stream Chat using `stream_chat()` API
+### Example 2: Stream Chat using `stream_chat()` API
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a ChatGLM2 model to stream chat, with IPEX-LLM INT4 optimizations.
-### 1. Install
-#### 1.1 Installation on Linux
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11
-conda activate llm
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-```
-#### 1.2 Installation on Windows
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11 libuv
-conda activate llm
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-```
-
-
-### 2. Configures OneAPI environment variables for Linux
-
-> [!NOTE]
-> Skip this step if you are running on Windows.
-
-This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
-
-```bash
-source /opt/intel/oneapi/setvars.sh
-```
-
-### 3. Runtime Configurations
-For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
-
-```bash
-export USE_XETLA=OFF
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Data Center GPU Max Series</summary>
-
-```bash
-export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-export ENABLE_SDP_FUSION=1
-```
-> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
-</details>
-
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```bash
-export SYCL_CACHE_PERSISTENT=1
-export BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-#### 3.2 Configurations for Windows
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-set BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-> [!NOTE]
-> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
 
-### 4. Running examples
 **Stream Chat using `stream_chat()` API**:
 ```
 python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION
diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3/README.md
index c4aca0e9977..70b66b022a0 100644
--- a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3/README.md
+++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3/README.md
@@ -5,10 +5,8 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
 ## 0. Requirements
 To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
-### 1. Install
-#### 1.1 Installation on Linux
+## 1. Install
+### 1.1 Installation on Linux
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11
@@ -17,7 +15,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 ```
 
-#### 1.2 Installation on Windows
+### 1.2 Installation on Windows
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11 libuv
@@ -27,7 +25,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 ```
 
-### 2. Configures OneAPI environment variables for Linux
+## 2. Configures OneAPI environment variables for Linux
 
 > [!NOTE]
 > Skip this step if you are running on Windows.
@@ -38,9 +36,9 @@ This is a required step on Linux for APT or offline installed oneAPI. Skip this
 source /opt/intel/oneapi/setvars.sh
 ```
 
-### 3. Runtime Configurations
+## 3. Runtime Configurations
 For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
+### 3.1 Configurations for Linux
 <details>
 
 <summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
@@ -77,7 +75,7 @@ export BIGDL_LLM_XMX_DISABLED=1
 
 </details>
 
-#### 3.2 Configurations for Windows
+### 3.2 Configurations for Windows
 <details>
 
 <summary>For Intel iGPU</summary>
@@ -101,7 +99,10 @@ set SYCL_CACHE_PERSISTENT=1
 
 > [!NOTE]
 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
-### 4. Running examples
+## 4. Running examples
+
+### Example 1: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
 
 ```
 python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
@@ -139,103 +140,8 @@ What is AI?
 AI stands for Artificial Intelligence. It refers to the development of computer systems or machines that can perform tasks that would normally require human intelligence, such as recognizing patterns
 ```
 
-## Example 2: Stream Chat using `stream_chat()` API
+### Example 2: Stream Chat using `stream_chat()` API
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a ChatGLM3 model to stream chat, with IPEX-LLM INT4 optimizations.
-### 1. Install
-#### 1.1 Installation on Linux
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11
-conda activate llm
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-```
-
-#### 1.2 Installation on Windows
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11 libuv
-conda activate llm
-
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-```
-
-### 2. Configures OneAPI environment variables for Linux
-
-> [!NOTE]
-> Skip this step if you are running on Windows.
-
-This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
-
-```bash
-source /opt/intel/oneapi/setvars.sh
-```
-
-### 3. Runtime Configurations
-For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
-
-```bash
-export USE_XETLA=OFF
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Data Center GPU Max Series</summary>
-
-```bash
-export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-export ENABLE_SDP_FUSION=1
-```
-> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
-</details>
-
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```bash
-export SYCL_CACHE_PERSISTENT=1
-export BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-#### 3.2 Configurations for Windows
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-set BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-> [!NOTE]
-> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
-### 4. Running examples
 
 **Stream Chat using `stream_chat()` API**:
 ```
diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/README.md
index 3d0c5d584a3..14d3f645f61 100644
--- a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/README.md
+++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/README.md
@@ -4,10 +4,8 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
 ## 0. Requirements
 To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a GLM-4 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
-### 1. Install
-#### 1.1 Installation on Linux
+## 1. Install
+### 1.1 Installation on Linux
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11
@@ -16,10 +14,10 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 
 # install tiktoken required for GLM-4
-pip install tiktoken
+pip install "tiktoken>=0.7.0"
 ```
 
-#### 1.2 Installation on Windows
+### 1.2 Installation on Windows
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11 libuv
@@ -29,10 +27,10 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 
 # install tiktoken required for GLM-4
-pip install tiktoken
+pip install "tiktoken>=0.7.0"
 ```
 
-### 2. Configures OneAPI environment variables for Linux
+## 2. Configures OneAPI environment variables for Linux
 
 > [!NOTE]
 > Skip this step if you are running on Windows.
@@ -43,9 +41,9 @@ This is a required step on Linux for APT or offline installed oneAPI. Skip this
 source /opt/intel/oneapi/setvars.sh
 ```
 
-### 3. Runtime Configurations
+## 3. Runtime Configurations
 For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
+### 3.1 Configurations for Linux
 <details>
 
 <summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
@@ -82,7 +80,7 @@ export BIGDL_LLM_XMX_DISABLED=1
 
 </details>
 
-#### 3.2 Configurations for Windows
+### 3.2 Configurations for Windows
 <details>
 
 <summary>For Intel iGPU</summary>
@@ -106,7 +104,10 @@ set SYCL_CACHE_PERSISTENT=1
 
 > [!NOTE]
 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
-### 4. Running examples
+## 4. Running examples
+
+### Example 1: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a GLM-4 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
 
 ```
 python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
@@ -118,7 +119,7 @@ Arguments info:
 - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
 
 #### Sample Output
-##### [THUDM/glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat)
+#### [THUDM/glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat)
 ```log
 Inference time: xxxx s
 -------------------- Prompt --------------------
@@ -145,109 +146,8 @@ What is AI?
 Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term "art
 ```
 
-## Example 2: Stream Chat using `stream_chat()` API
+### Example 2: Stream Chat using `stream_chat()` API
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a GLM-4 model to stream chat, with IPEX-LLM INT4 optimizations.
-### 1. Install
-#### 1.1 Installation on Linux
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11
-conda activate llm
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-
-# install tiktoken required for GLM-4
-pip install tiktoken
-```
-
-#### 1.2 Installation on Windows
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11 libuv
-conda activate llm
-
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-
-# install tiktoken required for GLM-4
-pip install tiktoken
-```
-
-### 2. Configures OneAPI environment variables for Linux
-
-> [!NOTE]
-> Skip this step if you are running on Windows.
-
-This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
-
-```bash
-source /opt/intel/oneapi/setvars.sh
-```
-
-### 3. Runtime Configurations
-For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
-
-```bash
-export USE_XETLA=OFF
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Data Center GPU Max Series</summary>
-
-```bash
-export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-export ENABLE_SDP_FUSION=1
-```
-> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
-</details>
-
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```bash
-export SYCL_CACHE_PERSISTENT=1
-export BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-#### 3.2 Configurations for Windows
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-set BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-> [!NOTE]
-> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
-### 4. Running examples
 
 **Stream Chat using `stream_chat()` API**:
 ```
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/chatglm2/README.md b/python/llm/example/GPU/PyTorch-Models/Model/chatglm2/README.md
index ace1a38b167..f3a10f37a6b 100644
--- a/python/llm/example/GPU/PyTorch-Models/Model/chatglm2/README.md
+++ b/python/llm/example/GPU/PyTorch-Models/Model/chatglm2/README.md
@@ -4,10 +4,8 @@ In this directory, you will find examples on how you could use IPEX-LLM `optimiz
 ## Requirements
 To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
-### 1. Install
-#### 1.1 Installation on Linux
+## 1. Install
+### 1.1 Installation on Linux
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11
@@ -16,7 +14,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 ```
 
-#### 1.2 Installation on Windows
+### 1.2 Installation on Windows
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11 libuv
@@ -26,7 +24,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 ```
 
-### 2. Configures OneAPI environment variables for Linux
+## 2. Configures OneAPI environment variables for Linux
 
 > [!NOTE]
 > Skip this step if you are running on Windows.
@@ -37,9 +35,9 @@ This is a required step on Linux for APT or offline installed oneAPI. Skip this
 source /opt/intel/oneapi/setvars.sh
 ```
 
-### 3. Runtime Configurations
+## 3. Runtime Configurations
 For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
+### 3.1 Configurations for Linux
 <details>
 
 <summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
@@ -76,7 +74,7 @@ export BIGDL_LLM_XMX_DISABLED=1
 
 </details>
 
-#### 3.2 Configurations for Windows
+### 3.2 Configurations for Windows
 <details>
 
 <summary>For Intel iGPU</summary>
@@ -100,7 +98,11 @@ set SYCL_CACHE_PERSISTENT=1
 
 > [!NOTE]
 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
-### 4. Running examples
+
+## 4. Running examples
+
+### Example 1: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
 
 ```bash
 python ./generate.py --prompt 'AI是什么？'
@@ -112,8 +114,9 @@ In the example, several arguments can be passed to satisfy your requirements:
 - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
 - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
 
-#### 4.1 Sample Output
+#### Sample Output
 #### [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)
+
 ```log
 Inference time: xxxx s
 -------------------- Output --------------------
@@ -132,103 +135,8 @@ Inference time: xxxx s
 答： Artificial Intelligence (AI) refers to the ability of a computer or machine to perform tasks that typically require human-like intelligence, such as understanding language, recognizing patterns
 ```
 
-## Example 2: Stream Chat using `stream_chat()` API
+### Example 2: Stream Chat using `stream_chat()` API
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a ChatGLM2 model to stream chat, with IPEX-LLM INT4 optimizations.
-### 1. Install
-#### 1.1 Installation on Linux
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11
-conda activate llm
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-```
-
-#### 1.2 Installation on Windows
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11 libuv
-conda activate llm
-
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-```
-
-### 2. Configures OneAPI environment variables for Linux
-
-> [!NOTE]
-> Skip this step if you are running on Windows.
-
-This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
-
-```bash
-source /opt/intel/oneapi/setvars.sh
-```
-
-### 3. Runtime Configurations
-For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
-
-```bash
-export USE_XETLA=OFF
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Data Center GPU Max Series</summary>
-
-```bash
-export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-export ENABLE_SDP_FUSION=1
-```
-> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
-</details>
-
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```bash
-export SYCL_CACHE_PERSISTENT=1
-export BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-#### 3.2 Configurations for Windows
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-set BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-> [!NOTE]
-> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
-### 4. Running examples
 
 **Stream Chat using `stream_chat()` API**:
 ```
@@ -244,4 +152,4 @@ In the example, several arguments can be passed to satisfy your requirements:
 
 - `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the ChatGLM2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm2-6b'`.
 - `--question QUESTION`: argument defining the question to ask. It is default to be `"晚上睡不着应该怎么办"`.
-- `--disable-stream`: argument defining whether to stream chat. If include `--disable-stream` when running the script, the stream chat is disabled and `chat()` API is used.
\ No newline at end of file
+- `--disable-stream`: argument defining whether to stream chat. If include `--disable-stream` when running the script, the stream chat is disabled and `chat()` API is used.
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/chatglm3/README.md b/python/llm/example/GPU/PyTorch-Models/Model/chatglm3/README.md
index e85d070a8e2..6c198bfd5dd 100644
--- a/python/llm/example/GPU/PyTorch-Models/Model/chatglm3/README.md
+++ b/python/llm/example/GPU/PyTorch-Models/Model/chatglm3/README.md
@@ -4,10 +4,8 @@ In this directory, you will find examples on how you could use IPEX-LLM `optimiz
 ## Requirements
 To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
-### 1. Install
-#### 1.1 Installation on Linux
+## 1. Install
+### 1.1 Installation on Linux
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11
@@ -16,7 +14,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 ```
 
-#### 1.2 Installation on Windows
+### 1.2 Installation on Windows
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.11 libuv
@@ -26,7 +24,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 ```
 
-### 2. Configures OneAPI environment variables for Linux
+## 2. Configures OneAPI environment variables for Linux
 
 > [!NOTE]
 > Skip this step if you are running on Windows.
@@ -37,9 +35,9 @@ This is a required step on Linux for APT or offline installed oneAPI. Skip this
 source /opt/intel/oneapi/setvars.sh
 ```
 
-### 3. Runtime Configurations
+## 3. Runtime Configurations
 For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
+### 3.1 Configurations for Linux
 <details>
 
 <summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
@@ -76,7 +74,7 @@ export BIGDL_LLM_XMX_DISABLED=1
 
 </details>
 
-#### 3.2 Configurations for Windows
+### 3.2 Configurations for Windows
 <details>
 
 <summary>For Intel iGPU</summary>
@@ -100,7 +98,10 @@ set SYCL_CACHE_PERSISTENT=1
 
 > [!NOTE]
 > For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
-### 4. Running examples
+## 4. Running examples
+
+### Example 1: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
 
 ```bash
 python ./generate.py --prompt 'AI是什么？'
@@ -112,7 +113,7 @@ In the example, several arguments can be passed to satisfy your requirements:
 - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
 - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
 
-#### 4.1 Sample Output
+#### Sample Output
 #### [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b)
 ```log
 Inference time: xxxx s
@@ -131,103 +132,9 @@ What is AI?
 AI stands for Artificial Intelligence. It refers to the development of computer systems or machines that can perform tasks that would normally require human intelligence, such as recognizing patterns
 ```
 
-## Example 2: Stream Chat using `stream_chat()` API
+### Example 2: Stream Chat using `stream_chat()` API
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a ChatGLM3 model to stream chat, with IPEX-LLM INT4 optimizations.
-### 1. Install
-#### 1.1 Installation on Linux
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11
-conda activate llm
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-```
-
-#### 1.2 Installation on Windows
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.11 libuv
-conda activate llm
-
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-```
-
-### 2. Configures OneAPI environment variables for Linux
-
-> [!NOTE]
-> Skip this step if you are running on Windows.
 
-This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
-
-```bash
-source /opt/intel/oneapi/setvars.sh
-```
-
-### 3. Runtime Configurations
-For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 3.1 Configurations for Linux
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
-
-```bash
-export USE_XETLA=OFF
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Data Center GPU Max Series</summary>
-
-```bash
-export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
-export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
-export SYCL_CACHE_PERSISTENT=1
-export ENABLE_SDP_FUSION=1
-```
-> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
-</details>
-
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```bash
-export SYCL_CACHE_PERSISTENT=1
-export BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-#### 3.2 Configurations for Windows
-<details>
-
-<summary>For Intel iGPU</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-set BIGDL_LLM_XMX_DISABLED=1
-```
-
-</details>
-
-<details>
-
-<summary>For Intel Arc™ A-Series Graphics</summary>
-
-```cmd
-set SYCL_CACHE_PERSISTENT=1
-```
-
-</details>
-
-> [!NOTE]
-> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
-### 4. Running examples
 **Stream Chat using `stream_chat()` API**:
 ```
 python ./streamchat.py
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/glm4/README.md b/python/llm/example/GPU/PyTorch-Models/Model/glm4/README.md
index 6993df68438..f7e95fddacc 100644
--- a/python/llm/example/GPU/PyTorch-Models/Model/glm4/README.md
+++ b/python/llm/example/GPU/PyTorch-Models/Model/glm4/README.md
@@ -16,7 +16,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 
 # install tiktoken required for GLM-4
-pip install tiktoken
+pip install "tiktoken>=0.7.0"
 ```
 
 #### 1.2 Installation on Windows
@@ -29,7 +29,7 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 
 # install tiktoken required for GLM-4
-pip install tiktoken
+pip install "tiktoken>=0.7.0"
 ```
 
 ### 2. Configures OneAPI environment variables for Linux