migrate bigdl-llm to ipex-llm

docs/docs/integrations/llms/bigdl.ipynb → docs/docs/integrations/llms/ipex_llm.ipynb

@@ -4,11 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# BigDL-LLM\n",
+    "# IPEX-LLM\n",
     "\n",
-    "> [BigDL-LLM](https://github.com/intel-analytics/BigDL/) is a low-bit LLM optimization library on Intel XPU (Xeon/Core/Flex/Arc/Max). It can make LLMs run extremely fast and consume much less memory on Intel platforms. It is open sourced under Apache 2.0 License.\n",
+    "> [IPEX-LLM](https://github.com/intel-analytics/ipex-llm/) is a low-bit LLM optimization library on Intel XPU (Xeon/Core/Flex/Arc/Max). It can make LLMs run extremely fast and consume much less memory on Intel platforms. It is open sourced under Apache 2.0 License.\n",
     "\n",
-    "This example goes over how to use LangChain to interact with BigDL-LLM for text generation. \n"
+    "This example goes over how to use LangChain to interact with IPEX-LLM for text generation. \n"
    ]
   },
   {
@@ -33,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Install BigDL-LLM for running LLMs locally on Intel CPU."
+    "Install IEPX-LLM for running LLMs locally on Intel CPU."
    ]
   },
   {
@@ -42,8 +42,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install BigDL\n",
-    "%pip install --pre --upgrade bigdl-llm[all]"
+    "%pip install --pre --upgrade ipex-llm[all]"
    ]
   },
   {
@@ -60,7 +59,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain_community.llms.bigdl import BigdlLLM\n",
+    "from langchain_community.llms.ipex_llm import IPEXLLM\n",
     "from langchain_core.prompts import PromptTemplate"
    ]
   },
@@ -110,7 +109,7 @@
     }
    ],
    "source": [
-    "llm = BigdlLLM.from_model_id(\n",
+    "llm = IPEXLLM.from_model_id(\n",
     "    model_id=\"lmsys/vicuna-7b-v1.5\",\n",
     "    model_kwargs={\"temperature\": 0, \"max_length\": 64, \"trust_remote_code\": True},\n",
     ")"

libs/community/langchain_community/llms/bigdl.py → libs/community/langchain_community/llms/ipex_llm.py

@@ -10,22 +10,22 @@
 logger = logging.getLogger(__name__)
 
 
-class BigdlLLM(LLM):
-    """Wrapper around the BigDL-LLM Transformer-INT4 model
+class IPEXLLM(LLM):
+    """Wrapper around the IPEX-LLM model
 
     Example:
         .. code-block:: python
 
-            from langchain.llms import TransformersLLM
-            llm = TransformersLLM.from_model_id(model_id="THUDM/chatglm-6b")
+            from langchain.llms import IPEXLLM
+            llm = IPEXLLM.from_model_id(model_id="THUDM/chatglm-6b")
     """
 
     model_id: str = DEFAULT_MODEL_ID
     """Model name or model path to use."""
     model_kwargs: Optional[dict] = None
     """Keyword arguments passed to the model."""
     model: Any  #: :meta private:
-    """BigDL-LLM Transformers-INT4 model."""
+    """IPEXLLM model."""
     tokenizer: Any  #: :meta private:
     """Huggingface tokenizer model."""
     streaming: bool = True
@@ -56,16 +56,16 @@ def from_model_id(
             An object of TransformersLLM.
         """
         try:
-            from bigdl.llm.transformers import (
+            from ipex_llm.transformers import (
                 AutoModel,
                 AutoModelForCausalLM,
             )
             from transformers import AutoTokenizer, LlamaTokenizer
 
         except ImportError:
             raise ValueError(
-                "Could not import bigdl-llm or transformers. "
-                "Please install it with `pip install --pre --upgrade bigdl-llm[all]`."
+                "Could not import ipex_llm or transformers. "
+                "Please install it with `pip install --pre --upgrade ipex_llm[all]`."
             )
 
         _model_kwargs = model_kwargs or {}
@@ -109,24 +109,24 @@ def from_model_id_low_bit(
 
         Args:
 
-            model_id: Path for the bigdl transformers low-bit model checkpoint folder.
+            model_id: Path for the ipex-llm transformers low-bit model checkpoint folder.
             model_kwargs: Keyword arguments to pass to the model and tokenizer.
             kwargs: Extra arguments to pass to the model and tokenizer.
 
         Returns:
             An object of TransformersLLM.
         """
         try:
-            from bigdl.llm.transformers import (
+            from ipex_llm.transformers import (
                 AutoModel,
                 AutoModelForCausalLM,
             )
             from transformers import AutoTokenizer, LlamaTokenizer
 
         except ImportError:
             raise ValueError(
-                "Could not import bigdl-llm or transformers. "
-                "Please install it with `pip install --pre --upgrade bigdl-llm[all]`"
+                "Could not import ipex_llm or transformers. "
+                "Please install it with `pip install --pre --upgrade ipex_llm[all]`"
             )
 
         _model_kwargs = model_kwargs or {}
@@ -163,7 +163,7 @@ def _identifying_params(self) -> Mapping[str, Any]:
 
     @property
     def _llm_type(self) -> str:
-        return "BigDL-llm"
+        return "ipex-llm"
 
     def _call(
         self,

libs/community/tests/integration_tests/llms/test_bigdl.py → libs/community/tests/integration_tests/llms/test_ipex_llm.py

@@ -1,12 +1,12 @@
-"""Test BigDL LLM"""
+"""Test IPEX LLM"""
 from langchain_core.outputs import LLMResult
 
-from langchain_community.llms.bigdl import BigdlLLM
+from langchain_community.llms.ipex_llm import IPEXLLM
 
 
 def test_call() -> None:
-    """Test valid call to baichuan."""
-    llm = BigdlLLM.from_model_id(
+    """Test valid call to ipex-llm."""
+    llm = IPEXLLM.from_model_id(
         model_id="lmsys/vicuna-7b-v1.5",
         model_kwargs={"temperature": 0, "max_length": 16, "trust_remote_code": True},
     )
@@ -15,8 +15,8 @@ def test_call() -> None:
 
 
 def test_generate() -> None:
-    """Test valid call to baichuan."""
-    llm = BigdlLLM.from_model_id(
+    """Test valid call to ipex-llm."""
+    llm = IPEXLLM.from_model_id(
         model_id="lmsys/vicuna-7b-v1.5",
         model_kwargs={"temperature": 0, "max_length": 16, "trust_remote_code": True},
     )