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## IPEX-LLM
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> [!IMPORTANT]
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> ***`bigdl-llm` has now become `ipex-llm` (see the migration guide [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/bigdl_llm_migration.html)); you may find the original `BigDL` project [here](https://github.com/intel-analytics/BigDL-2.x).***
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**`ipex-llm`** is a library for running **LLM** (large language model) on Intel **XPU** (from *Laptop* to *GPU* to *Cloud*) using **INT4/FP4/INT8/FP8** with very low latency[^1] (for any **PyTorch** model).
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---
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> *It is built on the excellent work of [llama.cpp](https://github.com/ggerganov/llama.cpp), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), [qlora](https://github.com/artidoro/qlora), [gptq](https://github.com/IST-DASLab/gptq), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [awq](https://github.com/mit-han-lab/llm-awq), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [vLLM](https://github.com/vllm-project/vllm), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [gptq_for_llama](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [chatglm.cpp](https://github.com/li-plus/chatglm.cpp), [redpajama.cpp](https://github.com/togethercomputer/redpajama.cpp), [gptneox.cpp](https://github.com/byroneverson/gptneox.cpp), [bloomz.cpp](https://github.com/NouamaneTazi/bloomz.cpp/), etc.*
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# 💫 IPEX-LLM
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**`IPEX-LLM`** is a PyTorch library for running **LLM** on Intel CPU and GPU *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)* with very low latency[^1].
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> [!NOTE]
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> - *It is built on top of **Intel Extension for PyTorch** (**`IPEX`**), as well as the excellent work of **`llama.cpp`**, **`bitsandbytes`**, **`vLLM`**, **`qlora`**, **`AutoGPTQ`**, **`AutoAWQ`**, etc.*
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> - *It provides seamless integration with [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html), [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html), [HuggingFace tansformers](python/llm/example/GPU/HF-Transformers-AutoModels), [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning), [LangChain](python/llm/example/GPU/LangChain), [LlamaIndex](python/llm/example/GPU/LlamaIndex), [DeepSpeed-AutoTP](python/llm/example/GPU/Deepspeed-AutoTP), [vLLM](python/llm/example/GPU/vLLM-Serving), [FastChat](python/llm/src/ipex_llm/serving/fastchat), [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO), [AutoGen](python/llm/example/CPU/Applications/autogen), [ModeScope](python/llm/example/GPU/ModelScope-Models), etc.*
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> - ***50+ models** have been optimized/verified on `ipex-llm` (including LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, RWKV, and more); see the complete list [here](#verified-models).*
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### Latest update 🔥
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- [2024/03] **LangChain** added support for `ipex-llm`; see the details [here](https://python.langchain.com/docs/integrations/llms/bigdl).
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## Latest Update 🔥
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- [2024/03] `bigdl-llm` has now become `ipex-llm` (see the migration guide [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/bigdl_llm_migration.html)); you may find the original `BigDL` project [here](https://github.com/intel-analytics/bigdl-2.x).
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- [2024/02] `ipex-llm` now supports directly loading model from [ModelScope](python/llm/example/GPU/ModelScope-Models) ([魔搭](python/llm/example/CPU/ModelScope-Models)).
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- [2024/02] `ipex-llm` added inital **INT2** support (based on llama.cpp [IQ2](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2) mechanism), which makes it possible to run large-size LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM.
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- [2024/02] Users can now use `ipex-llm` through [Text-Generation-WebUI](https://github.com/intel-analytics/text-generation-webui) GUI.
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- [2024/02] `ipex-llm` now supports *[Self-Speculative Decoding](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Inference/Self_Speculative_Decoding.html)*, which in practice brings **~30% speedup** for FP16 and BF16 inference latency on Intel [GPU](python/llm/example/GPU/Speculative-Decoding) and [CPU](python/llm/example/CPU/Speculative-Decoding) respectively.
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- [2024/02] `ipex-llm` now supports a comprehensive list of LLM finetuning on Intel GPU (including [LoRA](python/llm/example/GPU/LLM-Finetuning/LoRA), [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), [DPO](python/llm/example/GPU/LLM-Finetuning/DPO), [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) and [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora)).
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- [2024/02] `ipex-llm` now supports a comprehensive list of LLM **finetuning** on Intel GPU (including [LoRA](python/llm/example/GPU/LLM-Finetuning/LoRA), [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), [DPO](python/llm/example/GPU/LLM-Finetuning/DPO), [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) and [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora)).
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- [2024/01] Using `ipex-llm` [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), we managed to finetune LLaMA2-7B in **21 minutes** and LLaMA2-70B in **3.14 hours** on 8 Intel Max 1550 GPU for [Standford-Alpaca](python/llm/example/GPU/LLM-Finetuning/QLoRA/alpaca-qlora) (see the blog [here](https://www.intel.com/content/www/us/en/developer/articles/technical/finetuning-llms-on-intel-gpus-using-bigdl-llm.html)).
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- [2024/01] 🔔🔔🔔 ***The default `ipex-llm` GPU Linux installation has switched from PyTorch 2.0 to PyTorch 2.1, which requires new oneAPI and GPU driver versions. (See the [GPU installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html) for more details.)***
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<details><summary>More updates</summary>
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<br/>
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- [2023/12] `ipex-llm` now supports [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora) (see *["ReLoRA: High-Rank Training Through Low-Rank Updates"](https://arxiv.org/abs/2307.05695)*).
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- [2023/12] `ipex-llm` now supports [Mixtral-8x7B](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral) on both Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral) and [CPU](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral).
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- [2023/12] `ipex-llm` now supports [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) (see *["QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models"](https://arxiv.org/abs/2309.14717)*).
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@ -22,10 +30,13 @@
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- [2023/10] `ipex-llm` now supports [QLoRA finetuning](python/llm/example/GPU/LLM-Finetuning/QLoRA) on both Intel [GPU](python/llm/example/GPU/LLM-Finetuning/QLoRA) and [CPU](python/llm/example/CPU/QLoRA-FineTuning).
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- [2023/10] `ipex-llm` now supports [FastChat serving](python/llm/src/ipex_llm/llm/serving) on on both Intel CPU and GPU.
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- [2023/09] `ipex-llm` now supports [Intel GPU](python/llm/example/GPU) (including iGPU, Arc, Flex and MAX).
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- [2023/09] `ipex-llm` [tutorial](https://github.com/intel-analytics/ipex-llm-tutorial) is released.
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- [2023/09] Over 40 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaMA2, ChatGLM2/ChatGLM3, Mistral, Falcon, MPT, LLaVA, WizardCoder, Dolly, Whisper, Baichuan/Baichuan2, InternLM, Skywork, QWen/Qwen-VL, Aquila, MOSS,* and more; see the complete list [here](#verified-models).
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- [2023/09] `ipex-llm` [tutorial](https://github.com/intel-analytics/bigdl-llm-tutorial) is released.
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### `ipex-llm` Demos
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</details>
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[^1]: Performance varies by use, configuration and other factors. `ipex-llm` may not optimize to the same degree for non-Intel products. Learn more at www.Intel.com/PerformanceIndex.
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## `ipex-llm` Demos
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See the ***optimized performance*** of `chatglm2-6b` and `llama-2-13b-chat` models on 12th Gen Intel Core CPU and Intel Arc GPU below.
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<table width="100%">
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@ -55,96 +66,59 @@ See the ***optimized performance*** of `chatglm2-6b` and `llama-2-13b-chat` mode
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</tr>
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</table>
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### `ipex-llm` quickstart
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## `ipex-llm` Quickstart
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### Install `ipex-llm`
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- [Windows GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html): installing `ipex-llm` on Windows with Intel GPU
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- [Linux GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html): installing `ipex-llm` on Linux with Intel GPU
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- [Docker](docker/llm): using `ipex-llm` dockers on Intel CPU and GPU
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- *For more details, please refer to the [installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install.html)*
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- [Windows GPU installation](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html)
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- [Run IPEX-LLM in Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html)
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- [Run IPEX-LLM using Docker](docker/llm)
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- [CPU INT4](#cpu-int4)
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- [GPU INT4](#gpu-int4)
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- [More Low-Bit support](#more-low-bit-support)
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- [Verified models](#verified-models)
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### Run `ipex-llm`
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- [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html): running **ipex-llm for llama.cpp** (*using C++ interface of `ipex-llm` as an accelerated backend for `llama.cpp` on Intel GPU*)
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- [vLLM](python/llm/example/GPU/vLLM-Serving): running `ipex-llm` in `vLLM` on both Intel [GPU](python/llm/example/GPU/vLLM-Serving) and [CPU](python/llm/example/CPU/vLLM-Serving)
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- [FastChat](python/llm/src/ipex_llm/serving/fastchat): running `ipex-llm` in `FastChat` serving on on both Intel GPU and CPU
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- [LangChain-Chatchat RAG](https://github.com/intel-analytics/Langchain-Chatchat): running `ipex-llm` in `LangChain-Chatchat` (*Knowledge Base QA using **RAG** pipeline*)
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- [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html): running `ipex-llm` in `oobabooga` **WebUI**
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- [Benchmarking](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/benchmark_quickstart.html): running (latency and throughput) benchmarks for `ipex-llm` on Intel CPU and GPU
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#### CPU INT4
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##### Install
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You may install **`ipex-llm`** on Intel CPU as follows:
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> Note: See the [CPU installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_cpu.html) for more details.
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```bash
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pip install --pre --upgrade ipex-llm[all]
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```
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> Note: `ipex-llm` has been tested on Python 3.9, 3.10 and 3.11
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### Code Examples
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- Low bit inference
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- [INT4 inference](python/llm/example/GPU/HF-Transformers-AutoModels/Model): **INT4** LLM inference on Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Model) and [CPU](python/llm/example/CPU/HF-Transformers-AutoModels/Model)
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- [FP8/FP4 inference](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types): **FP8** and **FP4** LLM inference on Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types)
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- [INT8 inference](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types): **INT8** LLM inference on Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types) and [CPU](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types)
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- [INT2 inference](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2): **INT2** LLM inference (based on llama.cpp IQ2 mechanism) on Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2)
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- FP16/BF16 inference
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- **FP16** LLM inference on Intel [GPU](python/llm/example/GPU/Speculative-Decoding), with possible [self-speculative decoding](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Inference/Self_Speculative_Decoding.html) optimization
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- **BF16** LLM inference on Intel [CPU](python/llm/example/CPU/Speculative-Decoding), with possible [self-speculative decoding](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Inference/Self_Speculative_Decoding.html) optimization
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- Save and load
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- [Low-bit models](python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load): saving and loading `ipex-llm` low-bit models
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- [GGUF](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF): directly loading GGUF models into `ipex-llm`
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- [AWQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ): directly loading AWQ models into `ipex-llm`
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- [GPTQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/QPTQ): directly loading GPTQ models into `ipex-llm`
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- Finetuning
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- LLM finetuning on Intel [GPU](python/llm/example/GPU/LLM-Finetuning), including [LoRA](python/llm/example/GPU/LLM-Finetuning/LoRA), [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), [DPO](python/llm/example/GPU/LLM-Finetuning/DPO), [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) and [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora)
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- QLoRA finetuning on Intel [CPU](python/llm/example/CPU/QLoRA-FineTuning)
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- Integration with community libraries
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- [HuggingFace tansformers](python/llm/example/GPU/HF-Transformers-AutoModels)
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- [Standard PyTorch model](python/llm/example/GPU/PyTorch-Models)
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- [DeepSpeed-AutoTP](python/llm/example/GPU/Deepspeed-AutoTP)
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- [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning/HF-PEFT)
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- [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO)
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- [LangChain](python/llm/example/GPU/LangChain)
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- [LlamaIndex](python/llm/example/GPU/LlamaIndex)
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- [AutoGen](python/llm/example/CPU/Applications/autogen)
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- [ModeScope](python/llm/example/GPU/ModelScope-Models)
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- [Tutorials](https://github.com/intel-analytics/bigdl-llm-tutorial)
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##### Run Model
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You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
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*For more details, please refer to the `ipex-llm` document [website](https://ipex-llm.readthedocs.io/).*
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```python
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#load Hugging Face Transformers model with INT4 optimizations
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from ipex_llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model on CPU
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...)
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids)
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```
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*See the complete examples [here](python/llm/example/CPU/HF-Transformers-AutoModels/Model).*
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#### GPU INT4
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##### Install
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You may install **`ipex-llm`** on Intel GPU as follows:
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> Note: See the [GPU installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html) for more details.
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```bash
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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```
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> Note: `ipex-llm` has been tested on Python 3.9, 3.10 and 3.11
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##### Run Model
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You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
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```python
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#load Hugging Face Transformers model with INT4 optimizations
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from ipex_llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model on Intel GPU
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model = model.to('xpu')
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...).to('xpu')
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids.cpu())
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```
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*See the complete examples [here](python/llm/example/GPU).*
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#### More Low-Bit Support
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##### Save and load
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After the model is optimized using `ipex-llm`, you may save and load the model as follows:
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```python
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model.save_low_bit(model_path)
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new_model = AutoModelForCausalLM.load_low_bit(model_path)
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```
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*See the complete example [here](python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load).*
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##### Additonal data types
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In addition to INT4, You may apply other low bit optimizations (such as *INT8*, *INT5*, *NF4*, etc.) as follows:
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```python
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int8")
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```
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*See the complete example [here](python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types).*
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#### Verified Models
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Over 40 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaMA2, ChatGLM/ChatGLM2, Mistral, Falcon, MPT, Baichuan/Baichuan2, InternLM, QWen* and more; see the example list below.
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## Verified Models
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Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM2/ChatGLM3, Baichuan/Baichuan2, Qwen/Qwen-1.5, InternLM* and more; see the list below.
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| Model | CPU Example | GPU Example |
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| ---------------------------------------- | ---------------------------------------- | ---------------------------------------- |
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| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/vicuna) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/vicuna) |
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| LLaMA 2 | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2) | [link1](python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama2), [link2-low GPU memory example](python/llm/example/GPU/PyTorch-Models/Model/llama2#example-2---low-memory-version-predict-tokens-using-generate-api) |
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|------------|----------------------------------------------------------------|-----------------------------------------------------------------|
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| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/vicuna) |[link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/vicuna)|
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| LLaMA 2 | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama2) |
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| ChatGLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm) | |
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| ChatGLM2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2) |
|
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| ChatGLM3 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3) |
|
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|
|
@ -154,7 +128,7 @@ Over 40 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
|
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| MPT | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mpt) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mpt) |
|
||||
| Dolly-v1 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v1) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/dolly-v1) |
|
||||
| Dolly-v2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/dolly-v2) |
|
||||
| Replit Code | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/replit) |
|
||||
| Replit Code| [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/replit) |
|
||||
| RedPajama | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/redpajama) | |
|
||||
| Phoenix | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phoenix) | |
|
||||
| StarCoder | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/starcoder) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/starcoder) |
|
||||
|
|
@ -195,6 +169,3 @@ Over 40 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
|
|||
| Gemma | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/gemma) |
|
||||
| DeciLM-7B | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/deciLM-7b) |
|
||||
| Deepseek | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/deepseek) |
|
||||
|
||||
|
||||
***For more details, please refer to the `ipex-llm` [Document](https://test-ipex-llm.readthedocs.io/en/main/doc/LLM/index.html), [Readme](python/llm), [Tutorial](https://github.com/intel-analytics/ipex-llm-tutorial) and [API Doc](https://ipex-llm.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).***
|
||||
|
|
|
|||
|
|
@ -1,28 +1,17 @@
|
|||
# bigdl-llm upgrade guide
|
||||
## Upgrade bigdl-llm to ipex-llm
|
||||
Need to uninstall bigdl-llm and install ipex-llm first.
|
||||
# `bigdl-llm` Migration Guide
|
||||
## Upgrade `bigdl-llm` package to `ipex-llm`
|
||||
First uninstall `bigdl-llm` and install `ipex-llm`.
|
||||
```bash
|
||||
pip uninstall -y bigdl-llm
|
||||
pip install --pre --upgrade ipex-llm[all] # for cpu
|
||||
pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu # for xpu
|
||||
```
|
||||
## Run bigdl-llm example code
|
||||
Choose a way to modify the example code to use.
|
||||
## Migrate `bigdl-llm` code to `ipex-llm`
|
||||
There are two options to migrate `bigdl-llm` code to `ipex-llm`.
|
||||
|
||||
### 1.Run bigdl-llm example code with ipex-llm in compatible mode
|
||||
Minimal change: Use `BIGDL_COMPATIBLE_MODE` to run, need to add `import ipex_llm` at the
|
||||
beginning of code with bigdl-llm examples.
|
||||
```python
|
||||
# need to add before import bigdl.llm
|
||||
import ipex_llm
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
load_in_4bit=True,
|
||||
trust_remote_code=True)
|
||||
```
|
||||
### 1. Upgrade `bigdl-llm` code to `ipex-llm`
|
||||
To upgrade `bigdl-llm` code to `ipex-llm`, simply replace all `bigdl.llm` with `ipex_llm`:
|
||||
|
||||
### 2.Upgrade bigdl-llm example code to ipex-llm
|
||||
Need to replace all `bigdl.llm` with `ipex_llm`.
|
||||
```python
|
||||
#from bigdl.llm.transformers import AutoModelForCausalLM
|
||||
from ipex_llm.transformers import AutoModelForCausalLM
|
||||
|
|
@ -31,3 +20,15 @@ model = AutoModelForCausalLM.from_pretrained(model_path,
|
|||
trust_remote_code=True)
|
||||
```
|
||||
|
||||
### 2. Run `bigdl-llm` code in compatible mode (experimental)
|
||||
To run in the compatible mode, simply add `import ipex_llm` at the beginning of the existing `bigdl-llm` code:
|
||||
|
||||
```python
|
||||
# need to add the below line before "import bigdl.llm"
|
||||
import ipex_llm
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
load_in_4bit=True,
|
||||
trust_remote_code=True)
|
||||
```
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue