> [!IMPORTANT] > ***`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).*** --- # 💫 Intel® LLM library for PyTorch* **`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]. > [!NOTE] > - *It runs on top of Intel Extension for PyTorch (**`IPEX`**), and is built on top of the excellent work of **`llama.cpp`**, **`transformers`**, **`bitsandbytes`**, **`vLLM`**, **`qlora`**, **`AutoGPTQ`**, **`AutoAWQ`**, etc.* > - *It provides seamless integration with [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html), [Ollama](https://ipex-llm.readthedocs.io/en/main/doc/LLM/Quickstart/ollama_quickstart.html), [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html), [HuggingFace transformers](python/llm/example/GPU/HF-Transformers-AutoModels), [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), [Axolotl](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/axolotl_quickstart.html), [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning), [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO), [AutoGen](python/llm/example/CPU/Applications/autogen), [ModeScope](python/llm/example/GPU/ModelScope-Models), etc.* > - ***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).* ## `ipex-llm` Demo See the demo of running [*Text-Generation-WebUI*](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html), [*local RAG using LangChain-Chatchat*](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/chatchat_quickstart.html), [*llama.cpp*](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html) and [*Ollama*](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/ollama_quickstart.html) *(on either Intel Core Ultra laptop or Arc GPU)* with `ipex-llm` below.
Intel Core Ultra Laptop Intel Arc GPU
Text-Generation-WebUI Local RAG using LangChain-Chatchat llama.cpp Ollama
## Latest Update 🔥 - [2024/05] You can now easily run `ipex-llm` inference, serving and finetuning using [Docker](#docker). - [2024/05] You can now install `ipex-llm` on Windows using just "*[one command](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html#install-ipex-llm)*". - [2024/05] `ipex-llm` now supports **Axolotl** for LLM finetuning on Intel GPU; see the quickstart [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/axolotl_quickstart.html). - [2024/04] You can now run **Open WebUI** on Intel GPU using `ipex-llm`; see the quickstart [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/open_webui_with_ollama_quickstart.html). - [2024/04] You can now run **Llama 3** on Intel GPU using `llama.cpp` and `ollama` with `ipex-llm`; see the quickstart [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama3_llamacpp_ollama_quickstart.html). - [2024/04] `ipex-llm` now supports **Llama 3** on both Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama3) and [CPU](python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3). - [2024/04] `ipex-llm` now provides C++ interface, which can be used as an accelerated backend for running [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html) and [ollama](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/ollama_quickstart.html) on Intel GPU. - [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). - [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)). - [2024/02] `ipex-llm` added initial **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. - [2024/02] Users can now use `ipex-llm` through [Text-Generation-WebUI](https://github.com/intel-analytics/text-generation-webui) GUI. - [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. - [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)). - [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)).
More updates
- [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)*). - [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). - [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)*). - [2023/12] `ipex-llm` now supports [FP8 and FP4 inference](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types) on Intel ***GPU***. - [2023/11] Initial support for directly loading [GGUF](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF), [AWQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ) and [GPTQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GPTQ) models into `ipex-llm` is available. - [2023/11] `ipex-llm` now supports [vLLM continuous batching](python/llm/example/GPU/vLLM-Serving) on both Intel [GPU](python/llm/example/GPU/vLLM-Serving) and [CPU](python/llm/example/CPU/vLLM-Serving). - [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). - [2023/10] `ipex-llm` now supports [FastChat serving](python/llm/src/ipex_llm/llm/serving) on on both Intel CPU and GPU. - [2023/09] `ipex-llm` now supports [Intel GPU](python/llm/example/GPU) (including iGPU, Arc, Flex and MAX). - [2023/09] `ipex-llm` [tutorial](https://github.com/intel-analytics/ipex-llm-tutorial) is released.
[^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. ## `ipex-llm` Quickstart ### Docker - [GPU Inference in C++](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/docker_cpp_xpu_quickstart.html): running `llama.cpp`, `ollama`, `OpenWebUI`, etc., with `ipex-llm` on Intel GPU - [GPU Inference in Python](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/docker_pytorch_inference_gpu.html) : running HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`, etc. with `ipex-llm` on Intel GPU - [vLLM on GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/vllm_docker_quickstart.html): running `vLLM` serving with `ipex-llm` on Intel GPU - [FastChat on GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/fastchat_docker_quickstart.html): running `FastChat` serving with `ipex-llm` on Intel GPU ### Use - [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html): running **llama.cpp** (*using C++ interface of `ipex-llm` as an accelerated backend for `llama.cpp`*) on Intel GPU - [Ollama](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/ollama_quickstart.html): running **ollama** (*using C++ interface of `ipex-llm` as an accelerated backend for `ollama`*) on Intel GPU - [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) - [FastChat](python/llm/src/ipex_llm/serving/fastchat): running `ipex-llm` in **FastChat** serving on on both Intel GPU and CPU - [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html): running `ipex-llm` in `oobabooga` **WebUI** - [Axolotl](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/axolotl_quickstart.html): running `ipex-llm` in **Axolotl** for LLM finetuning - [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 ### Applications - [Local RAG](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/chatchat_quickstart.html): running `LangChain-Chatchat` (*Knowledge Base QA using **RAG** pipeline*) with `ipex-llm` - [Coding copilot](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/continue_quickstart.html): running `Continue` (coding copilot in VSCode) with `ipex-llm` - [Open WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/open_webui_with_ollama_quickstart.html): running `Open WebUI` with `ipex-llm` - [PrivateGPT](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/privateGPT_quickstart.html): running `PrivateGPT` to interact with documents with `ipex-llm` - [Dify platform](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/dify_quickstart.html): running `ipex-llm` in `Dify`(*production-ready LLM app development platform*) ### Install - [Windows GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html): installing `ipex-llm` on Windows with Intel GPU - [Linux GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html): installing `ipex-llm` on Linux with Intel GPU - *For more details, please refer to the [installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install.html)* ### Code Examples - Low bit inference - [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) - [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) - [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/CPU/HF-Transformers-AutoModels/More-Data-Types) - [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) - FP16/BF16 inference - **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 - **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 - Save and load - [Low-bit models](python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load): saving and loading `ipex-llm` low-bit models - [GGUF](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF): directly loading GGUF models into `ipex-llm` - [AWQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ): directly loading AWQ models into `ipex-llm` - [GPTQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GPTQ): directly loading GPTQ models into `ipex-llm` - Finetuning - 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) - QLoRA finetuning on Intel [CPU](python/llm/example/CPU/QLoRA-FineTuning) - Integration with community libraries - [HuggingFace transformers](python/llm/example/GPU/HF-Transformers-AutoModels) - [Standard PyTorch model](python/llm/example/GPU/PyTorch-Models) - [LangChain](python/llm/example/GPU/LangChain) - [LlamaIndex](python/llm/example/GPU/LlamaIndex) - [DeepSpeed-AutoTP](python/llm/example/GPU/Deepspeed-AutoTP) - [Axolotl](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/axolotl_quickstart.html) - [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning/HF-PEFT) - [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO) - [AutoGen](python/llm/example/CPU/Applications/autogen) - [ModeScope](python/llm/example/GPU/ModelScope-Models) - [Tutorials](https://github.com/intel-analytics/ipex-llm-tutorial) *For more details, please refer to the `ipex-llm` document [website](https://ipex-llm.readthedocs.io/).* ## Verified Models 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. | Model | CPU Example | GPU Example | |------------|----------------------------------------------------------------|-----------------------------------------------------------------| | 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)| | 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) | | LLaMA 3 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama3) | | ChatGLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm) | | | ChatGLM2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2) | | ChatGLM3 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3) | | Mistral | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mistral) | | Mixtral | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral) | | Falcon | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/falcon) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/falcon) | | 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) | | 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) | | Baichuan | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan) | | Baichuan2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan2) | | InternLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm) | | Qwen | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen) | | Qwen1.5 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) | | Qwen2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwe2) | | Qwen-VL | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen-vl) | | Aquila | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/aquila) | | Aquila2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/aquila2) | | MOSS | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/moss) | | | Whisper | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/whisper) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/whisper) | | Phi-1_5 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-1_5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-1_5) | | Flan-t5 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/flan-t5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/flan-t5) | | LLaVA | [link](python/llm/example/CPU/PyTorch-Models/Model/llava) | [link](python/llm/example/GPU/PyTorch-Models/Model/llava) | | CodeLlama | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codellama) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/codellama) | | Skywork | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/skywork) | | | InternLM-XComposer | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer) | | | WizardCoder-Python | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) | | | CodeShell | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codeshell) | | | Fuyu | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/fuyu) | | | Distil-Whisper | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/distil-whisper) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/distil-whisper) | | Yi | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yi) | | BlueLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/bluelm) | | Mamba | [link](python/llm/example/CPU/PyTorch-Models/Model/mamba) | [link](python/llm/example/GPU/PyTorch-Models/Model/mamba) | | SOLAR | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/solar) | | Phixtral | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phixtral) | | InternLM2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm2) | | RWKV4 | | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/rwkv4) | | RWKV5 | | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/rwkv5) | | Bark | [link](python/llm/example/CPU/PyTorch-Models/Model/bark) | [link](python/llm/example/GPU/PyTorch-Models/Model/bark) | | SpeechT5 | | [link](python/llm/example/GPU/PyTorch-Models/Model/speech-t5) | | DeepSeek-MoE | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek-moe) | | | Ziya-Coding-34B-v1.0 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya) | | | Phi-2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-2) | | Phi-3 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-3) | | Phi-3-vision | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3-vision) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-3-vision) | | Yuan2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yuan2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yuan2) | | 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) | | StableLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/stablelm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/stablelm) | | CodeGemma | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegemma) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/codegemma) | | Command-R/cohere | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/cohere) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/cohere) | | CodeGeeX2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegeex2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/codegeex2) | | MiniCPM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/minicpm) | ## Get Support - Please report a bug or raise a feature request by opening a [Github Issue](https://github.com/intel-analytics/ipex-llm/issues) - Please report a vulnerability by opening a draft [GitHub Security Advisory](https://github.com/intel-analytics/ipex-llm/security/advisories)